xszheng2020/the_stack_dedup_python · Datasets at Hugging Face

79a565e7d7928d619d4922162412c3aac164285d

3,016

py

Python

nonebot_plugin_bam/database/helper.py

7sDream/nonebot_plugin_bam

9d19856661a75484440efff8d77094390230f4c9

[ "MIT" ]

4

2021-02-08T16:18:12.000Z

2021-12-28T07:13:51.000Z

nonebot_plugin_bam/database/helper.py

7sDream/nonebot_plugin_bam

9d19856661a75484440efff8d77094390230f4c9

[ "MIT" ]

null

nonebot_plugin_bam/database/helper.py

7sDream/nonebot_plugin_bam

9d19856661a75484440efff8d77094390230f4c9

[ "MIT" ]

null

from collections import defaultdict from typing import Dict from nonebot.log import logger from peewee import JOIN from .db import DB from .tables import BilibiliUser, BilibiliUserStatus, FollowLink, Group def log_sql(s): # logger.debug(f"[DB:SQL] {s.sql()}") return s def get_all_groups(): yield from log_sql(Group.select()) def get_group(gid: int) -> Group: for group in log_sql(Group.select().where(Group.gid == gid)): return group return None def add_group(gid: int, group_suid: int): return log_sql( Group.insert(gid=gid, super_user=group_suid).on_conflict_replace() ).execute() def remove_group(group: Group): group.delete_instance(recursive=True, delete_nullable=True) def get_users_with_linked_groups_and_status() -> Dict[int, BilibiliUser]: users = {} for user in log_sql( BilibiliUser.select(BilibiliUser, FollowLink, BilibiliUserStatus) .join(FollowLink, JOIN.LEFT_OUTER) .switch(BilibiliUser) .join(BilibiliUserStatus, JOIN.LEFT_OUTER, attr="status") ): users[user.uid] = user return users def clean_users_live_status(): log_sql(BilibiliUserStatus.update(live_status=False)).execute(None) def clean_user_live_status_in(users): if len(users) > 0: log_sql( BilibiliUserStatus.update(live_status=False).where( BilibiliUserStatus.bilibili_user.in_(users) ) ).execute() def set_user_live_status_in(users): if len(users) > 0: log_sql( BilibiliUserStatus.update(live_status=True).where( BilibiliUserStatus.bilibili_user.in_(users) ) ).execute() def get_group_with_following_users(gid): for group in log_sql( Group.select() .where(Group.gid == gid) .join(FollowLink, JOIN.LEFT_OUTER) .join(BilibiliUser, JOIN.LEFT_OUTER) ): return group return None def get_user(uid): for user in log_sql(BilibiliUser.select().where(BilibiliUser.uid == uid)): return user return None def add_user(uid, nickname, rid): user, created = BilibiliUser.get_or_create( uid=uid, defaults={"nickname": nickname, "rid": rid} ) if created: BilibiliUserStatus.create( bilibili_user=user, newest_activity_id=0, live_status=False ) else: user.nickname = nickname user.rid = rid user.save() return user def add_link(group, user): FollowLink.create(group=group, bilibili_user=user) def remove_link(gid, uid): log_sql( FollowLink.delete().where( (FollowLink.group == gid) & (FollowLink.bilibili_user == uid) ) ).execute() def update_user_newest_activity_id(data: dict[int, int]): with DB.atomic(): for user, act_id in data.items(): BilibiliUserStatus.update(newest_activity_id=act_id).where( BilibiliUserStatus.bilibili_user == user ).execute()

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null

0

null

0

1

0

79a69b808745f05349b2ede483ce4782883293d0

1,536

py

Python

monitor.py

hletrd/Facebook-Autopoker

18735eebd4a34992a43a0987d390bbcfc0050d96

[ "MIT" ]

5

2015-07-14T17:11:24.000Z

2016-07-28T11:52:03.000Z

monitor.py

hletrd/Facebook-Autopoker

18735eebd4a34992a43a0987d390bbcfc0050d96

[ "MIT" ]

null

monitor.py

hletrd/Facebook-Autopoker

18735eebd4a34992a43a0987d390bbcfc0050d96

[ "MIT" ]

null

db = 'log.db' import sqlite3 import time dbc = sqlite3.connect(db, check_same_thread=False) dbc.text_factory = str c = dbc.cursor() def key(obj): return obj[2] while True: c.execute('SELECT userid, name, COUNT(`date`) FROM log WHERE `date` > \'' + time.strftime('%Y-%m-%d 00:00:00') + '\' AND result=1 GROUP BY userid;') result = c.fetchall() result.sort(key=key, reverse=True) total = 0 for i in result: total += i[2] print('Poked ' + str(i[1]) + '(' + str(i[0]) + ') ' + str(i[2]) + ' time' + ('s' if (i[2] > 1) else '') + ' today') print('Total: ' + str(total) + ' poke' + ('s' if (total > 1) else '')) c.execute('SELECT COUNT(`date`) FROM log WHERE `date` > datetime(\'' + time.strftime('%Y-%m-%d %H:%M:%S') + '\', \'-24 hours\') AND result=1;') print(str((c.fetchone()[0] * 100 / 1440) / 100.0) + ' ppm for last 24 hours') c.execute('SELECT COUNT(`date`) FROM log WHERE `date` > datetime(\'' + time.strftime('%Y-%m-%d %H:%M:%S') + '\', \'-6 hours\') AND result=1;') print(str((c.fetchone()[0] * 100 / 360) / 100.0) + ' ppm for last 6 hours') c.execute('SELECT COUNT(`date`) FROM log WHERE `date` > datetime(\'' + time.strftime('%Y-%m-%d %H:%M:%S') + '\', \'-1 hours\') AND result=1;') print(str((c.fetchone()[0] * 100 / 60) / 100.0) + ' ppm for last 1 hour') c.execute('SELECT COUNT(`date`) FROM log WHERE `date` > datetime(\'' + time.strftime('%Y-%m-%d %H:%M:%S') + '\', \'-5 minutes\') AND result=1;') print(str((c.fetchone()[0] * 100 / 5) / 100.0) + ' ppm for last 5 minutes') print('') time.sleep(5)

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0.259259

0

null

0

null

0

1

0

79a7ef7c11468985df93521d767d16d3db7e7f54

1,949

py

Python

annoTree/subs/parsSyms.py

jvfNontools/jvfNontools

60b3c2643f6cabbcad342b5f6b3e5490e89f31f5

[ "Apache-2.0" ]

null

annoTree/subs/parsSyms.py

jvfNontools/jvfNontools

60b3c2643f6cabbcad342b5f6b3e5490e89f31f5

[ "Apache-2.0" ]

null

annoTree/subs/parsSyms.py

jvfNontools/jvfNontools

60b3c2643f6cabbcad342b5f6b3e5490e89f31f5

[ "Apache-2.0" ]

null

#!/usr/bin/python3 #Copyright 2018 Jim Van Fleet #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. class SearchFileForSyms: def doSearch(self, openFile, searchItem): symb = "sym=" startb = "start-address=0x" commb = "," spacb = " " parab = "(" brackb = "[" allSyms = [] symIndex = 0 with open(openFile) as symFile: for line in symFile: if (line.find(searchItem) == -1): continue # want exception if search items not found si = line.index(symb) ei = line.index(commb, (si+1)) li = line.rfind(parab, (si+1), ei) if (li == -1): li = line.rfind(brackb, (si+1), ei) if li == -1: sy0 = line[(si+4): (ei)] else: sy0 = line[(si+4): li] else: sy0 = line[(si+4): li] line = symFile.readline() line = symFile.readline() si = line.index(startb) ei = line.index(spacb, si) star10 = line[(si+16): ei] star1 = star10.lstrip("0") allSyms.append(sy0) allSyms.append(star1) return allSyms def __init__(self, openFile, searchItem): self.symData = self.doSearch(openFile, searchItem)

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false

0

0.111111

0

null

0

null

0

1

0

79aa3668bb043f0729ae0d753b69ad0de26cb30d

2,270

py

Python

models/networkgcn.py

Byomyyt/GnTCN

b4cc9e97fc0b0438deb0a7e118817a7ab73ae93c

[ "MIT" ]

91

2021-04-06T15:33:11.000Z

2022-03-31T05:16:27.000Z

models/networkgcn.py

ddddwee1/GnTCN

e1abb8c526b2a9904d6f964b0084b54f123b82c9

[ "MIT" ]

17

2021-01-04T09:08:20.000Z

2022-03-17T11:45:27.000Z

models/networkgcn.py

ddddwee1/GnTCN

e1abb8c526b2a9904d6f964b0084b54f123b82c9

[ "MIT" ]

15

2021-01-18T01:54:23.000Z

2021-09-24T01:29:32.000Z

import numpy as np import torch import torch.nn.functional as F from TorchSUL import Model as M from torch.nn.parameter import Parameter import torch.nn.init as init class PropLayer(M.Model): def initialize(self, outdim, usebias=True): self.outdim = outdim self.act = torch.nn.ReLU() self.act2 = torch.nn.ReLU() self.usebias = usebias def build(self, *inp): # inp: [Bsize, num_pts, 2] num_pts = inp[0].shape[1] indim = inp[0].shape[2] self.weight = Parameter(torch.Tensor(num_pts, indim, self.outdim)) self.weight2 = Parameter(torch.Tensor(num_pts, self.outdim, self.outdim)) init.kaiming_uniform_(self.weight, a=np.sqrt(5)) init.kaiming_uniform_(self.weight2, a=np.sqrt(5)) if self.usebias: print('initialize bias') self.bias = Parameter(torch.Tensor(num_pts, self.outdim)) self.bias2 = Parameter(torch.Tensor(num_pts, self.outdim)) init.uniform_(self.bias, -0.1, 0.1) init.uniform_(self.bias2, -0.1, 0.1) def forward(self, inp, aff=None, act=True): if aff is not None: # propagate the keypoints x = torch.einsum('ikl,ijk->ijl', inp, aff) else: x = inp x = torch.einsum('ijk,jkl->ijl', x, self.weight) if self.usebias: x = x + self.bias if act: x = self.act(x) # x = F.dropout(x, 0.25, self.training, False) x = torch.einsum('ijk,jkl->ijl', x, self.weight2) if self.usebias: x = x + self.bias2 if act: x = self.act2(x) #x = F.dropout(x, 0.25, self.training, False) if aff is not None: x = torch.cat([inp, x], dim=-1) return x class TransNet(M.Model): def initialize(self, outdim, num_pts): self.num_pts = num_pts self.c1 = PropLayer(outdim) self.c2 = PropLayer(outdim) self.c3 = PropLayer(outdim) self.b2 = PropLayer(outdim) self.b3 = PropLayer(outdim) self.c8 = PropLayer(outdim) self.c9 = PropLayer(3) def forward(self, x, aff, aff_bone, inc, inc_inv): x = feat = self.c1(x) x = self.c2(x, aff) x = self.c3(x, aff) feat = torch.einsum('ijk,lj->ilk', feat, inc) feat = self.b2(feat, aff_bone) feat = self.b3(feat, aff_bone) feat = torch.einsum('ijk,lj->ilk', feat, inc_inv) x = torch.cat([x, feat], dim=-1) x = self.c8(x) x = self.c9(x, act=False) # print(x.shape) x = x.reshape(-1, self.num_pts, 3) return x

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2,270

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false

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0.015152

0

null

0

null

0

1

0

79ac7f2c83fe009a0c2f95dafc3599ebde6411df

307

py

Python

tests/backends/test_init.py

benkrikler/fast-carpenter-github-test

b6f7e1b218d3a1f39fcbe739c8bab19af63aabb8

[ "Apache-2.0" ]

12

2019-05-17T13:02:20.000Z

2020-08-31T08:16:47.000Z

tests/backends/test_init.py

FAST-HEP/fast-carpenter

b6f7e1b218d3a1f39fcbe739c8bab19af63aabb8

[ "Apache-2.0" ]

104

2019-05-17T16:25:35.000Z

2022-03-28T16:11:10.000Z

tests/backends/test_init.py

benkrikler/fast-carpenter-github-test

b6f7e1b218d3a1f39fcbe739c8bab19af63aabb8

[ "Apache-2.0" ]

16

2019-05-20T16:57:48.000Z

2020-09-28T16:36:21.000Z

import pytest import fast_carpenter.backends as backends def test_get_backend(): coffea_back = backends.get_backend("coffea:dask") assert hasattr(coffea_back, "execute") with pytest.raises(ValueError) as e: backends.get_backend("doesn't exist") assert "Unknown backend" in str(e)

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null

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null

0

1

0

79ae5d267641860a50ba60429c85299cdeeef14d

1,534

py

Python

serving_patterns/src/api_composition_proxy/helpers.py

shibuiwilliam/ml-system-in-action

0aa9d6bc4a4346236b9c971ec90afad04bcf5cca

[ "MIT" ]

10

2020-08-30T03:19:10.000Z

2021-08-08T17:38:06.000Z

serving_patterns/src/api_composition_proxy/helpers.py

shibuiwilliam/ml-system-in-action

0aa9d6bc4a4346236b9c971ec90afad04bcf5cca

[ "MIT" ]

null

serving_patterns/src/api_composition_proxy/helpers.py

shibuiwilliam/ml-system-in-action

0aa9d6bc4a4346236b9c971ec90afad04bcf5cca

[ "MIT" ]

6

2020-08-30T03:19:13.000Z

2021-11-26T23:32:42.000Z

from typing import Dict import logging logger = logging.getLogger(__name__) def path_builder(url: str, path: str) -> str: if path == "" or path is None: return url if path.startswith("/"): path = path[1:] if url.endswith("/"): url = f"{url}{path}" else: url = f"{url}/{path}" return url def url_builder(hostname: str, https: bool = False) -> str: if not (hostname.startswith("http://") or hostname.startswith("https://")): hostname = f"https://{hostname}" if https else f"http://{hostname}" return hostname def url_path_builder(hostname: str, path: str, https: bool = False) -> str: hostname = url_builder(hostname, https) url = path_builder(hostname, path) return url def customized_redirect_builder(alias: str, url: str, redirect_path: str, customized_redirect_map: Dict[str, Dict[str, str]] = None) -> str: """ customized_redirect_map { ALIAS_0: { REDIRECT_PATH_0: redirect_path_0, REDIRECT_PATH_1: redirect_path_1, }, ALIAS_1: { REDIRECT_PATH_0: redirect_path_0, REDIRECT_PATH_2: redirect_path_2, } } """ path = path_builder(url, redirect_path) if customized_redirect_map is None: return path if alias in customized_redirect_map.keys(): if redirect_path in customized_redirect_map[alias].keys(): path = path_builder(url, customized_redirect_map[alias][redirect_path]) return path

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1,534

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0

0.413793

0

null

0

null

0

1

0

79ae9cb10f166f65649baf95240f5d262fca4fa9

1,856

py

Python

rsa/rsa/common.py

andrew-kulikov/crypto

c81cf7965d58da23ce234435676c8516daf3c649

[ "MIT" ]

null

rsa/rsa/common.py

andrew-kulikov/crypto

c81cf7965d58da23ce234435676c8516daf3c649

[ "MIT" ]

null

rsa/rsa/common.py

andrew-kulikov/crypto

c81cf7965d58da23ce234435676c8516daf3c649

[ "MIT" ]

null

import typing class NotRelativePrimeError(ValueError): def __init__(self, a, b, d, msg=''): super().__init__(msg or "%d and %d are not relatively prime, divider=%i" % (a, b, d)) self.a = a self.b = b self.d = d def bit_size(num: int) -> int: try: return num.bit_length() except AttributeError: raise TypeError('bit_size(num) only supports integers, not %r' % type(num)) def byte_size(number: int) -> int: if number == 0: return 1 return ceil_div(bit_size(number), 8) def ceil_div(num: int, div: int) -> int: quanta, mod = divmod(num, div) if mod: quanta += 1 return quanta def extended_gcd(a: int, b: int) -> typing.Tuple[int, int, int]: """Returns a tuple (r, i, j) such that r = gcd(a, b) = ia + jb """ # r = gcd(a,b) i = multiplicitive inverse of a mod b # or j = multiplicitive inverse of b mod a # Neg return values for i or j are made positive mod b or a respectively # Iterateive Version is faster and uses much less stack space x = 0 y = 1 lx = 1 ly = 0 oa = a # Remember original a/b to remove ob = b # negative values from return results while b != 0: q = a // b (a, b) = (b, a % b) (x, lx) = ((lx - (q * x)), x) (y, ly) = ((ly - (q * y)), y) if lx < 0: lx += ob # If neg wrap modulo orignal b if ly < 0: ly += oa # If neg wrap modulo orignal a return a, lx, ly # Return only positive values def inverse(x: int, n: int) -> int: """Returns the inverse of x % n under multiplication, a.k.a x^-1 (mod n) >>> inverse(7, 4) 3 >>> (inverse(143, 4) * 143) % 4 1 """ (divider, inv, _) = extended_gcd(x, n) if divider != 1: raise NotRelativePrimeError(x, n, divider) return inv

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79af2c5e0250f4d13af181fe19d4ed482ecdc804

12,217

py

Python

tests/unit/core/test_datasetprofile.py

bernease/whylogs-python

cfd2a2f71280537aae584cbd40a752fbe7da647b

[ "Apache-2.0" ]

null

tests/unit/core/test_datasetprofile.py

bernease/whylogs-python

cfd2a2f71280537aae584cbd40a752fbe7da647b

[ "Apache-2.0" ]

null

tests/unit/core/test_datasetprofile.py

bernease/whylogs-python

cfd2a2f71280537aae584cbd40a752fbe7da647b

[ "Apache-2.0" ]

null

import datetime import json import os from uuid import uuid4 import pytest import numpy as np from pandas import util from whylogs.core.datasetprofile import DatasetProfile, array_profile, dataframe_profile from whylogs.core.model_profile import ModelProfile from whylogs.util import time from whylogs.util.protobuf import message_to_dict, message_to_json from whylogs.util.time import to_utc_ms def test_all_zeros_returns_summary_with_stats(): stats = ("min", "max", "stddev", "mean") array = np.zeros([100, 1]) prof = array_profile(array) msg = prof.to_summary() d = message_to_dict(msg) d1 = json.loads(message_to_json(msg)) number_summary = d["columns"]["0"]["numberSummary"] missing_stats = [k for k in stats if k not in number_summary] if len(missing_stats) > 0: raise RuntimeError(f"Stats missing from number summary: {missing_stats}") assert d == d1 def test_empty_valid_datasetprofiles_empty(): now = datetime.datetime.utcnow() shared_session_id = uuid4().hex x1 = DatasetProfile(name="test", session_id=shared_session_id, session_timestamp=now, tags={ "key": "value"}, metadata={"key": "value"}, ) x2 = DatasetProfile(name="test", session_id=shared_session_id, session_timestamp=now, tags={ "key": "value"}, metadata={"key": "value"}, ) merged = x1.merge(x2) assert merged.name == "test" assert merged.session_id == shared_session_id assert merged.session_timestamp == now assert merged.columns == {} def test_merge_different_columns(): now = datetime.datetime.utcnow() shared_session_id = uuid4().hex x1 = DatasetProfile(name="test", session_id=shared_session_id, session_timestamp=now, tags={ "key": "value"}, metadata={"key": "x1"}, ) x1.track("col1", "value") x2 = DatasetProfile(name="test", session_id=shared_session_id, session_timestamp=now, tags={ "key": "value"}, metadata={"key": "x2"}, ) x2.track("col2", "value") merged = x1.merge(x2) assert merged.name == "test" assert merged.session_id == shared_session_id assert merged.session_timestamp == now assert set(list(merged.columns.keys())) == {"col1", "col2"} assert merged.columns["col1"].counters.count == 1 assert merged.columns["col2"].counters.count == 1 assert merged.tags == dict({"name": "test", "key": "value"}) assert merged.metadata == dict({"key": "x1"}) def test_merge_lhs_no_profile(): now = datetime.datetime.utcnow() shared_session_id = uuid4().hex x1 = DatasetProfile(name="test", session_id=shared_session_id, session_timestamp=now, tags={ "key": "value"}, metadata={"key": "value"}, ) x2 = DatasetProfile(name="test", session_id=shared_session_id, session_timestamp=now, tags={ "key": "value"}, metadata={"key": "value"}, model_profile=ModelProfile()) merged = x1.merge(x2) assert merged.name == "test" assert merged.session_id == shared_session_id assert merged.session_timestamp == now assert merged.columns == {} assert merged.model_profile is not None def test_merge_rhs_no_profile(): now = datetime.datetime.utcnow() shared_session_id = uuid4().hex x1 = DatasetProfile(name="test", session_id=shared_session_id, session_timestamp=now, tags={ "key": "value"}, metadata={"key": "value"}, model_profile=ModelProfile()) x2 = DatasetProfile(name="test", session_id=shared_session_id, session_timestamp=now, tags={ "key": "value"}, metadata={"key": "value"}, ) merged = x1.merge(x2) assert merged.name == "test" assert merged.session_id == shared_session_id assert merged.session_timestamp == now assert merged.columns == {} assert merged.model_profile is not None def test_merge_same_columns(): now = datetime.datetime.utcnow() shared_session_id = uuid4().hex x1 = DatasetProfile(name="test", session_id=shared_session_id, session_timestamp=now, tags={ "key": "value"}, metadata={"key": "value"}, ) x1.track("col1", "value1") x2 = DatasetProfile(name="test", session_id=shared_session_id, session_timestamp=now, tags={ "key": "value"}, metadata={"key": "value"}, ) x2.track("col1", "value1") x2.track("col2", "value") merged = x1.merge(x2) assert merged.name == "test" assert merged.session_id == shared_session_id assert merged.session_timestamp == now assert set(list(merged.columns.keys())) == {"col1", "col2"} assert merged.columns["col1"].counters.count == 2 assert merged.columns["col2"].counters.count == 1 def test_protobuf_round_trip(): now = datetime.datetime.utcnow() tags = {"k1": "rock", "k2": "scissors", "k3": "paper"} original = DatasetProfile(name="test", dataset_timestamp=now, tags=tags, ) original.track("col1", "value") original.track("col2", "value") msg = original.to_protobuf() roundtrip = DatasetProfile.from_protobuf(msg) assert roundtrip.name == "test" assert roundtrip.session_id == original.session_id assert to_utc_ms(roundtrip.session_timestamp) == to_utc_ms( original.session_timestamp) assert set(list(roundtrip.columns.keys())) == {"col1", "col2"} assert roundtrip.columns["col1"].counters.count == 1 assert roundtrip.columns["col2"].counters.count == 1 tags["name"] = "test" assert set(roundtrip.tags) == set(tags) assert roundtrip.metadata == original.metadata def test_non_string_tag_raises_assert_error(): now = datetime.datetime.utcnow() tags = {"key": "value"} x = DatasetProfile("test", now, tags=tags) x.validate() # Include a non-string tag x._tags["number"] = 1 try: x.validate() raise RuntimeError("validate should raise an AssertionError") except AssertionError: pass def test_mismatched_tags_raises_assertion_error(): now = datetime.datetime.utcnow() x1 = DatasetProfile("test", now, tags={"key": "foo"}) x2 = DatasetProfile("test", now, tags={"key": "bar"}) try: x1.merge_strict(x2) raise RuntimeError("Assertion error not raised") except AssertionError: pass def test_mismatched_tags_merge_succeeds(): now = datetime.datetime.utcnow() x1 = DatasetProfile("test", now, tags={"key": "foo"}) x2 = DatasetProfile("test2", now, tags={"key": "bar"}) result = x1.merge(x2) assert result.tags.get("key") == "foo" def test_name_always_appear_in_tags(): x1 = DatasetProfile(name="test") assert x1.tags["name"] == "test" def test_parse_delimited_from_java_single(): dir_path = os.path.dirname(os.path.realpath(__file__)) with open(os.path.join(dir_path, "output_from_java_08242020.bin"), "rb") as f: data = f.read() assert DatasetProfile.parse_delimited_single(data) is not None with open(os.path.join(dir_path, "output_from_java_01212021.bin"), "rb") as f: data = f.read() assert DatasetProfile.parse_delimited_single(data) is not None def test_parse_from_protobuf(): dir_path = os.path.dirname(os.path.realpath(__file__)) DatasetProfile.read_protobuf(os.path.join( dir_path, "output_from_java_08242020.bin")) def test_parse_delimited_from_java_multiple(): dir_path = os.path.dirname(os.path.realpath(__file__)) with open(os.path.join(dir_path, "output_from_java_08242020.bin"), "rb") as f: data = f.read() multiple = data + data result = DatasetProfile.parse_delimited(multiple) assert len(result) == 2 def test_write_delimited_single(): now = datetime.datetime.utcnow() original = DatasetProfile(name="test", session_id="test.session.id", session_timestamp=now, tags={ "key": "value"}, metadata={"key": "value"}, ) original.track("col1", "value") output_bytes = original.serialize_delimited() pos, roundtrip = DatasetProfile.parse_delimited_single(output_bytes) assert roundtrip.session_id == original.session_id # Python time precision includes nanoseconds assert time.to_utc_ms(roundtrip.session_timestamp) == time.to_utc_ms( original.session_timestamp) assert roundtrip.tags == original.tags assert roundtrip.metadata == original.metadata def test_write_delimited_multiple(): now = datetime.datetime.utcnow() original = DatasetProfile(name="test", session_id="test.session.id", session_timestamp=now, tags={ "key": "value"}, metadata={"key": "value"}, ) original.track("col1", "value") output_bytes = original.serialize_delimited() multiple_entries = output_bytes for i in range(1, 5): multiple_entries += output_bytes entries = DatasetProfile.parse_delimited(multiple_entries) assert len(entries) == 5 for entry in entries: assert entry.session_id == original.session_id # Python time precisions are different assert time.to_utc_ms(entry.session_timestamp) == time.to_utc_ms( original.session_timestamp) assert entry.tags == original.tags assert entry.metadata == original.metadata def test_verify_schema_version(): dp = DatasetProfile(name="test", session_id="test.session.id", session_timestamp=datetime.datetime.now( ), tags={"key": "value"}, metadata={"key": "value"}, ) props = dp.to_properties() assert props.schema_major_version == 1 assert props.schema_minor_version == 1 def tests_timestamp(): time = datetime.datetime.now() dp = DatasetProfile(name="test", session_id="test.session.id", session_timestamp=datetime.datetime.now( ), tags={"key": "value"}, metadata={"key": "value"}, ) time_2 = dp.session_timestamp_ms assert time_2 == int(time.replace( tzinfo=datetime.timezone.utc).timestamp() * 1000.0) def test_dataframe_profile(): time = datetime.datetime.now() df = util.testing.makeDataFrame() profile = DatasetProfile("test", time) profile.track_dataframe(df) profile_factory = dataframe_profile(df, name="test", timestamp=time) assert profile_factory.columns["A"].number_tracker.variance.mean == profile.columns[ "A"].number_tracker.variance.mean profile_factory_2 = dataframe_profile(df) assert profile_factory_2.columns["A"].number_tracker.variance.mean == profile.columns[ "A"].number_tracker.variance.mean profile_factory_3 = dataframe_profile(df, timestamp=103433) assert profile_factory_3.columns["A"].number_tracker.variance.mean == profile.columns[ "A"].number_tracker.variance.mean def test_track(): now = datetime.datetime.utcnow() original = DatasetProfile(name="test", session_id="test.session.id", session_timestamp=now, tags={ "key": "value"}, metadata={"key": "value"}, ) data = { "rows": 1, "names": "roger roger", } original.track(columns=data) def test_errors(): now = datetime.datetime.utcnow() original = DatasetProfile(name="test", session_id="test.session.id", session_timestamp=now, tags={ "key": "value"}, metadata={"key": "value"}, ) with pytest.raises(TypeError): original.track(columns=1, data=34) def test_flat_summary(): now = datetime.datetime.utcnow() original = DatasetProfile(name="test", session_id="test.session.id", session_timestamp=now, tags={ "key": "value"}, metadata={"key": "value"}, ) flat_summary = original.flat_summary() assert flat_summary is not None assert len(original.flat_summary()) == 4 def test_chunk_iterator(): now = datetime.datetime.utcnow() original = DatasetProfile(name="test", session_id="test.session.id", session_timestamp=now, tags={ "key": "value"}, metadata={"key": "value"}, ) data = { "rows": 1, "names": "roger roger", } original.track(columns=data) for each_chuck in original.chunk_iterator(): assert each_chuck is not None def test_array(): now = datetime.datetime.utcnow() original = DatasetProfile(name="test", session_id="test.session.id", session_timestamp=now, tags={ "key": "value"}, metadata={"key": "value"}, ) with pytest.raises(ValueError): original.track_array(np.random.rand(3))

35.106322

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0.681919

1,535

12,217

5.227362

0.134853

0.066176

0.045364

0.068669

0.645937

0.617896

0.600698

0.559696

0.547233

0.542498

0

0.014706

0.17623

12,217

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0.782591

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0

0.139535

0

null

0

null

0

1

0

79af4b15407f4473ba60b0d4c07074b41824263f

2,645

py

Python

canvas/cli/api.py

robinsax/canvas

6e8b9b260fdda868d687b562926a2038736ec56c

[ "Apache-2.0" ]

4

2018-01-24T01:34:39.000Z

2021-01-14T21:29:47.000Z

canvas/cli/api.py

robinsax/canvas

6e8b9b260fdda868d687b562926a2038736ec56c

[ "Apache-2.0" ]

2

2018-06-09T22:28:56.000Z

2018-06-12T01:40:10.000Z

canvas/cli/api.py

robinsax/canvas

6e8b9b260fdda868d687b562926a2038736ec56c

[ "Apache-2.0" ]

null

# coding: utf-8 ''' The CLI API definition, available to both the core and plugins. ''' import sys from ..exceptions import NotInstalled from .. import __installed__ # Define the global name to launcher function map. _launchers = dict() # Define a single character to launcher function map. _shortforms = dict() def launcher(name, **info): ''' Register a launcher function to be referenced from the CLI as `name`. An abbreviation will be automatically assigned if one is available. The `info` keyword arguments can contain one or more of: * `description` - A textual description of the launch mode. * `argspec` - A CLI argument specification. * `init` - A flag indicating a full initialization is required before the handler is invoked. ''' def launcher_wrap(func): ref_name, char = name, name[0] func.__info__ = info if char not in _shortforms: # Assign a short form alias. ref_name = ''.join(('(', char, ')', name[1:])) _shortforms[char] = func info['ref_name'] = ref_name _launchers[name] = func return func return launcher_wrap def launch_cli(args): '''Launch the CLI given the commandline arguments `args`.''' # Define the incorrect usage handler. def print_usage(): # Define the argument representation generatior. def write_one(name, launcher): ref_name = launcher.__info__['ref_name'] string = ' '.join( (''.join(('--', ref_name)), launcher.__info__.get('argspec', '')) ) string = ''.join(( string, ' '*(35 - len(string)), launcher.__info__.get('description', '') )) return string # Sort launch options alphabetically. alpha_order = sorted(_launchers.keys()) print(' '.join(( 'Usage:', 'python3 canvas [', '\n\t' + '\n\t'.join( write_one(name, _launchers[name]) for name in alpha_order ), '\n]' ))) # Exit. sys.exit(1) # Define an asserted initializer. def safe_initialize(): if not __installed__: raise NotInstalled('Run python3 canvas --init') from ..core import initialize initialize() if args and args[0] == '-!': # The -i switch causes eager initialization. safe_initialize() args = args[1:] # Nothing supplied, show usage. if not args: print_usage() # Look up the launcher. launcher = None if args[0].startswith('--'): launcher = _launchers.get(args[0][2:]) elif args[0].startswith('-'): launcher = _shortforms.get(args[0][1:]) if not launcher: print_usage() if launcher.__info__.get('init', False): # This launcher requires initialization. safe_initialize() if launcher(args[1:]) is False: # The launch function reported incorrect usage. print_usage()

25.432692

76

0.677127

345

2,645

5.023188

0.37971

0.028275

0.025967

0.024235

0

0.007955

0.19206

2,645

103

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1

0.101695

false

0

0.067797

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0.220339

0.084746

0

null

0

null

0

1

0

79b1464dc8a1a2223cbbc525bfa7851ed4e2bee9

1,408

py

Python

lego/apps/gallery/migrations/0006_auto_20171210_1610.py

ollfkaih/lego

b15aacaf09efe90e7f984d25b0e7bddbe12647e8

[ "MIT" ]

45

2017-10-24T12:09:06.000Z

2021-11-03T21:21:03.000Z

lego/apps/gallery/migrations/0006_auto_20171210_1610.py

ollfkaih/lego

b15aacaf09efe90e7f984d25b0e7bddbe12647e8

[ "MIT" ]

980

2017-10-24T12:29:07.000Z

2022-03-31T04:04:31.000Z

lego/apps/gallery/migrations/0006_auto_20171210_1610.py

wahello/lego

a0b02f3abc997fe96326e9c9c05b49847170041b

[ "MIT" ]

23

2018-04-11T16:34:22.000Z

2021-11-23T12:28:30.000Z

# Generated by Django 2.0 on 2017-12-10 16:10 import django.db.models.deletion from django.conf import settings from django.db import migrations, models class Migration(migrations.Migration): dependencies = [("gallery", "0005_auto_20170912_1708")] operations = [ migrations.AlterField( model_name="gallery", name="created_by", field=models.ForeignKey( default=None, editable=False, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name="gallery_created", to=settings.AUTH_USER_MODEL, ), ), migrations.AlterField( model_name="gallery", name="event", field=models.ForeignKey( null=True, on_delete=django.db.models.deletion.SET_NULL, related_name="galleries", to="events.Event", ), ), migrations.AlterField( model_name="gallery", name="updated_by", field=models.ForeignKey( default=None, editable=False, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name="gallery_updated", to=settings.AUTH_USER_MODEL, ), ), ]

29.333333

61

0.52983

132

1,408

5.477273

0.371212

0.055325

0.077455

0.121715

0.611342

0.547718

0.381743

0

0.034091

0.375

1,408

47

62

29.957447

0.7875

0.03054

0

0.658537

1

0

0.093177

0.016875

0

1

0

false

0

0.073171

0

0.146341

0

null

0

1

0

null

0

1

79b154f9526abf942504a9812110e0bdc124d139

1,898

py

Python

tests/run/test_config_file.py

vincent99/rio

018dac19be47ee20ae47bcd8eea71c8c4f07a1af

[ "Apache-2.0" ]

1

2019-05-28T11:32:11.000Z

tests/run/test_config_file.py

vincent99/rio

018dac19be47ee20ae47bcd8eea71c8c4f07a1af

[ "Apache-2.0" ]

null

tests/run/test_config_file.py

vincent99/rio

018dac19be47ee20ae47bcd8eea71c8c4f07a1af

[ "Apache-2.0" ]

null

from os import unlink from random import randint import util import tempfile def config_setup(stack, *configs): config_name = "tconfig" + str(randint(1000, 5000)) fp = tempfile.NamedTemporaryFile(delete=False) for c in configs: fp.write(bytes(c+"\n", 'utf8')) fp.close() util.run(f"rio config create {stack}/{config_name} {fp.name}") unlink(fp.name) return config_name def run_config(stack, config_names): name = "tsrv" + str(randint(1000, 5000)) fullName = "%s/%s" % (stack, name) cmd = (f'rio run -n {fullName}') for c in config_names: tempdir = ":/temp" + str(randint(100, 999)) cmd += " --config " + c + tempdir cmd += " nginx" print(cmd) util.run(cmd) util.run(f"rio wait {fullName}") print(name) return name def rio_chk(stack, sname): fullName = "%s/%s" % (stack, sname) inspect = util.rioInspect(fullName) out = [] for item in inspect["configs"]: out.append(item["source"]) out.sort() return out def kube_chk(stack, service_name): fullName = "%s/%s" % (stack, service_name) id = util.rioInspect(fullName, "id") namespace = id.split(":")[0] obj = util.kubectl(namespace, "deployment", service_name) out = [] for item in obj['spec']['template']['spec']['volumes']: if 'configMap' in item: out.append(str(item['configMap']['name']).split("-")[0]) out.sort() print(out) return out def test_content(stack): config_name1 = config_setup(stack, "1foo=1bar", "1foo2=1bar2") config_setup(stack, "2foo=2bar", "2foo1=2bar2") expect = [config_name1] expect.sort() servicename = run_config(stack, expect) print(stack, servicename) gotrio = rio_chk(stack, servicename) assert expect == gotrio gotk8s = kube_chk(stack, servicename) assert expect == gotk8s

21.325843

68

0.615385

245

1,898

4.685714

0.35102

0.027875

0.041812

0.039199

0.054007

0

0.028082

0.230769

1,898

88

69

21.568182

0.758219

0

0.105263

0

0.134352

0.011064

0

0.035088

1

0.087719

false

0

0.070175

0

0.22807

0.070175

0

null

0

null

0

1

0

79b1746a37fe8892a2af50ecacc257f9f91b14cd

1,090

py

Python

Semana5/lab05/tienda/models.py

SPFelipe/TECSUP-DAE-2021-2

ec218d0a7fa66a73e3e0a8889e325cf2ce2a74d3

[ "MIT" ]

null

Semana5/lab05/tienda/models.py

SPFelipe/TECSUP-DAE-2021-2

ec218d0a7fa66a73e3e0a8889e325cf2ce2a74d3

[ "MIT" ]

null

Semana5/lab05/tienda/models.py

SPFelipe/TECSUP-DAE-2021-2

ec218d0a7fa66a73e3e0a8889e325cf2ce2a74d3

[ "MIT" ]

null

from django.db import models # Create your models here. class Categoria(models.Model): nombre = models.CharField(max_length=200) pub_date = models.DateTimeField('Fecha Creación') def __str__(self): return self.nombre class Product(models.Model): categoria = models.ForeignKey(Categoria, on_delete=models.CASCADE) nombre = models.CharField(max_length=200) precio = models.DecimalField(max_digits=6, decimal_places=2) stock = models.IntegerField(default=0) pub_date = models.DateTimeField('date published') def __str__(self): return self.nombre class Cliente(models.Model): nombre = models.CharField(max_length=30) apellido = models.CharField(max_length=30) dni = models.CharField(max_length=8) telefono = models.CharField(max_length=9) direccion = models.CharField(max_length=50) email = models.EmailField(max_length=100) fecha_nacimiento = models.DateField("Fecha de Nacimiento") pub_date = models.DateTimeField('Fecha Creación') def __str__(self): return self.nombre+ " " + self.apellido

34.0625

70

0.725688

136

1,090

5.617647

0.419118

0.094241

0.164921

0.219895

0.408377

0.371728

0.324607

0.170157

0

0.022075

0.168807

1,090

32

71

34.0625

0.821192

0.022018

0

0.36

0

0.058216

0

1

0.12

false

0

0.04

0.12

1

0

null

0

1

0

null

0

1

0

2

79b4abdae3efe0c4ff65adf1b7ab722b6fbb2d46

71

py

Python

PyUdemy/Day1/variables.py

JoseArtur/phyton-exercices

f3da4447044e445222233960f991fb2e36311131

[ "MIT" ]

null

PyUdemy/Day1/variables.py

JoseArtur/phyton-exercices

f3da4447044e445222233960f991fb2e36311131

[ "MIT" ]

null

PyUdemy/Day1/variables.py

JoseArtur/phyton-exercices

f3da4447044e445222233960f991fb2e36311131

[ "MIT" ]

null

a=input("a= ") b=input("b= ") aa=a a=b b=aa print("a=",a) print("b=",b)

10.142857

14

0.507042

18

71

2

0.277778

0.111111

0

0.112676

71

7

15

10.142857

0.571429

0

0.138889

0

1

0

false

0

0.285714

1

0

null

0

1

0

null

0

2

79b4dd93658058c4c08b578922c3ee4d84c4d4e5

5,548

py

Python

vivareal.py

erlancassiano/portal_crawler

bcbda7871d74080b926b0f59c05d813385286173

[ "MIT" ]

null

vivareal.py

erlancassiano/portal_crawler

bcbda7871d74080b926b0f59c05d813385286173

[ "MIT" ]

null

vivareal.py

erlancassiano/portal_crawler

bcbda7871d74080b926b0f59c05d813385286173

[ "MIT" ]

null

import os import datetime import csv import time import random from time import sleep from selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.support import expected_conditions as EC from selenium.webdriver.common.by import By from selenium.common.exceptions import NoSuchElementException from selenium.common.exceptions import StaleElementReferenceException import undetected_chromedriver as uc class Vivareal: timestamp = str(datetime.datetime.now()).replace(".","").replace("-","").replace(":","") filename = "results_{}".format(timestamp)+".csv" chromeOptions = uc.ChromeOptions() #chromeOptions.add_argument('--headless') driver = uc.Chrome(options=chromeOptions) def __init__(self): self.csvCreater() url = "https://www.vivareal.com.br/aluguel" self.driver.get(url) while True: # driver.implicitly_wait(10) WebDriverWait(self.driver, 60).until(EC.presence_of_element_located((By.CSS_SELECTOR, 'span.property-card__title.js-cardLink.js-card-title'))) self.ScrollPage() result_div = self.driver.find_element_by_css_selector(".results-list.js-results-list") result_cards_list = result_div.find_elements_by_css_selector("article.property-card__container.js-property-card") for item in result_cards_list: try: title = item.find_element_by_css_selector("span.property-card__title.js-cardLink.js-card-title").text except NoSuchElementException: title = "-" try: address = item.find_element_by_css_selector("span.property-card__address").text except NoSuchElementException: address = "-" try: price = item.find_element_by_css_selector(".property-card__price.js-property-card-prices.js-property-card__price-small").text except: price = "-" try: price_details = item.find_element_by_css_selector(".property-card__price-details--condo") price_details = str(price_details.text).replace("Condomínio:","").strip() except NoSuchElementException: price_details = "-" try: area = item.find_element_by_css_selector("li.property-card__detail-item.property-card__detail-area") area = str(area.text).replace(" ","").strip() except NoSuchElementException: area = "-" try: rooms = item.find_element_by_css_selector("li.property-card__detail-item.property-card__detail-room.js-property-detail-rooms") rooms = str(rooms.text).replace(" Quarto","").replace("s","").strip() except NoSuchElementException: rooms = "-" try: garages = item.find_element_by_css_selector("li.property-card__detail-item.property-card__detail-garage.js-property-detail-garages") garages = str(garages.text).replace("Vaga","").replace("s","").strip() except NoSuchElementException: garages = "-" try: bathrooms = item.find_element_by_css_selector("li.property-card__detail-item.property-card__detail-bathroom.js-property-detail-bathroom") bathrooms = str(bathrooms.text).replace(" Banheiro","").replace("s","").strip() except NoSuchElementException: bathrooms = "-" self.csvupdate(title,address,price,price_details,area,rooms,bathrooms,garages) print(title,"\n",address,"\n",price,"\n",price_details,"\n",area,"\n",rooms,"\n",bathrooms,"\n",garages,"\n\n") self.driver.find_element_by_xpath("//a[@class='js-change-page' and contains(text(), 'Próxima página')]").click() time.sleep(5) def csvCreater(self): with open(self.filename,'w' ,newline='') as file: fieldNames = ['Title','Address','Rent','Admin Fee','Area','Rooms','Bathrooms','Parking'] thewriter = csv.DictWriter(file, fieldnames=fieldNames) thewriter.writeheader() def csvupdate(self,title,address,price,price_details,area,rooms,bathrooms,garages): with open(self.filename,'a' ,newline='') as file: fieldNames = ['Title','Address','Rent','Admin Fee','Area','Rooms','Bathrooms','Parking'] thewriter = csv.DictWriter(file, fieldnames=fieldNames) thewriter.writerow({'Title': str(title),'Address': str(address),'Rent': price,'Admin Fee': price_details,'Area': area,'Rooms':rooms,'Bathrooms': bathrooms,'Parking': garages}) def ScrollPage(self): lenOfPage = self.driver.execute_script("window.scrollTo(0, document.body.scrollHeight);var lenOfPage=document.body.scrollHeight;return lenOfPage;") match=False while(match==False): lastCount = lenOfPage sleep(3) lenOfPage = self.driver.execute_script("window.scrollTo(0, document.body.scrollHeight);var lenOfPage=document.body.scrollHeight;return lenOfPage;") if lastCount==lenOfPage: match=True bot = Vivareal()

