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dss/storage/bundles.py | DataBiosphere/data-store | 3 | 6630451 | import io
from functools import lru_cache
import json
import typing
import time
import re
from collections import OrderedDict
import cachetools
from cloud_blobstore import BlobNotFoundError, BlobStore
from dss import Config, Replica
from dss.api.search import PerPageBounds
from dss.storage.identifiers import (DSS_BUNDLE_KEY_REGEX, TOMBSTONE_SUFFIX, BUNDLE_PREFIX, BundleTombstoneID,
DSS_VERSIONED_BUNDLE_TOMBSTONE_KEY_REGEX, BundleFQID, UUID_PATTERN,
DSS_UNVERSIONED_BUNDLE_TOMBSTONE_KEY_REGEX, VERSION_PATTERN,)
from dss.storage.blobstore import test_object_exists, idempotent_save
from dss.util import multipart_parallel_upload
_cache_key_template = "{replica}{fqid}"
_bundle_manifest_cache = cachetools.LRUCache(maxsize=4)
def get_bundle_manifest(
uuid: str,
replica: Replica,
version: typing.Optional[str],
*,
bucket: typing.Optional[str] = None) -> typing.Optional[dict]:
cache_key = _cache_key_template.format(replica=replica.name, fqid=BundleFQID(uuid, version).to_key())
if cache_key in _bundle_manifest_cache:
return _bundle_manifest_cache[cache_key]
else:
bundle = _get_bundle_manifest(uuid, replica, version, bucket=bucket)
if bundle is not None:
_bundle_manifest_cache[cache_key] = bundle
return bundle
def _get_bundle_manifest(
uuid: str,
replica: Replica,
version: typing.Optional[str],
*,
bucket: typing.Optional[str] = None) -> typing.Optional[dict]:
"""
Return the contents of the bundle manifest file from cloud storage, subject to the rules of tombstoning. If version
is None, return the latest version, once again, subject to the rules of tombstoning.
If the bundle cannot be found, return None
"""
uuid = uuid.lower()
handle = Config.get_blobstore_handle(replica)
default_bucket = replica.bucket
# need the ability to use fixture bucket for testing
bucket = default_bucket if bucket is None else bucket
def tombstone_exists(uuid: str, version: typing.Optional[str]):
return test_object_exists(handle, bucket, BundleTombstoneID(uuid=uuid, version=version).to_key())
# handle the following deletion cases
# 1. the whole bundle is deleted
# 2. the specific version of the bundle is deleted
if tombstone_exists(uuid, None) or (version and tombstone_exists(uuid, version)):
return None
# handle the following deletion case
# 3. no version is specified, we want the latest _non-deleted_ version
if version is None:
# list the files and find the one that is the most recent.
prefix = f"bundles/{uuid}."
object_names = handle.list(bucket, prefix)
version = _latest_version_from_object_names(object_names)
if version is None:
# no matches!
return None
bundle_fqid = BundleFQID(uuid=uuid, version=version)
# retrieve the bundle metadata.
try:
bundle_manifest_blob = handle.get(bucket, bundle_fqid.to_key()).decode("utf-8")
return json.loads(bundle_manifest_blob)
except BlobNotFoundError:
return None
def save_bundle_manifest(replica: Replica, uuid: str, version: str, bundle: dict) -> typing.Tuple[bool, bool]:
handle = Config.get_blobstore_handle(replica)
data = json.dumps(bundle).encode("utf-8")
fqid = BundleFQID(uuid, version).to_key()
created, idempotent = idempotent_save(handle, replica.bucket, fqid, data)
if created and idempotent:
cache_key = _cache_key_template.format(replica=replica.name, fqid=fqid)
_bundle_manifest_cache[cache_key] = bundle
return created, idempotent
def _latest_version_from_object_names(object_names: typing.Iterator[str]) -> str:
dead_versions = set() # type: typing.Set[str]
all_versions = set() # type: typing.Set[str]
set_checks = [
(DSS_VERSIONED_BUNDLE_TOMBSTONE_KEY_REGEX, dead_versions),
(DSS_BUNDLE_KEY_REGEX, all_versions),
]
for object_name in object_names:
for regex, version_set in set_checks:
match = regex.match(object_name)
if match:
_, version = match.groups()
version_set.add(version)
break
version = None
for current_version in (all_versions - dead_versions):
if version is None or current_version > version:
version = current_version
return version
def enumerate_available_bundles(replica: str = None,
prefix: typing.Optional[str] = None,
per_page: int = PerPageBounds.per_page_max,
search_after: typing.Optional[str] = None,
token: typing.Optional[str] = None):
"""
:returns: dictionary with bundles that are available, provides context of cloud providers internal pagination
mechanism.
:rtype: dictionary
"""
kwargs = dict(bucket=Replica[replica].bucket, prefix=prefix, k_page_max=per_page)
if search_after:
kwargs['start_after_key'] = search_after
if token:
kwargs['token'] = token
storage_handler = Config.get_blobstore_handle(Replica[replica])
prefix_iterator = Living(storage_handler.list_v2(**kwargs)) # note dont wrap this in enumerate; it looses the token
uuid_list = list()
for fqid in prefix_iterator:
uuid_list.append(dict(uuid=fqid.uuid, version=fqid.version))
if len(uuid_list) >= per_page:
break
return dict(search_after=prefix_iterator.start_after_key,
bundles=uuid_list,
token=prefix_iterator.token,
page_count=len(uuid_list))
class Living():
"""
This utility class takes advantage of lexicographical ordering on object storage to list non-tombstoned bundles.
"""
def __init__(self, paged_iter):
self.paged_iter = paged_iter
self._init_bundle_info()
self.start_after_key = None
self.token = None
def _init_bundle_info(self, fqid=None):
self.bundle_info = dict(contains_unversioned_tombstone=False, uuid=None, fqids=OrderedDict())
if fqid:
self.bundle_info['uuid'] = fqid.uuid
self.bundle_info['fqids'][fqid] = False
def _living_fqids_in_bundle_info(self):
if not self.bundle_info['contains_unversioned_tombstone']:
for fqid, is_dead in self.bundle_info['fqids'].items():
if not is_dead:
yield fqid
def _keys(self):
for key, _ in self.paged_iter:
yield key
self.start_after_key = key
self.token = getattr(self.paged_iter, "token", None)
def __iter__(self):
for key in self._keys():
fqid = BundleFQID.from_key(key)
if fqid.uuid != self.bundle_info['uuid']:
for bundle_fqid in self._living_fqids_in_bundle_info():
yield bundle_fqid
self._init_bundle_info(fqid)
else:
if not fqid.is_fully_qualified():
self.bundle_info['contains_unversioned_tombstone'] = True
else:
self.bundle_info['fqids'][fqid] = isinstance(fqid, BundleTombstoneID)
for bundle_fqid in self._living_fqids_in_bundle_info():
yield bundle_fqid
def get_tombstoned_bundles(replica: Replica, tombstone_key: str) -> typing.Iterator[str]:
"""
Return the bundle fqid(s) associated with a versioned or unversioned tombstone, as verified on object storage.
Note that an unversioned tombstone returns keys associated with bundles not previously, as show in the example
below.
bundles/uuid.version1
bundles/uuid.version2
bundles/uuid.version2.dead
bundles/uuid.version3
bundles/uuid.version3.dead
bundles/uuid.dead
For the above listing:
`get_tombstoned_bundles(replica, bundles/uuid.version2.dead)` -> `[bundles/uuid.version2]`
`get_tombstoned_bundles(replica, bundles/uuid.dead)` -> `[bundles/uuid.version1]`
"""
handle = Config.get_blobstore_handle(replica)
if DSS_VERSIONED_BUNDLE_TOMBSTONE_KEY_REGEX.match(tombstone_key):
pfx = tombstone_key.split(f".{TOMBSTONE_SUFFIX}")[0]
prev_key = ""
for key in handle.list(replica.bucket, pfx):
if key == f"{prev_key}.{TOMBSTONE_SUFFIX}":
yield prev_key
prev_key = key
elif DSS_UNVERSIONED_BUNDLE_TOMBSTONE_KEY_REGEX.match(tombstone_key):
pfx = tombstone_key.split(f".{TOMBSTONE_SUFFIX}")[0]
prev_key = ""
for key in handle.list(replica.bucket, pfx):
if key != f"{prev_key}.{TOMBSTONE_SUFFIX}" and not prev_key.endswith(TOMBSTONE_SUFFIX):
if prev_key:
yield prev_key
prev_key = key
else:
raise ValueError(f"{tombstone_key} is not a tombstone key")
| import io
from functools import lru_cache
import json
import typing
import time
import re
from collections import OrderedDict
import cachetools
from cloud_blobstore import BlobNotFoundError, BlobStore
from dss import Config, Replica
from dss.api.search import PerPageBounds
from dss.storage.identifiers import (DSS_BUNDLE_KEY_REGEX, TOMBSTONE_SUFFIX, BUNDLE_PREFIX, BundleTombstoneID,
DSS_VERSIONED_BUNDLE_TOMBSTONE_KEY_REGEX, BundleFQID, UUID_PATTERN,
DSS_UNVERSIONED_BUNDLE_TOMBSTONE_KEY_REGEX, VERSION_PATTERN,)
from dss.storage.blobstore import test_object_exists, idempotent_save
from dss.util import multipart_parallel_upload
_cache_key_template = "{replica}{fqid}"
_bundle_manifest_cache = cachetools.LRUCache(maxsize=4)
def get_bundle_manifest(
uuid: str,
replica: Replica,
version: typing.Optional[str],
*,
bucket: typing.Optional[str] = None) -> typing.Optional[dict]:
cache_key = _cache_key_template.format(replica=replica.name, fqid=BundleFQID(uuid, version).to_key())
if cache_key in _bundle_manifest_cache:
return _bundle_manifest_cache[cache_key]
else:
bundle = _get_bundle_manifest(uuid, replica, version, bucket=bucket)
if bundle is not None:
_bundle_manifest_cache[cache_key] = bundle
return bundle
def _get_bundle_manifest(
uuid: str,
replica: Replica,
version: typing.Optional[str],
*,
bucket: typing.Optional[str] = None) -> typing.Optional[dict]:
"""
Return the contents of the bundle manifest file from cloud storage, subject to the rules of tombstoning. If version
is None, return the latest version, once again, subject to the rules of tombstoning.
If the bundle cannot be found, return None
"""
uuid = uuid.lower()
handle = Config.get_blobstore_handle(replica)
default_bucket = replica.bucket
# need the ability to use fixture bucket for testing
bucket = default_bucket if bucket is None else bucket
def tombstone_exists(uuid: str, version: typing.Optional[str]):
return test_object_exists(handle, bucket, BundleTombstoneID(uuid=uuid, version=version).to_key())
# handle the following deletion cases
# 1. the whole bundle is deleted
# 2. the specific version of the bundle is deleted
if tombstone_exists(uuid, None) or (version and tombstone_exists(uuid, version)):
return None
# handle the following deletion case
# 3. no version is specified, we want the latest _non-deleted_ version
if version is None:
# list the files and find the one that is the most recent.
prefix = f"bundles/{uuid}."
object_names = handle.list(bucket, prefix)
version = _latest_version_from_object_names(object_names)
if version is None:
# no matches!
return None
bundle_fqid = BundleFQID(uuid=uuid, version=version)
# retrieve the bundle metadata.
try:
bundle_manifest_blob = handle.get(bucket, bundle_fqid.to_key()).decode("utf-8")
return json.loads(bundle_manifest_blob)
except BlobNotFoundError:
return None
def save_bundle_manifest(replica: Replica, uuid: str, version: str, bundle: dict) -> typing.Tuple[bool, bool]:
handle = Config.get_blobstore_handle(replica)
data = json.dumps(bundle).encode("utf-8")
fqid = BundleFQID(uuid, version).to_key()
created, idempotent = idempotent_save(handle, replica.bucket, fqid, data)
if created and idempotent:
cache_key = _cache_key_template.format(replica=replica.name, fqid=fqid)
_bundle_manifest_cache[cache_key] = bundle
return created, idempotent
def _latest_version_from_object_names(object_names: typing.Iterator[str]) -> str:
dead_versions = set() # type: typing.Set[str]
all_versions = set() # type: typing.Set[str]
set_checks = [
(DSS_VERSIONED_BUNDLE_TOMBSTONE_KEY_REGEX, dead_versions),
(DSS_BUNDLE_KEY_REGEX, all_versions),
]
for object_name in object_names:
for regex, version_set in set_checks:
match = regex.match(object_name)
if match:
_, version = match.groups()
version_set.add(version)
break
version = None
for current_version in (all_versions - dead_versions):
if version is None or current_version > version:
version = current_version
return version
def enumerate_available_bundles(replica: str = None,
prefix: typing.Optional[str] = None,
per_page: int = PerPageBounds.per_page_max,
search_after: typing.Optional[str] = None,
token: typing.Optional[str] = None):
"""
:returns: dictionary with bundles that are available, provides context of cloud providers internal pagination
mechanism.
:rtype: dictionary
"""
kwargs = dict(bucket=Replica[replica].bucket, prefix=prefix, k_page_max=per_page)
if search_after:
kwargs['start_after_key'] = search_after
if token:
kwargs['token'] = token
storage_handler = Config.get_blobstore_handle(Replica[replica])
prefix_iterator = Living(storage_handler.list_v2(**kwargs)) # note dont wrap this in enumerate; it looses the token
uuid_list = list()
for fqid in prefix_iterator:
uuid_list.append(dict(uuid=fqid.uuid, version=fqid.version))
if len(uuid_list) >= per_page:
break
return dict(search_after=prefix_iterator.start_after_key,
bundles=uuid_list,
token=prefix_iterator.token,
page_count=len(uuid_list))
class Living():
"""
This utility class takes advantage of lexicographical ordering on object storage to list non-tombstoned bundles.
"""
def __init__(self, paged_iter):
self.paged_iter = paged_iter
self._init_bundle_info()
self.start_after_key = None
self.token = None
def _init_bundle_info(self, fqid=None):
self.bundle_info = dict(contains_unversioned_tombstone=False, uuid=None, fqids=OrderedDict())
if fqid:
self.bundle_info['uuid'] = fqid.uuid
self.bundle_info['fqids'][fqid] = False
def _living_fqids_in_bundle_info(self):
if not self.bundle_info['contains_unversioned_tombstone']:
for fqid, is_dead in self.bundle_info['fqids'].items():
if not is_dead:
yield fqid
def _keys(self):
for key, _ in self.paged_iter:
yield key
self.start_after_key = key
self.token = getattr(self.paged_iter, "token", None)
def __iter__(self):
for key in self._keys():
fqid = BundleFQID.from_key(key)
if fqid.uuid != self.bundle_info['uuid']:
for bundle_fqid in self._living_fqids_in_bundle_info():
yield bundle_fqid
self._init_bundle_info(fqid)
else:
if not fqid.is_fully_qualified():
self.bundle_info['contains_unversioned_tombstone'] = True
else:
self.bundle_info['fqids'][fqid] = isinstance(fqid, BundleTombstoneID)
for bundle_fqid in self._living_fqids_in_bundle_info():
yield bundle_fqid
def get_tombstoned_bundles(replica: Replica, tombstone_key: str) -> typing.Iterator[str]:
"""
Return the bundle fqid(s) associated with a versioned or unversioned tombstone, as verified on object storage.
Note that an unversioned tombstone returns keys associated with bundles not previously, as show in the example
below.
bundles/uuid.version1
bundles/uuid.version2
bundles/uuid.version2.dead
bundles/uuid.version3
bundles/uuid.version3.dead
bundles/uuid.dead
For the above listing:
`get_tombstoned_bundles(replica, bundles/uuid.version2.dead)` -> `[bundles/uuid.version2]`
`get_tombstoned_bundles(replica, bundles/uuid.dead)` -> `[bundles/uuid.version1]`
"""
handle = Config.get_blobstore_handle(replica)
if DSS_VERSIONED_BUNDLE_TOMBSTONE_KEY_REGEX.match(tombstone_key):
pfx = tombstone_key.split(f".{TOMBSTONE_SUFFIX}")[0]
prev_key = ""
for key in handle.list(replica.bucket, pfx):
if key == f"{prev_key}.{TOMBSTONE_SUFFIX}":
yield prev_key
prev_key = key
elif DSS_UNVERSIONED_BUNDLE_TOMBSTONE_KEY_REGEX.match(tombstone_key):
pfx = tombstone_key.split(f".{TOMBSTONE_SUFFIX}")[0]
prev_key = ""
for key in handle.list(replica.bucket, pfx):
if key != f"{prev_key}.{TOMBSTONE_SUFFIX}" and not prev_key.endswith(TOMBSTONE_SUFFIX):
if prev_key:
yield prev_key
prev_key = key
else:
raise ValueError(f"{tombstone_key} is not a tombstone key")
| en | 0.753998 | Return the contents of the bundle manifest file from cloud storage, subject to the rules of tombstoning. If version is None, return the latest version, once again, subject to the rules of tombstoning. If the bundle cannot be found, return None # need the ability to use fixture bucket for testing # handle the following deletion cases # 1. the whole bundle is deleted # 2. the specific version of the bundle is deleted # handle the following deletion case # 3. no version is specified, we want the latest _non-deleted_ version # list the files and find the one that is the most recent. # no matches! # retrieve the bundle metadata. # type: typing.Set[str] # type: typing.Set[str] :returns: dictionary with bundles that are available, provides context of cloud providers internal pagination mechanism. :rtype: dictionary # note dont wrap this in enumerate; it looses the token This utility class takes advantage of lexicographical ordering on object storage to list non-tombstoned bundles. Return the bundle fqid(s) associated with a versioned or unversioned tombstone, as verified on object storage. Note that an unversioned tombstone returns keys associated with bundles not previously, as show in the example below. bundles/uuid.version1 bundles/uuid.version2 bundles/uuid.version2.dead bundles/uuid.version3 bundles/uuid.version3.dead bundles/uuid.dead For the above listing: `get_tombstoned_bundles(replica, bundles/uuid.version2.dead)` -> `[bundles/uuid.version2]` `get_tombstoned_bundles(replica, bundles/uuid.dead)` -> `[bundles/uuid.version1]` | 2.076172 | 2 |
tests/test_ezbee.py | ffreemt/ezbee | 0 | 6630452 | <gh_stars>0
"""Test ezbee."""
# pylint: disable=broad-except
from ezbee import __version__
from ezbee import ezbee
from ezbee.loadtext import loadtext
def test_version():
"""Test version."""
assert __version__[:3] == "0.1"
def test_sanity():
"""Sanity check."""
try:
assert not ezbee([""], [""])
except Exception:
assert True
def test_data_en_zh():
"""Test data/test-en/zh.txt.
pytest tests\test_ezbee.py -k en_zh
poetry remove holoviews plotly seaborn
holoviews 1.14.8 plotly 5.6.0 seaborn 0.11.2
du -sh . # 452M
poetry add -E holoviews plotly seaborn
"""
text_en = loadtext("data/test-en.txt")
text_zh = loadtext("data/test-zh.txt")
list1 = [elm.strip() for elm in text_en.splitlines() if elm.strip()]
list2 = [elm.strip() for elm in text_zh.splitlines() if elm.strip()]
# (len(list1), len(list2)) == (33, 36)
res = ezbee(list1, list2)
assert len(res) == 36
assert res[-1] == (35, 32, 0.5)
assert res[15] == (15, 14, 0.31)
assert [elm[0] for elm in res if not isinstance(elm[0], str)] == [*range(36)]
[elm[1] for elm in res if not isinstance(elm[1], str)] == [*range(33)]
| """Test ezbee."""
# pylint: disable=broad-except
from ezbee import __version__
from ezbee import ezbee
from ezbee.loadtext import loadtext
def test_version():
"""Test version."""
assert __version__[:3] == "0.1"
def test_sanity():
"""Sanity check."""
try:
assert not ezbee([""], [""])
except Exception:
assert True
def test_data_en_zh():
"""Test data/test-en/zh.txt.
pytest tests\test_ezbee.py -k en_zh
poetry remove holoviews plotly seaborn
holoviews 1.14.8 plotly 5.6.0 seaborn 0.11.2
du -sh . # 452M
poetry add -E holoviews plotly seaborn
"""
text_en = loadtext("data/test-en.txt")
text_zh = loadtext("data/test-zh.txt")
list1 = [elm.strip() for elm in text_en.splitlines() if elm.strip()]
list2 = [elm.strip() for elm in text_zh.splitlines() if elm.strip()]
# (len(list1), len(list2)) == (33, 36)
res = ezbee(list1, list2)
assert len(res) == 36
assert res[-1] == (35, 32, 0.5)
assert res[15] == (15, 14, 0.31)
assert [elm[0] for elm in res if not isinstance(elm[0], str)] == [*range(36)]
[elm[1] for elm in res if not isinstance(elm[1], str)] == [*range(33)] | en | 0.302364 | Test ezbee. # pylint: disable=broad-except Test version. Sanity check. Test data/test-en/zh.txt. pytest tests\test_ezbee.py -k en_zh poetry remove holoviews plotly seaborn holoviews 1.14.8 plotly 5.6.0 seaborn 0.11.2 du -sh . # 452M poetry add -E holoviews plotly seaborn # (len(list1), len(list2)) == (33, 36) | 2.221175 | 2 |
inputs/trace_to_flow.py | flaviovdf/playlist-analysis | 0 | 6630453 | import pandas as pd
df = pd.read_csv('./transitions-mood-sound-gender-collaps.csv')
df = df[['playlist_id', 'source', 'tags', 'mood_source', 'mood_target',
'sound_source', 'sound_target', 'playlist_name', 'genre_source',
'genre_target']]
for i in range(len(df)):
row = df.ix[i]
try:
if isinstance(row['tags'], str):
tags = row['tags']
else:
tags = 'NT'
if isinstance(row['playlist_name'], str):
name = row['playlist_name']
else:
name = 'NN'
t = str(row['playlist_id']) + '/' + tags + '/' + name + '/' + \
row['source']
print(0, end='\t')
print(t, end='\t')
print(row['mood_source'], end='/')
print(row['sound_source'], end='/')
print(row['genre_source'], end='\t')
print(row['mood_target'], end='/')
print(row['sound_target'], end='/')
print(row['genre_target'])
except:
pass
| import pandas as pd
df = pd.read_csv('./transitions-mood-sound-gender-collaps.csv')
df = df[['playlist_id', 'source', 'tags', 'mood_source', 'mood_target',
'sound_source', 'sound_target', 'playlist_name', 'genre_source',
'genre_target']]
for i in range(len(df)):
row = df.ix[i]
try:
if isinstance(row['tags'], str):
tags = row['tags']
else:
tags = 'NT'
if isinstance(row['playlist_name'], str):
name = row['playlist_name']
else:
name = 'NN'
t = str(row['playlist_id']) + '/' + tags + '/' + name + '/' + \
row['source']
print(0, end='\t')
print(t, end='\t')
print(row['mood_source'], end='/')
print(row['sound_source'], end='/')
print(row['genre_source'], end='\t')
print(row['mood_target'], end='/')
print(row['sound_target'], end='/')
print(row['genre_target'])
except:
pass
| none | 1 | 2.920804 | 3 |
|
station/__init__.py | mrbenjones/coffeetalk-python | 1 | 6630454 | <gh_stars>1-10
from flask import Flask
from config import Config
from flask_sqlalchemy import SQLAlchemy
from flask_migrate import Migrate
from flask_cors import CORS
app = Flask(__name__)
app.config.from_object(Config)
#cors = CORS(app, resources={r"/get_calls/*": {"origins": "*"}})
cors = CORS(app)
db = SQLAlchemy(app)
migrate = Migrate(app,db)
from station import primary,models
| from flask import Flask
from config import Config
from flask_sqlalchemy import SQLAlchemy
from flask_migrate import Migrate
from flask_cors import CORS
app = Flask(__name__)
app.config.from_object(Config)
#cors = CORS(app, resources={r"/get_calls/*": {"origins": "*"}})
cors = CORS(app)
db = SQLAlchemy(app)
migrate = Migrate(app,db)
from station import primary,models | en | 0.241252 | #cors = CORS(app, resources={r"/get_calls/*": {"origins": "*"}}) | 1.970525 | 2 |
HelpDesk/UpdateGEPrefix.py | flopezag/fiware-scrum-reports | 0 | 6630455 | <gh_stars>0
__author__ = '<NAME>'
import re
from jira.client import JIRA
from kernel.Settings import settings
server = settings.server['JIRA']
options = {'server': 'https://{}'.format(server.domain), 'verify': False}
# jira = JIRA(options, basic_auth=(server.username, server.password))
users = ('aalonsog', 'fermin', 'frb', 'aarranz', 'amagan', 'fdelavega', 'meth', 'henar', 'knagin', 'tali',
'ckan-fiware-okfn', 'llopez', 'artusio', 'ralli', 'gessler', 'telsaleh', 'cdangerville', 'olivier.bettan',
'ariokkon', 'cvetan', 'jhyvarinen', 'sami_jylkk', 'jonne.vaisanen', 'loorni', 'tospie', 'glikson',
'jesus.movilla', 'jesus.perezgonzalez')
user = 'sergg'
chapter = 'Data'
keyword = 'CKAN'
query = 'project = HELP and status in (Closed, Done, Dismissed) AND component = FIWARE-TECH-HELP and assignee = {}'\
.format(user)
issues = jira.search_issues(query, maxResults=False)
issues = [issue for issue in issues if not re.match(r'FIWARE\.(Request|Question)\.Tech\.Data', issue.fields.summary)]
for n, issue in enumerate(issues):
print(n, issue, issue.fields.summary)
# exit()
for n, issue in enumerate(issues):
if re.match(r'FIWARE\.(Question|Request)\.Tech\.{}\.{}'.format(chapter, keyword), issue.fields.summary):
# print('->right', n, issue.fields.summary)
continue
if re.match(r'FIWARE\.(Question|Request)\.{}'.format('Tech'), issue.fields.summary):
if re.match(r'FIWARE\.(Question|Request)\.Tech\.FIWARE\.(Question|Request)\.Tech', issue.fields.summary):
summary = re.sub(r'FIWARE\.(Question|Request)\.Tech\.FIWARE\.', 'FIWARE.', issue.fields.summary)
issue.update(fields={'summary': summary})
print('updated ->', issue, issue.fields.summary)
else:
summary = issue.fields.summary.strip().split('.')
prefix = '.'.join(summary[0:3])
issuename = '.'.join(summary[3:]).strip()
summary = prefix + '.{}.{}.'.format(chapter, keyword) + issuename
issue.update(fields={'summary': summary})
print('updated ->', issue, issue.fields.summary)
continue
if re.match(r'FIWARE\.(Question|Request)\.Lab', issue.fields.summary.strip()):
# print(n, issue, issue.fields.summary)
summary = re.sub(r'\.Lab\.', '.Tech.', issue.fields.summary.strip())
issue.update(fields={'summary': summary})
print('updated ->', issue, issue.fields.summary)
continue
# print(n, issue, issue.fields.issuetype, issue.fields.summary)
summary = re.sub(r'\[[^\]]+?\]', '', issue.fields.summary)
if issue.fields.issuetype.name == 'Monitor':
summary = 'FIWARE.Question.Tech.{}'.format(summary.strip())
if issue.fields.issuetype.name == 'extRequest':
summary = 'FIWARE.Request.Tech.{}'.format(summary.strip())
issue.update(fields={'summary': summary})
print('updated->', issue, summary)
| __author__ = '<NAME>'
import re
from jira.client import JIRA
from kernel.Settings import settings
server = settings.server['JIRA']
options = {'server': 'https://{}'.format(server.domain), 'verify': False}
# jira = JIRA(options, basic_auth=(server.username, server.password))
users = ('aalonsog', 'fermin', 'frb', 'aarranz', 'amagan', 'fdelavega', 'meth', 'henar', 'knagin', 'tali',
'ckan-fiware-okfn', 'llopez', 'artusio', 'ralli', 'gessler', 'telsaleh', 'cdangerville', 'olivier.bettan',
'ariokkon', 'cvetan', 'jhyvarinen', 'sami_jylkk', 'jonne.vaisanen', 'loorni', 'tospie', 'glikson',
'jesus.movilla', 'jesus.perezgonzalez')
user = 'sergg'
chapter = 'Data'
keyword = 'CKAN'
query = 'project = HELP and status in (Closed, Done, Dismissed) AND component = FIWARE-TECH-HELP and assignee = {}'\
.format(user)
issues = jira.search_issues(query, maxResults=False)
issues = [issue for issue in issues if not re.match(r'FIWARE\.(Request|Question)\.Tech\.Data', issue.fields.summary)]
for n, issue in enumerate(issues):
print(n, issue, issue.fields.summary)
# exit()
for n, issue in enumerate(issues):
if re.match(r'FIWARE\.(Question|Request)\.Tech\.{}\.{}'.format(chapter, keyword), issue.fields.summary):
# print('->right', n, issue.fields.summary)
continue
if re.match(r'FIWARE\.(Question|Request)\.{}'.format('Tech'), issue.fields.summary):
if re.match(r'FIWARE\.(Question|Request)\.Tech\.FIWARE\.(Question|Request)\.Tech', issue.fields.summary):
summary = re.sub(r'FIWARE\.(Question|Request)\.Tech\.FIWARE\.', 'FIWARE.', issue.fields.summary)
issue.update(fields={'summary': summary})
print('updated ->', issue, issue.fields.summary)
else:
summary = issue.fields.summary.strip().split('.')
prefix = '.'.join(summary[0:3])
issuename = '.'.join(summary[3:]).strip()
summary = prefix + '.{}.{}.'.format(chapter, keyword) + issuename
issue.update(fields={'summary': summary})
print('updated ->', issue, issue.fields.summary)
continue
if re.match(r'FIWARE\.(Question|Request)\.Lab', issue.fields.summary.strip()):
# print(n, issue, issue.fields.summary)
summary = re.sub(r'\.Lab\.', '.Tech.', issue.fields.summary.strip())
issue.update(fields={'summary': summary})
print('updated ->', issue, issue.fields.summary)
continue
# print(n, issue, issue.fields.issuetype, issue.fields.summary)
summary = re.sub(r'\[[^\]]+?\]', '', issue.fields.summary)
if issue.fields.issuetype.name == 'Monitor':
summary = 'FIWARE.Question.Tech.{}'.format(summary.strip())
if issue.fields.issuetype.name == 'extRequest':
summary = 'FIWARE.Request.Tech.{}'.format(summary.strip())
issue.update(fields={'summary': summary})
print('updated->', issue, summary) | en | 0.604633 | # jira = JIRA(options, basic_auth=(server.username, server.password)) # exit() # print('->right', n, issue.fields.summary) # print(n, issue, issue.fields.summary) # print(n, issue, issue.fields.issuetype, issue.fields.summary) | 2.271983 | 2 |
Errors/RequestFailedException.py | pjpmosteiro/DiscordGPT-3 | 49 | 6630456 | <filename>Errors/RequestFailedException.py
from string import Template
from Errors import *
from Errors.OpenAIError import OpenAIError
from Errors.TokenExhaustedError import TokenExhaustedError
from Errors.TokenInvalidError import TokenInvalidError
class RequestFailedException(Error):
def __init__(self, status_code: int):
self.status_code = status_code
self.try_handle()
def __str__(self):
return repr(self.status_code)
def try_handle(self):
if self.status_code == 429:
raise TokenExhaustedError
elif self.status_code == 400:
raise TokenInvalidError
elif self.status_code == 500:
raise OpenAIError
raise NotImplementedError(
Template("$input status code has no exception handler.").substitute(input=self.status_code))
| <filename>Errors/RequestFailedException.py
from string import Template
from Errors import *
from Errors.OpenAIError import OpenAIError
from Errors.TokenExhaustedError import TokenExhaustedError
from Errors.TokenInvalidError import TokenInvalidError
class RequestFailedException(Error):
def __init__(self, status_code: int):
self.status_code = status_code
self.try_handle()
def __str__(self):
return repr(self.status_code)
def try_handle(self):
if self.status_code == 429:
raise TokenExhaustedError
elif self.status_code == 400:
raise TokenInvalidError
elif self.status_code == 500:
raise OpenAIError
raise NotImplementedError(
Template("$input status code has no exception handler.").substitute(input=self.status_code))
| none | 1 | 2.786871 | 3 |
|
qubekit/torsions/fitting/forcebalance_wrapper.py | qubekit/QUBEK | 45 | 6630457 | """
Classes that help with parameter fitting using ForceBalance.
"""
import abc
import copy
import os
import subprocess
from typing import Any, Dict, List, Tuple
from pydantic import BaseModel, Field, PositiveFloat, PositiveInt
from qcelemental.util import which_import
from typing_extensions import Literal
from qubekit.molecules import Ligand, TorsionDriveData
from qubekit.torsions.utils import forcebalance_setup
from qubekit.utils.datastructures import StageBase
from qubekit.utils.exceptions import ForceBalanceError, MissingReferenceData
from qubekit.utils.file_handling import get_data, make_and_change_into
from qubekit.utils.helpers import export_torsiondrive_data
class Priors(BaseModel):
"""
A class which controls the forcebalance force field prior values.
"""
Proper_k: float = Field(
6.0, description="The initial prior for the proper torsion k values."
)
def format_priors(self) -> Dict[str, Any]:
"""
Returns:
A formatted dict version of the prior that can be consumed by forcebalance.
"""
data = {}
for prior, value in self.__dict__.items():
prior = prior.split("_")
prior = "/".join(prior)
data[prior] = value
return data
class TargetBase(BaseModel, abc.ABC):
"""
A base class which each forcebalnce target should overwrite.
This should control the run time settings used during fitting and implement a file setup method which is called before fitting.
"""
target_name: str
description: str
writelevel: PositiveInt = 2
keywords: Dict[str, Any] = {}
openmm_platform: Literal["Reference"] = "Reference"
class Config:
validate_assignment = True
fields = {
"target_name": {
"description": "The name of the forcebalance target to be fit"
},
"entries": {"description": "a list of target entries to be optimised."},
"writelevel": {
"description": "The write level controls the types of intermedate information which is saved from an optimisation."
},
"keywords": {
"description": "Any keyword information which should be passed to forcebalance as a run time setting."
},
"openmm_platform": {
"description": "The openmm platform that should be used in the fitting."
},
}
@abc.abstractmethod
def prep_for_fitting(self, molecule: Ligand) -> None:
"""
The target will need to convert the input reference data to some format ready for fitting, this method should be implimented to take
each molecule and assume it has the correct reference data.
"""
...
def fb_options(self) -> Dict[str, Any]:
"""
Format the target class run time options into a dict that can be consumed by forcebalance.
"""
data = self.dict(exclude={"target_name", "description", "keywords"})
data.update(self.keywords)
return data
class TorsionProfile(TargetBase):
"""
This helps set up the files required to perform the torsion profile fitting target in forcebalance.
For each ligand passed the input files are prepared for each target torsion, the optimize in file is also updated with the target info.
"""
target_name: Literal["TorsionProfile_OpenMM"] = "TorsionProfile_OpenMM"
description = "Relaxed energy and RMSD fitting for torsion drives only."
energy_denom: PositiveFloat = Field(
1.0,
description="The energy denominator used by forcebalance to weight the energies contribution to the objective function.",
)
energy_upper: PositiveFloat = Field(
10.0,
description="The upper limit for energy differences in kcal/mol which are included in fitting. Relative energies above this value do not contribute to the objective.",
)
attenuate: bool = Field(
False,
description="If the weights should be attenuated as a function of the energy above the minimum.",
)
restrain_k: float = Field(
1.0,
description="The strength of the harmonic restraint in kcal/mol used in the mm relaxation on all non-torsion atoms.",
)
keywords: Dict[str, Any] = {"pdb": "molecule.pdb", "coords": "scan.xyz"}
def prep_for_fitting(self, molecule: Ligand) -> List[str]:
"""
For the given ligand prep the input files ready for torsion profile fitting.
Args:
molecule: The molecule object that we need to prep for fitting, this should have qm reference data stored in molecule.qm_scans.
Note:
We assume we are already in the targets folder.
Returns:
A list of target folder names made by this target.
Raises:
MissingReferenceData: If the molecule does not have any torsion drive reference data saved in molecule.qm_scans.
"""
# make sure we have data
if not molecule.qm_scans:
raise MissingReferenceData(
f"Can not prepare a forcebalance fitting target for {molecule.name} as the reference data is missing!"
)
# write out the qdata and other input files for each scan
target_folders = []
# keep track of where we start
base_folder = os.getcwd()
# loop over each scanned bond and make a target folder
for scan in molecule.qm_scans:
task_name = (
f"{self.target_name}_{scan.central_bond[0]}_{scan.central_bond[1]}"
)
target_folders.append(task_name)
make_and_change_into(name=task_name)
# make the pdb topology file
if molecule.has_ub_terms():
molecule._to_ub_pdb(file_name="molecule")
else:
molecule.to_file(file_name="molecule.pdb")
# write the qdata file
export_torsiondrive_data(molecule=molecule, tdrive_data=scan)
# make the metadata
self.make_metadata(torsiondrive_data=scan)
# now move back to the base
os.chdir(base_folder)
return target_folders
@staticmethod
def make_metadata(torsiondrive_data: TorsionDriveData) -> None:
"""
Create the metadata.json required to run a torsion profile target, this details the constrained optimisations to be done.
Args:
torsiondrive_data: Create the torsion metadata file which describes the angle to be scanned and the dihedral grid points.
"""
import json
json_data = {
"dihedrals": [
torsiondrive_data.dihedral,
],
"grid_spacing": [
torsiondrive_data.grid_spacing,
],
"dihedral_ranges": [
torsiondrive_data.torsion_drive_range,
],
"torsion_grid_ids": [
[
data.angle,
]
for data in torsiondrive_data.reference_data.values()
],
}
# now dump to file
with open("metadata.json", "w") as meta:
meta.write(json.dumps(json_data, indent=2))
class ForceBalanceFitting(StageBase):
"""
This class interfaces with forcebalance <https://github.com/leeping/forcebalance> and allows users to fit multiple force field parameters
to multiple different targets such as optimised geometries vibration frequencies and relaxed torsion profiles.
Note:
We only support relaxed torsion profile fitting so far.
All targets are fitted using OpenMM.
"""
class Config:
validate_assignment = True
arbitrary_types_allowed = True
type: Literal["ForceBalanceFitting"] = "ForceBalanceFitting"
penalty_type: Literal["L1", "L2"] = "L1"
job_type: str = "optimize"
max_iterations: PositiveInt = 10
convergence_step_criteria: PositiveFloat = 0.01
convergence_objective_criteria: PositiveFloat = 0.01
convergence_gradient_criteria: PositiveFloat = 0.01
n_criteria: PositiveInt = 1
eig_lowerbound: PositiveFloat = 0.01
finite_difference_h: PositiveFloat = 0.01
penalty_additive: PositiveFloat = 0.1
constrain_charge: bool = False
initial_trust_radius: float = -0.25
minimum_trust_radius: float = 0.05
error_tolerance: PositiveFloat = 1.0
adaptive_factor: PositiveFloat = 0.2
adaptive_damping: PositiveFloat = 1.0
normalize_weights: bool = False
extras: Dict[str, Any] = {}
priors: Priors = Priors()
targets: Dict[str, TorsionProfile] = {"TorsionProfile_OpenMM": TorsionProfile()}
def start_message(self, **kwargs) -> str:
return "Performing torsion optimisations using ForceBalance."
def finish_message(self, **kwargs) -> str:
return "Torsion optimisation complete."
@classmethod
def is_available(cls) -> bool:
"""Make sure forcebalance can be imported."""
fb = which_import(
"forcebalance",
return_bool=True,
raise_error=True,
raise_msg="Please install via `conda install forcebalance -c conda-forge`.",
)
openmm = which_import(
".openmm",
return_bool=True,
raise_error=True,
package="simtk",
raise_msg="Please install via `conda install openmm -c conda-forge`.",
)
return fb and openmm
def run(self, molecule: "Ligand", **kwargs) -> "Ligand":
"""
The main run method of the ForceBalance torsion optimisation stage.
Args:
molecule: The molecule that should be optimised with its QM reference data.
Important:
We work on a copy of the molecule as we have to change some parameters.
"""
# check we have targets to fit
if not self.targets:
raise ForceBalanceError(
"No fitting targets have been set for forcebalance, please set at least one target."
)
# now we have validated the data run the optimiser
return self._optimise(molecule=molecule)
def add_target(self, target: TargetBase) -> None:
"""
Try and add the given target class to the forcebalance optimiser to be executed when optimise is called.
Args:
target: The ForceBalance optimisation target that should be added.
"""
if issubclass(type(target), TargetBase):
self.targets[target.target_name] = target
def _optimise(self, molecule: Ligand) -> Ligand:
"""
For the given input molecule run the forcebalance fitting for the list of targets and run time settings.
Note:
The list of optimisation targets should be set before running.
"""
# set up the master fitting folder
with forcebalance_setup(folder_name="ForceBalance"):
fitting_folder = os.getcwd()
fitting_targets = {}
# prep the target folders
os.chdir("targets")
for target in self.targets.values():
target_folders = target.prep_for_fitting(molecule=molecule)
fitting_targets[target.target_name] = target_folders
# back to fitting folder
os.chdir(fitting_folder)
# now we can make the optimize in file
self.generate_optimise_in(target_data=fitting_targets)
# now make the forcefield file
self.generate_forcefield(molecule=molecule)
# now execute forcebalance
with open("log.txt", "w") as log:
subprocess.run(
"ForceBalance optimize.in", shell=True, stdout=log, stderr=log
)
result_ligand = self.collect_results(molecule=molecule)
return result_ligand
def generate_forcefield(self, molecule: Ligand) -> None:
"""
For the given molecule generate the fitting forcefield with the target torsion terms tagged with the parameterize keyword.
Args:
molecule: The molecule whose torsion parameters should be optimised.
Note:
We currently hard code to only fit dihedrals that pass through the targeted rotatable bond.
Important:
We work with a copy of the molecule here as we add attributes to the forcefield and change default values.
"""
copy_mol = copy.deepcopy(molecule)
# now we need to find all of the dihedrals for a central bond which should be optimised
for torsiondrive_data in copy_mol.qm_scans:
central_bond = torsiondrive_data.central_bond
# now we can get all dihedrals for this bond
try:
dihedrals = copy_mol.dihedrals[central_bond]
except KeyError:
dihedrals = copy_mol.dihedrals[tuple(reversed(central_bond))]
dihedral_groups = self.group_by_symmetry(
molecule=copy_mol, dihedrals=dihedrals
)
for dihedral_group in dihedral_groups:
# add parametrise flags for forcebalance to one dihedral in the group
# the rest get parameter eval tags referenced to this master dihedral
master_dihedral = dihedral_group[0]
master_parameter = copy_mol.TorsionForce[master_dihedral]
# use the parameter atom order to be consistent
dihedral_string = ".".join(str(i) for i in master_parameter.atoms)
eval_tags = [
f"k{i}=PARM['Proper/k{i}/{dihedral_string}']" for i in range(1, 5)
]
# we need to make sure all parameters are bigger than 0 to be loaded into an OpenMM system
parameter_data = {"attributes": {"k1", "k2", "k3", "k4"}}
for k in ["k1", "k2", "k3", "k4"]:
if getattr(master_parameter, k) == 0:
parameter_data[k] = 1e-6
master_parameter.update(**parameter_data)
# now add parameter evals
for dihedral in dihedral_group[1:]:
parameter = copy_mol.TorsionForce[dihedral]
parameter.parameter_eval = eval_tags
# now we have all of the dihedrals build the force field
copy_mol.write_parameters(file_name=os.path.join("forcefield", "bespoke.xml"))
def group_by_symmetry(
self, molecule: Ligand, dihedrals: List[Tuple[int, int, int, int]]
) -> List[List[Tuple[int, int, int, int]]]:
"""
For a list of target dihedrals to be optimised group them by symmetry type.
Note:
Dihedrals not in the target bond but in the same symmetry group are also included.
"""
dihedral_types = molecule.dihedral_types
atom_types = molecule.atom_types
dihedral_groups = {}
for dihedral in dihedrals:
# get the dihedral type
dihedral_type = "-".join(atom_types[i] for i in dihedral)
# now get all dihedrals of this type
if (
dihedral_type not in dihedral_groups
and dihedral_type[::-1] not in dihedral_groups
):
try:
dihedral_groups[dihedral_type] = dihedral_types[dihedral_type]
except KeyError:
dihedral_groups[dihedral_type[::-1]] = dihedral_types[
dihedral_type[::-1]
]
return list(dihedral_groups.values())
def generate_optimise_in(self, target_data: Dict[str, List[str]]) -> None:
"""
For the given list of targets and entries produce an optimize.in file which contains all of the run time settings to be used in the optimization.
this uses jinja templates to generate the required file from the template distributed with qubekit.
Args:
target_data: A dictionary mapping the target name to the target folders that have been created.
"""
from jinja2 import Template
template_file = get_data(os.path.join("templates", "optimize.txt"))
with open(template_file) as file:
template = Template(file.read())
data = self.dict(exclude={"targets", "priors"})
data["priors"] = self.priors.format_priors()
data["fitting_targets"] = target_data
target_options = {}
for target in self.targets.values():
target_options[target.target_name] = target.fb_options()
data["target_options"] = target_options
rendered_template = template.render(**data)
with open("optimize.in", "w") as opt_in:
opt_in.write(rendered_template)
@staticmethod
def check_converged() -> bool:
"""
Read the output from a forcebalance run to determine the exit status of the optimisation.
Returns:
`True` if the optimisation has converged else `False`
"""
converged = False
with open("optimize.out") as log:
for line in log.readlines():
if "optimization converged" in line.lower():
converged = True
break
elif "convergence failure" in line.lower():
converged = False
break
return converged
def collect_results(self, molecule: Ligand) -> Ligand:
"""
Collect the results of an optimisation by checking the exit status and then transferring the optimised parameters from the final
xml forcefield back into the ligand.
Args:
molecule: The molecule that was optimised and where the parameters should be stored.
Returns:
The input molecule with the optimised parameters saved.
Raises:
ForceBalanceError: If the optimisation did not converge or exit properly.
"""
# first check the exit status
status = self.check_converged()
if not status:
raise ForceBalanceError(
f"The optimisation for molecule {molecule.name} did not converge so the parameters could not be updated."
)
else:
from qubekit.parametrisation import XML
# load the new parameters into the ligand
# xml needs a pdb file
xml = XML()
xml.run(
molecule=molecule,
input_files=[os.path.join("result", self.job_type, "bespoke.xml")],
)
return molecule
| """
Classes that help with parameter fitting using ForceBalance.
"""
import abc
import copy
import os
import subprocess
from typing import Any, Dict, List, Tuple
from pydantic import BaseModel, Field, PositiveFloat, PositiveInt
from qcelemental.util import which_import
from typing_extensions import Literal
from qubekit.molecules import Ligand, TorsionDriveData
from qubekit.torsions.utils import forcebalance_setup
from qubekit.utils.datastructures import StageBase
from qubekit.utils.exceptions import ForceBalanceError, MissingReferenceData
from qubekit.utils.file_handling import get_data, make_and_change_into
from qubekit.utils.helpers import export_torsiondrive_data
class Priors(BaseModel):
"""
A class which controls the forcebalance force field prior values.
"""
Proper_k: float = Field(
6.0, description="The initial prior for the proper torsion k values."
)
def format_priors(self) -> Dict[str, Any]:
"""
Returns:
A formatted dict version of the prior that can be consumed by forcebalance.
"""
data = {}
for prior, value in self.__dict__.items():
prior = prior.split("_")
prior = "/".join(prior)
data[prior] = value
return data
class TargetBase(BaseModel, abc.ABC):
"""
A base class which each forcebalnce target should overwrite.
This should control the run time settings used during fitting and implement a file setup method which is called before fitting.
"""
target_name: str
description: str
writelevel: PositiveInt = 2
keywords: Dict[str, Any] = {}
openmm_platform: Literal["Reference"] = "Reference"
class Config:
validate_assignment = True
fields = {
"target_name": {
"description": "The name of the forcebalance target to be fit"
},
"entries": {"description": "a list of target entries to be optimised."},
"writelevel": {
"description": "The write level controls the types of intermedate information which is saved from an optimisation."
},
"keywords": {
"description": "Any keyword information which should be passed to forcebalance as a run time setting."
},
"openmm_platform": {
"description": "The openmm platform that should be used in the fitting."
},
}
@abc.abstractmethod
def prep_for_fitting(self, molecule: Ligand) -> None:
"""
The target will need to convert the input reference data to some format ready for fitting, this method should be implimented to take
each molecule and assume it has the correct reference data.
"""
...
def fb_options(self) -> Dict[str, Any]:
"""
Format the target class run time options into a dict that can be consumed by forcebalance.
"""
data = self.dict(exclude={"target_name", "description", "keywords"})
data.update(self.keywords)
return data
class TorsionProfile(TargetBase):
"""
This helps set up the files required to perform the torsion profile fitting target in forcebalance.
For each ligand passed the input files are prepared for each target torsion, the optimize in file is also updated with the target info.
"""
target_name: Literal["TorsionProfile_OpenMM"] = "TorsionProfile_OpenMM"
description = "Relaxed energy and RMSD fitting for torsion drives only."
energy_denom: PositiveFloat = Field(
1.0,
description="The energy denominator used by forcebalance to weight the energies contribution to the objective function.",
)
energy_upper: PositiveFloat = Field(
10.0,
description="The upper limit for energy differences in kcal/mol which are included in fitting. Relative energies above this value do not contribute to the objective.",
)
attenuate: bool = Field(
False,
description="If the weights should be attenuated as a function of the energy above the minimum.",
)
restrain_k: float = Field(
1.0,
description="The strength of the harmonic restraint in kcal/mol used in the mm relaxation on all non-torsion atoms.",
)
keywords: Dict[str, Any] = {"pdb": "molecule.pdb", "coords": "scan.xyz"}
def prep_for_fitting(self, molecule: Ligand) -> List[str]:
"""
For the given ligand prep the input files ready for torsion profile fitting.
Args:
molecule: The molecule object that we need to prep for fitting, this should have qm reference data stored in molecule.qm_scans.
Note:
We assume we are already in the targets folder.
Returns:
A list of target folder names made by this target.
Raises:
MissingReferenceData: If the molecule does not have any torsion drive reference data saved in molecule.qm_scans.
"""
# make sure we have data
if not molecule.qm_scans:
raise MissingReferenceData(
f"Can not prepare a forcebalance fitting target for {molecule.name} as the reference data is missing!"
)
# write out the qdata and other input files for each scan
target_folders = []
# keep track of where we start
base_folder = os.getcwd()
# loop over each scanned bond and make a target folder
for scan in molecule.qm_scans:
task_name = (
f"{self.target_name}_{scan.central_bond[0]}_{scan.central_bond[1]}"
)
target_folders.append(task_name)
make_and_change_into(name=task_name)
# make the pdb topology file
if molecule.has_ub_terms():
molecule._to_ub_pdb(file_name="molecule")
else:
molecule.to_file(file_name="molecule.pdb")
# write the qdata file
export_torsiondrive_data(molecule=molecule, tdrive_data=scan)
# make the metadata
self.make_metadata(torsiondrive_data=scan)
# now move back to the base
os.chdir(base_folder)
return target_folders
@staticmethod
def make_metadata(torsiondrive_data: TorsionDriveData) -> None:
"""
Create the metadata.json required to run a torsion profile target, this details the constrained optimisations to be done.
Args:
torsiondrive_data: Create the torsion metadata file which describes the angle to be scanned and the dihedral grid points.
"""
import json
json_data = {
"dihedrals": [
torsiondrive_data.dihedral,
],
"grid_spacing": [
torsiondrive_data.grid_spacing,
],
"dihedral_ranges": [
torsiondrive_data.torsion_drive_range,
],
"torsion_grid_ids": [
[
data.angle,
]
for data in torsiondrive_data.reference_data.values()
],
}
# now dump to file
with open("metadata.json", "w") as meta:
meta.write(json.dumps(json_data, indent=2))
class ForceBalanceFitting(StageBase):
"""
This class interfaces with forcebalance <https://github.com/leeping/forcebalance> and allows users to fit multiple force field parameters
to multiple different targets such as optimised geometries vibration frequencies and relaxed torsion profiles.
Note:
We only support relaxed torsion profile fitting so far.
All targets are fitted using OpenMM.
"""
class Config:
validate_assignment = True
arbitrary_types_allowed = True
type: Literal["ForceBalanceFitting"] = "ForceBalanceFitting"
penalty_type: Literal["L1", "L2"] = "L1"
job_type: str = "optimize"
max_iterations: PositiveInt = 10
convergence_step_criteria: PositiveFloat = 0.01
convergence_objective_criteria: PositiveFloat = 0.01
convergence_gradient_criteria: PositiveFloat = 0.01
n_criteria: PositiveInt = 1
eig_lowerbound: PositiveFloat = 0.01
finite_difference_h: PositiveFloat = 0.01
penalty_additive: PositiveFloat = 0.1
constrain_charge: bool = False
initial_trust_radius: float = -0.25
minimum_trust_radius: float = 0.05
error_tolerance: PositiveFloat = 1.0
adaptive_factor: PositiveFloat = 0.2
adaptive_damping: PositiveFloat = 1.0
normalize_weights: bool = False
extras: Dict[str, Any] = {}
priors: Priors = Priors()
targets: Dict[str, TorsionProfile] = {"TorsionProfile_OpenMM": TorsionProfile()}
def start_message(self, **kwargs) -> str:
return "Performing torsion optimisations using ForceBalance."
def finish_message(self, **kwargs) -> str:
return "Torsion optimisation complete."
@classmethod
def is_available(cls) -> bool:
"""Make sure forcebalance can be imported."""
fb = which_import(
"forcebalance",
return_bool=True,
raise_error=True,
raise_msg="Please install via `conda install forcebalance -c conda-forge`.",
)
openmm = which_import(
".openmm",
return_bool=True,
raise_error=True,
package="simtk",
raise_msg="Please install via `conda install openmm -c conda-forge`.",
)
return fb and openmm
def run(self, molecule: "Ligand", **kwargs) -> "Ligand":
"""
The main run method of the ForceBalance torsion optimisation stage.
Args:
molecule: The molecule that should be optimised with its QM reference data.
Important:
We work on a copy of the molecule as we have to change some parameters.
"""
# check we have targets to fit
if not self.targets:
raise ForceBalanceError(
"No fitting targets have been set for forcebalance, please set at least one target."
)
# now we have validated the data run the optimiser
return self._optimise(molecule=molecule)
def add_target(self, target: TargetBase) -> None:
"""
Try and add the given target class to the forcebalance optimiser to be executed when optimise is called.
Args:
target: The ForceBalance optimisation target that should be added.
"""
if issubclass(type(target), TargetBase):
self.targets[target.target_name] = target
def _optimise(self, molecule: Ligand) -> Ligand:
"""
For the given input molecule run the forcebalance fitting for the list of targets and run time settings.
Note:
The list of optimisation targets should be set before running.
"""
# set up the master fitting folder
with forcebalance_setup(folder_name="ForceBalance"):
fitting_folder = os.getcwd()
fitting_targets = {}
# prep the target folders
os.chdir("targets")
for target in self.targets.values():
target_folders = target.prep_for_fitting(molecule=molecule)
fitting_targets[target.target_name] = target_folders
# back to fitting folder
os.chdir(fitting_folder)
# now we can make the optimize in file
self.generate_optimise_in(target_data=fitting_targets)
# now make the forcefield file
self.generate_forcefield(molecule=molecule)
# now execute forcebalance
with open("log.txt", "w") as log:
subprocess.run(
"ForceBalance optimize.in", shell=True, stdout=log, stderr=log
)
result_ligand = self.collect_results(molecule=molecule)
return result_ligand
def generate_forcefield(self, molecule: Ligand) -> None:
"""
For the given molecule generate the fitting forcefield with the target torsion terms tagged with the parameterize keyword.
Args:
molecule: The molecule whose torsion parameters should be optimised.
Note:
We currently hard code to only fit dihedrals that pass through the targeted rotatable bond.
Important:
We work with a copy of the molecule here as we add attributes to the forcefield and change default values.
"""
copy_mol = copy.deepcopy(molecule)
# now we need to find all of the dihedrals for a central bond which should be optimised
for torsiondrive_data in copy_mol.qm_scans:
central_bond = torsiondrive_data.central_bond
# now we can get all dihedrals for this bond
try:
dihedrals = copy_mol.dihedrals[central_bond]
except KeyError:
dihedrals = copy_mol.dihedrals[tuple(reversed(central_bond))]
dihedral_groups = self.group_by_symmetry(
molecule=copy_mol, dihedrals=dihedrals
)
for dihedral_group in dihedral_groups:
# add parametrise flags for forcebalance to one dihedral in the group
# the rest get parameter eval tags referenced to this master dihedral
master_dihedral = dihedral_group[0]
master_parameter = copy_mol.TorsionForce[master_dihedral]
# use the parameter atom order to be consistent
dihedral_string = ".".join(str(i) for i in master_parameter.atoms)
eval_tags = [
f"k{i}=PARM['Proper/k{i}/{dihedral_string}']" for i in range(1, 5)
]
# we need to make sure all parameters are bigger than 0 to be loaded into an OpenMM system
parameter_data = {"attributes": {"k1", "k2", "k3", "k4"}}
for k in ["k1", "k2", "k3", "k4"]:
if getattr(master_parameter, k) == 0:
parameter_data[k] = 1e-6
master_parameter.update(**parameter_data)
# now add parameter evals
for dihedral in dihedral_group[1:]:
parameter = copy_mol.TorsionForce[dihedral]
parameter.parameter_eval = eval_tags
# now we have all of the dihedrals build the force field
copy_mol.write_parameters(file_name=os.path.join("forcefield", "bespoke.xml"))
def group_by_symmetry(
self, molecule: Ligand, dihedrals: List[Tuple[int, int, int, int]]
) -> List[List[Tuple[int, int, int, int]]]:
"""
For a list of target dihedrals to be optimised group them by symmetry type.
Note:
Dihedrals not in the target bond but in the same symmetry group are also included.
"""
dihedral_types = molecule.dihedral_types
atom_types = molecule.atom_types
dihedral_groups = {}
for dihedral in dihedrals:
# get the dihedral type
dihedral_type = "-".join(atom_types[i] for i in dihedral)
# now get all dihedrals of this type
if (
dihedral_type not in dihedral_groups
and dihedral_type[::-1] not in dihedral_groups
):
try:
dihedral_groups[dihedral_type] = dihedral_types[dihedral_type]
except KeyError:
dihedral_groups[dihedral_type[::-1]] = dihedral_types[
dihedral_type[::-1]
]
return list(dihedral_groups.values())
def generate_optimise_in(self, target_data: Dict[str, List[str]]) -> None:
"""
For the given list of targets and entries produce an optimize.in file which contains all of the run time settings to be used in the optimization.
this uses jinja templates to generate the required file from the template distributed with qubekit.
Args:
target_data: A dictionary mapping the target name to the target folders that have been created.
"""
from jinja2 import Template
template_file = get_data(os.path.join("templates", "optimize.txt"))
with open(template_file) as file:
template = Template(file.read())
data = self.dict(exclude={"targets", "priors"})
data["priors"] = self.priors.format_priors()
data["fitting_targets"] = target_data
target_options = {}
for target in self.targets.values():
target_options[target.target_name] = target.fb_options()
data["target_options"] = target_options
rendered_template = template.render(**data)
with open("optimize.in", "w") as opt_in:
opt_in.write(rendered_template)
@staticmethod
def check_converged() -> bool:
"""
Read the output from a forcebalance run to determine the exit status of the optimisation.
Returns:
`True` if the optimisation has converged else `False`
"""
converged = False
with open("optimize.out") as log:
for line in log.readlines():
if "optimization converged" in line.lower():
converged = True
break
elif "convergence failure" in line.lower():
converged = False
break
return converged
def collect_results(self, molecule: Ligand) -> Ligand:
"""
Collect the results of an optimisation by checking the exit status and then transferring the optimised parameters from the final
xml forcefield back into the ligand.
Args:
molecule: The molecule that was optimised and where the parameters should be stored.
Returns:
The input molecule with the optimised parameters saved.
Raises:
ForceBalanceError: If the optimisation did not converge or exit properly.
"""
# first check the exit status
status = self.check_converged()
if not status:
raise ForceBalanceError(
f"The optimisation for molecule {molecule.name} did not converge so the parameters could not be updated."
)
else:
from qubekit.parametrisation import XML
# load the new parameters into the ligand
# xml needs a pdb file
xml = XML()
xml.run(
molecule=molecule,
input_files=[os.path.join("result", self.job_type, "bespoke.xml")],
)
return molecule
| en | 0.817635 | Classes that help with parameter fitting using ForceBalance. A class which controls the forcebalance force field prior values. Returns: A formatted dict version of the prior that can be consumed by forcebalance. A base class which each forcebalnce target should overwrite. This should control the run time settings used during fitting and implement a file setup method which is called before fitting. The target will need to convert the input reference data to some format ready for fitting, this method should be implimented to take each molecule and assume it has the correct reference data. Format the target class run time options into a dict that can be consumed by forcebalance. This helps set up the files required to perform the torsion profile fitting target in forcebalance. For each ligand passed the input files are prepared for each target torsion, the optimize in file is also updated with the target info. For the given ligand prep the input files ready for torsion profile fitting. Args: molecule: The molecule object that we need to prep for fitting, this should have qm reference data stored in molecule.qm_scans. Note: We assume we are already in the targets folder. Returns: A list of target folder names made by this target. Raises: MissingReferenceData: If the molecule does not have any torsion drive reference data saved in molecule.qm_scans. # make sure we have data # write out the qdata and other input files for each scan # keep track of where we start # loop over each scanned bond and make a target folder # make the pdb topology file # write the qdata file # make the metadata # now move back to the base Create the metadata.json required to run a torsion profile target, this details the constrained optimisations to be done. Args: torsiondrive_data: Create the torsion metadata file which describes the angle to be scanned and the dihedral grid points. # now dump to file This class interfaces with forcebalance <https://github.com/leeping/forcebalance> and allows users to fit multiple force field parameters to multiple different targets such as optimised geometries vibration frequencies and relaxed torsion profiles. Note: We only support relaxed torsion profile fitting so far. All targets are fitted using OpenMM. Make sure forcebalance can be imported. The main run method of the ForceBalance torsion optimisation stage. Args: molecule: The molecule that should be optimised with its QM reference data. Important: We work on a copy of the molecule as we have to change some parameters. # check we have targets to fit # now we have validated the data run the optimiser Try and add the given target class to the forcebalance optimiser to be executed when optimise is called. Args: target: The ForceBalance optimisation target that should be added. For the given input molecule run the forcebalance fitting for the list of targets and run time settings. Note: The list of optimisation targets should be set before running. # set up the master fitting folder # prep the target folders # back to fitting folder # now we can make the optimize in file # now make the forcefield file # now execute forcebalance For the given molecule generate the fitting forcefield with the target torsion terms tagged with the parameterize keyword. Args: molecule: The molecule whose torsion parameters should be optimised. Note: We currently hard code to only fit dihedrals that pass through the targeted rotatable bond. Important: We work with a copy of the molecule here as we add attributes to the forcefield and change default values. # now we need to find all of the dihedrals for a central bond which should be optimised # now we can get all dihedrals for this bond # add parametrise flags for forcebalance to one dihedral in the group # the rest get parameter eval tags referenced to this master dihedral # use the parameter atom order to be consistent # we need to make sure all parameters are bigger than 0 to be loaded into an OpenMM system # now add parameter evals # now we have all of the dihedrals build the force field For a list of target dihedrals to be optimised group them by symmetry type. Note: Dihedrals not in the target bond but in the same symmetry group are also included. # get the dihedral type # now get all dihedrals of this type For the given list of targets and entries produce an optimize.in file which contains all of the run time settings to be used in the optimization. this uses jinja templates to generate the required file from the template distributed with qubekit. Args: target_data: A dictionary mapping the target name to the target folders that have been created. Read the output from a forcebalance run to determine the exit status of the optimisation. Returns: `True` if the optimisation has converged else `False` Collect the results of an optimisation by checking the exit status and then transferring the optimised parameters from the final xml forcefield back into the ligand. Args: molecule: The molecule that was optimised and where the parameters should be stored. Returns: The input molecule with the optimised parameters saved. Raises: ForceBalanceError: If the optimisation did not converge or exit properly. # first check the exit status # load the new parameters into the ligand # xml needs a pdb file | 2.788165 | 3 |
modules/remoteprocessing/remotescript/pydas/__init__.py | jcfr/Midas | 20 | 6630458 | <reponame>jcfr/Midas
import json
import urllib
from communicator import Communicator
| import json
import urllib
from communicator import Communicator | none | 1 | 1.156469 | 1 |
|
GeneralClassesFunctions/simulation_functions.py | EAOgroup/FEAST | 6 | 6630459 | """
All functions that are required in the simulations and independent of input data and detection method are stored here.
"""
import numpy as np
import random
import os
import pickle
def sample_wr(data, n_samples=1):
"""
Create a list of data by random sampling from a data set with replacement
Inputs:
data List of data
n_samples number of samples to draw
Return:
sample list of samples drawn
"""
sample = []
for ind in range(0, n_samples):
sample.append(random.choice(data))
return sample
def new_leak_count(time, gas_field):
"""
Calculate the number of new leaks to generate
Inputs:
time a Time object
gas_field a GasField object
Return:
Number of new leaks
"""
return np.random.poisson(gas_field.leak_production_rate * time.delta_t * gas_field.component_count)
def save_results(dir_out, results):
"""
Save results to a file
Inputs:
dir_out Name of output file to save
results A results object
"""
if not os.path.exists(dir_out):
os.makedirs(dir_out)
n_realization = len(os.listdir(dir_out))
file_out = dir_out + '/realization' + str(n_realization) + '.p'
pickle.dump(results, open(file_out, 'wb'))
def set_kwargs_attrs(obj_in, kwargs, only_existing=True):
"""
Function for overwriting parameters with key word arguments
Inputs:
obj_in Object with parameters to be updated
kwargs Dict containing new parameter values
only_existing Determines with new parameters can be created with kwargs
"""
for key in kwargs.keys():
# If only_existing is true, only set attributes that already exist
if only_existing:
if not hasattr(obj_in, key):
raise ValueError("Tried to set invalid attribute. Class: ", type(obj_in), 'attempted attribute:', key)
setattr(obj_in, key, kwargs[key])
# Calculate the concentration of methane at arbitrary points downwind of a leak source.
def gauss_leak_model(x, y, z, leak, atm, current_time, index=None):
"""
Returns the concentration of methane at a position downwind of the leak
Inputs:
x, y, z array of points at which to calculate the concentration [m]
x is measured parallel to the wind, z is vertical distance above the ground and y completes a right
hand coordinate system
leak Leak object containing numpy arrays of leak data
atm Atmosphere object containing atmosphere data
index index of leak to be analyzed from within 'leak.'
Outputs:
phi array storing the concentration of methane at the desired
coordinates [g/m^3]
"""
# Definitions:
# sigmay is the standard deviation of the concentration in the y direction.
# sigmaz is the standard deviation of the concentration in the z direction.
# sigmay and sigmaz are calculated as a function of x based on the Pasquill
# stability category. They are linear fits to the 100 meter data on the
# Pasquill Gifford curves.
n_loc = len(x)
# If an index (or list of indexes) is passed in, only consider those leaks. Otherwise, compute phi for all leaks
if index is None:
rng = np.array(range(0, leak.n_leaks))
phi = np.zeros([n_loc, leak.n_leaks])
elif type(index) is not np.ndarray:
try:
len(index)
rng = np.array(index)
except TypeError:
rng = np.array([index])
phi = np.zeros([n_loc, len(rng)])
else:
rng = index
phi = np.zeros([n_loc, len(rng)])
# Iterate through each location
for loc in range(0, n_loc):
if z[loc] < 0:
continue
normx = x[loc]-leak.x[rng]
# poslocations limits the calculation to points downwind of the leak and above ground.
poslocations = np.where(normx > 0)
normx = normx[poslocations]
temp_rng = rng[poslocations]
# sigmay and sigmaz are arrays of dimension normx
sigmaz = atm.l[current_time]*normx/(1+normx/atm.a[current_time])**atm.q[current_time]
sigmay = atm.k[current_time]*normx/(1+normx/atm.a[current_time])**atm.p[current_time]
# a and b are variables used in later calculations to account for
# the buoyancy of the plume.
b = -leak.z[temp_rng]-1.6 * leak.f_one_third[temp_rng] * normx**(2/3)/atm.wind_speed[current_time]
a = z[loc]+b
# Concentration Calculation
fy = np.exp(-(y[loc]-leak.y[temp_rng])**2 / (2*sigmay**2))
fz = np.exp(-a**2./(2*sigmaz**2))
fg = np.exp(-(a-2*b)**2./(2*sigmaz**2))
phi[loc, poslocations] = leak.flux[temp_rng]/(atm.wind_speed[current_time] * 2*np.pi*sigmaz*sigmay)*fy*(fz+fg)
# g/m^3
return phi
| """
All functions that are required in the simulations and independent of input data and detection method are stored here.
"""
import numpy as np
import random
import os
import pickle
def sample_wr(data, n_samples=1):
"""
Create a list of data by random sampling from a data set with replacement
Inputs:
data List of data
n_samples number of samples to draw
Return:
sample list of samples drawn
"""
sample = []
for ind in range(0, n_samples):
sample.append(random.choice(data))
return sample
def new_leak_count(time, gas_field):
"""
Calculate the number of new leaks to generate
Inputs:
time a Time object
gas_field a GasField object
Return:
Number of new leaks
"""
return np.random.poisson(gas_field.leak_production_rate * time.delta_t * gas_field.component_count)
def save_results(dir_out, results):
"""
Save results to a file
Inputs:
dir_out Name of output file to save
results A results object
"""
if not os.path.exists(dir_out):
os.makedirs(dir_out)
n_realization = len(os.listdir(dir_out))
file_out = dir_out + '/realization' + str(n_realization) + '.p'
pickle.dump(results, open(file_out, 'wb'))
def set_kwargs_attrs(obj_in, kwargs, only_existing=True):
"""
Function for overwriting parameters with key word arguments
Inputs:
obj_in Object with parameters to be updated
kwargs Dict containing new parameter values
only_existing Determines with new parameters can be created with kwargs
"""
for key in kwargs.keys():
# If only_existing is true, only set attributes that already exist
if only_existing:
if not hasattr(obj_in, key):
raise ValueError("Tried to set invalid attribute. Class: ", type(obj_in), 'attempted attribute:', key)
setattr(obj_in, key, kwargs[key])
# Calculate the concentration of methane at arbitrary points downwind of a leak source.
def gauss_leak_model(x, y, z, leak, atm, current_time, index=None):
"""
Returns the concentration of methane at a position downwind of the leak
Inputs:
x, y, z array of points at which to calculate the concentration [m]
x is measured parallel to the wind, z is vertical distance above the ground and y completes a right
hand coordinate system
leak Leak object containing numpy arrays of leak data
atm Atmosphere object containing atmosphere data
index index of leak to be analyzed from within 'leak.'
Outputs:
phi array storing the concentration of methane at the desired
coordinates [g/m^3]
"""
# Definitions:
# sigmay is the standard deviation of the concentration in the y direction.
# sigmaz is the standard deviation of the concentration in the z direction.
# sigmay and sigmaz are calculated as a function of x based on the Pasquill
# stability category. They are linear fits to the 100 meter data on the
# Pasquill Gifford curves.
n_loc = len(x)
# If an index (or list of indexes) is passed in, only consider those leaks. Otherwise, compute phi for all leaks
if index is None:
rng = np.array(range(0, leak.n_leaks))
phi = np.zeros([n_loc, leak.n_leaks])
elif type(index) is not np.ndarray:
try:
len(index)
rng = np.array(index)
except TypeError:
rng = np.array([index])
phi = np.zeros([n_loc, len(rng)])
else:
rng = index
phi = np.zeros([n_loc, len(rng)])
# Iterate through each location
for loc in range(0, n_loc):
if z[loc] < 0:
continue
normx = x[loc]-leak.x[rng]
# poslocations limits the calculation to points downwind of the leak and above ground.
poslocations = np.where(normx > 0)
normx = normx[poslocations]
temp_rng = rng[poslocations]
# sigmay and sigmaz are arrays of dimension normx
sigmaz = atm.l[current_time]*normx/(1+normx/atm.a[current_time])**atm.q[current_time]
sigmay = atm.k[current_time]*normx/(1+normx/atm.a[current_time])**atm.p[current_time]
# a and b are variables used in later calculations to account for
# the buoyancy of the plume.
b = -leak.z[temp_rng]-1.6 * leak.f_one_third[temp_rng] * normx**(2/3)/atm.wind_speed[current_time]
a = z[loc]+b
# Concentration Calculation
fy = np.exp(-(y[loc]-leak.y[temp_rng])**2 / (2*sigmay**2))
fz = np.exp(-a**2./(2*sigmaz**2))
fg = np.exp(-(a-2*b)**2./(2*sigmaz**2))
phi[loc, poslocations] = leak.flux[temp_rng]/(atm.wind_speed[current_time] * 2*np.pi*sigmaz*sigmay)*fy*(fz+fg)
# g/m^3
return phi
| en | 0.816776 | All functions that are required in the simulations and independent of input data and detection method are stored here. Create a list of data by random sampling from a data set with replacement
Inputs:
data List of data
n_samples number of samples to draw
Return:
sample list of samples drawn Calculate the number of new leaks to generate
Inputs:
time a Time object
gas_field a GasField object
Return:
Number of new leaks Save results to a file
Inputs:
dir_out Name of output file to save
results A results object Function for overwriting parameters with key word arguments
Inputs:
obj_in Object with parameters to be updated
kwargs Dict containing new parameter values
only_existing Determines with new parameters can be created with kwargs # If only_existing is true, only set attributes that already exist # Calculate the concentration of methane at arbitrary points downwind of a leak source. Returns the concentration of methane at a position downwind of the leak
Inputs:
x, y, z array of points at which to calculate the concentration [m]
x is measured parallel to the wind, z is vertical distance above the ground and y completes a right
hand coordinate system
leak Leak object containing numpy arrays of leak data
atm Atmosphere object containing atmosphere data
index index of leak to be analyzed from within 'leak.'
Outputs:
phi array storing the concentration of methane at the desired
coordinates [g/m^3] # Definitions: # sigmay is the standard deviation of the concentration in the y direction. # sigmaz is the standard deviation of the concentration in the z direction. # sigmay and sigmaz are calculated as a function of x based on the Pasquill # stability category. They are linear fits to the 100 meter data on the # Pasquill Gifford curves. # If an index (or list of indexes) is passed in, only consider those leaks. Otherwise, compute phi for all leaks # Iterate through each location # poslocations limits the calculation to points downwind of the leak and above ground. # sigmay and sigmaz are arrays of dimension normx # a and b are variables used in later calculations to account for # the buoyancy of the plume. # Concentration Calculation # g/m^3 | 2.989686 | 3 |
test/test_mpc.py | JJusti/CrypTen | 0 | 6630460 | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import itertools
import logging
import math
import unittest
import crypten
import crypten.communicator as comm
import torch
import torch.nn.functional as F
from crypten.common.functions.pooling import _pool2d_reshape
from crypten.common.rng import generate_kbit_random_tensor, generate_random_ring_element
from crypten.common.tensor_types import is_float_tensor
from crypten.config import cfg
from crypten.mpc import MPCTensor, ptype as Ptype
from crypten.mpc.primitives import ArithmeticSharedTensor, BinarySharedTensor
from test.multiprocess_test_case import MultiProcessTestCase, get_random_test_tensor
class TestMPC(object):
"""
This class tests all functions of MPCTensor.
"""
def _get_random_test_tensor(self, *args, **kwargs):
return get_random_test_tensor(device=self.device, *args, **kwargs)
def _check(self, encrypted_tensor, reference, msg, dst=None, tolerance=None):
if tolerance is None:
tolerance = getattr(self, "default_tolerance", 0.05)
tensor = encrypted_tensor.get_plain_text(dst=dst)
if dst is not None and dst != self.rank:
self.assertIsNone(tensor)
return
# Check sizes match
self.assertTrue(tensor.size() == reference.size(), msg)
self.assertTrue(is_float_tensor(reference), "reference must be a float")
if tensor.device != reference.device:
tensor = tensor.cpu()
reference = reference.cpu()
diff = (tensor - reference).abs_()
norm_diff = diff.div(tensor.abs() + reference.abs()).abs_()
test_passed = norm_diff.le(tolerance) + diff.le(tolerance * 0.1)
test_passed = test_passed.gt(0).all().item() == 1
if not test_passed:
logging.info(msg)
logging.info("Result %s" % tensor)
logging.info("Reference %s" % reference)
logging.info("Result - Reference = %s" % (tensor - reference))
self.assertTrue(test_passed, msg=msg)
def _check_tuple(self, encrypted_tuple, reference, msg, tolerance=None):
self.assertTrue(isinstance(encrypted_tuple, tuple))
self.assertEqual(len(encrypted_tuple), len(reference))
for i in range(len(reference)):
self._check(encrypted_tuple[i], reference[i], msg, tolerance=tolerance)
def test_repr(self):
a = self._get_random_test_tensor(size=(1,))
arithmetic = MPCTensor(a, ptype=Ptype.arithmetic)
binary = MPCTensor(a, ptype=Ptype.binary)
# Make sure these don't crash
print(arithmetic)
repr(arithmetic)
print(binary)
repr(binary)
def test_from_shares(self):
"""Tests MPCTensor.from_shares() functionality."""
# settings for test:
num_parties = int(self.world_size)
size = (5, 4)
def _generate_tensor(ptype):
reference = self._get_random_test_tensor(size=size, is_float=False)
# generate arithmetic sharing of reference tensor:
if ptype == Ptype.arithmetic:
zero_shares = generate_random_ring_element(
(num_parties, *size), device=self.device
)
zero_shares = zero_shares - zero_shares.roll(1, dims=0)
shares = list(zero_shares.unbind(0))
shares[0] += reference
# generate binary sharing of reference tensor:
else:
zero_shares = generate_kbit_random_tensor(
(num_parties, *size), device=self.device
)
zero_shares = zero_shares ^ zero_shares.roll(1, dims=0)
shares = list(zero_shares.unbind(0))
shares[0] ^= reference
# return shares and reference:
return shares, reference
# test both types:
for ptype in [Ptype.arithmetic, Ptype.binary]:
# generate shares, sync them between parties, and create tensor:
shares, reference = _generate_tensor(ptype)
share = comm.get().scatter(shares, 0)
encrypted_tensor = MPCTensor.from_shares(share, ptype=ptype)
# check resulting tensor:
self.assertIsInstance(encrypted_tensor, MPCTensor)
self.assertEqual(encrypted_tensor.ptype, ptype)
self.assertIsInstance(encrypted_tensor._tensor, ptype.to_tensor())
decrypted_tensor = encrypted_tensor.reveal()
self.assertTrue(torch.all(decrypted_tensor.eq(reference)).item())
def test_share_attr(self):
"""Tests share attribute getter and setter"""
for is_float in (True, False):
reference = self._get_random_test_tensor(is_float=is_float)
encrypted_tensor = MPCTensor(reference)
underlying_tensor = encrypted_tensor.share
self.assertTrue(
torch.equal(encrypted_tensor.share, underlying_tensor),
"share getter failed",
)
new_share = self._get_random_test_tensor(is_float=False)
encrypted_tensor.share = new_share
self.assertTrue(
torch.equal(encrypted_tensor.share, new_share), "share setter failed"
)
def test_encrypt_decrypt(self):
"""
Tests tensor encryption and decryption for both positive
and negative values.
"""
sizes = [
(),
(1,),
(5,),
(1, 1),
(1, 5),
(5, 1),
(5, 5),
(1, 5, 5),
(5, 1, 5),
(5, 5, 1),
(5, 5, 5),
(1, 3, 32, 32),
(5, 3, 32, 32),
]
for size in sizes:
# encryption and decryption without source:
reference = self._get_random_test_tensor(size=size, is_float=True)
encrypted_tensor = MPCTensor(reference)
self._check(encrypted_tensor, reference, "en/decryption failed")
for dst in range(self.world_size):
self._check(
encrypted_tensor, reference, "en/decryption failed", dst=dst
)
# test creation via new() function:
encrypted_tensor2 = encrypted_tensor.new(reference)
self.assertIsInstance(
encrypted_tensor2, MPCTensor, "new() returns incorrect type"
)
self._check(encrypted_tensor2, reference, "en/decryption failed")
# TODO: Implement broadcast_size on GPU
if self.device.type == "cuda":
continue
# encryption and decryption with source:
for src in range(self.world_size):
input_tensor = reference if src == self.rank else []
encrypted_tensor = MPCTensor(input_tensor, src=src, broadcast_size=True)
for dst in range(self.world_size):
self._check(
encrypted_tensor,
reference,
"en/decryption with broadcast_size failed",
dst=dst,
)
# MPCTensors cannot be initialized with None:
with self.assertRaises(ValueError):
_ = MPCTensor(None)
def test_arithmetic(self):
"""Tests arithmetic functions on encrypted tensor."""
arithmetic_functions = ["add", "add_", "sub", "sub_", "mul", "mul_"]
for func in arithmetic_functions:
for tensor_type in [lambda x: x, MPCTensor]:
tensor1 = self._get_random_test_tensor(is_float=True)
tensor2 = self._get_random_test_tensor(is_float=True)
encrypted = MPCTensor(tensor1)
encrypted2 = tensor_type(tensor2)
reference = getattr(tensor1, func)(tensor2)
encrypted_out = getattr(encrypted, func)(encrypted2)
self._check(
encrypted_out,
reference,
"%s %s failed"
% ("private" if tensor_type == MPCTensor else "public", func),
)
if "_" in func:
# Check in-place op worked
self._check(
encrypted,
reference,
"%s %s failed"
% ("private" if tensor_type == MPCTensor else "public", func),
)
else:
# Check original is not modified
self._check(
encrypted,
tensor1,
"%s %s failed"
% ("private" if tensor_type == MPCTensor else "public", func),
)
# Check encrypted vector with encrypted scalar works.
tensor1 = self._get_random_test_tensor(is_float=True)
tensor2 = self._get_random_test_tensor(is_float=True, size=(1,))
encrypted1 = MPCTensor(tensor1)
encrypted2 = MPCTensor(tensor2)
reference = getattr(tensor1, func)(tensor2)
encrypted_out = getattr(encrypted1, func)(encrypted2)
self._check(encrypted_out, reference, "private %s failed" % func)
tensor = self._get_random_test_tensor(is_float=True)
reference = tensor * tensor
encrypted = MPCTensor(tensor)
encrypted_out = encrypted.square()
self._check(encrypted_out, reference, "square failed")
# Test radd, rsub, and rmul
reference = 2 + tensor1
encrypted = MPCTensor(tensor1)
encrypted_out = 2 + encrypted
self._check(encrypted_out, reference, "right add failed")
reference = 2 - tensor1
encrypted_out = 2 - encrypted
self._check(encrypted_out, reference, "right sub failed")
reference = 2 * tensor1
encrypted_out = 2 * encrypted
self._check(encrypted_out, reference, "right mul failed")
def test_sum(self):
"""Tests sum reduction on encrypted tensor."""
tensor = self._get_random_test_tensor(size=(100, 100), is_float=True)
encrypted = MPCTensor(tensor)
self._check(encrypted.sum(), tensor.sum(), "sum failed")
for dim in [0, 1]:
reference = tensor.sum(dim)
encrypted_out = encrypted.sum(dim)
self._check(encrypted_out, reference, "sum failed")
def test_prod(self):
"""Tests prod reduction on encrypted tensor."""
tensor = self._get_random_test_tensor(size=(3, 3), max_value=3, is_float=False)
encrypted = MPCTensor(tensor)
self._check(encrypted.prod(), tensor.prod().float(), "prod failed")
tensor = self._get_random_test_tensor(
size=(5, 5, 5), max_value=3, is_float=False
)
encrypted = MPCTensor(tensor)
for dim in [0, 1, 2]:
reference = tensor.prod(dim).float()
encrypted_out = encrypted.prod(dim)
self._check(encrypted_out, reference, "prod failed")
def test_ptype(self):
"""Test that ptype attribute creates the correct type of encrypted tensor"""
ptype_values = [crypten.mpc.arithmetic, crypten.mpc.binary]
tensor_types = [ArithmeticSharedTensor, BinarySharedTensor]
for i, curr_ptype in enumerate(ptype_values):
tensor = self._get_random_test_tensor(is_float=False)
encr_tensor = crypten.cryptensor(tensor, ptype=curr_ptype)
assert isinstance(encr_tensor._tensor, tensor_types[i]), "ptype test failed"
def test_div(self):
"""Tests division of encrypted tensor by scalar and tensor."""
for function in ["div", "div_"]:
for scalar in [2, 2.0]:
tensor = self._get_random_test_tensor(is_float=True)
reference = tensor.float().div(scalar)
encrypted_tensor = MPCTensor(tensor)
encrypted_tensor = getattr(encrypted_tensor, function)(scalar)
self._check(encrypted_tensor, reference, "scalar division failed")
# multiply denominator by 10 to avoid dividing by small num
divisor = self._get_random_test_tensor(is_float=True, ex_zero=True) * 10
reference = tensor.div(divisor)
encrypted_tensor = MPCTensor(tensor)
encrypted_tensor = getattr(encrypted_tensor, function)(divisor)
self._check(encrypted_tensor, reference, "tensor division failed")
def test_mean(self):
"""Tests computing means of encrypted tensors."""
tensor = self._get_random_test_tensor(size=(5, 10, 15), is_float=True)
encrypted = MPCTensor(tensor)
self._check(encrypted.mean(), tensor.mean(), "mean failed")
for dim in [0, 1, 2]:
reference = tensor.mean(dim)
encrypted_out = encrypted.mean(dim)
self._check(encrypted_out, reference, "mean failed")
def test_var(self):
"""Tests computing variances of encrypted tensors."""
tensor = self._get_random_test_tensor(size=(5, 10, 15), is_float=True)
encrypted = MPCTensor(tensor)
self._check(encrypted.var(), tensor.var(), "var failed")
for dim in [0, 1, 2]:
reference = tensor.var(dim)
encrypted_out = encrypted.var(dim)
self._check(encrypted_out, reference, "var failed")
def test_matmul(self):
"""Test matrix multiplication."""
for tensor_type in [lambda x: x, MPCTensor]:
tensor = self._get_random_test_tensor(max_value=7, is_float=True)
for width in range(2, tensor.nelement()):
matrix_size = (tensor.nelement(), width)
matrix = self._get_random_test_tensor(
max_value=7, size=matrix_size, is_float=True
)
reference = tensor.matmul(matrix)
encrypted_tensor = MPCTensor(tensor)
matrix = tensor_type(matrix)
encrypted_tensor = encrypted_tensor.matmul(matrix)
self._check(
encrypted_tensor,
reference,
"Private-%s matrix multiplication failed"
% ("private" if tensor_type == MPCTensor else "public"),
)
def test_dot_ger(self):
"""Test dot product of vector and encrypted tensor."""
for tensor_type in [lambda x: x, MPCTensor]:
tensor1 = self._get_random_test_tensor(is_float=True).squeeze()
tensor2 = self._get_random_test_tensor(is_float=True).squeeze()
dot_reference = tensor1.dot(tensor2)
ger_reference = torch.ger(tensor1, tensor2)
tensor2 = tensor_type(tensor2)
# dot
encrypted_tensor = MPCTensor(tensor1)
encrypted_out = encrypted_tensor.dot(tensor2)
self._check(
encrypted_out,
dot_reference,
"%s dot product failed" % "private"
if tensor_type == MPCTensor
else "public",
)
# ger
encrypted_tensor = MPCTensor(tensor1)
encrypted_out = encrypted_tensor.ger(tensor2)
self._check(
encrypted_out,
ger_reference,
"%s outer product failed" % "private"
if tensor_type == MPCTensor
else "public",
)
def test_squeeze(self):
tensor = self._get_random_test_tensor(is_float=True)
for dim in [0, 1, 2]:
# Test unsqueeze
reference = tensor.unsqueeze(dim)
encrypted = MPCTensor(tensor)
encrypted_out = encrypted.unsqueeze(dim)
self._check(encrypted_out, reference, "unsqueeze failed")
# Test squeeze
encrypted = MPCTensor(tensor.unsqueeze(0))
encrypted_out = encrypted.squeeze()
self._check(encrypted_out, reference.squeeze(), "squeeze failed")
# Check that the encrypted_out and encrypted point to the same
# thing.
encrypted_out[0:2] = torch.tensor(
[0, 1], dtype=torch.float, device=self.device
)
ref = encrypted.squeeze().get_plain_text()
self._check(encrypted_out, ref, "squeeze failed")
def test_transpose(self):
sizes = [
(1,),
(5,),
(1, 1),
(1, 5),
(5, 1),
(5, 5),
(1, 5, 5),
(5, 1, 5),
(5, 5, 1),
(5, 5, 5),
(1, 3, 32, 32),
(5, 3, 32, 32),
]
for size in sizes:
tensor = self._get_random_test_tensor(size=size, is_float=True)
encrypted_tensor = MPCTensor(tensor)
if len(size) == 2: # t() asserts dim == 2
reference = tensor.t()
encrypted_out = encrypted_tensor.t()
self._check(encrypted_out, reference, "t() failed")
for dim0 in range(len(size)):
for dim1 in range(len(size)):
reference = tensor.transpose(dim0, dim1)
encrypted_out = encrypted_tensor.transpose(dim0, dim1)
self._check(encrypted_out, reference, "transpose failed")
def test_conv1d_smaller_signal_one_channel(self):
self._conv1d(5, 1)
def test_conv1d_smaller_signal_many_channels(self):
self._conv1d(5, 5)
def test_conv1d_larger_signal_one_channel(self):
self._conv1d(16, 1)
def test_conv1d_larger_signal_many_channels(self):
self._conv1d(16, 5)
def _conv1d(self, signal_size, in_channels):
"""Test convolution of encrypted tensor with public/private tensors."""
nbatches = [1, 3]
kernel_sizes = [1, 2, 3]
ochannels = [1, 3, 6]
paddings = [0, 1]
strides = [1, 2]
dilations = [1, 2]
groupings = [1, 2]
for func_name in ["conv1d", "conv_transpose1d"]:
for kernel_type in [lambda x: x, MPCTensor]:
for (
batches,
kernel_size,
out_channels,
padding,
stride,
dilation,
groups,
) in itertools.product(
nbatches,
kernel_sizes,
ochannels,
paddings,
strides,
dilations,
groupings,
):
# group convolution is not supported on GPU
if self.device.type == "cuda" and groups > 1:
continue
input_size = (batches, in_channels * groups, signal_size)
signal = self._get_random_test_tensor(
size=input_size, is_float=True
)
if func_name == "conv1d":
k_size = (out_channels * groups, in_channels, kernel_size)
else:
k_size = (in_channels * groups, out_channels, kernel_size)
kernel = self._get_random_test_tensor(size=k_size, is_float=True)
reference = getattr(F, func_name)(
signal,
kernel,
padding=padding,
stride=stride,
dilation=dilation,
groups=groups,
)
encrypted_signal = MPCTensor(signal)
encrypted_kernel = kernel_type(kernel)
encrypted_conv = getattr(encrypted_signal, func_name)(
encrypted_kernel,
padding=padding,
stride=stride,
dilation=dilation,
groups=groups,
)
self._check(encrypted_conv, reference, f"{func_name} failed")
def test_conv2d_square_image_one_channel(self):
self._conv2d((5, 5), 1, "conv2d")
def test_conv_transpose2d_square_image_one_channel(self):
self._conv2d((5, 5), 1, "conv_transpose2d")
def test_conv2d_square_image_many_channels(self):
self._conv2d((5, 5), 5, "conv2d")
def test_conv_transpose2d_square_image_many_channels(self):
self._conv2d((5, 5), 5, "conv_transpose2d")
def test_conv2d_rectangular_image_one_channel(self):
self._conv2d((16, 7), 1, "conv2d")
def test_conv_transpose2d_rectangular_image_one_channel(self):
self._conv2d((16, 7), 1, "conv_transpose2d")
def test_conv2d_rectangular_image_many_channels(self):
self._conv2d((16, 7), 5, "conv2d")
def test_conv_transpose2d_rectangular_image_many_channels(self):
self._conv2d((16, 7), 5, "conv_transpose2d")
def _conv2d(self, image_size, in_channels, func_name):
"""Test convolution of encrypted tensor with public/private tensors."""
nbatches = [1, 3]
kernel_sizes = [(1, 1), (2, 2), (2, 3)]
ochannels = [1, 3]
paddings = [0, 1, (0, 1)]
strides = [1, 2, (1, 2)]
dilations = [1, 2]
groupings = [1, 2]
assert func_name in [
"conv2d",
"conv_transpose2d",
], f"Invalid func_name: {func_name}"
for kernel_type in [lambda x: x, MPCTensor]:
for (
batches,
kernel_size,
out_channels,
padding,
stride,
dilation,
groups,
) in itertools.product(
nbatches,
kernel_sizes,
ochannels,
paddings,
strides,
dilations,
groupings,
):
# group convolution is not supported on GPU
if self.device.type == "cuda" and groups > 1:
continue
# sample input:
input_size = (batches, in_channels * groups, *image_size)
input = self._get_random_test_tensor(size=input_size, is_float=True)
# sample filtering kernel:
if func_name == "conv2d":
k_size = (out_channels * groups, in_channels, *kernel_size)
else:
k_size = (in_channels * groups, out_channels, *kernel_size)
kernel = self._get_random_test_tensor(size=k_size, is_float=True)
# perform filtering:
encr_matrix = MPCTensor(input)
encr_kernel = kernel_type(kernel)
encr_conv = getattr(encr_matrix, func_name)(
encr_kernel,
padding=padding,
stride=stride,
dilation=dilation,
groups=groups,
)
# check that result is correct:
reference = getattr(F, func_name)(
input,
kernel,
padding=padding,
stride=stride,
dilation=dilation,
groups=groups,
)
self._check(encr_conv, reference, "%s failed" % func_name)
def test_max_pooling(self):
"""Test max_pool of encrypted tensor."""
def _assert_index_match(
indices,
encrypted_indices,
matrix_size,
kernel_size,
**kwargs,
):
# Assert each kernel is one-hot
self.assertTrue(
encrypted_indices.get_plain_text()
.sum(-1)
.sum(-1)
.eq(torch.ones_like(indices))
.all(),
"Encrypted indices are not one-hot",
)
# Populate tensor with kernel indices
arange_size = matrix_size[-2:]
index_values = torch.arange(arange_size.numel(), device=indices.device)
index_values = index_values.view(arange_size)
index_values = index_values.expand(matrix_size)
# Ensure encrypted indices are correct
index_mask, size = _pool2d_reshape(index_values, kernel_size, **kwargs)
index_mask = index_mask.view(*size, kernel_size, kernel_size)
crypten_indices = encrypted_indices.mul(index_mask).sum(-1).sum(-1)
self._check(
crypten_indices, indices.float(), "max_pool2d indexing is incorrect"
)
dilations = [1, 2]
for width in range(2, 5):
for kernel_size in range(1, width):
matrix_size = (1, 4, 5, width)
matrix = self._get_random_test_tensor(size=matrix_size, is_float=True)
strides = list(range(1, kernel_size + 1)) + [(1, kernel_size)]
paddings = range(kernel_size // 2 + 1)
for (
stride,
padding,
dilation,
ceil_mode,
return_indices,
) in itertools.product(
strides,
paddings,
dilations,
[False, True],
[False, True],
):
kwargs = {
"stride": stride,
"padding": padding,
"dilation": dilation,
"ceil_mode": ceil_mode,
"return_indices": return_indices,
}
# Skip kernels that lead to 0-size outputs
if (kernel_size - 1) * dilation > width - 1:
continue
reference = F.max_pool2d(matrix, kernel_size, **kwargs)
encrypted_matrix = MPCTensor(matrix)
encrypted_pool = encrypted_matrix.max_pool2d(kernel_size, **kwargs)
if return_indices:
indices = reference[1]
encrypted_indices = encrypted_pool[1]
kwargs.pop("return_indices")
_assert_index_match(
indices,
encrypted_indices,
matrix.size(),
kernel_size,
**kwargs,
)
encrypted_pool = encrypted_pool[0]
reference = reference[0]
self._check(encrypted_pool, reference, "max_pool2d failed")
def test_avg_pooling(self):
"""Test avg_pool of encrypted tensor."""
for width in range(2, 5):
for kernel_size in range(1, width):
matrix_size = (1, 4, 5, width)
matrix = self._get_random_test_tensor(size=matrix_size, is_float=True)
strides = list(range(1, kernel_size + 1)) + [(1, kernel_size)]
paddings = range(kernel_size // 2 + 1)
for stride, padding in itertools.product(strides, paddings):
kwargs = {"stride": stride, "padding": padding}
reference = F.avg_pool2d(matrix, kernel_size, **kwargs)
encrypted_matrix = MPCTensor(matrix)
encrypted_pool = encrypted_matrix.avg_pool2d(kernel_size, **kwargs)
self._check(encrypted_pool, reference, "avg_pool2d failed")
def test_adaptive_pooling(self):
"""test adaptive_avg_pool2d and adaptive_max_pool2d"""
for in_size in range(1, 11):
for out_size in list(range(1, in_size + 1)) + [None]:
input_size = (1, in_size, in_size)
output_size = (out_size, out_size)
tensor = self._get_random_test_tensor(
size=input_size, is_float=True
).unsqueeze(0)
encrypted = MPCTensor(tensor)
# Test adaptive_avg_pool2d
reference = F.adaptive_avg_pool2d(tensor, output_size)
encrypted_out = encrypted.adaptive_avg_pool2d(output_size)
self._check(encrypted_out, reference, "adaptive_avg_pool2d failed")
# Test adapvite_max_pool2d
for return_indices in [False, True]:
reference = F.adaptive_max_pool2d(
tensor, output_size, return_indices=return_indices
)
encrypted_out = encrypted.adaptive_max_pool2d(
output_size, return_indices=return_indices
)
if return_indices:
encrypted_out = encrypted_out[0]
reference = reference[0]
self._check(encrypted_out, reference, "adaptive_max_pool2d failed")
def test_take(self):
"""Tests take function on encrypted tensor"""
tensor_size = [5, 5, 5, 5]
index = torch.tensor(
[[[1, 2], [3, 4]], [[4, 2], [1, 3]]], dtype=torch.long, device=self.device
)
tensor = self._get_random_test_tensor(size=tensor_size, is_float=True)
# Test when dimension!=None
for dimension in range(0, 4):
ndarray = tensor.cpu().numpy()
reference = torch.from_numpy(ndarray.take(index.cpu(), dimension))
encrypted_tensor = MPCTensor(tensor)
encrypted_out = encrypted_tensor.take(index, dimension)
self._check(encrypted_out, reference, "take function failed: dimension set")
# Test when dimension is default (i.e. None)
sizes = [(15,), (5, 10), (15, 10, 5)]
for size in sizes:
tensor = self._get_random_test_tensor(size=size, is_float=True)
encrypted_tensor = MPCTensor(tensor)
take_indices = [[0], [10], [0, 5, 10]]
for indices in take_indices:
indices = torch.tensor(indices, device=self.device)
self._check(
encrypted_tensor.take(indices),
tensor.take(indices),
f"take failed with indices {indices}",
)
def test_neg(self):
"""Test negative on encrypted tensor."""
for width in range(2, 5):
matrix_size = (5, width)
matrix = self._get_random_test_tensor(size=matrix_size, is_float=True)
encrypted_matrix = MPCTensor(matrix)
self._check(-encrypted_matrix, -matrix, "__neg__ failed")
for func_name in ["neg", "neg_"]:
reference = getattr(matrix, func_name)()
encrypted_output = getattr(encrypted_matrix, func_name)()
self._check(encrypted_output, reference, "%s failed" % func_name)
def test_relu(self):
"""Test relu on encrypted tensor."""
for width in range(2, 5):
matrix_size = (5, width)
matrix = self._get_random_test_tensor(size=matrix_size, is_float=True)
# Generate some negative values
matrix2 = self._get_random_test_tensor(size=matrix_size, is_float=True)
matrix = matrix - matrix2
encrypted_matrix = MPCTensor(matrix)
reference = F.relu_(matrix)
encrypted_matrix = encrypted_matrix.relu()
self._check(encrypted_matrix, reference, "relu failed")
def test_comparators(self):
"""Test comparators (>, >=, <, <=, ==, !=)"""
for comp in ["gt", "ge", "lt", "le", "eq", "ne"]:
for tensor_type in [lambda x: x, MPCTensor]:
tensor1 = self._get_random_test_tensor(is_float=True)
tensor2 = self._get_random_test_tensor(is_float=True)
encrypted_tensor1 = MPCTensor(tensor1)
encrypted_tensor2 = tensor_type(tensor2)
reference = getattr(tensor1, comp)(tensor2).float()
encrypted_out = getattr(encrypted_tensor1, comp)(encrypted_tensor2)
self._check(encrypted_out, reference, "%s comparator failed" % comp)
# Check deterministic example to guarantee all combinations
tensor1 = torch.tensor([2.0, 3.0, 1.0, 2.0, 2.0])
tensor2 = torch.tensor([2.0, 2.0, 2.0, 3.0, 1.0])
encrypted_tensor1 = MPCTensor(tensor1)
encrypted_tensor2 = tensor_type(tensor2)
reference = getattr(tensor1, comp)(tensor2).float()
encrypted_out = getattr(encrypted_tensor1, comp)(encrypted_tensor2)
self._check(encrypted_out, reference, "%s comparator failed" % comp)
def test_max_min_pairwise(self):
"""Tests max and min for the deterministic constant (n^2) algorithm"""
self._max_min("pairwise")
def test_max_min_log_reduction(self):
"""Tests max and min for log reduction algorithm"""
self._max_min("log_reduction")
def test_max_min_double_log_reduction(self):
"""Tests max and min for double log reduction algorithm"""
self._max_min("double_log_reduction")
def test_max_min_accelerated_cascade(self):
"""Tests max and min for accelerated cascading algorithm"""
self._max_min("accelerated_cascade")
def _max_min(self, method):
"""Test max and min for the specified algorithm"""
sizes = [
(),
(1,),
(5,),
(1, 1),
(1, 5),
(5, 5),
(1, 1, 1),
(5, 5, 5),
(1, 1, 1, 1),
(5, 5, 5, 5),
]
test_cases = [
torch.tensor(
[[1, 1, 2, 1, 4, 1, 3, 4]], dtype=torch.float, device=self.device
)
] + [self._get_random_test_tensor(size=size, is_float=False) for size in sizes]
for tensor in test_cases:
tensor = tensor.float()
encrypted_tensor = MPCTensor(tensor)
for comp in ["max", "min"]:
reference = getattr(tensor, comp)()
with cfg.temp_override({"functions.max_method": method}):
encrypted_out = getattr(encrypted_tensor, comp)()
self._check(encrypted_out, reference, "%s reduction failed" % comp)
for dim in range(tensor.dim()):
for keepdim in [False, True]:
reference = getattr(tensor, comp)(dim, keepdim=keepdim)
# Test with one_hot = False
with cfg.temp_override({"functions.max_method": method}):
encrypted_out = getattr(encrypted_tensor, comp)(
dim, keepdim=keepdim, one_hot=False
)
# Check max / min values are correct
self._check(
encrypted_out[0], reference[0], "%s reduction failed" % comp
)
# Test argmax / argmin values are correct
out_encr = encrypted_out[1]
out_decr = out_encr.get_plain_text().long()
argmax_ref = reference[1]
# Must index into tensor since ties are broken randomly
# so crypten and PyTorch can return different indices.
# This checks that they index to the same value.
if not keepdim:
out_decr = out_decr.unsqueeze(dim)
argmax_ref = argmax_ref.unsqueeze(dim)
mpc_result = tensor.gather(dim, out_decr)
torch_result = tensor.gather(dim, argmax_ref)
self.assertTrue(
(mpc_result == torch_result).all().item(),
"%s reduction failed" % comp,
)
# Test indices with one_hot = True
with cfg.temp_override({"functions.max_method": method}):
encrypted_out = getattr(encrypted_tensor, comp)(
dim, keepdim=keepdim, one_hot=True
)
# Check argmax results
val_ref = reference[0]
out_encr = encrypted_out[1]
out_decr = out_encr.get_plain_text()
self.assertTrue((out_decr.sum(dim) == 1).all())
self.assertTrue(
(
out_decr.mul(tensor).sum(dim, keepdim=keepdim)
== val_ref
).all()
)
def test_argmax_argmin_pairwise(self):
"""Tests argmax and argmin for the deterministic constant (n^2) algorithm"""
self._argmax_argmin("pairwise")
def test_argmax_argmin_log_reduction(self):
"""Tests argmax and argmin for log reduction algorithm"""
self._argmax_argmin("log_reduction")
def test_argmax_argmin_double_log_reduction(self):
"""Tests argmax and argmin for double log reduction algorithm"""
self._argmax_argmin("double_log_reduction")
def test_argmax_argmin_accelerated_cascade(self):
"""Tests max and min for accelerated cascading algorithm"""
self._max_min("accelerated_cascade")
def _argmax_argmin(self, method):
"""Test argmax and argmin for specified algorithm"""
sizes = [
(),
(1,),
(5,),
(1, 1),
(1, 5),
(5, 5),
(1, 1, 1),
(5, 5, 5),
(1, 1, 1, 1),
(5, 5, 5, 5),
]
test_cases = [
torch.tensor(
[[1, 1, 2, 1, 4, 1, 3, 4]], dtype=torch.float, device=self.device
)
] + [self._get_random_test_tensor(size=size, is_float=False) for size in sizes]
for tensor in test_cases:
tensor = tensor.float()
encrypted_tensor = MPCTensor(tensor)
for comp in ["argmax", "argmin"]:
cmp = comp[3:]
value = getattr(tensor, cmp)()
# test with one_hot = False
with cfg.temp_override({"functions.max_method": method}):
encrypted_out = getattr(encrypted_tensor, comp)(one_hot=False)
# Must index into tensor since ties are broken randomly
# so crypten and PyTorch can return different indices.
# This checks that they index to the same value.
decrypted_out = encrypted_out.get_plain_text()
if tensor.dim() == 0: # if input is 0-d, argmax should be 0
self.assertEqual(decrypted_out, 0)
else:
decrypted_val = tensor.flatten()[decrypted_out.long()]
self.assertTrue(decrypted_val.eq(value).all().item())
# test with one_hot = False
with cfg.temp_override({"functions.max_method": method}):
encrypted_out = getattr(encrypted_tensor, comp)(one_hot=True)
one_hot_indices = (tensor == value).float()
decrypted_out = encrypted_out.get_plain_text()
self.assertTrue(decrypted_out.sum() == 1)
self.assertTrue(decrypted_out.mul(one_hot_indices).sum() == 1)
for dim in range(tensor.dim()):
for keepdim in [False, True]:
# Compute one-hot argmax/min reference in plaintext
values, indices = getattr(tensor, cmp)(dim, keepdim=keepdim)
# test with one_hot = False
with cfg.temp_override({"functions.max_method": method}):
encrypted_out = getattr(encrypted_tensor, comp)(
dim, keepdim=keepdim, one_hot=False
)
# Must index into tensor since ties are broken randomly
# so crypten and PyTorch can return different indices.
# This checks that they index to the same value.abs
decrypted_out = encrypted_out.get_plain_text()
if not keepdim:
decrypted_out = decrypted_out.unsqueeze(dim)
indices = indices.unsqueeze(dim)
decrypted_val = tensor.gather(dim, decrypted_out.long())
reference = tensor.gather(dim, indices)
self.assertTrue(decrypted_val.eq(reference).all().item())
# test with one_hot = True
with cfg.temp_override({"functions.max_method": method}):
encrypted_out = getattr(encrypted_tensor, comp)(
dim, keepdim=keepdim, one_hot=True
)
decrypted_out = encrypted_out.get_plain_text()
if not keepdim:
values = values.unsqueeze(dim)
one_hot_indices = tensor.eq(values).float()
self.assertTrue(decrypted_out.sum(dim).eq(1).all())
self.assertTrue(
decrypted_out.mul(one_hot_indices).sum(dim).eq(1).all()
)
def test_abs_sign(self):
"""Test absolute value function"""
for op in ["abs", "sign"]:
tensor = self._get_random_test_tensor(is_float=True)
if op == "sign":
# do not test on 0 since torch.tensor([0]).sign() = 0
tensor = tensor + (tensor == 0).float()
encrypted_tensor = MPCTensor(tensor)
reference = getattr(tensor, op)()
encrypted_out = getattr(encrypted_tensor, op)()
self._check(encrypted_out, reference, "%s failed" % op)
def test_approximations(self):
"""Test appoximate functions (exp, log, sqrt, reciprocal, pos_pow)"""
def test_with_inputs(func, input):
encrypted_tensor = MPCTensor(input)
reference = getattr(tensor, func)()
encrypted_out = getattr(encrypted_tensor, func)()
self._check(encrypted_out, reference, "%s failed" % func)
# Test on [-10, 10] range
full_range_cases = ["exp"]
tensor = torch.tensor(
[0.01 * i for i in range(-1000, 1001, 1)], device=self.device
)
for func in full_range_cases:
test_with_inputs(func, tensor)
# Test on [0, 10] range
tensor[tensor == 0] = 1.0
non_zero_cases = ["reciprocal"]
for func in non_zero_cases:
test_with_inputs(func, tensor)
# Test on [0, 10] range
tensor = tensor[1001:]
pos_cases = ["log", "sqrt"]
for func in pos_cases:
test_with_inputs(func, tensor)
# Test pos_pow with several exponents
encrypted_tensor = MPCTensor(tensor)
# Reduced the max_value so approximations have less absolute error
tensor_exponent = self._get_random_test_tensor(
max_value=2, size=tensor.size(), is_float=True
)
exponents = [-3, -2, -1, 0, 1, 2, 3, tensor_exponent]
exponents += [MPCTensor(tensor_exponent)]
for p in exponents:
if isinstance(p, MPCTensor):
reference = tensor.pow(p.get_plain_text())
else:
reference = tensor.pow(p)
encrypted_out = encrypted_tensor.pos_pow(p)
self._check(encrypted_out, reference, f"pos_pow failed with power {p}")
def test_norm(self):
"""Tests p-norm"""
for p in [1, 1.5, 2, 3, float("inf"), "fro"]:
for dim in [None, 0, 1, 2]:
tensor = self._get_random_test_tensor(size=(3, 3, 3), is_float=True) / 5
if dim is None:
reference = tensor.norm(p=p)
else:
reference = tensor.norm(p=p, dim=dim)
encrypted = MPCTensor(tensor)
encrypted_out = encrypted.norm(p=p, dim=dim)
self._check(encrypted_out, reference, f"{p}-norm failed", tolerance=0.5)
def test_logistic(self):
"""Tests logistic functions (sigmoid, tanh)"""
tensor = torch.tensor(
[0.01 * i for i in range(-1000, 1001, 1)], device=self.device
)
encrypted_tensor = MPCTensor(tensor)
cases = ["sigmoid", "tanh"]
for func in cases:
reference = getattr(tensor, func)()
encrypted_out = getattr(encrypted_tensor, func)()
self._check(encrypted_out, reference, "%s failed" % func)
def test_hardtanh(self):
tensor = torch.arange(-10, 10, dtype=torch.float32)
encrypted = MPCTensor(tensor)
for minval in range(-10, 10):
for maxval in range(minval, 11):
reference = torch.nn.functional.hardtanh(tensor, minval, maxval)
encrypted_out = encrypted.hardtanh(minval, maxval)
self._check(encrypted_out, reference, "hardtanh failed")
def test_inplace_warning(self):
"""Tests that a warning is thrown that indicates that the `inplace` kwarg
is ignored when a function is called with `inplace=True`
"""
tensor = get_random_test_tensor(is_float=True)
encrypted = MPCTensor(tensor)
functions = ["dropout", "_feature_dropout"]
for func in functions:
warning_str = (
f"CrypTen {func} does not support inplace computation during training."
)
with self.assertLogs(logger=logging.getLogger(), level="WARNING") as cm:
getattr(encrypted, func)(inplace=True)
self.assertTrue(f"WARNING:root:{warning_str}" in cm.output)
def test_cos_sin(self):
"""Tests trigonometric functions (cos, sin)"""
tensor = torch.tensor(
[0.01 * i for i in range(-1000, 1001, 1)], device=self.device
)
encrypted_tensor = MPCTensor(tensor)
cases = ["cos", "sin"]
for func in cases:
reference = getattr(tensor, func)()
encrypted_out = getattr(encrypted_tensor, func)()
self._check(encrypted_out, reference, "%s failed" % func)
def test_softmax(self):
"""Test softmax and log_softmax function"""
for softmax_fn in ["softmax", "log_softmax"]:
# Test 0-dim tensor:
tensor = self._get_random_test_tensor(size=(), is_float=True)
reference = getattr(tensor, softmax_fn)(0)
encrypted_tensor = MPCTensor(tensor)
encrypted_out = getattr(encrypted_tensor, softmax_fn)(0)
self._check(encrypted_out, reference, "0-dim tensor %s failed" % softmax_fn)
# Test all other sizes
sizes = [
(1,),
(5,),
(1, 1),
(1, 5),
(5, 1),
(5, 5),
(1, 5, 5),
(5, 1, 5),
(5, 5, 1),
(5, 5, 5),
(1, 5, 5, 5),
(5, 5, 5, 5),
]
for size in sizes:
tensor = self._get_random_test_tensor(size=size, is_float=True) / 5
encrypted_tensor = MPCTensor(tensor)
for dim in range(tensor.dim()):
reference = getattr(tensor, softmax_fn)(dim)
encrypted_out = getattr(encrypted_tensor, softmax_fn)(dim)
self._check(encrypted_out, reference, "%s failed" % softmax_fn)
def test_get_set(self):
"""Tests element setting and getting by index"""
for tensor_type in [lambda x: x, MPCTensor]:
for size in range(1, 5):
# Test __getitem__
tensor = self._get_random_test_tensor(size=(size, size), is_float=True)
reference = tensor[:, 0]
encrypted_tensor = MPCTensor(tensor)
encrypted_out = encrypted_tensor[:, 0]
self._check(encrypted_out, reference, "getitem failed")
reference = tensor[0, :]
encrypted_out = encrypted_tensor[0, :]
self._check(encrypted_out, reference, "getitem failed")
# Test __setitem__
tensor2 = self._get_random_test_tensor(size=(size,), is_float=True)
reference = tensor.clone()
reference[:, 0] = tensor2
encrypted_out = MPCTensor(tensor)
encrypted2 = tensor_type(tensor2)
encrypted_out[:, 0] = encrypted2
self._check(
encrypted_out, reference, "%s setitem failed" % type(encrypted2)
)
reference = tensor.clone()
reference[0, :] = tensor2
encrypted_out = MPCTensor(tensor)
encrypted2 = tensor_type(tensor2)
encrypted_out[0, :] = encrypted2
self._check(
encrypted_out, reference, "%s setitem failed" % type(encrypted2)
)
def test_pad(self):
"""Tests padding"""
sizes = [(1,), (5,), (1, 1), (5, 5), (5, 5, 5), (5, 3, 32, 32)]
pads = [
(0, 0, 0, 0),
(1, 0, 0, 0),
(0, 1, 0, 0),
(0, 0, 1, 0),
(0, 0, 0, 1),
(1, 1, 1, 1),
(2, 2, 1, 1),
(2, 2, 2, 2),
]
for size in sizes:
tensor = self._get_random_test_tensor(size=size, is_float=True)
encrypted_tensor = MPCTensor(tensor)
for pad in pads:
for value in [0, 1, 10]:
if tensor.dim() < 2:
pad = pad[:2]
reference = torch.nn.functional.pad(tensor, pad, value=value)
encrypted_value = MPCTensor(value, device=self.device)
encrypted_out = encrypted_tensor.pad(pad, value=encrypted_value)
encrypted_out2 = encrypted_tensor.pad(pad, value=value)
self._check(encrypted_out, reference, "pad failed")
self._check(encrypted_out2, reference, "pad failed")
def test_index_add(self):
"""Test index_add function of encrypted tensor"""
index_add_functions = ["index_add", "index_add_"]
tensor_size1 = [5, 5, 5, 5]
index = torch.tensor(
[1, 2, 3, 4, 4, 2, 1, 3], dtype=torch.long, device=self.device
)
for dimension in range(0, 4):
tensor_size2 = [5, 5, 5, 5]
tensor_size2[dimension] = index.size(0)
for func in index_add_functions:
for tensor_type in [lambda x: x, MPCTensor]:
tensor1 = self._get_random_test_tensor(
size=tensor_size1, is_float=True
)
tensor2 = self._get_random_test_tensor(
size=tensor_size2, is_float=True
)
encrypted = MPCTensor(tensor1)
encrypted2 = tensor_type(tensor2)
reference = getattr(tensor1, func)(dimension, index, tensor2)
encrypted_out = getattr(encrypted, func)(
dimension, index, encrypted2
)
private_type = tensor_type == MPCTensor
self._check(
encrypted_out,
reference,
"%s %s failed"
% ("private" if private_type else "public", func),
)
if func.endswith("_"):
# Check in-place index_add worked
self._check(
encrypted,
reference,
"%s %s failed"
% ("private" if private_type else "public", func),
)
else:
# Check original is not modified
self._check(
encrypted,
tensor1,
"%s %s failed"
% (
"private" if tensor_type == MPCTensor else "public",
func,
),
)
def test_scatter(self):
"""Test scatter/scatter_add function of encrypted tensor"""
funcs = ["scatter", "scatter_", "scatter_add", "scatter_add_"]
sizes = [(5, 5), (5, 5, 5), (5, 5, 5, 5)]
for func in funcs:
for size in sizes:
for tensor_type in [lambda x: x, MPCTensor]:
for dim in range(len(size)):
tensor1 = self._get_random_test_tensor(size=size, is_float=True)
tensor2 = self._get_random_test_tensor(size=size, is_float=True)
index = self._get_random_test_tensor(size=size, is_float=False)
index = index.abs().clamp(0, 4)
encrypted = MPCTensor(tensor1)
encrypted2 = tensor_type(tensor2)
reference = getattr(tensor1, func)(dim, index, tensor2)
encrypted_out = getattr(encrypted, func)(dim, index, encrypted2)
private = tensor_type == MPCTensor
self._check(
encrypted_out,
reference,
"%s %s failed" % ("private" if private else "public", func),
)
if func.endswith("_"):
# Check in-place scatter/scatter_add worked
self._check(
encrypted,
reference,
"%s %s failed"
% ("private" if private else "public", func),
)
else:
# Check original is not modified
self._check(
encrypted,
tensor1,
"%s %s failed"
% ("private" if private else "public", func),
)
def test_broadcast_arithmetic_ops(self):
"""Test broadcast of arithmetic functions."""
arithmetic_functions = ["add", "sub", "mul", "div"]
# TODO: Add broadcasting for pos_pow since it can take a tensor argument
arithmetic_sizes = [
(),
(1,),
(2,),
(1, 1),
(1, 2),
(2, 1),
(2, 2),
(1, 1, 1),
(1, 1, 2),
(1, 2, 1),
(2, 1, 1),
(2, 2, 2),
(1, 1, 1, 1),
(1, 1, 1, 2),
(1, 1, 2, 1),
(1, 2, 1, 1),
(2, 1, 1, 1),
(2, 2, 2, 2),
]
for tensor_type in [lambda x: x, MPCTensor]:
for func in arithmetic_functions:
for size1, size2 in itertools.combinations(arithmetic_sizes, 2):
exclude_zero = True if func == "div" else False
# multiply denominator by 10 to avoid dividing by small num
const = 10 if func == "div" else 1
tensor1 = self._get_random_test_tensor(size=size1, is_float=True)
tensor2 = self._get_random_test_tensor(
size=size2, is_float=True, ex_zero=exclude_zero
)
tensor2 *= const
encrypted1 = MPCTensor(tensor1)
encrypted2 = tensor_type(tensor2)
reference = getattr(tensor1, func)(tensor2)
encrypted_out = getattr(encrypted1, func)(encrypted2)
private = isinstance(encrypted2, MPCTensor)
self._check(
encrypted_out,
reference,
"%s %s broadcast failed"
% ("private" if private else "public", func),
)
# Test with integer tensor
tensor2 = self._get_random_test_tensor(
size=size2, is_float=False, ex_zero=exclude_zero
)
tensor2 *= const
reference = getattr(tensor1, func)(tensor2.float())
encrypted_out = getattr(encrypted1, func)(tensor2)
self._check(
encrypted_out,
reference,
"%s broadcast failed with public integer tensor" % func,
)
def test_broadcast_matmul(self):
"""Test broadcast of matmul."""
matmul_sizes = [(1, 1), (1, 5), (5, 1), (5, 5)]
batch_dims = [(), (1,), (5,), (1, 1), (1, 5), (5, 5)]
for tensor_type in [lambda x: x, MPCTensor]:
for size in matmul_sizes:
for batch1, batch2 in itertools.combinations(batch_dims, 2):
size1 = (*batch1, *size)
size2 = (*batch2, *size)
tensor1 = self._get_random_test_tensor(size=size1, is_float=True)
tensor2 = self._get_random_test_tensor(size=size2, is_float=True)
tensor2 = tensor2.transpose(-2, -1)
encrypted1 = MPCTensor(tensor1)
encrypted2 = tensor_type(tensor2)
reference = tensor1.matmul(tensor2)
encrypted_out = encrypted1.matmul(encrypted2)
private = isinstance(encrypted2, MPCTensor)
self._check(
encrypted_out,
reference,
"%s matmul broadcast failed"
% ("private" if private else "public"),
)
# Test with integer tensor
tensor2 = self._get_random_test_tensor(size=size2, is_float=False)
tensor2 = tensor2.float().transpose(-2, -1)
reference = tensor1.matmul(tensor2)
encrypted_out = encrypted1.matmul(tensor2)
self._check(
encrypted_out,
reference,
"matmul broadcast failed with public integer tensor",
)
def test_inplace(self):
"""Test inplace vs. out-of-place functions"""
for op in ["add", "sub", "mul", "div"]:
for tensor_type in [lambda x: x, MPCTensor]:
tensor1 = self._get_random_test_tensor(is_float=True)
tensor2 = self._get_random_test_tensor(is_float=True)
reference = getattr(torch, op)(tensor1, tensor2)
encrypted1 = MPCTensor(tensor1)
encrypted2 = tensor_type(tensor2)
input_tensor_id = id(encrypted1._tensor)
input_encrypted_id = id(encrypted1)
# Test that out-of-place functions do not modify the input
private = isinstance(encrypted2, MPCTensor)
encrypted_out = getattr(encrypted1, op)(encrypted2)
self._check(
encrypted1,
tensor1,
"%s out-of-place %s modifies input"
% ("private" if private else "public", op),
)
self._check(
encrypted_out,
reference,
"%s out-of-place %s produces incorrect output"
% ("private" if private else "public", op),
)
self.assertFalse(id(encrypted_out._tensor) == input_tensor_id)
self.assertFalse(id(encrypted_out) == input_encrypted_id)
# Test that in-place functions modify the input
encrypted_out = getattr(encrypted1, op + "_")(encrypted2)
self._check(
encrypted1,
reference,
"%s in-place %s_ does not modify input"
% ("private" if private else "public", op),
)
self._check(
encrypted_out,
reference,
"%s in-place %s_ produces incorrect output"
% ("private" if private else "public", op),
)
self.assertTrue(id(encrypted_out._tensor) == input_tensor_id)
self.assertTrue(id(encrypted_out) == input_encrypted_id)
def test_copy_clone(self):
"""Tests shallow_copy and clone of encrypted tensors."""
sizes = [(5,), (1, 5), (5, 10, 15)]
for size in sizes:
tensor = self._get_random_test_tensor(size=size, is_float=True)
encrypted_tensor = MPCTensor(tensor)
# test shallow_copy
encrypted_tensor_shallow = encrypted_tensor.shallow_copy()
self.assertEqual(
id(encrypted_tensor_shallow._tensor), id(encrypted_tensor._tensor)
)
self._check(encrypted_tensor_shallow, tensor, "shallow_copy failed")
# test clone
encrypted_tensor_clone = encrypted_tensor.clone()
self.assertNotEqual(
id(encrypted_tensor_clone._tensor), id(encrypted_tensor._tensor)
)
self._check(encrypted_tensor_clone, tensor, "clone failed")
def test_copy_(self):
"""Tests copy_ function."""
sizes = [(5,), (1, 5), (5, 10, 15)]
for size in sizes:
tensor1 = self._get_random_test_tensor(size=size, is_float=True)
tensor2 = self._get_random_test_tensor(size=size, is_float=True)
encrypted_tensor1 = MPCTensor(tensor1)
encrypted_tensor2 = MPCTensor(tensor2)
encrypted_tensor1.copy_(encrypted_tensor2)
self._check(encrypted_tensor1, tensor2, "copy_ failed")
def test_index_select(self):
"""Tests index_select of encrypted tensors."""
sizes = [(5,), (5, 10), (5, 10, 15)]
for size in sizes:
tensor = self._get_random_test_tensor(size=size, is_float=True)
encrypted_tensor = MPCTensor(tensor)
indices = [[0], [0, 3], [0, 2, 4]]
for dim in range(tensor.dim()):
for index in indices:
index_tensor = torch.tensor(
index, dtype=torch.long, device=self.device
)
reference = tensor.index_select(dim, index_tensor)
encrypted_out = encrypted_tensor.index_select(dim, index_tensor)
self._check(
encrypted_out,
reference,
"index_select failed at dim {dim} and index {index}",
)
def test_narrow(self):
"""Tests narrow function."""
sizes = [(5, 6), (5, 6, 7), (6, 7, 8, 9)]
for size in sizes:
tensor = self._get_random_test_tensor(size=size, is_float=True)
encr_tensor = MPCTensor(tensor)
for dim in range(len(size)):
for start in range(size[dim] - 2):
for length in range(1, size[dim] - start):
tensor_narrow = tensor.narrow(dim, start, length)
encr_tensor_narrow = encr_tensor.narrow(dim, start, length)
self._check(
encr_tensor_narrow,
tensor_narrow,
"narrow failed along dimension %d" % dim,
)
def test_repeat_expand(self):
"""Tests repeat and expand of encrypted tensors."""
sizes = [(1, 8), (4, 1, 8)]
repeat_dims = [(4, 2, 1), (4, 2, 10)]
expand_dims = [(4, 2, 8), (4, 5, 8), (10, 4, 5, 8)]
for size in sizes:
tensor = self._get_random_test_tensor(size=size, is_float=True)
encrypted_tensor = MPCTensor(tensor)
for dims in repeat_dims:
encrypted_tensor_repeated = encrypted_tensor.repeat(*dims)
# test that repeat copies tensor's data
self.assertNotEqual(
id(encrypted_tensor_repeated._tensor), id(encrypted_tensor._tensor)
)
self._check(
encrypted_tensor_repeated,
tensor.repeat(*dims),
f"repeat failed with dims {dims}",
)
for dims in expand_dims:
encrypted_tensor_expanded = encrypted_tensor.expand(*dims)
# test that expand creates a view into the same underlying tensor
self.assertNotEqual(
id(encrypted_tensor_expanded.share), id(encrypted_tensor.share)
)
self._check(
encrypted_tensor_expanded,
tensor.expand(*dims),
f"repeat failed with dims {dims}",
)
def test_view_flatten(self):
"""Tests view and flatten of encrypted tensors."""
sizes = [(100,), (4, 25), (2, 5, 10)]
for size in sizes:
tensor = self._get_random_test_tensor(size=size, is_float=True)
encrypted_tensor = MPCTensor(tensor)
for dim in range(tensor.dim()):
self._check(
encrypted_tensor.flatten(start_dim=dim),
tensor.flatten(start_dim=dim),
f"flatten failed with dim {dim}",
)
shapes = [100, (5, 20), (10, 2, 5), (-1, 10)]
for shape in shapes:
self._check(
encrypted_tensor.view(shape),
tensor.view(shape),
f"view failed with shape {shape}",
)
def test_roll(self):
"""Tests roll of encrypted tensors."""
sizes = [(10, 1), (5, 2), (5, 10, 15)]
for size in sizes:
tensor = self._get_random_test_tensor(size=size, is_float=True)
encrypted_tensor = MPCTensor(tensor)
roll_shifts = [1, 2, 3, (2, 1)]
roll_dims = [0, 1, 0, (0, 1)]
for shifts, dims in zip(roll_shifts, roll_dims):
encrypted_tensor_rolled = encrypted_tensor.roll(shifts, dims=dims)
self.assertEqual(encrypted_tensor_rolled.numel(), tensor.numel())
self._check(
encrypted_tensor_rolled,
tensor.roll(shifts, dims=dims),
f"roll failed with shift {shifts} and dims {dims}",
)
def test_unfold(self):
"""Tests unfold of encrypted tensors."""
tensor_sizes = [(8,), (15, 10, 5), (5, 10, 15, 20)]
for tensor_size in tensor_sizes:
tensor = self._get_random_test_tensor(size=tensor_size, is_float=True)
encrypted_tensor = MPCTensor(tensor)
for size, step in itertools.product(range(1, 4), range(1, 4)):
# check unfold along higher dimension if possible
for dim in range(tensor.dim()):
self._check(
encrypted_tensor.unfold(dim, size, step),
tensor.unfold(dim, size, step),
"unfold failed with dim "
f"{dim}, size {size}, and step {step}",
)
def test_to(self):
"""Tests Arithemetic/Binary SharedTensor type conversions."""
from crypten.mpc.ptype import ptype as Ptype
tensor_sizes = [(), (1,), (5,), (1, 1), (5, 5), (1, 1, 1), (5, 5, 5)]
for size in tensor_sizes:
tensor = self._get_random_test_tensor(size=size, is_float=True)
encrypted_tensor = MPCTensor(tensor)
self.assertEqual(encrypted_tensor.ptype, Ptype.arithmetic)
binary_encrypted_tensor = encrypted_tensor.to(Ptype.binary)
self.assertEqual(binary_encrypted_tensor.ptype, Ptype.binary)
# check original encrypted_tensor was not modified after conversion
self._check(
encrypted_tensor,
tensor,
"encrypted_tensor was modified during conversion to BinarySharedTensor.",
)
encrypted_from_binary = binary_encrypted_tensor.to(Ptype.arithmetic)
self._check(
encrypted_from_binary,
tensor,
"to failed from BinarySharedTensor to ArithmeticSharedTensor",
)
# Test API
tensor = self._get_random_test_tensor(size=(5,), is_float=True)
encrypted_tensor = MPCTensor(tensor)
if torch.cuda.is_available():
encrypted_tensor = encrypted_tensor.to("cuda")
self.assertEqual(encrypted_tensor.device.type, "cuda")
self.assertEqual(encrypted_tensor.ptype, Ptype.arithmetic)
self._check(
encrypted_tensor,
tensor,
"encrypted_tensor was modified during conversion to cuda",
)
encrypted_tensor = encrypted_tensor.to(device="cuda")
self.assertEqual(encrypted_tensor.device.type, "cuda")
self.assertEqual(encrypted_tensor.ptype, Ptype.arithmetic)
self._check(
encrypted_tensor,
tensor,
"encrypted_tensor was modified during conversion to cuda",
)
encrypted_tensor = encrypted_tensor.to("cpu")
self.assertEqual(encrypted_tensor.device.type, "cpu")
self.assertEqual(encrypted_tensor.ptype, Ptype.arithmetic)
self._check(
encrypted_tensor,
tensor,
"encrypted_tensor was modified during conversion to cpu",
)
encrypted_tensor = encrypted_tensor.to(device="cpu")
self.assertEqual(encrypted_tensor.device.type, "cpu")
self.assertEqual(encrypted_tensor.ptype, Ptype.arithmetic)
self._check(
encrypted_tensor,
tensor,
"encrypted_tensor was modified during conversion to cpu",
)
encrypted_tensor = encrypted_tensor.to(ptype=Ptype.binary)
self.assertEqual(encrypted_tensor.device.type, "cpu")
self.assertEqual(encrypted_tensor.ptype, Ptype.binary)
self._check(
encrypted_tensor,
tensor,
"encrypted_tensor was modified during conversion to BinarySharedTensor.",
)
encrypted_tensor = encrypted_tensor.to(ptype=Ptype.arithmetic)
self.assertEqual(encrypted_tensor.device.type, "cpu")
self.assertEqual(encrypted_tensor.ptype, Ptype.arithmetic)
self._check(
encrypted_tensor,
tensor,
"encrypted_tensor was modified during conversion to ArithmeticSharedTensor.",
)
def test_cumsum(self):
"""Tests cumulative sum on encrypted tensors."""
sizes = [(8,), (5, 10), (15, 10, 5)]
for size in sizes:
tensor = self._get_random_test_tensor(size=size, is_float=True)
encrypted_tensor = MPCTensor(tensor)
for dim in range(tensor.dim()):
self._check(
encrypted_tensor.cumsum(dim),
tensor.cumsum(dim),
f"cumsum failed along {dim} dim",
)
def test_trace(self):
"""Tests trace operation on 2D encrypted tensors."""
sizes = [(3, 3), (10, 10), (2, 3)]
for size in sizes:
tensor = self._get_random_test_tensor(size=size, is_float=True)
encrypted_tensor = MPCTensor(tensor)
self._check(encrypted_tensor.trace(), tensor.trace(), "trace failed")
def test_flip(self):
"""Tests flip operation on encrypted tensors."""
sizes = [(5,), (5, 10), (5, 10, 15)]
for size in sizes:
tensor = self._get_random_test_tensor(size=size, is_float=True)
encrypted_tensor = MPCTensor(tensor)
flip_dims = [(0,), (0, 1), (0, 1, 2)]
for dims in flip_dims:
if len(dims) <= tensor.dim():
self._check(
encrypted_tensor.flip(dims),
tensor.flip(dims),
f"flip failed with {dims} dims",
)
def test_control_flow_failure(self):
"""Tests that control flow fails as expected"""
tensor = self._get_random_test_tensor(is_float=True)
encrypted_tensor = MPCTensor(tensor)
with self.assertRaises(RuntimeError):
if encrypted_tensor:
pass
with self.assertRaises(RuntimeError):
tensor = 5 if encrypted_tensor else 0
with self.assertRaises(RuntimeError):
if False:
pass
elif encrypted_tensor:
pass
def test_where(self):
"""Tests where() conditional element selection"""
sizes = [(10,), (5, 10), (1, 5, 10)]
y_types = [lambda x: x, MPCTensor]
for size, y_type in itertools.product(sizes, y_types):
tensor1 = self._get_random_test_tensor(size=size, is_float=True)
encrypted_tensor1 = MPCTensor(tensor1)
tensor2 = self._get_random_test_tensor(size=size, is_float=True)
encrypted_tensor2 = y_type(tensor2)
condition_tensor = (
self._get_random_test_tensor(max_value=1, size=size, is_float=False) + 1
)
condition_encrypted = MPCTensor(condition_tensor)
condition_bool = condition_tensor.bool()
reference_out = tensor1.where(condition_bool, tensor2)
encrypted_out = encrypted_tensor1.where(condition_bool, encrypted_tensor2)
y_is_private = y_type == MPCTensor
self._check(
encrypted_out,
reference_out,
f"{'private' if y_is_private else 'public'} y "
"where failed with public condition",
)
encrypted_out = encrypted_tensor1.where(
condition_encrypted, encrypted_tensor2
)
self._check(
encrypted_out,
reference_out,
f"{'private' if y_is_private else 'public'} y "
"where failed with private condition",
)
# test scalar y
scalar = self._get_random_test_tensor(max_value=0, size=[1], is_float=True)
self._check(
encrypted_tensor1.where(condition_bool, scalar),
tensor1.where(condition_bool, scalar),
"where failed against scalar y with public condition",
)
self._check(
encrypted_tensor1.where(condition_encrypted, scalar),
tensor1.where(condition_bool, scalar),
"where failed against scalar y with private condition",
)
def test_unbind(self):
"""Tests unbind"""
sizes = [
(1,),
(5,),
(1, 1),
(1, 5),
(5, 5),
(1, 1, 1),
(5, 5, 5),
(1, 1, 1, 1),
(5, 5, 5, 5),
]
for size in sizes:
tensor = self._get_random_test_tensor(size=size, is_float=True)
encrypted = MPCTensor(tensor)
for dim in range(tensor.dim()):
reference = tensor.unbind(dim)
encrypted_out = encrypted.unbind(dim)
self._check_tuple(encrypted_out, reference, "unbind failed")
def test_split(self):
"""Tests split"""
sizes = [
(1,),
(5,),
(1, 1),
(1, 5),
(5, 5),
(1, 1, 1),
(5, 5, 5),
(1, 1, 1, 1),
(5, 5, 5, 5),
]
for size in sizes:
tensor = self._get_random_test_tensor(size=size, is_float=True)
encrypted = MPCTensor(tensor)
for dim in range(tensor.dim()):
# Get random split
split = self._get_random_test_tensor(
size=(), max_value=tensor.size(dim)
)
split = split.abs().clamp(0, tensor.size(dim) - 1)
split = split.item()
# Test int split
int_split = 1 if split == 0 else split
reference = tensor.split(int_split, dim=dim)
encrypted_out = encrypted.split(int_split, dim=dim)
self._check_tuple(encrypted_out, reference, "split failed")
# Test list split
split = [split, tensor.size(dim) - split]
reference = tensor.split(split, dim=dim)
encrypted_out = encrypted.split(split, dim=dim)
self._check_tuple(encrypted_out, reference, "split failed")
def test_set(self):
"""Tests set correctly re-assigns encrypted shares"""
sizes = [(1, 5), (5, 10), (15, 10, 5)]
for size in sizes:
tensor1 = self._get_random_test_tensor(size=size, is_float=True)
encrypted1 = MPCTensor(tensor1)
tensor2 = self._get_random_test_tensor(size=size, is_float=True)
encrypted2 = MPCTensor(tensor2)
# check encrypted set
encrypted1.set(encrypted2)
self._check(
encrypted1, tensor2, f"set with encrypted other failed with size {size}"
)
# check plain text set
encrypted1 = MPCTensor(tensor1)
encrypted1.set(tensor2)
self._check(
encrypted1,
tensor2,
f"set with unencrypted other failed with size {size}",
)
def test_polynomial(self):
"""Tests polynomial function"""
sizes = [
(1,),
(5,),
(1, 1),
(1, 5),
(5, 5),
(1, 1, 1),
(5, 5, 5),
(1, 1, 1, 1),
(5, 5, 5, 5),
]
for size in sizes:
tensor = self._get_random_test_tensor(size=size, max_value=3, is_float=True)
encrypted = MPCTensor(tensor)
for terms in range(1, 5):
coeffs = self._get_random_test_tensor(
size=(terms,), max_value=3, is_float=True
)
reference = torch.zeros(size=tensor.size(), device=self.device)
for i, term in enumerate(coeffs.tolist()):
reference += term * tensor.pow(i + 1)
# Test list coeffs
encrypted_out = encrypted.polynomial(coeffs.tolist())
self._check(encrypted_out, reference, "polynomial failed")
# Test plaintext tensor coeffs
encrypted_out = encrypted.polynomial(coeffs)
self._check(encrypted_out, reference, "polynomial failed")
# Test encrypted tensor coeffs
coeffs_enc = MPCTensor(coeffs)
encrypted_out = encrypted.polynomial(coeffs_enc)
self._check(encrypted_out, reference, "polynomial failed")
def test_gather(self):
"""Test gather function of encrypted tensor"""
sizes = [(5, 5), (5, 5, 5), (5, 5, 5, 5)]
for size in sizes:
for dim in range(len(size)):
tensor = self._get_random_test_tensor(size=size, is_float=True)
index = self._get_random_test_tensor(size=size, is_float=False)
index = index.abs().clamp(0, 4)
encrypted = MPCTensor(tensor)
reference = tensor.gather(dim, index)
encrypted_out = encrypted.gather(dim, index)
self._check(encrypted_out, reference, f"gather failed with size {size}")
def test_dropout(self):
"""
Tests the dropout functions. Directly compares the zero and non-zero
entries of the input tensor, since we cannot force the encrypted and
unencrypted versions to generate identical random output. Also confirms
that the number of zeros in the encrypted dropout function is as expected.
"""
all_prob_values = [x * 0.2 for x in range(5)]
def get_first_nonzero_value(x):
x = x.flatten()
x = x[x.abs().ge(1e-4)]
x = x.take(torch.tensor(0))
return x
# check that the encrypted and plaintext versions scale
# identically, by testing on all-ones tensor
for prob in all_prob_values:
tensor = torch.ones([10, 10, 10], device=self.device).float()
encr_tensor = MPCTensor(tensor)
dropout_encr = encr_tensor.dropout(prob, training=True)
dropout_decr = dropout_encr.get_plain_text()
dropout_plain = F.dropout(tensor, prob, training=True)
# All non-zero values should be identical in both tensors, so
# compare any one of them
decr_nonzero_value = get_first_nonzero_value(dropout_decr)
plaintext_nonzero_value = get_first_nonzero_value(dropout_plain)
self.assertTrue(
math.isclose(
decr_nonzero_value,
plaintext_nonzero_value,
rel_tol=1e-2,
abs_tol=1e-2,
)
)
for dropout_fn in ["dropout", "_feature_dropout"]:
for prob in all_prob_values:
for size in [(5, 10), (5, 10, 15), (5, 10, 15, 20)]:
for inplace in [False, True]:
for training in [False, True]:
tensor = self._get_random_test_tensor(
size=size, ex_zero=True, min_value=1.0, is_float=True
)
encr_tensor = MPCTensor(tensor)
dropout_encr = getattr(encr_tensor, dropout_fn)(
prob, inplace=inplace, training=training
)
if training:
# Check the scaling for non-zero elements
dropout_decr = dropout_encr.get_plain_text()
scaled_tensor = tensor / (1 - prob)
reference = dropout_decr.where(
dropout_decr == 0, scaled_tensor
)
else:
reference = tensor
self._check(
dropout_encr,
reference,
f"dropout failed with size {size} and probability "
f"{prob}",
)
if inplace:
self._check(
encr_tensor,
reference,
f"in-place dropout failed with size {size} and "
f"probability {prob}",
)
else:
self._check(
encr_tensor,
tensor,
"out-of-place dropout modifies input",
)
# Check that channels that are zeroed are all zeros
if dropout_fn in [
"dropout2d",
"dropout3d",
"feature_dropout",
]:
dropout_encr_flat = dropout_encr.flatten(
start_dim=0, end_dim=1
)
dropout_flat = dropout_encr_flat.get_plain_text()
for i in range(0, dropout_flat.size(0)):
all_zeros = (dropout_flat[i] == 0).all()
all_nonzeros = (dropout_flat[i] != 0).all()
self.assertTrue(
all_zeros or all_nonzeros,
f"{dropout_fn} failed for size {size} with "
f"training {training} and inplace {inplace}",
)
# Check the expected number of zero elements
# For speed, restrict test to single p = 0.4
encr_tensor = MPCTensor(torch.empty((int(1e5), 2, 2)).fill_(1).to(self.device))
dropout_encr = encr_tensor.dropout(0.4)
dropout_tensor = dropout_encr.get_plain_text()
frac_zero = float((dropout_tensor == 0).sum()) / dropout_tensor.nelement()
self.assertTrue(math.isclose(frac_zero, 0.4, rel_tol=1e-2, abs_tol=1e-2))
def test_tuple_cache(self):
# Skip RSS setting since it does not generate tuples
if cfg.mpc.protocol == "replicated":
return
# TODO: encorporate wrap_rng for 3PC+ settings
if comm.get().get_world_size() > 2:
return
provider = crypten.mpc.get_default_provider()
# Test tracing attribute
crypten.trace()
self.assertTrue(provider.tracing)
x = get_random_test_tensor(is_float=True)
x = crypten.cryptensor(x)
_ = x.square()
_ = x * x
_ = x.matmul(x.t())
_ = x.relu()
y = x.unsqueeze(0)
_ = y.conv1d(y, stride=2)
# Populate reference requests
ref_names = ["square"]
ref_names += ["generate_additive_triple"] * 2
ref_names += ["generate_binary_triple"] * 7 + ["B2A_rng"]
ref_names += ["generate_additive_triple"] * 2
ref_args = [
(torch.Size([1, 5]),),
(torch.Size([1, 5]), torch.Size([1, 5]), "mul"),
(torch.Size([1, 5]), torch.Size([5, 1]), "matmul"),
(torch.Size([1, 1, 5]), torch.Size([1, 1, 5])),
]
ref_args += [(torch.Size([2, 1, 1, 5]), torch.Size([2, 1, 1, 5]))] * 6
ref_args += [(torch.Size([1, 5]),)]
ref_args += [(torch.Size([1, 5]), torch.Size([1, 5]), "mul")]
ref_args += [(torch.Size([1, 1, 5]), torch.Size([1, 1, 5]), "conv1d")]
kwargs = {"device": torch.device("cpu")}
conv_kwargs = {"device": torch.device("cpu"), "stride": 2}
requests = [(ref_names[i], ref_args[i], kwargs) for i in range(12)]
requests += [(ref_names[12], ref_args[12], conv_kwargs)]
self.assertEqual(
provider.request_cache,
requests,
"TupleProvider request cache incorrect",
)
crypten.trace(False)
self.assertFalse(provider.tracing)
# Check that cache populates as expected
crypten.fill_cache()
kwargs = frozenset(kwargs.items())
conv_kwargs = frozenset(conv_kwargs.items())
keys = [(ref_names[i], ref_args[i], kwargs) for i in range(12)]
keys += [(ref_names[12], ref_args[12], conv_kwargs)]
self.assertEqual(
set(provider.tuple_cache.keys()),
set(keys),
"TupleProvider tuple_cache populated incorrectly",
)
# Test that function calls return from cache when trace is off
crypten.trace(False)
_ = x.square()
_ = x * x
_ = x.matmul(x.t())
_ = x.relu()
y = x.unsqueeze(0)
_ = y.conv1d(y, stride=2)
for v in provider.tuple_cache.values():
self.assertEqual(
len(v), 0, msg="TupleProvider is not popping tuples properly from cache"
)
# Run all unit tests with both TFP and TTP providers
class TestTFP(MultiProcessTestCase, TestMPC):
def setUp(self):
self._original_provider = cfg.mpc.provider
crypten.CrypTensor.set_grad_enabled(False)
cfg.mpc.provider = "TFP"
super(TestTFP, self).setUp()
def tearDown(self):
cfg.mpc.provider = self._original_provider
crypten.CrypTensor.set_grad_enabled(True)
super(TestTFP, self).tearDown()
class TestTTP(MultiProcessTestCase, TestMPC):
def setUp(self):
self._original_provider = cfg.mpc.provider
crypten.CrypTensor.set_grad_enabled(False)
cfg.mpc.provider = "TTP"
super(TestTTP, self).setUp()
def tearDown(self):
cfg.mpc.provider = self._original_provider
crypten.CrypTensor.set_grad_enabled(True)
super(TestTTP, self).tearDown()
class Test3PC(MultiProcessTestCase, TestMPC):
def setUp(self):
super(Test3PC, self).setUp(world_size=3)
class TestRSS(MultiProcessTestCase, TestMPC):
def setUp(self):
self._original_protocol = cfg.mpc.protocol
cfg.mpc.protocol = "replicated"
super(TestRSS, self).setUp(world_size=3)
def tearDown(self):
cfg.mpc.protocol = self._original_protocol
super(TestRSS, self).tearDown()
# This code only runs when executing the file outside the test harness (e.g.
# via the buck target of another test)
if __name__ == "__main__":
unittest.main()
| #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import itertools
import logging
import math
import unittest
import crypten
import crypten.communicator as comm
import torch
import torch.nn.functional as F
from crypten.common.functions.pooling import _pool2d_reshape
from crypten.common.rng import generate_kbit_random_tensor, generate_random_ring_element
from crypten.common.tensor_types import is_float_tensor
from crypten.config import cfg
from crypten.mpc import MPCTensor, ptype as Ptype
from crypten.mpc.primitives import ArithmeticSharedTensor, BinarySharedTensor
from test.multiprocess_test_case import MultiProcessTestCase, get_random_test_tensor
class TestMPC(object):
"""
This class tests all functions of MPCTensor.
