xszheng2020/the_stack_dedup_python_hits_0 · Datasets at Hugging Face

7bbdf574388c84658ffc5b1e989b4bad6ddb075e

9,045

py

Python

befh/exchanges/okex_spot.py

philsong/BitcoinExchangeFH

3c45d4be2ea2a258f132d982f62f69d649e0b083

[ "Apache-2.0" ]

32

2017-12-15T07:30:11.000Z

2020-07-16T10:15:18.000Z

befh/exchanges/okex_spot.py

bijiasuo/BitcoinExchangeFH

9aa7b790cf74cf9fe48662147c30fc05e045e9ed

[ "Apache-2.0" ]

null

befh/exchanges/okex_spot.py

bijiasuo/BitcoinExchangeFH

9aa7b790cf74cf9fe48662147c30fc05e045e9ed

[ "Apache-2.0" ]

20

2017-11-09T15:28:39.000Z

2019-12-10T01:02:57.000Z

from befh.ws_api_socket import WebSocketApiClient from befh.market_data import L2Depth, Trade from befh.exchanges.gateway import ExchangeGateway from befh.instrument import Instrument from befh.util import Logger from befh.clients.sql_template import SqlClientTemplate import time import threading import json from functools import partial from datetime import datetime class ExchGwApiOkexSpotWs(WebSocketApiClient): """ Exchange socket """ def __init__(self): """ Constructor """ WebSocketApiClient.__init__(self, 'ExchGwOkexSpot') @classmethod def get_timestamp_offset(cls): return 1000 @classmethod def get_order_book_timestamp_field_name(cls): return 'timestamp' @classmethod def get_bids_field_name(cls): return 'bids' @classmethod def get_asks_field_name(cls): return 'asks' @classmethod def get_link(cls): return 'wss://real.okex.com:10441/websocket' @classmethod def get_order_book_subscription_string(cls, instmt): return json.dumps({"event":"addChannel", "channel": instmt.get_order_book_channel_id()}) @classmethod def get_trades_subscription_string(cls, instmt): return json.dumps({"event":"addChannel", "channel": instmt.get_trades_channel_id()}) @classmethod def parse_l2_depth(cls, instmt, raw): """ Parse raw data to L2 depth :param instmt: Instrument :param raw: Raw data in JSON """ # l2_depth = instmt.get_l2_depth() l2_depth = L2Depth() keys = list(raw.keys()) if cls.get_order_book_timestamp_field_name() in keys and \ cls.get_bids_field_name() in keys and \ cls.get_asks_field_name() in keys: # Date time timestamp = float(raw[cls.get_order_book_timestamp_field_name()])/cls.get_timestamp_offset() l2_depth.date_time = datetime.utcfromtimestamp(timestamp).strftime("%Y%m%d %H:%M:%S.%f") # Bids bids = raw[cls.get_bids_field_name()] bids = sorted(bids, key=lambda x: x[0], reverse=True) max_bid_len = min(len(bids), 5) for i in range(0, max_bid_len): l2_depth.bids[i].price = float(bids[i][0]) if type(bids[i][0]) != float else bids[i][0] l2_depth.bids[i].volume = float(bids[i][1]) if type(bids[i][1]) != float else bids[i][1] # Asks asks = raw[cls.get_asks_field_name()] asks = sorted(asks, key=lambda x: x[0]) max_ask_len = min(len(asks), 5) for i in range(0, max_ask_len): l2_depth.asks[i].price = float(asks[i][0]) if type(asks[i][0]) != float else asks[i][0] l2_depth.asks[i].volume = float(asks[i][1]) if type(asks[i][1]) != float else asks[i][1] else: raise Exception('Does not contain order book keys in instmt %s-%s.\nOriginal:\n%s' % \ (instmt.get_exchange_name(), instmt.get_instmt_name(), \ raw)) return l2_depth @classmethod def parse_trade(cls, instmt, raw): """ :param instmt: Instrument :param raw: Raw data in JSON :return: """ trade = Trade() trade_id = raw[0] trade_price = float(raw[1]) trade_volume = float(raw[2]) date_time = raw[3] trade_side = raw[4] # trade.date_time = date_time trade.trade_id = str(trade_id) trade.trade_price = trade_price trade.trade_volume = trade_volume trade.trade_side = Trade.parse_side(trade_side) return trade class ExchGwOkexSpot(ExchangeGateway): """ Exchange gateway """ def __init__(self, db_clients): """ Constructor :param db_client: Database client """ ExchangeGateway.