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null

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null

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79b52191dc7ea7de4c6237513c7af4e22ce1b28f

3,339

py

Python

seahub/base/profile.py

gzy403999903/seahub

992e5852579a6d9e0cfdaf18c77ce0191cb64449

[ "Apache-2.0" ]

null

seahub/base/profile.py

gzy403999903/seahub

992e5852579a6d9e0cfdaf18c77ce0191cb64449

[ "Apache-2.0" ]

6

2019-12-13T09:55:45.000Z

2022-03-11T23:47:29.000Z

seahub/base/profile.py

gzy403999903/seahub

992e5852579a6d9e0cfdaf18c77ce0191cb64449

[ "Apache-2.0" ]

1

2019-05-16T06:58:16.000Z

# Copyright (c) 2012-2016 Seafile Ltd. """ The MIT License (MIT) Copyright (c) 2013 Omar Bohsali 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. """ try: import cProfile as profile except ImportError: import profile import pstats from cStringIO import StringIO from django.conf import settings class ProfilerMiddleware(object): """ Simple profile middleware to profile django views. To run it, add ?prof to the URL like this: http://localhost:8000/view/?__prof__=true Optionally pass the following to modify the output: ?sort => Sort the output by a given metric. Default is time. See http://docs.python.org/2/library/profile.html#pstats.Stats.sort_stats for all sort options. quick reference: - time: sort by function execution time - cum: the cumulative time spent in this and all subfunctions (from invocation till exit). This figure is accurate even for recursive functions. ?count => The number of rows to display. Default is 100. ?fullpath=<true|false> default false. True to show full path of the source file of each function ?callee=<true|false> default false. True to show the time of a function spent on its sub function. This is adapted from an example found here: http://www.slideshare.net/zeeg/django-con-high-performance-django-presentation. """ def can(self, request): return settings.DEBUG and request.GET.get('__prof__', False) == 'true' def process_view(self, request, callback, callback_args, callback_kwargs): if self.can(request): self.profiler = profile.Profile() args = (request,) + callback_args return self.profiler.runcall(callback, *args, **callback_kwargs) def process_response(self, request, response): if self.can(request): self.profiler.create_stats() io = StringIO() stats = pstats.Stats(self.profiler, stream=io) if not request.GET.get('fullpath', False): stats.strip_dirs() stats.sort_stats(request.GET.get('sort', 'time')) if request.GET.get('callee', False): stats.print_callees() stats.print_stats(int(request.GET.get('count', 100))) response.content = '<pre>%s</pre>' % io.getvalue() return response

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null

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null

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79b68a6d1a405ace81c0d0f659613828d57db047

3,203

py

Python

data/make_stterror_data/tts.py

gcunhase/StackedDeBERT

82777114fd99cafc6e2a3d760e774f007c563245

[ "MIT" ]

32

2020-01-03T09:53:03.000Z

2021-09-07T07:23:26.000Z

data/make_stterror_data/tts.py

gcunhase/StackedDeBERT

82777114fd99cafc6e2a3d760e774f007c563245

[ "MIT" ]

null

data/make_stterror_data/tts.py

gcunhase/StackedDeBERT

82777114fd99cafc6e2a3d760e774f007c563245

[ "MIT" ]

6

2020-01-21T06:50:21.000Z

2021-01-22T08:04:00.000Z

import data.make_stterror_data.utils as utils import os import sys import subprocess # TTS imports from gtts import gTTS import pyttsx3 # sys.path.append("~/PycharmProjects/pyfestival") # https://github.com/techiaith/pyfestival/pull/4 # import festival __author__ = 'Gwena Cunha' """ Text-To-Speech Module """ class TTS: def __init__(self, data_dir="", result_dir="", audio_type=".wav"): print("Initializing TTS Module") self.audio_type = audio_type self.project_dir = utils.project_dir_name() self.data_dir = data_dir self.result_dir = result_dir utils.ensure_dir(self.project_dir + self.result_dir) def set_data_dir(self, data_dir): self.data_dir = data_dir def set_result_dir(self, result_dir): self.result_dir = result_dir def read_text(self, text, sentence_id, tts_type="gtts"): if "macsay" in tts_type: # Mac self.audio_from_mac_say(text, sentence_id) else: # google gtts self.audio_from_google(text, sentence_id) def audio_from_google(self, text, sentence_id): gtts = gTTS(text=text, lang='en') # , slow=False) partial_saved_audio_filename = self.project_dir + self.result_dir + "gtts_" + sentence_id tmp_saved_audio_filename = partial_saved_audio_filename + "_tmp" + self.audio_type final_saved_audio_filename = partial_saved_audio_filename + self.audio_type gtts.save(tmp_saved_audio_filename) # fix_missing_riff_header = "ffmpeg - i "+tmp_saved_audio_filename+" -y "+final_saved_audio_filename # Making ffmpeg quieter (less verbose): ffmpeg -nostats -loglevel 0 -i 2.mp3 ~/PycharmProjects/STTError/assets/2.mp3 subprocess.call( ["ffmpeg", "-nostats", "-loglevel", "0", "-i", tmp_saved_audio_filename, "-y", final_saved_audio_filename]) # os.system("ffmpeg -nostats -loglevel 0 -i {} -y {}".format(tmp_saved_audio_filename, final_saved_audio_filename)) # Remove tmp_saved_audio_filename subprocess.call(["rm", tmp_saved_audio_filename]) return final_saved_audio_filename def audio_from_mac_say(self, text, sentence_id): """ Mac's say command: Mac Systems """ for voice in ['Fred']: # ['Alex', 'Fred', 'Victoria'] partial_saved_audio_filename = self.project_dir + self.result_dir + "macsay_" + sentence_id tmp_saved_audio_filename = partial_saved_audio_filename + "_tmp.aiff" final_saved_audio_filename = partial_saved_audio_filename + self.audio_type subprocess.call(["say", "-o", tmp_saved_audio_filename, "-v", voice, text]) subprocess.call(["ffmpeg", "-nostats", "-loglevel", "0", "-i", tmp_saved_audio_filename, "-y", final_saved_audio_filename]) # os.system("say -o {} -v {} {}".format(tmp_saved_audio_filename, voice, text)) # os.system("ffmpeg -nostats -loglevel 0 -i {} -y {}".format(tmp_saved_audio_filename, final_saved_audio_filename)) # Remove tmp_saved_audio_filename subprocess.call(["rm", tmp_saved_audio_filename]) return final_saved_audio_filename

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null

0

1

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null

0

1

79b7a93216b116c4fe2b33e6f3183397b498a763

1,047

py

Python

year2019/day21/code.py

romainvigneres/advent_of_code

2ae38617706cb1041ab3950cdec3713176dc3633

[ "MIT" ]

null

year2019/day21/code.py

romainvigneres/advent_of_code

2ae38617706cb1041ab3950cdec3713176dc3633

[ "MIT" ]

null

year2019/day21/code.py

romainvigneres/advent_of_code

2ae38617706cb1041ab3950cdec3713176dc3633

[ "MIT" ]

null

from year2019.intcode_v2 import Intcode from common import input_integer_sep def part_one(inp_list): program1 = ( "NOT A J\n" "NOT B T\n" "OR T J\n" "NOT C T\n" "OR T J\n" "AND D J\n" "WALK\n" ) p = Intcode( inp_list, [ord(char) for char in program1] ) while not p.done: out = p.run_until_output() if out > 999: return out def part_two(inp_list): program2 = ( "NOT C J\n" "NOT B T\n" "OR T J\n" "NOT A T\n" "OR T J\n" "AND D J\n" "NOT E T\n" "NOT T T\n" "OR H T\n" "AND T J\n" "RUN\n" ) p = Intcode( inp_list, [ord(char) for char in program2] ) while not p.done: out = p.run_until_output() if out > 257: return out def get_result(): inp = input_integer_sep("2019", "21") print("Part one", part_one(inp.copy())) print("Part two", part_two(inp.copy()))

20.529412

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2.92638

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0.041929

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0.403056

1,047

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0.043478

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null

0

null

0

1

0

79b7cbd53300df46238acfe16835276ec2f45c5e

2,252

py

Python

july/management/commands/fix_locations.py

kanika-art/julython.org

557b29e5d69a772b684fb6073a616f06b97d0a48

[ "MIT" ]

7

2015-07-01T18:01:40.000Z

2019-12-27T02:04:07.000Z

july/management/commands/fix_locations.py

kanika-art/julython.org

557b29e5d69a772b684fb6073a616f06b97d0a48

[ "MIT" ]

6

2015-07-01T11:32:34.000Z

2021-06-10T20:35:32.000Z

july/management/commands/fix_locations.py

kanika-art/julython.org

557b29e5d69a772b684fb6073a616f06b97d0a48

[ "MIT" ]

10

2015-07-01T11:20:35.000Z

2020-10-02T18:58:07.000Z

import logging from django.core.management.base import BaseCommand from django.template.defaultfilters import slugify from july.models import User from july.people.models import Location from july.utils import check_location from optparse import make_option class Command(BaseCommand): help = 'fix locations' option_list = BaseCommand.option_list + ( make_option( '--commit', action='store_true', dest='commit', default=False, help='Actually move the items.'), ) def handle(self, *args, **options): commit = options['commit'] empty = 0 fine = 0 fixable = 0 bad = [] for location in Location.objects.all(): user_count = User.objects.filter(location=location).count() if not user_count: logging.info("Empty location: %s", location) if commit: location.delete() logging.info('Deleted') empty += 1 continue l = check_location(location.name) if l == location.name: logging.info('Location fine: %s', location) fine += 1 continue if not commit: if l: fixable += 1 else: bad.append((location, user_count)) continue elif l is not None: new_loc = Location.create(l) User.objects.filter(location=location).update(location=new_loc) user_count = User.objects.filter(location=location).count() if not user_count: logging.error("missed users!") else: location.delete() elif l is None: logging.info('Bad location: %s', location) location.approved = False location.save() if not commit: [logging.error('Bad Loc: %s, count: %s', l, c) for l, c in bad] logging.info('Empty: %s, Fine: %s, fixable: %s', empty, fine, fixable) logging.info('Add --commit to fix locations')

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2,252

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0

0.116667

0

0.183333

0

null

0

null

0

1

0

79b9401f776d3b7758cf615a45ec455370cc2331

42

py

Python

tests/identifiers/__init__.py

jparsai/cvejob

8f9462a1ecdf1d4de877ac5f44e772239ffcb379

[ "Apache-2.0" ]

8

2019-09-25T14:45:28.000Z

2021-11-08T10:30:03.000Z

tests/identifiers/__init__.py

jparsai/cvejob

8f9462a1ecdf1d4de877ac5f44e772239ffcb379

[ "Apache-2.0" ]

113

2018-07-10T12:58:16.000Z

2020-12-09T22:33:15.000Z

tests/identifiers/__init__.py

jparsai/cvejob

8f9462a1ecdf1d4de877ac5f44e772239ffcb379

[ "Apache-2.0" ]

12

2018-07-10T11:00:02.000Z

2021-01-27T12:19:56.000Z

"""Tests for package name identifiers."""

21

41

0.714286

5

42

6

1

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42

1

42

0.810811

0.833333

0

null

0

null

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null

0

null

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null

true

0

null

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null

0

1

0

1

0

null

0

1

0

5

79b9db085b3980a703000d7ded2a0b497ec1fcdd

6,338

py

Python

core/feature/gps_location_daywise/gps_location_daywise.py

MD2Korg/CerebralCortex-DataAnalysis

73f5ea2430bc7c23de422dccb7b65ef9f8917595

[ "BSD-2-Clause" ]

1

2018-04-24T18:11:24.000Z

core/feature/gps_location_daywise/gps_location_daywise.py

Boris69bg/CerebralCortex-DataAnalysis

49565bdff348d69153bd5d3a37e73f1645f82b32

[ "BSD-2-Clause" ]

10

2018-03-13T19:04:09.000Z

2018-05-12T01:40:03.000Z

core/feature/gps_location_daywise/gps_location_daywise.py

Boris69bg/CerebralCortex-DataAnalysis

49565bdff348d69153bd5d3a37e73f1645f82b32

[ "BSD-2-Clause" ]

42

2017-12-07T17:08:14.000Z

2019-06-02T08:25:12.000Z

# Copyright (c) 2018, MD2K Center of Excellence # - Alina Zaman <azaman@memphis.edu> # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright notice, this # list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE # DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE # FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL # DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR # SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER # CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. from cerebralcortex.core.data_manager.raw.stream_handler import DataSet from cerebralcortex.cerebralcortex import CerebralCortex from cerebralcortex.core.datatypes.datastream import DataStream from cerebralcortex.core.datatypes.datastream import DataPoint from datetime import datetime, timedelta, time from core.computefeature import ComputeFeatureBase from typing import List import pprint as pp import numpy as np import pdb import pickle import uuid import json import traceback feature_class_name = 'GpsLocationDaywise' GPS_EPISODES_AND_SEMANTIC_lOCATION_STREAM = "org.md2k.data_analysis.gps_episodes_and_semantic_location_from_model" class GpsLocationDaywise(ComputeFeatureBase): """ Produce feature from gps location from "org.md2k.data_analysis.gps_episodes_and_semantic_location" data stream. One data point is split into two when it starts from one day and ends in other day. In that way, we are getting semantic location of daily data """ def listing_all_gps_location_daywise(self, user_id: str, all_days: List[str]): """ Produce and save the gps location of participant's in day basis :param str user_id: UUID of the stream owner :param List(str) all_days: All days of the user in the format 'YYYYMMDD' """ self.CC.logging.log('%s started processing for user_id %s' % (self.__class__.__name__, str(user_id))) gps_data = [] stream_ids = self.get_latest_stream_id(user_id, GPS_EPISODES_AND_SEMANTIC_lOCATION_STREAM) for stream_id in stream_ids: for day in all_days: location_data_stream = \ self.CC.get_stream(stream_id["identifier"], user_id, day, localtime=False) for data in set(location_data_stream.data): if data.start_time.date() != data.end_time.date(): temp = DataPoint(data.start_time, data.end_time, data.offset, data.sample) start_day = data.start_time.date() end_time = datetime.combine(start_day, time.max) end_time = end_time.replace(tzinfo=data.start_time.tzinfo) temp.end_time = end_time gps_data.append(temp) end_day = data.end_time.date() start_day += timedelta(days=1) while start_day != end_day: temp = DataPoint(data.start_time, data.end_time, data.offset, data.sample) start_time = datetime.combine(start_day, time.min) start_time = start_time.replace(tzinfo=data.start_time.tzinfo) temp.start_time = start_time end_time = datetime.combine(start_day, time.max) end_time = end_time.replace(tzinfo=data.start_time.tzinfo) temp.end_time = end_time gps_data.append(temp) start_day += timedelta(days=1) temp = DataPoint(data.start_time, data.end_time, data.offset, data.sample) start_time = datetime.combine(start_day, time.min) start_time = start_time.replace(tzinfo=data.start_time.tzinfo) temp.start_time = start_time gps_data.append(temp) else: gps_data.append(data) try: if len(gps_data): streams = self.CC.get_user_streams(user_id) for stream_name, stream_metadata in streams.items(): if stream_name == GPS_EPISODES_AND_SEMANTIC_lOCATION_STREAM: self.store_stream(filepath="gps_location_daywise.json", input_streams=[stream_metadata], user_id=user_id, data=gps_data) break except Exception as e: self.CC.logging.log("Exception:", str(e)) self.CC.logging.log(traceback.format_exc()) self.CC.logging.log('%s finished processing for user_id %s saved %d ' 'data points' % (self.__class__.__name__, str(user_id), len(gps_data))) def process(self, user_id: str, all_days: List[str]): """ Main processing function inherited from ComputerFeatureBase :param str user_id: UUID of the user :param List(str) all_days: List of days with format 'YYYYMMDD' """ if self.CC is not None: self.CC.logging.log("Processing Working Days") self.listing_all_gps_location_daywise(user_id, all_days)

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0.057998

0.021749

0

1

0.026667

false

0

0.186667

0

0.226667

0.013333

0

null

0

null

0

1

0

79bc0b508670cb51847c1e5dbe41b345258f4db3

263

py

Python

Python Advanced/Advanced/Multidimensional Lists/Lab/Task04.py

IvanTodorovBG/SoftUni

7b667f6905d9f695ab1484efbb02b6715f6d569e

[ "MIT" ]

1

2022-03-16T10:23:04.000Z

Python Advanced/Advanced/Multidimensional Lists/Lab/Task04.py

IvanTodorovBG/SoftUni

7b667f6905d9f695ab1484efbb02b6715f6d569e

[ "MIT" ]

null

Python Advanced/Advanced/Multidimensional Lists/Lab/Task04.py

IvanTodorovBG/SoftUni

7b667f6905d9f695ab1484efbb02b6715f6d569e

[ "MIT" ]

null

rows, columns = [int(x) for x in input().split(", ")] matrix = [[int(i) for i in input().split()] for _ in range(rows)] for column in range(columns): sum_column = 0 for row in range(rows): sum_column += matrix[row][column] print(sum_column)

26.3

65

0.619772

42

263

3.785714

0.380952

0.132075

0.150943

0

0.004854

0.21673

263

9

66

29.222222

0.76699

0

0.007605

0

1

0

false

0

0.142857

0

null

0

1

0

null

0

1

79bc48970d40d17cad24372399c2613c8f57a896

2,939

py

Python

benchmark/invocation.py

zanderhavgaard/thesis-code

d9f193e622b8b98ec88c33006f8e0e1dbb3d17fc

[ "MIT" ]

null

benchmark/invocation.py

zanderhavgaard/thesis-code

d9f193e622b8b98ec88c33006f8e0e1dbb3d17fc

[ "MIT" ]

2

2020-04-28T07:59:30.000Z

2020-05-17T15:36:04.000Z

benchmark/invocation.py

zanderhavgaard/thesis-code

d9f193e622b8b98ec88c33006f8e0e1dbb3d17fc

[ "MIT" ]

null

import sys import uuid import psutil import time from datetime import datetime # remove for production from pprint import pprint from functools import reduce import function_lib as lib class Invocation: def __init__(self, exp_uuid: str, root: str, data: dict): self.exp_id = exp_uuid self.root_identifier = root # parse data to self self.parse_data(data) def get_data(self): return self.__dict__ def dev_print(self): pprint(self.get_data()) def parse_data(self, data: dict): for i in map(lambda x: setattr(self, x, data[x]), list(data)): pass # invocation can be either a success or an error, this will be marked if('error' in data): self.is_error = True self.type = lib.str_replace(self.error['type'],[('\'',''),('\"','')]) self.trace = lib.str_replace(self.error['trace'],[('\'',''),('\"','')]) self.message = lib.str_replace( self.error['message'], [('\'',''),('\"','')]) delattr(self, 'error') else: self.is_error = False self.execution_total = self.execution_end - self.execution_start self.invocation_total = self.invocation_end - self.invocation_start def create_monolith_query(self, invo_dict:dict): keys = 'exp_id,invo_id,seed,function_argument,function_called,monolith_result' values = """'{0}','{1}',{2},{3},'{4}','{5}'""".format( self.exp_id, invo_dict['identifier'], invo_dict.pop('seed'), invo_dict.pop('function_argument'), invo_dict.pop('function_called'), invo_dict.pop('monolith_result')) if 'process_time_matrix' in invo_dict: keys += ',process_time_matrix,running_time_matrix' values += """,{0},{1}""".format(invo_dict.pop('process_time_matrix'),invo_dict.pop('running_time_matrix')) return [f'INSERT INTO Monolith ({keys}) VALUES ({values});'] def get_query_string(self): key_values = self.__dict__.copy() monolith = [] if 'monolith_result' not in key_values else self.create_monolith_query(key_values) is_error = key_values.pop('is_error') list(map(lambda x: x if x[1] != None else key_values.pop(x[0]), key_values.copy().items())) (keys,vals) = reduce(lambda x,y: ( f'{x[0]}{y[0]},', f'{x[1]}{y[1]},') if not isinstance(y[1],str) else ( f'{x[0]}{y[0]},', f"""{x[1]}'{y[1]}',""") ,[('','')] + list(key_values.items())) return ['INSERT INTO {0} ({1}) VALUES ({2});'.format('Error' if is_error else 'Invocation', keys[:-1], vals[:-1])]+monolith

40.819444

131

0.543042

361

2,939

4.204986

0.277008

0.047431

0.043478

0.033597

0.056653

0.013175

0

0.011667

0.300102

2,939

71

132

41.394366

0.7263

0.036747

0

0.171621

0.04954

0

1

0.117647

false

0.019608

0.156863

0.019608

0.352941

0.058824

0

null

0

null

0

1

0

79bc840d240556a6ff5b245bea2de77b90bf2da0

470

py

Python

mysite/news/forms.py

rsg33/testsite

939e5c25f2e128c30d4a8593337059971587dd3c

[ "MIT" ]

null

mysite/news/forms.py

rsg33/testsite

939e5c25f2e128c30d4a8593337059971587dd3c

[ "MIT" ]

null

mysite/news/forms.py

rsg33/testsite

939e5c25f2e128c30d4a8593337059971587dd3c

[ "MIT" ]

null

from django import forms from .models import News class NewsForm(forms.ModelForm): class Meta: model = News # fields = '__all__' fields = ['title', 'content', 'is_published', 'category'] widgets = { 'title': forms.TextInput(attrs={'class': 'form-control'}), 'content': forms.Textarea(attrs={'class': 'form-control', 'rows': 5}), 'category': forms.Select(attrs={'class': 'form-control'}) }

33.571429

82

0.574468

48

470

5.520833

0.5625

0.113208

0.158491

0.237736

0

0.002865

0.257447

470

14

83

33.571429

0.756447

0.038298

0

0.237251

0

1

0

false

0

0.181818

0

0.363636

0

null

0

1

0

null

0

1

79bcabef33a714ea5bd9e55eb07ea14a99365d51

4,123

py

Python

src/pymor/tools/io/vtk.py

kinnala/pymor

9d2a8ee5f7a71482e62952257332d269d50678e9

[ "Unlicense" ]

2

2022-03-22T11:47:12.000Z

2022-03-22T11:48:23.000Z

src/pymor/tools/io/vtk.py

kinnala/pymor

9d2a8ee5f7a71482e62952257332d269d50678e9

[ "Unlicense" ]

14

2022-01-05T09:25:11.000Z

2022-03-31T17:07:10.000Z

src/pymor/tools/io/vtk.py

moro1111/pymor

aa03f2521ee3c7b8a9e7da4cb109caea4c788b29

[ "Unlicense" ]

1

2022-03-28T10:58:18.000Z

# This file is part of the pyMOR project (https://www.pymor.org). # Copyright pyMOR developers and contributors. All rights reserved. # License: BSD 2-Clause License (https://opensource.org/licenses/BSD-2-Clause) from pymor.core.config import config config.require('VTKIO') from pathlib import Path import meshio from xml.etree.ElementTree import fromstring from collections import OrderedDict from xmljson import BadgerFish from lxml import etree from pymor.core.exceptions import IOLibsMissing def _read_collection(xml, metadata_key): collection = xml['VTKFile']['Collection'] files = collection['DataSet'] data = [(fl[f'@{metadata_key}'], _read_single(fl['@file'])) for fl in files] data.sort(key=lambda t: t[0]) return data def _read_single(filename): mesh = meshio.read(filename) assert len(mesh.points) return mesh def _get_collection_data(filename): path = Path(filename) assert path.is_file() bf = BadgerFish(dict_type=OrderedDict) return path, bf.data(fromstring(open(path, 'rb').read())) def _get_vtk_type(path): """Parse given file until a VTKFile element is found. We use the incremental event emitting parser here since we can expect to encounter appended binary data in the xml which lxml cannot parse. Parameters ---------- path vtk file to peek into Returns ------- None if no VTKFile element found, else the type attribute of the VTKFile element """ parser = etree.XMLPullParser(events=('start',)) with open(path, 'rb') as xml: for lines in xml.readlines(): parser.feed(lines) for action, element in parser.read_events(): if element.tag == 'VTKFile': return element.get('type') return None def read_vtkfile(filename, metadata_key='timestep'): """Try to read a given file into a Sequence of meshio.Mesh instances Parameters ---------- metadata_key Which metadata to extract and return alongside the meshio.Mesh instances. Returns ------- A list of (metadata_value, meshio.Mesh) tuples. The length of the list is either 1 for a singular vtk/vtu/vtp input file (None is returned as metadata), or however many members are in the collection file (pvd). """ from pymor.tools.io import change_to_directory vtk_type = _get_vtk_type(filename) if vtk_type == 'Collection': path, xml = _get_collection_data(filename) with change_to_directory(path.parent): return _read_collection(xml, metadata_key=metadata_key) return [(None, _read_single(filename, vtk_type))] def write_vtk_collection(filename_base, meshes, metadata=None): """Output grid-associated data in vtk format filename_base common component for output files in collection meshes Sequence of meshio.Mesh objects metadata dict of {key1: sequence1, key2: sequence2} where sequence must be of len(meshes) or len == 1 currently supported keys are "timestep", "name", "group" and "part" used to describe datapoints in Vtk collection file defaults to { 'timestep': list(range(len(meshes))) } Returns ------- full filename of saved file """ if not config.HAVE_VTKIO: raise IOLibsMissing() from pyevtk.vtk import VtkGroup fn_tpl = '{}_{:08d}.vtu' metadata = metadata or {'timestep': list(range(len(meshes)))} def _meta(key, i): if key in metadata.keys(): return metadata[key][0] if len(metadata[key]) == 1 else metadata[key][i] # carry over defaults from pyevtk to not break backwards compat return {'timestep': 0, 'group': '', 'name': '', 'part': '0'}[key] group = VtkGroup(filename_base) for i, mesh in enumerate(meshes): fn = fn_tpl.format(filename_base, i) mesh.write(fn) group.addFile(filepath=fn, sim_time=_meta('timestep', i), group=_meta('group', i), name=_meta('name', i), part=_meta('part', i)) group.save() return f'{filename_base}.pvd'

32.464567

113

0.66408

551

4,123

4.865699

0.352087

0.036927

0.00746

0.01865

0.040283

0

0.004717

0.228717

4,123

126

114

32.722222

0.838365

0.369634

0

0.06953

0

0.033898

1

0.118644

false

0

0.169492

0

0.457627

0

null

0

null

0

1

0

79be6504986834ffb4fc631ea1dcce3c139c7fb4

161

py

Python

wsmode/exceptions.py

29527/OKExPyWebsocket

d084373e0bf18ca533bcc8f4fc1ba051d6be0209

[ "MIT" ]

2

2021-08-20T10:01:22.000Z

2021-11-07T21:41:35.000Z

wsmode/exceptions.py

29527/OKExPyWebsocket

d084373e0bf18ca533bcc8f4fc1ba051d6be0209

[ "MIT" ]

null

wsmode/exceptions.py

29527/OKExPyWebsocket

d084373e0bf18ca533bcc8f4fc1ba051d6be0209

[ "MIT" ]

3

2021-08-18T09:07:15.000Z

2022-03-11T08:09:06.000Z

class MessageTypeNotExist(Exception): """ 消息类型不存在 """ pass class TopicMessageTypeNotExist(Exception): """ 主题消息类型不存在 """ pass

10.733333

42

0.590062

10

161

9.5

0.7

0

0.298137

161

14

43

11.5

0.840708

0.10559

0

0.5

0

1

0

true

0.5

0

0.5

0

1

0

null

0

null

0

1

0

3

79bf7da39552fc06aca988e7aec5e90f151e4f97

159

py

Python

cart/admin.py

shaongitt/boihut

d93c6b503dd7ad4f37dc572a6dec21f593bf7b35

[ "BSD-2-Clause" ]

null

cart/admin.py

shaongitt/boihut

d93c6b503dd7ad4f37dc572a6dec21f593bf7b35

[ "BSD-2-Clause" ]

null

cart/admin.py

shaongitt/boihut

d93c6b503dd7ad4f37dc572a6dec21f593bf7b35

[ "BSD-2-Clause" ]

null

from django.contrib import admin from .models import Cart,CartItems # Register your models here. admin.site.register(CartItems) admin.site.register(Cart)

26.5

35

0.792453

22

159

5.727273

0.545455

0.142857

0.269841

0

0.125786

159

5

36

31.8

0.906475

0.163522

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

79c3715dd99e77bde511c274c350ef404bb7cf2e

1,887

py

Python

models/vanilla_cnn.py

mamaheux/pytorch-exemple-calcul-canada

41bd1769aaf30bd3786589bd3e3252bb115fdd69

[ "MIT" ]

null

models/vanilla_cnn.py

mamaheux/pytorch-exemple-calcul-canada

41bd1769aaf30bd3786589bd3e3252bb115fdd69

[ "MIT" ]

null

models/vanilla_cnn.py

mamaheux/pytorch-exemple-calcul-canada

41bd1769aaf30bd3786589bd3e3252bb115fdd69

[ "MIT" ]

null

import torch.nn as nn from models.blocks import GlobalAvgPool2d class _VanillaConvBlock(nn.Module): def __init__(self, in_channels, out_channels, kernel_size): super(_VanillaConvBlock, self).__init__() self._block = nn.Sequential( nn.Conv2d(in_channels, out_channels, kernel_size, stride=1, padding=kernel_size // 2, bias=False), nn.BatchNorm2d(out_channels), nn.ReLU(inplace=True) ) def forward(self, x): return self._block(x) class VanillaCnn(nn.Module): def __init__(self, class_count=10, use_softmax=True): super(VanillaCnn, self).__init__() self._features = nn.Sequential(_VanillaConvBlock(in_channels=3, out_channels=8, kernel_size=3), _VanillaConvBlock(in_channels=8, out_channels=16, kernel_size=3), nn.MaxPool2d(kernel_size=2, stride=2), _VanillaConvBlock(in_channels=16, out_channels=32, kernel_size=3), _VanillaConvBlock(in_channels=32, out_channels=64, kernel_size=3), nn.MaxPool2d(kernel_size=2, stride=2), _VanillaConvBlock(in_channels=64, out_channels=128, kernel_size=3), _VanillaConvBlock(in_channels=128, out_channels=256, kernel_size=3), nn.MaxPool2d(kernel_size=2, stride=2)) classifier_layers = [ GlobalAvgPool2d(), nn.Conv2d(256, class_count, kernel_size=1) ] if use_softmax: classifier_layers.append(nn.Softmax(dim=1)) self._classifier = nn.Sequential(*classifier_layers) def forward(self, x): y = self._features(x) return self._classifier(y)[:, :, 0, 0]

39.3125

110

0.581346

206

1,887

5

0.276699

0.126214

0.151456

0.078641

0.371845

0.334951

0.16699

0

0.042868

0.320085

1,887

47

111

40.148936

0.759938

0

0.117647

0

1

0.117647

false

0

0.058824

0.029412

0.294118

0

null

0

null

0

1

0

79c4397925c5e818a6ed0d6ef4f084f5009de3b4

1,079

py

Python

routemaster_sdk/exceptions.py

thread/routemaster-sdk

1300508525a3e1495c640f9c7ff689bb6f621d7e

[ "MIT" ]

null

routemaster_sdk/exceptions.py

thread/routemaster-sdk

1300508525a3e1495c640f9c7ff689bb6f621d7e

[ "MIT" ]

null

routemaster_sdk/exceptions.py

thread/routemaster-sdk

1300508525a3e1495c640f9c7ff689bb6f621d7e

[ "MIT" ]

null

"""Well known exceptions.""" from routemaster_sdk.types import LabelRef, StateMachine class UnknownLabel(ValueError): """Represents a label unknown in the given state machine.""" deleted = False def __init__(self, label: LabelRef) -> None: self.label = label def __str__(self): return "{0}: {1}".format(self.__class__.__name__, self.label) class DeletedLabel(UnknownLabel): """Represents a label deleted in the given state machine.""" deleted = True class UnknownStateMachine(ValueError): """Represents a state machine not in the system.""" def __init__(self, state_machine: StateMachine) -> None: self.state_machine = state_machine def __str__(self): return "{0}: {1}".format(self.__class__.__name__, self.state_machine) class LabelAlreadyExists(ValueError): """Thrown when a label already exists in the state machine.""" def __init__(self, label: LabelRef) -> None: self.label = label def __str__(self): return "{0}: {1}".format(self.__class__.__name__, self.label)

26.975

77

0.682113

129

1,079

5.294574

0.341085

0.140556

0.048316

0.070278

0.402635

0.317716

0

0.006936

0.198332

1,079

39

78

27.666667

0.782659

0.217794

0

0.473684

0

0.02934

0

1

0.315789

false

0

0.052632

0.157895

0.842105

0

null

0

null

0

1

0

1

0

2

79c473b0364a6ca1e495e68f26cc755757c8686b

604

py

Python

servicebox/platforms/api/views.py

FlxPeters/servicebox

2fc39fa5ec6e629a0794fda003a7a0e4adf05202

[ "Apache-2.0" ]

null

servicebox/platforms/api/views.py

FlxPeters/servicebox

2fc39fa5ec6e629a0794fda003a7a0e4adf05202

[ "Apache-2.0" ]

null

servicebox/platforms/api/views.py

FlxPeters/servicebox

2fc39fa5ec6e629a0794fda003a7a0e4adf05202

[ "Apache-2.0" ]

null

from platforms.models import PlatformGroup, Platform from rest_framework import viewsets from platforms.api.serializers import PlatformGroupSerializer, PlatformSerializer class PlatformViewSet(viewsets.ModelViewSet): """ API endpoint that allows tenants to be viewed or edited. """ queryset = Platform.objects.all() serializer_class = PlatformSerializer class PlatformGroupViewSet(viewsets.ModelViewSet): """ API endpoint that allows tenant groups to be viewed or edited. """ queryset = PlatformGroup.objects.all() serializer_class = PlatformGroupSerializer

27.454545

81

0.768212

61

604

7.557377

0.52459

0.056399

0.099783

0.13449

0.290672

0

0.167219

604

21

82

28.761905

0.916501

0.19702

0

1

0

false

0

0.333333

0

1

0

null

0

null

0

1

0

1

79c4bc7411a8ceae834135ef0832c81c48a8f427

5,528

py

Python

transforms.py

amitkumarj441/TGS_Kaggle

a4f613046cc36f3f6dbec28adb35f97a63c2a994

[ "MIT" ]

1

2019-03-20T07:10:08.000Z

transforms.py

amitkumarj441/TGS_Kaggle

a4f613046cc36f3f6dbec28adb35f97a63c2a994

[ "MIT" ]

null

transforms.py

amitkumarj441/TGS_Kaggle

a4f613046cc36f3f6dbec28adb35f97a63c2a994

[ "MIT" ]

null

import cv2 import numpy as np from scipy.ndimage.filters import gaussian_filter from scipy.ndimage.interpolation import map_coordinates def upsample(image, image_size_target): padding0 = (image_size_target - image.shape[0]) / 2 padding1 = (image_size_target - image.shape[1]) / 2 padding_start0 = int(np.ceil(padding0)) padding_end0 = int(np.floor(padding0)) padding_start1 = int(np.ceil(padding1)) padding_end1 = int(np.floor(padding1)) return cv2.copyMakeBorder(image, padding_start0, padding_end0, padding_start1, padding_end1, cv2.BORDER_REFLECT_101) def downsample(image, image_size_original): padding = (image.shape[0] - image_size_original) / 2 padding_start = int(np.ceil(padding)) return image[padding_start:padding_start + image_size_original, padding_start:padding_start + image_size_original] def augment(image, mask): if np.random.rand() < 0.5: image = np.fliplr(image) mask = np.fliplr(mask) if np.random.rand() < 0.5: c = np.random.choice(2) if c == 0: image = multiply_brightness(image, np.random.uniform(1 - 0.1, 1 + 0.1)) elif c == 1: image = adjust_gamma(image, np.random.uniform(1 - 0.1, 1 + 0.1)) if np.random.rand() < 0.5: c = np.random.choice(3) if c == 0: image, mask = apply_elastic_transform(image, mask, alpha=150, sigma=8, alpha_affine=0) elif c == 1: image, mask = apply_elastic_transform(image, mask, alpha=0, sigma=0, alpha_affine=8) elif c == 2: image, mask = apply_elastic_transform(image, mask, alpha=150, sigma=10, alpha_affine=5) if np.random.rand() < 0.5: image, mask = random_crop_and_pad(image, mask) return image, mask def multiply_brightness(image, coefficient): image_HLS = cv2.cvtColor(image, cv2.COLOR_RGB2HLS) image_HLS = np.array(image_HLS, dtype=np.float64) image_HLS[:, :, 1] = image_HLS[:, :, 1] * coefficient image_HLS[:, :, 1][image_HLS[:, :, 1] > 255] = 255 image_HLS = np.array(image_HLS, dtype=np.uint8) return cv2.cvtColor(image_HLS, cv2.COLOR_HLS2RGB) def adjust_gamma(image, gamma): # build a lookup table mapping the pixel values [0, 255] to # their adjusted gamma values invGamma = 1.0 / gamma table = np.array([((i / 255.0) ** invGamma) * 255 for i in np.arange(0, 256)]).astype("uint8") # apply gamma correction using the lookup table return cv2.LUT(image, table) # Function to distort image def elastic_transform(image, alpha, sigma, alpha_affine, random_state=None): """Elastic deformation of images as described in [Simard2003]_ (with modifications). .. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for Convolutional Neural Networks applied to Visual Document Analysis", in Proc. of the International Conference on Document Analysis and Recognition, 2003. Based on https://gist.github.com/erniejunior/601cdf56d2b424757de5 """ if random_state is None: random_state = np.random.RandomState(None) shape = image.shape shape_size = shape[:2] # Random affine center_square = np.float32(shape_size) // 2 square_size = min(shape_size) // 3 pts1 = np.float32([center_square + square_size, [center_square[0] + square_size, center_square[1] - square_size], center_square - square_size]) pts2 = pts1 + random_state.uniform(-alpha_affine, alpha_affine, size=pts1.shape).astype(np.float32) M = cv2.getAffineTransform(pts1, pts2) image = cv2.warpAffine(image, M, shape_size[::-1], borderMode=cv2.BORDER_REFLECT_101) dx = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma) * alpha dy = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma) * alpha dz = np.zeros_like(dx) x, y, z = np.meshgrid(np.arange(shape[1]), np.arange(shape[0]), np.arange(shape[2])) indices = np.reshape(y + dy, (-1, 1)), np.reshape(x + dx, (-1, 1)), np.reshape(z, (-1, 1)) return map_coordinates(image, indices, order=1, mode='reflect').reshape(shape) def apply_elastic_transform(image, mask, alpha, sigma, alpha_affine): channels = np.concatenate((image, mask[..., None]), axis=2) result = elastic_transform(channels, alpha, sigma, alpha_affine, random_state=np.random.RandomState(None)) image_result = result[..., 0:3] mask_result = result[..., 3] mask_result = (mask_result > 0.5).astype(mask.dtype) return image_result, mask_result def random_crop_and_pad(image, mask): max_crop = 40 crop_x_total = np.random.randint(max_crop) crop_x0 = np.random.randint(crop_x_total + 1) crop_x1 = crop_x_total - crop_x0 crop_y_total = np.random.randint(max_crop) crop_y0 = np.random.randint(crop_y_total + 1) crop_y1 = crop_y_total - crop_y0 cropped_image = image[crop_x0:image.shape[0] - crop_x1, crop_y0:image.shape[1] - crop_y1, :] cropped_mask = mask[crop_x0:mask.shape[0] - crop_x1, crop_y0:mask.shape[1] - crop_y1] cropped_padded_image = upsample(cropped_image, image.shape[0]) cropped_padded_mask = upsample(cropped_mask, mask.shape[0]) return cropped_padded_image, cropped_padded_mask def random_crop_to_size(image, mask, size): dmax = image.shape[0] - size dx = np.random.randint(dmax + 1) dy = np.random.randint(dmax + 1) cropped_image = image[dx:dx + size, dy:dy + size, :] cropped_mask = mask[dx:dx + size, dy:dy + size] return cropped_image, cropped_mask

38.929577

120

0.680174

806

5,528

4.475186

0.219603

0.035487

0.024951

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0

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0.193741

5,528

141

121

39.205674

0.764191

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0

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1

0.096774

false

0

0.043011

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0.236559

0

null

0

null

0

1

0

79c67d96a3b58ae9b3f7d1e5efc7a2527f181276

1,663

py

Python

taskobra/monitor/system_info.py

Vipyr/taskobra

d9884f006ef9c735852075912d5a945543de52f5

[ "MIT" ]

null

taskobra/monitor/system_info.py

Vipyr/taskobra

d9884f006ef9c735852075912d5a945543de52f5

[ "MIT" ]

43

2020-02-06T22:23:42.000Z

2020-04-29T23:56:43.000Z

taskobra/monitor/system_info.py

Vipyr/taskobra

d9884f006ef9c735852075912d5a945543de52f5

[ "MIT" ]

2

2020-02-06T21:01:42.000Z

2020-02-06T23:43:11.000Z

from taskobra.orm import * import platform import cpuinfo import subprocess def create_system(args, database_engine): system = System(name=platform.node()) cpu_info = cpuinfo.get_cpu_info() system.add_component(OperatingSystem( name=platform.system(), version=platform.platform(), )) system.add_component(CPU( manufacturer=cpu_info.get('vendor_id', ''), model=cpu_info.get('brand', ''), isa=cpu_info.get('arch', ''), core_count=cpu_info.get('count', 1), threads_per_core=1, nominal_frequency=(cpu_info.get('hz_actual_raw')[0] / 1000000000), )) with get_session(bind=database_engine) as session: current_system = session.query(System).filter( System.name == platform.node(), ).first() if current_system is None: session.add(system) session.commit() #gpu = GPU( # manufacturer="NVIDIA", # model="1070", # architecture="CUDA", # tdp=105, # core_count=1920, # memory=8.0, #) #memory = Memory( # manufacturer="G-Skill", # model="Trident", # standard="DDR4", # capacity=16.0, # frequency=3600, # cas_latency=16, # t_rcd=19, # t_rp=19, # t_ras=39, #) #storage = Storage( # manufacturer="Sabrent", # model="Rocket", # standard="NVMe PCIe 4.0", # capacity=500.0, # max_read=5000, # max_write=2500, #) #system.add_component(gpu) #system.add_component(memory) #system.add_component(memory) #system.add_component(storage)

25.984375

74

0.578473

185

1,663

5.016216

0.491892

0.052802

0.116379

0.047414

0.071121

0

0.046823

0.280818

1,663

63

75

26.396825

0.729097

0.338545

0

0.076923

0

0.033676

0

1

0.038462

false

0

0.153846

0

0.192308

0

null

0

null

0

1

0

79ca87eadda13d9fdb7282bf224ad560fc96b076

2,392

py

Python

expression_builder/tests/test_string_replace.py

django-advance-utils/expression-builder

08dab5780ae3a8be90c4daa6b9950ad1af4d87a4

[ "MIT" ]

null

expression_builder/tests/test_string_replace.py

django-advance-utils/expression-builder

08dab5780ae3a8be90c4daa6b9950ad1af4d87a4

[ "MIT" ]

null

expression_builder/tests/test_string_replace.py

django-advance-utils/expression-builder

08dab5780ae3a8be90c4daa6b9950ad1af4d87a4

[ "MIT" ]

null

import unittest from expression_builder.exceptions import ExpressionError from expression_builder.expression_builder import ExpressionBuilder class StringReplaceTests(unittest.TestCase): # noinspection PyPep8Naming def setUp(self): self.exp = ExpressionBuilder() self.exp.add_to_global("fred", 1234) self.exp.add_to_global_string_statement("path", ";[tom];[tom]#") self.exp.add_to_global_string_statement("path2", ";[tom];[tom+$]#") def test_simple(self): result = self.exp.string_replace("fred") self.assertEqual('fred', result) def test_simple_math(self): result = self.exp.string_replace(";[1+5];#") self.assertEqual(';6;#', result) def test_simple_variable(self): result = self.exp.string_replace(";[tom];#", variables={'tom': 5}) self.assertEqual(';5;#', result) def test_bool_variable_true(self): result = self.exp.string_replace(";[tom];#", variables={'tom': True}) self.assertEqual(';1;#', result) def test_bool_variable_false(self): result = self.exp.string_replace(";[tom];#", variables={'tom': False}) self.assertEqual(';0;#', result) def test_simple_variable2(self): result = self.exp.string_replace(";[tom];[tom]#", variables={'tom': 5}) self.assertEqual(';5;5#', result) def test_simple_global_variable(self): result = self.exp.string_replace(";[fred];[fred]#") self.assertEqual(';1234;1234#', result) def test_string_variable(self): result = self.exp.string_replace(";[bill];#", {'bill': 'hello'}) self.assertEqual(';hello;#', result) def test_unknown_variable(self): with self.assertRaises(ExpressionError) as cm: self.exp.string_replace(";[tom];[tom]#") the_exception = cm.exception self.assertEqual(the_exception.value, 'No variable named tom') def test_global(self): result = self.exp.run_statement("path", {'tom': 10}) self.assertEqual(";10;10#", result) def test_global_with_replace(self): result = self.exp.run_statement("path2", {'tom': 10}, replace_values={'$': 5}) self.assertEqual(";10;15#", result) def test_inline_replace(self): result = self.exp.run_statement('result = ^"ten = [ten]"', {'ten': 10}) self.assertEqual("ten = 10", result['result'])