"""
def _get_random_test_tensor(self, *args, **kwargs):
return get_random_test_tensor(device=self.device, *args, **kwargs)
def _check(self, encrypted_tensor, reference, msg, dst=None, tolerance=None):
if tolerance is None:
tolerance = getattr(self, "default_tolerance", 0.05)
tensor = encrypted_tensor.get_plain_text(dst=dst)
if dst is not None and dst != self.rank:
self.assertIsNone(tensor)
return
# Check sizes match
self.assertTrue(tensor.size() == reference.size(), msg)
self.assertTrue(is_float_tensor(reference), "reference must be a float")
if tensor.device != reference.device:
tensor = tensor.cpu()
reference = reference.cpu()
diff = (tensor - reference).abs_()
norm_diff = diff.div(tensor.abs() + reference.abs()).abs_()
test_passed = norm_diff.le(tolerance) + diff.le(tolerance * 0.1)
test_passed = test_passed.gt(0).all().item() == 1
if not test_passed:
logging.info(msg)
logging.info("Result %s" % tensor)
logging.info("Reference %s" % reference)
logging.info("Result - Reference = %s" % (tensor - reference))
self.assertTrue(test_passed, msg=msg)
def _check_tuple(self, encrypted_tuple, reference, msg, tolerance=None):
self.assertTrue(isinstance(encrypted_tuple, tuple))
self.assertEqual(len(encrypted_tuple), len(reference))
for i in range(len(reference)):
self._check(encrypted_tuple[i], reference[i], msg, tolerance=tolerance)
def test_repr(self):
a = self._get_random_test_tensor(size=(1,))
arithmetic = MPCTensor(a, ptype=Ptype.arithmetic)
binary = MPCTensor(a, ptype=Ptype.binary)
# Make sure these don't crash
print(arithmetic)
repr(arithmetic)
print(binary)
repr(binary)
def test_from_shares(self):
"""Tests MPCTensor.from_shares() functionality."""
# settings for test:
num_parties = int(self.world_size)
size = (5, 4)
def _generate_tensor(ptype):
reference = self._get_random_test_tensor(size=size, is_float=False)
# generate arithmetic sharing of reference tensor:
if ptype == Ptype.arithmetic:
zero_shares = generate_random_ring_element(
(num_parties, *size), device=self.device
)
zero_shares = zero_shares - zero_shares.roll(1, dims=0)
shares = list(zero_shares.unbind(0))
shares[0] += reference
# generate binary sharing of reference tensor:
else:
zero_shares = generate_kbit_random_tensor(
(num_parties, *size), device=self.device
)
zero_shares = zero_shares ^ zero_shares.roll(1, dims=0)
shares = list(zero_shares.unbind(0))
shares[0] ^= reference
# return shares and reference:
return shares, reference
# test both types:
for ptype in [Ptype.arithmetic, Ptype.binary]:
# generate shares, sync them between parties, and create tensor:
shares, reference = _generate_tensor(ptype)
share = comm.get().scatter(shares, 0)
encrypted_tensor = MPCTensor.from_shares(share, ptype=ptype)
# check resulting tensor:
self.assertIsInstance(encrypted_tensor, MPCTensor)
self.assertEqual(encrypted_tensor.ptype, ptype)
self.assertIsInstance(encrypted_tensor._tensor, ptype.to_tensor())
decrypted_tensor = encrypted_tensor.reveal()
self.assertTrue(torch.all(decrypted_tensor.eq(reference)).item())
def test_share_attr(self):
"""Tests share attribute getter and setter"""
for is_float in (True, False):
reference = self._get_random_test_tensor(is_float=is_float)
encrypted_tensor = MPCTensor(reference)
underlying_tensor = encrypted_tensor.share
self.assertTrue(
torch.equal(encrypted_tensor.share, underlying_tensor),
"share getter failed",
)
new_share = self._get_random_test_tensor(is_float=False)
encrypted_tensor.share = new_share
self.assertTrue(
torch.equal(encrypted_tensor.share, new_share), "share setter failed"
)
def test_encrypt_decrypt(self):
"""
Tests tensor encryption and decryption for both positive
and negative values.
"""
sizes = [
(),
(1,),
(5,),
(1, 1),
(1, 5),
(5, 1),
(5, 5),
(1, 5, 5),
(5, 1, 5),
(5, 5, 1),
(5, 5, 5),
(1, 3, 32, 32),
(5, 3, 32, 32),
]
for size in sizes:
# encryption and decryption without source:
reference = self._get_random_test_tensor(size=size, is_float=True)
encrypted_tensor = MPCTensor(reference)
self._check(encrypted_tensor, reference, "en/decryption failed")
for dst in range(self.world_size):
self._check(
encrypted_tensor, reference, "en/decryption failed", dst=dst
)
# test creation via new() function:
encrypted_tensor2 = encrypted_tensor.new(reference)
self.assertIsInstance(
encrypted_tensor2, MPCTensor, "new() returns incorrect type"
)
self._check(encrypted_tensor2, reference, "en/decryption failed")
# TODO: Implement broadcast_size on GPU
if self.device.type == "cuda":
continue
# encryption and decryption with source:
for src in range(self.world_size):
input_tensor = reference if src == self.rank else []
encrypted_tensor = MPCTensor(input_tensor, src=src, broadcast_size=True)
for dst in range(self.world_size):
self._check(
encrypted_tensor,
reference,
"en/decryption with broadcast_size failed",
dst=dst,
)
# MPCTensors cannot be initialized with None:
with self.assertRaises(ValueError):
_ = MPCTensor(None)
def test_arithmetic(self):
"""Tests arithmetic functions on encrypted tensor."""
arithmetic_functions = ["add", "add_", "sub", "sub_", "mul", "mul_"]
for func in arithmetic_functions:
for tensor_type in [lambda x: x, MPCTensor]:
tensor1 = self._get_random_test_tensor(is_float=True)
tensor2 = self._get_random_test_tensor(is_float=True)
encrypted = MPCTensor(tensor1)
encrypted2 = tensor_type(tensor2)
reference = getattr(tensor1, func)(tensor2)
encrypted_out = getattr(encrypted, func)(encrypted2)
self._check(
encrypted_out,
reference,
"%s %s failed"
% ("private" if tensor_type == MPCTensor else "public", func),
)
if "_" in func:
# Check in-place op worked
self._check(
encrypted,
reference,
"%s %s failed"
% ("private" if tensor_type == MPCTensor else "public", func),
)
else:
# Check original is not modified
self._check(
encrypted,
tensor1,
"%s %s failed"
% ("private" if tensor_type == MPCTensor else "public", func),
)
# Check encrypted vector with encrypted scalar works.
tensor1 = self._get_random_test_tensor(is_float=True)
tensor2 = self._get_random_test_tensor(is_float=True, size=(1,))
encrypted1 = MPCTensor(tensor1)
encrypted2 = MPCTensor(tensor2)
reference = getattr(tensor1, func)(tensor2)
encrypted_out = getattr(encrypted1, func)(encrypted2)
self._check(encrypted_out, reference, "private %s failed" % func)
tensor = self._get_random_test_tensor(is_float=True)
reference = tensor * tensor
encrypted = MPCTensor(tensor)
encrypted_out = encrypted.square()
self._check(encrypted_out, reference, "square failed")
# Test radd, rsub, and rmul
reference = 2 + tensor1
encrypted = MPCTensor(tensor1)
encrypted_out = 2 + encrypted
self._check(encrypted_out, reference, "right add failed")
reference = 2 - tensor1
encrypted_out = 2 - encrypted
self._check(encrypted_out, reference, "right sub failed")
reference = 2 * tensor1
encrypted_out = 2 * encrypted
self._check(encrypted_out, reference, "right mul failed")
def test_sum(self):
"""Tests sum reduction on encrypted tensor."""
tensor = self._get_random_test_tensor(size=(100, 100), is_float=True)
encrypted = MPCTensor(tensor)
self._check(encrypted.sum(), tensor.sum(), "sum failed")
for dim in [0, 1]:
reference = tensor.sum(dim)
encrypted_out = encrypted.sum(dim)
self._check(encrypted_out, reference, "sum failed")
def test_prod(self):
"""Tests prod reduction on encrypted tensor."""
tensor = self._get_random_test_tensor(size=(3, 3), max_value=3, is_float=False)
encrypted = MPCTensor(tensor)
self._check(encrypted.prod(), tensor.prod().float(), "prod failed")
tensor = self._get_random_test_tensor(
size=(5, 5, 5), max_value=3, is_float=False
)
encrypted = MPCTensor(tensor)
for dim in [0, 1, 2]:
reference = tensor.prod(dim).float()
encrypted_out = encrypted.prod(dim)
self._check(encrypted_out, reference, "prod failed")
def test_ptype(self):
"""Test that ptype attribute creates the correct type of encrypted tensor"""
ptype_values = [crypten.mpc.arithmetic, crypten.mpc.binary]
tensor_types = [ArithmeticSharedTensor, BinarySharedTensor]
for i, curr_ptype in enumerate(ptype_values):
tensor = self._get_random_test_tensor(is_float=False)
encr_tensor = crypten.cryptensor(tensor, ptype=curr_ptype)
assert isinstance(encr_tensor._tensor, tensor_types[i]), "ptype test failed"
def test_div(self):
"""Tests division of encrypted tensor by scalar and tensor."""
for function in ["div", "div_"]:
for scalar in [2, 2.0]:
tensor = self._get_random_test_tensor(is_float=True)
reference = tensor.float().div(scalar)
encrypted_tensor = MPCTensor(tensor)
encrypted_tensor = getattr(encrypted_tensor, function)(scalar)
self._check(encrypted_tensor, reference, "scalar division failed")
# multiply denominator by 10 to avoid dividing by small num
divisor = self._get_random_test_tensor(is_float=True, ex_zero=True) * 10
reference = tensor.div(divisor)
encrypted_tensor = MPCTensor(tensor)
encrypted_tensor = getattr(encrypted_tensor, function)(divisor)
self._check(encrypted_tensor, reference, "tensor division failed")
def test_mean(self):
"""Tests computing means of encrypted tensors."""
tensor = self._get_random_test_tensor(size=(5, 10, 15), is_float=True)
encrypted = MPCTensor(tensor)
self._check(encrypted.mean(), tensor.mean(), "mean failed")
for dim in [0, 1, 2]:
reference = tensor.mean(dim)
encrypted_out = encrypted.mean(dim)
self._check(encrypted_out, reference, "mean failed")
def test_var(self):
"""Tests computing variances of encrypted tensors."""
tensor = self._get_random_test_tensor(size=(5, 10, 15), is_float=True)
encrypted = MPCTensor(tensor)
self._check(encrypted.var(), tensor.var(), "var failed")
for dim in [0, 1, 2]:
reference = tensor.var(dim)
encrypted_out = encrypted.var(dim)
self._check(encrypted_out, reference, "var failed")
def test_matmul(self):
"""Test matrix multiplication."""
for tensor_type in [lambda x: x, MPCTensor]:
tensor = self._get_random_test_tensor(max_value=7, is_float=True)
for width in range(2, tensor.nelement()):
matrix_size = (tensor.nelement(), width)
matrix = self._get_random_test_tensor(
max_value=7, size=matrix_size, is_float=True
)
reference = tensor.matmul(matrix)
encrypted_tensor = MPCTensor(tensor)
matrix = tensor_type(matrix)
encrypted_tensor = encrypted_tensor.matmul(matrix)
self._check(
encrypted_tensor,
reference,
"Private-%s matrix multiplication failed"
% ("private" if tensor_type == MPCTensor else "public"),
)
def test_dot_ger(self):
"""Test dot product of vector and encrypted tensor."""
for tensor_type in [lambda x: x, MPCTensor]:
tensor1 = self._get_random_test_tensor(is_float=True).squeeze()
tensor2 = self._get_random_test_tensor(is_float=True).squeeze()
dot_reference = tensor1.dot(tensor2)
ger_reference = torch.ger(tensor1, tensor2)
tensor2 = tensor_type(tensor2)
# dot
encrypted_tensor = MPCTensor(tensor1)
encrypted_out = encrypted_tensor.dot(tensor2)
self._check(
encrypted_out,
dot_reference,
"%s dot product failed" % "private"
if tensor_type == MPCTensor
else "public",
)
# ger
encrypted_tensor = MPCTensor(tensor1)
encrypted_out = encrypted_tensor.ger(tensor2)
self._check(
encrypted_out,
ger_reference,
"%s outer product failed" % "private"
if tensor_type == MPCTensor
else "public",
)
def test_squeeze(self):
tensor = self._get_random_test_tensor(is_float=True)
for dim in [0, 1, 2]:
# Test unsqueeze
reference = tensor.unsqueeze(dim)
encrypted = MPCTensor(tensor)
encrypted_out = encrypted.unsqueeze(dim)
self._check(encrypted_out, reference, "unsqueeze failed")
# Test squeeze
encrypted = MPCTensor(tensor.unsqueeze(0))
encrypted_out = encrypted.squeeze()
self._check(encrypted_out, reference.squeeze(), "squeeze failed")
# Check that the encrypted_out and encrypted point to the same
# thing.
encrypted_out[0:2] = torch.tensor(
[0, 1], dtype=torch.float, device=self.device
)
ref = encrypted.squeeze().get_plain_text()
self._check(encrypted_out, ref, "squeeze failed")
def test_transpose(self):
sizes = [
(1,),
(5,),
(1, 1),
(1, 5),
(5, 1),
(5, 5),
(1, 5, 5),
(5, 1, 5),
(5, 5, 1),
(5, 5, 5),
(1, 3, 32, 32),
(5, 3, 32, 32),
]
for size in sizes:
tensor = self._get_random_test_tensor(size=size, is_float=True)
encrypted_tensor = MPCTensor(tensor)
if len(size) == 2: # t() asserts dim == 2
reference = tensor.t()
encrypted_out = encrypted_tensor.t()
self._check(encrypted_out, reference, "t() failed")
for dim0 in range(len(size)):
for dim1 in range(len(size)):
reference = tensor.transpose(dim0, dim1)
encrypted_out = encrypted_tensor.transpose(dim0, dim1)
self._check(encrypted_out, reference, "transpose failed")
def test_conv1d_smaller_signal_one_channel(self):
self._conv1d(5, 1)
def test_conv1d_smaller_signal_many_channels(self):
self._conv1d(5, 5)
def test_conv1d_larger_signal_one_channel(self):
self._conv1d(16, 1)
def test_conv1d_larger_signal_many_channels(self):
self._conv1d(16, 5)
def _conv1d(self, signal_size, in_channels):
"""Test convolution of encrypted tensor with public/private tensors."""
nbatches = [1, 3]
kernel_sizes = [1, 2, 3]
ochannels = [1, 3, 6]
paddings = [0, 1]
strides = [1, 2]
dilations = [1, 2]
groupings = [1, 2]
for func_name in ["conv1d", "conv_transpose1d"]:
for kernel_type in [lambda x: x, MPCTensor]:
for (
batches,
kernel_size,
out_channels,
padding,
stride,
dilation,
groups,
) in itertools.product(
nbatches,
kernel_sizes,
ochannels,
paddings,
strides,
dilations,
groupings,
):
# group convolution is not supported on GPU
if self.device.type == "cuda" and groups > 1:
continue
input_size = (batches, in_channels * groups, signal_size)
signal = self._get_random_test_tensor(
size=input_size, is_float=True
)
if func_name == "conv1d":
k_size = (out_channels * groups, in_channels, kernel_size)
else:
k_size = (in_channels * groups, out_channels, kernel_size)
kernel = self._get_random_test_tensor(size=k_size, is_float=True)
reference = getattr(F, func_name)(
signal,
kernel,
padding=padding,
stride=stride,
dilation=dilation,
groups=groups,
)
encrypted_signal = MPCTensor(signal)
encrypted_kernel = kernel_type(kernel)
encrypted_conv = getattr(encrypted_signal, func_name)(
encrypted_kernel,
padding=padding,
stride=stride,
dilation=dilation,
groups=groups,
)
self._check(encrypted_conv, reference, f"{func_name} failed")
def test_conv2d_square_image_one_channel(self):
self._conv2d((5, 5), 1, "conv2d")
def test_conv_transpose2d_square_image_one_channel(self):
self._conv2d((5, 5), 1, "conv_transpose2d")
def test_conv2d_square_image_many_channels(self):
self._conv2d((5, 5), 5, "conv2d")
def test_conv_transpose2d_square_image_many_channels(self):
self._conv2d((5, 5), 5, "conv_transpose2d")
def test_conv2d_rectangular_image_one_channel(self):
self._conv2d((16, 7), 1, "conv2d")
def test_conv_transpose2d_rectangular_image_one_channel(self):
self._conv2d((16, 7), 1, "conv_transpose2d")
def test_conv2d_rectangular_image_many_channels(self):
self._conv2d((16, 7), 5, "conv2d")
def test_conv_transpose2d_rectangular_image_many_channels(self):
self._conv2d((16, 7), 5, "conv_transpose2d")
def _conv2d(self, image_size, in_channels, func_name):
"""Test convolution of encrypted tensor with public/private tensors."""
nbatches = [1, 3]
kernel_sizes = [(1, 1), (2, 2), (2, 3)]
ochannels = [1, 3]
paddings = [0, 1, (0, 1)]
strides = [1, 2, (1, 2)]
dilations = [1, 2]
groupings = [1, 2]
assert func_name in [
"conv2d",
"conv_transpose2d",
], f"Invalid func_name: {func_name}"
for kernel_type in [lambda x: x, MPCTensor]:
for (
batches,
kernel_size,
out_channels,
padding,
stride,
dilation,
groups,
) in itertools.product(
nbatches,
kernel_sizes,
ochannels,
paddings,
strides,
dilations,
groupings,
):
# group convolution is not supported on GPU
if self.device.type == "cuda" and groups > 1:
continue
# sample input:
input_size = (batches, in_channels * groups, *image_size)
input = self._get_random_test_tensor(size=input_size, is_float=True)
# sample filtering kernel:
if func_name == "conv2d":
k_size = (out_channels * groups, in_channels, *kernel_size)
else:
k_size = (in_channels * groups, out_channels, *kernel_size)
kernel = self._get_random_test_tensor(size=k_size, is_float=True)
# perform filtering:
encr_matrix = MPCTensor(input)
encr_kernel = kernel_type(kernel)
encr_conv = getattr(encr_matrix, func_name)(
encr_kernel,
padding=padding,
stride=stride,
dilation=dilation,
groups=groups,
)
# check that result is correct:
reference = getattr(F, func_name)(
input,
kernel,
padding=padding,
stride=stride,
dilation=dilation,
groups=groups,
)
self._check(encr_conv, reference, "%s failed" % func_name)
def test_max_pooling(self):
"""Test max_pool of encrypted tensor."""
def _assert_index_match(
indices,
encrypted_indices,
matrix_size,
kernel_size,
**kwargs,
):
# Assert each kernel is one-hot
self.assertTrue(
encrypted_indices.get_plain_text()
.sum(-1)
.sum(-1)
.eq(torch.ones_like(indices))
.all(),
"Encrypted indices are not one-hot",
)
# Populate tensor with kernel indices
arange_size = matrix_size[-2:]
index_values = torch.arange(arange_size.numel(), device=indices.device)
index_values = index_values.view(arange_size)
index_values = index_values.expand(matrix_size)
# Ensure encrypted indices are correct
index_mask, size = _pool2d_reshape(index_values, kernel_size, **kwargs)
index_mask = index_mask.view(*size, kernel_size, kernel_size)
crypten_indices = encrypted_indices.mul(index_mask).sum(-1).sum(-1)
self._check(
crypten_indices, indices.float(), "max_pool2d indexing is incorrect"
)
dilations = [1, 2]
for width in range(2, 5):
for kernel_size in range(1, width):
matrix_size = (1, 4, 5, width)
matrix = self._get_random_test_tensor(size=matrix_size, is_float=True)
strides = list(range(1, kernel_size + 1)) + [(1, kernel_size)]
paddings = range(kernel_size // 2 + 1)
for (
stride,
padding,
dilation,
ceil_mode,
return_indices,
) in itertools.product(
strides,
paddings,
dilations,
[False, True],
[False, True],
):
kwargs = {
"stride": stride,
"padding": padding,
"dilation": dilation,
"ceil_mode": ceil_mode,
"return_indices": return_indices,
}
# Skip kernels that lead to 0-size outputs
if (kernel_size - 1) * dilation > width - 1:
continue
reference = F.max_pool2d(matrix, kernel_size, **kwargs)
encrypted_matrix = MPCTensor(matrix)
encrypted_pool = encrypted_matrix.max_pool2d(kernel_size, **kwargs)
if return_indices:
indices = reference[1]
encrypted_indices = encrypted_pool[1]
kwargs.pop("return_indices")
_assert_index_match(
indices,
encrypted_indices,
matrix.size(),
kernel_size,
**kwargs,
)
encrypted_pool = encrypted_pool[0]
reference = reference[0]
self._check(encrypted_pool, reference, "max_pool2d failed")
def test_avg_pooling(self):
"""Test avg_pool of encrypted tensor."""
for width in range(2, 5):
for kernel_size in range(1, width):
matrix_size = (1, 4, 5, width)
matrix = self._get_random_test_tensor(size=matrix_size, is_float=True)
strides = list(range(1, kernel_size + 1)) + [(1, kernel_size)]
paddings = range(kernel_size // 2 + 1)
for stride, padding in itertools.product(strides, paddings):
kwargs = {"stride": stride, "padding": padding}
reference = F.avg_pool2d(matrix, kernel_size, **kwargs)
encrypted_matrix = MPCTensor(matrix)
encrypted_pool = encrypted_matrix.avg_pool2d(kernel_size, **kwargs)
self._check(encrypted_pool, reference, "avg_pool2d failed")
def test_adaptive_pooling(self):
"""test adaptive_avg_pool2d and adaptive_max_pool2d"""
for in_size in range(1, 11):
for out_size in list(range(1, in_size + 1)) + [None]:
input_size = (1, in_size, in_size)
output_size = (out_size, out_size)
tensor = self._get_random_test_tensor(
size=input_size, is_float=True
).unsqueeze(0)
encrypted = MPCTensor(tensor)
# Test adaptive_avg_pool2d
reference = F.adaptive_avg_pool2d(tensor, output_size)
encrypted_out = encrypted.adaptive_avg_pool2d(output_size)
self._check(encrypted_out, reference, "adaptive_avg_pool2d failed")
# Test adapvite_max_pool2d
for return_indices in [False, True]:
reference = F.adaptive_max_pool2d(
tensor, output_size, return_indices=return_indices
)
encrypted_out = encrypted.adaptive_max_pool2d(
output_size, return_indices=return_indices
)
if return_indices:
encrypted_out = encrypted_out[0]
reference = reference[0]
self._check(encrypted_out, reference, "adaptive_max_pool2d failed")
def test_take(self):
"""Tests take function on encrypted tensor"""
tensor_size = [5, 5, 5, 5]
index = torch.tensor(
[[[1, 2], [3, 4]], [[4, 2], [1, 3]]], dtype=torch.long, device=self.device
)
tensor = self._get_random_test_tensor(size=tensor_size, is_float=True)
# Test when dimension!=None
for dimension in range(0, 4):
ndarray = tensor.cpu().numpy()
reference = torch.from_numpy(ndarray.take(index.cpu(), dimension))
encrypted_tensor = MPCTensor(tensor)
encrypted_out = encrypted_tensor.take(index, dimension)
self._check(encrypted_out, reference, "take function failed: dimension set")
# Test when dimension is default (i.e. None)
sizes = [(15,), (5, 10), (15, 10, 5)]
for size in sizes:
tensor = self._get_random_test_tensor(size=size, is_float=True)
encrypted_tensor = MPCTensor(tensor)
take_indices = [[0], [10], [0, 5, 10]]
for indices in take_indices:
indices = torch.tensor(indices, device=self.device)
self._check(
encrypted_tensor.take(indices),
tensor.take(indices),
f"take failed with indices {indices}",
)
def test_neg(self):
"""Test negative on encrypted tensor."""
for width in range(2, 5):
matrix_size = (5, width)
matrix = self._get_random_test_tensor(size=matrix_size, is_float=True)
encrypted_matrix = MPCTensor(matrix)
self._check(-encrypted_matrix, -matrix, "__neg__ failed")
for func_name in ["neg", "neg_"]:
reference = getattr(matrix, func_name)()
encrypted_output = getattr(encrypted_matrix, func_name)()
self._check(encrypted_output, reference, "%s failed" % func_name)
def test_relu(self):
"""Test relu on encrypted tensor."""
for width in range(2, 5):
matrix_size = (5, width)
matrix = self._get_random_test_tensor(size=matrix_size, is_float=True)
# Generate some negative values
matrix2 = self._get_random_test_tensor(size=matrix_size, is_float=True)
matrix = matrix - matrix2
encrypted_matrix = MPCTensor(matrix)
reference = F.relu_(matrix)
encrypted_matrix = encrypted_matrix.relu()
self._check(encrypted_matrix, reference, "relu failed")
def test_comparators(self):
"""Test comparators (>, >=, <, <=, ==, !=)"""
for comp in ["gt", "ge", "lt", "le", "eq", "ne"]:
for tensor_type in [lambda x: x, MPCTensor]:
tensor1 = self._get_random_test_tensor(is_float=True)
tensor2 = self._get_random_test_tensor(is_float=True)
encrypted_tensor1 = MPCTensor(tensor1)
encrypted_tensor2 = tensor_type(tensor2)
reference = getattr(tensor1, comp)(tensor2).float()
encrypted_out = getattr(encrypted_tensor1, comp)(encrypted_tensor2)
self._check(encrypted_out, reference, "%s comparator failed" % comp)
# Check deterministic example to guarantee all combinations
tensor1 = torch.tensor([2.0, 3.0, 1.0, 2.0, 2.0])
tensor2 = torch.tensor([2.0, 2.0, 2.0, 3.0, 1.0])
encrypted_tensor1 = MPCTensor(tensor1)
encrypted_tensor2 = tensor_type(tensor2)
reference = getattr(tensor1, comp)(tensor2).float()
encrypted_out = getattr(encrypted_tensor1, comp)(encrypted_tensor2)
self._check(encrypted_out, reference, "%s comparator failed" % comp)
def test_max_min_pairwise(self):
"""Tests max and min for the deterministic constant (n^2) algorithm"""
self._max_min("pairwise")
def test_max_min_log_reduction(self):
"""Tests max and min for log reduction algorithm"""
self._max_min("log_reduction")
def test_max_min_double_log_reduction(self):
"""Tests max and min for double log reduction algorithm"""
self._max_min("double_log_reduction")
def test_max_min_accelerated_cascade(self):
"""Tests max and min for accelerated cascading algorithm"""
self._max_min("accelerated_cascade")
def _max_min(self, method):
"""Test max and min for the specified algorithm"""
sizes = [
(),
(1,),
(5,),
(1, 1),
(1, 5),
(5, 5),
(1, 1, 1),
(5, 5, 5),
(1, 1, 1, 1),
(5, 5, 5, 5),
]
test_cases = [
torch.tensor(
[[1, 1, 2, 1, 4, 1, 3, 4]], dtype=torch.float, device=self.device
)
] + [self._get_random_test_tensor(size=size, is_float=False) for size in sizes]
for tensor in test_cases:
tensor = tensor.float()
encrypted_tensor = MPCTensor(tensor)
for comp in ["max", "min"]:
reference = getattr(tensor, comp)()
with cfg.temp_override({"functions.max_method": method}):
encrypted_out = getattr(encrypted_tensor, comp)()
self._check(encrypted_out, reference, "%s reduction failed" % comp)
for dim in range(tensor.dim()):
for keepdim in [False, True]:
reference = getattr(tensor, comp)(dim, keepdim=keepdim)
# Test with one_hot = False
with cfg.temp_override({"functions.max_method": method}):
encrypted_out = getattr(encrypted_tensor, comp)(
dim, keepdim=keepdim, one_hot=False
)
# Check max / min values are correct
self._check(
encrypted_out[0], reference[0], "%s reduction failed" % comp
)
# Test argmax / argmin values are correct
out_encr = encrypted_out[1]
out_decr = out_encr.get_plain_text().long()
argmax_ref = reference[1]
# Must index into tensor since ties are broken randomly
# so crypten and PyTorch can return different indices.
# This checks that they index to the same value.
if not keepdim:
out_decr = out_decr.unsqueeze(dim)
argmax_ref = argmax_ref.unsqueeze(dim)
mpc_result = tensor.gather(dim, out_decr)
torch_result = tensor.gather(dim, argmax_ref)
self.assertTrue(
(mpc_result == torch_result).all().item(),
"%s reduction failed" % comp,
)
# Test indices with one_hot = True
with cfg.temp_override({"functions.max_method": method}):
encrypted_out = getattr(encrypted_tensor, comp)(
dim, keepdim=keepdim, one_hot=True
)
# Check argmax results
val_ref = reference[0]
out_encr = encrypted_out[1]
out_decr = out_encr.get_plain_text()
self.assertTrue((out_decr.sum(dim) == 1).all())
self.assertTrue(
(
out_decr.mul(tensor).sum(dim, keepdim=keepdim)
== val_ref
).all()
)
def test_argmax_argmin_pairwise(self):
"""Tests argmax and argmin for the deterministic constant (n^2) algorithm"""
self._argmax_argmin("pairwise")
def test_argmax_argmin_log_reduction(self):
"""Tests argmax and argmin for log reduction algorithm"""
self._argmax_argmin("log_reduction")
def test_argmax_argmin_double_log_reduction(self):
"""Tests argmax and argmin for double log reduction algorithm"""
self._argmax_argmin("double_log_reduction")
def test_argmax_argmin_accelerated_cascade(self):
"""Tests max and min for accelerated cascading algorithm"""
self._max_min("accelerated_cascade")
def _argmax_argmin(self, method):
"""Test argmax and argmin for specified algorithm"""
sizes = [
(),
(1,),
(5,),
(1, 1),
(1, 5),
(5, 5),
(1, 1, 1),
(5, 5, 5),
(1, 1, 1, 1),
(5, 5, 5, 5),
]
test_cases = [
torch.tensor(
[[1, 1, 2, 1, 4, 1, 3, 4]], dtype=torch.float, device=self.device
)
] + [self._get_random_test_tensor(size=size, is_float=False) for size in sizes]
for tensor in test_cases:
tensor = tensor.float()
encrypted_tensor = MPCTensor(tensor)
for comp in ["argmax", "argmin"]:
cmp = comp[3:]
value = getattr(tensor, cmp)()
# test with one_hot = False
with cfg.temp_override({"functions.max_method": method}):
encrypted_out = getattr(encrypted_tensor, comp)(one_hot=False)
# Must index into tensor since ties are broken randomly
# so crypten and PyTorch can return different indices.
# This checks that they index to the same value.
decrypted_out = encrypted_out.get_plain_text()
if tensor.dim() == 0: # if input is 0-d, argmax should be 0
self.assertEqual(decrypted_out, 0)
else:
decrypted_val = tensor.flatten()[decrypted_out.long()]
self.assertTrue(decrypted_val.eq(value).all().item())
# test with one_hot = False
with cfg.temp_override({"functions.max_method": method}):
encrypted_out = getattr(encrypted_tensor, comp)(one_hot=True)
one_hot_indices = (tensor == value).float()
decrypted_out = encrypted_out.get_plain_text()
self.assertTrue(decrypted_out.sum() == 1)
self.assertTrue(decrypted_out.mul(one_hot_indices).sum() == 1)
for dim in range(tensor.dim()):
for keepdim in [False, True]:
# Compute one-hot argmax/min reference in plaintext
values, indices = getattr(tensor, cmp)(dim, keepdim=keepdim)
# test with one_hot = False
with cfg.temp_override({"functions.max_method": method}):
encrypted_out = getattr(encrypted_tensor, comp)(
dim, keepdim=keepdim, one_hot=False
)
# Must index into tensor since ties are broken randomly
# so crypten and PyTorch can return different indices.
# This checks that they index to the same value.abs
decrypted_out = encrypted_out.get_plain_text()
if not keepdim:
decrypted_out = decrypted_out.unsqueeze(dim)
indices = indices.unsqueeze(dim)
decrypted_val = tensor.gather(dim, decrypted_out.long())
reference = tensor.gather(dim, indices)
self.assertTrue(decrypted_val.eq(reference).all().item())
# test with one_hot = True
with cfg.temp_override({"functions.max_method": method}):
encrypted_out = getattr(encrypted_tensor, comp)(
dim, keepdim=keepdim, one_hot=True
)
decrypted_out = encrypted_out.get_plain_text()
if not keepdim:
values = values.unsqueeze(dim)
one_hot_indices = tensor.eq(values).float()
self.assertTrue(decrypted_out.sum(dim).eq(1).all())
self.assertTrue(
decrypted_out.mul(one_hot_indices).sum(dim).eq(1).all()
)
def test_abs_sign(self):
"""Test absolute value function"""
for op in ["abs", "sign"]:
tensor = self._get_random_test_tensor(is_float=True)
if op == "sign":
# do not test on 0 since torch.tensor([0]).sign() = 0
tensor = tensor + (tensor == 0).float()
encrypted_tensor = MPCTensor(tensor)
reference = getattr(tensor, op)()
encrypted_out = getattr(encrypted_tensor, op)()
self._check(encrypted_out, reference, "%s failed" % op)
def test_approximations(self):
"""Test appoximate functions (exp, log, sqrt, reciprocal, pos_pow)"""
def test_with_inputs(func, input):
encrypted_tensor = MPCTensor(input)
reference = getattr(tensor, func)()
encrypted_out = getattr(encrypted_tensor, func)()
self._check(encrypted_out, reference, "%s failed" % func)
# Test on [-10, 10] range
full_range_cases = ["exp"]
tensor = torch.tensor(
[0.01 * i for i in range(-1000, 1001, 1)], device=self.device
)
for func in full_range_cases:
test_with_inputs(func, tensor)
# Test on [0, 10] range
tensor[tensor == 0] = 1.0
non_zero_cases = ["reciprocal"]
for func in non_zero_cases:
test_with_inputs(func, tensor)
# Test on [0, 10] range
tensor = tensor[1001:]
pos_cases = ["log", "sqrt"]
for func in pos_cases:
test_with_inputs(func, tensor)
# Test pos_pow with several exponents
encrypted_tensor = MPCTensor(tensor)
# Reduced the max_value so approximations have less absolute error
tensor_exponent = self._get_random_test_tensor(
max_value=2, size=tensor.size(), is_float=True
)
exponents = [-3, -2, -1, 0, 1, 2, 3, tensor_exponent]
exponents += [MPCTensor(tensor_exponent)]
for p in exponents:
if isinstance(p, MPCTensor):
reference = tensor.pow(p.get_plain_text())
else:
reference = tensor.pow(p)
encrypted_out = encrypted_tensor.pos_pow(p)
self._check(encrypted_out, reference, f"pos_pow failed with power {p}")
def test_norm(self):
"""Tests p-norm"""
for p in [1, 1.5, 2, 3, float("inf"), "fro"]:
for dim in [None, 0, 1, 2]:
tensor = self._get_random_test_tensor(size=(3, 3, 3), is_float=True) / 5
if dim is None:
reference = tensor.norm(p=p)
else:
reference = tensor.norm(p=p, dim=dim)
encrypted = MPCTensor(tensor)
encrypted_out = encrypted.norm(p=p, dim=dim)
self._check(encrypted_out, reference, f"{p}-norm failed", tolerance=0.5)
def test_logistic(self):
"""Tests logistic functions (sigmoid, tanh)"""
tensor = torch.tensor(
[0.01 * i for i in range(-1000, 1001, 1)], device=self.device
)
encrypted_tensor = MPCTensor(tensor)
cases = ["sigmoid", "tanh"]
for func in cases:
reference = getattr(tensor, func)()
encrypted_out = getattr(encrypted_tensor, func)()
self._check(encrypted_out, reference, "%s failed" % func)
def test_hardtanh(self):
tensor = torch.arange(-10, 10, dtype=torch.float32)
encrypted = MPCTensor(tensor)
for minval in range(-10, 10):
for maxval in range(minval, 11):
reference = torch.nn.functional.hardtanh(tensor, minval, maxval)
encrypted_out = encrypted.hardtanh(minval, maxval)
self._check(encrypted_out, reference, "hardtanh failed")
def test_inplace_warning(self):
"""Tests that a warning is thrown that indicates that the `inplace` kwarg
is ignored when a function is called with `inplace=True`
"""
tensor = get_random_test_tensor(is_float=True)
encrypted = MPCTensor(tensor)
functions = ["dropout", "_feature_dropout"]
for func in functions:
warning_str = (
f"CrypTen {func} does not support inplace computation during training."
)
with self.assertLogs(logger=logging.getLogger(), level="WARNING") as cm:
getattr(encrypted, func)(inplace=True)
self.assertTrue(f"WARNING:root:{warning_str}" in cm.output)
def test_cos_sin(self):
"""Tests trigonometric functions (cos, sin)"""
tensor = torch.tensor(
[0.01 * i for i in range(-1000, 1001, 1)], device=self.device
)
encrypted_tensor = MPCTensor(tensor)
cases = ["cos", "sin"]
for func in cases:
reference = getattr(tensor, func)()
encrypted_out = getattr(encrypted_tensor, func)()
self._check(encrypted_out, reference, "%s failed" % func)
def test_softmax(self):
"""Test softmax and log_softmax function"""
for softmax_fn in ["softmax", "log_softmax"]:
# Test 0-dim tensor:
tensor = self._get_random_test_tensor(size=(), is_float=True)
reference = getattr(tensor, softmax_fn)(0)
encrypted_tensor = MPCTensor(tensor)
encrypted_out = getattr(encrypted_tensor, softmax_fn)(0)
self._check(encrypted_out, reference, "0-dim tensor %s failed" % softmax_fn)
# Test all other sizes
sizes = [
(1,),
(5,),
(1, 1),
(1, 5),
(5, 1),
(5, 5),
(1, 5, 5),
(5, 1, 5),
(5, 5, 1),
(5, 5, 5),
(1, 5, 5, 5),
(5, 5, 5, 5),
]
for size in sizes:
tensor = self._get_random_test_tensor(size=size, is_float=True) / 5
encrypted_tensor = MPCTensor(tensor)
for dim in range(tensor.dim()):
reference = getattr(tensor, softmax_fn)(dim)
encrypted_out = getattr(encrypted_tensor, softmax_fn)(dim)
self._check(encrypted_out, reference, "%s failed" % softmax_fn)
def test_get_set(self):
"""Tests element setting and getting by index"""
for tensor_type in [lambda x: x, MPCTensor]:
for size in range(1, 5):
# Test __getitem__
tensor = self._get_random_test_tensor(size=(size, size), is_float=True)
reference = tensor[:, 0]
encrypted_tensor = MPCTensor(tensor)
encrypted_out = encrypted_tensor[:, 0]
self._check(encrypted_out, reference, "getitem failed")
reference = tensor[0, :]
encrypted_out = encrypted_tensor[0, :]
self._check(encrypted_out, reference, "getitem failed")
# Test __setitem__
tensor2 = self._get_random_test_tensor(size=(size,), is_float=True)
reference = tensor.clone()
reference[:, 0] = tensor2
encrypted_out = MPCTensor(tensor)
encrypted2 = tensor_type(tensor2)
encrypted_out[:, 0] = encrypted2
self._check(
encrypted_out, reference, "%s setitem failed" % type(encrypted2)
)
reference = tensor.clone()
reference[0, :] = tensor2
encrypted_out = MPCTensor(tensor)
encrypted2 = tensor_type(tensor2)
encrypted_out[0, :] = encrypted2
self._check(
encrypted_out, reference, "%s setitem failed" % type(encrypted2)
)
def test_pad(self):
"""Tests padding"""
sizes = [(1,), (5,), (1, 1), (5, 5), (5, 5, 5), (5, 3, 32, 32)]
pads = [
(0, 0, 0, 0),
(1, 0, 0, 0),
(0, 1, 0, 0),
(0, 0, 1, 0),
(0, 0, 0, 1),
(1, 1, 1, 1),
(2, 2, 1, 1),
(2, 2, 2, 2),
]
for size in sizes:
tensor = self._get_random_test_tensor(size=size, is_float=True)
encrypted_tensor = MPCTensor(tensor)
for pad in pads:
for value in [0, 1, 10]:
if tensor.dim() < 2:
pad = pad[:2]
reference = torch.nn.functional.pad(tensor, pad, value=value)
encrypted_value = MPCTensor(value, device=self.device)
encrypted_out = encrypted_tensor.pad(pad, value=encrypted_value)
encrypted_out2 = encrypted_tensor.pad(pad, value=value)
self._check(encrypted_out, reference, "pad failed")
self._check(encrypted_out2, reference, "pad failed")
def test_index_add(self):
"""Test index_add function of encrypted tensor"""
index_add_functions = ["index_add", "index_add_"]
tensor_size1 = [5, 5, 5, 5]
index = torch.tensor(
[1, 2, 3, 4, 4, 2, 1, 3], dtype=torch.long, device=self.device
)
for dimension in range(0, 4):
tensor_size2 = [5, 5, 5, 5]
tensor_size2[dimension] = index.size(0)
for func in index_add_functions:
for tensor_type in [lambda x: x, MPCTensor]:
tensor1 = self._get_random_test_tensor(
size=tensor_size1, is_float=True
)
tensor2 = self._get_random_test_tensor(
size=tensor_size2, is_float=True
)
encrypted = MPCTensor(tensor1)
encrypted2 = tensor_type(tensor2)
reference = getattr(tensor1, func)(dimension, index, tensor2)
encrypted_out = getattr(encrypted, func)(
dimension, index, encrypted2
)
private_type = tensor_type == MPCTensor
self._check(
encrypted_out,
reference,
"%s %s failed"
% ("private" if private_type else "public", func),
)
if func.endswith("_"):
# Check in-place index_add worked
self._check(
encrypted,
reference,
"%s %s failed"
% ("private" if private_type else "public", func),
)
else:
# Check original is not modified
self._check(
encrypted,
tensor1,
"%s %s failed"
% (
"private" if tensor_type == MPCTensor else "public",
func,
),
)
def test_scatter(self):
"""Test scatter/scatter_add function of encrypted tensor"""
funcs = ["scatter", "scatter_", "scatter_add", "scatter_add_"]
sizes = [(5, 5), (5, 5, 5), (5, 5, 5, 5)]
for func in funcs:
for size in sizes:
for tensor_type in [lambda x: x, MPCTensor]:
for dim in range(len(size)):
tensor1 = self._get_random_test_tensor(size=size, is_float=True)
tensor2 = self._get_random_test_tensor(size=size, is_float=True)
index = self._get_random_test_tensor(size=size, is_float=False)
index = index.abs().clamp(0, 4)
encrypted = MPCTensor(tensor1)
encrypted2 = tensor_type(tensor2)
reference = getattr(tensor1, func)(dim, index, tensor2)
encrypted_out = getattr(encrypted, func)(dim, index, encrypted2)
private = tensor_type == MPCTensor
self._check(
encrypted_out,
reference,
"%s %s failed" % ("private" if private else "public", func),
)
if func.endswith("_"):
# Check in-place scatter/scatter_add worked
self._check(
encrypted,
reference,
"%s %s failed"
% ("private" if private else "public", func),
)
else:
# Check original is not modified
self._check(
encrypted,
tensor1,
"%s %s failed"
% ("private" if private else "public", func),
)
def test_broadcast_arithmetic_ops(self):
"""Test broadcast of arithmetic functions."""
arithmetic_functions = ["add", "sub", "mul", "div"]
# TODO: Add broadcasting for pos_pow since it can take a tensor argument
arithmetic_sizes = [
(),
(1,),
(2,),
(1, 1),
(1, 2),
(2, 1),
(2, 2),
(1, 1, 1),
(1, 1, 2),
(1, 2, 1),
(2, 1, 1),
(2, 2, 2),
(1, 1, 1, 1),
(1, 1, 1, 2),
(1, 1, 2, 1),
(1, 2, 1, 1),
(2, 1, 1, 1),
(2, 2, 2, 2),
]
for tensor_type in [lambda x: x, MPCTensor]:
for func in arithmetic_functions:
for size1, size2 in itertools.combinations(arithmetic_sizes, 2):
exclude_zero = True if func == "div" else False
# multiply denominator by 10 to avoid dividing by small num
const = 10 if func == "div" else 1
tensor1 = self._get_random_test_tensor(size=size1, is_float=True)
tensor2 = self._get_random_test_tensor(
size=size2, is_float=True, ex_zero=exclude_zero
)
tensor2 *= const
encrypted1 = MPCTensor(tensor1)
encrypted2 = tensor_type(tensor2)
reference = getattr(tensor1, func)(tensor2)
encrypted_out = getattr(encrypted1, func)(encrypted2)
private = isinstance(encrypted2, MPCTensor)
self._check(
encrypted_out,
reference,
"%s %s broadcast failed"
% ("private" if private else "public", func),
)
# Test with integer tensor
tensor2 = self._get_random_test_tensor(
size=size2, is_float=False, ex_zero=exclude_zero
)
tensor2 *= const
reference = getattr(tensor1, func)(tensor2.float())
encrypted_out = getattr(encrypted1, func)(tensor2)
self._check(
encrypted_out,
reference,
"%s broadcast failed with public integer tensor" % func,
)
def test_broadcast_matmul(self):
"""Test broadcast of matmul."""
matmul_sizes = [(1, 1), (1, 5), (5, 1), (5, 5)]
batch_dims = [(), (1,), (5,), (1, 1), (1, 5), (5, 5)]
for tensor_type in [lambda x: x, MPCTensor]:
for size in matmul_sizes:
for batch1, batch2 in itertools.combinations(batch_dims, 2):
size1 = (*batch1, *size)
size2 = (*batch2, *size)
tensor1 = self._get_random_test_tensor(size=size1, is_float=True)
tensor2 = self._get_random_test_tensor(size=size2, is_float=True)
tensor2 = tensor2.transpose(-2, -1)
encrypted1 = MPCTensor(tensor1)
encrypted2 = tensor_type(tensor2)
reference = tensor1.matmul(tensor2)
encrypted_out = encrypted1.matmul(encrypted2)
private = isinstance(encrypted2, MPCTensor)
self._check(
encrypted_out,
reference,
"%s matmul broadcast failed"
% ("private" if private else "public"),
)
# Test with integer tensor
tensor2 = self._get_random_test_tensor(size=size2, is_float=False)
tensor2 = tensor2.float().transpose(-2, -1)
reference = tensor1.matmul(tensor2)
encrypted_out = encrypted1.matmul(tensor2)
self._check(
encrypted_out,
reference,
"matmul broadcast failed with public integer tensor",
)
def test_inplace(self):
"""Test inplace vs. out-of-place functions"""
for op in ["add", "sub", "mul", "div"]:
for tensor_type in [lambda x: x, MPCTensor]:
tensor1 = self._get_random_test_tensor(is_float=True)
tensor2 = self._get_random_test_tensor(is_float=True)
reference = getattr(torch, op)(tensor1, tensor2)
encrypted1 = MPCTensor(tensor1)
encrypted2 = tensor_type(tensor2)
input_tensor_id = id(encrypted1._tensor)
input_encrypted_id = id(encrypted1)
# Test that out-of-place functions do not modify the input
private = isinstance(encrypted2, MPCTensor)
encrypted_out = getattr(encrypted1, op)(encrypted2)
self._check(
encrypted1,
tensor1,
"%s out-of-place %s modifies input"
% ("private" if private else "public", op),
)
self._check(
encrypted_out,
reference,
"%s out-of-place %s produces incorrect output"
% ("private" if private else "public", op),
)
self.assertFalse(id(encrypted_out._tensor) == input_tensor_id)
self.assertFalse(id(encrypted_out) == input_encrypted_id)
# Test that in-place functions modify the input
encrypted_out = getattr(encrypted1, op + "_")(encrypted2)
self._check(
encrypted1,
reference,
"%s in-place %s_ does not modify input"
% ("private" if private else "public", op),
)
self._check(
encrypted_out,
reference,
"%s in-place %s_ produces incorrect output"
% ("private" if private else "public", op),
)
self.assertTrue(id(encrypted_out._tensor) == input_tensor_id)
self.assertTrue(id(encrypted_out) == input_encrypted_id)
def test_copy_clone(self):
"""Tests shallow_copy and clone of encrypted tensors."""
sizes = [(5,), (1, 5), (5, 10, 15)]
for size in sizes:
tensor = self._get_random_test_tensor(size=size, is_float=True)
encrypted_tensor = MPCTensor(tensor)
# test shallow_copy
encrypted_tensor_shallow = encrypted_tensor.shallow_copy()
self.assertEqual(
id(encrypted_tensor_shallow._tensor), id(encrypted_tensor._tensor)
)
self._check(encrypted_tensor_shallow, tensor, "shallow_copy failed")
# test clone
encrypted_tensor_clone = encrypted_tensor.clone()
self.assertNotEqual(
id(encrypted_tensor_clone._tensor), id(encrypted_tensor._tensor)
)
self._check(encrypted_tensor_clone, tensor, "clone failed")
def test_copy_(self):
"""Tests copy_ function."""
sizes = [(5,), (1, 5), (5, 10, 15)]
for size in sizes:
tensor1 = self._get_random_test_tensor(size=size, is_float=True)
tensor2 = self._get_random_test_tensor(size=size, is_float=True)
encrypted_tensor1 = MPCTensor(tensor1)
encrypted_tensor2 = MPCTensor(tensor2)
encrypted_tensor1.copy_(encrypted_tensor2)
self._check(encrypted_tensor1, tensor2, "copy_ failed")
def test_index_select(self):
"""Tests index_select of encrypted tensors."""
sizes = [(5,), (5, 10), (5, 10, 15)]
for size in sizes:
tensor = self._get_random_test_tensor(size=size, is_float=True)
encrypted_tensor = MPCTensor(tensor)
indices = [[0], [0, 3], [0, 2, 4]]
for dim in range(tensor.dim()):
for index in indices:
index_tensor = torch.tensor(
index, dtype=torch.long, device=self.device
)
reference = tensor.index_select(dim, index_tensor)
encrypted_out = encrypted_tensor.index_select(dim, index_tensor)
self._check(
encrypted_out,
reference,
"index_select failed at dim {dim} and index {index}",
)
def test_narrow(self):
"""Tests narrow function."""
sizes = [(5, 6), (5, 6, 7), (6, 7, 8, 9)]
for size in sizes:
tensor = self._get_random_test_tensor(size=size, is_float=True)
encr_tensor = MPCTensor(tensor)
for dim in range(len(size)):
for start in range(size[dim] - 2):
for length in range(1, size[dim] - start):
tensor_narrow = tensor.narrow(dim, start, length)
encr_tensor_narrow = encr_tensor.narrow(dim, start, length)
self._check(
encr_tensor_narrow,
tensor_narrow,
"narrow failed along dimension %d" % dim,
)
def test_repeat_expand(self):
"""Tests repeat and expand of encrypted tensors."""
sizes = [(1, 8), (4, 1, 8)]
repeat_dims = [(4, 2, 1), (4, 2, 10)]
expand_dims = [(4, 2, 8), (4, 5, 8), (10, 4, 5, 8)]
for size in sizes:
tensor = self._get_random_test_tensor(size=size, is_float=True)
encrypted_tensor = MPCTensor(tensor)
for dims in repeat_dims:
encrypted_tensor_repeated = encrypted_tensor.repeat(*dims)
# test that repeat copies tensor's data
self.assertNotEqual(
id(encrypted_tensor_repeated._tensor), id(encrypted_tensor._tensor)
)
self._check(
encrypted_tensor_repeated,
tensor.repeat(*dims),
f"repeat failed with dims {dims}",
)
for dims in expand_dims:
encrypted_tensor_expanded = encrypted_tensor.expand(*dims)
# test that expand creates a view into the same underlying tensor
self.assertNotEqual(
id(encrypted_tensor_expanded.share), id(encrypted_tensor.share)
)
self._check(
encrypted_tensor_expanded,
tensor.expand(*dims),
f"repeat failed with dims {dims}",
)
def test_view_flatten(self):
"""Tests view and flatten of encrypted tensors."""
sizes = [(100,), (4, 25), (2, 5, 10)]
for size in sizes:
tensor = self._get_random_test_tensor(size=size, is_float=True)
encrypted_tensor = MPCTensor(tensor)
for dim in range(tensor.dim()):
self._check(
encrypted_tensor.flatten(start_dim=dim),
tensor.flatten(start_dim=dim),
f"flatten failed with dim {dim}",
)
shapes = [100, (5, 20), (10, 2, 5), (-1, 10)]
for shape in shapes:
self._check(
encrypted_tensor.view(shape),
tensor.view(shape),
f"view failed with shape {shape}",
)
def test_roll(self):
"""Tests roll of encrypted tensors."""
sizes = [(10, 1), (5, 2), (5, 10, 15)]
for size in sizes:
tensor = self._get_random_test_tensor(size=size, is_float=True)
encrypted_tensor = MPCTensor(tensor)
roll_shifts = [1, 2, 3, (2, 1)]
roll_dims = [0, 1, 0, (0, 1)]
for shifts, dims in zip(roll_shifts, roll_dims):
encrypted_tensor_rolled = encrypted_tensor.roll(shifts, dims=dims)
self.assertEqual(encrypted_tensor_rolled.numel(), tensor.numel())
self._check(
encrypted_tensor_rolled,
tensor.roll(shifts, dims=dims),
f"roll failed with shift {shifts} and dims {dims}",
)
def test_unfold(self):
"""Tests unfold of encrypted tensors."""
tensor_sizes = [(8,), (15, 10, 5), (5, 10, 15, 20)]
for tensor_size in tensor_sizes:
tensor = self._get_random_test_tensor(size=tensor_size, is_float=True)
encrypted_tensor = MPCTensor(tensor)
for size, step in itertools.product(range(1, 4), range(1, 4)):
# check unfold along higher dimension if possible
for dim in range(tensor.dim()):
self._check(
encrypted_tensor.unfold(dim, size, step),
tensor.unfold(dim, size, step),
"unfold failed with dim "
f"{dim}, size {size}, and step {step}",
)
def test_to(self):
"""Tests Arithemetic/Binary SharedTensor type conversions."""
from crypten.mpc.ptype import ptype as Ptype
tensor_sizes = [(), (1,), (5,), (1, 1), (5, 5), (1, 1, 1), (5, 5, 5)]
for size in tensor_sizes:
tensor = self._get_random_test_tensor(size=size, is_float=True)
encrypted_tensor = MPCTensor(tensor)
self.assertEqual(encrypted_tensor.ptype, Ptype.arithmetic)
binary_encrypted_tensor = encrypted_tensor.to(Ptype.binary)
self.assertEqual(binary_encrypted_tensor.ptype, Ptype.binary)
# check original encrypted_tensor was not modified after conversion
self._check(
encrypted_tensor,
tensor,
"encrypted_tensor was modified during conversion to BinarySharedTensor.",
)
encrypted_from_binary = binary_encrypted_tensor.to(Ptype.arithmetic)
self._check(
encrypted_from_binary,
tensor,
"to failed from BinarySharedTensor to ArithmeticSharedTensor",
)
# Test API
tensor = self._get_random_test_tensor(size=(5,), is_float=True)
encrypted_tensor = MPCTensor(tensor)
if torch.cuda.is_available():
encrypted_tensor = encrypted_tensor.to("cuda")
self.assertEqual(encrypted_tensor.device.type, "cuda")
self.assertEqual(encrypted_tensor.ptype, Ptype.arithmetic)
self._check(
encrypted_tensor,
tensor,
"encrypted_tensor was modified during conversion to cuda",
)
encrypted_tensor = encrypted_tensor.to(device="cuda")
self.assertEqual(encrypted_tensor.device.type, "cuda")
self.assertEqual(encrypted_tensor.ptype, Ptype.arithmetic)
self._check(
encrypted_tensor,
tensor,
"encrypted_tensor was modified during conversion to cuda",
)
encrypted_tensor = encrypted_tensor.to("cpu")
self.assertEqual(encrypted_tensor.device.type, "cpu")
self.assertEqual(encrypted_tensor.ptype, Ptype.arithmetic)
self._check(
encrypted_tensor,
tensor,
"encrypted_tensor was modified during conversion to cpu",
)
encrypted_tensor = encrypted_tensor.to(device="cpu")
self.assertEqual(encrypted_tensor.device.type, "cpu")
self.assertEqual(encrypted_tensor.ptype, Ptype.arithmetic)
self._check(
encrypted_tensor,
tensor,
"encrypted_tensor was modified during conversion to cpu",
)
encrypted_tensor = encrypted_tensor.to(ptype=Ptype.binary)
self.assertEqual(encrypted_tensor.device.type, "cpu")
self.assertEqual(encrypted_tensor.ptype, Ptype.binary)
self._check(
encrypted_tensor,
tensor,
"encrypted_tensor was modified during conversion to BinarySharedTensor.",
)
encrypted_tensor = encrypted_tensor.to(ptype=Ptype.arithmetic)
self.assertEqual(encrypted_tensor.device.type, "cpu")
self.assertEqual(encrypted_tensor.ptype, Ptype.arithmetic)
self._check(
encrypted_tensor,
tensor,
"encrypted_tensor was modified during conversion to ArithmeticSharedTensor.",
)
def test_cumsum(self):
"""Tests cumulative sum on encrypted tensors."""
sizes = [(8,), (5, 10), (15, 10, 5)]
for size in sizes:
tensor = self._get_random_test_tensor(size=size, is_float=True)
encrypted_tensor = MPCTensor(tensor)
for dim in range(tensor.dim()):
self._check(
encrypted_tensor.cumsum(dim),
tensor.cumsum(dim),
f"cumsum failed along {dim} dim",
)
def test_trace(self):
"""Tests trace operation on 2D encrypted tensors."""
sizes = [(3, 3), (10, 10), (2, 3)]
for size in sizes:
tensor = self._get_random_test_tensor(size=size, is_float=True)
encrypted_tensor = MPCTensor(tensor)
self._check(encrypted_tensor.trace(), tensor.trace(), "trace failed")
def test_flip(self):
"""Tests flip operation on encrypted tensors."""
sizes = [(5,), (5, 10), (5, 10, 15)]
for size in sizes:
tensor = self._get_random_test_tensor(size=size, is_float=True)
encrypted_tensor = MPCTensor(tensor)
flip_dims = [(0,), (0, 1), (0, 1, 2)]
for dims in flip_dims:
if len(dims) <= tensor.dim():
self._check(
encrypted_tensor.flip(dims),
tensor.flip(dims),
f"flip failed with {dims} dims",
)
def test_control_flow_failure(self):
"""Tests that control flow fails as expected"""
tensor = self._get_random_test_tensor(is_float=True)
encrypted_tensor = MPCTensor(tensor)
with self.assertRaises(RuntimeError):
if encrypted_tensor:
pass
with self.assertRaises(RuntimeError):
tensor = 5 if encrypted_tensor else 0
with self.assertRaises(RuntimeError):
if False:
pass
elif encrypted_tensor:
pass
def test_where(self):
"""Tests where() conditional element selection"""
sizes = [(10,), (5, 10), (1, 5, 10)]
y_types = [lambda x: x, MPCTensor]
for size, y_type in itertools.product(sizes, y_types):
tensor1 = self._get_random_test_tensor(size=size, is_float=True)
encrypted_tensor1 = MPCTensor(tensor1)
tensor2 = self._get_random_test_tensor(size=size, is_float=True)
encrypted_tensor2 = y_type(tensor2)
condition_tensor = (
self._get_random_test_tensor(max_value=1, size=size, is_float=False) + 1
)
condition_encrypted = MPCTensor(condition_tensor)
condition_bool = condition_tensor.bool()
reference_out = tensor1.where(condition_bool, tensor2)
encrypted_out = encrypted_tensor1.where(condition_bool, encrypted_tensor2)
y_is_private = y_type == MPCTensor
self._check(
encrypted_out,
reference_out,
f"{'private' if y_is_private else 'public'} y "
"where failed with public condition",
)
encrypted_out = encrypted_tensor1.where(
condition_encrypted, encrypted_tensor2
)
self._check(
encrypted_out,
reference_out,
f"{'private' if y_is_private else 'public'} y "
"where failed with private condition",
)
# test scalar y
scalar = self._get_random_test_tensor(max_value=0, size=[1], is_float=True)
self._check(
encrypted_tensor1.where(condition_bool, scalar),
tensor1.where(condition_bool, scalar),
"where failed against scalar y with public condition",
)
self._check(
encrypted_tensor1.where(condition_encrypted, scalar),
tensor1.where(condition_bool, scalar),
"where failed against scalar y with private condition",
)
def test_unbind(self):
"""Tests unbind"""
sizes = [
(1,),
(5,),
(1, 1),
(1, 5),
(5, 5),
(1, 1, 1),
(5, 5, 5),
(1, 1, 1, 1),
(5, 5, 5, 5),
]
for size in sizes:
tensor = self._get_random_test_tensor(size=size, is_float=True)
encrypted = MPCTensor(tensor)
for dim in range(tensor.dim()):
reference = tensor.unbind(dim)
encrypted_out = encrypted.unbind(dim)
self._check_tuple(encrypted_out, reference, "unbind failed")
def test_split(self):
"""Tests split"""
sizes = [
(1,),
(5,),
(1, 1),
(1, 5),
(5, 5),
(1, 1, 1),
(5, 5, 5),
(1, 1, 1, 1),
(5, 5, 5, 5),
]
for size in sizes:
tensor = self._get_random_test_tensor(size=size, is_float=True)
encrypted = MPCTensor(tensor)
for dim in range(tensor.dim()):
# Get random split
split = self._get_random_test_tensor(
size=(), max_value=tensor.size(dim)
)
split = split.abs().clamp(0, tensor.size(dim) - 1)
split = split.item()
# Test int split
int_split = 1 if split == 0 else split
reference = tensor.split(int_split, dim=dim)
encrypted_out = encrypted.split(int_split, dim=dim)
self._check_tuple(encrypted_out, reference, "split failed")
# Test list split
split = [split, tensor.size(dim) - split]
reference = tensor.split(split, dim=dim)
encrypted_out = encrypted.split(split, dim=dim)
self._check_tuple(encrypted_out, reference, "split failed")
def test_set(self):
"""Tests set correctly re-assigns encrypted shares"""
sizes = [(1, 5), (5, 10), (15, 10, 5)]
for size in sizes:
tensor1 = self._get_random_test_tensor(size=size, is_float=True)
encrypted1 = MPCTensor(tensor1)
tensor2 = self._get_random_test_tensor(size=size, is_float=True)
encrypted2 = MPCTensor(tensor2)
# check encrypted set
encrypted1.set(encrypted2)
self._check(
encrypted1, tensor2, f"set with encrypted other failed with size {size}"
)
# check plain text set
encrypted1 = MPCTensor(tensor1)
encrypted1.set(tensor2)
self._check(
encrypted1,
tensor2,
f"set with unencrypted other failed with size {size}",
)
def test_polynomial(self):
"""Tests polynomial function"""
sizes = [
(1,),
(5,),
(1, 1),
(1, 5),
(5, 5),
(1, 1, 1),
(5, 5, 5),
(1, 1, 1, 1),
(5, 5, 5, 5),
]
for size in sizes:
tensor = self._get_random_test_tensor(size=size, max_value=3, is_float=True)
encrypted = MPCTensor(tensor)
for terms in range(1, 5):
coeffs = self._get_random_test_tensor(
size=(terms,), max_value=3, is_float=True
)
reference = torch.zeros(size=tensor.size(), device=self.device)
for i, term in enumerate(coeffs.tolist()):
reference += term * tensor.pow(i + 1)
# Test list coeffs
encrypted_out = encrypted.polynomial(coeffs.tolist())
self._check(encrypted_out, reference, "polynomial failed")
# Test plaintext tensor coeffs
encrypted_out = encrypted.polynomial(coeffs)
self._check(encrypted_out, reference, "polynomial failed")
# Test encrypted tensor coeffs
coeffs_enc = MPCTensor(coeffs)
encrypted_out = encrypted.polynomial(coeffs_enc)
self._check(encrypted_out, reference, "polynomial failed")
def test_gather(self):
"""Test gather function of encrypted tensor"""
sizes = [(5, 5), (5, 5, 5), (5, 5, 5, 5)]
for size in sizes:
for dim in range(len(size)):
tensor = self._get_random_test_tensor(size=size, is_float=True)
index = self._get_random_test_tensor(size=size, is_float=False)
index = index.abs().clamp(0, 4)
encrypted = MPCTensor(tensor)
reference = tensor.gather(dim, index)
encrypted_out = encrypted.gather(dim, index)
self._check(encrypted_out, reference, f"gather failed with size {size}")
def test_dropout(self):
"""
Tests the dropout functions. Directly compares the zero and non-zero
entries of the input tensor, since we cannot force the encrypted and
unencrypted versions to generate identical random output. Also confirms
that the number of zeros in the encrypted dropout function is as expected.
"""
all_prob_values = [x * 0.2 for x in range(5)]
def get_first_nonzero_value(x):
x = x.flatten()
x = x[x.abs().ge(1e-4)]
x = x.take(torch.tensor(0))
return x
# check that the encrypted and plaintext versions scale
# identically, by testing on all-ones tensor
for prob in all_prob_values:
tensor = torch.ones([10, 10, 10], device=self.device).float()
encr_tensor = MPCTensor(tensor)
dropout_encr = encr_tensor.dropout(prob, training=True)
dropout_decr = dropout_encr.get_plain_text()
dropout_plain = F.dropout(tensor, prob, training=True)
# All non-zero values should be identical in both tensors, so
# compare any one of them
decr_nonzero_value = get_first_nonzero_value(dropout_decr)
plaintext_nonzero_value = get_first_nonzero_value(dropout_plain)
self.assertTrue(
math.isclose(
decr_nonzero_value,
plaintext_nonzero_value,
rel_tol=1e-2,
abs_tol=1e-2,
)
)
for dropout_fn in ["dropout", "_feature_dropout"]:
for prob in all_prob_values:
for size in [(5, 10), (5, 10, 15), (5, 10, 15, 20)]:
for inplace in [False, True]:
for training in [False, True]:
tensor = self._get_random_test_tensor(
size=size, ex_zero=True, min_value=1.0, is_float=True
)
encr_tensor = MPCTensor(tensor)
dropout_encr = getattr(encr_tensor, dropout_fn)(
prob, inplace=inplace, training=training
)
if training:
# Check the scaling for non-zero elements
dropout_decr = dropout_encr.get_plain_text()
scaled_tensor = tensor / (1 - prob)
reference = dropout_decr.where(
dropout_decr == 0, scaled_tensor
)
else:
reference = tensor
self._check(
dropout_encr,
reference,
f"dropout failed with size {size} and probability "
f"{prob}",
)
if inplace:
self._check(
encr_tensor,
reference,
f"in-place dropout failed with size {size} and "
f"probability {prob}",
)
else:
self._check(
encr_tensor,
tensor,
"out-of-place dropout modifies input",
)
# Check that channels that are zeroed are all zeros
if dropout_fn in [
"dropout2d",
"dropout3d",
"feature_dropout",
]:
dropout_encr_flat = dropout_encr.flatten(
start_dim=0, end_dim=1
)
dropout_flat = dropout_encr_flat.get_plain_text()
for i in range(0, dropout_flat.size(0)):
all_zeros = (dropout_flat[i] == 0).all()
all_nonzeros = (dropout_flat[i] != 0).all()
self.assertTrue(
all_zeros or all_nonzeros,
f"{dropout_fn} failed for size {size} with "
f"training {training} and inplace {inplace}",
)
# Check the expected number of zero elements
# For speed, restrict test to single p = 0.4
encr_tensor = MPCTensor(torch.empty((int(1e5), 2, 2)).fill_(1).to(self.device))
dropout_encr = encr_tensor.dropout(0.4)
dropout_tensor = dropout_encr.get_plain_text()
frac_zero = float((dropout_tensor == 0).sum()) / dropout_tensor.nelement()
self.assertTrue(math.isclose(frac_zero, 0.4, rel_tol=1e-2, abs_tol=1e-2))
def test_tuple_cache(self):
# Skip RSS setting since it does not generate tuples
if cfg.mpc.protocol == "replicated":
return
# TODO: encorporate wrap_rng for 3PC+ settings
if comm.get().get_world_size() > 2:
return
provider = crypten.mpc.get_default_provider()
# Test tracing attribute
crypten.trace()
self.assertTrue(provider.tracing)
x = get_random_test_tensor(is_float=True)
x = crypten.cryptensor(x)
_ = x.square()
_ = x * x
_ = x.matmul(x.t())
_ = x.relu()
y = x.unsqueeze(0)
_ = y.conv1d(y, stride=2)
# Populate reference requests
ref_names = ["square"]
ref_names += ["generate_additive_triple"] * 2
ref_names += ["generate_binary_triple"] * 7 + ["B2A_rng"]
ref_names += ["generate_additive_triple"] * 2
ref_args = [
(torch.Size([1, 5]),),
(torch.Size([1, 5]), torch.Size([1, 5]), "mul"),
(torch.Size([1, 5]), torch.Size([5, 1]), "matmul"),
(torch.Size([1, 1, 5]), torch.Size([1, 1, 5])),
]
ref_args += [(torch.Size([2, 1, 1, 5]), torch.Size([2, 1, 1, 5]))] * 6
ref_args += [(torch.Size([1, 5]),)]
ref_args += [(torch.Size([1, 5]), torch.Size([1, 5]), "mul")]
ref_args += [(torch.Size([1, 1, 5]), torch.Size([1, 1, 5]), "conv1d")]
kwargs = {"device": torch.device("cpu")}
conv_kwargs = {"device": torch.device("cpu"), "stride": 2}
requests = [(ref_names[i], ref_args[i], kwargs) for i in range(12)]
requests += [(ref_names[12], ref_args[12], conv_kwargs)]
self.assertEqual(
provider.request_cache,
requests,
"TupleProvider request cache incorrect",
)
crypten.trace(False)
self.assertFalse(provider.tracing)
# Check that cache populates as expected
crypten.fill_cache()
kwargs = frozenset(kwargs.items())
conv_kwargs = frozenset(conv_kwargs.items())
keys = [(ref_names[i], ref_args[i], kwargs) for i in range(12)]
keys += [(ref_names[12], ref_args[12], conv_kwargs)]
self.assertEqual(
set(provider.tuple_cache.keys()),
set(keys),
"TupleProvider tuple_cache populated incorrectly",
)
# Test that function calls return from cache when trace is off
crypten.trace(False)
_ = x.square()
_ = x * x
_ = x.matmul(x.t())
_ = x.relu()
y = x.unsqueeze(0)
_ = y.conv1d(y, stride=2)
for v in provider.tuple_cache.values():
self.assertEqual(
len(v), 0, msg="TupleProvider is not popping tuples properly from cache"
)
# Run all unit tests with both TFP and TTP providers
class TestTFP(MultiProcessTestCase, TestMPC):
def setUp(self):
self._original_provider = cfg.mpc.provider
crypten.CrypTensor.set_grad_enabled(False)
cfg.mpc.provider = "TFP"
super(TestTFP, self).setUp()
def tearDown(self):
cfg.mpc.provider = self._original_provider
crypten.CrypTensor.set_grad_enabled(True)
super(TestTFP, self).tearDown()
class TestTTP(MultiProcessTestCase, TestMPC):
def setUp(self):
self._original_provider = cfg.mpc.provider
crypten.CrypTensor.set_grad_enabled(False)
cfg.mpc.provider = "TTP"
super(TestTTP, self).setUp()
def tearDown(self):
cfg.mpc.provider = self._original_provider
crypten.CrypTensor.set_grad_enabled(True)
super(TestTTP, self).tearDown()
class Test3PC(MultiProcessTestCase, TestMPC):
def setUp(self):
super(Test3PC, self).setUp(world_size=3)
class TestRSS(MultiProcessTestCase, TestMPC):
def setUp(self):
self._original_protocol = cfg.mpc.protocol
cfg.mpc.protocol = "replicated"
super(TestRSS, self).setUp(world_size=3)
def tearDown(self):
cfg.mpc.protocol = self._original_protocol
super(TestRSS, self).tearDown()
# This code only runs when executing the file outside the test harness (e.g.
# via the buck target of another test)
if __name__ == "__main__":
unittest.main()
| en | 0.819829 | #!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. This class tests all functions of MPCTensor. # Check sizes match # Make sure these don't crash Tests MPCTensor.from_shares() functionality. # settings for test: # generate arithmetic sharing of reference tensor: # generate binary sharing of reference tensor: # return shares and reference: # test both types: # generate shares, sync them between parties, and create tensor: # check resulting tensor: Tests share attribute getter and setter Tests tensor encryption and decryption for both positive and negative values. # encryption and decryption without source: # test creation via new() function: # TODO: Implement broadcast_size on GPU # encryption and decryption with source: # MPCTensors cannot be initialized with None: Tests arithmetic functions on encrypted tensor. # Check in-place op worked # Check original is not modified # Check encrypted vector with encrypted scalar works. # Test radd, rsub, and rmul Tests sum reduction on encrypted tensor. Tests prod reduction on encrypted tensor. Test that ptype attribute creates the correct type of encrypted tensor Tests division of encrypted tensor by scalar and tensor. # multiply denominator by 10 to avoid dividing by small num Tests computing means of encrypted tensors. Tests computing variances of encrypted tensors. Test matrix multiplication. Test dot product of vector and encrypted tensor. # dot # ger # Test unsqueeze # Test squeeze # Check that the encrypted_out and encrypted point to the same # thing. # t() asserts dim == 2 Test convolution of encrypted tensor with public/private tensors. # group convolution is not supported on GPU Test convolution of encrypted tensor with public/private tensors. # group convolution is not supported on GPU # sample input: # sample filtering kernel: # perform filtering: # check that result is correct: Test max_pool of encrypted tensor. # Assert each kernel is one-hot # Populate tensor with kernel indices # Ensure encrypted indices are correct # Skip kernels that lead to 0-size outputs Test avg_pool of encrypted tensor. test adaptive_avg_pool2d and adaptive_max_pool2d # Test adaptive_avg_pool2d # Test adapvite_max_pool2d Tests take function on encrypted tensor # Test when dimension!=None # Test when dimension is default (i.e. None) Test negative on encrypted tensor. Test relu on encrypted tensor. # Generate some negative values Test comparators (>, >=, <, <=, ==, !=) # Check deterministic example to guarantee all combinations Tests max and min for the deterministic constant (n^2) algorithm Tests max and min for log reduction algorithm Tests max and min for double log reduction algorithm Tests max and min for accelerated cascading algorithm Test max and min for the specified algorithm # Test with one_hot = False # Check max / min values are correct # Test argmax / argmin values are correct # Must index into tensor since ties are broken randomly # so crypten and PyTorch can return different indices. # This checks that they index to the same value. # Test indices with one_hot = True # Check argmax results Tests argmax and argmin for the deterministic constant (n^2) algorithm Tests argmax and argmin for log reduction algorithm Tests argmax and argmin for double log reduction algorithm Tests max and min for accelerated cascading algorithm Test argmax and argmin for specified algorithm # test with one_hot = False # Must index into tensor since ties are broken randomly # so crypten and PyTorch can return different indices. # This checks that they index to the same value. # if input is 0-d, argmax should be 0 # test with one_hot = False # Compute one-hot argmax/min reference in plaintext # test with one_hot = False # Must index into tensor since ties are broken randomly # so crypten and PyTorch can return different indices. # This checks that they index to the same value.abs # test with one_hot = True Test absolute value function # do not test on 0 since torch.tensor([0]).sign() = 0 Test appoximate functions (exp, log, sqrt, reciprocal, pos_pow) # Test on [-10, 10] range # Test on [0, 10] range # Test on [0, 10] range # Test pos_pow with several exponents # Reduced the max_value so approximations have less absolute error Tests p-norm Tests logistic functions (sigmoid, tanh) Tests that a warning is thrown that indicates that the `inplace` kwarg is ignored when a function is called with `inplace=True` Tests trigonometric functions (cos, sin) Test softmax and log_softmax function # Test 0-dim tensor: # Test all other sizes Tests element setting and getting by index # Test __getitem__ # Test __setitem__ Tests padding Test index_add function of encrypted tensor # Check in-place index_add worked # Check original is not modified Test scatter/scatter_add function of encrypted tensor # Check in-place scatter/scatter_add worked # Check original is not modified Test broadcast of arithmetic functions. # TODO: Add broadcasting for pos_pow since it can take a tensor argument # multiply denominator by 10 to avoid dividing by small num # Test with integer tensor Test broadcast of matmul. # Test with integer tensor Test inplace vs. out-of-place functions # Test that out-of-place functions do not modify the input # Test that in-place functions modify the input Tests shallow_copy and clone of encrypted tensors. # test shallow_copy # test clone Tests copy_ function. Tests index_select of encrypted tensors. Tests narrow function. Tests repeat and expand of encrypted tensors. # test that repeat copies tensor's data # test that expand creates a view into the same underlying tensor Tests view and flatten of encrypted tensors. Tests roll of encrypted tensors. Tests unfold of encrypted tensors. # check unfold along higher dimension if possible Tests Arithemetic/Binary SharedTensor type conversions. # check original encrypted_tensor was not modified after conversion # Test API Tests cumulative sum on encrypted tensors. Tests trace operation on 2D encrypted tensors. Tests flip operation on encrypted tensors. Tests that control flow fails as expected Tests where() conditional element selection # test scalar y Tests unbind Tests split # Get random split # Test int split # Test list split Tests set correctly re-assigns encrypted shares # check encrypted set # check plain text set Tests polynomial function # Test list coeffs # Test plaintext tensor coeffs # Test encrypted tensor coeffs Test gather function of encrypted tensor Tests the dropout functions. Directly compares the zero and non-zero entries of the input tensor, since we cannot force the encrypted and unencrypted versions to generate identical random output. Also confirms that the number of zeros in the encrypted dropout function is as expected. # check that the encrypted and plaintext versions scale # identically, by testing on all-ones tensor # All non-zero values should be identical in both tensors, so # compare any one of them # Check the scaling for non-zero elements # Check that channels that are zeroed are all zeros # Check the expected number of zero elements # For speed, restrict test to single p = 0.4 # Skip RSS setting since it does not generate tuples # TODO: encorporate wrap_rng for 3PC+ settings # Test tracing attribute # Populate reference requests # Check that cache populates as expected # Test that function calls return from cache when trace is off # Run all unit tests with both TFP and TTP providers # This code only runs when executing the file outside the test harness (e.g. # via the buck target of another test) | 2.069287 | 2 |
src/python/turicreate/__init__.py | rplom/turicreate | 1 | 6630461 | <filename>src/python/turicreate/__init__.py
# -*- coding: utf-8 -*-
# Copyright © 2017 Apple Inc. All rights reserved.
#
# Use of this source code is governed by a BSD-3-clause license that can
# be found in the LICENSE.txt file or at https://opensource.org/licenses/BSD-3-Clause
'''
@package turicreate
...
Turi Create is a machine learning platform that enables data scientists and app
developers to easily create intelligent applications at scale.
'''
from __future__ import print_function as _
from __future__ import division as _
from __future__ import absolute_import as _
from ._deps import LazyModuleLoader as _LazyModuleLoader
from ._deps import LazyCallable as _LazyCallable
__version__ = '{{VERSION_STRING}}'
from turicreate.version_info import __version__
from turicreate.toolkits import evaluation
# expose name aggregate as global variable
# this won't interfere with import turicreate.aggregate as agg
aggregate = _LazyModuleLoader('turicreate.aggregate')
toolkits = _LazyModuleLoader('turicreate.toolkits')
# must load
from turicreate.data_structures.sgraph import Vertex, Edge
from turicreate.data_structures.sgraph import SGraph
from turicreate.data_structures.sarray import SArray
from turicreate.data_structures.sframe import SFrame
from turicreate.data_structures.sketch import Sketch
from turicreate.data_structures.image import Image
from .data_structures.sarray_builder import SArrayBuilder
from .data_structures.sframe_builder import SFrameBuilder
## bring load functions to the top level
from turicreate.data_structures.sgraph import load_sgraph
from turicreate.data_structures.sframe import load_sframe
from turicreate.data_structures.sarray import load_sarray
# lazy import under leaf module level
import turicreate.toolkits.clustering as clustering
from turicreate.toolkits.clustering import kmeans
from turicreate.toolkits.clustering import dbscan
# lazy import under leaf module level
import turicreate.toolkits.graph_analytics as graph_analytics
from turicreate.toolkits.graph_analytics import connected_components
from turicreate.toolkits.graph_analytics import shortest_path
from turicreate.toolkits.graph_analytics import kcore
from turicreate.toolkits.graph_analytics import pagerank
from turicreate.toolkits.graph_analytics import graph_coloring
from turicreate.toolkits.graph_analytics import triangle_counting
from turicreate.toolkits.graph_analytics import degree_counting
from turicreate.toolkits.graph_analytics import label_propagation
# lazy import under leaf module level
import turicreate.toolkits.recommender as recommender
from turicreate.toolkits.recommender import popularity_recommender
from turicreate.toolkits.recommender import item_similarity_recommender
from turicreate.toolkits.recommender import ranking_factorization_recommender
from turicreate.toolkits.recommender import item_content_recommender
from turicreate.toolkits.recommender import factorization_recommender
# lazy load under leaf node level
import turicreate.toolkits.regression as regression
from turicreate.toolkits.regression import boosted_trees_regression
from turicreate.toolkits.regression import random_forest_regression
from turicreate.toolkits.regression import decision_tree_regression
from turicreate.toolkits.regression import linear_regression
# lazy load under leaf node level
import turicreate.toolkits.classifier as classifier
from turicreate.toolkits.classifier import svm_classifier
from turicreate.toolkits.classifier import logistic_classifier
from turicreate.toolkits.classifier import boosted_trees_classifier
from turicreate.toolkits.classifier import random_forest_classifier
from turicreate.toolkits.classifier import decision_tree_classifier
from turicreate.toolkits.classifier import nearest_neighbor_classifier
# lazy load under leaf node level
from turicreate.toolkits.image_analysis import image_analysis
# self-encaps modules without from import statements
# we can use top-level lazy import for them
distances = _LazyModuleLoader('turicreate.toolkits.distances')
nearest_neighbors = _LazyModuleLoader('turicreate.toolkits.nearest_neighbors')
topci_model = _LazyModuleLoader('turicreate.toolkits.topci_model')
text_analytics = _LazyModuleLoader('turicreate.toolkits.text_analytics')
text_classifier = _LazyModuleLoader('turicreate.toolkits.text_classifier')
image_classifier = _LazyModuleLoader('turicreate.toolkits.image_classifier')
image_similarity = _LazyModuleLoader('turicreate.toolkits.image_similarity')
object_detector = _LazyModuleLoader('turicreate.toolkits.object_detector')
one_shot_object_detector = _LazyModuleLoader('turicreate.toolkits.one_shot_object_detector')
style_transfer = _LazyModuleLoader('turicreate.toolkits.style_transfer')
activity_classifier = _LazyModuleLoader('turicreate.toolkits.activity_classifier')
drawing_classifier = _LazyModuleLoader('turicreate.toolkits.drawing_classifier')
# modules that don't expose attributes from __init__.py
sound_classifier = _LazyModuleLoader('turicreate.toolkits.sound_classifier.sound_classifier')
audio_analysis = _LazyModuleLoader('turicreate.toolkits.audio_analysis.audio_analysis')
# lazy callable
load_images = _LazyCallable(image_analysis, 'load_images')
load_audio = _LazyCallable(audio_analysis, 'load_audio')
load_model = _LazyCallable(_LazyModuleLoader(
'turicreate.toolkits._model'), 'load_model')
################### Extension Importing ########################
import turicreate.extensions
from turicreate.extensions import ext_import
turicreate.extensions._add_meta_path()
# rewrite the extensions module
class _extensions_wrapper(object):
def __init__(self, wrapped):
self._wrapped = wrapped
self.__doc__ = wrapped.__doc__
def __getattr__(self, name):
try:
return getattr(self._wrapped, name)
except:
pass
turicreate._connect.main.get_unity()
return getattr(self._wrapped, name)
import sys as _sys
_sys.modules["turicreate.extensions"] = _extensions_wrapper(_sys.modules["turicreate.extensions"])
# rewrite the import
extensions = _sys.modules["turicreate.extensions"]
from .visualization import plot, show
# internal util
from turicreate._connect.main import launch as _launch
_launch()
| <filename>src/python/turicreate/__init__.py
# -*- coding: utf-8 -*-
# Copyright © 2017 Apple Inc. All rights reserved.
#
# Use of this source code is governed by a BSD-3-clause license that can
# be found in the LICENSE.txt file or at https://opensource.org/licenses/BSD-3-Clause
'''
@package turicreate
...
Turi Create is a machine learning platform that enables data scientists and app
developers to easily create intelligent applications at scale.
'''
from __future__ import print_function as _
from __future__ import division as _
from __future__ import absolute_import as _
from ._deps import LazyModuleLoader as _LazyModuleLoader
from ._deps import LazyCallable as _LazyCallable
__version__ = '{{VERSION_STRING}}'
from turicreate.version_info import __version__
from turicreate.toolkits import evaluation
# expose name aggregate as global variable
# this won't interfere with import turicreate.aggregate as agg
aggregate = _LazyModuleLoader('turicreate.aggregate')
toolkits = _LazyModuleLoader('turicreate.toolkits')
# must load
from turicreate.data_structures.sgraph import Vertex, Edge
from turicreate.data_structures.sgraph import SGraph
from turicreate.data_structures.sarray import SArray
from turicreate.data_structures.sframe import SFrame
from turicreate.data_structures.sketch import Sketch
from turicreate.data_structures.image import Image
from .data_structures.sarray_builder import SArrayBuilder
from .data_structures.sframe_builder import SFrameBuilder
## bring load functions to the top level
from turicreate.data_structures.sgraph import load_sgraph
from turicreate.data_structures.sframe import load_sframe
from turicreate.data_structures.sarray import load_sarray
# lazy import under leaf module level
import turicreate.toolkits.clustering as clustering
from turicreate.toolkits.clustering import kmeans
from turicreate.toolkits.clustering import dbscan
# lazy import under leaf module level
import turicreate.toolkits.graph_analytics as graph_analytics
from turicreate.toolkits.graph_analytics import connected_components
from turicreate.toolkits.graph_analytics import shortest_path
from turicreate.toolkits.graph_analytics import kcore
from turicreate.toolkits.graph_analytics import pagerank
from turicreate.toolkits.graph_analytics import graph_coloring
from turicreate.toolkits.graph_analytics import triangle_counting
from turicreate.toolkits.graph_analytics import degree_counting
from turicreate.toolkits.graph_analytics import label_propagation
# lazy import under leaf module level
import turicreate.toolkits.recommender as recommender
from turicreate.toolkits.recommender import popularity_recommender
from turicreate.toolkits.recommender import item_similarity_recommender
from turicreate.toolkits.recommender import ranking_factorization_recommender
from turicreate.toolkits.recommender import item_content_recommender
from turicreate.toolkits.recommender import factorization_recommender
# lazy load under leaf node level
import turicreate.toolkits.regression as regression
from turicreate.toolkits.regression import boosted_trees_regression
from turicreate.toolkits.regression import random_forest_regression
from turicreate.toolkits.regression import decision_tree_regression
from turicreate.toolkits.regression import linear_regression
# lazy load under leaf node level
import turicreate.toolkits.classifier as classifier
from turicreate.toolkits.classifier import svm_classifier
from turicreate.toolkits.classifier import logistic_classifier
from turicreate.toolkits.classifier import boosted_trees_classifier
from turicreate.toolkits.classifier import random_forest_classifier
from turicreate.toolkits.classifier import decision_tree_classifier
from turicreate.toolkits.classifier import nearest_neighbor_classifier
# lazy load under leaf node level
from turicreate.toolkits.image_analysis import image_analysis
# self-encaps modules without from import statements
# we can use top-level lazy import for them
distances = _LazyModuleLoader('turicreate.toolkits.distances')
nearest_neighbors = _LazyModuleLoader('turicreate.toolkits.nearest_neighbors')
topci_model = _LazyModuleLoader('turicreate.toolkits.topci_model')
text_analytics = _LazyModuleLoader('turicreate.toolkits.text_analytics')
text_classifier = _LazyModuleLoader('turicreate.toolkits.text_classifier')
image_classifier = _LazyModuleLoader('turicreate.toolkits.image_classifier')
image_similarity = _LazyModuleLoader('turicreate.toolkits.image_similarity')
object_detector = _LazyModuleLoader('turicreate.toolkits.object_detector')
one_shot_object_detector = _LazyModuleLoader('turicreate.toolkits.one_shot_object_detector')
style_transfer = _LazyModuleLoader('turicreate.toolkits.style_transfer')
activity_classifier = _LazyModuleLoader('turicreate.toolkits.activity_classifier')
drawing_classifier = _LazyModuleLoader('turicreate.toolkits.drawing_classifier')
# modules that don't expose attributes from __init__.py
sound_classifier = _LazyModuleLoader('turicreate.toolkits.sound_classifier.sound_classifier')
audio_analysis = _LazyModuleLoader('turicreate.toolkits.audio_analysis.audio_analysis')
# lazy callable
load_images = _LazyCallable(image_analysis, 'load_images')
load_audio = _LazyCallable(audio_analysis, 'load_audio')
load_model = _LazyCallable(_LazyModuleLoader(
'turicreate.toolkits._model'), 'load_model')
################### Extension Importing ########################
import turicreate.extensions
from turicreate.extensions import ext_import
turicreate.extensions._add_meta_path()
# rewrite the extensions module
class _extensions_wrapper(object):
def __init__(self, wrapped):
self._wrapped = wrapped
self.__doc__ = wrapped.__doc__
def __getattr__(self, name):
try:
return getattr(self._wrapped, name)
except:
pass
turicreate._connect.main.get_unity()
return getattr(self._wrapped, name)
import sys as _sys
_sys.modules["turicreate.extensions"] = _extensions_wrapper(_sys.modules["turicreate.extensions"])
# rewrite the import
extensions = _sys.modules["turicreate.extensions"]
from .visualization import plot, show
# internal util
from turicreate._connect.main import launch as _launch
_launch()
| en | 0.76344 | # -*- coding: utf-8 -*- # Copyright © 2017 Apple Inc. All rights reserved. # # Use of this source code is governed by a BSD-3-clause license that can # be found in the LICENSE.txt file or at https://opensource.org/licenses/BSD-3-Clause @package turicreate ... Turi Create is a machine learning platform that enables data scientists and app developers to easily create intelligent applications at scale. # expose name aggregate as global variable # this won't interfere with import turicreate.aggregate as agg # must load ## bring load functions to the top level # lazy import under leaf module level # lazy import under leaf module level # lazy import under leaf module level # lazy load under leaf node level # lazy load under leaf node level # lazy load under leaf node level # self-encaps modules without from import statements # we can use top-level lazy import for them # modules that don't expose attributes from __init__.py # lazy callable ################### Extension Importing ######################## # rewrite the extensions module # rewrite the import # internal util | 1.724162 | 2 |
main/threadpool.py | haruhi5/Advance-python-example | 0 | 6630462 | import logging
import threading
from concurrent.futures import ThreadPoolExecutor
import time
import random
# Test function
def test(item):
s = random.randrange(1, 10)
logging.info(f'Thread {item}: id = {threading.get_ident()}')
logging.info(f'Thread {item}: name = {threading.current_thread().getName()}')
time.sleep(s)
logging.info(f'Thread {item}: finished')
def main():
logging.basicConfig(format='%(levelname)s - %(asctime)s: %(message)s', datefmt='%H:%M:%S', level=logging.DEBUG)
logging.info('Test Starting...')
workers = 5
items = 15
with ThreadPoolExecutor(max_workers=workers) as executor:
executor.map(test, range(items))
for i in range(5):
print(i)
logging.info('Test Finished')
if __name__ == '__main__':
main()
| import logging
import threading
from concurrent.futures import ThreadPoolExecutor
import time
import random
# Test function
def test(item):
s = random.randrange(1, 10)
logging.info(f'Thread {item}: id = {threading.get_ident()}')
logging.info(f'Thread {item}: name = {threading.current_thread().getName()}')
time.sleep(s)
logging.info(f'Thread {item}: finished')
def main():
logging.basicConfig(format='%(levelname)s - %(asctime)s: %(message)s', datefmt='%H:%M:%S', level=logging.DEBUG)
logging.info('Test Starting...')
workers = 5
items = 15
with ThreadPoolExecutor(max_workers=workers) as executor:
executor.map(test, range(items))
for i in range(5):
print(i)
logging.info('Test Finished')
if __name__ == '__main__':
main()
| en | 0.1013 | # Test function | 3.003041 | 3 |
astropy/io/fits/tests/test_fitsdiff.py | MatiasRepetto/astropy | 0 | 6630463 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
import numpy as np
import pytest
import os
from . import FitsTestCase
from astropy.io.fits.convenience import writeto
from astropy.io.fits.hdu import PrimaryHDU, hdulist
from astropy.io.fits import Header, ImageHDU, HDUList, FITSDiff
from astropy.io.fits.scripts import fitsdiff
from astropy import __version__ as version
class TestFITSDiff_script(FitsTestCase):
def test_help(self):
with pytest.raises(SystemExit) as e:
fitsdiff.main(['-h'])
assert e.value.code == 0
def test_version(self, capsys):
with pytest.raises(SystemExit) as e:
fitsdiff.main(['--version'])
out = capsys.readouterr()[0]
assert out == f'fitsdiff {version}'
assert e.value.code == 0
def test_noargs(self):
with pytest.raises(SystemExit) as e:
fitsdiff.main([""])
assert e.value.code == 2
def test_oneargargs(self):
with pytest.raises(SystemExit) as e:
fitsdiff.main(["file1"])
assert e.value.code == 2
def test_nodiff(self):
a = np.arange(100).reshape(10, 10)
hdu_a = PrimaryHDU(data=a)
b = a.copy()
hdu_b = PrimaryHDU(data=b)
tmp_a = self.temp('testa.fits')
tmp_b = self.temp('testb.fits')
hdu_a.writeto(tmp_a)
hdu_b.writeto(tmp_b)
numdiff = fitsdiff.main([tmp_a, tmp_b])
assert numdiff == 0
def test_onediff(self):
a = np.arange(100).reshape(10, 10)
hdu_a = PrimaryHDU(data=a)
b = a.copy()
b[1, 0] = 12
hdu_b = PrimaryHDU(data=b)
tmp_a = self.temp('testa.fits')
tmp_b = self.temp('testb.fits')
hdu_a.writeto(tmp_a)
hdu_b.writeto(tmp_b)
numdiff = fitsdiff.main([tmp_a, tmp_b])
assert numdiff == 1
def test_manydiff(self, capsys):
a = np.arange(100).reshape(10, 10)
hdu_a = PrimaryHDU(data=a)
b = a + 1
hdu_b = PrimaryHDU(data=b)
tmp_a = self.temp('testa.fits')
tmp_b = self.temp('testb.fits')
hdu_a.writeto(tmp_a)
hdu_b.writeto(tmp_b)
numdiff = fitsdiff.main([tmp_a, tmp_b])
out, err = capsys.readouterr()
assert numdiff == 1
assert out.splitlines()[-4:] == [
' a> 9',
' b> 10',
' ...',
' 100 different pixels found (100.00% different).']
numdiff = fitsdiff.main(['-n', '1', tmp_a, tmp_b])
out, err = capsys.readouterr()
assert numdiff == 1
assert out.splitlines()[-4:] == [
' a> 0',
' b> 1',
' ...',
' 100 different pixels found (100.00% different).']
def test_outputfile(self):
a = np.arange(100).reshape(10, 10)
hdu_a = PrimaryHDU(data=a)
b = a.copy()
b[1, 0] = 12
hdu_b = PrimaryHDU(data=b)
tmp_a = self.temp('testa.fits')
tmp_b = self.temp('testb.fits')
hdu_a.writeto(tmp_a)
hdu_b.writeto(tmp_b)
numdiff = fitsdiff.main(['-o', self.temp('diff.txt'), tmp_a, tmp_b])
assert numdiff == 1
with open(self.temp('diff.txt')) as f:
out = f.read()
assert out.splitlines()[-4:] == [
' Data differs at [1, 2]:',
' a> 10',
' b> 12',
' 1 different pixels found (1.00% different).']
def test_atol(self):
a = np.arange(100, dtype=float).reshape(10, 10)
hdu_a = PrimaryHDU(data=a)
b = a.copy()
b[1, 0] = 11
hdu_b = PrimaryHDU(data=b)
tmp_a = self.temp('testa.fits')
tmp_b = self.temp('testb.fits')
hdu_a.writeto(tmp_a)
hdu_b.writeto(tmp_b)
numdiff = fitsdiff.main(["-a", "1", tmp_a, tmp_b])
assert numdiff == 0
numdiff = fitsdiff.main(["--exact", "-a", "1", tmp_a, tmp_b])
assert numdiff == 1
def test_rtol(self):
a = np.arange(100, dtype=float).reshape(10, 10)
hdu_a = PrimaryHDU(data=a)
b = a.copy()
b[1, 0] = 11
hdu_b = PrimaryHDU(data=b)
tmp_a = self.temp('testa.fits')
tmp_b = self.temp('testb.fits')
hdu_a.writeto(tmp_a)
hdu_b.writeto(tmp_b)
numdiff = fitsdiff.main(["-r", "1e-1", tmp_a, tmp_b])
assert numdiff == 0
def test_rtol_diff(self, capsys):
a = np.arange(100, dtype=float).reshape(10, 10)
hdu_a = PrimaryHDU(data=a)
b = a.copy()
b[1, 0] = 11
hdu_b = PrimaryHDU(data=b)
tmp_a = self.temp('testa.fits')
tmp_b = self.temp('testb.fits')
hdu_a.writeto(tmp_a)
hdu_b.writeto(tmp_b)
numdiff = fitsdiff.main(["-r", "1e-2", tmp_a, tmp_b])
assert numdiff == 1
out, err = capsys.readouterr()
assert out == f"""
fitsdiff: {version}
a: {tmp_a}
b: {tmp_b}
Maximum number of different data values to be reported: 10
Relative tolerance: 0.01, Absolute tolerance: 0.0
Primary HDU:
Data contains differences:
Data differs at [1, 2]:
a> 10.0
? ^
b> 11.0
? ^
1 different pixels found (1.00% different).
"""
assert err == ""
def test_wildcard(self):
tmp1 = self.temp("tmp_file1")
with pytest.raises(SystemExit) as e:
fitsdiff.main([tmp1+"*", "ACME"])
assert e.value.code == 2
def test_not_quiet(self, capsys):
a = np.arange(100).reshape(10, 10)
hdu_a = PrimaryHDU(data=a)
b = a.copy()
hdu_b = PrimaryHDU(data=b)
tmp_a = self.temp('testa.fits')
tmp_b = self.temp('testb.fits')
hdu_a.writeto(tmp_a)
hdu_b.writeto(tmp_b)
numdiff = fitsdiff.main([tmp_a, tmp_b])
assert numdiff == 0
out, err = capsys.readouterr()
assert out == f"""
fitsdiff: {version}
a: {tmp_a}
b: {tmp_b}
Maximum number of different data values to be reported: 10
Relative tolerance: 0.0, Absolute tolerance: 0.0
No differences found.
"""
assert err == ""
def test_quiet(self, capsys):
a = np.arange(100).reshape(10, 10)
hdu_a = PrimaryHDU(data=a)
b = a.copy()
hdu_b = PrimaryHDU(data=b)
tmp_a = self.temp('testa.fits')
tmp_b = self.temp('testb.fits')
hdu_a.writeto(tmp_a)
hdu_b.writeto(tmp_b)
numdiff = fitsdiff.main(["-q", tmp_a, tmp_b])
assert numdiff == 0
out, err = capsys.readouterr()
assert out == ""
assert err == ""
def test_path(self, capsys):
os.mkdir(self.temp('sub/'))
tmp_b = self.temp('sub/ascii.fits')
tmp_g = self.temp('sub/group.fits')
tmp_h = self.data('group.fits')
with hdulist.fitsopen(tmp_h) as hdu_b:
hdu_b.writeto(tmp_g)
writeto(tmp_b, np.arange(100).reshape(10, 10))
# one modified file and a directory
assert fitsdiff.main(["-q", self.data_dir, tmp_b]) == 1
assert fitsdiff.main(["-q", tmp_b, self.data_dir]) == 1
# two directories
tmp_d = self.temp('sub/')
assert fitsdiff.main(["-q", self.data_dir, tmp_d]) == 1
assert fitsdiff.main(["-q", tmp_d, self.data_dir]) == 1
with pytest.warns(UserWarning, match=r"Field 'ORBPARM' has a repeat "
r"count of 0 in its format code"):
assert fitsdiff.main(["-q", self.data_dir, self.data_dir]) == 0
# no match
tmp_c = self.data('arange.fits')
fitsdiff.main([tmp_c, tmp_d])
out, err = capsys.readouterr()
assert "'arange.fits' has no match in" in err
# globbing
with pytest.warns(UserWarning, match=r"Field 'ORBPARM' has a repeat "
r"count of 0 in its format code"):
assert fitsdiff.main(["-q", self.data_dir+'/*.fits',
self.data_dir]) == 0
assert fitsdiff.main(["-q", self.data_dir+'/g*.fits', tmp_d]) == 0
# one file and a directory
tmp_f = self.data('tb.fits')
assert fitsdiff.main(["-q", tmp_f, self.data_dir]) == 0
assert fitsdiff.main(["-q", self.data_dir, tmp_f]) == 0
def test_ignore_hdus(self):
a = np.arange(100).reshape(10, 10)
b = a.copy() + 1
ha = Header([('A', 1), ('B', 2), ('C', 3)])
phdu_a = PrimaryHDU(header=ha)
phdu_b = PrimaryHDU(header=ha)
ihdu_a = ImageHDU(data=a, name='SCI')
ihdu_b = ImageHDU(data=b, name='SCI')
hdulist_a = HDUList([phdu_a, ihdu_a])
hdulist_b = HDUList([phdu_b, ihdu_b])
tmp_a = self.temp('testa.fits')
tmp_b = self.temp('testb.fits')
hdulist_a.writeto(tmp_a)
hdulist_b.writeto(tmp_b)
numdiff = fitsdiff.main([tmp_a, tmp_b])
assert numdiff == 1
numdiff = fitsdiff.main([tmp_a, tmp_b, "-u", "SCI"])
assert numdiff == 0
def test_ignore_hdus_report(self, capsys):
a = np.arange(100).reshape(10, 10)
b = a.copy() + 1
ha = Header([('A', 1), ('B', 2), ('C', 3)])
phdu_a = PrimaryHDU(header=ha)
phdu_b = PrimaryHDU(header=ha)
ihdu_a = ImageHDU(data=a, name='SCI')
ihdu_b = ImageHDU(data=b, name='SCI')
hdulist_a = HDUList([phdu_a, ihdu_a])
hdulist_b = HDUList([phdu_b, ihdu_b])
tmp_a = self.temp('testa.fits')
tmp_b = self.temp('testb.fits')
hdulist_a.writeto(tmp_a)
hdulist_b.writeto(tmp_b)
numdiff = fitsdiff.main([tmp_a, tmp_b, "-u", "SCI"])
assert numdiff == 0
out, err = capsys.readouterr()
assert "testa.fits" in out
assert "testb.fits" in out
@pytest.mark.skip(reason="fails intentionally to show open files (see PR #10159)")
def test_fitsdiff_openfile(tmpdir):
"""Make sure that failing FITSDiff doesn't leave open files."""
path1 = str(tmpdir.join("file1.fits"))
path2 = str(tmpdir.join("file2.fits"))
hdulist = HDUList([PrimaryHDU(), ImageHDU(data=np.zeros(5))])
hdulist.writeto(path1)
hdulist[1].data[0] = 1
hdulist.writeto(path2)
diff = FITSDiff(path1, path2)
assert diff.identical, diff.report()
| # Licensed under a 3-clause BSD style license - see LICENSE.rst
import numpy as np
import pytest
import os
from . import FitsTestCase
from astropy.io.fits.convenience import writeto
from astropy.io.fits.hdu import PrimaryHDU, hdulist
from astropy.io.fits import Header, ImageHDU, HDUList, FITSDiff
from astropy.io.fits.scripts import fitsdiff
from astropy import __version__ as version
class TestFITSDiff_script(FitsTestCase):
def test_help(self):
with pytest.raises(SystemExit) as e:
fitsdiff.main(['-h'])
assert e.value.code == 0
def test_version(self, capsys):
with pytest.raises(SystemExit) as e:
fitsdiff.main(['--version'])
out = capsys.readouterr()[0]
assert out == f'fitsdiff {version}'
assert e.value.code == 0
def test_noargs(self):
with pytest.raises(SystemExit) as e:
fitsdiff.main([""])
assert e.value.code == 2
def test_oneargargs(self):
with pytest.raises(SystemExit) as e:
fitsdiff.main(["file1"])
assert e.value.code == 2
def test_nodiff(self):
a = np.arange(100).reshape(10, 10)
hdu_a = PrimaryHDU(data=a)
b = a.copy()
hdu_b = PrimaryHDU(data=b)
tmp_a = self.temp('testa.fits')
tmp_b = self.temp('testb.fits')
hdu_a.writeto(tmp_a)
hdu_b.writeto(tmp_b)
numdiff = fitsdiff.main([tmp_a, tmp_b])
assert numdiff == 0
def test_onediff(self):
a = np.arange(100).reshape(10, 10)
hdu_a = PrimaryHDU(data=a)
b = a.copy()
b[1, 0] = 12
hdu_b = PrimaryHDU(data=b)
tmp_a = self.temp('testa.fits')
tmp_b = self.temp('testb.fits')
hdu_a.writeto(tmp_a)
hdu_b.writeto(tmp_b)
numdiff = fitsdiff.main([tmp_a, tmp_b])
assert numdiff == 1
def test_manydiff(self, capsys):
a = np.arange(100).reshape(10, 10)
hdu_a = PrimaryHDU(data=a)
b = a + 1
hdu_b = PrimaryHDU(data=b)
tmp_a = self.temp('testa.fits')
tmp_b = self.temp('testb.fits')
hdu_a.writeto(tmp_a)
hdu_b.writeto(tmp_b)
numdiff = fitsdiff.main([tmp_a, tmp_b])
out, err = capsys.readouterr()
assert numdiff == 1
assert out.splitlines()[-4:] == [
' a> 9',
' b> 10',
' ...',
' 100 different pixels found (100.00% different).']
numdiff = fitsdiff.main(['-n', '1', tmp_a, tmp_b])
out, err = capsys.readouterr()
assert numdiff == 1
assert out.splitlines()[-4:] == [
' a> 0',
' b> 1',
' ...',
' 100 different pixels found (100.00% different).']
def test_outputfile(self):
a = np.arange(100).reshape(10, 10)
hdu_a = PrimaryHDU(data=a)
b = a.copy()
b[1, 0] = 12
hdu_b = PrimaryHDU(data=b)
tmp_a = self.temp('testa.fits')
tmp_b = self.temp('testb.fits')
hdu_a.writeto(tmp_a)
hdu_b.writeto(tmp_b)
numdiff = fitsdiff.main(['-o', self.temp('diff.txt'), tmp_a, tmp_b])
assert numdiff == 1
with open(self.temp('diff.txt')) as f:
out = f.read()
assert out.splitlines()[-4:] == [
' Data differs at [1, 2]:',
' a> 10',
' b> 12',
' 1 different pixels found (1.00% different).']
def test_atol(self):
a = np.arange(100, dtype=float).reshape(10, 10)
hdu_a = PrimaryHDU(data=a)
b = a.copy()
b[1, 0] = 11
hdu_b = PrimaryHDU(data=b)
tmp_a = self.temp('testa.fits')
tmp_b = self.temp('testb.fits')
hdu_a.writeto(tmp_a)
hdu_b.writeto(tmp_b)
numdiff = fitsdiff.main(["-a", "1", tmp_a, tmp_b])
assert numdiff == 0
numdiff = fitsdiff.main(["--exact", "-a", "1", tmp_a, tmp_b])
assert numdiff == 1
def test_rtol(self):
a = np.arange(100, dtype=float).reshape(10, 10)
hdu_a = PrimaryHDU(data=a)
b = a.copy()
b[1, 0] = 11
hdu_b = PrimaryHDU(data=b)
tmp_a = self.temp('testa.fits')
tmp_b = self.temp('testb.fits')
hdu_a.writeto(tmp_a)
hdu_b.writeto(tmp_b)
numdiff = fitsdiff.main(["-r", "1e-1", tmp_a, tmp_b])
assert numdiff == 0
def test_rtol_diff(self, capsys):
a = np.arange(100, dtype=float).reshape(10, 10)
hdu_a = PrimaryHDU(data=a)
b = a.copy()
b[1, 0] = 11
hdu_b = PrimaryHDU(data=b)
tmp_a = self.temp('testa.fits')
tmp_b = self.temp('testb.fits')
hdu_a.writeto(tmp_a)
hdu_b.writeto(tmp_b)
numdiff = fitsdiff.main(["-r", "1e-2", tmp_a, tmp_b])
assert numdiff == 1
out, err = capsys.readouterr()
assert out == f"""
fitsdiff: {version}
a: {tmp_a}
b: {tmp_b}
Maximum number of different data values to be reported: 10
Relative tolerance: 0.01, Absolute tolerance: 0.0
Primary HDU:
Data contains differences:
Data differs at [1, 2]:
a> 10.0
? ^
b> 11.0
? ^
1 different pixels found (1.00% different).
"""
assert err == ""
def test_wildcard(self):
tmp1 = self.temp("tmp_file1")
with pytest.raises(SystemExit) as e:
fitsdiff.main([tmp1+"*", "ACME"])
assert e.value.code == 2
def test_not_quiet(self, capsys):
a = np.arange(100).reshape(10, 10)
hdu_a = PrimaryHDU(data=a)
b = a.copy()
hdu_b = PrimaryHDU(data=b)
tmp_a = self.temp('testa.fits')
tmp_b = self.temp('testb.fits')
hdu_a.writeto(tmp_a)
hdu_b.writeto(tmp_b)
numdiff = fitsdiff.main([tmp_a, tmp_b])
assert numdiff == 0
out, err = capsys.readouterr()
assert out == f"""
fitsdiff: {version}
a: {tmp_a}
b: {tmp_b}
Maximum number of different data values to be reported: 10
Relative tolerance: 0.0, Absolute tolerance: 0.0
No differences found.
"""
assert err == ""
def test_quiet(self, capsys):
a = np.arange(100).reshape(10, 10)
hdu_a = PrimaryHDU(data=a)
b = a.copy()
hdu_b = PrimaryHDU(data=b)
tmp_a = self.temp('testa.fits')
tmp_b = self.temp('testb.fits')
hdu_a.writeto(tmp_a)
hdu_b.writeto(tmp_b)
numdiff = fitsdiff.main(["-q", tmp_a, tmp_b])
assert numdiff == 0
out, err = capsys.readouterr()
assert out == ""
assert err == ""
def test_path(self, capsys):
os.mkdir(self.temp('sub/'))
tmp_b = self.temp('sub/ascii.fits')
tmp_g = self.temp('sub/group.fits')
tmp_h = self.data('group.fits')
with hdulist.fitsopen(tmp_h) as hdu_b:
hdu_b.writeto(tmp_g)
writeto(tmp_b, np.arange(100).reshape(10, 10))
# one modified file and a directory
assert fitsdiff.main(["-q", self.data_dir, tmp_b]) == 1
assert fitsdiff.main(["-q", tmp_b, self.data_dir]) == 1
# two directories
tmp_d = self.temp('sub/')
assert fitsdiff.main(["-q", self.data_dir, tmp_d]) == 1
assert fitsdiff.main(["-q", tmp_d, self.data_dir]) == 1
with pytest.warns(UserWarning, match=r"Field 'ORBPARM' has a repeat "
r"count of 0 in its format code"):
assert fitsdiff.main(["-q", self.data_dir, self.data_dir]) == 0
# no match
tmp_c = self.data('arange.fits')
fitsdiff.main([tmp_c, tmp_d])
out, err = capsys.readouterr()
assert "'arange.fits' has no match in" in err
# globbing
with pytest.warns(UserWarning, match=r"Field 'ORBPARM' has a repeat "
r"count of 0 in its format code"):
assert fitsdiff.main(["-q", self.data_dir+'/*.fits',
self.data_dir]) == 0
assert fitsdiff.main(["-q", self.data_dir+'/g*.fits', tmp_d]) == 0
# one file and a directory
tmp_f = self.data('tb.fits')
assert fitsdiff.main(["-q", tmp_f, self.data_dir]) == 0
assert fitsdiff.main(["-q", self.data_dir, tmp_f]) == 0
def test_ignore_hdus(self):
a = np.arange(100).reshape(10, 10)
b = a.copy() + 1
ha = Header([('A', 1), ('B', 2), ('C', 3)])
phdu_a = PrimaryHDU(header=ha)
phdu_b = PrimaryHDU(header=ha)
ihdu_a = ImageHDU(data=a, name='SCI')
ihdu_b = ImageHDU(data=b, name='SCI')
hdulist_a = HDUList([phdu_a, ihdu_a])
hdulist_b = HDUList([phdu_b, ihdu_b])
tmp_a = self.temp('testa.fits')
tmp_b = self.temp('testb.fits')
hdulist_a.writeto(tmp_a)
hdulist_b.writeto(tmp_b)
numdiff = fitsdiff.main([tmp_a, tmp_b])
assert numdiff == 1
numdiff = fitsdiff.main([tmp_a, tmp_b, "-u", "SCI"])
assert numdiff == 0
def test_ignore_hdus_report(self, capsys):
a = np.arange(100).reshape(10, 10)
b = a.copy() + 1
ha = Header([('A', 1), ('B', 2), ('C', 3)])
phdu_a = PrimaryHDU(header=ha)
phdu_b = PrimaryHDU(header=ha)
ihdu_a = ImageHDU(data=a, name='SCI')
ihdu_b = ImageHDU(data=b, name='SCI')
hdulist_a = HDUList([phdu_a, ihdu_a])
hdulist_b = HDUList([phdu_b, ihdu_b])
tmp_a = self.temp('testa.fits')
tmp_b = self.temp('testb.fits')
hdulist_a.writeto(tmp_a)
hdulist_b.writeto(tmp_b)
numdiff = fitsdiff.main([tmp_a, tmp_b, "-u", "SCI"])
assert numdiff == 0
out, err = capsys.readouterr()
assert "testa.fits" in out
assert "testb.fits" in out
@pytest.mark.skip(reason="fails intentionally to show open files (see PR #10159)")
def test_fitsdiff_openfile(tmpdir):
"""Make sure that failing FITSDiff doesn't leave open files."""
path1 = str(tmpdir.join("file1.fits"))
path2 = str(tmpdir.join("file2.fits"))
hdulist = HDUList([PrimaryHDU(), ImageHDU(data=np.zeros(5))])
hdulist.writeto(path1)
hdulist[1].data[0] = 1
hdulist.writeto(path2)
diff = FITSDiff(path1, path2)
assert diff.identical, diff.report()
| en | 0.748929 | # Licensed under a 3-clause BSD style license - see LICENSE.rst fitsdiff: {version} a: {tmp_a} b: {tmp_b} Maximum number of different data values to be reported: 10 Relative tolerance: 0.01, Absolute tolerance: 0.0 Primary HDU: Data contains differences: Data differs at [1, 2]: a> 10.0 ? ^ b> 11.0 ? ^ 1 different pixels found (1.00% different). fitsdiff: {version} a: {tmp_a} b: {tmp_b} Maximum number of different data values to be reported: 10 Relative tolerance: 0.0, Absolute tolerance: 0.0 No differences found. # one modified file and a directory # two directories # no match # globbing # one file and a directory #10159)") Make sure that failing FITSDiff doesn't leave open files. | 1.931735 | 2 |
weblogo-3.4_rd/test_corebio/test_nexus.py | go-bears/Final-Project | 0 | 6630464 | <reponame>go-bears/Final-Project
#!/usr/bin/env python
import unittest
from corebio.seq_io._nexus import Nexus
from test_corebio import *
class test_nexus(unittest.TestCase):
def test_create(self) :
n = Nexus()
self.assertNotEqual( n , None)
def test_parse_f0(self) :
f = testdata_stream("nexus/test_Nexus_input.nex")
n= Nexus(f)
#self.output_basics(n)
expected = ['t1',
"t2 the name",
"isn'that [a] strange name?",
"one should be punished, for (that)!",
"t5","t6","t7","t8","t9"]
taxa = n.taxlabels
self.assertEqual( taxa, expected)
f.close()
def test_parse_protein(self) :
f = testdata_stream("nexus/protein.nex")
n = Nexus(f)
f.close()
def test_parse_dna(self) :
f = testdata_stream("nexus/dna.nex")
n = Nexus(f)
taxa = n.taxlabels
taxa.sort()
self.assertEqual( len(taxa) ,10)
self.assertEqual( taxa[0], "Carp")
self.assertEqual( taxa[-1], "Whale")
f.close()
def test_TreeTest1(self):
"""Test Tree module."""
f = testdata_stream("nexus/test_Nexus_input.nex")
n=Nexus(f)
t3=n.trees[2]
t2=n.trees[2]
t3.root_with_outgroup(['t1','t5'])
# Return node_id of common ancestor if
# taxon_list is monophyletic, -1 otherwise.
self.assertEqual( t3.is_monophyletic(['t1','t5']), 13)
t3.split(parent_id=t3.search_taxon('t9'))
f.close()
if __name__ == '__main__':
unittest.main()
| #!/usr/bin/env python
import unittest
from corebio.seq_io._nexus import Nexus
from test_corebio import *
class test_nexus(unittest.TestCase):
def test_create(self) :
n = Nexus()
self.assertNotEqual( n , None)
def test_parse_f0(self) :
f = testdata_stream("nexus/test_Nexus_input.nex")
n= Nexus(f)
#self.output_basics(n)
expected = ['t1',
"t2 the name",
"isn'that [a] strange name?",
"one should be punished, for (that)!",
"t5","t6","t7","t8","t9"]
taxa = n.taxlabels
self.assertEqual( taxa, expected)
f.close()
def test_parse_protein(self) :
f = testdata_stream("nexus/protein.nex")
n = Nexus(f)
f.close()
def test_parse_dna(self) :
f = testdata_stream("nexus/dna.nex")
n = Nexus(f)
taxa = n.taxlabels
taxa.sort()
self.assertEqual( len(taxa) ,10)
self.assertEqual( taxa[0], "Carp")
self.assertEqual( taxa[-1], "Whale")
f.close()
def test_TreeTest1(self):
"""Test Tree module."""
f = testdata_stream("nexus/test_Nexus_input.nex")
n=Nexus(f)
t3=n.trees[2]
t2=n.trees[2]
t3.root_with_outgroup(['t1','t5'])
# Return node_id of common ancestor if
# taxon_list is monophyletic, -1 otherwise.
self.assertEqual( t3.is_monophyletic(['t1','t5']), 13)
t3.split(parent_id=t3.search_taxon('t9'))
f.close()
if __name__ == '__main__':
unittest.main() | en | 0.288472 | #!/usr/bin/env python #self.output_basics(n) Test Tree module. # Return node_id of common ancestor if # taxon_list is monophyletic, -1 otherwise. | 2.513697 | 3 |
src/plugins/id.py | SaloxiddinTursunaliev/TelegramTaxiBot | 0 | 6630465 | <reponame>SaloxiddinTursunaliev/TelegramTaxiBot
@bot.message_handler(commands=['id', 'Id'])
def send_id(message):
cur.execute('SELECT * from language where id = %s', [str(message.from_user.id)])
record = cur.fetchall()
for row in record:
userid = row[0]
userlang = row[1]
username = message.from_user.first_name.encode("utf-8")
userid = message.from_user.id
reply_msg = language[userlang]["ID_MSG"]
gpid = message.chat.id
if message.chat.type == "supergroup" or message.chat.type == "group":
reply_msg = reply_msg + language[userlang]["INGP_ID_MSG"]
if message.reply_to_message:
repliedid = message.reply_to_message.from_user.id
reply_msg = reply_msg + language[userlang]["REPLIED_ID_MSG"].format(repliedid)
if message.reply_to_message.forward_from:
reply_msg = reply_msg + language[userlang]["FORWARDED_ID_MSG"].format(message.reply_to_message.forward_from.id)
try:
reply_msg = reply_msg + language[userlang]["CHATID_ID_MSG"].format(message.reply_to_message.forward_from_chat.id)
except:
pass
bot.reply_to(message, reply_msg.format(username, userid, gpid), parse_mode="Markdown")
| @bot.message_handler(commands=['id', 'Id'])
def send_id(message):
cur.execute('SELECT * from language where id = %s', [str(message.from_user.id)])
record = cur.fetchall()
for row in record:
userid = row[0]
userlang = row[1]
username = message.from_user.first_name.encode("utf-8")
userid = message.from_user.id
reply_msg = language[userlang]["ID_MSG"]
gpid = message.chat.id
if message.chat.type == "supergroup" or message.chat.type == "group":
reply_msg = reply_msg + language[userlang]["INGP_ID_MSG"]
if message.reply_to_message:
repliedid = message.reply_to_message.from_user.id
reply_msg = reply_msg + language[userlang]["REPLIED_ID_MSG"].format(repliedid)
if message.reply_to_message.forward_from:
reply_msg = reply_msg + language[userlang]["FORWARDED_ID_MSG"].format(message.reply_to_message.forward_from.id)
try:
reply_msg = reply_msg + language[userlang]["CHATID_ID_MSG"].format(message.reply_to_message.forward_from_chat.id)
except:
pass
bot.reply_to(message, reply_msg.format(username, userid, gpid), parse_mode="Markdown") | none | 1 | 2.469614 | 2 |
|
christmasCTF/2016/house_of_daehee (unsolved)/debug.py | PurpEth/solved-hacking-problem | 1 | 6630466 | import gdb
class AgainCommand(gdb.Command):
def __init__(self):
super(AgainCommand, self).__init__(
"again",
gdb.COMMAND_RUNNING
)
def invoke(self, arg, from_tty):
gdb.execute('tbreak *unlink+173') # b
gdb.execute('tbreak *main+807') # c
gdb.execute('tbreak *main+825') # b
gdb.execute('tbreak *main+843') # a
f = open('payload', 'w')
f.write('12345678\xa1')
f.close()
gdb.execute('run < payload')
class QuickViewCommand(gdb.Command):
def __init__(self):
super(QuickViewCommand, self).__init__(
"qv",
gdb.COMMAND_DATA
)
def invoke(self, arg, from_tty):
gdb.execute('x/40wx 0x555555757000')
AgainCommand().invoke([], False)
QuickViewCommand()
| import gdb
class AgainCommand(gdb.Command):
def __init__(self):
super(AgainCommand, self).__init__(
"again",
gdb.COMMAND_RUNNING
)
def invoke(self, arg, from_tty):
gdb.execute('tbreak *unlink+173') # b
gdb.execute('tbreak *main+807') # c
gdb.execute('tbreak *main+825') # b
gdb.execute('tbreak *main+843') # a
f = open('payload', 'w')
f.write('12345678\xa1')
f.close()
gdb.execute('run < payload')
class QuickViewCommand(gdb.Command):
def __init__(self):
super(QuickViewCommand, self).__init__(
"qv",
gdb.COMMAND_DATA
)
def invoke(self, arg, from_tty):
gdb.execute('x/40wx 0x555555757000')
AgainCommand().invoke([], False)
QuickViewCommand()
| en | 0.38951 | # b # c # b # a | 2.487408 | 2 |
src/developer/ffx/build/gn_generate_cmd.py | EnderNightLord-ChromeBook/zircon-rpi | 14 | 6630467 | <filename>src/developer/ffx/build/gn_generate_cmd.py
#!/usr/bin/env python3.8
# Copyright 2020 The Fuchsia Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
#
import argparse
import os
import string
import sys
# Root dir is 5 levels up from here.
FUCHSIA_DIR = os.path.abspath(
os.path.join(
__file__, os.pardir, os.pardir, os.pardir, os.pardir, os.pardir))
sys.path += [os.path.join(FUCHSIA_DIR, 'third_party')]
from jinja2 import Environment, FileSystemLoader
def to_camel_case(snake_str):
components = snake_str.split('_')
return ''.join(x.title() for x in components[0:])
def wrap_deps(dep):
return {'enum': to_camel_case(dep), 'lib': dep + '_args'}
def main(args_list=None):
parser = argparse.ArgumentParser(description='Generate FFX Command struct')
parser.add_argument(
'--out', help='The output file to generate', required=True)
parser.add_argument(
'--deps',
help='Comma-seperated libraries to generate code from',
required=True)
parser.add_argument(
'--template',
help='The template file to use to generate code',
required=True)
if args_list:
args = parser.parse_args(args_list)
else:
args = parser.parse_args()
template_path = os.path.join(os.path.dirname(__file__), 'templates')
env = Environment(
loader=FileSystemLoader(template_path),
trim_blocks=True,
lstrip_blocks=True)
template = env.get_template(args.template)
libraries = args.deps.split(',')
deps = map(wrap_deps, libraries)
with open(args.out, 'w') as file:
file.write(template.render(deps=deps))
if __name__ == '__main__':
sys.exit(main())
| <filename>src/developer/ffx/build/gn_generate_cmd.py
#!/usr/bin/env python3.8
# Copyright 2020 The Fuchsia Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
#
import argparse
import os
import string
import sys
# Root dir is 5 levels up from here.
FUCHSIA_DIR = os.path.abspath(
os.path.join(
__file__, os.pardir, os.pardir, os.pardir, os.pardir, os.pardir))
sys.path += [os.path.join(FUCHSIA_DIR, 'third_party')]
from jinja2 import Environment, FileSystemLoader
def to_camel_case(snake_str):
components = snake_str.split('_')
return ''.join(x.title() for x in components[0:])
def wrap_deps(dep):
return {'enum': to_camel_case(dep), 'lib': dep + '_args'}
def main(args_list=None):
parser = argparse.ArgumentParser(description='Generate FFX Command struct')
parser.add_argument(
'--out', help='The output file to generate', required=True)
parser.add_argument(
'--deps',
help='Comma-seperated libraries to generate code from',
required=True)
parser.add_argument(
'--template',
help='The template file to use to generate code',
required=True)
if args_list:
args = parser.parse_args(args_list)
else:
args = parser.parse_args()
template_path = os.path.join(os.path.dirname(__file__), 'templates')
env = Environment(
loader=FileSystemLoader(template_path),
trim_blocks=True,
lstrip_blocks=True)
template = env.get_template(args.template)
libraries = args.deps.split(',')
deps = map(wrap_deps, libraries)
with open(args.out, 'w') as file:
file.write(template.render(deps=deps))
if __name__ == '__main__':
sys.exit(main())
| en | 0.902783 | #!/usr/bin/env python3.8 # Copyright 2020 The Fuchsia Authors. All rights reserved. # Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. # # Root dir is 5 levels up from here. | 2.186889 | 2 |
petl/test/io/test_sources.py | a-musing-moose/petl | 0 | 6630468 | # -*- coding: utf-8 -*-
from __future__ import absolute_import, print_function, division
import zipfile
from tempfile import NamedTemporaryFile
from petl.compat import PY2
from petl.test.helpers import ieq, eq_
import petl as etl
from petl.io.sources import StringSource, PopenSource, ZipSource, StdoutSource
def test_stringsource():
table1 = (('foo', 'bar'),
('a', '1'),
('b', '2'),
('c', '2'))
# test writing to a string buffer
ss = StringSource()
etl.tocsv(table1, ss)
expect = "foo,bar\r\na,1\r\nb,2\r\nc,2\r\n"
if not PY2:
expect = expect.encode('ascii')
actual = ss.getvalue()
eq_(expect, actual)
# test reading from a string buffer
table2 = etl.fromcsv(StringSource(actual))
ieq(table1, table2)
ieq(table1, table2)
# test appending
etl.appendcsv(table1, ss)
actual = ss.getvalue()
expect = "foo,bar\r\na,1\r\nb,2\r\nc,2\r\na,1\r\nb,2\r\nc,2\r\n"
if not PY2:
expect = expect.encode('ascii')
eq_(expect, actual)
def test_popensource():
expect = (('foo', 'bar'),)
delimiter = ' '
actual = etl.fromcsv(PopenSource(r'echo foo bar',
shell=True),
delimiter=delimiter)
ieq(expect, actual)
def test_zipsource():
# setup
table = [('foo', 'bar'), ('a', '1'), ('b', '2')]
fn_tsv = NamedTemporaryFile().name
etl.totsv(table, fn_tsv)
fn_zip = NamedTemporaryFile().name
z = zipfile.ZipFile(fn_zip, mode='w')
z.write(fn_tsv, 'data.tsv')
z.close()
# test
actual = etl.fromtsv(ZipSource(fn_zip, 'data.tsv'))
ieq(table, actual)
def test_stdoutsource():
table = [('foo', 'bar'), ('a', 1), ('b', 2)]
etl.tocsv(table, StdoutSource(), encoding='ascii')
etl.tohtml(table, StdoutSource(), encoding='ascii')
etl.topickle(table, StdoutSource())
def test_stdoutsource_unicode():
table = [('foo', 'bar'),
(u'Արամ Խաչատրյան', 1),
(u'<NAME>', 2)]
etl.tocsv(table, StdoutSource(), encoding='utf-8')
etl.tohtml(table, StdoutSource(), encoding='utf-8')
etl.topickle(table, StdoutSource())
| # -*- coding: utf-8 -*-
from __future__ import absolute_import, print_function, division
import zipfile
from tempfile import NamedTemporaryFile
from petl.compat import PY2
from petl.test.helpers import ieq, eq_
import petl as etl
from petl.io.sources import StringSource, PopenSource, ZipSource, StdoutSource
def test_stringsource():
table1 = (('foo', 'bar'),
('a', '1'),
('b', '2'),
('c', '2'))
# test writing to a string buffer
ss = StringSource()
etl.tocsv(table1, ss)
expect = "foo,bar\r\na,1\r\nb,2\r\nc,2\r\n"
if not PY2:
expect = expect.encode('ascii')
actual = ss.getvalue()
eq_(expect, actual)
# test reading from a string buffer
table2 = etl.fromcsv(StringSource(actual))
ieq(table1, table2)
ieq(table1, table2)
# test appending
etl.appendcsv(table1, ss)
actual = ss.getvalue()
expect = "foo,bar\r\na,1\r\nb,2\r\nc,2\r\na,1\r\nb,2\r\nc,2\r\n"
if not PY2:
expect = expect.encode('ascii')
eq_(expect, actual)
def test_popensource():
expect = (('foo', 'bar'),)
delimiter = ' '
actual = etl.fromcsv(PopenSource(r'echo foo bar',
shell=True),
delimiter=delimiter)
ieq(expect, actual)
def test_zipsource():
# setup
table = [('foo', 'bar'), ('a', '1'), ('b', '2')]
fn_tsv = NamedTemporaryFile().name
etl.totsv(table, fn_tsv)
fn_zip = NamedTemporaryFile().name
z = zipfile.ZipFile(fn_zip, mode='w')
z.write(fn_tsv, 'data.tsv')
z.close()
# test
actual = etl.fromtsv(ZipSource(fn_zip, 'data.tsv'))
ieq(table, actual)
def test_stdoutsource():
table = [('foo', 'bar'), ('a', 1), ('b', 2)]
etl.tocsv(table, StdoutSource(), encoding='ascii')
etl.tohtml(table, StdoutSource(), encoding='ascii')
etl.topickle(table, StdoutSource())
def test_stdoutsource_unicode():
table = [('foo', 'bar'),
(u'Արամ Խաչատրյան', 1),
(u'<NAME>', 2)]
etl.tocsv(table, StdoutSource(), encoding='utf-8')
etl.tohtml(table, StdoutSource(), encoding='utf-8')
etl.topickle(table, StdoutSource())
| en | 0.696918 | # -*- coding: utf-8 -*- # test writing to a string buffer # test reading from a string buffer # test appending # setup # test | 2.376603 | 2 |
Condor/Tools/Autorun/shell.py | OriolOriolOriol/Condor | 0 | 6630469 | <filename>Condor/Tools/Autorun/shell.py
# Import modules
import os.path
from os import remove
__startup_path = os.path.join(os.getenv("APPDATA"), "Microsoft\\Windows\\Start Menu\\Programs\\Startup")
""" Add to startup (startup dir) """
def Install(name, executable):
payload = (f"""
[InternetShortcut]
URL=file://{os.path.abspath(executable)}
""")
target = os.path.join(__startup_path, name + ".url")
with open(target, 'w') as file:
file.write(payload)
return True
""" Delete from startup (startup dir) """
def Uninstall(name):
target = os.path.join(__startup_path, name + ".url")
try: remove(target)
except FileNotFoundError:
pass
return not os.path.exists(name)
""" Check if exists (startup dir) """
def Exists(name):
target = os.path.join(__startup_path, name + ".url")
return os.path.exists(target) | <filename>Condor/Tools/Autorun/shell.py
# Import modules
import os.path
from os import remove
__startup_path = os.path.join(os.getenv("APPDATA"), "Microsoft\\Windows\\Start Menu\\Programs\\Startup")
""" Add to startup (startup dir) """
def Install(name, executable):
payload = (f"""
[InternetShortcut]
URL=file://{os.path.abspath(executable)}
""")
target = os.path.join(__startup_path, name + ".url")
with open(target, 'w') as file:
file.write(payload)
return True
""" Delete from startup (startup dir) """
def Uninstall(name):
target = os.path.join(__startup_path, name + ".url")
try: remove(target)
except FileNotFoundError:
pass
return not os.path.exists(name)
""" Check if exists (startup dir) """
def Exists(name):
target = os.path.join(__startup_path, name + ".url")
return os.path.exists(target) | en | 0.262843 | # Import modules Add to startup (startup dir) [InternetShortcut] URL=file://{os.path.abspath(executable)} Delete from startup (startup dir) Check if exists (startup dir) | 2.605238 | 3 |
tests/test_ulist.py | nclarey/pyg-base | 0 | 6630470 | from pyg_base import ulist, rng
def test_rng():
assert rng(3) == list(range(3))
def test_ulist():
assert ulist([1,3,2,1]) == list([1,3,2])
assert ulist([1,3,2,1]) + 4 == list([1,3,2,4])
assert ulist([1,3,2,1]) + [4,1] == list([1,3,2,4])
assert ulist([1,3,2,1]) + [4,1,5] == list([1,3,2,4,5])
assert ulist([1,3,2,1]) & [1,3,4] == [1,3]
assert ulist([1,3,2,1]) & 1 == [1]
assert ulist([1,3,2,1]) & 4 == []
assert ulist([1,3,2,1]) - [1,3,4] == [2]
assert ulist([1,3,2,1]) - 4 == ulist([1,3,2])
assert ulist([1,3,2,1]) - [4,5] == ulist([1,3,2])
def test_ulist_add():
assert ulist([1,2,3]) + 1 == ulist([1,2,3])
assert ulist([1,2,3]) + [1,2] == ulist([1,2,3])
assert ulist([1,2,3]).copy() == ulist([1,2,3])
| from pyg_base import ulist, rng
def test_rng():
assert rng(3) == list(range(3))
def test_ulist():
assert ulist([1,3,2,1]) == list([1,3,2])
assert ulist([1,3,2,1]) + 4 == list([1,3,2,4])
assert ulist([1,3,2,1]) + [4,1] == list([1,3,2,4])
assert ulist([1,3,2,1]) + [4,1,5] == list([1,3,2,4,5])
assert ulist([1,3,2,1]) & [1,3,4] == [1,3]
assert ulist([1,3,2,1]) & 1 == [1]
assert ulist([1,3,2,1]) & 4 == []
assert ulist([1,3,2,1]) - [1,3,4] == [2]
assert ulist([1,3,2,1]) - 4 == ulist([1,3,2])
assert ulist([1,3,2,1]) - [4,5] == ulist([1,3,2])
def test_ulist_add():
assert ulist([1,2,3]) + 1 == ulist([1,2,3])
assert ulist([1,2,3]) + [1,2] == ulist([1,2,3])
assert ulist([1,2,3]).copy() == ulist([1,2,3])
| none | 1 | 2.92488 | 3 |
|
Athos/Networks/SqueezeNetCIFAR10/Squeezenet_model.py | shas19/EzPC | 1 | 6630471 | '''
Authors: <NAME>.
Copyright:
Copyright (c) 2018 Microsoft Research
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.
**
Parts of this code including the model itself, the training code and some other parts
were taken from https://github.com/kaizouman/tensorsandbox/tree/master/cifar10/models/squeeze
**
'''
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import sys
import Util
import time
import numpy
import matplotlib
import tensorflow as tf
sys.path.append(os.path.join(os.path.dirname(__file__), '..', '..', 'TFCompiler'))
import DumpTFMtData
from argparse import ArgumentParser
class SqueezeNet1Orig:
def __init__(self):
self.all_weights = []
def inference(self, images):
# conv1
conv1 = self.conv_layer(images,
size=3,
filters=64,
stride=1,
decay=False,
name='conv1')
# pool1
pool1 = self.pool_layer(conv1,
size=3,
stride=2,
name='pool1')
# fire2
fire2 = self.fire_layer(pool1, 32, 64, 64, decay=False, name='fire2')
# fire3
fire3 = self.fire_layer(fire2, 32, 64, 64, decay=False, name='fire3')
# pool2
pool2 = self.pool_layer(fire3,
size=3,
stride=2,
name='pool2')
# fire4
fire4 = self.fire_layer(pool2, 32, 128, 128, decay=False, name='fire4')
# fire5
fire5 = self.fire_layer(fire4, 32, 128, 128, decay=False, name='fire5')
# Final squeeze to get ten classes
conv2 = self.conv_layer(fire5,
size=1,
filters=10,
stride=1,
decay=False,
name='squeeze')
# Average pooling on spatial dimensions
predictions = self.avg_layer(conv2, name='avg_pool')
return predictions
def pool_layer(self, inputs, size, stride, name):
with tf.variable_scope(name) as scope:
outputs = tf.nn.max_pool(inputs,
ksize=[1,size,size,1],
strides=[1,stride,stride,1],
padding='SAME',
name=name)
return outputs
def fire_layer(self, inputs, s1x1, e1x1, e3x3, name, decay=False):
with tf.variable_scope(name) as scope:
# Squeeze sub-layer
squeezed_inputs = self.conv_layer(inputs,
size=1,
filters=s1x1,
stride=1,
decay=decay,
name='s1x1')
# Expand 1x1 sub-layer
e1x1_outputs = self.conv_layer(squeezed_inputs,
size=1,
filters=e1x1,
stride=1,
decay=decay,
name='e1x1')
# Expand 3x3 sub-layer
e3x3_outputs = self.conv_layer(squeezed_inputs,
size=3,
filters=e3x3,
stride=1,
decay=decay,
name='e3x3')
# Concatenate outputs along the last dimension (channel)
return tf.concat([e1x1_outputs, e3x3_outputs], 3)
def avg_layer(self, inputs, name):
w = inputs.get_shape().as_list()[1]
h = inputs.get_shape().as_list()[2]
c = inputs.get_shape().as_list()[3]
with tf.variable_scope(name) as scope:
# Use current spatial dimensions as Kernel size to produce a scalar
avg = tf.nn.avg_pool(inputs,
ksize=[1,w,h,1],
strides=[1,1,1,1],
padding='VALID',
name=scope.name)
# Reshape output to remove spatial dimensions reduced to one
return tf.reshape(avg, shape=[-1,c])
def conv_layer(self, inputs, size, filters, stride, decay, name):
channels = inputs.shape[3]
shape = [size, size, channels, filters]
with tf.variable_scope(name + '/conv') as scope:
weights = self._get_weights_var('weights', shape=shape, decay=decay)
biases = self.get_cons_variable([filters], 0.0)
conv = tf.nn.conv2d(inputs,
weights,
strides=[1,stride,stride,1],
padding='SAME')
pre_activation = tf.nn.bias_add(conv, biases)
outputs= tf.nn.relu(pre_activation, name=scope.name)
return outputs
def get_cons_variable(self, shape, val):
initial = tf.constant(val, shape=shape)
temp = tf.Variable(initial)
self.all_weights.append(temp)
return temp
def _get_weights_var(self, name, shape, decay=False):
"""Helper to create an initialized Variable with weight decay.