__init__(self, ExchGwApiOkexSpotWs(), db_clients) @classmethod def get_exchange_name(cls): """ Get exchange name :return: Exchange name string """ return 'Okex' def on_open_handler(self, instmt, ws): """ Socket on open handler :param instmt: Instrument :param ws: Web socket """ Logger.info(self.__class__.__name__, "Instrument %s is subscribed in channel %s" % \ (instmt.get_instmt_code(), instmt.get_exchange_name())) if not instmt.get_subscribed(): instmt_code_split = instmt.get_instmt_code().split('_') if len(instmt_code_split) == 2: # Future instruments instmt.set_order_book_channel_id("ok_sub_spot_%s_%s_depth_5" % \ (instmt_code_split[0].lower(), instmt_code_split[1].lower())) instmt.set_trades_channel_id("ok_sub_spot_%s_%s_deals" % \ (instmt_code_split[0].lower(), instmt_code_split[1].lower())) else: # Spot instruments instmt.set_order_book_channel_id("ok_sub_spot_%s_depth_5" % instmt.get_instmt_code().lower()) instmt.set_trades_channel_id("ok_sub_spot_%s_deals" % instmt.get_instmt_code().lower()) ws.send(self.api_socket.get_order_book_subscription_string(instmt)) # ws.send(self.api_socket.get_trades_subscription_string(instmt)) instmt.set_subscribed(True) def on_close_handler(self, instmt, ws): """ Socket on close handler :param instmt: Instrument :param ws: Web socket """ Logger.info(self.__class__.__name__, "Instrument %s is unsubscribed in channel %s" % \ (instmt.get_instmt_code(), instmt.get_exchange_name())) instmt.set_subscribed(False) def on_message_handler(self, instmt, messages): """ Incoming message handler :param instmt: Instrument :param message: Message """ for message in messages: keys = message.keys() # print(keys) if 'channel' in keys: if 'data' in keys: if message['channel'] == instmt.get_order_book_channel_id(): data = message['data'] l2_depth = self.api_socket.parse_l2_depth(instmt, data) if l2_depth is not None: # Insert only if the first 5 levels are different # if l2_depth is not None and instmt.get_l2_depth().is_diff(instmt.get_prev_l2_depth()): instmt.set_prev_l2_depth(instmt.get_l2_depth()) instmt.set_l2_depth(l2_depth) instmt.incr_order_book_id() self.insert_order_book(instmt) elif message['channel'] == instmt.get_trades_channel_id(): for trade_raw in message['data']: trade = self.api_socket.parse_trade(instmt, trade_raw) if trade.trade_id != instmt.get_exch_trade_id(): instmt.incr_trade_id() instmt.set_exch_trade_id(trade.trade_id) self.insert_trade(instmt, trade) elif 'success' in keys: Logger.info(self.__class__.__name__, "Subscription to channel %s is %s" \ % (message['channel'], message['success'])) else: Logger.info(self.__class__.__name__, ' - ' + json.dumps(message)) def start(self, instmt): """ Start the exchange gateway :param instmt: Instrument :return List of threads """ instmt.set_prev_l2_depth(L2Depth(20)) instmt.set_l2_depth(L2Depth(20)) instmt.set_instmt_snapshot_table_name(self.get_instmt_snapshot_table_name(instmt.get_exchange_name(), instmt.get_instmt_name())) self.init_instmt_snapshot_table(instmt) return [self.api_socket.connect(self.api_socket.get_link(), on_message_handler=partial(self.on_message_handler, instmt), on_open_handler=partial(self.on_open_handler, instmt), on_close_handler=partial(self.on_close_handler, instmt))] if __name__ == '__main__': exchange_name = 'Okex' instmt_name = 'BCHBTC' instmt_code = 'BCH_BTC' instmt = Instrument(exchange_name, instmt_name, instmt_code) db_client = SqlClientTemplate() Logger.init_log() exch = ExchGwOkexSpot([db_client]) td = exch.start(instmt)