36.8

86

0.640886

285

2,392

5.189474

0.214035

0.075727

0.104124

0.126437

0.425963

0.379986

0.322515

0.091278

0

0.022234

0.191472

2,392

64

87

37.375

0.742503

0.010452

0

0.119662

0

0.276596

1

0.276596

false

0

0.06383

0

0.361702

0

null

0

null

0

1

0

1

79ca9ccc9aed4b417288aeae0d80662e45b6689d

309

py

Python

example/simple.py

yoophi/str_pic

e4ac65e819b50d7c8fb4bf94dd26aa5a97e4331b

[ "MIT" ]

null

example/simple.py

yoophi/str_pic

e4ac65e819b50d7c8fb4bf94dd26aa5a97e4331b

[ "MIT" ]

3

2021-06-08T19:34:37.000Z

2022-03-11T23:18:13.000Z

example/simple.py

yoophi/str_pic

e4ac65e819b50d7c8fb4bf94dd26aa5a97e4331b

[ "MIT" ]

null

from flask import Flask, render_template from flask_dummyimage import DummyImage app = Flask(__name__) dummyimage = DummyImage(app, url_prefix="/dm", endpoint="images", route="img") @app.route("/") def index(): return render_template("index.html") if __name__ == "__main__": app.run(debug=True)

19.3125

78

0.721683

40

309

5.175

0.6

0.086957

0

0.135922

309

15

79

20.6

0.775281

0

0.100324

0

1

0.111111

false

0

0.222222

0.111111

0.444444

0

null

0

null

0

1

0

1

79cc5960935ea7fbba4fb0eb6555e1ecb03c2fbf

1,562

py

Python

mints/args/typed.py

candy-kingdom/mints

e68a2351cf3ff6823e978bc6a4b740bd2a974ca3

[ "MIT" ]

4

2020-05-09T11:01:32.000Z

2020-06-03T14:44:06.000Z

mints/args/typed.py

candy-kingdom/cli

e68a2351cf3ff6823e978bc6a4b740bd2a974ca3

[ "MIT" ]

43

2020-01-27T21:14:16.000Z

2020-06-18T17:57:20.000Z

mints/args/typed.py

candy-kingdom/mints

e68a2351cf3ff6823e978bc6a4b740bd2a974ca3

[ "MIT" ]

null

from typing import Type, Any class Typed: """A typed command line argument. A typed command line argument is used for specifying the type to convert the value to. For example, consider the following code: @cli def double(number: Opt[int]('A number to double.')): print(number * 2) When this CLI is called as $ example.py double --number 5 the value '5' of the argument '--number' is converted to `int` and passed to the function. Note: The default type of arguments is `str`. Thus, if an argument is annotated as `Opt('A number to double.')`, a string value will be passed to the function. Attributes: kind: A kind of an argument (for example, `Arg`, `Opt` or `Flag`). type: A type of an argument (for example, `int`, `List[double]`, etc.). """ def __init__(self, kind: Type, type: Type): self.kind = kind self.type = type def __call__(self, *args: Any, **kwargs: Any) -> 'Typed': # Instantiate the parameter being wrapped. For example, # `Arg[int]` will return `Typed(Arg, int)`, and # `Typed(Arg, int)('Description.')` will instantiate # `self.kind = Arg('Description.')`. if isinstance(self.kind, type): self.kind = self.kind(*args, **kwargs) else: raise ValueError(f"Cannot instantiate {type(self.kind)} twice: it" f"is already instantiated as {repr(self.kind)}.") return self

31.877551

78

0.587068

206

1,562

4.412621

0.398058

0.070407

0.039604

0.037404

0.10341

0

0.002747

0.300896

1,562

48

79

32.541667

0.82967

0.604994

0

0.180791

0

1

0.166667

false

0

0.083333

0

0.416667

0

null

0

null

0

1

0

79cd97ed3020f125684d084c92be22793583d226

5,994

py

Python

lab3/es2/webservice.py

haraldmeister/Programming_for_IoT_applications

04ec13689caee1fca28bf4fb6a261c318ebd374d

[ "Apache-2.0" ]

null

lab3/es2/webservice.py

haraldmeister/Programming_for_IoT_applications

04ec13689caee1fca28bf4fb6a261c318ebd374d

[ "Apache-2.0" ]

null

lab3/es2/webservice.py

haraldmeister/Programming_for_IoT_applications

04ec13689caee1fca28bf4fb6a261c318ebd374d

[ "Apache-2.0" ]

null

import json import time import cherrypy class albums: def __init__(self,artist,year,title,num): self.artist=artist self.year=year self.title=title self.N=num class owner(albums): def __init__(self,nome,date): self.album_list=[] self.nome=nome self.last_upd=date self.result={"Artist":[],"Year":[],"Title":[],"Total songs":[]} self.discography={"Owner":self.nome,"Last update":self.last_upd,"Album List":self.album_list} def search_artist(self,key_artist): for i in range(len(self.album_list)): if(str(self.album_list[i].artist)==key_artist): self.result["Artist"]=self.album_list[i].artist self.result["Year"]=self.album_list[i].year self.result["Title"]=self.album_list[i].title self.result["Total songs"]=self.album_list[i].N return json.loads(json.dumps(self.result,default=lambda x: x.__dict__)) return json.loads(json.dumps(self.result,default=lambda x: x.__dict__)) def search_title(self,key_title): for i in range(len(self.album_list)): if(str(self.album_list[i].title)==key_title): self.result["Artist"]=self.album_list[i].artist self.result["Year"]=self.album_list[i].year self.result["Title"]=self.album_list[i].title self.result["Total songs"]=self.album_list[i].N return json.loads(json.dumps(self.result,default=lambda x: x.__dict__)) return json.loads(json.dumps(self.result,default=lambda x: x.__dict__)) def search_year(self,key_year): for i in range(len(self.album_list)): if(str(self.album_list[i].year)==key_year): self.result["Artist"]=self.album_list[i].artist self.result["Year"]=self.album_list[i].year self.result["Title"]=self.album_list[i].title self.result["Total songs"]=self.album_list[i].N return json.loads(json.dumps(self.result,default=lambda x: x.__dict__)) return json.loads(json.dumps(self.result,default=lambda x: x.__dict__)) def search_totalsong(self,key_nsong): for i in range(len(self.album_list)): if(str(self.album_list[i].N)==key_nsong): self.result["Artist"]=self.album_list[i].artist self.result["Year"]=self.album_list[i].year self.result["Title"]=self.album_list[i].title self.result["Total songs"]=self.album_list[i].N return self.result return json.loads(json.dumps(self.result,default=lambda x: x.__dict__)) def insert_album(self,artist,year,title,num): for i in range(len(self.album_list)): if(str(self.album_list[i].artist)==artist and str(self.album_list[i].title)==title ): self.album_list[i].N=num self.album_list[i].year=year self.last_upd=time.strftime('%d/%m/%Y')+' '+time.strftime('%H:%M:%S') return self.album_list.append(albums(artist,year,title,num)) self.last_upd=time.strftime('%d/%m/%Y')+' '+time.strftime('%H:%M:%S') def delete_album(self,artist,year,title,num): for i in range(len(self.album_list)): if(str(self.album_list[i].artist)==artist and str(self.album_list[i].title)==title ): self.album_list.remove(self.album_list[i]) self.last_upd=time.strftime('%d/%m/%Y')+' '+time.strftime('%H:%M:%S') def print_all(self): return json.loads(json.dumps(self.discography,default=lambda x: x.__dict__)) class Discography(owner): exposed=True def __init__(self): json_data=open("discography.txt") data = json.load(json_data) self.discogr=owner(data['discography_owner'],data['last_update']) for j in range(len(data['album_list'])): self.discogr.album_list.append(albums(data['album_list'][j]['artist'], data['album_list'][j]['publication_year'], data['album_list'][j]['title'], data['album_list'][j]['total_tracks'])) @cherrypy.tools.json_out() def GET(self,*uri,**params): if (len(uri)==0): return self.discogr.print_all() else: if uri[0]=="search_artist": return self.discogr.search_artist(uri[1]) elif uri[0]=="search_title": return self.discogr.search_title(uri[1]) elif uri[0]=="search_year": return self.discogr.search_year(uri[1]) elif uri[0]=="search_totalsong": return self.discogr.search_totalsong(uri[1]) elif uri[0]=="print": return self.discogr.print_all() @cherrypy.tools.json_in() def POST(self,*uri,**params): if uri[0]=="insert_album": input_json = cherrypy.request.json artist=input_json["artist"] year=int(input_json["year"]) title=input_json["title"] N=int(input_json["N"]) self.discogr.insert_album(artist,year,title,N) return if uri[0]=="delete_album": input_json = cherrypy.request.json artist=input_json["artist"] year=int(input_json["year"]) title=input_json["title"] N=int(input_json["N"]) self.discogr.delete_album(artist,year,title,N) return if __name__ == '__main__': conf = { '/': { 'request.dispatch': cherrypy.dispatch.MethodDispatcher(), 'tools.sessions.on': True } } cherrypy.tree.mount(Discography(), '/', conf) cherrypy.config.update({'server.socket_host': '0.0.0.0'}) cherrypy.config.update({'server.socket_port': 9090}) cherrypy.engine.start() cherrypy.engine.block()

45.067669

101

0.584918

790

5,994

4.249367

0.124051

0.117962

0.143283

0.112601

0.633006

0.569258

0.543342

0.526065

0

0.004514

0.260761

5,994

132

102

45.409091

0.753103

0

0.395161

0

0.089756

0

1

0.096774

false

0

0.024194

0.008065

0.298387

0.032258

0

null

0

null

0

1

0

79ce0327616efa9691f6ae8fe41ab9246d6bf9e6

143

py

Python

tests/redirects_tests/urls.py

Yoann-Vie/esgi-hearthstone

115d03426c7e8e80d89883b78ac72114c29bed12

[ "PSF-2.0", "BSD-3-Clause" ]

null

tests/redirects_tests/urls.py

Yoann-Vie/esgi-hearthstone

115d03426c7e8e80d89883b78ac72114c29bed12

[ "PSF-2.0", "BSD-3-Clause" ]

null

tests/redirects_tests/urls.py

Yoann-Vie/esgi-hearthstone

115d03426c7e8e80d89883b78ac72114c29bed12

[ "PSF-2.0", "BSD-3-Clause" ]

null

from django.conf.urls import url from django.http import HttpResponse urlpatterns = [ url(r'^$', lambda req: HttpResponse('OK')), ]

20.428571

48

0.678322

18

143

5.388889

0.722222

0.206186

0

0.188811

143

6

49

23.833333

0.836207

0

0.029197

0

1

0

false

0

0.4

0

0.4

0

1

0

null

1

0

1

0

null

0

1

0

4

79d29ea8f56cec3596c251c94d5aca0bfd3a1026

1,653

pyde

Python

examples/01_game_of_life/sketch_gameoflife.pyde

underwit/pyprocessing-examples

c6e84fded23dcdd5bf32d499aa91900d68ec213d

[ "MIT" ]

null

examples/01_game_of_life/sketch_gameoflife.pyde

underwit/pyprocessing-examples

c6e84fded23dcdd5bf32d499aa91900d68ec213d

[ "MIT" ]

null

examples/01_game_of_life/sketch_gameoflife.pyde

underwit/pyprocessing-examples

c6e84fded23dcdd5bf32d499aa91900d68ec213d

[ "MIT" ]

null

import random from itertools import product CS = 10 # cell size W = 600 # width H = 600 # height COLS = W // CS ROWS = H // CS DENSITY = 0.35 dirs = list(product((-1, 0, 1), repeat=2)) dirs.remove((0, 0)) points = [] new_points = [] run = False def xy2flat(x, y): x = (x + COLS) % COLS y = (y + ROWS) % ROWS return x + COLS * y def flat2xy(index): return index % COLS, index // COLS def setup(): frameRate(20) size(600, 600) for i in range(0, W * H, CS): points.append(random.random() < DENSITY) new_points.append(False) def mouseClicked(): x = mouseX // CS y = mouseY // CS index = xy2flat(x, y) points[index] = not points[index] def keyPressed(): global run if key == ' ': run = not run elif key == 'r': # randomly fill the board for i, _ in enumerate(points): points[i] = random.random() < DENSITY elif key == 'c': # clear the board for i, _ in enumerate(points): points[i] = False def calc_cell(index): x, y = flat2xy(index) nb = sum([points[xy2flat(x + _x, y + _y)] for _x, _y in dirs]) new_points[index] = points[index] if points[index] and (nb < 2 or nb > 3): new_points[index] = False elif nb == 3: new_points[index] = True def draw(): global points, new_points background(52, 63, 62) fill(220, 237, 255) for index, is_alive in enumerate(points): if is_alive: x, y = flat2xy(index) rect(x * CS, y * CS, CS, CS) if run: calc_cell(index) if run: points, new_points = new_points, points

21.467532

66

0.557774

241

1,653

3.751037

0.327801

0.079646

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0.079646

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0.30974

1,653

76

67

21.75

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0

0.1

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false

0

0.033333

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0.183333

0

null

0

null

0

1

0

79d585582066d6853246fd14d5eec7b556e67b85

5,064

py

Python

conanfile.py

madebr/conan-repo-actions-conan-libwebp

2f2eaad6e8de2cbec611f19de5205fc0b3267492

[ "MIT" ]

null

conanfile.py

madebr/conan-repo-actions-conan-libwebp

2f2eaad6e8de2cbec611f19de5205fc0b3267492

[ "MIT" ]

null

conanfile.py

madebr/conan-repo-actions-conan-libwebp

2f2eaad6e8de2cbec611f19de5205fc0b3267492

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- import os import shutil from conans import ConanFile, CMake, tools class LibwebpConan(ConanFile): name = "libwebp" version = "1.0.0" description = "library to encode and decode images in WebP format" url = "http://github.com/bincrafters/conan-libwebp" homepage = "https://github.com/webmproject/libwebp" author = "Bincrafters <bincrafters@gmail.com>" license = "BSD 3-Clause" exports = ["LICENSE.md"] exports_sources = ['CMakeLists.txt', '0001-install-pkg-config-files-during-the-CMake-build.patch'] generators = 'cmake' _source_subfolder = "source_subfolder" settings = "os", "compiler", "build_type", "arch" options = {"shared": [True, False], "fPIC": [True, False], "with_simd": [True, False], "near_lossless": [True, False], "swap_16bit_csp": [True, False]} default_options = {'shared': False, 'fPIC': True, 'with_simd': True, 'near_lossless': True, 'swap_16bit_csp': False} def source(self): source_url = "https://github.com/webmproject/libwebp" tools.get("{0}/archive/v{1}.tar.gz".format(source_url, self.version)) extracted_dir = self.name + "-" + self.version os.rename(extracted_dir, self._source_subfolder) tools.patch(base_path=self._source_subfolder, patch_file='0001-install-pkg-config-files-during-the-CMake-build.patch') os.rename(os.path.join(self._source_subfolder, "CMakeLists.txt"), os.path.join(self._source_subfolder, "CMakeListsOriginal.txt")) shutil.copy("CMakeLists.txt", os.path.join(self._source_subfolder, "CMakeLists.txt")) def configure(self): del self.settings.compiler.libcxx def config_options(self): if self.settings.os == 'Windows': del self.options.fPIC @property def _version_components(self): return [int(x) for x in self.version.split('.')] def _configure_cmake(self): cmake = CMake(self) # should be an option but it doesn't work yet cmake.definitions["WEBP_ENABLE_SIMD"] = self.options.with_simd if self._version_components[0] >= 1: cmake.definitions["WEBP_NEAR_LOSSLESS"] = self.options.near_lossless else: cmake.definitions["WEBP_ENABLE_NEAR_LOSSLESS"] = self.options.near_lossless cmake.definitions['WEBP_ENABLE_SWAP_16BIT_CSP'] = self.options.swap_16bit_csp # avoid finding system libs cmake.definitions['CMAKE_DISABLE_FIND_PACKAGE_GIF'] = True cmake.definitions['CMAKE_DISABLE_FIND_PACKAGE_PNG'] = True cmake.definitions['CMAKE_DISABLE_FIND_PACKAGE_TIFF'] = True cmake.definitions['CMAKE_DISABLE_FIND_PACKAGE_JPEG'] = True if self.settings.os == "Android": if 'CMAKE_ANDROID_ARCH_ABI' in cmake.definitions: cmake.definitions['ANDROID_ABI'] = cmake.definitions['CMAKE_ANDROID_ARCH_ABI'] if 'ANDROID_NDK_HOME' in os.environ: cmake.definitions['ANDROID_NDK'] = os.environ.get('ANDROID_NDK_HOME') cmake.configure(source_folder=self._source_subfolder) return cmake def build(self): # WEBP_EXTERN is not specified on Windows # Set it to dllexport for building (see CMakeLists.txt) and to dllimport otherwise if self.options.shared and self.settings.compiler == "Visual Studio": tools.replace_in_file(os.path.join(self._source_subfolder, 'src', 'webp', 'types.h'), '#ifndef WEBP_EXTERN', """#ifndef WEBP_EXTERN #ifdef _MSC_VER #ifdef WEBP_DLL #define WEBP_EXTERN __declspec(dllexport) #else #define WEBP_EXTERN __declspec(dllimport) #endif #endif /* _MSC_VER */ #endif #ifndef WEBP_EXTERN""") # cmake misses dll (RUNTIME) copy tools.replace_in_file(os.path.join(self._source_subfolder, "CMakeListsOriginal.txt"), "LIBRARY DESTINATION lib", "RUNTIME DESTINATION bin\nLIBRARY DESTINATION lib") if self._version_components[0] >= 1: # allow to build webpmux tools.replace_in_file(os.path.join(self._source_subfolder, "CMakeListsOriginal.txt"), "if(WEBP_BUILD_GIF2WEBP OR WEBP_BUILD_IMG2WEBP)", "if(TRUE)") cmake = self._configure_cmake() cmake.build() def package(self): cmake = self._configure_cmake() cmake.install() self.copy("COPYING", dst="licenses", src=self._source_subfolder) self.copy("FindWEBP.cmake", dst=".", src=".") def package_info(self): self.cpp_info.libs = ['webpmux', 'webpdemux', 'webpdecoder', 'webp'] if self.options.shared and self.settings.os == "Windows" and self.settings.compiler != 'Visual Studio': self.cpp_info.libs = [lib + '.dll' for lib in self.cpp_info.libs]

42.2

120

0.634874

598

5,064

5.16388

0.304348

0.05829

0.061529

0.027202

0.310557

0.271697

0.202396

0.138601

0.090997

0

0.007576

0.244076

5,064

119

121

42.554622

0.799112

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0

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0.267637

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false

0

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0.012195

0.341463

0

null

0

null

0

1

0

79d5867444343ac92dd71c753e06968277e1c875

5,184

py

Python

sjtwo-c/site_scons/site_tools/codegen/site_packages/can/broadcastmanager.py

seanlinc/Playmate

077877d172dd6b7beab910c52ec95ee300bc6480

[ "Apache-2.0" ]

2

2020-04-04T21:09:56.000Z

2020-04-08T17:00:58.000Z

sjtwo-c/site_scons/site_tools/codegen/site_packages/can/broadcastmanager.py

seanlinc/Playmate

077877d172dd6b7beab910c52ec95ee300bc6480

[ "Apache-2.0" ]

13

2020-04-11T21:50:57.000Z

2020-04-19T03:19:48.000Z

sjtwo-c/site_scons/site_tools/codegen/site_packages/can/broadcastmanager.py

seanlinc/Playmate

077877d172dd6b7beab910c52ec95ee300bc6480

[ "Apache-2.0" ]

null

# coding: utf-8 """ Exposes several methods for transmitting cyclic messages. The main entry point to these classes should be through :meth:`can.BusABC.send_periodic`. """ import abc import logging import threading import time import warnings log = logging.getLogger('can.bcm') class CyclicTask(object): """ Abstract Base for all cyclic tasks. """ @abc.abstractmethod def stop(self): """Cancel this periodic task. :raises can.CanError: If stop is called on an already stopped task. """ class CyclicSendTaskABC(CyclicTask): """ Message send task with defined period """ def __init__(self, message, period): """ :param can.Message message: The message to be sent periodically. :param float period: The rate in seconds at which to send the message. """ self.message = message self.can_id = message.arbitration_id self.arbitration_id = message.arbitration_id self.period = period super(CyclicSendTaskABC, self).__init__() class LimitedDurationCyclicSendTaskABC(CyclicSendTaskABC): def __init__(self, message, period, duration): """Message send task with a defined duration and period. :param can.Message message: The message to be sent periodically. :param float period: The rate in seconds at which to send the message. :param float duration: The duration to keep sending this message at given rate. """ super(LimitedDurationCyclicSendTaskABC, self).__init__(message, period) self.duration = duration class RestartableCyclicTaskABC(CyclicSendTaskABC): """Adds support for restarting a stopped cyclic task""" @abc.abstractmethod def start(self): """Restart a stopped periodic task. """ class ModifiableCyclicTaskABC(CyclicSendTaskABC): """Adds support for modifying a periodic message""" def modify_data(self, message): """Update the contents of this periodically sent message without altering the timing. :param can.Message message: The message with the new :attr:`can.Message.data`. Note: The arbitration ID cannot be changed. """ self.message = message class MultiRateCyclicSendTaskABC(CyclicSendTaskABC): """A Cyclic send task that supports switches send frequency after a set time. """ def __init__(self, channel, message, count, initial_period, subsequent_period): """ Transmits a message `count` times at `initial_period` then continues to transmit message at `subsequent_period`. :param channel: See interface specific documentation. :param can.Message message: :param int count: :param float initial_period: :param float subsequent_period: """ super(MultiRateCyclicSendTaskABC, self).__init__(channel, message, subsequent_period) class ThreadBasedCyclicSendTask(ModifiableCyclicTaskABC, LimitedDurationCyclicSendTaskABC, RestartableCyclicTaskABC): """Fallback cyclic send task using thread.""" def __init__(self, bus, lock, message, period, duration=None): super(ThreadBasedCyclicSendTask, self).__init__(message, period, duration) self.bus = bus self.lock = lock self.stopped = True self.thread = None self.end_time = time.time() + duration if duration else None self.start() def stop(self): self.stopped = True def start(self): self.stopped = False if self.thread is None or not self.thread.is_alive(): name = "Cyclic send task for 0x%X" % (self.message.arbitration_id) self.thread = threading.Thread(target=self._run, name=name) self.thread.daemon = True self.thread.start() def _run(self): while not self.stopped: # Prevent calling bus.send from multiple threads with self.lock: started = time.time() try: self.bus.send(self.message) except Exception as exc: log.exception(exc) break if self.end_time is not None and time.time() >= self.end_time: break # Compensate for the time it takes to send the message delay = self.period - (time.time() - started) time.sleep(max(0.0, delay)) def send_periodic(bus, message, period, *args, **kwargs): """ Send a :class:`~can.Message` every `period` seconds on the given bus. :param can.BusABC bus: A CAN bus which supports sending. :param can.Message message: Message to send periodically. :param float period: The minimum time between sending messages. :return: A started task instance """ warnings.warn("The function `can.send_periodic` is deprecated and will " + "be removed in an upcoming version. Please use `can.Bus.send_periodic` instead.", DeprecationWarning) return bus.send_periodic(message, period, *args, **kwargs)

32.603774

119

0.643711

590

5,184

5.561017

0.308475

0.034136

0.022859

0.033526

0.116733

0.078635

0.068881

0

0.001061

0.272569

5,184

158

120

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0.869

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0

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0

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false

0

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0

0.363636

0

null

0

null

0

1

0

79d996f9c7b739201903f7162ae39e85d80aae38

513

py

Python

accessible_output/braille/outputs/virgo.py

Timtam/cards-against-humanity

89ea61b5c9915198b845bbf8a93c3f7827323ceb

[ "MIT" ]

5

2017-04-11T00:18:42.000Z

2021-08-01T04:27:20.000Z

accessible_output/braille/outputs/virgo.py

Timtam/cards-against-humanity

89ea61b5c9915198b845bbf8a93c3f7827323ceb

[ "MIT" ]

47

2017-04-27T18:57:27.000Z

2017-07-16T21:18:28.000Z

accessible_output/braille/outputs/virgo.py

Timtam/cards-against-humanity

89ea61b5c9915198b845bbf8a93c3f7827323ceb

[ "MIT" ]

4

2018-05-17T12:33:59.000Z

2022-02-20T16:08:51.000Z

from pywintypes import com_error import win32com.client from main import OutputError, BrailleOutput class Virgo (BrailleOutput): """Braille output supporting the Virgo screen reader.""" name = 'Virgo' def __init__(self, *args, **kwargs): super (Virgo, self).__init__(*args, **kwargs) try: self.object = win32com.client.Dispatch("phoenix.BrailleSysClass") except com_error: raise OutputError def braille(self, text): self.object.sayonbraille(True,text) def canBraille(self): return True

23.318182

68

0.744639

63

513

5.904762

0.587302

0.043011

0

0.009132

0.146199

513

22

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0

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false

0

0.2

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0.6

0

null

0

null

0

1

0

79da26b04e69fcee30b862a1e5dd200b98e09556

3,050

py

Python

data_ai/comp3006/src/test.py

lonelyhentai/workspace

2a996af58d6b9be5d608ed040267398bcf72403b

[ "MIT" ]

2

2021-04-26T16:37:38.000Z

2022-03-15T01:26:19.000Z

data_ai/comp3006/src/test.py

lonelyhentai/workspace

2a996af58d6b9be5d608ed040267398bcf72403b

[ "MIT" ]

null

data_ai/comp3006/src/test.py

lonelyhentai/workspace

2a996af58d6b9be5d608ed040267398bcf72403b

[ "MIT" ]

1

2022-03-15T01:26:23.000Z

import pandas as pd import numpy as np from os import path from path_service import LOG_DIR, DATA_DIR from sklearn.metrics import log_loss import re prob_columns = list(map(lambda x: f"prob{x}", range(8))) prob_columns_without_end = list(map(lambda x: f"prob{x}", range(7))) def row_check(df: pd.DataFrame): df.loc[:,prob_columns]=df.loc[:,prob_columns].apply(lambda x: x/np.sum(x),axis=1,result_type='expand') df = df.round(5) sum7 = np.sum(df.loc[:,prob_columns_without_end],axis=1) df.loc[:,'prob7'] = 1.0 - sum7 return df def get_prob_res(file_name: str): df: pd.DataFrame = pd.DataFrame([]) with open(path.join(LOG_DIR, file_name), 'r') as prob_file: prob_lines = prob_file.readlines() probs = {} for i in range(8): probs[i] = [] for line in prob_lines: words = re.split(r"\s", line) for i in range(8): pos = i * 2 prob_index = int(words[pos][-1]) probs[prob_index].append(float(words[pos + 1])) df.loc[:, "file_id"] = pd.Series(list(range(1, len(probs[0]) + 1)), dtype=np.int) for i in range(8): df.loc[:, f"prob{i}"] = pd.Series(probs[i], dtype=np.float) return row_check(df) def get_single_res(file_name: str, true_mode: bool = True): df: pd.DataFrame = pd.DataFrame([]) with open(path.join(LOG_DIR if not true_mode else DATA_DIR, file_name), 'r') as prob_file: prob_lines = prob_file.readlines() probs = {} for i in range(8): probs[i] = [] j = 0 for line in prob_lines: label = int(str.strip(re.split(r"\s", line)[0])[-1]) for i in range(8): if i == label: probs[i].append(1.0) else: probs[i].append(0.0) df.loc[:, "file_id"] = pd.Series(list(range(1, len(probs[0]) + 1)), dtype=np.int) for i in range(8): df.loc[:, f"prob{i}"] = pd.Series(probs[i], dtype=np.float) return df def get_probs(df: pd.DataFrame) -> pd.DataFrame: return df.loc[:, list(map(lambda x: f"prob{x}", range(8)))] def check_valid_log_loss(): valid_prob_df = get_prob_res('valid_prob.log') labels = get_single_res('security.valid', True) print("prob mode: ", log_loss(get_probs(labels), get_probs(valid_prob_df))) def check_train_log_loss(): valid_prob_df = get_prob_res('train_prob.log') labels = get_single_res('new_train', True) print("prob mode: ", log_loss(get_probs(labels), get_probs(valid_prob_df))) def save_train_res(df: pd.DataFrame): df.to_csv(path.join(DATA_DIR, "test_submit.csv"), sep=",", index=False, float_format='%.5f') if __name__ == "__main__": check_valid_log_loss() check_train_log_loss() test_prob_df = get_prob_res("test_prob.log") save_train_res(test_prob_df) df = pd.read_csv(path.join(DATA_DIR, "test_submit.csv"), sep=",") for index, row in df.iterrows(): if np.abs(np.sum(row[list(map(lambda x: f"prob{x}", range(8)))]) - 1.0) > 1e-6: raise Exception(f"sum prob not equal 1.0 in {index}")

34.659091

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3,050

3.524366

0.216374

0.029867

0.019912

0.036504

0.53208

0.462389

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0.389934

0.309181

0

0.01697

0.207869

3,050

87

107

35.057471

0.731374

0

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0

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0

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0.098592

false

0

0.084507

0.014085

0.239437

0.028169

0

null

0

null

0

1

0

79daca46089122299df7193b935021df51332239

1,255

py

Python

tests/test_data.py

tappitikarrass/flask-ap

88b97bb522474ca3dc056c209640050af74cb5dc

[ "BSD-3-Clause" ]

null

tests/test_data.py

tappitikarrass/flask-ap

88b97bb522474ca3dc056c209640050af74cb5dc

[ "BSD-3-Clause" ]

null

tests/test_data.py

tappitikarrass/flask-ap

88b97bb522474ca3dc056c209640050af74cb5dc

[ "BSD-3-Clause" ]

null

import base64 # USER post_user_data_200 = { "username": "sbandera1", "firstname": "Stepan", "lastname": "Bandera", "email": "stepanko@liamg.com", "phone": "123", "password": "supersecret" } post_user_alt_data_200 = { "username": "ivanbahryanyi", "firstname": "Ivan", "lastname": "Bahryanyi", "email": "bahryanyi@liamg.com", "phone": "30", "password": "fcksovietunion" } post_user_data_400 = { "usernameeee": "sbandera1", "firstname": "Stepan", "lastname": "Bandera", "email": "stepanko@liamg.com", "phone": "123", "password": "supersecret" } update_user_data_200 = { "username": "sbandera1", "firstname": "Ivan", "lastname": "Franko", "email": "ivanko@liamg.com", "phone": "111", "password": "supersecret" } login_creds_200 = base64.b64encode(b'sbandera1:supersecret').decode('utf-8') login_creds_alt_200 = base64.b64encode(b'ivanbahryanyi:fcksovietunion').decode('utf-8') login_creds_403 = base64.b64encode(b'sbandera1:supersecreta').decode('utf-8') login_creds_404 = base64.b64encode(b'sbandera2:supersecret').decode('utf-8') # LIST post_list_data_200 = { "name": "watchlist" } update_list_data_200 = { "name": "newname" } post_list_anime_data_200 = { "mal_id": 47 }

22.818182

87

0.663745

142

1,255

5.633803

0.359155

0.0525

0.065

0.05625

0.3625

0.2875

0.2175

0

0.069224

0.148207

1,255

54

88

23.240741

0.679139

0.007171

0

0.326087

0

0.43041

0.074014

0

1

0

false

0.086957

0.021739

0

0.021739

0

null

0

null

0

1

0

1

79dc47eea0d2277e80b8015449551a4eef9526a7

235

py

Python

terrafirma/calendar/apps.py

AlexandraAlter/django-terrafirma

afce5946f173aded2b4bfea78cf1b1034ec32272

[ "MIT" ]

null

terrafirma/calendar/apps.py

AlexandraAlter/django-terrafirma

afce5946f173aded2b4bfea78cf1b1034ec32272

[ "MIT" ]

null

terrafirma/calendar/apps.py

AlexandraAlter/django-terrafirma

afce5946f173aded2b4bfea78cf1b1034ec32272

[ "MIT" ]

null

from django.apps import AppConfig from django.utils.translation import gettext_lazy as _ class CalendarConfig(AppConfig): name = 'terrafirma.calendar' label = 'terrafirma_calendar' verbose_name = _('Terrafirma Calendar')

26.111111

54

0.770213

26

235

6.769231

0.653846

0.306818

0.25

0

0.153191

235

8

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29.375

0.884422

0

0.242553

0

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false

0

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0

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1

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null

1

0

1

0

null

0

1

0

1

0

6

79dd7101e6c2dbca64177e87d238cd48079dd45d

6,466

py

Python

resources/lib/auth_routes.py

t43pasdf/plugin.video.espn_3

f111edf14f0344d248f0a62de3da4f15afc7d354

[ "MIT" ]

4

2019-10-18T01:27:48.000Z

2020-02-14T05:45:29.000Z

resources/lib/auth_routes.py

t43pasdf/plugin.video.espn_3

f111edf14f0344d248f0a62de3da4f15afc7d354

[ "MIT" ]

3

2020-02-10T05:58:30.000Z

2020-09-28T22:42:04.000Z

resources/lib/auth_routes.py

t43pasdf/plugin.video.espn_3

f111edf14f0344d248f0a62de3da4f15afc7d354

[ "MIT" ]

null

# Copyright 2019 https://github.com/kodi-addons # # 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. try: from urllib2 import HTTPError except ImportError: from urllib.error import HTTPError try: from Queue import Queue, Empty except ImportError: from queue import Queue, Empty import logging import threading import time import xbmcgui from resources.lib import adobe_activate_api, espnplus, player_config, util from resources.lib.plugin_routing import plugin from resources.lib.kodiutils import get_string, set_setting @plugin.route('/login-tv-provider') def login_tv_provider(): logging.debug('Authenticate Device') if adobe_activate_api.is_authenticated(): logging.debug('Device already authenticated, skipping authentication') dialog = xbmcgui.Dialog() dialog.ok(get_string(30037), get_string(30301)) set_setting('LoggedInToTvProvider', True) return True else: regcode = adobe_activate_api.get_regcode() dialog = xbmcgui.Dialog() ok = dialog.yesno(get_string(30310), get_string(30320), get_string(30330) % regcode, get_string(30340), get_string(30360), get_string(30350)) if ok: try: adobe_activate_api.authenticate(regcode) dialog.ok(get_string(30310), get_string(30370)) set_setting('LoggedInToTvProvider', True) return True except HTTPError as e: dialog.ok(get_string(30037), get_string(30420) % e) set_setting('LoggedInToTvProvider', False) return False @plugin.route('/view-tv-provider-details') def view_tv_provider_details(): dialog = xbmcgui.Dialog() dialog.ok(get_string(30380), get_string(30390) % adobe_activate_api.get_authentication_expires(), get_string(30700) % (player_config.get_dma(), player_config.get_timezone())) @plugin.route('/logout-tv-provider') def logout_tv_provider(): dialog = xbmcgui.Dialog() ok = dialog.yesno(get_string(30381), get_string(30382)) if ok: adobe_activate_api.deauthorize() set_setting('LoggedInToTvProvider', False) @plugin.route('/login-espn-plus') def login_espn_plus(): if not espnplus.have_valid_login_id_token(): logging.debug('Requesting login id token') semaphore = threading.Semaphore(0) result_queue = Queue() license_plate, ws = espnplus.perform_license_plate_auth_flow(semaphore, result_queue) progress_dialog = xbmcgui.DialogProgress() progress_dialog.create(get_string(40100), get_string(40110), license_plate) espnplus.start_websocket_thread(ws) times = 0 sleep_time = 1 max_time = 180 max_times = max_time / sleep_time # wait a maximum of 3 minutes while times < max_times: time.sleep(sleep_time) canceled = progress_dialog.iscanceled() acquired = semaphore.acquire(blocking=False) logging.debug('Canceled: %s Acquired: %s' % (canceled, acquired)) seconds_left = max_time - times * sleep_time minutes, seconds = divmod(seconds_left, 60) percent = int(times / max_times) progress_dialog.update(percent, get_string(40110), license_plate, get_string(40120) % (minutes, seconds)) if canceled or acquired: break times = times + 1 ws.close() progress_dialog.close() token = None try: token = result_queue.get(block=True, timeout=1) except Empty as e: logging.error('No result from websocket %s', e) if token is not None and 'id_token' in token: espnplus.handle_license_plate_token(token) else: dialog = xbmcgui.Dialog() dialog.ok(get_string(30037), get_string(40130)) set_setting('LoggedInToEspnPlus', False) return False if not espnplus.has_valid_bam_account_access_token(): espnplus.request_bam_account_access_token() logging.debug('Bam token %s' % espnplus.get_bam_account_access_token()) dialog = xbmcgui.Dialog() dialog.ok(get_string(40000), get_string(40101)) set_setting('LoggedInToEspnPlus', True) return True @plugin.route('/view-espn-plus-details') def view_espn_plus_details(): account_details = espnplus.get_bam_account_details() email = util.get_nested_value(account_details, ['attributes', 'email'], 'Unknown Email') profile_name = util.get_nested_value(account_details, ['activeProfile', 'profileName'], 'Unknown Profile Name') product_details = email + ' - ' + profile_name + '\n' sub_details = espnplus.get_bam_sub_details() for sub in sub_details: if sub['isActive']: product_name = '' for product in sub['products']: product_name = product_name + ' ' + product['name'] product_details = product_details + product_name + ' ' + sub['expirationDate'] + '\n' dialog = xbmcgui.Dialog() dialog.ok(get_string(40260), product_details) @plugin.route('/logout-espn-plus') def logout_espn_plus(): set_setting('LoggedInToEspnPlus', False) espnplus.config.reset_settings()

38.951807

115

0.665636

780

6,466

5.329487

0.324359

0.058456

0.031994

0.028626

0.156844

0.113784

0.077219

0.043782

0.024056

0

0.0299

0.244819

6,466

165

116

39.187879

0.821421

0.169193

0

0.225806

0

0.100149

0.008969

0

1

0.048387

false

0

0.104839

0

0.193548

0

null

0

null

0

1

0

79df34a92c6aa109d6fb09a1fbe24d44b829d071

3,440

py

Python

app.py

rbSparky/umit-hack-backend

a9402d35d07693b78498a2ba2d4ff08fcb6cab44

[ "MIT" ]

null

app.py

rbSparky/umit-hack-backend

a9402d35d07693b78498a2ba2d4ff08fcb6cab44

[ "MIT" ]

null

app.py

rbSparky/umit-hack-backend

a9402d35d07693b78498a2ba2d4ff08fcb6cab44

[ "MIT" ]

null

import pickle from flask import Flask, request, jsonify, session from flask_cors import CORS, cross_origin import sklearn from sklearn.decomposition import TruncatedSVD import pandas as pd import numpy as np ranks = [] app = Flask(__name__) cors = CORS(app) app.config['CORS_HEADERS'] = 'Content-Type' class Model: i = '0' lrank = 1 hrank = 2 r_names = None r_ID = None corr_ID = None recc = None cmat = [] fir = [] sec = [] final = [] def predict(self): self.sec = [] self.fir = [] flf = [] SVD = TruncatedSVD(n_components = 10) decompm = SVD.fit_transform(self.cmat) df = pd.DataFrame(decompm) corrm = np.corrcoef(decompm) p_names = list(self.cmat.index) p_ID = p_names.index(str(self.i)) c_ID = corrm[p_ID] Recommend = list(self.cmat.index[c_ID > 0.95]) fl = [] for i in range(len(c_ID)): if(c_ID[i] > 0.95): fl.append([c_ID[i], self.cmat.index[i]]) fl.sort(reverse=True) flf = [] for i in range(len(fl)): if (fl[i][0] > 0.95): flf.append(fl[i]) clgdf, clgds = {}, {} for i in flf: for j in self.cmat.loc[i[1]].items(): if ((j[1] == 5) and (j[0] not in self.fir) and (ranks[j[0]][1] >= self.lrank)):# and (self.hrank >= ranks[j[0]][0])): if(j[0] in clgdf): clgdf[j[0]] += 1 else: clgdf[j[0]] = 1 elif ((j[1] == 2) and (j[0] not in self.sec) and (ranks[j[0]][1] >= self.lrank)):# and (self.hrank >= ranks[j[0]][0])): if(j[0] in clgds): clgds[j[0]] += 1 else: clgds[j[0]] = 1 tf, ts = [], [] for k in clgdf: tf.append([clgdf[k], k]) for k in clgds: ts.append([clgds[k], k]) tf.sort(reverse=True) ts.sort(reverse=True) for i in tf: j = i[1] self.fir.append([(j.split())[0], j[len((j.split())[0]):], ranks[j][0], ranks[j][1]]) for i in ts: j = i[1] self.sec.append([(j.split())[0], j[len((j.split())[0]):], ranks[j][0], ranks[j][1]]) #print(self.fir, self.sec, sep = '\n\n\n') self.final = [] for i in self.fir: self.final.append(i) for i in self.sec: self.final.append(i) rfinal = [] [rfinal.append(x) for x in self.final if x not in rfinal] return jsonify(rfinal) @app.route('/') def hello(): return 'hi main' @app.route('/predict', methods=['POST','GET']) #or POST u see that @cross_origin() def predict(): #take all these as input from args global ranks #session.clear() req_dat = request.get_json() lrank = req_dat['lrank']#5000 hrank = req_dat['hrank']#7000 stream1 = req_dat['stream1']#'Computer Science' stream2 = req_dat['stream2']#'Electronics' ''' lrank = int(request.args.get("lrank")) hrank = int(request.args.get("hrank")) stream1 = request.args.get("p1") stream2 = request.args.get("p2") ''' f = open('essentials.pckl', 'rb') f1 = pickle.load(f) f.close() #print(f1) f = open('Model2.pckl', 'rb') f2 = pickle.load(f) f.close() wsc = f1[0] ranks = f1[1] f2.cmat = f1[2] f2.lrank = lrank f2.hrank = hrank f2.stream1 = stream1 f2.stream2 = stream2 f2.final = [] f2.i = wsc[(stream1, stream2)] return f2.predict() if __name__ == '__main__': #app.secret_key = 'super secret key' #app.config['SESSION_TYPE'] = 'filesystem' #session.init_app(app) app.run(debug=True)

23.888889

127

0.563081

540

3,440

3.514815

0.264815

0.014752

0.022129

0.017914

0.135933

0.103267

0.088514

0

0.034924

0.250872

3,440

143

128

24.055944

0.701591

0.094477

0

0.092593

0

0.037428

0

1

0.027778

false

0

0.064815

0.009259

0.231481

0

null

0

null

0

1

0

8dab135854cbf1898ed8a1808f3a10f5e2425b1b

235

py

Python

ccr/urls.py

nikhil96sher/coding_companion

bb5d9596dff74e342ca07b6d95c37fb491877224

[ "MIT" ]