The Variable is initialized using a normal distribution whose variance
is provided by the xavier formula (ie inversely proportional to the number
of inputs)
Args:
name: name of the tensor variable
shape: the tensor shape
decay: a boolean indicating if we apply decay to the tensor weights
using a regularization loss
Returns:
Variable Tensor
"""
# Declare an initializer for this variable
initializer = tf.contrib.layers.xavier_initializer(uniform=False,dtype=tf.float32)
# Declare variable (it is trainable by default)
var = tf.get_variable(name=name,
shape=shape,
initializer=initializer,
dtype=tf.float32)
if decay:
# We apply a weight decay to this tensor var that is equal to the
# model weight decay divided by the tensor size
weight_decay = self.wd
for x in shape:
weight_decay /= x
# Weight loss is L2 loss multiplied by weight decay
weight_loss = tf.multiply(tf.nn.l2_loss(var),
weight_decay,
name='weight_loss')
# Add weight loss for this variable to the global losses collection
tf.add_to_collection('losses', weight_loss)
self.all_weights.append(var)
return var
class SqueezeNet1:
def __init__(self, use_cons_init):
self.all_weights = []
self.debug_weights = []
self.use_cons_init = use_cons_init
def inference(self, images):
# conv1
conv1 = self.conv_layer(images,
size=3,
filters=64,
stride=1,
decay=False,
name='conv1')
# pool1
pool1 = self.pool_layer(conv1,
size=3,
stride=2,
name='pool1')
# fire2
fire2 = self.fire_layer(pool1, 32, 64, 64, decay=False, name='fire2')
# fire3
fire3 = self.fire_layer(fire2, 32, 64, 64, decay=False, name='fire3')
# pool2
pool2 = self.pool_layer(fire3,
size=3,
stride=2,
name='pool2')
# fire4
fire4 = self.fire_layer(pool2, 32, 128, 128, decay=False, name='fire4')
# fire5
fire5 = self.fire_layer(fire4, 32, 128, 128, decay=False, name='fire5')
# Final squeeze to get ten classes
conv2 = self.conv_layer(fire5,
size=1,
filters=10,
stride=1,
decay=False,
name='squeeze')
# Average pooling on spatial dimensions
predictions = self.avg_layer(conv2, name='avg_pool')
return predictions
def pool_layer(self, inputs, size, stride, name):
with tf.variable_scope(name) as scope:
outputs = tf.nn.max_pool(inputs,
ksize=[1,size,size,1],
strides=[1,stride,stride,1],
padding='SAME',
name=name)
return outputs
def fire_layer(self, inputs, s1x1, e1x1, e3x3, name, decay=False):
with tf.variable_scope(name) as scope:
# Squeeze sub-layer
squeezed_inputs = self.conv_layer(inputs,
size=1,
filters=s1x1,
stride=1,
decay=decay,
name='s1x1')
# Expand 1x1 sub-layer
e1x1_outputs = self.conv_layer(squeezed_inputs,
size=1,
filters=e1x1,
stride=1,
decay=decay,
name='e1x1')
# Expand 3x3 sub-layer
e3x3_outputs = self.conv_layer(squeezed_inputs,
size=3,
filters=e3x3,
stride=1,
decay=decay,
name='e3x3')
# Concatenate outputs along the last dimension (channel)
return tf.concat([e1x1_outputs, e3x3_outputs], 3)
def avg_layer(self, inputs, name):
w = inputs.get_shape().as_list()[1]
h = inputs.get_shape().as_list()[2]
c = inputs.get_shape().as_list()[3]
with tf.variable_scope(name) as scope:
# Use current spatial dimensions as Kernel size to produce a scalar
avg = tf.nn.avg_pool(inputs,
ksize=[1,w,h,1],
strides=[1,1,1,1],
padding='VALID',
name=scope.name)
# Reshape output to remove spatial dimensions reduced to one
return tf.reshape(avg, shape=[-1,c])
def conv_layer(self, inputs, size, filters, stride, decay, name):
channels = inputs.shape[3]
shape = [size, size, channels, filters]
with tf.variable_scope(name + '/conv') as scope:
# For getting performance numbers, don't need to use the actual activations - just use constant activations
if self.use_cons_init:
weights = self.get_cons_variable(shape, 0.01)
else:
weights = self._get_weights_var('weights', shape=shape, decay=decay)
biases = self.get_cons_variable([filters], 0.0)
conv = tf.nn.conv2d(inputs,
weights,
strides=[1,stride,stride,1],
padding='SAME')
pre_activation = tf.nn.bias_add(conv, biases)
outputs= tf.nn.relu(pre_activation, name=scope.name)
return outputs
def get_cons_variable(self, shape, val):
initial = tf.constant(val, shape=shape)
temp = tf.Variable(initial)
self.all_weights.append(temp)
return temp
def _get_weights_var(self, name, shape, decay=False):
"""Helper to create an initialized Variable with weight decay.
The Variable is initialized using a normal distribution whose variance
is provided by the xavier formula (ie inversely proportional to the number
of inputs)
Args:
name: name of the tensor variable
shape: the tensor shape
decay: a boolean indicating if we apply decay to the tensor weights
using a regularization loss
Returns:
Variable Tensor
"""
# Declare an initializer for this variable
initializer = tf.contrib.layers.xavier_initializer(uniform=False,dtype=tf.float32)
# Declare variable (it is trainable by default)
var = tf.get_variable(name=name,
shape=shape,
initializer=initializer,
dtype=tf.float32)
if decay:
# We apply a weight decay to this tensor var that is equal to the
# model weight decay divided by the tensor size
weight_decay = self.wd
for x in shape:
weight_decay /= x
# Weight loss is L2 loss multiplied by weight decay
weight_loss = tf.multiply(tf.nn.l2_loss(var),
weight_decay,
name='weight_loss')
# Add weight loss for this variable to the global losses collection
tf.add_to_collection('losses', weight_loss)
self.all_weights.append(var)
return var
def train(sqn, save_model_path):
print('Starting train...')
# Hyper parameters
epochs = 1
batch_size = 128
keep_probability = 0.7
learning_rate = 0.001
n_batches = 5 #CIFAR10 dataset in the python version has 5 batches
x = tf.placeholder(tf.float32, shape=(None, 32, 32, 3), name='input_x')
y = tf.placeholder(tf.float32, shape=(None, 10), name='output_y')
keep_prob = tf.placeholder(tf.float32, name='keep_prob')
logits = sqn.inference(x)
# Loss and Optimizer
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y))
l2Loss = sum(list(map(lambda x: tf.nn.l2_loss(x), sqn.all_weights)))
beta = 1e-5
cost = cost + beta*l2Loss
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
# Accuracy
correct_pred = tf.equal(tf.argmax(logits, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32), name='accuracy')
valid_features, valid_labels = Util.load_preprocess_validation_data()
testing_features, testing_labels = Util.load_preprocess_testing_data()
print('Training now...')
with tf.Session() as sess:
# Initializing the variables
sess.run(tf.global_variables_initializer())
# Training cycle
for epoch in range(epochs):
# Loop over all batches
for batch_i in range(1, n_batches + 1):
for batch_features, batch_labels in Util.load_preprocess_training_batch(batch_i, batch_size):
sess.run(optimizer, feed_dict={x: batch_features,
y: batch_labels,
keep_prob: keep_probability
})
print('Epoch {:>2}, CIFAR-10 Batch {}: '.format(epoch + 1, batch_i), end='')
# Print stats
loss = sess.run(cost, feed_dict={x: batch_features, y: batch_labels, keep_prob: keep_probability})
train_acc = sess.run(accuracy, feed_dict={x: batch_features, y: batch_labels, keep_prob: keep_probability})
valid_acc = sess.run(accuracy, feed_dict={x: valid_features, y: valid_labels, keep_prob: keep_probability})
testing_acc = sess.run(accuracy, feed_dict={x: testing_features, y: testing_labels, keep_prob: keep_probability})
print('Loss: {:>10.4f} Train Acc: {:.6f} Validation Accuracy: {:.6f} Testing Acc: {:.6f}'.format(loss, train_acc, valid_acc, testing_acc))
if (epoch % 10 == 0):
# Save Model
saver = tf.train.Saver()
save_path = saver.save(sess, save_model_path)
#outputArgMax should only be used when findAcc is False
def infer(sqn, sess, images, labels, restoreModelPath, findAccOrArgMaxOrPredVal=0, restoreWeights=True, onlysavegraph=False):
assert(findAccOrArgMaxOrPredVal>=0 and findAccOrArgMaxOrPredVal<=2)
if restoreWeights: assert(not(onlysavegraph))
if onlysavegraph: assert(findAccOrArgMaxOrPredVal==1)
x = tf.placeholder(tf.float32, shape=(None, 32, 32, 3), name='input_x')
if (not(onlysavegraph)):
y = tf.placeholder(tf.int32, shape=(None, 10), name='output_y')
logits = sqn.inference(x)
if findAccOrArgMaxOrPredVal==0:
correct_pred = tf.equal(tf.argmax(logits, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32), name='accuracy')
elif findAccOrArgMaxOrPredVal==1:
logits = tf.argmax(logits, axis=1)
elif findAccOrArgMaxOrPredVal==2:
pass
else:
assert False
print("Doing inference on ", len(images), " images.")
feed_dict = {x: images}
if not(onlysavegraph):
feed_dict[y] = labels
sess.run(tf.global_variables_initializer())
if onlysavegraph:
output_tensor = None
gg = tf.get_default_graph()
for node in gg.as_graph_def().node:
if node.name == 'ArgMax':
output_tensor = gg.get_operation_by_name(node.name).outputs[0]
optimized_graph_def = DumpTFMtData.save_graph_metadata(output_tensor, sess, feed_dict)
return
if restoreWeights:
saver = tf.train.Saver(sqn.all_weights)
saver.restore(sess, restoreModelPath)
print("*************** Starting Prediction****************")
start_time = time.time()
if findAccOrArgMaxOrPredVal==0:
predictions = sess.run([accuracy], feed_dict=feed_dict)
else:
predictions = sess.run([logits], feed_dict=feed_dict)
end_time = time.time()
print("*************** Done Prediction****************")
duration = end_time - start_time
print("Time taken in prediction : ", duration)
print("Inference result = ", predictions)
return predictions
def getTrainedWeightsStrForm(sess, evalTensors, scalingFac):
allWeightsStr = ''
finalParameters = map(lambda x : sess.run(x), evalTensors)
for curParameterVal in finalParameters:
for xx in numpy.nditer(curParameterVal, order='C'):
allWeightsStr += (str(int(xx * (1<<scalingFac))) + ' ')
allWeightsStr += '\n\n'
return allWeightsStr
def findAndSaveCorrectTestImg(pred, features, actualLabels, correctImgFolder, incorrectImgFolder, textFolder, sess, sqn, scalingFac):
#Run with findAcc=False and outputArgMax=False
assert(len(pred)==1 and len(pred[0].shape)==2)
modelPred = numpy.argmax(pred[0], axis=1)
trueLabel = numpy.argmax(actualLabels, axis=1)
print("Pred = ", pred)
print("actualLabels = ", actualLabels)
print("ModelPred = ", modelPred)
print("TrueLabel = ", trueLabel)
numImages = len(features)
allWeightsStr = getTrainedWeightsStrForm(sess, sqn.all_weights, scalingFac)
for ii in range(numImages):
curImage = features[ii]
if (modelPred[ii]==trueLabel[ii]):
matplotlib.image.imsave(os.path.join(correctImgFolder, str(ii)+'-test.png'), curImage)
else:
matplotlib.image.imsave(os.path.join(incorrectImgFolder, str(ii)+'-test.png'), curImage)
textInpFileName = os.path.join(textFolder, str(ii)+'-test-inp.txt')
dumpCifar10Image(curImage, textInpFileName, scalingFac, 'w')
with open(textInpFileName, 'a') as ff:
ff.write(allWeightsStr)
if (ii%10==0):
print("Processing ", ii, " images done.")
def main():
scalingFac = 12
findAccOrArgMaxOrPredVal = 0
restoreWeights = True
onlysavegraph = False
save_model_path = './TrainedModel/model'
doTraining = False
inp = None
if (len(sys.argv) > 1):
inp = sys.argv[1]
if (inp == 'train'):
doTraining = True
elif (inp == 'savegraph'):
findAccOrArgMaxOrPredVal = 1
restoreWeights = False
onlysavegraph = True
testing_features, testing_labels = Util.get_sample_points(2, 4555, 4556)
elif (inp == 'testSingleTestInp'):
testBatchInpNum = int(sys.argv[2])
findAccOrArgMaxOrPredVal = 1
restoreWeights = True
onlysavegraph = False
all_testing_features, all_testing_labels = Util.load_preprocess_testing_data()
testing_features, testing_labels = all_testing_features[testBatchInpNum:testBatchInpNum+1], all_testing_labels[testBatchInpNum:testBatchInpNum+1]
elif (inp == 'testSingleTestInpAndSaveData'):
testBatchInpNum = int(sys.argv[2])
findAccOrArgMaxOrPredVal = int(sys.argv[3])
restoreWeights = True
onlysavegraph = False
all_testing_features, all_testing_labels = Util.load_preprocess_testing_data()
testing_features, testing_labels = all_testing_features[testBatchInpNum:testBatchInpNum+1], all_testing_labels[testBatchInpNum:testBatchInpNum+1]
# testing_features, testing_labels = numpy.full((1,32,32,3),0.01), numpy.full((1,10),0.01)
elif (inp == 'savegraphAndDataBatch'):
batchNum = int(sys.argv[2])
imgStartNum = int(sys.argv[3])
imgEndNum = int(sys.argv[4])
findAccOrArgMaxOrPredVal = 1
restoreWeights = False
onlysavegraph = True
testing_features, testing_labels = Util.get_sample_points(batchNum, imgStartNum, imgEndNum)
elif (inp == 'testBatchInp'):
imgStartNum = int(sys.argv[2])
imgEndNum = int(sys.argv[3])
findAccOrArgMaxOrPredVal = 1
restoreWeights = True
onlysavegraph = False
all_testing_features, all_testing_labels = Util.load_preprocess_testing_data()
testing_features, testing_labels = all_testing_features[imgStartNum:imgEndNum], all_testing_labels[imgStartNum:imgEndNum]
elif (inp == 'findAndSaveCorrectTestImg'):
findAccOrArgMaxOrPredVal = 2
restoreWeights = True
onlysavegraph = False
testing_features, testing_labels = Util.load_preprocess_testing_data()
testing_features, testing_labels = testing_features[0:100], testing_labels[0:100]
else:
if (inp != ""):
print("WARNING : Given option didn't match any known value.")
testing_features, testing_labels = Util.load_preprocess_testing_data()
sqn = SqueezeNet1(use_cons_init=onlysavegraph)
if doTraining:
train(sqn, save_model_path)
else:
with tf.Session() as sess:
pred = infer(sqn, sess, testing_features, testing_labels, save_model_path, findAccOrArgMaxOrPredVal=findAccOrArgMaxOrPredVal,
restoreWeights=restoreWeights,
onlysavegraph=onlysavegraph)
if findAccOrArgMaxOrPredVal==1 and not(onlysavegraph):
print("Actual labels = ", testing_labels)
print("ArgMax in actual label : ", numpy.argmax(testing_labels, axis=1))
if (inp == 'findAndSaveCorrectTestImg'):
print('Running ' + inp)
print(pred[0].shape)
findAndSaveCorrectTestImg(pred, testing_features, testing_labels, './testPred/CorrectImg/', './testPred/IncorrectImg/', './testPred/TestInputs/', sess, sqn, scalingFac)
if (inp == 'savegraphAndDataBatch' or inp=='testSingleTestInpAndSaveData'):
imgFileName = 'SqNet_CIFAR_img.inp'
weightsFileName = 'SqNet_CIFAR_weights.inp'
for ii,curFeature in enumerate(testing_features):
if ii == 0 :
DumpTFMtData.dumpImageDataInt(curFeature, imgFileName, scalingFac, 'w')
else:
DumpTFMtData.dumpImageDataInt(curFeature, imgFileName, scalingFac, 'a')
DumpTFMtData.dumpTrainedWeightsInt(sess, sqn.all_weights, weightsFileName, scalingFac, 'w')
if __name__ == '__main__':
main()
| '''
Authors: <NAME>.
Copyright:
Copyright (c) 2018 Microsoft Research
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.
**
Parts of this code including the model itself, the training code and some other parts
were taken from https://github.com/kaizouman/tensorsandbox/tree/master/cifar10/models/squeeze
**
'''
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import sys
import Util
import time
import numpy
import matplotlib
import tensorflow as tf
sys.path.append(os.path.join(os.path.dirname(__file__), '..', '..', 'TFCompiler'))
import DumpTFMtData
from argparse import ArgumentParser
class SqueezeNet1Orig:
def __init__(self):
self.all_weights = []
def inference(self, images):
# conv1
conv1 = self.conv_layer(images,
size=3,
filters=64,
stride=1,
decay=False,
name='conv1')
# pool1
pool1 = self.pool_layer(conv1,
size=3,
stride=2,
name='pool1')
# fire2
fire2 = self.fire_layer(pool1, 32, 64, 64, decay=False, name='fire2')
# fire3
fire3 = self.fire_layer(fire2, 32, 64, 64, decay=False, name='fire3')
# pool2
pool2 = self.pool_layer(fire3,
size=3,
stride=2,
name='pool2')
# fire4
fire4 = self.fire_layer(pool2, 32, 128, 128, decay=False, name='fire4')
# fire5
fire5 = self.fire_layer(fire4, 32, 128, 128, decay=False, name='fire5')
# Final squeeze to get ten classes
conv2 = self.conv_layer(fire5,
size=1,
filters=10,
stride=1,
decay=False,
name='squeeze')
# Average pooling on spatial dimensions
predictions = self.avg_layer(conv2, name='avg_pool')
return predictions
def pool_layer(self, inputs, size, stride, name):
with tf.variable_scope(name) as scope:
outputs = tf.nn.max_pool(inputs,
ksize=[1,size,size,1],
strides=[1,stride,stride,1],
padding='SAME',
name=name)
return outputs
def fire_layer(self, inputs, s1x1, e1x1, e3x3, name, decay=False):
with tf.variable_scope(name) as scope:
# Squeeze sub-layer
squeezed_inputs = self.conv_layer(inputs,
size=1,
filters=s1x1,
stride=1,
decay=decay,
name='s1x1')
# Expand 1x1 sub-layer
e1x1_outputs = self.conv_layer(squeezed_inputs,
size=1,
filters=e1x1,
stride=1,
decay=decay,
name='e1x1')
# Expand 3x3 sub-layer
e3x3_outputs = self.conv_layer(squeezed_inputs,
size=3,
filters=e3x3,
stride=1,
decay=decay,
name='e3x3')
# Concatenate outputs along the last dimension (channel)
return tf.concat([e1x1_outputs, e3x3_outputs], 3)
def avg_layer(self, inputs, name):
w = inputs.get_shape().as_list()[1]
h = inputs.get_shape().as_list()[2]
c = inputs.get_shape().as_list()[3]
with tf.variable_scope(name) as scope:
# Use current spatial dimensions as Kernel size to produce a scalar
avg = tf.nn.avg_pool(inputs,
ksize=[1,w,h,1],
strides=[1,1,1,1],
padding='VALID',
name=scope.name)
# Reshape output to remove spatial dimensions reduced to one
return tf.reshape(avg, shape=[-1,c])
def conv_layer(self, inputs, size, filters, stride, decay, name):
channels = inputs.shape[3]
shape = [size, size, channels, filters]
with tf.variable_scope(name + '/conv') as scope:
weights = self._get_weights_var('weights', shape=shape, decay=decay)
biases = self.get_cons_variable([filters], 0.0)
conv = tf.nn.conv2d(inputs,
weights,
strides=[1,stride,stride,1],
padding='SAME')
pre_activation = tf.nn.bias_add(conv, biases)
outputs= tf.nn.relu(pre_activation, name=scope.name)
return outputs
def get_cons_variable(self, shape, val):
initial = tf.constant(val, shape=shape)
temp = tf.Variable(initial)
self.all_weights.append(temp)
return temp
def _get_weights_var(self, name, shape, decay=False):
"""Helper to create an initialized Variable with weight decay.
The Variable is initialized using a normal distribution whose variance
is provided by the xavier formula (ie inversely proportional to the number
of inputs)
Args:
name: name of the tensor variable
shape: the tensor shape
decay: a boolean indicating if we apply decay to the tensor weights
using a regularization loss
Returns:
Variable Tensor
"""
# Declare an initializer for this variable
initializer = tf.contrib.layers.xavier_initializer(uniform=False,dtype=tf.float32)
# Declare variable (it is trainable by default)
var = tf.get_variable(name=name,
shape=shape,
initializer=initializer,
dtype=tf.float32)
if decay:
# We apply a weight decay to this tensor var that is equal to the
# model weight decay divided by the tensor size
weight_decay = self.wd
for x in shape:
weight_decay /= x
# Weight loss is L2 loss multiplied by weight decay
weight_loss = tf.multiply(tf.nn.l2_loss(var),
weight_decay,
name='weight_loss')
# Add weight loss for this variable to the global losses collection
tf.add_to_collection('losses', weight_loss)
self.all_weights.append(var)
return var
class SqueezeNet1:
def __init__(self, use_cons_init):
self.all_weights = []
self.debug_weights = []
self.use_cons_init = use_cons_init
def inference(self, images):
# conv1
conv1 = self.conv_layer(images,
size=3,
filters=64,
stride=1,
decay=False,
name='conv1')
# pool1
pool1 = self.pool_layer(conv1,
size=3,
stride=2,
name='pool1')
# fire2
fire2 = self.fire_layer(pool1, 32, 64, 64, decay=False, name='fire2')
# fire3
fire3 = self.fire_layer(fire2, 32, 64, 64, decay=False, name='fire3')
# pool2
pool2 = self.pool_layer(fire3,
size=3,
stride=2,
name='pool2')
# fire4
fire4 = self.fire_layer(pool2, 32, 128, 128, decay=False, name='fire4')
# fire5
fire5 = self.fire_layer(fire4, 32, 128, 128, decay=False, name='fire5')
# Final squeeze to get ten classes
conv2 = self.conv_layer(fire5,
size=1,
filters=10,
stride=1,
decay=False,
name='squeeze')
# Average pooling on spatial dimensions
predictions = self.avg_layer(conv2, name='avg_pool')
return predictions
def pool_layer(self, inputs, size, stride, name):
with tf.variable_scope(name) as scope:
outputs = tf.nn.max_pool(inputs,
ksize=[1,size,size,1],
strides=[1,stride,stride,1],
padding='SAME',
name=name)
return outputs
def fire_layer(self, inputs, s1x1, e1x1, e3x3, name, decay=False):
with tf.variable_scope(name) as scope:
# Squeeze sub-layer
squeezed_inputs = self.conv_layer(inputs,
size=1,
filters=s1x1,
stride=1,
decay=decay,
name='s1x1')
# Expand 1x1 sub-layer
e1x1_outputs = self.conv_layer(squeezed_inputs,
size=1,
filters=e1x1,
stride=1,
decay=decay,
name='e1x1')
# Expand 3x3 sub-layer
e3x3_outputs = self.conv_layer(squeezed_inputs,
size=3,
filters=e3x3,
stride=1,
decay=decay,
name='e3x3')
# Concatenate outputs along the last dimension (channel)
return tf.concat([e1x1_outputs, e3x3_outputs], 3)
def avg_layer(self, inputs, name):
w = inputs.get_shape().as_list()[1]
h = inputs.get_shape().as_list()[2]
c = inputs.get_shape().as_list()[3]
with tf.variable_scope(name) as scope:
# Use current spatial dimensions as Kernel size to produce a scalar
avg = tf.nn.avg_pool(inputs,
ksize=[1,w,h,1],
strides=[1,1,1,1],
padding='VALID',
name=scope.name)
# Reshape output to remove spatial dimensions reduced to one
return tf.reshape(avg, shape=[-1,c])
def conv_layer(self, inputs, size, filters, stride, decay, name):
channels = inputs.shape[3]
shape = [size, size, channels, filters]
with tf.variable_scope(name + '/conv') as scope:
# For getting performance numbers, don't need to use the actual activations - just use constant activations
if self.use_cons_init:
weights = self.get_cons_variable(shape, 0.01)
else:
weights = self._get_weights_var('weights', shape=shape, decay=decay)
biases = self.get_cons_variable([filters], 0.0)
conv = tf.nn.conv2d(inputs,
weights,
strides=[1,stride,stride,1],
padding='SAME')
pre_activation = tf.nn.bias_add(conv, biases)
outputs= tf.nn.relu(pre_activation, name=scope.name)
return outputs
def get_cons_variable(self, shape, val):
initial = tf.constant(val, shape=shape)
temp = tf.Variable(initial)
self.all_weights.append(temp)
return temp
def _get_weights_var(self, name, shape, decay=False):
"""Helper to create an initialized Variable with weight decay.
The Variable is initialized using a normal distribution whose variance
is provided by the xavier formula (ie inversely proportional to the number
of inputs)
Args:
name: name of the tensor variable
shape: the tensor shape
decay: a boolean indicating if we apply decay to the tensor weights
using a regularization loss
Returns:
Variable Tensor
"""
# Declare an initializer for this variable
initializer = tf.contrib.layers.xavier_initializer(uniform=False,dtype=tf.float32)
# Declare variable (it is trainable by default)
var = tf.get_variable(name=name,
shape=shape,
initializer=initializer,
dtype=tf.float32)
if decay:
# We apply a weight decay to this tensor var that is equal to the
# model weight decay divided by the tensor size
weight_decay = self.wd
for x in shape:
weight_decay /= x
# Weight loss is L2 loss multiplied by weight decay
weight_loss = tf.multiply(tf.nn.l2_loss(var),
weight_decay,
name='weight_loss')
# Add weight loss for this variable to the global losses collection
tf.add_to_collection('losses', weight_loss)
self.all_weights.append(var)
return var
def train(sqn, save_model_path):
print('Starting train...')
# Hyper parameters
epochs = 1
batch_size = 128
keep_probability = 0.7
learning_rate = 0.001
n_batches = 5 #CIFAR10 dataset in the python version has 5 batches
x = tf.placeholder(tf.float32, shape=(None, 32, 32, 3), name='input_x')
y = tf.placeholder(tf.float32, shape=(None, 10), name='output_y')
keep_prob = tf.placeholder(tf.float32, name='keep_prob')
logits = sqn.inference(x)
# Loss and Optimizer
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y))
l2Loss = sum(list(map(lambda x: tf.nn.l2_loss(x), sqn.all_weights)))
beta = 1e-5
cost = cost + beta*l2Loss
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
# Accuracy
correct_pred = tf.equal(tf.argmax(logits, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32), name='accuracy')
valid_features, valid_labels = Util.load_preprocess_validation_data()
testing_features, testing_labels = Util.load_preprocess_testing_data()
print('Training now...')
with tf.Session() as sess:
# Initializing the variables
sess.run(tf.global_variables_initializer())
# Training cycle
for epoch in range(epochs):
# Loop over all batches
for batch_i in range(1, n_batches + 1):
for batch_features, batch_labels in Util.load_preprocess_training_batch(batch_i, batch_size):
sess.run(optimizer, feed_dict={x: batch_features,
y: batch_labels,
keep_prob: keep_probability
})
print('Epoch {:>2}, CIFAR-10 Batch {}: '.format(epoch + 1, batch_i), end='')
# Print stats
loss = sess.run(cost, feed_dict={x: batch_features, y: batch_labels, keep_prob: keep_probability})
train_acc = sess.run(accuracy, feed_dict={x: batch_features, y: batch_labels, keep_prob: keep_probability})
valid_acc = sess.run(accuracy, feed_dict={x: valid_features, y: valid_labels, keep_prob: keep_probability})
testing_acc = sess.run(accuracy, feed_dict={x: testing_features, y: testing_labels, keep_prob: keep_probability})
print('Loss: {:>10.4f} Train Acc: {:.6f} Validation Accuracy: {:.6f} Testing Acc: {:.6f}'.format(loss, train_acc, valid_acc, testing_acc))
if (epoch % 10 == 0):
# Save Model
saver = tf.train.Saver()
save_path = saver.save(sess, save_model_path)
#outputArgMax should only be used when findAcc is False
def infer(sqn, sess, images, labels, restoreModelPath, findAccOrArgMaxOrPredVal=0, restoreWeights=True, onlysavegraph=False):
assert(findAccOrArgMaxOrPredVal>=0 and findAccOrArgMaxOrPredVal<=2)
if restoreWeights: assert(not(onlysavegraph))
if onlysavegraph: assert(findAccOrArgMaxOrPredVal==1)
x = tf.placeholder(tf.float32, shape=(None, 32, 32, 3), name='input_x')
if (not(onlysavegraph)):
y = tf.placeholder(tf.int32, shape=(None, 10), name='output_y')
logits = sqn.inference(x)
if findAccOrArgMaxOrPredVal==0:
correct_pred = tf.equal(tf.argmax(logits, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32), name='accuracy')
elif findAccOrArgMaxOrPredVal==1:
logits = tf.argmax(logits, axis=1)
elif findAccOrArgMaxOrPredVal==2:
pass
else:
assert False
print("Doing inference on ", len(images), " images.")
feed_dict = {x: images}
if not(onlysavegraph):
feed_dict[y] = labels
sess.run(tf.global_variables_initializer())
if onlysavegraph:
output_tensor = None
gg = tf.get_default_graph()
for node in gg.as_graph_def().node:
if node.name == 'ArgMax':
output_tensor = gg.get_operation_by_name(node.name).outputs[0]
optimized_graph_def = DumpTFMtData.save_graph_metadata(output_tensor, sess, feed_dict)
return
if restoreWeights:
saver = tf.train.Saver(sqn.all_weights)
saver.restore(sess, restoreModelPath)
print("*************** Starting Prediction****************")
start_time = time.time()
if findAccOrArgMaxOrPredVal==0:
predictions = sess.run([accuracy], feed_dict=feed_dict)
else:
predictions = sess.run([logits], feed_dict=feed_dict)
end_time = time.time()
print("*************** Done Prediction****************")
duration = end_time - start_time
print("Time taken in prediction : ", duration)
print("Inference result = ", predictions)
return predictions
def getTrainedWeightsStrForm(sess, evalTensors, scalingFac):
allWeightsStr = ''
finalParameters = map(lambda x : sess.run(x), evalTensors)
for curParameterVal in finalParameters:
for xx in numpy.nditer(curParameterVal, order='C'):
allWeightsStr += (str(int(xx * (1<<scalingFac))) + ' ')
allWeightsStr += '\n\n'
return allWeightsStr
def findAndSaveCorrectTestImg(pred, features, actualLabels, correctImgFolder, incorrectImgFolder, textFolder, sess, sqn, scalingFac):
#Run with findAcc=False and outputArgMax=False
assert(len(pred)==1 and len(pred[0].shape)==2)
modelPred = numpy.argmax(pred[0], axis=1)
trueLabel = numpy.argmax(actualLabels, axis=1)
print("Pred = ", pred)
print("actualLabels = ", actualLabels)
print("ModelPred = ", modelPred)
print("TrueLabel = ", trueLabel)
numImages = len(features)
allWeightsStr = getTrainedWeightsStrForm(sess, sqn.all_weights, scalingFac)
for ii in range(numImages):
curImage = features[ii]
if (modelPred[ii]==trueLabel[ii]):
matplotlib.image.imsave(os.path.join(correctImgFolder, str(ii)+'-test.png'), curImage)
else:
matplotlib.image.imsave(os.path.join(incorrectImgFolder, str(ii)+'-test.png'), curImage)
textInpFileName = os.path.join(textFolder, str(ii)+'-test-inp.txt')
dumpCifar10Image(curImage, textInpFileName, scalingFac, 'w')
with open(textInpFileName, 'a') as ff:
ff.write(allWeightsStr)
if (ii%10==0):
print("Processing ", ii, " images done.")
def main():
scalingFac = 12
findAccOrArgMaxOrPredVal = 0
restoreWeights = True
onlysavegraph = False
save_model_path = './TrainedModel/model'
doTraining = False
inp = None
if (len(sys.argv) > 1):
inp = sys.argv[1]
if (inp == 'train'):
doTraining = True
elif (inp == 'savegraph'):
findAccOrArgMaxOrPredVal = 1
restoreWeights = False
onlysavegraph = True
testing_features, testing_labels = Util.get_sample_points(2, 4555, 4556)
elif (inp == 'testSingleTestInp'):
testBatchInpNum = int(sys.argv[2])
findAccOrArgMaxOrPredVal = 1
restoreWeights = True
onlysavegraph = False
all_testing_features, all_testing_labels = Util.load_preprocess_testing_data()
testing_features, testing_labels = all_testing_features[testBatchInpNum:testBatchInpNum+1], all_testing_labels[testBatchInpNum:testBatchInpNum+1]
elif (inp == 'testSingleTestInpAndSaveData'):
testBatchInpNum = int(sys.argv[2])
findAccOrArgMaxOrPredVal = int(sys.argv[3])
restoreWeights = True
onlysavegraph = False
all_testing_features, all_testing_labels = Util.load_preprocess_testing_data()
testing_features, testing_labels = all_testing_features[testBatchInpNum:testBatchInpNum+1], all_testing_labels[testBatchInpNum:testBatchInpNum+1]
# testing_features, testing_labels = numpy.full((1,32,32,3),0.01), numpy.full((1,10),0.01)
elif (inp == 'savegraphAndDataBatch'):
batchNum = int(sys.argv[2])
imgStartNum = int(sys.argv[3])
imgEndNum = int(sys.argv[4])
findAccOrArgMaxOrPredVal = 1
restoreWeights = False
onlysavegraph = True
testing_features, testing_labels = Util.get_sample_points(batchNum, imgStartNum, imgEndNum)
elif (inp == 'testBatchInp'):
imgStartNum = int(sys.argv[2])
imgEndNum = int(sys.argv[3])
findAccOrArgMaxOrPredVal = 1
restoreWeights = True
onlysavegraph = False
all_testing_features, all_testing_labels = Util.load_preprocess_testing_data()
testing_features, testing_labels = all_testing_features[imgStartNum:imgEndNum], all_testing_labels[imgStartNum:imgEndNum]
elif (inp == 'findAndSaveCorrectTestImg'):
findAccOrArgMaxOrPredVal = 2
restoreWeights = True
onlysavegraph = False
testing_features, testing_labels = Util.load_preprocess_testing_data()
testing_features, testing_labels = testing_features[0:100], testing_labels[0:100]
else:
if (inp != ""):
print("WARNING : Given option didn't match any known value.")
testing_features, testing_labels = Util.load_preprocess_testing_data()
sqn = SqueezeNet1(use_cons_init=onlysavegraph)
if doTraining:
train(sqn, save_model_path)
else:
with tf.Session() as sess:
pred = infer(sqn, sess, testing_features, testing_labels, save_model_path, findAccOrArgMaxOrPredVal=findAccOrArgMaxOrPredVal,
restoreWeights=restoreWeights,
onlysavegraph=onlysavegraph)
if findAccOrArgMaxOrPredVal==1 and not(onlysavegraph):
print("Actual labels = ", testing_labels)
print("ArgMax in actual label : ", numpy.argmax(testing_labels, axis=1))
if (inp == 'findAndSaveCorrectTestImg'):
print('Running ' + inp)
print(pred[0].shape)
findAndSaveCorrectTestImg(pred, testing_features, testing_labels, './testPred/CorrectImg/', './testPred/IncorrectImg/', './testPred/TestInputs/', sess, sqn, scalingFac)
if (inp == 'savegraphAndDataBatch' or inp=='testSingleTestInpAndSaveData'):
imgFileName = 'SqNet_CIFAR_img.inp'
weightsFileName = 'SqNet_CIFAR_weights.inp'
for ii,curFeature in enumerate(testing_features):
if ii == 0 :
DumpTFMtData.dumpImageDataInt(curFeature, imgFileName, scalingFac, 'w')
else:
DumpTFMtData.dumpImageDataInt(curFeature, imgFileName, scalingFac, 'a')
DumpTFMtData.dumpTrainedWeightsInt(sess, sqn.all_weights, weightsFileName, scalingFac, 'w')
if __name__ == '__main__':
main()
| en | 0.79513 | Authors: <NAME>.
Copyright:
Copyright (c) 2018 Microsoft Research
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.
**
Parts of this code including the model itself, the training code and some other parts
were taken from https://github.com/kaizouman/tensorsandbox/tree/master/cifar10/models/squeeze
** # conv1 # pool1 # fire2 # fire3 # pool2 # fire4 # fire5 # Final squeeze to get ten classes # Average pooling on spatial dimensions # Squeeze sub-layer # Expand 1x1 sub-layer # Expand 3x3 sub-layer # Concatenate outputs along the last dimension (channel) # Use current spatial dimensions as Kernel size to produce a scalar # Reshape output to remove spatial dimensions reduced to one Helper to create an initialized Variable with weight decay.
The Variable is initialized using a normal distribution whose variance
is provided by the xavier formula (ie inversely proportional to the number
of inputs)
Args:
name: name of the tensor variable
shape: the tensor shape
decay: a boolean indicating if we apply decay to the tensor weights
using a regularization loss
Returns:
Variable Tensor # Declare an initializer for this variable # Declare variable (it is trainable by default) # We apply a weight decay to this tensor var that is equal to the # model weight decay divided by the tensor size # Weight loss is L2 loss multiplied by weight decay # Add weight loss for this variable to the global losses collection # conv1 # pool1 # fire2 # fire3 # pool2 # fire4 # fire5 # Final squeeze to get ten classes # Average pooling on spatial dimensions # Squeeze sub-layer # Expand 1x1 sub-layer # Expand 3x3 sub-layer # Concatenate outputs along the last dimension (channel) # Use current spatial dimensions as Kernel size to produce a scalar # Reshape output to remove spatial dimensions reduced to one # For getting performance numbers, don't need to use the actual activations - just use constant activations Helper to create an initialized Variable with weight decay.
The Variable is initialized using a normal distribution whose variance
is provided by the xavier formula (ie inversely proportional to the number
of inputs)
Args:
name: name of the tensor variable
shape: the tensor shape
decay: a boolean indicating if we apply decay to the tensor weights
using a regularization loss
Returns:
Variable Tensor # Declare an initializer for this variable # Declare variable (it is trainable by default) # We apply a weight decay to this tensor var that is equal to the # model weight decay divided by the tensor size # Weight loss is L2 loss multiplied by weight decay # Add weight loss for this variable to the global losses collection # Hyper parameters #CIFAR10 dataset in the python version has 5 batches # Loss and Optimizer # Accuracy # Initializing the variables # Training cycle # Loop over all batches # Print stats # Save Model #outputArgMax should only be used when findAcc is False #Run with findAcc=False and outputArgMax=False # testing_features, testing_labels = numpy.full((1,32,32,3),0.01), numpy.full((1,10),0.01) | 1.426519 | 1 |
cnn_models/help_fun.py | lijian10086/quantized_distillation | 321 | 6630472 | import torch
import torch.nn as nn
from torch.autograd import Variable
import math
import time
import numpy as np
import helpers.functions as mhf
import random
USE_CUDA = torch.cuda.is_available()
def time_forward_pass(model, train_loader):
model.eval()
start_time = time.time()
for idx_minibatch, data in enumerate(train_loader):
# get the inputs
inputs, labels = data
inputs, labels = Variable(inputs, volatile=True), Variable(labels)
if USE_CUDA:
inputs = inputs.cuda()
labels = labels.cuda()
outputs = model(inputs)
end_time = time.time()
return end_time - start_time
def evaluateModel(model, testLoader, fastEvaluation=True, maxExampleFastEvaluation=10000, k=1):
'if fastEvaluation is True, it will only check a subset of *maxExampleFastEvaluation* images of the test set'
model.eval()
correctClass = 0
totalNumExamples = 0
for idx_minibatch, data in enumerate(testLoader):
# get the inputs
inputs, labels = data
inputs, labels = Variable(inputs, volatile=True), Variable(labels)
if USE_CUDA:
inputs = inputs.cuda()
labels = labels.cuda()
outputs = model(inputs)
_, topk_predictions = outputs.topk(k, dim=1, largest=True, sorted=True)
topk_predictions = topk_predictions.t()
correct = topk_predictions.eq(labels.view(1, -1).expand_as(topk_predictions))
correctClass += correct.view(-1).float().sum(0, keepdim=True).data[0]
totalNumExamples += len(labels)
if fastEvaluation is True and totalNumExamples > maxExampleFastEvaluation:
break
return correctClass / totalNumExamples
def forward_and_backward(model, batch, idx_batch, epoch, criterion=None,
use_distillation_loss=False, teacher_model=None,
temperature_distillation=2, ask_teacher_strategy='always',
return_more_info=False):
#TODO: return_more_info is just there for backward compatibility. A big refactoring is due here, and there one should
#remove the return_more_info flag
if criterion is None:
criterion = nn.CrossEntropyLoss()
if USE_CUDA:
criterion = criterion.cuda()
if use_distillation_loss is True and teacher_model is None:
raise ValueError('To compute distillation loss you need to pass the teacher model')
if not isinstance(ask_teacher_strategy, tuple):
ask_teacher_strategy = (ask_teacher_strategy, )
inputs, labels = batch
# wrap them in Variable
inputs, labels = Variable(inputs), Variable(labels)
if USE_CUDA:
inputs = inputs.cuda()
labels = labels.cuda()
# forward + backward + optimize
outputs = model(inputs)
count_asked_teacher = 0
if use_distillation_loss:
#if cutoff_entropy_value_distillation is not None, we use the distillation loss only on the examples
#whose entropy is higher than the cutoff.
weight_teacher_loss = 0.7
if 'entropy' in ask_teacher_strategy[0].lower():
prob_out = torch.nn.functional.softmax(outputs).data
entropy = [mhf.get_entropy(prob_out[idx_b, :]) for idx_b in range(prob_out.size(0))]
if ask_teacher_strategy[0].lower() == 'always':
mask_distillation_loss = torch.ByteTensor([True]*outputs.size(0))
elif ask_teacher_strategy[0].lower() == 'cutoff_entropy':
cutoff_entropy_value_distillation = ask_teacher_strategy[1]
mask_distillation_loss = torch.ByteTensor([entr > cutoff_entropy_value_distillation for entr in entropy])
elif ask_teacher_strategy[0].lower() == 'random_entropy':
max_entropy = math.log2(outputs.size(1)) #max possible entropy that happens with uniform distribution
mask_distillation_loss = torch.ByteTensor([random.random() < entr/max_entropy for entr in entropy])
elif ask_teacher_strategy[0].lower() == 'incorrect_labels':
_, predictions = outputs.max(dim=1)
mask_distillation_loss = (predictions != labels).data.cpu()
else:
raise ValueError('ask_teacher_strategy is incorrectly formatted')
index_distillation_loss = torch.arange(0, outputs.size(0))[mask_distillation_loss.view(-1, 1)].long()
inverse_idx_distill_loss = torch.arange(0, outputs.size(0))[1-mask_distillation_loss.view(-1, 1)].long()
if USE_CUDA:
index_distillation_loss = index_distillation_loss.cuda()
inverse_idx_distill_loss = inverse_idx_distill_loss.cuda()
# this criterion is the distillation criterion according to Hinton's paper:
# "Distilling the Knowledge in a Neural Network", Hinton et al.
softmaxFunction, logSoftmaxFunction, KLDivLossFunction = nn.Softmax(dim=1), nn.LogSoftmax(dim=1), nn.KLDivLoss()
if USE_CUDA:
softmaxFunction, logSoftmaxFunction = softmaxFunction.cuda(), logSoftmaxFunction.cuda(),
KLDivLossFunction = KLDivLossFunction.cuda()
if index_distillation_loss.size() != torch.Size():
count_asked_teacher = index_distillation_loss.numel()
# if index_distillation_loss is not empty
volatile_inputs = Variable(inputs.data[index_distillation_loss, :], requires_grad=False)
if USE_CUDA: volatile_inputs = volatile_inputs.cuda()
outputsTeacher = teacher_model(volatile_inputs).detach()
loss_masked = weight_teacher_loss * temperature_distillation**2 * KLDivLossFunction(
logSoftmaxFunction(outputs[index_distillation_loss, :]/ temperature_distillation),
softmaxFunction(outputsTeacher / temperature_distillation))
loss_masked += (1-weight_teacher_loss) * criterion(outputs[index_distillation_loss, :],
labels[index_distillation_loss])
else:
loss_masked = 0
if inverse_idx_distill_loss.size() != torch.Size():
#if inverse_idx_distill is not empty
loss_normal = criterion(outputs[inverse_idx_distill_loss, :], labels[inverse_idx_distill_loss])
else:
loss_normal = 0
loss = loss_masked + loss_normal
else:
loss = criterion(outputs, labels)
loss.backward()
if return_more_info:
count_total = inputs.size(0)
return loss.data[0], count_asked_teacher, count_total
else:
return loss.data[0]
def add_gradient_noise(model, idx_batch, epoch, number_minibatches_per_epoch):
# Adding random gaussian noise as in the paper "Adding gradient noise improves learning
# for very deep networks"
for p in model.parameters():
gaussianNoise = torch.Tensor(p.grad.size())
if USE_CUDA: gaussianNoise = gaussianNoise.cuda()
# here nu = 0.01, gamma = 0.55, t is the minibatch count = epoch*total_length +idx_minibatch
stdDev = (0.01 / (1 + epoch * number_minibatches_per_epoch + idx_batch) ** 0.55) ** 0.5
gaussianNoise.normal_(0, std=stdDev)
p.grad.data.add_(gaussianNoise)
class LearningRateScheduler:
def __init__(self, initial_learning_rate, learning_rate_type='generic'):
if learning_rate_type not in ('generic', 'cifar100', 'imagenet', 'quant_points_cifar100'):
raise ValueError('Wrong learning rate type specified')
self.initial_learning_rate = initial_learning_rate
self.learning_rate_type = learning_rate_type
self.current_learning_rate = initial_learning_rate
#variables for the generic method
self.old_validation_error = float('inf')
self.epochs_since_validation_error_dropped = 0
self.total_number_of_learning_rate_halves = 0
self.epochs_to_wait_for_halving = 0
#variables for quant_points_cifar100
self.best_validation_error = float('inf')
def update_learning_rate(self, epoch, validation_error):
if self.learning_rate_type == 'cifar100':
optim_factor = 0
if (epoch > 160):
optim_factor = 3
elif (epoch > 120):
optim_factor = 2
elif (epoch > 60):
optim_factor = 1
new_learning_rate = self.initial_learning_rate * math.pow(0.2, optim_factor)
self.current_learning_rate = new_learning_rate
stop_training = False
return new_learning_rate, stop_training
if self.learning_rate_type == 'imagenet':
new_learning_rate = self.initial_learning_rate * (0.1 ** (epoch // 30))
stop_training = False
return new_learning_rate, stop_training
if self.learning_rate_type in ('generic', 'quant_points_cifar100'):
if self.learning_rate_type == 'generic':
epoch_to_wait_before_reducing_rate = 10
epochs_to_wait_after_halving = 8
epoch_to_wait_before_stopping = 30
total_halves_before_stopping = 11
elif self.learning_rate_type == 'quant_points_cifar100':
epoch_to_wait_before_reducing_rate = 2
epochs_to_wait_after_halving = 0
epoch_to_wait_before_stopping = float('inf')
total_halves_before_stopping = float('inf')
else: raise ValueError
new_learning_rate = self.current_learning_rate
stop_training = False
# we have a 0.1% error band
if validation_error + 0.001 < self.old_validation_error:
self.old_validation_error = validation_error
self.epochs_since_validation_error_dropped = 0
else:
self.epochs_since_validation_error_dropped += 1
self.epochs_to_wait_for_halving = max(self.epochs_to_wait_for_halving - 1, 0)
if self.epochs_since_validation_error_dropped >= epoch_to_wait_before_reducing_rate and \
self.epochs_to_wait_for_halving == 0:
# if validation error does not drop for 10 epochs in a row, halve the learning rate
# but don't halve it for at least 8 epochs after halving.
self.epochs_to_wait_for_halving = epochs_to_wait_after_halving
self.total_number_of_learning_rate_halves += 1
new_learning_rate = self.current_learning_rate / 2
self.current_learning_rate = new_learning_rate
if self.epochs_since_validation_error_dropped > epoch_to_wait_before_stopping or \
self.total_number_of_learning_rate_halves > total_halves_before_stopping:
# stop training if validation rate hasn't dropped in 30 epochs or if learning rates was halved 11 times already
# i.e. it was reduced by 2048 times.
stop_training = True
return new_learning_rate, stop_training
| import torch
import torch.nn as nn
from torch.autograd import Variable
import math
import time
import numpy as np
import helpers.functions as mhf
import random
USE_CUDA = torch.cuda.is_available()
def time_forward_pass(model, train_loader):
model.eval()
start_time = time.time()
for idx_minibatch, data in enumerate(train_loader):
# get the inputs
inputs, labels = data
inputs, labels = Variable(inputs, volatile=True), Variable(labels)
if USE_CUDA:
inputs = inputs.cuda()
labels = labels.cuda()
outputs = model(inputs)
end_time = time.time()
return end_time - start_time
def evaluateModel(model, testLoader, fastEvaluation=True, maxExampleFastEvaluation=10000, k=1):
'if fastEvaluation is True, it will only check a subset of *maxExampleFastEvaluation* images of the test set'
model.eval()
correctClass = 0
totalNumExamples = 0
for idx_minibatch, data in enumerate(testLoader):
# get the inputs
inputs, labels = data
inputs, labels = Variable(inputs, volatile=True), Variable(labels)
if USE_CUDA:
inputs = inputs.cuda()
labels = labels.cuda()
outputs = model(inputs)
_, topk_predictions = outputs.topk(k, dim=1, largest=True, sorted=True)
topk_predictions = topk_predictions.t()
correct = topk_predictions.eq(labels.view(1, -1).expand_as(topk_predictions))
correctClass += correct.view(-1).float().sum(0, keepdim=True).data[0]
totalNumExamples += len(labels)
if fastEvaluation is True and totalNumExamples > maxExampleFastEvaluation:
break
return correctClass / totalNumExamples
def forward_and_backward(model, batch, idx_batch, epoch, criterion=None,
use_distillation_loss=False, teacher_model=None,
temperature_distillation=2, ask_teacher_strategy='always',
return_more_info=False):
#TODO: return_more_info is just there for backward compatibility. A big refactoring is due here, and there one should
#remove the return_more_info flag
if criterion is None:
criterion = nn.CrossEntropyLoss()
if USE_CUDA:
criterion = criterion.cuda()
if use_distillation_loss is True and teacher_model is None:
raise ValueError('To compute distillation loss you need to pass the teacher model')
if not isinstance(ask_teacher_strategy, tuple):
ask_teacher_strategy = (ask_teacher_strategy, )
inputs, labels = batch
# wrap them in Variable
inputs, labels = Variable(inputs), Variable(labels)
if USE_CUDA:
inputs = inputs.cuda()
labels = labels.cuda()
# forward + backward + optimize
outputs = model(inputs)
count_asked_teacher = 0
if use_distillation_loss:
#if cutoff_entropy_value_distillation is not None, we use the distillation loss only on the examples
#whose entropy is higher than the cutoff.
weight_teacher_loss = 0.7
if 'entropy' in ask_teacher_strategy[0].lower():
prob_out = torch.nn.functional.softmax(outputs).data
entropy = [mhf.get_entropy(prob_out[idx_b, :]) for idx_b in range(prob_out.size(0))]
if ask_teacher_strategy[0].lower() == 'always':
mask_distillation_loss = torch.ByteTensor([True]*outputs.size(0))
elif ask_teacher_strategy[0].lower() == 'cutoff_entropy':
cutoff_entropy_value_distillation = ask_teacher_strategy[1]
mask_distillation_loss = torch.ByteTensor([entr > cutoff_entropy_value_distillation for entr in entropy])
elif ask_teacher_strategy[0].lower() == 'random_entropy':
max_entropy = math.log2(outputs.size(1)) #max possible entropy that happens with uniform distribution
mask_distillation_loss = torch.ByteTensor([random.random() < entr/max_entropy for entr in entropy])
elif ask_teacher_strategy[0].lower() == 'incorrect_labels':
_, predictions = outputs.max(dim=1)
mask_distillation_loss = (predictions != labels).data.cpu()
else:
raise ValueError('ask_teacher_strategy is incorrectly formatted')
index_distillation_loss = torch.arange(0, outputs.size(0))[mask_distillation_loss.view(-1, 1)].long()
inverse_idx_distill_loss = torch.arange(0, outputs.size(0))[1-mask_distillation_loss.view(-1, 1)].long()
if USE_CUDA:
index_distillation_loss = index_distillation_loss.cuda()
inverse_idx_distill_loss = inverse_idx_distill_loss.cuda()
# this criterion is the distillation criterion according to Hinton's paper:
# "Distilling the Knowledge in a Neural Network", Hinton et al.
softmaxFunction, logSoftmaxFunction, KLDivLossFunction = nn.Softmax(dim=1), nn.LogSoftmax(dim=1), nn.KLDivLoss()
if USE_CUDA:
softmaxFunction, logSoftmaxFunction = softmaxFunction.cuda(), logSoftmaxFunction.cuda(),
KLDivLossFunction = KLDivLossFunction.cuda()
if index_distillation_loss.size() != torch.Size():
count_asked_teacher = index_distillation_loss.numel()
# if index_distillation_loss is not empty
volatile_inputs = Variable(inputs.data[index_distillation_loss, :], requires_grad=False)
if USE_CUDA: volatile_inputs = volatile_inputs.cuda()
outputsTeacher = teacher_model(volatile_inputs).detach()
loss_masked = weight_teacher_loss * temperature_distillation**2 * KLDivLossFunction(
logSoftmaxFunction(outputs[index_distillation_loss, :]/ temperature_distillation),
softmaxFunction(outputsTeacher / temperature_distillation))
loss_masked += (1-weight_teacher_loss) * criterion(outputs[index_distillation_loss, :],
labels[index_distillation_loss])
else:
loss_masked = 0
if inverse_idx_distill_loss.size() != torch.Size():
#if inverse_idx_distill is not empty
loss_normal = criterion(outputs[inverse_idx_distill_loss, :], labels[inverse_idx_distill_loss])
else:
loss_normal = 0
loss = loss_masked + loss_normal
else:
loss = criterion(outputs, labels)
loss.backward()
if return_more_info:
count_total = inputs.size(0)
return loss.data[0], count_asked_teacher, count_total
else:
return loss.data[0]
def add_gradient_noise(model, idx_batch, epoch, number_minibatches_per_epoch):
# Adding random gaussian noise as in the paper "Adding gradient noise improves learning
# for very deep networks"
for p in model.parameters():
gaussianNoise = torch.Tensor(p.grad.size())
if USE_CUDA: gaussianNoise = gaussianNoise.cuda()
# here nu = 0.01, gamma = 0.55, t is the minibatch count = epoch*total_length +idx_minibatch
stdDev = (0.01 / (1 + epoch * number_minibatches_per_epoch + idx_batch) ** 0.55) ** 0.5
gaussianNoise.normal_(0, std=stdDev)
p.grad.data.add_(gaussianNoise)
class LearningRateScheduler:
def __init__(self, initial_learning_rate, learning_rate_type='generic'):
if learning_rate_type not in ('generic', 'cifar100', 'imagenet', 'quant_points_cifar100'):
raise ValueError('Wrong learning rate type specified')
self.initial_learning_rate = initial_learning_rate
self.learning_rate_type = learning_rate_type
self.current_learning_rate = initial_learning_rate
#variables for the generic method
self.old_validation_error = float('inf')
self.epochs_since_validation_error_dropped = 0
self.total_number_of_learning_rate_halves = 0
self.epochs_to_wait_for_halving = 0
#variables for quant_points_cifar100
self.best_validation_error = float('inf')
def update_learning_rate(self, epoch, validation_error):
if self.learning_rate_type == 'cifar100':
optim_factor = 0
if (epoch > 160):
optim_factor = 3
elif (epoch > 120):
optim_factor = 2
elif (epoch > 60):
optim_factor = 1
new_learning_rate = self.initial_learning_rate * math.pow(0.2, optim_factor)
self.current_learning_rate = new_learning_rate
stop_training = False
return new_learning_rate, stop_training
if self.learning_rate_type == 'imagenet':
new_learning_rate = self.initial_learning_rate * (0.1 ** (epoch // 30))
stop_training = False
return new_learning_rate, stop_training
if self.learning_rate_type in ('generic', 'quant_points_cifar100'):
if self.learning_rate_type == 'generic':
epoch_to_wait_before_reducing_rate = 10
epochs_to_wait_after_halving = 8
epoch_to_wait_before_stopping = 30
total_halves_before_stopping = 11
elif self.learning_rate_type == 'quant_points_cifar100':
epoch_to_wait_before_reducing_rate = 2
epochs_to_wait_after_halving = 0
epoch_to_wait_before_stopping = float('inf')
total_halves_before_stopping = float('inf')
else: raise ValueError
new_learning_rate = self.current_learning_rate
stop_training = False
# we have a 0.1% error band
if validation_error + 0.001 < self.old_validation_error:
self.old_validation_error = validation_error
self.epochs_since_validation_error_dropped = 0
else:
self.epochs_since_validation_error_dropped += 1
self.epochs_to_wait_for_halving = max(self.epochs_to_wait_for_halving - 1, 0)
if self.epochs_since_validation_error_dropped >= epoch_to_wait_before_reducing_rate and \
self.epochs_to_wait_for_halving == 0:
# if validation error does not drop for 10 epochs in a row, halve the learning rate
# but don't halve it for at least 8 epochs after halving.
self.epochs_to_wait_for_halving = epochs_to_wait_after_halving
self.total_number_of_learning_rate_halves += 1
new_learning_rate = self.current_learning_rate / 2
self.current_learning_rate = new_learning_rate
if self.epochs_since_validation_error_dropped > epoch_to_wait_before_stopping or \
self.total_number_of_learning_rate_halves > total_halves_before_stopping:
# stop training if validation rate hasn't dropped in 30 epochs or if learning rates was halved 11 times already
# i.e. it was reduced by 2048 times.
stop_training = True
return new_learning_rate, stop_training
| en | 0.85981 | # get the inputs # get the inputs #TODO: return_more_info is just there for backward compatibility. A big refactoring is due here, and there one should #remove the return_more_info flag # wrap them in Variable # forward + backward + optimize #if cutoff_entropy_value_distillation is not None, we use the distillation loss only on the examples #whose entropy is higher than the cutoff. #max possible entropy that happens with uniform distribution # this criterion is the distillation criterion according to Hinton's paper: # "Distilling the Knowledge in a Neural Network", Hinton et al. # if index_distillation_loss is not empty #if inverse_idx_distill is not empty # Adding random gaussian noise as in the paper "Adding gradient noise improves learning # for very deep networks" # here nu = 0.01, gamma = 0.55, t is the minibatch count = epoch*total_length +idx_minibatch #variables for the generic method #variables for quant_points_cifar100 # we have a 0.1% error band # if validation error does not drop for 10 epochs in a row, halve the learning rate # but don't halve it for at least 8 epochs after halving. # stop training if validation rate hasn't dropped in 30 epochs or if learning rates was halved 11 times already # i.e. it was reduced by 2048 times. | 2.1504 | 2 |
src/Python/Visualization/NormalsDemo.py | cvandijck/VTKExamples | 309 | 6630473 | <filename>src/Python/Visualization/NormalsDemo.py
#!/usr/bin/env python
"""
"""
import vtk
def main():
colors = vtk.vtkNamedColors()
fileName = get_program_parameters()
polyData = ReadPolyData(fileName)
# A renderer.
renderer = vtk.vtkRenderer()
renderer.SetBackground(colors.GetColor3d("White"))
# Create background colors for each viewport.
backgroundColors = list()
backgroundColors.append(colors.GetColor3d("Cornsilk"))
backgroundColors.append(colors.GetColor3d("NavajoWhite"))
backgroundColors.append(colors.GetColor3d("Tan"))
# Create a renderer for each view port.
ren = list()
ren.append(vtk.vtkRenderer())
ren.append(vtk.vtkRenderer())
ren.append(vtk.vtkRenderer())
ren[0].SetViewport(0, 0, 1.0 / 3.0, 1) # Input
ren[1].SetViewport(1.0 / 3.0, 0, 2.0 / 3.0, 1) # Normals (no split)
ren[2].SetViewport(2.0 / 3.0, 0, 1, 1) # Normals (split)
# Shared camera.
camera = vtk.vtkCamera()
normals = vtk.vtkPolyDataNormals()
normals.SetInputData(polyData)
normals.SetFeatureAngle(30.0)
for i in range(0, 3):
if i == 0:
normals.ComputePointNormalsOff()
elif i == 1:
normals.ComputePointNormalsOn()
normals.SplittingOff()
else:
normals.ComputePointNormalsOn()
normals.SplittingOn()
normals.Update()
normalsPolyData = vtk.vtkPolyData()
normalsPolyData.DeepCopy(normals.GetOutput())
# mapper
mapper = vtk.vtkPolyDataMapper()
mapper.SetInputData(normalsPolyData)
mapper.ScalarVisibilityOff()
actor = vtk.vtkActor()
actor.SetMapper(mapper)
actor.GetProperty().SetDiffuseColor(colors.GetColor3d("Peacock"))
actor.GetProperty().SetDiffuse(.7)
actor.GetProperty().SetSpecularPower(20)
actor.GetProperty().SetSpecular(.5)
# add the actor
ren[i].SetBackground(backgroundColors[i])
ren[i].SetActiveCamera(camera)
ren[i].AddActor(actor)
# Render window.
renwin = vtk.vtkRenderWindow()
renwin.AddRenderer(ren[0])
renwin.AddRenderer(ren[1])
renwin.AddRenderer(ren[2])
# An interactor.
interactor = vtk.vtkRenderWindowInteractor()
interactor.SetRenderWindow(renwin)
renwin.SetSize(900, 300)
ren[0].GetActiveCamera().SetFocalPoint(0, 0, 0)
ren[0].GetActiveCamera().SetPosition(1, 0, 0)
ren[0].GetActiveCamera().SetViewUp(0, 0, -1)
ren[0].ResetCamera()
ren[0].GetActiveCamera().Azimuth(120)
ren[0].GetActiveCamera().Elevation(30)
ren[0].GetActiveCamera().Dolly(1.1)
ren[0].ResetCameraClippingRange()
renwin.Render()
ren[0].ResetCamera()
renwin.Render()
# Start.
interactor.Initialize()
interactor.Start()
def get_program_parameters():
import argparse
description = 'Surface normal generation.'
epilogue = '''
(a) Faceted model without normals.
(b) Polygons must be consistently oriented to accurately compute normals.
(c) Sharp edges are poorly represented using shared normals as shown on the corners of this model.
(d) Normal generation with sharp edges split.
'''
parser = argparse.ArgumentParser(description=description, epilog=epilogue,
formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument('filename1', help='42400-IDGH.stl.')
args = parser.parse_args()
return args.filename1
def ReadPolyData(file_name):
import os
path, extension = os.path.splitext(file_name)
extension = extension.lower()
if extension == ".ply":
reader = vtk.vtkPLYReader()
reader.SetFileName(file_name)
reader.Update()
poly_data = reader.GetOutput()
elif extension == ".vtp":
reader = vtk.vtkXMLpoly_dataReader()
reader.SetFileName(file_name)
reader.Update()
poly_data = reader.GetOutput()
elif extension == ".obj":
reader = vtk.vtkOBJReader()
reader.SetFileName(file_name)
reader.Update()
poly_data = reader.GetOutput()
elif extension == ".stl":
reader = vtk.vtkSTLReader()
reader.SetFileName(file_name)
reader.Update()
poly_data = reader.GetOutput()
elif extension == ".vtk":
reader = vtk.vtkpoly_dataReader()
reader.SetFileName(file_name)
reader.Update()
poly_data = reader.GetOutput()
elif extension == ".g":
reader = vtk.vtkBYUReader()
reader.SetGeometryFileName(file_name)
reader.Update()
poly_data = reader.GetOutput()
else:
# Return a None if the extension is unknown.
poly_data = None
return poly_data
if __name__ == '__main__':
main()
| <filename>src/Python/Visualization/NormalsDemo.py
#!/usr/bin/env python
"""
"""
import vtk
def main():
colors = vtk.vtkNamedColors()
fileName = get_program_parameters()
polyData = ReadPolyData(fileName)
# A renderer.
renderer = vtk.vtkRenderer()
renderer.SetBackground(colors.GetColor3d("White"))
# Create background colors for each viewport.
backgroundColors = list()
backgroundColors.append(colors.GetColor3d("Cornsilk"))
backgroundColors.append(colors.GetColor3d("NavajoWhite"))
backgroundColors.append(colors.GetColor3d("Tan"))
# Create a renderer for each view port.
ren = list()
ren.append(vtk.vtkRenderer())
ren.append(vtk.vtkRenderer())
ren.append(vtk.vtkRenderer())
ren[0].SetViewport(0, 0, 1.0 / 3.0, 1) # Input
ren[1].SetViewport(1.0 / 3.0, 0, 2.0 / 3.0, 1) # Normals (no split)
ren[2].SetViewport(2.0 / 3.0, 0, 1, 1) # Normals (split)
# Shared camera.
camera = vtk.vtkCamera()
normals = vtk.vtkPolyDataNormals()
normals.SetInputData(polyData)
normals.SetFeatureAngle(30.0)
for i in range(0, 3):
if i == 0:
normals.ComputePointNormalsOff()
elif i == 1:
normals.ComputePointNormalsOn()
normals.SplittingOff()
else:
normals.ComputePointNormalsOn()
normals.SplittingOn()
normals.Update()
normalsPolyData = vtk.vtkPolyData()
normalsPolyData.DeepCopy(normals.GetOutput())
# mapper
mapper = vtk.vtkPolyDataMapper()
mapper.SetInputData(normalsPolyData)
mapper.ScalarVisibilityOff()
actor = vtk.vtkActor()
actor.SetMapper(mapper)
actor.GetProperty().SetDiffuseColor(colors.GetColor3d("Peacock"))
actor.GetProperty().SetDiffuse(.7)
actor.GetProperty().SetSpecularPower(20)
actor.GetProperty().SetSpecular(.5)
# add the actor
ren[i].SetBackground(backgroundColors[i])
ren[i].SetActiveCamera(camera)
ren[i].AddActor(actor)
# Render window.
renwin = vtk.vtkRenderWindow()
renwin.AddRenderer(ren[0])
renwin.AddRenderer(ren[1])
renwin.AddRenderer(ren[2])
# An interactor.
interactor = vtk.vtkRenderWindowInteractor()
interactor.SetRenderWindow(renwin)
renwin.SetSize(900, 300)
ren[0].GetActiveCamera().SetFocalPoint(0, 0, 0)
ren[0].GetActiveCamera().SetPosition(1, 0, 0)
ren[0].GetActiveCamera().SetViewUp(0, 0, -1)
ren[0].ResetCamera()
ren[0].GetActiveCamera().Azimuth(120)
ren[0].GetActiveCamera().Elevation(30)
ren[0].GetActiveCamera().Dolly(1.1)
ren[0].ResetCameraClippingRange()
renwin.Render()
ren[0].ResetCamera()
renwin.Render()
# Start.
interactor.Initialize()
interactor.Start()
def get_program_parameters():
import argparse
description = 'Surface normal generation.'
epilogue = '''
(a) Faceted model without normals.
(b) Polygons must be consistently oriented to accurately compute normals.
(c) Sharp edges are poorly represented using shared normals as shown on the corners of this model.
(d) Normal generation with sharp edges split.
'''
parser = argparse.ArgumentParser(description=description, epilog=epilogue,
formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument('filename1', help='42400-IDGH.stl.')
args = parser.parse_args()
return args.filename1
def ReadPolyData(file_name):
import os
path, extension = os.path.splitext(file_name)
extension = extension.lower()
if extension == ".ply":
reader = vtk.vtkPLYReader()
reader.SetFileName(file_name)
reader.Update()
poly_data = reader.GetOutput()
elif extension == ".vtp":
reader = vtk.vtkXMLpoly_dataReader()
reader.SetFileName(file_name)
reader.Update()
poly_data = reader.GetOutput()
elif extension == ".obj":
reader = vtk.vtkOBJReader()
reader.SetFileName(file_name)
reader.Update()
poly_data = reader.GetOutput()
elif extension == ".stl":
reader = vtk.vtkSTLReader()
reader.SetFileName(file_name)
reader.Update()
poly_data = reader.GetOutput()
elif extension == ".vtk":
reader = vtk.vtkpoly_dataReader()
reader.SetFileName(file_name)
reader.Update()
poly_data = reader.GetOutput()
elif extension == ".g":
reader = vtk.vtkBYUReader()
reader.SetGeometryFileName(file_name)
reader.Update()
poly_data = reader.GetOutput()
else:
# Return a None if the extension is unknown.
poly_data = None
return poly_data
if __name__ == '__main__':
main()
| en | 0.808083 | #!/usr/bin/env python # A renderer. # Create background colors for each viewport. # Create a renderer for each view port. # Input # Normals (no split) # Normals (split) # Shared camera. # mapper # add the actor # Render window. # An interactor. # Start. (a) Faceted model without normals. (b) Polygons must be consistently oriented to accurately compute normals. (c) Sharp edges are poorly represented using shared normals as shown on the corners of this model. (d) Normal generation with sharp edges split. # Return a None if the extension is unknown. | 2.566708 | 3 |
src/api/pdi/domain/operation/DataOperation.py | ahmetcagriakca/pythondataintegrator | 1 | 6630474 | <filename>src/api/pdi/domain/operation/DataOperation.py
from typing import List
from pdip.data import Entity
from sqlalchemy import Column, String, Integer, ForeignKey
from sqlalchemy.orm import relationship
from pdi.domain.base import Base
from pdi.domain.base.operation.DataOperationBase import DataOperationBase
from pdi.domain.operation.DataOperationContact import DataOperationContact
from pdi.domain.operation.DataOperationIntegration import DataOperationIntegration
from pdi.domain.operation.DataOperationJob import DataOperationJob
class DataOperation(DataOperationBase, Entity, Base):
__tablename__ = "DataOperation"
__table_args__ = {"schema": "Operation"}
DefinitionId = Column(Integer, ForeignKey('Operation.Definition.Id'))
Name = Column(String(100), index=True, unique=False, nullable=False)
Definition = relationship("Definition", back_populates="DataOperations")
DataOperationJobs: List[DataOperationJob] = relationship("DataOperationJob", back_populates="DataOperation")
Integrations: List[DataOperationIntegration] = relationship("DataOperationIntegration",
back_populates="DataOperation")
Contacts: List[DataOperationContact] = relationship("DataOperationContact",
back_populates="DataOperation")
| <filename>src/api/pdi/domain/operation/DataOperation.py
from typing import List
from pdip.data import Entity
from sqlalchemy import Column, String, Integer, ForeignKey
from sqlalchemy.orm import relationship
from pdi.domain.base import Base
from pdi.domain.base.operation.DataOperationBase import DataOperationBase
from pdi.domain.operation.DataOperationContact import DataOperationContact
from pdi.domain.operation.DataOperationIntegration import DataOperationIntegration
from pdi.domain.operation.DataOperationJob import DataOperationJob
class DataOperation(DataOperationBase, Entity, Base):
__tablename__ = "DataOperation"
__table_args__ = {"schema": "Operation"}
DefinitionId = Column(Integer, ForeignKey('Operation.Definition.Id'))
Name = Column(String(100), index=True, unique=False, nullable=False)
Definition = relationship("Definition", back_populates="DataOperations")
DataOperationJobs: List[DataOperationJob] = relationship("DataOperationJob", back_populates="DataOperation")
Integrations: List[DataOperationIntegration] = relationship("DataOperationIntegration",
back_populates="DataOperation")
Contacts: List[DataOperationContact] = relationship("DataOperationContact",
back_populates="DataOperation")
| none | 1 | 2.252126 | 2 |
|
model/qalexnet.py | XueYue404/QNN | 0 | 6630475 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import seaborn as sns
import math
from torch.utils.data import DataLoader
from torch.autograd import Function
#从本项目中import
from model.quant import QuantConv2d
from model.quant import QuantLinear
from model.quant import QuantGrad
'''
AlexNet分类器，用于CIFAR10
用法：
network = AlexNet(isquant=True,bitA=4,bitW=8)
'''
class AlexNet(nn.Module):
def __init__(self,isquant=True,bitA=8,bitW=16,bitG=16):
super(AlexNet, self).__init__()
self.isquant = isquant
self.bitA = bitA
self.bitW = bitW
self.bitG = bitG
if self.isquant == True:
self.cnn = nn.Sequential(
# 卷积层1，3通道输入，96个卷积核，核大小7*7，步长2，填充2
# 经过该层图像大小变为32-7+2*2 / 2 +1，15*15
# 经3*3最大池化，2步长，图像变为15-3 / 2 + 1， 7*7
QuantConv2d(3, 96, 7, 2, 2,a_bits=self.bitA,w_bits=self.bitW),
QuantGrad(bitG = self.bitG),
nn.ReLU(inplace=True),
nn.MaxPool2d(3, 2, 0),
# 卷积层2，96输入通道，256个卷积核，核大小5*5，步长1，填充2
# 经过该层图像变为7-5+2*2 / 1 + 1，7*7
# 经3*3最大池化，2步长，图像变为7-3 / 2 + 1， 3*3
QuantConv2d(96, 256, 5, 1, 2,a_bits=self.bitA,w_bits=self.bitW),
QuantGrad(bitG = self.bitG),
nn.ReLU(inplace=True),
nn.MaxPool2d(3, 2, 0),
# 卷积层3，256输入通道，384个卷积核，核大小3*3，步长1，填充1
# 经过该层图像变为3-3+2*1 / 1 + 1，3*3
QuantConv2d(256, 384, 3, 1, 1,a_bits=self.bitA,w_bits=self.bitW),
QuantGrad(bitG = self.bitG),
nn.ReLU(inplace=True),
# 卷积层3，384输入通道，384个卷积核，核大小3*3，步长1，填充1
# 经过该层图像变为3-3+2*1 / 1 + 1，3*3
QuantConv2d(384, 384, 3, 1, 1,a_bits=self.bitA,w_bits=self.bitW),
QuantGrad(bitG = self.bitG),
nn.ReLU(inplace=True),
# 卷积层3，384输入通道，256个卷积核，核大小3*3，步长1，填充1
# 经过该层图像变为3-3+2*1 / 1 + 1，3*3
QuantConv2d(384, 256, 3, 1, 1,a_bits=self.bitA,w_bits=self.bitW),
QuantGrad(bitG = self.bitG),
nn.ReLU(inplace=True)
)
self.fc = nn.Sequential(
# 256个feature，每个feature 3*3
QuantLinear(256*3*3, 1024,a_bits=self.bitA,w_bits=self.bitW),
QuantGrad(bitG = self.bitG),
nn.ReLU(),
QuantLinear(1024, 512,a_bits=self.bitA,w_bits=self.bitW),
QuantGrad(bitG = self.bitG),
nn.ReLU(),
QuantLinear(512, 10,a_bits=self.bitA,w_bits=self.bitW)
)
else:
self.cnn = nn.Sequential(
nn.Conv2d(3, 96, 7, 2, 2),
nn.ReLU(inplace=True),
nn.MaxPool2d(3, 2, 0),
nn.Conv2d(96, 256, 5, 1, 2),
nn.ReLU(inplace=True),
nn.MaxPool2d(3, 2, 0),
nn.Conv2d(256, 384, 3, 1, 1),
nn.ReLU(inplace=True),
nn.Conv2d(384, 384, 3, 1, 1),
nn.ReLU(inplace=True),
nn.Conv2d(384, 256, 3, 1, 1),
nn.ReLU(inplace=True)
)
self.fc = nn.Sequential(
nn.Linear(256*3*3, 1024),
nn.ReLU(),
nn.Linear(1024, 512),
nn.ReLU(),
nn.Linear(512, 10)
)
def forward(self, x):
x = self.cnn(x)
x = x.view(x.size()[0], -1)
x = self.fc(x)
return x | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import seaborn as sns
import math
from torch.utils.data import DataLoader
from torch.autograd import Function
#从本项目中import
from model.quant import QuantConv2d
from model.quant import QuantLinear
from model.quant import QuantGrad
'''
AlexNet分类器，用于CIFAR10
用法：
network = AlexNet(isquant=True,bitA=4,bitW=8)
'''
class AlexNet(nn.Module):
def __init__(self,isquant=True,bitA=8,bitW=16,bitG=16):
super(AlexNet, self).__init__()
self.isquant = isquant
self.bitA = bitA
self.bitW = bitW
self.bitG = bitG
if self.isquant == True:
self.cnn = nn.Sequential(
# 卷积层1，3通道输入，96个卷积核，核大小7*7，步长2，填充2
# 经过该层图像大小变为32-7+2*2 / 2 +1，15*15
# 经3*3最大池化，2步长，图像变为15-3 / 2 + 1， 7*7
QuantConv2d(3, 96, 7, 2, 2,a_bits=self.bitA,w_bits=self.bitW),
QuantGrad(bitG = self.bitG),
nn.ReLU(inplace=True),
nn.MaxPool2d(3, 2, 0),
# 卷积层2，96输入通道，256个卷积核，核大小5*5，步长1，填充2
# 经过该层图像变为7-5+2*2 / 1 + 1，7*7
# 经3*3最大池化，2步长，图像变为7-3 / 2 + 1， 3*3
QuantConv2d(96, 256, 5, 1, 2,a_bits=self.bitA,w_bits=self.bitW),
QuantGrad(bitG = self.bitG),
nn.ReLU(inplace=True),
nn.MaxPool2d(3, 2, 0),
# 卷积层3，256输入通道，384个卷积核，核大小3*3，步长1，填充1
# 经过该层图像变为3-3+2*1 / 1 + 1，3*3
QuantConv2d(256, 384, 3, 1, 1,a_bits=self.bitA,w_bits=self.bitW),
QuantGrad(bitG = self.bitG),
nn.ReLU(inplace=True),
# 卷积层3，384输入通道，384个卷积核，核大小3*3，步长1，填充1
# 经过该层图像变为3-3+2*1 / 1 + 1，3*3
QuantConv2d(384, 384, 3, 1, 1,a_bits=self.bitA,w_bits=self.bitW),
QuantGrad(bitG = self.bitG),
nn.ReLU(inplace=True),
# 卷积层3，384输入通道，256个卷积核，核大小3*3，步长1，填充1
# 经过该层图像变为3-3+2*1 / 1 + 1，3*3
QuantConv2d(384, 256, 3, 1, 1,a_bits=self.bitA,w_bits=self.bitW),
QuantGrad(bitG = self.bitG),
nn.ReLU(inplace=True)
)
self.fc = nn.Sequential(
# 256个feature，每个feature 3*3
QuantLinear(256*3*3, 1024,a_bits=self.bitA,w_bits=self.bitW),
QuantGrad(bitG = self.bitG),
nn.ReLU(),
QuantLinear(1024, 512,a_bits=self.bitA,w_bits=self.bitW),
QuantGrad(bitG = self.bitG),
nn.ReLU(),
QuantLinear(512, 10,a_bits=self.bitA,w_bits=self.bitW)
)
else:
self.cnn = nn.Sequential(
nn.Conv2d(3, 96, 7, 2, 2),
nn.ReLU(inplace=True),
nn.MaxPool2d(3, 2, 0),
nn.Conv2d(96, 256, 5, 1, 2),
nn.ReLU(inplace=True),
nn.MaxPool2d(3, 2, 0),
nn.Conv2d(256, 384, 3, 1, 1),
nn.ReLU(inplace=True),
nn.Conv2d(384, 384, 3, 1, 1),
nn.ReLU(inplace=True),
nn.Conv2d(384, 256, 3, 1, 1),
nn.ReLU(inplace=True)
)
self.fc = nn.Sequential(
nn.Linear(256*3*3, 1024),
nn.ReLU(),
nn.Linear(1024, 512),
nn.ReLU(),
nn.Linear(512, 10)
)
def forward(self, x):
x = self.cnn(x)
x = x.view(x.size()[0], -1)
x = self.fc(x)
return x | zh | 0.729663 | #从本项目中import AlexNet分类器，用于CIFAR10 用法： network = AlexNet(isquant=True,bitA=4,bitW=8) # 卷积层1，3通道输入，96个卷积核，核大小7*7，步长2，填充2 # 经过该层图像大小变为32-7+2*2 / 2 +1，15*15 # 经3*3最大池化，2步长，图像变为15-3 / 2 + 1， 7*7 # 卷积层2，96输入通道，256个卷积核，核大小5*5，步长1，填充2 # 经过该层图像变为7-5+2*2 / 1 + 1，7*7 # 经3*3最大池化，2步长，图像变为7-3 / 2 + 1， 3*3 # 卷积层3，256输入通道，384个卷积核，核大小3*3，步长1，填充1 # 经过该层图像变为3-3+2*1 / 1 + 1，3*3 # 卷积层3，384输入通道，384个卷积核，核大小3*3，步长1，填充1 # 经过该层图像变为3-3+2*1 / 1 + 1，3*3 # 卷积层3，384输入通道，256个卷积核，核大小3*3，步长1，填充1 # 经过该层图像变为3-3+2*1 / 1 + 1，3*3 # 256个feature，每个feature 3*3 | 2.779815 | 3 |
scitbx/suffixtree/test/tst_single.py | rimmartin/cctbx_project | 0 | 6630476 | from __future__ import division
from scitbx import suffixtree
from scitbx.suffixtree import single
import unittest
class TestWord(unittest.TestCase):
def test_single_word(self):
word = single.word()
ld = word.length_descriptor()
self.assertEqual( len( word ), 0 )
self.assertEqual( ld(), 0 )
self.assertEqual( list( word.substring( 0, ld() ) ), [] )
c1 = object()
c2 = object()
word.append( c1 )
self.assertEqual( len( word ), 1 )
self.assertEqual( ld(), 1 )
self.assertEqual( word[0], c1 )
self.assertEqual( list( word ), [ c1 ] )
word.append( c2 )
self.assertEqual( len( word ), 2 )
self.assertEqual( ld(), 2 )
self.assertEqual( word[0], c1 )
self.assertEqual( word[1], c2 )
self.assertEqual( list( word ), [ c1, c2 ] )
self.assertEqual( list( word.substring( 0, 0 ) ), [] )
self.assertEqual( list( word.substring( 0, 1 ) ), [ c1 ] )
self.assertEqual( list( word.substring( 0, 2 ) ), [ c1, c2 ] )
self.assertEqual( list( word.substring( 1, 1 ) ), [] )
self.assertEqual( list( word.substring( 1, 2 ) ), [ c2 ] )
class TestEdge(unittest.TestCase):
def setUp(self):
self.root = single.edge.root()
self.branch = single.edge.branch( start = 1, stop = 2 )
self.word = single.word()
self.leaf = single.edge.leaf( start = 4, length = self.word.length_descriptor(), label = 6 )
def test_root(self):
self.assertEqual( self.root.start, 0 )
self.assertRaises( RuntimeError, setattr, self.root, "start", 1 )
self.assertEqual( self.root.stop, 0 )
self.assertRaises( RuntimeError, getattr, self.root, "label" )
self.assertRaises( RuntimeError, getattr, self.root, "parent" )
self.assertRaises( RuntimeError, setattr, self.root, "parent", self.branch )
self.assertRaises( RuntimeError, getattr, self.root, "suffix" )
self.assertRaises( RuntimeError, setattr, self.root, "suffix", self.branch )
self.assertTrue( self.root.is_root() )
self.assertFalse( self.root.is_leaf() )
self.assertTrue( self.root.is_empty() )
self.assertEqual( self.root.keys(), [] )
self.assertFalse( 1 in self.root )
self.assertTrue( 1 not in self.root )
self.assertRaises( KeyError, self.root.__getitem__, 1 )
self.root[1] = self.branch
self.root[1] = self.leaf
self.root[2] = self.branch
self.assertFalse( self.root.is_empty() )
self.assertEqual( set( self.root.keys() ), set( [ 1, 2 ] ) )
self.assertEqual( set( self.root.values() ), set( [ self.branch, self.leaf ] ) )
self.assertTrue( 1 in self.root )
self.assertTrue( 2 in self.root )
self.assertEqual( self.root[1], self.leaf )
self.assertEqual( self.root[2], self.branch )
def test_const_root(self):
cr = single.const_edge.from_edge( self.root )
self.assertEqual( self.root, cr )
self.assertEqual( hash( self.root ), hash( cr ) )
self.assertNotEqual( single.edge.root(), cr )
self.assertEqual( cr.start, 0 )
self.assertEqual( cr.stop, 0 )
self.assertRaises( RuntimeError, getattr, cr, "label" )
self.assertRaises( RuntimeError, getattr, cr, "parent" )
self.assertRaises( RuntimeError, getattr, cr, "suffix" )
self.assertTrue( cr.is_root() )
self.assertFalse( cr.is_leaf() )
self.assertTrue( cr.is_empty() )
self.assertEqual( cr.keys(), [] )
self.assertEqual( cr.values(), [] )
self.assertFalse( 1 in cr )
self.assertTrue( 1 not in cr )
self.assertRaises( KeyError, cr.__getitem__, 1 )
def test_branch(self):
self.assertEqual( self.branch.start, 1 )
self.branch.start = 3
self.assertEqual( self.branch.start, 3 )
self.assertEqual( self.branch.stop, 2 )
self.assertRaises( RuntimeError, getattr, self.branch, "label" )
self.assertEqual( self.branch.parent, None )
self.branch.parent = self.root
self.assertTrue( isinstance( self.branch.parent, single.edge ) )
self.assertEqual( self.branch.parent, self.root )
self.assertEqual( self.branch.suffix, None )
self.branch.suffix = self.root
self.assertTrue( isinstance( self.branch.parent, single.edge ) )
self.assertEqual( self.branch.suffix, self.root )
self.assertFalse( self.branch.is_root() )
self.assertFalse( self.branch.is_leaf() )
self.assertTrue( self.branch.is_empty() )
self.assertEqual( self.branch.keys(), [] )
self.assertFalse( 1 in self.branch )
self.assertTrue( 1 not in self.branch )
self.assertRaises( KeyError, self.branch.__getitem__, 1 )
self.branch[1] = self.root
self.branch[1] = self.leaf
self.branch[2] = self.root
self.assertFalse( self.branch.is_empty() )
self.assertEqual( set( self.branch.keys() ), set( [ 1, 2 ] ) )
self.assertEqual( set( self.branch.values() ), set( [ self.root, self.leaf ] ) )
self.assertTrue( 1 in self.branch )
self.assertTrue( 2 in self.branch )
self.assertEqual( self.branch[1], self.leaf )
self.assertEqual( self.branch[2], self.root )
def test_const_branch(self):
cb = single.const_edge.from_edge( self.branch )
self.assertEqual( self.branch, cb )
self.assertEqual( hash( self.branch ), hash( cb ) )
self.assertNotEqual( single.edge.branch( start = 1, stop = 2 ), cb )
self.assertEqual( cb.start, 1 )
self.assertEqual( cb.stop, 2 )
self.assertRaises( RuntimeError, getattr, cb, "label" )
self.assertEqual( cb.parent, None )
self.branch.parent = self.root
self.assertTrue( isinstance( cb.parent, single.const_edge ) )
self.assertEqual( cb.parent, self.root )
self.assertEqual( cb.suffix, None )
self.branch.suffix = self.root
self.assertTrue( isinstance( cb.suffix, single.const_edge ) )
self.assertEqual( cb.suffix, self.root )
self.assertFalse( cb.is_root() )
self.assertFalse( cb.is_leaf() )
self.assertTrue( cb.is_empty() )
self.assertEqual( cb.keys(), [] )
self.assertEqual( cb.values(), [] )
self.assertFalse( 1 in cb )
self.assertTrue( 1 not in cb )
self.assertRaises( KeyError, cb.__getitem__, 1 )
def test_leaf(self):
self.assertEqual( self.leaf.start, 4 )
self.leaf.start = 5
self.assertEqual( self.leaf.start, 5 )
ld = self.word.length_descriptor()
self.assertEqual( self.leaf.stop, ld() )
self.word.append( 1 )
self.word.append( 2 )
self.assertEqual( self.leaf.stop, ld() )
self.assertEqual( self.leaf.label, 6 )
self.assertEqual( self.leaf.parent, None )
self.leaf.parent = self.root
self.assertTrue( isinstance( self.leaf.parent, single.edge ) )
self.assertEqual( self.leaf.parent, self.root )
self.assertRaises( RuntimeError, getattr, self.leaf, "suffix" )
self.assertRaises( RuntimeError, setattr, self.leaf, "suffix", self.branch )
self.assertFalse( self.leaf.is_root() )
self.assertTrue( self.leaf.is_leaf() )
self.assertTrue( self.leaf.is_empty() )
self.assertEqual( self.leaf.keys(), [] )
self.assertEqual( self.leaf.values(), [] )
self.assertFalse( 1 in self.leaf )
self.assertTrue( 1 not in self.leaf )
self.assertRaises( RuntimeError, self.leaf.__getitem__, 1 )
self.assertRaises( RuntimeError, self.leaf.__setitem__, 1, self.root )
def test_const_leaf(self):
cl = single.const_edge.from_edge( self.leaf )
self.assertEqual( self.leaf, cl )
self.assertEqual( hash( self.leaf ), hash( cl ) )
self.assertNotEqual(
single.edge.leaf( start = 4, length = self.word.length_descriptor(), label = 6 ),
cl,
)
self.assertEqual( cl.start, 4 )
ld = self.word.length_descriptor()
self.assertEqual( cl.stop, ld() )
self.word.append( 1 )
self.word.append( 2 )
self.assertEqual( cl.stop, ld() )
self.assertEqual( cl.label, 6 )
self.assertEqual( cl.parent, None )
self.leaf.parent = self.root
self.assertTrue( isinstance( cl.parent, single.const_edge ) )
self.assertEqual( cl.parent, self.root )
self.assertRaises( RuntimeError, getattr, cl, "suffix" )
self.assertFalse( cl.is_root() )
self.assertTrue( cl.is_leaf() )
self.assertTrue( cl.is_empty() )
self.assertEqual( cl.keys(), [] )
self.assertEqual( cl.values(), [] )
self.assertFalse( 1 in cl )
self.assertTrue( 1 not in cl )
self.assertRaises( KeyError, cl.__getitem__, 1 )
class TestPreOrder(unittest.TestCase):
def setUp(self):
self.root = single.edge.root()
self.word = single.word()
length = self.word.length_descriptor()
self.edge_named = {
"r_b1": single.edge.branch( start = 0, stop = 3 ),
"r_b2": single.edge.branch( start = 1, stop = 3 ),
"r_l1": single.edge.leaf( start = 4, length = length, label = 6 ),
"r_b1_b1": single.edge.branch( start = 2, stop = 3 ),
"r_b1_l1": single.edge.leaf( start = 4, length = length, label = 7 ),
"r_b1_b1_l1": single.edge.leaf( start = 4, length = length, label = 8 ),
"r_b1_b1_l2": single.edge.leaf( start = 4, length = length, label = 9 ),
"r_b2_l1": single.edge.leaf( start = 4, length = length, label = 10 ),
"r_b2_l2": single.edge.leaf( start = 4, length = length, label = 11 ),
}
self.root[ "r_b1" ] = self.edge_named[ "r_b1" ]
self.root[ "r_b2" ] = self.edge_named[ "r_b2" ]
self.root[ "r_l1" ] = self.edge_named[ "r_l1" ]
self.edge_named[ "r_b1" ][ "r_b1_b1" ] = self.edge_named[ "r_b1_b1" ]
self.edge_named[ "r_b1" ][ "r_b1_l1" ] = self.edge_named[ "r_b1_l1" ]
self.edge_named[ "r_b1_b1" ][ "r_b1_b1_l1" ] = self.edge_named[ "r_b1_b1_l1" ]
self.edge_named[ "r_b1_b1" ][ "r_b1_b1_l2" ] = self.edge_named[ "r_b1_b1_l2" ]
self.edge_named[ "r_b2" ][ "r_b2_l1" ] = self.edge_named[ "r_b2_l1" ]
self.edge_named[ "r_b2" ][ "r_b2_l2" ] = self.edge_named[ "r_b2_l2" ]
def test_single_root(self):
root = single.edge.root()
res = list( root.preorder_iteration() )
self.assertEqual( res, [ root ] )
self.assertTrue( isinstance( res[0], single.edge ) )
cr = single.const_edge.from_edge( root )
res = list( cr.preorder_iteration() )
self.assertEqual( res, [ cr ] )
self.assertTrue( isinstance( res[0], single.const_edge ) )
def test_single_branch(self):
branch = single.edge.branch( start = 0, stop = 3 )
res = list( branch.preorder_iteration() )
self.assertEqual( res, [ branch ] )
self.assertTrue( isinstance( res[0], single.edge ) )
cb = single.const_edge.from_edge( branch )
res = list( cb.preorder_iteration() )
self.assertEqual( res, [ cb ] )
self.assertTrue( isinstance( res[0], single.const_edge ) )
def test_single_leaf(self):
word = single.word()
leaf = single.edge.leaf( start = 4, length = word.length_descriptor(), label = 6 )
res = list( leaf.preorder_iteration() )
self.assertEqual( res, [ leaf ] )
self.assertTrue( isinstance( res[0], single.edge ) )
cl = single.const_edge.from_edge( leaf )
res = list( cl.preorder_iteration() )
self.assertEqual( res, [ cl ] )
self.assertTrue( isinstance( res[0], single.const_edge ) )
def check_leaf_named(self, result, leafname):
self.assertEqual( result[0], self.edge_named[ leafname ] )
result.pop( 0 )
def check_r_b2(self, result):
action_for = {
"r_b2_l1": lambda result: self.check_leaf_named( result, "r_b2_l1" ),
"r_b2_l2": lambda result: self.check_leaf_named( result, "r_b2_l2" ),
}
self.assertEqual( result[0], self.edge_named[ "r_b2" ] )
result.pop( 0 )
for key in self.edge_named[ "r_b2" ].keys():
action_for[ key ]( result = result )
def check_r_b1_b1(self, result):
action_for = {
"r_b1_b1_l1": lambda result: self.check_leaf_named( result, "r_b1_b1_l1" ),
"r_b1_b1_l2": lambda result: self.check_leaf_named( result, "r_b1_b1_l2" ),
}
self.assertEqual( result[0], self.edge_named[ "r_b1_b1" ] )
result.pop( 0 )
for key in self.edge_named[ "r_b1_b1" ].keys():
action_for[ key ]( result = result )
def check_r_b1(self, result):
action_for = {
"r_b1_b1": self.check_r_b1_b1,
"r_b1_l1": lambda result: self.check_leaf_named( result, "r_b1_l1" ),
}
self.assertEqual( result[0], self.edge_named[ "r_b1" ] )
result.pop( 0 )
for key in self.edge_named[ "r_b1" ].keys():
action_for[ key ]( result = result )
def check_root(self, result):
action_for = {
"r_b1": self.check_r_b1,
"r_b2": self.check_r_b2,
"r_l1": lambda result: self.check_leaf_named( result, "r_l1" ),
}
self.assertEqual( result[0], self.root )
result.pop( 0 )
for key in self.root.keys():
action_for[ key ]( result = result )
def test_r_b2(self):
result = list( self.edge_named[ "r_b2" ].preorder_iteration() )
self.assertEqual( len( result ), 3 )
self.check_r_b2( result )
self.assertEqual( result, [] )
def test_r_b1_b1(self):
result = list( self.edge_named[ "r_b1_b1" ].preorder_iteration() )
self.assertEqual( len( result ), 3 )
self.check_r_b1_b1( result )
self.assertEqual( result, [] )
def test_r_b1(self):
result = list( self.edge_named[ "r_b1" ].preorder_iteration() )
self.assertEqual( len( result ), 5 )
self.check_r_b1( result )
self.assertEqual( result, [] )
def test_root(self):
result = list( self.root.preorder_iteration() )
self.assertEqual( len( result ), 10 )
self.assertTrue( all( isinstance( e, single.edge ) for e in result ) )
self.check_root( result )
self.assertEqual( result, [] )
def test_const_root(self):
cr = single.const_edge.from_edge( self.root )
result = list( cr.preorder_iteration() )
self.assertEqual( len( result ), 10 )
self.assertTrue( all( isinstance( e, single.const_edge ) for e in result ) )
self.check_root( result )
self.assertEqual( result, [] )
class TestPostOrder(unittest.TestCase):
def setUp(self):
self.root = single.edge.root()
self.word = single.word()
length = self.word.length_descriptor()
self.edge_named = {
"r_b1": single.edge.branch( start = 0, stop = 3 ),
"r_b2": single.edge.branch( start = 1, stop = 3 ),
"r_l1": single.edge.leaf( start = 4, length = length, label = 6 ),
"r_b1_b1": single.edge.branch( start = 2, stop = 3 ),
"r_b1_l1": single.edge.leaf( start = 4, length = length, label = 7 ),
"r_b1_b1_l1": single.edge.leaf( start = 4, length = length, label = 8 ),
"r_b1_b1_l2": single.edge.leaf( start = 4, length = length, label = 9 ),
"r_b2_l1": single.edge.leaf( start = 4, length = length, label = 10 ),
"r_b2_l2": single.edge.leaf( start = 4, length = length, label = 11 ),
}
self.root[ "r_b1" ] = self.edge_named[ "r_b1" ]
self.root[ "r_b2" ] = self.edge_named[ "r_b2" ]
self.root[ "r_l1" ] = self.edge_named[ "r_l1" ]
self.edge_named[ "r_b1" ][ "r_b1_b1" ] = self.edge_named[ "r_b1_b1" ]
self.edge_named[ "r_b1" ][ "r_b1_l1" ] = self.edge_named[ "r_b1_l1" ]
self.edge_named[ "r_b1_b1" ][ "r_b1_b1_l1" ] = self.edge_named[ "r_b1_b1_l1" ]
self.edge_named[ "r_b1_b1" ][ "r_b1_b1_l2" ] = self.edge_named[ "r_b1_b1_l2" ]
self.edge_named[ "r_b2" ][ "r_b2_l1" ] = self.edge_named[ "r_b2_l1" ]
self.edge_named[ "r_b2" ][ "r_b2_l2" ] = self.edge_named[ "r_b2_l2" ]
def test_single_root(self):
root = single.edge.root()
res = list( root.postorder_iteration() )
self.assertEqual( res, [ root ] )
self.assertTrue( isinstance( res[0], single.edge ) )
cr = single.const_edge.from_edge( root )
res = list( cr.postorder_iteration() )
self.assertEqual( res, [ cr ] )
self.assertTrue( isinstance( res[0], single.const_edge ) )
def test_single_branch(self):
branch = single.edge.branch( start = 0, stop = 3 )
res = list( branch.postorder_iteration() )
self.assertEqual( res, [ branch ] )
self.assertTrue( isinstance( res[0], single.edge ) )
cb = single.const_edge.from_edge( branch )
res = list( cb.postorder_iteration() )
self.assertEqual( res, [ cb ] )
self.assertTrue( isinstance( res[0], single.const_edge ) )
def test_single_leaf(self):
word = single.word()
leaf = single.edge.leaf( start = 4, length = word.length_descriptor(), label = 6 )
res = list( leaf.postorder_iteration() )
self.assertEqual( res, [ leaf ] )
self.assertTrue( isinstance( res[0], single.edge ) )
cl = single.const_edge.from_edge( leaf )
res = list( cl.postorder_iteration() )
self.assertEqual( res, [ cl ] )
self.assertTrue( isinstance( res[0], single.const_edge ) )
def check_leaf_named(self, result, leafname):
self.assertEqual( result[0], self.edge_named[ leafname ] )
result.pop( 0 )
def check_r_b2(self, result):
action_for = {
"r_b2_l1": lambda result: self.check_leaf_named( result, "r_b2_l1" ),
"r_b2_l2": lambda result: self.check_leaf_named( result, "r_b2_l2" ),
}
for key in self.edge_named[ "r_b2" ].keys():
action_for[ key ]( result = result )
self.assertEqual( result[0], self.edge_named[ "r_b2" ] )
result.pop( 0 )
def check_r_b1_b1(self, result):
action_for = {
"r_b1_b1_l1": lambda result: self.check_leaf_named( result, "r_b1_b1_l1" ),
"r_b1_b1_l2": lambda result: self.check_leaf_named( result, "r_b1_b1_l2" ),
}
for key in self.edge_named[ "r_b1_b1" ].keys():
action_for[ key ]( result = result )
self.assertEqual( result[0], self.edge_named[ "r_b1_b1" ] )
result.pop( 0 )
def check_r_b1(self, result):
action_for = {
"r_b1_b1": self.check_r_b1_b1,
"r_b1_l1": lambda result: self.check_leaf_named( result, "r_b1_l1" ),
}
for key in self.edge_named[ "r_b1" ].keys():
action_for[ key ]( result = result )
self.assertEqual( result[0], self.edge_named[ "r_b1" ] )
result.pop( 0 )
def check_root(self, result):
action_for = {
"r_b1": self.check_r_b1,
"r_b2": self.check_r_b2,
"r_l1": lambda result: self.check_leaf_named( result, "r_l1" ),
}
for key in self.root.keys():
action_for[ key ]( result = result )
self.assertEqual( result[0], self.root )
result.pop( 0 )
def test_r_b2(self):
result = list( self.edge_named[ "r_b2" ].postorder_iteration() )
self.assertEqual( len( result ), 3 )
self.check_r_b2( result )
self.assertEqual( result, [] )
def test_r_b1_b1(self):
result = list( self.edge_named[ "r_b1_b1" ].postorder_iteration() )
self.assertEqual( len( result ), 3 )
self.check_r_b1_b1( result )
self.assertEqual( result, [] )
def test_r_b1(self):
result = list( self.edge_named[ "r_b1" ].postorder_iteration() )
self.assertEqual( len( result ), 5 )
self.check_r_b1( result )
self.assertEqual( result, [] )
def test_root(self):
result = list( self.root.postorder_iteration() )
self.assertEqual( len( result ), 10 )
self.assertTrue( all( isinstance( e, single.edge ) for e in result ) )
self.check_root( result )
self.assertEqual( result, [] )
def test_const_root(self):
cr = single.const_edge.from_edge( self.root )
result = list( cr.postorder_iteration() )
self.assertEqual( len( result ), 10 )
self.assertTrue( all( isinstance( e, single.const_edge ) for e in result ) )
self.check_root( result )
self.assertEqual( result, [] )
class TestTree(unittest.TestCase):
def test_tree(self):
tree = single.tree()
self.assertEqual( tree.in_construction, False )
w = tree.word
self.assertTrue( isinstance( w, single.word ) )
self.assertEqual( w.length_descriptor()(), 0 )
r = tree.root
self.assertTrue( isinstance( r, single.const_edge ) )
self.assertEqual( r.keys(), [] )
def test_ukkonen1(self):
tree = single.tree()
self.assertEqual( tree.in_construction, False )
builder = single.ukkonen( tree )
self.assertTrue( tree.in_construction )
self.assertTrue( builder.is_attached )
self.assertTrue( builder.is_valid )
self.assertRaises( RuntimeError, single.ukkonen, tree )
builder.detach()
self.assertFalse( builder.is_attached )
self.assertTrue( builder.is_valid )
self.assertFalse( tree.in_construction )
self.assertRaises( RuntimeError, builder.append, "a" )
def test_ukkonen2(self):
tree = single.tree()
builder = single.ukkonen( tree )
builder.append( glyph = "a" )
self.assertTrue( builder.is_valid )
builder.append( glyph = "n" )
self.assertTrue( builder.is_valid )
builder.detach()
self.assertFalse( builder.is_attached )
self.assertFalse( tree.in_construction )
builder = single.ukkonen( tree )
self.assertTrue( builder.is_valid )
self.assertTrue( builder.is_attached )
self.assertTrue( tree.in_construction )
builder.append( glyph = "a" )
self.assertFalse( builder.is_valid )
self.assertRaises( builder.detach )
builder.append( glyph = "n" )
self.assertFalse( builder.is_valid )
self.assertRaises( builder.detach )
builder.append( glyph = "a" )
self.assertFalse( builder.is_valid )
self.assertRaises( builder.detach )
builder.append( glyph = "s" )
self.assertTrue( builder.is_valid )
builder.detach()
self.assertFalse( builder.is_attached )
self.assertFalse( tree.in_construction )
builder = single.ukkonen( tree )
self.assertTrue( builder.is_valid )
self.assertTrue( builder.is_attached )
self.assertTrue( tree.in_construction )
builder.append( glyph = "$" )
self.assertTrue( builder.is_valid )
builder.detach()
root = tree.root
self.assertTrue( root.is_root() )
self.assertEqual( set( root.keys() ), set( [ "a", "n", "s", "$" ] ) )
b_a = root[ "a" ]
self.assertFalse( b_a.is_root() )
self.assertFalse( b_a.is_leaf() )
self.assertEqual( b_a.start, 0 )
self.assertEqual( b_a.stop, 1 )
self.assertEqual( set( b_a.keys() ), set( [ "n", "s" ] ) )
self.assertEqual( b_a.parent, root )
b_a_n = b_a[ "n" ]
self.assertFalse( b_a_n.is_root() )
self.assertFalse( b_a_n.is_leaf() )
self.assertEqual( b_a_n.start, 1 )
self.assertEqual( b_a_n.stop, 3 )
self.assertEqual( set( b_a_n.keys() ), set( [ "n", "s" ] ) )
self.assertEqual( b_a_n.parent, b_a )
b_a_n_n = b_a_n[ "n" ]
self.assertTrue( b_a_n_n.is_leaf() )
self.assertEqual( b_a_n_n.start, 3 )
self.assertEqual( b_a_n_n.stop, 7 )
self.assertEqual( b_a_n_n.label, 0 )
self.assertEqual( b_a_n_n.parent, b_a_n )
b_a_n_s = b_a_n[ "s" ]
self.assertTrue( b_a_n_s.is_leaf() )
self.assertEqual( b_a_n_s.start, 5 )
self.assertEqual( b_a_n_s.stop, 7 )
self.assertEqual( b_a_n_s.label, 2 )
self.assertEqual( b_a_n_s.parent, b_a_n )
b_a_s = b_a[ "s" ]
self.assertTrue( b_a_s.is_leaf() )
self.assertEqual( b_a_s.start, 5 )
self.assertEqual( b_a_s.stop, 7 )
self.assertEqual( b_a_s.label, 4 )
self.assertEqual( b_a_s.parent, b_a )
b_n = root[ "n" ]
self.assertFalse( b_n.is_root() )
self.assertFalse( b_n.is_leaf() )
self.assertEqual( b_n.start, 1 )
self.assertEqual( b_n.stop, 3 )
self.assertEqual( set( b_n.keys() ), set( [ "n", "s" ] ) )
self.assertEqual( b_n.parent, root )
b_n_n = b_n[ "n" ]
self.assertTrue( b_n_n.is_leaf() )
self.assertEqual( b_n_n.start, 3 )
self.assertEqual( b_n_n.stop, 7 )
self.assertEqual( b_n_n.label, 1 )
self.assertEqual( b_n_n.parent, b_n )
b_n_s = b_n[ "s" ]
self.assertTrue( b_n_s.is_leaf() )
self.assertEqual( b_n_s.start, 5 )
self.assertEqual( b_n_s.stop, 7 )
self.assertEqual( b_n_s.label, 3 )
self.assertEqual( b_n_s.parent, b_n )
b_s = root[ "s" ]
self.assertTrue( b_s.is_leaf() )
self.assertEqual( b_s.start, 5 )
self.assertEqual( b_s.stop, 7 )
self.assertEqual( b_s.label, 5 )
self.assertEqual( b_s.parent, root )
b_dl = root[ "$" ]
self.assertTrue( b_dl.is_leaf() )
self.assertEqual( b_dl.start, 6 )
self.assertEqual( b_dl.stop, 7 )
self.assertEqual( b_dl.label, 6 )
self.assertEqual( b_dl.parent, root )
self.assertEqual( b_a.suffix, root )
self.assertEqual( b_n.suffix, b_a )
self.assertEqual( b_a_n.suffix, b_n )
class TestMatchingStatistics(unittest.TestCase):
def test(self):
tree = single.tree()
result = list( single.matching_statistics( tree, list( "ABC" ) ) )
self.assertEqual( result, [ ( 0, ( tree.root, 0 ) ) ] * 3 )
builder = single.ukkonen( tree = tree )
for c in "TTAGC$":
builder.append( c )
self.assertRaises( RuntimeError, single.matching_statistics, tree, list() );
builder.detach()
root = tree.root
branch_t = root[ "T" ]
leaf_0 = branch_t[ "T" ]
leaf_1 = branch_t[ "A" ]
leaf_2 = root[ "A" ]
leaf_3 = root[ "G" ]
leaf_4 = root[ "C" ]
result = list( single.matching_statistics( tree, [] ) )
self.assertEqual( result, [] )
result = list( single.matching_statistics( tree, list( "QTTATTATTTAGCQWTTAGFK" ) ) )
self.assertEqual(
result,
[
( 0, ( root, 0 ) ),
( 3, ( leaf_0, 3 ) ),
( 2, ( leaf_1, 3 ) ),
( 1, ( leaf_2, 3 ) ),
( 3, ( leaf_0, 3 ) ),
( 2, ( leaf_1, 3 ) ),
( 1, ( leaf_2, 3 ) ),
( 2, ( leaf_0, 2 ) ),
( 5, ( leaf_0, 5 ) ),
( 4, ( leaf_1, 5 ) ),
( 3, ( leaf_2, 5 ) ),
( 2, ( leaf_3, 5 ) ),
( 1, ( leaf_4, 5 ) ),
( 0, ( root, 0 ) ),
( 0, ( root, 0 ) ),
( 4, ( leaf_0, 4 ) ),
( 3, ( leaf_1, 4 ) ),
( 2, ( leaf_2, 4 ) ),
( 1, ( leaf_3, 4 ) ),
( 0, ( root, 0 ) ),
( 0, ( root, 0 ) ),
]
)
class TestLeafIndices(unittest.TestCase):
def test(self):
tree = single.tree()
result = list( single.matching_statistics( tree, list( "ABC" ) ) )
self.assertEqual( result, [ ( 0, ( tree.root, 0 ) ) ] * 3 )
builder = single.ukkonen( tree = tree )
for c in "TTAGC$":
builder.append( c )
builder.detach()
leaf_indices_below = suffixtree.calculate_leaf_indices( root = tree.root )
root = tree.root
branch_t = root[ "T" ]
leaf_0 = branch_t[ "T" ]
leaf_1 = branch_t[ "A" ]
leaf_2 = root[ "A" ]
leaf_3 = root[ "G" ]
leaf_4 = root[ "C" ]
leaf_5 = root[ "$" ]
self.assertEqual( leaf_indices_below[ leaf_0 ], [ 0 ] )
self.assertEqual( leaf_indices_below[ leaf_1 ], [ 1 ] )
self.assertEqual( leaf_indices_below[ leaf_2 ], [ 2 ] )
self.assertEqual( leaf_indices_below[ leaf_3 ], [ 3 ] )
self.assertEqual( leaf_indices_below[ leaf_4 ], [ 4 ] )
self.assertEqual( leaf_indices_below[ leaf_5 ], [ 5 ] )
self.assertEqual( sorted( leaf_indices_below[ branch_t ] ), [ 0, 1 ] )
self.assertEqual( sorted( leaf_indices_below[ root ] ), range( 6 ) )
suite_word = unittest.TestLoader().loadTestsFromTestCase(
TestWord
)
suite_edge = unittest.TestLoader().loadTestsFromTestCase(
TestEdge
)
suite_preorder = unittest.TestLoader().loadTestsFromTestCase(
TestPreOrder
)
suite_postorder = unittest.TestLoader().loadTestsFromTestCase(
TestPostOrder
)
suite_tree = unittest.TestLoader().loadTestsFromTestCase(
TestTree
)
suite_msi = unittest.TestLoader().loadTestsFromTestCase(
TestMatchingStatistics
)
suite_leaf_indices = unittest.TestLoader().loadTestsFromTestCase(
TestLeafIndices
)
alltests = unittest.TestSuite(
[
suite_word,
suite_edge,
suite_preorder,
suite_postorder,
suite_tree,
suite_msi,
suite_leaf_indices,
]
)
def load_tests(loader, tests, pattern):
return alltests
if __name__ == "__main__":
unittest.TextTestRunner( verbosity = 2 ).run( alltests )
| from __future__ import division
from scitbx import suffixtree
from scitbx.suffixtree import single
import unittest
class TestWord(unittest.TestCase):
def test_single_word(self):
word = single.word()
ld = word.length_descriptor()
self.assertEqual( len( word ), 0 )
self.assertEqual( ld(), 0 )
self.assertEqual( list( word.substring( 0, ld() ) ), [] )
c1 = object()
c2 = object()
word.append( c1 )
self.assertEqual( len( word ), 1 )
self.assertEqual( ld(), 1 )
self.assertEqual( word[0], c1 )
self.assertEqual( list( word ), [ c1 ] )
word.append( c2 )
self.assertEqual( len( word ), 2 )
self.assertEqual( ld(), 2 )
self.assertEqual( word[0], c1 )
self.assertEqual( word[1], c2 )
self.assertEqual( list( word ), [ c1, c2 ] )
self.assertEqual( list( word.substring( 0, 0 ) ), [] )
self.assertEqual( list( word.substring( 0, 1 ) ), [ c1 ] )
self.assertEqual( list( word.substring( 0, 2 ) ), [ c1, c2 ] )
self.assertEqual( list( word.substring( 1, 1 ) ), [] )
self.assertEqual( list( word.substring( 1, 2 ) ), [ c2 ] )
class TestEdge(unittest.TestCase):
def setUp(self):
self.root = single.edge.root()
self.branch = single.edge.branch( start = 1, stop = 2 )
self.word = single.word()
self.leaf = single.edge.leaf( start = 4, length = self.word.length_descriptor(), label = 6 )
def test_root(self):
self.assertEqual( self.root.start, 0 )
self.assertRaises( RuntimeError, setattr, self.root, "start", 1 )
self.assertEqual( self.root.stop, 0 )
self.assertRaises( RuntimeError, getattr, self.root, "label" )
self.assertRaises( RuntimeError, getattr, self.root, "parent" )
self.assertRaises( RuntimeError, setattr, self.root, "parent", self.branch )
self.assertRaises( RuntimeError, getattr, self.root, "suffix" )
self.assertRaises( RuntimeError, setattr, self.root, "suffix", self.branch )
self.assertTrue( self.root.is_root() )
self.assertFalse( self.root.is_leaf() )
self.assertTrue( self.root.is_empty() )
self.assertEqual( self.root.keys(), [] )
self.assertFalse( 1 in self.root )
self.assertTrue( 1 not in self.root )
self.assertRaises( KeyError, self.root.__getitem__, 1 )
self.root[1] = self.branch
self.root[1] = self.leaf
self.root[2] = self.branch
self.assertFalse( self.root.is_empty() )
self.assertEqual( set( self.root.keys() ), set( [ 1, 2 ] ) )
self.assertEqual( set( self.root.values() ), set( [ self.branch, self.leaf ] ) )
self.assertTrue( 1 in self.root )
self.assertTrue( 2 in self.root )
self.assertEqual( self.root[1], self.leaf )
self.assertEqual( self.root[2], self.branch )
def test_const_root(self):
cr = single.const_edge.from_edge( self.root )
self.assertEqual( self.root, cr )
self.assertEqual( hash( self.root ), hash( cr ) )
self.assertNotEqual( single.edge.root(), cr )
self.assertEqual( cr.start, 0 )
self.assertEqual( cr.stop, 0 )
self.assertRaises( RuntimeError, getattr, cr, "label" )
self.assertRaises( RuntimeError, getattr, cr, "parent" )
self.assertRaises( RuntimeError, getattr, cr, "suffix" )
self.assertTrue( cr.is_root() )
self.assertFalse( cr.is_leaf() )
self.assertTrue( cr.is_empty() )
self.assertEqual( cr.keys(), [] )
self.assertEqual( cr.values(), [] )
self.assertFalse( 1 in cr )
self.assertTrue( 1 not in cr )
self.assertRaises( KeyError, cr.__getitem__, 1 )
def test_branch(self):
self.assertEqual( self.branch.start, 1 )
self.branch.start = 3
self.assertEqual( self.branch.start, 3 )
self.assertEqual( self.branch.stop, 2 )
self.assertRaises( RuntimeError, getattr, self.branch, "label" )
self.assertEqual( self.branch.parent, None )
self.branch.parent = self.root
self.assertTrue( isinstance( self.branch.parent, single.edge ) )
self.assertEqual( self.branch.parent, self.root )
self.assertEqual( self.branch.suffix, None )
self.branch.suffix = self.root
self.assertTrue( isinstance( self.branch.parent, single.edge ) )
self.assertEqual( self.branch.suffix, self.root )
self.assertFalse( self.branch.is_root() )
self.assertFalse( self.branch.is_leaf() )
self.assertTrue( self.branch.is_empty() )
self.assertEqual( self.branch.keys(), [] )
self.assertFalse( 1 in self.branch )
self.assertTrue( 1 not in self.branch )
self.assertRaises( KeyError, self.branch.__getitem__, 1 )
self.branch[1] = self.root
self.branch[1] = self.leaf
self.branch[2] = self.root
self.assertFalse( self.branch.is_empty() )
self.assertEqual( set( self.branch.keys() ), set( [ 1, 2 ] ) )
self.assertEqual( set( self.branch.values() ), set( [ self.root, self.leaf ] ) )
self.assertTrue( 1 in self.branch )
self.assertTrue( 2 in self.branch )
self.assertEqual( self.branch[1], self.leaf )
self.assertEqual( self.branch[2], self.root )
def test_const_branch(self):
cb = single.const_edge.from_edge( self.branch )
self.assertEqual( self.branch, cb )
self.assertEqual( hash( self.branch ), hash( cb ) )
self.assertNotEqual( single.edge.branch( start = 1, stop = 2 ), cb )
self.assertEqual( cb.start, 1 )
self.assertEqual( cb.stop, 2 )
self.assertRaises( RuntimeError, getattr, cb, "label" )
self.assertEqual( cb.parent, None )
self.branch.parent = self.root
self.assertTrue( isinstance( cb.parent, single.const_edge ) )
self.assertEqual( cb.parent, self.root )
self.assertEqual( cb.suffix, None )
self.branch.suffix = self.root
self.assertTrue( isinstance( cb.suffix, single.const_edge ) )
self.assertEqual( cb.suffix, self.root )
self.assertFalse( cb.is_root() )
self.assertFalse( cb.is_leaf() )
self.assertTrue( cb.is_empty() )
self.assertEqual( cb.keys(), [] )
self.assertEqual( cb.values(), [] )
self.assertFalse( 1 in cb )
self.assertTrue( 1 not in cb )
self.assertRaises( KeyError, cb.__getitem__, 1 )
def test_leaf(self):
self.assertEqual( self.leaf.start, 4 )
self.leaf.start = 5
self.assertEqual( self.leaf.start, 5 )
ld = self.word.length_descriptor()
self.assertEqual( self.leaf.stop, ld() )
self.word.append( 1 )
self.word.append( 2 )
self.assertEqual( self.leaf.stop, ld() )
self.assertEqual( self.leaf.label, 6 )
self.assertEqual( self.leaf.parent, None )
self.leaf.parent = self.root
self.assertTrue( isinstance( self.leaf.parent, single.edge ) )
self.assertEqual( self.leaf.parent, self.root )
self.assertRaises( RuntimeError, getattr, self.leaf, "suffix" )
self.assertRaises( RuntimeError, setattr, self.leaf, "suffix", self.branch )
self.assertFalse( self.leaf.is_root() )
self.assertTrue( self.leaf.is_leaf() )
self.assertTrue( self.leaf.is_empty() )
self.assertEqual( self.leaf.keys(), [] )
self.assertEqual( self.leaf.values(), [] )
self.assertFalse( 1 in self.leaf )
self.assertTrue( 1 not in self.leaf )
self.assertRaises( RuntimeError, self.leaf.__getitem__, 1 )
self.assertRaises( RuntimeError, self.leaf.__setitem__, 1, self.root )
def test_const_leaf(self):
cl = single.const_edge.from_edge( self.leaf )
self.assertEqual( self.leaf, cl )
self.assertEqual( hash( self.leaf ), hash( cl ) )
self.assertNotEqual(
single.edge.leaf( start = 4, length = self.word.length_descriptor(), label = 6 ),
cl,
)
self.assertEqual( cl.start, 4 )
ld = self.word.length_descriptor()
self.assertEqual( cl.stop, ld() )
self.word.append( 1 )
self.word.append( 2 )
self.assertEqual( cl.stop, ld() )
self.assertEqual( cl.label, 6 )
self.assertEqual( cl.parent, None )
self.leaf.parent = self.root
self.assertTrue( isinstance( cl.parent, single.const_edge ) )
self.assertEqual( cl.parent, self.root )
self.assertRaises( RuntimeError, getattr, cl, "suffix" )
self.assertFalse( cl.is_root() )
self.assertTrue( cl.is_leaf() )
self.assertTrue( cl.is_empty() )
self.assertEqual( cl.keys(), [] )
self.assertEqual( cl.values(), [] )
self.assertFalse( 1 in cl )
self.assertTrue( 1 not in cl )
self.assertRaises( KeyError, cl.__getitem__, 1 )
class TestPreOrder(unittest.TestCase):
def setUp(self):
self.root = single.edge.root()
self.word = single.word()
length = self.word.length_descriptor()
self.edge_named = {
"r_b1": single.edge.branch( start = 0, stop = 3 ),
"r_b2": single.edge.branch( start = 1, stop = 3 ),
"r_l1": single.edge.leaf( start = 4, length = length, label = 6 ),
"r_b1_b1": single.edge.branch( start = 2, stop = 3 ),
"r_b1_l1": single.edge.leaf( start = 4, length = length, label = 7 ),
"r_b1_b1_l1": single.edge.leaf( start = 4, length = length, label = 8 ),
"r_b1_b1_l2": single.edge.leaf( start = 4, length = length, label = 9 ),
"r_b2_l1": single.edge.leaf( start = 4, length = length, label = 10 ),
"r_b2_l2": single.edge.leaf( start = 4, length = length, label = 11 ),
}
self.root[ "r_b1" ] = self.edge_named[ "r_b1" ]
self.root[ "r_b2" ] = self.edge_named[ "r_b2" ]
self.root[ "r_l1" ] = self.edge_named[ "r_l1" ]
self.edge_named[ "r_b1" ][ "r_b1_b1" ] = self.edge_named[ "r_b1_b1" ]
self.edge_named[ "r_b1" ][ "r_b1_l1" ] = self.edge_named[ "r_b1_l1" ]
self.edge_named[ "r_b1_b1" ][ "r_b1_b1_l1" ] = self.edge_named[ "r_b1_b1_l1" ]
self.edge_named[ "r_b1_b1" ][ "r_b1_b1_l2" ] = self.edge_named[ "r_b1_b1_l2" ]
self.edge_named[ "r_b2" ][ "r_b2_l1" ] = self.edge_named[ "r_b2_l1" ]
self.edge_named[ "r_b2" ][ "r_b2_l2" ] = self.edge_named[ "r_b2_l2" ]
def test_single_root(self):
root = single.edge.root()
res = list( root.preorder_iteration() )
self.assertEqual( res, [ root ] )
self.assertTrue( isinstance( res[0], single.edge ) )
cr = single.const_edge.from_edge( root )
res = list( cr.preorder_iteration() )
self.assertEqual( res, [ cr ] )
self.assertTrue( isinstance( res[0], single.const_edge ) )
def test_single_branch(self):
branch = single.edge.branch( start = 0, stop = 3 )
res = list( branch.preorder_iteration() )
self.assertEqual( res, [ branch ] )
self.assertTrue( isinstance( res[0], single.edge ) )
cb = single.const_edge.from_edge( branch )
res = list( cb.preorder_iteration() )
self.assertEqual( res, [ cb ] )
self.assertTrue( isinstance( res[0], single.const_edge ) )
def test_single_leaf(self):
word = single.word()
leaf = single.edge.leaf( start = 4, length = word.length_descriptor(), label = 6 )
res = list( leaf.preorder_iteration() )
self.assertEqual( res, [ leaf ] )
self.assertTrue( isinstance( res[0], single.edge ) )
cl = single.const_edge.from_edge( leaf )
res = list( cl.preorder_iteration() )
self.assertEqual( res, [ cl ] )
self.assertTrue( isinstance( res[0], single.const_edge ) )
def check_leaf_named(self, result, leafname):
self.assertEqual( result[0], self.edge_named[ leafname ] )
result.pop( 0 )
def check_r_b2(self, result):
action_for = {
"r_b2_l1": lambda result: self.check_leaf_named( result, "r_b2_l1" ),
"r_b2_l2": lambda result: self.check_leaf_named( result, "r_b2_l2" ),
}
self.assertEqual( result[0], self.edge_named[ "r_b2" ] )
result.pop( 0 )
for key in self.edge_named[ "r_b2" ].keys():
action_for[ key ]( result = result )
def check_r_b1_b1(self, result):
action_for = {
"r_b1_b1_l1": lambda result: self.check_leaf_named( result, "r_b1_b1_l1" ),
"r_b1_b1_l2": lambda result: self.check_leaf_named( result, "r_b1_b1_l2" ),
}
self.assertEqual( result[0], self.edge_named[ "r_b1_b1" ] )
result.pop( 0 )
for key in self.edge_named[ "r_b1_b1" ].keys():
action_for[ key ]( result = result )
def check_r_b1(self, result):
action_for = {
"r_b1_b1": self.check_r_b1_b1,
"r_b1_l1": lambda result: self.check_leaf_named( result, "r_b1_l1" ),
}
self.assertEqual( result[0], self.edge_named[ "r_b1" ] )
result.pop( 0 )
for key in self.edge_named[ "r_b1" ].keys():
action_for[ key ]( result = result )
def check_root(self, result):
action_for = {
"r_b1": self.check_r_b1,
"r_b2": self.check_r_b2,
"r_l1": lambda result: self.check_leaf_named( result, "r_l1" ),
}
self.assertEqual( result[0], self.root )
result.pop( 0 )
for key in self.root.keys():
action_for[ key ]( result = result )
def test_r_b2(self):
result = list( self.edge_named[ "r_b2" ].preorder_iteration() )
self.assertEqual( len( result ), 3 )
self.check_r_b2( result )
self.assertEqual( result, [] )
def test_r_b1_b1(self):
result = list( self.edge_named[ "r_b1_b1" ].preorder_iteration() )
self.assertEqual( len( result ), 3 )
self.check_r_b1_b1( result )
self.assertEqual( result, [] )
def test_r_b1(self):
result = list( self.edge_named[ "r_b1" ].preorder_iteration() )
self.assertEqual( len( result ), 5 )
self.check_r_b1( result )
self.assertEqual( result, [] )
def test_root(self):
result = list( self.root.preorder_iteration() )
self.assertEqual( len( result ), 10 )
self.assertTrue( all( isinstance( e, single.edge ) for e in result ) )
self.check_root( result )
self.assertEqual( result, [] )
def test_const_root(self):
cr = single.const_edge.from_edge( self.root )
result = list( cr.preorder_iteration() )
self.assertEqual( len( result ), 10 )
self.assertTrue( all( isinstance( e, single.const_edge ) for e in result ) )
self.check_root( result )
self.assertEqual( result, [] )
class TestPostOrder(unittest.TestCase):
def setUp(self):
self.root = single.edge.root()
self.word = single.word()
length = self.word.length_descriptor()
self.edge_named = {
"r_b1": single.edge.branch( start = 0, stop = 3 ),
"r_b2": single.edge.branch( start = 1, stop = 3 ),
"r_l1": single.edge.leaf( start = 4, length = length, label = 6 ),
"r_b1_b1": single.edge.branch( start = 2, stop = 3 ),
"r_b1_l1": single.edge.leaf( start = 4, length = length, label = 7 ),
"r_b1_b1_l1": single.edge.leaf( start = 4, length = length, label = 8 ),
"r_b1_b1_l2": single.edge.leaf( start = 4, length = length, label = 9 ),
"r_b2_l1": single.edge.leaf( start = 4, length = length, label = 10 ),
"r_b2_l2": single.edge.leaf( start = 4, length = length, label = 11 ),
}
self.root[ "r_b1" ] = self.edge_named[ "r_b1" ]
self.root[ "r_b2" ] = self.edge_named[ "r_b2" ]
self.root[ "r_l1" ] = self.edge_named[ "r_l1" ]
self.edge_named[ "r_b1" ][ "r_b1_b1" ] = self.edge_named[ "r_b1_b1" ]
self.edge_named[ "r_b1" ][ "r_b1_l1" ] = self.edge_named[ "r_b1_l1" ]
self.edge_named[ "r_b1_b1" ][ "r_b1_b1_l1" ] = self.edge_named[ "r_b1_b1_l1" ]
self.edge_named[ "r_b1_b1" ][ "r_b1_b1_l2" ] = self.edge_named[ "r_b1_b1_l2" ]
self.edge_named[ "r_b2" ][ "r_b2_l1" ] = self.edge_named[ "r_b2_l1" ]
self.edge_named[ "r_b2" ][ "r_b2_l2" ] = self.edge_named[ "r_b2_l2" ]
def test_single_root(self):
root = single.edge.root()
res = list( root.postorder_iteration() )
self.assertEqual( res, [ root ] )
self.assertTrue( isinstance( res[0], single.edge ) )
cr = single.const_edge.from_edge( root )
res = list( cr.postorder_iteration() )
self.assertEqual( res, [ cr ] )
self.assertTrue( isinstance( res[0], single.const_edge ) )
def test_single_branch(self):
branch = single.edge.branch( start = 0, stop = 3 )
res = list( branch.postorder_iteration() )
self.assertEqual( res, [ branch ] )
self.assertTrue( isinstance( res[0], single.edge ) )
cb = single.const_edge.from_edge( branch )
res = list( cb.postorder_iteration() )
self.assertEqual( res, [ cb ] )
self.assertTrue( isinstance( res[0], single.const_edge ) )
def test_single_leaf(self):
word = single.word()
leaf = single.edge.leaf( start = 4, length = word.length_descriptor(), label = 6 )
res = list( leaf.postorder_iteration() )
self.assertEqual( res, [ leaf ] )
self.assertTrue( isinstance( res[0], single.edge ) )
cl = single.const_edge.from_edge( leaf )
res = list( cl.postorder_iteration() )
self.assertEqual( res, [ cl ] )
self.assertTrue( isinstance( res[0], single.const_edge ) )
def check_leaf_named(self, result, leafname):
self.assertEqual( result[0], self.edge_named[ leafname ] )
result.pop( 0 )
def check_r_b2(self, result):
action_for = {
"r_b2_l1": lambda result: self.check_leaf_named( result, "r_b2_l1" ),
"r_b2_l2": lambda result: self.check_leaf_named( result, "r_b2_l2" ),
}
for key in self.edge_named[ "r_b2" ].keys():
action_for[ key ]( result = result )
self.assertEqual( result[0], self.edge_named[ "r_b2" ] )
result.pop( 0 )
def check_r_b1_b1(self, result):
action_for = {
"r_b1_b1_l1": lambda result: self.check_leaf_named( result, "r_b1_b1_l1" ),
"r_b1_b1_l2": lambda result: self.check_leaf_named( result, "r_b1_b1_l2" ),
}
for key in self.edge_named[ "r_b1_b1" ].keys():
action_for[ key ]( result = result )
self.assertEqual( result[0], self.edge_named[ "r_b1_b1" ] )
result.pop( 0 )
def check_r_b1(self, result):
action_for = {
"r_b1_b1": self.check_r_b1_b1,
"r_b1_l1": lambda result: self.check_leaf_named( result, "r_b1_l1" ),
}
for key in self.edge_named[ "r_b1" ].keys():
action_for[ key ]( result = result )
self.assertEqual( result[0], self.edge_named[ "r_b1" ] )
result.pop( 0 )
def check_root(self, result):
action_for = {
"r_b1": self.check_r_b1,
"r_b2": self.check_r_b2,
"r_l1": lambda result: self.check_leaf_named( result, "r_l1" ),
}
for key in self.root.keys():
action_for[ key ]( result = result )
self.assertEqual( result[0], self.root )
result.pop( 0 )
def test_r_b2(self):
result = list( self.edge_named[ "r_b2" ].postorder_iteration() )
self.assertEqual( len( result ), 3 )
self.check_r_b2( result )
self.assertEqual( result, [] )
def test_r_b1_b1(self):
result = list( self.edge_named[ "r_b1_b1" ].postorder_iteration() )
self.assertEqual( len( result ), 3 )
self.check_r_b1_b1( result )
self.assertEqual( result, [] )
def test_r_b1(self):
result = list( self.edge_named[ "r_b1" ].postorder_iteration() )
self.assertEqual( len( result ), 5 )
self.check_r_b1( result )
self.assertEqual( result, [] )
def test_root(self):
result = list( self.root.postorder_iteration() )
self.assertEqual( len( result ), 10 )
self.assertTrue( all( isinstance( e, single.edge ) for e in result ) )
self.check_root( result )
self.assertEqual( result, [] )
def test_const_root(self):
cr = single.const_edge.from_edge( self.root )
result = list( cr.postorder_iteration() )
self.assertEqual( len( result ), 10 )
self.assertTrue( all( isinstance( e, single.const_edge ) for e in result ) )
self.check_root( result )
self.assertEqual( result, [] )
class TestTree(unittest.TestCase):
def test_tree(self):
tree = single.tree()
self.assertEqual( tree.in_construction, False )
w = tree.word
self.assertTrue( isinstance( w, single.word ) )
self.assertEqual( w.length_descriptor()(), 0 )
r = tree.root
self.assertTrue( isinstance( r, single.const_edge ) )
self.assertEqual( r.keys(), [] )
def test_ukkonen1(self):
tree = single.tree()
self.assertEqual( tree.in_construction, False )
builder = single.ukkonen( tree )
self.assertTrue( tree.in_construction )
self.assertTrue( builder.is_attached )
self.assertTrue( builder.is_valid )
self.assertRaises( RuntimeError, single.ukkonen, tree )
builder.detach()
self.assertFalse( builder.is_attached )
self.assertTrue( builder.is_valid )
self.assertFalse( tree.in_construction )
self.assertRaises( RuntimeError, builder.append, "a" )
def test_ukkonen2(self):
tree = single.tree()
builder = single.ukkonen( tree )
builder.append( glyph = "a" )
self.assertTrue( builder.is_valid )
builder.append( glyph = "n" )
self.assertTrue( builder.is_valid )
builder.detach()
self.assertFalse( builder.is_attached )
self.assertFalse( tree.in_construction )
builder = single.ukkonen( tree )
self.assertTrue( builder.is_valid )
self.assertTrue( builder.is_attached )
self.assertTrue( tree.in_construction )
builder.append( glyph = "a" )
self.assertFalse( builder.is_valid )
self.assertRaises( builder.detach )
builder.append( glyph = "n" )
self.assertFalse( builder.is_valid )
self.assertRaises( builder.detach )
builder.append( glyph = "a" )
self.assertFalse( builder.is_valid )
self.assertRaises( builder.detach )
builder.append( glyph = "s" )
self.assertTrue( builder.is_valid )
builder.detach()
self.assertFalse( builder.is_attached )
self.assertFalse( tree.in_construction )
builder = single.ukkonen( tree )
self.assertTrue( builder.is_valid )
self.assertTrue( builder.is_attached )
self.assertTrue( tree.in_construction )
builder.append( glyph = "$" )
self.assertTrue( builder.is_valid )
builder.detach()
root = tree.root
self.assertTrue( root.is_root() )
self.assertEqual( set( root.keys() ), set( [ "a", "n", "s", "$" ] ) )
b_a = root[ "a" ]
self.assertFalse( b_a.is_root() )
self.assertFalse( b_a.is_leaf() )
self.assertEqual( b_a.start, 0 )
self.assertEqual( b_a.stop, 1 )
self.assertEqual( set( b_a.keys() ), set( [ "n", "s" ] ) )
self.assertEqual( b_a.parent, root )
b_a_n = b_a[ "n" ]
self.assertFalse( b_a_n.is_root() )
self.assertFalse( b_a_n.is_leaf() )
self.assertEqual( b_a_n.start, 1 )
self.assertEqual( b_a_n.stop, 3 )
self.assertEqual( set( b_a_n.keys() ), set( [ "n", "s" ] ) )
self.assertEqual( b_a_n.parent, b_a )
b_a_n_n = b_a_n[ "n" ]
self.assertTrue( b_a_n_n.is_leaf() )
self.assertEqual( b_a_n_n.start, 3 )
self.assertEqual( b_a_n_n.stop, 7 )
self.assertEqual( b_a_n_n.label, 0 )
self.assertEqual( b_a_n_n.parent, b_a_n )
b_a_n_s = b_a_n[ "s" ]
self.assertTrue( b_a_n_s.is_leaf() )
self.assertEqual( b_a_n_s.start, 5 )
self.assertEqual( b_a_n_s.stop, 7 )
self.assertEqual( b_a_n_s.label, 2 )
self.assertEqual( b_a_n_s.parent, b_a_n )
b_a_s = b_a[ "s" ]
self.assertTrue( b_a_s.is_leaf() )
self.assertEqual( b_a_s.start, 5 )
self.assertEqual( b_a_s.stop, 7 )
self.assertEqual( b_a_s.label, 4 )
self.assertEqual( b_a_s.parent, b_a )
b_n = root[ "n" ]
self.assertFalse( b_n.is_root() )
self.assertFalse( b_n.is_leaf() )
self.assertEqual( b_n.start, 1 )
self.assertEqual( b_n.stop, 3 )
self.assertEqual( set( b_n.keys() ), set( [ "n", "s" ] ) )
self.assertEqual( b_n.parent, root )
b_n_n = b_n[ "n" ]
self.assertTrue( b_n_n.is_leaf() )
self.assertEqual( b_n_n.start, 3 )
self.assertEqual( b_n_n.stop, 7 )
self.assertEqual( b_n_n.label, 1 )
self.assertEqual( b_n_n.parent, b_n )
b_n_s = b_n[ "s" ]
self.assertTrue( b_n_s.is_leaf() )
self.assertEqual( b_n_s.start, 5 )
self.assertEqual( b_n_s.stop, 7 )
self.assertEqual( b_n_s.label, 3 )
self.assertEqual( b_n_s.parent, b_n )
b_s = root[ "s" ]
self.assertTrue( b_s.is_leaf() )
self.assertEqual( b_s.start, 5 )
self.assertEqual( b_s.stop, 7 )
self.assertEqual( b_s.label, 5 )
self.assertEqual( b_s.parent, root )
b_dl = root[ "$" ]
self.assertTrue( b_dl.is_leaf() )
self.assertEqual( b_dl.start, 6 )
self.assertEqual( b_dl.stop, 7 )
self.assertEqual( b_dl.label, 6 )
self.assertEqual( b_dl.parent, root )
self.assertEqual( b_a.suffix, root )
self.assertEqual( b_n.suffix, b_a )
self.assertEqual( b_a_n.suffix, b_n )
class TestMatchingStatistics(unittest.TestCase):
def test(self):
tree = single.tree()
result = list( single.matching_statistics( tree, list( "ABC" ) ) )
self.assertEqual( result, [ ( 0, ( tree.root, 0 ) ) ] * 3 )
builder = single.ukkonen( tree = tree )
for c in "TTAGC$":
builder.append( c )
self.assertRaises( RuntimeError, single.matching_statistics, tree, list() );
builder.detach()
root = tree.root
branch_t = root[ "T" ]
leaf_0 = branch_t[ "T" ]
leaf_1 = branch_t[ "A" ]
leaf_2 = root[ "A" ]
leaf_3 = root[ "G" ]
leaf_4 = root[ "C" ]
result = list( single.matching_statistics( tree, [] ) )
self.assertEqual( result, [] )
result = list( single.matching_statistics( tree, list( "QTTATTATTTAGCQWTTAGFK" ) ) )
self.assertEqual(
result,
[
( 0, ( root, 0 ) ),
( 3, ( leaf_0, 3 ) ),
( 2, ( leaf_1, 3 ) ),
( 1, ( leaf_2, 3 ) ),
( 3, ( leaf_0, 3 ) ),
( 2, ( leaf_1, 3 ) ),
( 1, ( leaf_2, 3 ) ),
( 2, ( leaf_0, 2 ) ),
( 5, ( leaf_0, 5 ) ),
( 4, ( leaf_1, 5 ) ),
( 3, ( leaf_2, 5 ) ),
( 2, ( leaf_3, 5 ) ),
( 1, ( leaf_4, 5 ) ),
( 0, ( root, 0 ) ),
( 0, ( root, 0 ) ),
( 4, ( leaf_0, 4 ) ),
( 3, ( leaf_1, 4 ) ),
( 2, ( leaf_2, 4 ) ),
( 1, ( leaf_3, 4 ) ),
( 0, ( root, 0 ) ),
( 0, ( root, 0 ) ),
]
)
class TestLeafIndices(unittest.TestCase):
def test(self):
tree = single.tree()
result = list( single.matching_statistics( tree, list( "ABC" ) ) )
self.assertEqual( result, [ ( 0, ( tree.root, 0 ) ) ] * 3 )
builder = single.ukkonen( tree = tree )
for c in "TTAGC$":
builder.append( c )
builder.detach()
leaf_indices_below = suffixtree.calculate_leaf_indices( root = tree.root )
root = tree.root
branch_t = root[ "T" ]
leaf_0 = branch_t[ "T" ]
leaf_1 = branch_t[ "A" ]
leaf_2 = root[ "A" ]
leaf_3 = root[ "G" ]
leaf_4 = root[ "C" ]
leaf_5 = root[ "$" ]
self.assertEqual( leaf_indices_below[ leaf_0 ], [ 0 ] )
self.assertEqual( leaf_indices_below[ leaf_1 ], [ 1 ] )
self.assertEqual( leaf_indices_below[ leaf_2 ], [ 2 ] )
self.assertEqual( leaf_indices_below[ leaf_3 ], [ 3 ] )
self.assertEqual( leaf_indices_below[ leaf_4 ], [ 4 ] )
self.assertEqual( leaf_indices_below[ leaf_5 ], [ 5 ] )
self.assertEqual( sorted( leaf_indices_below[ branch_t ] ), [ 0, 1 ] )
self.assertEqual( sorted( leaf_indices_below[ root ] ), range( 6 ) )
suite_word = unittest.TestLoader().loadTestsFromTestCase(
TestWord
)
suite_edge = unittest.TestLoader().loadTestsFromTestCase(
TestEdge
)
suite_preorder = unittest.TestLoader().loadTestsFromTestCase(
TestPreOrder
)
suite_postorder = unittest.TestLoader().loadTestsFromTestCase(
TestPostOrder
)
suite_tree = unittest.TestLoader().loadTestsFromTestCase(
TestTree
)
suite_msi = unittest.TestLoader().loadTestsFromTestCase(
TestMatchingStatistics
)
suite_leaf_indices = unittest.TestLoader().loadTestsFromTestCase(
TestLeafIndices
)
alltests = unittest.TestSuite(
[
suite_word,
suite_edge,
suite_preorder,
suite_postorder,
suite_tree,
suite_msi,
suite_leaf_indices,
]
)
def load_tests(loader, tests, pattern):
return alltests
if __name__ == "__main__":
unittest.TextTestRunner( verbosity = 2 ).run( alltests )
| none | 1 | 2.883435 | 3 |
|
tests/examples/test_integrations.py | fstroth/lightning-flash | 1 | 6630477 | # Copyright The PyTorch Lightning team.
#
# 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 os
from pathlib import Path
from unittest import mock
import pytest
from flash.core.utilities.imports import _FIFTYONE_AVAILABLE
from tests.examples.utils import run_test
root = Path(__file__).parent.parent.parent
@mock.patch.dict(os.environ, {"FLASH_TESTING": "1"})
@pytest.mark.parametrize(
"folder, file", [
pytest.param(
"fiftyone",
"image_classification.py",
marks=pytest.mark.skipif(not _FIFTYONE_AVAILABLE, reason="fiftyone library isn't installed")
),
]
)
def test_integrations(tmpdir, folder, file):
run_test(str(root / "flash_examples" / "integrations" / folder / file))
| # Copyright The PyTorch Lightning team.