38.326271

116

0.570591

1,044

9,045

4.627395

0.165709

0.033326

0.028152

0.02691

0.336576

0.274477

0.217346

0.170358

0.153798

0.136411

0

0.01172

0.330238

9,045

235

117

38.489362

0.785738

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0

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0.048276

0.275862

0

null

0

null

0

1

0

7bbe5cef3d1aeca66fb6ca826edab503eb8c860b

587

py

Python

hardhat/recipes/python/twisted.py

stangelandcl/hardhat

1ad0c5dec16728c0243023acb9594f435ef18f9c

[ "MIT" ]

null

hardhat/recipes/python/twisted.py

stangelandcl/hardhat

1ad0c5dec16728c0243023acb9594f435ef18f9c

[ "MIT" ]

null

hardhat/recipes/python/twisted.py

stangelandcl/hardhat

1ad0c5dec16728c0243023acb9594f435ef18f9c

[ "MIT" ]

null

from .base import PipBaseRecipe class TwistedRecipe(PipBaseRecipe): def __init__(self, *args, **kwargs): super(TwistedRecipe, self).__init__(*args, **kwargs) self.sha256 = 'a4cc164a781859c74de47f17f0e85f4b' \ 'ce8a3321a9d0892c015c8f80c4158ad9' self.pythons = ['python3'] self.pydepends = ['Automat', 'constantly', 'hyperlink', 'incremental', 'zope.interface'] self.name = 'twisted' self.version = '18.4.0'

32.611111

60

0.524702

41

587

7.317073

0.731707

0.066667

0

0.13172

0.366269

587

17

61

34.529412

0.674731

0

0.229983

0.109029

0

1

0.071429

false

0

0.071429

0

0.214286

0

null

0

null

0

1

0

7bbf00877f721b0c24c4e63d13a17b9fddb98274

250

py

Python

EXC/CW1/task3/combiner.py

easyCZ/UoE-Projects

7651c8caf329c4f7b4562eba441bfc24124cfcfd

[ "BSD-2-Clause" ]

null

EXC/CW1/task3/combiner.py

easyCZ/UoE-Projects

7651c8caf329c4f7b4562eba441bfc24124cfcfd

[ "BSD-2-Clause" ]

1

2022-02-23T07:34:53.000Z

EXC/CW1/task3/combiner.py

easyCZ/UoE-Projects

7651c8caf329c4f7b4562eba441bfc24124cfcfd

[ "BSD-2-Clause" ]

null

#!/usr/bin/python # combiner.py import sys word_count = 0 line_count = 0 for line in sys.stdin: words, lines = line.strip().split('\t') word_count += int(words) line_count += int(lines) print("{0}\t{1}".format(word_count, line_count))

17.857143

48

0.66

41

250

3.878049

0.560976

0.169811

0

0.019231

0.168

250

14

48

17.857143

0.745192

0.112

0

0.045249

0

1

0

false

0

0.125

0

0.125

0

null

0

null

0

1

0

7bbf1685508e5466a589c9ca9ef370e0a3b9611c

1,376

py

Python

tests/exploratory/user_data/radish/steps.py

tuxrosi/radish

b21fa751f8dfc4309451476151c810b44975babb

[ "MIT" ]

null

tests/exploratory/user_data/radish/steps.py

tuxrosi/radish

b21fa751f8dfc4309451476151c810b44975babb

[ "MIT" ]

null

tests/exploratory/user_data/radish/steps.py

tuxrosi/radish

b21fa751f8dfc4309451476151c810b44975babb

[ "MIT" ]