12

2015-12-30T06:31:57.000Z

2017-12-26T01:42:18.000Z

ccr/urls.py

nikhilsheoran96/coding_companion

bb5d9596dff74e342ca07b6d95c37fb491877224

[ "MIT" ]

null

ccr/urls.py

nikhilsheoran96/coding_companion

bb5d9596dff74e342ca07b6d95c37fb491877224

[ "MIT" ]

5

2015-12-30T07:06:22.000Z

2019-04-24T05:46:01.000Z

from django.conf.urls import patterns,url from ccr import views urlpatterns=patterns( '', url(r'^$',views.main), url(r'^save/',views.save), url(r'^template/',views.template), url(r'^compile/',views.compile), url(r'^run/',views.run), )

21.363636

41

0.702128

37

235

4.459459

0.432432

0.121212

0

0.068085

235

11

42

21.363636

0.753425

0

0.135593

0

1

0

false

0

0.2

0

0.2

0

null

0

null

0

1

0

8daf3e4c8966ce1a6bddee1ab26929f7ffa13135

9,354

py

Python

tools/Project/UI_ModuleMgrDlg.py

wzhengsen/engine-x

f398b94a9a5bb9645c16d12d82d6366589db4e21

[ "MIT" ]

null

tools/Project/UI_ModuleMgrDlg.py

wzhengsen/engine-x

f398b94a9a5bb9645c16d12d82d6366589db4e21

[ "MIT" ]

null

tools/Project/UI_ModuleMgrDlg.py

wzhengsen/engine-x

f398b94a9a5bb9645c16d12d82d6366589db4e21

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Form implementation generated from reading ui file 'UI/ModuleMgrDlg.ui' # # Created by: PyQt5 UI code generator 5.15.2 # # WARNING: Any manual changes made to this file will be lost when pyuic5 is # run again. Do not edit this file unless you know what you are doing. from PyQt5 import QtCore, QtGui, QtWidgets class Ui_ModuleMgrDlg(object): def setupUi(self, ModuleMgrDlg): ModuleMgrDlg.setObjectName("ModuleMgrDlg") ModuleMgrDlg.setWindowModality(QtCore.Qt.WindowModal) ModuleMgrDlg.resize(600, 300) ModuleMgrDlg.setMinimumSize(QtCore.QSize(300, 150)) ModuleMgrDlg.setMaximumSize(QtCore.QSize(600, 300)) icon = QtGui.QIcon() icon.addPixmap(QtGui.QPixmap(":/res/IcoModule.ico"), QtGui.QIcon.Normal, QtGui.QIcon.Off) ModuleMgrDlg.setWindowIcon(icon) ModuleMgrDlg.setModal(True) self.horizontalLayout = QtWidgets.QHBoxLayout(ModuleMgrDlg) self.horizontalLayout.setContentsMargins(0, 0, 0, 0) self.horizontalLayout.setSpacing(2) self.horizontalLayout.setObjectName("horizontalLayout") self.gridLayout = QtWidgets.QGridLayout() self.gridLayout.setSpacing(2) self.gridLayout.setObjectName("gridLayout") self.NewModuleButton = QtWidgets.QPushButton(ModuleMgrDlg) icon1 = QtGui.QIcon() icon1.addPixmap(QtGui.QPixmap(":/res/ImgPlus.png"), QtGui.QIcon.Normal, QtGui.QIcon.Off) self.NewModuleButton.setIcon(icon1) self.NewModuleButton.setAutoDefault(False) self.NewModuleButton.setObjectName("NewModuleButton") self.gridLayout.addWidget(self.NewModuleButton, 1, 0, 1, 1) self.DelModuleButton = QtWidgets.QPushButton(ModuleMgrDlg) self.DelModuleButton.setEnabled(False) icon2 = QtGui.QIcon() icon2.addPixmap(QtGui.QPixmap(":/res/ImgDel.png"), QtGui.QIcon.Normal, QtGui.QIcon.Off) self.DelModuleButton.setIcon(icon2) self.DelModuleButton.setAutoDefault(False) self.DelModuleButton.setObjectName("DelModuleButton") self.gridLayout.addWidget(self.DelModuleButton, 1, 1, 1, 1) self.ModuleListWidget = QtWidgets.QListWidget(ModuleMgrDlg) self.ModuleListWidget.setObjectName("ModuleListWidget") self.gridLayout.addWidget(self.ModuleListWidget, 0, 0, 1, 2) self.horizontalLayout.addLayout(self.gridLayout) self.ModuleInfoTabWidget = QtWidgets.QTabWidget(ModuleMgrDlg) self.ModuleInfoTabWidget.setEnabled(False) self.ModuleInfoTabWidget.setObjectName("ModuleInfoTabWidget") self.tab = QtWidgets.QWidget() self.tab.setObjectName("tab") self.gridLayout_2 = QtWidgets.QGridLayout(self.tab) self.gridLayout_2.setContentsMargins(0, 0, 0, 0) self.gridLayout_2.setSpacing(0) self.gridLayout_2.setObjectName("gridLayout_2") self.InfoTableWidget = QtWidgets.QTableWidget(self.tab) self.InfoTableWidget.setSelectionMode(QtWidgets.QAbstractItemView.SingleSelection) self.InfoTableWidget.setGridStyle(QtCore.Qt.SolidLine) self.InfoTableWidget.setObjectName("InfoTableWidget") self.InfoTableWidget.setColumnCount(1) self.InfoTableWidget.setRowCount(4) item = QtWidgets.QTableWidgetItem() self.InfoTableWidget.setVerticalHeaderItem(0, item) item = QtWidgets.QTableWidgetItem() self.InfoTableWidget.setVerticalHeaderItem(1, item) item = QtWidgets.QTableWidgetItem() self.InfoTableWidget.setVerticalHeaderItem(2, item) item = QtWidgets.QTableWidgetItem() self.InfoTableWidget.setVerticalHeaderItem(3, item) item = QtWidgets.QTableWidgetItem() self.InfoTableWidget.setHorizontalHeaderItem(0, item) item = QtWidgets.QTableWidgetItem() self.InfoTableWidget.setItem(0, 0, item) item = QtWidgets.QTableWidgetItem() self.InfoTableWidget.setItem(1, 0, item) item = QtWidgets.QTableWidgetItem() self.InfoTableWidget.setItem(2, 0, item) item = QtWidgets.QTableWidgetItem() self.InfoTableWidget.setItem(3, 0, item) self.InfoTableWidget.horizontalHeader().setVisible(False) self.InfoTableWidget.horizontalHeader().setStretchLastSection(True) self.InfoTableWidget.verticalHeader().setDefaultSectionSize(20) self.InfoTableWidget.verticalHeader().setHighlightSections(False) self.gridLayout_2.addWidget(self.InfoTableWidget, 0, 0, 1, 1) self.ModuleInfoTabWidget.addTab(self.tab, "") self.tab_2 = QtWidgets.QWidget() self.tab_2.setObjectName("tab_2") self.gridLayout_3 = QtWidgets.QGridLayout(self.tab_2) self.gridLayout_3.setContentsMargins(0, 0, 0, 0) self.gridLayout_3.setSpacing(0) self.gridLayout_3.setObjectName("gridLayout_3") self.DelDirButton = QtWidgets.QPushButton(self.tab_2) self.DelDirButton.setEnabled(False) self.DelDirButton.setIcon(icon2) self.DelDirButton.setObjectName("DelDirButton") self.gridLayout_3.addWidget(self.DelDirButton, 1, 2, 1, 1) self.NewDirButton = QtWidgets.QPushButton(self.tab_2) self.NewDirButton.setIcon(icon1) self.NewDirButton.setObjectName("NewDirButton") self.gridLayout_3.addWidget(self.NewDirButton, 1, 1, 1, 1) spacerItem = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.gridLayout_3.addItem(spacerItem, 1, 0, 1, 1) self.DirsListWidget = QtWidgets.QListWidget(self.tab_2) self.DirsListWidget.setObjectName("DirsListWidget") self.gridLayout_3.addWidget(self.DirsListWidget, 0, 0, 1, 3) self.ModuleInfoTabWidget.addTab(self.tab_2, "") self.tab_3 = QtWidgets.QWidget() self.tab_3.setObjectName("tab_3") self.gridLayout_4 = QtWidgets.QGridLayout(self.tab_3) self.gridLayout_4.setContentsMargins(0, 0, 0, 0) self.gridLayout_4.setSpacing(0) self.gridLayout_4.setObjectName("gridLayout_4") self.DelFileButton = QtWidgets.QPushButton(self.tab_3) self.DelFileButton.setEnabled(False) self.DelFileButton.setIcon(icon2) self.DelFileButton.setObjectName("DelFileButton") self.gridLayout_4.addWidget(self.DelFileButton, 1, 2, 1, 1) self.NewFileButton = QtWidgets.QPushButton(self.tab_3) self.NewFileButton.setIcon(icon1) self.NewFileButton.setObjectName("NewFileButton") self.gridLayout_4.addWidget(self.NewFileButton, 1, 1, 1, 1) spacerItem1 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.gridLayout_4.addItem(spacerItem1, 1, 0, 1, 1) self.FilesListWidget = QtWidgets.QListWidget(self.tab_3) self.FilesListWidget.setObjectName("FilesListWidget") self.gridLayout_4.addWidget(self.FilesListWidget, 0, 0, 1, 3) self.ModuleInfoTabWidget.addTab(self.tab_3, "") self.horizontalLayout.addWidget(self.ModuleInfoTabWidget) self.horizontalLayout.setStretch(0, 4) self.horizontalLayout.setStretch(1, 12) self.retranslateUi(ModuleMgrDlg) self.ModuleInfoTabWidget.setCurrentIndex(0) QtCore.QMetaObject.connectSlotsByName(ModuleMgrDlg) def retranslateUi(self, ModuleMgrDlg): _translate = QtCore.QCoreApplication.translate ModuleMgrDlg.setWindowTitle(_translate("ModuleMgrDlg", "模块管理")) self.NewModuleButton.setText(_translate("ModuleMgrDlg", "新增模块")) self.DelModuleButton.setText(_translate("ModuleMgrDlg", "删除模块")) item = self.InfoTableWidget.verticalHeaderItem(0) item.setText(_translate("ModuleMgrDlg", "downloadUrl")) item.setToolTip(_translate("ModuleMgrDlg", "远程文件根目录")) item = self.InfoTableWidget.verticalHeaderItem(1) item.setText(_translate("ModuleMgrDlg", "uploadUrl")) item.setToolTip(_translate("ModuleMgrDlg", "上传根路径")) item = self.InfoTableWidget.verticalHeaderItem(2) item.setText(_translate("ModuleMgrDlg", "remoteVersionUrl")) item.setToolTip(_translate("ModuleMgrDlg", "远程版本文件")) item = self.InfoTableWidget.verticalHeaderItem(3) item.setText(_translate("ModuleMgrDlg", "remoteManifestUrl")) item.setToolTip(_translate("ModuleMgrDlg", "远程清单文件")) item = self.InfoTableWidget.horizontalHeaderItem(0) item.setText(_translate("ModuleMgrDlg", "值")) __sortingEnabled = self.InfoTableWidget.isSortingEnabled() self.InfoTableWidget.setSortingEnabled(False) self.InfoTableWidget.setSortingEnabled(__sortingEnabled) self.ModuleInfoTabWidget.setTabText(self.ModuleInfoTabWidget.indexOf(self.tab), _translate("ModuleMgrDlg", "常规")) self.DelDirButton.setText(_translate("ModuleMgrDlg", "删除目录")) self.NewDirButton.setText(_translate("ModuleMgrDlg", "新增目录")) self.ModuleInfoTabWidget.setTabText(self.ModuleInfoTabWidget.indexOf(self.tab_2), _translate("ModuleMgrDlg", "目录")) self.DelFileButton.setText(_translate("ModuleMgrDlg", "删除文件")) self.NewFileButton.setText(_translate("ModuleMgrDlg", "新增文件")) self.ModuleInfoTabWidget.setTabText(self.ModuleInfoTabWidget.indexOf(self.tab_3), _translate("ModuleMgrDlg", "文件")) import UI_rc

55.023529

123

0.715843

914

9,354

7.251641

0.195842

0.059143

0.04647

0.04481

0.272782

0.214544

0.168678

0.119191

0.040435

0.028365

0

0.023954

0.174364

9,354

169

124

55.349112

0.834261

0.029934

0

0.058065

1

0

0.070373

0

1

0.012903

false

0

0.012903

0

0.032258

0

null

0

1

0

null

0

1

8db06f6b303411c51b7e7ee7f461a4a3b7ef48b6

3,941

py

Python

spowtd/transmissivity.py

alex-cobb/python-spowtd

b841ce63a4ed168a6e1b4e17b689d8be9dc11318

[ "BSD-2-Clause" ]

null

spowtd/transmissivity.py

alex-cobb/python-spowtd

b841ce63a4ed168a6e1b4e17b689d8be9dc11318

[ "BSD-2-Clause" ]

null

spowtd/transmissivity.py

alex-cobb/python-spowtd

b841ce63a4ed168a6e1b4e17b689d8be9dc11318

[ "BSD-2-Clause" ]

2

2021-10-14T14:38:43.000Z

2022-03-21T16:21:06.000Z

"""Transmissivity classes """ import numpy as np import scipy.integrate as integrate_mod import spowtd.spline as spline_mod def create_transmissivity_function(parameters): """Create a transmissivity function Returns a callable object that returns transmissivity at a given water level. The class of the object depends on the "type" field in the parameters provided, and must be either "peatclsm" or "spline". """ if 'type' not in parameters: raise ValueError( '"type" field is required in parameters; got {}' .format(parameters)) sy_type = parameters.pop('type', None) return { 'peatclsm': PeatclsmTransmissivity, 'spline': SplineTransmissivity }[sy_type](**parameters) class SplineTransmissivity: """Transmissivity parameterized as a spline of log conductivity zeta_knots_mm: Sequence of water levels in mm K_knots: Condutivity values at those water levels Stores a set of knots representing hydraulic conductivity at water table heights (relative to surface) zeta. When called, takes a water table height and returns a transmissivity obtained by linear interpolation of log-conductivity. This is an extended value function that returns minimum_transmissivity below min(zeta) and extrapolates exponentially or linearly above max(zeta), according to whether the last two knots have the same or different conductivity. """ __slots__ = ['zeta_knots_mm', 'K_knots_km_d', 'minimum_transmissivity_m2_d', '_spline'] def __init__(self, zeta_knots_mm, K_knots_km_d, minimum_transmissivity_m2_d): self.zeta_knots_mm = np.asarray(zeta_knots_mm, dtype='float64') self.K_knots_km_d = np.asarray(K_knots_km_d, dtype='float64') self.minimum_transmissivity_m2_d = minimum_transmissivity_m2_d log_K_knots = np.log(K_knots_km_d) self._spline = spline_mod.Spline.from_points( zip(zeta_knots_mm, log_K_knots), order=1) def conductivity(self, water_level_mm): assert water_level_mm >= self.zeta_knots_mm.min() if water_level_mm >= self.zeta_knots_mm.max(): raise NotImplementedError('Extrapolation above highest knot') return np.exp(self._spline(water_level_mm)) def __call__(self, water_level_mm): if np.isscalar(water_level_mm): return self.call_scalar(water_level_mm) return np.array( [self.call_scalar(value) for value in water_level_mm], dtype='float64') def call_scalar(self, water_level_mm): """Compute transmissivity for a scalar argument """ if water_level_mm <= self.zeta_knots_mm.min(): return self.minimum_transmissivity_m2_d return ( self.minimum_transmissivity_m2_d + integrate_mod.quad( self.conductivity, self.zeta_knots_mm.min(), water_level_mm)[0]) class PeatclsmTransmissivity: """Transmissivity function used in PEATCLSM Computes transmissivity in m^2 / s from water level in mm. See equation 3 in Apers et al. 2022, JAMES. """ __slots__ = ['Ksmacz0', 'alpha', 'zeta_max_cm'] def __init__(self, Ksmacz0, alpha, zeta_max_cm): self.Ksmacz0 = Ksmacz0 self.alpha = alpha self.zeta_max_cm = zeta_max_cm def __call__(self, water_level_mm): Ksmacz0 = self.Ksmacz0 alpha = self.alpha zeta_max_cm = self.zeta_max_cm water_level_mm = np.asarray(water_level_mm) if (water_level_mm / 10 > zeta_max_cm).any(): raise ValueError('T undefined at water level > {} cm in {}' .format(zeta_max_cm, water_level_mm / 10)) return ( Ksmacz0 * (zeta_max_cm - water_level_mm / 10) ** (1 - alpha) ) / (100 * (alpha - 1))

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0.664298

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3,941

4.829746

0.295499

0.081037

0.082658

0.058347

0.200162

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0.01296

0.256026

3,941

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0.828786

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0

0.33871

0

null

0

null

0

1

0

8db1c50adc29a6ffdf16eef7eb3f4db1957e630c

1,737

py

Python

python/hillEquations.py

dhlee4/Tinkercell_new

c4d1848bbb905f0e1f9e011837268ac80aff8711

[ "BSD-3-Clause" ]

1

2021-01-07T13:12:51.000Z

python/hillEquations.py

dhlee4/Tinkercell_new

c4d1848bbb905f0e1f9e011837268ac80aff8711

[ "BSD-3-Clause" ]

7

2020-04-12T22:25:46.000Z

2020-04-13T07:50:40.000Z

python/hillEquations.py

daniel-anavaino/tinkercell

7896a7f809a0373ab3c848d25e3691d10a648437

[ "BSD-3-Clause" ]

2

2020-04-12T21:57:01.000Z

2020-04-12T21:59:29.000Z

""" category: Generate kinetics name: Hill equations description: automatically generate the equilibrium rate equation for transcription icon: hillequation.png menu: yes specific for: Coding tool: yes """ from tinkercell import * from tc2py import * items = tc_selectedItems(); genes = []; for i in range(0,items.length): if tc_isA( tc_getItem(items,i),"Coding"): genes.append( tc_getItem(items,i) ); tc_deleteItemsArray(items); if (len(genes) > 0): strList = toStrings(("AND","OR","XOR")); t = tc_getStringFromList("Select the logical function to approximate:",strList,"Auto"); tc_deleteStringsArray(strList); if t > -1: for i in genes: opnames = []; opname = ""; promoter = ""; upstream = tc_partsUpstream(i); for j in range(0,upstream.length): p = tc_getItem(upstream,j); if tc_isA(p,"Promoter"): promoter = tc_getUniqueName(p); if tc_isA(p,"Operator"): opname = tc_getUniqueName(p); if tc_getConnections(p).length > 0: opnames.append(opname); rate = "0.0"; if len(promoter) > 0: if len(opnames) < 1: rate = promoter + ".strength"; elif len(opnames) == 1: rate = promoter + ".strength * " + opnames[0]; else: if t == 0: #AND rate = " * ".join(opnames); elif t == 1: #OR rate = " + ".join(opnames) + " - " + " * ".join(opnames); elif t == 2: #XOR rate = " + ".join(opnames) + " - 2 * " + " * ".join(opnames); rate = promoter + ".strength * (" + rate + ")"; name = tc_getUniqueName(i) tc_print(name + " has rate : " + rate + "\n"); tc_addForcingFunction(i, name , rate); else: tc_print("no promoter found for this coding region\n"); else: tc_errorReport("please select a coding region");

28.47541

88

0.62061

224

1,737

4.727679

0.366071

0.051936

0.01983

0.028329

0.096317

0.058546

0

0.011773

0.217617

1,737

60

89

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0.767476

0.11802

0

0.06383

1

0

0.147929

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1

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0

0.042553

0

null

0

1

0

null

0

1

8db29e40510fc64c7655a39c604ac0d49c03c44b

795

py

Python

setup.py

Flowerowl/ici

7c3209ee0ddfae27bda76f586ac02545364a0c73

[ "MIT" ]

204

2015-01-03T14:29:43.000Z

2021-12-15T16:21:28.000Z

setup.py

QQ83076130/ici

7c3209ee0ddfae27bda76f586ac02545364a0c73

[ "MIT" ]

5

2015-05-14T10:34:24.000Z

2017-10-09T15:53:47.000Z

setup.py

QQ83076130/ici

7c3209ee0ddfae27bda76f586ac02545364a0c73

[ "MIT" ]

77

2015-01-13T01:44:16.000Z

2021-12-15T16:21:39.000Z

#encoding:utf-8 from setuptools import setup, find_packages import sys, os version = '0.4.3' setup(name='ici', version=version, description="方便程序员在terminal查询生词的小工具", long_description="""方便程序员在terminal查询生词的小工具""", classifiers=[], # Get strings from http://pypi.python.org/pypi?%3Aaction=list_classifiers keywords='python iciba dictionary terminal', author='yuzhe', author_email='lazynightz@gmail.com', url='https://github.com/Flowerowl/ici', license='', packages=find_packages(exclude=['ez_setup', 'examples', 'tests']), include_package_data=True, zip_safe=False, install_requires=[ 'termcolor', ], entry_points={ 'console_scripts':[ 'ici = ici.ici:main' ] }, )

27.413793

95

0.632704

83

795

5.915663

0.759036

0.04888

0

0.00813

0.226415

795

28

96

28.392857

0.790244

0.108176

0

0.288543

0.062235

0

1

0

false

0

0.08

0

0.08

0

null

0

null

0

1

0

8db3a905d27b52ca5f7ab31fe8496b3bc345779b

24,074

py

Python

src/son/monitor/son_sp.py

dang03/son-cli

3e29322d4556f3e02f7b15c43c5e66a1e7e07bd3

[ "Apache-2.0" ]

4

2017-02-08T22:50:28.000Z

2018-05-29T07:29:47.000Z

src/son/monitor/son_sp.py

dang03/son-cli

3e29322d4556f3e02f7b15c43c5e66a1e7e07bd3

[ "Apache-2.0" ]

81

2016-07-19T13:55:12.000Z

2021-05-07T15:03:05.000Z

src/son/monitor/son_sp.py

dang03/son-cli

3e29322d4556f3e02f7b15c43c5e66a1e7e07bd3

[ "Apache-2.0" ]

13

2016-07-19T13:33:19.000Z

2019-04-25T08:04:15.000Z

""" Copyright (c) 2015 SONATA-NFV 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. Neither the name of the SONATA-NFV [, ANY ADDITIONAL AFFILIATION] nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. This work has been performed in the framework of the SONATA project, funded by the European Commission under Grant number 671517 through the Horizon 2020 and 5G-PPP programmes. The authors would like to acknowledge the contributions of their colleagues of the SONATA partner consortium (www.sonata-nfv.eu). """ import logging from requests import Session, post, get import websocket import threading from subprocess import call, check_output import json from son.profile.helper import read_yaml, write_yaml from prometheus_client import start_http_server, Gauge import os import docker from time import gmtime, strftime import datetime """ This class implements the son-sp commands. These commands translate to the API's of the SONATA SP """ LOG = logging.getLogger('SP_monitor') LOG.setLevel(level=logging.INFO) prometheus_stream_port = 8082 prometheus_server_api = 'http://127.0.0.1:9090' prometheus_config_path = '/tmp/son-monitor/prometheus/prometheus_sdk.yml' GK_api = 'http://sp.int3.sonata-nfv.eu:32001/api/v2/' monitor_api = 'http://sp.int3.sonata-nfv.eu:8000/api/v1/' son_access_config_path = "/home/steven/.son-workspace" platform_id = 'sp1' class Service_Platform(): def __init__(self, export_port=8082, GK_api=None, **kwargs): self.monitor_api = kwargs.get('monitor_api', monitor_api) self.GK_api = kwargs.get('GK_api', GK_api) self.son_access_config_path = kwargs.get('son_access_config_path', son_access_config_path) self.platform_id = kwargs.get('platform_id', platform_id) # Build up our session self.session = Session() self.session.headers = { "Accept": "application/json; charset=UTF-8" } # global parameters needed for the SP_websocket Class global prometheus_stream_port prometheus_stream_port = export_port global prometheus_server_api prometheus_server_api = kwargs.get('prometheus_server_api', prometheus_server_api) global prometheus_config_path prometheus_config_path = kwargs.get('prometheus_config_path', prometheus_config_path) self.ws_thread = None # websocket in the SP self.ws = None # access token to auth the SDK user self.access_token = None def list(self, **kwargs): # if metric is specified, show the list of VNFs that export ths metric metric = kwargs.get('metric') if metric : url = self.monitor_api + 'prometheus/metrics/name/' + metric ret = self.session.get(url).json().get("metrics").get("result") else: url = self.monitor_api + 'prometheus/metrics/list' resp = self.session.get(url) ret = resp.json().get('metrics') return ret def query(self, **kwargs): verbose = kwargs.get("verbose", False) LOG.setLevel(level=logging.INFO) if verbose: LOG.setLevel(level=logging.DEBUG) # periodically refresh token self._get_token() service_name = kwargs.get("service") vnf_name = kwargs.get("vnf_name") vdu_id = kwargs.get("vdu_id") vnfc_id = kwargs.get("vnfc_id") metric = kwargs.get("metric") since = kwargs.get("since") until = kwargs.get("until") metric_list = [] service_desc_uuid = self._get_service_descriptor_uuid(service_name) vnf_instances = self._get_vnf_instances(service_desc_uuid) if len(vnf_instances) <= 0: LOG.warning("found no VNF instances for this service descriptor uuid: {0}".format(service_desc_uuid)) else: vnf_descriptor_uuid = self._get_VNF_descriptor_uuid(vnf_name) for vnf_instance_uuid in vnf_instances: vdu_id, vc_id = self._check_VNF_instance(vnf_instance_uuid, vnf_descriptor_uuid, vdu_id, vnfc_id) if vc_id: LOG.info("found VNF: {0} with instance uuid: {2}, vdu_id: {3} vnfc_id: {4} in service: {1} ".format( vnf_name, service_name, vnf_instance_uuid, vdu_id, vc_id)) metric_list = self._get_async_metric(vnf_instance_uuid, vdu_id, vc_id, metric, since, until) break return metric_list def stream_test(self, **kwargs): metric = kwargs.get('metric') vnf_name = kwargs.get('vnf_name') action = kwargs.get('action', 'start') if action == 'stop': SP_websocket._config_prometheus(remove=True) if self.ws: self.ws.close() # kill all running websocket streams call(['pkill', '-f', 'son-monitor stream']) return 'websocket closed' # create the websocket with a filter eg: {"metric":"vm_cpu_perc","filters":["exported_instance":"vtc-vnf"]} url = self.monitor_api + 'ws/new' data = {'metric':str(metric), 'filters':str(list("exported_instance={}".format(vnf_name)))} response = self.session.post(url, json=data) code = response.status_code if code == 200: ws_url = response.json().get('ws_url') LOG.info('ws_url: {}'.format(ws_url)) self.ws = SP_websocket(ws_url, vnf_name=vnf_name, metric=metric) self.ws_thread = threading.Thread(target=self.ws.run_forever) self.ws_thread.daemon = True self.ws_thread.start() self.ws_thread.join() return 'websocket thread started' def stream_auth(self, **kwargs): """ call the SONATA Gatekeeper API to request monitoring metrics :param kwargs: :return: """ verbose = kwargs.get("verbose", False) LOG.setLevel(level=logging.INFO) if verbose: LOG.setLevel(level=logging.DEBUG) action = kwargs.get('action', 'start') if action == 'stop': SP_websocket._config_prometheus(remove=True) if self.ws: self.ws.close() # kill all running websocket streams LOG.info('closing websocket') call(['pkill', '-f', 'son-monitor stream']) LOG.info('websocket closed') return 'websocket closed' # periodically refresh token self._get_token() service_name = kwargs.get("service","sonata-demo-12") vnf_name = kwargs.get("vnf_name","vtc-vnf2") vdu_id = kwargs.get("vdu_id") vnfc_id = kwargs.get("vnfc_id") metric = kwargs.get("metric") ws_url = None # first lookup if the service name is instantiated service_desc_uuid = self._get_service_descriptor_uuid(service_name) # then check if the service has an instance of this VNF vnf_instances = self._get_vnf_instances(service_desc_uuid) if len(vnf_instances) <= 0: LOG.warning("found no VNF instances for this service descriptor uuid: {0}".format(service_desc_uuid)) else: # get the descriptor uuid of this vnf vnf_descriptor_uuid = self._get_VNF_descriptor_uuid(vnf_name) for vnf_instance_uuid in vnf_instances: # check if this VNF instance has the correct vdu and vnfc vdu_id, vnfc_id = self._check_VNF_instance(vnf_instance_uuid, vnf_descriptor_uuid, vdu_id, vnfc_id) if vnfc_id: LOG.info("found VNF: {0} with instance uuid: {2}, vdu_id: {3} vnfc_id: {4} in service: {1} ".format( vnf_name, service_name, vnf_instance_uuid, vdu_id, vnfc_id)) ws_url = self._get_ws_url(vnf_instance_uuid, vdu_id, vnfc_id, metric) break if not vnfc_id: return 'No vnfc_id found in the record' if not ws_url: return 'No websocket url received' #ws_url = 'ws://10.30.0.112:8002/ws/98adab175fd64cc4bbe50ae9505fecf6' self.ws = SP_websocket(ws_url, vnf_name=vnf_name, metric=metric, vm_id=vnfc_id) self.ws_thread = threading.Thread(target=self.ws.run_forever) self.ws_thread.daemon = True self.ws_thread.start() self.ws_thread.join() return 'websocket thread started' # TODO: start background thread to refresh token def _get_token(self): # the credentials and token is fetched via son-access, the son-access config path must be given token_path = os.path.join(self.son_access_config_path, 'platforms', 'token.txt') output = check_output(['son-access', '-w', self.son_access_config_path, '-p', self.platform_id, 'auth']) #token_path = workspace_dir + '/' + token_file with open(token_path, 'r') as token: self.access_token = token.read() def _get_VNF_descriptor_uuid(self, vnf_name): headers = {'Authorization': "Bearer %s" % self.access_token} url = self.GK_api + "functions" resp = get(url, headers=headers) if resp.status_code >= 400: return 'error: {}'.format(resp.status_code) functions_list = resp.json() found_functions = [function.get("uuid") for function in functions_list if function["vnfd"]["name"] == vnf_name] if len(found_functions) > 1 or len(found_functions) == 0: LOG.warning("found {0} functions with name: {1}".format(len(found_functions), vnf_name)) return None else: uuid = found_functions[0] LOG.info("found function descriptor of {0} with uuid: {1}".format(vnf_name, uuid)) return uuid def _check_VNF_instance(self, vnf_instance_uuid, vnf_descriptor_uuid, vdu_id=None, vnfc_id=None): headers = {'Authorization': "Bearer %s" % self.access_token} url = self.GK_api + "records/functions" resp = get(url, headers=headers) if resp.status_code >= 400: return 'error: {}'.format(resp.status_code) LOG.debug('request VNF record, url:{0} json:{1}'.format(url, json.dumps(resp.json(), indent=2))) vnf_list = resp.json() vnf_list = [vnf for vnf in vnf_list if vnf.get("descriptor_reference") == vnf_descriptor_uuid and vnf.get("uuid") == vnf_instance_uuid] if len(vnf_list) > 1 : LOG.info("found multiple VNF instances with matching uuid: {0}".format(vnf_list)) return False elif len(vnf_list) == 0 : LOG.info("found no VNF instance with matching uuid: {0}".format(vnf_instance_uuid)) return False # we found 1 matching vnf instance, now check if it has a vdu LOG.info("found VNF instance with matching uuid: {0}".format(vnf_instance_uuid)) vnf_record = vnf_list[0] vdu_list = vnf_record["virtual_deployment_units"] if vdu_id: vdu_list = [vdu for vdu in vdu_list if vdu.get("id") == vdu_id] else: #pick by default first vdu vdu_list = [vdu_list[0]] vdu = vdu_list[0] vdu_id = vdu["id"] if len(vdu_list) > 1 : LOG.info("found multiple vdu_ids with matching id: {0} list: {1}".format(vdu_id, vdu_list)) return False elif len(vdu_list) == 0 : LOG.info("found no VDUs with matching id: {0}".format(vdu_id)) return False # we found 1 matching vdu id, now check if it has a vdu instance(vnfc) LOG.info("found VDU with matching id: {0}".format(vdu_id)) vdu = vdu_list[0] vnfc_list = vdu["vnfc_instance"] if vnfc_id: vnfc_list = [vnfc for vnfc in vnfc_list if vnfc.get("id") == vnfc_id] else: #pick by default first vnfc vnfc_list = [vnfc_list[0]] vnfc = vnfc_list[0] vnfc_id = vnfc["id"] if len(vnfc_list) > 1 : LOG.info("found multiple vnfc_ids with matching id: {0} list: {1}".format(vnfc_id, vnfc_list)) return False elif len(vnfc_list) == 0 : LOG.info("found no VNFCs with matching id: {0}".format(vnfc_id)) return False vnfc = vnfc_list[0] vc_id = vnfc["vc_id"] LOG.info("found VNFC with matching id: {0} and vc_id: {1}".format(vnfc_id, vc_id)) return vdu_id, vc_id # Get the list of all the service instances registered def _get_service_instance_list(self): headers = {'Authorization': "Bearer %s" % self.access_token} url = self.GK_api + "records/services" resp = get(url, headers=headers) LOG.info('request service instance uuid list, url:{0} json:{1}'.format(url, json.dumps(resp.json(), indent=2))) return resp.text # Gets a registered service instance def _get_vnf_instances(self, service_descriptor_uuid): headers = {'Authorization': "Bearer %s" % self.access_token} url = self.GK_api + "records/services" resp = get(url, headers=headers) if resp.status_code >= 400: return 'error: {}'.format(resp.status_code) LOG.debug('request service instances, url:{0} json:{1}'.format(url, json.dumps(resp.json(), indent=2))) services_list = resp.json() found_services = [service for service in services_list if service["descriptor_reference"] == service_descriptor_uuid] if len(found_services) > 1 or len(found_services) == 0 : LOG.warning("found {0} service instances with descriptor uuid: {1}". format(len(found_services), service_descriptor_uuid)) return [] else: service = found_services[0] service_instance_uuid = service["uuid"] vnfr_list = [vnf.get("vnfr_id") for vnf in service["network_functions"]] LOG.info("found VNF descriptors: {}".format(json.dumps(vnfr_list,indent=2))) return vnfr_list # Obtain the list of services that can be instantiated def _get_service_descriptor_uuid(self, service_name): headers = {'Authorization': "Bearer %s" % self.access_token} url = self.GK_api + "services" resp = get(url, headers=headers) if resp.status_code >= 400: return 'error: {}'.format(resp.status_code) LOG.debug('request service descriptor uuid, url:{0} json:{1}'.format(url, json.dumps(resp.json(), indent=2))) services_list = resp.json() found_services = [service.get("uuid") for service in services_list if service.get("nsd",{}).get("name") == service_name] if len(found_services) > 1 or len(found_services) == 0 : LOG.warning("found {0} services with name: {1}". format(len(found_services), service_name)) return None else: uuid = found_services[0] LOG.info("found service descriptor of service: {0} with uuid: {1}".format(service_name, uuid)) return uuid # get the websocket url where the metrocs will be streamed def _get_ws_url(self, vnf_instance_uuid, vdu_id, vc_id, metric): """ call Gatekeeper API …/functions/metrics/:inst_id/:vdu_id/:vnfc_id/synch-mon-data A metric is uniquely identified by vnf_instance + vdu_id + vnfc_id. A VNF can consist out of multiple VDU's, a VNFC is an instance of a VDU. the vnfc_id is only unique in the scope of the VNFR/VDU :param vnf_instance_uuid: vnf instance uuid of the VNF :param vdu_id: vdu id in the VNFD of the metric we want to monitor :param vc_id: vc id in the VNFR of the metric we want to monitor :param metric: :return: """ headers = {'Authorization': "Bearer %s" % self.access_token} #url = self.GK_api + "functions/" + function_uuid + "/instances/" + instance_uuid + "/synch-mon-data?metrics=" + \ # metric + "&for=10" url = self.GK_api + "functions/metrics/" + vnf_instance_uuid + "/" + vdu_id + "/" + vc_id +"/synch-mon-data" params = {"metrics": metric} response = get(url, headers=headers, params=params) code = response.status_code LOG.debug("url: {}".format(response.url)) LOG.debug("websocket request response: {}".format(response.json())) if code == 200: ws_url = response.json().get('ws_url') LOG.info('ws_url: {}'.format(ws_url)) return ws_url # Do a query to the SP Prometheus DB def _get_async_metric(self, vnf_instance_uuid, vdu_id, vc_id, metric, since=None, until=None, step='10s'): """ call Gatekeeper API …/functions/metrics/:inst_id/:vdu_id/:vnfc_id/asynch-mon-data :param vnf_instance_uuid: vnf instance uuid of the VNF :param vdu_id: vdu id in the VNFD of the metric we want to monitor :param vnfc_id: vnfc id in the VNFR of the metric we want to monitor :param metric: :param since: :param until: :return: """ # pick some default time values (since 1 min ago until now) (notation eg. 2017-05-05T17:10:22Z) # The SONATA integration env is UTC time if not until: #now = datetime.datetime.now() now = datetime.datetime.utcnow() until = now.strftime("%Y-%m-%dT%H:%M:%SZ") #until = '2017-06-19T10:06:00Z' if not since: #now = datetime.datetime.now() now = datetime.datetime.utcnow() now_minus_1 = now - datetime.timedelta(minutes=1) since = now_minus_1.strftime("%Y-%m-%dT%H:%M:%SZ") #since = '2017-06-19T10:05:00Z' LOG.info("since: {}".format(since)) LOG.info("until: {}".format(until)) LOG.info("step: {}".format(step)) headers = {'Authorization': "Bearer %s" % self.access_token} url = self.GK_api + "functions/metrics/" + vnf_instance_uuid + "/" + vdu_id + "/" + vc_id + "/asynch-mon-data" params = {"metrics":metric, "since":since, "until":until, "step":step} response = get(url, headers=headers, params=params) code = response.status_code LOG.debug("url: {}".format(response.url)) LOG.debug("metric request response: {}".format(response.text)) return response.json() class SP_websocket(websocket.WebSocketApp): def __init__(self, url, vnf_name=None, metric=None, vm_id=None, desc='exported metric from SP', print=True): self.vnf_name = vnf_name self.metric = metric self.vc_id = vm_id #the unique identifier of the vm, used by OpenStack self.desc = desc self.print = print self.metric_received = False self.prometheus_metric = None websocket.WebSocketApp.__init__(self, url, on_message=self._on_message, on_error=self._on_error, on_close=self._on_close, on_open=self._on_open ) def _on_message(self, ws, message): LOG.info('ws message: {}'.format(message)) metric_list = self.find_metric(message) # set the metric with the correct labels once, when first value is received if not self.metric_received: self.set_exported_metric(metric_list) if self.metric_received: for metric in metric_list: self.prometheus_metric.labels(**metric['labels']).set(metric["value"]) # some info printing if self.metric_received and self.print \ and self.vnf_name is not None and self.metric is not None: message = self.filter_output(message) def _on_error(self, ws, error): self._config_prometheus(remove=True) pass def _on_close(self, ws): self._config_prometheus(remove=True) pass def _on_open(self, ws): global prometheus_stream_port # start local http export server start_http_server(prometheus_stream_port) # make Prometheus scrape this server self._config_prometheus() LOG.info('websocket opened: {}'.format(self.url)) @staticmethod def _config_prometheus(remove=False): global prometheus_server_api global prometheus_config_path docker_cli = docker.from_env() # check if containers are already running c1 = docker_cli.containers.list(filters={'status': 'running', 'name': 'prometheus'}) if len(c1) < 1: LOG.info('Prometheus is not running') return "Prometheus DB is not running" # make Prometheus scrape this server config_file = read_yaml(prometheus_config_path) targets = config_file.get('scrape_configs', []) SP_stream_config = next((target for target in targets if target.get('job_name') == 'SP_stream'), None) # the SP http server is not yet added to the config file config_dict = {'job_name': 'SP_stream', 'scrape_interval': '1s', 'static_configs': [{'targets': ['172.17.0.1:{}'.format(prometheus_stream_port)]}]} if not SP_stream_config and not remove: config_file['scrape_configs'].append(config_dict) LOG.info('added SP stream to Prometheus') elif remove and SP_stream_config: config_file['scrape_configs'].remove(config_dict) LOG.info('removed SP stream from Prometheus') write_yaml(prometheus_config_path, config_file) post(prometheus_server_api + '/-/reload') def set_exported_metric(self, metric_list): for metric in metric_list: # metric is found and labels are set metric_name = self.metric labels = list(metric['labels']) self.prometheus_metric = Gauge(metric_name, self.desc, labels) self.metric_received = True LOG.info('exporting metric with labels: {}'.format(labels)) break def filter_output(self, message): data = json.loads(message) metric_list = data.get(self.metric, []) metric = {} for metric in metric_list: for label in metric.get('labels', []): if self.vc_id in label: LOG.info('label: {}'.format(label)) LOG.info('value: {}'.format(metric.get('value'))) LOG.info('time: {}'.format(metric.get('time'))) break return metric def find_metric(self, message): data = json.loads(message) metric_list = data.get(self.metric, []) metric_list_out = [] for metric in metric_list: metric_found = False labels = {} LOG.debug('metric found:{}'.format(metric)) for label in metric.get('labels', []): key, value = label.split('=') labels[key] = str(value).replace('"','') if self.vc_id in value: metric_found = True if metric_found: # metric is found and labels are set value = metric.get('value') metric = {'labels': labels, "value": value} metric_list_out.append(metric) return metric_list_out

43.533454

143

0.620005

3,160

24,074

4.537658

0.138608

0.012204

0.020922

0.007671

0.432039

0.40219

0.34577

0.316898

0.29758

0.290048

0

0.013361

0.272493

24,074

553

144

43.533454

0.805013

0.169436

0

0.378238

0

0.005181

0.157024

0.010645

0

0.001808

0

1

0.056995

false

0.005181

0.031088

0

0.173575

0.007772

0

null

0

null

0

1

0

8db3db553de8307aa88c5fde47c1bd6250050be2

2,246

py

Python

cogs/miscellaneous/avatar.py

AkshuAgarwal/Aperture-1.7

c55ffa68d3a4de0daaaad2c918173e5ebca9f006

[ "MIT" ]

2

2021-09-05T16:42:13.000Z

2021-09-09T18:41:14.000Z

cogs/miscellaneous/avatar.py

AkshuAgarwal/Aperture-1.7

c55ffa68d3a4de0daaaad2c918173e5ebca9f006

[ "MIT" ]

null

cogs/miscellaneous/avatar.py

AkshuAgarwal/Aperture-1.7

c55ffa68d3a4de0daaaad2c918173e5ebca9f006

[ "MIT" ]

null

from datetime import datetime from typing import Union from discord import Member, User, Embed from discord.ext import commands from bot.main import NewCommand class Avatar(commands.Cog): def __init__(self, client): self.client = client @commands.command( name='avatar', cls=NewCommand, aliases=['av'], brief='That Avatar looks cool!', description='Get the Avatar of a User', help="""This command is used to get the Avatar of a User/Member. The Member should be visible to Me. That means I need to share atleast 1 common Server with the user of whom I need to get the Avatar.""", usage='[user:name/id/@mention, default:command_invoker]', explained_usage=["**User:** User whose Avatar you need to get. Can be Name, ID or Mention."], examples=[ 'avatar', 'avatar 764462046032560128', 'avatar @Akshu' ] ) @commands.cooldown(1, 5, commands.BucketType.member) async def _avatar(self, ctx, user:Union[User, Member]=None): if not user: user = ctx.author if not user.avatar: desc = f"> **Download Avatar:**\n> [png]({user.avatar_url})" elif user.is_avatar_animated() is False: desc = f"> **Download Avatar:**\n> [webp]({user.avatar_url_as(format='webp')}) | [jpeg]({user.avatar_url_as(format='jpeg')}) | [jpg]({user.avatar_url_as(format='jpg')}) | [png]({user.avatar_url_as(format='png')})" elif user.is_avatar_animated() is True: desc = f"> **Download Avatar:**\n> [gif]({user.avatar_url_as(format='gif')}) | [webp]({user.avatar_url_as(format='webp')}) | [jpeg]({user.avatar_url_as(format='jpeg')}) | [jpg]({user.avatar_url_as(format='jpg')}) | [png]({user.avatar_url_as(format='png')})" embed = Embed(title=f"{user}'s Avatar", description=desc, color=0x00eeff, timestamp=datetime.utcnow()) embed.set_author(name=user, icon_url=user.avatar_url) embed.set_footer(text=f'Thanks for using {ctx.guild.me.name}', icon_url=ctx.guild.me.avatar_url) embed.set_image(url=user.avatar_url) await ctx.reply(embed=embed) def setup(client): client.add_cog(Avatar(client))