#
# 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 os
from pathlib import Path
from unittest import mock
import pytest
from flash.core.utilities.imports import _FIFTYONE_AVAILABLE
from tests.examples.utils import run_test
root = Path(__file__).parent.parent.parent
@mock.patch.dict(os.environ, {"FLASH_TESTING": "1"})
@pytest.mark.parametrize(
"folder, file", [
pytest.param(
"fiftyone",
"image_classification.py",
marks=pytest.mark.skipif(not _FIFTYONE_AVAILABLE, reason="fiftyone library isn't installed")
),
]
)
def test_integrations(tmpdir, folder, file):
run_test(str(root / "flash_examples" / "integrations" / folder / file))
| en | 0.856613 | # Copyright The PyTorch Lightning team. # # 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. | 1.945434 | 2 |
fedml_api/standalone/hierarchical_fl/trainer.py | xuwanwei/FedML | 1,120 | 6630478 | import logging
import numpy as np
from fedml_api.standalone.hierarchical_fl.group import Group
from fedml_api.standalone.hierarchical_fl.client import Client
from fedml_api.standalone.fedavg.fedavg_trainer import FedAvgTrainer
class Trainer(FedAvgTrainer):
def setup_clients(self, train_data_local_num_dict, train_data_local_dict, test_data_local_dict):
logging.info("############setup_clients (START)#############")
if self.args.group_method == 'random':
self.group_indexes = np.random.randint(0, self.args.group_num, self.args.client_num_in_total)
group_to_client_indexes = {}
for client_idx, group_idx in enumerate(self.group_indexes):
if not group_idx in group_to_client_indexes:
group_to_client_indexes[group_idx] = []
group_to_client_indexes[group_idx].append(client_idx)
else:
raise Exception(self.args.group_method)
self.group_dict = {}
for group_idx, client_indexes in group_to_client_indexes.items():
self.group_dict[group_idx] = Group(group_idx, client_indexes, train_data_local_dict, test_data_local_dict,
train_data_local_num_dict, self.args, self.device, self.model)
# maintain a dummy client to be used in FedAvgTrainer::local_test_on_all_clients()
self.client_list = [Client(client_idx, train_data_local_dict[0], test_data_local_dict[0],
train_data_local_num_dict[0], self.args, self.device, self.model)]
logging.info("############setup_clients (END)#############")
def client_sampling(self, global_round_idx, client_num_in_total, client_num_per_round):
sampled_client_indexes = super().client_sampling(global_round_idx, client_num_in_total, client_num_per_round)
group_to_client_indexes = {}
for client_idx in sampled_client_indexes:
group_idx = self.group_indexes[client_idx]
if not group_idx in group_to_client_indexes:
group_to_client_indexes[group_idx] = []
group_to_client_indexes[group_idx].append(client_idx)
logging.info("client_indexes of each group = {}".format(group_to_client_indexes))
return group_to_client_indexes
def train(self):
w_global = self.model.state_dict()
for global_round_idx in range(self.args.global_comm_round):
logging.info("################Global Communication Round : {}".format(global_round_idx))
group_to_client_indexes = self.client_sampling(global_round_idx, self.args.client_num_in_total,
self.args.client_num_per_round)
# train each group
w_groups_dict = {}
for group_idx in sorted(group_to_client_indexes.keys()):
sampled_client_indexes = group_to_client_indexes[group_idx]
group = self.group_dict[group_idx]
w_group_list = group.train(global_round_idx, w_global, sampled_client_indexes)
for global_epoch, w in w_group_list:
if not global_epoch in w_groups_dict: w_groups_dict[global_epoch] = []
w_groups_dict[global_epoch].append((group.get_sample_number(sampled_client_indexes), w))
# aggregate group weights into the global weight
for global_epoch in sorted(w_groups_dict.keys()):
w_groups = w_groups_dict[global_epoch]
w_global = self.aggregate(w_groups)
# evaluate performance
if global_epoch % self.args.frequency_of_the_test == 0 or \
global_epoch == self.args.global_comm_round*self.args.group_comm_round*self.args.epochs-1:
self.model.load_state_dict(w_global)
self.local_test_on_all_clients(self.model, global_epoch)
| import logging
import numpy as np
from fedml_api.standalone.hierarchical_fl.group import Group
from fedml_api.standalone.hierarchical_fl.client import Client
from fedml_api.standalone.fedavg.fedavg_trainer import FedAvgTrainer
class Trainer(FedAvgTrainer):
def setup_clients(self, train_data_local_num_dict, train_data_local_dict, test_data_local_dict):
logging.info("############setup_clients (START)#############")
if self.args.group_method == 'random':
self.group_indexes = np.random.randint(0, self.args.group_num, self.args.client_num_in_total)
group_to_client_indexes = {}
for client_idx, group_idx in enumerate(self.group_indexes):
if not group_idx in group_to_client_indexes:
group_to_client_indexes[group_idx] = []
group_to_client_indexes[group_idx].append(client_idx)
else:
raise Exception(self.args.group_method)
self.group_dict = {}
for group_idx, client_indexes in group_to_client_indexes.items():
self.group_dict[group_idx] = Group(group_idx, client_indexes, train_data_local_dict, test_data_local_dict,
train_data_local_num_dict, self.args, self.device, self.model)
# maintain a dummy client to be used in FedAvgTrainer::local_test_on_all_clients()
self.client_list = [Client(client_idx, train_data_local_dict[0], test_data_local_dict[0],
train_data_local_num_dict[0], self.args, self.device, self.model)]
logging.info("############setup_clients (END)#############")
def client_sampling(self, global_round_idx, client_num_in_total, client_num_per_round):
sampled_client_indexes = super().client_sampling(global_round_idx, client_num_in_total, client_num_per_round)
group_to_client_indexes = {}
for client_idx in sampled_client_indexes:
group_idx = self.group_indexes[client_idx]
if not group_idx in group_to_client_indexes:
group_to_client_indexes[group_idx] = []
group_to_client_indexes[group_idx].append(client_idx)
logging.info("client_indexes of each group = {}".format(group_to_client_indexes))
return group_to_client_indexes
def train(self):
w_global = self.model.state_dict()
for global_round_idx in range(self.args.global_comm_round):
logging.info("################Global Communication Round : {}".format(global_round_idx))
group_to_client_indexes = self.client_sampling(global_round_idx, self.args.client_num_in_total,
self.args.client_num_per_round)
# train each group
w_groups_dict = {}
for group_idx in sorted(group_to_client_indexes.keys()):
sampled_client_indexes = group_to_client_indexes[group_idx]
group = self.group_dict[group_idx]
w_group_list = group.train(global_round_idx, w_global, sampled_client_indexes)
for global_epoch, w in w_group_list:
if not global_epoch in w_groups_dict: w_groups_dict[global_epoch] = []
w_groups_dict[global_epoch].append((group.get_sample_number(sampled_client_indexes), w))
# aggregate group weights into the global weight
for global_epoch in sorted(w_groups_dict.keys()):
w_groups = w_groups_dict[global_epoch]
w_global = self.aggregate(w_groups)
# evaluate performance
if global_epoch % self.args.frequency_of_the_test == 0 or \
global_epoch == self.args.global_comm_round*self.args.group_comm_round*self.args.epochs-1:
self.model.load_state_dict(w_global)
self.local_test_on_all_clients(self.model, global_epoch)
| en | 0.431468 | ###########setup_clients (START)#############") # maintain a dummy client to be used in FedAvgTrainer::local_test_on_all_clients() ###########setup_clients (END)#############") ###############Global Communication Round : {}".format(global_round_idx)) # train each group # aggregate group weights into the global weight # evaluate performance | 2.156697 | 2 |
unreleased/azure-mgmt-machinelearning/azure/mgmt/machinelearning/models/column_specification.py | CharaD7/azure-sdk-for-python | 0 | 6630479 | <filename>unreleased/azure-mgmt-machinelearning/azure/mgmt/machinelearning/models/column_specification.py
# coding=utf-8
# --------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
#
# Code generated by Microsoft (R) AutoRest Code Generator.
# Changes may cause incorrect behavior and will be lost if the code is
# regenerated.
# --------------------------------------------------------------------------
from msrest.serialization import Model
class ColumnSpecification(Model):
"""Swagger 2.0 schema for a column within the data table representing a web
service input or output. See Swagger specification:
http://swagger.io/specification/.
:param type: Data type of the column. Possible values include: 'Boolean',
'Integer', 'Number', 'String'
:type type: str or :class:`ColumnType
<azure.mgmt.machinelearning.models.ColumnType>`
:param format: Additional format information for the data type. Possible
values include: 'Byte', 'Char', 'Datetime', 'Double', 'Duration',
'Float', 'Int8', 'Int16', 'Int32', 'Int64', 'Uint8', 'Uint16', 'Uint32',
'Uint64'
:type format: str or :class:`ColumnFormat
<azure.mgmt.machinelearning.models.ColumnFormat>`
:param enum: If the data type is categorical, this provides the list of
accepted categories.
:type enum: list of object
:param x_ms_isnullable: Flag indicating if the type supports null values
or not.
:type x_ms_isnullable: bool
:param x_ms_isordered: Flag indicating whether the categories are treated
as an ordered set or not, if this is a categorical column.
:type x_ms_isordered: bool
"""
_validation = {
'type': {'required': True},
}
_attribute_map = {
'type': {'key': 'type', 'type': 'str'},
'format': {'key': 'format', 'type': 'str'},
'enum': {'key': 'enum', 'type': '[object]'},
'x_ms_isnullable': {'key': 'x-ms-isnullable', 'type': 'bool'},
'x_ms_isordered': {'key': 'x-ms-isordered', 'type': 'bool'},
}
def __init__(self, type, format=None, enum=None, x_ms_isnullable=None, x_ms_isordered=None):
self.type = type
self.format = format
self.enum = enum
self.x_ms_isnullable = x_ms_isnullable
self.x_ms_isordered = x_ms_isordered
| <filename>unreleased/azure-mgmt-machinelearning/azure/mgmt/machinelearning/models/column_specification.py
# coding=utf-8
# --------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
#
# Code generated by Microsoft (R) AutoRest Code Generator.
# Changes may cause incorrect behavior and will be lost if the code is
# regenerated.
# --------------------------------------------------------------------------
from msrest.serialization import Model
class ColumnSpecification(Model):
"""Swagger 2.0 schema for a column within the data table representing a web
service input or output. See Swagger specification:
http://swagger.io/specification/.
:param type: Data type of the column. Possible values include: 'Boolean',
'Integer', 'Number', 'String'
:type type: str or :class:`ColumnType
<azure.mgmt.machinelearning.models.ColumnType>`
:param format: Additional format information for the data type. Possible
values include: 'Byte', 'Char', 'Datetime', 'Double', 'Duration',
'Float', 'Int8', 'Int16', 'Int32', 'Int64', 'Uint8', 'Uint16', 'Uint32',
'Uint64'
:type format: str or :class:`ColumnFormat
<azure.mgmt.machinelearning.models.ColumnFormat>`
:param enum: If the data type is categorical, this provides the list of
accepted categories.
:type enum: list of object
:param x_ms_isnullable: Flag indicating if the type supports null values
or not.
:type x_ms_isnullable: bool
:param x_ms_isordered: Flag indicating whether the categories are treated
as an ordered set or not, if this is a categorical column.
:type x_ms_isordered: bool
"""
_validation = {
'type': {'required': True},
}
_attribute_map = {
'type': {'key': 'type', 'type': 'str'},
'format': {'key': 'format', 'type': 'str'},
'enum': {'key': 'enum', 'type': '[object]'},
'x_ms_isnullable': {'key': 'x-ms-isnullable', 'type': 'bool'},
'x_ms_isordered': {'key': 'x-ms-isordered', 'type': 'bool'},
}
def __init__(self, type, format=None, enum=None, x_ms_isnullable=None, x_ms_isordered=None):
self.type = type
self.format = format
self.enum = enum
self.x_ms_isnullable = x_ms_isnullable
self.x_ms_isordered = x_ms_isordered
| en | 0.498983 | # coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. # # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is # regenerated. # -------------------------------------------------------------------------- Swagger 2.0 schema for a column within the data table representing a web service input or output. See Swagger specification: http://swagger.io/specification/. :param type: Data type of the column. Possible values include: 'Boolean', 'Integer', 'Number', 'String' :type type: str or :class:`ColumnType <azure.mgmt.machinelearning.models.ColumnType>` :param format: Additional format information for the data type. Possible values include: 'Byte', 'Char', 'Datetime', 'Double', 'Duration', 'Float', 'Int8', 'Int16', 'Int32', 'Int64', 'Uint8', 'Uint16', 'Uint32', 'Uint64' :type format: str or :class:`ColumnFormat <azure.mgmt.machinelearning.models.ColumnFormat>` :param enum: If the data type is categorical, this provides the list of accepted categories. :type enum: list of object :param x_ms_isnullable: Flag indicating if the type supports null values or not. :type x_ms_isnullable: bool :param x_ms_isordered: Flag indicating whether the categories are treated as an ordered set or not, if this is a categorical column. :type x_ms_isordered: bool | 2.023124 | 2 |
HER2/Practice Testing/Dropout Experiment/3_get_model_D7.py | raktim-mondol/DeepLearningCamelyon | 70 | 6630480 | def get_model():
model = Sequential()
model.add(Lambda(lambda x: x * 1./255., input_shape=(120, 160, 3), output_shape=(120, 160, 3)))
model.add(Conv2D(32, (3, 3), input_shape=(120, 160, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten()) # this converts our 3D feature maps to 1D feature vectors
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.875))
model.add(Dense(1))
# model.add(Dense(2))
model.add(Activation('sigmoid'))
# model.add(Activation('softmax'))
model.compile(loss='binary_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
# model.compile(loss='categorical_crossentropy',
# optimizer='rmsprop',
# metrics=['accuracy'])
return model
model = get_model()
print(model.summary())
| def get_model():
model = Sequential()
model.add(Lambda(lambda x: x * 1./255., input_shape=(120, 160, 3), output_shape=(120, 160, 3)))
model.add(Conv2D(32, (3, 3), input_shape=(120, 160, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten()) # this converts our 3D feature maps to 1D feature vectors
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.875))
model.add(Dense(1))
# model.add(Dense(2))
model.add(Activation('sigmoid'))
# model.add(Activation('softmax'))
model.compile(loss='binary_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
# model.compile(loss='categorical_crossentropy',
# optimizer='rmsprop',
# metrics=['accuracy'])
return model
model = get_model()
print(model.summary())
| en | 0.260105 | # this converts our 3D feature maps to 1D feature vectors # model.add(Dense(2)) # model.add(Activation('softmax')) # model.compile(loss='categorical_crossentropy', # optimizer='rmsprop', # metrics=['accuracy']) | 3.240985 | 3 |
items.py | legend-plus/LegendBot | 4 | 6630481 | <filename>items.py<gh_stars>1-10
class Item:
def __init__(self, base: str = "item", sprite: str = None, item_type: str = None, name: str = None,
description: str = None):
self.base = base
if sprite is not None:
self.sprite: str = sprite
if item_type is not None:
self.item_type: str = item_type
if name is not None:
self.name: str = name
if description is not None:
self.description: str = description
def get_sprite(self, base_items):
if hasattr(self, "sprite"):
return self.sprite
else:
return base_items[self.base]["sprite"]
def get_item_type(self, base_items):
if hasattr(self, "item_type"):
return self.item_type
else:
return base_items[self.base]["item_type"]
def get_name(self, base_items):
if hasattr(self, "name"):
return self.name
else:
return base_items[self.base]["name"]
def get_description(self, base_items):
if hasattr(self, "description"):
return self.description
else:
return base_items[self.base]["description"]
class Weapon(Item):
def __init__(self, base: str = "item", sprite: str = None, item_type: str = None, name: str = None,
description: str = None, weapon_class: str = None,
damage: float = None, damage_type: str = None):
super().__init__(base, sprite, item_type, name, description)
if weapon_class is not None:
self.weapon_class: str = weapon_class
if damage is not None:
self.damage: float = damage
if damage_type is not None:
self.damage_type: str = damage_type
def get_damage(self, base_items):
if hasattr(self, "damage"):
return self.damage
elif "damage" in base_items[self.base]:
return base_items[self.base]["damage"]
else:
return 0.0
def get_damage_type(self, base_items):
if hasattr(self, "damage_type"):
return self.damage_type
elif "damage_type" in base_items[self.base]:
return base_items[self.base]["damage_type"]
else:
return "void"
def get_weapon_class(self, base_items):
if hasattr(self, "weapon_class"):
return self.weapon_class
elif "weapon_class" in base_items[self.base]:
return base_items[self.base]["weapon_class"]
else:
return "unknown"
def from_dict(item_dict: dict, item_bases: dict) -> Item:
if ("item_type" in item_dict and item_dict["item_type"] == "weapon") or item_bases[item_dict["base"]]["item_type"] == "weapon":
return Weapon(
item_dict.get("base", "item"),
item_dict.get("sprite", None),
"weapon",
item_dict.get("name", None),
item_dict.get("description", None),
item_dict.get("weapon_class", None),
item_dict.get("damage", None),
item_dict.get("damage_type", None)
)
elif "item_type" in item_dict:
return Item(
item_dict.get("base", None),
item_dict.get("sprite", None),
item_dict["item_type"],
item_dict.get("name", None),
item_dict.get("description", None),
)
elif "item_type" in item_bases[item_dict["base"]]:
return Item(
item_dict.get("base", None),
item_dict.get("sprite", None),
item_bases[item_dict["base"]]["item_type"],
item_dict.get("name", None),
item_dict.get("description", None),
)
else:
return Item(
item_dict.get("base", None),
item_dict.get("sprite", None),
"misc",
item_dict.get("name", None),
item_dict.get("description", None),
) | <filename>items.py<gh_stars>1-10
class Item:
def __init__(self, base: str = "item", sprite: str = None, item_type: str = None, name: str = None,
description: str = None):
self.base = base
if sprite is not None:
self.sprite: str = sprite
if item_type is not None:
self.item_type: str = item_type
if name is not None:
self.name: str = name
if description is not None:
self.description: str = description
def get_sprite(self, base_items):
if hasattr(self, "sprite"):
return self.sprite
else:
return base_items[self.base]["sprite"]
def get_item_type(self, base_items):
if hasattr(self, "item_type"):
return self.item_type
else:
return base_items[self.base]["item_type"]
def get_name(self, base_items):
if hasattr(self, "name"):
return self.name
else:
return base_items[self.base]["name"]
def get_description(self, base_items):
if hasattr(self, "description"):
return self.description
else:
return base_items[self.base]["description"]
class Weapon(Item):
def __init__(self, base: str = "item", sprite: str = None, item_type: str = None, name: str = None,
description: str = None, weapon_class: str = None,
damage: float = None, damage_type: str = None):
super().__init__(base, sprite, item_type, name, description)
if weapon_class is not None:
self.weapon_class: str = weapon_class
if damage is not None:
self.damage: float = damage
if damage_type is not None:
self.damage_type: str = damage_type
def get_damage(self, base_items):
if hasattr(self, "damage"):
return self.damage
elif "damage" in base_items[self.base]:
return base_items[self.base]["damage"]
else:
return 0.0
def get_damage_type(self, base_items):
if hasattr(self, "damage_type"):
return self.damage_type
elif "damage_type" in base_items[self.base]:
return base_items[self.base]["damage_type"]
else:
return "void"
def get_weapon_class(self, base_items):
if hasattr(self, "weapon_class"):
return self.weapon_class
elif "weapon_class" in base_items[self.base]:
return base_items[self.base]["weapon_class"]
else:
return "unknown"
def from_dict(item_dict: dict, item_bases: dict) -> Item:
if ("item_type" in item_dict and item_dict["item_type"] == "weapon") or item_bases[item_dict["base"]]["item_type"] == "weapon":
return Weapon(
item_dict.get("base", "item"),
item_dict.get("sprite", None),
"weapon",
item_dict.get("name", None),
item_dict.get("description", None),
item_dict.get("weapon_class", None),
item_dict.get("damage", None),
item_dict.get("damage_type", None)
)
elif "item_type" in item_dict:
return Item(
item_dict.get("base", None),
item_dict.get("sprite", None),
item_dict["item_type"],
item_dict.get("name", None),
item_dict.get("description", None),
)
elif "item_type" in item_bases[item_dict["base"]]:
return Item(
item_dict.get("base", None),
item_dict.get("sprite", None),
item_bases[item_dict["base"]]["item_type"],
item_dict.get("name", None),
item_dict.get("description", None),
)
else:
return Item(
item_dict.get("base", None),
item_dict.get("sprite", None),
"misc",
item_dict.get("name", None),
item_dict.get("description", None),
) | none | 1 | 3.058458 | 3 |
|
Final_project/PostBoard/Board/views.py | GregTMJ/django-files | 1 | 6630482 | from django.contrib.auth.mixins import LoginRequiredMixin
from django.http import HttpResponseRedirect
from django.shortcuts import render, get_object_or_404
from django.urls import reverse
from .models import Post, Author, Category, Comments, User
from django.views.generic import ListView, CreateView, UpdateView, DeleteView, DetailView
from .forms import PostForm, EditForm, CommentForm, CommentEditForm
from .filters import SearchFilter
# Create your views here.
class Post_list(LoginRequiredMixin, ListView):
model = Post
template_name = 'Post/list_of_posts.html'
context_object_name = 'Posts'
paginate_by = 5
queryset = Post.objects.order_by('-id')
class Post_details(LoginRequiredMixin, DetailView):
model = Post
template_name = 'Post/post_details.html'
context_object_name = 'Post'
class Post_create(LoginRequiredMixin, CreateView):
model = Post
template_name = 'Post/post_create.html'
form_class = PostForm
class Post_update(LoginRequiredMixin, UpdateView):
template_name = 'Post/post_update.html'
form_class = EditForm
context_object_name = 'Post'
def get_object(self, **kwargs):
# Here we are getting the id so Django could stalk the change
ID = self.kwargs.get('pk')
return Post.objects.get(pk=ID)
class Post_delete(LoginRequiredMixin, DeleteView):
template_name = 'Post/post_delete.html'
queryset = Post.objects.all()
context_object_name = 'Post'
success_url = '/Posts'
class Add_comment(LoginRequiredMixin, CreateView):
model = Comments
template_name = 'Post/add_comment.html'
form_class = CommentForm
success_url = '/Posts'
def form_valid(self, form):
form.instance.comment_id = self.kwargs['pk']
return super().form_valid(form)
class List_of_Comments(LoginRequiredMixin, ListView):
model = Comments
template_name = 'Post/list_of_comments.html'
context_object_name = 'comments'
def get_context_data(self, *args, **kwargs):
context = super().get_context_data(**kwargs)
available_comments = Comments.objects.all()
user = User.objects.get(id=self.request.user.id)
context['filter'] = SearchFilter(self.request.GET,
queryset=self.get_queryset())
context['available_comments'] = available_comments
context['author_id'] = user
return context
class CommentEdit(LoginRequiredMixin, UpdateView):
template_name = 'Post/edit_comment.html'
form_class = CommentEditForm
context_object_name = 'comments'
def get_object(self, **kwargs):
# Here we are getting the id so Django could stalk the change
ID = self.kwargs.get('pk')
return Comments.objects.get(pk=ID)
class CommentDelete(LoginRequiredMixin, DeleteView):
model = Comments
template_name = 'Post/comment_delete.html'
queryset = Comments.objects.all()
context_object_name = 'comments'
success_url = '/Posts'
def AcceptedView(request, pk):
comment = get_object_or_404(Comments, id=request.POST.get('comment.id'))
if not comment.accepted:
comment.accepted = True
else:
comment.accepted = False
return HttpResponseRedirect(reverse('list_of_comments'))
| from django.contrib.auth.mixins import LoginRequiredMixin
from django.http import HttpResponseRedirect
from django.shortcuts import render, get_object_or_404
from django.urls import reverse
from .models import Post, Author, Category, Comments, User
from django.views.generic import ListView, CreateView, UpdateView, DeleteView, DetailView
from .forms import PostForm, EditForm, CommentForm, CommentEditForm
from .filters import SearchFilter
# Create your views here.
class Post_list(LoginRequiredMixin, ListView):
model = Post
template_name = 'Post/list_of_posts.html'
context_object_name = 'Posts'
paginate_by = 5
queryset = Post.objects.order_by('-id')
class Post_details(LoginRequiredMixin, DetailView):
model = Post
template_name = 'Post/post_details.html'
context_object_name = 'Post'
class Post_create(LoginRequiredMixin, CreateView):
model = Post
template_name = 'Post/post_create.html'
form_class = PostForm
class Post_update(LoginRequiredMixin, UpdateView):
template_name = 'Post/post_update.html'
form_class = EditForm
context_object_name = 'Post'
def get_object(self, **kwargs):
# Here we are getting the id so Django could stalk the change
ID = self.kwargs.get('pk')
return Post.objects.get(pk=ID)
class Post_delete(LoginRequiredMixin, DeleteView):
template_name = 'Post/post_delete.html'
queryset = Post.objects.all()
context_object_name = 'Post'
success_url = '/Posts'
class Add_comment(LoginRequiredMixin, CreateView):
model = Comments
template_name = 'Post/add_comment.html'
form_class = CommentForm
success_url = '/Posts'
def form_valid(self, form):
form.instance.comment_id = self.kwargs['pk']
return super().form_valid(form)
class List_of_Comments(LoginRequiredMixin, ListView):
model = Comments
template_name = 'Post/list_of_comments.html'
context_object_name = 'comments'
def get_context_data(self, *args, **kwargs):
context = super().get_context_data(**kwargs)
available_comments = Comments.objects.all()
user = User.objects.get(id=self.request.user.id)
context['filter'] = SearchFilter(self.request.GET,
queryset=self.get_queryset())
context['available_comments'] = available_comments
context['author_id'] = user
return context
class CommentEdit(LoginRequiredMixin, UpdateView):
template_name = 'Post/edit_comment.html'
form_class = CommentEditForm
context_object_name = 'comments'
def get_object(self, **kwargs):
# Here we are getting the id so Django could stalk the change
ID = self.kwargs.get('pk')
return Comments.objects.get(pk=ID)
class CommentDelete(LoginRequiredMixin, DeleteView):
model = Comments
template_name = 'Post/comment_delete.html'
queryset = Comments.objects.all()
context_object_name = 'comments'
success_url = '/Posts'
def AcceptedView(request, pk):
comment = get_object_or_404(Comments, id=request.POST.get('comment.id'))
if not comment.accepted:
comment.accepted = True
else:
comment.accepted = False
return HttpResponseRedirect(reverse('list_of_comments'))
| en | 0.956133 | # Create your views here. # Here we are getting the id so Django could stalk the change # Here we are getting the id so Django could stalk the change | 2.01855 | 2 |
src/hashable.py | josephnavarro/ascii-art-generator | 0 | 6630483 | <gh_stars>0
#! usr/bin/env python3
"""
:
: Container class for a PIL Image. Allows image data to be hashable.
:
:
"""
from PIL import Image
import uuid
class HashableImage:
"""
:
: Hashable PIL Image container. Enables PIL image data to be used as dictionary keys.
:
:
: Attrs:
: Image image : Image object
: UUID _hash : Uniquely hashable element
:
:
"""
__slots__ = [
"image",
"_hash",
]
def __init__(self, image: Image):
self.image = image # type: Image
self._hash = uuid.uuid4() # type: uuid.UUID
@property
def size(self) -> (int, int):
"""
:
: Gets size (i.e. width and height) of the contained image object.
:
:
"""
return self.image.size
def __hash__(self):
"""
:
: Returns hash(self).
:
:
"""
return hash(self._hash)
def convert(self, mode: str) -> Image:
"""
:
: Implements Image.convert(). Note that this returns a PIL Image, not a HashableImage.
:
:
"""
return self.image.convert(mode)
def crop(self, rect: (int, int, int, int)):
"""
:
: Implements Image.crop(). Note that this returns another HashableImage, not a PIL Image.
:
:
"""
return HashableImage(self.image.crop(rect))
def load(self):
"""
:
: Returns pixel access data for the contained image object.
:
:
"""
return self.image.load()
| #! usr/bin/env python3
"""
:
: Container class for a PIL Image. Allows image data to be hashable.
:
:
"""
from PIL import Image
import uuid
class HashableImage:
"""
:
: Hashable PIL Image container. Enables PIL image data to be used as dictionary keys.
:
:
: Attrs:
: Image image : Image object
: UUID _hash : Uniquely hashable element
:
:
"""
__slots__ = [
"image",
"_hash",
]
def __init__(self, image: Image):
self.image = image # type: Image
self._hash = uuid.uuid4() # type: uuid.UUID
@property
def size(self) -> (int, int):
"""
:
: Gets size (i.e. width and height) of the contained image object.
:
:
"""
return self.image.size
def __hash__(self):
"""
:
: Returns hash(self).
:
:
"""
return hash(self._hash)
def convert(self, mode: str) -> Image:
"""
:
: Implements Image.convert(). Note that this returns a PIL Image, not a HashableImage.
:
:
"""
return self.image.convert(mode)
def crop(self, rect: (int, int, int, int)):
"""
:
: Implements Image.crop(). Note that this returns another HashableImage, not a PIL Image.
:
:
"""
return HashableImage(self.image.crop(rect))
def load(self):
"""
:
: Returns pixel access data for the contained image object.
:
:
"""
return self.image.load() | fr | 0.318407 | #! usr/bin/env python3 : : Container class for a PIL Image. Allows image data to be hashable. : : : : Hashable PIL Image container. Enables PIL image data to be used as dictionary keys. : : : Attrs: : Image image : Image object : UUID _hash : Uniquely hashable element : : # type: Image # type: uuid.UUID : : Gets size (i.e. width and height) of the contained image object. : : : : Returns hash(self). : : : : Implements Image.convert(). Note that this returns a PIL Image, not a HashableImage. : : : : Implements Image.crop(). Note that this returns another HashableImage, not a PIL Image. : : : : Returns pixel access data for the contained image object. : : | 3.518299 | 4 |
HW1/q3.py | wzcwzcwzc/ML | 0 | 6630484 | # -*- coding: utf-8 -*-
"""Q3.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1nuVOMaV1Wr9RXpnbiyiNker8qGPQ86Nl
"""
import os
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# get 2-nd norms
def getNorms(arr):
norm = []
for i in range(len(arr)):
for j in range(i + 1, len(arr)):
temp = np.linalg.norm(arr[i] - arr[j])
norm.append(temp)
return norm
# create and initialize covariance matrix
def buildCovMatrix(d):
cov = [[0] * d for i in range(d)]
for i in range(0, d):
cov[i][i] = 1
return cov
"""d = 3 and sample_size = 10000"""
d = 3
mean = [0] * d
cov = buildCovMatrix(d)
# generate 10000 random samples, 10000 is too much, here use 1000 as an example
sample_size = 1000
mul_normal = np.random.multivariate_normal(mean, cov, sample_size)
# calculate the 2-nd norm of two data
norm = getNorms(mul_normal)
print(f'when d = 3, the norms are {norm}')
# calculate average and standard deviation of norm
norm = np.array(norm)
average = np.mean(norm)
standard = np.std(norm)
print(f'when d = 3, the average of norms is {average}')
print(f'when d = 3, the standard deviation of norms is {standard}')
# plot the histogram of Euclidean norms
plt.hist(norm, bins=100, density=0, facecolor="blue")
"""d = 3, 50, 100, 200, 500, 1000"""
d = [3, 50, 100, 200, 500, 1000]
# d = [3, 50]
average_ans = []
standard_ans = []
for x in range(0, len(d)):
mean = [0] * d[x]
cov = buildCovMatrix(d[x])
# get norms of each d
sample_size = 1000
arr = np.random.multivariate_normal(mean, cov, sample_size)
norm = np.array(getNorms(arr))
print(norm)
# calculate mean and standard deviation
average_ans.append(np.mean(norm))
standard_ans.append(np.std(norm))
print(average_ans)
print(standard_ans)
ax1 = plt.subplot(2,1,1)
ax2 = plt.subplot(2,1,2)
# plot the average and standard deviation
plt.sca(ax1)
plt.plot(d, average_ans)
plt.xlabel('d', fontsize=10)
plt.ylabel('average', fontsize=10)
plt.sca(ax2)
plt.plot(d, standard_ans)
plt.xlabel('d', fontsize=10)
plt.ylabel('standard deviation', fontsize=10)
"""what I find is that the average is going up but the standard deviation tends to be convergence""" | # -*- coding: utf-8 -*-
"""Q3.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1nuVOMaV1Wr9RXpnbiyiNker8qGPQ86Nl
"""
import os
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# get 2-nd norms
def getNorms(arr):
norm = []
for i in range(len(arr)):
for j in range(i + 1, len(arr)):
temp = np.linalg.norm(arr[i] - arr[j])
norm.append(temp)
return norm
# create and initialize covariance matrix
def buildCovMatrix(d):
cov = [[0] * d for i in range(d)]
for i in range(0, d):
cov[i][i] = 1
return cov
"""d = 3 and sample_size = 10000"""
d = 3
mean = [0] * d
cov = buildCovMatrix(d)
# generate 10000 random samples, 10000 is too much, here use 1000 as an example
sample_size = 1000
mul_normal = np.random.multivariate_normal(mean, cov, sample_size)
# calculate the 2-nd norm of two data
norm = getNorms(mul_normal)
print(f'when d = 3, the norms are {norm}')
# calculate average and standard deviation of norm
norm = np.array(norm)
average = np.mean(norm)
standard = np.std(norm)
print(f'when d = 3, the average of norms is {average}')
print(f'when d = 3, the standard deviation of norms is {standard}')
# plot the histogram of Euclidean norms
plt.hist(norm, bins=100, density=0, facecolor="blue")
"""d = 3, 50, 100, 200, 500, 1000"""
d = [3, 50, 100, 200, 500, 1000]
# d = [3, 50]
average_ans = []
standard_ans = []
for x in range(0, len(d)):
mean = [0] * d[x]
cov = buildCovMatrix(d[x])
# get norms of each d
sample_size = 1000
arr = np.random.multivariate_normal(mean, cov, sample_size)
norm = np.array(getNorms(arr))
print(norm)
# calculate mean and standard deviation
average_ans.append(np.mean(norm))
standard_ans.append(np.std(norm))
print(average_ans)
print(standard_ans)
ax1 = plt.subplot(2,1,1)
ax2 = plt.subplot(2,1,2)
# plot the average and standard deviation
plt.sca(ax1)
plt.plot(d, average_ans)
plt.xlabel('d', fontsize=10)
plt.ylabel('average', fontsize=10)
plt.sca(ax2)
plt.plot(d, standard_ans)
plt.xlabel('d', fontsize=10)
plt.ylabel('standard deviation', fontsize=10)
"""what I find is that the average is going up but the standard deviation tends to be convergence""" | en | 0.865371 | # -*- coding: utf-8 -*- Q3.ipynb Automatically generated by Colaboratory. Original file is located at https://colab.research.google.com/drive/1nuVOMaV1Wr9RXpnbiyiNker8qGPQ86Nl # get 2-nd norms # create and initialize covariance matrix d = 3 and sample_size = 10000 # generate 10000 random samples, 10000 is too much, here use 1000 as an example # calculate the 2-nd norm of two data # calculate average and standard deviation of norm # plot the histogram of Euclidean norms d = 3, 50, 100, 200, 500, 1000 # d = [3, 50] # get norms of each d # calculate mean and standard deviation # plot the average and standard deviation what I find is that the average is going up but the standard deviation tends to be convergence | 3.88354 | 4 |
stanCode_projects/my_drawing/my_drawing.py | shihjames/sc-projects | 0 | 6630485 | <gh_stars>0
"""
File: my_drawing.py
Name: <NAME>
----------------------
In this program, Karel the robot represents the students of stanCode,
and the beepers are what we have learned, what we are learning, and
what we will learn. When users click the mouse, Karel will move, this
means we are one step forward in becoming a professional software
engineer, data scientist, etc. Finally, Karel will walk through
the path, meaning that we have graduated from all the courses, and
we will feel very grateful and lucky to have the opportunity to receive
such a good programming education.
"""
from campy.graphics.gobjects import GRect, GLine, GOval, GLabel, GPolygon
from campy.graphics.gwindow import GWindow
from campy.gui.events.mouse import onmouseclicked
window = GWindow(500, 500, title='')
head = GOval(60, 40)
eye_l = GRect(10, 10)
eye_r = GRect(10, 10)
body = GRect(60, 50)
neck = GRect(30, 4)
arm_l = GRect(10, 30)
arm_r = GRect(10, 30)
leg_l = GOval(20, 15)
leg_r = GOval(20, 15)
word = GLabel('K')
count = 0
def main():
"""
The program will first create a canvas, a background, and lines.
The first click will show four beepers at the lower part of the
window. The second click will show a robot at the upper part of
the window. For the next few clicks will move the robot from the
upper part to the lower part of the window. The final click will
show a speech bubble.
"""
background()
draw_line()
build_s()
build_c()
onmouseclicked(create_beeper)
def background():
# Create a red background.
global window
back = GRect(window.width, window.height)
window.add(back)
def draw_line():
# Draw several vertical and horizontal lines.
for i in range(1, 10):
line_hor = GLine(0, i*50, window.width, i*50)
line_ver = GLine(i*50, 0, i*50, window.height)
window.add(line_hor)
window.add(line_ver)
def build_s():
# Create word: S.
global window
rect_l = 100
rect_s = 20
rect1 = GRect(rect_l, rect_s, x=100, y=150)
rect2 = GRect(rect_s, rect_l, x=100, y=150)
rect3 = GRect(rect_l, rect_s, x=100, y=230)
rect4 = GRect(rect_s, rect_l, x=180, y=230)
rect5 = GRect(rect_l, rect_s, x=100, y=310)
rect1.filled = True
rect1.fill_color = 'tomato'
rect1.color = 'tomato'
rect2.filled = True
rect2.fill_color = 'tomato'
rect2.color = 'tomato'
rect3.filled = True
rect3.fill_color = 'tomato'
rect3.color = 'tomato'
rect4.filled = True
rect4.fill_color = 'tomato'
rect4.color = 'tomato'
rect5.filled = True
rect5.fill_color = 'tomato'
rect5.color = 'tomato'
window.add(rect1)
window.add(rect2)
window.add(rect3)
window.add(rect4)
window.add(rect5)
def build_c():
# Create word: C.
global window
rect_l = 100
rect_s = 20
rect1 = GRect(rect_l, rect_s, x=300, y=150)
rect2 = GRect(rect_s, rect_l*2-rect_s, x=300, y=150)
rect3 = GRect(rect_l, rect_s, x=300, y=310)
rect1.filled = True
rect1.fill_color = 'tomato'
rect1.color = 'tomato'
rect2.filled = True
rect2.fill_color = 'tomato'
rect2.color = 'tomato'
rect3.filled = True
rect3.fill_color = 'tomato'
rect3.color = 'tomato'
window.add(rect1)
window.add(rect2)
window.add(rect3)
def build_karel(e):
# Build up the robot, Karel.
karel_head()
karel_eye()
karel_neck()
karel_body()
karel_limb()
karel_label()
onmouseclicked(move)
def create_beeper(e):
# create 4 beepers
size = 50
for i in (1, 3, 7, 9):
beeper = GOval(size, size, x=i*50-size/2, y=400-size/2)
beeper.filled = True
beeper.fill_color = 'blue'
window.add(beeper)
label1 = GLabel('001', x=50-size/2+9, y=400-size/2+37)
label2 = GLabel('101', x=150-size/2+9, y=400-size/2+37)
label3 = GLabel('201', x=350-size/2+9, y=400-size/2+37)
label4 = GLabel('202', x=450-size/2+9, y=400-size/2+37)
label1.font = '-15'
label2.font = '-15'
label3.font = '-15'
label4.font = '-15'
label1.color = 'white'
label2.color = 'white'
label3.color = 'white'
label4.color = 'white'
window.add(label1)
window.add(label2)
window.add(label3)
window.add(label4)
onmouseclicked(build_karel)
def karel_head():
# Build Karel's head.
global head
head = GOval(60, 40, x=220, y=20)
head.filled = True
head.fill_color = 'gray'
window.add(head)
return head
def karel_eye():
# Build Karel's eyes.
global eye_l, eye_r
eye_l = GRect(10, 10, x=235, y=35)
eye_r = GRect(10, 10, x=255, y=35)
eye_l.filled = True
eye_r.filled = True
eye_l.fill_color = 'blue'
eye_r.fill_color = 'blue'
window.add(eye_l)
window.add(eye_r)
def karel_neck():
# Build Karel's neck.
global neck
neck = GRect(30, 4, x=235, y=58)
neck.filled = True
neck.color = 'blue'
window.add(neck)
def karel_body():
# Build Karel's body.
global body
body = GRect(60, 50, x=220, y=62)
body.filled = True
body.fill_color = 'blue'
window.add(body)
def karel_limb():
# Build Karel's limbs.
global arm_l, arm_r, leg_l, leg_r
arm_l = GRect(10, 30, x=210, y=67)
arm_r = GRect(10, 30, x=280, y=67)
leg_l = GOval(20, 15, x=220, y=112)
leg_r = GOval(20, 15, x=260, y=112)
arm_l.filled = True
arm_r.filled = True
leg_l.filled = True
leg_r.filled = True
arm_l.fill_color = 'green'
arm_r.fill_color = 'green'
leg_l.fill_color = 'red'
leg_r.fill_color = 'red'
window.add(arm_l)
window.add(arm_r)
window.add(leg_l)
window.add(leg_r)
def karel_label():
# Show the word on Karel's body.
global word
word = GLabel('K', x=235, y=115)
word.font = '-35'
window.add(word)
def move(e):
# Move Karel down 100 units.
global count
if count < 6:
head.move(0, 50)
eye_l.move(0, 50)
eye_r.move(0, 50)
body.move(0, 50)
neck.move(0, 50)
arm_l.move(0, 50)
arm_r.move(0, 50)
leg_l.move(0, 50)
leg_r.move(0, 50)
word.move(0, 50)
count += 1
if count == 6:
speech_bub()
def speech_bub():
# Show what Karel says.
bub1 = GOval(10, 10, x=300, y=340)
bub2 = GOval(15, 15, x=320, y=330)
bub3 = GOval(150, 40, x=345, y=303)
bub1.filled = True
bub2.filled = True
bub3.filled = True
bub1.fill_color = 'white'
bub2.fill_color = 'white'
bub3.fill_color = 'white'
word1 = GLabel('I love stanCode!', x=365, y=325)
word2 = GLabel('I love Jerry !', x=375, y=338)
word1.font = 'Courier-8-bold'
word2.font = 'Courier-8-bold'
window.add(bub1)
window.add(bub2)
window.add(bub3)
window.add(word1)
window.add(word2)
if __name__ == '__main__':
main()
| """
File: my_drawing.py
Name: <NAME>
----------------------
In this program, Karel the robot represents the students of stanCode,
and the beepers are what we have learned, what we are learning, and
what we will learn. When users click the mouse, Karel will move, this
means we are one step forward in becoming a professional software
engineer, data scientist, etc. Finally, Karel will walk through
the path, meaning that we have graduated from all the courses, and
we will feel very grateful and lucky to have the opportunity to receive
such a good programming education.
"""
from campy.graphics.gobjects import GRect, GLine, GOval, GLabel, GPolygon
from campy.graphics.gwindow import GWindow
from campy.gui.events.mouse import onmouseclicked
window = GWindow(500, 500, title='')
head = GOval(60, 40)
eye_l = GRect(10, 10)
eye_r = GRect(10, 10)
body = GRect(60, 50)
neck = GRect(30, 4)
arm_l = GRect(10, 30)
arm_r = GRect(10, 30)
leg_l = GOval(20, 15)
leg_r = GOval(20, 15)
word = GLabel('K')
count = 0
def main():
"""
The program will first create a canvas, a background, and lines.
The first click will show four beepers at the lower part of the
window. The second click will show a robot at the upper part of
the window. For the next few clicks will move the robot from the
upper part to the lower part of the window. The final click will
show a speech bubble.
"""
background()
draw_line()
build_s()
build_c()
onmouseclicked(create_beeper)
def background():
# Create a red background.
global window
back = GRect(window.width, window.height)
window.add(back)
def draw_line():
# Draw several vertical and horizontal lines.
for i in range(1, 10):
line_hor = GLine(0, i*50, window.width, i*50)
line_ver = GLine(i*50, 0, i*50, window.height)
window.add(line_hor)
window.add(line_ver)
def build_s():
# Create word: S.
global window
rect_l = 100
rect_s = 20
rect1 = GRect(rect_l, rect_s, x=100, y=150)
rect2 = GRect(rect_s, rect_l, x=100, y=150)
rect3 = GRect(rect_l, rect_s, x=100, y=230)
rect4 = GRect(rect_s, rect_l, x=180, y=230)
rect5 = GRect(rect_l, rect_s, x=100, y=310)
rect1.filled = True
rect1.fill_color = 'tomato'
rect1.color = 'tomato'
rect2.filled = True
rect2.fill_color = 'tomato'
rect2.color = 'tomato'
rect3.filled = True
rect3.fill_color = 'tomato'
rect3.color = 'tomato'
rect4.filled = True
rect4.fill_color = 'tomato'
rect4.color = 'tomato'
rect5.filled = True
rect5.fill_color = 'tomato'
rect5.color = 'tomato'
window.add(rect1)
window.add(rect2)
window.add(rect3)
window.add(rect4)
window.add(rect5)
def build_c():
# Create word: C.
global window
rect_l = 100
rect_s = 20
rect1 = GRect(rect_l, rect_s, x=300, y=150)
rect2 = GRect(rect_s, rect_l*2-rect_s, x=300, y=150)
rect3 = GRect(rect_l, rect_s, x=300, y=310)
rect1.filled = True
rect1.fill_color = 'tomato'
rect1.color = 'tomato'
rect2.filled = True
rect2.fill_color = 'tomato'
rect2.color = 'tomato'
rect3.filled = True
rect3.fill_color = 'tomato'
rect3.color = 'tomato'
window.add(rect1)
window.add(rect2)
window.add(rect3)
def build_karel(e):
# Build up the robot, Karel.
karel_head()
karel_eye()
karel_neck()
karel_body()
karel_limb()
karel_label()
onmouseclicked(move)
def create_beeper(e):
# create 4 beepers
size = 50
for i in (1, 3, 7, 9):
beeper = GOval(size, size, x=i*50-size/2, y=400-size/2)
beeper.filled = True
beeper.fill_color = 'blue'
window.add(beeper)
label1 = GLabel('001', x=50-size/2+9, y=400-size/2+37)
label2 = GLabel('101', x=150-size/2+9, y=400-size/2+37)
label3 = GLabel('201', x=350-size/2+9, y=400-size/2+37)
label4 = GLabel('202', x=450-size/2+9, y=400-size/2+37)
label1.font = '-15'
label2.font = '-15'
label3.font = '-15'
label4.font = '-15'
label1.color = 'white'
label2.color = 'white'
label3.color = 'white'
label4.color = 'white'
window.add(label1)
window.add(label2)
window.add(label3)
window.add(label4)
onmouseclicked(build_karel)
def karel_head():
# Build Karel's head.
global head
head = GOval(60, 40, x=220, y=20)
head.filled = True
head.fill_color = 'gray'
window.add(head)
return head
def karel_eye():
# Build Karel's eyes.
global eye_l, eye_r
eye_l = GRect(10, 10, x=235, y=35)
eye_r = GRect(10, 10, x=255, y=35)
eye_l.filled = True
eye_r.filled = True
eye_l.fill_color = 'blue'
eye_r.fill_color = 'blue'
window.add(eye_l)
window.add(eye_r)
def karel_neck():
# Build Karel's neck.
global neck
neck = GRect(30, 4, x=235, y=58)
neck.filled = True
neck.color = 'blue'
window.add(neck)
def karel_body():
# Build Karel's body.
global body
body = GRect(60, 50, x=220, y=62)
body.filled = True
body.fill_color = 'blue'
window.add(body)
def karel_limb():
# Build Karel's limbs.
global arm_l, arm_r, leg_l, leg_r
arm_l = GRect(10, 30, x=210, y=67)
arm_r = GRect(10, 30, x=280, y=67)
leg_l = GOval(20, 15, x=220, y=112)
leg_r = GOval(20, 15, x=260, y=112)
arm_l.filled = True
arm_r.filled = True
leg_l.filled = True
leg_r.filled = True
arm_l.fill_color = 'green'
arm_r.fill_color = 'green'
leg_l.fill_color = 'red'
leg_r.fill_color = 'red'
window.add(arm_l)
window.add(arm_r)
window.add(leg_l)
window.add(leg_r)
def karel_label():
# Show the word on Karel's body.
global word
word = GLabel('K', x=235, y=115)
word.font = '-35'
window.add(word)
def move(e):
# Move Karel down 100 units.
global count
if count < 6:
head.move(0, 50)
eye_l.move(0, 50)
eye_r.move(0, 50)
body.move(0, 50)
neck.move(0, 50)
arm_l.move(0, 50)
arm_r.move(0, 50)
leg_l.move(0, 50)
leg_r.move(0, 50)
word.move(0, 50)
count += 1
if count == 6:
speech_bub()
def speech_bub():
# Show what Karel says.
bub1 = GOval(10, 10, x=300, y=340)
bub2 = GOval(15, 15, x=320, y=330)
bub3 = GOval(150, 40, x=345, y=303)
bub1.filled = True
bub2.filled = True
bub3.filled = True
bub1.fill_color = 'white'
bub2.fill_color = 'white'
bub3.fill_color = 'white'
word1 = GLabel('I love stanCode!', x=365, y=325)
word2 = GLabel('I love Jerry !', x=375, y=338)
word1.font = 'Courier-8-bold'
word2.font = 'Courier-8-bold'
window.add(bub1)
window.add(bub2)
window.add(bub3)
window.add(word1)
window.add(word2)
if __name__ == '__main__':
main() | en | 0.909421 | File: my_drawing.py Name: <NAME> ---------------------- In this program, Karel the robot represents the students of stanCode, and the beepers are what we have learned, what we are learning, and what we will learn. When users click the mouse, Karel will move, this means we are one step forward in becoming a professional software engineer, data scientist, etc. Finally, Karel will walk through the path, meaning that we have graduated from all the courses, and we will feel very grateful and lucky to have the opportunity to receive such a good programming education. The program will first create a canvas, a background, and lines. The first click will show four beepers at the lower part of the window. The second click will show a robot at the upper part of the window. For the next few clicks will move the robot from the upper part to the lower part of the window. The final click will show a speech bubble. # Create a red background. # Draw several vertical and horizontal lines. # Create word: S. # Create word: C. # Build up the robot, Karel. # create 4 beepers # Build Karel's head. # Build Karel's eyes. # Build Karel's neck. # Build Karel's body. # Build Karel's limbs. # Show the word on Karel's body. # Move Karel down 100 units. # Show what Karel says. | 4.033796 | 4 |
setup.py | Naminwang/VBDiarization | 0 | 6630486 | <reponame>Naminwang/VBDiarization
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright (C) 2018 Brno University of Technology FIT
# Author: <NAME> <<EMAIL>>
# All Rights Reserved
import os
import urllib
import tarfile
import logging
import tempfile
from distutils.core import setup
from subprocess import check_call
from setuptools import find_packages
from setuptools.command.install import install
from setuptools.command.develop import develop
from vbdiar.utils import mkdir_p
from vbdiar.kaldi import KALDI_ROOT_PATH
CDIR = os.path.dirname(os.path.realpath(__file__))
XVEC_MODELS_DIR = os.path.join(CDIR, 'models', 'x-vectors')
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
def install_scripts(directory):
""" Call cmd commands to install extra software/repositories.
Args:
directory (str): path
"""
if KALDI_ROOT_PATH is None or not os.path.isdir(KALDI_ROOT_PATH):
raise ValueError('Please, set path to correct kaldi installation.')
nnet_copy_binary = os.path.join(KALDI_ROOT_PATH, 'src', 'nnet3bin', 'nnet3-copy')
if not os.path.isfile(nnet_copy_binary):
raise ValueError('nnet3-copy binary not found in `{}`.'.format(os.path.dirname(nnet_copy_binary)))
copy_matrix_binary = os.path.join(KALDI_ROOT_PATH, 'src', 'bin', 'copy-matrix')
if not os.path.isfile(copy_matrix_binary):
raise ValueError('copy-matrix binary not found in `{}`.'.format(os.path.dirname(copy_matrix_binary)))
mkdir_p(XVEC_MODELS_DIR)
with tempfile.NamedTemporaryFile() as f:
urllib.urlretrieve(
'http://kaldi-asr.org/models/0003_sre16_v2_1a.tar.gz', f.name)
tar = tarfile.open(os.path.join(f.name), 'r:gz')
tar.extractall(XVEC_MODELS_DIR)
tar.close()
# replace input of the last layer, so we can easily extract xvectors
nnet_raw_path = os.path.join(XVEC_MODELS_DIR, '0003_sre16_v2_1a', 'exp', 'xvector_nnet_1a', 'final.raw')
old_line = 'output-node name=output input=output.log-softmax objective=linear'
new_line = 'output-node name=output input=tdnn6.affine objective=linear'
check_call(['sed', '-i', '-e', 's@{}@{}@g'.format(old_line, new_line), nnet_raw_path])
# convert LDA matrix to text format
lda_path = os.path.join(os.path.dirname(nnet_raw_path), '..', 'xvectors_sre_combined', 'transform.mat')
check_call([copy_matrix_binary, '--binary=false', lda_path, lda_path.replace('.mat', '.txt')])
class PostDevelopCommand(develop):
""" Post-installation for development mode."""
def run(self):
develop.run(self)
self.execute(install_scripts, (self.egg_path,), msg='Running post install scripts')
class PostInstallCommand(install):
""" Post-installation for installation mode."""
def run(self):
install.run(self)
self.execute(install_scripts, (self.install_lib,), msg='Running post install scripts')
setup(
name='vbdiar',
version='0.1',
packages=find_packages(),
url='',
license='',
author='profant',
author_email='<EMAIL>',
description='',
cmdclass={'install': PostInstallCommand, 'develop': PostDevelopCommand}
)
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| #!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright (C) 2018 Brno University of Technology FIT
# Author: <NAME> <<EMAIL>>
# All Rights Reserved
import os
import urllib
import tarfile
import logging
import tempfile
from distutils.core import setup
from subprocess import check_call
from setuptools import find_packages
from setuptools.command.install import install
from setuptools.command.develop import develop
from vbdiar.utils import mkdir_p
from vbdiar.kaldi import KALDI_ROOT_PATH
CDIR = os.path.dirname(os.path.realpath(__file__))
XVEC_MODELS_DIR = os.path.join(CDIR, 'models', 'x-vectors')
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
def install_scripts(directory):
""" Call cmd commands to install extra software/repositories.
Args:
directory (str): path
"""
if KALDI_ROOT_PATH is None or not os.path.isdir(KALDI_ROOT_PATH):
raise ValueError('Please, set path to correct kaldi installation.')
nnet_copy_binary = os.path.join(KALDI_ROOT_PATH, 'src', 'nnet3bin', 'nnet3-copy')
if not os.path.isfile(nnet_copy_binary):
raise ValueError('nnet3-copy binary not found in `{}`.'.format(os.path.dirname(nnet_copy_binary)))
copy_matrix_binary = os.path.join(KALDI_ROOT_PATH, 'src', 'bin', 'copy-matrix')
if not os.path.isfile(copy_matrix_binary):
raise ValueError('copy-matrix binary not found in `{}`.'.format(os.path.dirname(copy_matrix_binary)))
mkdir_p(XVEC_MODELS_DIR)
with tempfile.NamedTemporaryFile() as f:
urllib.urlretrieve(
'http://kaldi-asr.org/models/0003_sre16_v2_1a.tar.gz', f.name)
tar = tarfile.open(os.path.join(f.name), 'r:gz')
tar.extractall(XVEC_MODELS_DIR)
tar.close()
# replace input of the last layer, so we can easily extract xvectors
nnet_raw_path = os.path.join(XVEC_MODELS_DIR, '0003_sre16_v2_1a', 'exp', 'xvector_nnet_1a', 'final.raw')
old_line = 'output-node name=output input=output.log-softmax objective=linear'
new_line = 'output-node name=output input=tdnn6.affine objective=linear'
check_call(['sed', '-i', '-e', 's@{}@{}@g'.format(old_line, new_line), nnet_raw_path])
# convert LDA matrix to text format
lda_path = os.path.join(os.path.dirname(nnet_raw_path), '..', 'xvectors_sre_combined', 'transform.mat')
check_call([copy_matrix_binary, '--binary=false', lda_path, lda_path.replace('.mat', '.txt')])
class PostDevelopCommand(develop):
""" Post-installation for development mode."""
def run(self):
develop.run(self)
self.execute(install_scripts, (self.egg_path,), msg='Running post install scripts')
class PostInstallCommand(install):
""" Post-installation for installation mode."""
def run(self):
install.run(self)
self.execute(install_scripts, (self.install_lib,), msg='Running post install scripts')
setup(
name='vbdiar',
version='0.1',
packages=find_packages(),
url='',
license='',
author='profant',
author_email='<EMAIL>',
description='',
cmdclass={'install': PostInstallCommand, 'develop': PostDevelopCommand}
)
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# comment here, so github will not show, that this project is in perl
# comment here, so github will not show, that this project is in perl
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# comment here, so github will not show, that this project is in perl
# comment here, so github will not show, that this project is in perl
# comment here, so github will not show, that this project is in perl
# comment here, so github will not show, that this project is in perl
# comment here, so github will not show, that this project is in perl
# comment here, so github will not show, that this project is in perl
# comment here, so github will not show, that this project is in perl
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# comment here, so github will not show, that this project is in perl
# comment here, so github will not show, that this project is in perl | en | 0.880046 | #!/usr/bin/env python # -*- coding: utf-8 -*- # # Copyright (C) 2018 Brno University of Technology FIT # Author: <NAME> <<EMAIL>> # All Rights Reserved Call cmd commands to install extra software/repositories. Args: directory (str): path # replace input of the last layer, so we can easily extract xvectors # convert LDA matrix to text format Post-installation for development mode. Post-installation for installation mode. # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # 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comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # 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comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # 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comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl # comment here, so github will not show, that this project is in perl | 1.902782 | 2 |
old_versions/TCA_2_2/TCA_V_2_2_1/code/TCACore.py | OSADP/TCA | 1 | 6630487 | <reponame>OSADP/TCA
#standard
import math
import sys
import unittest
import time
import logging
import os
def set_logger(console_level = logging.INFO, file_level = logging.DEBUG, include_file = True, append_file = False):
if not append_file:
try:
os.remove('tca2.log')
except:
pass
logger = logging.getLogger('tca2')
logger.setLevel(file_level)
ch = logging.StreamHandler()
ch.setFormatter(logging.Formatter('%(message)s'))
ch.setLevel(console_level)
logger.addHandler(ch)
if include_file:
fh = logging.FileHandler('tca2.log')
fh.setFormatter(logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s'))
fh.setLevel(file_level)
logger.addHandler(fh)
return logger
logger = set_logger(include_file = True, file_level = logging.DEBUG)
#-------------------------------------------------------------------------
def Chk_Range(x1, y1, x2, y2):
unitValue = 1 #1 = linear distance #2 - Spherical distance
ran = None
if unitValue ==1:
ran = math.sqrt((float(y2) - float(y1)) ** 2 + (float(x2) - float(x1)) ** 2)
elif unitValue==2:
rad = 3958.76 #Earth radius in Miles
#Convert to radians
p1lat = float(x1) / 180 * math.pi
p1long = float(y1) / 180 * math.pi
p2lat = float(x2) / 180 * math.pi
p2long = float(y2) / 180 * math.pi
if (p1lat == p2lat) and (p1long == p2long):
ran = float(0)
else:
ran = math.acos(math.sin(p1lat) * math.sin(p2lat) + math.cos(p1lat) * math.cos(p2lat)
* math.cos(p2long - p1long)) * rad
return ran
#-------------------------------------------------------------------------
# (x1,y1) : Top left of rectangle, (x2,y2) : Bottom right of rectangle, (x3,y3) : Point in question
def Chk_Cellular_Range(x1, y1, x2, y2, x3, y3):
if((x3>=x1) & (y3<=y1) & (x3<=x2) & (y3>=y2)):
return True
else:
return False
#-------------------------------------------------------------------------
def Get_Heading(lat1, long1, lat2, long2):
heading = 0
y = math.sin(long2-long1) * math.cos(lat2)
x = math.cos(lat1) * math.sin(lat2) - math.sin(lat1) * math.cos(lat2) * math.cos(long2-long1)
heading = (math.degrees(math.atan2(y,x)) + 360) % 360
return heading
#-------------------------------------------------------------------------
def report_errors(errors):
if len(errors) > 0:
for error in errors:
print error
sys.exit(0)
class Timer:
def __init__(self, enabled=False):
self.timers = {}
self.last_start = ''
self.enabled = enabled
def valid_title(self, title):
if title in self.timers.keys() and isinstance(title, str):
return True
else:
return False
def __getitem__(self, title):
if self.valid_title(title) :
# print title
return title + ',' + \
str(self.stats(title)) + ',' + \
str(self.stats(title, type='max')) + ',' + \
str(self.stats(title, type='avg')) + ',' + \
str(len(self.timers[title]['values'])) + ',' + \
str(self.timers[title]['values'][-1])
def current(self, title):
if self.valid_title(title):
return self.timers[title]['values'][-1]
else:
return 0
def stats(self, title, type='SUM'):
if self.valid_title(title):
if len(self.timers[title]['values']) > 0:
if type.upper() == 'SUM':
return sum(self.timers[title]['values'])
elif type.upper() == 'MAX':
return max(self.timers[title]['values'])
elif type.upper() == 'AVG':
return sum(self.timers[title]['values']) / len(self.timers[title]['values'])
def start(self, title):
if self.enabled:
if title not in self.timers.keys():
self.timers[title] = {'st' : time.time(),
'values': []}
else:
self.timers[title]['st'] = time.time()
self.last_start = title
def stop(self, title=''):
if self.enabled:
if title == '':
title = self.last_start
if self.timers.has_key(title):
end_time = time.time()
self.timers[title]['values'].append(end_time - self.timers[title]['st'])
def drop(self,title):
if self.enabled:
del self.timers[title]
def header(self):
return 'title,sum,max,avg,len,last\n'
def write(self):
if self.enabled:
l = []
for title in self.timers:
l.append(self.__getitem__(title))
return '\n'.join(l)
else:
return ''
#*************************************************************************
class TestTimer(unittest.TestCase):
def test_timer(self):
t1=Timer(enabled = True)
t1.start('test1')
time.sleep(2)
t1.stop()
assert (t1.current('test1') >= 2.0) and (t1.current('test1') < 2.1)
t1.start('test2')
time.sleep(3)
t1.stop('test2')
assert (t1.current('test2') >= 3.0) and (t1.current('test2') < 3.1)
t1.start('test2')
time.sleep(2)
t1.stop()
assert (t1.stats('test2') >= 5.0) and (t1.stats('test2') < 5.1)
class CoreTests(unittest.TestCase):
def test_range(self):
assert 1 == int(Chk_Range(1,1,2,2))
assert 5.0 == Chk_Range(-2,1,1,5)
assert 0.0 == Chk_Range(1,1,1,1)
def test_errors(self):
e = []
report_errors(e)
def test_cellular_range(self):
assert True == Chk_Cellular_Range(-2,1,0,0,-1,1)
assert False == Chk_Cellular_Range(-2,1,0,0,1,0)
def test_heading(self):
assert 0 == Get_Heading(-1,0,0,0)
assert 90 == Get_Heading(0,-1,0,0)
assert 180 == Get_Heading(0,0,-1,0)
assert 270 == Get_Heading(0,0,0,-1)
if __name__ == '__main__':
unittest.main()
| #standard
import math
import sys
import unittest
import time
import logging
import os
def set_logger(console_level = logging.INFO, file_level = logging.DEBUG, include_file = True, append_file = False):
if not append_file:
try:
os.remove('tca2.log')
except:
pass
logger = logging.getLogger('tca2')
logger.setLevel(file_level)
ch = logging.StreamHandler()
ch.setFormatter(logging.Formatter('%(message)s'))
ch.setLevel(console_level)
logger.addHandler(ch)
if include_file:
fh = logging.FileHandler('tca2.log')
fh.setFormatter(logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s'))
fh.setLevel(file_level)
logger.addHandler(fh)
return logger
logger = set_logger(include_file = True, file_level = logging.DEBUG)
#-------------------------------------------------------------------------
def Chk_Range(x1, y1, x2, y2):
unitValue = 1 #1 = linear distance #2 - Spherical distance
ran = None
if unitValue ==1:
ran = math.sqrt((float(y2) - float(y1)) ** 2 + (float(x2) - float(x1)) ** 2)
elif unitValue==2:
rad = 3958.76 #Earth radius in Miles
#Convert to radians
p1lat = float(x1) / 180 * math.pi
p1long = float(y1) / 180 * math.pi
p2lat = float(x2) / 180 * math.pi
p2long = float(y2) / 180 * math.pi
if (p1lat == p2lat) and (p1long == p2long):
ran = float(0)
else:
ran = math.acos(math.sin(p1lat) * math.sin(p2lat) + math.cos(p1lat) * math.cos(p2lat)
* math.cos(p2long - p1long)) * rad
return ran
#-------------------------------------------------------------------------
# (x1,y1) : Top left of rectangle, (x2,y2) : Bottom right of rectangle, (x3,y3) : Point in question
def Chk_Cellular_Range(x1, y1, x2, y2, x3, y3):
if((x3>=x1) & (y3<=y1) & (x3<=x2) & (y3>=y2)):
return True
else:
return False
#-------------------------------------------------------------------------
def Get_Heading(lat1, long1, lat2, long2):
heading = 0
y = math.sin(long2-long1) * math.cos(lat2)
x = math.cos(lat1) * math.sin(lat2) - math.sin(lat1) * math.cos(lat2) * math.cos(long2-long1)
heading = (math.degrees(math.atan2(y,x)) + 360) % 360
return heading
#-------------------------------------------------------------------------
def report_errors(errors):
if len(errors) > 0:
for error in errors:
print error
sys.exit(0)
class Timer:
def __init__(self, enabled=False):
self.timers = {}
self.last_start = ''
self.enabled = enabled
def valid_title(self, title):
if title in self.timers.keys() and isinstance(title, str):
return True
else:
return False
def __getitem__(self, title):
if self.valid_title(title) :
# print title
return title + ',' + \
str(self.stats(title)) + ',' + \
str(self.stats(title, type='max')) + ',' + \
str(self.stats(title, type='avg')) + ',' + \
str(len(self.timers[title]['values'])) + ',' + \
str(self.timers[title]['values'][-1])
def current(self, title):
if self.valid_title(title):
return self.timers[title]['values'][-1]
else:
return 0
def stats(self, title, type='SUM'):
if self.valid_title(title):
if len(self.timers[title]['values']) > 0:
if type.upper() == 'SUM':
return sum(self.timers[title]['values'])
elif type.upper() == 'MAX':
return max(self.timers[title]['values'])
elif type.upper() == 'AVG':
return sum(self.timers[title]['values']) / len(self.timers[title]['values'])
def start(self, title):
if self.enabled:
if title not in self.timers.keys():
self.timers[title] = {'st' : time.time(),
'values': []}
else:
self.timers[title]['st'] = time.time()
self.last_start = title
def stop(self, title=''):
if self.enabled:
if title == '':
title = self.last_start
if self.timers.has_key(title):
end_time = time.time()
self.timers[title]['values'].append(end_time - self.timers[title]['st'])
def drop(self,title):
if self.enabled:
del self.timers[title]
def header(self):
return 'title,sum,max,avg,len,last\n'
def write(self):
if self.enabled:
l = []
for title in self.timers:
l.append(self.__getitem__(title))
return '\n'.join(l)
else:
return ''
#*************************************************************************
class TestTimer(unittest.TestCase):
def test_timer(self):
t1=Timer(enabled = True)
t1.start('test1')
time.sleep(2)
t1.stop()
assert (t1.current('test1') >= 2.0) and (t1.current('test1') < 2.1)
t1.start('test2')
time.sleep(3)
t1.stop('test2')
assert (t1.current('test2') >= 3.0) and (t1.current('test2') < 3.1)
t1.start('test2')
time.sleep(2)
t1.stop()
assert (t1.stats('test2') >= 5.0) and (t1.stats('test2') < 5.1)
class CoreTests(unittest.TestCase):
def test_range(self):
assert 1 == int(Chk_Range(1,1,2,2))
assert 5.0 == Chk_Range(-2,1,1,5)
assert 0.0 == Chk_Range(1,1,1,1)
def test_errors(self):
e = []
report_errors(e)
def test_cellular_range(self):
assert True == Chk_Cellular_Range(-2,1,0,0,-1,1)
assert False == Chk_Cellular_Range(-2,1,0,0,1,0)
def test_heading(self):
assert 0 == Get_Heading(-1,0,0,0)
assert 90 == Get_Heading(0,-1,0,0)
assert 180 == Get_Heading(0,0,-1,0)
assert 270 == Get_Heading(0,0,0,-1)
if __name__ == '__main__':
unittest.main() | en | 0.234517 | #standard #------------------------------------------------------------------------- #1 = linear distance #2 - Spherical distance #Earth radius in Miles #Convert to radians #------------------------------------------------------------------------- # (x1,y1) : Top left of rectangle, (x2,y2) : Bottom right of rectangle, (x3,y3) : Point in question #------------------------------------------------------------------------- #------------------------------------------------------------------------- # print title #************************************************************************* | 2.777546 | 3 |
tests/fixtures/internal/subpackage_a/subpackage_1/module_i.py | gitter-badger/dependenpy | 10 | 6630488 | <reponame>gitter-badger/dependenpy
class ClassI(object):
from ..module_1 import Class1
def function():
from ...module_a import ClassA
def inner_function():
from ...subpackage_a.module_1 import Class1
class InnerClass(object):
from external import module_a
return inner_function
| class ClassI(object):
from ..module_1 import Class1
def function():
from ...module_a import ClassA
def inner_function():
from ...subpackage_a.module_1 import Class1
class InnerClass(object):
from external import module_a
return inner_function | none | 1 | 2.683448 | 3 |
|
tremendous/__init__.py | ryansb/tremendous | 0 | 6630489 | from tremendous.colors import __funcs
# This is gross. Sorry.
globals().update(__funcs)
| from tremendous.colors import __funcs
# This is gross. Sorry.
globals().update(__funcs)
| en | 0.949875 | # This is gross. Sorry. | 1.082042 | 1 |
src/comments/views.py | LABETE/srvup_and_drf | 0 | 6630490 | from django.contrib import messages
from django.core.urlresolvers import reverse
from django.http import HttpResponseRedirect
from django.shortcuts import render
from django.views.generic.detail import DetailView
from django.views.generic.edit import FormView
from braces.views import LoginRequiredMixin
from comments.models import Comment
from notifications.signals import notify
from videos.forms import CommentForm
class CommentDetailView(LoginRequiredMixin, FormView, DetailView):
model = Comment
form_class = CommentForm
def get_context_data(self, *args, **kwargs):
context = super(CommentDetailView, self).get_context_data(
*args, **kwargs)
context["comments"] = self.get_object().get_children()
context["form"] = self.form_class
return context
def post(self, *args, **kwargs):
form = self.get_form()
if form.is_valid():
clean_text = form.cleaned_data["text"]
Comment.objects.create_comment(
user=self.request.user,
text=clean_text,
path=self.get_object().get_path,
video=self.get_object().video,
parent=self.get_object())
affected_users = self.get_object().get_affected_users()
#print(self.get_object().get)
notify.send(self.request.user,
recipient=self.get_object().user,
action=self.get_object(),
target=self.get_object().video,
verb=u'commented',
affected_users=affected_users,
)
messages.success(self.request, "Thank you for your comment")
return HttpResponseRedirect(self.get_object().get_absolute_url())
else:
messages.error(self.request, "There was an error")
return HttpResponseRedirect(self.get_object().get_absolute_url())
| from django.contrib import messages
from django.core.urlresolvers import reverse
from django.http import HttpResponseRedirect
from django.shortcuts import render
from django.views.generic.detail import DetailView
from django.views.generic.edit import FormView
from braces.views import LoginRequiredMixin
from comments.models import Comment
from notifications.signals import notify
from videos.forms import CommentForm
class CommentDetailView(LoginRequiredMixin, FormView, DetailView):
model = Comment
form_class = CommentForm
def get_context_data(self, *args, **kwargs):
context = super(CommentDetailView, self).get_context_data(
*args, **kwargs)
context["comments"] = self.get_object().get_children()
context["form"] = self.form_class
return context
def post(self, *args, **kwargs):
form = self.get_form()
if form.is_valid():
clean_text = form.cleaned_data["text"]
Comment.objects.create_comment(
user=self.request.user,
text=clean_text,
path=self.get_object().get_path,
video=self.get_object().video,
parent=self.get_object())
affected_users = self.get_object().get_affected_users()
#print(self.get_object().get)
notify.send(self.request.user,
recipient=self.get_object().user,
action=self.get_object(),
target=self.get_object().video,
verb=u'commented',
affected_users=affected_users,
)
messages.success(self.request, "Thank you for your comment")
return HttpResponseRedirect(self.get_object().get_absolute_url())
else:
messages.error(self.request, "There was an error")
return HttpResponseRedirect(self.get_object().get_absolute_url())
| ru | 0.145106 | #print(self.get_object().get) | 2.065968 | 2 |
setup.py | jakirkham/dask-distance | 9 | 6630491 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import sys
import setuptools
from setuptools import setup
from setuptools.command.test import test as TestCommand
import versioneer
class PyTest(TestCommand):
description = "Run test suite with pytest"
def finalize_options(self):
TestCommand.finalize_options(self)
self.test_args = []
self.test_suite = True
def run_tests(self):
import pytest
sys.exit(pytest.main(self.test_args))
with open("README.rst") as readme_file:
readme = readme_file.read()
requirements = [
"dask",
"numpy",
]
test_requirements = [
"pytest",
"scipy",
]
cmdclasses = {
"test": PyTest,
}
cmdclasses.update(versioneer.get_cmdclass())
setup(
name="dask-distance",
version=versioneer.get_version(),
description=(
"Distance computations with Dask (akin to scipy.spatial.distance)"
),
long_description=readme,
author="<NAME>",
author_email="<EMAIL>",
url="https://github.com/jakirkham/dask-distance",
cmdclass=cmdclasses,
packages=setuptools.find_packages(exclude=["tests*"]),
include_package_data=True,
install_requires=requirements,
license="BSD 3-Clause",
zip_safe=False,
keywords="dask-distance",
classifiers=[
"Development Status :: 2 - Pre-Alpha",
"Intended Audience :: Developers",
"License :: OSI Approved :: BSD License",
"Natural Language :: English",
"Programming Language :: Python :: 2",
"Programming Language :: Python :: 2.7",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.4",
"Programming Language :: Python :: 3.5",
"Programming Language :: Python :: 3.6",
],
tests_require=test_requirements
)
| #!/usr/bin/env python
# -*- coding: utf-8 -*-
import sys
import setuptools
from setuptools import setup
from setuptools.command.test import test as TestCommand
import versioneer
class PyTest(TestCommand):
description = "Run test suite with pytest"
def finalize_options(self):
TestCommand.finalize_options(self)
self.test_args = []
self.test_suite = True
def run_tests(self):
import pytest
sys.exit(pytest.main(self.test_args))
with open("README.rst") as readme_file:
readme = readme_file.read()
requirements = [
"dask",
"numpy",
]
test_requirements = [
"pytest",
"scipy",
]
cmdclasses = {
"test": PyTest,
}
cmdclasses.update(versioneer.get_cmdclass())
setup(
name="dask-distance",
version=versioneer.get_version(),
description=(
"Distance computations with Dask (akin to scipy.spatial.distance)"
),
long_description=readme,
author="<NAME>",
author_email="<EMAIL>",
url="https://github.com/jakirkham/dask-distance",
cmdclass=cmdclasses,
packages=setuptools.find_packages(exclude=["tests*"]),
include_package_data=True,
install_requires=requirements,
license="BSD 3-Clause",
zip_safe=False,
keywords="dask-distance",
classifiers=[
"Development Status :: 2 - Pre-Alpha",
"Intended Audience :: Developers",
"License :: OSI Approved :: BSD License",
"Natural Language :: English",
"Programming Language :: Python :: 2",
"Programming Language :: Python :: 2.7",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.4",
"Programming Language :: Python :: 3.5",
"Programming Language :: Python :: 3.6",
],
tests_require=test_requirements
)
| en | 0.352855 | #!/usr/bin/env python # -*- coding: utf-8 -*- | 1.719538 | 2 |
setup.py | Breakend/cule | 0 | 6630492 | <reponame>Breakend/cule
import argparse
import os
import sys
from Cython.Distutils import build_ext
from distutils.cmd import Command
from setuptools import find_packages, setup, Extension
from examples.utils.runtime import Runtime
try:
from examples.utils.runtime import get_device_props
codes = sorted(set([str(p.major) + str(p.minor) for p in get_device_props()]))
arch_gencode = ['-arch=sm_' + codes[0]] + ['-gencode=arch=compute_{0},code=sm_{0}'.format(code) for code in codes]
except:
print('Warning: Could not find nvcc in path. Compiling with default support for all architectures.'
'This may result in exceedingly long startup times during initialization on the GPU.')
arch_gencode = []
base_dir = os.path.dirname(os.path.realpath(__file__))
runtime = Runtime()
CUDA = runtime._locate()
sources = [os.path.join('torchcule', f) for f in ['frontend.cpp', 'backend.cu']]
third_party_dir = os.path.join(base_dir, 'third_party')
include_dirs = [base_dir, os.path.join(third_party_dir, 'agency'), os.path.join(third_party_dir, 'pybind11', 'include'), CUDA['include']]
libraries = ['gomp', 'z']
cxx_flags = ['-std=c++11']
nvcc_flags = arch_gencode + ['-O3', '-Xptxas=-v', '-Xcompiler=-Wall,-Wextra,-fPIC', '-std=c++11']
parser = argparse.ArgumentParser('CuLE', add_help=False)
parser.add_argument('--fastbuild', action='store_true', default=False, help='Build CuLE supporting only 2K roms')
parser.add_argument('--compiler', type=str, default='gcc', help='Host compiler (default: gcc)')
args, remaining_argv = parser.parse_known_args()
sys.argv = ['setup.py'] + remaining_argv
if args.fastbuild:
nvcc_flags += ['-DCULE_FAST_COMPILE']
nvcc_flags += ['-ccbin={}'.format(args.compiler)]
def customize_compiler_for_nvcc(self):
"""Inject deep into distutils to customize how the dispatch
to gcc/nvcc works.
If you subclass UnixCCompiler, it's not trivial to get your subclass
injected in, and still have the right customizations (i.e.
distutils.sysconfig.customize_compiler) run on it. So instead of going
the OO route, I have this. Note, it's kindof like a wierd functional
subclassing going on.
"""
# Tell the compiler it can processes .cu
self.src_extensions.append('.cu')
# Save references to the default compiler_so and _comple methods
default_compiler_so = self.compiler_so
super = self._compile
# Now redefine the _compile method. This gets executed for each
# object but distutils doesn't have the ability to change compilers
# based on source extension: we add it.
def _compile(obj, src, ext, cc_args, extra_postargs, pp_opts):
if os.path.splitext(src)[1] == '.cu':
# use the cuda for .cu files
self.set_executable('compiler_so', CUDA['nvcc'])
# use only a subset of the extra_postargs, which are 1-1
# translated from the extra_compile_args in the Extension class
postargs = extra_postargs['nvcc']
else:
postargs = extra_postargs['gcc']
super(obj, src, ext, cc_args, postargs, pp_opts)
# Reset the default compiler_so, which we might have changed for cuda
self.compiler_so = default_compiler_so
# Inject our redefined _compile method into the class
self._compile = _compile
# Run the customize_compiler
class custom_build_ext(build_ext):
def build_extensions(self):
customize_compiler_for_nvcc(self.compiler)
build_ext.build_extensions(self)
setup(name='torchcule',
version='0.1.0',
description='A GPU RL environment package for PyTorch',
url='https://github.com/NVlabs/cule',
author='<NAME>',
author_email='<EMAIL>',
install_requires=['gym>=0.9.5'],
ext_modules=[
Extension('torchcule_atari',
sources=sources,
include_dirs=include_dirs,
libraries=libraries,
extra_compile_args={'gcc': cxx_flags, 'nvcc': nvcc_flags}
)
],
# Exclude the build files.
packages=find_packages(exclude=['build']),
cmdclass={
'build_ext': custom_build_ext,
}
)
| import argparse
import os
import sys
from Cython.Distutils import build_ext
from distutils.cmd import Command
from setuptools import find_packages, setup, Extension
from examples.utils.runtime import Runtime
try:
from examples.utils.runtime import get_device_props
codes = sorted(set([str(p.major) + str(p.minor) for p in get_device_props()]))
arch_gencode = ['-arch=sm_' + codes[0]] + ['-gencode=arch=compute_{0},code=sm_{0}'.format(code) for code in codes]
except:
print('Warning: Could not find nvcc in path. Compiling with default support for all architectures.'
'This may result in exceedingly long startup times during initialization on the GPU.')
arch_gencode = []
base_dir = os.path.dirname(os.path.realpath(__file__))
runtime = Runtime()
CUDA = runtime._locate()
sources = [os.path.join('torchcule', f) for f in ['frontend.cpp', 'backend.cu']]
third_party_dir = os.path.join(base_dir, 'third_party')
include_dirs = [base_dir, os.path.join(third_party_dir, 'agency'), os.path.join(third_party_dir, 'pybind11', 'include'), CUDA['include']]
libraries = ['gomp', 'z']
cxx_flags = ['-std=c++11']
nvcc_flags = arch_gencode + ['-O3', '-Xptxas=-v', '-Xcompiler=-Wall,-Wextra,-fPIC', '-std=c++11']
parser = argparse.ArgumentParser('CuLE', add_help=False)
parser.add_argument('--fastbuild', action='store_true', default=False, help='Build CuLE supporting only 2K roms')
parser.add_argument('--compiler', type=str, default='gcc', help='Host compiler (default: gcc)')
args, remaining_argv = parser.parse_known_args()
sys.argv = ['setup.py'] + remaining_argv
if args.fastbuild:
nvcc_flags += ['-DCULE_FAST_COMPILE']
nvcc_flags += ['-ccbin={}'.format(args.compiler)]
def customize_compiler_for_nvcc(self):
"""Inject deep into distutils to customize how the dispatch
to gcc/nvcc works.
If you subclass UnixCCompiler, it's not trivial to get your subclass
injected in, and still have the right customizations (i.e.
distutils.sysconfig.customize_compiler) run on it. So instead of going
the OO route, I have this. Note, it's kindof like a wierd functional
subclassing going on.
"""
# Tell the compiler it can processes .cu
self.src_extensions.append('.cu')
# Save references to the default compiler_so and _comple methods
default_compiler_so = self.compiler_so
super = self._compile
# Now redefine the _compile method. This gets executed for each
# object but distutils doesn't have the ability to change compilers
# based on source extension: we add it.
def _compile(obj, src, ext, cc_args, extra_postargs, pp_opts):
if os.path.splitext(src)[1] == '.cu':
# use the cuda for .cu files
self.set_executable('compiler_so', CUDA['nvcc'])
# use only a subset of the extra_postargs, which are 1-1
# translated from the extra_compile_args in the Extension class
postargs = extra_postargs['nvcc']
else:
postargs = extra_postargs['gcc']
super(obj, src, ext, cc_args, postargs, pp_opts)
# Reset the default compiler_so, which we might have changed for cuda
self.compiler_so = default_compiler_so
# Inject our redefined _compile method into the class
self._compile = _compile
# Run the customize_compiler
class custom_build_ext(build_ext):
def build_extensions(self):
customize_compiler_for_nvcc(self.compiler)
build_ext.build_extensions(self)
setup(name='torchcule',
version='0.1.0',
description='A GPU RL environment package for PyTorch',
url='https://github.com/NVlabs/cule',
author='<NAME>',
author_email='<EMAIL>',
install_requires=['gym>=0.9.5'],
ext_modules=[
Extension('torchcule_atari',
sources=sources,
include_dirs=include_dirs,
libraries=libraries,
extra_compile_args={'gcc': cxx_flags, 'nvcc': nvcc_flags}
)
],
# Exclude the build files.
packages=find_packages(exclude=['build']),
cmdclass={
'build_ext': custom_build_ext,
}
) | en | 0.832827 | Inject deep into distutils to customize how the dispatch to gcc/nvcc works. If you subclass UnixCCompiler, it's not trivial to get your subclass injected in, and still have the right customizations (i.e. distutils.sysconfig.customize_compiler) run on it. So instead of going the OO route, I have this. Note, it's kindof like a wierd functional subclassing going on. # Tell the compiler it can processes .cu # Save references to the default compiler_so and _comple methods # Now redefine the _compile method. This gets executed for each # object but distutils doesn't have the ability to change compilers # based on source extension: we add it. # use the cuda for .cu files # use only a subset of the extra_postargs, which are 1-1 # translated from the extra_compile_args in the Extension class # Reset the default compiler_so, which we might have changed for cuda # Inject our redefined _compile method into the class # Run the customize_compiler # Exclude the build files. | 1.755793 | 2 |
loglib/change_detect.py | alanmitchell/mini-monitor | 7 | 6630493 | <reponame>alanmitchell/mini-monitor
"""Functions related to detecting and posting changes in
a set of sensor readings.
"""
def find_changes(vals, threshold=None, change_pct=0.02, max_interval=None):
"""Returns an array of index values that point at the values in the 'vals'
array that represent signficant changes. 'threshold' is the absolute amount
that a value must change before being included. If 'threshold' is None,
'change_pct' is used to determine a threshold change by multiplying
'change_pct' by the difference between the minimum and maximum value. If
'max_interval' is provided, it will be the maximum allowed separation between
returned index values. This forces some some points to be returned even if
no signficant change has occurred.
Index 0, pointing at the first value is always returned.
"""
# special case of no values passed
if len(vals) == 0:
return []
if threshold is None:
# compute the threshold difference that constitutes a change
# from the change_pct parameter
threshold = (max(vals) - min(vals)) * change_pct
if threshold == 0.0:
threshold = 0.01 # keep it from showing every point
# Start with the first value
final_ixs = [0]
last_val = vals[0]
last_ix = 0
for ix in range(1, len(vals)):
v = vals[ix]
include_this_index = False
if abs(v - last_val) >= threshold:
# if the prior value was not included, include it
if last_ix != ix - 1:
final_ixs.append(ix - 1)
include_this_index = True
elif max_interval is not None and ix - last_ix >= max_interval:
include_this_index = True
if include_this_index:
final_ixs.append(ix)
last_val = v
last_ix = ix
return final_ixs
def make_post_lines(sensor_id, time_stamps, values, change_threshold, max_interval):
"""Returns an array of lines to post to the MQTT broker. Only posts changes in
values.
'sensor_id': sensor ID to use for each line
'time_stamps': numpy array of time stamps associated with the values
'values': numpy array of values to scan for changes and post accordingly
'change_threshold': amount of change that needs to occur before inclusion
'max_interval': maximum number of points to separate posts. Will post w/o a change to meet this.
"""
# find the indexes to include in the post lines
ixs = find_changes(values, change_threshold, max_interval=max_interval)
ts_incl = time_stamps[ixs]
val_incl = values[ixs]
return ['%s\t%s\t%s' % (ts, sensor_id, val) for ts, val in zip(ts_incl, val_incl)]
| """Functions related to detecting and posting changes in
a set of sensor readings.
"""
def find_changes(vals, threshold=None, change_pct=0.02, max_interval=None):
"""Returns an array of index values that point at the values in the 'vals'
array that represent signficant changes. 'threshold' is the absolute amount
that a value must change before being included. If 'threshold' is None,
'change_pct' is used to determine a threshold change by multiplying
'change_pct' by the difference between the minimum and maximum value. If
'max_interval' is provided, it will be the maximum allowed separation between
returned index values. This forces some some points to be returned even if
no signficant change has occurred.
Index 0, pointing at the first value is always returned.
"""
# special case of no values passed
if len(vals) == 0:
return []
if threshold is None:
# compute the threshold difference that constitutes a change
# from the change_pct parameter
threshold = (max(vals) - min(vals)) * change_pct
if threshold == 0.0:
threshold = 0.01 # keep it from showing every point
# Start with the first value
final_ixs = [0]
last_val = vals[0]
last_ix = 0
for ix in range(1, len(vals)):
v = vals[ix]
include_this_index = False
if abs(v - last_val) >= threshold:
# if the prior value was not included, include it
if last_ix != ix - 1:
final_ixs.append(ix - 1)
include_this_index = True
elif max_interval is not None and ix - last_ix >= max_interval:
include_this_index = True
if include_this_index:
final_ixs.append(ix)
last_val = v
last_ix = ix
return final_ixs
def make_post_lines(sensor_id, time_stamps, values, change_threshold, max_interval):
"""Returns an array of lines to post to the MQTT broker. Only posts changes in
values.
'sensor_id': sensor ID to use for each line
'time_stamps': numpy array of time stamps associated with the values
'values': numpy array of values to scan for changes and post accordingly
'change_threshold': amount of change that needs to occur before inclusion
'max_interval': maximum number of points to separate posts. Will post w/o a change to meet this.
"""
# find the indexes to include in the post lines
ixs = find_changes(values, change_threshold, max_interval=max_interval)
ts_incl = time_stamps[ixs]
val_incl = values[ixs]
return ['%s\t%s\t%s' % (ts, sensor_id, val) for ts, val in zip(ts_incl, val_incl)] | en | 0.87073 | Functions related to detecting and posting changes in a set of sensor readings. Returns an array of index values that point at the values in the 'vals' array that represent signficant changes. 'threshold' is the absolute amount that a value must change before being included. If 'threshold' is None, 'change_pct' is used to determine a threshold change by multiplying 'change_pct' by the difference between the minimum and maximum value. If 'max_interval' is provided, it will be the maximum allowed separation between returned index values. This forces some some points to be returned even if no signficant change has occurred. Index 0, pointing at the first value is always returned. # special case of no values passed # compute the threshold difference that constitutes a change # from the change_pct parameter # keep it from showing every point # Start with the first value # if the prior value was not included, include it Returns an array of lines to post to the MQTT broker. Only posts changes in values. 'sensor_id': sensor ID to use for each line 'time_stamps': numpy array of time stamps associated with the values 'values': numpy array of values to scan for changes and post accordingly 'change_threshold': amount of change that needs to occur before inclusion 'max_interval': maximum number of points to separate posts. Will post w/o a change to meet this. # find the indexes to include in the post lines | 3.50892 | 4 |
zoo/vgg/vgg_c.py | sujeet-ap/keras-idiomatic-programmer | 3 | 6630494 | <reponame>sujeet-ap/keras-idiomatic-programmer<gh_stars>1-10
# Copyright 2019 Google LLC
#
# 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
#
# https://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.
# VGG (16 and 19 & Composable) (2014)
# Trainable params: 138,357,544
# Paper: https://arxiv.org/pdf/1409.1556.pdf
import tensorflow as tf
from tensorflow.keras import Input, Model
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Activation
from tensorflow.keras.regularizers import l2
import sys
sys.path.append('../')
from models_c import Composable
class VGG(Composable):
""" VGG (composable)
"""
# Meta-parameter: list of groups: number of layers and filter size
groups = { 16 : [ { 'n_layers': 1, 'n_filters': 64 },
{ 'n_layers': 2, 'n_filters': 128 },
{ 'n_layers': 3, 'n_filters': 256 },
{ 'n_layers': 3, 'n_filters': 512 },
{ 'n_layers': 3, 'n_filters': 512 } ], # VGG16
19 : [ { 'n_layers': 1, 'n_filters': 64 },
{ 'n_layers': 2, 'n_filters': 128 },
{ 'n_layers': 4, 'n_filters': 256 },
{ 'n_layers': 4, 'n_filters': 512 },
{ 'n_layers': 4, 'n_filters': 512 } ] } # VGG19
def __init__(self, n_layers,
input_shape=(224, 224, 3), n_classes=1000, include_top=True,
reg=None, init_weights='glorot_uniform', relu=None, bias=True):
""" Construct a VGG model
n_layers : number of layers (16 or 19) or metaparameter for blocks
input_shape : input shape to the model
n_classes: : number of output classes
include_top : whether to include classifier
reg : kernel regularizer
init_weights: kernel initializer
relu : max value for ReLU
bias : whether to use bias
"""
# Configure the base (super) class
super().__init__(init_weights=init_weights, reg=reg, relu=relu, bias=bias)
# predefined
if isinstance(n_layers, int):
if n_layers not in [16, 19]:
raise Exception("VGG: Invalid value for n_layers")
blocks = list(self.groups[n_layers])
# user defined
else:
blocks = n_layers
# The input vector
inputs = Input(input_shape)
# The stem group
x = self.stem(inputs)
# The learner
outputs = self.learner(x, blocks=blocks)
# The classifier
if include_top:
outputs = self.classifier(outputs, n_classes)
# Instantiate the Model
self._model = Model(inputs, outputs)
def stem(self, inputs):
""" Construct the Stem Convolutional Group
inputs : the input vector
"""
x = self.Conv2D(inputs, 64, (3, 3), strides=(1, 1), padding="same")
x = self.ReLU(x)
return x
def learner(self, x, **metaparameters):
""" Construct the (Feature) Learner
x : input to the learner
blocks : list of groups: filter size and number of conv layers
"""
blocks = metaparameters['blocks']
# The convolutional groups
for block in blocks:
x = self.group(x, **block, **metaparameters)
return x
def group(self, x, **metaparameters):
""" Construct a Convolutional Group
x : input to the group
n_layers : number of convolutional layers
n_filters: number of filters
"""
n_filters = metaparameters['n_filters']
n_layers = metaparameters['n_layers']
del metaparameters['n_filters']
# Block of convolutional layers
for n in range(n_layers):
x = self.Conv2D(x, n_filters, (3, 3), strides=(1, 1), padding="same",
activation=self.ReLU, **metaparameters)
# Max pooling at the end of the block
x = MaxPooling2D(2, strides=(2, 2))(x)
return x
def classifier(self, x, n_classes):
""" Construct the Classifier
x : input to the classifier
n_classes : number of output classes
"""
# Save the encoding layer
self.encoding = x
# Flatten the feature maps
x = Flatten()(x)
# Save the embedding layer
self.embedding = x
# Two fully connected dense layers
x = self.Dense(x, 4096, activation=self.ReLU)
x = self.Dense(x, 4096, activation=self.ReLU)
outputs = super().classifier(x, n_classes, pooling=None)
return outputs
# Example stock VGG16
# vgg = VGG(16)
def example():
''' Example for constructing/training a VGG model on CIFAR-10
'''
# Example of constructing a mini-VGG
groups = [ { 'n_layers': 1, 'n_filters': 64 },
{ 'n_layers': 2, 'n_filters': 128 },
{ 'n_layers': 2, 'n_filters': 256 } ]
vgg = VGG(groups, input_shape=(32, 32, 3), n_classes=10)
vgg.model.summary()
# train on CIFAR-10
vgg.cifar10()
# example()
| # Copyright 2019 Google LLC
#
# 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
#
# https://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.
# VGG (16 and 19 & Composable) (2014)
# Trainable params: 138,357,544
# Paper: https://arxiv.org/pdf/1409.1556.pdf
import tensorflow as tf
from tensorflow.keras import Input, Model
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Activation
from tensorflow.keras.regularizers import l2
import sys
sys.path.append('../')
from models_c import Composable
class VGG(Composable):
""" VGG (composable)
"""
# Meta-parameter: list of groups: number of layers and filter size
groups = { 16 : [ { 'n_layers': 1, 'n_filters': 64 },
{ 'n_layers': 2, 'n_filters': 128 },
{ 'n_layers': 3, 'n_filters': 256 },
{ 'n_layers': 3, 'n_filters': 512 },
{ 'n_layers': 3, 'n_filters': 512 } ], # VGG16
19 : [ { 'n_layers': 1, 'n_filters': 64 },
{ 'n_layers': 2, 'n_filters': 128 },
{ 'n_layers': 4, 'n_filters': 256 },
{ 'n_layers': 4, 'n_filters': 512 },
{ 'n_layers': 4, 'n_filters': 512 } ] } # VGG19
def __init__(self, n_layers,
input_shape=(224, 224, 3), n_classes=1000, include_top=True,
reg=None, init_weights='glorot_uniform', relu=None, bias=True):
""" Construct a VGG model
n_layers : number of layers (16 or 19) or metaparameter for blocks
input_shape : input shape to the model
n_classes: : number of output classes
include_top : whether to include classifier
reg : kernel regularizer
init_weights: kernel initializer
relu : max value for ReLU
bias : whether to use bias
"""
# Configure the base (super) class
super().__init__(init_weights=init_weights, reg=reg, relu=relu, bias=bias)
# predefined
if isinstance(n_layers, int):
if n_layers not in [16, 19]:
raise Exception("VGG: Invalid value for n_layers")
blocks = list(self.groups[n_layers])
# user defined
else:
blocks = n_layers
# The input vector
inputs = Input(input_shape)
# The stem group
x = self.stem(inputs)
# The learner
outputs = self.learner(x, blocks=blocks)
# The classifier
if include_top:
outputs = self.classifier(outputs, n_classes)
# Instantiate the Model
self._model = Model(inputs, outputs)
def stem(self, inputs):
""" Construct the Stem Convolutional Group
inputs : the input vector
"""
x = self.Conv2D(inputs, 64, (3, 3), strides=(1, 1), padding="same")
x = self.ReLU(x)
return x
def learner(self, x, **metaparameters):
""" Construct the (Feature) Learner
x : input to the learner
blocks : list of groups: filter size and number of conv layers
"""
blocks = metaparameters['blocks']
# The convolutional groups
for block in blocks:
x = self.group(x, **block, **metaparameters)
return x
def group(self, x, **metaparameters):
""" Construct a Convolutional Group
x : input to the group
n_layers : number of convolutional layers
n_filters: number of filters
"""
n_filters = metaparameters['n_filters']
n_layers = metaparameters['n_layers']
del metaparameters['n_filters']
# Block of convolutional layers
for n in range(n_layers):
x = self.Conv2D(x, n_filters, (3, 3), strides=(1, 1), padding="same",
activation=self.ReLU, **metaparameters)
# Max pooling at the end of the block
x = MaxPooling2D(2, strides=(2, 2))(x)
return x
def classifier(self, x, n_classes):
""" Construct the Classifier
x : input to the classifier
n_classes : number of output classes
"""
# Save the encoding layer
self.encoding = x
# Flatten the feature maps
x = Flatten()(x)
# Save the embedding layer
self.embedding = x
# Two fully connected dense layers
x = self.Dense(x, 4096, activation=self.ReLU)
x = self.Dense(x, 4096, activation=self.ReLU)
outputs = super().classifier(x, n_classes, pooling=None)
return outputs
# Example stock VGG16
# vgg = VGG(16)
def example():
''' Example for constructing/training a VGG model on CIFAR-10
'''
# Example of constructing a mini-VGG
groups = [ { 'n_layers': 1, 'n_filters': 64 },
{ 'n_layers': 2, 'n_filters': 128 },
{ 'n_layers': 2, 'n_filters': 256 } ]
vgg = VGG(groups, input_shape=(32, 32, 3), n_classes=10)
vgg.model.summary()
# train on CIFAR-10
vgg.cifar10()
# example() | en | 0.691981 | # Copyright 2019 Google LLC # # 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 # # https://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. # VGG (16 and 19 & Composable) (2014) # Trainable params: 138,357,544 # Paper: https://arxiv.org/pdf/1409.1556.pdf VGG (composable) # Meta-parameter: list of groups: number of layers and filter size # VGG16 # VGG19 Construct a VGG model n_layers : number of layers (16 or 19) or metaparameter for blocks input_shape : input shape to the model n_classes: : number of output classes include_top : whether to include classifier reg : kernel regularizer init_weights: kernel initializer relu : max value for ReLU bias : whether to use bias # Configure the base (super) class # predefined # user defined # The input vector # The stem group # The learner # The classifier # Instantiate the Model Construct the Stem Convolutional Group inputs : the input vector Construct the (Feature) Learner x : input to the learner blocks : list of groups: filter size and number of conv layers # The convolutional groups Construct a Convolutional Group x : input to the group n_layers : number of convolutional layers n_filters: number of filters # Block of convolutional layers # Max pooling at the end of the block Construct the Classifier x : input to the classifier n_classes : number of output classes # Save the encoding layer # Flatten the feature maps # Save the embedding layer # Two fully connected dense layers # Example stock VGG16 # vgg = VGG(16) Example for constructing/training a VGG model on CIFAR-10 # Example of constructing a mini-VGG # train on CIFAR-10 # example() | 1.860335 | 2 |
Python3/789.escape-the-ghosts.py | 610yilingliu/leetcode | 0 | 6630495 | #
# @lc app=leetcode id=789 lang=python3
#
# [789] Escape The Ghosts
#
# @lc code=start
class Solution:
def escapeGhosts(self, ghosts: List[List[int]], target: List[int]):
my_dist = abs(target[0]) + abs(target[1])
for ghost in ghosts:
curdist = abs(ghost[0] - target[0]) + abs(ghost[1] - target[1])
if curdist < my_dist:
return False
return True
# @lc code=end
| #
# @lc app=leetcode id=789 lang=python3
#
# [789] Escape The Ghosts
#
# @lc code=start
class Solution:
def escapeGhosts(self, ghosts: List[List[int]], target: List[int]):
my_dist = abs(target[0]) + abs(target[1])
for ghost in ghosts:
curdist = abs(ghost[0] - target[0]) + abs(ghost[1] - target[1])
if curdist < my_dist:
return False
return True
# @lc code=end
| en | 0.478972 | # # @lc app=leetcode id=789 lang=python3 # # [789] Escape The Ghosts # # @lc code=start # @lc code=end | 2.97045 | 3 |
test/discovery/test_stub.py | ezh/cloudselect | 4 | 6630496 | <gh_stars>1-10
# Copyright 2019 <NAME> and individual contributors
# See the LICENSE.txt file at the top-level directory of this distribution.
#
# Licensed under the MIT license
# <LICENSE-MIT or http://opensource.org/licenses/MIT>
# This file may not be copied, modified, or distributed
# except according to those terms.
"""This module is used for testing Stub discovery plugin."""
from cloudselect import Container
from cloudselect.cloudselect import CloudSelect
from cloudselect.discovery import DiscoveryServiceProvider
from cloudselect.discovery.stub import Stub
def test_stub_discovery(cfgdir):
"""
Testing Stub initializaion.
Is fabric creating Stub by default?
Does Stub return []?
Is Stub singleton?
"""
cloud = CloudSelect(cfgdir)
# Read shared part
profile = cloud.configuration_read()
args = cloud.parse_args([])
cloud.fabric(profile, args)
assert Container.discovery().__class__.__name__ == "Stub"
assert Container.discovery().run() == []
assert Container.discovery() == Container.discovery()
def test_stub_behaviour(mocker, cfgdir):
"""Assert calling run() for "cloudselect.discovery.stub" plugin."""
cloud = CloudSelect(cfgdir)
service_provider = cloud.plugin(
"cloudselect.discovery.stub", DiscoveryServiceProvider,
)
stub = service_provider()
mocker.patch.object(Stub, "run")
stub.run()
Stub.run.assert_called_once_with()
| # Copyright 2019 <NAME> and individual contributors
# See the LICENSE.txt file at the top-level directory of this distribution.
#
# Licensed under the MIT license
# <LICENSE-MIT or http://opensource.org/licenses/MIT>
# This file may not be copied, modified, or distributed
# except according to those terms.
"""This module is used for testing Stub discovery plugin."""
from cloudselect import Container
from cloudselect.cloudselect import CloudSelect
from cloudselect.discovery import DiscoveryServiceProvider
from cloudselect.discovery.stub import Stub
def test_stub_discovery(cfgdir):
"""
Testing Stub initializaion.
Is fabric creating Stub by default?
Does Stub return []?
Is Stub singleton?
"""
cloud = CloudSelect(cfgdir)
# Read shared part
profile = cloud.configuration_read()
args = cloud.parse_args([])
cloud.fabric(profile, args)
assert Container.discovery().__class__.__name__ == "Stub"
assert Container.discovery().run() == []
assert Container.discovery() == Container.discovery()
def test_stub_behaviour(mocker, cfgdir):
"""Assert calling run() for "cloudselect.discovery.stub" plugin."""
cloud = CloudSelect(cfgdir)
service_provider = cloud.plugin(
"cloudselect.discovery.stub", DiscoveryServiceProvider,
)
stub = service_provider()
mocker.patch.object(Stub, "run")
stub.run()
Stub.run.assert_called_once_with() | en | 0.719138 | # Copyright 2019 <NAME> and individual contributors # See the LICENSE.txt file at the top-level directory of this distribution. # # Licensed under the MIT license # <LICENSE-MIT or http://opensource.org/licenses/MIT> # This file may not be copied, modified, or distributed # except according to those terms. This module is used for testing Stub discovery plugin. Testing Stub initializaion. Is fabric creating Stub by default? Does Stub return []? Is Stub singleton? # Read shared part Assert calling run() for "cloudselect.discovery.stub" plugin. | 1.92073 | 2 |
EngVer/UpdateGoogleDrive.py | 0xf3cd/Chinese-Resume | 0 | 6630497 | import os
from pydrive import auth, drive
def UpdateFile(file_id, src_path):
f = drive.CreateFile({'id': file_id})
f.SetContentFile(src_path)
f.Upload()
def CreateFile(parent_folder_id, file_name, src_path):
f = drive.CreateFile({
'title': file_name,
'parents': [{'id': parent_folder_id}],
})
f.SetContentFile(src_path)
f.Upload()
FOLDER_ID = '10CgNO2GlpE6MgC4B-WWOFdWxPMBMfeIp'
RESUME_PATH = './EngVer.pdf'
DEST_FILE_NAME = 'Resume-NingqiWang.pdf'
g_auth = auth.GoogleAuth()
g_auth.LocalWebserverAuth() # Creates local webserver and auto handles authentication.
drive = drive.GoogleDrive(g_auth)
# Create or update the resume
file_list = drive.ListFile({
'q': f"'{FOLDER_ID}' in parents and trashed=false"
}).GetList()
for f_data in file_list:
f_name = f_data['title']
if f_name == DEST_FILE_NAME:
UpdateFile(f_data['id'], RESUME_PATH)
os._exit(0)
# At this point, we can make sure that the .pdf file has not been uploaded yet
CreateFile(FOLDER_ID, DEST_FILE_NAME, RESUME_PATH)
| import os
from pydrive import auth, drive
def UpdateFile(file_id, src_path):
f = drive.CreateFile({'id': file_id})
f.SetContentFile(src_path)
f.Upload()
def CreateFile(parent_folder_id, file_name, src_path):
f = drive.CreateFile({
'title': file_name,
'parents': [{'id': parent_folder_id}],
})
f.SetContentFile(src_path)
f.Upload()
FOLDER_ID = '10CgNO2GlpE6MgC4B-WWOFdWxPMBMfeIp'
RESUME_PATH = './EngVer.pdf'
DEST_FILE_NAME = 'Resume-NingqiWang.pdf'
g_auth = auth.GoogleAuth()
g_auth.LocalWebserverAuth() # Creates local webserver and auto handles authentication.
drive = drive.GoogleDrive(g_auth)
# Create or update the resume
file_list = drive.ListFile({
'q': f"'{FOLDER_ID}' in parents and trashed=false"
}).GetList()
for f_data in file_list:
f_name = f_data['title']
if f_name == DEST_FILE_NAME:
UpdateFile(f_data['id'], RESUME_PATH)
os._exit(0)
# At this point, we can make sure that the .pdf file has not been uploaded yet
CreateFile(FOLDER_ID, DEST_FILE_NAME, RESUME_PATH)
| en | 0.937672 | # Creates local webserver and auto handles authentication. # Create or update the resume # At this point, we can make sure that the .pdf file has not been uploaded yet | 2.761962 | 3 |
projects/VISOLO/visolo/visolo.py | SuHoHan95/VISOLO | 0 | 6630498 | <filename>projects/VISOLO/visolo/visolo.py<gh_stars>0
import math
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
import copy
import cv2
from scipy.ndimage.morphology import binary_dilation
from PIL import Image, ImageDraw, ImageFont
import os
from detectron2.modeling import META_ARCH_REGISTRY, build_backbone
from detectron2.structures import Boxes, ImageList, Instances
from .models.visolo_model import VISOLO, SetCriterion, DataUtils
__all__ = ["Visolo"]
@META_ARCH_REGISTRY.register()
class Visolo(nn.Module):
def __init__(self, cfg):
super().__init__()
self.num_frames = cfg.INPUT.SAMPLING_FRAME_NUM
self.data_eps = cfg.INPUT.SAMPLING_EPS
self.device = torch.device(cfg.MODEL.DEVICE)
self.num_class = cfg.MODEL.VISOLO.NUM_CLASSES
self.mask_weight = cfg.MODEL.VISOLO.MASK_WEIGHT
self.FL_alpha = cfg.MODEL.VISOLO.FOCAL_LOSS_ALPHA
self.FL_gamma = cfg.MODEL.VISOLO.FOCAL_LOSS_GAMMA
self.DL_eps = cfg.MODEL.VISOLO.DICE_LOSS_EPS
self.S = cfg.MODEL.VISOLO.GRID_NUM
self.indim = cfg.MODEL.VISOLO.INDIM
self.outdim = cfg.MODEL.VISOLO.OUTDIM
self.norm = cfg.MODEL.VISOLO.NORM
self.tracking_thr = cfg.MODEL.VISOLO.TRACKING_THR
self.score_thr = cfg.MODEL.VISOLO.SCORE_THR
self.mask_thr = cfg.MODEL.VISOLO.MASK_THR
self.update_thr = cfg.MODEL.VISOLO.UPDATE_THR
self.kernel = cfg.MODEL.VISOLO.KERNEL
self.sigma = cfg.MODEL.VISOLO.SIGMA
self.nms_pre = cfg.MODEL.VISOLO.NMS_PRE
backbone = build_backbone(cfg)
backbone_features = cfg.MODEL.RESNETS.OUT_FEATURES
self.model = VISOLO(backbone, backbone_features, self.S, self.num_class, self.indim, self.outdim, self.norm)
self.criterion = SetCriterion(self.FL_alpha, self.FL_gamma, self.DL_eps, self.mask_weight)
self.data_utils = DataUtils(self.device, self.num_class, self.S, self.data_eps)
self.tracking_module = self.model.Tracking_branch
pixel_mean = torch.Tensor(cfg.MODEL.PIXEL_MEAN).to(self.device).view(3, 1, 1)
pixel_std = torch.Tensor(cfg.MODEL.PIXEL_STD).to(self.device).view(3, 1, 1)
self.normalizer = lambda x: (x - pixel_mean) / pixel_std
self.to(self.device)
def preprocess_image(self, batched_inputs, is_eval=False):
B = len(batched_inputs)
N = len(batched_inputs[0]['image'])
dim, H, W = batched_inputs[0]['image'][0].size()
frames = torch.zeros((B, dim, N, H, W), dtype=torch.float32, device=self.device)
for b in range(B):
for n in range(N):
frames[b,:,n,:,:] = self.normalizer(batched_inputs[b]['image'][n].to(self.device))
if is_eval:
return frames, batched_inputs[0]['height'], batched_inputs[0]['width'], batched_inputs[0]['video_id']
else:
return frames
def getCandidateDict(self, f_idx, valid_idx, tra_feature, idx_to_inst):
candidate = {}
candidate['f_idx'] = f_idx
candidate['valid_idx'] = valid_idx
candidate['tra_feature'] = tra_feature
candidate['idx_mapping'] = idx_to_inst
return candidate
def forward(self, batched_inputs):
if self.training:
frames = self.preprocess_image(batched_inputs)
pred_masks, pred_kernels, pred_cats, pred_tracking = self.model(frames)
GT_masks, GT_classes, GT_tracking = self.data_utils.getGridGT(batched_inputs)
loss_dict = self.criterion(pred_cats, pred_masks, pred_kernels, pred_tracking,
GT_classes, GT_masks, GT_tracking)
return loss_dict
else:
frames, v_h, v_w, v_id = self.preprocess_image(batched_inputs, is_eval=True)
N = frames.size()[2]
tra_candidates = []
pred_masks_0, pred_kernel_0, pred_cats_0, frame_f, cat_f, kernel_f = self.model(frames[:, :, 0, :, :], None, None, None)
m_frame_f = frame_f.unsqueeze(2)
m_cat_f = cat_f.unsqueeze(2)
m_kernel_f = kernel_f.unsqueeze(2)
pred_masks_1, pred_kernel_1, pred_cats_1, pred_tracking, frame_f, cat_f, kernel_f = self.model(frames[:, :, 1, :, :], frame_f,
cat_f, kernel_f)
grid_weight = None
inst_masks, inst_cats, inst_cat_scores, inst_cat_scores_ori, f0_result, valid_tra, valid_ind_map_to_inst, \
_, _, tra_candidates, f0_valid_ind \
= self.getTestResultV4(pred_masks_0[0], pred_kernel_0[0], pred_cats_0[0], pred_tracking[0],
None,
m_frame_f[:, :, -1, :, :], N, 0, [], [], [], [],
None, None, [], tra_candidates)
m_frame_f = torch.cat((m_frame_f, frame_f.unsqueeze(2)), dim=2)
m_cat_f = torch.cat((m_cat_f, cat_f.unsqueeze(2)), dim=2)
m_kernel_f = torch.cat((m_kernel_f, kernel_f.unsqueeze(2)), dim=2)
pred_masks_2, pred_kernel_2, pred_cats_2, pred_tracking, frame_f, cat_f, kernel_f = self.model(frames[:, :, 2, :, :],
m_frame_f,
m_cat_f, m_kernel_f)
grid_weight = self.model(m_frame_f[:, :, -2, :, :], m_frame_f[:, :, -1, :, :], None)
inst_masks, inst_cats, inst_cat_scores, inst_cat_scores_ori, f0_result, valid_tra, valid_ind_map_to_inst, \
pre_inst_tra_check, pre_ind_map_inst, tra_candidates, f1_valid_ind \
= self.getTestResultV4(pred_masks_1[0], pred_kernel_1[0], pred_cats_1[0], pred_tracking[0],
grid_weight,
m_frame_f[:, :, -1, :, :], N, 1, inst_masks, inst_cats, inst_cat_scores,
inst_cat_scores_ori,
f0_result, valid_tra, valid_ind_map_to_inst, tra_candidates)
if pre_inst_tra_check is not None and (pre_inst_tra_check == 0).sum() > 0:
pre_valid_ind = f0_valid_ind[pre_inst_tra_check == 0]
tra_candidates.append(self.getCandidateDict(0, pre_valid_ind, m_frame_f[:, :, -2, :, :], pre_ind_map_inst))
m_frame_f = torch.cat((m_frame_f, frame_f.unsqueeze(2)), dim=2)
m_cat_f = torch.cat((m_cat_f, cat_f.unsqueeze(2)), dim=2)
m_kernel_f = torch.cat((m_kernel_f, kernel_f.unsqueeze(2)), dim=2)
pred_masks_1 = pred_masks_2.clone()
pred_kernel_1 = pred_kernel_2.clone()
pred_cats_1 = pred_cats_2.clone()
if f1_valid_ind is not None:
f0_valid_ind = f1_valid_ind.clone()
else:
f0_valid_ind = f1_valid_ind
for n in range(3, N + 1):
if n == N:
grid_weight = self.model(m_frame_f[:,:,-2,:,:], m_frame_f[:,:,-1,:,:], m_frame_f[:,:,-3,:,:])
inst_masks, inst_cats, inst_cat_scores, inst_cat_scores_ori, f0_result, valid_tra, valid_ind_map_to_inst, \
pre_inst_tra_check, pre_ind_map_inst, tra_candidates, f1_valid_ind \
= self.getTestResultV4(pred_masks_1[0], pred_kernel_1[0], pred_cats_1[0], None, grid_weight,
m_frame_f[:,:,-1,:,:], N, n - 1, inst_masks, inst_cats, inst_cat_scores,
inst_cat_scores_ori,
f0_result, valid_tra, valid_ind_map_to_inst, tra_candidates)
continue
pred_masks_2, pred_kernel_2, pred_cats_2, pred_tracking, frame_f, cat_f, kernel_f = self.model(frames[:, :, n, :, :], m_frame_f,
m_cat_f, m_kernel_f) # B,S**2,1,H,W / B,C,1,S,S / B,1,S**2,S**2
grid_weight = self.model(m_frame_f[:,:,-2,:,:], m_frame_f[:,:,-1,:,:], m_frame_f[:,:,-3,:,:])
inst_masks, inst_cats, inst_cat_scores, inst_cat_scores_ori, f0_result, valid_tra, valid_ind_map_to_inst, \
pre_inst_tra_check, pre_ind_map_inst, tra_candidates, f1_valid_ind \
= self.getTestResultV4(pred_masks_1[0], pred_kernel_1[0], pred_cats_1[0], pred_tracking[0], grid_weight, m_frame_f[:,:,-1,:,:],
N, n - 1, inst_masks, inst_cats, inst_cat_scores, inst_cat_scores_ori,
f0_result, valid_tra, valid_ind_map_to_inst, tra_candidates)
if pre_inst_tra_check is not None and (pre_inst_tra_check == 0).sum() > 0:
pre_valid_ind = f0_valid_ind[pre_inst_tra_check == 0]
tra_candidates.append(self.getCandidateDict(n - 2, pre_valid_ind, m_frame_f[:,:,-2,:,:], pre_ind_map_inst))
if n % 5 == 2:
m_frame_f = torch.cat((m_frame_f, frame_f.unsqueeze(2)), dim=2)
m_cat_f = torch.cat((m_cat_f, cat_f.unsqueeze(2)), dim=2)
m_kernel_f = torch.cat((m_kernel_f, kernel_f.unsqueeze(2)), dim=2)
else:
m_frame_f = torch.cat((m_frame_f[:, :, :-2], m_frame_f[:, :, -1:], frame_f.unsqueeze(2)),
dim=2)
m_cat_f = torch.cat((m_cat_f[:, :, :-2], m_cat_f[:, :, -1:], cat_f.unsqueeze(2)), dim=2)
m_kernel_f = torch.cat((m_kernel_f[:, :, :-2], m_kernel_f[:, :, -1:], kernel_f.unsqueeze(2)), dim=2)
pred_masks_1 = pred_masks_2.clone()
pred_kernel_1 = pred_kernel_2.clone()
pred_cats_1 = pred_cats_2.clone()
if f1_valid_ind is not None:
f0_valid_ind = f1_valid_ind.clone()
else:
f0_valid_ind = f1_valid_ind
if isinstance(inst_masks, list):
return None
inst_masks = F.interpolate(inst_masks, (v_h, v_w), mode='bilinear', align_corners=False)
inst_masks = (inst_masks >= self.mask_thr).float()
new_inst_cat_scores, new_inst_cats = self.getAverageCat(inst_cat_scores_ori)
video_output = {
"pred_scores": new_inst_cat_scores,
"pred_labels": new_inst_cats,
"pred_masks": inst_masks,
}
return video_output
def getAverageCat(self, cat_scores_ori):
# cat_scores_ori: K, v_l, 40
K, L, _ = cat_scores_ori.size()
valid_frame_num = torch.count_nonzero(torch.sum(cat_scores_ori, dim=2), dim=1).view(K, 1)
avg_scores = torch.div(torch.sum(cat_scores_ori, dim=1), valid_frame_num.expand(K, self.num_class))
cat_scores, cats = torch.max(avg_scores, dim=1)
return cat_scores, cats
def getTestResultV4(self, N_pred_masks, N_pred_kernels, N_pred_cats, N_pred_tra, grid_weight, tra_feature, N, f_idx,
inst_masks, inst_cats, inst_cat_scores, inst_cat_scores_ori, f0_result=None,
valid_tra=None, valid_ind_map_to_inst=None, tra_candidates=None):
# N_pred_masks : dim, 1, H/4, W/4
# N_pred_kernels : dim, 1, S1, S2
# N_pred_cats : C, 1, S1, S2
# N_pred_tra : 1, S1*S2, S1*S2
inst_masks = inst_masks # K, N, H, W
inst_cats = inst_cats # K, N
inst_cat_scores = inst_cat_scores # K, N
inst_cat_scores_ori = inst_cat_scores_ori # K, N, 40
f0_result = f0_result
valid_tra = valid_tra
valid_ind_map_to_inst = valid_ind_map_to_inst
N = N
f_idx = f_idx
pre_inst_tra_check = None
pre_ind_map_inst = None
f0_valid_ind = None
if grid_weight is not None:
grid_weight = grid_weight[0,0]
if f0_result is None:
f0_result = self.getSegMaskV5(N_pred_masks[:,0,:,:], N_pred_kernels[:,0,:,:], N_pred_cats[:,0,:,:], grid_weight)
if f0_result is not None:
f0_seg_masks, f0_cat_labels, f0_cat_scores, f0_cat_scores_ori, f0_valid_ind = f0_result
k0, _, _ = f0_seg_masks.size()
if f_idx != N - 1:
valid_tra = N_pred_tra[0, f0_valid_ind, :] # k0, S**2
inst_num = len(inst_masks)
no_match_ind = [x for x in range(k0)]
inst_idx, tra_candidates = self.getTrackInfo(tra_candidates, tra_feature, no_match_ind,
valid_ind=f0_valid_ind)
map_num = 0
for i in range(k0):
if inst_idx[i] != -1:
if inst_masks[inst_idx[i]][f_idx].sum() != 0:
print('Error and mask in inst track!!!')
exit()
else:
inst_masks[inst_idx[i]][f_idx] = f0_seg_masks[i, :, :]
inst_cats[inst_idx[i]][f_idx] = f0_cat_labels[i]
inst_cat_scores[inst_idx[i]][f_idx] = f0_cat_scores[i]
inst_cat_scores_ori[inst_idx[i]][f_idx] = f0_cat_scores_ori[i]
valid_ind_map_to_inst.append(inst_idx[i])
else:
_, H, W = f0_seg_masks.size()
masks = torch.zeros((1, N, H, W), device=self.device)
cats = torch.full((1, N), -1, device=self.device)
cat_scores = torch.zeros((1, N), device=self.device)
cat_scores_ori = torch.zeros((1, N, self.num_class), device=self.device)
masks[0, f_idx] = f0_seg_masks[i, :, :]
cats[0, f_idx] = f0_cat_labels[i]
cat_scores[0, f_idx] = f0_cat_scores[i]
cat_scores_ori[0, f_idx] = f0_cat_scores_ori[i]
if isinstance(inst_masks, list):
inst_masks = masks
inst_cats = cats
inst_cat_scores = cat_scores
inst_cat_scores_ori = cat_scores_ori
else:
inst_masks = torch.cat((inst_masks, masks), dim=0)
inst_cats = torch.cat((inst_cats, cats), dim=0)
inst_cat_scores = torch.cat((inst_cat_scores, cat_scores), dim=0)
inst_cat_scores_ori = torch.cat((inst_cat_scores_ori, cat_scores_ori), dim=0)
valid_ind_map_to_inst.append(inst_num + map_num)
map_num+=1
else:
f0_result = self.getSegMaskV5(N_pred_masks[:,0,:,:], N_pred_kernels[:,0,:,:], N_pred_cats[:,0,:,:], grid_weight)
if f0_result is not None:
f0_seg_masks, f0_cat_labels, f0_cat_scores, f0_cat_scores_ori, f0_valid_ind = f0_result
k1, _, _ = f0_seg_masks.size()
no_match_ind = []
temp_map_ind = [0 for _ in range(k1)]
pre_inst_tra_check = torch.zeros((valid_tra.size()[0]))
inst_num = len(inst_masks)
valid_tra = valid_tra[:, f0_valid_ind] # k0, k1
for i in range(k1):
tra_sort_ind = torch.argsort(valid_tra[:, i], descending=True)
check_match = 0
for ind in tra_sort_ind:
inst_map_ind = valid_ind_map_to_inst[int(ind)]
if inst_masks[inst_map_ind][f_idx].sum() == 0 and valid_tra[int(ind), i] >= self.tracking_thr:
inst_masks[inst_map_ind][f_idx] = f0_seg_masks[i, :, :]
inst_cats[inst_map_ind][f_idx] = f0_cat_labels[i]
inst_cat_scores[inst_map_ind][f_idx] = f0_cat_scores[i]
inst_cat_scores_ori[inst_map_ind][f_idx] = f0_cat_scores_ori[i]
check_match = 1
temp_map_ind[i] = inst_map_ind
pre_inst_tra_check[int(ind)] = 1
break
if check_match == 0:
no_match_ind.append(i)
valid_ind = f0_valid_ind[no_match_ind]
inst_idx, tra_candidates = self.getTrackInfo(tra_candidates, tra_feature, no_match_ind,
valid_ind=valid_ind)
map_num = 0
for i in range(len(no_match_ind)):
ind = no_match_ind[i]
if inst_idx[i] != -1:
if inst_masks[inst_idx[i]][f_idx].sum() != 0:
print('Error add mask in inst track!!!')
exit()
else:
inst_masks[inst_idx[i]][f_idx] = f0_seg_masks[ind, :, :]
inst_cats[inst_idx[i]][f_idx] = f0_cat_labels[ind]
inst_cat_scores[inst_idx[i]][f_idx] = f0_cat_scores[ind]
inst_cat_scores_ori[inst_idx[i]][f_idx] = f0_cat_scores_ori[ind]
temp_map_ind[ind] = inst_idx[i]
else:
_, H, W = f0_seg_masks.size()
masks = torch.zeros((1, N, H, W), device=self.device)
cats = torch.full((1, N), -1, device=self.device)
cat_scores = torch.zeros((1, N), device=self.device)
cat_scores_ori = torch.zeros((1, N, self.num_class), device=self.device)
masks[0, f_idx] = f0_seg_masks[ind, :, :]
cats[0, f_idx] = f0_cat_labels[ind]
cat_scores[0, f_idx] = f0_cat_scores[ind]
cat_scores_ori[0, f_idx] = f0_cat_scores_ori[ind]
inst_masks = torch.cat((inst_masks, masks), dim=0)
inst_cats = torch.cat((inst_cats, cats), dim=0)
inst_cat_scores = torch.cat((inst_cat_scores, cat_scores), dim=0)
inst_cat_scores_ori = torch.cat((inst_cat_scores_ori, cat_scores_ori), dim=0)
temp_map_ind[ind] = inst_num + map_num
map_num += 1
pre_ind_map_inst = [k for idx, k in enumerate(valid_ind_map_to_inst)
if pre_inst_tra_check[idx] == 0]
valid_ind_map_to_inst = temp_map_ind
if f_idx != N - 1:
valid_tra = N_pred_tra[0, f0_valid_ind, :] # k1, S**2
else:
pre_inst_tra_check = torch.zeros((valid_tra.size()[0]))
pre_ind_map_inst = copy.deepcopy(valid_ind_map_to_inst)
valid_tra = None
valid_ind_map_to_inst = []
return inst_masks, inst_cats, inst_cat_scores, inst_cat_scores_ori,\
f0_result, valid_tra, valid_ind_map_to_inst,\
pre_inst_tra_check, pre_ind_map_inst, tra_candidates, f0_valid_ind
def getSegMaskV5(self, pred_masks, pred_kernels, pred_cats, grid_weight):
# pred_masks : dim, H/4, W/4
# pred_kernels : dim, S1, S2
# pred_cats : C, S1, S2
# grid_weight : S1*S2
_, H, W = pred_masks.size()
C, S1, S2 = pred_cats.size()
cat_scores = pred_cats.reshape(-1, S1 * S2).transpose(1, 0) # S**2, C
cat_scores_ori = cat_scores.clone()
cat_scores, cat_labels = cat_scores.max(1) # S**2
if grid_weight is not None:
cat_scores *= grid_weight
cat_scores[cat_scores < self.score_thr] = 0.
valid_ind = cat_scores.nonzero()[:, 0]
if valid_ind.sum() == 0:
return None
kernels = pred_kernels.reshape(-1, S1*S2).transpose(1, 0) # S1*S2, dim
kernels = kernels[valid_ind]
seg_preds = self.getMaskMap(pred_masks, kernels)
seg_masks = (seg_preds > self.mask_thr).float()
cat_scores = cat_scores[valid_ind]
cat_labels = cat_labels[valid_ind]
sum_masks = seg_masks.sum((1, 2)).float()
seg_scores = (seg_preds * seg_masks.float()).sum((1, 2)) / sum_masks
cat_scores *= seg_scores
sort_ind = torch.argsort(cat_scores, descending=True)
if sort_ind.size()[0] > self.nms_pre:
sort_ind = sort_ind[:self.nms_pre]
seg_masks = seg_masks[sort_ind, :, :]
cat_scores = cat_scores[sort_ind]
cat_labels = cat_labels[sort_ind]
valid_ind = valid_ind[sort_ind]
cat_scores = self.matrix_nms(seg_masks, cat_labels, cat_scores)
keep = cat_scores >= self.update_thr
if keep.sum() == 0:
return None
seg_masks = seg_masks[keep, :, :]
cat_scores = cat_scores[keep]
cat_labels = cat_labels[keep]
valid_ind = valid_ind[keep]
sort_ind = torch.argsort(cat_scores, descending=True)
if sort_ind.size()[0] > 100:
sort_ind = sort_ind[:100]
seg_masks = seg_masks[sort_ind, :, :]
cat_scores = cat_scores[sort_ind]
cat_labels = cat_labels[sort_ind]
valid_ind = valid_ind[sort_ind]
cat_scores_ori = cat_scores_ori[valid_ind, :]
for i in range(len(valid_ind) - 1):
if seg_masks[i].sum() == 0:
continue
for j in range(i + 1, len(valid_ind)):
inter_region = (seg_masks[i] * seg_masks[j]).sum()
mask_region = seg_masks[j].sum()
if inter_region / mask_region > 0.5:
seg_masks[j] = 0
final_valid_ind = (seg_masks.sum((1, 2)) > 0)
seg_masks = seg_masks[final_valid_ind, :, :]
cat_scores = cat_scores[final_valid_ind]
cat_labels = cat_labels[final_valid_ind]
cat_scores_ori = cat_scores_ori[final_valid_ind, :]
valid_ind = valid_ind[final_valid_ind]
return seg_masks, cat_labels, cat_scores, cat_scores_ori, valid_ind
def matrix_nms(self, seg_masks, cate_labels, cate_scores, sum_masks=None):
"""Matrix NMS for multi-class masks.
Args:
seg_masks (Tensor): shape (n, h, w)
cate_labels (Tensor): shape (n), mask labels in descending order
cate_scores (Tensor): shape (n), mask scores in descending order
self.kernel (str): 'linear' or 'gauss'
self.sigma (float): std in gaussian method
sum_masks (Tensor): The sum of seg_masks
Returns:
Tensor: cate_scores_update, tensors of shape (n)
"""
n_samples = len(cate_labels)
if n_samples == 0:
return []
if sum_masks is None:
sum_masks = seg_masks.sum((1, 2)).float()
seg_masks = seg_masks.reshape(n_samples, -1).float()
# inter.
inter_matrix = torch.mm(seg_masks, seg_masks.transpose(1, 0))
# union.
sum_masks_x = sum_masks.expand(n_samples, n_samples)
# iou.
iou_matrix = (inter_matrix / (sum_masks_x + sum_masks_x.transpose(1, 0) - inter_matrix)).triu(diagonal=1)
# label_specific matrix.
cate_labels_x = cate_labels.expand(n_samples, n_samples)
label_matrix = (cate_labels_x == cate_labels_x.transpose(1, 0))
label_matrix = label_matrix.float().triu(diagonal=1)
# IoU compensation
compensate_iou, _ = (iou_matrix * label_matrix).max(0)
compensate_iou = compensate_iou.expand(n_samples, n_samples).transpose(1, 0)
# IoU decay
decay_iou = iou_matrix * label_matrix
# matrix nms
if self.kernel == 'gaussian':
decay_matrix = torch.exp(-1 * self.sigma * (decay_iou ** 2))
compensate_matrix = torch.exp(-1 * self.sigma * (compensate_iou ** 2))
decay_coefficient, _ = (decay_matrix / compensate_matrix).min(0)
elif self.kernel == 'linear':
decay_matrix = (1 - decay_iou) / (1 - compensate_iou)
decay_coefficient, _ = decay_matrix.min(0)
else:
raise NotImplementedError
# update the score.
cate_scores_update = cate_scores * decay_coefficient
return cate_scores_update
def getTrackInfo(self, tra_candidates, tra_feature, idxs, valid_ind=None):
can_num = len(tra_candidates)
inst_idx = [-1 for _ in range(len(idxs))]
if can_num == 0:
return inst_idx, tra_candidates
[k1] = valid_ind.size()
tra_candidate_check = []
for c in range(can_num-1, -1, -1):
tra_check = []
tra_candidate = tra_candidates[c]
[k0] = tra_candidate['valid_idx'].size()
pred_tra = torch.sigmoid(self.tracking_module(tra_candidate['tra_feature'], tra_feature))
pred_tra = pred_tra[0,0]
valid_tra = pred_tra[tra_candidate['valid_idx'],:]
valid_tra = valid_tra[:, valid_ind]
for i in range(k1):
if inst_idx[i] == -1:
tra_sort_ind = torch.argsort(valid_tra[:, i], descending=True)
for ind in tra_sort_ind:
if valid_tra[int(ind), i] >= self.tracking_thr and int(ind) not in tra_check:
inst_idx[i] = tra_candidate['idx_mapping'][int(ind)]
tra_check.append(int(ind))
break
valid_masks_idx = [x for x in range(k0) if x not in tra_check]
tra_candidate['valid_idx'] = tra_candidate['valid_idx'][valid_masks_idx]
tra_candidate['idx_mapping'] = [k for idx, k in enumerate(tra_candidate['idx_mapping'])
if idx not in tra_check]
if len(tra_candidate['idx_mapping']) == 0:
tra_candidate_check.append(c)
if -1 not in inst_idx:
break
tra_candidates = [k for idx, k in enumerate(tra_candidates)
if idx not in tra_candidate_check]
return inst_idx, tra_candidates
def getMaskMap(self, mask, kernel):
# mask: dim, H/4, W/4
# kernel: valid_idxs, dim
# out_device = mask.device
if not mask.is_cuda:
mask = mask.to('cuda')
kernel = kernel.to('cuda')
num_kernel, _ = kernel.size()
dim, H, W = mask.size()
mask = mask.unsqueeze(0) # 1, dim, H/4, W/4
mask_map = F.conv2d(mask, kernel.view(num_kernel, dim, 1, 1))
mask_map = F.interpolate(mask_map, scale_factor=4, mode='bilinear', align_corners=False).squeeze(0)
mask_map = torch.sigmoid(mask_map)
return mask_map
| <filename>projects/VISOLO/visolo/visolo.py<gh_stars>0
import math
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
import copy
import cv2
from scipy.ndimage.morphology import binary_dilation
from PIL import Image, ImageDraw, ImageFont
import os
from detectron2.modeling import META_ARCH_REGISTRY, build_backbone
from detectron2.structures import Boxes, ImageList, Instances
from .models.visolo_model import VISOLO, SetCriterion, DataUtils
__all__ = ["Visolo"]
@META_ARCH_REGISTRY.register()
class Visolo(nn.Module):
def __init__(self, cfg):
super().__init__()
self.num_frames = cfg.INPUT.SAMPLING_FRAME_NUM
self.data_eps = cfg.INPUT.SAMPLING_EPS
self.device = torch.device(cfg.MODEL.DEVICE)
self.num_class = cfg.MODEL.VISOLO.NUM_CLASSES
self.mask_weight = cfg.MODEL.VISOLO.MASK_WEIGHT
self.FL_alpha = cfg.MODEL.VISOLO.FOCAL_LOSS_ALPHA
self.FL_gamma = cfg.MODEL.VISOLO.FOCAL_LOSS_GAMMA
self.DL_eps = cfg.MODEL.VISOLO.DICE_LOSS_EPS
self.S = cfg.MODEL.VISOLO.GRID_NUM
self.indim = cfg.MODEL.VISOLO.INDIM
self.outdim = cfg.MODEL.VISOLO.OUTDIM
self.norm = cfg.MODEL.VISOLO.NORM
self.tracking_thr = cfg.MODEL.VISOLO.TRACKING_THR
self.score_thr = cfg.MODEL.VISOLO.SCORE_THR
self.mask_thr = cfg.MODEL.VISOLO.MASK_THR
self.update_thr = cfg.MODEL.VISOLO.UPDATE_THR
self.kernel = cfg.MODEL.VISOLO.KERNEL
self.sigma = cfg.MODEL.VISOLO.SIGMA
self.nms_pre = cfg.MODEL.VISOLO.NMS_PRE
backbone = build_backbone(cfg)
backbone_features = cfg.MODEL.RESNETS.OUT_FEATURES
self.model = VISOLO(backbone, backbone_features, self.S, self.num_class, self.indim, self.outdim, self.norm)
self.criterion = SetCriterion(self.FL_alpha, self.FL_gamma, self.DL_eps, self.mask_weight)
self.data_utils = DataUtils(self.device, self.num_class, self.S, self.data_eps)
self.tracking_module = self.model.Tracking_branch
pixel_mean = torch.Tensor(cfg.MODEL.PIXEL_MEAN).to(self.device).view(3, 1, 1)
pixel_std = torch.Tensor(cfg.MODEL.PIXEL_STD).to(self.device).view(3, 1, 1)
self.normalizer = lambda x: (x - pixel_mean) / pixel_std
self.to(self.device)
def preprocess_image(self, batched_inputs, is_eval=False):
B = len(batched_inputs)
N = len(batched_inputs[0]['image'])
dim, H, W = batched_inputs[0]['image'][0].size()
frames = torch.zeros((B, dim, N, H, W), dtype=torch.float32, device=self.device)
for b in range(B):
for n in range(N):
frames[b,:,n,:,:] = self.normalizer(batched_inputs[b]['image'][n].to(self.device))
if is_eval:
return frames, batched_inputs[0]['height'], batched_inputs[0]['width'], batched_inputs[0]['video_id']
else:
return frames
def getCandidateDict(self, f_idx, valid_idx, tra_feature, idx_to_inst):
candidate = {}
candidate['f_idx'] = f_idx
candidate['valid_idx'] = valid_idx
candidate['tra_feature'] = tra_feature
candidate['idx_mapping'] = idx_to_inst
return candidate
def forward(self, batched_inputs):
if self.training:
frames = self.preprocess_image(batched_inputs)
pred_masks, pred_kernels, pred_cats, pred_tracking = self.model(frames)
GT_masks, GT_classes, GT_tracking = self.data_utils.getGridGT(batched_inputs)
loss_dict = self.criterion(pred_cats, pred_masks, pred_kernels, pred_tracking,
GT_classes, GT_masks, GT_tracking)
return loss_dict
else:
frames, v_h, v_w, v_id = self.preprocess_image(batched_inputs, is_eval=True)
N = frames.size()[2]
tra_candidates = []
pred_masks_0, pred_kernel_0, pred_cats_0, frame_f, cat_f, kernel_f = self.model(frames[:, :, 0, :, :], None, None, None)
m_frame_f = frame_f.unsqueeze(2)
m_cat_f = cat_f.unsqueeze(2)
m_kernel_f = kernel_f.unsqueeze(2)
pred_masks_1, pred_kernel_1, pred_cats_1, pred_tracking, frame_f, cat_f, kernel_f = self.model(frames[:, :, 1, :, :], frame_f,
cat_f, kernel_f)
grid_weight = None
inst_masks, inst_cats, inst_cat_scores, inst_cat_scores_ori, f0_result, valid_tra, valid_ind_map_to_inst, \
_, _, tra_candidates, f0_valid_ind \
= self.getTestResultV4(pred_masks_0[0], pred_kernel_0[0], pred_cats_0[0], pred_tracking[0],
None,
m_frame_f[:, :, -1, :, :], N, 0, [], [], [], [],
None, None, [], tra_candidates)
m_frame_f = torch.cat((m_frame_f, frame_f.unsqueeze(2)), dim=2)
m_cat_f = torch.cat((m_cat_f, cat_f.unsqueeze(2)), dim=2)
m_kernel_f = torch.cat((m_kernel_f, kernel_f.unsqueeze(2)), dim=2)
pred_masks_2, pred_kernel_2, pred_cats_2, pred_tracking, frame_f, cat_f, kernel_f = self.model(frames[:, :, 2, :, :],
m_frame_f,
m_cat_f, m_kernel_f)
grid_weight = self.model(m_frame_f[:, :, -2, :, :], m_frame_f[:, :, -1, :, :], None)
inst_masks, inst_cats, inst_cat_scores, inst_cat_scores_ori, f0_result, valid_tra, valid_ind_map_to_inst, \
pre_inst_tra_check, pre_ind_map_inst, tra_candidates, f1_valid_ind \
= self.getTestResultV4(pred_masks_1[0], pred_kernel_1[0], pred_cats_1[0], pred_tracking[0],
grid_weight,
m_frame_f[:, :, -1, :, :], N, 1, inst_masks, inst_cats, inst_cat_scores,
inst_cat_scores_ori,
f0_result, valid_tra, valid_ind_map_to_inst, tra_candidates)
if pre_inst_tra_check is not None and (pre_inst_tra_check == 0).sum() > 0:
pre_valid_ind = f0_valid_ind[pre_inst_tra_check == 0]
tra_candidates.append(self.getCandidateDict(0, pre_valid_ind, m_frame_f[:, :, -2, :, :], pre_ind_map_inst))
m_frame_f = torch.cat((m_frame_f, frame_f.unsqueeze(2)), dim=2)
m_cat_f = torch.cat((m_cat_f, cat_f.unsqueeze(2)), dim=2)
m_kernel_f = torch.cat((m_kernel_f, kernel_f.unsqueeze(2)), dim=2)
pred_masks_1 = pred_masks_2.clone()
pred_kernel_1 = pred_kernel_2.clone()
pred_cats_1 = pred_cats_2.clone()
if f1_valid_ind is not None:
f0_valid_ind = f1_valid_ind.clone()
else:
f0_valid_ind = f1_valid_ind
for n in range(3, N + 1):
if n == N:
grid_weight = self.model(m_frame_f[:,:,-2,:,:], m_frame_f[:,:,-1,:,:], m_frame_f[:,:,-3,:,:])
inst_masks, inst_cats, inst_cat_scores, inst_cat_scores_ori, f0_result, valid_tra, valid_ind_map_to_inst, \
pre_inst_tra_check, pre_ind_map_inst, tra_candidates, f1_valid_ind \
= self.getTestResultV4(pred_masks_1[0], pred_kernel_1[0], pred_cats_1[0], None, grid_weight,
m_frame_f[:,:,-1,:,:], N, n - 1, inst_masks, inst_cats, inst_cat_scores,
inst_cat_scores_ori,
f0_result, valid_tra, valid_ind_map_to_inst, tra_candidates)
continue
pred_masks_2, pred_kernel_2, pred_cats_2, pred_tracking, frame_f, cat_f, kernel_f = self.model(frames[:, :, n, :, :], m_frame_f,
m_cat_f, m_kernel_f) # B,S**2,1,H,W / B,C,1,S,S / B,1,S**2,S**2
grid_weight = self.model(m_frame_f[:,:,-2,:,:], m_frame_f[:,:,-1,:,:], m_frame_f[:,:,-3,:,:])
inst_masks, inst_cats, inst_cat_scores, inst_cat_scores_ori, f0_result, valid_tra, valid_ind_map_to_inst, \
pre_inst_tra_check, pre_ind_map_inst, tra_candidates, f1_valid_ind \
= self.getTestResultV4(pred_masks_1[0], pred_kernel_1[0], pred_cats_1[0], pred_tracking[0], grid_weight, m_frame_f[:,:,-1,:,:],
N, n - 1, inst_masks, inst_cats, inst_cat_scores, inst_cat_scores_ori,
f0_result, valid_tra, valid_ind_map_to_inst, tra_candidates)
if pre_inst_tra_check is not None and (pre_inst_tra_check == 0).sum() > 0:
pre_valid_ind = f0_valid_ind[pre_inst_tra_check == 0]
tra_candidates.append(self.getCandidateDict(n - 2, pre_valid_ind, m_frame_f[:,:,-2,:,:], pre_ind_map_inst))
if n % 5 == 2:
m_frame_f = torch.cat((m_frame_f, frame_f.unsqueeze(2)), dim=2)
m_cat_f = torch.cat((m_cat_f, cat_f.unsqueeze(2)), dim=2)
m_kernel_f = torch.cat((m_kernel_f, kernel_f.unsqueeze(2)), dim=2)
else:
m_frame_f = torch.cat((m_frame_f[:, :, :-2], m_frame_f[:, :, -1:], frame_f.unsqueeze(2)),
dim=2)
m_cat_f = torch.cat((m_cat_f[:, :, :-2], m_cat_f[:, :, -1:], cat_f.unsqueeze(2)), dim=2)
m_kernel_f = torch.cat((m_kernel_f[:, :, :-2], m_kernel_f[:, :, -1:], kernel_f.unsqueeze(2)), dim=2)
pred_masks_1 = pred_masks_2.clone()
pred_kernel_1 = pred_kernel_2.clone()
pred_cats_1 = pred_cats_2.clone()
if f1_valid_ind is not None:
f0_valid_ind = f1_valid_ind.clone()
else:
f0_valid_ind = f1_valid_ind
if isinstance(inst_masks, list):
return None
inst_masks = F.interpolate(inst_masks, (v_h, v_w), mode='bilinear', align_corners=False)
inst_masks = (inst_masks >= self.mask_thr).float()
new_inst_cat_scores, new_inst_cats = self.getAverageCat(inst_cat_scores_ori)
video_output = {
"pred_scores": new_inst_cat_scores,
"pred_labels": new_inst_cats,
"pred_masks": inst_masks,
}
return video_output
def getAverageCat(self, cat_scores_ori):
# cat_scores_ori: K, v_l, 40
K, L, _ = cat_scores_ori.size()
valid_frame_num = torch.count_nonzero(torch.sum(cat_scores_ori, dim=2), dim=1).view(K, 1)
avg_scores = torch.div(torch.sum(cat_scores_ori, dim=1), valid_frame_num.expand(K, self.num_class))
cat_scores, cats = torch.max(avg_scores, dim=1)
return cat_scores, cats
def getTestResultV4(self, N_pred_masks, N_pred_kernels, N_pred_cats, N_pred_tra, grid_weight, tra_feature, N, f_idx,
inst_masks, inst_cats, inst_cat_scores, inst_cat_scores_ori, f0_result=None,
valid_tra=None, valid_ind_map_to_inst=None, tra_candidates=None):
# N_pred_masks : dim, 1, H/4, W/4
# N_pred_kernels : dim, 1, S1, S2
# N_pred_cats : C, 1, S1, S2
# N_pred_tra : 1, S1*S2, S1*S2
inst_masks = inst_masks # K, N, H, W
inst_cats = inst_cats # K, N
inst_cat_scores = inst_cat_scores # K, N
inst_cat_scores_ori = inst_cat_scores_ori # K, N, 40
f0_result = f0_result
valid_tra = valid_tra
valid_ind_map_to_inst = valid_ind_map_to_inst
N = N
f_idx = f_idx
pre_inst_tra_check = None
pre_ind_map_inst = None
f0_valid_ind = None
if grid_weight is not None:
grid_weight = grid_weight[0,0]
if f0_result is None:
f0_result = self.getSegMaskV5(N_pred_masks[:,0,:,:], N_pred_kernels[:,0,:,:], N_pred_cats[:,0,:,:], grid_weight)
if f0_result is not None:
f0_seg_masks, f0_cat_labels, f0_cat_scores, f0_cat_scores_ori, f0_valid_ind = f0_result
k0, _, _ = f0_seg_masks.size()
if f_idx != N - 1:
valid_tra = N_pred_tra[0, f0_valid_ind, :] # k0, S**2
inst_num = len(inst_masks)
no_match_ind = [x for x in range(k0)]
inst_idx, tra_candidates = self.getTrackInfo(tra_candidates, tra_feature, no_match_ind,
valid_ind=f0_valid_ind)
map_num = 0
for i in range(k0):
if inst_idx[i] != -1:
if inst_masks[inst_idx[i]][f_idx].sum() != 0:
print('Error and mask in inst track!!!')
exit()
else:
inst_masks[inst_idx[i]][f_idx] = f0_seg_masks[i, :, :]
inst_cats[inst_idx[i]][f_idx] = f0_cat_labels[i]
inst_cat_scores[inst_idx[i]][f_idx] = f0_cat_scores[i]
inst_cat_scores_ori[inst_idx[i]][f_idx] = f0_cat_scores_ori[i]
valid_ind_map_to_inst.append(inst_idx[i])
else:
_, H, W = f0_seg_masks.size()
masks = torch.zeros((1, N, H, W), device=self.device)
cats = torch.full((1, N), -1, device=self.device)
cat_scores = torch.zeros((1, N), device=self.device)
cat_scores_ori = torch.zeros((1, N, self.num_class), device=self.device)
masks[0, f_idx] = f0_seg_masks[i, :, :]
cats[0, f_idx] = f0_cat_labels[i]
cat_scores[0, f_idx] = f0_cat_scores[i]
cat_scores_ori[0, f_idx] = f0_cat_scores_ori[i]
if isinstance(inst_masks, list):
inst_masks = masks
inst_cats = cats
inst_cat_scores = cat_scores
inst_cat_scores_ori = cat_scores_ori
else:
inst_masks = torch.cat((inst_masks, masks), dim=0)
inst_cats = torch.cat((inst_cats, cats), dim=0)
inst_cat_scores = torch.cat((inst_cat_scores, cat_scores), dim=0)
inst_cat_scores_ori = torch.cat((inst_cat_scores_ori, cat_scores_ori), dim=0)
valid_ind_map_to_inst.append(inst_num + map_num)
map_num+=1
else:
f0_result = self.getSegMaskV5(N_pred_masks[:,0,:,:], N_pred_kernels[:,0,:,:], N_pred_cats[:,0,:,:], grid_weight)
if f0_result is not None:
f0_seg_masks, f0_cat_labels, f0_cat_scores, f0_cat_scores_ori, f0_valid_ind = f0_result
k1, _, _ = f0_seg_masks.size()
no_match_ind = []
temp_map_ind = [0 for _ in range(k1)]
pre_inst_tra_check = torch.zeros((valid_tra.size()[0]))
inst_num = len(inst_masks)
valid_tra = valid_tra[:, f0_valid_ind] # k0, k1
for i in range(k1):
tra_sort_ind = torch.argsort(valid_tra[:, i], descending=True)
check_match = 0
for ind in tra_sort_ind:
inst_map_ind = valid_ind_map_to_inst[int(ind)]
if inst_masks[inst_map_ind][f_idx].sum() == 0 and valid_tra[int(ind), i] >= self.tracking_thr:
inst_masks[inst_map_ind][f_idx] = f0_seg_masks[i, :, :]
inst_cats[inst_map_ind][f_idx] = f0_cat_labels[i]
inst_cat_scores[inst_map_ind][f_idx] = f0_cat_scores[i]
inst_cat_scores_ori[inst_map_ind][f_idx] = f0_cat_scores_ori[i]
check_match = 1
temp_map_ind[i] = inst_map_ind
pre_inst_tra_check[int(ind)] = 1
break
if check_match == 0:
no_match_ind.append(i)
valid_ind = f0_valid_ind[no_match_ind]
inst_idx, tra_candidates = self.getTrackInfo(tra_candidates, tra_feature, no_match_ind,
valid_ind=valid_ind)
map_num = 0
for i in range(len(no_match_ind)):
ind = no_match_ind[i]
if inst_idx[i] != -1:
if inst_masks[inst_idx[i]][f_idx].sum() != 0:
print('Error add mask in inst track!!!')
exit()
else:
inst_masks[inst_idx[i]][f_idx] = f0_seg_masks[ind, :, :]
inst_cats[inst_idx[i]][f_idx] = f0_cat_labels[ind]
inst_cat_scores[inst_idx[i]][f_idx] = f0_cat_scores[ind]
inst_cat_scores_ori[inst_idx[i]][f_idx] = f0_cat_scores_ori[ind]
temp_map_ind[ind] = inst_idx[i]
else:
_, H, W = f0_seg_masks.size()
masks = torch.zeros((1, N, H, W), device=self.device)
cats = torch.full((1, N), -1, device=self.device)
cat_scores = torch.zeros((1, N), device=self.device)
cat_scores_ori = torch.zeros((1, N, self.num_class), device=self.device)
masks[0, f_idx] = f0_seg_masks[ind, :, :]
cats[0, f_idx] = f0_cat_labels[ind]
cat_scores[0, f_idx] = f0_cat_scores[ind]
cat_scores_ori[0, f_idx] = f0_cat_scores_ori[ind]
inst_masks = torch.cat((inst_masks, masks), dim=0)
inst_cats = torch.cat((inst_cats, cats), dim=0)
inst_cat_scores = torch.cat((inst_cat_scores, cat_scores), dim=0)
inst_cat_scores_ori = torch.cat((inst_cat_scores_ori, cat_scores_ori), dim=0)
temp_map_ind[ind] = inst_num + map_num
map_num += 1
pre_ind_map_inst = [k for idx, k in enumerate(valid_ind_map_to_inst)
if pre_inst_tra_check[idx] == 0]
valid_ind_map_to_inst = temp_map_ind
if f_idx != N - 1:
valid_tra = N_pred_tra[0, f0_valid_ind, :] # k1, S**2
else:
pre_inst_tra_check = torch.zeros((valid_tra.size()[0]))
pre_ind_map_inst = copy.deepcopy(valid_ind_map_to_inst)
valid_tra = None
valid_ind_map_to_inst = []
return inst_masks, inst_cats, inst_cat_scores, inst_cat_scores_ori,\
f0_result, valid_tra, valid_ind_map_to_inst,\
pre_inst_tra_check, pre_ind_map_inst, tra_candidates, f0_valid_ind
def getSegMaskV5(self, pred_masks, pred_kernels, pred_cats, grid_weight):
# pred_masks : dim, H/4, W/4
# pred_kernels : dim, S1, S2
# pred_cats : C, S1, S2
# grid_weight : S1*S2
_, H, W = pred_masks.size()
C, S1, S2 = pred_cats.size()
cat_scores = pred_cats.reshape(-1, S1 * S2).transpose(1, 0) # S**2, C
cat_scores_ori = cat_scores.clone()
cat_scores, cat_labels = cat_scores.max(1) # S**2
if grid_weight is not None:
cat_scores *= grid_weight
cat_scores[cat_scores < self.score_thr] = 0.
valid_ind = cat_scores.nonzero()[:, 0]
if valid_ind.sum() == 0:
return None
kernels = pred_kernels.reshape(-1, S1*S2).transpose(1, 0) # S1*S2, dim
kernels = kernels[valid_ind]
seg_preds = self.getMaskMap(pred_masks, kernels)
seg_masks = (seg_preds > self.mask_thr).float()
cat_scores = cat_scores[valid_ind]
cat_labels = cat_labels[valid_ind]
sum_masks = seg_masks.sum((1, 2)).float()
seg_scores = (seg_preds * seg_masks.float()).sum((1, 2)) / sum_masks
cat_scores *= seg_scores
sort_ind = torch.argsort(cat_scores, descending=True)
if sort_ind.size()[0] > self.nms_pre:
sort_ind = sort_ind[:self.nms_pre]
seg_masks = seg_masks[sort_ind, :, :]
cat_scores = cat_scores[sort_ind]
cat_labels = cat_labels[sort_ind]
valid_ind = valid_ind[sort_ind]
cat_scores = self.matrix_nms(seg_masks, cat_labels, cat_scores)
keep = cat_scores >= self.update_thr
if keep.sum() == 0:
return None
seg_masks = seg_masks[keep, :, :]
cat_scores = cat_scores[keep]
cat_labels = cat_labels[keep]
valid_ind = valid_ind[keep]
sort_ind = torch.argsort(cat_scores, descending=True)
if sort_ind.size()[0] > 100:
sort_ind = sort_ind[:100]
seg_masks = seg_masks[sort_ind, :, :]
cat_scores = cat_scores[sort_ind]
cat_labels = cat_labels[sort_ind]
valid_ind = valid_ind[sort_ind]
cat_scores_ori = cat_scores_ori[valid_ind, :]
for i in range(len(valid_ind) - 1):
if seg_masks[i].sum() == 0:
continue
for j in range(i + 1, len(valid_ind)):
inter_region = (seg_masks[i] * seg_masks[j]).sum()
mask_region = seg_masks[j].sum()
if inter_region / mask_region > 0.5:
seg_masks[j] = 0
final_valid_ind = (seg_masks.sum((1, 2)) > 0)
seg_masks = seg_masks[final_valid_ind, :, :]
cat_scores = cat_scores[final_valid_ind]
cat_labels = cat_labels[final_valid_ind]
cat_scores_ori = cat_scores_ori[final_valid_ind, :]
valid_ind = valid_ind[final_valid_ind]
return seg_masks, cat_labels, cat_scores, cat_scores_ori, valid_ind
def matrix_nms(self, seg_masks, cate_labels, cate_scores, sum_masks=None):
"""Matrix NMS for multi-class masks.