null

import re from radish.stepregistry import step from radish import when, then from radish.terrain import world @step(re.compile("I have the number in user data as (.+)")) def have_number(step, input_variable): if world.config.user_data: if input_variable in world.config.user_data: step.context.numbers.append(int(world.config.user_data[input_variable])) else: msg = "Variable [{0}] is not in the user data (-u/--user-data) specified on the command-line." assert False, msg.format(input_variable) else: assert ( False ), "There is no user data (-u/--user-data) specified on the command-line." @when("I sum them") def sum_numbers(step): step.context.result = sum(step.context.numbers) @then(re.compile("I expect the result to be the value in user data as (.+)")) def expect_result(step, result_variable): if world.config.user_data: if result_variable in world.config.user_data: assert step.context.result == int(world.config.user_data[result_variable]) else: msg = "Variable [{0}] is not in the user data (-u/--user-data) specified on the command-line." assert False, msg.format(input_variable) else: assert ( False ), "There is no user data (-u/--user-data) specified on the command-line."

35.282051

106

0.652616

198

1,376

4.449495

0.252525

0.145289

0.102157

0.129398

0.61748

0.533485

0.46765

0.397276

0

0.001899

0.234738

1,376

38

107

36.210526

0.834758

0

0.516129

0

0.064516

0.300872

0

0.16129

1

0.096774

false

0

0.129032

0

0.225806

0

null

0

null

0

1

0

7bbf1d84d1d1e722a857754d78ceb86118a7eadb

3,462

py

Python

django/core/views.py

andreyvpng/askme

65139c347a6b80f0a660ca24d6dd864e4531903a

[ "Apache-2.0" ]

2

2018-10-29T09:37:47.000Z

2019-11-28T14:11:12.000Z

django/core/views.py

andreyvpng/askme

65139c347a6b80f0a660ca24d6dd864e4531903a

[ "Apache-2.0" ]

null

django/core/views.py

andreyvpng/askme

65139c347a6b80f0a660ca24d6dd864e4531903a

[ "Apache-2.0" ]

2

2018-09-18T14:09:46.000Z

2019-11-28T14:11:14.000Z

from django.contrib.auth import get_user_model from django.contrib.auth.mixins import LoginRequiredMixin from django.core.exceptions import ObjectDoesNotExist, PermissionDenied from django.http.response import HttpResponseBadRequest, HttpResponseRedirect from django.urls import reverse_lazy from django.urls.base import reverse from django.views.generic import CreateView, DeleteView, DetailView, View from .forms import AnswerForm, QuestionForm from .models import Answer, Like, Question User = get_user_model() class AnswerDetailView(DetailView): queryset = Answer.objects.all_with_question() class AnswerCreateView(LoginRequiredMixin, CreateView): model = Answer form_class = AnswerForm def form_valid(self, form): self.object = form.save(commit=False) self.object.question = self.get_question() if self.object.question.asked_to != self.request.user: return HttpResponseBadRequest() self.object.save() return super().form_valid(form) def get_question(self): return Question.objects.get(id=self.kwargs['pk']) class AnswerDeleteView(LoginRequiredMixin, DeleteView): model = Answer success_url = reverse_lazy('user:my-profile') def dispatch(self, *args, **kwargs): answer = self.get_object() if answer.question.asked_to != self.request.user: raise PermissionDenied return super().dispatch(*args, **kwargs) class PrivateQuestionDetailView(DetailView): model = Question def dispatch(self, *args, **kwargs): question = self.get_object() if question.asked_to != self.request.user: raise PermissionDenied try: return reverse(question.answer.get_absolute_url()) except ObjectDoesNotExist: pass return super().dispatch(*args, **kwargs) def get_context_data(self, **kwargs): ctx = super().get_context_data(**kwargs) answer_form = AnswerForm() ctx.update({'answer_form': answer_form}) return ctx class QuestionCreateView(LoginRequiredMixin, CreateView): model = Question form_class = QuestionForm def form_valid(self, form): self.object = form.save(commit=False) self.object.asked_by = self.request.user self.object.asked_to = self.get_user() self.object.save() return super().form_valid(form) def get_success_url(self): return reverse('user:profile', kwargs={ 'pk': self.get_user().id }) def get_user(self): return User.objects.get(id=self.kwargs['pk']) class QuestionDeleteView(LoginRequiredMixin, DeleteView): model = Question success_url = reverse_lazy('user:inbox') def dispatch(self, *args, **kwargs): question = self.get_object() if question.asked_to != self.request.user: raise PermissionDenied return super().dispatch(*args, **kwargs) class LikeView(LoginRequiredMixin, View): def post(self, request, pk): answer = Answer.objects.get(id=pk) like = Like.objects.filter(answer=answer, liked_by=request.user) if like: like.delete() else: like = Like.objects.create(answer=answer, liked_by=request.user) like.save() return HttpResponseRedirect(request.META.get('HTTP_REFERER'))