45.836735

269

0.638023

312

2,246

4.467949

0.355769

0.093257

0.111908

0.096844

0.283357

0.225251

0.160689

0

0.01359

0.213713

2,246

49

270

45.836735

0.775764

0

0.073171

0.427681

0.199377

0

0.00356

0

1

0.04878

false

0

0.121951

0

0.195122

0

null

0

null

0

1

0

8db417da99710aab489ce1f6e872bf13a1887670

323

py

Python

pylinkedin/exceptions.py

johnjoo1/scrape-linkedin

4860e90e65aa776ce84afa3041a5bd826790ec0a

[ "MIT" ]

160

2016-06-27T12:55:20.000Z

2021-10-02T12:38:55.000Z

pylinkedin/exceptions.py

johnjoo1/scrape-linkedin

4860e90e65aa776ce84afa3041a5bd826790ec0a

[ "MIT" ]

16

2017-03-23T08:38:32.000Z

2020-02-24T22:39:19.000Z

pylinkedin/exceptions.py

johnjoo1/scrape-linkedin

4860e90e65aa776ce84afa3041a5bd826790ec0a

[ "MIT" ]

55

2017-02-23T14:29:45.000Z

2021-05-03T09:28:19.000Z

class ProfileNotFound(Exception): """ Exception if the linkedin url points to the linkedin not found page """ pass class NotAProfile(Exception): """ Exception raised if you pass a non linkedin profile as url """ pass class ServerIpBlacklisted(Exception): pass class BadStatusCode(Exception): pass

24.846154

79

0.724458

39

323

6

0.564103

0.115385

0

0.201238

323

13

80

24.846154

0.906977

0.393189

0

0.5

0

1

0

true

0.5

0

0.5

0

null

0

null

0

1

0

2

8db5993b3ba09fcfb72c92ea6f0805e8ba07d24f

810

py

Python

Basics II/Lists2.py

marinaoliveira96/python-exercises

13fc0ec30dec9bb6531cdeb41c80726971975835

[ "MIT" ]

null

Basics II/Lists2.py

marinaoliveira96/python-exercises

13fc0ec30dec9bb6531cdeb41c80726971975835

[ "MIT" ]

null

Basics II/Lists2.py

marinaoliveira96/python-exercises

13fc0ec30dec9bb6531cdeb41c80726971975835

[ "MIT" ]

null

print(isinstance(3, int)) lista = ['marina', 2, 'jujuba'] lista2 = [] for i in lista: if isinstance(i, str): lista2.append(i) print(lista2) myList = ['marina', 123, 9.5] print(isinstance(9.5, int)) #strings items = ['marina', 123, 9.5] print(isinstance(9.5, float)) str_items = ['abc', 'Abc','def', 'BBBB','ghi', 'AAAA'] str_items.sort(key=str.lower, reverse=True) print(str_items) new_items = sorted(str_items) print(new_items) #numbers int_numbers = [123, 13.44, 5436, 324.54, 9034] int_numbers.sort() print(f'sort.() = {int_numbers}') int_numbers.sort(reverse=True) print(f'sort.(reverse=True) = {int_numbers}') #esse sorted ta relacionado a lista n aos numeros new_numbers = sorted(int_numbers, reverse=False) print(f'new numbers = {new_numbers}') total = sum(int_numbers) print(total)

18.837209

54

0.691358

128

810

4.257813

0.40625

0.12844

0.036697

0.040367

0.102752

0

0.055241

0.128395

810

42

55

19.285714

0.716714

0.076543

0

0.173154

0

1

0

false

0

0.4

0

null

0

null

0

1

0

8db72819dbae785cf03bf81e31c9e2232cea71f2

1,283

py

Python

webapp/config.py

rustprooflabs/psycopg3-connpool

5576fd89ed986afb24fa2f229d52925e7a6d845c

[ "MIT" ]

3

2021-03-13T14:07:25.000Z

2022-03-12T01:51:49.000Z

webapp/config.py

rustprooflabs/psycopg3-connpool

5576fd89ed986afb24fa2f229d52925e7a6d845c

[ "MIT" ]

1

2021-09-12T15:03:12.000Z

webapp/config.py

rustprooflabs/psycopg3-connpool

5576fd89ed986afb24fa2f229d52925e7a6d845c

[ "MIT" ]

null

import os import logging APP_NAME = 'psycopg3-connpool' # Set to False to force reporting queries to share pool with non-reporting queries REPORTING_POOL = True POOL_MIN_SIZE = 1 POOL_MAX_SIZE = 10 POOL_MAX_IDLE = 60 POOL_STAT_SLEEP = 300 if not REPORTING_POOL: pool_max_size += 5 CURR_PATH = os.path.abspath(os.path.dirname(__file__)) PROJECT_BASE_PATH = os.path.abspath(os.path.join(CURR_PATH, os.pardir)) try: LOG_PATH = os.environ['LOG_PATH'] except KeyError: LOG_PATH = PROJECT_BASE_PATH + '/webapp.log' # Required for CSRF protection in Flask, please change to something secret! try: APP_SECRET_KEY = os.environ['APP_SECRET_KEY'] except KeyError: ERR_MSG = '\nSECURITY WARNING: To ensure security please set the APP_SECRET_KEY' ERR_MSG += ' environment variable.\n' #LOGGER.warning(ERR_MSG) print(ERR_MSG) APP_SECRET_KEY = 'S$332sgajg9GHKL14jklsjfkjasglmssajfsdgGADAAJj77j@neHMld' try: DATABASE_STRING = os.environ['PG_CONN'] except KeyError: key_msg = 'Database environment variable not set. Need PG_CONN string' sys.exit(key_msg) try: APP_DEBUG_RAW = os.environ['APP_DEBUG'] if APP_DEBUG_RAW == 'False': APP_DEBUG = False else: APP_DEBUG = True except KeyError: APP_DEBUG = False

23.759259

84

0.731878

189

1,283

4.698413

0.433862

0.054054

0.038288

0.051802

0

0.01711

0.180047

1,283

53

85

24.207547

0.826996

0.137958

0

0.277778

0

0.251589

0.049955

0

1

0

false

0

0.055556

0

0.055556

0.027778

0

null

0

null

0

1

0

8db775d1dd9bd4e0cb2fd047201ecfb22122ee56

247

py

Python

toughradius/common/__init__.py

capitek-wangsj/toughradius

ee0e6c20d32262ff7a6ace653af5a78340db62a2

[ "Apache-2.0" ]

null

toughradius/common/__init__.py

capitek-wangsj/toughradius

ee0e6c20d32262ff7a6ace653af5a78340db62a2

[ "Apache-2.0" ]

null

toughradius/common/__init__.py

capitek-wangsj/toughradius

ee0e6c20d32262ff7a6ace653af5a78340db62a2

[ "Apache-2.0" ]

null

# coding: utf-8 class ObjectDict(dict): def __getattr__(self, name): try: return self[name] except KeyError: raise AttributeError(name) def __setattr__(self, name, value): self[name] = value

24.7

39

0.587045

27

247

5.074074

0.666667

0.233577

0.189781

0

0.005917

0.315789

247

10

40

24.7

0.804734

0.052632

0

1

0.25

false

0

0.5

0

1

0

null

1

0

null

0

1

0

4

8db868f6631d93b30648549794d251ef271627af

3,695

py

Python

mnist.py

xiaoxinyi/tfrecord

6f39e3dbd5b1ffb3df8636b3163dbe2161469075

[ "Apache-2.0" ]

null

mnist.py

xiaoxinyi/tfrecord

6f39e3dbd5b1ffb3df8636b3163dbe2161469075

[ "Apache-2.0" ]

null

mnist.py

xiaoxinyi/tfrecord

6f39e3dbd5b1ffb3df8636b3163dbe2161469075

[ "Apache-2.0" ]

null

import os import tensorflow as tf import tensorflow.contrib.slim as slim from tensorflow.examples.tutorials.mnist import mnist TRAIN_FILE = 'train.tfrecords' VALIDATION_FILE = 'train.tfrecords' def lenet(images): net = slim.layers.conv2d(images, 20, [5,5], scope='conv1') net = slim.layers.max_pool2d(net, [2,2], scope='pool1') net = slim.layers.conv2d(net, 50, [5,5], scope='conv2') net = slim.layers.max_pool2d(net, [2,2], scope='pool2') net = slim.layers.flatten(net, scope='flatten3') net = slim.layers.fully_connected(net, 500, scope='fully_connected4') net = slim.layers.fully_connected(net, 10, activation_fn=None, scope='fully_connected5') return net def read_and_decode(filename_queue): reader = tf.TFRecordReader() _, serialized_example = reader.read(filename_queue) features = tf.parse_single_example( serialized_example, # Defaults are not specified since both keys are required. features={ 'image_raw': tf.FixedLenFeature([], tf.string), 'label': tf.FixedLenFeature([], tf.int64), }) # Convert from a scalar string tensor (whose single string has # length mnist.IMAGE_PIXELS) to a uint8 tensor with shape # [mnist.IMAGE_PIXELS]. image = tf.decode_raw(features['image_raw'], tf.uint8) image.set_shape([mnist.IMAGE_PIXELS]) image = tf.cast(image, tf.float32) * (1. / 255) - 0.5 image = tf.reshape(image, [mnist.IMAGE_SIZE, mnist.IMAGE_SIZE, 1]) # OPTIONAL: Could reshape into a 28x28 image and apply distortions # here. Since we are not applying any distortions in this # example, and the next step expects the image to be flattened # into a vector, we don't bother. # Convert label from a scalar uint8 tensor to an int32 scalar. label = tf.cast(features['label'], tf.int32) return image, label def inputs(train_dir, train, batch_size, num_epochs, one_hot_labels=False): """Reads input data num_epochs times. Args: train: Selects between the training (True) and validation (False) data. batch_size: Number of examples per returned batch. num_epochs: Number of times to read the input data, or 0/None to train forever. Returns: A tuple (images, labels), where: * images is a float tensor with shape [batch_size, mnist.IMAGE_PIXELS] in the range [-0.5, 0.5]. * labels is an int32 tensor with shape [batch_size] with the true label, a number in the range [0, mnist.NUM_CLASSES). Note that an tf.train.QueueRunner is added to the graph, which must be run using e.g. tf.train.start_queue_runners(). """ if not num_epochs: num_epochs = None filename = os.path.join(train_dir, TRAIN_FILE if train else VALIDATION_FILE) with tf.name_scope('input'): filename_queue = tf.train.string_input_producer( [filename], num_epochs=num_epochs) # Even when reading in multiple threads, share the filename # queue. image, label = read_and_decode(filename_queue) if one_hot_labels: label = tf.one_hot(label, mnist.NUM_CLASSES, dtype=tf.int32) # Shuffle the examples and collect them into batch_size batches. # (Internally uses a RandomShuffleQueue.) # We run this in two threads to avoid being a bottleneck. images, sparse_labels = tf.train.shuffle_batch( [image, label], batch_size=batch_size, num_threads=2, capacity=1000 + 3 * batch_size, # Ensures a minimum amount of shuffling of examples. min_after_dequeue=1000) return images, sparse_labels

39.731183

91

0.673884

518

3,695

4.673745

0.372587

0.02974

0.037588

0.015696

0.115655

0.074349

0.026435

0

0.024613

0.230311

3,695

92

40.163043

0.826653

0.381867

0

0.055732

0

1

0.066667

false

0

0.088889

0

0.222222

0

null

0

null

0

1

0

8db9b3ff0897bce11d0fb7fc945e79ab18d1a305

2,635

py

Python

setup.py

BenFrankel/hgf

78ec6a1e4eaa62005cc3914e8a554d2f1401ac37

[ "Apache-2.0" ]

null

setup.py

BenFrankel/hgf

78ec6a1e4eaa62005cc3914e8a554d2f1401ac37

[ "Apache-2.0" ]

2

2017-12-27T17:38:18.000Z

2017-12-27T17:42:10.000Z

setup.py

BenFrankel/hgf

78ec6a1e4eaa62005cc3914e8a554d2f1401ac37

[ "Apache-2.0" ]

null

#!/usr/bin/env python ############################################################################### # # # Copyright 2017 - Ben Frankel # # # # Licensed under the Apache License, Version 2.0 (the "License"); # # you may not use this file except in compliance with the License. # # You may obtain a copy of the License at # # # # http://www.apache.org/licenses/LICENSE-2.0 # # # # Unless required by applicable law or agreed to in writing, software # # distributed under the License is distributed on an "AS IS" BASIS, # # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # # See the License for the specific language governing permissions and # # limitations under the License. # # # ############################################################################### from setuptools import setup, find_packages version = '0.2.2' with open('README.md') as f: long_description = f.read() setup( name='hgf', version=version, description='A framework for building hierarchical GUIs', long_description=long_description, author='Ben Frankel', author_email='ben.frankel7@gmail.com', license='Apache 2.0', url='https://www.github.com/BenFrankel/hgf', download_url='https://www.github.com/BenFrankel/hgf/tarball/' + version, keywords='hgf hierarchical gui framework', classifiers=[ 'Development Status :: 2 - Pre-Alpha', 'Intended Audience :: Developers', 'License :: OSI Approved :: Apache Software License', 'Natural Language :: English', 'Operating System :: OS Independent', 'Programming Language :: Python :: 3 :: Only', 'Topic :: Software Development :: Libraries :: Application Frameworks', 'Topic :: Software Development :: Libraries :: pygame', ], packages=find_packages(), install_requires=[ 'pygame (>=1.9.1)', 'pyperclip (>=1.6.0)', ], provides=['hgf'] )

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null

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0

8db9c18f5e2082b747c4a03ec17f797125c796c9

605

py

Python

tools/ml/get_email.py

Xowap/Maiznet

bd564d4c93eb28dc87135e9d31dad9a921ea8cf6

[ "WTFPL" ]

1

2015-05-04T09:28:14.000Z

tools/ml/get_email.py

Xowap/Maiznet

bd564d4c93eb28dc87135e9d31dad9a921ea8cf6

[ "WTFPL" ]

null

tools/ml/get_email.py

Xowap/Maiznet

bd564d4c93eb28dc87135e9d31dad9a921ea8cf6

[ "WTFPL" ]

null

#!/usr/bin/python from django.core.management import setup_environ import sys sys.path.append('/var/wsgi/maiznet') sys.path.append('/var/wsgi') from maiznet import settings setup_environ(settings) from maiznet.register.models import Presence wfile_announces = open("/var/wsgi/maiznet/tools/ml/emails_announces","w") wfile_talkings = open("/var/wsgi/maiznet/tools/ml/emails_talkings","w") presence = Presence.objects.all() for p in presence : if p.talkings==1 : wfile_talkings.write(p.user.email + "\n") wfile_announces.write(p.user.email + "\n") wfile_announces.close() wfile_talkings.close()

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8dbb58ecd3a0dc98c548334dbc3b8ca871d442dd

1,368

py

Python

tests/functional/test_actions.py

AKhodus/adcm

98dbf22af3f1c6afa94505e9acaff0ac4088a602

[ "Apache-2.0" ]

null

tests/functional/test_actions.py

AKhodus/adcm

98dbf22af3f1c6afa94505e9acaff0ac4088a602

[ "Apache-2.0" ]

null

tests/functional/test_actions.py

AKhodus/adcm

98dbf22af3f1c6afa94505e9acaff0ac4088a602

[ "Apache-2.0" ]

null

# 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 allure import pytest from adcm_client.objects import ADCMClient from adcm_pytest_plugin import utils from tests.ui_tests.test_actions_page import check_verbosity @pytest.mark.parametrize("verbose_state", [True, False], ids=["verbose_state_true", "verbose_state_false"]) def test_check_verbose_option_of_action_run(sdk_client_fs: ADCMClient, verbose_state): """Test action run with verbose switch""" bundle_dir = utils.get_data_dir(__file__, "verbose_state") bundle = sdk_client_fs.upload_from_fs(bundle_dir) cluster = bundle.cluster_create(utils.random_string()) task = cluster.action(name="dummy_action").run(verbose=verbose_state) with allure.step(f"Check if verbosity is {verbose_state}"): task.wait() log = task.job().log() check_verbosity(log, verbose_state)

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8dbbf348283d5d908174cdee1b01595e478b5b7d

11,798

py

Python

src/network/graph_module.py

andrewliao11/env-aware-program-gen

bc50b788c35e8e8545b8af9127c279a7387146d6

[ "MIT" ]

5

2019-08-17T07:53:02.000Z

2022-02-26T07:17:37.000Z

src/network/graph_module.py

andrewliao11/env-aware-program-gen

bc50b788c35e8e8545b8af9127c279a7387146d6

[ "MIT" ]

9

2019-06-28T07:36:10.000Z

2022-03-11T23:48:39.000Z

src/network/graph_module.py

andrewliao11/env-aware-program-gen

bc50b788c35e8e8545b8af9127c279a7387146d6

[ "MIT" ]

1

2020-04-14T12:48:40.000Z

import torch import torch.nn as nn from program.graph_utils import * from helper import fc_block, LayerNormGRUCell # helper class for GraphEncoder class AttrProxy(object): """ Translates index lookups into attribute lookups. To implement some trick which able to use list of nn.Module in a nn.Module see https://discuss.pytorch.org/t/list-of-nn-module-in-a-nn-module/219/2 """ def __init__(self, module, prefix): self.module = module self.prefix = prefix def __getitem__(self, i): return getattr(self.module, self.prefix + str(i)) class VanillaGraphEncoder(nn.Module): def __init__( self, n_timesteps, n_edge_types, graph_hidden, embedding_dim, hidden): super(VanillaGraphEncoder, self).__init__() layernorm = True self.n_timesteps = n_timesteps self.n_edge_types = n_edge_types self.embedding_dim = embedding_dim self.input_dim = n_edge_types + embedding_dim self.graph_hidden = graph_hidden node_init2hidden = nn.Sequential() node_init2hidden.add_module( 'fc1', fc_block( 3 * embedding_dim, graph_hidden, False, nn.Tanh)) node_init2hidden.add_module( 'fc2', fc_block( graph_hidden, graph_hidden, False, nn.Tanh)) for i in range(n_edge_types): hidden2message_in = fc_block( graph_hidden, graph_hidden, False, nn.Tanh) self.add_module( "hidden2message_in_{}".format(i), hidden2message_in) hidden2message_out = fc_block( graph_hidden, graph_hidden, False, nn.Tanh) self.add_module( "hidden2message_out_{}".format(i), hidden2message_out) if layernorm: self.gru_cell = LayerNormGRUCell else: self.gru_cell = nn.GRUCell propagator = self.gru_cell( input_size=2 * n_edge_types * graph_hidden, hidden_size=graph_hidden) self.node_init2hidden = node_init2hidden self.hidden2message_in = AttrProxy(self, "hidden2message_in_") self.hidden2message_out = AttrProxy(self, "hidden2message_out_") self.propagator = propagator def forward( self, edge_adjacency_matrix, node_state_prev, related_mask=None): """edge_adjacency_matrix: e, b, v, v object_state_arry: b, v, p state: b, v, h """ B, V, H = node_state_prev.size() node_state_prev = node_state_prev.view(B * V, -1) node_state = node_state_prev edge_adjacency_matrix = edge_adjacency_matrix.float() edge_adjacency_matrix_out = edge_adjacency_matrix # convert the outgoing edges to incoming edges edge_adjacency_matrix_in = edge_adjacency_matrix.permute(0, 1, 3, 2) for i in range(self.n_timesteps): message_out = [] for j in range(self.n_edge_types): node_state_hidden = self.hidden2message_out[j]( node_state) # b*v, h node_state_hidden = node_state_hidden.view(B, V, -1) message_out.append( torch.bmm( edge_adjacency_matrix_out[j], node_state_hidden)) # b, v, h # concatenate the message from each edges message_out = torch.stack(message_out, 2) # b, v, e, h message_out = message_out.view(B * V, -1) # b, v, e*h message_in = [] for j in range(self.n_edge_types): node_state_hidden = self.hidden2message_in[j]( node_state) # b*v, h node_state_hidden = node_state_hidden.view(B, V, -1) message_in.append( torch.bmm( edge_adjacency_matrix_in[j], node_state_hidden)) # concatenate the message from each edges message_in = torch.stack(message_in, 2) # b, v, e, h message_in = message_in.view(B * V, -1) # b, v, e*h message = torch.cat([message_out, message_in], 1) node_state = self.propagator(message, node_state) if related_mask is not None: # mask out un-related changes related_mask_expand = related_mask.unsqueeze( 2).repeat(1, 1, self.graph_hidden).float() related_mask_expand = related_mask_expand.view(B * V, -1) node_state = node_state * related_mask_expand + \ node_state_prev * (-related_mask_expand + 1) node_state = node_state.view(B, V, -1) return node_state class ResidualActionGraphEncoder(VanillaGraphEncoder): def __init__( self, n_edge_types, n_touch, graph_hidden, embedding_dim, hidden): super( ResidualActionGraphEncoder, self).__init__( 0, n_edge_types, graph_hidden, embedding_dim, hidden) self.n_touch = n_touch action2hidden = nn.Sequential() action2hidden.add_module( 'fc1', fc_block( embedding_dim + n_touch, graph_hidden, False, nn.Tanh)) action2hidden.add_module( 'fc2', fc_block( graph_hidden, graph_hidden, False, nn.Tanh)) compute_residual = nn.Sequential() compute_residual.add_module( 'fc1', fc_block( 2 * graph_hidden, graph_hidden, False, nn.Tanh)) compute_residual.add_module( 'fc2', fc_block( graph_hidden, graph_hidden, False, nn.Tanh)) self.compute_residual = compute_residual self.action2hidden = action2hidden def action_applier( self, action_embedding, batch_touch_idx, batch_node_state_prev, batch_touch_mask): """ action_embedding: b, emb batch_touch_idx: b, n, touch_type, batch_node_state_prev: b, n, h batch_touch_mask: b, n """ B, N, _ = batch_touch_idx.size() action_embedding = action_embedding.unsqueeze(1).repeat(1, N, 1) graph_input = torch.cat([action_embedding, batch_touch_idx], 2) graph_input = self.action2hidden(graph_input) graph_input = graph_input.view(B * N, -1) batch_node_state_prev = batch_node_state_prev.view(B * N, -1) residual = self.compute_residual( torch.cat([graph_input, batch_node_state_prev], 1)) batch_touch_mask = batch_touch_mask.unsqueeze( 2).repeat(1, 1, self.graph_hidden) batch_touch_mask = batch_touch_mask.view(B * N, -1) batch_node_state = batch_node_state_prev + residual * batch_touch_mask batch_node_state = batch_node_state.view(B, N, -1) return batch_node_state class FCActionGraphEncoder(VanillaGraphEncoder): def __init__( self, n_edge_types, n_touch, graph_hidden, embedding_dim, hidden): super(FCActionGraphEncoder, self).__init__( 0, n_edge_types, graph_hidden, embedding_dim, hidden) self.n_touch = n_touch action2hidden = nn.Sequential() action2hidden.add_module('fc1', fc_block(embedding_dim + n_touch, graph_hidden, False, nn.Tanh)) action2hidden.add_module('fc2', fc_block(graph_hidden, graph_hidden, False, nn.Tanh)) compute_residual = nn.Sequential() compute_residual.add_module('fc1', fc_block(2*graph_hidden, graph_hidden, False, nn.Tanh)) compute_residual.add_module('fc2', fc_block(graph_hidden, graph_hidden, False, nn.Tanh)) self.compute_residual = compute_residual self.action2hidden = action2hidden def action_applier( self, action_embedding, batch_touch_idx, batch_node_state_prev, batch_touch_mask): """ action_embedding: b, emb batch_touch_idx: b, n, touch_type, batch_node_state_prev: b, n, h batch_touch_mask: b, n """ B, N, _ = batch_touch_idx.size() action_embedding = action_embedding.unsqueeze(1).repeat(1, N, 1) graph_input = torch.cat([action_embedding, batch_touch_idx], 2) graph_input = self.action2hidden(graph_input) graph_input = graph_input.view(B * N, -1) batch_node_state_prev = batch_node_state_prev.view(B * N, -1) batch_node_state = self.compute_residual(torch.cat([graph_input, batch_node_state_prev], 1)) batch_touch_mask = batch_touch_mask.unsqueeze(2).repeat(1, 1, self.graph_hidden) batch_touch_mask = batch_touch_mask.view(B * N, -1) batch_node_state = batch_node_state * batch_touch_mask + batch_node_state_prev * (-batch_touch_mask + 1) batch_node_state = batch_node_state.view(B, N, -1) return batch_node_state class GRUActionGraphEncoder(VanillaGraphEncoder): def __init__( self, n_edge_types, n_touch, graph_hidden, embedding_dim, hidden): super( GRUActionGraphEncoder, self).__init__( 0, n_edge_types, graph_hidden, embedding_dim, hidden) self.n_touch = n_touch action2hidden = nn.Sequential() action2hidden.add_module('fc1', fc_block(embedding_dim + n_touch, graph_hidden, False, nn.Tanh)) action2hidden.add_module('fc2', fc_block(graph_hidden, graph_hidden, False, nn.Tanh)) temporal_propagator = self.gru_cell(input_size=graph_hidden, hidden_size=graph_hidden) self.temporal_propagator = temporal_propagator self.action2hidden = action2hidden def action_applier( self, action_embedding, batch_touch_idx, batch_node_state_prev, batch_touch_mask): """ action_embedding: b, emb batch_touch_idx: b, n, touch_type, batch_node_state_prev: b, n, h batch_touch_mask: b, n """ B, N, _ = batch_touch_idx.size() action_embedding = action_embedding.unsqueeze(1).repeat(1, N, 1) graph_input = torch.cat([action_embedding, batch_touch_idx], 2) graph_input = self.action2hidden(graph_input) graph_input = graph_input.view(B * N, -1) batch_node_state_prev = batch_node_state_prev.view(B * N, -1) batch_node_state = self.temporal_propagator(graph_input, batch_node_state_prev) batch_touch_mask = batch_touch_mask.unsqueeze(2).repeat(1, 1, self.graph_hidden) batch_touch_mask = batch_touch_mask.view(B * N, -1) batch_node_state = batch_node_state * batch_touch_mask + batch_node_state_prev * (-batch_touch_mask + 1) batch_node_state = batch_node_state.view(B, N, -1) return batch_node_state

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null

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null

0

1

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8dbd46f30d6dfa6a1507fd5a331fbdfbe41f94f6

744

py

Python

adam_io/utils.py

barcesat/adam_io

c9b36a696dca2338de02925cc9af78bc59b55fce

[ "MIT" ]

null

adam_io/utils.py

barcesat/adam_io

c9b36a696dca2338de02925cc9af78bc59b55fce

[ "MIT" ]

null

adam_io/utils.py

barcesat/adam_io

c9b36a696dca2338de02925cc9af78bc59b55fce

[ "MIT" ]

null

# check with status codes # exceptions # check 'error' and 'status' from socket import inet_pton, inet_aton, AF_INET, error def valid_ipv4(address: str): """ :param address: ip address string :return: if the address is a valid dotted ipv4 address """ try: inet_pton(AF_INET, address) except AttributeError: try: inet_aton(address) except error: return False return address.count('.') == 3 except error: return False return True class URI: DIGITAL_INPUT = "/digitalinput" DIGITAL_OUTPUT = "/digitaloutput" ANALOG_INPUT = "/analoginput" ANALOG_OUTPUT = "/analogoutput" ALL = "/all" VALUE = "/value" RANGE = "/range"

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8dbf72711729ecd397489eebb67c03ca4962e12f

182

py

Python

core/__init__.py

xmings/IdeaNote

a538d4bf012255a19583f9acc57576c44105283e

[ "Apache-2.0" ]

25

2019-11-13T03:35:34.000Z

2022-03-26T04:13:50.000Z

core/__init__.py

xmings/IdeaNote

a538d4bf012255a19583f9acc57576c44105283e

[ "Apache-2.0" ]

3

2019-11-04T06:23:35.000Z

2021-02-08T07:21:04.000Z

core/__init__.py

xmings/IdeaNote

a538d4bf012255a19583f9acc57576c44105283e

[ "Apache-2.0" ]

null

#!/bin/python # -*- coding: utf-8 -*- # @File : __init__.py # @Author: wangms # @Date : 2018/8/7 from flask import Blueprint core = Blueprint('core', __name__) from . import view

18.2

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0

5

8dc0d85e7869f55ab1de18e5ff3544c306008e2b

3,241

py

Python

doc/examples/doc_code/raysgd_torch_signatures.py

thedrow/ray

584645cc7da2bfd7d341d52b59c9c8561dbd119b

[ "Apache-2.0" ]

1

2020-02-25T08:43:46.000Z

doc/examples/doc_code/raysgd_torch_signatures.py

mwbrulhardt/ray

b97b8c2be1c7d01e2c93ca97a0b87120bfa2bd1a

[ "Apache-2.0" ]

1

2019-03-16T07:08:57.000Z

doc/examples/doc_code/raysgd_torch_signatures.py

gehring/ray

d8eeb9641314740572e81f9836cbce3e5b8f2b73

[ "Apache-2.0" ]

null

# flake8: noqa """ This file holds code for the Pytorch Trainer creator signatures. It ignores yapf because yapf doesn't allow comments right after code blocks, but we put comments right after code blocks to prevent large white spaces in the documentation. """ # yapf: disable # __torch_model_start__ import torch.nn as nn def model_creator(config): """Constructor function for the model(s) to be optimized. You will also need to provide a custom training function to specify the optimization procedure for multiple models. Args: config (dict): Configuration dictionary passed into ``PyTorchTrainer``. Returns: One or more torch.nn.Module objects. """ return nn.Linear(1, 1) # __torch_model_end__ # __torch_optimizer_start__ import torch def optimizer_creator(model, config): """Constructor of one or more Torch optimizers. Args: models: The return values from ``model_creator``. This can be one or more torch nn modules. config (dict): Configuration dictionary passed into ``PyTorchTrainer``. Returns: One or more Torch optimizer objects. """ return torch.optim.SGD(model.parameters(), lr=config.get("lr", 1e-4)) # __torch_optimizer_end__ # __torch_data_start__ from ray.util.sgd.pytorch.examples.train_example import LinearDataset def data_creator(config): """Constructs torch.utils.data.Dataset objects. Note that even though two Dataset objects can be returned, only one dataset will be used for training. Args: config: Configuration dictionary passed into ``PyTorchTrainer`` Returns: One or Two Dataset objects. If only one Dataset object is provided, ``trainer.validate()`` will throw a ValueError. """ return LinearDataset(2, 5), LinearDataset(2, 5, size=400) # __torch_data_end__ # __torch_loss_start__ import torch def loss_creator(config): """Constructs the Torch Loss object. Note that optionally, you can pass in a Torch Loss constructor directly into the PyTorchTrainer (i.e., ``PyTorchTrainer(loss_creator=nn.BCELoss, ...)``). Args: config: Configuration dictionary passed into ``PyTorchTrainer`` Returns: Torch Loss object. """ return torch.nn.BCELoss() # __torch_loss_end__ # __torch_scheduler_start__ import torch def scheduler_creator(optimizer, config): """Constructor of one or more Torch optimizer schedulers. Args: optimizers: The return values from ``optimizer_creator``. This can be one or more torch optimizer objects. config: Configuration dictionary passed into ``PyTorchTrainer`` Returns: One or more Torch scheduler objects. """ return torch.optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.9) # __torch_scheduler_end__ # __torch_ray_start__ import ray ray.init() # or ray.init(address="auto") to connect to a running cluster. # __torch_ray_end__ # __torch_trainer_start__ from ray.util.sgd import PyTorchTrainer trainer = PyTorchTrainer( model_creator, data_creator, optimizer_creator, loss_creator=nn.MSELoss, scheduler_creator=scheduler_creator, config={"lr": 0.001}) # __torch_trainer_end__

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8dc3232c7e5f402c1d0748b307d531d49a9423ea

144

py

Python

lib/abridger/extraction_model/table.py

willangenent/abridger

6daa80f7360339376b38544ce60694c5addaa30f

[ "MIT" ]

8

2016-10-19T14:15:34.000Z

2020-06-23T09:37:02.000Z

lib/abridger/extraction_model/table.py

freewilll/abridger

6daa80f7360339376b38544ce60694c5addaa30f

[ "MIT" ]

null

lib/abridger/extraction_model/table.py

freewilll/abridger

6daa80f7360339376b38544ce60694c5addaa30f

[ "MIT" ]

null

class Table(object): def __init__(self, table, col, values): self.table = table self.col = col self.values = values

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8dc3361defa92720863211b21b935ff651f2bf8d

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py

Python

tests/test_view.py

takos22/baguette

36c6cafa793ff4be057ca2f8a5c7129baf8a5ab8

[ "MIT" ]

20

2021-04-13T06:23:33.000Z

2021-12-12T13:52:50.000Z

tests/test_view.py

takos22/baguette

36c6cafa793ff4be057ca2f8a5c7129baf8a5ab8

[ "MIT" ]

4

2021-04-17T23:17:36.000Z

2021-05-23T14:20:08.000Z

tests/test_view.py

takos22/baguette

36c6cafa793ff4be057ca2f8a5c7129baf8a5ab8

[ "MIT" ]

3

2021-04-23T00:01:45.000Z

2021-04-29T22:48:33.000Z

import pytest from baguette.app import Baguette from baguette.httpexceptions import MethodNotAllowed from baguette.responses import make_response from baguette.view import View @pytest.mark.asyncio async def test_view_create(): class TestView(View): async def get(self, request): return "GET" async def post(self, request): return "POST" async def put(self, request): return "PUT" async def delete(self, request): return "DELETE" async def nonexistent_method(self, request): return "NONEXISTENT" view = TestView(Baguette()) assert view.methods == ["GET", "POST", "PUT", "DELETE"] assert await view.get(None) == "GET" assert await view.post(None) == "POST" assert await view.put(None) == "PUT" assert await view.delete(None) == "DELETE" assert await view.nonexistent_method(None) == "NONEXISTENT" @pytest.fixture(name="view") def create_view(): class TestView(View): async def get(self, request): return "GET" async def post(self, request): return "POST" async def put(self, request): return "PUT" async def delete(self, request): return "DELETE" return TestView(Baguette()) @pytest.mark.asyncio async def test_view_call(view, test_request): result = await view(test_request) response = make_response(result) assert response.status_code == 200 assert response.body == "GET" @pytest.mark.asyncio @pytest.mark.parametrize( ["method", "method_allowed"], [ ["GET", True], ["POST", True], ["PUT", True], ["DELETE", True], ["PATCH", False], ["NONEXISTENT", False], ], ) async def test_view_dispatch(view, test_request, method, method_allowed): test_request.method = method if method_allowed: result = await view.dispatch(test_request) response = make_response(result) assert response.status_code == 200 assert response.body == method else: with pytest.raises(MethodNotAllowed): await view.dispatch(test_request)

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null

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1

0

8dc386d6b5d927e8b934f386d034dfa885867aad

5,167

py

Python

tests/conftest.py

adrien-berchet/luigi-tools

5de731714db38656db06e39acdb0b9e53ed612bf

[ "Apache-2.0" ]

2

2021-07-20T13:08:44.000Z

2021-07-23T13:08:05.000Z

tests/conftest.py

adrien-berchet/luigi-tools

5de731714db38656db06e39acdb0b9e53ed612bf

[ "Apache-2.0" ]

3

2021-10-04T11:48:34.000Z

2022-03-18T15:48:00.000Z

tests/conftest.py

adrien-berchet/luigi-tools

5de731714db38656db06e39acdb0b9e53ed612bf

[ "Apache-2.0" ]

1

2022-03-21T15:15:23.000Z

# Copyright 2021 Blue Brain Project / EPFL # # 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. """Fixtures for luigi-tools test suite.""" import os import luigi import pytest import luigi_tools.task from luigi_tools.util import set_luigi_config from .tools import create_not_empty_file @pytest.fixture(scope="function") def tmp_working_dir(tmp_path): """Change working directory before a test and change it back when the test is finished""" cwd = os.getcwd() os.chdir(tmp_path) yield tmp_path os.chdir(cwd) @pytest.fixture def luigi_tools_params(): return {"TaskA": {"a_cfg": "default_value_in_cfg"}} @pytest.fixture def luigi_tools_working_directory(tmp_working_dir, luigi_tools_params): # Set config with set_luigi_config(luigi_tools_params): yield tmp_working_dir @pytest.fixture def task_collection(tmpdir): class TaskClasses: """Class with some luigi tasks to test""" def __init__(self): self.tmpdir = tmpdir self.reset_classes() self.classes = self._classes() self.targets = self._targets() self.reset_classes() # Reset again to return classes that are not registered by luigi def reset_classes(self): class TaskA(luigi_tools.task.WorkflowTask): """""" counter = luigi.IntParameter(default=0) def run(self): for i in luigi.task.flatten(self.output()): create_not_empty_file(i.path) def output(self): return luigi.LocalTarget(tmpdir / "TaskA.target") class TaskB(luigi_tools.task.WorkflowTask): """""" def requires(self): return TaskA() def run(self): for i in luigi.task.flatten(self.output()): create_not_empty_file(i.path) def output(self): return [ luigi.LocalTarget(tmpdir / "TaskB.target"), [ luigi.LocalTarget(tmpdir / "TaskB2.target"), luigi.LocalTarget(tmpdir / "TaskB3.target"), ], ] class TaskC(luigi_tools.task.WorkflowTask): """""" def requires(self): return TaskA() def run(self): for i in luigi.task.flatten(self.output()): create_not_empty_file(i.path) def output(self): return { "first_target": luigi.LocalTarget(tmpdir / "TaskC.target"), "second_target": luigi.LocalTarget(tmpdir / "TaskC2.target"), } class TaskD(luigi_tools.task.WorkflowTask): """""" def requires(self): return [TaskB(), TaskC()] def run(self): for i in luigi.task.flatten(self.output()): create_not_empty_file(i.path) def output(self): return [ luigi.LocalTarget(tmpdir / "TaskD.target"), luigi.LocalTarget(tmpdir / "TaskD2.target"), ] class TaskE(luigi_tools.task.WorkflowTask): """""" def requires(self): return TaskD() def run(self): for i in luigi.task.flatten(self.output()): create_not_empty_file(i.path) def output(self): return { "first_target": luigi.LocalTarget(tmpdir / "TaskE.target"), "other_targets": { "second_target": luigi.LocalTarget(tmpdir / "TaskE2.target"), "third_target": luigi.LocalTarget(tmpdir / "TaskE3.target"), }, } self.TaskA = TaskA self.TaskB = TaskB self.TaskC = TaskC self.TaskD = TaskD self.TaskE = TaskE def _classes(self): return [ self.TaskA, self.TaskB, self.TaskC, self.TaskD, self.TaskE, ] def _targets(self): targets = {} for task in self.classes: targets[task.__name__] = task().output() return targets return TaskClasses()

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1

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2

8dc3acc4aaba2f96a5e6b63f6e6498fe13dc2b2d

2,633

py

Python

HubMovrmentChallenge.py

lhsalud/hub-movement

351fb430bb5e8540d4215415154c1d9f8d0730fe

[ "Apache-2.0" ]

null

HubMovrmentChallenge.py

lhsalud/hub-movement

351fb430bb5e8540d4215415154c1d9f8d0730fe

[ "Apache-2.0" ]

null

HubMovrmentChallenge.py

lhsalud/hub-movement

351fb430bb5e8540d4215415154c1d9f8d0730fe

[ "Apache-2.0" ]

null

# # Author: L. Salud, April 26.2018 # import pandas as pd import os import matplotlib.pyplot as plt from sklearn.decomposition import PCA from sklearn.preprocessing import StandardScaler os.getcwd() # Get and place .py in same directory as .xls initially os.chdir('./') # Path to .xls file from pandas import read_excel df = read_excel('rssi_data_challenge2.xls') df.dropna(how="all", inplace=True) # drops the empty line at file-end df.head(n=5) df.tail() df.describe(include = 'all') pca = PCA(n_components=3) X = df[['attributesfirstnodemeanrssi','attributessecondnodemeanrssi', 'attributesthirdnodemeanrssi','attributesfourthnodemeanrssi','attributesfifthnodemeanrssi','attributessixthnodemeanrssi']] X.loc[1:10] list(df) S = df[['attributesfirstnodestddevrssi','attributessecondnodestddevrssi', 'attributesthirdnodestddevrssi','attributesfourthnodestddevrssi','attributesfifthnodestddevrssi','attributessixthnodestddevrssi']] S.loc[1:10] S.columns = X.columns S1 = S.replace(0.00, 0.01) CD = (X*X) + 300*S1 # TODO: Refine # TODO: See if any of these and other publications may be applicable # https://www.hindawi.com/journals/jcnc/2013/185138/abs/ # https://www.ncbi.nlm.nih.gov/pubmed/28895879 # https://dl.acm.org/citation.cfm?id=2790093 # https://en.wikipedia.org/wiki/Short-time_Fourier_transform # Standardizing the features CD = StandardScaler().fit_transform(CD) CD[1:10] principalComponents = pca.fit_transform(CD) principalDf = pd.DataFrame(data = principalComponents , columns = ['principal_component_1', 'principal_component_2', 'principal_component_3']) principalDf = principalDf[principalDf['principal_component_1'] < 18 ] max(principalDf['principal_component_1']) finalDf = pd.concat([principalDf, df[['movestate']]], axis = 1) # Visualize fig = plt.figure(figsize = (26, 26)) #ax = fig.add_subplot(111, projection='3d') ax = fig.add_subplot(1,1,1) ax.set_xlabel('Principal Component 2', fontsize = 15) ax.set_ylabel('Principal Component 1', fontsize = 15) #ax.set_zlabel('Principal Component 3', fontsize = 15) ax.set_title('3 component PCA', fontsize = 20) targets = ['nonmove', 'move'] colors = ['r', 'b', 'g'] for target, color in zip(targets,colors): indicesToKeep = finalDf['movestate'] == target ax.scatter(finalDf.loc[indicesToKeep, 'principal_component_2'] , finalDf.loc[indicesToKeep, 'principal_component_1'] # , finalDf.loc[indicesToKeep, 'principal_component_3'] , c = color , alpha=0.5, label="Point") ax.legend(targets) ax.grid() # TODO: Run SVM for cluster separation