Args:
seg_masks (Tensor): shape (n, h, w)
cate_labels (Tensor): shape (n), mask labels in descending order
cate_scores (Tensor): shape (n), mask scores in descending order
self.kernel (str): 'linear' or 'gauss'
self.sigma (float): std in gaussian method
sum_masks (Tensor): The sum of seg_masks
Returns:
Tensor: cate_scores_update, tensors of shape (n)
"""
n_samples = len(cate_labels)
if n_samples == 0:
return []
if sum_masks is None:
sum_masks = seg_masks.sum((1, 2)).float()
seg_masks = seg_masks.reshape(n_samples, -1).float()
# inter.
inter_matrix = torch.mm(seg_masks, seg_masks.transpose(1, 0))
# union.
sum_masks_x = sum_masks.expand(n_samples, n_samples)
# iou.
iou_matrix = (inter_matrix / (sum_masks_x + sum_masks_x.transpose(1, 0) - inter_matrix)).triu(diagonal=1)
# label_specific matrix.
cate_labels_x = cate_labels.expand(n_samples, n_samples)
label_matrix = (cate_labels_x == cate_labels_x.transpose(1, 0))
label_matrix = label_matrix.float().triu(diagonal=1)
# IoU compensation
compensate_iou, _ = (iou_matrix * label_matrix).max(0)
compensate_iou = compensate_iou.expand(n_samples, n_samples).transpose(1, 0)
# IoU decay
decay_iou = iou_matrix * label_matrix
# matrix nms
if self.kernel == 'gaussian':
decay_matrix = torch.exp(-1 * self.sigma * (decay_iou ** 2))
compensate_matrix = torch.exp(-1 * self.sigma * (compensate_iou ** 2))
decay_coefficient, _ = (decay_matrix / compensate_matrix).min(0)
elif self.kernel == 'linear':
decay_matrix = (1 - decay_iou) / (1 - compensate_iou)
decay_coefficient, _ = decay_matrix.min(0)
else:
raise NotImplementedError
# update the score.
cate_scores_update = cate_scores * decay_coefficient
return cate_scores_update
def getTrackInfo(self, tra_candidates, tra_feature, idxs, valid_ind=None):
can_num = len(tra_candidates)
inst_idx = [-1 for _ in range(len(idxs))]
if can_num == 0:
return inst_idx, tra_candidates
[k1] = valid_ind.size()
tra_candidate_check = []
for c in range(can_num-1, -1, -1):
tra_check = []
tra_candidate = tra_candidates[c]
[k0] = tra_candidate['valid_idx'].size()
pred_tra = torch.sigmoid(self.tracking_module(tra_candidate['tra_feature'], tra_feature))
pred_tra = pred_tra[0,0]
valid_tra = pred_tra[tra_candidate['valid_idx'],:]
valid_tra = valid_tra[:, valid_ind]
for i in range(k1):
if inst_idx[i] == -1:
tra_sort_ind = torch.argsort(valid_tra[:, i], descending=True)
for ind in tra_sort_ind:
if valid_tra[int(ind), i] >= self.tracking_thr and int(ind) not in tra_check:
inst_idx[i] = tra_candidate['idx_mapping'][int(ind)]
tra_check.append(int(ind))
break
valid_masks_idx = [x for x in range(k0) if x not in tra_check]
tra_candidate['valid_idx'] = tra_candidate['valid_idx'][valid_masks_idx]
tra_candidate['idx_mapping'] = [k for idx, k in enumerate(tra_candidate['idx_mapping'])
if idx not in tra_check]
if len(tra_candidate['idx_mapping']) == 0:
tra_candidate_check.append(c)
if -1 not in inst_idx:
break
tra_candidates = [k for idx, k in enumerate(tra_candidates)
if idx not in tra_candidate_check]
return inst_idx, tra_candidates
def getMaskMap(self, mask, kernel):
# mask: dim, H/4, W/4
# kernel: valid_idxs, dim
# out_device = mask.device
if not mask.is_cuda:
mask = mask.to('cuda')
kernel = kernel.to('cuda')
num_kernel, _ = kernel.size()
dim, H, W = mask.size()
mask = mask.unsqueeze(0) # 1, dim, H/4, W/4
mask_map = F.conv2d(mask, kernel.view(num_kernel, dim, 1, 1))
mask_map = F.interpolate(mask_map, scale_factor=4, mode='bilinear', align_corners=False).squeeze(0)
mask_map = torch.sigmoid(mask_map)
return mask_map
| en | 0.427679 | # B,S**2,1,H,W / B,C,1,S,S / B,1,S**2,S**2 # cat_scores_ori: K, v_l, 40 # N_pred_masks : dim, 1, H/4, W/4 # N_pred_kernels : dim, 1, S1, S2 # N_pred_cats : C, 1, S1, S2 # N_pred_tra : 1, S1*S2, S1*S2 # K, N, H, W # K, N # K, N # K, N, 40 # k0, S**2 # k0, k1 # k1, S**2 # pred_masks : dim, H/4, W/4 # pred_kernels : dim, S1, S2 # pred_cats : C, S1, S2 # grid_weight : S1*S2 # S**2, C # S**2 # S1*S2, dim Matrix NMS for multi-class masks. Args: seg_masks (Tensor): shape (n, h, w) cate_labels (Tensor): shape (n), mask labels in descending order cate_scores (Tensor): shape (n), mask scores in descending order self.kernel (str): 'linear' or 'gauss' self.sigma (float): std in gaussian method sum_masks (Tensor): The sum of seg_masks Returns: Tensor: cate_scores_update, tensors of shape (n) # inter. # union. # iou. # label_specific matrix. # IoU compensation # IoU decay # matrix nms # update the score. # mask: dim, H/4, W/4 # kernel: valid_idxs, dim # out_device = mask.device # 1, dim, H/4, W/4 | 1.903676 | 2 |
zlogging/enum/Tunnel.py | JarryShaw/zlogging | 0 | 6630499 | <gh_stars>0
# -*- coding: utf-8 -*-
# pylint: disable=line-too-long,import-error
"""Namespace: ``Tunnel``."""
from zlogging._compat import enum
@enum.unique
class Type(enum.IntFlag):
"""Enum: ``Tunnel::Type``.
See Also:
`base/bif/types.bif.zeek <https://docs.zeek.org/en/stable/scripts/base/bif/types.bif.zeek.html#type-Tunnel::Type>`__
"""
_ignore_ = 'Type _'
Type = vars()
Type['NONE'] = enum.auto()
Type['IP'] = enum.auto()
Type['AYIYA'] = enum.auto()
Type['TEREDO'] = enum.auto()
Type['SOCKS'] = enum.auto()
Type['GTPv1'] = enum.auto()
Type['HTTP'] = enum.auto()
Type['GRE'] = enum.auto()
Type['VXLAN'] = enum.auto()
@enum.unique
class Action(enum.IntFlag):
"""Enum: ``Tunnel::Action``.
Types of interesting activity that can occur with a tunnel.
See Also:
`base/frameworks/tunnels/main.zeek <https://docs.zeek.org/en/stable/scripts/base/frameworks/tunnels/main.zeek.html#type-Tunnel::Action>`__
"""
_ignore_ = 'Action _'
Action = vars()
#: A new tunnel (encapsulating “connection”) has been seen.
Action['DISCOVER'] = enum.auto()
#: A tunnel connection has closed.
Action['CLOSE'] = enum.auto()
#: No new connections over a tunnel happened in the amount of
#: time indicated by Tunnel::expiration\_interval.
Action['EXPIRE'] = enum.auto()
| # -*- coding: utf-8 -*-
# pylint: disable=line-too-long,import-error
"""Namespace: ``Tunnel``."""
from zlogging._compat import enum
@enum.unique
class Type(enum.IntFlag):
"""Enum: ``Tunnel::Type``.
See Also:
`base/bif/types.bif.zeek <https://docs.zeek.org/en/stable/scripts/base/bif/types.bif.zeek.html#type-Tunnel::Type>`__
"""
_ignore_ = 'Type _'
Type = vars()
Type['NONE'] = enum.auto()
Type['IP'] = enum.auto()
Type['AYIYA'] = enum.auto()
Type['TEREDO'] = enum.auto()
Type['SOCKS'] = enum.auto()
Type['GTPv1'] = enum.auto()
Type['HTTP'] = enum.auto()
Type['GRE'] = enum.auto()
Type['VXLAN'] = enum.auto()
@enum.unique
class Action(enum.IntFlag):
"""Enum: ``Tunnel::Action``.
Types of interesting activity that can occur with a tunnel.
See Also:
`base/frameworks/tunnels/main.zeek <https://docs.zeek.org/en/stable/scripts/base/frameworks/tunnels/main.zeek.html#type-Tunnel::Action>`__
"""
_ignore_ = 'Action _'
Action = vars()
#: A new tunnel (encapsulating “connection”) has been seen.
Action['DISCOVER'] = enum.auto()
#: A tunnel connection has closed.
Action['CLOSE'] = enum.auto()
#: No new connections over a tunnel happened in the amount of
#: time indicated by Tunnel::expiration\_interval.
Action['EXPIRE'] = enum.auto() | en | 0.72016 | # -*- coding: utf-8 -*- # pylint: disable=line-too-long,import-error Namespace: ``Tunnel``. Enum: ``Tunnel::Type``. See Also: `base/bif/types.bif.zeek <https://docs.zeek.org/en/stable/scripts/base/bif/types.bif.zeek.html#type-Tunnel::Type>`__ Enum: ``Tunnel::Action``. Types of interesting activity that can occur with a tunnel. See Also: `base/frameworks/tunnels/main.zeek <https://docs.zeek.org/en/stable/scripts/base/frameworks/tunnels/main.zeek.html#type-Tunnel::Action>`__ #: A new tunnel (encapsulating “connection”) has been seen. #: A tunnel connection has closed. #: No new connections over a tunnel happened in the amount of #: time indicated by Tunnel::expiration\_interval. | 2.003443 | 2 |
examples/hello.py | concurrence/concurrence | 27 | 6630500 | <filename>examples/hello.py
from concurrence import dispatch
def hello():
print "Hello World!"
if __name__ == '__main__':
dispatch(hello)
| <filename>examples/hello.py
from concurrence import dispatch
def hello():
print "Hello World!"
if __name__ == '__main__':
dispatch(hello)
| none | 1 | 1.556847 | 2 |
|
testing/example_scripts/dataclasses/test_compare_dataclasses.py | tinkerlin/pytest | 5,079 | 6630501 | <reponame>tinkerlin/pytest
# -*- coding: utf-8 -*-
from dataclasses import dataclass
from dataclasses import field
def test_dataclasses():
@dataclass
class SimpleDataObject(object):
field_a: int = field()
field_b: int = field()
left = SimpleDataObject(1, "b")
right = SimpleDataObject(1, "c")
assert left == right
| # -*- coding: utf-8 -*-
from dataclasses import dataclass
from dataclasses import field
def test_dataclasses():
@dataclass
class SimpleDataObject(object):
field_a: int = field()
field_b: int = field()
left = SimpleDataObject(1, "b")
right = SimpleDataObject(1, "c")
assert left == right | en | 0.769321 | # -*- coding: utf-8 -*- | 3.067268 | 3 |
src/run_scripts/repro/dvc.py | prhuppertz/Burned_Area_Detection | 5 | 6630502 | <filename>src/run_scripts/repro/dvc.py
"""Usage:
dvc.py [--cache=<cache_path>] [--link=None] [--link2=None]
@ <NAME> & <NAME> 2020, Cervest
Creates symlinks/DVC structure
Options:
-h --help Show help.
--version Show version.
--cache=<cache_path> Inference mode. 'roi' or 'wsi'. [default: data/]
--link=<link> Source path for symlink to points towards, if using remote storage
"""
import os
import subprocess
from docopt import docopt
def set_cache(cache_dir):
"""
Sets dvc cache given config file
:param cache_dir: path to cache directory
:return:
"""
p = subprocess.Popen("dvc cache dir {} --local".format(cache_dir), shell=True)
p.communicate()
def set_symlink():
p = subprocess.Popen("dvc config cache.type symlink --local", shell=True)
p.communicate()
p = subprocess.Popen("dvc config cache.protected true --local", shell=True)
p.communicate()
def main(cache_path, link_path):
"""
Sets up dvc for large data
:return:
"""
set_symlink()
if cache_path:
os.makedirs(cache_path, exist_ok=True)
set_cache(cache_path)
# Get directory of current project
cur_project_dir = os.getcwd()
# Make a path where raw_data is stored, if data is stored externally
if link_path:
to_folder = os.path.join(cur_project_dir, "data/raw_data/tiles")
from_folder = link_path
os.makedirs(from_folder, exist_ok=True)
os.makedirs(to_folder, exist_ok=True)
#Create a symlink
os.symlink(from_folder, to_folder)
if __name__ == "__main__":
arguments = docopt(__doc__)
cache_path = arguments["--cache"]
link_path = arguments["--link"]
main(cache_path, link_path)
| <filename>src/run_scripts/repro/dvc.py
"""Usage:
dvc.py [--cache=<cache_path>] [--link=None] [--link2=None]
@ <NAME> & <NAME> 2020, Cervest
Creates symlinks/DVC structure
Options:
-h --help Show help.
--version Show version.
--cache=<cache_path> Inference mode. 'roi' or 'wsi'. [default: data/]
--link=<link> Source path for symlink to points towards, if using remote storage
"""
import os
import subprocess
from docopt import docopt
def set_cache(cache_dir):
"""
Sets dvc cache given config file
:param cache_dir: path to cache directory
:return:
"""
p = subprocess.Popen("dvc cache dir {} --local".format(cache_dir), shell=True)
p.communicate()
def set_symlink():
p = subprocess.Popen("dvc config cache.type symlink --local", shell=True)
p.communicate()
p = subprocess.Popen("dvc config cache.protected true --local", shell=True)
p.communicate()
def main(cache_path, link_path):
"""
Sets up dvc for large data
:return:
"""
set_symlink()
if cache_path:
os.makedirs(cache_path, exist_ok=True)
set_cache(cache_path)
# Get directory of current project
cur_project_dir = os.getcwd()
# Make a path where raw_data is stored, if data is stored externally
if link_path:
to_folder = os.path.join(cur_project_dir, "data/raw_data/tiles")
from_folder = link_path
os.makedirs(from_folder, exist_ok=True)
os.makedirs(to_folder, exist_ok=True)
#Create a symlink
os.symlink(from_folder, to_folder)
if __name__ == "__main__":
arguments = docopt(__doc__)
cache_path = arguments["--cache"]
link_path = arguments["--link"]
main(cache_path, link_path)
| en | 0.506802 | Usage: dvc.py [--cache=<cache_path>] [--link=None] [--link2=None] @ <NAME> & <NAME> 2020, Cervest Creates symlinks/DVC structure Options: -h --help Show help. --version Show version. --cache=<cache_path> Inference mode. 'roi' or 'wsi'. [default: data/] --link=<link> Source path for symlink to points towards, if using remote storage Sets dvc cache given config file :param cache_dir: path to cache directory :return: Sets up dvc for large data :return: # Get directory of current project # Make a path where raw_data is stored, if data is stored externally #Create a symlink | 2.490375 | 2 |
utils/host_info.py | wanjun-group-seu/AQUARIUM | 0 | 6630503 | <gh_stars>0
# !/usr/bin/env python
# -*- coding:utf-8 -*-
# author : zerodel
# Readme:
#
import argparse
import os
import os.path
import sys
upper_root = os.path.abspath(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
sys.path.append(upper_root)
import pysrc.sub_module.summary_quant as sq
parser = argparse.ArgumentParser()
parser.add_argument("anno", help="""path to annotation file or quantification result directory with each sample has a final.gtf in
its sub-dir""",
default="",
nargs="+")
parser.add_argument("-o", help="output file location", default="")
__doc__ = ''' summarize the host gene information from
'''
__author__ = 'zerodel'
if __name__ == "__main__":
args = parser.parse_args()
if args.o and args.anno:
isoform_ownership = sq.TranscriptOwnership()
for entry_annotation in args.anno:
if os.path.isfile(entry_annotation):
isoform_ownership.parse_gtf(entry_annotation)
elif os.path.isdir(entry_annotation):
# print(entry_annotation)
gtf_under_this_folder = [os.path.join(entry_annotation, x, "final.gtf") for x in os.listdir(entry_annotation) if
os.path.isdir(os.path.join(entry_annotation, x)) and not x.startswith(".")]
# for x in os.listdir(entry_annotation):
# print(x)
# if os.path.isdir(os.path.join(entry_annotation, x)) and not x.startswith("."):
# print(os.path.join(entry_annotation, x, "final.gtf"))
# print(gtf_under_this_folder)
for path_gtf in gtf_under_this_folder:
if os.path.exists(path_gtf) and os.path.isfile(path_gtf):
isoform_ownership.parse_gtf(path_gtf)
else:
print("a wrong path is given : {}".format(entry_annotation))
continue
with open(args.o, "w") as dump_it:
for line in isoform_ownership.to_text_table_lines():
dump_it.write("{}\n".format(line.strip()))
else:
print("wrong arguments given , please check usage by using '-h' option")
exit(-1)
| # !/usr/bin/env python
# -*- coding:utf-8 -*-
# author : zerodel
# Readme:
#
import argparse
import os
import os.path
import sys
upper_root = os.path.abspath(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
sys.path.append(upper_root)
import pysrc.sub_module.summary_quant as sq
parser = argparse.ArgumentParser()
parser.add_argument("anno", help="""path to annotation file or quantification result directory with each sample has a final.gtf in
its sub-dir""",
default="",
nargs="+")
parser.add_argument("-o", help="output file location", default="")
__doc__ = ''' summarize the host gene information from
'''
__author__ = 'zerodel'
if __name__ == "__main__":
args = parser.parse_args()
if args.o and args.anno:
isoform_ownership = sq.TranscriptOwnership()
for entry_annotation in args.anno:
if os.path.isfile(entry_annotation):
isoform_ownership.parse_gtf(entry_annotation)
elif os.path.isdir(entry_annotation):
# print(entry_annotation)
gtf_under_this_folder = [os.path.join(entry_annotation, x, "final.gtf") for x in os.listdir(entry_annotation) if
os.path.isdir(os.path.join(entry_annotation, x)) and not x.startswith(".")]
# for x in os.listdir(entry_annotation):
# print(x)
# if os.path.isdir(os.path.join(entry_annotation, x)) and not x.startswith("."):
# print(os.path.join(entry_annotation, x, "final.gtf"))
# print(gtf_under_this_folder)
for path_gtf in gtf_under_this_folder:
if os.path.exists(path_gtf) and os.path.isfile(path_gtf):
isoform_ownership.parse_gtf(path_gtf)
else:
print("a wrong path is given : {}".format(entry_annotation))
continue
with open(args.o, "w") as dump_it:
for line in isoform_ownership.to_text_table_lines():
dump_it.write("{}\n".format(line.strip()))
else:
print("wrong arguments given , please check usage by using '-h' option")
exit(-1) | en | 0.549246 | # !/usr/bin/env python # -*- coding:utf-8 -*- # author : zerodel # Readme: # path to annotation file or quantification result directory with each sample has a final.gtf in its sub-dir summarize the host gene information from # print(entry_annotation) # for x in os.listdir(entry_annotation): # print(x) # if os.path.isdir(os.path.join(entry_annotation, x)) and not x.startswith("."): # print(os.path.join(entry_annotation, x, "final.gtf")) # print(gtf_under_this_folder) | 2.239162 | 2 |
subir/query.py | xyla-io/subir | 0 | 6630504 | from data_layer import Redshift as SQL
from typing import Optional, Dict, List
class ColumnTypeQuery(SQL.GeneratedQuery, SQL.ResultQuery[Dict[str, str]]):
database: str
schema: str
table: str
def __init__(self, database: str, schema: str, table: str):
self.database = database
self.schema = schema
self.table = table
super().__init__()
def generate_query(self):
self.query = '''
select column_name, data_type, character_maximum_length
from information_schema.columns
where table_catalog = %s
and table_schema = %s
and table_name = %s;
'''
self.substitution_parameters=(
self.database,
self.schema,
self.table,
)
def cursor_to_result(self, cursor: any) -> Optional[Dict[str, str]]:
result = cursor.fetchall()
return {
r[0]: f'{r[1]}({r[2]})' if r[1] == 'character varying' else r[1]
for r in result
}
class CreateTableQuery(SQL.GeneratedQuery):
schema: str
table: str
column_types: Dict[str, str]
read_only_groups: List[str]
def __init__(self, schema: str, table: str, column_types: Dict[str, str], read_only_groups: List[str]=[]):
self.schema = schema
self.table = table
self.column_types = column_types
self.read_only_groups = read_only_groups
super().__init__()
def generate_query(self):
columns_definition = ',\n'.join(f' "{c}" {t}' for c, t in self.column_types.items())
self.query = f'''
create table {self.schema}.{self.table} (
{columns_definition}
);
'''
for group in self.read_only_groups:
self.query += f'\ngrant select on table {self.schema}.{self.table} to group {group};'
class DropTableQuery(SQL.GeneratedQuery):
schema: str
table: str
def __init__(self, schema: str, table: str):
self.schema = schema
self.table = table
super().__init__()
def generate_query(self):
self.query = f'drop table {self.schema}.{self.table}'
class UploadQuery(SQL.GeneratedQuery):
schema: str
table: str
def __init__(self, schema: str, table: str):
self.schema = schema
self.table = table
super().__init__()
@property
def upload_table(self) -> str:
return f'flx_upload_{self.table}'
class PrepareUploadTableQuery(UploadQuery):
schema: str
table: str
def generate_query(self):
self.query = f'''
create temporary table {self.upload_table} (like {self.schema}.{self.table} including defaults);
'''
class AppendUploadQuery(UploadQuery):
def generate_query(self):
self.query = f'insert into {self.schema}.{self.table} select * from {self.upload_table};'
class ReplaceUploadQuery(UploadQuery):
def generate_query(self):
append_query = AppendUploadQuery(schema=self.schema, table=self.table)
self.query = f'''
truncate table {self.schema}.{self.table};
{append_query.query}
'''
self.substitution_parameters = append_query.substitution_parameters
class MergeUploadQuery(SQL.MergeQuery):
schema: str
table: str
def __init__(self, join_columns: List[str], update_columns: List[str], schema: str, table: str):
self.schema = schema
self.table = table
super().__init__(
join_columns=join_columns,
update_columns=update_columns,
source_table=self.upload_table,
target_table=table,
source_schema=None,
target_schema=schema
)
@property
def upload_table(self) -> str:
return f'flx_upload_{self.table}'
class MergeReplaceUploadQuery(SQL.MergeReplaceQuery):
schema: str
table: str
def __init__(self, join_columns: List[str], schema: str, table: str):
self.schema = schema
self.table = table
super().__init__(
join_columns=join_columns,
source_table=self.upload_table,
target_table=table,
source_schema=None,
target_schema=schema
)
@property
def upload_table(self) -> str:
return f'flx_upload_{self.table}' | from data_layer import Redshift as SQL
from typing import Optional, Dict, List
class ColumnTypeQuery(SQL.GeneratedQuery, SQL.ResultQuery[Dict[str, str]]):
database: str
schema: str
table: str
def __init__(self, database: str, schema: str, table: str):
self.database = database
self.schema = schema
self.table = table
super().__init__()
def generate_query(self):
self.query = '''
select column_name, data_type, character_maximum_length
from information_schema.columns
where table_catalog = %s
and table_schema = %s
and table_name = %s;
'''
self.substitution_parameters=(
self.database,
self.schema,
self.table,
)
def cursor_to_result(self, cursor: any) -> Optional[Dict[str, str]]:
result = cursor.fetchall()
return {
r[0]: f'{r[1]}({r[2]})' if r[1] == 'character varying' else r[1]
for r in result
}
class CreateTableQuery(SQL.GeneratedQuery):
schema: str
table: str
column_types: Dict[str, str]
read_only_groups: List[str]
def __init__(self, schema: str, table: str, column_types: Dict[str, str], read_only_groups: List[str]=[]):
self.schema = schema
self.table = table
self.column_types = column_types
self.read_only_groups = read_only_groups
super().__init__()
def generate_query(self):
columns_definition = ',\n'.join(f' "{c}" {t}' for c, t in self.column_types.items())
self.query = f'''
create table {self.schema}.{self.table} (
{columns_definition}
);
'''
for group in self.read_only_groups:
self.query += f'\ngrant select on table {self.schema}.{self.table} to group {group};'
class DropTableQuery(SQL.GeneratedQuery):
schema: str
table: str
def __init__(self, schema: str, table: str):
self.schema = schema
self.table = table
super().__init__()
def generate_query(self):
self.query = f'drop table {self.schema}.{self.table}'
class UploadQuery(SQL.GeneratedQuery):
schema: str
table: str
def __init__(self, schema: str, table: str):
self.schema = schema
self.table = table
super().__init__()
@property
def upload_table(self) -> str:
return f'flx_upload_{self.table}'
class PrepareUploadTableQuery(UploadQuery):
schema: str
table: str
def generate_query(self):
self.query = f'''
create temporary table {self.upload_table} (like {self.schema}.{self.table} including defaults);
'''
class AppendUploadQuery(UploadQuery):
def generate_query(self):
self.query = f'insert into {self.schema}.{self.table} select * from {self.upload_table};'
class ReplaceUploadQuery(UploadQuery):
def generate_query(self):
append_query = AppendUploadQuery(schema=self.schema, table=self.table)
self.query = f'''
truncate table {self.schema}.{self.table};
{append_query.query}
'''
self.substitution_parameters = append_query.substitution_parameters
class MergeUploadQuery(SQL.MergeQuery):
schema: str
table: str
def __init__(self, join_columns: List[str], update_columns: List[str], schema: str, table: str):
self.schema = schema
self.table = table
super().__init__(
join_columns=join_columns,
update_columns=update_columns,
source_table=self.upload_table,
target_table=table,
source_schema=None,
target_schema=schema
)
@property
def upload_table(self) -> str:
return f'flx_upload_{self.table}'
class MergeReplaceUploadQuery(SQL.MergeReplaceQuery):
schema: str
table: str
def __init__(self, join_columns: List[str], schema: str, table: str):
self.schema = schema
self.table = table
super().__init__(
join_columns=join_columns,
source_table=self.upload_table,
target_table=table,
source_schema=None,
target_schema=schema
)
@property
def upload_table(self) -> str:
return f'flx_upload_{self.table}' | en | 0.290867 | select column_name, data_type, character_maximum_length from information_schema.columns where table_catalog = %s and table_schema = %s and table_name = %s; create table {self.schema}.{self.table} ( {columns_definition} ); create temporary table {self.upload_table} (like {self.schema}.{self.table} including defaults); truncate table {self.schema}.{self.table}; {append_query.query} | 2.638931 | 3 |
Hotel_Management_System_python/Registration.py | Mastermind-sap/HotelManagementSystem-python | 1 | 6630505 | <filename>Hotel_Management_System_python/Registration.py
import Hotel_Management_Software
class Registration:
def main(self):
print("")
print("\t\t\t\t\t\t\t\t\tRegistration")
print("\t\t\t\t\t\t\t\t\t************")
print("")
ob=Hotel_Management_Software()
g=0
n1=0
while g==0:
#Input for number of guests to be registered
try:
n1 = int(input("enter number of guests"))
except ValueError:
print("Invalid input")
else:
g = 1
break
finally:
if (n1==0) or (n1<1):
g=0
print("Invalid input")
if n1<=ob.remaining_rooms:
i = (ob.no_rooms - ob.remaining_rooms)
for d in range(0,n1):
ob.remaining_rooms=ob.remaining_rooms-1
ob.name[i]=input("enter name of the guest") #input for the name of the guest to be registered
f=0
#input for the gender of the guest to be registered
while f==0:
choice=input("enter 'M' if guest is a male\n 'F' if guest is a female")[0].upper()
if choice=='M':
ob.gender[i]="male"
break
elif choice=='F':
ob.gender[i]="female"
break
else:
print("Invalid input")
#input for the age of the guest to be registered
g1=0
while g1==0:
try:
ob.age[i]=int(input("enter age of the guest in years"))
except ValueError:
print("Invalid age")
print("Try Again!")
else:
if(ob.age[i]==0) or (ob.age[i]<1) or (ob.age[i]>110):
print("Invalid age")
print("enter age of the guest again")
else:
g1=1
break
#input for the address of the guest to be registered
ob.address[i]=input("enter address of the guest")
print("enter PAN number of the guest") #input for the PAN number of the guest to be registered
int p=0
do
{
ob.pan[i]=sc.next()
ob.pan[i]+=sc.nextLine()
ob.pan[i]=ob.pan[i].toUpperCase()
if((ob.pan[i].length()==10)&&(Character.isLetter(ob.pan[i].charAt(0)))&&(Character.isLetter(ob.pan[i].charAt(1)))
&&(Character.isLetter(ob.pan[i].charAt(2)))&&(Character.isLetter(ob.pan[i].charAt(3)))
&&(Character.isLetter(ob.pan[i].charAt(4)))&&(Character.isDigit(ob.pan[i].charAt(5)))
&&(Character.isDigit(ob.pan[i].charAt(6)))&&(Character.isDigit(ob.pan[i].charAt(7)))
&&(Character.isDigit(ob.pan[i].charAt(8)))&&(Character.isLetter(ob.pan[i].charAt(9))))
p=0
else
{print("invalid PAN number")
print("PAN structure is as follows: AAAPL1234C")
print("please enter the PAN number again")
p=1
}
}while(p==1)
DateFormat df = new SimpleDateFormat("dd/MM/yy HH:mm:ss") #Take the system date and time as the check in date and time
Date dateobj = new Date()
ob.checkin_date_time[i]=df.format(dateobj)
int w=0 #Input for the type of suit the guest wishes to live in
while(w==0)
{
print("Enter 1 for Deluxe suit-- Rs.2000/day \n 2 for Super Deluxe suit-- Rs.4000/day \n 3 for Executive suit-- Rs.6000/day \n 4 for Presidential suite-- Rs.8000/day ")
String s1=sc.next()
s1+=sc.nextLine()
int s=0
try
{ s=Integer.parseInt(s1)
}catch(Exception e)
{
}
switch(s)
{
case 1:
ob.suite[i]="Deluxe"
w=1
break
case 2:
ob.suite[i]="Super Deluxe"
w=1
break
case 3:
ob.suite[i]="Executive"
w=1
break
case 4:
ob.suite[i]="Presidential"
w=1
break
default:
print("Wrong choice")
}
}
}
print("")
print("\t\t\t\t\t\t\t\tRegistration is complete!")
print("\t\t\t\t\t\t\t\t*************************")
print("")
}
else
{
print("Sorry ,we are out of rooms") #inavailibity of rooms in the hotel
print("We have only "+ob.remaining_rooms+" rooms at the moment")
}
}
}
| <filename>Hotel_Management_System_python/Registration.py
import Hotel_Management_Software
class Registration:
def main(self):
print("")
print("\t\t\t\t\t\t\t\t\tRegistration")
print("\t\t\t\t\t\t\t\t\t************")
print("")
ob=Hotel_Management_Software()
g=0
n1=0
while g==0:
#Input for number of guests to be registered
try:
n1 = int(input("enter number of guests"))
except ValueError:
print("Invalid input")
else:
g = 1
break
finally:
if (n1==0) or (n1<1):
g=0
print("Invalid input")
if n1<=ob.remaining_rooms:
i = (ob.no_rooms - ob.remaining_rooms)
for d in range(0,n1):
ob.remaining_rooms=ob.remaining_rooms-1
ob.name[i]=input("enter name of the guest") #input for the name of the guest to be registered
f=0
#input for the gender of the guest to be registered
while f==0:
choice=input("enter 'M' if guest is a male\n 'F' if guest is a female")[0].upper()
if choice=='M':
ob.gender[i]="male"
break
elif choice=='F':
ob.gender[i]="female"
break
else:
print("Invalid input")
#input for the age of the guest to be registered
g1=0
while g1==0:
try:
ob.age[i]=int(input("enter age of the guest in years"))
except ValueError:
print("Invalid age")
print("Try Again!")
else:
if(ob.age[i]==0) or (ob.age[i]<1) or (ob.age[i]>110):
print("Invalid age")
print("enter age of the guest again")
else:
g1=1
break
#input for the address of the guest to be registered
ob.address[i]=input("enter address of the guest")
print("enter PAN number of the guest") #input for the PAN number of the guest to be registered
int p=0
do
{
ob.pan[i]=sc.next()
ob.pan[i]+=sc.nextLine()
ob.pan[i]=ob.pan[i].toUpperCase()
if((ob.pan[i].length()==10)&&(Character.isLetter(ob.pan[i].charAt(0)))&&(Character.isLetter(ob.pan[i].charAt(1)))
&&(Character.isLetter(ob.pan[i].charAt(2)))&&(Character.isLetter(ob.pan[i].charAt(3)))
&&(Character.isLetter(ob.pan[i].charAt(4)))&&(Character.isDigit(ob.pan[i].charAt(5)))
&&(Character.isDigit(ob.pan[i].charAt(6)))&&(Character.isDigit(ob.pan[i].charAt(7)))
&&(Character.isDigit(ob.pan[i].charAt(8)))&&(Character.isLetter(ob.pan[i].charAt(9))))
p=0
else
{print("invalid PAN number")
print("PAN structure is as follows: AAAPL1234C")
print("please enter the PAN number again")
p=1
}
}while(p==1)
DateFormat df = new SimpleDateFormat("dd/MM/yy HH:mm:ss") #Take the system date and time as the check in date and time
Date dateobj = new Date()
ob.checkin_date_time[i]=df.format(dateobj)
int w=0 #Input for the type of suit the guest wishes to live in
while(w==0)
{
print("Enter 1 for Deluxe suit-- Rs.2000/day \n 2 for Super Deluxe suit-- Rs.4000/day \n 3 for Executive suit-- Rs.6000/day \n 4 for Presidential suite-- Rs.8000/day ")
String s1=sc.next()
s1+=sc.nextLine()
int s=0
try
{ s=Integer.parseInt(s1)
}catch(Exception e)
{
}
switch(s)
{
case 1:
ob.suite[i]="Deluxe"
w=1
break
case 2:
ob.suite[i]="Super Deluxe"
w=1
break
case 3:
ob.suite[i]="Executive"
w=1
break
case 4:
ob.suite[i]="Presidential"
w=1
break
default:
print("Wrong choice")
}
}
}
print("")
print("\t\t\t\t\t\t\t\tRegistration is complete!")
print("\t\t\t\t\t\t\t\t*************************")
print("")
}
else
{
print("Sorry ,we are out of rooms") #inavailibity of rooms in the hotel
print("We have only "+ob.remaining_rooms+" rooms at the moment")
}
}
}
| en | 0.804681 | #Input for number of guests to be registered #input for the name of the guest to be registered #input for the gender of the guest to be registered #input for the age of the guest to be registered #input for the address of the guest to be registered #input for the PAN number of the guest to be registered #Take the system date and time as the check in date and time #Input for the type of suit the guest wishes to live in #inavailibity of rooms in the hotel | 3.896387 | 4 |
server/products/models.py | tanvirtin/tinmart | 2 | 6630506 | <filename>server/products/models.py
from mongoengine import Document, StringField, IntField, DictField, FloatField, ListField
# model responsible for interacting with crawler_documents collection in walmartcrawler database in mongodb
class CrawlerDocument(Document):
docId = IntField()
url = StringField()
title = StringField()
price = FloatField()
category = StringField()
productImgUrl = StringField()
date = StringField()
metadata = DictField()
meta = {'db_alias': 'fillmyfridge'}
# model responsible for interacting with transaction collection in
class Transaction(Document):
transactionId = StringField()
products = ListField()
meta = {'db_alias': 'tinmart'} | <filename>server/products/models.py
from mongoengine import Document, StringField, IntField, DictField, FloatField, ListField
# model responsible for interacting with crawler_documents collection in walmartcrawler database in mongodb
class CrawlerDocument(Document):
docId = IntField()
url = StringField()
title = StringField()
price = FloatField()
category = StringField()
productImgUrl = StringField()
date = StringField()
metadata = DictField()
meta = {'db_alias': 'fillmyfridge'}
# model responsible for interacting with transaction collection in
class Transaction(Document):
transactionId = StringField()
products = ListField()
meta = {'db_alias': 'tinmart'} | en | 0.84403 | # model responsible for interacting with crawler_documents collection in walmartcrawler database in mongodb # model responsible for interacting with transaction collection in | 2.213203 | 2 |
http-bench/python_test.py | vieyahn2017/go-test | 0 | 6630507 | import pytest
import testutil
@pytest.mark.parametrize("variant", ["httplib", "requests"])
@pytest.mark.parametrize("blocksize", [
8, 32, 64, 128, 256, 512, 1024, 2048, 4096
])
def test_python(server, variant, blocksize):
print(testutil.upload(variant, testutil.SIZE_MB, blocksize))
@pytest.mark.parametrize("variant", ["httplib", "requests"])
@pytest.mark.parametrize("blocksize", [32, 64, 128, 256, 512])
def test_python_parallel(server, variant, blocksize):
print(testutil.upload_parallel(variant, testutil.SIZE_MB, blocksize))
| import pytest
import testutil
@pytest.mark.parametrize("variant", ["httplib", "requests"])
@pytest.mark.parametrize("blocksize", [
8, 32, 64, 128, 256, 512, 1024, 2048, 4096
])
def test_python(server, variant, blocksize):
print(testutil.upload(variant, testutil.SIZE_MB, blocksize))
@pytest.mark.parametrize("variant", ["httplib", "requests"])
@pytest.mark.parametrize("blocksize", [32, 64, 128, 256, 512])
def test_python_parallel(server, variant, blocksize):
print(testutil.upload_parallel(variant, testutil.SIZE_MB, blocksize))
| none | 1 | 2.001779 | 2 |
|
psctb/utils/model_comparing.py | PySCeS/PyscesToolbox | 3 | 6630508 | import copy
import warnings
from os import path
import numpy as np
import pandas as pd
from pysces import Scanner, ParScanner
from pysces import model as pysces_model
from matplotlib import pyplot as plt
from .misc import scanner_range_setup, DotDict, cc_dict, rc_dict, ec_dict, prc_dict, is_parameter, is_species, \
is_mca_coef, silence_print
__all__ = ['compare_models',
'SteadyStateComparer',
'SimulationComparer',
'ParameterScanComparer',
'ClosedOpenComparer']
def compare_models(model_list, model_mapping = None, augment_mapping=True, comparison_type = 'ss'):
"""
Returns a specified model comparer for a list of models.
The returned model comparer object considers the first model in
`model_list` as the "base model" to which the other models are
compared. Since models may have a different naming scheme for
reactions, species, etc. `model_mapping` allows the user to specify
the names of attributes of other models as they relate to the base
model's attribute names.
Parameters
----------
model_list : list of PysMod
A list of PysMod (Pysces model) objects to be compared.
model_mapping : array, optional (Default : None)
An array that maps the names attributes of the base model to
others. Each column relates to a different model in `model_list`
in order. The first column contains the names of attributes of
the base model, whereas each other column contains the name as
it appears in the other models. Only species, reactions and
parameters are needed as other attributes (e.g. control
coefficients) are derived from these three categories. If None
is supplied, all attributes are assumed to be named in the
same way.
augment_mapping : boolean, optional (Default : True)
This parameter specifies if `model_mapping` should be used to
augment a model_mapping which assumes all attributes are named
in the same way. This is useful if only a few model attributes
differ in their naming. If set to false, `model_mapping` must
specify the relationships between all species, reaction and
parameter names.
comparison_type : str, optional (Default : "ss")
A string that specifies which type of comparison should be made.
Options are 'ss' for SteadyStateComparer, "parscan" for
ParameterScanComparer, 'sim' for SimulationComparer, and
'closed_open' for 'ClosedOpenComparer'.
Returns
-------
values: ModelComparer
A model comparer object instantiated with the arguments supplied
above.
"""
if comparison_type == 'ss':
comparer = SteadyStateComparer
elif comparison_type == 'parscan':
comparer = ParameterScanComparer
elif comparison_type == 'sim':
comparer = SimulationComparer
elif comparison_type == 'closed_open':
comparer = ClosedOpenComparer
else:
raise AssertionError('Incorrect comparison_type')
return comparer(model_list, model_mapping, augment_mapping)
class ModelMapper(object):
@staticmethod
def map_models(model_list, model_mapping=None, augment_mapping=True):
# Replace duplicate models with clones
model_list = ModelMapper.replace_with_clones(model_list)
base_model = model_list[0]
# Generate unique model names
model_names = ModelMapper.generate_unique_model_names(model_list)
if augment_mapping and model_mapping is not None:
augment_map_dict_list = ModelMapper.make_map_dict_list(model_mapping)
model_mapping = None
else:
augment_map_dict_list = None
# Generate model mapping if needed
if model_mapping is None:
model_mapping = ModelMapper.auto_map_models(model_list)
# Ensure that supplied (or generated) model mapping
# at least has the correct shape
assert model_mapping.shape[1] == len(model_list)
# Make list of model_dicts
map_dict_list = ModelMapper.make_map_dict_list(model_mapping)
if augment_map_dict_list:
for map_dict, augment_map_dict in zip(map_dict_list,
augment_map_dict_list):
map_dict.update(augment_map_dict)
# Get newly fixed parameters for other models
fixed_param_list = [None]
for i, map_dict in enumerate(map_dict_list[1:]):
i = 1 + i
fixed_params = ModelMapper.get_now_fixed(base_model,
model_list[i],
map_dict)
fixed_param_list.append(fixed_params)
# Add mca coefficients to model_dicts
base_mca_dict = ModelMapper.make_mca_dict(base_model)
for map_dict in map_dict_list:
mca_map_dict = ModelMapper.get_mca_dict_mapping(base_mca_dict,
map_dict)
map_dict.update(mca_map_dict)
model_map_params = list(zip(model_list, model_names,
map_dict_list, fixed_param_list))
model_maps = []
for model, model_name, map_dict, fixed_params in model_map_params:
model_maps.append(ModelMap(model, model_name,
map_dict, fixed_params))
return model_maps
@staticmethod
def make_map_dict_list(mapping_array):
"""
For a model mapping array, returns a list of dictionaries where
the keys correspond to the fist column and the values correspond to
each column (including the first). A 4 column array will thus produce
a list of dicts of len 4 with the first having the same keys
as values.
"""
base_vals = mapping_array[:, 0]
comp_vals = mapping_array[:, 1:]
dict_mapping = [dict(list(zip(base_vals, base_vals)))]
for col in comp_vals.T:
dict_mapping.append(dict(list(zip(base_vals, col))))
return dict_mapping
@staticmethod
def replace_with_clones(model_list):
"""
For a list of models potentially containing duplicate PySMod
instantiations, returns a list of unique objects.
"""
new_model_list = []
for model in model_list:
if model in new_model_list:
new_model_list.append(ModelMapper.clone_model(model))
else:
new_model_list.append(model)
return new_model_list
@staticmethod
def generate_unique_model_names(model_list):
"""
Returns a list of unique model names for a list of models.
"""
model_names = [path.split(model.ModelFile)[-1][:-4] \
for model in model_list]
for i, model_name in enumerate(model_names):
indices = [j for j, v in enumerate(model_names) \
if v == model_name]
if len(indices) > 1:
for j, index in enumerate(indices):
model_names[index] = model_name + '_' + str(j)
return model_names
@staticmethod
def auto_map_models(model_list):
"""
For a list of models returns an array where each column maps contains
the names of model attributes as they appear in each model. Missing
species, parameters, and reactions are replaced with None.
"""
base_model = model_list[0]
comparison_models = model_list[1:]
base_attributes = list(base_model.species) + \
list(base_model.reactions) + \
list(base_model.parameters)
mapping_array = [base_attributes]
for model in comparison_models:
match_attributes = [attr if hasattr(model, attr) \
else None \
for attr in base_attributes]
mapping_array.append(match_attributes)
return np.array(mapping_array).T
@staticmethod
def clone_model(model):
"""
Given a model this method returns a new instantiation of the same
model.
See also:
replace_with_clones
"""
new_model_path = path.join(model.ModelDir,
model.ModelFile)
new_model = pysces_model(new_model_path)
return new_model
@staticmethod
def get_mca_dict_mapping(mca_dict, map_dict):
"""
Given an mca dictionary of the base model and a map dictionary of any
comparison model, this returns a dictionary that maps the names of
mca coefficients in the base model to those in the comparison.
See also:
get_equiv_coeff
"""
return {k: ModelMapper.get_equiv_coeff(k, mca_dict, map_dict)
for k in mca_dict.keys()}
@staticmethod
def get_equiv_coeff(coeff, mca_dict, map_dict):
"""
Given an mca coefficient, an mca dictionary of the base model and a map
dictionary of any comparison model, this returns the name of the
equivalent mca coefficient in the comparison model.
See also:
get_mca_dict_mapping
"""
coeff_type = ModelMapper.get_coeff_type(coeff)
coeff_components = mca_dict[coeff]
equiv_components = [map_dict[component] for component \
in coeff_components]
if None in equiv_components:
return None
else:
if coeff_type in ['cc', 'prc', 'rc']:
prefix = coeff_type + 'J'
else:
prefix = coeff_type
if coeff_type == 'prc':
return prefix + '{}_{}_{}'.format(*equiv_components)
else:
return prefix + '{}_{}'.format(*equiv_components)
@staticmethod
def make_mca_dict(model):
"""
Returns a dictionary with the mca coefficients of a model as keys
and a tuple of the components that make up each coefficient as values.
"""
full_dict = {}
full_dict.update(ec_dict(model))
full_dict.update(cc_dict(model))
full_dict.update(rc_dict(model))
full_dict.update(prc_dict(model))
return full_dict
@staticmethod
def get_coeff_type(coefficient):
"""
Returns the coefficient type.
"""
if coefficient.startswith('prc'):
return 'prc'
elif coefficient.startswith('rc'):
return 'rc'
elif coefficient.startswith('ec'):
return 'ec'
elif coefficient.startswith('cc'):
return 'cc'
@staticmethod
def get_now_fixed(base_model, comparison_model, map_dict):
now_fixed_list = []
for k, v in map_dict.items():
if is_species(k, base_model) and is_parameter(v, comparison_model):
now_fixed_list.append(k)
return now_fixed_list
class ModelMap(object):
def __init__(self, model, model_name, attr_dict, is_now_fixed=None):
self.model = model
self.model_name = model_name
self.attr_dict = attr_dict
self._rev_attr_dict = ModelMap._reverse_dict(self.attr_dict)
if not is_now_fixed:
is_now_fixed = []
self.is_now_fixed = is_now_fixed
@staticmethod
def _reverse_dict(dict_):
return {v: k for k, v in dict_.items()}
def getattrname(self, key):
prefix, suffix, key = ModelMap._prefix_suffix_getter(key)
attr = self.attr_dict[key]
if attr is None:
return None
elif key in self.is_now_fixed:
return attr
else:
return prefix + attr + suffix
def hasattr(self, item):
_, _, item = ModelMap._prefix_suffix_getter(item)
if item in list(self.attr_dict.keys()):
return True
else:
return False
def setattr(self, key, value):
if self.hasattr(key):
true_key = self.getattrname(key)
if true_key is not None and hasattr(self.model, true_key):
setattr(self.model, true_key, value)
def getattr(self, item):
true_key = self.getattrname(item)
if true_key is None:
return np.NaN
else:
return getattr(self.model, true_key)
def attr_names_from_base_names(self, key_list, include_nones=False):
converted_attrs = []
for key in key_list:
true_attr = self.getattrname(key)
if true_attr is None:
if include_nones:
converted_attrs.append(true_attr)
else:
converted_attrs.append(true_attr)
return converted_attrs
def base_names_from_attr_names(self, attr_list, add_ss = False):
new_attr_list = [ModelMap._prefix_suffix_getter(attr) for \
attr in attr_list]
for i, p_s_a in enumerate(new_attr_list):
prefix, suffix, attr = p_s_a
base_attr = self._rev_attr_dict[attr]
if base_attr in self.is_now_fixed and add_ss:
suffix = '_ss'
new_attr_list[i] = (prefix, suffix, attr)
converted_attrs = [prefix + self._rev_attr_dict[attr] + suffix for \
prefix, suffix, attr in new_attr_list]
return converted_attrs
def setattrs_from_dict(self, attr_dict):
for key, value in attr_dict.items():
self.setattr(key, value)
def getattrs_from_list(self, attr_list):
return [self.getattr(key) for key in attr_list]
@staticmethod
def _prefix_suffix_getter(key):
prefix = ''
suffix = ''
flux_prefix = 'J_'
ssconc_suffix = '_ss'
init_prefix = '_init'
if key.startswith(flux_prefix):
key = key[2:]
prefix = flux_prefix
elif key.endswith(ssconc_suffix):
key = key[:-3]
suffix = ssconc_suffix
elif key.endswith(init_prefix):
key = key[:-5]
suffix = init_prefix
return prefix, suffix, key
class ResultsGenerator(object):
@staticmethod
def percentage_change(a, b, abs_values = False):
with warnings.catch_warnings():
warnings.simplefilter("ignore")
if abs_values:
return np.divide(np.abs(np.subtract(b, a)), np.abs(a)) * 100
else:
return np.divide(np.subtract(b, a), a) * 100
@staticmethod
def do_par_scan(model, scan_in, scan_out, start, end, points, is_log_range, par_scan, par_engine):
if par_scan:
scanner = ParScanner(model, engine=par_engine)
else:
scanner = Scanner(model)
scan_out = [str(each) for each in scan_out]
scanner.addScanParameter(scan_in, start=start, end=end, points=points, log=is_log_range)
scanner.addUserOutput(*scan_out)
scanner.Run()
result = scanner.getResultMatrix()
return result
@staticmethod
def do_simulation(model, time_range, sim_out):
model.sim_time = time_range
model.Simulate(userinit=1)
results, labels = model.data_sim.getAllSimData(lbls=True)
sim_out = ['Time'] + sim_out
sim_out_index = [labels.index(out_label) for out_label in sim_out]
results_to_keep = results[:,sim_out_index]
return results_to_keep
class BaseModelComparer(object):
def __init__(self, model_list, model_mapping=None, augment_mapping=True):
super(BaseModelComparer, self).__init__()
assert len(model_list) > 1, 'Two or more models are needed to make a comparison'
self.mmap_list = ModelMapper.map_models(model_list=model_list,
model_mapping=model_mapping,
augment_mapping=augment_mapping)
self.raw_data = None
self.comparison = None
@silence_print
def do_compare(self, output_list=None, custom_init=None, uniform_init=True):
"""
Performs the comparison of the models.
Results are stored in `self.raw_data` and `self.comparison`.
Parameters
----------
output_list : list of str, optional (Default : None)
A list of model attributes to compare. Valid options are:
species, reactions, parameters, and mca coefficients.
custom_init : dict or list of dict, optional (Default : None)
A dictionary specifying the initial conditions of the models.
Keys specify model attributes (in str) and values are float
values of these attributes. A list of dictionaries can also
be supplied in order to give each model a different set of
initial conditions. The list follows the same order as
the `model_list` supplied during object instantiation.
uniform_init : boolean, optional (Default : True)
If set to True, the attributes of each model will be set to
that of the base model prior to comparison. `custom_inits`
are applied after making model attributes uniform.
"""
if output_list is None:
output_list = self._get_default_outputs()
self._uniform_init(uniform_init)
self._custom_init(custom_init)
self._generate_raw_data(output_list)
self._compare_raw_data()
@silence_print
def _get_default_outputs(self):
base_model = self.mmap_list[0].model
return list(base_model.species) + list(base_model.reactions)
@silence_print
def _custom_init(self, custom_init):
if custom_init:
if type(custom_init) is dict:
for mmap in self.mmap_list:
mmap.setattrs_from_dict(custom_init)
elif type(custom_init) is list:
num_of_models = len(self.mmap_list)
all_dicts = all([type(each) is dict for each in custom_init])
correct_num_of_dicts = len(custom_init) == num_of_models
assert all_dicts and correct_num_of_dicts, \
"list of custom inits must contain {} dictionaries".format(num_of_models)
for custom_init_dict, mmap in zip(custom_init, self.mmap_list):
mmap.setattrs_from_dict(custom_init_dict)
else:
raise AssertionError("custom_inits must be a dictionary or a list of dictionaries")
@silence_print
def _uniform_init(self, uniform_init):
if uniform_init:
base_model = self.mmap_list[0].model
base_parameter_dict = {k:getattr(base_model, k) for k in base_model.parameters}
base_species_dict = {k:getattr(base_model, k) for k in base_model.species}
base_attribute_dict = {}
base_attribute_dict.update(base_parameter_dict)
base_attribute_dict.update(base_species_dict)
for mmap in self.mmap_list[1:]:
mmap.setattrs_from_dict(base_attribute_dict)
def _compare_raw_data(self):
raise NotImplementedError
def _generate_raw_data(self, output_list):
raise NotImplementedError
@silence_print
def _output_to_ss(self, output_list):
base_model = self.mmap_list[0].model
new_out_list = []
for each in output_list:
if each in base_model.species:
new_out_list.append(each + '_ss')
elif each in base_model.reactions:
new_out_list.append('J_' + each)
else:
new_out_list.append(each)
return new_out_list
class SteadyStateComparer(BaseModelComparer):
@silence_print
def _generate_raw_data(self, output_list):
output_list = self._output_to_ss(output_list)
all_results = []
base_model = self.mmap_list[0].model
state_method = 'mca' if any([is_mca_coef(attr, base_model) for attr in output_list]) else 'ss'
for mmap in self.mmap_list:
if state_method == 'mca':
mmap.model.doMca()
else:
mmap.model.doState()
model_results = [mmap.getattr(attr) for attr in output_list]
all_results.append(model_results)
all_results = np.array(all_results).T
self.raw_data = pd.DataFrame(data=all_results,
columns=[mmap.model_name for mmap in self.mmap_list],
index=output_list)
@silence_print
def _compare_raw_data(self):
raw_data = self.raw_data
data_matrix = raw_data.as_matrix()
reference_values = data_matrix[:,0]
change_values = data_matrix[:,1:]
comparison_names = []
all_results = []
for i, change_col in enumerate(change_values.T):
comparison_name = '{}_{}'.format(self.mmap_list[0].model_name,
self.mmap_list[i+1].model_name)
comparison_names.append(comparison_name)
comparison_result = ResultsGenerator.percentage_change(reference_values,
change_col)
all_results.append(comparison_result)
all_results = np.array(all_results).T
self.comparison = pd.DataFrame(data=all_results,
columns=comparison_names,
index=raw_data.index)
class ParameterScanComparer(BaseModelComparer):
@silence_print
def do_compare(self, scan_in, scan_range, output_list=None, custom_init=None,
uniform_init=True, par_scan=False, par_engine='multiproc'):
"""
Performs the comparison of the models.
Results are stored in `self.raw_data` and `self.comparison`.
Parameters
----------
scan_in : str
A string that specifies the parameter to be varied
scan_range : array-like
An array that specifies the range of values over which
`scan_in` should be varied
output_list : list of str, optional (Default : None)
A list of model attributes to compare. Valid options are:
species, reactions, parameters, and mca coefficients.
custom_init : dict or list of dict, optional (Default : None)
A dictionary specifying the initial conditions of the models.
Keys specify model attributes (in str) and values are float
values of these attributes. A list of dictionaries can also
be supplied in order to give each model a different set of
initial conditions. The list follows the same order as
the `model_list` supplied during object instantiation.
uniform_init : boolean, optional (Default : True)
If set to True, the attributes of each model will be set to
that of the base model prior to comparison. `custom_inits`
are applied after making model attributes uniform.
par_scan : boolean, optional (Default : False)
If True the parameter scan will be executed using parallel
processing.
par_enging: str, optional (Default : 'multiproc')
The parallel engine to be used. Options are dictated by
PySCeS's ParScanner.
"""
if output_list is None:
output_list = self._get_default_outputs()
self._uniform_init(uniform_init)
self._custom_init(custom_init)
self._generate_raw_data(scan_in, output_list, scan_range, par_scan, par_engine)
self._compare_raw_data()
@silence_print
def _generate_raw_data(self, scan_in, output_list, scan_range, par_scan, par_engine):
output_list = self._output_to_ss(output_list)
main_column_labels = [scan_in] + output_list
start, end, points, is_log_range = scanner_range_setup(scan_range)
all_results = DotDict()
for i, mmap in enumerate(self.mmap_list):
current_scan_in = mmap.getattrname(scan_in)
current_scan_out = mmap.attr_names_from_base_names(output_list)
current_col_labels = [scan_in] + mmap.base_names_from_attr_names(current_scan_out, add_ss=True)
# parameter scan
raw_result = ResultsGenerator.do_par_scan(model=mmap.model,
scan_in=current_scan_in,
scan_out=current_scan_out,
start=start,
end=end,
points=points,
is_log_range=is_log_range,
par_scan=par_scan,
par_engine=par_engine)
complete_results = pd.DataFrame(data=raw_result,
columns=current_col_labels)
complete_results = complete_results.reindex(columns=main_column_labels)
complete_results = complete_results.set_index(scan_in)
all_results[mmap.model_name] = complete_results
self.raw_data = all_results
@silence_print
def _compare_raw_data(self):
raw_data = self.raw_data
reference_model_name = self.mmap_list[0].model_name
reference_matrix = raw_data[reference_model_name].reset_index().as_matrix()
column_labels = raw_data[reference_model_name].reset_index().columns
all_results = DotDict()
for mmap in self.mmap_list[1:]:
current_model_name = mmap.model_name
comparison_name = '{}_{}'.format(reference_model_name,
current_model_name)
change_matrix = raw_data[current_model_name].reset_index().as_matrix()
comparison_result = ResultsGenerator.percentage_change(reference_matrix[:,1:],change_matrix[:,1:])
comparison_result = np.hstack([reference_matrix[:,0][:,np.newaxis],comparison_result])
df = pd.DataFrame(data=comparison_result,
columns=column_labels)
index = df.columns[0]
df = df.set_index(index)
all_results[comparison_name] = df
self.comparison = all_results
def plot(self,cols=3, width=None, height=None):
"""
Produces a simple plot that allows for the visual comparison of
results for each model.
Parameters
----------
cols : int, optional (Default : 3)
The number of columns to use when plotting subplots.
width : float, optional (Default : 15.0)
The width of the figure in inches.
height : float, optional (Default : 5.0*number of subplot rows)
The height of the figure in inches. By default the height is
dynamic in will be adjusted based on the number of rows of
subplots where each row is assigned 5 inches.
Returns
-------
f: matplotlib figure
A matplotlib figure on which results are plotted.
axxarr: 2-dimensional numpy array of AxesSubplots
An array that contains the axes associate with the figure.
"""
columns = list(self.raw_data.values())[0].columns
index = list(self.raw_data.values())[0].index
assert cols >= 2
rows = int(np.ceil(len(columns)/float(cols)))
if not height:
height = 5.*rows
if not width:
width = 15.
f, axarr = plt.subplots(rows,cols)
f.set_size_inches(w=width, h=height)
for i in range(cols*rows - len(columns)):
f.delaxes(axarr.flat[-1-i])
for column, ax in zip(columns,axarr.flat):
for k, v in self.raw_data.items():
ax.plot(index, v[column], '-',label=k)
ax.set_xlabel(index.name)
ax.set_title(column)
ax.legend()
return f,axarr
class SimulationComparer(ParameterScanComparer):
@silence_print
def do_compare(self, time_range, output_list=None, custom_init=None,
uniform_init=True):
"""
Performs the comparison of the models.
Results are stored in `self.raw_data` and `self.comparison`.
Parameters
----------
time_range : array-like
An array that specifies the range of values over which
`Time` should be varied. It allows for starting points other
than zero.
output_list : list of str, optional (Default : None)
A list of model attributes to compare. Valid options are:
species, reactions, parameters, and mca coefficients.
custom_init : dict or list of dict, optional (Default : None)
A dictionary specifying the initial conditions of the models.
Keys specify model attributes (in str) and values are float
values of these attributes. A list of dictionaries can also
be supplied in order to give each model a different set of
initial conditions. The list follows the same order as
the `model_list` supplied during object instantiation.
uniform_init : boolean, optional (Default : True)
If set to True, the attributes of each model will be set to
that of the base model prior to comparison. `custom_inits`
are applied after making model attributes uniform.
"""
if output_list is None:
output_list = self._get_default_outputs()
self._uniform_init(uniform_init)
self._custom_init(custom_init)
self._generate_raw_data(output_list, time_range)
self._compare_raw_data()
@silence_print
def _generate_raw_data(self, output_list, time_range):
main_column_labels = ['Time'] + output_list
all_results = DotDict()
for i, mmap in enumerate(self.mmap_list):
now_fixed_indicies = []
for i,out in enumerate(output_list):
if out in mmap.is_now_fixed:
now_fixed_indicies.append(i)
new_output_list = copy.deepcopy(output_list)
param_cols = []
for ind in reversed(now_fixed_indicies):
out_val = mmap.getattr(output_list[ind])
new_output_list.pop(ind)
param_cols.append([out_val for _ in range(time_range.shape[0])])
param_cols = param_cols
current_sim_out = mmap.attr_names_from_base_names(new_output_list)
real_sim_out = mmap.attr_names_from_base_names(output_list)
current_col_labels = ['Time'] + mmap.base_names_from_attr_names(real_sim_out )
# parameter scan
raw_result = ResultsGenerator.do_simulation(model=mmap.model,
time_range=time_range,
sim_out=current_sim_out)
new_raw_result = [col for col in raw_result.T]
for ind, col in zip(reversed(now_fixed_indicies), param_cols):
new_raw_result.insert(ind, col)
new_raw_result = np.array(new_raw_result).T
complete_results = pd.DataFrame(data=new_raw_result,
columns=current_col_labels)
complete_results = complete_results.reindex(columns=main_column_labels)
complete_results = complete_results.set_index('Time')
all_results[mmap.model_name] = complete_results
self.raw_data = all_results
class ClosedOpenComparer(SimulationComparer):
@silence_print
def do_compare(self, time_range, output_list=None, custom_init=None,
uniform_init=True):
"""
Performs the comparison of the models.
Results are stored in `self.raw_data` and `self.comparison`.
Parameters
----------
time_range : array-like
An array that specifies the range of values over which
`Time` should be varied. It allows for starting points other
than zero.
output_list : list of str, optional (Default : None)
A list of model attributes to compare. Valid options are:
species, reactions, parameters, and mca coefficients.
custom_init : dict or list of dict, optional (Default : None)
A dictionary specifying the initial conditions of the models.
Keys specify model attributes (in str) and values are float
values of these attributes. A list of dictionaries can also
be supplied in order to give each model a different set of
initial conditions. The list follows the same order as
the `model_list` supplied during object instantiation.
uniform_init : boolean, optional (Default : True)
If set to True, the attributes of each model will be set to
that of the base model prior to comparison. `custom_inits`
are applied after making model attributes uniform.
"""
if output_list is None:
output_list = self._get_default_outputs()
self._uniform_init(uniform_init)
self._custom_init(custom_init)
self._generate_raw_data(output_list, time_range)
self._compare_raw_data()
@silence_print
def _generate_raw_data(self, output_list, time_range):
base_mmap = self.mmap_list[0]
full_scan_column_names = []
for mmap in self.mmap_list:
full_scan_column_names = full_scan_column_names + mmap.is_now_fixed
full_scan_column_names = list(set(full_scan_column_names))
base_sim_out_list = full_scan_column_names + [out for out in output_list \
if out not in full_scan_column_names]
base_column_names = ['Time'] + base_sim_out_list
all_results = DotDict()
simulation_results = ResultsGenerator.do_simulation(model=base_mmap.model,
time_range=time_range,
sim_out=base_sim_out_list)
base_complete_results = pd.DataFrame(data=simulation_results,
columns=base_column_names)
base_complete_results = base_complete_results.set_index('Time')
all_results[base_mmap.model_name] = base_complete_results
for i, mmap in enumerate(self.mmap_list[1:]):
scan_column_names = mmap.is_now_fixed
output_partial = [out for out in output_list if out not in scan_column_names]
outputs = self._output_to_ss(output_partial)
output_col_names = ['Time'] + scan_column_names + output_partial
time_col = base_complete_results.reset_index()['Time'].as_matrix()
input_val_list = [base_complete_results[col_name].as_matrix() for col_name in scan_column_names]
raw_results = []
for input_vals in zip(*input_val_list):
current_row = [] + list(input_vals)
dict_to_set = dict(list(zip(scan_column_names, input_vals)))
mmap.setattrs_from_dict(dict_to_set)
mmap.model.doState()
current_row = current_row + mmap.getattrs_from_list(outputs)
raw_results.append(current_row)
raw_results = np.hstack((time_col[:,np.newaxis],np.array(raw_results)))
complete_results = pd.DataFrame(data=raw_results,
columns=output_col_names)
complete_results = complete_results.reindex(columns=base_column_names)
complete_results = complete_results.set_index('Time')
all_results[mmap.model_name] = complete_results
self.raw_data = all_results
| import copy
import warnings
from os import path
import numpy as np
import pandas as pd
from pysces import Scanner, ParScanner
from pysces import model as pysces_model
from matplotlib import pyplot as plt
from .misc import scanner_range_setup, DotDict, cc_dict, rc_dict, ec_dict, prc_dict, is_parameter, is_species, \
is_mca_coef, silence_print
__all__ = ['compare_models',
'SteadyStateComparer',
'SimulationComparer',
'ParameterScanComparer',
'ClosedOpenComparer']
def compare_models(model_list, model_mapping = None, augment_mapping=True, comparison_type = 'ss'):
"""
Returns a specified model comparer for a list of models.
The returned model comparer object considers the first model in
`model_list` as the "base model" to which the other models are
compared. Since models may have a different naming scheme for
reactions, species, etc. `model_mapping` allows the user to specify
the names of attributes of other models as they relate to the base
model's attribute names.
Parameters
----------
model_list : list of PysMod
A list of PysMod (Pysces model) objects to be compared.
model_mapping : array, optional (Default : None)
An array that maps the names attributes of the base model to
others. Each column relates to a different model in `model_list`
in order. The first column contains the names of attributes of
the base model, whereas each other column contains the name as
it appears in the other models. Only species, reactions and
parameters are needed as other attributes (e.g. control
coefficients) are derived from these three categories. If None
is supplied, all attributes are assumed to be named in the
same way.
augment_mapping : boolean, optional (Default : True)
This parameter specifies if `model_mapping` should be used to
augment a model_mapping which assumes all attributes are named
in the same way. This is useful if only a few model attributes
differ in their naming. If set to false, `model_mapping` must
specify the relationships between all species, reaction and
parameter names.
comparison_type : str, optional (Default : "ss")
A string that specifies which type of comparison should be made.
Options are 'ss' for SteadyStateComparer, "parscan" for
ParameterScanComparer, 'sim' for SimulationComparer, and
'closed_open' for 'ClosedOpenComparer'.
Returns
-------
values: ModelComparer
A model comparer object instantiated with the arguments supplied
above.
"""
if comparison_type == 'ss':
comparer = SteadyStateComparer
elif comparison_type == 'parscan':
comparer = ParameterScanComparer
elif comparison_type == 'sim':
comparer = SimulationComparer
elif comparison_type == 'closed_open':
comparer = ClosedOpenComparer
else:
raise AssertionError('Incorrect comparison_type')
return comparer(model_list, model_mapping, augment_mapping)
class ModelMapper(object):
@staticmethod
def map_models(model_list, model_mapping=None, augment_mapping=True):
# Replace duplicate models with clones
model_list = ModelMapper.replace_with_clones(model_list)
base_model = model_list[0]
# Generate unique model names
model_names = ModelMapper.generate_unique_model_names(model_list)
if augment_mapping and model_mapping is not None:
augment_map_dict_list = ModelMapper.make_map_dict_list(model_mapping)
model_mapping = None
else:
augment_map_dict_list = None
# Generate model mapping if needed
if model_mapping is None:
model_mapping = ModelMapper.auto_map_models(model_list)
# Ensure that supplied (or generated) model mapping
# at least has the correct shape
assert model_mapping.shape[1] == len(model_list)
# Make list of model_dicts
map_dict_list = ModelMapper.make_map_dict_list(model_mapping)
if augment_map_dict_list:
for map_dict, augment_map_dict in zip(map_dict_list,
augment_map_dict_list):
map_dict.update(augment_map_dict)
# Get newly fixed parameters for other models
fixed_param_list = [None]
for i, map_dict in enumerate(map_dict_list[1:]):
i = 1 + i
fixed_params = ModelMapper.get_now_fixed(base_model,
model_list[i],
map_dict)
fixed_param_list.append(fixed_params)
# Add mca coefficients to model_dicts
base_mca_dict = ModelMapper.make_mca_dict(base_model)
for map_dict in map_dict_list:
mca_map_dict = ModelMapper.get_mca_dict_mapping(base_mca_dict,
map_dict)
map_dict.update(mca_map_dict)
model_map_params = list(zip(model_list, model_names,
map_dict_list, fixed_param_list))
model_maps = []
for model, model_name, map_dict, fixed_params in model_map_params:
model_maps.append(ModelMap(model, model_name,
map_dict, fixed_params))
return model_maps
@staticmethod
def make_map_dict_list(mapping_array):
"""
For a model mapping array, returns a list of dictionaries where
the keys correspond to the fist column and the values correspond to
each column (including the first). A 4 column array will thus produce
a list of dicts of len 4 with the first having the same keys
as values.
"""
base_vals = mapping_array[:, 0]
comp_vals = mapping_array[:, 1:]
dict_mapping = [dict(list(zip(base_vals, base_vals)))]
for col in comp_vals.T:
dict_mapping.append(dict(list(zip(base_vals, col))))
return dict_mapping
@staticmethod
def replace_with_clones(model_list):
"""
For a list of models potentially containing duplicate PySMod
instantiations, returns a list of unique objects.
"""
new_model_list = []
for model in model_list:
if model in new_model_list:
new_model_list.append(ModelMapper.clone_model(model))
else:
new_model_list.append(model)
return new_model_list
@staticmethod
def generate_unique_model_names(model_list):
"""
Returns a list of unique model names for a list of models.
"""
model_names = [path.split(model.ModelFile)[-1][:-4] \
for model in model_list]
for i, model_name in enumerate(model_names):
indices = [j for j, v in enumerate(model_names) \
if v == model_name]
if len(indices) > 1:
for j, index in enumerate(indices):
model_names[index] = model_name + '_' + str(j)
return model_names
@staticmethod
def auto_map_models(model_list):
"""
For a list of models returns an array where each column maps contains
the names of model attributes as they appear in each model. Missing
species, parameters, and reactions are replaced with None.
"""
base_model = model_list[0]
comparison_models = model_list[1:]
base_attributes = list(base_model.species) + \
list(base_model.reactions) + \
list(base_model.parameters)
mapping_array = [base_attributes]
for model in comparison_models:
match_attributes = [attr if hasattr(model, attr) \
else None \
for attr in base_attributes]
mapping_array.append(match_attributes)
return np.array(mapping_array).T
@staticmethod
def clone_model(model):
"""
Given a model this method returns a new instantiation of the same
model.
See also:
replace_with_clones
"""
new_model_path = path.join(model.ModelDir,
model.ModelFile)
new_model = pysces_model(new_model_path)
return new_model
@staticmethod
def get_mca_dict_mapping(mca_dict, map_dict):
"""
Given an mca dictionary of the base model and a map dictionary of any
comparison model, this returns a dictionary that maps the names of
mca coefficients in the base model to those in the comparison.
See also:
get_equiv_coeff
"""
return {k: ModelMapper.get_equiv_coeff(k, mca_dict, map_dict)
for k in mca_dict.keys()}
@staticmethod
def get_equiv_coeff(coeff, mca_dict, map_dict):
"""
Given an mca coefficient, an mca dictionary of the base model and a map
dictionary of any comparison model, this returns the name of the
equivalent mca coefficient in the comparison model.
See also:
get_mca_dict_mapping
"""
coeff_type = ModelMapper.get_coeff_type(coeff)
coeff_components = mca_dict[coeff]
equiv_components = [map_dict[component] for component \
in coeff_components]
if None in equiv_components:
return None
else:
if coeff_type in ['cc', 'prc', 'rc']:
prefix = coeff_type + 'J'
else:
prefix = coeff_type
if coeff_type == 'prc':
return prefix + '{}_{}_{}'.format(*equiv_components)
else:
return prefix + '{}_{}'.format(*equiv_components)
@staticmethod
def make_mca_dict(model):
"""
Returns a dictionary with the mca coefficients of a model as keys
and a tuple of the components that make up each coefficient as values.
"""
full_dict = {}
full_dict.update(ec_dict(model))
full_dict.update(cc_dict(model))
full_dict.update(rc_dict(model))
full_dict.update(prc_dict(model))
return full_dict
@staticmethod
def get_coeff_type(coefficient):
"""
Returns the coefficient type.
"""
if coefficient.startswith('prc'):
return 'prc'
elif coefficient.startswith('rc'):
return 'rc'
elif coefficient.startswith('ec'):
return 'ec'
elif coefficient.startswith('cc'):
return 'cc'
@staticmethod
def get_now_fixed(base_model, comparison_model, map_dict):
now_fixed_list = []
for k, v in map_dict.items():
if is_species(k, base_model) and is_parameter(v, comparison_model):
now_fixed_list.append(k)
return now_fixed_list
class ModelMap(object):
def __init__(self, model, model_name, attr_dict, is_now_fixed=None):
self.model = model
self.model_name = model_name
self.attr_dict = attr_dict
self._rev_attr_dict = ModelMap._reverse_dict(self.attr_dict)
if not is_now_fixed:
is_now_fixed = []
self.is_now_fixed = is_now_fixed
@staticmethod
def _reverse_dict(dict_):
return {v: k for k, v in dict_.items()}
def getattrname(self, key):
prefix, suffix, key = ModelMap._prefix_suffix_getter(key)
attr = self.attr_dict[key]
if attr is None:
return None
elif key in self.is_now_fixed:
return attr
else:
return prefix + attr + suffix
def hasattr(self, item):
_, _, item = ModelMap._prefix_suffix_getter(item)
if item in list(self.attr_dict.keys()):
return True
else:
return False
def setattr(self, key, value):
if self.hasattr(key):
true_key = self.getattrname(key)
if true_key is not None and hasattr(self.model, true_key):
setattr(self.model, true_key, value)
def getattr(self, item):
true_key = self.getattrname(item)
if true_key is None:
return np.NaN
else:
return getattr(self.model, true_key)
def attr_names_from_base_names(self, key_list, include_nones=False):
converted_attrs = []
for key in key_list:
true_attr = self.getattrname(key)
if true_attr is None:
if include_nones:
converted_attrs.append(true_attr)
else:
converted_attrs.append(true_attr)
return converted_attrs
def base_names_from_attr_names(self, attr_list, add_ss = False):
new_attr_list = [ModelMap._prefix_suffix_getter(attr) for \
attr in attr_list]
for i, p_s_a in enumerate(new_attr_list):
prefix, suffix, attr = p_s_a
base_attr = self._rev_attr_dict[attr]
if base_attr in self.is_now_fixed and add_ss:
suffix = '_ss'
new_attr_list[i] = (prefix, suffix, attr)
converted_attrs = [prefix + self._rev_attr_dict[attr] + suffix for \
prefix, suffix, attr in new_attr_list]
return converted_attrs
def setattrs_from_dict(self, attr_dict):
for key, value in attr_dict.items():
self.setattr(key, value)
def getattrs_from_list(self, attr_list):
return [self.getattr(key) for key in attr_list]
@staticmethod
def _prefix_suffix_getter(key):
prefix = ''
suffix = ''
flux_prefix = 'J_'
ssconc_suffix = '_ss'
init_prefix = '_init'
if key.startswith(flux_prefix):
key = key[2:]
prefix = flux_prefix
elif key.endswith(ssconc_suffix):
key = key[:-3]
suffix = ssconc_suffix
elif key.endswith(init_prefix):
key = key[:-5]
suffix = init_prefix
return prefix, suffix, key
class ResultsGenerator(object):
@staticmethod
def percentage_change(a, b, abs_values = False):
with warnings.catch_warnings():
warnings.simplefilter("ignore")
if abs_values:
return np.divide(np.abs(np.subtract(b, a)), np.abs(a)) * 100
else:
return np.divide(np.subtract(b, a), a) * 100
@staticmethod
def do_par_scan(model, scan_in, scan_out, start, end, points, is_log_range, par_scan, par_engine):
if par_scan:
scanner = ParScanner(model, engine=par_engine)
else:
scanner = Scanner(model)
scan_out = [str(each) for each in scan_out]
scanner.addScanParameter(scan_in, start=start, end=end, points=points, log=is_log_range)
scanner.addUserOutput(*scan_out)
scanner.Run()
result = scanner.getResultMatrix()
return result
@staticmethod
def do_simulation(model, time_range, sim_out):
model.sim_time = time_range
model.Simulate(userinit=1)
results, labels = model.data_sim.getAllSimData(lbls=True)
sim_out = ['Time'] + sim_out
sim_out_index = [labels.index(out_label) for out_label in sim_out]
results_to_keep = results[:,sim_out_index]
return results_to_keep
class BaseModelComparer(object):
def __init__(self, model_list, model_mapping=None, augment_mapping=True):
super(BaseModelComparer, self).__init__()
assert len(model_list) > 1, 'Two or more models are needed to make a comparison'
self.mmap_list = ModelMapper.map_models(model_list=model_list,
model_mapping=model_mapping,
augment_mapping=augment_mapping)
self.raw_data = None
self.comparison = None
@silence_print
def do_compare(self, output_list=None, custom_init=None, uniform_init=True):
"""
Performs the comparison of the models.
Results are stored in `self.raw_data` and `self.comparison`.
Parameters
----------
output_list : list of str, optional (Default : None)
A list of model attributes to compare. Valid options are:
species, reactions, parameters, and mca coefficients.
custom_init : dict or list of dict, optional (Default : None)
A dictionary specifying the initial conditions of the models.
Keys specify model attributes (in str) and values are float
values of these attributes. A list of dictionaries can also
be supplied in order to give each model a different set of
initial conditions. The list follows the same order as
the `model_list` supplied during object instantiation.
uniform_init : boolean, optional (Default : True)
If set to True, the attributes of each model will be set to
that of the base model prior to comparison. `custom_inits`
are applied after making model attributes uniform.
"""
if output_list is None:
output_list = self._get_default_outputs()
self._uniform_init(uniform_init)
self._custom_init(custom_init)
self._generate_raw_data(output_list)
self._compare_raw_data()
@silence_print
def _get_default_outputs(self):
base_model = self.mmap_list[0].model
return list(base_model.species) + list(base_model.reactions)
@silence_print
def _custom_init(self, custom_init):
if custom_init:
if type(custom_init) is dict:
for mmap in self.mmap_list:
mmap.setattrs_from_dict(custom_init)
elif type(custom_init) is list:
num_of_models = len(self.mmap_list)
all_dicts = all([type(each) is dict for each in custom_init])
correct_num_of_dicts = len(custom_init) == num_of_models
assert all_dicts and correct_num_of_dicts, \
"list of custom inits must contain {} dictionaries".format(num_of_models)
for custom_init_dict, mmap in zip(custom_init, self.mmap_list):
mmap.setattrs_from_dict(custom_init_dict)
else:
raise AssertionError("custom_inits must be a dictionary or a list of dictionaries")
@silence_print
def _uniform_init(self, uniform_init):
if uniform_init:
base_model = self.mmap_list[0].model
base_parameter_dict = {k:getattr(base_model, k) for k in base_model.parameters}
base_species_dict = {k:getattr(base_model, k) for k in base_model.species}
base_attribute_dict = {}
base_attribute_dict.update(base_parameter_dict)
base_attribute_dict.update(base_species_dict)
for mmap in self.mmap_list[1:]:
mmap.setattrs_from_dict(base_attribute_dict)
def _compare_raw_data(self):
raise NotImplementedError
def _generate_raw_data(self, output_list):
raise NotImplementedError
@silence_print
def _output_to_ss(self, output_list):
base_model = self.mmap_list[0].model
new_out_list = []
for each in output_list:
if each in base_model.species:
new_out_list.append(each + '_ss')
elif each in base_model.reactions:
new_out_list.append('J_' + each)
else:
new_out_list.append(each)
return new_out_list
class SteadyStateComparer(BaseModelComparer):
@silence_print
def _generate_raw_data(self, output_list):
output_list = self._output_to_ss(output_list)
all_results = []
base_model = self.mmap_list[0].model
state_method = 'mca' if any([is_mca_coef(attr, base_model) for attr in output_list]) else 'ss'
for mmap in self.mmap_list:
if state_method == 'mca':
mmap.model.doMca()
else:
mmap.model.doState()
model_results = [mmap.getattr(attr) for attr in output_list]
all_results.append(model_results)
all_results = np.array(all_results).T
self.raw_data = pd.DataFrame(data=all_results,
columns=[mmap.model_name for mmap in self.mmap_list],
index=output_list)
@silence_print
def _compare_raw_data(self):
raw_data = self.raw_data
data_matrix = raw_data.as_matrix()
reference_values = data_matrix[:,0]
change_values = data_matrix[:,1:]
comparison_names = []
all_results = []
for i, change_col in enumerate(change_values.T):
comparison_name = '{}_{}'.format(self.mmap_list[0].model_name,
self.mmap_list[i+1].model_name)
comparison_names.append(comparison_name)
comparison_result = ResultsGenerator.percentage_change(reference_values,
change_col)
all_results.append(comparison_result)
all_results = np.array(all_results).T
self.comparison = pd.DataFrame(data=all_results,
columns=comparison_names,
index=raw_data.index)
class ParameterScanComparer(BaseModelComparer):
@silence_print
def do_compare(self, scan_in, scan_range, output_list=None, custom_init=None,
uniform_init=True, par_scan=False, par_engine='multiproc'):
"""
Performs the comparison of the models.
Results are stored in `self.raw_data` and `self.comparison`.
Parameters
----------
scan_in : str
A string that specifies the parameter to be varied
scan_range : array-like
An array that specifies the range of values over which
`scan_in` should be varied
output_list : list of str, optional (Default : None)
A list of model attributes to compare. Valid options are:
species, reactions, parameters, and mca coefficients.
custom_init : dict or list of dict, optional (Default : None)
A dictionary specifying the initial conditions of the models.
Keys specify model attributes (in str) and values are float
values of these attributes. A list of dictionaries can also
be supplied in order to give each model a different set of
initial conditions. The list follows the same order as
the `model_list` supplied during object instantiation.
uniform_init : boolean, optional (Default : True)
If set to True, the attributes of each model will be set to
that of the base model prior to comparison. `custom_inits`
are applied after making model attributes uniform.
par_scan : boolean, optional (Default : False)
If True the parameter scan will be executed using parallel
processing.
par_enging: str, optional (Default : 'multiproc')
The parallel engine to be used. Options are dictated by
PySCeS's ParScanner.
"""
if output_list is None:
output_list = self._get_default_outputs()
self._uniform_init(uniform_init)
self._custom_init(custom_init)
self._generate_raw_data(scan_in, output_list, scan_range, par_scan, par_engine)
self._compare_raw_data()
@silence_print
def _generate_raw_data(self, scan_in, output_list, scan_range, par_scan, par_engine):
output_list = self._output_to_ss(output_list)
main_column_labels = [scan_in] + output_list
start, end, points, is_log_range = scanner_range_setup(scan_range)
all_results = DotDict()
for i, mmap in enumerate(self.mmap_list):
current_scan_in = mmap.getattrname(scan_in)
current_scan_out = mmap.attr_names_from_base_names(output_list)
current_col_labels = [scan_in] + mmap.base_names_from_attr_names(current_scan_out, add_ss=True)
# parameter scan
raw_result = ResultsGenerator.do_par_scan(model=mmap.model,
scan_in=current_scan_in,
scan_out=current_scan_out,
start=start,
end=end,
points=points,
is_log_range=is_log_range,
par_scan=par_scan,
par_engine=par_engine)
complete_results = pd.DataFrame(data=raw_result,
columns=current_col_labels)
complete_results = complete_results.reindex(columns=main_column_labels)
complete_results = complete_results.set_index(scan_in)
all_results[mmap.model_name] = complete_results
self.raw_data = all_results
@silence_print
def _compare_raw_data(self):
raw_data = self.raw_data
reference_model_name = self.mmap_list[0].model_name
reference_matrix = raw_data[reference_model_name].reset_index().as_matrix()
column_labels = raw_data[reference_model_name].reset_index().columns
all_results = DotDict()
for mmap in self.mmap_list[1:]:
current_model_name = mmap.model_name
comparison_name = '{}_{}'.format(reference_model_name,
current_model_name)
change_matrix = raw_data[current_model_name].reset_index().as_matrix()
comparison_result = ResultsGenerator.percentage_change(reference_matrix[:,1:],change_matrix[:,1:])
comparison_result = np.hstack([reference_matrix[:,0][:,np.newaxis],comparison_result])
df = pd.DataFrame(data=comparison_result,
columns=column_labels)
index = df.columns[0]
df = df.set_index(index)
all_results[comparison_name] = df
self.comparison = all_results
def plot(self,cols=3, width=None, height=None):
"""
Produces a simple plot that allows for the visual comparison of
results for each model.
Parameters
----------
cols : int, optional (Default : 3)
The number of columns to use when plotting subplots.
width : float, optional (Default : 15.0)
The width of the figure in inches.
height : float, optional (Default : 5.0*number of subplot rows)
The height of the figure in inches. By default the height is
dynamic in will be adjusted based on the number of rows of
subplots where each row is assigned 5 inches.
Returns
-------
f: matplotlib figure
A matplotlib figure on which results are plotted.
axxarr: 2-dimensional numpy array of AxesSubplots
An array that contains the axes associate with the figure.
"""
columns = list(self.raw_data.values())[0].columns
index = list(self.raw_data.values())[0].index
assert cols >= 2
rows = int(np.ceil(len(columns)/float(cols)))
if not height:
height = 5.*rows
if not width:
width = 15.
f, axarr = plt.subplots(rows,cols)
f.set_size_inches(w=width, h=height)
for i in range(cols*rows - len(columns)):
f.delaxes(axarr.flat[-1-i])
for column, ax in zip(columns,axarr.flat):
for k, v in self.raw_data.items():
ax.plot(index, v[column], '-',label=k)
ax.set_xlabel(index.name)
ax.set_title(column)
ax.legend()
return f,axarr
class SimulationComparer(ParameterScanComparer):
@silence_print
def do_compare(self, time_range, output_list=None, custom_init=None,
uniform_init=True):
"""
Performs the comparison of the models.
Results are stored in `self.raw_data` and `self.comparison`.
Parameters
----------
time_range : array-like
An array that specifies the range of values over which
`Time` should be varied. It allows for starting points other
than zero.
output_list : list of str, optional (Default : None)
A list of model attributes to compare. Valid options are:
species, reactions, parameters, and mca coefficients.
custom_init : dict or list of dict, optional (Default : None)
A dictionary specifying the initial conditions of the models.
Keys specify model attributes (in str) and values are float
values of these attributes. A list of dictionaries can also
be supplied in order to give each model a different set of
initial conditions. The list follows the same order as
the `model_list` supplied during object instantiation.
uniform_init : boolean, optional (Default : True)
If set to True, the attributes of each model will be set to
that of the base model prior to comparison. `custom_inits`
are applied after making model attributes uniform.
"""
if output_list is None:
output_list = self._get_default_outputs()
self._uniform_init(uniform_init)
self._custom_init(custom_init)
self._generate_raw_data(output_list, time_range)
self._compare_raw_data()
@silence_print
def _generate_raw_data(self, output_list, time_range):
main_column_labels = ['Time'] + output_list
all_results = DotDict()
for i, mmap in enumerate(self.mmap_list):
now_fixed_indicies = []
for i,out in enumerate(output_list):
if out in mmap.is_now_fixed:
now_fixed_indicies.append(i)
new_output_list = copy.deepcopy(output_list)
param_cols = []
for ind in reversed(now_fixed_indicies):
out_val = mmap.getattr(output_list[ind])
new_output_list.pop(ind)
param_cols.append([out_val for _ in range(time_range.shape[0])])
param_cols = param_cols
current_sim_out = mmap.attr_names_from_base_names(new_output_list)
real_sim_out = mmap.attr_names_from_base_names(output_list)
current_col_labels = ['Time'] + mmap.base_names_from_attr_names(real_sim_out )
# parameter scan
raw_result = ResultsGenerator.do_simulation(model=mmap.model,
time_range=time_range,
sim_out=current_sim_out)
new_raw_result = [col for col in raw_result.T]
for ind, col in zip(reversed(now_fixed_indicies), param_cols):
new_raw_result.insert(ind, col)
new_raw_result = np.array(new_raw_result).T
complete_results = pd.DataFrame(data=new_raw_result,
columns=current_col_labels)
complete_results = complete_results.reindex(columns=main_column_labels)
complete_results = complete_results.set_index('Time')
all_results[mmap.model_name] = complete_results
self.raw_data = all_results
class ClosedOpenComparer(SimulationComparer):
@silence_print
def do_compare(self, time_range, output_list=None, custom_init=None,
uniform_init=True):
"""
Performs the comparison of the models.
Results are stored in `self.raw_data` and `self.comparison`.
Parameters
----------
time_range : array-like
An array that specifies the range of values over which
`Time` should be varied. It allows for starting points other
than zero.
output_list : list of str, optional (Default : None)
A list of model attributes to compare. Valid options are:
species, reactions, parameters, and mca coefficients.
custom_init : dict or list of dict, optional (Default : None)
A dictionary specifying the initial conditions of the models.
Keys specify model attributes (in str) and values are float
values of these attributes. A list of dictionaries can also
be supplied in order to give each model a different set of
initial conditions. The list follows the same order as
the `model_list` supplied during object instantiation.
uniform_init : boolean, optional (Default : True)
If set to True, the attributes of each model will be set to
that of the base model prior to comparison. `custom_inits`
are applied after making model attributes uniform.
"""
if output_list is None:
output_list = self._get_default_outputs()
self._uniform_init(uniform_init)
self._custom_init(custom_init)
self._generate_raw_data(output_list, time_range)
self._compare_raw_data()
@silence_print
def _generate_raw_data(self, output_list, time_range):
base_mmap = self.mmap_list[0]
full_scan_column_names = []
for mmap in self.mmap_list:
full_scan_column_names = full_scan_column_names + mmap.is_now_fixed
full_scan_column_names = list(set(full_scan_column_names))
base_sim_out_list = full_scan_column_names + [out for out in output_list \
if out not in full_scan_column_names]
base_column_names = ['Time'] + base_sim_out_list
all_results = DotDict()
simulation_results = ResultsGenerator.do_simulation(model=base_mmap.model,
time_range=time_range,
sim_out=base_sim_out_list)
base_complete_results = pd.DataFrame(data=simulation_results,
columns=base_column_names)
base_complete_results = base_complete_results.set_index('Time')
all_results[base_mmap.model_name] = base_complete_results
for i, mmap in enumerate(self.mmap_list[1:]):
scan_column_names = mmap.is_now_fixed
output_partial = [out for out in output_list if out not in scan_column_names]
outputs = self._output_to_ss(output_partial)
output_col_names = ['Time'] + scan_column_names + output_partial
time_col = base_complete_results.reset_index()['Time'].as_matrix()
input_val_list = [base_complete_results[col_name].as_matrix() for col_name in scan_column_names]
raw_results = []
for input_vals in zip(*input_val_list):
current_row = [] + list(input_vals)
dict_to_set = dict(list(zip(scan_column_names, input_vals)))
mmap.setattrs_from_dict(dict_to_set)
mmap.model.doState()
current_row = current_row + mmap.getattrs_from_list(outputs)
raw_results.append(current_row)
raw_results = np.hstack((time_col[:,np.newaxis],np.array(raw_results)))
complete_results = pd.DataFrame(data=raw_results,
columns=output_col_names)
complete_results = complete_results.reindex(columns=base_column_names)
complete_results = complete_results.set_index('Time')
all_results[mmap.model_name] = complete_results
self.raw_data = all_results
| en | 0.723177 | Returns a specified model comparer for a list of models. The returned model comparer object considers the first model in `model_list` as the "base model" to which the other models are compared. Since models may have a different naming scheme for reactions, species, etc. `model_mapping` allows the user to specify the names of attributes of other models as they relate to the base model's attribute names. Parameters ---------- model_list : list of PysMod A list of PysMod (Pysces model) objects to be compared. model_mapping : array, optional (Default : None) An array that maps the names attributes of the base model to others. Each column relates to a different model in `model_list` in order. The first column contains the names of attributes of the base model, whereas each other column contains the name as it appears in the other models. Only species, reactions and parameters are needed as other attributes (e.g. control coefficients) are derived from these three categories. If None is supplied, all attributes are assumed to be named in the same way. augment_mapping : boolean, optional (Default : True) This parameter specifies if `model_mapping` should be used to augment a model_mapping which assumes all attributes are named in the same way. This is useful if only a few model attributes differ in their naming. If set to false, `model_mapping` must specify the relationships between all species, reaction and parameter names. comparison_type : str, optional (Default : "ss") A string that specifies which type of comparison should be made. Options are 'ss' for SteadyStateComparer, "parscan" for ParameterScanComparer, 'sim' for SimulationComparer, and 'closed_open' for 'ClosedOpenComparer'. Returns ------- values: ModelComparer A model comparer object instantiated with the arguments supplied above. # Replace duplicate models with clones # Generate unique model names # Generate model mapping if needed # Ensure that supplied (or generated) model mapping # at least has the correct shape # Make list of model_dicts # Get newly fixed parameters for other models # Add mca coefficients to model_dicts For a model mapping array, returns a list of dictionaries where the keys correspond to the fist column and the values correspond to each column (including the first). A 4 column array will thus produce a list of dicts of len 4 with the first having the same keys as values. For a list of models potentially containing duplicate PySMod instantiations, returns a list of unique objects. Returns a list of unique model names for a list of models. For a list of models returns an array where each column maps contains the names of model attributes as they appear in each model. Missing species, parameters, and reactions are replaced with None. Given a model this method returns a new instantiation of the same model. See also: replace_with_clones Given an mca dictionary of the base model and a map dictionary of any comparison model, this returns a dictionary that maps the names of mca coefficients in the base model to those in the comparison. See also: get_equiv_coeff Given an mca coefficient, an mca dictionary of the base model and a map dictionary of any comparison model, this returns the name of the equivalent mca coefficient in the comparison model. See also: get_mca_dict_mapping Returns a dictionary with the mca coefficients of a model as keys and a tuple of the components that make up each coefficient as values. Returns the coefficient type. Performs the comparison of the models. Results are stored in `self.raw_data` and `self.comparison`. Parameters ---------- output_list : list of str, optional (Default : None) A list of model attributes to compare. Valid options are: species, reactions, parameters, and mca coefficients. custom_init : dict or list of dict, optional (Default : None) A dictionary specifying the initial conditions of the models. Keys specify model attributes (in str) and values are float values of these attributes. A list of dictionaries can also be supplied in order to give each model a different set of initial conditions. The list follows the same order as the `model_list` supplied during object instantiation. uniform_init : boolean, optional (Default : True) If set to True, the attributes of each model will be set to that of the base model prior to comparison. `custom_inits` are applied after making model attributes uniform. Performs the comparison of the models. Results are stored in `self.raw_data` and `self.comparison`. Parameters ---------- scan_in : str A string that specifies the parameter to be varied scan_range : array-like An array that specifies the range of values over which `scan_in` should be varied output_list : list of str, optional (Default : None) A list of model attributes to compare. Valid options are: species, reactions, parameters, and mca coefficients. custom_init : dict or list of dict, optional (Default : None) A dictionary specifying the initial conditions of the models. Keys specify model attributes (in str) and values are float values of these attributes. A list of dictionaries can also be supplied in order to give each model a different set of initial conditions. The list follows the same order as the `model_list` supplied during object instantiation. uniform_init : boolean, optional (Default : True) If set to True, the attributes of each model will be set to that of the base model prior to comparison. `custom_inits` are applied after making model attributes uniform. par_scan : boolean, optional (Default : False) If True the parameter scan will be executed using parallel processing. par_enging: str, optional (Default : 'multiproc') The parallel engine to be used. Options are dictated by PySCeS's ParScanner. # parameter scan Produces a simple plot that allows for the visual comparison of results for each model. Parameters ---------- cols : int, optional (Default : 3) The number of columns to use when plotting subplots. width : float, optional (Default : 15.0) The width of the figure in inches. height : float, optional (Default : 5.0*number of subplot rows) The height of the figure in inches. By default the height is dynamic in will be adjusted based on the number of rows of subplots where each row is assigned 5 inches. Returns ------- f: matplotlib figure A matplotlib figure on which results are plotted. axxarr: 2-dimensional numpy array of AxesSubplots An array that contains the axes associate with the figure. Performs the comparison of the models. Results are stored in `self.raw_data` and `self.comparison`. Parameters ---------- time_range : array-like An array that specifies the range of values over which `Time` should be varied. It allows for starting points other than zero. output_list : list of str, optional (Default : None) A list of model attributes to compare. Valid options are: species, reactions, parameters, and mca coefficients. custom_init : dict or list of dict, optional (Default : None) A dictionary specifying the initial conditions of the models. Keys specify model attributes (in str) and values are float values of these attributes. A list of dictionaries can also be supplied in order to give each model a different set of initial conditions. The list follows the same order as the `model_list` supplied during object instantiation. uniform_init : boolean, optional (Default : True) If set to True, the attributes of each model will be set to that of the base model prior to comparison. `custom_inits` are applied after making model attributes uniform. # parameter scan Performs the comparison of the models. Results are stored in `self.raw_data` and `self.comparison`. Parameters ---------- time_range : array-like An array that specifies the range of values over which `Time` should be varied. It allows for starting points other than zero. output_list : list of str, optional (Default : None) A list of model attributes to compare. Valid options are: species, reactions, parameters, and mca coefficients. custom_init : dict or list of dict, optional (Default : None) A dictionary specifying the initial conditions of the models. Keys specify model attributes (in str) and values are float values of these attributes. A list of dictionaries can also be supplied in order to give each model a different set of initial conditions. The list follows the same order as the `model_list` supplied during object instantiation. uniform_init : boolean, optional (Default : True) If set to True, the attributes of each model will be set to that of the base model prior to comparison. `custom_inits` are applied after making model attributes uniform. | 2.256458 | 2 |
vipsevents/eventapp/apps.py | ACE-VSIT/vips-events | 0 | 6630509 | from django.apps import AppConfig
class EventappConfig(AppConfig):
name = 'vipsevents.eventapp'
| from django.apps import AppConfig
class EventappConfig(AppConfig):
name = 'vipsevents.eventapp'
| none | 1 | 1.079371 | 1 |
|
dcorch/common/endpoint_cache.py | DadeCoderh/starlingx-stagingm | 1 | 6630510 | # Copyright 2015 Huawei Technologies Co., Ltd.
# 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.
import collections
from keystoneauth1 import loading
from keystoneauth1 import session
from keystoneclient.v3 import client as keystone_client
from oslo_config import cfg
from oslo_log import log as logging
from dcorch.common import consts
LOG = logging.getLogger(__name__)
class EndpointCache(object):
def __init__(self, region_name=None, auth_url=None):
self.endpoint_map = collections.defaultdict(dict)
self.admin_session = None
self.keystone_client = None
# if auth_url is provided use that otherwise use the one
# defined in the config
if auth_url:
self.external_auth_url = auth_url
else:
self.external_auth_url = cfg.CONF.cache.auth_uri
self._initialize_keystone_client(region_name, auth_url)
self._update_endpoints()
def _initialize_keystone_client(self, region_name=None, auth_url=None):
loader = loading.get_plugin_loader(
cfg.CONF.keystone_authtoken.auth_type)
auth = loader.load_from_options(
auth_url=self.external_auth_url,
username=cfg.CONF.cache.admin_username,
user_domain_name=cfg.CONF.cache.admin_user_domain_name,
password=cfg.CONF.cache.admin_password,
project_name=cfg.CONF.cache.admin_tenant,
project_domain_name=cfg.CONF.cache.admin_project_domain_name,
)
self.admin_session = session.Session(
auth=auth, additional_headers=consts.USER_HEADER)
self.keystone_client = keystone_client.Client(
session=self.admin_session,
region_name=consts.VIRTUAL_MASTER_CLOUD)
# if Endpoint cache is intended for a subcloud then
# we need to retrieve the subcloud token and session.
# Skip this if auth_url was provided as its assumed that the
# auth_url would correspond to a subcloud so session was
# set up above
if (not auth_url and region_name and
region_name not in
[consts.CLOUD_0, consts.VIRTUAL_MASTER_CLOUD]):
identity_service = self.keystone_client.services.list(
name='keystone', type='identity')
sc_auth_url = self.keystone_client.endpoints.list(
service=identity_service[0].id,
interface=consts.KS_ENDPOINT_INTERNAL,
region=region_name)
try:
sc_auth_url = sc_auth_url[0].url
except IndexError:
LOG.error("Cannot find identity auth_url for %s", region_name)
raise
sc_loader = loading.get_plugin_loader(
cfg.CONF.keystone_authtoken.auth_type)
# We assume that the Admin user names and passwords are the same
# on this subcloud since this is an audited resource
sc_auth = sc_loader.load_from_options(
auth_url=sc_auth_url,
username=cfg.CONF.cache.admin_username,
user_domain_name=cfg.CONF.cache.admin_user_domain_name,
password=cfg.CONF.cache.admin_password,
project_name=cfg.CONF.cache.admin_tenant,
project_domain_name=cfg.CONF.cache.admin_project_domain_name,
)
self.admin_session = session.Session(
auth=sc_auth, additional_headers=consts.USER_HEADER)
self.keystone_client = keystone_client.Client(
session=self.admin_session,
region_name=region_name)
self.external_auth_url = sc_auth_url
@staticmethod
def _get_endpoint_from_keystone(self):
service_id_name_map = {}
for service in self.keystone_client.services.list():
service_dict = service.to_dict()
service_id_name_map[service_dict['id']] = service_dict['name']
region_service_endpoint_map = {}
for endpoint in self.keystone_client.endpoints.list():
endpoint_dict = endpoint.to_dict()
if endpoint_dict['interface'] != consts.KS_ENDPOINT_INTERNAL:
continue
region_id = endpoint_dict['region']
service_id = endpoint_dict['service_id']
url = endpoint_dict['url']
service_name = service_id_name_map[service_id]
if region_id not in region_service_endpoint_map:
region_service_endpoint_map[region_id] = {}
region_service_endpoint_map[region_id][service_name] = url
return region_service_endpoint_map
def _get_endpoint(self, region, service, retry):
if service not in self.endpoint_map[region]:
if retry:
self.update_endpoints()
return self._get_endpoint(region, service, False)
else:
return ''
else:
return self.endpoint_map[region][service]
def _update_endpoints(self):
endpoint_map = EndpointCache._get_endpoint_from_keystone(self)
for region in endpoint_map:
for service in endpoint_map[region]:
self.endpoint_map[region][
service] = endpoint_map[region][service]
def get_endpoint(self, region, service):
"""Get service endpoint url.
:param region: region the service belongs to
:param service: service type
:return: url of the service
"""
return self._get_endpoint(region, service, True)
def update_endpoints(self):
"""Update endpoint cache from Keystone.
:return: None
"""
self._update_endpoints()
def get_all_regions(self):
"""Get region list.
return: List of regions
"""
return self.endpoint_map.keys()
def get_session_from_token(self, token, project_id):
"""Get session based on token to communicate with openstack services.
:param token: token with which the request is triggered.
:param project_id: UUID of the project.
:return: session object.
"""
loader = loading.get_plugin_loader('token')
auth = loader.load_from_options(auth_url=self.external_auth_url,
token=token, project_id=project_id)
sess = session.Session(auth=auth)
return sess
| # Copyright 2015 Huawei Technologies Co., Ltd.
# 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.
import collections
from keystoneauth1 import loading
from keystoneauth1 import session
from keystoneclient.v3 import client as keystone_client
from oslo_config import cfg
from oslo_log import log as logging
from dcorch.common import consts
LOG = logging.getLogger(__name__)
class EndpointCache(object):
def __init__(self, region_name=None, auth_url=None):
self.endpoint_map = collections.defaultdict(dict)
self.admin_session = None
self.keystone_client = None
# if auth_url is provided use that otherwise use the one
# defined in the config
if auth_url:
self.external_auth_url = auth_url
else:
self.external_auth_url = cfg.CONF.cache.auth_uri
self._initialize_keystone_client(region_name, auth_url)
self._update_endpoints()
def _initialize_keystone_client(self, region_name=None, auth_url=None):
loader = loading.get_plugin_loader(
cfg.CONF.keystone_authtoken.auth_type)
auth = loader.load_from_options(
auth_url=self.external_auth_url,
username=cfg.CONF.cache.admin_username,
user_domain_name=cfg.CONF.cache.admin_user_domain_name,
password=cfg.CONF.cache.admin_password,
project_name=cfg.CONF.cache.admin_tenant,
project_domain_name=cfg.CONF.cache.admin_project_domain_name,
)
self.admin_session = session.Session(
auth=auth, additional_headers=consts.USER_HEADER)
self.keystone_client = keystone_client.Client(
session=self.admin_session,
region_name=consts.VIRTUAL_MASTER_CLOUD)
# if Endpoint cache is intended for a subcloud then
# we need to retrieve the subcloud token and session.
# Skip this if auth_url was provided as its assumed that the
# auth_url would correspond to a subcloud so session was
# set up above
if (not auth_url and region_name and
region_name not in
[consts.CLOUD_0, consts.VIRTUAL_MASTER_CLOUD]):
identity_service = self.keystone_client.services.list(
name='keystone', type='identity')
sc_auth_url = self.keystone_client.endpoints.list(
service=identity_service[0].id,
interface=consts.KS_ENDPOINT_INTERNAL,
region=region_name)
try:
sc_auth_url = sc_auth_url[0].url
except IndexError:
LOG.error("Cannot find identity auth_url for %s", region_name)
raise
sc_loader = loading.get_plugin_loader(
cfg.CONF.keystone_authtoken.auth_type)
# We assume that the Admin user names and passwords are the same
# on this subcloud since this is an audited resource
sc_auth = sc_loader.load_from_options(
auth_url=sc_auth_url,
username=cfg.CONF.cache.admin_username,
user_domain_name=cfg.CONF.cache.admin_user_domain_name,
password=cfg.CONF.cache.admin_password,
project_name=cfg.CONF.cache.admin_tenant,
project_domain_name=cfg.CONF.cache.admin_project_domain_name,
)
self.admin_session = session.Session(
auth=sc_auth, additional_headers=consts.USER_HEADER)
self.keystone_client = keystone_client.Client(
session=self.admin_session,
region_name=region_name)
self.external_auth_url = sc_auth_url
@staticmethod
def _get_endpoint_from_keystone(self):
service_id_name_map = {}
for service in self.keystone_client.services.list():
service_dict = service.to_dict()
service_id_name_map[service_dict['id']] = service_dict['name']
region_service_endpoint_map = {}
for endpoint in self.keystone_client.endpoints.list():
endpoint_dict = endpoint.to_dict()
if endpoint_dict['interface'] != consts.KS_ENDPOINT_INTERNAL:
continue
region_id = endpoint_dict['region']
service_id = endpoint_dict['service_id']
url = endpoint_dict['url']
service_name = service_id_name_map[service_id]
if region_id not in region_service_endpoint_map:
region_service_endpoint_map[region_id] = {}
region_service_endpoint_map[region_id][service_name] = url
return region_service_endpoint_map
def _get_endpoint(self, region, service, retry):
if service not in self.endpoint_map[region]:
if retry:
self.update_endpoints()
return self._get_endpoint(region, service, False)
else:
return ''
else:
return self.endpoint_map[region][service]
def _update_endpoints(self):
endpoint_map = EndpointCache._get_endpoint_from_keystone(self)
for region in endpoint_map:
for service in endpoint_map[region]:
self.endpoint_map[region][
service] = endpoint_map[region][service]
def get_endpoint(self, region, service):
"""Get service endpoint url.
:param region: region the service belongs to
:param service: service type
:return: url of the service
"""
return self._get_endpoint(region, service, True)
def update_endpoints(self):
"""Update endpoint cache from Keystone.
:return: None
"""
self._update_endpoints()
def get_all_regions(self):
"""Get region list.
return: List of regions
"""
return self.endpoint_map.keys()
def get_session_from_token(self, token, project_id):
"""Get session based on token to communicate with openstack services.
:param token: token with which the request is triggered.
:param project_id: UUID of the project.
:return: session object.
"""
loader = loading.get_plugin_loader('token')
auth = loader.load_from_options(auth_url=self.external_auth_url,
token=token, project_id=project_id)
sess = session.Session(auth=auth)
return sess
| en | 0.87691 | # Copyright 2015 Huawei Technologies Co., Ltd. # 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. # if auth_url is provided use that otherwise use the one # defined in the config # if Endpoint cache is intended for a subcloud then # we need to retrieve the subcloud token and session. # Skip this if auth_url was provided as its assumed that the # auth_url would correspond to a subcloud so session was # set up above # We assume that the Admin user names and passwords are the same # on this subcloud since this is an audited resource Get service endpoint url. :param region: region the service belongs to :param service: service type :return: url of the service Update endpoint cache from Keystone. :return: None Get region list. return: List of regions Get session based on token to communicate with openstack services. :param token: token with which the request is triggered. :param project_id: UUID of the project. :return: session object. | 1.700357 | 2 |
checkov/secrets/runner.py | jasonckeating/checkov | 0 | 6630511 | import linecache
import logging
import os
import re
from typing import Optional, List
from detect_secrets import SecretsCollection
from detect_secrets.core import scan
from detect_secrets.core.potential_secret import PotentialSecret
from detect_secrets.settings import transient_settings
from typing_extensions import TypedDict
from checkov.common.bridgecrew.platform_integration import bc_integration
from checkov.common.comment.enum import COMMENT_REGEX
from checkov.common.parallelizer.parallel_runner import parallel_runner
from checkov.common.models.consts import SUPPORTED_FILE_EXTENSIONS
from checkov.common.models.enums import CheckResult
from checkov.common.output.record import Record
from checkov.common.output.report import Report
from checkov.common.runners.base_runner import BaseRunner, filter_ignored_paths
from checkov.common.runners.base_runner import ignored_directories
from checkov.common.util.consts import DEFAULT_EXTERNAL_MODULES_DIR
from checkov.runner_filter import RunnerFilter
SECRET_TYPE_TO_ID = {
'Artifactory Credentials': 'CKV_SECRET_1',
'AWS Access Key': 'CKV_SECRET_2',
'Azure Storage Account access key': 'CKV_SECRET_3',
'Basic Auth Credentials': 'CKV_SECRET_4',
'Cloudant Credentials': 'CKV_SECRET_5',
'Base64 High Entropy String': 'CKV_SECRET_6',
'IBM Cloud IAM Key': 'CKV_SECRET_7',
'IBM COS HMAC Credentials': 'CKV_SECRET_8',
'JSON Web Token': 'CKV_SECRET_9',
# 'Secret Keyword': 'CKV_SECRET_10',
'Mailchimp Access Key': 'CKV_SECRET_11',
'NPM tokens': 'CKV_SECRET_12',
'Private Key': 'CKV_SECRET_13',
'Slack Token': '<PASSWORD>',
'SoftLayer Credentials': 'CKV_SECRET_15',
'Square OAuth Secret': 'CKV_SECRET_16',
'Stripe Access Key': 'CKV_SECRET_17',
'Twilio API Key': 'CKV_SECRET_18',
'Hex High Entropy String': 'CKV_SECRET_19'
}
CHECK_ID_TO_SECRET_TYPE = {v: k for k, v in SECRET_TYPE_TO_ID.items()}
ENTROPY_KEYWORD_LIMIT = 3
PROHIBITED_FILES = ['Pipfile.lock', 'yarn.lock', 'package-lock.json', 'requirements.txt']
class _CheckResult(TypedDict, total=False):
result: CheckResult
suppress_comment: str
class Runner(BaseRunner):
check_type = 'secrets'
def run(
self,
root_folder: str,
external_checks_dir: Optional[List[str]] = None,
files: Optional[List[str]] = None,
runner_filter: RunnerFilter = RunnerFilter(),
collect_skip_comments: bool = True
) -> Report:
current_dir = os.path.dirname(os.path.realpath(__file__))
secrets = SecretsCollection()
with transient_settings({
# Only run scans with only these plugins.
'plugins_used': [
{
'name': 'AWSKeyDetector'
},
{
'name': 'ArtifactoryDetector'
},
{
'name': 'AzureStorageKeyDetector'
},
{
'name': 'BasicAuthDetector'
},
{
'name': 'CloudantDetector'
},
{
'name': 'IbmCloudIamDetector'
},
{
'name': 'MailchimpDetector'
},
{
'name': 'PrivateKeyDetector'
},
{
'name': 'SlackDetector'
},
{
'name': 'SoftlayerDetector'
},
{
'name': 'SquareOAuthDetector'
},
{
'name': 'StripeDetector'
},
{
'name': 'TwilioKeyDetector'
},
{
'name': 'EntropyKeywordCombinator',
'path': f'file://{current_dir}/plugins/entropy_keyword_combinator.py',
'limit': ENTROPY_KEYWORD_LIMIT
}
]
}) as settings:
report = Report(self.check_type)
# Implement non IaC files (including .terraform dir)
files_to_scan = files or []
excluded_paths = (runner_filter.excluded_paths or []) + ignored_directories + [DEFAULT_EXTERNAL_MODULES_DIR]
if root_folder:
for root, d_names, f_names in os.walk(root_folder):
filter_ignored_paths(root, d_names, excluded_paths)
filter_ignored_paths(root, f_names, excluded_paths)
for file in f_names:
if file not in PROHIBITED_FILES and f".{file.split('.')[-1]}" in SUPPORTED_FILE_EXTENSIONS:
files_to_scan.append(os.path.join(root, file))
logging.info(f'Secrets scanning will scan {len(files_to_scan)} files')
settings.disable_filters(*['detect_secrets.filters.heuristic.is_indirect_reference'])
Runner._scan_files(files_to_scan, secrets)
for _, secret in iter(secrets):
check_id = SECRET_TYPE_TO_ID.get(secret.type)
bc_check_id = bc_integration.ckv_to_bc_id_mapping.get(check_id) if bc_integration.ckv_to_bc_id_mapping else None
if not check_id:
continue
if runner_filter.checks and not runner_filter.should_run_check(check_id, bc_check_id):
continue
result: _CheckResult = {'result': CheckResult.FAILED}
line_text = linecache.getline(secret.filename, secret.line_number)
if line_text != "" and len(line_text.split()) > 0 and line_text.split()[0] == 'git_commit':
continue
result = self.search_for_suppression(
check_id=check_id,
bc_check_id=bc_check_id,
secret=secret,
runner_filter=runner_filter,
) or result
report.add_resource(f'{secret.filename}:{secret.secret_hash}')
report.add_record(Record(
check_id=check_id,
bc_check_id=bc_check_id,
check_name=secret.type,
check_result=result,
code_block=[(secret.line_number, line_text)],
file_path=f'/{os.path.relpath(secret.filename, root_folder)}',
file_line_range=[secret.line_number, secret.line_number + 1],
resource=secret.secret_hash,
check_class=None,
evaluations=None,
file_abs_path=os.path.abspath(secret.filename)
))
return report
@staticmethod
def _scan_files(files_to_scan, secrets):
# implemented the scan function like secrets.scan_files
results = parallel_runner.run_function(
lambda f: list(scan.scan_file(os.path.join(secrets.root, f))), files_to_scan)
for secrets_results in results:
for secret in secrets_results:
secrets[os.path.relpath(secret.filename, secrets.root)].add(secret)
@staticmethod
def search_for_suppression(
check_id: str,
bc_check_id: str,
secret: PotentialSecret,
runner_filter: RunnerFilter
) -> Optional[_CheckResult]:
if not runner_filter.should_run_check(check_id, bc_check_id) and check_id in CHECK_ID_TO_SECRET_TYPE.keys():
return {
"result": CheckResult.SKIPPED,
"suppress_comment": f"Secret scan {check_id} is skipped"
}
# Check for suppression comment in the line before, the line of, and the line after the secret
for line_number in [secret.line_number, secret.line_number - 1, secret.line_number + 1]:
lt = linecache.getline(secret.filename, line_number)
skip_search = re.search(COMMENT_REGEX, lt)
if skip_search and (skip_search.group(2) == check_id or skip_search.group(2) == bc_check_id):
return {
"result": CheckResult.SKIPPED,
"suppress_comment": skip_search.group(3)[1:] if skip_search.group(3) else "No comment provided"
}
return None
| import linecache
import logging
import os
import re
from typing import Optional, List
from detect_secrets import SecretsCollection
from detect_secrets.core import scan
from detect_secrets.core.potential_secret import PotentialSecret
from detect_secrets.settings import transient_settings
from typing_extensions import TypedDict
from checkov.common.bridgecrew.platform_integration import bc_integration
from checkov.common.comment.enum import COMMENT_REGEX
from checkov.common.parallelizer.parallel_runner import parallel_runner
from checkov.common.models.consts import SUPPORTED_FILE_EXTENSIONS
from checkov.common.models.enums import CheckResult
from checkov.common.output.record import Record
from checkov.common.output.report import Report
from checkov.common.runners.base_runner import BaseRunner, filter_ignored_paths
from checkov.common.runners.base_runner import ignored_directories
from checkov.common.util.consts import DEFAULT_EXTERNAL_MODULES_DIR
from checkov.runner_filter import RunnerFilter
SECRET_TYPE_TO_ID = {
'Artifactory Credentials': 'CKV_SECRET_1',
'AWS Access Key': 'CKV_SECRET_2',
'Azure Storage Account access key': 'CKV_SECRET_3',
'Basic Auth Credentials': 'CKV_SECRET_4',
'Cloudant Credentials': 'CKV_SECRET_5',
'Base64 High Entropy String': 'CKV_SECRET_6',
'IBM Cloud IAM Key': 'CKV_SECRET_7',
'IBM COS HMAC Credentials': 'CKV_SECRET_8',
'JSON Web Token': 'CKV_SECRET_9',
# 'Secret Keyword': 'CKV_SECRET_10',
'Mailchimp Access Key': 'CKV_SECRET_11',
'NPM tokens': 'CKV_SECRET_12',
'Private Key': 'CKV_SECRET_13',
'Slack Token': '<PASSWORD>',
'SoftLayer Credentials': 'CKV_SECRET_15',
'Square OAuth Secret': 'CKV_SECRET_16',
'Stripe Access Key': 'CKV_SECRET_17',
'Twilio API Key': 'CKV_SECRET_18',
'Hex High Entropy String': 'CKV_SECRET_19'
}
CHECK_ID_TO_SECRET_TYPE = {v: k for k, v in SECRET_TYPE_TO_ID.items()}
ENTROPY_KEYWORD_LIMIT = 3
PROHIBITED_FILES = ['Pipfile.lock', 'yarn.lock', 'package-lock.json', 'requirements.txt']
class _CheckResult(TypedDict, total=False):
result: CheckResult
suppress_comment: str
class Runner(BaseRunner):
check_type = 'secrets'
def run(
self,
root_folder: str,
external_checks_dir: Optional[List[str]] = None,
files: Optional[List[str]] = None,
runner_filter: RunnerFilter = RunnerFilter(),
collect_skip_comments: bool = True
) -> Report:
current_dir = os.path.dirname(os.path.realpath(__file__))
secrets = SecretsCollection()
with transient_settings({
# Only run scans with only these plugins.
'plugins_used': [
{
'name': 'AWSKeyDetector'
},
{
'name': 'ArtifactoryDetector'
},
{
'name': 'AzureStorageKeyDetector'
},
{
'name': 'BasicAuthDetector'
},
{
'name': 'CloudantDetector'
},
{
'name': 'IbmCloudIamDetector'
},
{
'name': 'MailchimpDetector'
},
{
'name': 'PrivateKeyDetector'
},
{
'name': 'SlackDetector'
},
{
'name': 'SoftlayerDetector'
},
{
'name': 'SquareOAuthDetector'
},
{
'name': 'StripeDetector'
},
{
'name': 'TwilioKeyDetector'
},
{
'name': 'EntropyKeywordCombinator',
'path': f'file://{current_dir}/plugins/entropy_keyword_combinator.py',
'limit': ENTROPY_KEYWORD_LIMIT
}
]
}) as settings:
report = Report(self.check_type)
# Implement non IaC files (including .terraform dir)
files_to_scan = files or []
excluded_paths = (runner_filter.excluded_paths or []) + ignored_directories + [DEFAULT_EXTERNAL_MODULES_DIR]
if root_folder:
for root, d_names, f_names in os.walk(root_folder):
filter_ignored_paths(root, d_names, excluded_paths)
filter_ignored_paths(root, f_names, excluded_paths)
for file in f_names:
if file not in PROHIBITED_FILES and f".{file.split('.')[-1]}" in SUPPORTED_FILE_EXTENSIONS:
files_to_scan.append(os.path.join(root, file))
logging.info(f'Secrets scanning will scan {len(files_to_scan)} files')
settings.disable_filters(*['detect_secrets.filters.heuristic.is_indirect_reference'])
Runner._scan_files(files_to_scan, secrets)
for _, secret in iter(secrets):
check_id = SECRET_TYPE_TO_ID.get(secret.type)
bc_check_id = bc_integration.ckv_to_bc_id_mapping.get(check_id) if bc_integration.ckv_to_bc_id_mapping else None
if not check_id:
continue
if runner_filter.checks and not runner_filter.should_run_check(check_id, bc_check_id):
continue
result: _CheckResult = {'result': CheckResult.FAILED}
line_text = linecache.getline(secret.filename, secret.line_number)
if line_text != "" and len(line_text.split()) > 0 and line_text.split()[0] == 'git_commit':
continue
result = self.search_for_suppression(
check_id=check_id,
bc_check_id=bc_check_id,
secret=secret,
runner_filter=runner_filter,
) or result
report.add_resource(f'{secret.filename}:{secret.secret_hash}')
report.add_record(Record(
check_id=check_id,
bc_check_id=bc_check_id,
check_name=secret.type,
check_result=result,
code_block=[(secret.line_number, line_text)],
file_path=f'/{os.path.relpath(secret.filename, root_folder)}',
file_line_range=[secret.line_number, secret.line_number + 1],
resource=secret.secret_hash,
check_class=None,
evaluations=None,
file_abs_path=os.path.abspath(secret.filename)
))
return report
@staticmethod
def _scan_files(files_to_scan, secrets):
# implemented the scan function like secrets.scan_files
results = parallel_runner.run_function(
lambda f: list(scan.scan_file(os.path.join(secrets.root, f))), files_to_scan)
for secrets_results in results:
for secret in secrets_results:
secrets[os.path.relpath(secret.filename, secrets.root)].add(secret)
@staticmethod
def search_for_suppression(
check_id: str,
bc_check_id: str,
secret: PotentialSecret,
runner_filter: RunnerFilter
) -> Optional[_CheckResult]:
if not runner_filter.should_run_check(check_id, bc_check_id) and check_id in CHECK_ID_TO_SECRET_TYPE.keys():
return {
"result": CheckResult.SKIPPED,
"suppress_comment": f"Secret scan {check_id} is skipped"
}
# Check for suppression comment in the line before, the line of, and the line after the secret
for line_number in [secret.line_number, secret.line_number - 1, secret.line_number + 1]:
lt = linecache.getline(secret.filename, line_number)
skip_search = re.search(COMMENT_REGEX, lt)
if skip_search and (skip_search.group(2) == check_id or skip_search.group(2) == bc_check_id):
return {
"result": CheckResult.SKIPPED,
"suppress_comment": skip_search.group(3)[1:] if skip_search.group(3) else "No comment provided"
}
return None
| en | 0.726259 | # 'Secret Keyword': 'CKV_SECRET_10', # Only run scans with only these plugins. # Implement non IaC files (including .terraform dir) # implemented the scan function like secrets.scan_files # Check for suppression comment in the line before, the line of, and the line after the secret | 1.497397 | 1 |
waymo_open_dataset/utils/box_utils_test.py | DKandrew/waymo-open-dataset | 8 | 6630512 | <gh_stars>1-10
# Copyright 2019 The Waymo Open Dataset 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 waymo_open_dataset.utils.box_utils."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import tensorflow as tf
from waymo_open_dataset.utils import box_utils
from waymo_open_dataset.utils import test_utils
from waymo_open_dataset.utils import transform_utils
class BoxUtilsTest(tf.test.TestCase):
def test_is_within_box_3d(self):
# Unit size boxes centered at (center, 0, 2)
box, _, num_box = test_utils.generate_boxes([5.0, 19.6], 1)
box = box[0, 0:num_box[0], :]
point = tf.constant(
[
[5.0, 0.0, 1.5], # in
[20.0, 0.0, 1.6], # in
[20.0, 0.0, 2.6], # not in
],
dtype=tf.float32)
point_in_box = box_utils.is_within_box_3d(point, box)
with self.test_session() as sess:
point_in_box = sess.run(point_in_box)
self.assertAllEqual(point_in_box,
[[True, False], [False, True], [False, False]])
def test_compute_num_points_in_box_3d(self):
# Unit size boxes centered at (center, 0, 2)
box, _, num_box = test_utils.generate_boxes([5.0, 5.5, 19.6, 50], 1)
box = box[0, 0:num_box[0], :]
point = tf.constant(
[
[5.0, 0.0, 1.5], # in
[5.0, 0.0, 2.5], # in
[20.0, 0.0, 1.6], # in
[20.0, 0.0, 2.6], # not in
],
dtype=tf.float32)
num_points_in_box = box_utils.compute_num_points_in_box_3d(point, box)
with self.test_session() as sess:
num_points_in_box = sess.run(num_points_in_box)
self.assertAllEqual(num_points_in_box, [2, 2, 1, 0])
def test_transform_point_among_frames(self):
p = tf.constant([[1.0, 0, 0]], dtype=tf.float32)
from_frame_pose = transform_utils.get_transform(
transform_utils.get_yaw_rotation(math.pi * 0.5),
tf.constant([0.0, 0.0, 2.0], dtype=tf.float32))
to_frame_pose = transform_utils.get_transform(
transform_utils.get_yaw_rotation(math.pi * 0.1),
tf.constant([0.0, 0.0, 0.0], dtype=tf.float32))
p = p[tf.newaxis, tf.newaxis, tf.newaxis, ...]
from_frame_pose = from_frame_pose[tf.newaxis, tf.newaxis, tf.newaxis, ...]
to_frame_pose = to_frame_pose[tf.newaxis, tf.newaxis, tf.newaxis, ...]
pp = box_utils.transform_point(p, from_frame_pose, to_frame_pose)
with self.test_session():
self.assertAllClose(
pp[0, 0, 0, ...].eval(),
[[math.cos(math.pi * 0.4),
math.sin(math.pi * 0.4), 2.0]])
def test_transform_box_among_frames(self):
b = tf.constant([[1.0, 0, 0, 2.0, 2.0, 2.0, math.pi * 0.1]],
dtype=tf.float32)
from_frame_pose = transform_utils.get_transform(
transform_utils.get_yaw_rotation(math.pi * 0.5),
tf.constant([0.0, 0.0, 1.0], dtype=tf.float32))
to_frame_pose = transform_utils.get_transform(
transform_utils.get_yaw_rotation(math.pi * 0.25),
tf.constant([0.0, 0.0, 0.0], dtype=tf.float32))
b = b[tf.newaxis, tf.newaxis, tf.newaxis, ...]
from_frame_pose = from_frame_pose[tf.newaxis, tf.newaxis, tf.newaxis, ...]
to_frame_pose = to_frame_pose[tf.newaxis, tf.newaxis, tf.newaxis, ...]
bb = box_utils.transform_box(b, from_frame_pose, to_frame_pose)
with self.test_session():
self.assertAllClose(bb[0, 0, 0, ...].eval(), [[
math.cos(math.pi * 0.25),
math.sin(math.pi * 0.25), 1.0, 2.0, 2.0, 2.0, math.pi * 0.35
]])
if __name__ == "__main__":
tf.compat.v1.disable_eager_execution()
tf.test.main()
| # Copyright 2019 The Waymo Open Dataset 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 waymo_open_dataset.utils.box_utils."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import tensorflow as tf
from waymo_open_dataset.utils import box_utils
from waymo_open_dataset.utils import test_utils
from waymo_open_dataset.utils import transform_utils
class BoxUtilsTest(tf.test.TestCase):
def test_is_within_box_3d(self):
# Unit size boxes centered at (center, 0, 2)
box, _, num_box = test_utils.generate_boxes([5.0, 19.6], 1)
box = box[0, 0:num_box[0], :]
point = tf.constant(
[
[5.0, 0.0, 1.5], # in
[20.0, 0.0, 1.6], # in
[20.0, 0.0, 2.6], # not in
],
dtype=tf.float32)
point_in_box = box_utils.is_within_box_3d(point, box)
with self.test_session() as sess:
point_in_box = sess.run(point_in_box)
self.assertAllEqual(point_in_box,
[[True, False], [False, True], [False, False]])
def test_compute_num_points_in_box_3d(self):
# Unit size boxes centered at (center, 0, 2)
box, _, num_box = test_utils.generate_boxes([5.0, 5.5, 19.6, 50], 1)
box = box[0, 0:num_box[0], :]
point = tf.constant(
[
[5.0, 0.0, 1.5], # in
[5.0, 0.0, 2.5], # in
[20.0, 0.0, 1.6], # in
[20.0, 0.0, 2.6], # not in
],
dtype=tf.float32)
num_points_in_box = box_utils.compute_num_points_in_box_3d(point, box)
with self.test_session() as sess:
num_points_in_box = sess.run(num_points_in_box)
self.assertAllEqual(num_points_in_box, [2, 2, 1, 0])
def test_transform_point_among_frames(self):
p = tf.constant([[1.0, 0, 0]], dtype=tf.float32)
from_frame_pose = transform_utils.get_transform(
transform_utils.get_yaw_rotation(math.pi * 0.5),
tf.constant([0.0, 0.0, 2.0], dtype=tf.float32))
to_frame_pose = transform_utils.get_transform(
transform_utils.get_yaw_rotation(math.pi * 0.1),
tf.constant([0.0, 0.0, 0.0], dtype=tf.float32))
p = p[tf.newaxis, tf.newaxis, tf.newaxis, ...]
from_frame_pose = from_frame_pose[tf.newaxis, tf.newaxis, tf.newaxis, ...]
to_frame_pose = to_frame_pose[tf.newaxis, tf.newaxis, tf.newaxis, ...]
pp = box_utils.transform_point(p, from_frame_pose, to_frame_pose)
with self.test_session():
self.assertAllClose(
pp[0, 0, 0, ...].eval(),
[[math.cos(math.pi * 0.4),
math.sin(math.pi * 0.4), 2.0]])
def test_transform_box_among_frames(self):
b = tf.constant([[1.0, 0, 0, 2.0, 2.0, 2.0, math.pi * 0.1]],
dtype=tf.float32)
from_frame_pose = transform_utils.get_transform(
transform_utils.get_yaw_rotation(math.pi * 0.5),
tf.constant([0.0, 0.0, 1.0], dtype=tf.float32))
to_frame_pose = transform_utils.get_transform(
transform_utils.get_yaw_rotation(math.pi * 0.25),
tf.constant([0.0, 0.0, 0.0], dtype=tf.float32))
b = b[tf.newaxis, tf.newaxis, tf.newaxis, ...]
from_frame_pose = from_frame_pose[tf.newaxis, tf.newaxis, tf.newaxis, ...]
to_frame_pose = to_frame_pose[tf.newaxis, tf.newaxis, tf.newaxis, ...]