27.696

77

0.662334

383

3,462

5.869452

0.232376

0.035587

0.024466

0.033808

0.344751

0.298488

0.258452

0.232651

0

0.237146

3,462

124

78

27.919355

0.851193

0

0.337349

0

0.019064

0

1

0.120482

false

0.012048

0.108434

0.036145

0.578313

0

null

0

null

0

1

0

7bc111fc110f0ab3862581da0b6b979e7a706d1e

3,234

py

Python

drowsiness_detector.py

zhww-107/drowsiness_detector

855995e1da36ffc0ec1fda7df8ea1aafc35c416d

[ "BSD-2-Clause" ]

1

2020-05-12T12:31:51.000Z

drowsiness_detector.py

zhww-107/drowsiness_detector

855995e1da36ffc0ec1fda7df8ea1aafc35c416d

[ "BSD-2-Clause" ]

null

drowsiness_detector.py

zhww-107/drowsiness_detector

855995e1da36ffc0ec1fda7df8ea1aafc35c416d

[ "BSD-2-Clause" ]

null

from imutils import face_utils from scipy.spatial import distance import cv2 import dlib import imutils import pygame import time # Initializing the alert sound pygame.mixer.init() alert_sound = pygame.mixer.Sound("alert_sound.wav") default_volume = 0.2 # Eye-Aspect-Ratio data EAR_threshhold = 0.17 # One valid frame is counted when EAR is lower than this value frame_count = 0 # Number of frames when EAR is lower than EAR_threshhold EAR_total_frame = 25 # Having frame_count larger than this value is considered drowsiness # Play the alarm in a given volume def alert(volume): alert_sound.set_volume(volume) alert_sound.play() # Given an eye landmark, compute its eye_aspect_ratio def eye_aspect_ratio(eye): v1 = distance.euclidean(eye[1], eye[5]) v2 = distance.euclidean(eye[2], eye[4]) h1 = distance.euclidean(eye[0], eye[3]) return (v1 + v2) / (2 * h1) # Initialize the face detector and Facial landmark predictor detector = dlib.get_frontal_face_detector() predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat") # Access the camera cap = cv2.VideoCapture(0) # Main loop for drowsiness detection while True: # Read the camera input, resize it, and concert it to grayscale frame ret, frame = cap.read() frame = imutils.resize(frame, width=600) raw = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # Detect faces in grayscale frame bounds = detector(raw,0) for bound in bounds: # Predict facial landmarks for each detected face shape = predictor(raw,bound) # Convert the facial lanmarks into a 1-D numpy array (x, y) shape = face_utils.shape_to_np(shape) # Left and right eyes' indexes for facial landmarks left_eye = shape[42:48] right_eye = shape[36:42] # The main EAR is the average of left and right eye's EAR left_EAR = eye_aspect_ratio(left_eye) right_EAR = eye_aspect_ratio(right_eye) EAR = (left_EAR + right_EAR) / 2 # Draw the facial landmarks for left eye for (x, y) in left_eye: cv2.circle(frame, (x, y), 1, (0, 255, 0), -1) # Draw the facial landmarks for right eye for (x, y) in right_eye: cv2.circle(frame, (x, y), 1, (0, 255, 0), -1) # Alarm when drowsiness is detected if EAR < EAR_threshhold: frame_count += 1 # Volume increases gradually if frame_count >= EAR_total_frame: alert(0.2 + (frame_count - 25) * 0.2) time.sleep(3) else: frame_count = 0 # Display informations cv2.putText(frame, "Frame: {:.0f}".format(frame_count), (30, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2) cv2.putText(frame, "Eye-Aspect-Ratio: {:.2f}".format(EAR), (30, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2) cv2.putText(frame, "Press Q to exit.", (410, 320), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2) # Display the frame cv2.imshow("Drowsiness_Detector", frame) # Provide a way to exit the program -- pressing "Q" key = cv2.waitKey(1) & 0xFF if key == ord("q"): break cv2.destroyAllWindows()