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null

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1

0

1

8dc61223149b3158489a5e9ccf76ac85256384c3

1,278

py

Python

utils/filter_empty_lines.py

LeCongThuong/deep-text-recognition-benchmark

b9f4e5dab9a991435d9ba9e71a89dd6fce20f468

[ "Apache-2.0" ]

null

utils/filter_empty_lines.py

LeCongThuong/deep-text-recognition-benchmark

b9f4e5dab9a991435d9ba9e71a89dd6fce20f468

[ "Apache-2.0" ]

null

utils/filter_empty_lines.py

LeCongThuong/deep-text-recognition-benchmark

b9f4e5dab9a991435d9ba9e71a89dd6fce20f468

[ "Apache-2.0" ]

null

import re def read_from_file(file_path): with open(file_path, 'r', encoding='utf-8') as f: content = f.read().splitlines() return content def write_to_file(corpus, dest_path): with open(dest_path, 'w', encoding='utf-8') as f: for item in corpus: f.write("%s\n" % item) def filter_emtpy_lines(content, character_vocab): out_of_vocab = f'[^{character_vocab}]' count = 0 filtered_content = [] for line in content: print(f'\r{line}', end='') filtered_line = re.sub(out_of_vocab, '', line) if len(filtered_line) == 0: count = count + 1 else: filtered_content.extend(line) print("Done") print("Num of invalid lines: ", count) return filtered_content def main(): file_path = '/home/love_you/ocr-gen/vi.txt' character_vocab = 'hjbóẺoÝLvÚẼÁÂẩởĨỈtgKứẾmŨÒWsăỷịơIÔỀửãùaXP9ẰẳỉẹỶzầẪâỸỎảệyOựỬẵỘxCỐlỲD6ộỦỒĂƠÌồ1áTFnỆpHẽờếỏẢYẨUắƯẦíÃẤJèýẲ2i4ẬỊÊÓớR7ÙÕàGỨềỳecêSéừqQạòấỮ0ốẫ5õfỗđỡúNũỤợỖỠMằẸôỚặuỌỞụÀEkĐÉBưẮ3ỂễAìỜủỔỢổọwậdZĩẻ8ỄỰểrÈẴÍỪẶẠữỹV ' dest_path = '/home/love_you/ocr-gen/filtered_vi.txt' content = read_from_file(file_path) filtered_content = filter_emtpy_lines(content, character_vocab) write_to_file(filtered_content, dest_path) main()

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null

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1

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8dc6bc8eb5fc6294a76cfdda9f4e036dfe3da0de

3,680

py

Python

cltk/data.py

fractaledmind/cltk

78c7259c1845a4ae8bbd33935ffbae34da23234b

[ "MIT" ]

1

2020-08-02T19:35:06.000Z

cltk/data.py

fractaledmind/cltk

78c7259c1845a4ae8bbd33935ffbae34da23234b

[ "MIT" ]

null

cltk/data.py

fractaledmind/cltk

78c7259c1845a4ae8bbd33935ffbae34da23234b

[ "MIT" ]

null

"""Classes to access the `cltk_data/` directory tree""" __author__ = 'Stephen Margheim <stephen.margheim@gmail.com>' __license__ = 'MIT License. See LICENSE.' import os import site from cltk.cltk import CLTK_DATA_DIR from cltk.cltk.corpus.wrappers.logger import logger class CorpusError(Exception): pass class CLTKData(object): """This class provides access to the full directory tree of `cltk_data/`. The basic structure of the `cltk_data/` directory is: ``` cltk_data/ {language}/ text_corpora/ originals/ {corpus}/ structured/ {corpus}/ plain/ {corpus}/ readable/ {corpus}/ treebank/ {corpus}/ training_set/ {corpus}/ ``` Users can set the path to `cltk_data/` via the ``data_path`` property. When dealing with a particular corpus, users will also need to set the ``language_dir`` property properly in order to access the corpus. """ def __init__(self): self._data_path = None self._language_dir = None ## Base `cltk_data/` directory -------------------------------------------- @property def data_path(self): if self._data_path: return self.resolve_path(self._data_path) else: return self.resolve_path(CLTK_DATA_DIR) @data_path.setter def data_path(self, value): self._data_path = value ## 2nd level language directories ----------------------------------------- @property def language_dir(self): if self._language_dir: return self.resolve_path(os.path.join(self.data_path, self._language_dir)) else: # TODO: Fix error message raise CorpusError('Define `language_dir`!') @language_dir.setter def language_dir(self, value): self._language_dir = value ## 3rd level corpus type directories -------------------------------------- @property def corpora_dir(self): return self.resolve_path(os.path.join(self.language_dir, 'text_corpora')) @property def treebank_dir(self): return self.resolve_path(os.path.join(self.language_dir, 'treebank')) @property def training_dir(self): return self.resolve_path(os.path.join(self.language_dir, 'training_set')) ## Misc. ------------------------------------------------------------------ # What does this do? @property def bin_path(self): return os.path.join(site.getsitepackages()[0], 'cltk') def resolve_path(self, path): # Resolve absolute path if os.path.isabs(path): full_path = path elif path.startswith('~'): full_path = os.path.expanduser(path) elif path.startswith('.'): full_path = os.path.abspath(path) # Ensure absolute path exists if not os.path.exists(full_path): # If directory if os.path.splitext(full_path)[1] == '': os.makedirs(full_path) logger.info('Directory created at : {}'.format(full_path)) # If file else: open(full_path).close() logger.info('File created at : {}'.format(full_path)) return full_path # Alias cltk_data = CLTKData()

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8dc7d59fdd262802a1ded4d3d7416e6bb94d267d

118

py

Python

Executor/Tasks/PostRun/__init__.py

EVOLVED-5G/ELCM

07d07a114b667e8c6915ee3ef125dd4864dd2247

[ "Apache-2.0" ]

1

2020-04-16T17:07:46.000Z

Executor/Tasks/PostRun/__init__.py

EVOLVED-5G/ELCM

07d07a114b667e8c6915ee3ef125dd4864dd2247

[ "Apache-2.0" ]

3

2020-03-06T11:22:09.000Z

2020-03-06T11:22:10.000Z

Executor/Tasks/PostRun/__init__.py

EVOLVED-5G/ELCM

07d07a114b667e8c6915ee3ef125dd4864dd2247

[ "Apache-2.0" ]

1

2022-02-01T07:56:44.000Z

from .decommission import Decommission from .release_resources import ReleaseResources from .farewell import Farewell

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5

8dc7dd2aecae51adb10cd582c54a3498d17a6890

3,482

py

Python

qa/L0_backend_python/model_control/model_control_test.py

galv/server

071eb2c6c9a8f1bba380c0e69592f50a857c5c42

[ "BSD-3-Clause" ]

2,159

2020-08-26T06:21:38.000Z

2022-03-31T16:13:46.000Z

qa/L0_backend_python/model_control/model_control_test.py

galv/server

071eb2c6c9a8f1bba380c0e69592f50a857c5c42

[ "BSD-3-Clause" ]

1,482

2020-08-26T08:26:36.000Z

2022-03-31T23:11:19.000Z

qa/L0_backend_python/model_control/model_control_test.py

galv/server

071eb2c6c9a8f1bba380c0e69592f50a857c5c42

[ "BSD-3-Clause" ]

592

2020-08-26T06:09:25.000Z

2022-03-31T00:37:41.000Z

# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions # are met: # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # * Neither the name of NVIDIA CORPORATION nor the names of its # contributors may be used to endorse or promote products derived # from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY # EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR # PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR # CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, # EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, # PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR # PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY # OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. import sys sys.path.append("../../common") import test_util as tu import tritonclient.http as httpclient from tritonclient.utils import * import numpy as np import unittest class ExplicitModelTest(tu.TestResultCollector): def send_identity_request(self, client, model_name): inputs = [] inputs.append(httpclient.InferInput('INPUT0', [1, 16], "FP32")) input0_data = np.arange(start=0, stop=16, dtype=np.float32) input0_data = np.expand_dims(input0_data, axis=0) inputs[0].set_data_from_numpy(input0_data) result = client.infer( model_name=model_name, inputs=inputs, outputs=[httpclient.InferRequestedOutput('OUTPUT0')]) output_numpy = result.as_numpy('OUTPUT0') self.assertTrue(np.all(input0_data == output_numpy)) def test_model_reload(self): model_name = "identity_fp32" ensemble_model_name = 'simple_' + "identity_fp32" with httpclient.InferenceServerClient("localhost:8000") as client: for _ in range(5): self.assertFalse(client.is_model_ready(model_name)) # Load the model before the ensemble model to make sure reloading the # model works properly in Python backend. client.load_model(model_name) client.load_model(ensemble_model_name) self.assertTrue(client.is_model_ready(model_name)) self.assertTrue(client.is_model_ready(ensemble_model_name)) self.send_identity_request(client, model_name) self.send_identity_request(client, ensemble_model_name) client.unload_model(ensemble_model_name) client.unload_model(model_name) self.assertFalse(client.is_model_ready(model_name)) self.assertFalse(client.is_model_ready(ensemble_model_name)) if __name__ == '__main__': unittest.main()

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null

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null

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1

0

8dc7f5a5c998df601fc1435d5e14c66275786aee

19,093

py

Python

learnable_primitives/primitives.py

ianhuang0630/CSQ

5f1fe99a8d9da73692643b3911d675dce269a03d

[ "MIT" ]

null

learnable_primitives/primitives.py

ianhuang0630/CSQ

5f1fe99a8d9da73692643b3911d675dce269a03d

[ "MIT" ]

null

learnable_primitives/primitives.py

ianhuang0630/CSQ

5f1fe99a8d9da73692643b3911d675dce269a03d

[ "MIT" ]

null

import numpy as np import torch def fexp(x, p): return torch.sign(x)*(torch.abs(x)**p) def cuboid_inside_outside_function(X, shape_params, epsilon=0.25): """ Arguments: ---------- X: Tensor with size BxNxMx3, containing the 3D points, where B is the batch size and N is the number of points shape_params: Tensor with size BxMx3, containing the shape along each axis for the M primitives epsilon: int, the shape of the SQ along the latitude and longitude Returns: --------- F: Tensor with size BxNxM, containing the values of the inside-outside function """ # Make sure that both tensors have the right shape assert X.shape[0] == shape_params.shape[0] # batch size assert X.shape[2] == shape_params.shape[1] # number of primitives assert X.shape[-1] == 3 # 3D points # Tensor that holds the values of the inside-outside function F = shape_params.new_zeros(X.shape[:-1]) shape_params = shape_params.unsqueeze(1) for i in range(3): F += (X[:, :, :, i] / shape_params[:, :, :, i])**(2.0/epsilon) return F**(epsilon) def inside_outside_function(X, shape_params, epsilons): """ Arguments: ---------- X: Tensor with size BxNxMx3, containing the 3D points, where B is the batch size and N is the number of points shape_params: Tensor with size BxMx3, containing the shape along each axis for the M primitives epsilons: Tensor with size BxMx2, containing the shape along the longitude and the latitude for the M primitives Returns: --------- F: Tensor with size BxNxM, containing the values of the inside-outside function """ B = X.shape[0] # batch_size N = X.shape[1] # number of points on target object M = X.shape[2] # number of primitives # Make sure that both tensors have the right shape assert shape_params.shape[0] == B # batch size assert epsilons.shape[0] == B # batch size assert shape_params.shape[1] == M # number of primitives assert shape_params.shape[1] == epsilons.shape[1] assert shape_params.shape[-1] == 3 # number of shape parameters assert epsilons.shape[-1] == 2 # number of shape parameters assert X.shape[-1] == 3 # 3D points # Declare some variables a1 = shape_params[:, :, 0].unsqueeze(1) # size Bx1xM a2 = shape_params[:, :, 1].unsqueeze(1) # size Bx1xM a3 = shape_params[:, :, 2].unsqueeze(1) # size Bx1xM e1 = epsilons[:, :, 0].unsqueeze(1) # size Bx1xM e2 = epsilons[:, :, 1].unsqueeze(1) # size Bx1xM # Add a small constant to points that are completely dead center to avoid # numerical issues in computing the gradient # zeros = X == 0 # X[zeros] = X[zeros] + 1e-6 X = ((X > 0).float() * 2 - 1) * torch.max(torch.abs(X), X.new_tensor(1e-6)) F = ((X[:, :, :, 0] / a1)**2)**(1./e2) # F += ((X[:, :, :, 1] / a2)**2)**(1./e2) F = F+((X[:, :, :, 1] / a2)**2)**(1./e2) F = F**(e2 / e1) # F += ((X[:, :, :, 2] / a3)**2)**(1./e1) F = F+((X[:, :, :, 2] / a3)**2)**(1./e1) # Sanity check to make sure that we have the expected size assert F.shape == (B, N, M) return F**e1 # return F def points_to_cuboid_distances(X, shape_params): """ Arguments: ---------- X: Tensor with size BxNxMx3, containing the 3D points, where B is the batch size and N is the number of points shape_params: Tensor with size BxMx3, containing the shape along each axis for the M primitives Returns: --------- F: Tensor with size BxNxM, containing the distances of each point to every primitive """ # Make sure that everything has the right size assert X.shape[0] == shape_params.shape[0] # batch size assert X.shape[2] == shape_params.shape[1] # number of primitives assert X.shape[-1] == 3 # 3D points # The distance between a point (x, y, z) to a cuboid with dimensions # (a1, a2, a3) is sqrt(max(0, abs(x) - a1)^2 + max(0, abs(y) - a2)^2 + # max(0, abs(z) - a3)^2). Technically, F=0 for all points either inside or # on the surface of the primitive, while we only want F=0 for the points on # the surface of the cuboid. F = (torch.max( X.abs() - shape_params.unsqueeze(1), torch.zeros_like(X) )**2).sum(-1) return F def euler_angles_to_rotation_matrices(angles): """ Arguments: --------- angles: Tensor with size Kx3, where K is the number of Euler angles we want to transform to rotation matrices Returns: ------- rotation_matrices: Tensor with size Kx3x3, that contains the computed rotation matrices """ K = angles.shape[0] # Allocate memory for a Tensor of size Kx3x3 that will hold the rotation # matrix along the x-axis r_x = angles.new_zeros((K, 3, 3)) r_x[:, 0, 0] = 1.0 c = torch.cos(angles[:, 0]) s = torch.sin(angles[:, 0]) r_x[torch.arange(K), 1, 1] = c r_x[torch.arange(K), 2, 2] = c r_x[torch.arange(K), 1, 2] = -s r_x[torch.arange(K), 2, 1] = s # Similar for the rotation matrices along the y-axis and z-axis r_y = angles.new_zeros((K, 3, 3)) r_y[:, 1, 1] = 1.0 c = torch.cos(angles[:, 1]) s = torch.sin(angles[:, 1]) r_y[torch.arange(K), 0, 0] = c r_y[torch.arange(K), 2, 2] = c r_y[torch.arange(K), 2, 0] = -s r_y[torch.arange(K), 0, 2] = s r_z = angles.new_zeros((K, 3, 3)) r_z[:, 2, 2] = 1.0 c = torch.cos(angles[:, 2]) s = torch.sin(angles[:, 2]) r_z[torch.arange(K), 0, 0] = c r_z[torch.arange(K), 1, 1] = c r_z[torch.arange(K), 0, 1] = -s r_z[torch.arange(K), 1, 0] = s return r_z.bmm(r_y.bmm(r_x)) def quaternions_to_rotation_matrices(quaternions): """ Arguments: --------- quaternions: Tensor with size Kx4, where K is the number of quaternions we want to transform to rotation matrices Returns: ------- rotation_matrices: Tensor with size Kx3x3, that contains the computed rotation matrices """ K = quaternions.shape[0] # Allocate memory for a Tensor of size Kx3x3 that will hold the rotation # matrix along the x-axis R = quaternions.new_zeros((K, 3, 3)) # A unit quaternion is q = w + xi + yj + zk xx = quaternions[:, 1]**2 yy = quaternions[:, 2]**2 zz = quaternions[:, 3]**2 ww = quaternions[:, 0]**2 n = (ww + xx + yy + zz).unsqueeze(-1) s = quaternions.new_zeros((K, 1)) s[n != 0] = 2 / n[n != 0] xy = s[:, 0] * quaternions[:, 1] * quaternions[:, 2] xz = s[:, 0] * quaternions[:, 1] * quaternions[:, 3] yz = s[:, 0] * quaternions[:, 2] * quaternions[:, 3] xw = s[:, 0] * quaternions[:, 1] * quaternions[:, 0] yw = s[:, 0] * quaternions[:, 2] * quaternions[:, 0] zw = s[:, 0] * quaternions[:, 3] * quaternions[:, 0] xx = s[:, 0] * xx yy = s[:, 0] * yy zz = s[:, 0] * zz idxs = torch.arange(K).to(quaternions.device) R[idxs, 0, 0] = 1 - yy - zz R[idxs, 0, 1] = xy - zw R[idxs, 0, 2] = xz + yw R[idxs, 1, 0] = xy + zw R[idxs, 1, 1] = 1 - xx - zz R[idxs, 1, 2] = yz - xw R[idxs, 2, 0] = xz - yw R[idxs, 2, 1] = yz + xw R[idxs, 2, 2] = 1 - xx - yy return R def transform_to_primitives_centric_system(X, translations, rotation_angles): """ Arguments: ---------- X: Tensor with size BxNx3, containing the 3D points, where B is the batch size and N is the number of points translations: Tensor with size BxMx3, containing the translation vectors for the M primitives rotation_angles: Tensor with size BxMx4 containing the 4 quaternion values for the M primitives Returns: -------- X_transformed: Tensor with size BxNxMx3 containing the N points transformed in the M primitive centric coordinate systems. """ # Make sure that all tensors have the right shape assert X.shape[0] == translations.shape[0] assert translations.shape[0] == rotation_angles.shape[0] assert translations.shape[1] == rotation_angles.shape[1] assert X.shape[-1] == 3 assert translations.shape[-1] == 3 assert rotation_angles.shape[-1] == 4 # Subtract the translation and get X_transformed with size BxNxMx3 X_transformed = X.unsqueeze(2) - translations.unsqueeze(1) # R = euler_angles_to_rotation_matrices(rotation_angles.view(-1, 3)).view( R = quaternions_to_rotation_matrices(rotation_angles.view(-1, 4)).view( rotation_angles.shape[0], rotation_angles.shape[1], 3, 3 ) # Let as denote a point x_p in the primitive-centric coordinate system and # its corresponding point in the world coordinate system x_w. We denote the # transformation from the point in the world coordinate system to a point # in the primitive-centric coordinate system as x_p = R(x_w - t) X_transformed = R.unsqueeze(1).matmul(X_transformed.unsqueeze(-1)) X_signs = (X_transformed > 0).float() * 2 - 1 X_abs = X_transformed.abs() X_transformed = X_signs * torch.max(X_abs, X_abs.new_tensor(1e-5)) return X_transformed.squeeze(-1) def transform_to_world_coordinates_system(X_SQ, translations, rotation_angles): """ Arguments: ---------- X_SQ: Tensor with size BxMxSx3, containing the 3D points, where B is the batch size, M is the number of primitives and S is the number of points on each primitive-centric system translations: Tensor with size BxMx3, containing the translation vectors for the M primitives rotation_angles: Tensor with size BxMx3 containing the 3 Euler angles for the M primitives Returns: -------- X_SQ_w: Tensor with size BxMxSx3 containing the N points transformed in the M primitive centric coordinate systems. """ # Make sure that all tensors have the right shape assert X_SQ.shape[0] == translations.shape[0] assert translations.shape[0] == rotation_angles.shape[0] assert translations.shape[1] == rotation_angles.shape[1] assert X_SQ.shape[1] == translations.shape[1] assert X_SQ.shape[-1] == 3 assert translations.shape[-1] == 3 assert rotation_angles.shape[-1] == 4 # Compute the rotation matrices to every primitive centric coordinate # system (R has size BxMx3x3) R = quaternions_to_rotation_matrices(rotation_angles.view(-1, 4)).view( rotation_angles.shape[0], rotation_angles.shape[1], 3, 3 ) # We need the R.T to get the rotation matrix from the primitive-centric # coordinate system to the world coordinate system. R_t = torch.einsum("...ij->...ji", (R,)) assert R.shape == R_t.shape X_SQ_w = R.unsqueeze(2).matmul(X_SQ.unsqueeze(-1)) X_SQ_w = X_SQ_w.squeeze(-1) + translations.unsqueeze(2) return X_SQ_w def deform(X, shape_params, tapering_params, bending_params=None): """ Arguments: ---------- X: Tensor with size BxMxSx3 containing the S points sampled on the surfaces of each SQ shape_params: Tensor with size BxMx3, containing the shape along each axis for the M primitives tapering_params: Tensor with size BxMx2, containing the tapering_params for every primitive bending_params: Tensor with size BxMx2, containing the bending_params for every primitive Returns: -------- X_deformed: Tensor with size BxMxSx3 containing the N points transformed in the M primitive centric coordinate systems after the deformations. """ B, M, S, _ = X.shape # Make sure that all tensors have the right shape assert X.shape[0] == shape_params.shape[0] # batch size assert X.shape[0] == tapering_params.shape[0] # batch size assert shape_params.shape[-1] == 3 assert tapering_params.shape[-1] == 2 assert X.shape[1] == shape_params.shape[1] assert X.shape[1] == tapering_params.shape[1] # Compute the two linear tapering functions K = tapering_params / shape_params[:, :, -1].unsqueeze(-1) assert tapering_params.shape == K.shape f = K.unsqueeze(2) * X[:, :, :, -1].unsqueeze(-1) + 1.0 assert f.shape == (B, M, S, 2) f = torch.cat([ f, f.new_ones(B, M, S, 1) ], -1) assert f.shape == X.shape X_d = X * f X_d = apply_bending(X_d, bending_params) return X_d def apply_bending(X, bending_params): """ Arguments: ---------- X: Tensor with size BxMxSx3 containing the S points sampled on the surfaces of each SQ bending_params: Tensor with size BxMx2, containing the bending_params for every primitive Returns: -------- X_d: Tensor with size BxMxSx3 containing the N points transformed in the M primitive centric coordinate systems after the deformations. """ # If there no bending params specified return the input as is if bending_params is None: return X B, M, S, _ = X.shape # Make sure that all tensors have the right shape assert X.shape[0] == bending_params.shape[0] # batch size assert bending_params.shape[-1] == 2 # Apply the bending operation bending_params = bending_params.unsqueeze(2) # BXMX2 -> BxMx1x2 k = bending_params[:, :, :, 0].unsqueeze(-1) # BxMx1x1 a = bending_params[:, :, :, 1].unsqueeze(-1) # BxMx1x1 b = torch.atan2(X[:, :, :, 1].unsqueeze(-1), X[:, :, :, 0].unsqueeze(-1)) assert b.shape == (B, M, S, 1) r = torch.sqrt( X_d[:, :, :, 0].unsqueeze(-1)**2 + X_d[:, :, :, 1].unsqueeze(-1)**2 ) * torch.cos(a - b) assert r.shape == (B, M, S, 1) k_inv = 1 / k # BxMx1x1 gamma = X_d[:, :, :, -1].unsqueeze(-1) / k R = k_inv - (k_inv - r) * torch.cos(gamma) assert R.shape == (B, M, S, 1) X_d = X.new_zeros(X.shape) X_d[:, :, :, 0] = X_d[:, :, :, 0] + (R - r)*torch.cos(a) X_d[:, :, :, 1] = X_d[:, :, :, 1] + (R - r)*torch.sin(a) X_d[:, :, :, 2] = (k_inv - r)*(R - r)*torch.sin(gamma) return X_d def distance(F, shape_params=None, use_chamfer=False): """ Arguments: ---------- F: Tensor of size BxNxM, with the values of the inside-outside function for the N points w.r.t. the M primitives shape_params: Tensor with size BxMx3, containing the shape along each axis for the M primitives Returns: -------- C: Tensor of size BxNxM, with the distance between points and primitives primitive_idxs: Tensor of size BxNxM, with the indices of the primitives in the original tensor F """ # Minimization of the distances between points and primitives if use_chamfer: C = (F-1.0)**2.0 else: a1a2a3 = torch.sqrt(shape_params.prod(-1)).unsqueeze(1) # C = torch.max(a1a2a3*(F - 1.0), torch.zeros_like(F)) # C = torch.max(torch.sqrt(F) - 1.0, torch.zeros_like(F)) C = torch.max((F - 1.0), torch.zeros_like(F)) return torch.sort(C, dim=-1) def ray_plane_intersections(P, V, normals, exp1, exp2): """ Find the interesection between a set of rays and a set of planes. Rays are defined as two points and normals as points and planes. We we want to compute rs = n (Vo - Po) ----------- n (P1 - Po) n and Vo define the plane and Po and P1 the ray Arguments: ---------- P: Tensor of size BxMx?x3 containing the start of each ray (P1 - Po) V: Tensor of size BxMxSxNx3 with the differences between the ray_starts and the points of the planes (Vo - Po) normals: Tensor of size BxMx?x3 N normals transformed in the M primitive-centric coordinate systems Returns: -------- r: Tensor of size BxMxSxN with the squared_distances """ B, M, S, N, _ = V.shape t1 = torch.einsum(exp1, [normals, V]) t2 = torch.einsum(exp2, [normals, P]) rs = torch.div(t1, t2) assert rs.shape == (B, M, S, N) return torch.pow(rs, 2) def beta_stirling(x, y): sqrt2pi = float(np.sqrt(2*np.pi)) return sqrt2pi * (x**(x-0.5) * y**(y-0.5)) / (x+y)**(x+y-0.5) def sq_volumes(parameters): a1a2a3 = parameters[3].view(-1, 3).prod(-1) e = parameters[4].view(-1, 2) e1 = e[:, 0] e2 = e[:, 1] e1e2 = e.prod(-1) b1 = beta_stirling(e1/2 + 1, e1) b2 = beta_stirling(e2/2, e2/2) volumes = 2 * a1a2a3 * e1e2 * b1 * b2 return volumes def sq_areas(shapes, epsilons): """Approximate area of the superquadric. We use Knud Thomsen's formula for ellipsoids. """ p = 1.6075 a = shapes[:, :, 0] b = shapes[:, :, 1] c = shapes[:, :, 2] return 4 * np.pi * (((a*b)**p + (a*c)**p + (b*c)**p)/3)**(1/p) def sample_points_inside_primitives(X_SQ, N, rotations, translations): """Sample points inside the primitives, given S points on their surface Arguments: ---------- X_SQ: Tensor of size BxMxSx3 containing S points sampled on the surface of each primitive rotations: Tensor of size BxMx4 containing the quaternions of the SQs translations: Tensor of size BxMx4 containing the translation vectors of the SQs N: number of points to be generated internally in each primitive Returns: -------- X_world: Tensor of size BxMxNx3 containing N points sampled uniformly inside and on the surface of the SQs """ B, M, S, _ = X_SQ.shape assert rotations.shape == (B, M, 4) assert translations.shape == (B, M, 3) # Create points inside the primitives device = X_SQ.device batch = (torch.arange(B*M*N) / (M*N)).view(B, M, N).to(device) prim = ((torch.arange(B*M*N) / N) % M).view(B, M, N).to(device) pointsA = torch.randint(0, S, (B, M, N), dtype=torch.long).to(device) pointsB = torch.randint(0, S, (B, M, N), dtype=torch.long).to(device) t = torch.rand(B, M, N, 1).to(device) X_a = X_SQ[batch, prim, pointsA] X_b = X_SQ[batch, prim, pointsB] X = X_a + t * (X_b-X_a) # Transform the points to world coordinates # R = quaternions_to_rotation_matrices(rotations.view(-1, 4)) # R = R.view(B, M, 3, 3) # X_world = X.view(B, M, N, 1, 3).matmul(R.view(B, M, 1, 3, 3)) # X_world = X_world.view(B, M, N, 3) # X_world = X_world + translations.view(B, M, 1, 3) X_world = transform_to_world_coordinates_system( X, translations, rotations ) assert X_world.shape == (B, M, N, 3) return X_world

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8dc85eed480d432c4899171384c4da5e6df7a236

6,206

py

Python

spring semester 2 course/computer_graphics_labs/3D OpenGL/lab.py

andrwnv/study-progs

902c4ede0b273d91fd87c93e861b40439847c1a9

[ "MIT" ]

4

2020-01-02T08:38:55.000Z

2020-11-12T19:46:22.000Z

spring semester 2 course/computer_graphics_labs/3D OpenGL/lab.py

andrwnv/StudyProgs

902c4ede0b273d91fd87c93e861b40439847c1a9

[ "MIT" ]

null

spring semester 2 course/computer_graphics_labs/3D OpenGL/lab.py

andrwnv/StudyProgs

902c4ede0b273d91fd87c93e861b40439847c1a9

[ "MIT" ]

null

from PyQt5.QtOpenGL import * from OpenGL.GL import * from PyQt5 import QtWidgets, QtCore class FigureWidget(QGLWidget): """ Main OpenGL widget. """ def __init__(self, parent): super(FigureWidget, self).__init__() self.setMinimumSize(1280, 720) self.__rotate_angle_y = 70 self.__rotate_angle_x = 15 self.__rotate_angle_z = 0 self.__zoom_coefficient = -5 self.setFocusPolicy(QtCore.Qt.StrongFocus) self.__timer = QtCore.QTimer() self.__timer.setInterval(30) self.__timer.timeout.connect(lambda: self.idle()) self.__timer.start() def idle(self): self.__rotate_angle_y += 0.5 self.update() def paintGL(self) -> None: """ Draw scene. """ glClear(GL_COLOR_BUFFER_BIT) glClearColor(0, 0, 0, 1.0) glColor3f(1.0, 1.0, 1.0) glMatrixMode(GL_PROJECTION) glLoadIdentity() glFrustum(-3, 3, -2, 2, 1.2, 40) glMatrixMode(GL_MODELVIEW) glLoadIdentity() glTranslatef(0, 0, self.__zoom_coefficient) glRotatef(self.__rotate_angle_x, 1, 0, 0) glRotatef(self.__rotate_angle_y, 0, 1, 0) glRotatef(self.__rotate_angle_z, 0, 0, 1) glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT) glColor3f(1.0, 1.0, 1.0) glBegin(GL_LINES) i: float = -2.5 # Draw coordinate grid. while i <= 2.5: glVertex3f(i, -4, 2.5) glVertex3f(i, -4, -2.5) glVertex3f(2.5, -4, i) glVertex3f(-2.5, -4, i) i += 0.25 glEnd() # Draw up pyramid. glBegin(GL_TRIANGLE_STRIP) # 1st face. glColor3f(1, 0, 1) glVertex3f(0, 3, 0) glColor3f(0.5, 0, 0.5) glVertex3f(-1, 1, -1) glColor3f(0.5, 1, 1) glVertex3f(-1, 1, 1) # 2nd face. glColor3f(0, 0, 1) glVertex3f(0, 3, 0) glColor3f(0.5, 0.5, 1) glVertex3f(-1, 1, 1) glColor3f(0.5, 0.3, 0.2) glVertex3f(1, 1, 1) # 3th face. glColor3f(0, 1, 1) glVertex3f(0, 3, 0) glColor3f(0.5, 0, 0.5) glVertex3f(1, 1, 1) glColor3f(0, 1, 1) glVertex3f(1, 1, -1) # 4sth face. glColor3f(0, 1, 0) glVertex3f(0, 3, 0) glColor3f(0.5, 0.7, 0.3) glVertex3f(1, 1, -1) glColor3f(0.1, 0.4, 0.3) glVertex3f(-1, 1, -1) glEnd() # Draw cube. glBegin(GL_QUAD_STRIP) glColor3f(1, 1, 0) glVertex3f(-1, 1, -1) glColor3f(0.5, 1, 0) glVertex3f(-1, 1, 1) glColor3f(1, 0, 1) glVertex3f(-1, -1, -1) glVertex3f(-1, -1, 1) glColor3f(1, 0, 0) glVertex3f(-1, 1, 1) glColor3f(1, 1, 0) glVertex3f(1, 1, 1) glVertex3f(-1, -1, 1) glColor3f(0.5, 1, 0) glVertex3f(1, -1, 1) glColor3f(0, 0.5, 1) glVertex3f(1, 1, 1) glVertex3f(1, 1, -1) glColor3f(1, 0, 1) glVertex3f(1, -1, 1) glColor3f(0, 1, 0) glVertex3f(1, -1, -1) glColor3f(1, 0, 1) glVertex3f(1, 1, -1) glColor3f(0.5, 1, 0) glVertex3f(-1, 1, -1) glColor3f(1, 1, 0) glVertex3f(1, -1, -1) glVertex3f(-1, -1, -1) glEnd() # Draw down pyramid. glBegin(GL_TRIANGLE_STRIP) # 1st face. glColor3f(1, 0, 1) glVertex3f(0, -3, 0) glColor3f(0.2, 0.7, 1) glVertex3f(-1, -1, -1) glColor3f(0.1, 0.7, 0.8) glVertex3f(-1, -1, 1) # 2nd face. glColor3f(0, 0, 1) glVertex3f(0, -3, 0) glColor3f(0.1, 0, 0.8) glVertex3f(-1, -1, 1) glColor3f(0.8, 0, 0.8) glVertex3f(1, -1, 1) # 3th face. glColor3f(0, 1, 1) glVertex3f(0, -3, 0) glColor3f(0.1, 0.7, 0.8) glVertex3f(1, -1, 1) glColor3f(0.2, 0.7, 1) glVertex3f(1, -1, -1) # 4sth face. glColor3f(0, 1, 0) glVertex3f(0, -3, 0) glColor3f(0, 0, 1) glVertex3f(1, -1, -1) glColor3f(0.8, 0, 0.8) glVertex3f(-1, -1, -1) glEnd() glFlush() def resizeGL(self, w, h) -> None: """ Resize event. """ glViewport(50, 50, w - 100, h - 100) def initializeGL(self) -> None: """ Init OpenGL. """ # glEnable(GL_CULL_FACE) # glCullFace(GL_FRONT) # Enable depth. glEnable(GL_DEPTH_TEST) glClearColor(0.1, 0.39, 0.88, 1.0) glColor3f(1.0, 1.0, 1.0) glMatrixMode(GL_PROJECTION) glLoadIdentity() glFrustum(-2, 2, -1.5, 1.5, 1, 40) glMatrixMode(GL_MODELVIEW) glLoadIdentity() glTranslatef(0, 0, -3) glRotatef(70, 0, 1, 0) glDisable(GL_BLEND) def keyPressEvent(self, event): if event.key() == QtCore.Qt.Key_A: self.__rotate_angle_y -= 0.5 self.update() elif event.key() == QtCore.Qt.Key_D: self.__rotate_angle_y += 0.5 self.update() elif event.key() == QtCore.Qt.Key_W: self.__rotate_angle_x += 0.5 self.update() elif event.key() == QtCore.Qt.Key_S: self.__rotate_angle_x -= 0.5 self.update() elif event.key() == QtCore.Qt.Key_Q: self.__rotate_angle_z += 0.5 self.update() elif event.key() == QtCore.Qt.Key_E: self.__rotate_angle_z -= 0.5 self.update() elif event.key() == QtCore.Qt.Key_Plus: self.__zoom_coefficient += 0.5 self.update() elif event.key() == QtCore.Qt.Key_Minus: self.__zoom_coefficient -= 0.5 self.update() class App(QtWidgets.QMainWindow): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.__glWidget = FigureWidget(self) self.setCentralWidget(self.__glWidget) if __name__ == '__main__': app = QtWidgets.QApplication(['3D OpenGL']) window = App() window.show() app.exec_()

24.626984

58

0.508701

824

6,206

3.667476

0.163835

0.046989

0.127068

0.137657

0.656188

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251

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0

0.068571

0

null

0

null

0

1

0

8dc89f352fa436e39e052c93cd7afb303171ceba

2,485

py

Python

src/passari/scripts/submit_sip.py

finnish-heritage-agency/passari

17af68b07435a40eaabaacf8c34e54517edc9153

[ "MIT" ]

1

2020-06-17T11:05:00.000Z

src/passari/scripts/submit_sip.py

finnish-heritage-agency/passari

17af68b07435a40eaabaacf8c34e54517edc9153

[ "MIT" ]

2

2021-03-31T19:50:58.000Z

2021-05-17T20:52:03.000Z

src/passari/scripts/submit_sip.py

finnish-heritage-agency/passari

17af68b07435a40eaabaacf8c34e54517edc9153

[ "MIT" ]

null

""" Submit a SIP archive to the digital preservation service """ from pathlib import Path import click from passari.config import CONFIG from passari.dpres.package import MuseumObjectPackage from passari.dpres.ssh import connect_dpres_sftp from passari.utils import debugger_enabled def submit_sip(package_dir, object_id: int, sip_id: str = None): """ Submit SIP to the DPRES service :param package_dir: Path to directory containing objects under processing :param archive_dir: Path to directory containing logs for processed SIPs :param object_id: Object ID of the object to process :param sip_id: Optional SIP ID used to generate multiple SIPs from the same object version """ with connect_dpres_sftp() as sftp: museum_package = MuseumObjectPackage.from_path_sync( path=package_dir / str(object_id), sip_id=sip_id ) # DPRES service won't process files with the suffix '.incomplete' temp_filename = f"{museum_package.sip_filename}.incomplete" dest_path = Path(CONFIG["ssh"]["home_path"]) / "transfer" print(f"Uploading to {dest_path / temp_filename}") sftp.put( museum_package.sip_archive_path, str(dest_path / temp_filename) ) print("Renaming uploaded file") sftp.rename( str(dest_path / temp_filename), str(dest_path / museum_package.sip_filename) ) return museum_package @click.command() @click.option( "--package-dir", help="Directory used to process and store the objects", type=click.Path(exists=True, file_okay=False, dir_okay=True), default=Path.home() / "MuseumObjects" ) @click.option( "--debug/--no-debug", default=False, envvar="MUSEUMPLUS_DEBUG", help=( "Enable debug mode. Any unhandled exception will launch a debugger." ) ) @click.option( "--sip-id", help=( "Optional SIP ID allowing multiple SIPs to be generated for the " "same package." ), type=str, default=None ) @click.argument("object_id", nargs=1) def cli(package_dir, object_id, debug, sip_id): main(package_dir, object_id, debug, sip_id) def main(package_dir, object_id, debug=False, sip_id=None): package_dir = Path(package_dir) with debugger_enabled(debug): return submit_sip( package_dir=package_dir, object_id=object_id, sip_id=sip_id ) if __name__ == "__main__": cli()

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77

0.677666

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2,485

4.908815

0.322188

0.037152

0.049536

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0.139319

0.076161

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0.228571

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0

0.089286

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0.020182

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0.053571

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0.071429

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0

null

0

null

0

1

0

1

8dc9eea63f0db427ca8d82b08f280d3e1c15f968

2,931

py

Python

alipay/aop/api/domain/AlipayOfflineProviderIndirectisvActivityCreateModel.py

antopen/alipay-sdk-python-all

8e51c54409b9452f8d46c7bb10eea7c8f7e8d30c

[ "Apache-2.0" ]

null

alipay/aop/api/domain/AlipayOfflineProviderIndirectisvActivityCreateModel.py

antopen/alipay-sdk-python-all

8e51c54409b9452f8d46c7bb10eea7c8f7e8d30c

[ "Apache-2.0" ]

null

alipay/aop/api/domain/AlipayOfflineProviderIndirectisvActivityCreateModel.py

antopen/alipay-sdk-python-all

8e51c54409b9452f8d46c7bb10eea7c8f7e8d30c

[ "Apache-2.0" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- import json from alipay.aop.api.constant.ParamConstants import * from alipay.aop.api.domain.IndirectIsvTerminalInfo import IndirectIsvTerminalInfo class AlipayOfflineProviderIndirectisvActivityCreateModel(object): def __init__(self): self._ext_info = None self._indirect_isv_terminal_info = None self._merchant_id = None @property def ext_info(self): return self._ext_info @ext_info.setter def ext_info(self, value): self._ext_info = value @property def indirect_isv_terminal_info(self): return self._indirect_isv_terminal_info @indirect_isv_terminal_info.setter def indirect_isv_terminal_info(self, value): if isinstance(value, list): self._indirect_isv_terminal_info = list() for i in value: if isinstance(i, IndirectIsvTerminalInfo): self._indirect_isv_terminal_info.append(i) else: self._indirect_isv_terminal_info.append(IndirectIsvTerminalInfo.from_alipay_dict(i)) @property def merchant_id(self): return self._merchant_id @merchant_id.setter def merchant_id(self, value): self._merchant_id = value def to_alipay_dict(self): params = dict() if self.ext_info: if hasattr(self.ext_info, 'to_alipay_dict'): params['ext_info'] = self.ext_info.to_alipay_dict() else: params['ext_info'] = self.ext_info if self.indirect_isv_terminal_info: if isinstance(self.indirect_isv_terminal_info, list): for i in range(0, len(self.indirect_isv_terminal_info)): element = self.indirect_isv_terminal_info[i] if hasattr(element, 'to_alipay_dict'): self.indirect_isv_terminal_info[i] = element.to_alipay_dict() if hasattr(self.indirect_isv_terminal_info, 'to_alipay_dict'): params['indirect_isv_terminal_info'] = self.indirect_isv_terminal_info.to_alipay_dict() else: params['indirect_isv_terminal_info'] = self.indirect_isv_terminal_info if self.merchant_id: if hasattr(self.merchant_id, 'to_alipay_dict'): params['merchant_id'] = self.merchant_id.to_alipay_dict() else: params['merchant_id'] = self.merchant_id return params @staticmethod def from_alipay_dict(d): if not d: return None o = AlipayOfflineProviderIndirectisvActivityCreateModel() if 'ext_info' in d: o.ext_info = d['ext_info'] if 'indirect_isv_terminal_info' in d: o.indirect_isv_terminal_info = d['indirect_isv_terminal_info'] if 'merchant_id' in d: o.merchant_id = d['merchant_id'] return o

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104

0.639713

349

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5

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0.132378

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0

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2,931

82

105

35.743902

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0.036049

0

1

0.134328

false

0

0.044776

0.283582

0

null

0

1

0

null

0

2

8dca2da6c8645bf9fdd19dabbdf05a6549cdbc55

4,220

py

Python

mate/environments/environment.py

thomyphan/emergent-cooperation

2406b8679ddbebba745f1026ca3689f1ba181e28

[ "MIT" ]

null

mate/environments/environment.py

thomyphan/emergent-cooperation

2406b8679ddbebba745f1026ca3689f1ba181e28

[ "MIT" ]

null

mate/environments/environment.py

thomyphan/emergent-cooperation

2406b8679ddbebba745f1026ca3689f1ba181e28

[ "MIT" ]

null

import numpy class Environment: def __init__(self, params) -> None: self.domain_value_labels = params["domain_value_labels"] self.observation_dim = params["observation_dim"] self.nr_agents = params["nr_agents"] self.nr_actions = params["nr_actions"] self.time_limit = params["time_limit"] self.gamma = params["gamma"] self.time_step = 0 self.sent_gifts = numpy.zeros(self.nr_agents) self.discounted_returns = numpy.zeros(self.nr_agents) self.undiscounted_returns = numpy.zeros(self.nr_agents) self.domain_counts = numpy.zeros(len(self.domain_value_labels)) self.last_joint_action = -numpy.ones(self.nr_agents, dtype=numpy.int) """ Performs the joint action in order to change the environment. Returns the reward for each agent in a list sorted by agent ID. """ def perform_step(self, joint_action): assert not self.is_done(), "Episode terminated at time step {}. Please, reset before calling 'step'."\ .format(self.time_step) return numpy.zeros(self.nr_agents), {} """ Indicates if an episode is done and the environments needs to be reset. """ def is_done(self): return self.time_step >= self.time_limit def action_as_vector(self, action): if action < self.nr_actions: vector = numpy.zeros(self.nr_actions) if action >= 0: vector[action] = 1 else: vector = numpy.ones(self.nr_actions) return vector """ Performs a joint action to change the state of the environment. Returns the joint observation, the joint reward, a done flag, and other optional information (e.g., logged data). Note: The joint action must be a list ordered according to the agent ID!. """ def step(self, joint_action): assert len(joint_action) == self.nr_agents, "Length of 'joint_action' is {}, expected {}"\ .format(len(joint_action), self.nr_agents) assert not self.is_done(), "Episode terminated at time step {}. Please, reset before calling 'step'."\ .format(self.time_step) rewards, infos = self.perform_step(joint_action) for i, a in enumerate(joint_action): self.last_joint_action[i] = a if a >= self.nr_actions: self.sent_gifts[i] += 1 assert len(rewards) == self.nr_agents, "Length of 'rewards' is {}, expected {}"\ .format(len(rewards), self.nr_agents) observations = self.joint_observation() assert len(observations) == self.nr_agents, "Length of 'observations' is {}, expected {}"\ .format(len(observations), self.nr_agents) self.time_step += 1 self.domain_counts[0] += 1.0 self.undiscounted_returns += rewards self.discounted_returns += (self.gamma**self.time_step)*rewards if "neighbor_agents" not in infos: infos["neighbor_agents"] = [[j for j in range(self.nr_agents) if j != i] for i in range(self.nr_agents)] return observations, rewards, self.is_done(), infos def get_index(self, label): return self.domain_value_labels.index(label) """ The local observation for a specific agent. Only visible for the corresponding agent and private to others. """ def local_observation(self, agent_id): pass """ Returns the observations of all agents in a listed sorted by agent ids. """ def joint_observation(self): return [numpy.array(self.local_observation(i)).reshape(self.observation_dim) for i in range(self.nr_agents)] """ Returns a high-level value which is domain-specific. """ def domain_values(self): return self.domain_counts def domain_value_debugging_indices(self): return 0,1 """ Re-Setup of the environment for a new episode. """ def reset(self): self.time_step = 0 self.discounted_returns[:] = 0 self.undiscounted_returns[:] = 0 self.last_joint_action[:] = -1 self.domain_counts[:] = 0 self.sent_gifts[:] = 0 return self.joint_observation()