bb = box_utils.transform_box(b, from_frame_pose, to_frame_pose)
with self.test_session():
self.assertAllClose(bb[0, 0, 0, ...].eval(), [[
math.cos(math.pi * 0.25),
math.sin(math.pi * 0.25), 1.0, 2.0, 2.0, 2.0, math.pi * 0.35
]])
if __name__ == "__main__":
tf.compat.v1.disable_eager_execution()
tf.test.main() | en | 0.809498 | # Copyright 2019 The Waymo Open Dataset 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 waymo_open_dataset.utils.box_utils. # Unit size boxes centered at (center, 0, 2) # in # in # not in # Unit size boxes centered at (center, 0, 2) # in # in # in # not in | 2.048139 | 2 |
FEArena/feaapi/models/play/ActiveUnit.py | Superbird11/FEArena | 1 | 6630513 | <filename>FEArena/feaapi/models/play/ActiveUnit.py
from django.db import models
from .._util import BaseModel
from ..build.BuiltUnit import BuiltUnit
from .ActiveWeapon import ActiveWeapon
from .ActiveItem import ActiveItem
from ..core import Skill
class ActiveUnit(BaseModel):
id: int = models.AutoField(primary_key=True)
# demographic information should be contained in the template unit
template: BuiltUnit = models.ForeignKey(BuiltUnit, on_delete=models.CASCADE)
# for skill use. This should be reset at the beginning of each turn.
restricted_actions: str = models.TextField(default='')
# otherwise, current state information
current_hp: int = models.IntegerField(default=0)
mod_max_hp: int = models.IntegerField(default=0)
mod_str: int = models.IntegerField(default=0)
mod_mag: int = models.IntegerField(default=0, null=True, blank=True)
mod_skl: int = models.IntegerField(default=0)
mod_spd: int = models.IntegerField(default=0)
mod_luk: int = models.IntegerField(default=0)
mod_def: int = models.IntegerField(default=0)
mod_res: int = models.IntegerField(default=0)
mod_cha: int = models.IntegerField(default=0, null=True, blank=True)
mod_mov: int = models.IntegerField(default=0)
mod_con: int = models.IntegerField(default=0, null=True, blank=True)
# inventory (limit should be in template unit)
weapons = models.ManyToManyField(ActiveWeapon)
items = models.ManyToManyField(ActiveItem)
# weapon rank modifiers
mod_rank_sword: int = models.IntegerField(default=0)
mod_rank_lance: int = models.IntegerField(default=0)
mod_rank_axe: int = models.IntegerField(default=0)
mod_rank_bow: int = models.IntegerField(default=0)
mod_rank_gauntlet: int = models.IntegerField(default=0, null=True, blank=True)
mod_rank_hidden: int = models.IntegerField(default=0, null=True, blank=True)
mod_rank_tome: int = models.IntegerField(default=0, null=True, blank=True)
mod_rank_fire: int = models.IntegerField(default=0, null=True, blank=True)
mod_rank_wind: int = models.IntegerField(default=0, null=True, blank=True)
mod_rank_thunder: int = models.IntegerField(default=0, null=True, blank=True)
mod_rank_dark: int = models.IntegerField(default=0, null=True, blank=True)
mod_rank_light: int = models.IntegerField(default=0, null=True, blank=True)
mod_rank_anima: int = models.IntegerField(default=0, null=True, blank=True)
mod_rank_black: int = models.IntegerField(default=0, null=True, blank=True)
mod_rank_white: int = models.IntegerField(default=0, null=True, blank=True)
mod_rank_staff: int = models.IntegerField(default=0, null=True, blank=True)
mod_rank_dragonstone: int = models.IntegerField(default=0, null=True, blank=True)
mod_rank_beast: int = models.IntegerField(default=0, null=True, blank=True)
mod_rank_special: int = models.IntegerField(default=0, null=True, blank=True)
# temporary skills
temp_skills = models.ManyToManyField(Skill)
def to_dict(self):
# extract all_classes to its own variable at once, to keep the order consistent
all_classes = self.template.unit_class_history.all()
return {
'id': self.id,
'nickname': self.template.nickname,
'name': self.template.unit.name,
'description': self.template.unit.description,
'sex': self.template.unit.sex,
'game': self.template.unit.game.name,
'route': self.template.unit.route.name if self.template.unit.route else None,
'class': self.template.unit_class.to_dict(),
'level': self.template.unit_level,
'all_class_levels': [pc.levels for pc in all_classes],
'current_hp': self.current_hp,
'stats': {
'level': self.template.unit_level,
'hp': {
'unit_base': self.template.unit.base_hp,
'class_base': self.template.unit_class.base_hp,
'unit_growth': self.template.unit.growth_hp,
'all_class_growths': [pc.template.growth_hp for pc in all_classes],
'boosts': self.template.boosts_hp,
'modifiers': self.mod_max_hp,
'unit_max': self.template.unit.max_hp,
'unit_max_mod': self.template.unit.mod_max_hp,
'class_max': self.template.unit_class.max_hp
},
'str': {
'unit_base': self.template.unit.base_str,
'class_base': self.template.unit_class.base_str,
'unit_growth': self.template.unit.growth_str,
'all_class_growths': [pc.template.growth_str for pc in all_classes],
'boosts': self.template.boosts_str,
'modifiers': self.mod_str,
'unit_max': self.template.unit.max_str,
'unit_max_mod': self.template.unit.mod_max_str,
'class_max': self.template.unit_class.max_str
},
'mag': {
'unit_base': self.template.unit.base_mag,
'class_base': self.template.unit_class.base_mag,
'unit_growth': self.template.unit.growth_mag,
'all_class_growths': [pc.template.growth_mag for pc in all_classes],
'boosts': self.template.boosts_mag,
'modifiers': self.mod_mag,
'unit_max': self.template.unit.max_mag,
'unit_max_mod': self.template.unit.mod_max_mag,
'class_max': self.template.unit_class.max_mag
},
'skl': {
'unit_base': self.template.unit.base_skl,
'class_base': self.template.unit_class.base_skl,
'unit_growth': self.template.unit.growth_skl,
'all_class_growths': [pc.template.growth_skl for pc in all_classes],
'boosts': self.template.boosts_skl,
'modifiers': self.mod_skl,
'unit_max': self.template.unit.max_skl,
'unit_max_mod': self.template.unit.mod_max_skl,
'class_max': self.template.unit_class.max_skl
},
'spd': {
'unit_base': self.template.unit.base_spd,
'class_base': self.template.unit_class.base_spd,
'unit_growth': self.template.unit.growth_spd,
'all_class_growths': [pc.template.growth_spd for pc in all_classes],
'boosts': self.template.boosts_spd,
'modifiers': self.mod_spd,
'unit_max': self.template.unit.max_spd,
'unit_max_mod': self.template.unit.mod_max_spd,
'class_max': self.template.unit_class.max_spd
},
'luk': {
'unit_base': self.template.unit.base_luk,
'class_base': self.template.unit_class.base_luk,
'unit_growth': self.template.unit.growth_luk,
'all_class_growths': [pc.template.growth_luk for pc in all_classes],
'boosts': self.template.boosts_luk,
'modifiers': self.mod_luk,
'unit_max': self.template.unit.max_luk,
'unit_max_mod': self.template.unit.mod_max_luk,
'class_max': self.template.unit_class.max_luk
},
'def': {
'unit_base': self.template.unit.base_def,
'class_base': self.template.unit_class.base_def,
'unit_growth': self.template.unit.growth_def,
'all_class_growths': [pc.template.growth_def for pc in all_classes],
'boosts': self.template.boosts_def,
'modifiers': self.mod_def,
'unit_max': self.template.unit.max_def,
'unit_max_mod': self.template.unit.mod_max_def,
'class_max': self.template.unit_class.max_def
},
'res': {
'unit_base': self.template.unit.base_res,
'class_base': self.template.unit_class.base_res,
'unit_growth': self.template.unit.growth_res,
'all_class_growths': [pc.template.growth_res for pc in all_classes],
'boosts': self.template.boosts_res,
'modifiers': self.mod_res,
'unit_max': self.template.unit.max_res,
'unit_max_mod': self.template.unit.mod_max_res,
'class_max': self.template.unit_class.max_res
},
'cha': {
'unit_base': self.template.unit.base_cha,
'class_base': self.template.unit_class.base_cha,
'unit_growth': self.template.unit.growth_cha,
'all_class_growths': [pc.template.growth_cha for pc in all_classes],
'boosts': self.template.boosts_cha,
'modifiers': self.mod_cha,
'unit_max': self.template.unit.max_cha,
'unit_max_mod': self.template.unit.mod_max_cha,
'class_max': self.template.unit_class.max_cha
},
'con': {
'unit_base': self.template.unit.base_con,
'class_base': self.template.unit_class.base_con,
'unit_growth': self.template.unit.growth_con,
'all_class_growths': [pc.template.growth_con for pc in all_classes],
'boosts': self.template.boosts_con,
'modifiers': self.mod_con,
'unit_max': self.template.unit.max_con,
'unit_max_mod': self.template.unit.mod_max_con,
'class_max': self.template.unit_class.max_con
},
'mov': {
'unit_base': self.template.unit.base_mov,
'class_base': self.template.unit_class.base_mov,
'unit_growth': self.template.unit.growth_mov,
'all_class_growths': [pc.template.growth_mov for pc in all_classes],
'boosts': self.template.boosts_mov,
'modifiers': self.mod_mov,
'unit_max': self.template.unit.max_mov,
'unit_max_mod': self.template.unit.mod_max_mov,
'class_max': self.template.unit_class.max_mov
},
},
'weapon_ranks': {
'sword': {
'unit_base': self.template.unit.base_rank_sword,
'class_base': self.template.unit_class.base_rank_sword,
'boosts': self.template.boost_rank_sword,
'modifiers': self.mod_rank_sword,
},
'lance': {
'unit_base': self.template.unit.base_rank_lance,
'class_base': self.template.unit_class.base_rank_lance,
'boosts': self.template.boost_rank_lance,
'modifiers': self.mod_rank_lance,
},
'axe': {
'unit_base': self.template.unit.base_rank_axe,
'class_base': self.template.unit_class.base_rank_axe,
'boosts': self.template.boost_rank_axe,
'modifiers': self.mod_rank_axe,
},
'bow': {
'unit_base': self.template.unit.base_rank_bow,
'class_base': self.template.unit_class.base_rank_bow,
'boosts': self.template.boost_rank_bow,
'modifiers': self.mod_rank_bow,
},
'gauntlet': {
'unit_base': self.template.unit.base_rank_gauntlet,
'class_base': self.template.unit_class.base_rank_gauntlet,
'boosts': self.template.boost_rank_gauntlet,
'modifiers': self.mod_rank_gauntlet,
},
'hidden': {
'unit_base': self.template.unit.base_rank_hidden,
'class_base': self.template.unit_class.base_rank_hidden,
'boosts': self.template.boost_rank_hidden,
'modifiers': self.mod_rank_hidden,
},
'tome': {
'unit_base': self.template.unit.base_rank_tome,
'class_base': self.template.unit_class.base_rank_tome,
'boosts': self.template.boost_rank_tome,
'modifiers': self.mod_rank_tome,
},
'fire': {
'unit_base': self.template.unit.base_rank_fire,
'class_base': self.template.unit_class.base_rank_fire,
'boosts': self.template.boost_rank_fire,
'modifiers': self.mod_rank_fire,
},
'wind': {
'unit_base': self.template.unit.base_rank_wind,
'class_base': self.template.unit_class.base_rank_wind,
'boosts': self.template.boost_rank_wind,
'modifiers': self.mod_rank_wind,
},
'thunder': {
'unit_base': self.template.unit.base_rank_thunder,
'class_base': self.template.unit_class.base_rank_thunder,
'boosts': self.template.boost_rank_thunder,
'modifiers': self.mod_rank_thunder,
},
'dark': {
'unit_base': self.template.unit.base_rank_dark,
'class_base': self.template.unit_class.base_rank_dark,
'boosts': self.template.boost_rank_dark,
'modifiers': self.mod_rank_dark,
},
'light': {
'unit_base': self.template.unit.base_rank_light,
'class_base': self.template.unit_class.base_rank_light,
'boosts': self.template.boost_rank_light,
'modifiers': self.mod_rank_light,
},
'anima': {
'unit_base': self.template.unit.base_rank_anima,
'class_base': self.template.unit_class.base_rank_anima,
'boosts': self.template.boost_rank_anima,
'modifiers': self.mod_rank_anima,
},
'black': {
'unit_base': self.template.unit.base_rank_black,
'class_base': self.template.unit_class.base_rank_black,
'boosts': self.template.boost_rank_black,
'modifiers': self.mod_rank_black,
},
'white': {
'unit_base': self.template.unit.base_rank_white,
'class_base': self.template.unit_class.base_rank_white,
'boosts': self.template.boost_rank_white,
'modifiers': self.mod_rank_white,
},
'staff': {
'unit_base': self.template.unit.base_rank_staff,
'class_base': self.template.unit_class.base_rank_staff,
'boosts': self.template.boost_rank_staff,
'modifiers': self.mod_rank_staff,
},
'dragonstone': {
'unit_base': self.template.unit.base_rank_dragonstone,
'class_base': self.template.unit_class.base_rank_dragonstone,
'boosts': self.template.boost_rank_dragonstone,
'modifiers': self.mod_rank_dragonstone,
},
'beast': {
'unit_base': self.template.unit.base_rank_beast,
'class_base': self.template.unit_class.base_rank_beast,
'boosts': self.template.boost_rank_beast,
'modifiers': self.mod_rank_beast,
},
'special': {
'unit_base': self.template.unit.base_rank_special,
'class_base': self.template.unit_class.base_rank_special,
'boosts': self.template.boost_rank_special,
'modifiers': self.mod_rank_special,
},
},
'inventory': {
'weapons': [weapon.to_dict() for weapon in self.weapons.all()],
'items': [item.to_dict() for item in self.items.all()],
},
'personal_skills': [skill.to_dict() for skill in self.template.unit.personal_skills.all()],
'extra_skills': [skill.to_dict() for skill in self.template.extra_skills.all()],
'temporary_skills': [skill.to_dict() for skill in self.temp_skills.all()]
}
| <filename>FEArena/feaapi/models/play/ActiveUnit.py
from django.db import models
from .._util import BaseModel
from ..build.BuiltUnit import BuiltUnit
from .ActiveWeapon import ActiveWeapon
from .ActiveItem import ActiveItem
from ..core import Skill
class ActiveUnit(BaseModel):
id: int = models.AutoField(primary_key=True)
# demographic information should be contained in the template unit
template: BuiltUnit = models.ForeignKey(BuiltUnit, on_delete=models.CASCADE)
# for skill use. This should be reset at the beginning of each turn.
restricted_actions: str = models.TextField(default='')
# otherwise, current state information
current_hp: int = models.IntegerField(default=0)
mod_max_hp: int = models.IntegerField(default=0)
mod_str: int = models.IntegerField(default=0)
mod_mag: int = models.IntegerField(default=0, null=True, blank=True)
mod_skl: int = models.IntegerField(default=0)
mod_spd: int = models.IntegerField(default=0)
mod_luk: int = models.IntegerField(default=0)
mod_def: int = models.IntegerField(default=0)
mod_res: int = models.IntegerField(default=0)
mod_cha: int = models.IntegerField(default=0, null=True, blank=True)
mod_mov: int = models.IntegerField(default=0)
mod_con: int = models.IntegerField(default=0, null=True, blank=True)
# inventory (limit should be in template unit)
weapons = models.ManyToManyField(ActiveWeapon)
items = models.ManyToManyField(ActiveItem)
# weapon rank modifiers
mod_rank_sword: int = models.IntegerField(default=0)
mod_rank_lance: int = models.IntegerField(default=0)
mod_rank_axe: int = models.IntegerField(default=0)
mod_rank_bow: int = models.IntegerField(default=0)
mod_rank_gauntlet: int = models.IntegerField(default=0, null=True, blank=True)
mod_rank_hidden: int = models.IntegerField(default=0, null=True, blank=True)
mod_rank_tome: int = models.IntegerField(default=0, null=True, blank=True)
mod_rank_fire: int = models.IntegerField(default=0, null=True, blank=True)
mod_rank_wind: int = models.IntegerField(default=0, null=True, blank=True)
mod_rank_thunder: int = models.IntegerField(default=0, null=True, blank=True)
mod_rank_dark: int = models.IntegerField(default=0, null=True, blank=True)
mod_rank_light: int = models.IntegerField(default=0, null=True, blank=True)
mod_rank_anima: int = models.IntegerField(default=0, null=True, blank=True)
mod_rank_black: int = models.IntegerField(default=0, null=True, blank=True)
mod_rank_white: int = models.IntegerField(default=0, null=True, blank=True)
mod_rank_staff: int = models.IntegerField(default=0, null=True, blank=True)
mod_rank_dragonstone: int = models.IntegerField(default=0, null=True, blank=True)
mod_rank_beast: int = models.IntegerField(default=0, null=True, blank=True)
mod_rank_special: int = models.IntegerField(default=0, null=True, blank=True)
# temporary skills
temp_skills = models.ManyToManyField(Skill)
def to_dict(self):
# extract all_classes to its own variable at once, to keep the order consistent
all_classes = self.template.unit_class_history.all()
return {
'id': self.id,
'nickname': self.template.nickname,
'name': self.template.unit.name,
'description': self.template.unit.description,
'sex': self.template.unit.sex,
'game': self.template.unit.game.name,
'route': self.template.unit.route.name if self.template.unit.route else None,
'class': self.template.unit_class.to_dict(),
'level': self.template.unit_level,
'all_class_levels': [pc.levels for pc in all_classes],
'current_hp': self.current_hp,
'stats': {
'level': self.template.unit_level,
'hp': {
'unit_base': self.template.unit.base_hp,
'class_base': self.template.unit_class.base_hp,
'unit_growth': self.template.unit.growth_hp,
'all_class_growths': [pc.template.growth_hp for pc in all_classes],
'boosts': self.template.boosts_hp,
'modifiers': self.mod_max_hp,
'unit_max': self.template.unit.max_hp,
'unit_max_mod': self.template.unit.mod_max_hp,
'class_max': self.template.unit_class.max_hp
},
'str': {
'unit_base': self.template.unit.base_str,
'class_base': self.template.unit_class.base_str,
'unit_growth': self.template.unit.growth_str,
'all_class_growths': [pc.template.growth_str for pc in all_classes],
'boosts': self.template.boosts_str,
'modifiers': self.mod_str,
'unit_max': self.template.unit.max_str,
'unit_max_mod': self.template.unit.mod_max_str,
'class_max': self.template.unit_class.max_str
},
'mag': {
'unit_base': self.template.unit.base_mag,
'class_base': self.template.unit_class.base_mag,
'unit_growth': self.template.unit.growth_mag,
'all_class_growths': [pc.template.growth_mag for pc in all_classes],
'boosts': self.template.boosts_mag,
'modifiers': self.mod_mag,
'unit_max': self.template.unit.max_mag,
'unit_max_mod': self.template.unit.mod_max_mag,
'class_max': self.template.unit_class.max_mag
},
'skl': {
'unit_base': self.template.unit.base_skl,
'class_base': self.template.unit_class.base_skl,
'unit_growth': self.template.unit.growth_skl,
'all_class_growths': [pc.template.growth_skl for pc in all_classes],
'boosts': self.template.boosts_skl,
'modifiers': self.mod_skl,
'unit_max': self.template.unit.max_skl,
'unit_max_mod': self.template.unit.mod_max_skl,
'class_max': self.template.unit_class.max_skl
},
'spd': {
'unit_base': self.template.unit.base_spd,
'class_base': self.template.unit_class.base_spd,
'unit_growth': self.template.unit.growth_spd,
'all_class_growths': [pc.template.growth_spd for pc in all_classes],
'boosts': self.template.boosts_spd,
'modifiers': self.mod_spd,
'unit_max': self.template.unit.max_spd,
'unit_max_mod': self.template.unit.mod_max_spd,
'class_max': self.template.unit_class.max_spd
},
'luk': {
'unit_base': self.template.unit.base_luk,
'class_base': self.template.unit_class.base_luk,
'unit_growth': self.template.unit.growth_luk,
'all_class_growths': [pc.template.growth_luk for pc in all_classes],
'boosts': self.template.boosts_luk,
'modifiers': self.mod_luk,
'unit_max': self.template.unit.max_luk,
'unit_max_mod': self.template.unit.mod_max_luk,
'class_max': self.template.unit_class.max_luk
},
'def': {
'unit_base': self.template.unit.base_def,
'class_base': self.template.unit_class.base_def,
'unit_growth': self.template.unit.growth_def,
'all_class_growths': [pc.template.growth_def for pc in all_classes],
'boosts': self.template.boosts_def,
'modifiers': self.mod_def,
'unit_max': self.template.unit.max_def,
'unit_max_mod': self.template.unit.mod_max_def,
'class_max': self.template.unit_class.max_def
},
'res': {
'unit_base': self.template.unit.base_res,
'class_base': self.template.unit_class.base_res,
'unit_growth': self.template.unit.growth_res,
'all_class_growths': [pc.template.growth_res for pc in all_classes],
'boosts': self.template.boosts_res,
'modifiers': self.mod_res,
'unit_max': self.template.unit.max_res,
'unit_max_mod': self.template.unit.mod_max_res,
'class_max': self.template.unit_class.max_res
},
'cha': {
'unit_base': self.template.unit.base_cha,
'class_base': self.template.unit_class.base_cha,
'unit_growth': self.template.unit.growth_cha,
'all_class_growths': [pc.template.growth_cha for pc in all_classes],
'boosts': self.template.boosts_cha,
'modifiers': self.mod_cha,
'unit_max': self.template.unit.max_cha,
'unit_max_mod': self.template.unit.mod_max_cha,
'class_max': self.template.unit_class.max_cha
},
'con': {
'unit_base': self.template.unit.base_con,
'class_base': self.template.unit_class.base_con,
'unit_growth': self.template.unit.growth_con,
'all_class_growths': [pc.template.growth_con for pc in all_classes],
'boosts': self.template.boosts_con,
'modifiers': self.mod_con,
'unit_max': self.template.unit.max_con,
'unit_max_mod': self.template.unit.mod_max_con,
'class_max': self.template.unit_class.max_con
},
'mov': {
'unit_base': self.template.unit.base_mov,
'class_base': self.template.unit_class.base_mov,
'unit_growth': self.template.unit.growth_mov,
'all_class_growths': [pc.template.growth_mov for pc in all_classes],
'boosts': self.template.boosts_mov,
'modifiers': self.mod_mov,
'unit_max': self.template.unit.max_mov,
'unit_max_mod': self.template.unit.mod_max_mov,
'class_max': self.template.unit_class.max_mov
},
},
'weapon_ranks': {
'sword': {
'unit_base': self.template.unit.base_rank_sword,
'class_base': self.template.unit_class.base_rank_sword,
'boosts': self.template.boost_rank_sword,
'modifiers': self.mod_rank_sword,
},
'lance': {
'unit_base': self.template.unit.base_rank_lance,
'class_base': self.template.unit_class.base_rank_lance,
'boosts': self.template.boost_rank_lance,
'modifiers': self.mod_rank_lance,
},
'axe': {
'unit_base': self.template.unit.base_rank_axe,
'class_base': self.template.unit_class.base_rank_axe,
'boosts': self.template.boost_rank_axe,
'modifiers': self.mod_rank_axe,
},
'bow': {
'unit_base': self.template.unit.base_rank_bow,
'class_base': self.template.unit_class.base_rank_bow,
'boosts': self.template.boost_rank_bow,
'modifiers': self.mod_rank_bow,
},
'gauntlet': {
'unit_base': self.template.unit.base_rank_gauntlet,
'class_base': self.template.unit_class.base_rank_gauntlet,
'boosts': self.template.boost_rank_gauntlet,
'modifiers': self.mod_rank_gauntlet,
},
'hidden': {
'unit_base': self.template.unit.base_rank_hidden,
'class_base': self.template.unit_class.base_rank_hidden,
'boosts': self.template.boost_rank_hidden,
'modifiers': self.mod_rank_hidden,
},
'tome': {
'unit_base': self.template.unit.base_rank_tome,
'class_base': self.template.unit_class.base_rank_tome,
'boosts': self.template.boost_rank_tome,
'modifiers': self.mod_rank_tome,
},
'fire': {
'unit_base': self.template.unit.base_rank_fire,
'class_base': self.template.unit_class.base_rank_fire,
'boosts': self.template.boost_rank_fire,
'modifiers': self.mod_rank_fire,
},
'wind': {
'unit_base': self.template.unit.base_rank_wind,
'class_base': self.template.unit_class.base_rank_wind,
'boosts': self.template.boost_rank_wind,
'modifiers': self.mod_rank_wind,
},
'thunder': {
'unit_base': self.template.unit.base_rank_thunder,
'class_base': self.template.unit_class.base_rank_thunder,
'boosts': self.template.boost_rank_thunder,
'modifiers': self.mod_rank_thunder,
},
'dark': {
'unit_base': self.template.unit.base_rank_dark,
'class_base': self.template.unit_class.base_rank_dark,
'boosts': self.template.boost_rank_dark,
'modifiers': self.mod_rank_dark,
},
'light': {
'unit_base': self.template.unit.base_rank_light,
'class_base': self.template.unit_class.base_rank_light,
'boosts': self.template.boost_rank_light,
'modifiers': self.mod_rank_light,
},
'anima': {
'unit_base': self.template.unit.base_rank_anima,
'class_base': self.template.unit_class.base_rank_anima,
'boosts': self.template.boost_rank_anima,
'modifiers': self.mod_rank_anima,
},
'black': {
'unit_base': self.template.unit.base_rank_black,
'class_base': self.template.unit_class.base_rank_black,
'boosts': self.template.boost_rank_black,
'modifiers': self.mod_rank_black,
},
'white': {
'unit_base': self.template.unit.base_rank_white,
'class_base': self.template.unit_class.base_rank_white,
'boosts': self.template.boost_rank_white,
'modifiers': self.mod_rank_white,
},
'staff': {
'unit_base': self.template.unit.base_rank_staff,
'class_base': self.template.unit_class.base_rank_staff,
'boosts': self.template.boost_rank_staff,
'modifiers': self.mod_rank_staff,
},
'dragonstone': {
'unit_base': self.template.unit.base_rank_dragonstone,
'class_base': self.template.unit_class.base_rank_dragonstone,
'boosts': self.template.boost_rank_dragonstone,
'modifiers': self.mod_rank_dragonstone,
},
'beast': {
'unit_base': self.template.unit.base_rank_beast,
'class_base': self.template.unit_class.base_rank_beast,
'boosts': self.template.boost_rank_beast,
'modifiers': self.mod_rank_beast,
},
'special': {
'unit_base': self.template.unit.base_rank_special,
'class_base': self.template.unit_class.base_rank_special,
'boosts': self.template.boost_rank_special,
'modifiers': self.mod_rank_special,
},
},
'inventory': {
'weapons': [weapon.to_dict() for weapon in self.weapons.all()],
'items': [item.to_dict() for item in self.items.all()],
},
'personal_skills': [skill.to_dict() for skill in self.template.unit.personal_skills.all()],
'extra_skills': [skill.to_dict() for skill in self.template.extra_skills.all()],
'temporary_skills': [skill.to_dict() for skill in self.temp_skills.all()]
}
| en | 0.790269 | # demographic information should be contained in the template unit # for skill use. This should be reset at the beginning of each turn. # otherwise, current state information # inventory (limit should be in template unit) # weapon rank modifiers # temporary skills # extract all_classes to its own variable at once, to keep the order consistent | 2.130676 | 2 |
src/cryptography/hazmat/primitives/serialization/ssh.py | Capfly/cryptography | 1 | 6630514 | <reponame>Capfly/cryptography<gh_stars>1-10
# This file is dual licensed under the terms of the Apache License, Version
# 2.0, and the BSD License. See the LICENSE file in the root of this repository
# for complete details.
import binascii
import os
import re
import typing
from base64 import encodebytes as _base64_encode
from cryptography import utils
from cryptography.exceptions import UnsupportedAlgorithm
from cryptography.hazmat.primitives.asymmetric import dsa, ec, ed25519, rsa
from cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes
from cryptography.hazmat.primitives.serialization import (
Encoding,
NoEncryption,
PrivateFormat,
PublicFormat,
)
try:
from bcrypt import kdf as _bcrypt_kdf
_bcrypt_supported = True
except ImportError:
_bcrypt_supported = False
def _bcrypt_kdf(
password: <PASSWORD>,
salt: bytes,
desired_key_bytes: int,
rounds: int,
ignore_few_rounds: bool = False,
) -> bytes:
raise UnsupportedAlgorithm("Need bcrypt module")
_SSH_ED25519 = b"ssh-ed25519"
_SSH_RSA = b"ssh-rsa"
_SSH_DSA = b"ssh-dss"
_ECDSA_NISTP256 = b"ecdsa-sha2-nistp256"
_ECDSA_NISTP384 = b"ecdsa-sha2-nistp384"
_ECDSA_NISTP521 = b"ecdsa-sha2-nistp521"
_CERT_SUFFIX = b"-<EMAIL>"
_SSH_PUBKEY_RC = re.compile(br"\A(\S+)[ \t]+(\S+)")
_SK_MAGIC = b"openssh-key-v1\0"
_SK_START = b"-----BEGIN OPENSSH PRIVATE KEY-----"
_SK_END = b"-----END OPENSSH PRIVATE KEY-----"
_BCRYPT = b"bcrypt"
_NONE = b"none"
_DEFAULT_CIPHER = b"aes256-ctr"
_DEFAULT_ROUNDS = 16
_MAX_PASSWORD = 72
# re is only way to work on bytes-like data
_PEM_RC = re.compile(_SK_START + b"(.*?)" + _SK_END, re.DOTALL)
# padding for max blocksize
_PADDING = memoryview(bytearray(range(1, 1 + 16)))
# ciphers that are actually used in key wrapping
_SSH_CIPHERS: typing.Dict[
bytes,
typing.Tuple[
typing.Type[algorithms.AES],
int,
typing.Union[typing.Type[modes.CTR], typing.Type[modes.CBC]],
int,
],
] = {
b"aes256-ctr": (algorithms.AES, 32, modes.CTR, 16),
b"aes256-cbc": (algorithms.AES, 32, modes.CBC, 16),
}
# map local curve name to key type
_ECDSA_KEY_TYPE = {
"secp256r1": _ECDSA_NISTP256,
"secp384r1": _ECDSA_NISTP384,
"secp521r1": _ECDSA_NISTP521,
}
def _ecdsa_key_type(public_key: ec.EllipticCurvePublicKey) -> bytes:
"""Return SSH key_type and curve_name for private key."""
curve = public_key.curve
if curve.name not in _ECDSA_KEY_TYPE:
raise ValueError(
"Unsupported curve for ssh private key: %r" % curve.name
)
return _ECDSA_KEY_TYPE[curve.name]
def _ssh_pem_encode(
data: bytes,
prefix: bytes = _SK_START + b"\n",
suffix: bytes = _SK_END + b"\n",
) -> bytes:
return b"".join([prefix, _base64_encode(data), suffix])
def _check_block_size(data: bytes, block_len: int) -> None:
"""Require data to be full blocks"""
if not data or len(data) % block_len != 0:
raise ValueError("Corrupt data: missing padding")
def _check_empty(data: bytes) -> None:
"""All data should have been parsed."""
if data:
raise ValueError("Corrupt data: unparsed data")
def _init_cipher(
ciphername: bytes,
password: typing.Optional[bytes],
salt: bytes,
rounds: int,
) -> Cipher[typing.Union[modes.CBC, modes.CTR]]:
"""Generate key + iv and return cipher."""
if not password:
raise ValueError("Key is password-protected.")
algo, key_len, mode, iv_len = _SSH_CIPHERS[ciphername]
seed = _bcrypt_kdf(password, salt, key_len + iv_len, rounds, True)
return Cipher(algo(seed[:key_len]), mode(seed[key_len:]))
def _get_u32(data: memoryview) -> typing.Tuple[int, memoryview]:
"""Uint32"""
if len(data) < 4:
raise ValueError("Invalid data")
return int.from_bytes(data[:4], byteorder="big"), data[4:]
def _get_u64(data: memoryview) -> typing.Tuple[int, memoryview]:
"""Uint64"""
if len(data) < 8:
raise ValueError("Invalid data")
return int.from_bytes(data[:8], byteorder="big"), data[8:]
def _get_sshstr(data: memoryview) -> typing.Tuple[memoryview, memoryview]:
"""Bytes with u32 length prefix"""
n, data = _get_u32(data)
if n > len(data):
raise ValueError("Invalid data")
return data[:n], data[n:]
def _get_mpint(data: memoryview) -> typing.Tuple[int, memoryview]:
"""Big integer."""
val, data = _get_sshstr(data)
if val and val[0] > 0x7F:
raise ValueError("Invalid data")
return int.from_bytes(val, "big"), data
def _to_mpint(val: int) -> bytes:
"""Storage format for signed bigint."""
if val < 0:
raise ValueError("negative mpint not allowed")
if not val:
return b""
nbytes = (val.bit_length() + 8) // 8
return utils.int_to_bytes(val, nbytes)
class _FragList(object):
"""Build recursive structure without data copy."""
flist: typing.List[bytes]
def __init__(self, init: typing.List[bytes] = None) -> None:
self.flist = []
if init:
self.flist.extend(init)
def put_raw(self, val: bytes) -> None:
"""Add plain bytes"""
self.flist.append(val)
def put_u32(self, val: int) -> None:
"""Big-endian uint32"""
self.flist.append(val.to_bytes(length=4, byteorder="big"))
def put_sshstr(self, val: typing.Union[bytes, "_FragList"]) -> None:
"""Bytes prefixed with u32 length"""
if isinstance(val, (bytes, memoryview, bytearray)):
self.put_u32(len(val))
self.flist.append(val)
else:
self.put_u32(val.size())
self.flist.extend(val.flist)
def put_mpint(self, val: int) -> None:
"""Big-endian bigint prefixed with u32 length"""
self.put_sshstr(_to_mpint(val))
def size(self) -> int:
"""Current number of bytes"""
return sum(map(len, self.flist))
def render(self, dstbuf: memoryview, pos: int = 0) -> int:
"""Write into bytearray"""
for frag in self.flist:
flen = len(frag)
start, pos = pos, pos + flen
dstbuf[start:pos] = frag
return pos
def tobytes(self) -> bytes:
"""Return as bytes"""
buf = memoryview(bytearray(self.size()))
self.render(buf)
return buf.tobytes()
class _SSHFormatRSA(object):
"""Format for RSA keys.
Public:
mpint e, n
Private:
mpint n, e, d, iqmp, p, q
"""
def get_public(self, data: memoryview):
"""RSA public fields"""
e, data = _get_mpint(data)
n, data = _get_mpint(data)
return (e, n), data
def load_public(
self, data: memoryview
) -> typing.Tuple[rsa.RSAPublicKey, memoryview]:
"""Make RSA public key from data."""
(e, n), data = self.get_public(data)
public_numbers = rsa.RSAPublicNumbers(e, n)
public_key = public_numbers.public_key()
return public_key, data
def load_private(
self, data: memoryview, pubfields
) -> typing.Tuple[rsa.RSAPrivateKey, memoryview]:
"""Make RSA private key from data."""
n, data = _get_mpint(data)
e, data = _get_mpint(data)
d, data = _get_mpint(data)
iqmp, data = _get_mpint(data)
p, data = _get_mpint(data)
q, data = _get_mpint(data)
if (e, n) != pubfields:
raise ValueError("Corrupt data: rsa field mismatch")
dmp1 = rsa.rsa_crt_dmp1(d, p)
dmq1 = rsa.rsa_crt_dmq1(d, q)
public_numbers = rsa.RSAPublicNumbers(e, n)
private_numbers = rsa.RSAPrivateNumbers(
p, q, d, dmp1, dmq1, iqmp, public_numbers
)
private_key = private_numbers.private_key()
return private_key, data
def encode_public(
self, public_key: rsa.RSAPublicKey, f_pub: _FragList
) -> None:
"""Write RSA public key"""
pubn = public_key.public_numbers()
f_pub.put_mpint(pubn.e)
f_pub.put_mpint(pubn.n)
def encode_private(
self, private_key: rsa.RSAPrivateKey, f_priv: _FragList
) -> None:
"""Write RSA private key"""
private_numbers = private_key.private_numbers()
public_numbers = private_numbers.public_numbers
f_priv.put_mpint(public_numbers.n)
f_priv.put_mpint(public_numbers.e)
f_priv.put_mpint(private_numbers.d)
f_priv.put_mpint(private_numbers.iqmp)
f_priv.put_mpint(private_numbers.p)
f_priv.put_mpint(private_numbers.q)
class _SSHFormatDSA(object):
"""Format for DSA keys.
Public:
mpint p, q, g, y
Private:
mpint p, q, g, y, x
"""
def get_public(
self, data: memoryview
) -> typing.Tuple[typing.Tuple, memoryview]:
"""DSA public fields"""
p, data = _get_mpint(data)
q, data = _get_mpint(data)
g, data = _get_mpint(data)
y, data = _get_mpint(data)
return (p, q, g, y), data
def load_public(
self, data: memoryview
) -> typing.Tuple[dsa.DSAPublicKey, memoryview]:
"""Make DSA public key from data."""
(p, q, g, y), data = self.get_public(data)
parameter_numbers = dsa.DSAParameterNumbers(p, q, g)
public_numbers = dsa.DSAPublicNumbers(y, parameter_numbers)
self._validate(public_numbers)
public_key = public_numbers.public_key()
return public_key, data
def load_private(
self, data: memoryview, pubfields
) -> typing.Tuple[dsa.DSAPrivateKey, memoryview]:
"""Make DSA private key from data."""
(p, q, g, y), data = self.get_public(data)
x, data = _get_mpint(data)
if (p, q, g, y) != pubfields:
raise ValueError("Corrupt data: dsa field mismatch")
parameter_numbers = dsa.DSAParameterNumbers(p, q, g)
public_numbers = dsa.DSAPublicNumbers(y, parameter_numbers)
self._validate(public_numbers)
private_numbers = dsa.DSAPrivateNumbers(x, public_numbers)
private_key = private_numbers.private_key()
return private_key, data
def encode_public(
self, public_key: dsa.DSAPublicKey, f_pub: _FragList
) -> None:
"""Write DSA public key"""
public_numbers = public_key.public_numbers()
parameter_numbers = public_numbers.parameter_numbers
self._validate(public_numbers)
f_pub.put_mpint(parameter_numbers.p)
f_pub.put_mpint(parameter_numbers.q)
f_pub.put_mpint(parameter_numbers.g)
f_pub.put_mpint(public_numbers.y)
def encode_private(
self, private_key: dsa.DSAPrivateKey, f_priv: _FragList
) -> None:
"""Write DSA private key"""
self.encode_public(private_key.public_key(), f_priv)
f_priv.put_mpint(private_key.private_numbers().x)
def _validate(self, public_numbers: dsa.DSAPublicNumbers) -> None:
parameter_numbers = public_numbers.parameter_numbers
if parameter_numbers.p.bit_length() != 1024:
raise ValueError("SSH supports only 1024 bit DSA keys")
class _SSHFormatECDSA(object):
"""Format for ECDSA keys.
Public:
str curve
bytes point
Private:
str curve
bytes point
mpint secret
"""
def __init__(self, ssh_curve_name: bytes, curve: ec.EllipticCurve):
self.ssh_curve_name = ssh_curve_name
self.curve = curve
def get_public(
self, data: memoryview
) -> typing.Tuple[typing.Tuple, memoryview]:
"""ECDSA public fields"""
curve, data = _get_sshstr(data)
point, data = _get_sshstr(data)
if curve != self.ssh_curve_name:
raise ValueError("Curve name mismatch")
if point[0] != 4:
raise NotImplementedError("Need uncompressed point")
return (curve, point), data
def load_public(
self, data: memoryview
) -> typing.Tuple[ec.EllipticCurvePublicKey, memoryview]:
"""Make ECDSA public key from data."""
(curve_name, point), data = self.get_public(data)
public_key = ec.EllipticCurvePublicKey.from_encoded_point(
self.curve, point.tobytes()
)
return public_key, data
def load_private(
self, data: memoryview, pubfields
) -> typing.Tuple[ec.EllipticCurvePrivateKey, memoryview]:
"""Make ECDSA private key from data."""
(curve_name, point), data = self.get_public(data)
secret, data = _get_mpint(data)
if (curve_name, point) != pubfields:
raise ValueError("Corrupt data: ecdsa field mismatch")
private_key = ec.derive_private_key(secret, self.curve)
return private_key, data
def encode_public(
self, public_key: ec.EllipticCurvePublicKey, f_pub: _FragList
) -> None:
"""Write ECDSA public key"""
point = public_key.public_bytes(
Encoding.X962, PublicFormat.UncompressedPoint
)
f_pub.put_sshstr(self.ssh_curve_name)
f_pub.put_sshstr(point)
def encode_private(
self, private_key: ec.EllipticCurvePrivateKey, f_priv: _FragList
) -> None:
"""Write ECDSA private key"""
public_key = private_key.public_key()
private_numbers = private_key.private_numbers()
self.encode_public(public_key, f_priv)
f_priv.put_mpint(private_numbers.private_value)
class _SSHFormatEd25519(object):
"""Format for Ed25519 keys.
Public:
bytes point
Private:
bytes point
bytes secret_and_point
"""
def get_public(
self, data: memoryview
) -> typing.Tuple[typing.Tuple, memoryview]:
"""Ed25519 public fields"""
point, data = _get_sshstr(data)
return (point,), data
def load_public(
self, data: memoryview
) -> typing.Tuple[ed25519.Ed25519PublicKey, memoryview]:
"""Make Ed25519 public key from data."""
(point,), data = self.get_public(data)
public_key = ed25519.Ed25519PublicKey.from_public_bytes(
point.tobytes()
)
return public_key, data
def load_private(
self, data: memoryview, pubfields
) -> typing.Tuple[ed25519.Ed25519PrivateKey, memoryview]:
"""Make Ed25519 private key from data."""
(point,), data = self.get_public(data)
keypair, data = _get_sshstr(data)
secret = keypair[:32]
point2 = keypair[32:]
if point != point2 or (point,) != pubfields:
raise ValueError("Corrupt data: ed25519 field mismatch")
private_key = ed25519.Ed25519PrivateKey.from_private_bytes(secret)
return private_key, data
def encode_public(
self, public_key: ed25519.Ed25519PublicKey, f_pub: _FragList
) -> None:
"""Write Ed25519 public key"""
raw_public_key = public_key.public_bytes(
Encoding.Raw, PublicFormat.Raw
)
f_pub.put_sshstr(raw_public_key)
def encode_private(
self, private_key: ed25519.Ed25519PrivateKey, f_priv: _FragList
) -> None:
"""Write Ed25519 private key"""
public_key = private_key.public_key()
raw_private_key = private_key.private_bytes(
Encoding.Raw, PrivateFormat.Raw, NoEncryption()
)
raw_public_key = public_key.public_bytes(
Encoding.Raw, PublicFormat.Raw
)
f_keypair = _FragList([raw_private_key, raw_public_key])
self.encode_public(public_key, f_priv)
f_priv.put_sshstr(f_keypair)
_KEY_FORMATS = {
_SSH_RSA: _SSHFormatRSA(),
_SSH_DSA: _SSHFormatDSA(),
_SSH_ED25519: _SSHFormatEd25519(),
_ECDSA_NISTP256: _SSHFormatECDSA(b"nistp256", ec.SECP256R1()),
_ECDSA_NISTP384: _SSHFormatECDSA(b"nistp384", ec.SECP384R1()),
_ECDSA_NISTP521: _SSHFormatECDSA(b"nistp521", ec.SECP521R1()),
}
def _lookup_kformat(key_type: bytes):
"""Return valid format or throw error"""
if not isinstance(key_type, bytes):
key_type = memoryview(key_type).tobytes()
if key_type in _KEY_FORMATS:
return _KEY_FORMATS[key_type]
raise UnsupportedAlgorithm("Unsupported key type: %r" % key_type)
_SSH_PRIVATE_KEY_TYPES = typing.Union[
ec.EllipticCurvePrivateKey,
rsa.RSAPrivateKey,
dsa.DSAPrivateKey,
ed25519.Ed25519PrivateKey,
]
def load_ssh_private_key(
data: bytes,
password: typing.Optional[bytes],
backend: typing.Any = None,
) -> _SSH_PRIVATE_KEY_TYPES:
"""Load private key from OpenSSH custom encoding."""
utils._check_byteslike("data", data)
if password is not None:
utils._check_bytes("password", password)
m = _PEM_RC.search(data)
if not m:
raise ValueError("Not OpenSSH private key format")
p1 = m.start(1)
p2 = m.end(1)
data = binascii.a2b_base64(memoryview(data)[p1:p2])
if not data.startswith(_SK_MAGIC):
raise ValueError("Not OpenSSH private key format")
data = memoryview(data)[len(_SK_MAGIC) :]
# parse header
ciphername, data = _get_sshstr(data)
kdfname, data = _get_sshstr(data)
kdfoptions, data = _get_sshstr(data)
nkeys, data = _get_u32(data)
if nkeys != 1:
raise ValueError("Only one key supported")
# load public key data
pubdata, data = _get_sshstr(data)
pub_key_type, pubdata = _get_sshstr(pubdata)
kformat = _lookup_kformat(pub_key_type)
pubfields, pubdata = kformat.get_public(pubdata)
_check_empty(pubdata)
# load secret data
edata, data = _get_sshstr(data)
_check_empty(data)
if (ciphername, kdfname) != (_NONE, _NONE):
ciphername_bytes = ciphername.tobytes()
if ciphername_bytes not in _SSH_CIPHERS:
raise UnsupportedAlgorithm(
"Unsupported cipher: %r" % ciphername_bytes
)
if kdfname != _BCRYPT:
raise UnsupportedAlgorithm("Unsupported KDF: %r" % kdfname)
blklen = _SSH_CIPHERS[ciphername_bytes][3]
_check_block_size(edata, blklen)
salt, kbuf = _get_sshstr(kdfoptions)
rounds, kbuf = _get_u32(kbuf)
_check_empty(kbuf)
ciph = _init_cipher(ciphername_bytes, password, salt.tobytes(), rounds)
edata = memoryview(ciph.decryptor().update(edata))
else:
blklen = 8
_check_block_size(edata, blklen)
ck1, edata = _get_u32(edata)
ck2, edata = _get_u32(edata)
if ck1 != ck2:
raise ValueError("Corrupt data: broken checksum")
# load per-key struct
key_type, edata = _get_sshstr(edata)
if key_type != pub_key_type:
raise ValueError("Corrupt data: key type mismatch")
private_key, edata = kformat.load_private(edata, pubfields)
comment, edata = _get_sshstr(edata)
# yes, SSH does padding check *after* all other parsing is done.
# need to follow as it writes zero-byte padding too.
if edata != _PADDING[: len(edata)]:
raise ValueError("Corrupt data: invalid padding")
return private_key
def serialize_ssh_private_key(
private_key: _SSH_PRIVATE_KEY_TYPES,
password: typing.Optional[bytes] = None,
) -> bytes:
"""Serialize private key with OpenSSH custom encoding."""
if password is not None:
utils._check_bytes("password", password)
if password and len(password) > _MAX_PASSWORD:
raise ValueError(
"Passwords longer than 72 bytes are not supported by "
"OpenSSH private key format"
)
if isinstance(private_key, ec.EllipticCurvePrivateKey):
key_type = _ecdsa_key_type(private_key.public_key())
elif isinstance(private_key, rsa.RSAPrivateKey):
key_type = _SSH_RSA
elif isinstance(private_key, dsa.DSAPrivateKey):
key_type = _SSH_DSA
elif isinstance(private_key, ed25519.Ed25519PrivateKey):
key_type = _SSH_ED25519
else:
raise ValueError("Unsupported key type")
kformat = _lookup_kformat(key_type)
# setup parameters
f_kdfoptions = _FragList()
if password:
ciphername = _DEFAULT_CIPHER
blklen = _SSH_CIPHERS[ciphername][3]
kdfname = _BCRYPT
rounds = _DEFAULT_ROUNDS
salt = os.urandom(16)
f_kdfoptions.put_sshstr(salt)
f_kdfoptions.put_u32(rounds)
ciph = _init_cipher(ciphername, password, salt, rounds)
else:
ciphername = kdfname = _NONE
blklen = 8
ciph = None
nkeys = 1
checkval = os.urandom(4)
comment = b""
# encode public and private parts together
f_public_key = _FragList()
f_public_key.put_sshstr(key_type)
kformat.encode_public(private_key.public_key(), f_public_key)
f_secrets = _FragList([checkval, checkval])
f_secrets.put_sshstr(key_type)
kformat.encode_private(private_key, f_secrets)
f_secrets.put_sshstr(comment)
f_secrets.put_raw(_PADDING[: blklen - (f_secrets.size() % blklen)])
# top-level structure
f_main = _FragList()
f_main.put_raw(_SK_MAGIC)
f_main.put_sshstr(ciphername)
f_main.put_sshstr(kdfname)
f_main.put_sshstr(f_kdfoptions)
f_main.put_u32(nkeys)
f_main.put_sshstr(f_public_key)
f_main.put_sshstr(f_secrets)
# copy result info bytearray
slen = f_secrets.size()
mlen = f_main.size()
buf = memoryview(bytearray(mlen + blklen))
f_main.render(buf)
ofs = mlen - slen
# encrypt in-place
if ciph is not None:
ciph.encryptor().update_into(buf[ofs:mlen], buf[ofs:])
txt = _ssh_pem_encode(buf[:mlen])
buf[ofs:mlen] = bytearray(slen)
return txt
_SSH_PUBLIC_KEY_TYPES = typing.Union[
ec.EllipticCurvePublicKey,
rsa.RSAPublicKey,
dsa.DSAPublicKey,
ed25519.Ed25519PublicKey,
]
def load_ssh_public_key(
data: bytes, backend: typing.Any = None
) -> _SSH_PUBLIC_KEY_TYPES:
"""Load public key from OpenSSH one-line format."""
utils._check_byteslike("data", data)
m = _SSH_PUBKEY_RC.match(data)
if not m:
raise ValueError("Invalid line format")
key_type = orig_key_type = m.group(1)
key_body = m.group(2)
with_cert = False
if _CERT_SUFFIX == key_type[-len(_CERT_SUFFIX) :]:
with_cert = True
key_type = key_type[: -len(_CERT_SUFFIX)]
kformat = _lookup_kformat(key_type)
try:
rest = memoryview(binascii.a2b_base64(key_body))
except (TypeError, binascii.Error):
raise ValueError("Invalid key format")
inner_key_type, rest = _get_sshstr(rest)
if inner_key_type != orig_key_type:
raise ValueError("Invalid key format")
if with_cert:
nonce, rest = _get_sshstr(rest)
public_key, rest = kformat.load_public(rest)
if with_cert:
serial, rest = _get_u64(rest)
cctype, rest = _get_u32(rest)
key_id, rest = _get_sshstr(rest)
principals, rest = _get_sshstr(rest)
valid_after, rest = _get_u64(rest)
valid_before, rest = _get_u64(rest)
crit_options, rest = _get_sshstr(rest)
extensions, rest = _get_sshstr(rest)
reserved, rest = _get_sshstr(rest)
sig_key, rest = _get_sshstr(rest)
signature, rest = _get_sshstr(rest)
_check_empty(rest)
return public_key
def serialize_ssh_public_key(public_key: _SSH_PUBLIC_KEY_TYPES) -> bytes:
"""One-line public key format for OpenSSH"""
if isinstance(public_key, ec.EllipticCurvePublicKey):
key_type = _ecdsa_key_type(public_key)
elif isinstance(public_key, rsa.RSAPublicKey):
key_type = _SSH_RSA
elif isinstance(public_key, dsa.DSAPublicKey):
key_type = _SSH_DSA
elif isinstance(public_key, ed25519.Ed25519PublicKey):
key_type = _SSH_ED25519
else:
raise ValueError("Unsupported key type")
kformat = _lookup_kformat(key_type)
f_pub = _FragList()
f_pub.put_sshstr(key_type)
kformat.encode_public(public_key, f_pub)
pub = binascii.b2a_base64(f_pub.tobytes()).strip()
return b"".join([key_type, b" ", pub])
| # This file is dual licensed under the terms of the Apache License, Version
# 2.0, and the BSD License. See the LICENSE file in the root of this repository
# for complete details.
import binascii
import os
import re
import typing
from base64 import encodebytes as _base64_encode
from cryptography import utils
from cryptography.exceptions import UnsupportedAlgorithm
from cryptography.hazmat.primitives.asymmetric import dsa, ec, ed25519, rsa
from cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes
from cryptography.hazmat.primitives.serialization import (
Encoding,
NoEncryption,
PrivateFormat,
PublicFormat,
)
try:
from bcrypt import kdf as _bcrypt_kdf
_bcrypt_supported = True
except ImportError:
_bcrypt_supported = False
def _bcrypt_kdf(
password: <PASSWORD>,
salt: bytes,
desired_key_bytes: int,
rounds: int,
ignore_few_rounds: bool = False,
) -> bytes:
raise UnsupportedAlgorithm("Need bcrypt module")
_SSH_ED25519 = b"ssh-ed25519"
_SSH_RSA = b"ssh-rsa"
_SSH_DSA = b"ssh-dss"
_ECDSA_NISTP256 = b"ecdsa-sha2-nistp256"
_ECDSA_NISTP384 = b"ecdsa-sha2-nistp384"
_ECDSA_NISTP521 = b"ecdsa-sha2-nistp521"
_CERT_SUFFIX = b"-<EMAIL>"
_SSH_PUBKEY_RC = re.compile(br"\A(\S+)[ \t]+(\S+)")
_SK_MAGIC = b"openssh-key-v1\0"
_SK_START = b"-----BEGIN OPENSSH PRIVATE KEY-----"
_SK_END = b"-----END OPENSSH PRIVATE KEY-----"
_BCRYPT = b"bcrypt"
_NONE = b"none"
_DEFAULT_CIPHER = b"aes256-ctr"
_DEFAULT_ROUNDS = 16
_MAX_PASSWORD = 72
# re is only way to work on bytes-like data
_PEM_RC = re.compile(_SK_START + b"(.*?)" + _SK_END, re.DOTALL)
# padding for max blocksize
_PADDING = memoryview(bytearray(range(1, 1 + 16)))
# ciphers that are actually used in key wrapping
_SSH_CIPHERS: typing.Dict[
bytes,
typing.Tuple[
typing.Type[algorithms.AES],
int,
typing.Union[typing.Type[modes.CTR], typing.Type[modes.CBC]],
int,
],
] = {
b"aes256-ctr": (algorithms.AES, 32, modes.CTR, 16),
b"aes256-cbc": (algorithms.AES, 32, modes.CBC, 16),
}
# map local curve name to key type
_ECDSA_KEY_TYPE = {
"secp256r1": _ECDSA_NISTP256,
"secp384r1": _ECDSA_NISTP384,
"secp521r1": _ECDSA_NISTP521,
}
def _ecdsa_key_type(public_key: ec.EllipticCurvePublicKey) -> bytes:
"""Return SSH key_type and curve_name for private key."""
curve = public_key.curve
if curve.name not in _ECDSA_KEY_TYPE:
raise ValueError(
"Unsupported curve for ssh private key: %r" % curve.name
)
return _ECDSA_KEY_TYPE[curve.name]
def _ssh_pem_encode(
data: bytes,
prefix: bytes = _SK_START + b"\n",
suffix: bytes = _SK_END + b"\n",
) -> bytes:
return b"".join([prefix, _base64_encode(data), suffix])
def _check_block_size(data: bytes, block_len: int) -> None:
"""Require data to be full blocks"""
if not data or len(data) % block_len != 0:
raise ValueError("Corrupt data: missing padding")
def _check_empty(data: bytes) -> None:
"""All data should have been parsed."""
if data:
raise ValueError("Corrupt data: unparsed data")
def _init_cipher(
ciphername: bytes,
password: typing.Optional[bytes],
salt: bytes,
rounds: int,
) -> Cipher[typing.Union[modes.CBC, modes.CTR]]:
"""Generate key + iv and return cipher."""
if not password:
raise ValueError("Key is password-protected.")
algo, key_len, mode, iv_len = _SSH_CIPHERS[ciphername]
seed = _bcrypt_kdf(password, salt, key_len + iv_len, rounds, True)
return Cipher(algo(seed[:key_len]), mode(seed[key_len:]))
def _get_u32(data: memoryview) -> typing.Tuple[int, memoryview]:
"""Uint32"""
if len(data) < 4:
raise ValueError("Invalid data")
return int.from_bytes(data[:4], byteorder="big"), data[4:]
def _get_u64(data: memoryview) -> typing.Tuple[int, memoryview]:
"""Uint64"""
if len(data) < 8:
raise ValueError("Invalid data")
return int.from_bytes(data[:8], byteorder="big"), data[8:]
def _get_sshstr(data: memoryview) -> typing.Tuple[memoryview, memoryview]:
"""Bytes with u32 length prefix"""
n, data = _get_u32(data)
if n > len(data):
raise ValueError("Invalid data")
return data[:n], data[n:]
def _get_mpint(data: memoryview) -> typing.Tuple[int, memoryview]:
"""Big integer."""
val, data = _get_sshstr(data)
if val and val[0] > 0x7F:
raise ValueError("Invalid data")
return int.from_bytes(val, "big"), data
def _to_mpint(val: int) -> bytes:
"""Storage format for signed bigint."""
if val < 0:
raise ValueError("negative mpint not allowed")
if not val:
return b""
nbytes = (val.bit_length() + 8) // 8
return utils.int_to_bytes(val, nbytes)
class _FragList(object):
"""Build recursive structure without data copy."""
flist: typing.List[bytes]
def __init__(self, init: typing.List[bytes] = None) -> None:
self.flist = []
if init:
self.flist.extend(init)
def put_raw(self, val: bytes) -> None:
"""Add plain bytes"""
self.flist.append(val)
def put_u32(self, val: int) -> None:
"""Big-endian uint32"""
self.flist.append(val.to_bytes(length=4, byteorder="big"))
def put_sshstr(self, val: typing.Union[bytes, "_FragList"]) -> None:
"""Bytes prefixed with u32 length"""
if isinstance(val, (bytes, memoryview, bytearray)):
self.put_u32(len(val))
self.flist.append(val)
else:
self.put_u32(val.size())
self.flist.extend(val.flist)
def put_mpint(self, val: int) -> None:
"""Big-endian bigint prefixed with u32 length"""
self.put_sshstr(_to_mpint(val))
def size(self) -> int:
"""Current number of bytes"""
return sum(map(len, self.flist))
def render(self, dstbuf: memoryview, pos: int = 0) -> int:
"""Write into bytearray"""
for frag in self.flist:
flen = len(frag)
start, pos = pos, pos + flen
dstbuf[start:pos] = frag
return pos
def tobytes(self) -> bytes:
"""Return as bytes"""
buf = memoryview(bytearray(self.size()))
self.render(buf)
return buf.tobytes()
class _SSHFormatRSA(object):
"""Format for RSA keys.
Public:
mpint e, n
Private:
mpint n, e, d, iqmp, p, q
"""
def get_public(self, data: memoryview):
"""RSA public fields"""
e, data = _get_mpint(data)
n, data = _get_mpint(data)
return (e, n), data
def load_public(
self, data: memoryview
) -> typing.Tuple[rsa.RSAPublicKey, memoryview]:
"""Make RSA public key from data."""
(e, n), data = self.get_public(data)
public_numbers = rsa.RSAPublicNumbers(e, n)
public_key = public_numbers.public_key()
return public_key, data
def load_private(
self, data: memoryview, pubfields
) -> typing.Tuple[rsa.RSAPrivateKey, memoryview]:
"""Make RSA private key from data."""
n, data = _get_mpint(data)
e, data = _get_mpint(data)
d, data = _get_mpint(data)
iqmp, data = _get_mpint(data)
p, data = _get_mpint(data)
q, data = _get_mpint(data)
if (e, n) != pubfields:
raise ValueError("Corrupt data: rsa field mismatch")
dmp1 = rsa.rsa_crt_dmp1(d, p)
dmq1 = rsa.rsa_crt_dmq1(d, q)
public_numbers = rsa.RSAPublicNumbers(e, n)
private_numbers = rsa.RSAPrivateNumbers(
p, q, d, dmp1, dmq1, iqmp, public_numbers
)
private_key = private_numbers.private_key()
return private_key, data
def encode_public(
self, public_key: rsa.RSAPublicKey, f_pub: _FragList
) -> None:
"""Write RSA public key"""
pubn = public_key.public_numbers()
f_pub.put_mpint(pubn.e)
f_pub.put_mpint(pubn.n)
def encode_private(
self, private_key: rsa.RSAPrivateKey, f_priv: _FragList
) -> None:
"""Write RSA private key"""
private_numbers = private_key.private_numbers()
public_numbers = private_numbers.public_numbers
f_priv.put_mpint(public_numbers.n)
f_priv.put_mpint(public_numbers.e)
f_priv.put_mpint(private_numbers.d)
f_priv.put_mpint(private_numbers.iqmp)
f_priv.put_mpint(private_numbers.p)
f_priv.put_mpint(private_numbers.q)
class _SSHFormatDSA(object):
"""Format for DSA keys.
Public:
mpint p, q, g, y
Private:
mpint p, q, g, y, x
"""
def get_public(
self, data: memoryview
) -> typing.Tuple[typing.Tuple, memoryview]:
"""DSA public fields"""
p, data = _get_mpint(data)
q, data = _get_mpint(data)
g, data = _get_mpint(data)
y, data = _get_mpint(data)
return (p, q, g, y), data
def load_public(
self, data: memoryview
) -> typing.Tuple[dsa.DSAPublicKey, memoryview]:
"""Make DSA public key from data."""
(p, q, g, y), data = self.get_public(data)
parameter_numbers = dsa.DSAParameterNumbers(p, q, g)
public_numbers = dsa.DSAPublicNumbers(y, parameter_numbers)
self._validate(public_numbers)
public_key = public_numbers.public_key()
return public_key, data
def load_private(
self, data: memoryview, pubfields
) -> typing.Tuple[dsa.DSAPrivateKey, memoryview]:
"""Make DSA private key from data."""
(p, q, g, y), data = self.get_public(data)
x, data = _get_mpint(data)
if (p, q, g, y) != pubfields:
raise ValueError("Corrupt data: dsa field mismatch")
parameter_numbers = dsa.DSAParameterNumbers(p, q, g)
public_numbers = dsa.DSAPublicNumbers(y, parameter_numbers)
self._validate(public_numbers)
private_numbers = dsa.DSAPrivateNumbers(x, public_numbers)
private_key = private_numbers.private_key()
return private_key, data
def encode_public(
self, public_key: dsa.DSAPublicKey, f_pub: _FragList
) -> None:
"""Write DSA public key"""
public_numbers = public_key.public_numbers()
parameter_numbers = public_numbers.parameter_numbers
self._validate(public_numbers)
f_pub.put_mpint(parameter_numbers.p)
f_pub.put_mpint(parameter_numbers.q)
f_pub.put_mpint(parameter_numbers.g)
f_pub.put_mpint(public_numbers.y)
def encode_private(
self, private_key: dsa.DSAPrivateKey, f_priv: _FragList
) -> None:
"""Write DSA private key"""
self.encode_public(private_key.public_key(), f_priv)
f_priv.put_mpint(private_key.private_numbers().x)
def _validate(self, public_numbers: dsa.DSAPublicNumbers) -> None:
parameter_numbers = public_numbers.parameter_numbers
if parameter_numbers.p.bit_length() != 1024:
raise ValueError("SSH supports only 1024 bit DSA keys")
class _SSHFormatECDSA(object):
"""Format for ECDSA keys.
Public:
str curve
bytes point
Private:
str curve
bytes point
mpint secret
"""
def __init__(self, ssh_curve_name: bytes, curve: ec.EllipticCurve):
self.ssh_curve_name = ssh_curve_name
self.curve = curve
def get_public(
self, data: memoryview
) -> typing.Tuple[typing.Tuple, memoryview]:
"""ECDSA public fields"""
curve, data = _get_sshstr(data)
point, data = _get_sshstr(data)
if curve != self.ssh_curve_name:
raise ValueError("Curve name mismatch")
if point[0] != 4:
raise NotImplementedError("Need uncompressed point")
return (curve, point), data
def load_public(
self, data: memoryview
) -> typing.Tuple[ec.EllipticCurvePublicKey, memoryview]:
"""Make ECDSA public key from data."""
(curve_name, point), data = self.get_public(data)
public_key = ec.EllipticCurvePublicKey.from_encoded_point(
self.curve, point.tobytes()
)
return public_key, data
def load_private(
self, data: memoryview, pubfields
) -> typing.Tuple[ec.EllipticCurvePrivateKey, memoryview]:
"""Make ECDSA private key from data."""
(curve_name, point), data = self.get_public(data)
secret, data = _get_mpint(data)
if (curve_name, point) != pubfields:
raise ValueError("Corrupt data: ecdsa field mismatch")
private_key = ec.derive_private_key(secret, self.curve)
return private_key, data
def encode_public(
self, public_key: ec.EllipticCurvePublicKey, f_pub: _FragList
) -> None:
"""Write ECDSA public key"""
point = public_key.public_bytes(
Encoding.X962, PublicFormat.UncompressedPoint
)
f_pub.put_sshstr(self.ssh_curve_name)
f_pub.put_sshstr(point)
def encode_private(
self, private_key: ec.EllipticCurvePrivateKey, f_priv: _FragList
) -> None:
"""Write ECDSA private key"""
public_key = private_key.public_key()
private_numbers = private_key.private_numbers()
self.encode_public(public_key, f_priv)
f_priv.put_mpint(private_numbers.private_value)
class _SSHFormatEd25519(object):
"""Format for Ed25519 keys.
Public:
bytes point
Private:
bytes point
bytes secret_and_point
"""
def get_public(
self, data: memoryview
) -> typing.Tuple[typing.Tuple, memoryview]:
"""Ed25519 public fields"""
point, data = _get_sshstr(data)
return (point,), data
def load_public(
self, data: memoryview
) -> typing.Tuple[ed25519.Ed25519PublicKey, memoryview]:
"""Make Ed25519 public key from data."""
(point,), data = self.get_public(data)
public_key = ed25519.Ed25519PublicKey.from_public_bytes(
point.tobytes()
)
return public_key, data
def load_private(
self, data: memoryview, pubfields
) -> typing.Tuple[ed25519.Ed25519PrivateKey, memoryview]:
"""Make Ed25519 private key from data."""
(point,), data = self.get_public(data)
keypair, data = _get_sshstr(data)
secret = keypair[:32]
point2 = keypair[32:]
if point != point2 or (point,) != pubfields:
raise ValueError("Corrupt data: ed25519 field mismatch")
private_key = ed25519.Ed25519PrivateKey.from_private_bytes(secret)
return private_key, data
def encode_public(
self, public_key: ed25519.Ed25519PublicKey, f_pub: _FragList
) -> None:
"""Write Ed25519 public key"""
raw_public_key = public_key.public_bytes(
Encoding.Raw, PublicFormat.Raw
)
f_pub.put_sshstr(raw_public_key)
def encode_private(
self, private_key: ed25519.Ed25519PrivateKey, f_priv: _FragList
) -> None:
"""Write Ed25519 private key"""
public_key = private_key.public_key()
raw_private_key = private_key.private_bytes(
Encoding.Raw, PrivateFormat.Raw, NoEncryption()
)
raw_public_key = public_key.public_bytes(
Encoding.Raw, PublicFormat.Raw
)
f_keypair = _FragList([raw_private_key, raw_public_key])
self.encode_public(public_key, f_priv)
f_priv.put_sshstr(f_keypair)
_KEY_FORMATS = {
_SSH_RSA: _SSHFormatRSA(),
_SSH_DSA: _SSHFormatDSA(),
_SSH_ED25519: _SSHFormatEd25519(),
_ECDSA_NISTP256: _SSHFormatECDSA(b"nistp256", ec.SECP256R1()),
_ECDSA_NISTP384: _SSHFormatECDSA(b"nistp384", ec.SECP384R1()),
_ECDSA_NISTP521: _SSHFormatECDSA(b"nistp521", ec.SECP521R1()),
}
def _lookup_kformat(key_type: bytes):
"""Return valid format or throw error"""
if not isinstance(key_type, bytes):
key_type = memoryview(key_type).tobytes()
if key_type in _KEY_FORMATS:
return _KEY_FORMATS[key_type]
raise UnsupportedAlgorithm("Unsupported key type: %r" % key_type)
_SSH_PRIVATE_KEY_TYPES = typing.Union[
ec.EllipticCurvePrivateKey,
rsa.RSAPrivateKey,
dsa.DSAPrivateKey,
ed25519.Ed25519PrivateKey,
]
def load_ssh_private_key(
data: bytes,
password: typing.Optional[bytes],
backend: typing.Any = None,
) -> _SSH_PRIVATE_KEY_TYPES:
"""Load private key from OpenSSH custom encoding."""
utils._check_byteslike("data", data)
if password is not None:
utils._check_bytes("password", password)
m = _PEM_RC.search(data)
if not m:
raise ValueError("Not OpenSSH private key format")
p1 = m.start(1)
p2 = m.end(1)
data = binascii.a2b_base64(memoryview(data)[p1:p2])
if not data.startswith(_SK_MAGIC):
raise ValueError("Not OpenSSH private key format")
data = memoryview(data)[len(_SK_MAGIC) :]
# parse header
ciphername, data = _get_sshstr(data)
kdfname, data = _get_sshstr(data)
kdfoptions, data = _get_sshstr(data)
nkeys, data = _get_u32(data)
if nkeys != 1:
raise ValueError("Only one key supported")
# load public key data
pubdata, data = _get_sshstr(data)
pub_key_type, pubdata = _get_sshstr(pubdata)
kformat = _lookup_kformat(pub_key_type)
pubfields, pubdata = kformat.get_public(pubdata)
_check_empty(pubdata)
# load secret data
edata, data = _get_sshstr(data)
_check_empty(data)
if (ciphername, kdfname) != (_NONE, _NONE):
ciphername_bytes = ciphername.tobytes()
if ciphername_bytes not in _SSH_CIPHERS:
raise UnsupportedAlgorithm(
"Unsupported cipher: %r" % ciphername_bytes
)
if kdfname != _BCRYPT:
raise UnsupportedAlgorithm("Unsupported KDF: %r" % kdfname)
blklen = _SSH_CIPHERS[ciphername_bytes][3]
_check_block_size(edata, blklen)
salt, kbuf = _get_sshstr(kdfoptions)
rounds, kbuf = _get_u32(kbuf)
_check_empty(kbuf)
ciph = _init_cipher(ciphername_bytes, password, salt.tobytes(), rounds)
edata = memoryview(ciph.decryptor().update(edata))
else:
blklen = 8
_check_block_size(edata, blklen)
ck1, edata = _get_u32(edata)
ck2, edata = _get_u32(edata)
if ck1 != ck2:
raise ValueError("Corrupt data: broken checksum")
# load per-key struct
key_type, edata = _get_sshstr(edata)
if key_type != pub_key_type:
raise ValueError("Corrupt data: key type mismatch")
private_key, edata = kformat.load_private(edata, pubfields)
comment, edata = _get_sshstr(edata)
# yes, SSH does padding check *after* all other parsing is done.
# need to follow as it writes zero-byte padding too.
if edata != _PADDING[: len(edata)]:
raise ValueError("Corrupt data: invalid padding")
return private_key
def serialize_ssh_private_key(
private_key: _SSH_PRIVATE_KEY_TYPES,
password: typing.Optional[bytes] = None,
) -> bytes:
"""Serialize private key with OpenSSH custom encoding."""
if password is not None:
utils._check_bytes("password", password)
if password and len(password) > _MAX_PASSWORD:
raise ValueError(
"Passwords longer than 72 bytes are not supported by "
"OpenSSH private key format"
)
if isinstance(private_key, ec.EllipticCurvePrivateKey):
key_type = _ecdsa_key_type(private_key.public_key())
elif isinstance(private_key, rsa.RSAPrivateKey):
key_type = _SSH_RSA
elif isinstance(private_key, dsa.DSAPrivateKey):
key_type = _SSH_DSA
elif isinstance(private_key, ed25519.Ed25519PrivateKey):
key_type = _SSH_ED25519
else:
raise ValueError("Unsupported key type")
kformat = _lookup_kformat(key_type)
# setup parameters
f_kdfoptions = _FragList()
if password:
ciphername = _DEFAULT_CIPHER
blklen = _SSH_CIPHERS[ciphername][3]
kdfname = _BCRYPT
rounds = _DEFAULT_ROUNDS
salt = os.urandom(16)
f_kdfoptions.put_sshstr(salt)
f_kdfoptions.put_u32(rounds)
ciph = _init_cipher(ciphername, password, salt, rounds)
else:
ciphername = kdfname = _NONE
blklen = 8
ciph = None
nkeys = 1
checkval = os.urandom(4)
comment = b""
# encode public and private parts together
f_public_key = _FragList()
f_public_key.put_sshstr(key_type)
kformat.encode_public(private_key.public_key(), f_public_key)
f_secrets = _FragList([checkval, checkval])
f_secrets.put_sshstr(key_type)
kformat.encode_private(private_key, f_secrets)
f_secrets.put_sshstr(comment)
f_secrets.put_raw(_PADDING[: blklen - (f_secrets.size() % blklen)])
# top-level structure
f_main = _FragList()
f_main.put_raw(_SK_MAGIC)
f_main.put_sshstr(ciphername)
f_main.put_sshstr(kdfname)
f_main.put_sshstr(f_kdfoptions)
f_main.put_u32(nkeys)
f_main.put_sshstr(f_public_key)
f_main.put_sshstr(f_secrets)
# copy result info bytearray
slen = f_secrets.size()
mlen = f_main.size()
buf = memoryview(bytearray(mlen + blklen))
f_main.render(buf)
ofs = mlen - slen
# encrypt in-place
if ciph is not None:
ciph.encryptor().update_into(buf[ofs:mlen], buf[ofs:])
txt = _ssh_pem_encode(buf[:mlen])
buf[ofs:mlen] = bytearray(slen)
return txt
_SSH_PUBLIC_KEY_TYPES = typing.Union[
ec.EllipticCurvePublicKey,
rsa.RSAPublicKey,
dsa.DSAPublicKey,
ed25519.Ed25519PublicKey,
]
def load_ssh_public_key(
data: bytes, backend: typing.Any = None
) -> _SSH_PUBLIC_KEY_TYPES:
"""Load public key from OpenSSH one-line format."""
utils._check_byteslike("data", data)
m = _SSH_PUBKEY_RC.match(data)
if not m:
raise ValueError("Invalid line format")
key_type = orig_key_type = m.group(1)
key_body = m.group(2)
with_cert = False
if _CERT_SUFFIX == key_type[-len(_CERT_SUFFIX) :]:
with_cert = True
key_type = key_type[: -len(_CERT_SUFFIX)]
kformat = _lookup_kformat(key_type)
try:
rest = memoryview(binascii.a2b_base64(key_body))
except (TypeError, binascii.Error):
raise ValueError("Invalid key format")
inner_key_type, rest = _get_sshstr(rest)
if inner_key_type != orig_key_type:
raise ValueError("Invalid key format")
if with_cert:
nonce, rest = _get_sshstr(rest)
public_key, rest = kformat.load_public(rest)
if with_cert:
serial, rest = _get_u64(rest)
cctype, rest = _get_u32(rest)
key_id, rest = _get_sshstr(rest)
principals, rest = _get_sshstr(rest)
valid_after, rest = _get_u64(rest)
valid_before, rest = _get_u64(rest)
crit_options, rest = _get_sshstr(rest)
extensions, rest = _get_sshstr(rest)
reserved, rest = _get_sshstr(rest)
sig_key, rest = _get_sshstr(rest)
signature, rest = _get_sshstr(rest)
_check_empty(rest)
return public_key
def serialize_ssh_public_key(public_key: _SSH_PUBLIC_KEY_TYPES) -> bytes:
"""One-line public key format for OpenSSH"""
if isinstance(public_key, ec.EllipticCurvePublicKey):
key_type = _ecdsa_key_type(public_key)
elif isinstance(public_key, rsa.RSAPublicKey):
key_type = _SSH_RSA
elif isinstance(public_key, dsa.DSAPublicKey):
key_type = _SSH_DSA
elif isinstance(public_key, ed25519.Ed25519PublicKey):
key_type = _SSH_ED25519
else:
raise ValueError("Unsupported key type")
kformat = _lookup_kformat(key_type)
f_pub = _FragList()
f_pub.put_sshstr(key_type)
kformat.encode_public(public_key, f_pub)
pub = binascii.b2a_base64(f_pub.tobytes()).strip()
return b"".join([key_type, b" ", pub]) | en | 0.713052 | # This file is dual licensed under the terms of the Apache License, Version # 2.0, and the BSD License. See the LICENSE file in the root of this repository # for complete details. # re is only way to work on bytes-like data # padding for max blocksize # ciphers that are actually used in key wrapping # map local curve name to key type Return SSH key_type and curve_name for private key. Require data to be full blocks All data should have been parsed. Generate key + iv and return cipher. Uint32 Uint64 Bytes with u32 length prefix Big integer. Storage format for signed bigint. Build recursive structure without data copy. Add plain bytes Big-endian uint32 Bytes prefixed with u32 length Big-endian bigint prefixed with u32 length Current number of bytes Write into bytearray Return as bytes Format for RSA keys. Public: mpint e, n Private: mpint n, e, d, iqmp, p, q RSA public fields Make RSA public key from data. Make RSA private key from data. Write RSA public key Write RSA private key Format for DSA keys. Public: mpint p, q, g, y Private: mpint p, q, g, y, x DSA public fields Make DSA public key from data. Make DSA private key from data. Write DSA public key Write DSA private key Format for ECDSA keys. Public: str curve bytes point Private: str curve bytes point mpint secret ECDSA public fields Make ECDSA public key from data. Make ECDSA private key from data. Write ECDSA public key Write ECDSA private key Format for Ed25519 keys. Public: bytes point Private: bytes point bytes secret_and_point Ed25519 public fields Make Ed25519 public key from data. Make Ed25519 private key from data. Write Ed25519 public key Write Ed25519 private key Return valid format or throw error Load private key from OpenSSH custom encoding. # parse header # load public key data # load secret data # load per-key struct # yes, SSH does padding check *after* all other parsing is done. # need to follow as it writes zero-byte padding too. Serialize private key with OpenSSH custom encoding. # setup parameters # encode public and private parts together # top-level structure # copy result info bytearray # encrypt in-place Load public key from OpenSSH one-line format. One-line public key format for OpenSSH | 2.102288 | 2 |
examples/hacker_news/hacker_news/legacy/repo.py | geoHeil/dagster | 0 | 6630515 | from dagster import repository
from ..schedules.hourly_hn_download_schedule import hourly_hn_download_schedule
from ..sensors.download_pipeline_finished_sensor import dbt_on_hn_download_finished
from ..sensors.hn_tables_updated_sensor import story_recommender_on_hn_table_update
from ..sensors.slack_on_failure_sensor import make_pipeline_failure_sensor
from .pipelines.dbt_pipeline import dbt_pipeline
from .pipelines.download_pipeline import download_pipeline
from .pipelines.story_recommender import story_recommender
# Creates a hacker news reference repository using the legacy dagster APIs of pipeline, solid,
# and mode.
@repository
def hacker_news_legacy():
pipelines = [
download_pipeline,
story_recommender,
dbt_pipeline,
]
schedules = [
hourly_hn_download_schedule,
]
sensors = [
make_pipeline_failure_sensor(base_url="my_dagit_url.com"),
story_recommender_on_hn_table_update,
dbt_on_hn_download_finished,
]
return pipelines + schedules + sensors
| from dagster import repository
from ..schedules.hourly_hn_download_schedule import hourly_hn_download_schedule
from ..sensors.download_pipeline_finished_sensor import dbt_on_hn_download_finished
from ..sensors.hn_tables_updated_sensor import story_recommender_on_hn_table_update
from ..sensors.slack_on_failure_sensor import make_pipeline_failure_sensor
from .pipelines.dbt_pipeline import dbt_pipeline
from .pipelines.download_pipeline import download_pipeline
from .pipelines.story_recommender import story_recommender
# Creates a hacker news reference repository using the legacy dagster APIs of pipeline, solid,
# and mode.
@repository
def hacker_news_legacy():
pipelines = [
download_pipeline,
story_recommender,
dbt_pipeline,
]
schedules = [
hourly_hn_download_schedule,
]
sensors = [
make_pipeline_failure_sensor(base_url="my_dagit_url.com"),
story_recommender_on_hn_table_update,
dbt_on_hn_download_finished,
]
return pipelines + schedules + sensors
| en | 0.494526 | # Creates a hacker news reference repository using the legacy dagster APIs of pipeline, solid, # and mode. | 1.866747 | 2 |
clean_mws.py | Wonqu/SMAA2019_Team5 | 0 | 6630516 | <gh_stars>0
import uuid
from extract_sentences import extract_sentences, extract_chapters
FRANKENSTEIN_CHAPTERS = {
x.upper().strip() for x in
"""Letter 1
Letter 2
Letter 3
Letter 4
Chapter 1
Chapter 2
Chapter 3
Chapter 4
Chapter 5
Chapter 6
Chapter 7
Chapter 8
Chapter 9
Chapter 10
Chapter 11
Chapter 12
Chapter 13
Chapter 14
Chapter 15
Chapter 16
Chapter 17
Chapter 18
Chapter 19
Chapter 20
Chapter 21
Chapter 22
Chapter 23
Chapter 24
""".split('\n') if x.upper().strip()
}
if __name__ == '__main__':
sentences = [
sentence
for poem in extract_chapters(FRANKENSTEIN_CHAPTERS, 'mws_clean.txt')
for sentence in extract_sentences(poem)
]
with open('./data/out/mws.csv', 'w', encoding='UTF-8') as mws:
newline = '\n'
mws.write(f'"id","text","author"{newline}')
for s in sentences:
mws.write(f'"{uuid.uuid4()}","{s}","MWS"{newline}')
| import uuid
from extract_sentences import extract_sentences, extract_chapters
FRANKENSTEIN_CHAPTERS = {
x.upper().strip() for x in
"""Letter 1
Letter 2
Letter 3
Letter 4
Chapter 1
Chapter 2
Chapter 3
Chapter 4
Chapter 5
Chapter 6
Chapter 7
Chapter 8
Chapter 9
Chapter 10
Chapter 11
Chapter 12
Chapter 13
Chapter 14
Chapter 15
Chapter 16
Chapter 17
Chapter 18
Chapter 19
Chapter 20
Chapter 21
Chapter 22
Chapter 23
Chapter 24
""".split('\n') if x.upper().strip()
}
if __name__ == '__main__':
sentences = [
sentence
for poem in extract_chapters(FRANKENSTEIN_CHAPTERS, 'mws_clean.txt')
for sentence in extract_sentences(poem)
]
with open('./data/out/mws.csv', 'w', encoding='UTF-8') as mws:
newline = '\n'
mws.write(f'"id","text","author"{newline}')
for s in sentences:
mws.write(f'"{uuid.uuid4()}","{s}","MWS"{newline}') | en | 0.682626 | Letter 1 Letter 2 Letter 3 Letter 4 Chapter 1 Chapter 2 Chapter 3 Chapter 4 Chapter 5 Chapter 6 Chapter 7 Chapter 8 Chapter 9 Chapter 10 Chapter 11 Chapter 12 Chapter 13 Chapter 14 Chapter 15 Chapter 16 Chapter 17 Chapter 18 Chapter 19 Chapter 20 Chapter 21 Chapter 22 Chapter 23 Chapter 24 | 2.689702 | 3 |
rhea/vendor/_output_serdes.py | meetps/rhea | 1 | 6630517 | <filename>rhea/vendor/_output_serdes.py
from myhdl import Signal, intbv, concat, always, always_comb
from . import SERDESInterface
| <filename>rhea/vendor/_output_serdes.py
from myhdl import Signal, intbv, concat, always, always_comb
from . import SERDESInterface
| none | 1 | 1.30917 | 1 |
|
bayesmark/xgb_cuml.py | daxiongshu/bayesmark | 3 | 6630518 | <filename>bayesmark/xgb_cuml.py<gh_stars>1-10
import xgboost as xgb
import cupy as cp
from cuml.preprocessing.model_selection import train_test_split
def get_default_params():
return {
'objective': 'multi:softprob',
'eval_metric': 'mlogloss',
'tree_method': 'gpu_hist',
'verbosity': 0,
'early_stopping':False,
'validation_fraction':0.1,
'early_stopping_rounds':10,
}
def print_shape(*x):
for i in x:
print(i.shape, end=' ')
print()
class XGBbase:
def __init__(self, **params):
self.params = get_default_params()
self.params.update(params)
def fit(self, X, y):
test_size = self.params['validation_fraction']
num_boost_round = self.params['n_estimators']
#ext_params = ['early_stopping', 'early_stopping_rounds',
# 'n_estimators', 'silent', 'validation_fraction', 'verbose']
if test_size >0:
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = test_size)
dtrain = xgb.DMatrix(data=X_train, label=y_train)
dvalid = xgb.DMatrix(data=X_test, label=y_test)
watchlist = [(dtrain, 'train'), (dvalid, 'eval')]
early_stopping_rounds = self.params['early_stopping_rounds']
self.clf = xgb.train(self.params, dtrain=dtrain,
num_boost_round=num_boost_round,evals=watchlist,
early_stopping_rounds=early_stopping_rounds,
verbose_eval=1000)
else:
dtrain = xgb.DMatrix(data=X, label=y)
self.clf = xgb.train(self.params, dtrain=dtrain,
num_boost_round=num_boost_round)
return self
def predict(self, X):
self.clf.set_param({'predictor': 'gpu_predictor'})
dtest = xgb.DMatrix(data=X)
yp = self.clf.predict(dtest)
yp = cp.asarray(yp)
return yp
class XGBClassifier(XGBbase):
def __init__(self, **params):
super().__init__(**params)
def fit(self, X, y):
num_class = int(y.max()+1)
if num_class > 2:
self.params['num_class'] = num_class
else:
self.params['objective'] = 'binary:logistic'
self.params['eval_metric'] = 'logloss'
return super().fit(X, y)
def predict_proba(self, X):
return super().predict(X)
def predict(self, X):
yp = super().predict(X)
if len(yp.shape) == 2:
yp = cp.argmax(yp, axis=1)
else:
yp = yp>0.5
return yp
class XGBRegressor(XGBbase):
def __init__(self, **params):
params['objective'] = 'reg:squarederror'
params['eval_metric'] = 'rmse'
super().__init__(**params)
| <filename>bayesmark/xgb_cuml.py<gh_stars>1-10
import xgboost as xgb
import cupy as cp
from cuml.preprocessing.model_selection import train_test_split
def get_default_params():
return {
'objective': 'multi:softprob',
'eval_metric': 'mlogloss',
'tree_method': 'gpu_hist',
'verbosity': 0,
'early_stopping':False,
'validation_fraction':0.1,
'early_stopping_rounds':10,
}
def print_shape(*x):
for i in x:
print(i.shape, end=' ')
print()
class XGBbase:
def __init__(self, **params):
self.params = get_default_params()
self.params.update(params)
def fit(self, X, y):
test_size = self.params['validation_fraction']
num_boost_round = self.params['n_estimators']
#ext_params = ['early_stopping', 'early_stopping_rounds',
# 'n_estimators', 'silent', 'validation_fraction', 'verbose']
if test_size >0:
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = test_size)
dtrain = xgb.DMatrix(data=X_train, label=y_train)
dvalid = xgb.DMatrix(data=X_test, label=y_test)
watchlist = [(dtrain, 'train'), (dvalid, 'eval')]
early_stopping_rounds = self.params['early_stopping_rounds']
self.clf = xgb.train(self.params, dtrain=dtrain,
num_boost_round=num_boost_round,evals=watchlist,
early_stopping_rounds=early_stopping_rounds,
verbose_eval=1000)
else:
dtrain = xgb.DMatrix(data=X, label=y)
self.clf = xgb.train(self.params, dtrain=dtrain,
num_boost_round=num_boost_round)
return self
def predict(self, X):
self.clf.set_param({'predictor': 'gpu_predictor'})
dtest = xgb.DMatrix(data=X)
yp = self.clf.predict(dtest)
yp = cp.asarray(yp)
return yp
class XGBClassifier(XGBbase):
def __init__(self, **params):
super().__init__(**params)
def fit(self, X, y):
num_class = int(y.max()+1)
if num_class > 2:
self.params['num_class'] = num_class
else:
self.params['objective'] = 'binary:logistic'
self.params['eval_metric'] = 'logloss'
return super().fit(X, y)
def predict_proba(self, X):
return super().predict(X)
def predict(self, X):
yp = super().predict(X)
if len(yp.shape) == 2:
yp = cp.argmax(yp, axis=1)
else:
yp = yp>0.5
return yp
class XGBRegressor(XGBbase):
def __init__(self, **params):
params['objective'] = 'reg:squarederror'
params['eval_metric'] = 'rmse'
super().__init__(**params)
| en | 0.247735 | #ext_params = ['early_stopping', 'early_stopping_rounds', # 'n_estimators', 'silent', 'validation_fraction', 'verbose'] | 2.249122 | 2 |
netnir/plugins/facts.py | jtdub/netnir | 0 | 6630519 | from nornir.core.task import Task, Result
from typing import Any
def inventory_facts(task: Task, **kwargs: Any) -> Result:
"""gather inventory facts
:params task: type object
:returns: inventory host facts dictionary
"""
return Result(
host=task.host, result={"facts": task.host.data, "groups": task.host.groups},
)
| from nornir.core.task import Task, Result
from typing import Any
def inventory_facts(task: Task, **kwargs: Any) -> Result:
"""gather inventory facts
:params task: type object
:returns: inventory host facts dictionary
"""
return Result(
host=task.host, result={"facts": task.host.data, "groups": task.host.groups},
)
| en | 0.51312 | gather inventory facts :params task: type object :returns: inventory host facts dictionary | 2.334316 | 2 |
microcosm_flask/tests/conventions/test_create_collection_conflict.py | Sinon/microcosm-flask | 11 | 6630520 | from hamcrest import assert_that, calling, raises
from marshmallow import Schema
from microcosm.api import create_object_graph
from microcosm_flask.conventions.base import EndpointDefinition, RouteAlreadyRegisteredException
from microcosm_flask.conventions.crud import configure_crud
from microcosm_flask.namespaces import Namespace
from microcosm_flask.operations import Operation
class TestCreateCollectionConflict:
def setup(self):
self.graph = create_object_graph(name="example", testing=True)
self.ns = Namespace(subject="foo")
self.client = self.graph.flask.test_client()
def test_register_both_create_and_create_collection(self):
mappings = {
Operation.Create: EndpointDefinition(
func=lambda x: x,
request_schema=Schema(),
response_schema=Schema(),
),
Operation.CreateCollection: EndpointDefinition(
func=lambda x: x,
request_schema=Schema(),
response_schema=Schema(),
),
}
assert_that(
calling(configure_crud).with_args(self.graph, self.ns, mappings),
raises(RouteAlreadyRegisteredException),
)
| from hamcrest import assert_that, calling, raises
from marshmallow import Schema
from microcosm.api import create_object_graph
from microcosm_flask.conventions.base import EndpointDefinition, RouteAlreadyRegisteredException
from microcosm_flask.conventions.crud import configure_crud
from microcosm_flask.namespaces import Namespace
from microcosm_flask.operations import Operation
class TestCreateCollectionConflict:
def setup(self):
self.graph = create_object_graph(name="example", testing=True)
self.ns = Namespace(subject="foo")
self.client = self.graph.flask.test_client()
def test_register_both_create_and_create_collection(self):
mappings = {
Operation.Create: EndpointDefinition(
func=lambda x: x,
request_schema=Schema(),
response_schema=Schema(),
),
Operation.CreateCollection: EndpointDefinition(
func=lambda x: x,
request_schema=Schema(),
response_schema=Schema(),
),
}
assert_that(
calling(configure_crud).with_args(self.graph, self.ns, mappings),
raises(RouteAlreadyRegisteredException),
)
| none | 1 | 2.114926 | 2 |
|
scripts/recon_enum3/nfsrecon.py | r4b3rt/OSCPRepo | 2,109 | 6630521 | #!/usr/bin/python
import sys
import os
import subprocess
import argparse
import errno
import pathlib
# TODO: this relied on nfspy to do certain checks. Look into dockerizing nfspy and re-implementing
# mkdir_p function updated for >= python 3.5
def mkdir_p(path):
pathlib.Path(path).mkdir(parents=True, exist_ok=True)
#Running
#nfs-ls: Attempts to get useful info about files from NFS Exports
#nfs-showmount: Show NFS explorts like 'showmount -e'
#nfs-statfs: Retrieves disk space from NFS like 'df'
def doNmap(ip_address, port):
print("INFO: Starting nfs nmap on %s:%s" % (ip_address, port))
if len(port.split(",")) > 1:
for ports in port.split(","):
outfileNmap = "/root/scripts/recon_enum/results/exam/nfs/%s_%s_nfsnmap" % (ip_address, ports)
subprocess.check_output(['nmap','-n','-sV','-Pn','-vv','-p',ports,'--script','nfs-ls,nfs-showmount,nfs-statfs,vulners',"-oA",outfileNmap,ip_address],encoding='utf8')
else:
outfileNmap = "/root/scripts/recon_enum/results/exam/nfs/%s_%s_nfsnmap" % (ip_address, port)
subprocess.check_output(['nmap','-n','-sV','-Pn','-vv','-p',port,'--script','nfs-ls,nfs-showmount,nfs-statfs,vulners',"-oA",outfileNmap,ip_address],encoding='utf8')
print("INFO: Nfs nmap completed on %s:%s" % (ip_address, port))
return
def doSysCommands(ip_address, port):
print("INFO: Starting nfs sysCommands on %s:%s" % (ip_address, port))
f = open(outfile,'w')
DEVNULL = open(os.devnull, 'w')
try:
results1 = subprocess.check_output(['showmount','-a',ip_address],encoding='utf8',stderr=DEVNULL)
if results1:
f.write("Showmount -a: " + "\n")
f.write(results1)
f.write("\n")
except subprocess.CalledProcessError as e:
if e.returncode == 1:
print("Unable to showmount -a, try manually")
else:
print("Unexpected error in nfsrecon doSysCommands 1")
try:
results2 = subprocess.check_output(['showmount','-e',ip_address],encoding='utf8',stderr=DEVNULL)
DEVNULL.close()
if results2:
f.write("Showmount -e: " + "\n")
f.write(results2)
f.write("\n")
f.close()
results = results2.split("\n")
doNfspy(results)
except subprocess.CalledProcessError as e:
if e.returncode == 1:
print("Unable to showmount -e, try manually")
else:
print("Unexpected error in nfsrecon doSysCommands 2")
def doNfspy(results):
for res in results:
if "/" in res:
try:
sharename = res.split(" ")[0] #grab just the mount/share
fqsharename = "%s:%s" % (ip_address, sharename)
if os.path.isfile('/bin/nfspy'):
try:
dir = sharename.split("/")[-1] #grab last element so we can make a name for it
dir = "/mnt/%s" % dir
if not os.path.isdir(dir):
mkdir_p(dir)
subprocess.check_output(['nfspy',dir,'-o','server=%s,getroot,hide,allow_root,rw' % (fqsharename)],encoding='utf8')
print("INFO: %s should be mounted at %s" % (sharename, dir))
except:
print("Error in NFSRecon, nfspy failed")
except:
print("Something went wrong with nfspy or creation. Try manually for: %s" % res)
print("INFO: nfs sysCommands completed on %s:%s" % (ip_address, port))
return
if __name__=='__main__':
parser = argparse.ArgumentParser(description='Rough script to handle nfs enumeration. Usage: nfsrecon.py ip {port}')
parser.add_argument('ip', help="IP address of target")
parser.add_argument('--port', default='111,2049', help="Port. Default is 111,2049")
args = parser.parse_args()
ip_address = args.ip
if args.port != '111,2049':
port = '111,2049,%s' % args.port #need rpc for nmap scripts
else:
port = '111,2049'
BASE = "/root/scripts/recon_enum/results/exam/nfs"
mkdir_p(BASE)
outfile = "/root/scripts/recon_enum/results/exam/nfs/%s_%s_nfsrecon.txt" % (ip_address, port)
doNmap(ip_address, port)
doSysCommands(ip_address, port)
print("INFO: nfsRecon completed for %s:%s" % (ip_address, port))
| #!/usr/bin/python
import sys
import os
import subprocess
import argparse
import errno
import pathlib
# TODO: this relied on nfspy to do certain checks. Look into dockerizing nfspy and re-implementing
# mkdir_p function updated for >= python 3.5
def mkdir_p(path):
pathlib.Path(path).mkdir(parents=True, exist_ok=True)
#Running
#nfs-ls: Attempts to get useful info about files from NFS Exports
#nfs-showmount: Show NFS explorts like 'showmount -e'
#nfs-statfs: Retrieves disk space from NFS like 'df'
def doNmap(ip_address, port):
print("INFO: Starting nfs nmap on %s:%s" % (ip_address, port))
if len(port.split(",")) > 1:
for ports in port.split(","):
outfileNmap = "/root/scripts/recon_enum/results/exam/nfs/%s_%s_nfsnmap" % (ip_address, ports)
subprocess.check_output(['nmap','-n','-sV','-Pn','-vv','-p',ports,'--script','nfs-ls,nfs-showmount,nfs-statfs,vulners',"-oA",outfileNmap,ip_address],encoding='utf8')
else:
outfileNmap = "/root/scripts/recon_enum/results/exam/nfs/%s_%s_nfsnmap" % (ip_address, port)
subprocess.check_output(['nmap','-n','-sV','-Pn','-vv','-p',port,'--script','nfs-ls,nfs-showmount,nfs-statfs,vulners',"-oA",outfileNmap,ip_address],encoding='utf8')
print("INFO: Nfs nmap completed on %s:%s" % (ip_address, port))
return
def doSysCommands(ip_address, port):
print("INFO: Starting nfs sysCommands on %s:%s" % (ip_address, port))
f = open(outfile,'w')
DEVNULL = open(os.devnull, 'w')
try:
results1 = subprocess.check_output(['showmount','-a',ip_address],encoding='utf8',stderr=DEVNULL)
if results1:
f.write("Showmount -a: " + "\n")
f.write(results1)
f.write("\n")
except subprocess.CalledProcessError as e:
if e.returncode == 1:
print("Unable to showmount -a, try manually")
else:
print("Unexpected error in nfsrecon doSysCommands 1")
try:
results2 = subprocess.check_output(['showmount','-e',ip_address],encoding='utf8',stderr=DEVNULL)
DEVNULL.close()
if results2:
f.write("Showmount -e: " + "\n")
f.write(results2)
f.write("\n")
f.close()
results = results2.split("\n")
doNfspy(results)
except subprocess.CalledProcessError as e:
if e.returncode == 1:
print("Unable to showmount -e, try manually")
else:
print("Unexpected error in nfsrecon doSysCommands 2")
def doNfspy(results):
for res in results:
if "/" in res:
try:
sharename = res.split(" ")[0] #grab just the mount/share
fqsharename = "%s:%s" % (ip_address, sharename)
if os.path.isfile('/bin/nfspy'):
try:
dir = sharename.split("/")[-1] #grab last element so we can make a name for it
dir = "/mnt/%s" % dir
if not os.path.isdir(dir):
mkdir_p(dir)
subprocess.check_output(['nfspy',dir,'-o','server=%s,getroot,hide,allow_root,rw' % (fqsharename)],encoding='utf8')
print("INFO: %s should be mounted at %s" % (sharename, dir))
except:
print("Error in NFSRecon, nfspy failed")
except:
print("Something went wrong with nfspy or creation. Try manually for: %s" % res)
print("INFO: nfs sysCommands completed on %s:%s" % (ip_address, port))
return
if __name__=='__main__':
parser = argparse.ArgumentParser(description='Rough script to handle nfs enumeration. Usage: nfsrecon.py ip {port}')
parser.add_argument('ip', help="IP address of target")
parser.add_argument('--port', default='111,2049', help="Port. Default is 111,2049")
args = parser.parse_args()
ip_address = args.ip
if args.port != '111,2049':
port = '111,2049,%s' % args.port #need rpc for nmap scripts
else:
port = '111,2049'
BASE = "/root/scripts/recon_enum/results/exam/nfs"
mkdir_p(BASE)
outfile = "/root/scripts/recon_enum/results/exam/nfs/%s_%s_nfsrecon.txt" % (ip_address, port)
doNmap(ip_address, port)
doSysCommands(ip_address, port)
print("INFO: nfsRecon completed for %s:%s" % (ip_address, port))
| en | 0.709396 | #!/usr/bin/python # TODO: this relied on nfspy to do certain checks. Look into dockerizing nfspy and re-implementing # mkdir_p function updated for >= python 3.5 #Running #nfs-ls: Attempts to get useful info about files from NFS Exports #nfs-showmount: Show NFS explorts like 'showmount -e' #nfs-statfs: Retrieves disk space from NFS like 'df' #grab just the mount/share #grab last element so we can make a name for it #need rpc for nmap scripts | 2.335058 | 2 |
stacked_capsule_autoencoders/capsules/data/constellation.py | rmitra/google-research | 1 | 6630522 | <gh_stars>1-10
# coding=utf-8
# Copyright 2019 The Google Research Authors.
#
# 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.
"""Constellation dataset."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import numpy as np
import tensorflow.compat.v1 as tf
ConstellationTuple = collections.namedtuple('ConstellationTuple',
'presence corners pattern_presence '
'pattern_id')
def create_numpy(
size_n=1,
shuffle_corners=True,
gaussian_noise=0.,
max_translation=1.,
rotation_percent=0.0,
max_upscale=0.0,
which_patterns='basic',
drop_prob=0.0,
):
"""Creates a batch of data using numpy."""
sm = 1
if which_patterns == 'basic':
which_patterns = [[0], [1]]
elif which_patterns == 'all':
which_patterns = [[0], [1], [2], [3]]
elif isinstance(which_patterns, str):
raise ValueError('Pattern "{}" has not been '
'implemented.'.format(which_patterns))
pattern = [
np.array([[1 + 2 * sm, 1 + 2 * sm, 1], [1, 1, 1], [1 + 2 * sm, 1, 1],
[1, 1 + 2 * sm, 1]]), # square
np.array([[1, 1 + sm, 1], [1 + 2 * sm, 1, 1], [1 + 2 * sm, 1 + 2 * sm,
1]]), # triangle
np.array([[1, 1, 1], [1 + sm, 1 - 2 * sm, 1], [1 + 2 * sm, 1 - sm, 1],
[1 + 2 * sm, 1 + sm, 1], [1 + sm, 1 + 2 * sm, 1]]), # pentagon
np.array([[1, 1, 1], [1 + sm, 1, 1], [1 + 2 * sm, 1, 1],
[1 + 2 * sm, 1 + sm, 1], [1 + 2 * sm, 1 + 2 * sm, 1]]), # L
]
caps_dim = pattern[0].shape[1]
transformations = []
all_corners = []
all_corner_presence = []
all_pattern_presence = []
centers = np.array([0, 0, 1])
for i in range(len(which_patterns)):
corners = centers.copy()
for j in range(len(which_patterns[i])):
corner_trans = np.zeros((pattern[which_patterns[i][j]].shape[0], caps_dim,
caps_dim))
corner_trans[:, -1, :] = pattern[which_patterns[i][j]] * (j + 1)
corner_trans[:, :-1, :-1] = np.eye(caps_dim - 1)
corners = np.matmul(corners, corner_trans)
corners = corners.reshape(-1, caps_dim)
transformation = np.zeros((size_n, caps_dim, caps_dim))
transformation[:, :, -1] = [0, 0, 1]
# [pi/2, pi]
degree = (np.random.random((size_n)) - .5) * 2. * np.pi * rotation_percent
scale = 1. + np.random.random((size_n)) * max_upscale
translation = np.random.random((size_n, 2)) * 24. * max_translation
transformation[:, 0, 0] = np.cos(degree) * scale
transformation[:, 1, 1] = np.cos(degree) * scale
transformation[:, 0, 1] = np.sin(degree) * scale
transformation[:, 1, 0] = -np.sin(degree) * scale
transformation[:, -1, :-1] = translation / scale[Ellipsis, np.newaxis]
corners = np.matmul(corners, transformation)
random_pattern_choice = np.random.binomial(1, 1. - drop_prob,
(corners.shape[0], 1))
random_corer_choice = np.tile(random_pattern_choice, (1, corners.shape[1]))
all_corner_presence.append(random_corer_choice)
all_pattern_presence.append(random_pattern_choice)
transformations.append(transformation)
all_corners.append(corners)
capsules = np.concatenate(all_corners, axis=1)[Ellipsis, :2]
all_corner_presence = np.concatenate(all_corner_presence, axis=1)
all_pattern_presence = np.concatenate(all_pattern_presence, axis=1)
pattern_ids = []
current_pattern_id = 0
for i in range(len(which_patterns)):
for j in which_patterns[i]:
corner_ids = [current_pattern_id] * len(pattern[j])
pattern_ids.extend(corner_ids)
current_pattern_id += 1
pattern_ids = np.asarray(pattern_ids)[np.newaxis]
pattern_ids = np.tile(pattern_ids, [capsules.shape[0], 1])
capsules, all_corner_presence, all_pattern_presence = [
i.astype(np.float32)
for i in (capsules, all_corner_presence, all_pattern_presence)
]
if shuffle_corners:
for i in range(capsules.shape[0]):
p = np.random.permutation(len(capsules[i]))
capsules[i] = capsules[i][p]
all_corner_presence[i] = all_corner_presence[i][p]
pattern_ids[i] = pattern_ids[i][p]
if gaussian_noise > 0.:
capsules += np.random.normal(scale=gaussian_noise, size=capsules.shape)
# normalize corners
min_d, max_d = capsules.min(), capsules.max()
capsules = (capsules - min_d) / (max_d - min_d + 1e-8) * 2 - 1.
minibatch = dict(corners=capsules, presence=all_corner_presence,
pattern_presence=all_pattern_presence,
pattern_id=pattern_ids)
return minibatch
def create(
batch_size=32,
subset=None,
shuffle_corners=True,
gaussian_noise=0.,
max_translation=1.,
rotation_percent=0.0,
which_patterns='basic',
drop_prob=0.0,
max_scale=3.,
min_scale=.1,
use_scale_schedule=False,
schedule_steps=100000.,
):
"""Creates the dataset loader."""
del subset
def generator(n_batches=100):
"""Generates `n_batches` of data."""
step = 0
n = n_batches * batch_size
while True:
if use_scale_schedule:
scale = (max_scale - min_scale) * float(step) / schedule_steps
scale = min(scale + min_scale, max_scale)
step += n_batches
else:
scale = max_scale
batch = create_numpy(n, shuffle_corners, gaussian_noise, max_translation,
rotation_percent, scale, which_patterns, drop_prob)
for i in range(n_batches):
st = i * batch_size
ed = st + batch_size
b = {k: v[st:ed] for k, v in batch.items()}
yield b
minibatch = next(generator(n_batches=1))
dtypes = {k: v.dtype for k, v in minibatch.items()}
shapes = {k: v.shape for k, v in minibatch.items()}
dataset = tf.data.Dataset.from_generator(generator, dtypes, shapes)
iter_data = dataset.make_one_shot_iterator()
input_batch = iter_data.get_next()
for _, v in input_batch.items():
v.set_shape([batch_size] + v.shape[1:].as_list())
return ConstellationTuple(**input_batch)
| # coding=utf-8
# Copyright 2019 The Google Research Authors.
#
# 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.
"""Constellation dataset."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import numpy as np
import tensorflow.compat.v1 as tf
ConstellationTuple = collections.namedtuple('ConstellationTuple',
'presence corners pattern_presence '
'pattern_id')
def create_numpy(
size_n=1,
shuffle_corners=True,
gaussian_noise=0.,
max_translation=1.,
rotation_percent=0.0,
max_upscale=0.0,
which_patterns='basic',
drop_prob=0.0,
):
"""Creates a batch of data using numpy."""
sm = 1
if which_patterns == 'basic':
which_patterns = [[0], [1]]
elif which_patterns == 'all':
which_patterns = [[0], [1], [2], [3]]
elif isinstance(which_patterns, str):
raise ValueError('Pattern "{}" has not been '
'implemented.'.format(which_patterns))
pattern = [
np.array([[1 + 2 * sm, 1 + 2 * sm, 1], [1, 1, 1], [1 + 2 * sm, 1, 1],
[1, 1 + 2 * sm, 1]]), # square
np.array([[1, 1 + sm, 1], [1 + 2 * sm, 1, 1], [1 + 2 * sm, 1 + 2 * sm,
1]]), # triangle
np.array([[1, 1, 1], [1 + sm, 1 - 2 * sm, 1], [1 + 2 * sm, 1 - sm, 1],
[1 + 2 * sm, 1 + sm, 1], [1 + sm, 1 + 2 * sm, 1]]), # pentagon
np.array([[1, 1, 1], [1 + sm, 1, 1], [1 + 2 * sm, 1, 1],
[1 + 2 * sm, 1 + sm, 1], [1 + 2 * sm, 1 + 2 * sm, 1]]), # L
]
caps_dim = pattern[0].shape[1]
transformations = []
all_corners = []
all_corner_presence = []
all_pattern_presence = []
centers = np.array([0, 0, 1])
for i in range(len(which_patterns)):
corners = centers.copy()
for j in range(len(which_patterns[i])):
corner_trans = np.zeros((pattern[which_patterns[i][j]].shape[0], caps_dim,
caps_dim))
corner_trans[:, -1, :] = pattern[which_patterns[i][j]] * (j + 1)
corner_trans[:, :-1, :-1] = np.eye(caps_dim - 1)
corners = np.matmul(corners, corner_trans)
corners = corners.reshape(-1, caps_dim)
transformation = np.zeros((size_n, caps_dim, caps_dim))
transformation[:, :, -1] = [0, 0, 1]
# [pi/2, pi]
degree = (np.random.random((size_n)) - .5) * 2. * np.pi * rotation_percent
scale = 1. + np.random.random((size_n)) * max_upscale
translation = np.random.random((size_n, 2)) * 24. * max_translation
transformation[:, 0, 0] = np.cos(degree) * scale
transformation[:, 1, 1] = np.cos(degree) * scale
transformation[:, 0, 1] = np.sin(degree) * scale
transformation[:, 1, 0] = -np.sin(degree) * scale
transformation[:, -1, :-1] = translation / scale[Ellipsis, np.newaxis]
corners = np.matmul(corners, transformation)
random_pattern_choice = np.random.binomial(1, 1. - drop_prob,
(corners.shape[0], 1))
random_corer_choice = np.tile(random_pattern_choice, (1, corners.shape[1]))
all_corner_presence.append(random_corer_choice)
all_pattern_presence.append(random_pattern_choice)
transformations.append(transformation)
all_corners.append(corners)
capsules = np.concatenate(all_corners, axis=1)[Ellipsis, :2]
all_corner_presence = np.concatenate(all_corner_presence, axis=1)
all_pattern_presence = np.concatenate(all_pattern_presence, axis=1)
pattern_ids = []
current_pattern_id = 0
for i in range(len(which_patterns)):
for j in which_patterns[i]:
corner_ids = [current_pattern_id] * len(pattern[j])
pattern_ids.extend(corner_ids)
current_pattern_id += 1
pattern_ids = np.asarray(pattern_ids)[np.newaxis]
pattern_ids = np.tile(pattern_ids, [capsules.shape[0], 1])
capsules, all_corner_presence, all_pattern_presence = [
i.astype(np.float32)
for i in (capsules, all_corner_presence, all_pattern_presence)
]
if shuffle_corners:
for i in range(capsules.shape[0]):
p = np.random.permutation(len(capsules[i]))
capsules[i] = capsules[i][p]
all_corner_presence[i] = all_corner_presence[i][p]
pattern_ids[i] = pattern_ids[i][p]
if gaussian_noise > 0.:
capsules += np.random.normal(scale=gaussian_noise, size=capsules.shape)
# normalize corners
min_d, max_d = capsules.min(), capsules.max()
capsules = (capsules - min_d) / (max_d - min_d + 1e-8) * 2 - 1.
minibatch = dict(corners=capsules, presence=all_corner_presence,
pattern_presence=all_pattern_presence,
pattern_id=pattern_ids)
return minibatch
def create(
batch_size=32,
subset=None,
shuffle_corners=True,
gaussian_noise=0.,
max_translation=1.,
rotation_percent=0.0,
which_patterns='basic',
drop_prob=0.0,
max_scale=3.,
min_scale=.1,
use_scale_schedule=False,
schedule_steps=100000.,
):
"""Creates the dataset loader."""
del subset
def generator(n_batches=100):
"""Generates `n_batches` of data."""
step = 0
n = n_batches * batch_size
while True:
if use_scale_schedule:
scale = (max_scale - min_scale) * float(step) / schedule_steps
scale = min(scale + min_scale, max_scale)
step += n_batches
else:
scale = max_scale
batch = create_numpy(n, shuffle_corners, gaussian_noise, max_translation,
rotation_percent, scale, which_patterns, drop_prob)
for i in range(n_batches):
st = i * batch_size
ed = st + batch_size
b = {k: v[st:ed] for k, v in batch.items()}
yield b
minibatch = next(generator(n_batches=1))
dtypes = {k: v.dtype for k, v in minibatch.items()}
shapes = {k: v.shape for k, v in minibatch.items()}
dataset = tf.data.Dataset.from_generator(generator, dtypes, shapes)
iter_data = dataset.make_one_shot_iterator()
input_batch = iter_data.get_next()
for _, v in input_batch.items():
v.set_shape([batch_size] + v.shape[1:].as_list())
return ConstellationTuple(**input_batch) | en | 0.793324 | # coding=utf-8 # Copyright 2019 The Google Research Authors. # # 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. Constellation dataset. Creates a batch of data using numpy. # square # triangle # pentagon # L # [pi/2, pi] # normalize corners Creates the dataset loader. Generates `n_batches` of data. | 2.144808 | 2 |
examples/matplotlib/mpl_plot_kde_2d_hdi.py | sudojarvis/arviz | 1,159 | 6630523 | <reponame>sudojarvis/arviz
"""
2d KDE with HDI Contours
========================
_thumb: .5, .8
_example_title: 2d KDE with HDI Contours
"""
import matplotlib.pyplot as plt
import numpy as np
import arviz as az
az.style.use("arviz-darkgrid")
rng = np.random.default_rng()
data = rng.multivariate_normal([2, 2], [[1, 0.4], [0.4, 0.8]], 1000000)
ax = az.plot_kde(
data[:, 0],
data[:, 1],
hdi_probs=[0.393, 0.865, 0.989], # 1, 2 and 3 sigma contours
contourf_kwargs={"cmap": "Blues"},
)
ax.set_aspect("equal")
plt.show()
| """
2d KDE with HDI Contours
========================
_thumb: .5, .8
_example_title: 2d KDE with HDI Contours
"""
import matplotlib.pyplot as plt
import numpy as np
import arviz as az
az.style.use("arviz-darkgrid")
rng = np.random.default_rng()
data = rng.multivariate_normal([2, 2], [[1, 0.4], [0.4, 0.8]], 1000000)
ax = az.plot_kde(
data[:, 0],
data[:, 1],
hdi_probs=[0.393, 0.865, 0.989], # 1, 2 and 3 sigma contours
contourf_kwargs={"cmap": "Blues"},
)
ax.set_aspect("equal")
plt.show() | en | 0.556357 | 2d KDE with HDI Contours ======================== _thumb: .5, .8 _example_title: 2d KDE with HDI Contours # 1, 2 and 3 sigma contours | 2.507939 | 3 |
setup.py | himanshu-dutta/pycoder | 1 | 6630524 | <reponame>himanshu-dutta/pycoder
from setuptools import setup, find_packages
from pycoder.version import __version__
from pathlib import Path
PACKAGE_DIR = (Path(__file__).parent / "pycoder").absolute()
HTML_PATH = PACKAGE_DIR / "api" / "index.html"
if not HTML_PATH.exists():
raise FileNotFoundError(HTML_PATH)
with open("README.md") as f:
long_description = f.read()
setup(
name="pycoder",
version=__version__,
packages=find_packages(),
description="A package to generate Python code from given topics and description",
long_description=long_description,
long_description_content_type="text/markdown",
author="<NAME>",
author_email="<EMAIL>",
url="https://github.com/himanshu-dutta/pycoder",
install_requires=[
"torch==1.8.1",
"typer==0.3.2",
"transformers==4.6.1",
"markdown==3.3.4",
"fastapi==0.65.1",
"uvicorn==0.13.4",
"pygments==2.9.0",
"click-spinner==0.1.10",
],
extras_require={"dev": ["pytest", "black", "wandb", "pygithub"]},
entry_points={
"console_scripts": [
"pycoder=pycoder.api.cli:cli",
],
},
include_package_data=True,
package_data={
"pycoder": [
str(HTML_PATH.relative_to(PACKAGE_DIR)),
]
},
python_requires=">3.7",
license="MIT License",
classifiers=[
"Programming Language :: Python :: 3.7",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
],
platforms=["linux", "unix", "windows"],
)
| from setuptools import setup, find_packages
from pycoder.version import __version__
from pathlib import Path
PACKAGE_DIR = (Path(__file__).parent / "pycoder").absolute()
HTML_PATH = PACKAGE_DIR / "api" / "index.html"
if not HTML_PATH.exists():
raise FileNotFoundError(HTML_PATH)
with open("README.md") as f:
long_description = f.read()
setup(
name="pycoder",
version=__version__,
packages=find_packages(),
description="A package to generate Python code from given topics and description",
long_description=long_description,
long_description_content_type="text/markdown",
author="<NAME>",
author_email="<EMAIL>",
url="https://github.com/himanshu-dutta/pycoder",
install_requires=[
"torch==1.8.1",
"typer==0.3.2",
"transformers==4.6.1",
"markdown==3.3.4",
"fastapi==0.65.1",
"uvicorn==0.13.4",
"pygments==2.9.0",
"click-spinner==0.1.10",
],
extras_require={"dev": ["pytest", "black", "wandb", "pygithub"]},
entry_points={
"console_scripts": [
"pycoder=pycoder.api.cli:cli",
],
},
include_package_data=True,
package_data={
"pycoder": [
str(HTML_PATH.relative_to(PACKAGE_DIR)),
]
},
python_requires=">3.7",
license="MIT License",
classifiers=[
"Programming Language :: Python :: 3.7",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
],
platforms=["linux", "unix", "windows"],
) | none | 1 | 2.045601 | 2 |
|
VUsbTools/Style.py | scanlime/vusb-analyzer | 34 | 6630525 | <filename>VUsbTools/Style.py
#
# VUsbTools.Views
# <NAME> <<EMAIL>>
#
# A container for color and font preferences
#
# Copyright (C) 2005-2010 VMware, Inc. Licensed under the MIT
# License, please see the README.txt. All rights reserved.
#
import math
from VUsbTools.Types import Color
#
# Use the default monospace font. If this is too large/small for your
# tastes, you can try a specific font name and size like "courier 9".
#
monospaceFont = "monospace"
def toMonospaceMarkup(text):
"""Convert arbitrary text to pango markup in our monospace font"""
return '<span font_desc="%s">%s</span>' % (
monospaceFont,
text.replace("&", "&").replace("<", "<"))
directionColors = {
"Up": Color(0x00, 0x00, 0xFF),
"Down": Color(0x00, 0x80, 0x00),
}
directionIcons = {
"Down": "gtk-go-forward",
"Up": "gtk-go-back",
}
errorMarkerColor = Color(0xFF, 0x00, 0x00, 0x80)
diffMarkerColor = Color(0x00, 0xA0, 0x00, 0x30)
diffBorderColor = Color(0x00, 0xA0, 0x00, 0xA0)
emptyTransactionColor = Color(0x80, 0x80, 0x80)
smallTransactionColor = Color(0x80, 0x80, 0xFF)
largeTransactionColor = Color(0xFF, 0xFF, 0x80)
frameMarkerColor = Color(0x00, 0x00, 0xFF)
duplicateFrameColor = Color(0x80, 0x00, 0x00)
def getBarColor(transaction):
"""Get the color to use for a transaction's bar on the timing diagram.
This implementation bases the color on a transaction's size.
"""
s = transaction.datalen or 0
if not s:
return emptyTransactionColor
# For non-empty transactions, the color is actually proportional
# to size on a logarithmic scale.
return smallTransactionColor.lerp(
math.log(s) / math.log(4096), largeTransactionColor)
| <filename>VUsbTools/Style.py
#
# VUsbTools.Views
# <NAME> <<EMAIL>>
#
# A container for color and font preferences
#
# Copyright (C) 2005-2010 VMware, Inc. Licensed under the MIT
# License, please see the README.txt. All rights reserved.
#
import math
from VUsbTools.Types import Color
#
# Use the default monospace font. If this is too large/small for your
# tastes, you can try a specific font name and size like "courier 9".
#
monospaceFont = "monospace"
def toMonospaceMarkup(text):
"""Convert arbitrary text to pango markup in our monospace font"""
return '<span font_desc="%s">%s</span>' % (
monospaceFont,
text.replace("&", "&").replace("<", "<"))
directionColors = {
"Up": Color(0x00, 0x00, 0xFF),
"Down": Color(0x00, 0x80, 0x00),
}
directionIcons = {
"Down": "gtk-go-forward",
"Up": "gtk-go-back",
}
errorMarkerColor = Color(0xFF, 0x00, 0x00, 0x80)
diffMarkerColor = Color(0x00, 0xA0, 0x00, 0x30)
diffBorderColor = Color(0x00, 0xA0, 0x00, 0xA0)
emptyTransactionColor = Color(0x80, 0x80, 0x80)
smallTransactionColor = Color(0x80, 0x80, 0xFF)
largeTransactionColor = Color(0xFF, 0xFF, 0x80)
frameMarkerColor = Color(0x00, 0x00, 0xFF)
duplicateFrameColor = Color(0x80, 0x00, 0x00)
def getBarColor(transaction):
"""Get the color to use for a transaction's bar on the timing diagram.
This implementation bases the color on a transaction's size.
"""
s = transaction.datalen or 0
if not s:
return emptyTransactionColor
# For non-empty transactions, the color is actually proportional
# to size on a logarithmic scale.
return smallTransactionColor.lerp(
math.log(s) / math.log(4096), largeTransactionColor)
| en | 0.670161 | # # VUsbTools.Views # <NAME> <<EMAIL>> # # A container for color and font preferences # # Copyright (C) 2005-2010 VMware, Inc. Licensed under the MIT # License, please see the README.txt. All rights reserved. # # # Use the default monospace font. If this is too large/small for your # tastes, you can try a specific font name and size like "courier 9". # Convert arbitrary text to pango markup in our monospace font Get the color to use for a transaction's bar on the timing diagram. This implementation bases the color on a transaction's size. # For non-empty transactions, the color is actually proportional # to size on a logarithmic scale. | 2.272134 | 2 |
tests/test_pep8.py | sdrees/jupytext | 11 | 6630526 | import pytest
from nbformat.v4.nbbase import new_code_cell, new_notebook
from jupytext import read, reads, writes
from jupytext.compare import compare
from jupytext.pep8 import (
cell_ends_with_code,
cell_ends_with_function_or_class,
cell_has_code,
next_instruction_is_function_or_class,
pep8_lines_between_cells,
)
from .utils import list_notebooks
def test_next_instruction_is_function_or_class():
text = """@pytest.mark.parametrize('py_file',
[py_file for py_file in list_notebooks('../jupytext') + list_notebooks('.') if
py_file.endswith('.py')])
def test_no_metadata_when_py_is_pep8(py_file):
pass
"""
assert next_instruction_is_function_or_class(text.splitlines())
def test_cell_ends_with_code():
assert not cell_ends_with_code([])
def test_cell_ends_with_function_or_class():
text = """class A:
__init__():
'''A docstring
with two lines or more'''
self.a = 0
"""
assert cell_ends_with_function_or_class(text.splitlines())
lines = ["#", "#"]
assert not cell_ends_with_function_or_class(lines)
text = """# two blank line after this class
class A:
pass
# so we do not need to insert two blank lines below this cell
"""
assert not cell_ends_with_function_or_class(text.splitlines())
text = """# All lines
# are commented"""
assert not cell_ends_with_function_or_class(text.splitlines())
text = """# Two blank lines after function
def f(x):
return x
# And a comment here"""
assert not cell_ends_with_function_or_class(text.splitlines())
assert not cell_ends_with_function_or_class(["", "#"])
def test_pep8_lines_between_cells():
prev_lines = """a = a_long_instruction(
over_two_lines=True)""".splitlines()
next_lines = """def f(x):
return x""".splitlines()
assert cell_ends_with_code(prev_lines)
assert next_instruction_is_function_or_class(next_lines)
assert pep8_lines_between_cells(prev_lines, next_lines, ".py") == 2
def test_pep8_lines_between_cells_bis():
prev_lines = """def f(x):
return x""".splitlines()
next_lines = """# A markdown cell
# An instruction
a = 5
""".splitlines()
assert cell_ends_with_function_or_class(prev_lines)
assert cell_has_code(next_lines)
assert pep8_lines_between_cells(prev_lines, next_lines, ".py") == 2
next_lines = """# A markdown cell
# Only markdown here
# And here
""".splitlines()
assert cell_ends_with_function_or_class(prev_lines)
assert not cell_has_code(next_lines)
assert pep8_lines_between_cells(prev_lines, next_lines, ".py") == 1
def test_pep8_lines_between_cells_ter():
prev_lines = ["from jupytext.cell_to_text import RMarkdownCellExporter"]
next_lines = '''@pytest.mark.parametrize(
"lines",
[
"# text",
"""# # %%R
# # comment
# 1 + 1
# 2 + 2
""",
],
)
def test_paragraph_is_fully_commented(lines):
assert paragraph_is_fully_commented(
lines.splitlines(), comment="#", main_language="python"
)'''.splitlines()
assert cell_ends_with_code(prev_lines)
assert next_instruction_is_function_or_class(next_lines)
assert pep8_lines_between_cells(prev_lines, next_lines, ".py") == 2
def test_pep8():
text = """import os
path = os.path
# code cell #1, with a comment on f
def f(x):
return x + 1
# markdown cell #1
# code cell #2 - an instruction
a = 4
# markdown cell #2
# code cell #3 with a comment on g
def g(x):
return x + 1
# markdown cell #3
# the two lines are:
# - right below the function/class
# - below the last python paragraph (i.e. NOT ABOVE g)
# code cell #4
x = 4
"""
nb = reads(text, "py")
for cell in nb.cells:
assert not cell.metadata
text2 = writes(nb, "py")
compare(text2, text)
def test_pep8_bis():
text = """# This is a markdown cell
# a code cell
def f(x):
return x + 1
# And another markdown cell
# Separated from f by just one line
# As there is no code here
"""
nb = reads(text, "py")
for cell in nb.cells:
assert not cell.metadata
text2 = writes(nb, "py")
compare(text2, text)
@pytest.mark.parametrize(
"py_file",
[
py_file
for py_file in list_notebooks("../jupytext") + list_notebooks(".")
if py_file.endswith(".py") and "folding_markers" not in py_file
],
)
def test_no_metadata_when_py_is_pep8(py_file):
"""This test assumes that all Python files in the jupytext folder follow PEP8 rules"""
nb = read(py_file)
for i, cell in enumerate(nb.cells):
if "title" in cell.metadata:
cell.metadata.pop("title") # pragma: no cover
if i == 0 and not cell.source:
assert cell.metadata == {
"lines_to_next_cell": 0
}, py_file # pragma: no cover
else:
assert not cell.metadata, (py_file, cell.source)
def test_notebook_ends_with_exactly_one_empty_line_682():
"""(Issue #682)
Steps to reproduce:
Have a notebook that ends in a python code cell (with no empty lines at the end of the cell).
run jupytext --to py:percent notebookWithCodeCell.ipynb.
See that the generated python code file has two empty lines at the end.
I would expect there to just be one new line."""
nb = new_notebook(
cells=[new_code_cell("1+1")], metadata={"jupytext": {"main_language": "python"}}
)
py = writes(nb, "py:percent")
assert py.endswith("1+1\n")
| import pytest
from nbformat.v4.nbbase import new_code_cell, new_notebook
from jupytext import read, reads, writes
from jupytext.compare import compare
from jupytext.pep8 import (
cell_ends_with_code,
cell_ends_with_function_or_class,
cell_has_code,
next_instruction_is_function_or_class,
pep8_lines_between_cells,
)
from .utils import list_notebooks
def test_next_instruction_is_function_or_class():
text = """@pytest.mark.parametrize('py_file',
[py_file for py_file in list_notebooks('../jupytext') + list_notebooks('.') if
py_file.endswith('.py')])
def test_no_metadata_when_py_is_pep8(py_file):
pass
"""
assert next_instruction_is_function_or_class(text.splitlines())
def test_cell_ends_with_code():
assert not cell_ends_with_code([])
def test_cell_ends_with_function_or_class():
text = """class A:
__init__():
'''A docstring
with two lines or more'''
self.a = 0
"""
assert cell_ends_with_function_or_class(text.splitlines())
lines = ["#", "#"]
assert not cell_ends_with_function_or_class(lines)
text = """# two blank line after this class
class A:
pass
# so we do not need to insert two blank lines below this cell
"""
assert not cell_ends_with_function_or_class(text.splitlines())
text = """# All lines
# are commented"""
assert not cell_ends_with_function_or_class(text.splitlines())
text = """# Two blank lines after function
def f(x):
return x
# And a comment here"""
assert not cell_ends_with_function_or_class(text.splitlines())
assert not cell_ends_with_function_or_class(["", "#"])
def test_pep8_lines_between_cells():
prev_lines = """a = a_long_instruction(
over_two_lines=True)""".splitlines()
next_lines = """def f(x):
return x""".splitlines()
assert cell_ends_with_code(prev_lines)
assert next_instruction_is_function_or_class(next_lines)
assert pep8_lines_between_cells(prev_lines, next_lines, ".py") == 2
def test_pep8_lines_between_cells_bis():
prev_lines = """def f(x):
return x""".splitlines()
next_lines = """# A markdown cell
# An instruction
a = 5
""".splitlines()
assert cell_ends_with_function_or_class(prev_lines)
assert cell_has_code(next_lines)
assert pep8_lines_between_cells(prev_lines, next_lines, ".py") == 2
next_lines = """# A markdown cell
# Only markdown here
# And here
""".splitlines()
assert cell_ends_with_function_or_class(prev_lines)
assert not cell_has_code(next_lines)
assert pep8_lines_between_cells(prev_lines, next_lines, ".py") == 1
def test_pep8_lines_between_cells_ter():
prev_lines = ["from jupytext.cell_to_text import RMarkdownCellExporter"]
next_lines = '''@pytest.mark.parametrize(
"lines",
[
"# text",
"""# # %%R
# # comment
# 1 + 1
# 2 + 2
""",
],
)
def test_paragraph_is_fully_commented(lines):
assert paragraph_is_fully_commented(
lines.splitlines(), comment="#", main_language="python"
)'''.splitlines()
assert cell_ends_with_code(prev_lines)
assert next_instruction_is_function_or_class(next_lines)
assert pep8_lines_between_cells(prev_lines, next_lines, ".py") == 2
def test_pep8():
text = """import os
path = os.path
# code cell #1, with a comment on f
def f(x):
return x + 1
# markdown cell #1
# code cell #2 - an instruction
a = 4
# markdown cell #2
# code cell #3 with a comment on g
def g(x):
return x + 1
# markdown cell #3
# the two lines are:
# - right below the function/class
# - below the last python paragraph (i.e. NOT ABOVE g)
# code cell #4
x = 4
"""
nb = reads(text, "py")
for cell in nb.cells:
assert not cell.metadata
text2 = writes(nb, "py")
compare(text2, text)
def test_pep8_bis():
text = """# This is a markdown cell
# a code cell
def f(x):
return x + 1
# And another markdown cell
# Separated from f by just one line
# As there is no code here
"""
nb = reads(text, "py")
for cell in nb.cells:
assert not cell.metadata
text2 = writes(nb, "py")
compare(text2, text)
@pytest.mark.parametrize(
"py_file",
[
py_file
for py_file in list_notebooks("../jupytext") + list_notebooks(".")
if py_file.endswith(".py") and "folding_markers" not in py_file
],
)
def test_no_metadata_when_py_is_pep8(py_file):
"""This test assumes that all Python files in the jupytext folder follow PEP8 rules"""
nb = read(py_file)
for i, cell in enumerate(nb.cells):
if "title" in cell.metadata:
cell.metadata.pop("title") # pragma: no cover
if i == 0 and not cell.source:
assert cell.metadata == {
"lines_to_next_cell": 0
}, py_file # pragma: no cover
else:
assert not cell.metadata, (py_file, cell.source)
def test_notebook_ends_with_exactly_one_empty_line_682():
"""(Issue #682)
Steps to reproduce:
Have a notebook that ends in a python code cell (with no empty lines at the end of the cell).
run jupytext --to py:percent notebookWithCodeCell.ipynb.
See that the generated python code file has two empty lines at the end.
I would expect there to just be one new line."""
nb = new_notebook(
cells=[new_code_cell("1+1")], metadata={"jupytext": {"main_language": "python"}}
)
py = writes(nb, "py:percent")
assert py.endswith("1+1\n")
| en | 0.664204 | @pytest.mark.parametrize('py_file', [py_file for py_file in list_notebooks('../jupytext') + list_notebooks('.') if py_file.endswith('.py')]) def test_no_metadata_when_py_is_pep8(py_file): pass class A: __init__(): '''A docstring with two lines or more''' self.a = 0 # two blank line after this class class A: pass # so we do not need to insert two blank lines below this cell # All lines # are commented # Two blank lines after function def f(x): return x # And a comment here a = a_long_instruction( over_two_lines=True) def f(x): return x def f(x): return x # A markdown cell # An instruction a = 5 # A markdown cell # Only markdown here # And here @pytest.mark.parametrize( "lines", [ "# text", """# # %%R # # comment # 1 + 1 # 2 + 2 """, ], ) def test_paragraph_is_fully_commented(lines): assert paragraph_is_fully_commented( lines.splitlines(), comment="#", main_language="python" ) import os path = os.path # code cell #1, with a comment on f def f(x): return x + 1 # markdown cell #1 # code cell #2 - an instruction a = 4 # markdown cell #2 # code cell #3 with a comment on g def g(x): return x + 1 # markdown cell #3 # the two lines are: # - right below the function/class # - below the last python paragraph (i.e. NOT ABOVE g) # code cell #4 x = 4 # This is a markdown cell # a code cell def f(x): return x + 1 # And another markdown cell # Separated from f by just one line # As there is no code here This test assumes that all Python files in the jupytext folder follow PEP8 rules # pragma: no cover # pragma: no cover (Issue #682) Steps to reproduce: Have a notebook that ends in a python code cell (with no empty lines at the end of the cell). run jupytext --to py:percent notebookWithCodeCell.ipynb. See that the generated python code file has two empty lines at the end. I would expect there to just be one new line. | 2.723639 | 3 |
Module/dnsrecon/lib/gooenum.py | vishnudxb/Yuki-Chan-The-Auto-Pentest | 655 | 6630527 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright (C) 2010 <NAME>
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; Applies version 2 of the License.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software
# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA
import urllib
import re
import time
try:
url_opener = urllib.FancyURLopener
except AttributeError:
import urllib.request
url_opener = urllib.request.FancyURLopener
class AppURLopener(url_opener):
version = 'Mozilla/5.0 (compatible; Googlebot/2.1; + http://www.google.com/bot.html)'
def scrape_google(dom):
"""
Function for enumerating sub-domains and hosts by scrapping Google. It returns a unique
list if host name extracted from the HREF entries from the Google search.
"""
results = []
filtered = []
searches = ["100", "200", "300", "400", "500"]
data = ""
urllib._urlopener = AppURLopener()
user_agent = 'Mozilla/5.0 (Windows; U; Windows NT 5.1; en-US; rv:1.9.0.7) Gecko/2009021910 Firefox/3.0.7'
headers = {'User-Agent': user_agent, }
#opener.addheaders = [('User-Agent','Mozilla/5.0 (compatible; Googlebot/2.1; +http://www.google.com/bot.html)')]
for n in searches:
url = "http://google.com/search?hl=en&lr=&ie=UTF-8&q=%2B" + dom + "&start=" + n + "&sa=N&filter=0&num=100"
try:
sock = urllib.urlopen(url)
data += sock.read()
sock.close()
except AttributeError:
request = urllib.request.Request(url, None, headers)
response = urllib.request.urlopen(request)
data += str(response.read())
results.extend(unique(re.findall("href=\"htt\w{1,2}:\/\/([^:?]*[a-b0-9]*[^:?]*\." + dom + ")\/", data)))
# Make sure we are only getting the host
for f in results:
filtered.extend(re.findall("^([a-z.0-9^]*" + dom + ")", f))
time.sleep(2)
return unique(filtered)
def unique(seq, idfun=repr):
"""
Function to remove duplicates in an array. Returns array with duplicates
removed.
"""
seen = {}
return [seen.setdefault(idfun(e), e) for e in seq if idfun(e) not in seen]
| #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright (C) 2010 <NAME>
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; Applies version 2 of the License.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software
# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA
import urllib
import re
import time
try:
url_opener = urllib.FancyURLopener
except AttributeError:
import urllib.request
url_opener = urllib.request.FancyURLopener
class AppURLopener(url_opener):
version = 'Mozilla/5.0 (compatible; Googlebot/2.1; + http://www.google.com/bot.html)'
def scrape_google(dom):
"""
Function for enumerating sub-domains and hosts by scrapping Google. It returns a unique
list if host name extracted from the HREF entries from the Google search.
"""
results = []
filtered = []
searches = ["100", "200", "300", "400", "500"]
data = ""
urllib._urlopener = AppURLopener()
user_agent = 'Mozilla/5.0 (Windows; U; Windows NT 5.1; en-US; rv:1.9.0.7) Gecko/2009021910 Firefox/3.0.7'
headers = {'User-Agent': user_agent, }
#opener.addheaders = [('User-Agent','Mozilla/5.0 (compatible; Googlebot/2.1; +http://www.google.com/bot.html)')]
for n in searches:
url = "http://google.com/search?hl=en&lr=&ie=UTF-8&q=%2B" + dom + "&start=" + n + "&sa=N&filter=0&num=100"
try:
sock = urllib.urlopen(url)
data += sock.read()
sock.close()
except AttributeError:
request = urllib.request.Request(url, None, headers)
response = urllib.request.urlopen(request)
data += str(response.read())
results.extend(unique(re.findall("href=\"htt\w{1,2}:\/\/([^:?]*[a-b0-9]*[^:?]*\." + dom + ")\/", data)))
# Make sure we are only getting the host
for f in results:
filtered.extend(re.findall("^([a-z.0-9^]*" + dom + ")", f))
time.sleep(2)
return unique(filtered)
def unique(seq, idfun=repr):
"""
Function to remove duplicates in an array. Returns array with duplicates
removed.
"""
seen = {}
return [seen.setdefault(idfun(e), e) for e in seq if idfun(e) not in seen]
| en | 0.814775 | #!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright (C) 2010 <NAME> # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; Applies version 2 of the License. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software # Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA Function for enumerating sub-domains and hosts by scrapping Google. It returns a unique list if host name extracted from the HREF entries from the Google search. #opener.addheaders = [('User-Agent','Mozilla/5.0 (compatible; Googlebot/2.1; +http://www.google.com/bot.html)')] # Make sure we are only getting the host Function to remove duplicates in an array. Returns array with duplicates removed. | 2.7092 | 3 |
scripts/house_contacts.py | jeffgeee/congress-legislators | 0 | 6630528 | <gh_stars>0
#!/usr/bin/env python
# Update current congressmember's contact info from clerk XML feed
import requests
import lxml
import re
from datetime import datetime
from utils import load_data, save_data, parse_date
def run():
today = datetime.now().date()
y = load_data("legislators-current.yaml")
# TODO use download util?
xml = requests.get("http://clerk.house.gov/xml/lists/MemberData.xml")
root=lxml.etree.fromstring(xml.content)
for moc in y:
try:
term = moc["terms"][-1]
except IndexError:
print("Member has no terms", moc)
continue
if term["type"] != "rep": continue
if today < parse_date(term["start"]) or today > parse_date(term["end"]):
print("Member's last listed term is not current", moc, term["start"])
continue
if "class" in term: del term["class"]
ssdd = "%s%02d" % (term["state"], term["district"])
query_str = "./members/member/[statedistrict='%s']" % ssdd
# TODO: Follow up
query_str = query_str.replace("AS00", "AQ00")
#print(query_str)
mi = root.findall(query_str)[0].find('member-info')
if (mi.find('bioguideID').text != moc['id'].get('bioguide')):
print("Warning: Bioguide ID did not match for %s%02d (%s != %s)" % (
term["state"], term["district"],
mi.find('bioguideID').text, moc['id']['bioguide']))
# for now, no automatic name updates since there is disagremeent on how to handle
# firstname = mi.find('firstname').text
# middlename = mi.find('middlename').text #could be empty
# lastname = mi.find('lastname').text
#TODO: follow up, why no official name?
if mi.find('official-name') is None or mi.find('official-name').text is None:
print("Warning: No official-name tag for %s" % ssdd)
officialname = None
else:
officialname = re.sub("'", "’", mi.find('official-name').text)
office_room = mi.find('office-room').text
office_building = mi.find('office-building').text
office_building_full = office_building.replace("RHOB", "Rayburn House Office Building")
office_building_full = office_building_full.replace("CHOB", "Cannon House Office Building")
office_building_full = office_building_full.replace("LHOB", "Longworth House Office Building")
office_zip = mi.find('office-zip').text
office_zip_suffix = mi.find('office-zip-suffix').text
office = "{} {}".format(office_room, office_building_full)
address = "{} {} Washington DC {}-{}".format(office_room, office_building_full, office_zip, office_zip_suffix)
phone = mi.find('phone').text
phone_parsed = re.sub("^\((\d\d\d)\) ", lambda m : m.group(1) + "-", phone) # replace (XXX) area code with XXX- for compatibility w/ existing format
#for now, no automatic name updates since there is disagremeent on how to handle
# moc["name"]["first"] = firstname
# if (middlename):
# moc["name"]["middle"] = middlename
# else:
# if ("middle" in moc["name"]):
# del moc["name"]["middle"]
# moc["name"]["last"] = lastname
# TODO: leave if none?
if (officialname):
moc["name"]["official_full"] = officialname
term["address"] = address
term["office"] = office
term["phone"] = phone_parsed
save_data(y, "legislators-current.yaml")
if __name__ == '__main__':
run()
| #!/usr/bin/env python
# Update current congressmember's contact info from clerk XML feed
import requests
import lxml
import re
from datetime import datetime
from utils import load_data, save_data, parse_date
def run():
today = datetime.now().date()
y = load_data("legislators-current.yaml")
# TODO use download util?
xml = requests.get("http://clerk.house.gov/xml/lists/MemberData.xml")
root=lxml.etree.fromstring(xml.content)
for moc in y:
try:
term = moc["terms"][-1]
except IndexError:
print("Member has no terms", moc)
continue
if term["type"] != "rep": continue
if today < parse_date(term["start"]) or today > parse_date(term["end"]):
print("Member's last listed term is not current", moc, term["start"])
continue
if "class" in term: del term["class"]
ssdd = "%s%02d" % (term["state"], term["district"])
query_str = "./members/member/[statedistrict='%s']" % ssdd
# TODO: Follow up
query_str = query_str.replace("AS00", "AQ00")
#print(query_str)
mi = root.findall(query_str)[0].find('member-info')
if (mi.find('bioguideID').text != moc['id'].get('bioguide')):
print("Warning: Bioguide ID did not match for %s%02d (%s != %s)" % (
term["state"], term["district"],
mi.find('bioguideID').text, moc['id']['bioguide']))
# for now, no automatic name updates since there is disagremeent on how to handle
# firstname = mi.find('firstname').text
# middlename = mi.find('middlename').text #could be empty
# lastname = mi.find('lastname').text
#TODO: follow up, why no official name?
if mi.find('official-name') is None or mi.find('official-name').text is None:
print("Warning: No official-name tag for %s" % ssdd)
officialname = None
else:
officialname = re.sub("'", "’", mi.find('official-name').text)
office_room = mi.find('office-room').text
office_building = mi.find('office-building').text
office_building_full = office_building.replace("RHOB", "Rayburn House Office Building")
office_building_full = office_building_full.replace("CHOB", "Cannon House Office Building")
office_building_full = office_building_full.replace("LHOB", "Longworth House Office Building")
office_zip = mi.find('office-zip').text
office_zip_suffix = mi.find('office-zip-suffix').text
office = "{} {}".format(office_room, office_building_full)
address = "{} {} Washington DC {}-{}".format(office_room, office_building_full, office_zip, office_zip_suffix)
phone = mi.find('phone').text
phone_parsed = re.sub("^\((\d\d\d)\) ", lambda m : m.group(1) + "-", phone) # replace (XXX) area code with XXX- for compatibility w/ existing format
#for now, no automatic name updates since there is disagremeent on how to handle
# moc["name"]["first"] = firstname
# if (middlename):
# moc["name"]["middle"] = middlename
# else:
# if ("middle" in moc["name"]):
# del moc["name"]["middle"]
# moc["name"]["last"] = lastname
# TODO: leave if none?
if (officialname):
moc["name"]["official_full"] = officialname
term["address"] = address
term["office"] = office
term["phone"] = phone_parsed
save_data(y, "legislators-current.yaml")
if __name__ == '__main__':
run() | en | 0.538497 | #!/usr/bin/env python # Update current congressmember's contact info from clerk XML feed # TODO use download util? # TODO: Follow up #print(query_str) # for now, no automatic name updates since there is disagremeent on how to handle # firstname = mi.find('firstname').text # middlename = mi.find('middlename').text #could be empty # lastname = mi.find('lastname').text #TODO: follow up, why no official name? # replace (XXX) area code with XXX- for compatibility w/ existing format #for now, no automatic name updates since there is disagremeent on how to handle # moc["name"]["first"] = firstname # if (middlename): # moc["name"]["middle"] = middlename # else: # if ("middle" in moc["name"]): # del moc["name"]["middle"] # moc["name"]["last"] = lastname # TODO: leave if none? | 2.831433 | 3 |
ScopusScrapus/ScopusSearch.py | neoflex/ScopusScrapus | 1 | 6630529 | <reponame>neoflex/ScopusScrapus
import requests as r
import urllib.parse as purl
import json
def StartScopusSearch(key, params):
ssq = ScopusSearchQuery(key, params)
return ssq
class ScopusSearchQuery:
_defaultParams = {'count': 100,
'view': 'COMPLETE',
'httpAccept': 'application/json'}
_baseUrl = "http://api.elsevier.com/content/search/scopus?"