38.363636

116

0.638389

555

4,220

4.684685

0.225225

0.046154

0.069231

0.030769

0.250385

0.143846

0.113846

0.070769

0

0.005099

0.256398

4,220

109

117

38.715596

0.823454

0

0.086957

0

0.109352

0

0.072464

1

0.15942

false

0.014493

0.072464

0.318841

0

null

0

null

0

1

0

8dca97c9199a78c34139496f0e9ce4927d4d5e8f

1,651

py

Python

boa3/model/attribute.py

hal0x2328/neo3-boa

6825a3533384cb01660773050719402a9703065b

[ "Apache-2.0" ]

25

2020-07-22T19:37:43.000Z

2022-03-08T03:23:55.000Z

boa3/model/attribute.py

hal0x2328/neo3-boa

6825a3533384cb01660773050719402a9703065b

[ "Apache-2.0" ]

419

2020-04-23T17:48:14.000Z

2022-03-31T13:17:45.000Z

boa3/model/attribute.py

hal0x2328/neo3-boa

6825a3533384cb01660773050719402a9703065b

[ "Apache-2.0" ]

15

2020-05-21T21:54:24.000Z

2021-11-18T06:17:24.000Z

import ast from typing import Optional, Tuple, Union from boa3.model.expression import IExpression from boa3.model.imports.package import Package from boa3.model.symbol import ISymbol from boa3.model.type.classes.classtype import ClassType from boa3.model.type.itype import IType class Attribute(IExpression): """ A class used to represent an attribute :ivar value: the origin expression that has the attribute :ivar attr_name: the name of the attribute :ivar attr_symbol: the found symbol for the attribute """ def __init__(self, value: Union[ast.AST, IExpression, Package], attr_name: str, attr_symbol: Optional[ISymbol] = None, origin: Optional[ast.AST] = None): super().__init__(origin) self.value: Union[ast.AST, IExpression, Package] = value self.attr_name: str = attr_name obj_with_symbols = value.type if isinstance(value, IExpression) else value if (isinstance(value, (IExpression, ClassType, Package)) and hasattr(obj_with_symbols, 'symbols') and attr_name in obj_with_symbols.symbols): attr_symbol = obj_with_symbols.symbols[attr_name] self.attr_symbol: Optional[ISymbol] = attr_symbol @property def shadowing_name(self) -> str: return 'attribute' @property def type(self) -> IType: from boa3.model.type.type import Type return self.attr_symbol.type if isinstance(self.attr_symbol, IExpression) else Type.none @property def values(self) -> Tuple[Union[ast.AST, IExpression], Optional[ISymbol], str]: return self.value, self.attr_symbol, self.attr_name

35.891304

100

0.706239

218

1,651

5.201835

0.256881

0.070547

0.068783

0.044974

0.111111

0.067019

0

0.004577

0.205936

1,651

45

101

36.688889

0.860412

0.117505

0

0.107143

0

0.011197

0

1

0.142857

false

0

0.285714

0.071429

0.571429

0

null

0

null

0

1

0

1

8dcb21a58f1185a80b68bc21fae747a3384bef5d

2,409

py

Python

craynn/viz/imgs.py

maxim-borisyak/craynn

fceabd33f5969033fb3605f894778c42c42f3e08

[ "MIT" ]

null

craynn/viz/imgs.py

maxim-borisyak/craynn

fceabd33f5969033fb3605f894778c42c42f3e08

[ "MIT" ]

null

craynn/viz/imgs.py

maxim-borisyak/craynn

fceabd33f5969033fb3605f894778c42c42f3e08

[ "MIT" ]

null

import os import os.path as osp __all__ = [ 'pack_images', 'plot_and_pack', 'save_images' ] def pack_images(output, imgs, vmax=1024.0, archive=None, name="image_%d.png", **data): from scipy.misc import toimage try: os.makedirs(output) except: pass for i in range(imgs.shape[0]): args = dict([ (k, v[i]) for k, v in data.items()]) args['index'] = i path = osp.join(output, name.format(**args)) toimage(imgs[i], cmin=0.0, cmax=vmax, channel_axis=0).save(path) if archive is not None: import subprocess as sb if sb.check_call(['tar', '-czvf', archive, output]): os.removedirs(output) def save_images(cycle, version, original, transformed, outdir='output', pack=True): import matplotlib.pyplot as plt import os import os.path as osp path = osp.join(outdir, 'images_%012d_%s' % (cycle, str(version))) os.system('rm -rf %s' % path) os.system('mkdir -p %s' % path) plt.ioff() for i in range(original.shape[0]): fig = plt.figure(figsize=(10, 4)) ax = fig.add_subplot(1, 2, 1) ax.grid('off') im = ax.imshow(original[i, 0], interpolation='None', cmap=plt.cm.gray) cb = fig.colorbar(im) ax = fig.add_subplot(1, 2, 2) ax.grid('off') im = ax.imshow(transformed[i, 0], interpolation='None', cmap=plt.cm.gray) cb = fig.colorbar(im) plt.savefig(osp.join(path, 'test_%06d.png' % i), dpi=80) plt.close(fig) plt.ion() if pack: tar_path = osp.join(outdir, 'test_images_%s.tar.gz' % version) os.system('tar -czf %s %s ' % (tar_path, path)) def plot_and_pack(imgs, outdir='output', pack=True, name="image_{index}.png", figsize=(5, 4), cmap='Grey', **data): import matplotlib.pyplot as plt import os import os.path as osp os.system('rm -rf %s' % outdir) os.system('mkdir -p %s' % outdir) plt.ioff() for i in range(imgs.shape[0]): fig = plt.figure(figsize=figsize) plt.grid('off') plt.imshow(imgs[i, 0], interpolation='None', cmap=cmap) plt.colorbar() args = dict([(k, v[i]) for k, v in data.items()]) args['index'] = i filename = name.format(**args) plt.savefig(osp.join(outdir, filename), dpi=80) plt.close(fig) plt.ion() if pack: basedir, cwd = osp.split(outdir) tar_path = osp.join(basedir, '%s.tar.gz' % cwd) print('Archive', tar_path) return os.system('tar -czf %s %s ' % (tar_path, outdir))

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8dcbe319893f08c9fec316e62b3f5815719b8547

34,085

py

Python

shield.py

sidsen/VRL_CodeReview

6ee6114215ee49570c47ae41789f5d9bd11ce820

[ "MIT" ]

10

2019-10-22T16:58:43.000Z

2021-12-05T06:12:53.000Z

shield.py

sidsen/VRL_CodeReview

6ee6114215ee49570c47ae41789f5d9bd11ce820

[ "MIT" ]

1

2019-02-23T05:44:06.000Z

shield.py

sidsen/VRL_CodeReview

6ee6114215ee49570c47ae41789f5d9bd11ce820

[ "MIT" ]

5

2019-02-23T04:10:06.000Z

2020-09-06T22:59:26.000Z

import metrics from metrics import timeit from main import * import os class Shield(object): def __init__(self, env, actor, model_path=None, force_learning=False, debug=False): """init Args: env (Environment): environment actor (ActorNetwork): actor force_learning (bool, optional): if true, even there are model stored in model path, still train. """ self.env = env self.actor = actor self.model_path = model_path self.K = None self.K_list = [] self.initial_range_list = [] if not force_learning and os.path.isfile(str(self.model_path)): self.K_list = [_K for _K in loadK(self.model_path)] self.continuous = env.continuous self.shield_count = 0 self.debug = debug self.step_count = 0 self.last_B_value = 0 self.keep_increasing = False @timeit def train_shield(self, learning_method, number_of_rollouts, simulation_steps, eq_err=1e-2, rewardf=None, testf=None, explore_mag = .04, step_size = .05, names=None, coffset=None, bias=False, discretization=False, lqr_start=False, degree=4, without_nn_guide=False, enable_jit=False): """train shield Args: learning_method (string): learning method string number_of_rollouts (int): number of rollouts simulation_steps (int): simulation steps timestep (float, optional): timestep for continuous control eq_err (float, optional): amount of guassian error rewardf (None, optional): reward function testf (None, optional): reward function for draw controller explore_mag (float, optional): explore mag step_size (float, optional): step size names (None, optional): names of state """ # continuous if self.env.continuous: self.B_str_list = [] self.B_list = [] self.last_B_result = [] self.B = None if self.K_list == []: #assert names is not None x0 = self.env.reset() def default_testf_continous(x, u): if self.env.unsafe: if ((np.array(x) < self.env.x_max)*(np.array(x) > self.env.x_min)).all(axis=1).any(): return -1 else: return 0 else: if ((x < self.env.x_max).all() and (x > self.env.x_min).all()): return 0 else: return -1 def learning_oracle_continuous(x): self.K = learn_shield(self.env.A, self.env.B, self.env.Q, self.env.R, x, eq_err,\ learning_method, number_of_rollouts, simulation_steps, self.actor, self.env.x_min, self.env.x_max, rewardf=rewardf, \ continuous=True, timestep=self.env.timestep, explore_mag = explore_mag, step_size = step_size, coffset=coffset, bias=bias, \ unsafe_flag=self.env.unsafe, lqr_start=lqr_start, without_nn_guide=without_nn_guide) return self.K def draw_oracle_continuous(x, K): # draw_controller (self.env.A, self.env.B, self.K, x, simulation_steps*shield_testing_on_x_ep_len, names, True, 0.01) test_reward = testf if testf is not None else default_testf_continous result = test_controller (self.env.A, self.env.B, self.K, x, simulation_steps*shield_testing_on_x_ep_len, rewardf=test_reward, \ continuous=True, timestep=self.env.timestep, coffset=coffset, bias=bias) return result #Iteratively search polcies that can cover all initial states ''' Fixme: the verification approach does not consider the case under which x_min and x_max ''' def verification_oracle_continuous(x, initial_size, Theta, K): #Theta and K is useless here but required by the API #Generate the closed loop system for verification Acl = self.env.A + self.env.B.dot(self.K) print "Learned Closed Loop System: {}".format(Acl) if (discretization): S0 = Polyhedron.from_bounds(self.env.s_min, self.env.s_max) self.O_inf = verify_controller_via_discretization(Acl, self.env.timestep, self.env.x_min, self.env.x_max) min = np.array([[x[i,0] - initial_size[i]] for i in range(self.env.state_dim)]) max = np.array([[x[i,0] + initial_size[i]] for i in range(self.env.state_dim)]) S = Polyhedron.from_bounds(min, max) S = S.intersection(S0) ce = S.is_included_in_with_ce(self.O_inf) return (ce is None) else: #Specs for initial conditions init = [] initSOSPoly = [] init_cnstr = [] for i in range(self.env.state_dim): init.append("init" + str(i+1) + " = (x[" + str(i+1) + "] - " + str(self.env.s_min[i,0]) + ")*(" + str(self.env.s_max[i,0]) + "-x[" + str(i+1) + "])") for i in range(self.env.state_dim): initSOSPoly.append("@variable m Zinit" + str(i+1) + " SOSPoly(Z)") for i in range(self.env.state_dim): init_cnstr.append(" - Zinit" + str(i+1) + "*init" + str(i+1)) #Specs for initial conditions subject to intial_size for i in range(self.env.state_dim): l = x[i,0] - initial_size[i] h = x[i,0] + initial_size[i] init.append("init" + str(self.env.state_dim+i+1) + " = (x[" + str(i+1) + "] - (" + str(l) + "))*((" + str(h) + ")-x[" + str(i+1) + "])") for i in range(self.env.state_dim): initSOSPoly.append("@variable m Zinit" + str(self.env.state_dim+i+1) + " SOSPoly(Z)") for i in range(self.env.state_dim): init_cnstr.append(" - Zinit" + str(self.env.state_dim+i+1) + "*init" + str(self.env.state_dim+i+1)) #Specs for unsafe condions depends on env.unsafe unsafe = [] unsafeSOSPoly = [] unsafe_cnstr = [] if (self.env.unsafe): #unsafe is given either via unsafe regions or unsfe properties in the env if (self.env.unsafe_property is not None): unsafes = self.env.unsafe_property () unsafe = [] unsafeSOSPoly = [] unsafe_cnstr = [] for i in range(len(unsafes)): unsafe.append("unsafe" + str(i+1) + " = " + unsafes[i]) unsafeSOSPoly.append("@variable m Zunsafe" + str(i+1) + " SOSPoly(Z)") unsafe_cnstr.append(" - Zunsafe" + str(i+1) + "*unsafe" + str(i+1)) if (self.env.x_min is not None): for j in range(len(self.env.x_min)): unsafe_query = "" unsafe_x_min = self.env.x_min[j] unsafe_x_max = self.env.x_max[j] for i in range(self.env.state_dim): if unsafe_x_min[i, 0] != np.NINF and unsafe_x_max[i, 0] != np.inf: unsafe.append("unsafe" + str(i+1) + " = (x[" + str(i+1) + "] - " + str(unsafe_x_min[i,0]) + ")*(" + str(unsafe_x_max[i,0]) + "-x[" + str(i+1) + "])") unsafeSOSPoly.append("@variable m Zunsafe" + str(i+1) + " SOSPoly(Z)") unsafe_query += " - Zunsafe" + str(i+1) + "*unsafe" + str(i+1) elif unsafe_x_min[i, 0] != np.NINF: unsafe.append("unsafe" + str(i+1) + " = (x[" + str(i+1) + "] - " + str(unsafe_x_min[i,0]) + ")*(" + str(unsafe_x_max[i,0]) + "-x[" + str(i+1) + "])") unsafeSOSPoly.append("@variable m Zunsafe" + str(i+1) + " SOSPoly(Z)") unsafe_query += " - Zunsafe" + str(i+1) + "*unsafe" + str(i+1) elif unsafe_x_max[i, 0] != np.inf: unsafe.append("unsafe" + str(i+1) + " = (x[" + str(i+1) + "] - " + str(unsafe_x_min[i,0]) + ")*(" + str(unsafe_x_max[i,0]) + "-x[" + str(i+1) + "])") unsafeSOSPoly.append("@variable m Zunsafe" + str(i+1) + " SOSPoly(Z)") unsafe_query += " - Zunsafe" + str(i+1) + "*unsafe" + str(i+1) if unsafe_query != "": unsafe_cnstr.append(unsafe_query) else: for i in range(self.env.state_dim): mid = (self.env.x_min[i, 0] + self.env.x_max[i, 0]) / 2 radium = self.env.x_max[i, 0] - mid unsafe.append("unsafe" + str(i+1) + " = (x[" + str(i+1) + "] - " + str(mid) + ")^2 - " + str(pow(radium, 2))) unsafeSOSPoly.append("@variable m Zunsafe" + str(i+1) + " SOSPoly(Z)") unsafe_cnstr.append(" - Zunsafe" + str(i+1) + "*unsafe" + str(i+1)) # Now we have init, unsafe and sysdynamics for verification sos = genSOSContinuousAsDiscreteMultipleUnsafes( self.env.timestep, self.env.state_dim, ",".join(dxdt(Acl)), "\n".join(init), "\n".join(unsafe), "\n".join(initSOSPoly), "\n".join(unsafeSOSPoly), "".join(init_cnstr), unsafe_cnstr, degree=degree) verified = verifySOS(writeSOS("SOS.jl", sos), False, 900) print verified if verified.split("#")[0].find("Optimal") >= 0: # returns Verified and the inductive invariant return True, verified.split("#")[1] else: return False, None #return (verified.find("Optimal") >= 0) Theta = (self.env.s_min, self.env.s_max) result, resultList = verify_controller_z3(x0, Theta, verification_oracle_continuous, learning_oracle_continuous, draw_oracle_continuous, continuous=True) print ("Shield synthesis result: {}".format(result)) if result: for (x, initial_size, inv, K) in resultList: self.B_str_list.append(inv+"\n") self.B_list.append(barrier_certificate_str2func(inv, self.env.state_dim, enable_jit)) self.K_list.append(K) initial_range = np.array([x-initial_size.reshape(len(initial_size), 1), x+initial_size.reshape(len(initial_size), 1)]) self.initial_range_list.append(initial_range) self.save_shield(os.path.split(self.model_path)[0]) else: self.load_shield(os.path.split(self.model_path)[0], enable_jit) # discrete else: self.O_inf_list = [] self.last_O_inf_result = [] self.O_inf = None if self.K_list == []: x0 = self.env.reset() S0 = Polyhedron.from_bounds(self.env.s_min, self.env.s_max) def default_testf_discrete(x, u): if self.env.unsafe: if ((np.array(x) < self.env.x_max)*(np.array(x) > self.env.x_min)).all(axis=1).any(): return -1 else: return 0 else: if ((x < self.env.x_max).all() and (x > self.env.x_min).all()) and ((u < self.env.u_max).all() and (u > self.env.u_min).all()): return 0 else: return -1 def learning_oracle_discrete(x): self.K = learn_shield(self.env.A, self.env.B, self.env.Q, self.env.R, x, eq_err,\ learning_method, number_of_rollouts, simulation_steps, self.actor, self.env.x_min, self.env.x_max, rewardf=rewardf,\ continuous=False, timestep=self.env.timestep, explore_mag = explore_mag, step_size = step_size, coffset=coffset, bias=bias, \ unsafe_flag=self.env.unsafe, lqr_start=lqr_start, without_nn_guide=without_nn_guide) return self.K def draw_oracle_discrete(x, K): # draw_controller (self.env.A, self.env.B, self.K, x, simulation_steps*shield_testing_on_x_ep_len, names, True, 0.01) test_reward = testf if testf is not None else default_testf_discrete result = test_controller (self.env.A, self.env.B, self.K, x, simulation_steps*shield_testing_on_x_ep_len, rewardf=test_reward, \ coffset=coffset, bias=bias) return result #Iteratively search polcies that can cover all initial states def verification_oracle_discrete(x, initial_size, Theta, K): self.O_inf = verify_controller(np.asarray(self.env.A), np.asarray(self.env.B), np.asarray(self.K), self.env.x_min, self.env.x_max, self.env.u_min, self.env.u_max) min = np.array([[x[i,0] - initial_size[i]] for i in range(self.env.state_dim)]) max = np.array([[x[i,0] + initial_size[i]] for i in range(self.env.state_dim)]) S = Polyhedron.from_bounds(min, max) S = S.intersection(S0) ce = S.is_included_in_with_ce(self.O_inf) if ce is None: self.K_list.append(K) self.O_inf_list.append(self.O_inf) initial_range = np.array([x-initial_size.reshape(len(initial_size), 1), x+initial_size.reshape(len(initial_size), 1)]) self.initial_range_list.append(initial_range) return (ce is None) Theta = (self.env.s_min, self.env.s_max) result = verify_controller_z3(x0, Theta, verification_oracle_discrete, learning_oracle_discrete, draw_oracle_discrete, continuous=False) print ("Shield synthesis result: {}".format(result)) if result: self.save_shield(os.path.split(self.model_path)[0]) else: self.load_shield(os.path.split(self.model_path)[0], enable_jit) @timeit def train_polysys_shield(self, learning_method, number_of_rollouts, simulation_steps, eq_err=1e-2, explore_mag = .04, step_size = .05, names=None, coffset=None, bias=False, degree=4, aggressive=False, without_nn_guide=False, enable_jit=False): """train shield Args: learning_method (string): learning method string number_of_rollouts (int): number of rollouts simulation_steps (int): simulation steps timestep (float, optional): timestep for continuous control eq_err (float, optional): amount of guassian error rewardf (None, optional): reward function testf (None, optional): reward function for draw controller explore_mag (float, optional): explore mag step_size (float, optional): step size names (None, optional): names of state """ """ Additional arguments in line 2 of the function signature: polyf: describe polynomial system dynamics in python polyf_to_str(K): describe polynomial system dynamics in string rewardf describe polynomial system reward function testf describe polynomial system test function unsafe_string(): describe polynomial unsafe conditions in string """ self.B_str_list = [] self.B_list = [] self.last_B_result = [] self.B = None self.initial_range_list = [] if self.K_list == []: #assert names is not None x0 = self.env.reset() def learning_oracle_continuous(x): self.K = learn_polysys_shield(self.env.polyf, self.env.state_dim, self.env.action_dim, self.env.Q, self.env.R, x, eq_err,\ learning_method, number_of_rollouts, simulation_steps, self.actor, rewardf=self.env.rewardf, \ continuous=True, timestep=self.env.timestep, explore_mag = explore_mag, step_size = step_size, coffset=coffset, bias=bias, without_nn_guide=without_nn_guide) return self.K def draw_oracle_continuous(x, K): result = test_controller_helper(self.env.polyf, self.K, x, simulation_steps*shield_testing_on_x_ep_len, rewardf=self.env.testf, continuous=True, timestep=self.env.timestep,\ coffset=coffset, bias=bias) if (result >= 0): # Find *a new piece of* controller saveK(self.model_path, self.K) return result #Iteratively search polcies that can cover all initial states def verification_oracle_continuous(x, initial_size, Theta, K): #Theta and K is useless here but required by the API #Specs for initial conditions init = [] initSOSPoly = [] init_cnstr = [] for i in range(self.env.state_dim): init.append("init" + str(i+1) + " = (x[" + str(i+1) + "] - " + str(self.env.s_min[i,0]) + ")*(" + str(self.env.s_max[i,0]) + "-x[" + str(i+1) + "])") for i in range(self.env.state_dim): initSOSPoly.append("@variable m Zinit" + str(i+1) + " SOSPoly(Z)") for i in range(self.env.state_dim): init_cnstr.append(" - Zinit" + str(i+1) + "*init" + str(i+1)) #Specs for initial conditions subject to initial_size for i in range(self.env.state_dim): l = x[i,0] - initial_size[i] h = x[i,0] + initial_size[i] init.append("init" + str(self.env.state_dim+i+1) + " = (x[" + str(i+1) + "] - (" + str(l) + "))*((" + str(h) + ")-x[" + str(i+1) + "])") for i in range(self.env.state_dim): initSOSPoly.append("@variable m Zinit" + str(self.env.state_dim+i+1) + " SOSPoly(Z)") for i in range(self.env.state_dim): init_cnstr.append(" - Zinit" + str(self.env.state_dim+i+1) + "*init" + str(self.env.state_dim+i+1)) #Specs for unsafe condions unsafes = self.env.unsafe_property() unsafe = [] unsafeSOSPoly = [] unsafe_cnstr = [] for i in range(len(unsafes)): unsafe.append("unsafe" + str(i+1) + " = " + unsafes[i]) for i in range(len(unsafes)): unsafeSOSPoly.append("@variable m Zunsafe" + str(i+1) + " SOSPoly(Z)") for i in range(len(unsafes)): unsafe_cnstr.append(" - Zunsafe" + str(i+1) + "*unsafe" + str(i+1)) #Specs for bounded state space bound = [] boundSOSPoly = [] bound_cnstr = [] if (self.env.bound_x_min is not None and self.env.bound_x_max is not None): for i in range(self.env.state_dim): if (self.env.bound_x_min[i,0] is not None and self.env.bound_x_max[i,0] is not None): bound.append("bound" + str(i+1) + " = (x[" + str(i+1) + "] - " + str(self.env.bound_x_min[i,0]) + ")*(" + str(self.env.bound_x_max[i,0]) + "-x[" + str(i+1) + "])") for i in range(self.env.state_dim): if (self.env.bound_x_min[i,0] is not None and self.env.bound_x_max[i,0] is not None): boundSOSPoly.append("@variable m Zbound" + str(i+1) + " SOSPoly(Z)") for i in range(self.env.state_dim): if (self.env.bound_x_min[i,0] is not None and self.env.bound_x_max[i,0] is not None): bound_cnstr.append(" - Zbound" + str(i+1) + "*bound" + str(i+1)) #Specs for bounded environment disturbance disturbance = [] disturbanceSOSPoly = [] disturbance_cnstr = [] if (self.env.disturbance_x_min is not None and self.env.disturbance_x_max is not None): for i in range(self.env.state_dim): if (self.env.disturbance_x_min[i,0] is not None and self.env.disturbance_x_max[i,0] is not None): disturbance.append("disturbance" + str(i+1) + " = (d[" + str(i+1) + "] - " + str(self.env.disturbance_x_min[i,0]) + ")*(" + str(self.env.disturbance_x_max[i,0]) + "-d[" + str(i+1) + "])") for i in range(self.env.state_dim): if (self.env.disturbance_x_min[i,0] is not None and self.env.disturbance_x_max[i,0] is not None): disturbanceSOSPoly.append("@variable m Zdisturbance" + str(i+1) + " SOSPoly(D)") for i in range(self.env.state_dim): if (self.env.disturbance_x_min[i,0] is not None and self.env.disturbance_x_max[i,0] is not None): disturbance_cnstr.append(" - Zdisturbance" + str(i+1) + "*disturbance" + str(i+1)) # Now we have init, unsafe and sysdynamics for verification sos = None if (self.env.bound_x_min is not None and self.env.bound_x_max is not None): sos = genSOSwithBound(self.env.state_dim, ",".join(self.env.polyf_to_str(K)), "\n".join(init), "\n".join(unsafe), "\n".join(bound), "\n".join(initSOSPoly), "\n".join(unsafeSOSPoly), "\n".join(boundSOSPoly), "".join(init_cnstr), "".join(unsafe_cnstr), "".join(bound_cnstr), degree=degree) elif (self.env.disturbance_x_min is not None and self.env.disturbance_x_max is not None): sos = genSOSwithDisturbance(self.env.state_dim, ",".join(self.env.polyf_to_str(K)), "\n".join(init), "\n".join(unsafe), "\n".join(disturbance), "\n".join(initSOSPoly), "\n".join(unsafeSOSPoly), "\n".join(disturbanceSOSPoly), "".join(init_cnstr), "".join(unsafe_cnstr), "".join(disturbance_cnstr), degree=degree) else: sos = genSOS(self.env.state_dim, ",".join(self.env.polyf_to_str(K)), "\n".join(init), "\n".join(unsafe), "\n".join(initSOSPoly), "\n".join(unsafeSOSPoly), "".join(init_cnstr), "".join(unsafe_cnstr), degree=degree) verified = verifySOS(writeSOS("SOS.jl", sos), False, 900, aggressive=aggressive) print verified if verified.split("#")[0].find("Optimal") >= 0: return True, verified.split("#")[1] else: return False, None Theta = (self.env.s_min, self.env.s_max) result, resultList = verify_controller_z3(x0, Theta, verification_oracle_continuous, learning_oracle_continuous, draw_oracle_continuous, continuous=True) print ("Shield synthesis result: {}".format(result)) if result: for (x, initial_size, inv, K) in resultList: self.B_str_list.append(inv+"\n") self.B_list.append(barrier_certificate_str2func(inv, self.env.state_dim, enable_jit)) self.K_list.append(K) initial_range = np.array([x-initial_size.reshape(len(initial_size), 1), x+initial_size.reshape(len(initial_size), 1)]) self.initial_range_list.append(initial_range) self.save_shield(os.path.split(self.model_path)[0]) else: self.load_shield(os.path.split(self.model_path)[0], enable_jit) def save_shield(self, model_path): if self.env.continuous: with open(model_path+"/shield.model", "w") as f: for B_str in self.B_str_list: f.write(B_str) # print B_str print "store shield to "+model_path+"/shield.model" saveK(model_path+"/K.model", np.array(self.K_list)) print "store K to "+model_path+"/K.model.npy" saveK(model_path+"/initial_range.model", np.array(self.initial_range_list)) print "store initial_range to "+model_path+"/initial_range.model.npy" else: saveK(model_path+"/K.model", np.array(self.K_list)) print "store K to "+model_path+"/K.model.npy" saveK(model_path+"/initial_range.model", np.array(self.initial_range_list)) print "store initial_range to "+model_path+"/initial_range.model.npy" def load_shield(self, model_path, enable_jit): if self.env.continuous: with open(model_path+"/shield.model", "r") as f: for B_str in f: self.B_list.append(barrier_certificate_str2func(B_str, self.env.state_dim, enable_jit)) print "load barrier from " + model_path + "/shield.model" self.K_list = [K for K in loadK(model_path+"/K.model.npy")] print "load K from "+model_path+"/K.model.npy" self.initial_range_list = [initr for initr in loadK(model_path+"/initial_range.model.npy")] print "load initial range to "+model_path+"/initial_range.model.npy" else: self.K_list = [K for K in loadK(model_path+"/K.model.npy")] print "load K from "+model_path+"/K.model.npy" self.initial_range_list = [initr for initr in loadK(model_path+"/initial_range.model.npy")] print "load initial range to "+model_path+"/initial_range.model.npy" for K in self.K_list: O_inf = verify_controller(np.asarray(self.env.A), np.asarray(self.env.B), np.asarray(K), self.env.x_min, self.env.x_max, self.env.u_min, self.env.u_max) self.O_inf_list.append(O_inf) def select_shield(self): i = -1 if (len(self.initial_range_list) > 1): lowboundaries = np.array([item[0] for item in self.initial_range_list]) upboundaries = np.array([item[1] for item in self.initial_range_list]) if self.debug: print "x0: \n", self.env.x0 print "low boundary: \n", lowboundaries print "up boundary: \n", upboundaries select_list = [(self.env.x0>low).all()*(self.env.x0<high).all() for low, high in zip(lowboundaries, upboundaries)] i = select_list.index(True) if self.debug: print "select list", select_list elif (len(self.initial_range_list) == 1): i == 0 else: print "Error: No shield available!" assert (False) self.K = self.K_list[i] if self.continuous: self.B = self.B_list[i] return self.B else: self.O_inf = self.O_inf_list[i] return self.O_inf def detactor(self, x, u, mode="single", loss_compensation=0.0, increase_step=-1): """detact if there are dangerous state in furture Args: x: current state u: current action mode (str, optional): single(faster, more calls) -> choose one shield according to the initial state. all(slower, less calls) -> use all shield at run time, if all the B > 0, call shield. loss_compensation (float, optional): The compensation for loss in calculating barrier increase_step (int, optional): if B's value keep increase this step, call shield until the vale stop increasing, now only support the single mode. Returns: Bool: True -> call shield False -> call neural network """ mode_tuple = ("single", "all") assert mode in mode_tuple xk = self.env.simulation(u) # single shield model if mode == mode_tuple[0]: # continuous if self.env.continuous: if self.B is None: self.select_shield() B_value = self.B(*state2list(xk)) if self.debug: print B_value if increase_step >= 0: if B_value > self.last_B_value: self.step_count += 1 else: self.keep_increasing = False self.last_B_value = B_value if self.step_count >= increase_step: self.step_count = 0 self.keep_increasing = True if self.keep_increasing: return True if B_value > -loss_compensation: return True return False # discrete else: self.select_shield() if self.O_inf.contains(xk): return False return True # all shield model elif mode == mode_tuple[1]: # continuous if self.env.continuous: current_B_result = [] if self.last_B_result == []: lowboundaries = np.array([i[0] for i in self.initial_range_list]) upboundaries = np.array([i[1] for i in self.initial_range_list]) self.last_B_result = [np.logical_not((self.env.x0>low).all()*(self.env.x0<high).all()) for low, high in zip(lowboundaries, upboundaries)] debug_list = [] for B in self.B_list: B_value = B(*state2list(xk)) if self.debug: debug_list.append(B_value) res = B_value > -loss_compensation current_B_result.append(res) if self.debug: print debug_list if np.array(current_B_result).all(): # The K will be called latter self.K = self.K_list[self.last_B_result.index(False)] return True self.last_B_result = current_B_result return False # discrete else: current_O_inf_result = [] if self.last_O_inf_result == []: lowboundaries = np.array([i[0] for i in self.initial_range_list]) upboundaries = np.array([i[1] for i in self.initial_range_list]) self.last_O_inf_result = [np.logical_not((self.env.x0>low).all()*(self.env.x0<high).all()) for low, high in zip(lowboundaries, upboundaries)] for O_inf in self.O_inf_list: res = not O_inf.contains(xk) current_O_inf_result.append(res) if self.debug: print xk print current_O_inf_result if np.array(current_O_inf_result).all(): # The K will be called latter self.K = self.K_list[self.last_O_inf_result.index(False)] return True self.last_O_inf_result = current_O_inf_result return False def call_shield(self, x, mute=False): """call shield Args: x : current state mute (bool, optional): print !shield or not Returns: shield action """ u = self.K.dot(x) if not mute: print 'Shield! in state: \n', x self.shield_count += 1 return u @timeit def test_shield(self, test_ep=1, test_step=5000, x0=None, mode="single", loss_compensation=0, shield_combo=1, mute=False): """test if shield works Args: test_ep (int, optional): test episodes test_step (int, optional): test step in each episode """ assert shield_combo > 0 assert loss_compensation >= 0 fail_time = 0 success_time = 0 fail_list = [] self.shield_count = 0 combo_remain = 0 for ep in xrange(test_ep): if x0 is not None: x = self.env.reset(x0) else: x = self.env.reset() init_x = x for i in xrange(test_step): u = np.reshape(self.actor.predict(np.reshape(np.array(x), \ (1, self.actor.s_dim))), (self.actor.a_dim, 1)) # safe or not if self.detactor(x, u, mode=mode, loss_compensation=loss_compensation) or (combo_remain > 0): if combo_remain == 0: combo_remain = shield_combo u = self.call_shield(x, mute=mute) if not mute: print "!shield at step {}".format(i) combo_remain -= 1 # step x, _, terminal = self.env.step(u) # success or fail if terminal: if np.sum(np.power(self.env.xk, 2)) < self.env.terminal_err: success_time += 1 else: fail_time += 1 fail_list.append((init_x, x)) break if i == test_step-1: success_time += 1 print "----epoch: {} ----".format(ep) print 'initial state:\n', init_x, '\nterminal state:\n', x, '\nlast action:\n', self.env.last_u print "----step: {} ----".format(i) print 'Success: {}, Fail: {}'.format(success_time, fail_time) print '#############Fail List:###############' for (i, e) in fail_list: print 'initial state:\n{}\nend state: \n{}\n----'.format(i, e) print 'shield times: {}, shield ratio: {}'.format(self.shield_count, float(self.shield_count)/(test_ep*test_step)) @timeit def shield_boundary(self, sample_ep=500, sample_step=100): """sample to find the state bound of shield Args: sample_ep (int, optional): epsoides sample_step (int, optional): step in each epsoide """ max_boundary = np.zeros([self.env.state_dim, 1]) min_boundary = np.zeros([self.env.state_dim, 1]) for ep in xrange(sample_ep): x = self.env.reset() for i in xrange(sample_step): u = self.call_shield(x, mute=True) max_boundary, min_boundary = metrics.find_boundary(x, max_boundary, min_boundary) # step x, _, terminal = self.env.step(u) print 'max_boundary:\n{}\nmin_boundary:\n{}'.format(max_boundary, min_boundary) def learn_shield_gd(self, lr=0.00001, epsoides=100, steps=1000): K = np.random.random(self.env.state_dim) grad = np.zeros(self.env.state_dim) for ep in xrange(epsoides): self.env.reset() loss = 0 for step in xrange(steps): u = self.actor.predict(np.reshape(np.array(self.env.xk), (1, self.actor.s_dim))) grad += np.array(((K.dot(self.env.xk)-u).dot(self.env.xk.T)))[0] loss += np.sum(np.power((K.dot(self.env.xk)-u), 2)) self.env.step(u) K -= lr*grad print loss return K import re def barrier_certificate_str2func(bc_str, vars_num, enable_jit=False): """transform julia barrier string to function Args: bc_str (str): string vars_num (int): the dimension number of state enable_jit: enable jit, the performance of B will increase, but it takes time to preprocess B """ eval_str = re.sub("\^", r"**", bc_str) variables = ["x"+str(i+1) for i in xrange(vars_num)] var_pattern = re.compile(r"(?P<var>x\d*)") eval_str = var_pattern.sub(r'*\g<var>', eval_str) # This way is much much slower # def B(state): # values_name=get_values_name(len(state)) # assert len(variables) == len(values_name) # eval_str1 = eval_str # for var, val in zip(variables, values_name): # eval_str1 = re.sub(var, val, eval_str1) # return eval(eval_str1) args_str = "" for arg in variables: args_str += (arg+",") args_str = args_str[:-1] if enable_jit: from numba import jit, float64 exec(("@jit"+"(float64 ({}))"+"\ndef B({}): return {}").format(("float64,"*vars_num)[:-1], args_str, eval_str)) else: exec("""def B({}): return {}""".format(args_str, eval_str)) return B def barrier_certificate_str2z3(bc_str, vars_num): """transform julia barrier string to what z3 and python can understand Args: bc_str (str): string """ eval_str = re.sub("\^", r"**", bc_str) var_pattern = re.compile(r"(?P<var>x\d*)") eval_str = var_pattern.sub(r'*\g<var>', eval_str) # substitute x1 to x[0], ..., x[n] to x[n-1] for i in range(vars_num): eval_str = eval_str.replace("x"+str(i+1), "x[" + str(i) + "]") # polynomial function's value should be less than 0. eval_str = eval_str + " <= 0" return eval_str def get_values_name(vars_num): return ["state["+str(i)+"][0]" for i in xrange(vars_num)] def state2list(state): return [x[0] for x in state.tolist()]

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null

0

null

0

1

0

1

8dcf634f294734dd35e17dc999018f8d298536e2

171

py

Python

palindrome_partitioning.py

spencercjh/sync-leetcode-today-problem-python3-example

4957e5eadb697334741df0fc297bec2edaa9e2ab

[ "Apache-2.0" ]

null

palindrome_partitioning.py

spencercjh/sync-leetcode-today-problem-python3-example

4957e5eadb697334741df0fc297bec2edaa9e2ab

[ "Apache-2.0" ]

null

palindrome_partitioning.py

spencercjh/sync-leetcode-today-problem-python3-example

4957e5eadb697334741df0fc297bec2edaa9e2ab

[ "Apache-2.0" ]

null

class PalindromePartitioning: """ https://leetcode-cn.com/problems/palindrome-partitioning/ """ def partition(self, s: str) -> List[List[str]]:

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8dd01c14c4996542c62dcb370ac32dc38f1f238a

3,922

py

Python

Chapter 09/exercise9_10/exercise9_10.py

nescience8/starting-out-with-python-global-4th-edition

c16f93b7cbb4c7ae7b57653a7190bf192fe6b472

[ "MIT" ]

35

2019-05-03T00:30:31.000Z

2022-01-20T06:57:25.000Z

Chapter 09/exercise9_10/exercise9_10.py

nescience8/starting-out-with-python-global-4th-edition

c16f93b7cbb4c7ae7b57653a7190bf192fe6b472

[ "MIT" ]

null

Chapter 09/exercise9_10/exercise9_10.py

nescience8/starting-out-with-python-global-4th-edition

c16f93b7cbb4c7ae7b57653a7190bf192fe6b472

[ "MIT" ]

22

2020-05-13T21:20:02.000Z

2021-12-21T08:35:59.000Z

##Write a program that reads the contents of a text file. The program should create a diction- ##ary in which the key-value pairs are described as follows: ##• Key. The keys are the individual words found in the file. ##• Values. Each value is a list that contains the line numbers in the file where the word ##(the key) is found. ##For example, suppose the word “robot” is found in lines 7, 18, 94, and 138. The dictionary ##would contain an element in which the key was the string “robot”, and the value was a list ##containing the numbers 7, 18, 94, and 138. ##Once the dictionary is built, the program should create another text file, known as a word ##index, listing the contents of the dictionary. The word index file should contain an alpha- ##betical listing of the words that are stored as keys in the dictionary, along with the line ##numbers where the words appear in the original file. Figure 9-1 shows an example of an ##original text file ( Kennedy.txt ) and its index file ( index.txt ). # Open a text file. # Read the contents # Every time a new word appers, create a new key with value a list with a single # element, which will be the lien where the word appeared. # Every time a word that already exists appears, add to the list of that key, # the line that appeared. # Create another text file # Sort the keys alphabetically # Write each word and the times it appeared with a colon (:) in between. def get_textname(): # Ask the use the name of the text file to create an index for. name = input('For which file would you like me to create a word index? ') return name def create_dictionary(filename): infile = open(filename, 'r', encoding='utf8') # Open the file word_index = dict() # Create an empty dictionary to store the words and line numbers counter = 0 # Set a counter to count the line we found the word for line in infile: wordlinelist = line.rstrip('\n').split() # remove \n and split the line into words. counter += 1 # advance the counter to reflect the line we are in for word in wordlinelist: # for every word in the line if word not in word_index: # If we haven't yet encountered it, word_index[word] = [str(counter)] # Start a key/value pair with value being a list with the line number. elif word in word_index: # If the word was found, word_index[word].append(str(counter)) # Append the line number to the list in the value infile.close() # Close the file since we are done reading. return word_index def create_index_file(dict): outfile = open('index.txt', 'w', encoding='utf8') # create a file to store the word index. a_list = [] # Create a list to store the word the index. index = 0 # Create a counter to control the index we are checking. for key in dict.keys(): # Ever word found in the dictionary a_list.append(key) # Append it to the a_list for value in dict[key]: # Ever value found for that word/key a_list[index] = a_list[index] + ' ' + value # Add it to the a_list, in the same index, seperated by a space index += 1 # Advance the index by one to continue to the next word. a_list.sort() # Sort the finished list. for element in a_list: outfile.write(element + '\n') # Extract the list, element by element to the file index.txt. outfile.close() # Close the file def main(): print('This program creates a word index of the file you request.') print('----------------------------------------------------------') print() file = get_textname() # Ask the user for the file to create an index for. dictionary = create_dictionary(file) # Create the dictionary for the file. create_index_file(dictionary) # Write the the word index to a file. print('Word index is created. File name is: index.txt') # Call the main function. main()

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null

0

null

0

1

0

8dd2ec97b90cb2d15b0a054348250dd7d5121065

982

py

Python

problems4/4A.py

Lopa10ko/ITMO-algo-2021-2022

fa1ae8571e9cccd54faf1680fad21ffc6dbcef49

[ "MIT" ]

1

2021-11-11T12:08:14.000Z

problems4/4A.py

Lopa10ko/ITMO-algo-2021-2022

fa1ae8571e9cccd54faf1680fad21ffc6dbcef49

[ "MIT" ]

null

problems4/4A.py

Lopa10ko/ITMO-algo-2021-2022

fa1ae8571e9cccd54faf1680fad21ffc6dbcef49

[ "MIT" ]

null

file_input = open('stack.in', 'r') file_output = open('stack.out', 'w') def validate_stack(top_index): return True if (top_index == -1) else False class ImplementedStack(object): def __init__(self): self.stack = [] self.top = -1 def push_value(self, value): self.top += 1 self.stack += [''] self.stack[self.top] = value def pop_value(self): try: if validate_stack(self.top): return else: self.top -= 1 return self.stack[self.top + 1] except IndexError: return if __name__ == '__main__': arr = ImplementedStack() for i in range(int(file_input.readline())): current= file_input.readline().split() if current[0] == '+': arr.push_value(int(current[1])) else: print(arr.pop_value(), file=file_output) file_output.close()