_support_pagination = True
_root_key = 'search-results'
def __init__(self, key, params, timeout=60, apikey_return=False):
self._apiKey = key
self._keys = None
if isinstance(key, list):
self._keys = key
self._keyCount = 0
self._apiKey = key[0]
self._state = "empty"
self._params = params
self._data = []
self._nextUrl = None
self._i = 0
self._count = 0
self._timeout = timeout
self._apikey_return = apikey_return # Return (self._data[self._i-1], self._apiKey) instead of self._data[self._i-1]
def _make_search_url(self):
params = self._params
defParams = self._defaultParams
pSet = set(params.keys()).union(set(defParams.keys()))
parameters = {key: params[key] if key in params else defParams[key] for key in pSet}
querystring = purl.urlencode(parameters)
apiKeyString = purl.urlencode({'apiKey': self._apiKey})
url = "{}{}{}{}".format(self._baseUrl, querystring, '&', apiKeyString)
return url
def _manageQuotaExcess(self, raiseOnQE=False):
print("Managing quota exess...")
if raiseOnQE or self._keys is None:
raise Exception("QuotaExceeded - You must wait 7 days until quotas are reset")
self._nextUrl = None
self._keyCount = self._keyCount + 1
print("Key was: "+self._apiKey)
try:
self._apiKey = self._keys[self._keyCount]
print("Key is: "+self._apiKey)
return self._run_search()
except IndexError:
return self._run_search(True) # If we fail again, we surrender
def _run_search(self, raiseOnQE=False):
url = self._nextUrl
if url is None: url = self._make_search_url()
if url == "done": raise StopIteration()
qRes = r.get(url,timeout=self._timeout)
if qRes.status_code in [429, 401]:
# If Invalid API Key or exceeding quota for the API Key, change it
return self._manageQuotaExcess(raiseOnQE)
dta = qRes.json()
if qRes.status_code != 200:
raise Exception("{} {} {} {}".format("Error: ",
dta['service-error']['status']['statusText'],
"URL is:", url)) # Fix this
# KeyError hazard: If no 'next' url is available, we need to error out anyway
nxtLink = [ln for ln in dta[self._root_key]['link'] if ln['@ref'] == 'next']
if len(nxtLink) > 0: self._nextUrl = nxtLink[0]['@href']
else: self._nextUrl = "done" # Nasty? Sorry : )
return dta[self._root_key]['entry'] # Returning only the obtained results
def __iter__(self):
return self
def next(self):
return self.__next__()
def __next__(self):
if self._i == len(self._data):
self._data = self._run_search()
self._i = 0
self._i += 1
if self._apikey_return:
return self._data[self._i-1], self._apiKey
elif not self.apikey_return:
return self._data[self._i-1]
class ScopusSerialTitle(ScopusSearchQuery):
_defaultParams = {'count': 100,
'view': 'CITESCORE',
'httpAccept': 'application/json'}
_baseUrl = "http://api.elsevier.com/content/serial/title"
_support_pagination = False
_root_key = 'serial-metadata-response'
def _make_search_url(self):
params = self._params
defParams = self._defaultParams
pSet = set(params.keys()).union(set(defParams.keys()))
parameters = {key: params[key] if key in params else defParams[key] for key in pSet}
if "issn" in parameters:
base_url = self._baseUrl + '/issn/' + parameters['issn'] + '?'
if 'title' in parameters:
parameters.pop('title')
parameters.pop('issn')
else:
base_url = self._baseUrl + '?'
querystring = purl.urlencode(parameters)
apiKeyString = purl.urlencode({'apiKey': self._apiKey})
url = "{}{}{}{}".format(self._baseUrl, querystring, '&', apiKeyString)
return url
class SerialTitleQuery:
# Serial title search
_defaultParams = {'count': 200,
'view': 'CITESCORE',
'httpAccept': 'application/json'}
_baseUrl = "http://api.elsevier.com/content/serial/title?"
_support_pagination = False
_root_key = 'serial-metadata-response'
def __init__(self, key, params, timeout=60):
self._apiKey = key
self._keys = None
if isinstance(key, list):
self._keys = key
self._keyCount = 0
self._apiKey = key[0]
self._state = "empty"
self._params = params
self._data = []
self._nextUrl = None
self._i = 0
self._count = 0
self._timeout = timeout
def _make_search_url(self):
params = self._params
pset = set(params.keys()).union(set(self._defaultParams.keys()))
parameters = {key: params[key] if key in params else self._defaultParams[key] for key in pset}
# make query
querystring = purl.urlencode(parameters)
apiKeyString = purl.urlencode({'apiKey': self._apiKey})
url = "{}{}{}{}".format(self._base_url, querystring, '&', apiKeyString)
return url
def _manageQuotaExcess(self,
raiseOnQE=False):
print("Managing quota exess...")
if raiseOnQE or self._keys is None:
raise Exception("QuotaExceeded - You must wait 7 days until quotas are reset")
self._nextUrl = None
self._keyCount = self._keyCount + 1
print("Key was: " + self._apiKey)
try:
self._apiKey = self._keys[self._keyCount]
print("Key is: " + self._apiKey)
return self._run_search()
except IndexError:
return self._run_search(True) # If we fail again, we surrender
def _run_search(self, raiseOnQE=False):
url = self._nextUrl
if url is None:
url = self._make_search_url()
if url == "done":
raise StopIteration()
qRes = r.get(url, timeout=self._timeout)
if qRes.status_code in [429, 401]:
# If Invalid API Key or exceeding quota for the API Key, change it
return self._manageQuotaExcess(raiseOnQE)
dta = qRes.json()
if qRes.status_code != 200:
logger.info("{} {} {} {}".format("Error: ",
dta['service-error']['status']['statusText'], "URL is:", url))
raise StopIteration()
if 'entry' not in dta[self._root_key]:
raise StopIteration()
if _defaultParams['count'] <= len(dta[self._root_key]['entry']):
next_url = dta[self._root_key]['link'][0]['@href']
parsed = urlparse.urlparse(next_url)
parsed_query = urlparse.parse_qs(parsed.query)
start = int(parsed_query['start'][0]) + int(parsed_query['count'][0])
self._nextUrl = "{0}&{1}={2}".format(self._make_search_url(), 'start', start)
if start >= 10000:
self._nextUrl = "done"
else:
self._nextUrl = "done"
return dta[self._root_key]['entry']
def __iter__(self):
return self
def next(self):
return self.__next__()
def __next__(self):
if self._i == len(self._data):
self._data = self._run_search()
self._i = 0
if len(self._data) == self._i:
pass # Raise error
self._i += 1
return self._data[self._i - 1]
| import requests as r
import urllib.parse as purl
import json
def StartScopusSearch(key, params):
ssq = ScopusSearchQuery(key, params)
return ssq
class ScopusSearchQuery:
_defaultParams = {'count': 100,
'view': 'COMPLETE',
'httpAccept': 'application/json'}
_baseUrl = "http://api.elsevier.com/content/search/scopus?"
_support_pagination = True
_root_key = 'search-results'
def __init__(self, key, params, timeout=60, apikey_return=False):
self._apiKey = key
self._keys = None
if isinstance(key, list):
self._keys = key
self._keyCount = 0
self._apiKey = key[0]
self._state = "empty"
self._params = params
self._data = []
self._nextUrl = None
self._i = 0
self._count = 0
self._timeout = timeout
self._apikey_return = apikey_return # Return (self._data[self._i-1], self._apiKey) instead of self._data[self._i-1]
def _make_search_url(self):
params = self._params
defParams = self._defaultParams
pSet = set(params.keys()).union(set(defParams.keys()))
parameters = {key: params[key] if key in params else defParams[key] for key in pSet}
querystring = purl.urlencode(parameters)
apiKeyString = purl.urlencode({'apiKey': self._apiKey})
url = "{}{}{}{}".format(self._baseUrl, querystring, '&', apiKeyString)
return url
def _manageQuotaExcess(self, raiseOnQE=False):
print("Managing quota exess...")
if raiseOnQE or self._keys is None:
raise Exception("QuotaExceeded - You must wait 7 days until quotas are reset")
self._nextUrl = None
self._keyCount = self._keyCount + 1
print("Key was: "+self._apiKey)
try:
self._apiKey = self._keys[self._keyCount]
print("Key is: "+self._apiKey)
return self._run_search()
except IndexError:
return self._run_search(True) # If we fail again, we surrender
def _run_search(self, raiseOnQE=False):
url = self._nextUrl
if url is None: url = self._make_search_url()
if url == "done": raise StopIteration()
qRes = r.get(url,timeout=self._timeout)
if qRes.status_code in [429, 401]:
# If Invalid API Key or exceeding quota for the API Key, change it
return self._manageQuotaExcess(raiseOnQE)
dta = qRes.json()
if qRes.status_code != 200:
raise Exception("{} {} {} {}".format("Error: ",
dta['service-error']['status']['statusText'],
"URL is:", url)) # Fix this
# KeyError hazard: If no 'next' url is available, we need to error out anyway
nxtLink = [ln for ln in dta[self._root_key]['link'] if ln['@ref'] == 'next']
if len(nxtLink) > 0: self._nextUrl = nxtLink[0]['@href']
else: self._nextUrl = "done" # Nasty? Sorry : )
return dta[self._root_key]['entry'] # Returning only the obtained results
def __iter__(self):
return self
def next(self):
return self.__next__()
def __next__(self):
if self._i == len(self._data):
self._data = self._run_search()
self._i = 0
self._i += 1
if self._apikey_return:
return self._data[self._i-1], self._apiKey
elif not self.apikey_return:
return self._data[self._i-1]
class ScopusSerialTitle(ScopusSearchQuery):
_defaultParams = {'count': 100,
'view': 'CITESCORE',
'httpAccept': 'application/json'}
_baseUrl = "http://api.elsevier.com/content/serial/title"
_support_pagination = False
_root_key = 'serial-metadata-response'
def _make_search_url(self):
params = self._params
defParams = self._defaultParams
pSet = set(params.keys()).union(set(defParams.keys()))
parameters = {key: params[key] if key in params else defParams[key] for key in pSet}
if "issn" in parameters:
base_url = self._baseUrl + '/issn/' + parameters['issn'] + '?'
if 'title' in parameters:
parameters.pop('title')
parameters.pop('issn')
else:
base_url = self._baseUrl + '?'
querystring = purl.urlencode(parameters)
apiKeyString = purl.urlencode({'apiKey': self._apiKey})
url = "{}{}{}{}".format(self._baseUrl, querystring, '&', apiKeyString)
return url
class SerialTitleQuery:
# Serial title search
_defaultParams = {'count': 200,
'view': 'CITESCORE',
'httpAccept': 'application/json'}
_baseUrl = "http://api.elsevier.com/content/serial/title?"
_support_pagination = False
_root_key = 'serial-metadata-response'
def __init__(self, key, params, timeout=60):
self._apiKey = key
self._keys = None
if isinstance(key, list):
self._keys = key
self._keyCount = 0
self._apiKey = key[0]
self._state = "empty"
self._params = params
self._data = []
self._nextUrl = None
self._i = 0
self._count = 0
self._timeout = timeout
def _make_search_url(self):
params = self._params
pset = set(params.keys()).union(set(self._defaultParams.keys()))
parameters = {key: params[key] if key in params else self._defaultParams[key] for key in pset}
# make query
querystring = purl.urlencode(parameters)
apiKeyString = purl.urlencode({'apiKey': self._apiKey})
url = "{}{}{}{}".format(self._base_url, querystring, '&', apiKeyString)
return url
def _manageQuotaExcess(self,
raiseOnQE=False):
print("Managing quota exess...")
if raiseOnQE or self._keys is None:
raise Exception("QuotaExceeded - You must wait 7 days until quotas are reset")
self._nextUrl = None
self._keyCount = self._keyCount + 1
print("Key was: " + self._apiKey)
try:
self._apiKey = self._keys[self._keyCount]
print("Key is: " + self._apiKey)
return self._run_search()
except IndexError:
return self._run_search(True) # If we fail again, we surrender
def _run_search(self, raiseOnQE=False):
url = self._nextUrl
if url is None:
url = self._make_search_url()
if url == "done":
raise StopIteration()
qRes = r.get(url, timeout=self._timeout)
if qRes.status_code in [429, 401]:
# If Invalid API Key or exceeding quota for the API Key, change it
return self._manageQuotaExcess(raiseOnQE)
dta = qRes.json()
if qRes.status_code != 200:
logger.info("{} {} {} {}".format("Error: ",
dta['service-error']['status']['statusText'], "URL is:", url))
raise StopIteration()
if 'entry' not in dta[self._root_key]:
raise StopIteration()
if _defaultParams['count'] <= len(dta[self._root_key]['entry']):
next_url = dta[self._root_key]['link'][0]['@href']
parsed = urlparse.urlparse(next_url)
parsed_query = urlparse.parse_qs(parsed.query)
start = int(parsed_query['start'][0]) + int(parsed_query['count'][0])
self._nextUrl = "{0}&{1}={2}".format(self._make_search_url(), 'start', start)
if start >= 10000:
self._nextUrl = "done"
else:
self._nextUrl = "done"
return dta[self._root_key]['entry']
def __iter__(self):
return self
def next(self):
return self.__next__()
def __next__(self):
if self._i == len(self._data):
self._data = self._run_search()
self._i = 0
if len(self._data) == self._i:
pass # Raise error
self._i += 1
return self._data[self._i - 1] | en | 0.476615 | # Return (self._data[self._i-1], self._apiKey) instead of self._data[self._i-1] # If we fail again, we surrender # If Invalid API Key or exceeding quota for the API Key, change it # Fix this # KeyError hazard: If no 'next' url is available, we need to error out anyway # Nasty? Sorry : ) # Returning only the obtained results # Serial title search # make query # If we fail again, we surrender # If Invalid API Key or exceeding quota for the API Key, change it # Raise error | 2.937572 | 3 |
django_smartfarm_git/main/migrations/0006_remove_temp_date.py | jyjy318/smartfarm | 0 | 6630530 | # Generated by Django 2.0.13 on 2020-09-03 13:19
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('main', '0005_auto_20200903_2215'),
]
operations = [
migrations.RemoveField(
model_name='temp',
name='date',
),
]
| # Generated by Django 2.0.13 on 2020-09-03 13:19
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('main', '0005_auto_20200903_2215'),
]
operations = [
migrations.RemoveField(
model_name='temp',
name='date',
),
]
| en | 0.831999 | # Generated by Django 2.0.13 on 2020-09-03 13:19 | 1.302781 | 1 |
server/grade_view/blue_prints/register.py | jw3329/grade-view | 0 | 6630531 | <filename>server/grade_view/blue_prints/register.py
from flask import Blueprint, request, jsonify
from flask_login import login_required, current_user
from grade_view import db, app
from grade_view.models import User, Course, GPA, Major
register = Blueprint('register', __name__)
@register.route('/gpa',methods=['GET','POST'])
@login_required
def profile():
if request.method == 'GET':
gpas = GPA.query.filter_by(user_id=current_user.id).all()
json = {
'status': True,
'info': []
}
for gpa in gpas:
course = gpa.course
json['info'].append({
'course': course.major.major,
'course_number': course.course_number,
'gpa': gpa.gpa
})
return jsonify(json)
elif request.method == 'POST':
registered_data = request.get_json()
print(registered_data)
try:
course = registered_data['course']
course_number = registered_data['course_number']
# handle course number if it is more than 3 digits
if len(course_number) > 3: raise Exception('Invalid course number')
gpa = registered_data['gpa']
major_id = Major.query.filter_by(major=course).first().id
course_data = Course.query.filter_by(major_id=major_id).filter_by(course_number=course_number).first()
# if there's no filter result, then add it
if not course_data:
course_data = Course(major_id,course_number)
db.session.add(course_data)
db.session.commit()
# after commit, add gpa data
grade_map = {
'A+/A': 4.00,
'A-': 3.67,
'B+': 3.33,
'B': 3.00,
'B-': 2.67,
'C+': 2.33,
'C': 2.00,
'C-': 1.67,
'D+': 1.33,
'D': 1.00,
'D-': 0.67,
'F': 0.00,
}
# handle error case if the grade does not belong to the category
if gpa not in grade_map: raise Exception('Given grade does not belong to the grade map')
gpa_data = GPA(current_user.id, course_data.id, grade_map[gpa])
db.session.add(gpa_data)
db.session.commit()
status = True
message = 'Successfully registered gpa information'
except Exception as e:
status = False
message = str(e)
return jsonify({
'status':status,
'message':message
})
app.register_blueprint(register, url_prefix='/register')
| <filename>server/grade_view/blue_prints/register.py
from flask import Blueprint, request, jsonify
from flask_login import login_required, current_user
from grade_view import db, app
from grade_view.models import User, Course, GPA, Major
register = Blueprint('register', __name__)
@register.route('/gpa',methods=['GET','POST'])
@login_required
def profile():
if request.method == 'GET':
gpas = GPA.query.filter_by(user_id=current_user.id).all()
json = {
'status': True,
'info': []
}
for gpa in gpas:
course = gpa.course
json['info'].append({
'course': course.major.major,
'course_number': course.course_number,
'gpa': gpa.gpa
})
return jsonify(json)
elif request.method == 'POST':
registered_data = request.get_json()
print(registered_data)
try:
course = registered_data['course']
course_number = registered_data['course_number']
# handle course number if it is more than 3 digits
if len(course_number) > 3: raise Exception('Invalid course number')
gpa = registered_data['gpa']
major_id = Major.query.filter_by(major=course).first().id
course_data = Course.query.filter_by(major_id=major_id).filter_by(course_number=course_number).first()
# if there's no filter result, then add it
if not course_data:
course_data = Course(major_id,course_number)
db.session.add(course_data)
db.session.commit()
# after commit, add gpa data
grade_map = {
'A+/A': 4.00,
'A-': 3.67,
'B+': 3.33,
'B': 3.00,
'B-': 2.67,
'C+': 2.33,
'C': 2.00,
'C-': 1.67,
'D+': 1.33,
'D': 1.00,
'D-': 0.67,
'F': 0.00,
}
# handle error case if the grade does not belong to the category
if gpa not in grade_map: raise Exception('Given grade does not belong to the grade map')
gpa_data = GPA(current_user.id, course_data.id, grade_map[gpa])
db.session.add(gpa_data)
db.session.commit()
status = True
message = 'Successfully registered gpa information'
except Exception as e:
status = False
message = str(e)
return jsonify({
'status':status,
'message':message
})
app.register_blueprint(register, url_prefix='/register')
| en | 0.735311 | # handle course number if it is more than 3 digits # if there's no filter result, then add it # after commit, add gpa data # handle error case if the grade does not belong to the category | 2.915622 | 3 |
tests/beacon/test_aggregation.py | Bhargavasomu/py-evm | 0 | 6630532 | <reponame>Bhargavasomu/py-evm
import pytest
from hypothesis import (
given,
settings,
strategies as st,
)
from eth._utils import bls
from eth._utils.bitfield import (
get_empty_bitfield,
has_voted,
)
from eth.beacon.aggregation import (
aggregate_votes,
verify_votes,
)
@settings(max_examples=1)
@given(random=st.randoms())
@pytest.mark.parametrize(
(
'votes_count'
),
[
(0),
(9),
],
)
def test_aggregate_votes(votes_count, random, privkeys, pubkeys):
bit_count = 10
pre_bitfield = get_empty_bitfield(bit_count)
pre_sigs = ()
domain = 0
random_votes = random.sample(range(bit_count), votes_count)
message = b'hello'
# Get votes: (committee_index, sig, public_key)
votes = [
(
committee_index,
bls.sign(message, privkeys[committee_index], domain),
pubkeys[committee_index],
)
for committee_index in random_votes
]
# Verify
sigs, committee_indices = verify_votes(message, votes, domain)
# Aggregate the votes
bitfield, sigs = aggregate_votes(
bitfield=pre_bitfield,
sigs=pre_sigs,
voting_sigs=sigs,
voting_committee_indices=committee_indices
)
try:
_, _, pubs = zip(*votes)
except ValueError:
pubs = ()
voted_index = [
committee_index
for committee_index in random_votes
if has_voted(bitfield, committee_index)
]
assert len(voted_index) == len(votes)
aggregated_pubs = bls.aggregate_pubkeys(pubs)
assert bls.verify(message, aggregated_pubs, sigs, domain)
| import pytest
from hypothesis import (
given,
settings,
strategies as st,
)
from eth._utils import bls
from eth._utils.bitfield import (
get_empty_bitfield,
has_voted,
)
from eth.beacon.aggregation import (
aggregate_votes,
verify_votes,
)
@settings(max_examples=1)
@given(random=st.randoms())
@pytest.mark.parametrize(
(
'votes_count'
),
[
(0),
(9),
],
)
def test_aggregate_votes(votes_count, random, privkeys, pubkeys):
bit_count = 10
pre_bitfield = get_empty_bitfield(bit_count)
pre_sigs = ()
domain = 0
random_votes = random.sample(range(bit_count), votes_count)
message = b'hello'
# Get votes: (committee_index, sig, public_key)
votes = [
(
committee_index,
bls.sign(message, privkeys[committee_index], domain),
pubkeys[committee_index],
)
for committee_index in random_votes
]
# Verify
sigs, committee_indices = verify_votes(message, votes, domain)
# Aggregate the votes
bitfield, sigs = aggregate_votes(
bitfield=pre_bitfield,
sigs=pre_sigs,
voting_sigs=sigs,
voting_committee_indices=committee_indices
)
try:
_, _, pubs = zip(*votes)
except ValueError:
pubs = ()
voted_index = [
committee_index
for committee_index in random_votes
if has_voted(bitfield, committee_index)
]
assert len(voted_index) == len(votes)
aggregated_pubs = bls.aggregate_pubkeys(pubs)
assert bls.verify(message, aggregated_pubs, sigs, domain) | en | 0.649237 | # Get votes: (committee_index, sig, public_key) # Verify # Aggregate the votes | 2.248288 | 2 |
problem0055.py | kmarcini/Project-Euler-Python | 0 | 6630533 | ###########################
#
# #55 Lychrel numbers - Project Euler
# https://projecteuler.net/problem=55
#
# Code by <NAME>
#
###########################
| ###########################
#
# #55 Lychrel numbers - Project Euler
# https://projecteuler.net/problem=55
#
# Code by <NAME>
#
###########################
| de | 0.362785 | ########################### # # #55 Lychrel numbers - Project Euler # https://projecteuler.net/problem=55 # # Code by <NAME> # ########################### | 1.126742 | 1 |
migrations/versions/e9f614a8dce6_.py | Ahmad-Zaky/fuyyr_udacity_proj | 0 | 6630534 | """empty message
Revision ID: e9f614a8dce6
Revises: <PASSWORD>
Create Date: 2021-02-28 05:23:44.254402
"""
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision = 'e9f614a8dce6'
down_revision = '<PASSWORD>'
branch_labels = None
depends_on = None
def upgrade():
# ### commands auto generated by Alembic - please adjust! ###
op.create_table('shows',
sa.Column('venue_id', sa.Integer(), nullable=False),
sa.Column('artist_id', sa.Integer(), nullable=False),
sa.Column('start_time', sa.DateTime(), server_default=sa.text('now()'), nullable=True),
sa.ForeignKeyConstraint(['artist_id'], ['artists.id'], ),
sa.ForeignKeyConstraint(['venue_id'], ['venues.id'], ),
sa.PrimaryKeyConstraint('venue_id', 'artist_id')
)
op.add_column('artists', sa.Column('created_time', sa.DateTime(), server_default=sa.text('now()'), nullable=True))
op.add_column('artists', sa.Column('modified_time', sa.DateTime(), server_default=sa.text('now()'), nullable=True))
op.add_column('venues', sa.Column('created_time', sa.DateTime(), server_default=sa.text('now()'), nullable=True))
op.add_column('venues', sa.Column('modified_time', sa.DateTime(), server_default=sa.text('now()'), nullable=True))
# ### end Alembic commands ###
def downgrade():
# ### commands auto generated by Alembic - please adjust! ###
op.drop_column('venues', 'modified_time')
op.drop_column('venues', 'created_time')
op.drop_column('artists', 'modified_time')
op.drop_column('artists', 'created_time')
op.drop_table('shows')
# ### end Alembic commands ###
| """empty message
Revision ID: e9f614a8dce6
Revises: <PASSWORD>
Create Date: 2021-02-28 05:23:44.254402
"""
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision = 'e9f614a8dce6'
down_revision = '<PASSWORD>'
branch_labels = None
depends_on = None
def upgrade():
# ### commands auto generated by Alembic - please adjust! ###
op.create_table('shows',
sa.Column('venue_id', sa.Integer(), nullable=False),
sa.Column('artist_id', sa.Integer(), nullable=False),
sa.Column('start_time', sa.DateTime(), server_default=sa.text('now()'), nullable=True),
sa.ForeignKeyConstraint(['artist_id'], ['artists.id'], ),
sa.ForeignKeyConstraint(['venue_id'], ['venues.id'], ),
sa.PrimaryKeyConstraint('venue_id', 'artist_id')
)
op.add_column('artists', sa.Column('created_time', sa.DateTime(), server_default=sa.text('now()'), nullable=True))
op.add_column('artists', sa.Column('modified_time', sa.DateTime(), server_default=sa.text('now()'), nullable=True))
op.add_column('venues', sa.Column('created_time', sa.DateTime(), server_default=sa.text('now()'), nullable=True))
op.add_column('venues', sa.Column('modified_time', sa.DateTime(), server_default=sa.text('now()'), nullable=True))
# ### end Alembic commands ###
def downgrade():
# ### commands auto generated by Alembic - please adjust! ###
op.drop_column('venues', 'modified_time')
op.drop_column('venues', 'created_time')
op.drop_column('artists', 'modified_time')
op.drop_column('artists', 'created_time')
op.drop_table('shows')
# ### end Alembic commands ###
| en | 0.472817 | empty message Revision ID: e9f614a8dce6 Revises: <PASSWORD> Create Date: 2021-02-28 05:23:44.254402 # revision identifiers, used by Alembic. # ### commands auto generated by Alembic - please adjust! ### # ### end Alembic commands ### # ### commands auto generated by Alembic - please adjust! ### # ### end Alembic commands ### | 1.847127 | 2 |
test_flood.py | henrywall56/Lab-Group-1-U-H | 0 | 6630535 | <reponame>henrywall56/Lab-Group-1-U-H
from numpy import float32
from floodsystem.flood import stations_level_over_threshold, stations_highest_rel_level
from floodsystem.stationdata import build_station_list, update_water_levels
from floodsystem.station import MonitoringStation
def test_stations_level_over_threshold(): #checking the output of the function is as intended
stations = build_station_list()
update_water_levels(stations)
over_threshold_list = stations_level_over_threshold(stations, tol=0.8)
assert isinstance(over_threshold_list, list)
for i in over_threshold_list:
assert isinstance(i, tuple)
assert isinstance(i[0], MonitoringStation)
assert isinstance(i[1], float)
def test_stations_highest_rel_level():
stations = build_station_list()
update_water_levels(stations)
N=10
first_10_stations = stations_highest_rel_level(stations, N)
assert isinstance(first_10_stations, list)
assert len(first_10_stations) == 10
for i in first_10_stations:
assert isinstance(i, tuple)
assert isinstance(i[0], MonitoringStation)
assert isinstance(i[1], float)
| from numpy import float32
from floodsystem.flood import stations_level_over_threshold, stations_highest_rel_level
from floodsystem.stationdata import build_station_list, update_water_levels
from floodsystem.station import MonitoringStation
def test_stations_level_over_threshold(): #checking the output of the function is as intended
stations = build_station_list()
update_water_levels(stations)
over_threshold_list = stations_level_over_threshold(stations, tol=0.8)
assert isinstance(over_threshold_list, list)
for i in over_threshold_list:
assert isinstance(i, tuple)
assert isinstance(i[0], MonitoringStation)
assert isinstance(i[1], float)
def test_stations_highest_rel_level():
stations = build_station_list()
update_water_levels(stations)
N=10
first_10_stations = stations_highest_rel_level(stations, N)
assert isinstance(first_10_stations, list)
assert len(first_10_stations) == 10
for i in first_10_stations:
assert isinstance(i, tuple)
assert isinstance(i[0], MonitoringStation)
assert isinstance(i[1], float) | en | 0.909578 | #checking the output of the function is as intended | 2.844965 | 3 |
binstar_client/inspect_package/tests/test_pypi.py | rpk101/anaconda-client | 98 | 6630536 | from __future__ import print_function, unicode_literals
import os
import shutil
import tempfile
import unittest
from pprint import pprint
from binstar_client.inspect_package import pypi
from binstar_client.utils.test.utils import data_dir
expected_package_data = {'name': 'test-package34',
'license': 'custom',
'summary': 'Python test package for binstar client'}
expected_version_data = {'home_page': 'http://github.com/binstar/binstar_pypi',
'version': '0.3.1',
'description':'longer description of the package'}
expected_dependencies = {'depends': [ {'name': u'python-dateutil', 'specs': []},
{'name': u'pytz', 'specs': []},
{'name': u'pyyaml', 'specs': []},
{'name': u'requests', 'specs': [(u'>=', u'2.0'), (u'<=', u'3.0')]}, ],
'extras': [{'depends': [{'name': u'argparse',
'specs': []}],
'name': u':python_version=="2.6"'},
{'depends': [{'name': u'reportlab',
'specs': [(u'>=', u'1.2')]},
{'name': u'rxp', 'specs': []}],
'name': u'PDF'},
{'depends': [{'name': u'docutils',
'specs': [(u'>=', u'0.3')]}],
'name': u'reST'}],
'has_dep_errors': False}
expected_whl_dependencies = {u'depends': [{u'name': u'python-dateutil', u'specs': []},
{u'name': u'pytz', u'specs': []},
{u'name': u'pyyaml', u'specs': []},
{u'name': u'requests',
u'specs': [(u'>=', u'2.0'),
(u'<=', u'3.0')]}],
u'environments': [{u'depends': [{u'name': u'argparse',
u'specs': []}],
u'name': u'python_version=="2.6"'}],
u'extras': [{u'depends': [{u'name': u'reportlab',
u'specs': [(u'>=', u'1.2')]},
{u'name': u'rxp',
u'specs': []},
],
u'name': u'PDF'},
{u'depends': [{u'name': u'docutils',
u'specs': [(u'>=', u'0.3')]}],
u'name': u'reST'}],
u'has_dep_errors': False}
expected_egg_file_data = {'attrs': {'packagetype': 'bdist_egg', 'python_version': 'source'},
'basename': 'test_package34-0.3.1-py2.7.egg',
'dependencies': expected_dependencies,
'platform': None}
class Test(unittest.TestCase):
maxDiff = None
def test_sdist(self):
filename = data_dir('test_package34-0.3.1.tar.gz')
with open(filename, 'rb') as fd:
package_data, version_data, file_data = pypi.inspect_pypi_package(filename, fd)
expected_file_data = {'attrs': {'packagetype': 'sdist', 'python_version': 'source'},
'basename': 'test_package34-0.3.1.tar.gz',
'dependencies': expected_dependencies}
self.assertEqual(expected_package_data, package_data)
self.assertEqual(expected_version_data, version_data)
self.assertEqual(set(expected_file_data), set(file_data))
for key in expected_file_data:
self.assertEqual(expected_file_data[key], file_data[key])
def test_bdist_wheel(self):
filename = data_dir('test_package34-0.3.1-py2-none-any.whl')
with open(filename, 'rb') as fd:
package_data, version_data, file_data = pypi.inspect_pypi_package(filename, fd)
expected_file_data = {'attrs': {'abi': None, 'build_no': 0,
'packagetype': 'bdist_wheel',
'python_version': 'py2'},
'basename': 'test_package34-0.3.1-py2-none-any.whl',
'dependencies': expected_whl_dependencies,
'platform': None}
self.assertEqual(expected_package_data, package_data)
self.assertEqual(expected_version_data, version_data)
self.assertEqual(set(expected_file_data), set(file_data))
for key in expected_file_data:
print(expected_file_data[key])
print(file_data[key])
self.assertEqual(expected_file_data[key], file_data[key])
def test_bdist_wheel_newer_version(self):
filename_whl = 'azure_cli_extension-0.2.1-py2.py3-none-any.whl'
filename = data_dir(filename_whl)
with open(filename, 'rb') as fd:
package_data, version_data, file_data = pypi.inspect_pypi_package(filename, fd)
expected_file_data = {
'platform': None,
'basename': filename_whl,
'dependencies': {
u'depends': [
{u'name': u'azure-cli-command-modules-nspkg', u'specs': [(u'>=', u'2.0.0')]},
{u'name': u'azure-cli-core', u'specs': []}, {u'name': u'pip', u'specs': []},
{u'name': u'wheel', u'specs': [(u'==', u'0.30.0')]}
],
u'extras': [],
u'has_dep_errors': False,
u'environments': []},
u'attrs': {
'abi': None,
'packagetype': u'bdist_wheel',
'python_version': u'py2.py3',
'build_no': 0
}
}
expected_package_data = {
u'name': 'azure-cli-extension',
u'license': 'MIT',
u'summary': 'Microsoft Azure Command-Line Tools Extension Command Module',
}
expected_version_data = {
u'home_page': 'https://github.com/Azure/azure-cli',
u'version': '0.2.1',
u'description': u"Microsoft Azure CLI 'extension' Command Module",
}
self.assertEqual(expected_package_data, package_data)
self.assertEqual(expected_version_data, version_data)
self.assertEqual(set(expected_file_data), set(file_data))
for key in expected_file_data:
print(expected_file_data[key])
print(file_data[key])
self.assertEqual(expected_file_data[key], file_data[key])
def test_bdist_egg(self):
filename = data_dir('test_package34-0.3.1-py2.7.egg')
with open(filename, 'rb') as fd:
package_data, version_data, file_data = pypi.inspect_pypi_package(filename, fd)
self.assertEqual(expected_package_data, package_data)
self.assertEqual(expected_version_data, version_data)
self.assertEqual(set(expected_egg_file_data), set(file_data))
for key in expected_egg_file_data:
self.assertEqual(expected_egg_file_data[key], file_data[key])
def test_bdist_egg_dashed_path(self):
filename = data_dir('test_package34-0.3.1-py2.7.egg')
tmpdir = tempfile.gettempdir()
dash_count = tmpdir.count('-')
if dash_count == 0:
tmpdir = os.path.join(tmpdir, 'has-dash')
try:
os.mkdir(tmpdir)
except (IOError, OSError):
raise unittest.SkipTest('Cannot create temporary directory %r' % tmpdir)
elif dash_count > 1:
raise unittest.SkipTest('Too many dashes in temporary directory path %r' % tmpdir)
try:
shutil.copy(filename, tmpdir)
except (IOError, OSError):
raise unittest.SkipTest('Cannot copy package to temporary directory')
tmpfilename = os.path.join(tmpdir, 'test_package34-0.3.1-py2.7.egg')
with open(tmpfilename, 'rb') as fd:
package_data, version_data, file_data = pypi.inspect_pypi_package(tmpfilename, fd)
# If we could create this file, we ought to be able to delete it
os.remove(tmpfilename)
if dash_count == 0:
# We created a temporary directory like /tmp/has-dash, delete it
os.rmdir(tmpdir)
self.assertEqual(expected_package_data, package_data)
self.assertEqual(expected_version_data, version_data)
self.assertEqual(set(expected_egg_file_data), set(file_data))
self.assertEqual(expected_egg_file_data['platform'], file_data['platform'])
self.assertEqual(expected_egg_file_data['attrs']['python_version'],
file_data['attrs']['python_version'])
def test_sdist_distutils(self):
filename = data_dir('test_package34-distutils-0.3.1.tar.gz')
with open(filename, 'rb') as fd:
package_data, version_data, file_data = pypi.inspect_pypi_package(filename, fd)
expected_file_data = {'attrs': {'packagetype': 'sdist', 'python_version': 'source'},
'basename': 'test_package34-distutils-0.3.1.tar.gz',
'dependencies': {'depends': [{'name': 'requests',
'specs': [('>=', '2.0'), ('<=', '3.0')]},
{'name': 'pyyaml', 'specs': [('==', '2.0')]},
{'name': 'pytz', 'specs': []}],
'extras': [],
'has_dep_errors': False}}
dexpected_package_data = expected_package_data.copy()
dexpected_package_data['name'] = dexpected_package_data['name'].replace('-', '_')
self.assertEqual(dexpected_package_data, package_data)
self.assertEqual(expected_version_data, version_data)
self.assertEqual(set(expected_file_data), set(file_data))
for key in expected_file_data:
print(expected_file_data[key])
print(file_data[key])
self.assertEqual(expected_file_data[key], file_data[key])
if __name__ == "__main__":
# import sys;sys.argv = ['', 'Test.testName']
unittest.main()
| from __future__ import print_function, unicode_literals
import os
import shutil
import tempfile
import unittest
from pprint import pprint
from binstar_client.inspect_package import pypi
from binstar_client.utils.test.utils import data_dir
expected_package_data = {'name': 'test-package34',
'license': 'custom',
'summary': 'Python test package for binstar client'}
expected_version_data = {'home_page': 'http://github.com/binstar/binstar_pypi',
'version': '0.3.1',
'description':'longer description of the package'}
expected_dependencies = {'depends': [ {'name': u'python-dateutil', 'specs': []},
{'name': u'pytz', 'specs': []},
{'name': u'pyyaml', 'specs': []},
{'name': u'requests', 'specs': [(u'>=', u'2.0'), (u'<=', u'3.0')]}, ],
'extras': [{'depends': [{'name': u'argparse',
'specs': []}],
'name': u':python_version=="2.6"'},
{'depends': [{'name': u'reportlab',
'specs': [(u'>=', u'1.2')]},
{'name': u'rxp', 'specs': []}],
'name': u'PDF'},
{'depends': [{'name': u'docutils',
'specs': [(u'>=', u'0.3')]}],
'name': u'reST'}],
'has_dep_errors': False}
expected_whl_dependencies = {u'depends': [{u'name': u'python-dateutil', u'specs': []},
{u'name': u'pytz', u'specs': []},
{u'name': u'pyyaml', u'specs': []},
{u'name': u'requests',
u'specs': [(u'>=', u'2.0'),
(u'<=', u'3.0')]}],
u'environments': [{u'depends': [{u'name': u'argparse',
u'specs': []}],
u'name': u'python_version=="2.6"'}],
u'extras': [{u'depends': [{u'name': u'reportlab',
u'specs': [(u'>=', u'1.2')]},
{u'name': u'rxp',
u'specs': []},
],
u'name': u'PDF'},
{u'depends': [{u'name': u'docutils',
u'specs': [(u'>=', u'0.3')]}],
u'name': u'reST'}],
u'has_dep_errors': False}
expected_egg_file_data = {'attrs': {'packagetype': 'bdist_egg', 'python_version': 'source'},
'basename': 'test_package34-0.3.1-py2.7.egg',
'dependencies': expected_dependencies,
'platform': None}
class Test(unittest.TestCase):
maxDiff = None
def test_sdist(self):
filename = data_dir('test_package34-0.3.1.tar.gz')
with open(filename, 'rb') as fd:
package_data, version_data, file_data = pypi.inspect_pypi_package(filename, fd)
expected_file_data = {'attrs': {'packagetype': 'sdist', 'python_version': 'source'},
'basename': 'test_package34-0.3.1.tar.gz',
'dependencies': expected_dependencies}
self.assertEqual(expected_package_data, package_data)
self.assertEqual(expected_version_data, version_data)
self.assertEqual(set(expected_file_data), set(file_data))
for key in expected_file_data:
self.assertEqual(expected_file_data[key], file_data[key])
def test_bdist_wheel(self):
filename = data_dir('test_package34-0.3.1-py2-none-any.whl')
with open(filename, 'rb') as fd:
package_data, version_data, file_data = pypi.inspect_pypi_package(filename, fd)
expected_file_data = {'attrs': {'abi': None, 'build_no': 0,
'packagetype': 'bdist_wheel',
'python_version': 'py2'},
'basename': 'test_package34-0.3.1-py2-none-any.whl',
'dependencies': expected_whl_dependencies,
'platform': None}
self.assertEqual(expected_package_data, package_data)
self.assertEqual(expected_version_data, version_data)
self.assertEqual(set(expected_file_data), set(file_data))
for key in expected_file_data:
print(expected_file_data[key])
print(file_data[key])
self.assertEqual(expected_file_data[key], file_data[key])
def test_bdist_wheel_newer_version(self):
filename_whl = 'azure_cli_extension-0.2.1-py2.py3-none-any.whl'
filename = data_dir(filename_whl)
with open(filename, 'rb') as fd:
package_data, version_data, file_data = pypi.inspect_pypi_package(filename, fd)
expected_file_data = {
'platform': None,
'basename': filename_whl,
'dependencies': {
u'depends': [
{u'name': u'azure-cli-command-modules-nspkg', u'specs': [(u'>=', u'2.0.0')]},
{u'name': u'azure-cli-core', u'specs': []}, {u'name': u'pip', u'specs': []},
{u'name': u'wheel', u'specs': [(u'==', u'0.30.0')]}
],
u'extras': [],
u'has_dep_errors': False,
u'environments': []},
u'attrs': {
'abi': None,
'packagetype': u'bdist_wheel',
'python_version': u'py2.py3',
'build_no': 0
}
}
expected_package_data = {
u'name': 'azure-cli-extension',
u'license': 'MIT',
u'summary': 'Microsoft Azure Command-Line Tools Extension Command Module',
}
expected_version_data = {
u'home_page': 'https://github.com/Azure/azure-cli',
u'version': '0.2.1',
u'description': u"Microsoft Azure CLI 'extension' Command Module",
}
self.assertEqual(expected_package_data, package_data)
self.assertEqual(expected_version_data, version_data)
self.assertEqual(set(expected_file_data), set(file_data))
for key in expected_file_data:
print(expected_file_data[key])
print(file_data[key])
self.assertEqual(expected_file_data[key], file_data[key])
def test_bdist_egg(self):
filename = data_dir('test_package34-0.3.1-py2.7.egg')
with open(filename, 'rb') as fd:
package_data, version_data, file_data = pypi.inspect_pypi_package(filename, fd)
self.assertEqual(expected_package_data, package_data)
self.assertEqual(expected_version_data, version_data)
self.assertEqual(set(expected_egg_file_data), set(file_data))
for key in expected_egg_file_data:
self.assertEqual(expected_egg_file_data[key], file_data[key])
def test_bdist_egg_dashed_path(self):
filename = data_dir('test_package34-0.3.1-py2.7.egg')
tmpdir = tempfile.gettempdir()
dash_count = tmpdir.count('-')
if dash_count == 0:
tmpdir = os.path.join(tmpdir, 'has-dash')
try:
os.mkdir(tmpdir)
except (IOError, OSError):
raise unittest.SkipTest('Cannot create temporary directory %r' % tmpdir)
elif dash_count > 1:
raise unittest.SkipTest('Too many dashes in temporary directory path %r' % tmpdir)
try:
shutil.copy(filename, tmpdir)
except (IOError, OSError):
raise unittest.SkipTest('Cannot copy package to temporary directory')
tmpfilename = os.path.join(tmpdir, 'test_package34-0.3.1-py2.7.egg')
with open(tmpfilename, 'rb') as fd:
package_data, version_data, file_data = pypi.inspect_pypi_package(tmpfilename, fd)
# If we could create this file, we ought to be able to delete it
os.remove(tmpfilename)
if dash_count == 0:
# We created a temporary directory like /tmp/has-dash, delete it
os.rmdir(tmpdir)
self.assertEqual(expected_package_data, package_data)
self.assertEqual(expected_version_data, version_data)
self.assertEqual(set(expected_egg_file_data), set(file_data))
self.assertEqual(expected_egg_file_data['platform'], file_data['platform'])
self.assertEqual(expected_egg_file_data['attrs']['python_version'],
file_data['attrs']['python_version'])
def test_sdist_distutils(self):
filename = data_dir('test_package34-distutils-0.3.1.tar.gz')
with open(filename, 'rb') as fd:
package_data, version_data, file_data = pypi.inspect_pypi_package(filename, fd)
expected_file_data = {'attrs': {'packagetype': 'sdist', 'python_version': 'source'},
'basename': 'test_package34-distutils-0.3.1.tar.gz',
'dependencies': {'depends': [{'name': 'requests',
'specs': [('>=', '2.0'), ('<=', '3.0')]},
{'name': 'pyyaml', 'specs': [('==', '2.0')]},
{'name': 'pytz', 'specs': []}],
'extras': [],
'has_dep_errors': False}}
dexpected_package_data = expected_package_data.copy()
dexpected_package_data['name'] = dexpected_package_data['name'].replace('-', '_')
self.assertEqual(dexpected_package_data, package_data)
self.assertEqual(expected_version_data, version_data)
self.assertEqual(set(expected_file_data), set(file_data))
for key in expected_file_data:
print(expected_file_data[key])
print(file_data[key])
self.assertEqual(expected_file_data[key], file_data[key])
if __name__ == "__main__":
# import sys;sys.argv = ['', 'Test.testName']
unittest.main()
| en | 0.897283 | # If we could create this file, we ought to be able to delete it # We created a temporary directory like /tmp/has-dash, delete it # import sys;sys.argv = ['', 'Test.testName'] | 1.909 | 2 |
uspto_tools/parse/__init__.py | clicumu/uspto-tools | 0 | 6630537 | <reponame>clicumu/uspto-tools
from . import aps, patent, patft_html, sgml, xml | from . import aps, patent, patft_html, sgml, xml | none | 1 | 1.057074 | 1 |
|
src/tools.py/mapKeyboard.py | Mambix/Keyduino | 3 | 6630538 | <filename>src/tools.py/mapKeyboard.py
#!/usr/bin/env python
# Filename: mapKeyboard.py
'''
Created on 24 Jul 2016
@author: <EMAIL>
'''
import serial
import json
keys = {}
if __name__ == '__main__':
ser = serial.Serial('COM9', 115200)
cnt = 0
try:
if ser.is_open == False:
ser.open()
while cnt<100:
ln1 = ser.readline()
ln2 = ser.readline()
# ln3 = ser.readline()
# ln4 = ser.readline()
keys['KEY_%0.2X' % cnt] = [ln1, ln2] # , ln3, ln4]
cnt = cnt + 1
except Exception as msg:
print(msg)
if ser.is_open == True:
ser.close()
with open('%s_decoded.json' % 'PK1306R1A08', 'w') as f:
f.write(json.dumps(keys, indent=2, sort_keys=True))
| <filename>src/tools.py/mapKeyboard.py
#!/usr/bin/env python
# Filename: mapKeyboard.py
'''
Created on 24 Jul 2016
@author: <EMAIL>
'''
import serial
import json
keys = {}
if __name__ == '__main__':
ser = serial.Serial('COM9', 115200)
cnt = 0
try:
if ser.is_open == False:
ser.open()
while cnt<100:
ln1 = ser.readline()
ln2 = ser.readline()
# ln3 = ser.readline()
# ln4 = ser.readline()
keys['KEY_%0.2X' % cnt] = [ln1, ln2] # , ln3, ln4]
cnt = cnt + 1
except Exception as msg:
print(msg)
if ser.is_open == True:
ser.close()
with open('%s_decoded.json' % 'PK1306R1A08', 'w') as f:
f.write(json.dumps(keys, indent=2, sort_keys=True))
| en | 0.29883 | #!/usr/bin/env python # Filename: mapKeyboard.py Created on 24 Jul 2016 @author: <EMAIL> # ln3 = ser.readline() # ln4 = ser.readline() # , ln3, ln4] | 2.597744 | 3 |
test_pypasshash.py | NeverMine17/pypasshash | 0 | 6630539 | <gh_stars>0
import unittest
import pypasshash
tests_version_1 = [
['test123', 'asd', 'w9XHj6TXSSC4'],
['qwerty', 'qwerty', 't8BvIfZIqw8i'],
['123', '123', 'd7JCE8mdIksv'],
['12345', '12345', 'f8Ew2fPdXNDB'],
['5agRrdKKPFWkT3pj5dwNddyA', 'jbuHzWA4ch7h73KugVHtqv6r', 'q1RjFnRTw2gq'],
['3VaY2WPxpfZU6z25MWhdexsK', 'V<KEY>', 'h3CUKhIANOS3'],
['NT2Sv5q7Ne4JVVyB4J8X3epX', 'bzBsz5LMtYdjK8Sv7Ntw72KS', 'b5FbaC4bCT5a'],
['eXsAeVTEBf9ZdqCnyfSfZYSy', '3afGM8nZpafjQxyFYS4bZjaG', 'w7JNYoywZIlS']
]
tests_version_2 = [
['test123', 'asd', 'u8NFNm8i4y0I'],
['qwerty', 'qwerty', 'n7XKKsaXAYnk'],
['123', '123', 'j8CC1DMjHpuJ'],
['12345', '12345', 'v6ZawS4UvMTr'],
['5agRrdKKPFWkT3pj5dwNddyA', 'jbuHzWA4ch7h73KugVHtqv6r', 'l9Q920XQlkk9'],
['3VaY2WPxpfZU6z25MWhdexsK', '<KEY>', 'j9HCjIiJYUoE'],
['NT2Sv5q7Ne4JVVyB4J8X3epX', 'bzBsz5LMtYdjK8Sv7Ntw72KS', 'n0PATXNeKgnG'],
['eXsAeVTEBf9ZdqCnyfSfZYSy', '3afGM8nZpafjQxyFYS4bZjaG', 'v5Nd1kEo4yvR']
]
class PassHashTest(unittest.TestCase):
def test_version_2(self):
for test in tests_version_2:
self.assertEqual(pypasshash.get_pass(test[0], test[1], 2), test[2])
def test_version_1(self):
for test in tests_version_1:
self.assertEqual(pypasshash.get_pass(test[0], test[1], 1), test[2])
if __name__ == '__main__':
unittest.main()
| import unittest
import pypasshash
tests_version_1 = [
['test123', 'asd', 'w9XHj6TXSSC4'],
['qwerty', 'qwerty', 't8BvIfZIqw8i'],
['123', '123', 'd7JCE8mdIksv'],
['12345', '12345', 'f8Ew2fPdXNDB'],
['5agRrdKKPFWkT3pj5dwNddyA', 'jbuHzWA4ch7h73KugVHtqv6r', 'q1RjFnRTw2gq'],
['3VaY2WPxpfZU6z25MWhdexsK', 'V<KEY>', 'h3CUKhIANOS3'],
['NT2Sv5q7Ne4JVVyB4J8X3epX', 'bzBsz5LMtYdjK8Sv7Ntw72KS', 'b5FbaC4bCT5a'],
['eXsAeVTEBf9ZdqCnyfSfZYSy', '3afGM8nZpafjQxyFYS4bZjaG', 'w7JNYoywZIlS']
]
tests_version_2 = [
['test123', 'asd', 'u8NFNm8i4y0I'],
['qwerty', 'qwerty', 'n7XKKsaXAYnk'],
['123', '123', 'j8CC1DMjHpuJ'],
['12345', '12345', 'v6ZawS4UvMTr'],
['5agRrdKKPFWkT3pj5dwNddyA', 'jbuHzWA4ch7h73KugVHtqv6r', 'l9Q920XQlkk9'],
['3VaY2WPxpfZU6z25MWhdexsK', '<KEY>', 'j9HCjIiJYUoE'],
['NT2Sv5q7Ne4JVVyB4J8X3epX', 'bzBsz5LMtYdjK8Sv7Ntw72KS', 'n0PATXNeKgnG'],
['eXsAeVTEBf9ZdqCnyfSfZYSy', '3afGM8nZpafjQxyFYS4bZjaG', 'v5Nd1kEo4yvR']
]
class PassHashTest(unittest.TestCase):
def test_version_2(self):
for test in tests_version_2:
self.assertEqual(pypasshash.get_pass(test[0], test[1], 2), test[2])
def test_version_1(self):
for test in tests_version_1:
self.assertEqual(pypasshash.get_pass(test[0], test[1], 1), test[2])
if __name__ == '__main__':
unittest.main() | none | 1 | 1.867559 | 2 |
|
VelodyneHDL/python/veloview/applogic.py | yajin1126/C-Users-yajin-Documents-veloview | 0 | 6630540 | <filename>VelodyneHDL/python/veloview/applogic.py
# Copyright 2013 Velodyne Acoustics, Inc.
#
# 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 os
import csv
import datetime
import time
import math
import paraview.simple as smp
from paraview import servermanager
from paraview import vtk
import PythonQt
from PythonQt import QtCore, QtGui
from vtkIOXMLPython import vtkXMLPolyDataWriter
import kiwiviewerExporter
import gridAdjustmentDialog
import planefit
from PythonQt.paraview import vvCalibrationDialog, vvCropReturnsDialog, vvSelectFramesDialog
from VelodyneHDLPluginPython import vtkVelodyneHDLReader
_repCache = {}
SAMPLE_PROCESSING_MODE = False
def cachedGetRepresentation(src, view):
try:
return _repCache[(src, view)]
except KeyError:
rep = smp.GetRepresentation(src, view)
_repCache[(src, view)] = rep
return rep
class AppLogic(object):
def __init__(self):
self.playing = False
self.playDirection = 1
self.seekPlayDirection = 1
self.seekPlay = False
self.targetFps = 30
self.renderIsPending = False
self.createStatusBarWidgets()
self.setupTimers()
self.mousePressed = False
mainView = smp.GetActiveView()
views = smp.GetRenderViews()
otherViews = [v for v in views if v != mainView]
assert len(otherViews) == 1
overheadView = otherViews[0]
self.mainView = mainView
self.overheadView = overheadView
self.transformMode = 0
self.relativeTransform = False
self.reader = None
self.position = (None, None, None)
self.sensor = None
self.fps = [0,0]
def setupTimers(self):
self.playTimer = QtCore.QTimer()
self.playTimer.setSingleShot(True)
self.playTimer.connect('timeout()', onPlayTimer)
self.seekTimer = QtCore.QTimer()
self.seekTimer.setSingleShot(True)
self.seekTimer.connect('timeout()', seekPressTimeout)
self.renderTimer = QtCore.QTimer()
self.renderTimer.setSingleShot(True)
self.renderTimer.connect('timeout()', forceRender)
def createStatusBarWidgets(self):
self.logoLabel = QtGui.QLabel()
self.logoLabel.setPixmap(QtGui.QPixmap(":/VelodyneHDLPlugin/velodyne_logo.png"))
self.logoLabel.setScaledContents(True)
self.filenameLabel = QtGui.QLabel()
self.statusLabel = QtGui.QLabel()
self.timeLabel = QtGui.QLabel()
class IconPaths(object):
trailingFrames = ':/VelodyneHDLPlugin/trailingframes.png'
play = ':/VelodyneHDLPlugin/media-playback-start.png'
pause =':/VelodyneHDLPlugin/media-playback-pause.png'
seekForward = ':/VelodyneHDLPlugin/media-seek-forward.png'
seekForward2x = ':/VelodyneHDLPlugin/media-seek-forward-2x.png'
seekForwardHalfx = ':/VelodyneHDLPlugin/media-seek-forward-0.5x.png'
seekForwardQuarterx = ':/VelodyneHDLPlugin/media-seek-forward-0.25x.png'
seekForward3x = ':/VelodyneHDLPlugin/media-seek-forward-3x.png'
seekBackward = ':/VelodyneHDLPlugin/media-seek-backward.png'
seekBackward2x = ':/VelodyneHDLPlugin/media-seek-backward-2x.png'
seekBackward3x = ':/VelodyneHDLPlugin/media-seek-backward-3x.png'
seekBackwardHalfx = ':/VelodyneHDLPlugin/media-seek-backward-0.5x.png'
seekBackwardQuarterx = ':/VelodyneHDLPlugin/media-seek-backward-0.25x.png'
def hasArrayName(sourceProxy, arrayName):
'''
Returns True if the data has non-zero points and has a point data
attribute with the given arrayName.
'''
info = sourceProxy.GetDataInformation().DataInformation
if info.GetNumberOfPoints() == 0:
return False
info = info.GetAttributeInformation(0)
for i in xrange(info.GetNumberOfArrays()):
if info.GetArrayInformation(i).GetName() == arrayName:
return True
return False
def openData(filename):
close()
reader = smp.OpenDataFile(filename, guiName='Data')
if not reader:
return
rep = smp.Show(reader)
rep.InterpolateScalarsBeforeMapping = 0
setDefaultLookupTables(reader)
colorByIntensity(reader)
showSourceInSpreadSheet(reader)
smp.GetActiveView().ViewTime = 0.0
app.reader = reader
app.filenameLabel.setText('File: %s' % os.path.basename(filename))
updateSliderTimeRange()
enablePlaybackActions()
enableSaveActions()
addRecentFile(filename)
app.actions['actionSavePCAP'].setEnabled(False)
app.actions['actionChoose_Calibration_File'].setEnabled(False)
app.actions['actionCropReturns'].setEnabled(False)
app.actions['actionRecord'].setEnabled(False)
app.actions['actionDualReturnModeDual'].enabled = True
app.actions['actionDualReturnDistanceNear'].enabled = True
app.actions['actionDualReturnDistanceFar'].enabled = True
app.actions['actionDualReturnIntensityHigh'].enabled = True
app.actions['actionDualReturnIntensityLow'].enabled = True
resetCamera()
def planeFit():
planefit.fitPlane()
def setDefaultLookupTables(sourceProxy):
# LUT for 'intensity'
smp.GetLookupTableForArray(
'intensity', 1,
ScalarRangeInitialized=1.0,
ColorSpace='HSV',
RGBPoints=[0.0, 0.0, 0.0, 1.0,
100.0, 1.0, 1.0, 0.0,
256.0, 1.0, 0.0, 0.0])
# LUT for 'dual_distance'
smp.GetLookupTableForArray(
'dual_distance', 1,
InterpretValuesAsCategories=True, NumberOfTableValues=3,
RGBPoints=[-1.0, 0.1, 0.5, 0.7,
0.0, 0.9, 0.9, 0.9,
+1.0, 0.8, 0.2, 0.3],
Annotations=['-1', 'near', '0', 'dual', '1', 'far'])
# LUT for 'dual_intensity'
smp.GetLookupTableForArray(
'dual_intensity', 1,
InterpretValuesAsCategories=True, NumberOfTableValues=3,
RGBPoints=[-1.0, 0.5, 0.2, 0.8,
0.0, 0.6, 0.6, 0.6,
+1.0, 1.0, 0.9, 0.4],
Annotations=['-1', 'low', '0', 'dual', '1', 'high'])
def colorByIntensity(sourceProxy):
if not hasArrayName(sourceProxy, 'intensity'):
return False
setDefaultLookupTables(sourceProxy)
rep = smp.GetDisplayProperties(sourceProxy)
rep.ColorArrayName = 'intensity'
rep.LookupTable = smp.GetLookupTableForArray('intensity', 1)
return True
def getTimeStamp():
format = '%Y-%m-%d-%H-%M-%S'
return datetime.datetime.now().strftime(format)
def getReaderFileName():
filename = getReader().FileName
return filename[0] if isinstance(filename, servermanager.FileNameProperty) else filename
def getDefaultSaveFileName(extension, suffix='', appendFrameNumber=False):
sensor = getSensor()
reader = getReader()
if sensor:
nchannels = sensor.GetPropertyValue('NumberOfChannels')
base = 'HDL-'
if nchannels <= 16:
base = 'VLP-'
sensortype = base + str(nchannels)
return '%s_Velodyne-%s-Data.%s' % (getTimeStamp(), sensortype, extension)
if reader:
basename = os.path.splitext(os.path.basename(getReaderFileName()))[0]
if appendFrameNumber:
suffix = '%s (Frame %04d)' % (suffix, int(app.scene.AnimationTime))
return '%s%s.%s' % (basename, suffix, extension)
def chooseCalibration():
class Calibration(object):
def __init__(self, dialog):
self.calibrationFile = dialog.selectedCalibrationFile()
self.gpsYaw = dialog.gpsYaw()
self.gpsRoll = dialog.gpsRoll()
self.gpsPitch = dialog.gpsPitch()
self.sensorTransform = vtk.vtkTransform()
qm = dialog.sensorTransform()
vm = vtk.vtkMatrix4x4()
for row in xrange(4):
vm.SetElement(row, 0, qm.row(row).x())
vm.SetElement(row, 1, qm.row(row).y())
vm.SetElement(row, 2, qm.row(row).z())
vm.SetElement(row, 3, qm.row(row).w())
self.sensorTransform.SetMatrix(vm)
dialog = vvCalibrationDialog(getMainWindow())
if not dialog.exec_():
return None
return Calibration(dialog)
def openSensor():
calibration = chooseCalibration()
if not calibration:
return
calibrationFile = calibration.calibrationFile
sensorTransform = calibration.sensorTransform
close()
sensor = smp.VelodyneHDLSource(guiName='Data', CalibrationFile=calibrationFile, CacheSize=100)
sensor.GetClientSideObject().SetSensorTransform(sensorTransform)
sensor.UpdatePipeline()
sensor.Start()
if SAMPLE_PROCESSING_MODE:
processor = smp.ProcessingSample(sensor)
smp.GetActiveView().ViewTime = 0.0
app.sensor = sensor
app.colorByInitialized = False
app.filenameLabel.setText('Live sensor stream.')
enablePlaybackActions()
enableSaveActions()
onCropReturns(False) # Dont show the dialog just restore settings
onLaserSelection(False)
rep = smp.Show(sensor)
rep.InterpolateScalarsBeforeMapping = 0
if SAMPLE_PROCESSING_MODE:
prep = smp.Show(processor)
smp.Render()
showSourceInSpreadSheet(sensor)
app.actions['actionDualReturnModeDual'].enabled = True
app.actions['actionDualReturnDistanceNear'].enabled = True
app.actions['actionDualReturnDistanceFar'].enabled = True
app.actions['actionDualReturnIntensityHigh'].enabled = True
app.actions['actionDualReturnIntensityLow'].enabled = True
play()
def openPCAP(filename, positionFilename=None):
calibration = chooseCalibration()
if not calibration:
return
calibrationFile = calibration.calibrationFile
sensorTransform = calibration.sensorTransform
close()
def onProgressEvent(o, e):
PythonQt.QtGui.QApplication.instance().processEvents()
progressDialog = QtGui.QProgressDialog('Reading packet file...', '', 0, 0, getMainWindow())
progressDialog.setCancelButton(None)
progressDialog.setModal(True)
progressDialog.show()
handler = servermanager.ActiveConnection.Session.GetProgressHandler()
handler.PrepareProgress()
freq = handler.GetProgressFrequency()
handler.SetProgressFrequency(0.05)
tag = handler.AddObserver('ProgressEvent', onProgressEvent)
# construct the reader, this calls UpdateInformation on the
# reader which scans the pcap file and emits progress events
reader = smp.VelodyneHDLReader(guiName='Data',
FileName=filename,
CalibrationFile=calibrationFile,
ApplyTransform=(app.transformMode > 0),
NumberOfTrailingFrames=app.trailingFramesSpinBox.value,
PointsSkip=app.trailingFramesSpinBox.value)
app.reader = reader
app.filenameLabel.setText('File: %s' % os.path.basename(filename))
onCropReturns(False) # Dont show the dialog just restore settings
onLaserSelection(False)
reader.GetClientSideObject().SetSensorTransform(sensorTransform)
if SAMPLE_PROCESSING_MODE:
processor = smp.ProcessingSample(reader)
handler.RemoveObserver(tag)
handler.SetProgressFrequency(freq)
progressDialog.close()
smp.GetActiveView().ViewTime = 0.0
rep = smp.Show(reader)
if SAMPLE_PROCESSING_MODE:
prep = smp.Show(processor)
app.scene.UpdateAnimationUsingDataTimeSteps()
# update overhead view
smp.SetActiveView(app.overheadView)
if positionFilename is None:
posreader = smp.VelodyneHDLPositionReader(guiName="Position",
FileName=filename)
else:
posreader = smp.ApplanixPositionReader(guiName="Position",
FileName=positionFilename)
posreader.BaseYaw = calibration.gpsYaw
posreader.BaseRoll = calibration.gpsRoll
posreader.BasePitch = calibration.gpsPitch
smp.Show(posreader)
# Create a sphere glyph
g = smp.Sphere()
g.Radius = 5.0
smp.Show(g)
if posreader.GetClientSideObject().GetOutput().GetNumberOfPoints():
reader.GetClientSideObject().SetInterpolator(
posreader.GetClientSideObject().GetInterpolator())
smp.Render(app.overheadView)
app.overheadView.ResetCamera()
trange = posreader.GetPointDataInformation().GetArray('time').GetRange()
# By construction time zero is at position 0,0,0
# Setup scalar bar
rep = smp.GetDisplayProperties(posreader)
rep.ColorArrayName = 'time'
rgbPoints = [trange[0], 0.0, 0.0, 1.0,
trange[1], 1.0, 0.0, 0.0]
rep.LookupTable = smp.GetLookupTableForArray('time', 1,
RGBPoints=rgbPoints,
ScalarRangeInitialized=1.0)
sb = smp.CreateScalarBar(LookupTable=rep.LookupTable, Title='Time')
sb.Orientation = 'Horizontal'
sb.Position, sb.Position2 = [.1, .05], [.8, .02]
app.overheadView.Representations.append(sb)
app.position = (posreader, None, g)
smp.Render(app.overheadView)
else:
if positionFilename is not None:
QtGui.QMessageBox.warning(getMainWindow(), 'Georeferncing data invalid',
'File %s is not supported' % positionFilename)
smp.Delete(posreader)
smp.SetActiveView(app.mainView)
smp.SetActiveSource(reader)
rep.InterpolateScalarsBeforeMapping = 0
setDefaultLookupTables(reader)
colorByIntensity(reader)
showSourceInSpreadSheet(reader)
updateSliderTimeRange()
updatePosition()
enablePlaybackActions()
enableSaveActions()
addRecentFile(filename)
app.actions['actionRecord'].setEnabled(False)
app.actions['actionDualReturnModeDual'].enabled = True
app.actions['actionDualReturnDistanceNear'].enabled = True
app.actions['actionDualReturnDistanceFar'].enabled = True
app.actions['actionDualReturnIntensityHigh'].enabled = True
app.actions['actionDualReturnIntensityLow'].enabled = True
resetCamera()
def hideMeasurementGrid():
rep = smp.GetDisplayProperties(app.grid)
rep.Visibility = 0
smp.Render()
def showMeasurementGrid():
rep = smp.GetDisplayProperties(app.grid)
rep.Visibility = 1
smp.Render()
# Start Functions related to ruler
def createRuler():
pxm = servermanager.ProxyManager()
distancerep = pxm.NewProxy('representations', 'DistanceWidgetRepresentation')
distancerepeasy = servermanager._getPyProxy(distancerep)
smp.GetActiveView().Representations.append(distancerepeasy)
distancerepeasy.Visibility = False
smp.Render()
return distancerepeasy
def hideRuler():
app.ruler.Visibility = False
smp.Render()
def showRuler():
app.ruler.Visibility = True
smp.Render()
def getPointFromCoordinate(coord, midPlaneDistance = 0.5):
assert len(coord) == 2
windowHeight = smp.GetActiveView().ViewSize[1]
displayPoint = [coord[0], windowHeight - coord[1], midPlaneDistance]
renderer = smp.GetActiveView().GetRenderer()
renderer.SetDisplayPoint(displayPoint)
renderer.DisplayToWorld()
world1 = renderer.GetWorldPoint()
return world1[:3]
def toggleRulerContext():
measurmentState = app.actions['actionMeasure'].isChecked()
mW = getMainWindow()
vtkW = mW.findChild('pqQVTKWidget')
if measurmentState == True:
vtkW.connect('mouseEvent(QMouseEvent*)', setRulerCoordinates)
elif measurmentState == False:
vtkW.disconnect('mouseEvent(QMouseEvent*)', setRulerCoordinates)
app.mousePressed = False
hideRuler()
def setRulerCoordinates(mouseEvent):
pqView = smp.GetActiveView()
rW = pqView.GetRenderWindow()
windowInteractor = rW.GetInteractor()
currentMouseState = mouseEvent.buttons()
currentKeyboardState = mouseEvent.modifiers()
if currentMouseState == 1: #Left button pressed
if app.mousePressed == False: #For the first time
if currentKeyboardState == 67108864: #Control key pressed
app.mousePressed = True
app.ruler.Point1WorldPosition = getPointFromCoordinate([mouseEvent.x(),mouseEvent.y()])
windowInteractor.Disable()
elif app.mousePressed == True: #Not for the first time
app.ruler.Point2WorldPosition = getPointFromCoordinate([mouseEvent.x(),mouseEvent.y()])
showRuler()
smp.Render()
elif currentMouseState == 0: #Left button released
windowInteractor.Enable()
if app.mousePressed == True: #For the first time
app.mousePressed = False
app.ruler.Point2WorldPosition = getPointFromCoordinate([mouseEvent.x(),mouseEvent.y()])
showRuler()
smp.Render()
# End Functions related to ruler
def rotateCSVFile(filename):
# read the csv file, move the last 3 columns to the
# front, and then overwrite the file with the result
csvFile = open(filename, 'rb')
reader = csv.reader(csvFile, quoting=csv.QUOTE_NONNUMERIC)
rows = [row[-3:] + row[:-3] for row in reader]
csvFile.close()
writer = csv.writer(open(filename, 'wb'), quoting=csv.QUOTE_NONNUMERIC, delimiter=',')
writer.writerows(rows)
def savePositionCSV(filename):
w = smp.CreateWriter(filename, getPosition())
w.Precision = 16
w.FieldAssociation = 'Points'
w.UpdatePipeline()
smp.Delete(w)
def saveCSVCurrentFrame(filename):
w = smp.CreateWriter(filename, smp.GetActiveSource())
w.Precision = 16
w.FieldAssociation = 'Points'
w.UpdatePipeline()
smp.Delete(w)
rotateCSVFile(filename)
def saveLASFrames(filename, first, last, transform):
reader = getReader().GetClientSideObject()
position = getPosition().GetClientSideObject().GetOutput()
PythonQt.paraview.pqVelodyneManager.saveFramesToLAS(
reader, position, first, last, filename, transform)
def saveLASCurrentFrame(filename, transform):
t = app.scene.AnimationTime
saveLASFrames(filename, t, t, transform)
def saveAllFrames(filename, saveFunction):
saveFunction(filename, getCurrentTimesteps())
def saveFrameRange(filename, frameStart, frameStop, saveFunction):
timesteps = range(frameStart, frameStop+1)
saveFunction(filename, timesteps)
def saveCSV(filename, timesteps):
tempDir = kiwiviewerExporter.tempfile.mkdtemp()
basenameWithoutExtension = os.path.splitext(os.path.basename(filename))[0]
outDir = os.path.join(tempDir, basenameWithoutExtension)
filenameTemplate = os.path.join(outDir, basenameWithoutExtension + ' (Frame %04d).csv')
os.makedirs(outDir)
writer = smp.CreateWriter('tmp.csv', getSensor() or getReader())
writer.FieldAssociation = 'Points'
writer.Precision = 16
for t in timesteps:
app.scene.AnimationTime = t
writer.FileName = filenameTemplate % t
writer.UpdatePipeline()
rotateCSVFile(writer.FileName)
smp.Delete(writer)
kiwiviewerExporter.zipDir(outDir, filename)
kiwiviewerExporter.shutil.rmtree(tempDir)
def saveLAS(filename, timesteps, transform):
tempDir = kiwiviewerExporter.tempfile.mkdtemp()
basenameWithoutExtension = os.path.splitext(os.path.basename(filename))[0]
outDir = os.path.join(tempDir, basenameWithoutExtension)
filenameTemplate = os.path.join(outDir, basenameWithoutExtension + ' (Frame %04d).csv')
os.makedirs(outDir)
for t in sorted(timesteps):
saveLASFrames(filenameTemplate % t, t, t, transform)
kiwiviewerExporter.zipDir(outDir, filename)
kiwiviewerExporter.shutil.rmtree(tempDir)
def getTimeStamp():
format = '%Y-%m-%d-%H-%M-%S'
return datetime.datetime.now().strftime(format)
def getSaveFileName(title, extension, defaultFileName=None):
settings = getPVSettings()
defaultDir = settings.value('VelodyneHDLPlugin/OpenData/DefaultDir', QtCore.QDir.homePath())
defaultFileName = defaultDir if not defaultFileName else os.path.join(defaultDir, defaultFileName)
nativeDialog = 0 if app.actions['actionNative_File_Dialogs'].isChecked() else QtGui.QFileDialog.DontUseNativeDialog
filters = '%s (*.%s)' % (extension, extension)
selectedFilter = '*.%s' % extension
fileName = QtGui.QFileDialog.getSaveFileName(getMainWindow(), title,
defaultFileName, filters, selectedFilter, nativeDialog)
if fileName:
settings.setValue('VelodyneHDLPlugin/OpenData/DefaultDir', QtCore.QFileInfo(fileName).absoluteDir().absolutePath())
return fileName
def restoreNativeFileDialogsAction():
settings = getPVSettings()
app.actions['actionNative_File_Dialogs'].setChecked(int(settings.value('VelodyneHDLPlugin/NativeFileDialogs', 1)))
def onNativeFileDialogsAction():
settings = getPVSettings()
defaultDir = settings.setValue('VelodyneHDLPlugin/NativeFileDialogs', int(app.actions['actionNative_File_Dialogs'].isChecked()))
def getFrameSelectionFromUser(frameStrideVisibility=False, framePackVisibility=False, frameTransformVisibility=False):
class FrameOptions(object):
pass
dialog = PythonQt.paraview.vvSelectFramesDialog(getMainWindow())
dialog.frameMinimum = app.scene.StartTime
dialog.frameMaximum = app.scene.EndTime
dialog.frameStrideVisibility = frameStrideVisibility
dialog.framePackVisibility = framePackVisibility
dialog.frameTransformVisibility = frameTransformVisibility
dialog.restoreState()
if not dialog.exec_():
return None
frameOptions = FrameOptions()
frameOptions.mode = dialog.frameMode
frameOptions.start = dialog.frameStart
frameOptions.stop = dialog.frameStop
frameOptions.stride = dialog.frameStride
frameOptions.pack = dialog.framePack
frameOptions.transform = dialog.frameTransform
dialog.setParent(None)
return frameOptions
def onSaveCSV():
frameOptions = getFrameSelectionFromUser()
if frameOptions is None:
return
if frameOptions.mode == vvSelectFramesDialog.CURRENT_FRAME:
fileName = getSaveFileName('Save CSV', 'csv', getDefaultSaveFileName('csv', appendFrameNumber=True))
if fileName:
oldTransform = transformMode()
setTransformMode(1 if frameOptions.transform else 0)
saveCSVCurrentFrame(fileName)
setTransformMode(oldTransform)
else:
fileName = getSaveFileName('Save CSV (to zip file)', 'zip', getDefaultSaveFileName('zip'))
if fileName:
oldTransform = transformMode()
setTransformMode(1 if frameOptions.transform else 0)
if frameOptions.mode == vvSelectFramesDialog.ALL_FRAMES:
saveAllFrames(fileName, saveCSV)
else:
start = frameOptions.start
stop = frameOptions.stop
saveFrameRange(fileName, start, stop, saveCSV)
setTransformMode(oldTransform)
def onSavePosition():
fileName = getSaveFileName('Save CSV', 'csv', getDefaultSaveFileName('csv', '-position'))
if fileName:
savePositionCSV(fileName)
def onSaveLAS():
frameOptions = getFrameSelectionFromUser(framePackVisibility=True, frameTransformVisibility=False)
if frameOptions is None:
return
if frameOptions.mode == vvSelectFramesDialog.CURRENT_FRAME:
frameOptions.start = frameOptions.stop = app.scene.AnimationTime
elif frameOptions.mode == vvSelectFramesDialog.ALL_FRAMES:
frameOptions.start = int(app.scene.StartTime)
frameOptions.stop = int(app.scene.EndTime)
if frameOptions.mode == vvSelectFramesDialog.CURRENT_FRAME:
fileName = getSaveFileName('Save LAS', 'las', getDefaultSaveFileName('las', appendFrameNumber=True))
if fileName:
oldTransform = transformMode()
setTransformMode(1 if frameOptions.transform else 0)
saveLASCurrentFrame(fileName, frameOptions.transform)
setTransformMode(oldTransform)
elif frameOptions.pack == vvSelectFramesDialog.FILE_PER_FRAME:
fileName = getSaveFileName('Save LAS (to zip file)', 'zip',
getDefaultSaveFileName('zip'))
if fileName:
oldTransform = transformMode()
setTransformMode(1 if frameOptions.transform else 0)
def saveTransformedLAS(filename, timesteps):
saveLAS(filename, timesteps, frameOptions.transform)
if frameOptions.mode == vvSelectFramesDialog.ALL_FRAMES:
saveAllFrames(fileName, saveTransformedLAS)
else:
start = frameOptions.start
stop = frameOptions.stop
saveFrameRange(fileName, start, stop, saveTransformedLAS)
setTransformMode(oldTransform)
else:
suffix = ' (Frame %d to %d)' % (frameOptions.start, frameOptions.stop)
defaultFileName = getDefaultSaveFileName('las', suffix=suffix)
fileName = getSaveFileName('Save LAS', 'las', defaultFileName)
if not fileName:
return
oldTransform = transformMode()
setTransformMode(1 if frameOptions.transform else 0)
saveLASFrames(fileName, frameOptions.start, frameOptions.stop,
frameOptions.transform)
setTransformMode(oldTransform)
def onSavePCAP():
frameOptions = getFrameSelectionFromUser(frameTransformVisibility=False)
if frameOptions is None:
return
if frameOptions.mode == vvSelectFramesDialog.CURRENT_FRAME:
frameOptions.start = frameOptions.stop = app.scene.AnimationTime
elif frameOptions.mode == vvSelectFramesDialog.ALL_FRAMES:
frameOptions.start = int(app.scene.StartTime)
frameOptions.stop = int(app.scene.EndTime)
defaultFileName = getDefaultSaveFileName('pcap', suffix=' (Frame %d to %d)' % (frameOptions.start, frameOptions.stop))
fileName = getSaveFileName('Save PCAP', 'pcap', defaultFileName)
if not fileName:
return
PythonQt.paraview.pqVelodyneManager.saveFramesToPCAP(getReader().SMProxy, frameOptions.start, frameOptions.stop, fileName)
def onSaveScreenshot():
fileName = getSaveFileName('Save Screenshot', 'png', getDefaultSaveFileName('png', appendFrameNumber=True))
if fileName:
saveScreenshot(fileName)
def onKiwiViewerExport():
frameOptions = getFrameSelectionFromUser(frameStrideVisibility=True,
frameTransformVisibility=False)
if frameOptions is None:
return
defaultFileName = getDefaultSaveFileName('zip', suffix=' (KiwiViewer)')
fileName = getSaveFileName('Export To KiwiViewer', 'zip', defaultFileName)
if not fileName:
return
if frameOptions.mode == vvSelectFramesDialog.CURRENT_FRAME:
timesteps = [app.scene.AnimationTime]
elif frameOptions.mode == vvSelectFramesDialog.ALL_FRAMES:
timesteps = range(int(app.scene.StartTime), int(app.scene.EndTime) + 1, frameOptions.stride)
else:
timesteps = range(frameOptions.start, frameOptions.stop+1, frameOptions.stride)
saveToKiwiViewer(fileName, timesteps)
def saveToKiwiViewer(filename, timesteps):
tempDir = kiwiviewerExporter.tempfile.mkdtemp()
outDir = os.path.join(tempDir, os.path.splitext(os.path.basename(filename))[0])
os.makedirs(outDir)
filenames = exportToDirectory(outDir, timesteps)
kiwiviewerExporter.writeJsonData(outDir, smp.GetActiveView(), smp.GetDisplayProperties(), filenames)
kiwiviewerExporter.zipDir(outDir, filename)
kiwiviewerExporter.shutil.rmtree(tempDir)
def exportToDirectory(outDir, timesteps):
filenames = []
alg = smp.GetActiveSource().GetClientSideObject()
writer = vtkXMLPolyDataWriter()
writer.SetDataModeToAppended()
writer.EncodeAppendedDataOff()
writer.SetCompressorTypeToZLib()
for t in timesteps:
filename = 'frame_%04d.vtp' % t
filenames.append(filename)
app.scene.AnimationTime = t
polyData = vtk.vtkPolyData()
polyData.ShallowCopy(alg.GetOutput())
writer.SetInputData(polyData)
writer.SetFileName(os.path.join(outDir, filename))
writer.Update()
return filenames
def getVersionString():
return QtGui.QApplication.instance().applicationVersion
def onDeveloperGuide():
basePath = PythonQt.QtGui.QApplication.instance().applicationDirPath()
paths = ['../Resources/VeloView_Developer_Guide.pdf']
for path in paths:
filename = os.path.join(basePath, path)
if os.path.isfile(filename):
QtGui.QDesktopServices.openUrl(QtCore.QUrl('file:///%s' % filename, QtCore.QUrl.TolerantMode))
def onUserGuide():
basePath = PythonQt.QtGui.QApplication.instance().applicationDirPath()
paths = ['../Resources/VeloView_User_Guide.pdf']
for path in paths:
filename = os.path.join(basePath, path)
if os.path.isfile(filename):
QtGui.QDesktopServices.openUrl(QtCore.QUrl('file:///%s' % filename, QtCore.QUrl.TolerantMode))
def onAbout():
title = 'About VeloView'
text = '''<h1>VeloView %s</h1><br/>Copyright (c) 2013, <NAME><br />
Sample Data Repository: <a href=http://midas3.kitware.com/midas/community/29>http://midas3.kitware.com/midas/community/29</a>'''% getVersionString()
QtGui.QMessageBox.about(getMainWindow(), title, text)
def close():
stop()
hideRuler()
unloadData()
smp.Render(app.overheadView)
app.scene.AnimationTime = 0
app.reader = None
app.sensor = None
smp.HideUnusedScalarBars()
resetCameraToForwardView()
app.filenameLabel.setText('')
app.statusLabel.setText('')
app.timeLabel.setText('')
updateSliderTimeRange()
disablePlaybackActions()
disableSaveActions()
app.actions['actionRecord'].setChecked(False)
app.actions['actionDualReturnModeDual'].setChecked(True)
app.actions['actionDualReturnModeDual'].enabled = False
app.actions['actionDualReturnDistanceNear'].enabled = False
app.actions['actionDualReturnDistanceFar'].enabled = False
app.actions['actionDualReturnIntensityHigh'].enabled = False
app.actions['actionDualReturnIntensityLow'].enabled = False
def seekForward():
if app.playing:
if app.playDirection < 0 or app.playDirection == 5:
app.playDirection = 0
app.playDirection += 1
updateSeekButtons()
else:
gotoNext()
def seekBackward():
if app.playing:
if app.playDirection > 0 or app.playDirection == -5:
app.playDirection = 0
app.playDirection -= 1
updateSeekButtons()
else:
gotoPrevious()
def seekPressTimeout():
app.seekPlay = True
onPlayTimer()
def seekForwardPressed():
app.seekPlayDirection = 1
if not app.playing:
app.seekTimer.start(500)
def seekForwardReleased():
app.seekTimer.stop()
app.seekPlay = False
def seekBackwardPressed():
app.seekPlayDirection = -1
if not app.playing:
app.seekTimer.start(500)
def seekBackwardReleased():
seekForwardReleased()
def updateSeekButtons():
icons = {
-5 : IconPaths.seekBackwardQuarterx,
-4 : IconPaths.seekBackwardHalfx,
-3 : IconPaths.seekBackward3x,
-2 : IconPaths.seekBackward2x,
-1 : IconPaths.seekBackward,
1 : IconPaths.seekForward,
2 : IconPaths.seekForward2x,
3 : IconPaths.seekForward3x,
4 : IconPaths.seekForwardHalfx,
5 : IconPaths.seekForwardQuarterx,
}
setActionIcon('actionSeek_Backward', icons[app.playDirection] if app.playDirection < 0 else IconPaths.seekBackward)
setActionIcon('actionSeek_Forward', icons[app.playDirection] if app.playDirection > 0 else IconPaths.seekForward)
fpsMap = {-5:5, 5:5, -4:11, 4:11}
fpsDefault = 30
app.targetFps = fpsMap.get(app.playDirection, fpsDefault)
def setPlaybackActionsEnabled(enabled):
for action in ('Play', 'Record', 'Seek_Forward', 'Seek_Backward', 'Go_To_Start', 'Go_To_End'):
app.actions['action'+action].setEnabled(enabled)
def enablePlaybackActions():
setPlaybackActionsEnabled(True)
def disablePlaybackActions():
setPlaybackActionsEnabled(False)
def _setSaveActionsEnabled(enabled):
for action in ('SaveCSV', 'SavePCAP', 'SaveLAS', 'Export_To_KiwiViewer',
'Close', 'Choose_Calibration_File', 'CropReturns'):
app.actions['action'+action].setEnabled(enabled)
getMainWindow().findChild('QMenu', 'menuSaveAs').enabled = enabled
def enableSaveActions():
_setSaveActionsEnabled(True)
if getPosition():
app.actions['actionSavePositionCSV'].setEnabled(True)
def disableSaveActions():
_setSaveActionsEnabled(False)
app.actions['actionSavePositionCSV'].setEnabled(False)
def recordFile(filename):
sensor = getSensor()
if sensor:
stopStream()
sensor.OutputFile = filename
app.statusLabel.setText(' Recording file: %s.' % os.path.basename(filename))
if app.playing:
startStream()
def onRecord():
recordAction = app.actions['actionRecord']
if not recordAction.isChecked():
stopRecording()
else:
fileName = getSaveFileName('Choose Output File', 'pcap', getDefaultSaveFileName('pcap'))
if not fileName:
recordAction.setChecked(False)
return
recordFile(fileName)
def stopRecording():
app.statusLabel.setText('')
sensor = getSensor()
if sensor:
stopStream()
sensor.OutputFile = ''
if app.playing:
startStream()
def startStream():
sensor = getSensor()
if sensor:
sensor.Start()
def stopStream():
sensor = getSensor()
if sensor:
sensor.Stop()
def pollSource():
source = getSensor()
reader = getReader()
if source is not None:
source.Poll()
source.UpdatePipelineInformation()
return source or reader
def getPointCloudData(attribute=None):
if attribute is not None:
data = getPointCloudData()
if data:
if attribute == 'points':
return data.GetPoints().GetData()
else:
return data.GetPointData().GetArray(attribute)
else:
source = getSensor() or getReader()
if source:
return source.GetClientSideObject().GetOutput()
def getCurrentTimesteps():
source = pollSource()
return list(source.TimestepValues) if source is not None else []
def getNumberOfTimesteps():
return getTimeKeeper().getNumberOfTimeStepValues()
def togglePlay():
setPlayMode(not app.playing)
def play():
setPlayMode(True)
def stop():
setPlayMode(False)
def onPlayTimer():
if app.playing or app.seekPlay:
startTime = vtk.vtkTimerLog.GetUniversalTime()
playbackTick()
fpsDelayMilliseconds = int(1000.0 / app.targetFps)
elapsedMilliseconds = int((vtk.vtkTimerLog.GetUniversalTime() - startTime)*1000.0)
if elapsedMilliseconds > 0:
fps = 1000.0/elapsedMilliseconds
app.fps[0] += fps
app.fps[1] += 1
waitMilliseconds = fpsDelayMilliseconds - elapsedMilliseconds
app.playTimer.start(max(waitMilliseconds,0))
def setPlayMode(mode):
if not getReader() and not getSensor():
return
app.playing = mode
if mode:
startStream()
setActionIcon('actionPlay', IconPaths.pause)
app.playTimer.start(33)
if app.scene.AnimationTime == app.scene.EndTime:
app.scene.AnimationTime = app.scene.StartTime
else:
stopStream()
setActionIcon('actionPlay', IconPaths.play)
app.playDirection = 1
updateSeekButtons()
def gotoStart():
pollSource()
app.scene.GoToFirst()
updatePosition()
def gotoEnd():
pollSource()
app.scene.GoToLast()
updatePosition()
def gotoNext():
pollSource()
app.scene.GoToNext()
updatePosition()
def gotoPrevious():
pollSource()
app.scene.GoToPrevious()
updatePosition()
def updatePosition():
reader = getReader()
pos = getPosition()
if reader and pos:
pointcloud = reader.GetClientSideObject().GetOutput()
if pointcloud.GetNumberOfPoints():
# Update the overhead view
# TODO: Approximate time, just grabbing the last
t = pointcloud.GetPointData().GetScalars('adjustedtime')
#currentTime = t.GetTuple1(t.GetNumberOfTuples() - 1)
currentTime = t.GetTuple1(0) * 1e-6
interp = getPosition().GetClientSideObject().GetInterpolator()
trange = [interp.GetMinimumT(), interp.GetMaximumT()]
# Clamp
currentTime = min(max(currentTime, trange[0]+1.0e-1), trange[1]-1.0e-1)
position = [0.0] * 3
transform = vtk.vtkTransform()
interp.InterpolateTransform(currentTime, transform)
transform.TransformPoint(position, position)
rep = cachedGetRepresentation(reader, view=app.mainView)
if app.relativeTransform:
rep.Position = transform.GetInverse().GetPosition()
rep.Orientation = transform.GetInverse().GetOrientation()
else:
rep.Position = [0.0, 0.0, 0.0]
rep.Orientation = [0.0, 0.0, 0.0]
g = getGlyph()
rep = cachedGetRepresentation(g, view=app.overheadView)
rep.Position = position[:3]
def playbackTick():
sensor = getSensor()
reader = getReader()
view = smp.GetActiveView()
if sensor is not None:
timesteps = getCurrentTimesteps()
if not timesteps:
return
if view.ViewTime == timesteps[-1]:
return
if not app.colorByInitialized:
sensor.UpdatePipeline()
if colorByIntensity(sensor):
app.colorByInitialized = True
resetCamera()
app.scene.GoToLast()
elif reader is not None:
numberOfTimesteps = getNumberOfTimesteps()
if not numberOfTimesteps:
return
step = app.seekPlayDirection if app.seekPlay else app.playDirection
stepMap = {4:1, 5:1, -4:-1, -5:-1}
step = stepMap.get(step, step)
newTime = app.scene.AnimationTime + step
if app.actions['actionLoop'].isChecked():
newTime = newTime % numberOfTimesteps
else:
newTime = max(app.scene.StartTime, min(newTime, app.scene.EndTime))
# stop playback when it reaches the first or last timestep
if newTime in (app.scene.StartTime, app.scene.EndTime):
stop()
app.scene.AnimationTime = newTime
# TODO: For sensor as well?
updatePosition()
def unloadData():
_repCache.clear()
for k, src in smp.GetSources().iteritems():
if src != app.grid:
smp.Delete(src)
toremove = [x for x in app.overheadView.Representations if type(x) == servermanager.rendering.ScalarBarWidgetRepresentation]
for t in toremove:
app.overheadView.Representations.remove(t)
app.reader = None
app.position = (None, None, None)
app.sensor = None
clearSpreadSheetView()
def getReader():
return getattr(app, 'reader', None)
def getSensor():
return getattr(app, 'sensor', None)
def getPosition():
return getattr(app, 'position', (None, None, None))[0]
def getGlyph():
return getattr(app, 'position', (None, None, None))[2]
def onChooseCalibrationFile():
calibration = chooseCalibration()
if not calibration:
return
calibrationFile = calibration.calibrationFile
sensorTransform = calibration.sensorTransform
reader = getReader()
sensor = getSensor()
if reader is not None:
reader.GetClientSideObject().SetSensorTransform(sensorTransform)
reader.CalibrationFile = calibrationFile
reader.DummyProperty = not reader.DummyProperty
smp.Render()
elif sensor is not None:
sensor.GetClientSideObject().SetSensorTransform(sensorTransform)
sensor.CalibrationFile = calibrationFile
# no need to render now, calibration file will be used on the next frame
def onCropReturns(show = True):
dialog = vvCropReturnsDialog(getMainWindow())
cropEnabled = False
cropInside = False
firstCorner = QtGui.QVector3D()
secondCorner = QtGui.QVector3D()
reader = getReader()
sensor = getSensor()
if reader is not None:
cropEnabled = reader.CropReturns
cropInside = reader.CropInside
firstCorner = QtGui.QVector3D(reader.CropRegion[0], reader.CropRegion[2], reader.CropRegion[4])
secondCorner = QtGui.QVector3D(reader.CropRegion[1], reader.CropRegion[3], reader.CropRegion[5])
if sensor is not None:
cropEnabled = sensor.CropReturns
cropInside = sensor.CropInside
firstCorner = QtGui.QVector3D(sensor.CropRegion[0], sensor.CropRegion[2], sensor.CropRegion[4])
secondCorner = QtGui.QVector3D(sensor.CropRegion[1], sensor.CropRegion[3], sensor.CropRegion[5])
if show:
dialog.croppingEnabled = cropEnabled
dialog.cropInside = cropInside
dialog.firstCorner = firstCorner
dialog.secondCorner = secondCorner
if not dialog.exec_():
return
if reader is not None:
reader.CropReturns = dialog.croppingEnabled
reader.CropInside = dialog.cropInside
p1 = dialog.firstCorner
p2 = dialog.secondCorner
reader.CropRegion = [p1.x(), p2.x(), p1.y(), p2.y(), p1.z(), p2.z()]
if show:
smp.Render()
if sensor is not None:
sensor.CropReturns = dialog.croppingEnabled
sensor.CropInside = dialog.cropInside
p1 = dialog.firstCorner
p2 = dialog.secondCorner
sensor.CropRegion = [p1.x(), p2.x(), p1.y(), p2.y(), p1.z(), p2.z()]
if show:
smp.Render()
def resetCamera():
def subtract(a, b):
result = range(3)
vtk.vtkMath.Subtract(a, b, result)
return result
def cross(a, b):
result = range(3)
vtk.vtkMath.Cross(a, b, result)
return result
view = smp.GetActiveView()
foc = list(view.CenterOfRotation)
pos = list(view.CameraPosition)
viewDirection = subtract(foc, pos)
view.CameraPosition = subtract([0, 0, 0], viewDirection)
view.CameraFocalPoint = [0, 0, 0]
view.CenterOfRotation = [0, 0, 0]
vtk.vtkMath.Normalize(viewDirection)
perp = cross(viewDirection, [0, 0, 1])
viewUp = cross(perp, viewDirection)
view.CameraViewUp = viewUp
view.StillRender()
def resetCameraToBirdsEyeView(view=None):
view = view or smp.GetActiveView()
view.CameraFocalPoint = [0, 0, 0]
view.CameraViewUp = [0, 1, 0]
view.CameraPosition = [0, 0, 40]
view.CenterOfRotation = [0, 0, 0]
smp.Render(view)
def resetCameraToForwardView(view=None):
view = view or smp.GetActiveView()
view.CameraFocalPoint = [0,0,0]
view.CameraViewUp = [0, 0.27, 0.96]
view.CameraPosition = [0, -72, 18.0]
view.CenterOfRotation = [0, 0, 0]
smp.Render(view)
def saveScreenshot(filename):
smp.WriteImage(filename)
# reload the saved screenshot as a pixmap
screenshot = QtGui.QPixmap()
screenshot.load(filename)
# create a new pixmap with the status bar widget painted at the bottom
statusBar = QtGui.QPixmap.grabWidget(getMainWindow().statusBar())
composite = QtGui.QPixmap(screenshot.width(), screenshot.height() + statusBar.height())
painter = QtGui.QPainter()
painter.begin(composite)
painter.drawPixmap(screenshot.rect(), screenshot, screenshot.rect())
painter.drawPixmap(statusBar.rect().translated(0, screenshot.height()), statusBar, statusBar.rect())
painter.end()
# save final screenshot
composite.save(filename)
def getSpreadSheetViewProxy():
for p in smp.servermanager.ProxyManager():
if p.GetXMLName() == 'SpreadSheetView':
return p
def clearSpreadSheetView():
view = getSpreadSheetViewProxy()
view.Representations = []
def showSourceInSpreadSheet(source):
spreadSheetView = getSpreadSheetViewProxy()
smp.Show(source, spreadSheetView)
# Work around a bug where the 'Showing' combobox doesn't update.
# Calling hide and show again will trigger the refresh.
smp.Hide(source, spreadSheetView)
smp.Show(source, spreadSheetView)
def createGrid(view=None):
view = view or smp.GetActiveView()
grid = smp.VelodyneHDLGridSource(guiName='Measurement Grid')
rep = smp.Show(grid, view)
rep.DiffuseColor = [0.2, 0.2, 0.2]
rep.Pickable = 0
rep.Visibility = 0
smp.SetActiveSource(None)
return grid
def hideGrid():
smp.GetDisplayProperties(app.grid).Hide()
def showGrid():
smp.GetDisplayProperties(app.grid).Show()
def getAnimationScene():
'''This function is a workaround because paraview.simple.GetAnimationScene()
has an issue where the returned proxy might not have its Cues property initialized'''
for proxy in servermanager.ProxyManager().GetProxiesInGroup("animation").values():
if proxy.GetXMLName() == 'AnimationScene' and len(proxy.Cues):
return proxy
def start():
global app
app = AppLogic()
app.scene = getAnimationScene()
view = smp.GetActiveView()
view.Background = [0.0, 0.0, 0.0]
view.Background2 = [0.0, 0.0, 0.2]
view.UseGradientBackground = True
smp._DisableFirstRenderCameraReset()
smp.GetActiveView().LODThreshold = 1e100
app.grid = createGrid()
app.ruler = createRuler()
resetCameraToForwardView()
setupActions()
disablePlaybackActions()
disableSaveActions()
app.actions['actionMeasure'].setEnabled(view.CameraParallelProjection)
setupStatusBar()
setupTimeSliderWidget()
hideColorByComponent()
restoreNativeFileDialogsAction()
updateRecentFiles()
getTimeKeeper().connect('timeChanged()', onTimeChanged)
def findQObjectByName(widgets, name):
for w in widgets:
if w.objectName == name:
return w
def getMainWindow():
return findQObjectByName(QtGui.QApplication.topLevelWidgets(), 'vvMainWindow')
def getPVApplicationCore():
return PythonQt.paraview.pqPVApplicationCore.instance()
def getPVSettings():
return getPVApplicationCore().settings()
def getTimeKeeper():
return getPVApplicationCore().getActiveServer().getTimeKeeper()
def getPlaybackToolBar():
return findQObjectByName(getMainWindow().children(), 'playbackToolbar')
def quit():
PythonQt.QtGui.QApplication.instance().quit()
exit = quit
def addShortcuts(keySequenceStr, function):
shortcut = PythonQt.QtGui.QShortcut(PythonQt.QtGui.QKeySequence(keySequenceStr), getMainWindow())
shortcut.connect("activated()", function)
def onTrailingFramesChanged(numFrames):
try:
app.reader.NumberOfTrailingFrames = numFrames
smp.Render()
smp.Render(getSpreadSheetViewProxy())
except AttributeError:
pass
def onPointsSkipChanged(pr):
try:
app.reader.PointsSkip = pr
smp.Render()
smp.Render(getSpreadSheetViewProxy())
except AttributeError:
pass
def setupTimeSliderWidget():
frame = QtGui.QWidget()
layout = QtGui.QHBoxLayout(frame)
spinBox = QtGui.QSpinBox()
spinBox.setMinimum(0)
spinBox.setMaximum(100)
spinBox.setValue(0)
slider = QtGui.QSlider(QtCore.Qt.Horizontal)
slider.setMaximumWidth(160)
slider.connect('valueChanged(int)', onTimeSliderChanged)
spinBox.connect('valueChanged(int)', onTimeSliderChanged)
slider.setEnabled(False)
spinBox.setEnabled(False)
layout.addWidget(slider)
layout.addWidget(spinBox)
layout.addStretch()
toolbar = getPlaybackToolBar()
toolbar.addWidget(frame)
app.timeSlider = slider
app.timeSpinBox = spinBox
def updateSliderTimeRange():
frame = int(app.scene.AnimationTime)
lastFrame = int(app.scene.EndTime)
for widget in (app.timeSlider, app.timeSpinBox):
widget.setMinimum(0)
widget.setMaximum(lastFrame)
widget.setSingleStep(1)
if hasattr(widget, 'setPageStep'):
widget.setPageStep(10)
widget.setValue(frame)
widget.setEnabled(getNumberOfTimesteps())
def scheduleRender():
if not app.renderIsPending:
app.renderIsPending = True
app.renderTimer.start(33)
def forceRender():
smp.Render()
app.renderIsPending = False
def onTimeSliderChanged(frame):
app.scene.AnimationTime = frame
updatePosition()
def setupStatusBar():
statusBar = getMainWindow().statusBar()
statusBar.addPermanentWidget(app.logoLabel)
statusBar.addWidget(app.filenameLabel)
statusBar.addWidget(app.statusLabel)
statusBar.addWidget(app.timeLabel)
def setActionIcon(actionName, iconPath):
app.actions[actionName].setIcon(QtGui.QIcon(QtGui.QPixmap(iconPath)))
def onTimeChanged():
frame = int(getTimeKeeper().getTime())
app.timeLabel.setText(' Frame: %s' % frame)
for widget in (app.timeSlider, app.timeSpinBox):
widget.blockSignals(True)
widget.setValue(frame)
widget.blockSignals(False)
def onGridProperties():
if gridAdjustmentDialog.showDialog(getMainWindow(), app.grid):
smp.Render()
def onLaserSelection(show = True):
oldmask = [1] * 64
reader = getReader()
sensor = getSensor()
corrections = [0] * 64
nchannels = 32
if reader:
reader.GetClientSideObject().GetLaserSelection(oldmask)
reader.GetClientSideObject().GetVerticalCorrections(corrections)
nchannels = reader.GetPropertyValue('NumberOfChannels')
elif sensor:
sensor.GetClientSideObject().GetLaserSelection(oldmask)
sensor.GetClientSideObject().GetVerticalCorrections(corrections)
nchannels = sensor.GetPropertyValue('NumberOfChannels')
# Need a way to initialize the mask
dialog = PythonQt.paraview.vvLaserSelectionDialog(getMainWindow())
if show:
dialog.setLaserSelectionSelector(oldmask)
dialog.setVerticalCorrections(corrections, nchannels)
if not dialog.exec_():
return
mask = dialog.getLaserSelectionSelector()
if reader:
reader.GetClientSideObject().SetLaserSelection(mask)
reader.DummyProperty = not reader.DummyProperty
if show:
smp.Render()
smp.Render(getSpreadSheetViewProxy())
if sensor:
sensor.GetClientSideObject().SetLaserSelection(mask)
sensor.DummyProperty = not sensor.DummyProperty
if show:
smp.Render()
smp.Render(getSpreadSheetViewProxy())
def hideColorByComponent():
getMainWindow().findChild('vvColorToolbar').findChild('pqDisplayColorWidget').findChildren('QComboBox')[1].hide()
def addRecentFile(filename):
recentFiles = getRecentFiles()
try:
recentFiles.remove(filename)
except ValueError:
pass
recentFiles = recentFiles[:4]
recentFiles.insert(0, filename)
getPVSettings().setValue('VelodyneHDLPlugin/RecentFiles', recentFiles)
updateRecentFiles()
def openRecentFile(filename):
if not os.path.isfile(filename):
QtGui.QMessageBox.warning(getMainWindow(), 'File not found', 'File not found: %s' % filename)
return
if os.path.splitext(filename)[1].lower() == '.pcap':
openPCAP(filename)
else:
openData(filename)
def getRecentFiles():
return list(getPVSettings().value('VelodyneHDLPlugin/RecentFiles', []) or [])
def updateRecentFiles():
settings = getPVSettings()
recentFiles = getRecentFiles()
recentFilesMenu = findQObjectByName(findQObjectByName(getMainWindow().menuBar().children(), 'menu_File').children(), 'menuRecent_Files')
clearMenuAction = app.actions['actionClear_Menu']
for action in recentFilesMenu.actions()[:-2]:
recentFilesMenu.removeAction(action)
def createActionFunction(filename):
def f():
openRecentFile(filename)
return f
actions = []
for filename in recentFiles:
actions.append(QtGui.QAction(os.path.basename(filename), recentFilesMenu))
actions[-1].connect('triggered()', createActionFunction(filename))
recentFilesMenu.insertActions(recentFilesMenu.actions()[0], actions)
def onClearMenu():
settings = getPVSettings()
settings.setValue('VelodyneHDLPlugin/RecentFiles', [])
updateRecentFiles()
def toggleProjectionType():
view = smp.GetActiveView()
view.CameraParallelProjection = not view.CameraParallelProjection
if app.actions['actionMeasure'].isChecked():
app.actions['actionMeasure'].trigger()
app.actions['actionMeasure'].toggle()
app.actions['actionMeasure'].setEnabled(view.CameraParallelProjection)
smp.Render()
def setViewTo(axis,sign):
view = smp.GetActiveView()
viewUp = view.CameraViewUp
position = view.CameraPosition
norm = math.sqrt(math.pow(position[0],2) + math.pow(position[1],2) + math.pow(position[2],2))
if axis == 'X':
view.CameraViewUp = [0,0,1]
view.CameraPosition = [-1*sign*norm,0,0]
elif axis == 'Y':
view.CameraViewUp = [0,0,1]
view.CameraPosition = [0,-1*sign*norm,0]
elif axis == 'Z':
view.CameraViewUp = [0,1,0]
view.CameraPosition = [0,0,-1*sign*norm]
view.CameraFocalPoint = [0,0,0]
view.CenterOfRotation = [0,0,0]
view.ResetCamera()
smp.Render()
def setViewToXPlus():
setViewTo('X',1)
def setViewToXMinus():
setViewTo('X',-1)
def setViewToYPlus():
setViewTo('Y',1)
def setViewToYMinus():
setViewTo('Y',-1)
def setViewToZPlus():
setViewTo('Z',1)
def setViewToZMinus():
setViewTo('Z',-1)
def setFilterToDual():
setFilterTo(0)
def setFilterToDistanceNear():
setFilterTo(vtkVelodyneHDLReader.DUAL_DISTANCE_NEAR)
def setFilterToDistanceFar():
setFilterTo(vtkVelodyneHDLReader.DUAL_DISTANCE_FAR)
def setFilterToIntensityHigh():
setFilterTo(vtkVelodyneHDLReader.DUAL_INTENSITY_HIGH)
def setFilterToIntensityLow():
setFilterTo(vtkVelodyneHDLReader.DUAL_INTENSITY_LOW)
def setFilterTo(mask):
reader = getReader()
if reader:
reader.DualReturnFilter = mask
smp.Render()
smp.Render(getSpreadSheetViewProxy())
sensor = getSensor()
if sensor:
sensor.DualReturnFilter = mask
smp.Render()
smp.Render(getSpreadSheetViewProxy())
def transformMode():
reader = getReader()
if not reader:
return None
if reader.ApplyTransform:
if app.relativeTransform:
return 2 # relative
else:
return 1 # absolute
return 0 # raw
def setTransformMode(mode):
# 0 - raw
# 1 - absolute
# 2 - relative
reader = getReader()
if reader:
reader.ApplyTransform = (mode > 0)
app.transformMode = mode
app.relativeTransform = (mode == 2)
def geolocationChanged(setting):
setTransformMode(setting)
updatePosition()
smp.Render(view=app.mainView)
def setupActions():
mW = getMainWindow()
actions = mW.findChildren('QAction')
app.actions = {}
for a in actions:
app.actions[a.objectName] = a
app.actions['actionPlaneFit'].connect('triggered()', planeFit)
app.actions['actionClose'].connect('triggered()', close)
app.actions['actionPlay'].connect('triggered()', togglePlay)
app.actions['actionRecord'].connect('triggered()', onRecord)
app.actions['actionSaveCSV'].connect('triggered()', onSaveCSV)
app.actions['actionSavePositionCSV'].connect('triggered()', onSavePosition)
app.actions['actionSaveLAS'].connect('triggered()', onSaveLAS)
app.actions['actionSavePCAP'].connect('triggered()', onSavePCAP)
app.actions['actionSaveScreenshot'].connect('triggered()', onSaveScreenshot)
app.actions['actionExport_To_KiwiViewer'].connect('triggered()', onKiwiViewerExport)
app.actions['actionReset_Camera'].connect('triggered()', resetCamera)
app.actions['actionGrid_Properties'].connect('triggered()', onGridProperties)
app.actions['actionLaserSelection'].connect('triggered()', onLaserSelection)
app.actions['actionChoose_Calibration_File'].connect('triggered()', onChooseCalibrationFile)
app.actions['actionCropReturns'].connect('triggered()', onCropReturns)
app.actions['actionSeek_Forward'].connect('triggered()', seekForward)
app.actions['actionSeek_Backward'].connect('triggered()', seekBackward)
app.actions['actionGo_To_End'].connect('triggered()', gotoEnd)
app.actions['actionGo_To_Start'].connect('triggered()', gotoStart)
app.actions['actionNative_File_Dialogs'].connect('triggered()', onNativeFileDialogsAction)
app.actions['actionAbout_VeloView'].connect('triggered()', onAbout)
app.actions['actionVeloViewDeveloperGuide'].connect('triggered()', onDeveloperGuide)
app.actions['actionClear_Menu'].connect('triggered()', onClearMenu)
app.actions['actionToggleProjection'].connect('triggered()', toggleProjectionType)
app.actions['actionMeasure'].connect('triggered()', toggleRulerContext)
app.actions['actionSetViewXPlus'].connect('triggered()', setViewToXPlus)
app.actions['actionSetViewXMinus'].connect('triggered()', setViewToXMinus)
app.actions['actionSetViewYPlus'].connect('triggered()', setViewToYPlus)
app.actions['actionSetViewYMinus'].connect('triggered()', setViewToYMinus)
app.actions['actionSetViewZPlus'].connect('triggered()', setViewToZPlus)
app.actions['actionSetViewZMinus'].connect('triggered()', setViewToZMinus)
app.actions['actionDualReturnModeDual'].connect('triggered()', setFilterToDual)
app.actions['actionDualReturnDistanceNear'].connect('triggered()', setFilterToDistanceNear)
app.actions['actionDualReturnDistanceFar'].connect('triggered()', setFilterToDistanceFar)
app.actions['actionDualReturnIntensityHigh'].connect('triggered()', setFilterToIntensityHigh)
app.actions['actionDualReturnIntensityLow'].connect('triggered()', setFilterToIntensityLow)
# Action created #
timeToolBar = mW.findChild('QToolBar','playbackToolbar')
geolocationToolBar = mW.findChild('QToolBar', 'geolocationToolbar')
comboBox = QtGui.QComboBox()
comboBox.addItem('Relative RAW')
comboBox.addItem('Absolute Geolocation')
comboBox.addItem('Relative Geolocation')
comboBox.connect('currentIndexChanged(int)', geolocationChanged)
geolocationToolBar.addWidget(comboBox)
spinBoxLabel = QtGui.QLabel('TF:')
spinBoxLabel.toolTip = "Number of trailing frames"
timeToolBar.addWidget(spinBoxLabel)
spinBox = QtGui.QSpinBox()
spinBox.toolTip = "Number of trailing frames"
spinBox.setMinimum(0)
spinBox.setMaximum(100)
spinBox.connect('valueChanged(int)', onTrailingFramesChanged)
app.trailingFramesSpinBox = spinBox
app.actions['actionTrailingFramesSelector'] = timeToolBar.addWidget(spinBox)
app.actions['actionTrailingFramesSelector'].setVisible(True)
pointsSkipLabel = QtGui.QLabel('Skip:')
pointsSkipLabel.toolTip = "Number of Points to Skip"
timeToolBar.addWidget(pointsSkipLabel)
pointsSkipBox = QtGui.QSpinBox()
pointsSkipBox.toolTip = "Number of Points to Skip"
pointsSkipBox.setMinimum(0)
pointsSkipBox.setMaximum(100)
pointsSkipBox.connect('valueChanged(int)', onPointsSkipChanged)
app.pointsSkipSpinBox = pointsSkipBox
app.actions['actionPointsSkipSelector'] = timeToolBar.addWidget(pointsSkipBox)
app.actions['actionPointsSkipSelector'].setVisible(True)
buttons = {}
for button in getPlaybackToolBar().findChildren('QToolButton'):
buttons[button.text] = button
buttons['Seek Forward'].connect('pressed()', seekForwardPressed)
buttons['Seek Forward'].connect('released()', seekForwardReleased)
buttons['Seek Backward'].connect('pressed()', seekBackwardPressed)
buttons['Seek Backward'].connect('released()', seekBackwardReleased)
| <filename>VelodyneHDL/python/veloview/applogic.py
# Copyright 2013 Velodyne Acoustics, Inc.
#
# 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 os
import csv
import datetime
import time
import math
import paraview.simple as smp
from paraview import servermanager
from paraview import vtk
import PythonQt
from PythonQt import QtCore, QtGui
from vtkIOXMLPython import vtkXMLPolyDataWriter
import kiwiviewerExporter
import gridAdjustmentDialog
import planefit
from PythonQt.paraview import vvCalibrationDialog, vvCropReturnsDialog, vvSelectFramesDialog
from VelodyneHDLPluginPython import vtkVelodyneHDLReader
_repCache = {}
SAMPLE_PROCESSING_MODE = False
def cachedGetRepresentation(src, view):
try:
return _repCache[(src, view)]
except KeyError:
rep = smp.GetRepresentation(src, view)
_repCache[(src, view)] = rep
return rep
class AppLogic(object):
def __init__(self):
self.playing = False
self.playDirection = 1
self.seekPlayDirection = 1
self.seekPlay = False
self.targetFps = 30
self.renderIsPending = False
self.createStatusBarWidgets()
self.setupTimers()
self.mousePressed = False
mainView = smp.GetActiveView()
views = smp.GetRenderViews()
otherViews = [v for v in views if v != mainView]
assert len(otherViews) == 1
overheadView = otherViews[0]
self.mainView = mainView
self.overheadView = overheadView
self.transformMode = 0
self.relativeTransform = False
self.reader = None
self.position = (None, None, None)
self.sensor = None
self.fps = [0,0]
def setupTimers(self):
self.playTimer = QtCore.QTimer()
self.playTimer.setSingleShot(True)
self.playTimer.connect('timeout()', onPlayTimer)
self.seekTimer = QtCore.QTimer()
self.seekTimer.setSingleShot(True)
self.seekTimer.connect('timeout()', seekPressTimeout)
self.renderTimer = QtCore.QTimer()
self.renderTimer.setSingleShot(True)
self.renderTimer.connect('timeout()', forceRender)
def createStatusBarWidgets(self):
self.logoLabel = QtGui.QLabel()
self.logoLabel.setPixmap(QtGui.QPixmap(":/VelodyneHDLPlugin/velodyne_logo.png"))
self.logoLabel.setScaledContents(True)
self.filenameLabel = QtGui.QLabel()
self.statusLabel = QtGui.QLabel()
self.timeLabel = QtGui.QLabel()
class IconPaths(object):
trailingFrames = ':/VelodyneHDLPlugin/trailingframes.png'
play = ':/VelodyneHDLPlugin/media-playback-start.png'
pause =':/VelodyneHDLPlugin/media-playback-pause.png'
seekForward = ':/VelodyneHDLPlugin/media-seek-forward.png'
seekForward2x = ':/VelodyneHDLPlugin/media-seek-forward-2x.png'
seekForwardHalfx = ':/VelodyneHDLPlugin/media-seek-forward-0.5x.png'
seekForwardQuarterx = ':/VelodyneHDLPlugin/media-seek-forward-0.25x.png'
seekForward3x = ':/VelodyneHDLPlugin/media-seek-forward-3x.png'
seekBackward = ':/VelodyneHDLPlugin/media-seek-backward.png'
seekBackward2x = ':/VelodyneHDLPlugin/media-seek-backward-2x.png'
seekBackward3x = ':/VelodyneHDLPlugin/media-seek-backward-3x.png'
seekBackwardHalfx = ':/VelodyneHDLPlugin/media-seek-backward-0.5x.png'
seekBackwardQuarterx = ':/VelodyneHDLPlugin/media-seek-backward-0.25x.png'
def hasArrayName(sourceProxy, arrayName):
'''
Returns True if the data has non-zero points and has a point data
attribute with the given arrayName.
'''
info = sourceProxy.GetDataInformation().DataInformation
if info.GetNumberOfPoints() == 0:
return False
info = info.GetAttributeInformation(0)
for i in xrange(info.GetNumberOfArrays()):
if info.GetArrayInformation(i).GetName() == arrayName:
return True
return False
def openData(filename):
close()
reader = smp.OpenDataFile(filename, guiName='Data')
if not reader:
return
rep = smp.Show(reader)
rep.InterpolateScalarsBeforeMapping = 0
setDefaultLookupTables(reader)
colorByIntensity(reader)
showSourceInSpreadSheet(reader)
smp.GetActiveView().ViewTime = 0.0
app.reader = reader
app.filenameLabel.setText('File: %s' % os.path.basename(filename))
updateSliderTimeRange()
enablePlaybackActions()
enableSaveActions()
addRecentFile(filename)
app.actions['actionSavePCAP'].setEnabled(False)
app.actions['actionChoose_Calibration_File'].setEnabled(False)
app.actions['actionCropReturns'].setEnabled(False)
app.actions['actionRecord'].setEnabled(False)
app.actions['actionDualReturnModeDual'].enabled = True
app.actions['actionDualReturnDistanceNear'].enabled = True
app.actions['actionDualReturnDistanceFar'].enabled = True
app.actions['actionDualReturnIntensityHigh'].enabled = True
app.actions['actionDualReturnIntensityLow'].enabled = True
resetCamera()
def planeFit():
planefit.fitPlane()
def setDefaultLookupTables(sourceProxy):
# LUT for 'intensity'
smp.GetLookupTableForArray(
'intensity', 1,
ScalarRangeInitialized=1.0,
ColorSpace='HSV',
RGBPoints=[0.0, 0.0, 0.0, 1.0,
100.0, 1.0, 1.0, 0.0,
256.0, 1.0, 0.0, 0.0])
# LUT for 'dual_distance'
smp.GetLookupTableForArray(
'dual_distance', 1,
InterpretValuesAsCategories=True, NumberOfTableValues=3,
RGBPoints=[-1.0, 0.1, 0.5, 0.7,
0.0, 0.9, 0.9, 0.9,
+1.0, 0.8, 0.2, 0.3],
Annotations=['-1', 'near', '0', 'dual', '1', 'far'])
# LUT for 'dual_intensity'
smp.GetLookupTableForArray(
'dual_intensity', 1,
InterpretValuesAsCategories=True, NumberOfTableValues=3,
RGBPoints=[-1.0, 0.5, 0.2, 0.8,
0.0, 0.6, 0.6, 0.6,
+1.0, 1.0, 0.9, 0.4],
Annotations=['-1', 'low', '0', 'dual', '1', 'high'])
def colorByIntensity(sourceProxy):
if not hasArrayName(sourceProxy, 'intensity'):
return False
setDefaultLookupTables(sourceProxy)
rep = smp.GetDisplayProperties(sourceProxy)
rep.ColorArrayName = 'intensity'
rep.LookupTable = smp.GetLookupTableForArray('intensity', 1)
return True
def getTimeStamp():
format = '%Y-%m-%d-%H-%M-%S'
return datetime.datetime.now().strftime(format)
def getReaderFileName():
filename = getReader().FileName
return filename[0] if isinstance(filename, servermanager.FileNameProperty) else filename
def getDefaultSaveFileName(extension, suffix='', appendFrameNumber=False):
sensor = getSensor()
reader = getReader()
if sensor:
nchannels = sensor.GetPropertyValue('NumberOfChannels')
base = 'HDL-'
if nchannels <= 16:
base = 'VLP-'
sensortype = base + str(nchannels)
return '%s_Velodyne-%s-Data.%s' % (getTimeStamp(), sensortype, extension)
if reader:
basename = os.path.splitext(os.path.basename(getReaderFileName()))[0]
if appendFrameNumber:
suffix = '%s (Frame %04d)' % (suffix, int(app.scene.AnimationTime))
return '%s%s.%s' % (basename, suffix, extension)
def chooseCalibration():
class Calibration(object):
def __init__(self, dialog):
self.calibrationFile = dialog.selectedCalibrationFile()
self.gpsYaw = dialog.gpsYaw()
self.gpsRoll = dialog.gpsRoll()
self.gpsPitch = dialog.gpsPitch()
self.sensorTransform = vtk.vtkTransform()
qm = dialog.sensorTransform()
vm = vtk.vtkMatrix4x4()
for row in xrange(4):
vm.SetElement(row, 0, qm.row(row).x())
vm.SetElement(row, 1, qm.row(row).y())
vm.SetElement(row, 2, qm.row(row).z())
vm.SetElement(row, 3, qm.row(row).w())
self.sensorTransform.SetMatrix(vm)
dialog = vvCalibrationDialog(getMainWindow())
if not dialog.exec_():
return None
return Calibration(dialog)
def openSensor():
calibration = chooseCalibration()
if not calibration:
return
calibrationFile = calibration.calibrationFile
sensorTransform = calibration.sensorTransform
close()
sensor = smp.VelodyneHDLSource(guiName='Data', CalibrationFile=calibrationFile, CacheSize=100)
sensor.GetClientSideObject().SetSensorTransform(sensorTransform)
sensor.UpdatePipeline()
sensor.Start()
if SAMPLE_PROCESSING_MODE:
processor = smp.ProcessingSample(sensor)
smp.GetActiveView().ViewTime = 0.0
app.sensor = sensor
app.colorByInitialized = False
app.filenameLabel.setText('Live sensor stream.')
enablePlaybackActions()
enableSaveActions()
onCropReturns(False) # Dont show the dialog just restore settings
onLaserSelection(False)
rep = smp.Show(sensor)
rep.InterpolateScalarsBeforeMapping = 0
if SAMPLE_PROCESSING_MODE:
prep = smp.Show(processor)
smp.Render()
showSourceInSpreadSheet(sensor)
app.actions['actionDualReturnModeDual'].enabled = True
app.actions['actionDualReturnDistanceNear'].enabled = True
app.actions['actionDualReturnDistanceFar'].enabled = True
app.actions['actionDualReturnIntensityHigh'].enabled = True
app.actions['actionDualReturnIntensityLow'].enabled = True
play()
def openPCAP(filename, positionFilename=None):
calibration = chooseCalibration()
if not calibration:
return
calibrationFile = calibration.calibrationFile
sensorTransform = calibration.sensorTransform
close()
def onProgressEvent(o, e):
PythonQt.QtGui.QApplication.instance().processEvents()
progressDialog = QtGui.QProgressDialog('Reading packet file...', '', 0, 0, getMainWindow())
progressDialog.setCancelButton(None)
progressDialog.setModal(True)
progressDialog.show()
handler = servermanager.ActiveConnection.Session.GetProgressHandler()
handler.PrepareProgress()
freq = handler.GetProgressFrequency()
handler.SetProgressFrequency(0.05)
tag = handler.AddObserver('ProgressEvent', onProgressEvent)
# construct the reader, this calls UpdateInformation on the
# reader which scans the pcap file and emits progress events
reader = smp.VelodyneHDLReader(guiName='Data',
FileName=filename,
CalibrationFile=calibrationFile,
ApplyTransform=(app.transformMode > 0),
NumberOfTrailingFrames=app.trailingFramesSpinBox.value,
PointsSkip=app.trailingFramesSpinBox.value)
app.reader = reader
app.filenameLabel.setText('File: %s' % os.path.basename(filename))
onCropReturns(False) # Dont show the dialog just restore settings
onLaserSelection(False)
reader.GetClientSideObject().SetSensorTransform(sensorTransform)
if SAMPLE_PROCESSING_MODE:
processor = smp.ProcessingSample(reader)
handler.RemoveObserver(tag)
handler.SetProgressFrequency(freq)
progressDialog.close()
smp.GetActiveView().ViewTime = 0.0
rep = smp.Show(reader)
if SAMPLE_PROCESSING_MODE:
prep = smp.Show(processor)
app.scene.UpdateAnimationUsingDataTimeSteps()
# update overhead view
smp.SetActiveView(app.overheadView)
if positionFilename is None:
posreader = smp.VelodyneHDLPositionReader(guiName="Position",
FileName=filename)
else:
posreader = smp.ApplanixPositionReader(guiName="Position",
FileName=positionFilename)
posreader.BaseYaw = calibration.gpsYaw
posreader.BaseRoll = calibration.gpsRoll
posreader.BasePitch = calibration.gpsPitch
smp.Show(posreader)
# Create a sphere glyph
g = smp.Sphere()
g.Radius = 5.0
smp.Show(g)
if posreader.GetClientSideObject().GetOutput().GetNumberOfPoints():
reader.GetClientSideObject().SetInterpolator(
posreader.GetClientSideObject().GetInterpolator())
smp.Render(app.overheadView)
app.overheadView.ResetCamera()
trange = posreader.GetPointDataInformation().GetArray('time').GetRange()
# By construction time zero is at position 0,0,0
# Setup scalar bar
rep = smp.GetDisplayProperties(posreader)
rep.ColorArrayName = 'time'
rgbPoints = [trange[0], 0.0, 0.0, 1.0,
trange[1], 1.0, 0.0, 0.0]
rep.LookupTable = smp.GetLookupTableForArray('time', 1,
RGBPoints=rgbPoints,
ScalarRangeInitialized=1.0)
sb = smp.CreateScalarBar(LookupTable=rep.LookupTable, Title='Time')
sb.Orientation = 'Horizontal'
sb.Position, sb.Position2 = [.1, .05], [.8, .02]
app.overheadView.Representations.append(sb)
app.position = (posreader, None, g)
smp.Render(app.overheadView)
else:
if positionFilename is not None:
QtGui.QMessageBox.warning(getMainWindow(), 'Georeferncing data invalid',
'File %s is not supported' % positionFilename)
smp.Delete(posreader)
smp.SetActiveView(app.mainView)
smp.SetActiveSource(reader)
rep.InterpolateScalarsBeforeMapping = 0
setDefaultLookupTables(reader)
colorByIntensity(reader)
showSourceInSpreadSheet(reader)
updateSliderTimeRange()
updatePosition()
enablePlaybackActions()
enableSaveActions()
addRecentFile(filename)
app.actions['actionRecord'].setEnabled(False)
app.actions['actionDualReturnModeDual'].enabled = True
app.actions['actionDualReturnDistanceNear'].enabled = True
app.actions['actionDualReturnDistanceFar'].enabled = True
app.actions['actionDualReturnIntensityHigh'].enabled = True
app.actions['actionDualReturnIntensityLow'].enabled = True
resetCamera()
def hideMeasurementGrid():
rep = smp.GetDisplayProperties(app.grid)
rep.Visibility = 0
smp.Render()
def showMeasurementGrid():
rep = smp.GetDisplayProperties(app.grid)
rep.Visibility = 1
smp.Render()
# Start Functions related to ruler
def createRuler():
pxm = servermanager.ProxyManager()
distancerep = pxm.NewProxy('representations', 'DistanceWidgetRepresentation')
distancerepeasy = servermanager._getPyProxy(distancerep)
smp.GetActiveView().Representations.append(distancerepeasy)
distancerepeasy.Visibility = False
smp.Render()
return distancerepeasy
def hideRuler():
app.ruler.Visibility = False
smp.Render()
def showRuler():
app.ruler.Visibility = True
smp.Render()
def getPointFromCoordinate(coord, midPlaneDistance = 0.5):
assert len(coord) == 2
windowHeight = smp.GetActiveView().ViewSize[1]
displayPoint = [coord[0], windowHeight - coord[1], midPlaneDistance]
renderer = smp.GetActiveView().GetRenderer()
renderer.SetDisplayPoint(displayPoint)
renderer.DisplayToWorld()
world1 = renderer.GetWorldPoint()
return world1[:3]
def toggleRulerContext():
measurmentState = app.actions['actionMeasure'].isChecked()
mW = getMainWindow()
vtkW = mW.findChild('pqQVTKWidget')
if measurmentState == True:
vtkW.connect('mouseEvent(QMouseEvent*)', setRulerCoordinates)
elif measurmentState == False:
vtkW.disconnect('mouseEvent(QMouseEvent*)', setRulerCoordinates)
app.mousePressed = False
hideRuler()
def setRulerCoordinates(mouseEvent):
pqView = smp.GetActiveView()
rW = pqView.GetRenderWindow()
windowInteractor = rW.GetInteractor()
currentMouseState = mouseEvent.buttons()
currentKeyboardState = mouseEvent.modifiers()
if currentMouseState == 1: #Left button pressed
if app.mousePressed == False: #For the first time
if currentKeyboardState == 67108864: #Control key pressed
app.mousePressed = True
app.ruler.Point1WorldPosition = getPointFromCoordinate([mouseEvent.x(),mouseEvent.y()])
windowInteractor.Disable()
elif app.mousePressed == True: #Not for the first time
app.ruler.Point2WorldPosition = getPointFromCoordinate([mouseEvent.x(),mouseEvent.y()])
showRuler()
smp.Render()
elif currentMouseState == 0: #Left button released
windowInteractor.Enable()
if app.mousePressed == True: #For the first time
app.mousePressed = False
app.ruler.Point2WorldPosition = getPointFromCoordinate([mouseEvent.x(),mouseEvent.y()])
showRuler()
smp.Render()
# End Functions related to ruler
def rotateCSVFile(filename):
# read the csv file, move the last 3 columns to the
# front, and then overwrite the file with the result
csvFile = open(filename, 'rb')
reader = csv.reader(csvFile, quoting=csv.QUOTE_NONNUMERIC)
rows = [row[-3:] + row[:-3] for row in reader]
csvFile.close()
writer = csv.writer(open(filename, 'wb'), quoting=csv.QUOTE_NONNUMERIC, delimiter=',')
writer.writerows(rows)
def savePositionCSV(filename):
w = smp.CreateWriter(filename, getPosition())
w.Precision = 16
w.FieldAssociation = 'Points'
w.UpdatePipeline()
smp.Delete(w)
def saveCSVCurrentFrame(filename):
w = smp.CreateWriter(filename, smp.GetActiveSource())
w.Precision = 16
w.FieldAssociation = 'Points'
w.UpdatePipeline()
smp.Delete(w)
rotateCSVFile(filename)
def saveLASFrames(filename, first, last, transform):
reader = getReader().GetClientSideObject()
position = getPosition().GetClientSideObject().GetOutput()
PythonQt.paraview.pqVelodyneManager.saveFramesToLAS(
reader, position, first, last, filename, transform)
def saveLASCurrentFrame(filename, transform):
t = app.scene.AnimationTime
saveLASFrames(filename, t, t, transform)
def saveAllFrames(filename, saveFunction):
saveFunction(filename, getCurrentTimesteps())
def saveFrameRange(filename, frameStart, frameStop, saveFunction):
timesteps = range(frameStart, frameStop+1)
saveFunction(filename, timesteps)
def saveCSV(filename, timesteps):
tempDir = kiwiviewerExporter.tempfile.mkdtemp()
basenameWithoutExtension = os.path.splitext(os.path.basename(filename))[0]
outDir = os.path.join(tempDir, basenameWithoutExtension)
filenameTemplate = os.path.join(outDir, basenameWithoutExtension + ' (Frame %04d).csv')
os.makedirs(outDir)
writer = smp.CreateWriter('tmp.csv', getSensor() or getReader())
writer.FieldAssociation = 'Points'
writer.Precision = 16
for t in timesteps:
app.scene.AnimationTime = t
writer.FileName = filenameTemplate % t
writer.UpdatePipeline()
rotateCSVFile(writer.FileName)
smp.Delete(writer)
kiwiviewerExporter.zipDir(outDir, filename)
kiwiviewerExporter.shutil.rmtree(tempDir)
def saveLAS(filename, timesteps, transform):
tempDir = kiwiviewerExporter.tempfile.mkdtemp()
basenameWithoutExtension = os.path.splitext(os.path.basename(filename))[0]
outDir = os.path.join(tempDir, basenameWithoutExtension)
filenameTemplate = os.path.join(outDir, basenameWithoutExtension + ' (Frame %04d).csv')
os.makedirs(outDir)
for t in sorted(timesteps):
saveLASFrames(filenameTemplate % t, t, t, transform)
kiwiviewerExporter.zipDir(outDir, filename)
kiwiviewerExporter.shutil.rmtree(tempDir)
def getTimeStamp():
format = '%Y-%m-%d-%H-%M-%S'
return datetime.datetime.now().strftime(format)
def getSaveFileName(title, extension, defaultFileName=None):
settings = getPVSettings()
defaultDir = settings.value('VelodyneHDLPlugin/OpenData/DefaultDir', QtCore.QDir.homePath())
defaultFileName = defaultDir if not defaultFileName else os.path.join(defaultDir, defaultFileName)
nativeDialog = 0 if app.actions['actionNative_File_Dialogs'].isChecked() else QtGui.QFileDialog.DontUseNativeDialog
filters = '%s (*.%s)' % (extension, extension)
selectedFilter = '*.%s' % extension
fileName = QtGui.QFileDialog.getSaveFileName(getMainWindow(), title,
defaultFileName, filters, selectedFilter, nativeDialog)
if fileName:
settings.setValue('VelodyneHDLPlugin/OpenData/DefaultDir', QtCore.QFileInfo(fileName).absoluteDir().absolutePath())
return fileName
def restoreNativeFileDialogsAction():
settings = getPVSettings()
app.actions['actionNative_File_Dialogs'].setChecked(int(settings.value('VelodyneHDLPlugin/NativeFileDialogs', 1)))
def onNativeFileDialogsAction():
settings = getPVSettings()
defaultDir = settings.setValue('VelodyneHDLPlugin/NativeFileDialogs', int(app.actions['actionNative_File_Dialogs'].isChecked()))
def getFrameSelectionFromUser(frameStrideVisibility=False, framePackVisibility=False, frameTransformVisibility=False):
class FrameOptions(object):
pass
dialog = PythonQt.paraview.vvSelectFramesDialog(getMainWindow())
dialog.frameMinimum = app.scene.StartTime
dialog.frameMaximum = app.scene.EndTime
dialog.frameStrideVisibility = frameStrideVisibility
dialog.framePackVisibility = framePackVisibility
dialog.frameTransformVisibility = frameTransformVisibility
dialog.restoreState()
if not dialog.exec_():
return None
frameOptions = FrameOptions()
frameOptions.mode = dialog.frameMode
frameOptions.start = dialog.frameStart
frameOptions.stop = dialog.frameStop
frameOptions.stride = dialog.frameStride
frameOptions.pack = dialog.framePack
frameOptions.transform = dialog.frameTransform
dialog.setParent(None)
return frameOptions
def onSaveCSV():
frameOptions = getFrameSelectionFromUser()
if frameOptions is None:
return
if frameOptions.mode == vvSelectFramesDialog.CURRENT_FRAME:
fileName = getSaveFileName('Save CSV', 'csv', getDefaultSaveFileName('csv', appendFrameNumber=True))
if fileName:
oldTransform = transformMode()
setTransformMode(1 if frameOptions.transform else 0)
saveCSVCurrentFrame(fileName)
setTransformMode(oldTransform)
else:
fileName = getSaveFileName('Save CSV (to zip file)', 'zip', getDefaultSaveFileName('zip'))
if fileName:
oldTransform = transformMode()
setTransformMode(1 if frameOptions.transform else 0)
if frameOptions.mode == vvSelectFramesDialog.ALL_FRAMES:
saveAllFrames(fileName, saveCSV)
else:
start = frameOptions.start
stop = frameOptions.stop
saveFrameRange(fileName, start, stop, saveCSV)
setTransformMode(oldTransform)
def onSavePosition():
fileName = getSaveFileName('Save CSV', 'csv', getDefaultSaveFileName('csv', '-position'))
if fileName:
savePositionCSV(fileName)
def onSaveLAS():
frameOptions = getFrameSelectionFromUser(framePackVisibility=True, frameTransformVisibility=False)
if frameOptions is None:
return
if frameOptions.mode == vvSelectFramesDialog.CURRENT_FRAME:
frameOptions.start = frameOptions.stop = app.scene.AnimationTime
elif frameOptions.mode == vvSelectFramesDialog.ALL_FRAMES:
frameOptions.start = int(app.scene.StartTime)
frameOptions.stop = int(app.scene.EndTime)
if frameOptions.mode == vvSelectFramesDialog.CURRENT_FRAME:
fileName = getSaveFileName('Save LAS', 'las', getDefaultSaveFileName('las', appendFrameNumber=True))
if fileName:
oldTransform = transformMode()
setTransformMode(1 if frameOptions.transform else 0)
saveLASCurrentFrame(fileName, frameOptions.transform)
setTransformMode(oldTransform)
elif frameOptions.pack == vvSelectFramesDialog.FILE_PER_FRAME:
fileName = getSaveFileName('Save LAS (to zip file)', 'zip',
getDefaultSaveFileName('zip'))
if fileName:
oldTransform = transformMode()
setTransformMode(1 if frameOptions.transform else 0)
def saveTransformedLAS(filename, timesteps):
saveLAS(filename, timesteps, frameOptions.transform)
if frameOptions.mode == vvSelectFramesDialog.ALL_FRAMES:
saveAllFrames(fileName, saveTransformedLAS)
else:
start = frameOptions.start
stop = frameOptions.stop
saveFrameRange(fileName, start, stop, saveTransformedLAS)
setTransformMode(oldTransform)
else:
suffix = ' (Frame %d to %d)' % (frameOptions.start, frameOptions.stop)
defaultFileName = getDefaultSaveFileName('las', suffix=suffix)
fileName = getSaveFileName('Save LAS', 'las', defaultFileName)
if not fileName:
return
oldTransform = transformMode()
setTransformMode(1 if frameOptions.transform else 0)
saveLASFrames(fileName, frameOptions.start, frameOptions.stop,
frameOptions.transform)
setTransformMode(oldTransform)
def onSavePCAP():
frameOptions = getFrameSelectionFromUser(frameTransformVisibility=False)
if frameOptions is None:
return
if frameOptions.mode == vvSelectFramesDialog.CURRENT_FRAME:
frameOptions.start = frameOptions.stop = app.scene.AnimationTime
elif frameOptions.mode == vvSelectFramesDialog.ALL_FRAMES:
frameOptions.start = int(app.scene.StartTime)
frameOptions.stop = int(app.scene.EndTime)
defaultFileName = getDefaultSaveFileName('pcap', suffix=' (Frame %d to %d)' % (frameOptions.start, frameOptions.stop))
fileName = getSaveFileName('Save PCAP', 'pcap', defaultFileName)
if not fileName:
return
PythonQt.paraview.pqVelodyneManager.saveFramesToPCAP(getReader().SMProxy, frameOptions.start, frameOptions.stop, fileName)
def onSaveScreenshot():
fileName = getSaveFileName('Save Screenshot', 'png', getDefaultSaveFileName('png', appendFrameNumber=True))
if fileName:
saveScreenshot(fileName)
def onKiwiViewerExport():
frameOptions = getFrameSelectionFromUser(frameStrideVisibility=True,
frameTransformVisibility=False)
if frameOptions is None:
return
defaultFileName = getDefaultSaveFileName('zip', suffix=' (KiwiViewer)')
fileName = getSaveFileName('Export To KiwiViewer', 'zip', defaultFileName)
if not fileName:
return
if frameOptions.mode == vvSelectFramesDialog.CURRENT_FRAME:
timesteps = [app.scene.AnimationTime]
elif frameOptions.mode == vvSelectFramesDialog.ALL_FRAMES:
timesteps = range(int(app.scene.StartTime), int(app.scene.EndTime) + 1, frameOptions.stride)
else:
timesteps = range(frameOptions.start, frameOptions.stop+1, frameOptions.stride)
saveToKiwiViewer(fileName, timesteps)
def saveToKiwiViewer(filename, timesteps):
tempDir = kiwiviewerExporter.tempfile.mkdtemp()
outDir = os.path.join(tempDir, os.path.splitext(os.path.basename(filename))[0])
os.makedirs(outDir)
filenames = exportToDirectory(outDir, timesteps)
kiwiviewerExporter.writeJsonData(outDir, smp.GetActiveView(), smp.GetDisplayProperties(), filenames)
kiwiviewerExporter.zipDir(outDir, filename)
kiwiviewerExporter.shutil.rmtree(tempDir)
def exportToDirectory(outDir, timesteps):
filenames = []
alg = smp.GetActiveSource().GetClientSideObject()
writer = vtkXMLPolyDataWriter()
writer.SetDataModeToAppended()
writer.EncodeAppendedDataOff()
writer.SetCompressorTypeToZLib()
for t in timesteps:
filename = 'frame_%04d.vtp' % t
filenames.append(filename)
app.scene.AnimationTime = t
polyData = vtk.vtkPolyData()
polyData.ShallowCopy(alg.GetOutput())
writer.SetInputData(polyData)
writer.SetFileName(os.path.join(outDir, filename))
writer.Update()
return filenames
def getVersionString():
return QtGui.QApplication.instance().applicationVersion
def onDeveloperGuide():
basePath = PythonQt.QtGui.QApplication.instance().applicationDirPath()
paths = ['../Resources/VeloView_Developer_Guide.pdf']
for path in paths:
filename = os.path.join(basePath, path)
if os.path.isfile(filename):
QtGui.QDesktopServices.openUrl(QtCore.QUrl('file:///%s' % filename, QtCore.QUrl.TolerantMode))
def onUserGuide():
basePath = PythonQt.QtGui.QApplication.instance().applicationDirPath()
paths = ['../Resources/VeloView_User_Guide.pdf']
for path in paths:
filename = os.path.join(basePath, path)
if os.path.isfile(filename):
QtGui.QDesktopServices.openUrl(QtCore.QUrl('file:///%s' % filename, QtCore.QUrl.TolerantMode))
def onAbout():
title = 'About VeloView'
text = '''<h1>VeloView %s</h1><br/>Copyright (c) 2013, <NAME><br />
Sample Data Repository: <a href=http://midas3.kitware.com/midas/community/29>http://midas3.kitware.com/midas/community/29</a>'''% getVersionString()
QtGui.QMessageBox.about(getMainWindow(), title, text)
def close():
stop()
hideRuler()
unloadData()
smp.Render(app.overheadView)
app.scene.AnimationTime = 0
app.reader = None
app.sensor = None
smp.HideUnusedScalarBars()
resetCameraToForwardView()
app.filenameLabel.setText('')
app.statusLabel.setText('')
app.timeLabel.setText('')
updateSliderTimeRange()
disablePlaybackActions()
disableSaveActions()
app.actions['actionRecord'].setChecked(False)
app.actions['actionDualReturnModeDual'].setChecked(True)
app.actions['actionDualReturnModeDual'].enabled = False
app.actions['actionDualReturnDistanceNear'].enabled = False
app.actions['actionDualReturnDistanceFar'].enabled = False
app.actions['actionDualReturnIntensityHigh'].enabled = False
app.actions['actionDualReturnIntensityLow'].enabled = False
def seekForward():
if app.playing:
if app.playDirection < 0 or app.playDirection == 5:
app.playDirection = 0
app.playDirection += 1
updateSeekButtons()
else:
gotoNext()
def seekBackward():
if app.playing:
if app.playDirection > 0 or app.playDirection == -5:
app.playDirection = 0
app.playDirection -= 1
updateSeekButtons()
else:
gotoPrevious()
def seekPressTimeout():
app.seekPlay = True
onPlayTimer()
def seekForwardPressed():
app.seekPlayDirection = 1
if not app.playing:
app.seekTimer.start(500)
def seekForwardReleased():
app.seekTimer.stop()
app.seekPlay = False
def seekBackwardPressed():
app.seekPlayDirection = -1
if not app.playing:
app.seekTimer.start(500)
def seekBackwardReleased():
seekForwardReleased()
def updateSeekButtons():
icons = {
-5 : IconPaths.seekBackwardQuarterx,
-4 : IconPaths.seekBackwardHalfx,
-3 : IconPaths.seekBackward3x,
-2 : IconPaths.seekBackward2x,
-1 : IconPaths.seekBackward,
1 : IconPaths.seekForward,
2 : IconPaths.seekForward2x,
3 : IconPaths.seekForward3x,
4 : IconPaths.seekForwardHalfx,
5 : IconPaths.seekForwardQuarterx,
}
setActionIcon('actionSeek_Backward', icons[app.playDirection] if app.playDirection < 0 else IconPaths.seekBackward)
setActionIcon('actionSeek_Forward', icons[app.playDirection] if app.playDirection > 0 else IconPaths.seekForward)
fpsMap = {-5:5, 5:5, -4:11, 4:11}
fpsDefault = 30
app.targetFps = fpsMap.get(app.playDirection, fpsDefault)
def setPlaybackActionsEnabled(enabled):
for action in ('Play', 'Record', 'Seek_Forward', 'Seek_Backward', 'Go_To_Start', 'Go_To_End'):
app.actions['action'+action].setEnabled(enabled)
def enablePlaybackActions():
setPlaybackActionsEnabled(True)
def disablePlaybackActions():
setPlaybackActionsEnabled(False)
def _setSaveActionsEnabled(enabled):
for action in ('SaveCSV', 'SavePCAP', 'SaveLAS', 'Export_To_KiwiViewer',
'Close', 'Choose_Calibration_File', 'CropReturns'):
app.actions['action'+action].setEnabled(enabled)
getMainWindow().findChild('QMenu', 'menuSaveAs').enabled = enabled
def enableSaveActions():
_setSaveActionsEnabled(True)
if getPosition():
app.actions['actionSavePositionCSV'].setEnabled(True)
def disableSaveActions():
_setSaveActionsEnabled(False)
app.actions['actionSavePositionCSV'].setEnabled(False)
def recordFile(filename):
sensor = getSensor()
if sensor:
stopStream()
sensor.OutputFile = filename
app.statusLabel.setText(' Recording file: %s.' % os.path.basename(filename))
if app.playing:
startStream()
def onRecord():
recordAction = app.actions['actionRecord']
if not recordAction.isChecked():
stopRecording()
else:
fileName = getSaveFileName('Choose Output File', 'pcap', getDefaultSaveFileName('pcap'))
if not fileName:
recordAction.setChecked(False)
return
recordFile(fileName)
def stopRecording():
app.statusLabel.setText('')
sensor = getSensor()
if sensor:
stopStream()
sensor.OutputFile = ''
if app.playing:
startStream()
def startStream():
sensor = getSensor()
if sensor:
sensor.Start()
def stopStream():
sensor = getSensor()
if sensor:
sensor.Stop()
def pollSource():
source = getSensor()
reader = getReader()
if source is not None:
source.Poll()
source.UpdatePipelineInformation()
return source or reader
def getPointCloudData(attribute=None):
if attribute is not None:
data = getPointCloudData()
if data:
if attribute == 'points':
return data.GetPoints().GetData()
else:
return data.GetPointData().GetArray(attribute)
else:
source = getSensor() or getReader()
if source:
return source.GetClientSideObject().GetOutput()
def getCurrentTimesteps():
source = pollSource()
return list(source.TimestepValues) if source is not None else []
def getNumberOfTimesteps():
return getTimeKeeper().getNumberOfTimeStepValues()
def togglePlay():
setPlayMode(not app.playing)
def play():
setPlayMode(True)
def stop():
setPlayMode(False)
def onPlayTimer():
if app.playing or app.seekPlay:
startTime = vtk.vtkTimerLog.GetUniversalTime()
playbackTick()
fpsDelayMilliseconds = int(1000.0 / app.targetFps)
elapsedMilliseconds = int((vtk.vtkTimerLog.GetUniversalTime() - startTime)*1000.0)
if elapsedMilliseconds > 0:
fps = 1000.0/elapsedMilliseconds
app.fps[0] += fps
app.fps[1] += 1
waitMilliseconds = fpsDelayMilliseconds - elapsedMilliseconds
app.playTimer.start(max(waitMilliseconds,0))
def setPlayMode(mode):
if not getReader() and not getSensor():
return
app.playing = mode
if mode:
startStream()
setActionIcon('actionPlay', IconPaths.pause)
app.playTimer.start(33)
if app.scene.AnimationTime == app.scene.EndTime:
app.scene.AnimationTime = app.scene.StartTime
else:
stopStream()
setActionIcon('actionPlay', IconPaths.play)
app.playDirection = 1
updateSeekButtons()
def gotoStart():
pollSource()
app.scene.GoToFirst()
updatePosition()
def gotoEnd():
pollSource()
app.scene.GoToLast()
updatePosition()
def gotoNext():
pollSource()
app.scene.GoToNext()
updatePosition()
def gotoPrevious():
pollSource()
app.scene.GoToPrevious()
updatePosition()
def updatePosition():
reader = getReader()
pos = getPosition()
if reader and pos:
pointcloud = reader.GetClientSideObject().GetOutput()
if pointcloud.GetNumberOfPoints():
# Update the overhead view
# TODO: Approximate time, just grabbing the last
t = pointcloud.GetPointData().GetScalars('adjustedtime')
#currentTime = t.GetTuple1(t.GetNumberOfTuples() - 1)
currentTime = t.GetTuple1(0) * 1e-6
interp = getPosition().GetClientSideObject().GetInterpolator()
trange = [interp.GetMinimumT(), interp.GetMaximumT()]
# Clamp
currentTime = min(max(currentTime, trange[0]+1.0e-1), trange[1]-1.0e-1)
position = [0.0] * 3
transform = vtk.vtkTransform()
interp.InterpolateTransform(currentTime, transform)
transform.TransformPoint(position, position)
rep = cachedGetRepresentation(reader, view=app.mainView)
if app.relativeTransform:
rep.Position = transform.GetInverse().GetPosition()
rep.Orientation = transform.GetInverse().GetOrientation()
else:
rep.Position = [0.0, 0.0, 0.0]
rep.Orientation = [0.0, 0.0, 0.0]
g = getGlyph()
rep = cachedGetRepresentation(g, view=app.overheadView)
rep.Position = position[:3]
def playbackTick():
sensor = getSensor()
reader = getReader()
view = smp.GetActiveView()
if sensor is not None:
timesteps = getCurrentTimesteps()
if not timesteps:
return
if view.ViewTime == timesteps[-1]:
return
if not app.colorByInitialized:
sensor.UpdatePipeline()
if colorByIntensity(sensor):
app.colorByInitialized = True
resetCamera()
app.scene.GoToLast()
elif reader is not None:
numberOfTimesteps = getNumberOfTimesteps()
if not numberOfTimesteps:
return
step = app.seekPlayDirection if app.seekPlay else app.playDirection
stepMap = {4:1, 5:1, -4:-1, -5:-1}
step = stepMap.get(step, step)
newTime = app.scene.AnimationTime + step
if app.actions['actionLoop'].isChecked():
newTime = newTime % numberOfTimesteps
else:
newTime = max(app.scene.StartTime, min(newTime, app.scene.EndTime))
# stop playback when it reaches the first or last timestep
if newTime in (app.scene.StartTime, app.scene.EndTime):
stop()
app.scene.AnimationTime = newTime
# TODO: For sensor as well?
updatePosition()
def unloadData():
_repCache.clear()
for k, src in smp.GetSources().iteritems():
if src != app.grid:
smp.Delete(src)
toremove = [x for x in app.overheadView.Representations if type(x) == servermanager.rendering.ScalarBarWidgetRepresentation]
for t in toremove:
app.overheadView.Representations.remove(t)
app.reader = None
app.position = (None, None, None)
app.sensor = None
clearSpreadSheetView()
def getReader():
return getattr(app, 'reader', None)
def getSensor():
return getattr(app, 'sensor', None)
def getPosition():
return getattr(app, 'position', (None, None, None))[0]
def getGlyph():
return getattr(app, 'position', (None, None, None))[2]
def onChooseCalibrationFile():
calibration = chooseCalibration()
if not calibration:
return
calibrationFile = calibration.calibrationFile
sensorTransform = calibration.sensorTransform
reader = getReader()
sensor = getSensor()
if reader is not None:
reader.GetClientSideObject().SetSensorTransform(sensorTransform)
reader.CalibrationFile = calibrationFile
reader.DummyProperty = not reader.DummyProperty
smp.Render()
elif sensor is not None:
sensor.GetClientSideObject().SetSensorTransform(sensorTransform)
sensor.CalibrationFile = calibrationFile
# no need to render now, calibration file will be used on the next frame
def onCropReturns(show = True):
dialog = vvCropReturnsDialog(getMainWindow())
cropEnabled = False
cropInside = False
firstCorner = QtGui.QVector3D()
secondCorner = QtGui.QVector3D()
reader = getReader()
sensor = getSensor()
if reader is not None:
cropEnabled = reader.CropReturns
cropInside = reader.CropInside
firstCorner = QtGui.QVector3D(reader.CropRegion[0], reader.CropRegion[2], reader.CropRegion[4])
secondCorner = QtGui.QVector3D(reader.CropRegion[1], reader.CropRegion[3], reader.CropRegion[5])
if sensor is not None:
cropEnabled = sensor.CropReturns
cropInside = sensor.CropInside
firstCorner = QtGui.QVector3D(sensor.CropRegion[0], sensor.CropRegion[2], sensor.CropRegion[4])
secondCorner = QtGui.QVector3D(sensor.CropRegion[1], sensor.CropRegion[3], sensor.CropRegion[5])
if show:
dialog.croppingEnabled = cropEnabled
dialog.cropInside = cropInside
dialog.firstCorner = firstCorner
dialog.secondCorner = secondCorner
if not dialog.exec_():
return
if reader is not None:
reader.CropReturns = dialog.croppingEnabled
reader.CropInside = dialog.cropInside
p1 = dialog.firstCorner
p2 = dialog.secondCorner
reader.CropRegion = [p1.x(), p2.x(), p1.y(), p2.y(), p1.z(), p2.z()]
if show:
smp.Render()
if sensor is not None:
sensor.CropReturns = dialog.croppingEnabled
sensor.CropInside = dialog.cropInside
p1 = dialog.firstCorner
p2 = dialog.secondCorner
sensor.CropRegion = [p1.x(), p2.x(), p1.y(), p2.y(), p1.z(), p2.z()]
if show:
smp.Render()
def resetCamera():
def subtract(a, b):
result = range(3)
vtk.vtkMath.Subtract(a, b, result)
return result
def cross(a, b):
result = range(3)
vtk.vtkMath.Cross(a, b, result)
return result
view = smp.GetActiveView()
foc = list(view.CenterOfRotation)
pos = list(view.CameraPosition)
viewDirection = subtract(foc, pos)
view.CameraPosition = subtract([0, 0, 0], viewDirection)
view.CameraFocalPoint = [0, 0, 0]
view.CenterOfRotation = [0, 0, 0]
vtk.vtkMath.Normalize(viewDirection)
perp = cross(viewDirection, [0, 0, 1])
viewUp = cross(perp, viewDirection)
view.CameraViewUp = viewUp
view.StillRender()
def resetCameraToBirdsEyeView(view=None):
view = view or smp.GetActiveView()
view.CameraFocalPoint = [0, 0, 0]
view.CameraViewUp = [0, 1, 0]
view.CameraPosition = [0, 0, 40]
view.CenterOfRotation = [0, 0, 0]
smp.Render(view)
def resetCameraToForwardView(view=None):
view = view or smp.GetActiveView()
view.CameraFocalPoint = [0,0,0]
view.CameraViewUp = [0, 0.27, 0.96]
view.CameraPosition = [0, -72, 18.0]
view.CenterOfRotation = [0, 0, 0]
smp.Render(view)
def saveScreenshot(filename):
smp.WriteImage(filename)
# reload the saved screenshot as a pixmap
screenshot = QtGui.QPixmap()
screenshot.load(filename)
# create a new pixmap with the status bar widget painted at the bottom
statusBar = QtGui.QPixmap.grabWidget(getMainWindow().statusBar())
composite = QtGui.QPixmap(screenshot.width(), screenshot.height() + statusBar.height())
painter = QtGui.QPainter()
painter.begin(composite)
painter.drawPixmap(screenshot.rect(), screenshot, screenshot.rect())
painter.drawPixmap(statusBar.rect().translated(0, screenshot.height()), statusBar, statusBar.rect())
painter.end()
# save final screenshot
composite.save(filename)
def getSpreadSheetViewProxy():
for p in smp.servermanager.ProxyManager():
if p.GetXMLName() == 'SpreadSheetView':
return p
def clearSpreadSheetView():
view = getSpreadSheetViewProxy()
view.Representations = []
def showSourceInSpreadSheet(source):
spreadSheetView = getSpreadSheetViewProxy()
smp.Show(source, spreadSheetView)