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null

0

1

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1

8dd45ea0a29eda5d4c2a5b4693e02c4c67831b90

4,151

py

Python

pynextion/hardware.py

cowo78/pynextion

40215761bc8abbd7cc53fefa68e8b78a67b73aed

[ "Apache-2.0" ]

null

pynextion/hardware.py

cowo78/pynextion

40215761bc8abbd7cc53fefa68e8b78a67b73aed

[ "Apache-2.0" ]

null

pynextion/hardware.py

cowo78/pynextion

40215761bc8abbd7cc53fefa68e8b78a67b73aed

[ "Apache-2.0" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- import collections import queue import threading import typing from .constants import S_END_OF_CMD import serial class AbstractSerialNex(object): INCOMING_BUFFER_SIZE = 1024 # Seems the Nextion buffer size, mentioned in official docs MIN_SIZE_READ = len(S_END_OF_CMD) + 1 # Minimum return data size TERMINATOR_SIZE = len(S_END_OF_CMD) def __init__(self): super().__init__() self._port_mutex = threading.Lock() # Queue of event objects self._events = queue.Queue() # Incoming serial buffer self._buffer = bytearray(self.INCOMING_BUFFER_SIZE) self._events_queue = collections.deque() # type: typing.Sequence[bytearray] def write(self, data: bytes) -> int: """ Raw write access to underlying transport. Threadsafe. :returns: Number of bytes written """ with self._port_mutex: nbytes = self.sp.write(data) return nbytes send = write def read_all(self) -> bytes: """ Read all buffered data. Threadsafe. """ with self._port_mutex: data = self.sp.read_all() return data def read_next(self) -> bytes: """ Read next message. Threadsafe. May return an empty array is no event is available. """ # At some point (along with editor 0.58) the Nextion firmware changed and now it returns # an "instruction successful" everytime, even after a string or numeric data event if self._events_queue: return self._events_queue.pop() buffer_size = 0 with self._port_mutex: # Reading one byte at a time is of course inefficient, so serial.read_until is not the best option # We know the minimal read should be 4 chars (i.e. Invalid Instruction) and must be prepared to # partial command reads since we have no guarantee that we will always have complete commands in the buffer if self.sp.in_waiting < self.MIN_SIZE_READ: # Partial event, unlikely at this point return b'' # Read bulk of data chunk = self.sp.read(self.sp.in_waiting) self._buffer[buffer_size:buffer_size+len(chunk)] = chunk buffer_size = len(chunk) while self._buffer[buffer_size - self.TERMINATOR_SIZE:buffer_size] != S_END_OF_CMD: # Trickle until end of event chunk = self.sp.read(1) self._buffer[buffer_size:buffer_size+1] = chunk buffer_size += 1 # Finished reading and we are sure we have complete event(s), now split into single events start = 0 while True: pos = self._buffer.find(S_END_OF_CMD, start, buffer_size) if pos == -1: break self._events_queue.appendleft(self._buffer[start:pos+self.TERMINATOR_SIZE]) start = pos + self.TERMINATOR_SIZE return self._events_queue.pop() def clear_events_queue(self): self._events_queue.clear() def close(self): return self.sp.close() class PySerialNex(AbstractSerialNex): def __init__(self, port_or_url: str, *args, **kwargs): super().__init__() self.sp = serial.serial_for_url(port_or_url, *args, **kwargs) self.sp.reset_input_buffer() self.sp.reset_output_buffer() @property def baudrate(self): return self.sp.baudrate @baudrate.setter def baudrate(self, val): self.sp.baudrate = val # TODO: rotten class NexSerialMock(AbstractSerialNex): def __init__(self, *args, **kwargs): super().__init__() def write(self, cmd): pass def read(self): return None def close(self): print("close") """ # PyBoard 1.1 # https://docs.micropython.org/en/latest/pyboard/pyboard/quickref.html # RED: VIN # BLACK: GND # YELLOW: X9 (Board TX) # BLUE: X10 (Board RX) import machine import time class uPyNexSerial(AbstractSerialNex): def __init__(self, *args, **kwargs): self.sp = machine.UART(*args, **kwargs) """

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null

0

null

0

1

0

8dd4c7ac514b07c8936d2d8818ce12d988742cca

1,762

py

Python

scripts/compute_stats.py

mikeshuser/TopicWordMap

7ed9df73d1b7dd8ded03361a662444c31fad70bc

[ "MIT" ]

null

scripts/compute_stats.py

mikeshuser/TopicWordMap

7ed9df73d1b7dd8ded03361a662444c31fad70bc

[ "MIT" ]

null

scripts/compute_stats.py

mikeshuser/TopicWordMap

7ed9df73d1b7dd8ded03361a662444c31fad70bc

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Compute essential stats(freq/tf-idf) on a corpus Dependencies: pandas == 0.23 gensim == 3.8 """ __author__ = "Mike Shuser" import pickle import numpy as np import pandas as pd from gensim.models import TfidfModel from gensim.corpora import Dictionary DATA_SRC = "../processed_corpus" MODEL_SRC = "../modelling" if __name__ == '__main__': files = ["positive_text","negative_text"] vecs = pd.read_csv(f"{MODEL_SRC}/imdb_wordvectors.csv", index_col=[0], na_filter=False) for filetype in files: with open(f"{DATA_SRC}/{filetype}.csv.bigrams.pkl", "rb") as handle: docs = pickle.load(handle) vocab = pd.DataFrame(index=vecs.index) dct = Dictionary(docs) corpus = [dct.doc2bow(line) for line in docs] tfidf = TfidfModel(corpus) #corpus statistics def lookup_mentions(x): try: return dct.cfs[dct.token2id[x]] except KeyError: return 0 vocab['mentions'] = vocab.index.map(lookup_mentions) vocab['log2_mentions'] = np.log2(vocab.mentions) #get tf-idfs for every word in each doc, then get average per word vocab_tfidf = {k : [] for k in vocab.index} for i, row in enumerate(docs): tmp = dict(tfidf[corpus[i]]) for word in row: if word in vocab_tfidf: vocab_tfidf[word].extend([tmp[dct.token2id[word]]]) for k, v in vocab_tfidf.items(): vocab_tfidf[k] = np.mean(v) vocab['avg_tfidf'] = vocab.index.map(lambda x: vocab_tfidf[x]) vocab.to_csv(f"{MODEL_SRC}/{filetype}_vocab_stats.csv")

28.419355

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1,762

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77

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0.216216

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null

0

null

0

1

0

8dd5699562650669f491fdd18d9c1edc25f11acb

2,937

py

Python

app/utils/images/linalg/utils.py

vinaykakkad/audio_and_image_compression

b5f7c767429f36805262ae87e8239434569fc372

[ "MIT" ]

1

2021-11-13T11:08:24.000Z

app/utils/images/linalg/utils.py

neelpopat242/audio_and_image_compression

b5f7c767429f36805262ae87e8239434569fc372

[ "MIT" ]

null

app/utils/images/linalg/utils.py

neelpopat242/audio_and_image_compression

b5f7c767429f36805262ae87e8239434569fc372

[ "MIT" ]

1

2021-11-13T11:07:54.000Z

import math def print_matrix(matrix): """ Function to print a matrix """ for row in matrix: for col in row: print("%.3f" % col, end=" ") print() def rows(matrix): """ Returns the no. of rows of a matrix """ if type(matrix) != list: return 1 return len(matrix) def cols(matrix): """ Returns the no. of columns of a matrix """ if type(matrix[0]) != list: return 1 return len(matrix[0]) def eye(size): """ Returns an identity matrix """ mat = list() for r in range(size): row = list() for c in range(size): if r == c: row.append(1) else: row.append(0) mat.append(row) return mat def pivot_index(row): """ Returns the index of pivot in a row """ counter = 0 for element in row: if element != float(0): return counter counter += 1 return counter def pivot_value(row): """ Returns the value of pivot in a row """ for element in row: if element > math.exp(-8): return element return 0 def swap(matrix, index_1, index_2): """ Function to swap two rows """ x = matrix[index_1] matrix[index_1] = matrix[index_2] matrix[index_2] = x def transpose(matrix): """ Returns the transpose of a matrix """ transpose_matrix = list() for i in range(cols(matrix)): row = list() for j in range(rows(matrix)): row.append(matrix[j][i]) transpose_matrix.append(row) return transpose_matrix def mat_multiply(a, b): """ Function to multiply two matrices """ c = [[0 for i in range(cols(b))] for j in range(rows(a))] for i in range(rows(a)): for j in range(cols(b)): for k in range(rows(b)): c[i][j] += a[i][k] * b[k][j] return c def mat_splice(matrix, r, c): """ Function which returns a matrix with the first r rows and first c columns of the original matrix """ result = list() for i in range(r): row = matrix[i] result.append(row[:c]) return result def to_int(matrix): """ Funciton to convert the eact element of the matrix to int """ for row in range(rows(matrix)): for col in range(cols(matrix)): for j in range(3): matrix[row][col][j] = int(matrix[row][col][j]) return matrix def clip(matrix): """ Function to clip each element to the range float[0, 1] """ for row in range(rows(matrix)): for col in range(cols(matrix)): for j in range(3): if matrix[row][col][j] > 1: matrix[row][col][j] = 1 if matrix[row][col][j] < 0: matrix[row][col][j] = 0 return matrix

17.908537

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2,937

3.690244

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0

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0.350649

0.038961

0

null

0

null

0

1

0

8dd581b173988521e90d63f2bb0417df587f5647

1,102

py

Python

testproject/manage.py

innovationinit/django-kazoo-locks

91ceb37ab92dad97659f24a9a7ace3bb9ae3ba10

[ "BSD-2-Clause" ]

null

testproject/manage.py

innovationinit/django-kazoo-locks

91ceb37ab92dad97659f24a9a7ace3bb9ae3ba10

[ "BSD-2-Clause" ]

null

testproject/manage.py

innovationinit/django-kazoo-locks

91ceb37ab92dad97659f24a9a7ace3bb9ae3ba10

[ "BSD-2-Clause" ]

1

2022-03-15T07:30:07.000Z

#!/usr/bin/env python import os import sys if __name__ == "__main__": try: from django.core.management import execute_from_command_line except ImportError: # The above import may fail for some other reason. Ensure that the # issue is really that Django is missing to avoid masking other # exceptions on Python 2. try: import django except ImportError: raise ImportError( "Couldn't import Django. Are you sure it's installed and " "available on your PYTHONPATH environment variable? Did you " "forget to activate a virtual environment?" ) raise try: import settings as settings_mod # Assumed to be in the same directory. except ImportError: sys.stderr.write("Error: Can't find the file 'settings.py' in the directory containing %r" % __file__) sys.exit(1) sys.path.insert(0, settings_mod.BASE_DIR) os.environ.setdefault("DJANGO_SETTINGS_MODULE", "testproject.settings") execute_from_command_line(sys.argv)

34.4375

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1,102

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1,102

31

111

35.548387

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0

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0

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1

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true

0

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0

null

0

null

0

1

0

1

0

2

8dd7751b946690a5f79059a3575a07f2c9cb06b8

1,443

py

Python

mos_ru_service/file_crypt.py

onlycska/depersonalization-of-data

d11497d0f0708496975d682ae447e97bfd9177d9

[ "MIT" ]

null

mos_ru_service/file_crypt.py

onlycska/depersonalization-of-data

d11497d0f0708496975d682ae447e97bfd9177d9

[ "MIT" ]

null

mos_ru_service/file_crypt.py

onlycska/depersonalization-of-data

d11497d0f0708496975d682ae447e97bfd9177d9

[ "MIT" ]

null

import hashlib from datetime import datetime def salsa_20_xor_bytes(): pass def n_string(string, n): hash = hashlib.sha512() hash.update(string.encode('utf-8')) return hash.digest()[:n] def encryption(iv: str, key: str, filename: str) -> bool: try: iv = n_string(iv, 8) key = n_string(key, 32) header_bytes = 50 with open(filename, "rb") as picture: picture.seek(header_bytes) picture_content = picture.read() cipher = salsa_20_xor_bytes(picture_content, key, iv) with open(filename + ".encr", "wb") as encryption: picture.seek(0) encryption.write(picture.read(header_bytes)) encryption.write(cipher) return True except Exception as e: return False def decryption(iv: str, key: str, filename: str) -> bool: try: iv = n_string(iv, 8) key = n_string(key, 32) header_bytes = 50 with open(filename + ".encr", "rb") as picture: picture.seek(header_bytes) encryption = picture.read() original = salsa_20_xor_bytes(encryption, key, iv) with open(filename, "wb") as decrypted: picture.seek(0) decrypted.write(picture.read(header_bytes)) decrypted.write(original) return True except Exception as e: return False print()

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66

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0

null

0

null

0

1

0

8dd86a18a4119d3adc2f367d73fae1d910601d27

3,747

py

Python

fitbenchmarking/utils/tests/test_create_dirs.py

fitbenchmarking/fitbenchmarking

ea398efa61f071dc64fe7c3b484d5bb4e1897856

[ "BSD-3-Clause" ]

6

2019-07-22T01:56:10.000Z

2021-12-10T05:29:30.000Z

fitbenchmarking/utils/tests/test_create_dirs.py

fitbenchmarking/fitbenchmarking

ea398efa61f071dc64fe7c3b484d5bb4e1897856

[ "BSD-3-Clause" ]

677

2019-04-29T10:23:49.000Z

2022-03-22T12:01:30.000Z

fitbenchmarking/utils/tests/test_create_dirs.py

fitbenchmarking/fitbenchmarking

ea398efa61f071dc64fe7c3b484d5bb4e1897856

[ "BSD-3-Clause" ]

8

2019-06-13T10:32:17.000Z

2020-12-09T15:08:40.000Z

""" This file contains tests on the creation of directories """ from __future__ import absolute_import, division, print_function import time import os import shutil import unittest from fitbenchmarking.utils.create_dirs import (figures, group_results, results, support_pages, css) class CreateDirsTests(unittest.TestCase): """ Tests for the creation of directories """ def setUp(self): """ Sets a temporary directory in which results are stored """ path = 'r{}'.format(int(time.time())) self.results_dir = os.path.join(os.getcwd(), path) def tearDown(self): """ Deletes the temporary folder """ if os.path.exists(self.results_dir): shutil.rmtree(self.results_dir) def test_results_throw_correct_error(self): """ Check that the correct error is raised """ self.assertRaises(TypeError, results, 123) self.assertRaises(TypeError, results, None) def test_results_create_correct_dir(self): """ Check that the correct directory is created """ results_dir = results(self.results_dir) results_dir_expected = self.results_dir self.assertEqual(results_dir_expected, results_dir) self.assertTrue(os.path.exists(results_dir_expected)) shutil.rmtree(results_dir_expected) def test_groupResults_create_correct_group_results(self): """ Check that the Group results directory is as expected """ results_dir = results(self.results_dir) group_results_dir = group_results(results_dir, "test_group") group_results_dir_expected = os.path.join(results_dir, "test_group") self.assertEqual(group_results_dir_expected, group_results_dir) self.assertTrue(os.path.exists(group_results_dir_expected)) shutil.rmtree(results_dir) def test_support_pages_create_correct_dir(self): """ Check that the support pages directory is as expected """ results_dir = results(self.results_dir) group_results_dir = group_results(results_dir, "test_group") support_pages_dir = support_pages(group_results_dir) support_pages_dir_expected = os.path.join(group_results_dir, 'support_pages') self.assertEqual(support_pages_dir_expected, support_pages_dir) self.assertTrue(os.path.exists(support_pages_dir_expected)) shutil.rmtree(results_dir) def test_figures_create_correct_dir(self): """ Check that the figures directory is as expected """ results_dir = results(self.results_dir) group_results_dir = group_results(results_dir, "test_group") support_pages_dir = support_pages(group_results_dir) figures_dir = figures(support_pages_dir) figures_dir_expected = os.path.join(support_pages_dir, 'figures') self.assertEqual(figures_dir_expected, figures_dir) self.assertTrue(os.path.exists(figures_dir_expected)) shutil.rmtree(results_dir) def test_css_create_correct_dir(self): """ Check that the css directory is as expected """ results_dir = results(self.results_dir) group_results_dir = group_results(results_dir, "test_group") css_dir = css(group_results_dir) css_dir_expected = os.path.join(group_results_dir, 'css') self.assertEqual(css_dir_expected, css_dir) self.assertTrue(os.path.exists(css_dir_expected)) shutil.rmtree(css_dir) if __name__ == "__main__": unittest.main()

32.868421

79

0.662397

446

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5.226457

0.174888

0.1716

0.083655

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0.443157

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0.204204

0

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3,747

113

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0

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1

0.135593

false

0

0.101695

0

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0.016949

0

null

0

null

0

1

0

8dd963b267b7e2d742f71f63075d5795850791fb

1,173

py

Python

router.py

laddge/cardAPI

770c5f8936f7b699ccaf386c82f7172e84292ecc

[ "MIT" ]

null

router.py

laddge/cardAPI

770c5f8936f7b699ccaf386c82f7172e84292ecc

[ "MIT" ]

null

router.py

laddge/cardAPI

770c5f8936f7b699ccaf386c82f7172e84292ecc

[ "MIT" ]

null

import os from urllib.parse import urlparse from fastapi import FastAPI, Request from fastapi.middleware.cors import CORSMiddleware from fastapi.responses import RedirectResponse, Response from fastapi.staticfiles import StaticFiles from typing import Optional import api app = FastAPI() app.mount("/files", StaticFiles(directory="files"), name="files") app.add_middleware( CORSMiddleware, allow_origins=["*"], allow_credentials=True, allow_methods=["*"], allow_headers=["*"], ) @app.middleware("http") async def middleware(request: Request, call_next): if request.method == "HEAD": response = Response() elif "herokuapp" in urlparse(str(request.url)).netloc: domain = os.getenv("DOMAIN") if domain: url = urlparse(str(request.url))._replace(netloc=domain).geturl() response = RedirectResponse(url) else: response = await call_next(request) else: response = await call_next(request) return response @app.get('/') async def getAPI(url: Optional[str] = None): if url: return api.main(url) else: return {'message': 'hello, world'}

24.957447

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0.672634

134

1,173

5.820896

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0.05641

0.046154

0.053846

0.082051

0

0.208014

1,173

46

78

25.5

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false

0

0.216216

0

0.297297

0

null

0

null

0

1

0

8ddada2583d4d1572a9c3857e2ac95af6c1ca1dd

1,976

py

Python

examples/list_alert_notifications.py

kenlavoie/python-sdc-client

cafb8f2279956c572bd2c01c8645895d0895716f

[ "MIT" ]

null

examples/list_alert_notifications.py

kenlavoie/python-sdc-client

cafb8f2279956c572bd2c01c8645895d0895716f

[ "MIT" ]

null

examples/list_alert_notifications.py

kenlavoie/python-sdc-client

cafb8f2279956c572bd2c01c8645895d0895716f

[ "MIT" ]

null

#!/usr/bin/env python # # Get alert notifications from Sysdig Cloud # import os import sys import time sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(sys.argv[0])), '..')) from sdcclient import SdcClient def print_notifications(notifications): for notification in notifications: values = [] for entity in notification['entities']: for value in entity['metricValues']: values.append(str(value['value'])) print "#%s, State: %s, Severity: %s, Scope: %s, Condition: %s, Value: %s, Resolved: %s" % \ (notification['id'], notification['state'], notification['severity'], notification['filter'], notification['condition'], ','.join(values), notification['resolved']) # # Parse arguments # if len(sys.argv) != 2: print 'usage: %s <sysdig-token>' % sys.argv[0] print 'You can find your token at https://app.sysdigcloud.com/#/settings/user' sys.exit(1) sdc_token = sys.argv[1] # # Instantiate the SDC client # sdclient = SdcClient(sdc_token) # # Get the notifications in the last day # res = sdclient.get_notifications(from_ts=int(time.time()-86400), to_ts=int(time.time())) print_notifications(res[1]['notifications']) if not res[0]: sys.exit(1) # # Get the notifications in the last day and active state # res = sdclient.get_notifications(from_ts=int(time.time()-86400), to_ts=int(time.time()), state='ACTIVE') print_notifications(res[1]['notifications']) if not res[0]: sys.exit(1) # # Get the notifications in the last day and active state # res = sdclient.get_notifications(from_ts=int(time.time()-86400), to_ts=int(time.time()), state='OK') print_notifications(res[1]['notifications']) if not res[0]: sys.exit(1) # # Get the notifications in the last day and resolved state # res = sdclient.get_notifications(from_ts=int(time.time()-86400), to_ts=int(time.time()), resolved=True) print_notifications(res[1]['notifications']) if not res[0]: sys.exit(1)

27.444444

176

0.688765

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1,976

4.742049

0.279152

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0.053651

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0.472429

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0

0.022673

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1,976

71

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0

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0

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0

0.114286

null

0.228571

0

null

0

null

0

1

0

1

8ddb9586e5b11a1deeec483163fc0ec9a71544d9

791

py

Python

Table/groupingDeviceMappingTable.py

tuanldchainos/HcPullData

65f89cfdcae135781aad4b3edf210c0ecd2d6a1c

[ "Apache-2.0" ]

null

Table/groupingDeviceMappingTable.py

tuanldchainos/HcPullData

65f89cfdcae135781aad4b3edf210c0ecd2d6a1c

[ "Apache-2.0" ]

null

Table/groupingDeviceMappingTable.py

tuanldchainos/HcPullData

65f89cfdcae135781aad4b3edf210c0ecd2d6a1c

[ "Apache-2.0" ]

null

from sqlalchemy import Column, Integer, String from sqlalchemy import DateTime from sqlalchemy import Table, Column, Integer, String, MetaData, ForeignKey class groupingDeviceMappingTable(): def __init__(self, metadata: MetaData): self.groupingDeviceMappingTable = Table('GroupingDeviceMapping', metadata, Column('GroupingId', String, primary_key=True, nullable=False), Column('GroupUnicastId', Integer, nullable=False), Column('DeviceId', String, primary_key=True, nullable=False), Column('DeviceUnicastId', Integer, nullable=False), )

56.5

111

0.548673

57

791

7.508772

0.438596

0.121495

0.140187

0.093458

0.182243

0

0.376738

791

13

112

60.846154

0.868154

0

0.085967

0.026549

0

1

0.090909

false

0

0.272727

0

0.454545

0

null

0

null

0

1

0

8ddb98d57d427980a09a0ebd40ba75c59e9df8f6

802

py

Python

nutils/__init__.py

wijnandhoitinga/nutils

7ad6793ca5e3a43f45dcc0a4a795b381d2a0b9d4

[ "MIT" ]

25

2015-04-29T13:10:22.000Z

2019-03-18T09:45:29.000Z

nutils/__init__.py

wijnandhoitinga/nutils

7ad6793ca5e3a43f45dcc0a4a795b381d2a0b9d4

[ "MIT" ]

330

2015-03-04T09:06:38.000Z

2019-06-11T10:31:54.000Z

nutils/__init__.py

wijnandhoitinga/nutils

7ad6793ca5e3a43f45dcc0a4a795b381d2a0b9d4

[ "MIT" ]

16

2015-03-23T08:00:46.000Z

2019-02-21T11:14:47.000Z

import sys import numpy from distutils.version import LooseVersion assert sys.version_info >= (3, 5) assert LooseVersion(numpy.version.version) >= LooseVersion('1.16'), 'nutils requires numpy 1.16 or higher, got {}'.format(numpy.version.version) version = '8.0a0' version_name = None long_version = ('{} "{}"' if version_name else '{}').format(version, version_name) __all__ = [ 'cache', 'cli', 'element', 'elementseq', 'evaluable', 'export', 'expression_v1', 'expression_v2', 'function', 'matrix', 'mesh', 'numeric', 'parallel', 'points', 'pointsseq', 'sample', 'solver', 'sparse', 'testing', 'topology', 'transform', 'transformseq', 'types', 'unit', 'util', 'warnings', ] # vim:sw=2:sts=2:et

19.095238

144

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0.119149

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0

0.024116

0.224439

802

41

145

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0.731511

0.021197

0

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1

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false

0

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0

0.083333

0

null

0

null

0

1

0

8ddbd28f219a98dd5d2cc0254cc240672cc2d246

4,871

py

Python

addutils/toc.py

addfor/AddUtils

2e2eb7ecd0718c7ec88a72b59313ab9084c0eaac

[ "MIT" ]

null

addutils/toc.py

addfor/AddUtils

2e2eb7ecd0718c7ec88a72b59313ab9084c0eaac

[ "MIT" ]

null

addutils/toc.py

addfor/AddUtils

2e2eb7ecd0718c7ec88a72b59313ab9084c0eaac

[ "MIT" ]

null

# The MIT License (MIT) # # Copyright (c) 2015 addfor s.r.l. # # 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. """IPython utility: automatic table of contents generation Functions: js - get the JavaScript script that generates the TOC of the document """ JS_SCRIPT = """ $(function() { function regenTOC(){ element = $("#toc-container"); var toc = document.createElement("div"); $(toc).attr("class", "table-of-contents"); var curLevel = 0; var containerStack = [toc]; var levelOfTag = {"h2": 1, "h3": 2, "h4": 3, "h5": 4}; function pushLevel() { var list = document.createElement("ul"); containerStack.push(list); curLevel++; } function popLevel() { var lastContainer = containerStack.pop(); $(lastContainer).appendTo(containerStack[containerStack.length - 1]); curLevel--; } $(".text_cell_render :header").each(function (i, elem) { var level = levelOfTag[ elem.tagName.toLowerCase() ]; if (level === undefined) return; while (curLevel < level) pushLevel(); while (curLevel > level) popLevel(); var listItem = document.createElement("li"); var link = document.createElement("a"); $(link) .text($(elem).contents().first().text()) // Remove the pilcrow sign .attr("href", "#" + $(elem).attr("id")) .appendTo(listItem); $(listItem).appendTo(containerStack[containerStack.length - 1]); }); while (curLevel > 0) popLevel(); $("<a class='btn-update' href='#'>Update</a>") .click(regenTOC).prependTo(toc); $(toc).prepend("<div class='title'>Contents</div>") .wrap("<div class='toc-headings'/>"); $(element).empty(); $(element).append(toc); } if (typeof(IPython) !== 'undefined') $([IPython.events]).on('notebook_loaded.Notebook', regenTOC); regenTOC(); }); """ def js(ipy_notebook=False): """Get the JavaScript script that generates the TOC of the document. The returned script uses JQuery to access the DOM, and looks at the heading tags (i.e. <h1>, <h2>, ...) to create a table of contents. The resulting table of contents is appended to the element #toc-container (which, in the case of an IPython notebook, is created in the output area of the cell). Parameters: ipy_notebook (bool) : When true, the script is returned wrapped in a IPython.display.HTML object. This makes it work automatically in any IPython notebook. Returns: (str or IPython.display.HTML) - The JS script The structure of the output is (if you want to style it with CSS, for example): div#table-of-contents div.title ("Contents") .toc-container ul First li First.1 li First.2 li First.3 ... ul Second li Second.1 li Second.2 ul Second.2.1 li Second.2.1.1 li Second.2.1.2 ... ... ... ... """ if ipy_notebook: from IPython.display import HTML return HTML(data=("<div id='toc-container'>" + "<script type='text/javascript'>" + JS_SCRIPT + "</script>" +"</div>")) else: return JS_SCRIPT

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4,871

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4,871

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null

0

1

0

null

0

1

8ddbf31bfb108f86a7acf1ea3203d6b23129570c

720

py

Python

portfolio/blog/models.py

SkullTech/portfolio-devclub

a3db4ab72464c82e9da6ee89ad51cecc082d9ff5

[ "MIT" ]

1

2017-03-02T22:38:26.000Z

portfolio/blog/models.py

SkullTech/portfolio-devclub

a3db4ab72464c82e9da6ee89ad51cecc082d9ff5

[ "MIT" ]

null

portfolio/blog/models.py

SkullTech/portfolio-devclub

a3db4ab72464c82e9da6ee89ad51cecc082d9ff5

[ "MIT" ]

null

from django.db import models from django.utils import timezone class Tag(models.Model): name = models.CharField(max_length=50) description = models.CharField(max_length=200, null=True, blank=True, default='') def __str__(self): return self.name class Post(models.Model): author = models.ForeignKey('auth.user') title = models.CharField(max_length=200) text = models.TextField() created_date = models.DateTimeField(default=timezone.now) published_date = models.DateTimeField(blank=True, null=True) tags = models.ManyToManyField(Tag) def publish(self): self.published_date = timezone.now() self.save() def __str__(self): return self.title

26.666667

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null

0

null

0

1

0

1

8ddd126c99d8feae03f19cc66e85504d9442c512

1,710

py

Python

ReadCifar.py

timestocome/ReadCifar10

ea6e70a982bdc923386327db648e038a63b1d55d

[ "MIT" ]

null

ReadCifar.py

timestocome/ReadCifar10

ea6e70a982bdc923386327db648e038a63b1d55d

[ "MIT" ]

null

ReadCifar.py

timestocome/ReadCifar10

ea6e70a982bdc923386327db648e038a63b1d55d

[ "MIT" ]

null

# http://github.com/timestocome # data # https://www.cs.toronto.edu/~kriz/cifar.html import numpy as np import pickle import matplotlib.pyplot as plt ################################################################################### # read in data ################################################################################## n_classes = 10 image_height = 32 image_width = 32 image_depth = 3 label_bytes = 1 def unpickle(file): fo = open(file, 'rb') dict = pickle.load(fo) fo.close() return dict def load_data(): xs = [] ys = [] # read in training files for i in range(5): # this is the directory you put the cifar batch files into filename = 'cifar-10/data_batch_%d' % (i+1) with open(filename, 'rb') as f: d = pickle.load(f, encoding='latin1') # needed for python2-python3 pickle x = d['data'] y = d['labels'] xs.append(x) ys.append(y) # read in test files filename = 'cifar-10/test_batch' with open(filename, 'rb') as f: d = pickle.load(f, encoding='latin1') xs.append(d['data']) ys.append(d['labels']) x = np.concatenate(xs) # images y = np.concatenate(ys) # labels x = x.reshape((x.shape[0], 3, 32, 32)).transpose(0,2,3,1) # Visualizing CIFAR 10 fig, axes1 = plt.subplots(5,5,figsize=(10,10)) for j in range(5): for k in range(5): i = np.random.choice(range(len(x))) axes1[j][k].set_axis_off() axes1[j][k].imshow(x[i:i+1][0]) plt.show() # scale images x = x / 255. load_data()

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null

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null

0

1

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8ddd3e9119c32c0e18dc330a4330c6e053f331a5

427

py

Python

app/blueprints/seo_arizona/errors.py

Anioko/TestApp

95fa8d27ca8e7a074e62f92609427a378844e621

[ "MIT" ]

null

app/blueprints/seo_arizona/errors.py

Anioko/TestApp

95fa8d27ca8e7a074e62f92609427a378844e621

[ "MIT" ]

1

2021-06-02T01:53:47.000Z

app/blueprints/seo_arizona/errors.py

Anioko/TestApp

95fa8d27ca8e7a074e62f92609427a378844e621

[ "MIT" ]

null

from flask import render_template from app.blueprints.seo_arizona.views import seo_arizona @seo_arizona.app_errorhandler(403) def forbidden(_): return render_template('errors/403.html'), 403 @seo_arizona.app_errorhandler(404) def page_not_found(_): return render_template('errors/404.html'), 404 @seo_arizona.app_errorhandler(500) def internal_server_error(_): return render_template('errors/500.html'), 500

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427

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null

0

1

0

null

0

1

0

1

0

4

8dde19fedd80853c00291936adb295b97fcd76bc

1,315

py

Python

tests.py

rudradatta/Flames

3692564ef1c3493eb2e1586be47ca997ede97cb4

[ "BSD-3-Clause" ]

null

tests.py

rudradatta/Flames

3692564ef1c3493eb2e1586be47ca997ede97cb4

[ "BSD-3-Clause" ]

null

tests.py

rudradatta/Flames

3692564ef1c3493eb2e1586be47ca997ede97cb4

[ "BSD-3-Clause" ]

null

import flames import unittest class TestFlamesMethods(unittest.TestCase): def test_flames_count(self): self.assertEqual(flames.flames_count('abhi','abhi'),0) self.assertEqual(flames.flames_count('abhi','a'),3) self.assertEqual(flames.flames_count('abhi','asd'),5) def test_flames_result(self): self.assertEqual(flames.flames_result(1),'S') self.assertEqual(flames.flames_result(2),'E') self.assertEqual(flames.flames_result(3),'F') self.assertEqual(flames.flames_result(7),'E') self.assertEqual(flames.flames_result(10),'L') self.assertEqual(flames.flames_result(15),'M') self.assertEqual(flames.flames_result(21),'F') self.assertEqual(flames.flames_result(28),'A') self.assertEqual(flames.flames_result(30),'A') def test_calculate(self): self.assertEqual(flames.calculate('abhi','abhil'),'S') self.assertEqual(flames.calculate('abhi','abhila'),'E') self.assertEqual(flames.calculate('abhi','abhilas'),'F') self.assertEqual(flames.calculate('abhi','abhilashdsm'),'E') self.assertEqual(flames.calculate('Abhi',' abHil '),'S') self.assertEqual(flames.calculate('abhi','abhi.l'),'S') if __name__ == '__main__': unittest.main()

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0.1764

0

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1,315

36

69

36.527778

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0

null

1

0

null

0

1

0

4

8ddf6ed4392a1c97342e5972d833a191e47b53d0

826

py

Python

app/api/errors/server.py

maxzhenzhera/my_vocab_backend

2e9f968374e0bc2fcc0ae40830ca40f3cf5754d1

[ "MIT" ]

null

app/api/errors/server.py

maxzhenzhera/my_vocab_backend

2e9f968374e0bc2fcc0ae40830ca40f3cf5754d1

[ "MIT" ]

null

app/api/errors/server.py

maxzhenzhera/my_vocab_backend

2e9f968374e0bc2fcc0ae40830ca40f3cf5754d1

[ "MIT" ]

null

import traceback from fastapi import Request from fastapi.responses import PlainTextResponse from starlette.status import HTTP_500_INTERNAL_SERVER_ERROR def internal_server_exception_handler( _: Request, exception: Exception ) -> PlainTextResponse: """ Return the traceback of the internal server error. """ exception_traceback = ''.join( traceback.format_exception( type(exception), value=exception, tb=exception.__traceback__ ) ) message = ( f'{"Internal server error has occurred.":<50}|\n' f'{"Please, check the traceback.":<50}|\n' f'{"-" * 50}x\n\n' ) message += exception_traceback return PlainTextResponse( status_code=HTTP_500_INTERNAL_SERVER_ERROR, content=message )

27.533333

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null

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null

0

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8de0588b8d216183bd189807faae11d1037b92e8

11,025

py

Python

dynamic_rcnn/utils/misc.py

yyzq1/bigwork

c2247abd2355b0f64ddfcc6e489e77b1eec55147

[ "MIT" ]

177

2020-04-14T01:16:26.000Z

2022-03-28T03:29:28.000Z

dynamic_rcnn/utils/misc.py

yyzq1/bigwork

c2247abd2355b0f64ddfcc6e489e77b1eec55147

[ "MIT" ]

10

2020-05-06T13:42:47.000Z

2021-02-06T13:35:27.000Z

dynamic_rcnn/utils/misc.py

yyzq1/bigwork

c2247abd2355b0f64ddfcc6e489e77b1eec55147

[ "MIT" ]

23

2020-04-14T05:41:25.000Z

2021-12-21T02:43:01.000Z

# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. """ helper class that supports empty tensors on some nn functions. Ideally, add support directly in PyTorch to empty tensors in those functions. This can be removed once https://github.com/pytorch/pytorch/issues/12013 is implemented """ import math import torch from torch import nn from torch.nn.modules.utils import _ntuple class _NewEmptyTensorOp(torch.autograd.Function): @staticmethod def forward(ctx, x, new_shape): ctx.shape = x.shape return x.new_empty(new_shape) @staticmethod def backward(ctx, grad): shape = ctx.shape return _NewEmptyTensorOp.apply(grad, shape), None class Conv2d(torch.nn.Conv2d): def forward(self, x): if x.numel() > 0: return super(Conv2d, self).forward(x) # get output shape output_shape = [ (i + 2 * p - (di * (k - 1) + 1)) // d + 1 for i, p, di, k, d in zip( x.shape[-2:], self.padding, self.dilation, self.kernel_size, self.stride ) ] output_shape = [x.shape[0], self.weight.shape[0]] + output_shape return _NewEmptyTensorOp.apply(x, output_shape) class ConvTranspose2d(torch.nn.ConvTranspose2d): def forward(self, x): if x.numel() > 0: return super(ConvTranspose2d, self).forward(x) # get output shape output_shape = [ (i - 1) * d - 2 * p + (di * (k - 1) + 1) + op for i, p, di, k, d, op in zip( x.shape[-2:], self.padding, self.dilation, self.kernel_size, self.stride, self.output_padding, ) ] output_shape = [x.shape[0], self.bias.shape[0]] + output_shape return _NewEmptyTensorOp.apply(x, output_shape) class BatchNorm2d(torch.nn.BatchNorm2d): def forward(self, x): if x.numel() > 0: return super(BatchNorm2d, self).forward(x) # get output shape output_shape = x.shape return _NewEmptyTensorOp.apply(x, output_shape) def interpolate( input, size=None, scale_factor=None, mode="nearest", align_corners=None ): if input.numel() > 0: return torch.nn.functional.interpolate( input, size, scale_factor, mode, align_corners ) def _check_size_scale_factor(dim): if size is None and scale_factor is None: raise ValueError("either size or scale_factor should be defined") if size is not None and scale_factor is not None: raise ValueError("only one of size or scale_factor should be defined") if ( scale_factor is not None and isinstance(scale_factor, tuple) and len(scale_factor) != dim ): raise ValueError( "scale_factor shape must match input shape. " "Input is {}D, scale_factor size is {}".format(dim, len(scale_factor)) ) def _output_size(dim): _check_size_scale_factor(dim) if size is not None: return size scale_factors = _ntuple(dim)(scale_factor) # math.floor might return float in py2.7 return [ int(math.floor(input.size(i + 2) * scale_factors[i])) for i in range(dim) ] output_shape = tuple(_output_size(2)) output_shape = input.shape[:-2] + output_shape return _NewEmptyTensorOp.apply(input, output_shape) class DFConv2d(nn.Module): """Deformable convolutional layer""" def __init__( self, in_channels, out_channels, with_modulated_dcn=True, kernel_size=3, stride=1, groups=1, dilation=1, deformable_groups=1, bias=False ): super(DFConv2d, self).__init__() if isinstance(kernel_size, (list, tuple)): assert isinstance(stride, (list, tuple)) assert isinstance(dilation, (list, tuple)) assert len(kernel_size) == 2 assert len(stride) == 2 assert len(dilation) == 2 padding = ( dilation[0] * (kernel_size[0] - 1) // 2, dilation[1] * (kernel_size[1] - 1) // 2 ) offset_base_channels = kernel_size[0] * kernel_size[1] else: padding = dilation * (kernel_size - 1) // 2 offset_base_channels = kernel_size * kernel_size if with_modulated_dcn: from dynamic_rcnn.kernels.ops.dcn import ModulatedDeformConv offset_channels = offset_base_channels * 3 #default: 27 conv_block = ModulatedDeformConv else: from dynamic_rcnn.kernels.ops.dcn import DeformConv offset_channels = offset_base_channels * 2 #default: 18 conv_block = DeformConv self.offset = Conv2d( in_channels, deformable_groups * offset_channels, kernel_size=kernel_size, stride=stride, padding=padding, groups=1, dilation=dilation ) for l in [self.offset,]: nn.init.kaiming_uniform_(l.weight, a=1) torch.nn.init.constant_(l.bias, 0.) self.conv = conv_block( in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, deformable_groups=deformable_groups, bias=bias ) self.with_modulated_dcn = with_modulated_dcn self.kernel_size = kernel_size self.stride = stride self.padding = padding self.dilation = dilation def forward(self, x): if x.numel() > 0: if not self.with_modulated_dcn: offset = self.offset(x) x = self.conv(x, offset) else: offset_mask = self.offset(x) offset = offset_mask[:, :18, :, :] mask = offset_mask[:, -9:, :, :].sigmoid() x = self.conv(x, offset, mask) return x # get output shape output_shape = [ (i + 2 * p - (di * (k - 1) + 1)) // d + 1 for i, p, di, k, d in zip( x.shape[-2:], self.padding, self.dilation, self.kernel_size, self.stride ) ] output_shape = [x.shape[0], self.conv.weight.shape[0]] + output_shape return _NewEmptyTensorOp.apply(x, output_shape) class FrozenBatchNorm2d(nn.Module): """ BatchNorm2d where the batch statistics and the affine parameters are fixed """ def __init__(self, n): super(FrozenBatchNorm2d, self).__init__() self.register_buffer("weight", torch.ones(n)) self.register_buffer("bias", torch.zeros(n)) self.register_buffer("running_mean", torch.zeros(n)) self.register_buffer("running_var", torch.ones(n)) def forward(self, x): # Cast all fixed parameters to half() if necessary if x.dtype == torch.float16: self.weight = self.weight.half() self.bias = self.bias.half() self.running_mean = self.running_mean.half() self.running_var = self.running_var.half() scale = self.weight * self.running_var.rsqrt() bias = self.bias - self.running_mean * scale scale = scale.reshape(1, -1, 1, 1) bias = bias.reshape(1, -1, 1, 1) return x * scale + bias def get_group_gn(dim, dim_per_gp, num_groups): """get number of groups used by GroupNorm, based on number of channels.""" assert dim_per_gp == -1 or num_groups == -1, \ "GroupNorm: can only specify G or C/G." if dim_per_gp > 0: assert dim % dim_per_gp == 0, \ "dim: {}, dim_per_gp: {}".format(dim, dim_per_gp) group_gn = dim // dim_per_gp else: assert dim % num_groups == 0, \ "dim: {}, num_groups: {}".format(dim, num_groups) group_gn = num_groups return group_gn # TODO, fix the cfg setting def group_norm(out_channels, affine=True, divisor=1, cfg=None): out_channels = out_channels // divisor if cfg: dim_per_gp = cfg.MODEL.GROUP_NORM.DIM_PER_GP // divisor num_groups = cfg.MODEL.GROUP_NORM.NUM_GROUPS // divisor eps = cfg.MODEL.GROUP_NORM.EPSILON # default: 1e-5 else: dim_per_gp = -1 num_groups = 32 eps = 1e-5 return torch.nn.GroupNorm( get_group_gn(out_channels, dim_per_gp, num_groups), out_channels, eps, affine ) def make_conv3x3( in_channels, out_channels, dilation=1, stride=1, use_gn=False, use_relu=False, kaiming_init=True ): conv = Conv2d( in_channels, out_channels, kernel_size=3, stride=stride, padding=dilation, dilation=dilation, bias=False if use_gn else True ) if kaiming_init: nn.init.kaiming_normal_( conv.weight, mode="fan_out", nonlinearity="relu" ) else: torch.nn.init.normal_(conv.weight, std=0.01) if not use_gn: nn.init.constant_(conv.bias, 0) module = [conv,] if use_gn: module.append(group_norm(out_channels)) if use_relu: module.append(nn.ReLU(inplace=True)) if len(module) > 1: return nn.Sequential(*module) return conv def make_fc(dim_in, hidden_dim, use_gn=False): ''' Caffe2 implementation uses XavierFill, which in fact corresponds to kaiming_uniform_ in PyTorch ''' if use_gn: fc = nn.Linear(dim_in, hidden_dim, bias=False) nn.init.kaiming_uniform_(fc.weight, a=1) return nn.Sequential(fc, group_norm(hidden_dim)) fc = nn.Linear(dim_in, hidden_dim) nn.init.kaiming_uniform_(fc.weight, a=1) nn.init.constant_(fc.bias, 0) return fc def conv_with_kaiming_uniform(use_gn=False, use_relu=False): def make_conv( in_channels, out_channels, kernel_size, stride=1, dilation=1 ): conv = Conv2d( in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=dilation * (kernel_size - 1) // 2, dilation=dilation, bias=False if use_gn else True ) # Caffe2 implementation uses XavierFill, which in fact # corresponds to kaiming_uniform_ in PyTorch nn.init.kaiming_uniform_(conv.weight, a=1) if not use_gn: nn.init.constant_(conv.bias, 0) module = [conv,] if use_gn: module.append(group_norm(out_channels)) if use_relu: module.append(nn.ReLU(inplace=True)) if len(module) > 1: return nn.Sequential(*module) return conv return make_conv

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