# Work around a bug where the 'Showing' combobox doesn't update.
# Calling hide and show again will trigger the refresh.
smp.Hide(source, spreadSheetView)
smp.Show(source, spreadSheetView)
def createGrid(view=None):
view = view or smp.GetActiveView()
grid = smp.VelodyneHDLGridSource(guiName='Measurement Grid')
rep = smp.Show(grid, view)
rep.DiffuseColor = [0.2, 0.2, 0.2]
rep.Pickable = 0
rep.Visibility = 0
smp.SetActiveSource(None)
return grid
def hideGrid():
smp.GetDisplayProperties(app.grid).Hide()
def showGrid():
smp.GetDisplayProperties(app.grid).Show()
def getAnimationScene():
'''This function is a workaround because paraview.simple.GetAnimationScene()
has an issue where the returned proxy might not have its Cues property initialized'''
for proxy in servermanager.ProxyManager().GetProxiesInGroup("animation").values():
if proxy.GetXMLName() == 'AnimationScene' and len(proxy.Cues):
return proxy
def start():
global app
app = AppLogic()
app.scene = getAnimationScene()
view = smp.GetActiveView()
view.Background = [0.0, 0.0, 0.0]
view.Background2 = [0.0, 0.0, 0.2]
view.UseGradientBackground = True
smp._DisableFirstRenderCameraReset()
smp.GetActiveView().LODThreshold = 1e100
app.grid = createGrid()
app.ruler = createRuler()
resetCameraToForwardView()
setupActions()
disablePlaybackActions()
disableSaveActions()
app.actions['actionMeasure'].setEnabled(view.CameraParallelProjection)
setupStatusBar()
setupTimeSliderWidget()
hideColorByComponent()
restoreNativeFileDialogsAction()
updateRecentFiles()
getTimeKeeper().connect('timeChanged()', onTimeChanged)
def findQObjectByName(widgets, name):
for w in widgets:
if w.objectName == name:
return w
def getMainWindow():
return findQObjectByName(QtGui.QApplication.topLevelWidgets(), 'vvMainWindow')
def getPVApplicationCore():
return PythonQt.paraview.pqPVApplicationCore.instance()
def getPVSettings():
return getPVApplicationCore().settings()
def getTimeKeeper():
return getPVApplicationCore().getActiveServer().getTimeKeeper()
def getPlaybackToolBar():
return findQObjectByName(getMainWindow().children(), 'playbackToolbar')
def quit():
PythonQt.QtGui.QApplication.instance().quit()
exit = quit
def addShortcuts(keySequenceStr, function):
shortcut = PythonQt.QtGui.QShortcut(PythonQt.QtGui.QKeySequence(keySequenceStr), getMainWindow())
shortcut.connect("activated()", function)
def onTrailingFramesChanged(numFrames):
try:
app.reader.NumberOfTrailingFrames = numFrames
smp.Render()
smp.Render(getSpreadSheetViewProxy())
except AttributeError:
pass
def onPointsSkipChanged(pr):
try:
app.reader.PointsSkip = pr
smp.Render()
smp.Render(getSpreadSheetViewProxy())
except AttributeError:
pass
def setupTimeSliderWidget():
frame = QtGui.QWidget()
layout = QtGui.QHBoxLayout(frame)
spinBox = QtGui.QSpinBox()
spinBox.setMinimum(0)
spinBox.setMaximum(100)
spinBox.setValue(0)
slider = QtGui.QSlider(QtCore.Qt.Horizontal)
slider.setMaximumWidth(160)
slider.connect('valueChanged(int)', onTimeSliderChanged)
spinBox.connect('valueChanged(int)', onTimeSliderChanged)
slider.setEnabled(False)
spinBox.setEnabled(False)
layout.addWidget(slider)
layout.addWidget(spinBox)
layout.addStretch()
toolbar = getPlaybackToolBar()
toolbar.addWidget(frame)
app.timeSlider = slider
app.timeSpinBox = spinBox
def updateSliderTimeRange():
frame = int(app.scene.AnimationTime)
lastFrame = int(app.scene.EndTime)
for widget in (app.timeSlider, app.timeSpinBox):
widget.setMinimum(0)
widget.setMaximum(lastFrame)
widget.setSingleStep(1)
if hasattr(widget, 'setPageStep'):
widget.setPageStep(10)
widget.setValue(frame)
widget.setEnabled(getNumberOfTimesteps())
def scheduleRender():
if not app.renderIsPending:
app.renderIsPending = True
app.renderTimer.start(33)
def forceRender():
smp.Render()
app.renderIsPending = False
def onTimeSliderChanged(frame):
app.scene.AnimationTime = frame
updatePosition()
def setupStatusBar():
statusBar = getMainWindow().statusBar()
statusBar.addPermanentWidget(app.logoLabel)
statusBar.addWidget(app.filenameLabel)
statusBar.addWidget(app.statusLabel)
statusBar.addWidget(app.timeLabel)
def setActionIcon(actionName, iconPath):
app.actions[actionName].setIcon(QtGui.QIcon(QtGui.QPixmap(iconPath)))
def onTimeChanged():
frame = int(getTimeKeeper().getTime())
app.timeLabel.setText(' Frame: %s' % frame)
for widget in (app.timeSlider, app.timeSpinBox):
widget.blockSignals(True)
widget.setValue(frame)
widget.blockSignals(False)
def onGridProperties():
if gridAdjustmentDialog.showDialog(getMainWindow(), app.grid):
smp.Render()
def onLaserSelection(show = True):
oldmask = [1] * 64
reader = getReader()
sensor = getSensor()
corrections = [0] * 64
nchannels = 32
if reader:
reader.GetClientSideObject().GetLaserSelection(oldmask)
reader.GetClientSideObject().GetVerticalCorrections(corrections)
nchannels = reader.GetPropertyValue('NumberOfChannels')
elif sensor:
sensor.GetClientSideObject().GetLaserSelection(oldmask)
sensor.GetClientSideObject().GetVerticalCorrections(corrections)
nchannels = sensor.GetPropertyValue('NumberOfChannels')
# Need a way to initialize the mask
dialog = PythonQt.paraview.vvLaserSelectionDialog(getMainWindow())
if show:
dialog.setLaserSelectionSelector(oldmask)
dialog.setVerticalCorrections(corrections, nchannels)
if not dialog.exec_():
return
mask = dialog.getLaserSelectionSelector()
if reader:
reader.GetClientSideObject().SetLaserSelection(mask)
reader.DummyProperty = not reader.DummyProperty
if show:
smp.Render()
smp.Render(getSpreadSheetViewProxy())
if sensor:
sensor.GetClientSideObject().SetLaserSelection(mask)
sensor.DummyProperty = not sensor.DummyProperty
if show:
smp.Render()
smp.Render(getSpreadSheetViewProxy())
def hideColorByComponent():
getMainWindow().findChild('vvColorToolbar').findChild('pqDisplayColorWidget').findChildren('QComboBox')[1].hide()
def addRecentFile(filename):
recentFiles = getRecentFiles()
try:
recentFiles.remove(filename)
except ValueError:
pass
recentFiles = recentFiles[:4]
recentFiles.insert(0, filename)
getPVSettings().setValue('VelodyneHDLPlugin/RecentFiles', recentFiles)
updateRecentFiles()
def openRecentFile(filename):
if not os.path.isfile(filename):
QtGui.QMessageBox.warning(getMainWindow(), 'File not found', 'File not found: %s' % filename)
return
if os.path.splitext(filename)[1].lower() == '.pcap':
openPCAP(filename)
else:
openData(filename)
def getRecentFiles():
return list(getPVSettings().value('VelodyneHDLPlugin/RecentFiles', []) or [])
def updateRecentFiles():
settings = getPVSettings()
recentFiles = getRecentFiles()
recentFilesMenu = findQObjectByName(findQObjectByName(getMainWindow().menuBar().children(), 'menu_File').children(), 'menuRecent_Files')
clearMenuAction = app.actions['actionClear_Menu']
for action in recentFilesMenu.actions()[:-2]:
recentFilesMenu.removeAction(action)
def createActionFunction(filename):
def f():
openRecentFile(filename)
return f
actions = []
for filename in recentFiles:
actions.append(QtGui.QAction(os.path.basename(filename), recentFilesMenu))
actions[-1].connect('triggered()', createActionFunction(filename))
recentFilesMenu.insertActions(recentFilesMenu.actions()[0], actions)
def onClearMenu():
settings = getPVSettings()
settings.setValue('VelodyneHDLPlugin/RecentFiles', [])
updateRecentFiles()
def toggleProjectionType():
view = smp.GetActiveView()
view.CameraParallelProjection = not view.CameraParallelProjection
if app.actions['actionMeasure'].isChecked():
app.actions['actionMeasure'].trigger()
app.actions['actionMeasure'].toggle()
app.actions['actionMeasure'].setEnabled(view.CameraParallelProjection)
smp.Render()
def setViewTo(axis,sign):
view = smp.GetActiveView()
viewUp = view.CameraViewUp
position = view.CameraPosition
norm = math.sqrt(math.pow(position[0],2) + math.pow(position[1],2) + math.pow(position[2],2))
if axis == 'X':
view.CameraViewUp = [0,0,1]
view.CameraPosition = [-1*sign*norm,0,0]
elif axis == 'Y':
view.CameraViewUp = [0,0,1]
view.CameraPosition = [0,-1*sign*norm,0]
elif axis == 'Z':
view.CameraViewUp = [0,1,0]
view.CameraPosition = [0,0,-1*sign*norm]
view.CameraFocalPoint = [0,0,0]
view.CenterOfRotation = [0,0,0]
view.ResetCamera()
smp.Render()
def setViewToXPlus():
setViewTo('X',1)
def setViewToXMinus():
setViewTo('X',-1)
def setViewToYPlus():
setViewTo('Y',1)
def setViewToYMinus():
setViewTo('Y',-1)
def setViewToZPlus():
setViewTo('Z',1)
def setViewToZMinus():
setViewTo('Z',-1)
def setFilterToDual():
setFilterTo(0)
def setFilterToDistanceNear():
setFilterTo(vtkVelodyneHDLReader.DUAL_DISTANCE_NEAR)
def setFilterToDistanceFar():
setFilterTo(vtkVelodyneHDLReader.DUAL_DISTANCE_FAR)
def setFilterToIntensityHigh():
setFilterTo(vtkVelodyneHDLReader.DUAL_INTENSITY_HIGH)
def setFilterToIntensityLow():
setFilterTo(vtkVelodyneHDLReader.DUAL_INTENSITY_LOW)
def setFilterTo(mask):
reader = getReader()
if reader:
reader.DualReturnFilter = mask
smp.Render()
smp.Render(getSpreadSheetViewProxy())
sensor = getSensor()
if sensor:
sensor.DualReturnFilter = mask
smp.Render()
smp.Render(getSpreadSheetViewProxy())
def transformMode():
reader = getReader()
if not reader:
return None
if reader.ApplyTransform:
if app.relativeTransform:
return 2 # relative
else:
return 1 # absolute
return 0 # raw
def setTransformMode(mode):
# 0 - raw
# 1 - absolute
# 2 - relative
reader = getReader()
if reader:
reader.ApplyTransform = (mode > 0)
app.transformMode = mode
app.relativeTransform = (mode == 2)
def geolocationChanged(setting):
setTransformMode(setting)
updatePosition()
smp.Render(view=app.mainView)
def setupActions():
mW = getMainWindow()
actions = mW.findChildren('QAction')
app.actions = {}
for a in actions:
app.actions[a.objectName] = a
app.actions['actionPlaneFit'].connect('triggered()', planeFit)
app.actions['actionClose'].connect('triggered()', close)
app.actions['actionPlay'].connect('triggered()', togglePlay)
app.actions['actionRecord'].connect('triggered()', onRecord)
app.actions['actionSaveCSV'].connect('triggered()', onSaveCSV)
app.actions['actionSavePositionCSV'].connect('triggered()', onSavePosition)
app.actions['actionSaveLAS'].connect('triggered()', onSaveLAS)
app.actions['actionSavePCAP'].connect('triggered()', onSavePCAP)
app.actions['actionSaveScreenshot'].connect('triggered()', onSaveScreenshot)
app.actions['actionExport_To_KiwiViewer'].connect('triggered()', onKiwiViewerExport)
app.actions['actionReset_Camera'].connect('triggered()', resetCamera)
app.actions['actionGrid_Properties'].connect('triggered()', onGridProperties)
app.actions['actionLaserSelection'].connect('triggered()', onLaserSelection)
app.actions['actionChoose_Calibration_File'].connect('triggered()', onChooseCalibrationFile)
app.actions['actionCropReturns'].connect('triggered()', onCropReturns)
app.actions['actionSeek_Forward'].connect('triggered()', seekForward)
app.actions['actionSeek_Backward'].connect('triggered()', seekBackward)
app.actions['actionGo_To_End'].connect('triggered()', gotoEnd)
app.actions['actionGo_To_Start'].connect('triggered()', gotoStart)
app.actions['actionNative_File_Dialogs'].connect('triggered()', onNativeFileDialogsAction)
app.actions['actionAbout_VeloView'].connect('triggered()', onAbout)
app.actions['actionVeloViewDeveloperGuide'].connect('triggered()', onDeveloperGuide)
app.actions['actionClear_Menu'].connect('triggered()', onClearMenu)
app.actions['actionToggleProjection'].connect('triggered()', toggleProjectionType)
app.actions['actionMeasure'].connect('triggered()', toggleRulerContext)
app.actions['actionSetViewXPlus'].connect('triggered()', setViewToXPlus)
app.actions['actionSetViewXMinus'].connect('triggered()', setViewToXMinus)
app.actions['actionSetViewYPlus'].connect('triggered()', setViewToYPlus)
app.actions['actionSetViewYMinus'].connect('triggered()', setViewToYMinus)
app.actions['actionSetViewZPlus'].connect('triggered()', setViewToZPlus)
app.actions['actionSetViewZMinus'].connect('triggered()', setViewToZMinus)
app.actions['actionDualReturnModeDual'].connect('triggered()', setFilterToDual)
app.actions['actionDualReturnDistanceNear'].connect('triggered()', setFilterToDistanceNear)
app.actions['actionDualReturnDistanceFar'].connect('triggered()', setFilterToDistanceFar)
app.actions['actionDualReturnIntensityHigh'].connect('triggered()', setFilterToIntensityHigh)
app.actions['actionDualReturnIntensityLow'].connect('triggered()', setFilterToIntensityLow)
# Action created #
timeToolBar = mW.findChild('QToolBar','playbackToolbar')
geolocationToolBar = mW.findChild('QToolBar', 'geolocationToolbar')
comboBox = QtGui.QComboBox()
comboBox.addItem('Relative RAW')
comboBox.addItem('Absolute Geolocation')
comboBox.addItem('Relative Geolocation')
comboBox.connect('currentIndexChanged(int)', geolocationChanged)
geolocationToolBar.addWidget(comboBox)
spinBoxLabel = QtGui.QLabel('TF:')
spinBoxLabel.toolTip = "Number of trailing frames"
timeToolBar.addWidget(spinBoxLabel)
spinBox = QtGui.QSpinBox()
spinBox.toolTip = "Number of trailing frames"
spinBox.setMinimum(0)
spinBox.setMaximum(100)
spinBox.connect('valueChanged(int)', onTrailingFramesChanged)
app.trailingFramesSpinBox = spinBox
app.actions['actionTrailingFramesSelector'] = timeToolBar.addWidget(spinBox)
app.actions['actionTrailingFramesSelector'].setVisible(True)
pointsSkipLabel = QtGui.QLabel('Skip:')
pointsSkipLabel.toolTip = "Number of Points to Skip"
timeToolBar.addWidget(pointsSkipLabel)
pointsSkipBox = QtGui.QSpinBox()
pointsSkipBox.toolTip = "Number of Points to Skip"
pointsSkipBox.setMinimum(0)
pointsSkipBox.setMaximum(100)
pointsSkipBox.connect('valueChanged(int)', onPointsSkipChanged)
app.pointsSkipSpinBox = pointsSkipBox
app.actions['actionPointsSkipSelector'] = timeToolBar.addWidget(pointsSkipBox)
app.actions['actionPointsSkipSelector'].setVisible(True)
buttons = {}
for button in getPlaybackToolBar().findChildren('QToolButton'):
buttons[button.text] = button
buttons['Seek Forward'].connect('pressed()', seekForwardPressed)
buttons['Seek Forward'].connect('released()', seekForwardReleased)
buttons['Seek Backward'].connect('pressed()', seekBackwardPressed)
buttons['Seek Backward'].connect('released()', seekBackwardReleased)
| en | 0.791256 | # Copyright 2013 Velodyne Acoustics, Inc. # # 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. Returns True if the data has non-zero points and has a point data attribute with the given arrayName. # LUT for 'intensity' # LUT for 'dual_distance' # LUT for 'dual_intensity' # Dont show the dialog just restore settings # construct the reader, this calls UpdateInformation on the # reader which scans the pcap file and emits progress events # Dont show the dialog just restore settings # update overhead view # Create a sphere glyph # By construction time zero is at position 0,0,0 # Setup scalar bar # Start Functions related to ruler #Left button pressed #For the first time #Control key pressed #Not for the first time #Left button released #For the first time # End Functions related to ruler # read the csv file, move the last 3 columns to the # front, and then overwrite the file with the result <h1>VeloView %s</h1><br/>Copyright (c) 2013, <NAME><br /> Sample Data Repository: <a href=http://midas3.kitware.com/midas/community/29>http://midas3.kitware.com/midas/community/29</a> # Update the overhead view # TODO: Approximate time, just grabbing the last #currentTime = t.GetTuple1(t.GetNumberOfTuples() - 1) # Clamp # stop playback when it reaches the first or last timestep # TODO: For sensor as well? # no need to render now, calibration file will be used on the next frame # reload the saved screenshot as a pixmap # create a new pixmap with the status bar widget painted at the bottom # save final screenshot # Work around a bug where the 'Showing' combobox doesn't update. # Calling hide and show again will trigger the refresh. This function is a workaround because paraview.simple.GetAnimationScene() has an issue where the returned proxy might not have its Cues property initialized # Need a way to initialize the mask # relative # absolute # raw # 0 - raw # 1 - absolute # 2 - relative # Action created # | 1.860877 | 2 |
examples/example_mapping.py | JulienLefevreMars/slam | 6 | 6630541 | """
.. _example_mapping:
===================================
Mapping example in slam
===================================
"""
# Authors:
# <NAME> <<EMAIL>>
# <NAME> <<EMAIL>>
# License: BSD (3-clause)
# sphinx_gallery_thumbnail_number = 2
###############################################################################
# Importation of slam modules
import slam.generate_parametric_surfaces as sps
import numpy as np
import slam.topology as stop
import slam.plot as splt
import slam.mapping as smap
import slam.distortion as sdst
from vispy.scene import Line
from visbrain.objects import VispyObj, SourceObj
###############################################################################
# Generation of an open mesh
K = [-1, -1]
open_mesh = sps.generate_quadric(K, nstep=[5, 5])
open_mesh_boundary = stop.mesh_boundary(open_mesh)
# Visualization
visb_sc = splt.visbrain_plot(mesh=open_mesh, caption='open mesh')
for bound in open_mesh_boundary:
points = open_mesh.vertices[bound]
s_rad = SourceObj('rad', points, color='red', symbol='square',
radius_min=10)
visb_sc.add_to_subplot(s_rad)
lines = Line(pos=open_mesh.vertices[bound], width=10, color='b')
# wrap the vispy object using visbrain
l_obj = VispyObj('line', lines)
visb_sc.add_to_subplot(l_obj)
visb_sc.preview()
###############################################################################
# Mapping onto a planar disk
disk_mesh = smap.disk_conformal_mapping(open_mesh)
# Visualization
visb_sc2 = splt.visbrain_plot(mesh=disk_mesh, caption='disk mesh')
for bound in open_mesh_boundary:
points = disk_mesh.vertices[bound]
s_rad = SourceObj('rad', points, color='red', symbol='square',
radius_min=10)
visb_sc2.add_to_subplot(s_rad)
lines = Line(pos=disk_mesh.vertices[bound], width=10, color='b')
# wrap the vispy object using visbrain
l_obj = VispyObj('line', lines)
visb_sc2.add_to_subplot(l_obj)
visb_sc2.preview()
###############################################################################
# Compute distortion measures between original and planar representations
angle_diff = sdst.angle_difference(disk_mesh, open_mesh)
area_diff = sdst.area_difference(disk_mesh, open_mesh)
edge_diff = sdst.edge_length_difference(disk_mesh, open_mesh)
print(np.mean(angle_diff))
print(np.mean(area_diff))
print(np.mean(edge_diff))
| """
.. _example_mapping:
===================================
Mapping example in slam
===================================
"""
# Authors:
# <NAME> <<EMAIL>>
# <NAME> <<EMAIL>>
# License: BSD (3-clause)
# sphinx_gallery_thumbnail_number = 2
###############################################################################
# Importation of slam modules
import slam.generate_parametric_surfaces as sps
import numpy as np
import slam.topology as stop
import slam.plot as splt
import slam.mapping as smap
import slam.distortion as sdst
from vispy.scene import Line
from visbrain.objects import VispyObj, SourceObj
###############################################################################
# Generation of an open mesh
K = [-1, -1]
open_mesh = sps.generate_quadric(K, nstep=[5, 5])
open_mesh_boundary = stop.mesh_boundary(open_mesh)
# Visualization
visb_sc = splt.visbrain_plot(mesh=open_mesh, caption='open mesh')
for bound in open_mesh_boundary:
points = open_mesh.vertices[bound]
s_rad = SourceObj('rad', points, color='red', symbol='square',
radius_min=10)
visb_sc.add_to_subplot(s_rad)
lines = Line(pos=open_mesh.vertices[bound], width=10, color='b')
# wrap the vispy object using visbrain
l_obj = VispyObj('line', lines)
visb_sc.add_to_subplot(l_obj)
visb_sc.preview()
###############################################################################
# Mapping onto a planar disk
disk_mesh = smap.disk_conformal_mapping(open_mesh)
# Visualization
visb_sc2 = splt.visbrain_plot(mesh=disk_mesh, caption='disk mesh')
for bound in open_mesh_boundary:
points = disk_mesh.vertices[bound]
s_rad = SourceObj('rad', points, color='red', symbol='square',
radius_min=10)
visb_sc2.add_to_subplot(s_rad)
lines = Line(pos=disk_mesh.vertices[bound], width=10, color='b')
# wrap the vispy object using visbrain
l_obj = VispyObj('line', lines)
visb_sc2.add_to_subplot(l_obj)
visb_sc2.preview()
###############################################################################
# Compute distortion measures between original and planar representations
angle_diff = sdst.angle_difference(disk_mesh, open_mesh)
area_diff = sdst.area_difference(disk_mesh, open_mesh)
edge_diff = sdst.edge_length_difference(disk_mesh, open_mesh)
print(np.mean(angle_diff))
print(np.mean(area_diff))
print(np.mean(edge_diff))
| de | 0.540745 | .. _example_mapping: =================================== Mapping example in slam =================================== # Authors: # <NAME> <<EMAIL>> # <NAME> <<EMAIL>> # License: BSD (3-clause) # sphinx_gallery_thumbnail_number = 2 ############################################################################### # Importation of slam modules ############################################################################### # Generation of an open mesh # Visualization # wrap the vispy object using visbrain ############################################################################### # Mapping onto a planar disk # Visualization # wrap the vispy object using visbrain ############################################################################### # Compute distortion measures between original and planar representations | 2.721205 | 3 |
reinforce/jax2.py | gebob19/rl_with_jax | 5 | 6630542 | ## different implementation version of batch REINFORCE (still works and is 2x faster)
#%%
import jax
import jax.numpy as np
import numpy as onp
import distrax
import optax
import gym
from functools import partial
from jax.config import config
config.update("jax_enable_x64", True)
config.update("jax_debug_nans", True) # break on nans
#%%
env_name = 'CartPole-v0'
env = gym.make(env_name)
n_actions = env.action_space.n
obs_dim = env.observation_space.shape[0]
print(f'[LOGGER] n_actions: {n_actions} obs_dim: {obs_dim}')
#%%
import haiku as hk
init_final = hk.initializers.RandomUniform(-3e-3, 3e-3)
def _policy_fcn(obs):
a_probs = hk.Sequential([
hk.Linear(32), jax.nn.relu,
hk.Linear(32), jax.nn.relu,
hk.Linear(n_actions, w_init=init_final), jax.nn.softmax
])(obs)
return a_probs
policy_fcn = hk.transform(_policy_fcn)
policy_fcn = hk.without_apply_rng(policy_fcn)
p_frwd = jax.jit(policy_fcn.apply)
@jax.jit
def update_step(params, grads, opt_state):
grads, opt_state = p_optim.update(grads, opt_state)
params = optax.apply_updates(params, grads)
return params, opt_state
def reward2go(r, gamma=0.99):
for i in range(len(r) - 1)[::-1]:
r[i] = r[i] + gamma * r[i+1]
r = (r - r.mean()) / (r.std() + 1e-8)
return r
@jax.jit
def policy(p_params, obs, rng):
a_probs = p_frwd(p_params, obs)
dist = distrax.Categorical(probs=a_probs)
a = dist.sample(seed=rng)
entropy = dist.entropy()
return a, entropy
def rollout(p_params, rng):
global step_count
observ, action, rew = [], [], []
obs = env.reset()
while True:
rng, subkey = jax.random.split(rng, 2)
a, entropy = policy(p_params, obs, subkey)
a = a.item()
writer.add_scalar('policy/entropy', entropy.item(), step_count)
obs2, r, done, _ = env.step(a)
step_count += 1
pbar.update(1)
observ.append(obs)
action.append(a)
rew.append(r)
if done: break
obs = obs2
obs = np.stack(observ)
a = np.stack(action)
r = onp.stack(rew) #
return obs, a, r
def reinforce_loss(p_params, obs, a, r):
a_probs = p_frwd(p_params, obs)
log_prob = distrax.Categorical(probs=a_probs).log_prob(a.astype(int))
loss = -(log_prob * r).sum()
return loss
from functools import partial
def batch_reinforce_loss(params, batch):
return jax.vmap(partial(reinforce_loss, params))(*batch).sum()
# %%
seed = onp.random.randint(1e5)
policy_lr = 1e-3
batch_size = 32
max_n_steps = 100000
rng = jax.random.PRNGKey(seed)
onp.random.seed(seed)
env.seed(seed)
obs = env.reset() # dummy input
p_params = policy_fcn.init(rng, obs)
## optimizers
p_optim = optax.sgd(policy_lr)
p_opt_state = p_optim.init(p_params)
# %%
from torch.utils.tensorboard import SummaryWriter
writer = SummaryWriter(comment=f'reinforce_{env_name}_seed={seed}')
# %%
from tqdm import tqdm
step_count = 0
epi_i = 0
pbar = tqdm(total=max_n_steps)
loss_grad_fcn = jax.jit(jax.value_and_grad(batch_reinforce_loss))
while step_count < max_n_steps:
trajs = []
for _ in range(batch_size):
rng, subkey = jax.random.split(rng, 2)
obs, a, r = rollout(p_params, subkey)
writer.add_scalar('rollout/reward', r.sum().item(), epi_i)
r = reward2go(r)
trajs.append((obs, a, r))
epi_i += 1
trajs = jax.tree_multimap(lambda *x: np.concatenate(x, 0), *trajs)
loss, grads = loss_grad_fcn(p_params, trajs)
p_params, p_opt_state = update_step(p_params, grads, p_opt_state)
writer.add_scalar('loss/loss', loss.item(), step_count)
step_count += 1
# %%
# %%
# %%
| ## different implementation version of batch REINFORCE (still works and is 2x faster)
#%%
import jax
import jax.numpy as np
import numpy as onp
import distrax
import optax
import gym
from functools import partial
from jax.config import config
config.update("jax_enable_x64", True)
config.update("jax_debug_nans", True) # break on nans
#%%
env_name = 'CartPole-v0'
env = gym.make(env_name)
n_actions = env.action_space.n
obs_dim = env.observation_space.shape[0]
print(f'[LOGGER] n_actions: {n_actions} obs_dim: {obs_dim}')
#%%
import haiku as hk
init_final = hk.initializers.RandomUniform(-3e-3, 3e-3)
def _policy_fcn(obs):
a_probs = hk.Sequential([
hk.Linear(32), jax.nn.relu,
hk.Linear(32), jax.nn.relu,
hk.Linear(n_actions, w_init=init_final), jax.nn.softmax
])(obs)
return a_probs
policy_fcn = hk.transform(_policy_fcn)
policy_fcn = hk.without_apply_rng(policy_fcn)
p_frwd = jax.jit(policy_fcn.apply)
@jax.jit
def update_step(params, grads, opt_state):
grads, opt_state = p_optim.update(grads, opt_state)
params = optax.apply_updates(params, grads)
return params, opt_state
def reward2go(r, gamma=0.99):
for i in range(len(r) - 1)[::-1]:
r[i] = r[i] + gamma * r[i+1]
r = (r - r.mean()) / (r.std() + 1e-8)
return r
@jax.jit
def policy(p_params, obs, rng):
a_probs = p_frwd(p_params, obs)
dist = distrax.Categorical(probs=a_probs)
a = dist.sample(seed=rng)
entropy = dist.entropy()
return a, entropy
def rollout(p_params, rng):
global step_count
observ, action, rew = [], [], []
obs = env.reset()
while True:
rng, subkey = jax.random.split(rng, 2)
a, entropy = policy(p_params, obs, subkey)
a = a.item()
writer.add_scalar('policy/entropy', entropy.item(), step_count)
obs2, r, done, _ = env.step(a)
step_count += 1
pbar.update(1)
observ.append(obs)
action.append(a)
rew.append(r)
if done: break
obs = obs2
obs = np.stack(observ)
a = np.stack(action)
r = onp.stack(rew) #
return obs, a, r
def reinforce_loss(p_params, obs, a, r):
a_probs = p_frwd(p_params, obs)
log_prob = distrax.Categorical(probs=a_probs).log_prob(a.astype(int))
loss = -(log_prob * r).sum()
return loss
from functools import partial
def batch_reinforce_loss(params, batch):
return jax.vmap(partial(reinforce_loss, params))(*batch).sum()
# %%
seed = onp.random.randint(1e5)
policy_lr = 1e-3
batch_size = 32
max_n_steps = 100000
rng = jax.random.PRNGKey(seed)
onp.random.seed(seed)
env.seed(seed)
obs = env.reset() # dummy input
p_params = policy_fcn.init(rng, obs)
## optimizers
p_optim = optax.sgd(policy_lr)
p_opt_state = p_optim.init(p_params)
# %%
from torch.utils.tensorboard import SummaryWriter
writer = SummaryWriter(comment=f'reinforce_{env_name}_seed={seed}')
# %%
from tqdm import tqdm
step_count = 0
epi_i = 0
pbar = tqdm(total=max_n_steps)
loss_grad_fcn = jax.jit(jax.value_and_grad(batch_reinforce_loss))
while step_count < max_n_steps:
trajs = []
for _ in range(batch_size):
rng, subkey = jax.random.split(rng, 2)
obs, a, r = rollout(p_params, subkey)
writer.add_scalar('rollout/reward', r.sum().item(), epi_i)
r = reward2go(r)
trajs.append((obs, a, r))
epi_i += 1
trajs = jax.tree_multimap(lambda *x: np.concatenate(x, 0), *trajs)
loss, grads = loss_grad_fcn(p_params, trajs)
p_params, p_opt_state = update_step(p_params, grads, p_opt_state)
writer.add_scalar('loss/loss', loss.item(), step_count)
step_count += 1
# %%
# %%
# %%
| en | 0.448458 | ## different implementation version of batch REINFORCE (still works and is 2x faster) #%% # break on nans #%% #%% # # %% # dummy input ## optimizers # %% # %% # %% # %% # %% | 1.871264 | 2 |
env/lib/python3.8/site-packages/faker/providers/internet/ru_RU/__init__.py | avdhari/enigma | 258 | 6630543 | <filename>env/lib/python3.8/site-packages/faker/providers/internet/ru_RU/__init__.py
# coding=utf-8
from __future__ import unicode_literals
from .. import Provider as InternetProvider
class Provider(InternetProvider):
user_name_formats = (
'{{last_name_female}}.{{first_name_female}}',
'{{last_name_male}}.{{first_name_male}}',
'{{last_name_male}}.{{first_name_male}}',
'{{first_name_male}}.{{last_name_male}}',
'{{first_name}}##',
'{{first_name}}_##',
'?{{last_name}}',
'{{first_name}}{{year}}',
'{{first_name}}_{{year}}',
)
email_formats = (
'{{user_name}}@{{free_email_domain}}',
'{{user_name}}@{{domain_name}}')
free_email_domains = (
'gmail.com',
'yahoo.com',
'hotmail.com',
'mail.ru',
'yandex.ru',
'rambler.ru')
tlds = ('ru', 'com', 'biz', 'info', 'net', 'org', 'edu')
replacements = (
('А', 'a'), ('Б', 'b'), ('В', 'v'), ('Г', 'g'), ('Д', 'd'), ('Е', 'e'),
('Ё', 'e'), ('Ж', 'zh'), ('З', 'z'), ('И', 'i'), ('Й', ''), ('К', 'k'),
('Л', 'l'), ('М', 'm'), ('Н', 'n'), ('О', 'o'), ('П', 'p'), ('Р', 'r'),
('С', 's'), ('Т', 't'), ('У', 'u'), ('Ф', 'f'), ('Х', 'h'), ('Ц', 'ts'),
('Ч', 'ch'), ('Ш', 'sh'), ('Щ', 'shch'), ('Ъ', ''), ('Ы', 'i'),
('Ь', ''), ('Э', 'e'), ('Ю', 'yu'), ('Я', 'ya'), ('а', 'a'), ('б', 'b'),
('в', 'v'), ('г', 'g'), ('д', 'd'), ('е', 'e'), ('ё', 'e'), ('ж', 'zh'),
('з', 'z'), ('и', 'i'), ('й', ''), ('к', 'k'), ('л', 'l'), ('м', 'm'),
('н', 'n'), ('о', 'o'), ('п', 'p'), ('р', 'r'), ('с', 's'), ('т', 't'),
('у', 'u'), ('ф', 'f'), ('х', 'h'), ('ц', 'ts'), ('ч', 'ch'),
('ш', 'sh'), ('щ', 'shch'), ('ъ', ''), ('ы', 'i'), ('ь', ''),
('э', 'e'), ('ю', 'ju'), ('я', 'ja'),
)
| <filename>env/lib/python3.8/site-packages/faker/providers/internet/ru_RU/__init__.py
# coding=utf-8
from __future__ import unicode_literals
from .. import Provider as InternetProvider
class Provider(InternetProvider):
user_name_formats = (
'{{last_name_female}}.{{first_name_female}}',
'{{last_name_male}}.{{first_name_male}}',
'{{last_name_male}}.{{first_name_male}}',
'{{first_name_male}}.{{last_name_male}}',
'{{first_name}}##',
'{{first_name}}_##',
'?{{last_name}}',
'{{first_name}}{{year}}',
'{{first_name}}_{{year}}',
)
email_formats = (
'{{user_name}}@{{free_email_domain}}',
'{{user_name}}@{{domain_name}}')
free_email_domains = (
'gmail.com',
'yahoo.com',
'hotmail.com',
'mail.ru',
'yandex.ru',
'rambler.ru')
tlds = ('ru', 'com', 'biz', 'info', 'net', 'org', 'edu')
replacements = (
('А', 'a'), ('Б', 'b'), ('В', 'v'), ('Г', 'g'), ('Д', 'd'), ('Е', 'e'),
('Ё', 'e'), ('Ж', 'zh'), ('З', 'z'), ('И', 'i'), ('Й', ''), ('К', 'k'),
('Л', 'l'), ('М', 'm'), ('Н', 'n'), ('О', 'o'), ('П', 'p'), ('Р', 'r'),
('С', 's'), ('Т', 't'), ('У', 'u'), ('Ф', 'f'), ('Х', 'h'), ('Ц', 'ts'),
('Ч', 'ch'), ('Ш', 'sh'), ('Щ', 'shch'), ('Ъ', ''), ('Ы', 'i'),
('Ь', ''), ('Э', 'e'), ('Ю', 'yu'), ('Я', 'ya'), ('а', 'a'), ('б', 'b'),
('в', 'v'), ('г', 'g'), ('д', 'd'), ('е', 'e'), ('ё', 'e'), ('ж', 'zh'),
('з', 'z'), ('и', 'i'), ('й', ''), ('к', 'k'), ('л', 'l'), ('м', 'm'),
('н', 'n'), ('о', 'o'), ('п', 'p'), ('р', 'r'), ('с', 's'), ('т', 't'),
('у', 'u'), ('ф', 'f'), ('х', 'h'), ('ц', 'ts'), ('ч', 'ch'),
('ш', 'sh'), ('щ', 'shch'), ('ъ', ''), ('ы', 'i'), ('ь', ''),
('э', 'e'), ('ю', 'ju'), ('я', 'ja'),
)
| zh | 0.286321 | # coding=utf-8 ##', ##', | 1.990412 | 2 |
project/test/date_range_test.py | majvazov/martin-ayvazov-employees | 0 | 6630544 | import unittest
import sys
#step back one directory
sys.path.append("..")
#import all functions in functions.py
from packages.functions import (date_range_overlap,
find_longest_time_couple,
find_all_ranges,
read_file)
class TestLongestPair(unittest.TestCase):
'''
Automated test scripts for functions in functions.py
'''
def test_date(self):
'''
Unit testing date_range_overlap function used for detecting range in days between two date ranges
'''
self.assertEqual(date_range_overlap('2012-03-28', '2016-06-02', '2013-04-02', '2018-10-29'), 1158)
self.assertEqual(date_range_overlap('not a date', 'not a date', 'not a date', 'not a date') , 'Wrong date format!')
#excepting integer values given as arguments
self.assertEqual(date_range_overlap(21, 23, 34, 45) , 'Wrong date format!')
def test_longest_couple(self):
'''
To do
'''
pass
def test_file_reading(self):
'''
To do
'''
pass
def test_all_ranges(self):
'''
To do
'''
pass
if __name__ == '__main__':
unittest.main()
| import unittest
import sys
#step back one directory
sys.path.append("..")
#import all functions in functions.py
from packages.functions import (date_range_overlap,
find_longest_time_couple,
find_all_ranges,
read_file)
class TestLongestPair(unittest.TestCase):
'''
Automated test scripts for functions in functions.py
'''
def test_date(self):
'''
Unit testing date_range_overlap function used for detecting range in days between two date ranges
'''
self.assertEqual(date_range_overlap('2012-03-28', '2016-06-02', '2013-04-02', '2018-10-29'), 1158)
self.assertEqual(date_range_overlap('not a date', 'not a date', 'not a date', 'not a date') , 'Wrong date format!')
#excepting integer values given as arguments
self.assertEqual(date_range_overlap(21, 23, 34, 45) , 'Wrong date format!')
def test_longest_couple(self):
'''
To do
'''
pass
def test_file_reading(self):
'''
To do
'''
pass
def test_all_ranges(self):
'''
To do
'''
pass
if __name__ == '__main__':
unittest.main()
| en | 0.665933 | #step back one directory #import all functions in functions.py Automated test scripts for functions in functions.py Unit testing date_range_overlap function used for detecting range in days between two date ranges #excepting integer values given as arguments To do To do To do | 3.218003 | 3 |
dxm/lib/DxTable/DxMetaList.py | arunbsar/dxm-toolkit | 0 | 6630545 | #
# 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.
#
# Copyright (c) 2018 by Delphix. All rights reserved.
#
# Author : <NAME>
# Date : April 2018
import logging
import sys
from dxm.lib.DxTable.DxTable import DxTable
from dxm.lib.DxTable.DxFile import DxFile
from dxm.lib.DxEngine.DxMaskingEngine import DxMaskingEngine
from dxm.lib.DxTools.DxTools import get_objref_by_val_and_attribute
from dxm.lib.DxTools.DxTools import paginator
from dxm.lib.DxLogging import print_error
class DxMetaList(object):
__tableList = {}
__engine = None
__logger = None
@classmethod
def __init__(self):
"""
Constructor
:param engine: DxMaskingEngine object
"""
self.__engine = DxMaskingEngine
self.__logger = logging.getLogger()
self.__logger.debug("creating DxTableList object")
@classmethod
def LoadMeta(self, ruleset_id=None):
"""
Load list of rule sets
Return None if OK
"""
notable = None
nofile = None
if (self.__engine.version_ge('6.0.0')):
from masking_api_60.api.table_metadata_api import TableMetadataApi
from masking_api_60.api.file_metadata_api import FileMetadataApi
from masking_api_60.rest import ApiException
else:
from masking_api_53.api.table_metadata_api import TableMetadataApi
from masking_api_53.api.file_metadata_api import FileMetadataApi
from masking_api_53.rest import ApiException
self.__api = TableMetadataApi
self.__fileapi = FileMetadataApi
self.__apiexc = ApiException
self.__tableList.clear()
try:
api_instance = self.__api(self.__engine.api_client)
if ruleset_id:
table_metadata = paginator(
api_instance,
"get_all_table_metadata",
ruleset_id=ruleset_id)
else:
table_metadata = paginator(
api_instance,
"get_all_table_metadata")
if table_metadata.response_list:
for c in table_metadata.response_list:
table = DxTable(self.__engine)
table.from_table(c)
self.__tableList[c.table_metadata_id] = table
else:
self.__logger.error("No table metadata found")
except self.__apiexc as e:
if (e.status == 404) and (ruleset_id is not None):
notable = 1
else:
print_error(e.body)
self.__logger.error(e.body)
return 1
try:
api_instance = self.__fileapi(self.__engine.api_client)
if ruleset_id:
file_metadata = paginator(
api_instance,
"get_all_file_metadata",
ruleset_id=ruleset_id)
else:
file_metadata = paginator(
api_instance,
"get_all_file_metadata")
if file_metadata.response_list:
for c in file_metadata.response_list:
file = DxFile(self.__engine)
file.from_file(c)
self.__tableList[c.file_metadata_id] = file
else:
self.__logger.error("No file metadata found")
except self.__apiexc as e:
if (e.status == 404) and (ruleset_id is not None):
nofile = 1
else:
print_error(e.body)
self.__logger.error(e.body)
return 1
if nofile and notable:
print_error("Ruleset not found")
return 1
else:
return None
@classmethod
def get_by_ref(self, reference):
"""
return a Table object by refrerence
"""
try:
self.__logger.debug("reference %s" % reference)
return self.__tableList[reference]
except KeyError as e:
self.__logger.debug("can't find Table object"
" for reference %s" % reference)
self.__logger.debug(e)
sys.exit(1)
@classmethod
def get_allref(self):
"""
return a list of all references
"""
return self.__tableList.keys()
@classmethod
def get_MetadataId_by_name(self, name, skip_out=None):
"""
Return metadata id by name.
:param1 name: name of environment
:param2 skip_out: disable message printing
return ref if OK
return None if ruleset not found or not unique
"""
reflist = self.get_MetadataId_by_name_worker(name, skip_out, 1)
# convert list to single value
# as there will be only one element in list
if reflist:
return reflist[0]
else:
return None
@classmethod
def get_all_MetadataId_by_name(self, name, skip_out=None):
"""
Return metadata id by name.
:param1 name: name of environment
:param2 skip_out: disable message printing
return list of references if OK
return None if ruleset not found
"""
return self.get_MetadataId_by_name_worker(name, skip_out, None)
@classmethod
def get_MetadataId_by_name_worker(self, name, skip_out=None,
check_uniqueness=1):
metalist = get_objref_by_val_and_attribute(name, self, 'meta_name')
if len(metalist) == 0:
self.__logger.error('Table or file %s not found' % name)
if not skip_out:
print_error('Table or file %s not found' % name)
return None
if check_uniqueness:
if len(metalist) > 1:
self.__logger.error('Table or file %s is not unique' % name)
if not skip_out:
print_error('Table or file %s is not unique' % name)
return None
return metalist
@classmethod
def add(self, metaobj):
"""
Add an Table/File to a list and Engine
:param metaobj: Table/File object to add to Engine and list
return None if OK
"""
if (metaobj.add() == 0):
self.__logger.debug("Adding table/file %s to list" % metaobj)
self.__tableList[metaobj.meta_id] = metaobj
return None
else:
return 1
@classmethod
def delete(self, tableMetadataId):
"""
Delete a ruleset from a list and Engine
:param databaseRulesetId: Ruleset id to delete from Engine and list
return None if OK
"""
table = self.get_by_ref(tableMetadataId)
if table is not None:
if table.delete() == 0:
return None
else:
return 1
else:
print_error("Table or File with id %s not found" % tableMetadataId)
return 1
@classmethod
def copymeta(self, meta_id, newruleset_id):
"""
Copy meta data from current RS to new Ruleset
:param1 meta_id: Metadata id to copy
:param2 newruleset_id: new RS to copy meta to
return None if OK
"""
metaobj = self.get_by_ref(meta_id)
if type(metaobj) == DxTable:
newmeta = DxTable(self.__engine)
newmeta.from_table(metaobj)
newmeta.ruleset_id = newruleset_id
if self.add(newmeta) is None:
return newmeta.table_metadata_id
else:
return None
else:
newmeta = DxFile(self.__engine)
newmeta.from_file(metaobj)
newmeta.ruleset_id = newruleset_id
if self.add(newmeta) is None:
return newmeta.file_metadata_id
else:
return None
| #
# 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.
#
# Copyright (c) 2018 by Delphix. All rights reserved.
#
# Author : <NAME>
# Date : April 2018
import logging
import sys
from dxm.lib.DxTable.DxTable import DxTable
from dxm.lib.DxTable.DxFile import DxFile
from dxm.lib.DxEngine.DxMaskingEngine import DxMaskingEngine
from dxm.lib.DxTools.DxTools import get_objref_by_val_and_attribute
from dxm.lib.DxTools.DxTools import paginator
from dxm.lib.DxLogging import print_error
class DxMetaList(object):
__tableList = {}
__engine = None
__logger = None
@classmethod
def __init__(self):
"""
Constructor
:param engine: DxMaskingEngine object
"""
self.__engine = DxMaskingEngine
self.__logger = logging.getLogger()
self.__logger.debug("creating DxTableList object")
@classmethod
def LoadMeta(self, ruleset_id=None):
"""
Load list of rule sets
Return None if OK
"""
notable = None
nofile = None
if (self.__engine.version_ge('6.0.0')):
from masking_api_60.api.table_metadata_api import TableMetadataApi
from masking_api_60.api.file_metadata_api import FileMetadataApi
from masking_api_60.rest import ApiException
else:
from masking_api_53.api.table_metadata_api import TableMetadataApi
from masking_api_53.api.file_metadata_api import FileMetadataApi
from masking_api_53.rest import ApiException
self.__api = TableMetadataApi
self.__fileapi = FileMetadataApi
self.__apiexc = ApiException
self.__tableList.clear()
try:
api_instance = self.__api(self.__engine.api_client)
if ruleset_id:
table_metadata = paginator(
api_instance,
"get_all_table_metadata",
ruleset_id=ruleset_id)
else:
table_metadata = paginator(
api_instance,
"get_all_table_metadata")
if table_metadata.response_list:
for c in table_metadata.response_list:
table = DxTable(self.__engine)
table.from_table(c)
self.__tableList[c.table_metadata_id] = table
else:
self.__logger.error("No table metadata found")
except self.__apiexc as e:
if (e.status == 404) and (ruleset_id is not None):
notable = 1
else:
print_error(e.body)
self.__logger.error(e.body)
return 1
try:
api_instance = self.__fileapi(self.__engine.api_client)
if ruleset_id:
file_metadata = paginator(
api_instance,
"get_all_file_metadata",
ruleset_id=ruleset_id)
else:
file_metadata = paginator(
api_instance,
"get_all_file_metadata")
if file_metadata.response_list:
for c in file_metadata.response_list:
file = DxFile(self.__engine)
file.from_file(c)
self.__tableList[c.file_metadata_id] = file
else:
self.__logger.error("No file metadata found")
except self.__apiexc as e:
if (e.status == 404) and (ruleset_id is not None):
nofile = 1
else:
print_error(e.body)
self.__logger.error(e.body)
return 1
if nofile and notable:
print_error("Ruleset not found")
return 1
else:
return None
@classmethod
def get_by_ref(self, reference):
"""
return a Table object by refrerence
"""
try:
self.__logger.debug("reference %s" % reference)
return self.__tableList[reference]
except KeyError as e:
self.__logger.debug("can't find Table object"
" for reference %s" % reference)
self.__logger.debug(e)
sys.exit(1)
@classmethod
def get_allref(self):
"""
return a list of all references
"""
return self.__tableList.keys()
@classmethod
def get_MetadataId_by_name(self, name, skip_out=None):
"""
Return metadata id by name.
:param1 name: name of environment
:param2 skip_out: disable message printing
return ref if OK
return None if ruleset not found or not unique
"""
reflist = self.get_MetadataId_by_name_worker(name, skip_out, 1)
# convert list to single value
# as there will be only one element in list
if reflist:
return reflist[0]
else:
return None
@classmethod
def get_all_MetadataId_by_name(self, name, skip_out=None):
"""
Return metadata id by name.
:param1 name: name of environment
:param2 skip_out: disable message printing
return list of references if OK
return None if ruleset not found
"""
return self.get_MetadataId_by_name_worker(name, skip_out, None)
@classmethod
def get_MetadataId_by_name_worker(self, name, skip_out=None,
check_uniqueness=1):
metalist = get_objref_by_val_and_attribute(name, self, 'meta_name')
if len(metalist) == 0:
self.__logger.error('Table or file %s not found' % name)
if not skip_out:
print_error('Table or file %s not found' % name)
return None
if check_uniqueness:
if len(metalist) > 1:
self.__logger.error('Table or file %s is not unique' % name)
if not skip_out:
print_error('Table or file %s is not unique' % name)
return None
return metalist
@classmethod
def add(self, metaobj):
"""
Add an Table/File to a list and Engine
:param metaobj: Table/File object to add to Engine and list
return None if OK
"""
if (metaobj.add() == 0):
self.__logger.debug("Adding table/file %s to list" % metaobj)
self.__tableList[metaobj.meta_id] = metaobj
return None
else:
return 1
@classmethod
def delete(self, tableMetadataId):
"""
Delete a ruleset from a list and Engine
:param databaseRulesetId: Ruleset id to delete from Engine and list
return None if OK
"""
table = self.get_by_ref(tableMetadataId)
if table is not None:
if table.delete() == 0:
return None
else:
return 1
else:
print_error("Table or File with id %s not found" % tableMetadataId)
return 1
@classmethod
def copymeta(self, meta_id, newruleset_id):
"""
Copy meta data from current RS to new Ruleset
:param1 meta_id: Metadata id to copy
:param2 newruleset_id: new RS to copy meta to
return None if OK
"""
metaobj = self.get_by_ref(meta_id)
if type(metaobj) == DxTable:
newmeta = DxTable(self.__engine)
newmeta.from_table(metaobj)
newmeta.ruleset_id = newruleset_id
if self.add(newmeta) is None:
return newmeta.table_metadata_id
else:
return None
else:
newmeta = DxFile(self.__engine)
newmeta.from_file(metaobj)
newmeta.ruleset_id = newruleset_id
if self.add(newmeta) is None:
return newmeta.file_metadata_id
else:
return None
| en | 0.750646 | # # 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. # # Copyright (c) 2018 by Delphix. All rights reserved. # # Author : <NAME> # Date : April 2018 Constructor :param engine: DxMaskingEngine object Load list of rule sets Return None if OK return a Table object by refrerence return a list of all references Return metadata id by name. :param1 name: name of environment :param2 skip_out: disable message printing return ref if OK return None if ruleset not found or not unique # convert list to single value # as there will be only one element in list Return metadata id by name. :param1 name: name of environment :param2 skip_out: disable message printing return list of references if OK return None if ruleset not found Add an Table/File to a list and Engine :param metaobj: Table/File object to add to Engine and list return None if OK Delete a ruleset from a list and Engine :param databaseRulesetId: Ruleset id to delete from Engine and list return None if OK Copy meta data from current RS to new Ruleset :param1 meta_id: Metadata id to copy :param2 newruleset_id: new RS to copy meta to return None if OK | 1.835967 | 2 |
tests/unit/resources/test_factory.py | nglange/ibm-cos-sdk-python | 33 | 6630546 | <reponame>nglange/ibm-cos-sdk-python
# Copyright 2014 Amazon.com, Inc. or its affiliates. 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. A copy of
# the License is located at
#
# http://aws.amazon.com/apache2.0/
#
# or in the 'license' file accompanying this file. This file 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 ibm_botocore.model import DenormalizedStructureBuilder, ServiceModel
from tests import BaseTestCase, mock
from ibm_boto3.exceptions import ResourceLoadException
from ibm_boto3.utils import ServiceContext
from ibm_boto3.resources.base import ServiceResource
from ibm_boto3.resources.collection import CollectionManager
from ibm_boto3.resources.factory import ResourceFactory
from ibm_boto3.resources.action import WaiterAction
class BaseTestResourceFactory(BaseTestCase):
def setUp(self):
super(BaseTestResourceFactory, self).setUp()
self.emitter = mock.Mock()
self.factory = ResourceFactory(self.emitter)
def load(self, resource_name, resource_json_definition=None,
resource_json_definitions=None, service_model=None):
if resource_json_definition is None:
resource_json_definition = {}
if resource_json_definitions is None:
resource_json_definitions = {}
service_context=ServiceContext(
service_name='test',
resource_json_definitions=resource_json_definitions,
service_model=service_model,
service_waiter_model=None
)
return self.factory.load_from_definition(
resource_name=resource_name,
single_resource_json_definition=resource_json_definition,
service_context=service_context
)
class TestResourceFactory(BaseTestResourceFactory):
def test_get_service_returns_resource_class(self):
TestResource = self.load('test')
self.assertIn(ServiceResource, TestResource.__bases__,
'Did not return a ServiceResource subclass for service')
def test_get_resource_returns_resource_class(self):
QueueResource = self.load('Queue')
self.assertIn(ServiceResource, QueueResource.__bases__,
'Did not return a ServiceResource subclass for resource')
def test_factory_sets_service_name(self):
QueueResource = self.load('Queue')
self.assertEqual(QueueResource.meta.service_name, 'test',
'Service name not set')
def test_factory_sets_identifiers(self):
model = {
'identifiers': [
{'name': 'QueueUrl'},
{'name': 'ReceiptHandle'},
],
}
MessageResource = self.load('Message', model)
self.assertIn('queue_url', MessageResource.meta.identifiers,
'Missing queue_url identifier from model')
self.assertIn('receipt_handle', MessageResource.meta.identifiers,
'Missing receipt_handle identifier from model')
def test_identifiers_in_repr(self):
model = {
'identifiers': [
{'name': 'QueueUrl'},
{'name': 'ReceiptHandle'},
],
}
defs = {
'Message': model
}
resource = self.load('Message', model, defs)('url', 'handle')
# Class name
self.assertIn('test.Message', repr(resource))
# Identifier names and values
self.assertIn('queue_url', repr(resource))
self.assertIn("'url'", repr(resource))
self.assertIn('receipt_handle', repr(resource))
self.assertIn("'handle'", repr(resource))
def test_factory_creates_dangling_resources(self):
model = {
'has': {
'Queue': {
'resource': {
'type': 'Queue',
'identifiers': [
{'target': 'Url', 'source': 'input'}
]
}
},
'Message': {
'resource': {
'type': 'Message',
'identifiers': [
{'target': 'QueueUrl', 'source': 'input'},
{'target': 'Handle', 'source': 'input'}
]
}
}
}
}
defs = {
'Queue': {},
'Message': {}
}
TestResource = self.load('test', model, defs)
self.assertTrue(hasattr(TestResource, 'Queue'),
'Missing Queue class from model')
self.assertTrue(hasattr(TestResource, 'Message'),
'Missing Message class from model')
def test_factory_creates_properties(self):
model = {
'shape': 'TestShape',
'load': {
'request': {
'operation': 'DescribeTest',
}
}
}
shape = DenormalizedStructureBuilder().with_members({
'ETag': {
'type': 'string',
},
'LastModified': {
'type': 'string'
}
}).build_model()
service_model = mock.Mock()
service_model.shape_for.return_value = shape
TestResource = self.load('test', model, service_model=service_model)
self.assertTrue(hasattr(TestResource, 'e_tag'),
'ETag shape member not available on resource')
self.assertTrue(hasattr(TestResource, 'last_modified'),
'LastModified shape member not available on resource')
def test_factory_renames_on_clobber_identifier(self):
model = {
'identifiers': [
{'name': 'Meta'}
]
}
# Each resource has a ``meta`` defined, so this identifier
# must be renamed.
cls = self.load('test', model)
self.assertTrue(hasattr(cls, 'meta_identifier'))
def test_factory_fails_on_clobber_action(self):
model = {
'identifiers': [
{'name': 'Test'},
{'name': 'TestAction'}
],
'actions': {
'Test': {
'request': {
'operation': 'GetTest'
}
}
}
}
# This fails because the resource has an identifier
# that would be clobbered by the action name.
with self.assertRaises(ValueError) as cm:
self.load('test', model)
self.assertIn('test', str(cm.exception))
self.assertIn('action', str(cm.exception))
def test_can_instantiate_service_resource(self):
TestResource = self.load('test')
resource = TestResource()
self.assertIsInstance(resource, ServiceResource,
'Object is not an instance of ServiceResource')
def test_non_service_resource_missing_defs(self):
# Only services should get dangling defs
defs = {
'Queue': {
'identifiers': [
{'name': 'Url'}
]
},
'Message': {
'identifiers': [
{'name': 'QueueUrl'},
{'name': 'ReceiptHandle'}
]
}
}
model = defs['Queue']
queue = self.load('Queue', model, defs)('url')
self.assertTrue(not hasattr(queue, 'Queue'))
self.assertTrue(not hasattr(queue, 'Message'))
def test_subresource_requires_only_identifier(self):
defs = {
'Queue': {
'identifiers': [
{'name': 'Url'}
],
'has': {
'Message': {
'resource': {
'type': 'Message',
'identifiers': [
{'target': 'QueueUrl', 'source': 'identifier',
'name': 'Url'},
{'target': 'ReceiptHandle', 'source': 'input'}
]
}
}
}
},
'Message': {
'identifiers': [
{'name': 'QueueUrl'},
{'name': 'ReceiptHandle'}
]
}
}
model = defs['Queue']
queue = self.load('Queue', model, defs)('url')
# Let's create a message and only give it a receipt handle
# The required queue_url identifier should be set from the
# queue itself.
message = queue.Message('receipt')
self.assertEqual(message.queue_url, 'url',
'Wrong queue URL set on the message resource instance')
self.assertEqual(message.receipt_handle, 'receipt',
'Wrong receipt handle set on the message resource instance')
def test_resource_meta_unique(self):
queue_cls = self.load('Queue')
queue1 = queue_cls()
queue2 = queue_cls()
self.assertEqual(queue1.meta, queue2.meta,
'Queue meta copies not equal after creation')
queue1.meta.data = {'id': 'foo'}
queue2.meta.data = {'id': 'bar'}
self.assertNotEqual(queue_cls.meta, queue1.meta,
'Modified queue instance data should not modify the class data')
self.assertNotEqual(queue1.meta, queue2.meta,
'Queue data should be unique to queue instance')
self.assertNotEqual(queue1.meta, 'bad-value')
def test_resource_meta_repr(self):
queue_cls = self.load('Queue')
queue = queue_cls()
self.assertEqual(repr(queue.meta),
'ResourceMeta(\'test\', identifiers=[])')
@mock.patch('ibm_boto3.resources.factory.ServiceAction')
def test_resource_calls_action(self, action_cls):
model = {
'actions': {
'GetMessageStatus': {
'request': {
'operation': 'DescribeMessageStatus'
}
}
}
}
action = action_cls.return_value
queue = self.load('Queue', model)()
queue.get_message_status('arg1', arg2=2)
action.assert_called_with(queue, 'arg1', arg2=2)
@mock.patch('ibm_boto3.resources.factory.ServiceAction')
def test_resource_action_clears_data(self, action_cls):
model = {
'load': {
'request': {
'operation': 'DescribeQueue'
}
},
'actions': {
'GetMessageStatus': {
'request': {
'operation': 'DescribeMessageStatus'
}
}
}
}
queue = self.load('Queue', model)()
# Simulate loaded data
queue.meta.data = {'some': 'data'}
# Perform a call
queue.get_message_status()
# Cached data should be cleared
self.assertIsNone(queue.meta.data)
@mock.patch('ibm_boto3.resources.factory.ServiceAction')
def test_resource_action_leaves_data(self, action_cls):
# This model has NO load method. Cached data should
# never be cleared since it cannot be reloaded!
model = {
'actions': {
'GetMessageStatus': {
'request': {
'operation': 'DescribeMessageStatus'
}
}
}
}
queue = self.load('Queue', model)()
# Simulate loaded data
queue.meta.data = {'some': 'data'}
# Perform a call
queue.get_message_status()
# Cached data should not be cleared
self.assertEqual(queue.meta.data, {'some': 'data'})
@mock.patch('ibm_boto3.resources.factory.ServiceAction')
def test_resource_lazy_loads_properties(self, action_cls):
model = {
'shape': 'TestShape',
'identifiers': [
{'name': 'Url'}
],
'load': {
'request': {
'operation': 'DescribeTest',
}
}
}
shape = DenormalizedStructureBuilder().with_members({
'ETag': {
'type': 'string',
'shape_name': 'ETag'
},
'LastModified': {
'type': 'string',
'shape_name': 'LastModified'
},
'Url': {
'type': 'string',
'shape_name': 'Url'
}
}).build_model()
service_model = mock.Mock()
service_model.shape_for.return_value = shape
action = action_cls.return_value
action.return_value = {'ETag': 'tag', 'LastModified': 'never'}
resource = self.load(
'test', model, service_model=service_model)('url')
# Accessing an identifier should not call load, even if it's in
# the shape members.
resource.url
action.assert_not_called()
# Accessing a property should call load
self.assertEqual(resource.e_tag, 'tag',
'ETag property returned wrong value')
self.assertEqual(action.call_count, 1)
# Both params should have been loaded into the data bag
self.assertIn('ETag', resource.meta.data)
self.assertIn('LastModified', resource.meta.data)
# Accessing another property should use cached value
# instead of making a second call.
self.assertEqual(resource.last_modified, 'never',
'LastModified property returned wrong value')
self.assertEqual(action.call_count, 1)
@mock.patch('ibm_boto3.resources.factory.ServiceAction')
def test_resource_lazy_properties_missing_load(self, action_cls):
model = {
'shape': 'TestShape',
'identifiers': [
{'name': 'Url'}
]
# Note the lack of a `load` method. These resources
# are usually loaded via a call on a parent resource.
}
shape = DenormalizedStructureBuilder().with_members({
'ETag': {
'type': 'string',
},
'LastModified': {
'type': 'string'
},
'Url': {
'type': 'string'
}
}).build_model()
service_model = mock.Mock()
service_model.shape_for.return_value = shape
action = action_cls.return_value
action.return_value = {'ETag': 'tag', 'LastModified': 'never'}
resource = self.load(
'test', model, service_model=service_model)('url')
with self.assertRaises(ResourceLoadException):
resource.last_modified
@mock.patch('ibm_boto3.resources.factory.ServiceAction')
def test_resource_aliases_identifiers(self, action_cls):
model = {
'shape': 'TestShape',
'identifiers': [
{'name': 'id', 'memberName': 'foo_id'}
]
}
shape = DenormalizedStructureBuilder().with_members({
'foo_id': {
'type': 'string',
},
'bar': {
'type': 'string'
},
}).build_model()
service_model = mock.Mock()
service_model.shape_for.return_value = shape
shape_id = 'baz'
resource = self.load(
'test', model, service_model=service_model)(shape_id)
try:
self.assertEqual(resource.id, shape_id)
self.assertEqual(resource.foo_id, shape_id)
except ResourceLoadException:
self.fail("Load attempted on identifier alias.")
def test_resource_loads_references(self):
model = {
'shape': 'InstanceShape',
'identifiers': [{'name': 'GroupId'}],
'has': {
'Subnet': {
'resource': {
'type': 'Subnet',
'identifiers': [
{'target': 'Id', 'source': 'data',
'path': 'SubnetId'}
]
}
},
'Vpcs': {
'resource': {
'type': 'Vpc',
'identifiers': [
{'target': 'Id', 'source': 'data',
'path': 'Vpcs[].Id'}
]
}
}
}
}
defs = {
'Subnet': {
'identifiers': [{'name': 'Id'}]
},
'Vpc': {
'identifiers': [{'name': 'Id'}]
}
}
service_model = ServiceModel({
'shapes': {
'InstanceShape': {
'type': 'structure',
'members': {
'SubnetId': {
'shape': 'String'
}
}
},
'String': {
'type': 'string'
}
}
})
resource = self.load('Instance', model, defs,
service_model)('group-id')
# Load the resource with no data
resource.meta.data = {}
self.assertTrue(
hasattr(resource, 'subnet'),
'Resource should have a subnet reference')
self.assertIsNone(
resource.subnet,
'Missing identifier, should return None')
self.assertIsNone(resource.vpcs)
# Load the resource with data to instantiate a reference
resource.meta.data = {
'SubnetId': 'abc123',
'Vpcs': [
{'Id': 'vpc1'},
{'Id': 'vpc2'}
]
}
self.assertIsInstance(resource.subnet, ServiceResource)
self.assertEqual(resource.subnet.id, 'abc123')
vpcs = resource.vpcs
self.assertIsInstance(vpcs, list)
self.assertEqual(len(vpcs), 2)
self.assertEqual(vpcs[0].id, 'vpc1')
self.assertEqual(vpcs[1].id, 'vpc2')
@mock.patch('ibm_boto3.resources.model.Collection')
def test_resource_loads_collections(self, mock_model):
model = {
'hasMany': {
u'Queues': {
'request': {
'operation': 'ListQueues'
},
'resource': {
'type': 'Queue'
}
}
}
}
defs = {
'Queue': {}
}
service_model = ServiceModel({})
mock_model.return_value.name = 'queues'
resource = self.load('test', model, defs, service_model)()
self.assertTrue(hasattr(resource, 'queues'),
'Resource should expose queues collection')
self.assertIsInstance(resource.queues, CollectionManager,
'Queues collection should be a collection manager')
def test_resource_loads_waiters(self):
model = {
"waiters": {
"Exists": {
"waiterName": "BucketExists",
"params": [
{"target": "Bucket", "source": "identifier",
"name": "Name"}]
}
}
}
defs = {
'Bucket': {}
}
service_model = ServiceModel({})
resource = self.load('test', model, defs, service_model)()
self.assertTrue(hasattr(resource, 'wait_until_exists'),
'Resource should expose resource waiter: wait_until_exists')
@mock.patch('ibm_boto3.resources.factory.WaiterAction')
def test_resource_waiter_calls_waiter_method(self, waiter_action_cls):
model = {
"waiters": {
"Exists": {
"waiterName": "BucketExists",
"params": [
{"target": "Bucket", "source": "identifier",
"name": "Name"}]
}
}
}
defs = {
'Bucket': {}
}
service_model = ServiceModel({})
waiter_action = waiter_action_cls.return_value
resource = self.load('test', model, defs, service_model)()
resource.wait_until_exists('arg1', arg2=2)
waiter_action.assert_called_with(resource, 'arg1', arg2=2)
class TestResourceFactoryDanglingResource(BaseTestResourceFactory):
def setUp(self):
super(TestResourceFactoryDanglingResource, self).setUp()
self.model = {
'has': {
'Queue': {
'resource': {
'type': 'Queue',
'identifiers': [
{'target': 'Url', 'source': 'input'}
]
}
}
}
}
self.defs = {
'Queue': {
'identifiers': [
{'name': 'Url'}
]
}
}
def test_dangling_resources_create_resource_instance(self):
resource = self.load('test', self.model, self.defs)()
q = resource.Queue('test')
self.assertIsInstance(q, ServiceResource,
'Dangling resource instance not a ServiceResource')
def test_hash_resource_equal(self):
resource = self.load('test', self.model, self.defs)()
p = resource.Queue('test')
q = resource.Queue('test')
self.assertEqual(p, q, "Should be equal resource")
self.assertEqual(hash(p), hash(q), "Hash values should be equal")
def test_hash_resource_not_equal(self):
resource = self.load('test', self.model, self.defs)()
p = resource.Queue('test1')
q = resource.Queue('test2')
self.assertNotEquals(p, q, "Should not be equal resource")
self.assertNotEquals(hash(p), hash(q), "Hash values should be different")
def test_dangling_resource_create_with_kwarg(self):
resource = self.load('test', self.model, self.defs)()
q = resource.Queue(url='test')
self.assertIsInstance(q, ServiceResource,
'Dangling resource created with kwargs is not a ServiceResource')
def test_dangling_resource_shares_client(self):
resource = self.load('test', self.model, self.defs)()
q = resource.Queue('test')
self.assertEqual(resource.meta.client, q.meta.client,
'Client was not shared to dangling resource instance')
def test_dangling_resource_requires_identifier(self):
resource = self.load('test', self.model, self.defs)()
with self.assertRaises(ValueError):
resource.Queue()
def test_dangling_resource_raises_for_unknown_arg(self):
resource = self.load('test', self.model, self.defs)()
with self.assertRaises(ValueError):
resource.Queue(url='foo', bar='baz')
def test_dangling_resource_identifier_is_immutable(self):
resource = self.load('test', self.model, self.defs)()
queue = resource.Queue('url')
# We should not be able to change the identifier's value
with self.assertRaises(AttributeError):
queue.url = 'foo'
def test_dangling_resource_equality(self):
resource = self.load('test', self.model, self.defs)()
q1 = resource.Queue('url')
q2 = resource.Queue('url')
self.assertEqual(q1, q2)
def test_dangling_resource_inequality(self):
self.defs = {
'Queue': {
'identifiers': [{'name': 'Url'}],
'has': {
'Message': {
'resource': {
'type': 'Message',
'identifiers': [
{'target': 'QueueUrl', 'source': 'identifier',
'name': 'Url'},
{'target': 'Handle', 'source': 'input'}
]
}
}
}
},
'Message': {
'identifiers': [{'name': 'QueueUrl'}, {'name': 'Handle'}]
}
}
resource = self.load('test', self.model, self.defs)()
q1 = resource.Queue('url')
q2 = resource.Queue('different')
m = q1.Message('handle')
self.assertNotEqual(q1, q2)
self.assertNotEqual(q1, m)
def test_dangling_resource_loads_data(self):
# Given a loadable resource instance that contains a reference
# to another resource which has a resource data path, the
# referenced resource should be loaded with all of the data
# contained at that path. This allows loading references
# which would otherwise not be loadable (missing load method)
# and prevents extra load calls for others when we already
# have the data available.
self.defs = {
'Instance': {
'identifiers': [{'name': 'Id'}],
'has': {
'NetworkInterface': {
'resource': {
'type': 'NetworkInterface',
'identifiers': [
{'target': 'Id', 'source': 'data',
'path': 'NetworkInterface.Id'}
],
'path': 'NetworkInterface'
}
}
}
},
'NetworkInterface': {
'identifiers': [{'name': 'Id'}],
'shape': 'NetworkInterfaceShape'
}
}
self.model = self.defs['Instance']
shape = DenormalizedStructureBuilder().with_members({
'Id': {
'type': 'string',
},
'PublicIp': {
'type': 'string'
}
}).build_model()
service_model = mock.Mock()
service_model.shape_for.return_value = shape
cls = self.load('Instance', self.model, self.defs, service_model)
instance = cls('instance-id')
# Set some data as if we had completed a load action.
def set_meta_data():
instance.meta.data = {
'NetworkInterface': {
'Id': 'network-interface-id',
'PublicIp': '127.0.0.1'
}
}
instance.load = mock.Mock(side_effect=set_meta_data)
# Now, get the reference and make sure it has its data
# set as expected.
interface = instance.network_interface
self.assertIsNotNone(interface.meta.data)
self.assertEqual(interface.public_ip, '127.0.0.1')
class TestServiceResourceSubresources(BaseTestResourceFactory):
def setUp(self):
super(TestServiceResourceSubresources, self).setUp()
self.model = {
'has': {
'QueueObject': {
'resource': {
'type': 'Queue',
'identifiers': [
{'target': 'Url', 'source': 'input'}
]
}
},
'PriorityQueue': {
'resource': {
'type': 'Queue',
'identifiers': [
{'target': 'Url', 'source': 'input'}
]
}
}
}
}
self.defs = {
'Queue': {
'identifiers': [
{'name': 'Url'}
]
},
'Message': {
'identifiers': [
{'name': 'QueueUrl'},
{'name': 'ReceiptHandle'}
]
}
}
def test_subresource_custom_name(self):
resource = self.load('test', self.model, self.defs)()
self.assertTrue(hasattr(resource, 'QueueObject'))
def test_contains_all_subresources(self):
resource = self.load('test', self.model, self.defs)()
self.assertIn('QueueObject', dir(resource))
self.assertIn('PriorityQueue', dir(resource))
self.assertIn('Message', dir(resource))
def test_get_available_subresources(self):
resource = self.load('test', self.model, self.defs)()
self.assertTrue(hasattr(resource, 'get_available_subresources'))
subresources = sorted(resource.get_available_subresources())
expected = sorted(['PriorityQueue', 'Message', 'QueueObject'])
self.assertEqual(subresources, expected)
def test_subresource_missing_all_subresources(self):
resource = self.load('test', self.model, self.defs)()
message = resource.Message('url', 'handle')
self.assertNotIn('QueueObject', dir(message))
self.assertNotIn('PriorityQueue', dir(message))
self.assertNotIn('Queue', dir(message))
self.assertNotIn('Message', dir(message))
def test_event_emitted_when_class_created(self):
self.load('test', self.model, self.defs)
self.assertTrue(self.emitter.emit.called)
call_args = self.emitter.emit.call_args
# Verify the correct event name emitted.
self.assertEqual(call_args[0][0],
'creating-resource-class.test.ServiceResource')
# Verify we send out the class attributes dict.
actual_class_attrs = sorted(call_args[1]['class_attributes'])
self.assertEqual(actual_class_attrs, [
'Message', 'PriorityQueue', 'QueueObject',
'get_available_subresources', 'meta'])
base_classes = sorted(call_args[1]['base_classes'])
self.assertEqual(base_classes, [ServiceResource])
| # Copyright 2014 Amazon.com, Inc. or its affiliates. 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. A copy of
# the License is located at
#
# http://aws.amazon.com/apache2.0/
#
# or in the 'license' file accompanying this file. This file 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 ibm_botocore.model import DenormalizedStructureBuilder, ServiceModel
from tests import BaseTestCase, mock
from ibm_boto3.exceptions import ResourceLoadException
from ibm_boto3.utils import ServiceContext
from ibm_boto3.resources.base import ServiceResource
from ibm_boto3.resources.collection import CollectionManager
from ibm_boto3.resources.factory import ResourceFactory
from ibm_boto3.resources.action import WaiterAction
class BaseTestResourceFactory(BaseTestCase):
def setUp(self):
super(BaseTestResourceFactory, self).setUp()
self.emitter = mock.Mock()
self.factory = ResourceFactory(self.emitter)
def load(self, resource_name, resource_json_definition=None,
resource_json_definitions=None, service_model=None):
if resource_json_definition is None:
resource_json_definition = {}
if resource_json_definitions is None:
resource_json_definitions = {}
service_context=ServiceContext(
service_name='test',
resource_json_definitions=resource_json_definitions,
service_model=service_model,
service_waiter_model=None
)
return self.factory.load_from_definition(
resource_name=resource_name,
single_resource_json_definition=resource_json_definition,
service_context=service_context
)
class TestResourceFactory(BaseTestResourceFactory):
def test_get_service_returns_resource_class(self):
TestResource = self.load('test')
self.assertIn(ServiceResource, TestResource.__bases__,
'Did not return a ServiceResource subclass for service')
def test_get_resource_returns_resource_class(self):
QueueResource = self.load('Queue')
self.assertIn(ServiceResource, QueueResource.__bases__,
'Did not return a ServiceResource subclass for resource')
def test_factory_sets_service_name(self):
QueueResource = self.load('Queue')
self.assertEqual(QueueResource.meta.service_name, 'test',
'Service name not set')
def test_factory_sets_identifiers(self):
model = {
'identifiers': [
{'name': 'QueueUrl'},
{'name': 'ReceiptHandle'},
],
}
MessageResource = self.load('Message', model)
self.assertIn('queue_url', MessageResource.meta.identifiers,
'Missing queue_url identifier from model')
self.assertIn('receipt_handle', MessageResource.meta.identifiers,
'Missing receipt_handle identifier from model')
def test_identifiers_in_repr(self):
model = {
'identifiers': [
{'name': 'QueueUrl'},
{'name': 'ReceiptHandle'},
],
}
defs = {
'Message': model
}
resource = self.load('Message', model, defs)('url', 'handle')
# Class name
self.assertIn('test.Message', repr(resource))
# Identifier names and values
self.assertIn('queue_url', repr(resource))
self.assertIn("'url'", repr(resource))
self.assertIn('receipt_handle', repr(resource))
self.assertIn("'handle'", repr(resource))
def test_factory_creates_dangling_resources(self):
model = {
'has': {
'Queue': {
'resource': {
'type': 'Queue',
'identifiers': [
{'target': 'Url', 'source': 'input'}
]
}
},
'Message': {
'resource': {
'type': 'Message',
'identifiers': [
{'target': 'QueueUrl', 'source': 'input'},
{'target': 'Handle', 'source': 'input'}
]
}
}
}
}
defs = {
'Queue': {},
'Message': {}
}
TestResource = self.load('test', model, defs)
self.assertTrue(hasattr(TestResource, 'Queue'),
'Missing Queue class from model')
self.assertTrue(hasattr(TestResource, 'Message'),
'Missing Message class from model')
def test_factory_creates_properties(self):
model = {
'shape': 'TestShape',
'load': {
'request': {
'operation': 'DescribeTest',
}
}
}
shape = DenormalizedStructureBuilder().with_members({
'ETag': {
'type': 'string',
},
'LastModified': {
'type': 'string'
}
}).build_model()
service_model = mock.Mock()
service_model.shape_for.return_value = shape
TestResource = self.load('test', model, service_model=service_model)
self.assertTrue(hasattr(TestResource, 'e_tag'),
'ETag shape member not available on resource')
self.assertTrue(hasattr(TestResource, 'last_modified'),
'LastModified shape member not available on resource')
def test_factory_renames_on_clobber_identifier(self):
model = {
'identifiers': [
{'name': 'Meta'}
]
}
# Each resource has a ``meta`` defined, so this identifier
# must be renamed.
cls = self.load('test', model)
self.assertTrue(hasattr(cls, 'meta_identifier'))
def test_factory_fails_on_clobber_action(self):
model = {
'identifiers': [
{'name': 'Test'},
{'name': 'TestAction'}
],
'actions': {
'Test': {
'request': {
'operation': 'GetTest'
}
}
}
}
# This fails because the resource has an identifier
# that would be clobbered by the action name.
with self.assertRaises(ValueError) as cm:
self.load('test', model)
self.assertIn('test', str(cm.exception))
self.assertIn('action', str(cm.exception))
def test_can_instantiate_service_resource(self):
TestResource = self.load('test')
resource = TestResource()
self.assertIsInstance(resource, ServiceResource,
'Object is not an instance of ServiceResource')
def test_non_service_resource_missing_defs(self):
# Only services should get dangling defs
defs = {
'Queue': {
'identifiers': [
{'name': 'Url'}
]
},
'Message': {
'identifiers': [
{'name': 'QueueUrl'},
{'name': 'ReceiptHandle'}
]
}
}
model = defs['Queue']
queue = self.load('Queue', model, defs)('url')
self.assertTrue(not hasattr(queue, 'Queue'))
self.assertTrue(not hasattr(queue, 'Message'))
def test_subresource_requires_only_identifier(self):
defs = {
'Queue': {
'identifiers': [
{'name': 'Url'}
],
'has': {
'Message': {
'resource': {
'type': 'Message',
'identifiers': [
{'target': 'QueueUrl', 'source': 'identifier',
'name': 'Url'},
{'target': 'ReceiptHandle', 'source': 'input'}
]
}
}
}
},
'Message': {
'identifiers': [
{'name': 'QueueUrl'},
{'name': 'ReceiptHandle'}
]
}
}
model = defs['Queue']
queue = self.load('Queue', model, defs)('url')
# Let's create a message and only give it a receipt handle
# The required queue_url identifier should be set from the
# queue itself.
message = queue.Message('receipt')
self.assertEqual(message.queue_url, 'url',
'Wrong queue URL set on the message resource instance')
self.assertEqual(message.receipt_handle, 'receipt',
'Wrong receipt handle set on the message resource instance')
def test_resource_meta_unique(self):
queue_cls = self.load('Queue')
queue1 = queue_cls()
queue2 = queue_cls()
self.assertEqual(queue1.meta, queue2.meta,
'Queue meta copies not equal after creation')
queue1.meta.data = {'id': 'foo'}
queue2.meta.data = {'id': 'bar'}
self.assertNotEqual(queue_cls.meta, queue1.meta,
'Modified queue instance data should not modify the class data')
self.assertNotEqual(queue1.meta, queue2.meta,
'Queue data should be unique to queue instance')
self.assertNotEqual(queue1.meta, 'bad-value')
def test_resource_meta_repr(self):
queue_cls = self.load('Queue')
queue = queue_cls()
self.assertEqual(repr(queue.meta),
'ResourceMeta(\'test\', identifiers=[])')
@mock.patch('ibm_boto3.resources.factory.ServiceAction')
def test_resource_calls_action(self, action_cls):
model = {
'actions': {
'GetMessageStatus': {
'request': {
'operation': 'DescribeMessageStatus'
}
}
}
}
action = action_cls.return_value
queue = self.load('Queue', model)()
queue.get_message_status('arg1', arg2=2)
action.assert_called_with(queue, 'arg1', arg2=2)
@mock.patch('ibm_boto3.resources.factory.ServiceAction')
def test_resource_action_clears_data(self, action_cls):
model = {
'load': {
'request': {
'operation': 'DescribeQueue'
}
},
'actions': {
'GetMessageStatus': {
'request': {
'operation': 'DescribeMessageStatus'
}
}
}
}
queue = self.load('Queue', model)()
# Simulate loaded data
queue.meta.data = {'some': 'data'}
# Perform a call
queue.get_message_status()
# Cached data should be cleared
self.assertIsNone(queue.meta.data)
@mock.patch('ibm_boto3.resources.factory.ServiceAction')
def test_resource_action_leaves_data(self, action_cls):
# This model has NO load method. Cached data should
# never be cleared since it cannot be reloaded!
model = {
'actions': {
'GetMessageStatus': {
'request': {
'operation': 'DescribeMessageStatus'
}
}
}
}
queue = self.load('Queue', model)()
# Simulate loaded data
queue.meta.data = {'some': 'data'}
# Perform a call
queue.get_message_status()
# Cached data should not be cleared
self.assertEqual(queue.meta.data, {'some': 'data'})
@mock.patch('ibm_boto3.resources.factory.ServiceAction')
def test_resource_lazy_loads_properties(self, action_cls):
model = {
'shape': 'TestShape',
'identifiers': [
{'name': 'Url'}
],
'load': {
'request': {
'operation': 'DescribeTest',
}
}
}
shape = DenormalizedStructureBuilder().with_members({
'ETag': {
'type': 'string',
'shape_name': 'ETag'
},
'LastModified': {
'type': 'string',
'shape_name': 'LastModified'
},
'Url': {
'type': 'string',
'shape_name': 'Url'
}
}).build_model()
service_model = mock.Mock()
service_model.shape_for.return_value = shape
action = action_cls.return_value
action.return_value = {'ETag': 'tag', 'LastModified': 'never'}
resource = self.load(
'test', model, service_model=service_model)('url')
# Accessing an identifier should not call load, even if it's in
# the shape members.
resource.url
action.assert_not_called()
# Accessing a property should call load
self.assertEqual(resource.e_tag, 'tag',
'ETag property returned wrong value')
self.assertEqual(action.call_count, 1)
# Both params should have been loaded into the data bag
self.assertIn('ETag', resource.meta.data)
self.assertIn('LastModified', resource.meta.data)
# Accessing another property should use cached value
# instead of making a second call.
self.assertEqual(resource.last_modified, 'never',
'LastModified property returned wrong value')
self.assertEqual(action.call_count, 1)
@mock.patch('ibm_boto3.resources.factory.ServiceAction')
def test_resource_lazy_properties_missing_load(self, action_cls):
model = {
'shape': 'TestShape',
'identifiers': [
{'name': 'Url'}
]
# Note the lack of a `load` method. These resources
# are usually loaded via a call on a parent resource.
}
shape = DenormalizedStructureBuilder().with_members({
'ETag': {
'type': 'string',
},
'LastModified': {
'type': 'string'
},
'Url': {
'type': 'string'
}
}).build_model()
service_model = mock.Mock()
service_model.shape_for.return_value = shape
action = action_cls.return_value
action.return_value = {'ETag': 'tag', 'LastModified': 'never'}
resource = self.load(
'test', model, service_model=service_model)('url')
with self.assertRaises(ResourceLoadException):
resource.last_modified
@mock.patch('ibm_boto3.resources.factory.ServiceAction')
def test_resource_aliases_identifiers(self, action_cls):
model = {
'shape': 'TestShape',
'identifiers': [
{'name': 'id', 'memberName': 'foo_id'}
]
}
shape = DenormalizedStructureBuilder().with_members({
'foo_id': {
'type': 'string',
},
'bar': {
'type': 'string'
},
}).build_model()
service_model = mock.Mock()
service_model.shape_for.return_value = shape
shape_id = 'baz'
resource = self.load(
'test', model, service_model=service_model)(shape_id)
try:
self.assertEqual(resource.id, shape_id)
self.assertEqual(resource.foo_id, shape_id)
except ResourceLoadException:
self.fail("Load attempted on identifier alias.")
def test_resource_loads_references(self):
model = {
'shape': 'InstanceShape',
'identifiers': [{'name': 'GroupId'}],
'has': {
'Subnet': {
'resource': {
'type': 'Subnet',
'identifiers': [
{'target': 'Id', 'source': 'data',
'path': 'SubnetId'}
]
}
},
'Vpcs': {
'resource': {
'type': 'Vpc',
'identifiers': [
{'target': 'Id', 'source': 'data',
'path': 'Vpcs[].Id'}
]
}
}
}
}
defs = {
'Subnet': {
'identifiers': [{'name': 'Id'}]
},
'Vpc': {
'identifiers': [{'name': 'Id'}]
}
}
service_model = ServiceModel({
'shapes': {
'InstanceShape': {
'type': 'structure',
'members': {
'SubnetId': {
'shape': 'String'
}
}
},
'String': {
'type': 'string'
}
}
})
resource = self.load('Instance', model, defs,
service_model)('group-id')
# Load the resource with no data
resource.meta.data = {}
self.assertTrue(
hasattr(resource, 'subnet'),
'Resource should have a subnet reference')
self.assertIsNone(
resource.subnet,
'Missing identifier, should return None')
self.assertIsNone(resource.vpcs)
# Load the resource with data to instantiate a reference
resource.meta.data = {
'SubnetId': 'abc123',
'Vpcs': [
{'Id': 'vpc1'},
{'Id': 'vpc2'}
]
}
self.assertIsInstance(resource.subnet, ServiceResource)
self.assertEqual(resource.subnet.id, 'abc123')
vpcs = resource.vpcs
self.assertIsInstance(vpcs, list)
self.assertEqual(len(vpcs), 2)
self.assertEqual(vpcs[0].id, 'vpc1')
self.assertEqual(vpcs[1].id, 'vpc2')
@mock.patch('ibm_boto3.resources.model.Collection')
def test_resource_loads_collections(self, mock_model):
model = {
'hasMany': {
u'Queues': {
'request': {
'operation': 'ListQueues'
},
'resource': {
'type': 'Queue'
}
}
}
}
defs = {
'Queue': {}
}
service_model = ServiceModel({})
mock_model.return_value.name = 'queues'
resource = self.load('test', model, defs, service_model)()
self.assertTrue(hasattr(resource, 'queues'),
'Resource should expose queues collection')
self.assertIsInstance(resource.queues, CollectionManager,
'Queues collection should be a collection manager')
def test_resource_loads_waiters(self):
model = {
"waiters": {
"Exists": {
"waiterName": "BucketExists",
"params": [
{"target": "Bucket", "source": "identifier",
"name": "Name"}]
}
}
}
defs = {
'Bucket': {}
}
service_model = ServiceModel({})
resource = self.load('test', model, defs, service_model)()
self.assertTrue(hasattr(resource, 'wait_until_exists'),
'Resource should expose resource waiter: wait_until_exists')
@mock.patch('ibm_boto3.resources.factory.WaiterAction')
def test_resource_waiter_calls_waiter_method(self, waiter_action_cls):
model = {
"waiters": {
"Exists": {
"waiterName": "BucketExists",
"params": [
{"target": "Bucket", "source": "identifier",
"name": "Name"}]
}
}
}
defs = {
'Bucket': {}
}
service_model = ServiceModel({})
waiter_action = waiter_action_cls.return_value
resource = self.load('test', model, defs, service_model)()
resource.wait_until_exists('arg1', arg2=2)
waiter_action.assert_called_with(resource, 'arg1', arg2=2)
class TestResourceFactoryDanglingResource(BaseTestResourceFactory):
def setUp(self):
super(TestResourceFactoryDanglingResource, self).setUp()
self.model = {
'has': {
'Queue': {
'resource': {
'type': 'Queue',
'identifiers': [
{'target': 'Url', 'source': 'input'}
]
}
}
}
}
self.defs = {
'Queue': {
'identifiers': [
{'name': 'Url'}
]
}
}
def test_dangling_resources_create_resource_instance(self):
resource = self.load('test', self.model, self.defs)()
q = resource.Queue('test')
self.assertIsInstance(q, ServiceResource,
'Dangling resource instance not a ServiceResource')
def test_hash_resource_equal(self):
resource = self.load('test', self.model, self.defs)()
p = resource.Queue('test')
q = resource.Queue('test')
self.assertEqual(p, q, "Should be equal resource")
self.assertEqual(hash(p), hash(q), "Hash values should be equal")
def test_hash_resource_not_equal(self):
resource = self.load('test', self.model, self.defs)()
p = resource.Queue('test1')
q = resource.Queue('test2')
self.assertNotEquals(p, q, "Should not be equal resource")
self.assertNotEquals(hash(p), hash(q), "Hash values should be different")
def test_dangling_resource_create_with_kwarg(self):
resource = self.load('test', self.model, self.defs)()
q = resource.Queue(url='test')
self.assertIsInstance(q, ServiceResource,
'Dangling resource created with kwargs is not a ServiceResource')
def test_dangling_resource_shares_client(self):
resource = self.load('test', self.model, self.defs)()
q = resource.Queue('test')
self.assertEqual(resource.meta.client, q.meta.client,
'Client was not shared to dangling resource instance')
def test_dangling_resource_requires_identifier(self):
resource = self.load('test', self.model, self.defs)()
with self.assertRaises(ValueError):
resource.Queue()
def test_dangling_resource_raises_for_unknown_arg(self):
resource = self.load('test', self.model, self.defs)()
with self.assertRaises(ValueError):
resource.Queue(url='foo', bar='baz')
def test_dangling_resource_identifier_is_immutable(self):
resource = self.load('test', self.model, self.defs)()
queue = resource.Queue('url')
# We should not be able to change the identifier's value
with self.assertRaises(AttributeError):
queue.url = 'foo'
def test_dangling_resource_equality(self):
resource = self.load('test', self.model, self.defs)()
q1 = resource.Queue('url')
q2 = resource.Queue('url')
self.assertEqual(q1, q2)
def test_dangling_resource_inequality(self):
self.defs = {
'Queue': {
'identifiers': [{'name': 'Url'}],
'has': {
'Message': {
'resource': {
'type': 'Message',
'identifiers': [
{'target': 'QueueUrl', 'source': 'identifier',
'name': 'Url'},
{'target': 'Handle', 'source': 'input'}
]
}
}
}
},
'Message': {
'identifiers': [{'name': 'QueueUrl'}, {'name': 'Handle'}]
}
}
resource = self.load('test', self.model, self.defs)()
q1 = resource.Queue('url')
q2 = resource.Queue('different')
m = q1.Message('handle')
self.assertNotEqual(q1, q2)
self.assertNotEqual(q1, m)
def test_dangling_resource_loads_data(self):
# Given a loadable resource instance that contains a reference
# to another resource which has a resource data path, the
# referenced resource should be loaded with all of the data
# contained at that path. This allows loading references
# which would otherwise not be loadable (missing load method)
# and prevents extra load calls for others when we already
# have the data available.
self.defs = {
'Instance': {
'identifiers': [{'name': 'Id'}],
'has': {
'NetworkInterface': {
'resource': {
'type': 'NetworkInterface',
'identifiers': [
{'target': 'Id', 'source': 'data',
'path': 'NetworkInterface.Id'}
],
'path': 'NetworkInterface'
}
}
}
},
'NetworkInterface': {
'identifiers': [{'name': 'Id'}],
'shape': 'NetworkInterfaceShape'
}
}
self.model = self.defs['Instance']
shape = DenormalizedStructureBuilder().with_members({
'Id': {
'type': 'string',
},
'PublicIp': {
'type': 'string'
}
}).build_model()
service_model = mock.Mock()
service_model.shape_for.return_value = shape
cls = self.load('Instance', self.model, self.defs, service_model)
instance = cls('instance-id')
# Set some data as if we had completed a load action.
def set_meta_data():
instance.meta.data = {
'NetworkInterface': {
'Id': 'network-interface-id',
'PublicIp': '127.0.0.1'
}
}
instance.load = mock.Mock(side_effect=set_meta_data)
# Now, get the reference and make sure it has its data
# set as expected.
interface = instance.network_interface
self.assertIsNotNone(interface.meta.data)
self.assertEqual(interface.public_ip, '127.0.0.1')
class TestServiceResourceSubresources(BaseTestResourceFactory):
def setUp(self):
super(TestServiceResourceSubresources, self).setUp()
self.model = {
'has': {
'QueueObject': {
'resource': {
'type': 'Queue',
'identifiers': [
{'target': 'Url', 'source': 'input'}
]
}
},
'PriorityQueue': {
'resource': {
'type': 'Queue',
'identifiers': [
{'target': 'Url', 'source': 'input'}
]
}
}
}
}
self.defs = {
'Queue': {
'identifiers': [
{'name': 'Url'}
]
},
'Message': {
'identifiers': [
{'name': 'QueueUrl'},
{'name': 'ReceiptHandle'}
]
}
}
def test_subresource_custom_name(self):
resource = self.load('test', self.model, self.defs)()
self.assertTrue(hasattr(resource, 'QueueObject'))
def test_contains_all_subresources(self):
resource = self.load('test', self.model, self.defs)()
self.assertIn('QueueObject', dir(resource))
self.assertIn('PriorityQueue', dir(resource))
self.assertIn('Message', dir(resource))
def test_get_available_subresources(self):
resource = self.load('test', self.model, self.defs)()
self.assertTrue(hasattr(resource, 'get_available_subresources'))
subresources = sorted(resource.get_available_subresources())
expected = sorted(['PriorityQueue', 'Message', 'QueueObject'])
self.assertEqual(subresources, expected)
def test_subresource_missing_all_subresources(self):
resource = self.load('test', self.model, self.defs)()
message = resource.Message('url', 'handle')
self.assertNotIn('QueueObject', dir(message))
self.assertNotIn('PriorityQueue', dir(message))
self.assertNotIn('Queue', dir(message))
self.assertNotIn('Message', dir(message))
def test_event_emitted_when_class_created(self):
self.load('test', self.model, self.defs)
self.assertTrue(self.emitter.emit.called)
call_args = self.emitter.emit.call_args
# Verify the correct event name emitted.
self.assertEqual(call_args[0][0],
'creating-resource-class.test.ServiceResource')
# Verify we send out the class attributes dict.
actual_class_attrs = sorted(call_args[1]['class_attributes'])
self.assertEqual(actual_class_attrs, [
'Message', 'PriorityQueue', 'QueueObject',
'get_available_subresources', 'meta'])
base_classes = sorted(call_args[1]['base_classes'])
self.assertEqual(base_classes, [ServiceResource]) | en | 0.896204 | # Copyright 2014 Amazon.com, Inc. or its affiliates. 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. A copy of # the License is located at # # http://aws.amazon.com/apache2.0/ # # or in the 'license' file accompanying this file. This file 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. # Class name # Identifier names and values # Each resource has a ``meta`` defined, so this identifier # must be renamed. # This fails because the resource has an identifier # that would be clobbered by the action name. # Only services should get dangling defs # Let's create a message and only give it a receipt handle # The required queue_url identifier should be set from the # queue itself. # Simulate loaded data # Perform a call # Cached data should be cleared # This model has NO load method. Cached data should # never be cleared since it cannot be reloaded! # Simulate loaded data # Perform a call # Cached data should not be cleared # Accessing an identifier should not call load, even if it's in # the shape members. # Accessing a property should call load # Both params should have been loaded into the data bag # Accessing another property should use cached value # instead of making a second call. # Note the lack of a `load` method. These resources # are usually loaded via a call on a parent resource. # Load the resource with no data # Load the resource with data to instantiate a reference # We should not be able to change the identifier's value # Given a loadable resource instance that contains a reference # to another resource which has a resource data path, the # referenced resource should be loaded with all of the data # contained at that path. This allows loading references # which would otherwise not be loadable (missing load method) # and prevents extra load calls for others when we already # have the data available. # Set some data as if we had completed a load action. # Now, get the reference and make sure it has its data # set as expected. # Verify the correct event name emitted. # Verify we send out the class attributes dict. | 1.822501 | 2 |
utils/Geometry.py | albertdow/zinv-analysis | 0 | 6630547 | import numpy as np
import numba as nb
@nb.vectorize
def BoundPhi(phi):
if phi >= np.pi:
phi -= 2*np.pi
elif phi < -np.pi:
phi += 2*np.pi
return phi
@nb.njit
def DeltaR2(deta, dphi):
return deta**2 + BoundPhi(dphi)**2
@nb.njit
def RadToCart2D(r, phi):
return r*np.cos(phi), r*np.sin(phi)
@nb.njit
def CartToRad2D(x, y):
return np.sqrt(x**2+y**2), BoundPhi(np.arctan2(y, x))
@nb.njit
def PartCoorToCart3D(pt, eta, phi):
x, y = RadToCart2D(pt, phi)
z = pt*np.sinh(eta)
return x, y, z
@nb.njit
def CartToPartCoor3D(x, y, z):
pt, phi = CartToRad2D(x, y)
eta = np.arctanh(z/np.sqrt(z**2+pt**2))
return pt, eta, phi
@nb.njit
def LorTHPMToXYZE(t, h, p, m):
x = t*np.cos(p)
y = t*np.sin(p)
z = t*np.sinh(h)
e = np.sqrt(m**2 + t**2 + z**2)
return x, y, z, e
@nb.njit
def LorXYZEToTHPM(x, y, z, e):
t = np.sqrt(x**2+y**2)
h = np.arctanh(z/np.sqrt(t**2+z**2)) if z!=0. else 0.
p = BoundPhi(np.arctan2(y, x))
m2 = e**2 - t**2 - z**2
m = np.sign(m2) * np.sqrt(abs(m2))
return t, h, p, m
| import numpy as np
import numba as nb
@nb.vectorize
def BoundPhi(phi):
if phi >= np.pi:
phi -= 2*np.pi
elif phi < -np.pi:
phi += 2*np.pi
return phi
@nb.njit
def DeltaR2(deta, dphi):
return deta**2 + BoundPhi(dphi)**2
@nb.njit
def RadToCart2D(r, phi):
return r*np.cos(phi), r*np.sin(phi)
@nb.njit
def CartToRad2D(x, y):
return np.sqrt(x**2+y**2), BoundPhi(np.arctan2(y, x))
@nb.njit
def PartCoorToCart3D(pt, eta, phi):
x, y = RadToCart2D(pt, phi)
z = pt*np.sinh(eta)
return x, y, z
@nb.njit
def CartToPartCoor3D(x, y, z):
pt, phi = CartToRad2D(x, y)
eta = np.arctanh(z/np.sqrt(z**2+pt**2))
return pt, eta, phi
@nb.njit
def LorTHPMToXYZE(t, h, p, m):
x = t*np.cos(p)
y = t*np.sin(p)
z = t*np.sinh(h)
e = np.sqrt(m**2 + t**2 + z**2)
return x, y, z, e
@nb.njit
def LorXYZEToTHPM(x, y, z, e):
t = np.sqrt(x**2+y**2)
h = np.arctanh(z/np.sqrt(t**2+z**2)) if z!=0. else 0.
p = BoundPhi(np.arctan2(y, x))
m2 = e**2 - t**2 - z**2
m = np.sign(m2) * np.sqrt(abs(m2))
return t, h, p, m
| none | 1 | 2.477593 | 2 |
|
Data Preparation Layer/Data Enchichment_ML prep_Outlier Detection module/modelling_main.py | marcroiglama/GIVO | 1 | 6630548 | from __future__ import division, print_function
import numpy as np
import pandas as pd
from sklearn.externals import joblib
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import modelling_functions as mf
def mainEvaluateInterpolation():
# 1.Get spark data and transform to pandas df
df = mf.readCSV('curated_sensors.csv')
print(df.columns)
variable = raw_input("\nCHOOSE A VARIABLE: ")
methods = ['time', 'polynomial1','polynomial2','polynomial3',
'polynomial5','polynomial7','polynomial9']
# 2. Create a validation set with some extra NaNs
df_target = df[[variable]].copy()
df_validation = mf.createValidationSet(df_target.dropna().copy())
cond = df_validation['value set to NaN'].index
print('\nEvaluation of filled NaNs with the mean\n')
df_mean = df_validation.fillna(df_validation[variable].mean())
mf.error(df_target.loc[cond], df_mean.loc[cond,variable])
for method in methods:
# 3. Interpolate to fill blanks
if method[0:4] == 'poly':
d = method[10]
df_ip_validation = mf.interpolate(df_validation[variable].copy(),
method, 4, int(d))
print('\nEvaluation of polynomial with degree ' + d + '\n')
else:
df_ip_validation = mf.interpolate(df_validation[variable].copy(),
method, 4, None)
print('\nEvaluation of ' + method +' interpolation method\n')
# 4. Compare results of the df_ip_validate and original df
mf.error(df_target.loc[cond],df_ip_validation.loc[cond,method])
del [df_ip_validation]
def mainApplyInterpolation():
df = mf.readCSV('curated_sensors.csv')
print(df.columns)
variable = raw_input("\nCHOOSE A VARIABLE: ")
# 5. Select to best method to apply to the data and keep these values
print('\nlist of methods: mean, time, polynomial1, polynomial2,\
polynomial3, polynomial5, polynomial7, polynomial9\n')
method = raw_input("CHOOSE THE BEST METHOD: ")
df_target = df[[variable]].copy()
if method == 'mean':
df_ip = df_target.fillna(df_target.mean())
elif method[0:4] == 'poly':
df_ip = mf.interpolate(df_target.copy(), method, 4, method[10])
else:
df_ip = mf.interpolate(df_target.copy(), method, 4, None)
blancs = df_target[variable].isnull()
df_target.loc[blancs] = df_ip.loc[blancs]
#6. Show results
mf.interplots(df[variable].copy(),df_target.copy(),method)
print('\n number of blancs on the original data:\n',
df[variable].isnull().sum())
print('\n number of blancs after interpolation:\n',
df_ip.isnull().sum())
print('\n if any blanc persist means that its sorrounded \
by more than other 3 blancs, they will be deleted \n')
# 7. Save modified data (UNCOMMENT!)
'''
df_full = mf.readCSV('interpolated_sensors.csv')
df_full[variable] = df_target
df_full.to_csv('interpolated_sensors.csv')
'''
#firts time that the script runs:
#df_target.to_csv('interpolated_sensors.csv')
print("interpolation done!")
def mainPreModel():
df_ip = mf.readCSV('interpolated_sensors.csv')
# df_ip = mf.dropNotInterpolatedBlancs(df_ip)
df_ip.to_csv('interpolated_sensors.csv')
df_train, df_test = mf.splitData(df_ip, 0.3)
mf.fitStandardScaler(df_train)
df_train = mf.standarizing(df_train)
df_test = mf.standarizing(df_test)
df_train.to_csv('train_test_sets/train_set.csv')
df_test.to_csv('train_test_sets/test_set.csv')
print('\nfind train and test sets as csv files\n')
def mainOutlierDetection(folder, model_file, train_pred_file, test_pred_file, c):
# 1.Load train and test sets
df_train = mf.readCSV('train_test_sets/train_set.csv')
df_test = mf.readCSV('train_test_sets/test_set.csv')
print(len(df_train))
# 2.Create sklearn model and save it
clf = mf.instanceModel(df_train, folder, model_file, c)
# 3.Load sklearn model
clf = joblib.load(folder + '/' + model_file)
# 4.Predict and save predictions
mf.predict(df_train, folder, train_pred_file, clf)
mf.predict(df_test, folder, test_pred_file, clf)
def mainOutlierMetrics(folder, train_pred_file, test_pred_file,):
# Load predictions
train_predicted = mf.readCSV(folder + '/' + train_pred_file)
test_predicted = mf.readCSV(folder + '/' + test_pred_file)
# Calcule outlier ratios
ratios = [mf.outlier_metrics(train_predicted, train_pred_file),
mf.outlier_metrics(test_predicted, test_pred_file)]
return ratios
def mainPlotOutliers(folder, model_file, train_pred_file, test_pred_file, v1, v2, v3, ratios):
# Load predictions
df_train = mf.readCSV('train_test_sets/train_set.csv')
df_test = mf.readCSV('train_test_sets/test_set.csv')
df_train = mf.inverseStandarizing(df_train)
df_test = mf.inverseStandarizing(df_test)
train_predicted = mf.readCSV(folder + '/' + train_pred_file)
test_predicted = mf.readCSV(folder + '/' + test_pred_file)
df_train['outliers'] = train_predicted['outliers']
df_test['outliers'] = test_predicted['outliers']
# Plot variables
if v3 != None:
mf.outlier_plot3D(df_train, df_test,
v1, v2, v3, model_file, ratios)
else:
mf.outlier_plot2D(df_train, df_test,
v1, v2, model_file, ratios)
def mainDimensionReduction(folder, model, train_pred_file, test_pred_file):
# Read predictions
df_train = mf.readCSV('train_test_sets/train_set.csv')
df_test = mf.readCSV('train_test_sets/test_set.csv')
train_predicted = mf.readCSV(folder + '/' + train_pred_file)
test_predicted = mf.readCSV(folder + '/' + test_pred_file)
# Plot PCA
f, (ax1,ax2) = plt.subplots(1,2)
df_pca_train, df_pca_test = mf.pca(df_train.copy(), df_test.copy())
df_pca_train['outliers'] = train_predicted['outliers']
columns_except_outliers = test_predicted.columns != 'outliers'
df_pca_test['outliers'] = test_predicted['outliers']
mf.plotDimensionReductionOutliers2D(df_pca_train, 'PCA', ax1)
mf.plotDimensionReductionOutliers2D(df_pca_test, 'PCA',ax2)
title1 = ('Train set | ' + model + ' | PCA')
ax1.set_title(title1, fontsize = 9)
ax1.set_xlim(-7,7)
ax1.set_ylim(-10,10)
title2 = ('Test set | ' + model + ' | PCA')
ax2.set_title(title2, fontsize = 9)
ax2.set_xlim(-7,7)
ax2.set_ylim(-10,10)
plt.show()
# Plot T-SNE
f, (ax1,ax2) = plt.subplots(1,2)
df_tsne_train, df_tsne_test = mf.tsne(df_train.copy(),
df_test.copy())
df_tsne_train['outliers'] = train_predicted['outliers']
mf.plotDimensionReductionOutliers2D(df_tsne_train, 't-SNE', ax1)
df_tsne_test['outliers'] = test_predicted['outliers']
mf.plotDimensionReductionOutliers2D(df_tsne_test, 't-SNE', ax2)
title1 = ('Train set | ' + model + ' | T-SNE')
title2 = ('Test set | ' + model + ' | T-SNE')
ax1.set_title(title1, fontsize = 9)
ax2.set_title(title2, fontsize = 9)
plt.show()
if __name__ == '__main__':
'''
# mainEvaluateInterpolation()
# mainApplyInterpolation()
# mainPreModel()
'''
# inizialize
algorithm = 'EllipticEnvelope'
# algorithm = 'IsolationForest'
# algorithm = 'LocalOutlierFactor'
# algorithm = 'OneClassSVM'
# algorithm = 'ensemble'
c = 0.04
folder = algorithm
model = folder + '_' + str(c)
model_file = model + '.sav'
train_pred_file = 'train_' + model + '.csv'
test_pred_file = 'test_' + model + '.csv'
# fit and predict
#mainOutlierDetection(folder, model_file, train_pred_file, test_pred_file, c)
# evaluation
plt.rcParams["figure.figsize"] = [8,4]
ratios = mainOutlierMetrics(folder, train_pred_file, test_pred_file,)
mainPlotOutliers(folder, model_file, train_pred_file, test_pred_file,
'co', 'humidity', None, ratios)
mainDimensionReduction(folder, model, train_pred_file, test_pred_file)
'''
algorithms = ['EllipticEnvelope', 'IsolationForest', 'LocalOutlierFactor']
mf.ensemble(algorithms, c,'train')
#mf.ensemble(algorithms, c,'test')
'''
print('\n############## WORKS FINE ##################')
| from __future__ import division, print_function
import numpy as np
import pandas as pd
from sklearn.externals import joblib
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import modelling_functions as mf
def mainEvaluateInterpolation():
# 1.Get spark data and transform to pandas df
df = mf.readCSV('curated_sensors.csv')
print(df.columns)
variable = raw_input("\nCHOOSE A VARIABLE: ")
methods = ['time', 'polynomial1','polynomial2','polynomial3',
'polynomial5','polynomial7','polynomial9']
# 2. Create a validation set with some extra NaNs
df_target = df[[variable]].copy()
df_validation = mf.createValidationSet(df_target.dropna().copy())
cond = df_validation['value set to NaN'].index
print('\nEvaluation of filled NaNs with the mean\n')
df_mean = df_validation.fillna(df_validation[variable].mean())
mf.error(df_target.loc[cond], df_mean.loc[cond,variable])
for method in methods:
# 3. Interpolate to fill blanks
if method[0:4] == 'poly':
d = method[10]
df_ip_validation = mf.interpolate(df_validation[variable].copy(),
method, 4, int(d))
print('\nEvaluation of polynomial with degree ' + d + '\n')
else:
df_ip_validation = mf.interpolate(df_validation[variable].copy(),
method, 4, None)
print('\nEvaluation of ' + method +' interpolation method\n')
# 4. Compare results of the df_ip_validate and original df
mf.error(df_target.loc[cond],df_ip_validation.loc[cond,method])
del [df_ip_validation]
def mainApplyInterpolation():
df = mf.readCSV('curated_sensors.csv')
print(df.columns)
variable = raw_input("\nCHOOSE A VARIABLE: ")
# 5. Select to best method to apply to the data and keep these values
print('\nlist of methods: mean, time, polynomial1, polynomial2,\
polynomial3, polynomial5, polynomial7, polynomial9\n')
method = raw_input("CHOOSE THE BEST METHOD: ")
df_target = df[[variable]].copy()
if method == 'mean':
df_ip = df_target.fillna(df_target.mean())
elif method[0:4] == 'poly':
df_ip = mf.interpolate(df_target.copy(), method, 4, method[10])
else:
df_ip = mf.interpolate(df_target.copy(), method, 4, None)
blancs = df_target[variable].isnull()
df_target.loc[blancs] = df_ip.loc[blancs]
#6. Show results
mf.interplots(df[variable].copy(),df_target.copy(),method)
print('\n number of blancs on the original data:\n',
df[variable].isnull().sum())
print('\n number of blancs after interpolation:\n',
df_ip.isnull().sum())
print('\n if any blanc persist means that its sorrounded \
by more than other 3 blancs, they will be deleted \n')
# 7. Save modified data (UNCOMMENT!)
'''
df_full = mf.readCSV('interpolated_sensors.csv')
df_full[variable] = df_target
df_full.to_csv('interpolated_sensors.csv')
'''
#firts time that the script runs:
#df_target.to_csv('interpolated_sensors.csv')
print("interpolation done!")
def mainPreModel():
df_ip = mf.readCSV('interpolated_sensors.csv')
# df_ip = mf.dropNotInterpolatedBlancs(df_ip)
df_ip.to_csv('interpolated_sensors.csv')
df_train, df_test = mf.splitData(df_ip, 0.3)
mf.fitStandardScaler(df_train)
df_train = mf.standarizing(df_train)
df_test = mf.standarizing(df_test)
df_train.to_csv('train_test_sets/train_set.csv')
df_test.to_csv('train_test_sets/test_set.csv')
print('\nfind train and test sets as csv files\n')
def mainOutlierDetection(folder, model_file, train_pred_file, test_pred_file, c):
# 1.Load train and test sets
df_train = mf.readCSV('train_test_sets/train_set.csv')
df_test = mf.readCSV('train_test_sets/test_set.csv')
print(len(df_train))
# 2.Create sklearn model and save it
clf = mf.instanceModel(df_train, folder, model_file, c)
# 3.Load sklearn model
clf = joblib.load(folder + '/' + model_file)
# 4.Predict and save predictions
mf.predict(df_train, folder, train_pred_file, clf)
mf.predict(df_test, folder, test_pred_file, clf)
def mainOutlierMetrics(folder, train_pred_file, test_pred_file,):
# Load predictions
train_predicted = mf.readCSV(folder + '/' + train_pred_file)
test_predicted = mf.readCSV(folder + '/' + test_pred_file)
# Calcule outlier ratios
ratios = [mf.outlier_metrics(train_predicted, train_pred_file),
mf.outlier_metrics(test_predicted, test_pred_file)]
return ratios
def mainPlotOutliers(folder, model_file, train_pred_file, test_pred_file, v1, v2, v3, ratios):
# Load predictions
df_train = mf.readCSV('train_test_sets/train_set.csv')
df_test = mf.readCSV('train_test_sets/test_set.csv')
df_train = mf.inverseStandarizing(df_train)
df_test = mf.inverseStandarizing(df_test)
train_predicted = mf.readCSV(folder + '/' + train_pred_file)
test_predicted = mf.readCSV(folder + '/' + test_pred_file)
df_train['outliers'] = train_predicted['outliers']
df_test['outliers'] = test_predicted['outliers']
# Plot variables
if v3 != None:
mf.outlier_plot3D(df_train, df_test,
v1, v2, v3, model_file, ratios)
else:
mf.outlier_plot2D(df_train, df_test,
v1, v2, model_file, ratios)
def mainDimensionReduction(folder, model, train_pred_file, test_pred_file):
# Read predictions
df_train = mf.readCSV('train_test_sets/train_set.csv')
df_test = mf.readCSV('train_test_sets/test_set.csv')
train_predicted = mf.readCSV(folder + '/' + train_pred_file)
test_predicted = mf.readCSV(folder + '/' + test_pred_file)
# Plot PCA
f, (ax1,ax2) = plt.subplots(1,2)
df_pca_train, df_pca_test = mf.pca(df_train.copy(), df_test.copy())
df_pca_train['outliers'] = train_predicted['outliers']
columns_except_outliers = test_predicted.columns != 'outliers'
df_pca_test['outliers'] = test_predicted['outliers']
mf.plotDimensionReductionOutliers2D(df_pca_train, 'PCA', ax1)
mf.plotDimensionReductionOutliers2D(df_pca_test, 'PCA',ax2)
title1 = ('Train set | ' + model + ' | PCA')
ax1.set_title(title1, fontsize = 9)
ax1.set_xlim(-7,7)
ax1.set_ylim(-10,10)
title2 = ('Test set | ' + model + ' | PCA')
ax2.set_title(title2, fontsize = 9)
ax2.set_xlim(-7,7)
ax2.set_ylim(-10,10)
plt.show()
# Plot T-SNE
f, (ax1,ax2) = plt.subplots(1,2)
df_tsne_train, df_tsne_test = mf.tsne(df_train.copy(),
df_test.copy())
df_tsne_train['outliers'] = train_predicted['outliers']
mf.plotDimensionReductionOutliers2D(df_tsne_train, 't-SNE', ax1)
df_tsne_test['outliers'] = test_predicted['outliers']
mf.plotDimensionReductionOutliers2D(df_tsne_test, 't-SNE', ax2)
title1 = ('Train set | ' + model + ' | T-SNE')
title2 = ('Test set | ' + model + ' | T-SNE')
ax1.set_title(title1, fontsize = 9)
ax2.set_title(title2, fontsize = 9)
plt.show()
if __name__ == '__main__':
'''
# mainEvaluateInterpolation()
# mainApplyInterpolation()
# mainPreModel()
'''
# inizialize
algorithm = 'EllipticEnvelope'
# algorithm = 'IsolationForest'
# algorithm = 'LocalOutlierFactor'
# algorithm = 'OneClassSVM'
# algorithm = 'ensemble'
c = 0.04
folder = algorithm
model = folder + '_' + str(c)
model_file = model + '.sav'
train_pred_file = 'train_' + model + '.csv'
test_pred_file = 'test_' + model + '.csv'
# fit and predict
#mainOutlierDetection(folder, model_file, train_pred_file, test_pred_file, c)
# evaluation
plt.rcParams["figure.figsize"] = [8,4]
ratios = mainOutlierMetrics(folder, train_pred_file, test_pred_file,)
mainPlotOutliers(folder, model_file, train_pred_file, test_pred_file,
'co', 'humidity', None, ratios)
mainDimensionReduction(folder, model, train_pred_file, test_pred_file)
'''
algorithms = ['EllipticEnvelope', 'IsolationForest', 'LocalOutlierFactor']
mf.ensemble(algorithms, c,'train')
#mf.ensemble(algorithms, c,'test')
'''
print('\n############## WORKS FINE ##################')
| en | 0.561146 | # 1.Get spark data and transform to pandas df # 2. Create a validation set with some extra NaNs # 3. Interpolate to fill blanks # 4. Compare results of the df_ip_validate and original df # 5. Select to best method to apply to the data and keep these values #6. Show results # 7. Save modified data (UNCOMMENT!) df_full = mf.readCSV('interpolated_sensors.csv') df_full[variable] = df_target df_full.to_csv('interpolated_sensors.csv') #firts time that the script runs: #df_target.to_csv('interpolated_sensors.csv') # df_ip = mf.dropNotInterpolatedBlancs(df_ip) # 1.Load train and test sets # 2.Create sklearn model and save it # 3.Load sklearn model # 4.Predict and save predictions # Load predictions # Calcule outlier ratios # Load predictions # Plot variables # Read predictions # Plot PCA # Plot T-SNE # mainEvaluateInterpolation() # mainApplyInterpolation() # mainPreModel() # inizialize # algorithm = 'IsolationForest' # algorithm = 'LocalOutlierFactor' # algorithm = 'OneClassSVM' # algorithm = 'ensemble' # fit and predict #mainOutlierDetection(folder, model_file, train_pred_file, test_pred_file, c) # evaluation algorithms = ['EllipticEnvelope', 'IsolationForest', 'LocalOutlierFactor'] mf.ensemble(algorithms, c,'train') #mf.ensemble(algorithms, c,'test') ############## WORKS FINE ##################') | 3.051198 | 3 |
redpanda/examples/python/kafka-produce.py | lbooker42/deephaven-core | 0 | 6630549 | <reponame>lbooker42/deephaven-core
#
# Test driver to produce kafka messages using single type serializers for key and value.
#
# To run this script, you need confluent-kafka libraries installed.
# To create a dedicated venv for it, you can do:
#
# $ mkdir confluent-kafka; cd confluent-kafka
# $ python3 -m venv confluent-kafka
# $ cd confluent-kafka
# $ source bin/activate
# $ pip3 install confluent-kafka
# $ pip3 install avro
#
# Note: On a Mac you may need to install the librdkafka package.
# You can use "brew install librdkafka" if the pip3 command fails
# with an error like "librdkafka/rdkafka.h' file not found"
# as found at confluentinc/confluent-kafka-python#166.
# You may also need the following (be sure to substitute the right version of librdkafka):
# export C_INCLUDE_PATH=/opt/homebrew/Cellar/librdkafka/1.9.0/include
# export LIBRARY_PATH=/opt/homebrew/Cellar/librdkafka/1.9.0/lib
#
# Examples of use for DH testing together with web UI.
#
# == Common to all:
#
# * Start the redpanda compose: (cd redpanda && docker-compose up --build)
# * From web UI do:
#
# > from deephaven import kafka_consumer as kc
# > from deephaven.stream.kafka.consumer import TableType
#
# == Example (1) Simple String Key and simple double Value
#
# From web UI do:
#
# > t = kc.consume({'bootstrap.servers':'redpanda:29092', 'deephaven.key.column.name':'Symbol', 'deephaven.value.column.name':'Price', 'deephaven.value.column.type':'double'}, 'quotes', table_type=TableType.append())
#
# You should see a table show up with columns [ KafkaPartition, KafkaOffset, KafkaTimestamp, symbol, price ]
#
# Run this script on the host (not on a docker image) to produce one row:
#
# $ python3 ./kafka-produce.py quotes 0 MSFT double:274.82
#
# You should see one row show up on the web UI table, data matching above.
#
# == Example (2) Simple String Key and simple long Value
#
# From web UI do:
# > t2 = kc.consume({'bootstrap.servers':'redpanda:29092', 'deephaven.key.column.name':'Metric', 'deephaven.value.column.name':'Value', 'deephaven.value.column.type':'long', 'deephaven.offset.column.name':'', 'deephaven.partition.column.name':''}, 'metrics', table_type=TableType.append())
#
# You should see a table show up with columns: [ KafkaTimestamp, Metric, Value ]
#
# Run this script on the host (not on a docker image) to produce one row:
#
# $ python3 ./kafka-produce.py metrics 0 us_west.latency.millis long:29
#
# == Example (3) JSON.
#
# From web UI do:
#
# > from deephaven import dtypes as dh
# > t3 = kc.consume({'bootstrap.servers' : 'redpanda:29092'}, 'orders', value_spec=kc.json_spec([ ('Symbol', dh.string), ('Side', dh.string), ('Price', dh.double), ('Qty', dh.int_), ('UserName', dh.string), ('UserId', dh.int_) ], mapping = { '/User/Name' : 'UserName', '/User/Id' : 'UserId'}), table_type=TableType.append())
#
# Run this script on the host (not on a docker image) to produce one row:
#
# $ python3 kafka-produce.py orders 0 '' 'str:{ "Symbol" : "MSFT", "Side" : "BUY", "Price" : "278.85", "Qty" : "200", "User" : { "Name" : "Pepo", "Id" : 1234 } }'
#
# You should see one row of data as per above showing up in the UI.
#
#
from confluent_kafka import Producer
import sys
import struct
data_arg_form = "type:value"
def usage():
print("Usage: " + sys.argv[0] + " topic-name partition " +
data_arg_form + " [" + data_arg_form + "]", file=sys.stderr)
sys.exit(1)
if len(sys.argv) < 4 or len(sys.argv) > 5 or (len(sys.argv) == 2 and sys.argv[1] == '-h'):
usage()
c = 1
topic_name = sys.argv[c]
c += 1
try:
partition = int(sys.argv[c])
except ValueError as err:
print('Failed to parse "' + sys.argv[c] + '" as a partition number: ' + str(err), file=sys.stderr)
usage()
if partition < 0:
print('Partition argument should be a non-negative integer, instead got: ' + str(partition), file=sys.stderr)
usage()
c += 1
def delivery_report(err, msg):
""" Called once for each message produced to indicate delivery result.
Triggered by poll() or flush(). """
if err is not None:
print('Message delivery failed: {}'.format(err))
else:
print('Message key=|{}|, value=|{}| delivered to topic {} partition {}'
.format(msg.key(), msg.value(), msg.topic(), msg.partition()))
#
producer = Producer({
'bootstrap.servers': 'localhost:9092',
'on_delivery': delivery_report,
})
def wrong_form(data_arg):
print(sys.argv[0] + ": Error, argument " + data_arg +
" is not of the form " + data_arg_form + ".", file=sys.stderr)
sys.exit(1)
def data_arg_to_data(data_arg):
s = data_arg.split(':', 1)
if len(s) != 2:
if len(s) == 1:
# Assume string.
return s[0]
else:
wrong_form(data_arg)
if (s[0] == "str"):
return s[1]
if s[0] == "float":
return struct.pack('>f', float(s[1]))
if s[0] == "double":
return struct.pack('>d', float(s[1]))
if s[0] == "int":
return int(s[1]).to_bytes(4, "big")
if s[0] == "long":
return int(s[1]).to_bytes(8, "big")
print(sys.argv[0] + ": Error, type " + s[0] + " not supported.", file=sys.stderr)
sys.exit(1)
if len(sys.argv) == 3:
key = None
value = data_arg_to_data(sys.argv[c])
c += 1
else:
key = data_arg_to_data(sys.argv[c])
c += 1
value = data_arg_to_data(sys.argv[c])
c += 1
producer.produce(topic=topic_name, partition=partition, key=key, value=value)
producer.flush()
| #
# Test driver to produce kafka messages using single type serializers for key and value.
#
# To run this script, you need confluent-kafka libraries installed.
# To create a dedicated venv for it, you can do:
#
# $ mkdir confluent-kafka; cd confluent-kafka
# $ python3 -m venv confluent-kafka
# $ cd confluent-kafka
# $ source bin/activate
# $ pip3 install confluent-kafka
# $ pip3 install avro
#
# Note: On a Mac you may need to install the librdkafka package.
# You can use "brew install librdkafka" if the pip3 command fails
# with an error like "librdkafka/rdkafka.h' file not found"
# as found at confluentinc/confluent-kafka-python#166.
# You may also need the following (be sure to substitute the right version of librdkafka):
# export C_INCLUDE_PATH=/opt/homebrew/Cellar/librdkafka/1.9.0/include
# export LIBRARY_PATH=/opt/homebrew/Cellar/librdkafka/1.9.0/lib
#
# Examples of use for DH testing together with web UI.
#
# == Common to all:
#
# * Start the redpanda compose: (cd redpanda && docker-compose up --build)
# * From web UI do:
#
# > from deephaven import kafka_consumer as kc
# > from deephaven.stream.kafka.consumer import TableType
#
# == Example (1) Simple String Key and simple double Value
#
# From web UI do:
#
# > t = kc.consume({'bootstrap.servers':'redpanda:29092', 'deephaven.key.column.name':'Symbol', 'deephaven.value.column.name':'Price', 'deephaven.value.column.type':'double'}, 'quotes', table_type=TableType.append())
#
# You should see a table show up with columns [ KafkaPartition, KafkaOffset, KafkaTimestamp, symbol, price ]
#
# Run this script on the host (not on a docker image) to produce one row:
#
# $ python3 ./kafka-produce.py quotes 0 MSFT double:274.82
#
# You should see one row show up on the web UI table, data matching above.
#
# == Example (2) Simple String Key and simple long Value
#
# From web UI do:
# > t2 = kc.consume({'bootstrap.servers':'redpanda:29092', 'deephaven.key.column.name':'Metric', 'deephaven.value.column.name':'Value', 'deephaven.value.column.type':'long', 'deephaven.offset.column.name':'', 'deephaven.partition.column.name':''}, 'metrics', table_type=TableType.append())
#
# You should see a table show up with columns: [ KafkaTimestamp, Metric, Value ]
#
# Run this script on the host (not on a docker image) to produce one row:
#
# $ python3 ./kafka-produce.py metrics 0 us_west.latency.millis long:29
#
# == Example (3) JSON.
#
# From web UI do:
#
# > from deephaven import dtypes as dh
# > t3 = kc.consume({'bootstrap.servers' : 'redpanda:29092'}, 'orders', value_spec=kc.json_spec([ ('Symbol', dh.string), ('Side', dh.string), ('Price', dh.double), ('Qty', dh.int_), ('UserName', dh.string), ('UserId', dh.int_) ], mapping = { '/User/Name' : 'UserName', '/User/Id' : 'UserId'}), table_type=TableType.append())
#
# Run this script on the host (not on a docker image) to produce one row:
#
# $ python3 kafka-produce.py orders 0 '' 'str:{ "Symbol" : "MSFT", "Side" : "BUY", "Price" : "278.85", "Qty" : "200", "User" : { "Name" : "Pepo", "Id" : 1234 } }'
#
# You should see one row of data as per above showing up in the UI.
#
#
from confluent_kafka import Producer
import sys
import struct
data_arg_form = "type:value"
def usage():
print("Usage: " + sys.argv[0] + " topic-name partition " +
data_arg_form + " [" + data_arg_form + "]", file=sys.stderr)
sys.exit(1)
if len(sys.argv) < 4 or len(sys.argv) > 5 or (len(sys.argv) == 2 and sys.argv[1] == '-h'):
usage()
c = 1
topic_name = sys.argv[c]
c += 1
try:
partition = int(sys.argv[c])
except ValueError as err:
print('Failed to parse "' + sys.argv[c] + '" as a partition number: ' + str(err), file=sys.stderr)
usage()
if partition < 0:
print('Partition argument should be a non-negative integer, instead got: ' + str(partition), file=sys.stderr)
usage()
c += 1
def delivery_report(err, msg):
""" Called once for each message produced to indicate delivery result.
Triggered by poll() or flush(). """
if err is not None:
print('Message delivery failed: {}'.format(err))
else:
print('Message key=|{}|, value=|{}| delivered to topic {} partition {}'
.format(msg.key(), msg.value(), msg.topic(), msg.partition()))
#
producer = Producer({
'bootstrap.servers': 'localhost:9092',
'on_delivery': delivery_report,
})
def wrong_form(data_arg):
print(sys.argv[0] + ": Error, argument " + data_arg +
" is not of the form " + data_arg_form + ".", file=sys.stderr)
sys.exit(1)
def data_arg_to_data(data_arg):
s = data_arg.split(':', 1)
if len(s) != 2:
if len(s) == 1:
# Assume string.
return s[0]
else:
wrong_form(data_arg)
if (s[0] == "str"):
return s[1]
if s[0] == "float":
return struct.pack('>f', float(s[1]))
if s[0] == "double":
return struct.pack('>d', float(s[1]))
if s[0] == "int":
return int(s[1]).to_bytes(4, "big")
if s[0] == "long":
return int(s[1]).to_bytes(8, "big")
print(sys.argv[0] + ": Error, type " + s[0] + " not supported.", file=sys.stderr)
sys.exit(1)
if len(sys.argv) == 3:
key = None
value = data_arg_to_data(sys.argv[c])
c += 1
else:
key = data_arg_to_data(sys.argv[c])
c += 1
value = data_arg_to_data(sys.argv[c])
c += 1
producer.produce(topic=topic_name, partition=partition, key=key, value=value)
producer.flush() | en | 0.440194 | # # Test driver to produce kafka messages using single type serializers for key and value. # # To run this script, you need confluent-kafka libraries installed. # To create a dedicated venv for it, you can do: # # $ mkdir confluent-kafka; cd confluent-kafka # $ python3 -m venv confluent-kafka # $ cd confluent-kafka # $ source bin/activate # $ pip3 install confluent-kafka # $ pip3 install avro # # Note: On a Mac you may need to install the librdkafka package. # You can use "brew install librdkafka" if the pip3 command fails # with an error like "librdkafka/rdkafka.h' file not found" # as found at confluentinc/confluent-kafka-python#166. # You may also need the following (be sure to substitute the right version of librdkafka): # export C_INCLUDE_PATH=/opt/homebrew/Cellar/librdkafka/1.9.0/include # export LIBRARY_PATH=/opt/homebrew/Cellar/librdkafka/1.9.0/lib # # Examples of use for DH testing together with web UI. # # == Common to all: # # * Start the redpanda compose: (cd redpanda && docker-compose up --build) # * From web UI do: # # > from deephaven import kafka_consumer as kc # > from deephaven.stream.kafka.consumer import TableType # # == Example (1) Simple String Key and simple double Value # # From web UI do: # # > t = kc.consume({'bootstrap.servers':'redpanda:29092', 'deephaven.key.column.name':'Symbol', 'deephaven.value.column.name':'Price', 'deephaven.value.column.type':'double'}, 'quotes', table_type=TableType.append()) # # You should see a table show up with columns [ KafkaPartition, KafkaOffset, KafkaTimestamp, symbol, price ] # # Run this script on the host (not on a docker image) to produce one row: # # $ python3 ./kafka-produce.py quotes 0 MSFT double:274.82 # # You should see one row show up on the web UI table, data matching above. # # == Example (2) Simple String Key and simple long Value # # From web UI do: # > t2 = kc.consume({'bootstrap.servers':'redpanda:29092', 'deephaven.key.column.name':'Metric', 'deephaven.value.column.name':'Value', 'deephaven.value.column.type':'long', 'deephaven.offset.column.name':'', 'deephaven.partition.column.name':''}, 'metrics', table_type=TableType.append()) # # You should see a table show up with columns: [ KafkaTimestamp, Metric, Value ] # # Run this script on the host (not on a docker image) to produce one row: # # $ python3 ./kafka-produce.py metrics 0 us_west.latency.millis long:29 # # == Example (3) JSON. # # From web UI do: # # > from deephaven import dtypes as dh # > t3 = kc.consume({'bootstrap.servers' : 'redpanda:29092'}, 'orders', value_spec=kc.json_spec([ ('Symbol', dh.string), ('Side', dh.string), ('Price', dh.double), ('Qty', dh.int_), ('UserName', dh.string), ('UserId', dh.int_) ], mapping = { '/User/Name' : 'UserName', '/User/Id' : 'UserId'}), table_type=TableType.append()) # # Run this script on the host (not on a docker image) to produce one row: # # $ python3 kafka-produce.py orders 0 '' 'str:{ "Symbol" : "MSFT", "Side" : "BUY", "Price" : "278.85", "Qty" : "200", "User" : { "Name" : "Pepo", "Id" : 1234 } }' # # You should see one row of data as per above showing up in the UI. # # Called once for each message produced to indicate delivery result. Triggered by poll() or flush(). # # Assume string. | 2.360703 | 2 |
utils/__init__.py | thearyadev/Python-Console-Logging | 0 | 6630550 | from .console import Console
| from .console import Console
| none | 1 | 1.054096 | 1 |
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