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0

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0.248918

3,234

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31.096154

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0

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false

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0

null

0

null

0

1

0

7bc160c90d8d420f5bacbdb3fbe421c84e36aaf4

11,809

py

Python

trunk-tap.py

schreiberstein/trunk-tap.py

aacf32816e2a558e31ebc431edf84e23ef22146d

[ "MIT" ]

15

2017-10-22T15:08:58.000Z

2022-01-03T22:21:12.000Z

trunk-tap.py

ideechaniz/trunk-tap.py

aacf32816e2a558e31ebc431edf84e23ef22146d

[ "MIT" ]

2

2018-04-04T18:52:54.000Z

2019-02-20T10:16:13.000Z

trunk-tap.py

ideechaniz/trunk-tap.py

aacf32816e2a558e31ebc431edf84e23ef22146d

[ "MIT" ]

6

2017-10-23T03:03:16.000Z

2021-07-03T16:28:29.000Z

#!/usr/bin/env python3 # < trunk-tap.py > # Version 1.0 < 20171022 > # Copyright 2017: Alexander Schreiber < schreiberstein[at]gmail.com > # https://github.com/schreiberstein/trunk-tap.py # MIT License: # ============ # 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. # See: https://opensource.org/licenses/MIT # Introduction: # ============= # trunk-tap.py is a Linux command line utility to connects a set of 802.1Q VLANs to a TINC VPN/OpenVPN TAP-interface and is designed to be invoked by ifup/ifdown scripts after starting or stopping a VPN connection. # Dependencies (on Debian): python3, iproute2, bridge-utils, vlan (including kernel module '8021q' in /etc/modules) # It reads the filenames from the content of a folder containing files corresponding to the VLAN ID (e.g. '100', '105', ...), then creates VLAN interfaces on a local Ethernet adapter used as "trunk port" (e.g. 'eth1.100', 'eth1.105', ...). # The script then proceeds to generate bridge interfaces for every VLAN ID. (e.g. "trunk0.100", "trunk0.105", ...) and attaches the respective Ethernet VLAN interfaces to the bridge. (e.g. 'trunk0.105 <-> eth1.105', ...) # After that, the local infrastructure is ready to be attached to the VPN layer 2 tunnel. # This is achieved by enabling the TAP interface ("up"), creating VLAN interfaces on the TAP adapter (e.g. 'tap0.100', 'tap0.105', ...) and attaching them to the respective bridge. # Illustration: # ============= # (TINC VPN / OpenVPN) # -------- SITE 1 ------- -------- SITE 2 ------- # eth1.100 <-> trunk0.100 <--\ ################ /--> trunk0.100 <-> eth1.100 # eth1.105 <-> trunk0.105 <--->> ---TAP-TUNNEL--- <<---> trunk0.105 <-> eth1.105 # eth1.110 <-> trunk0.110 <--/ ################ \--> trunk0.110 <-> eth1.110 # Hint: Interface names (ethernet adapter, bridge name, ...) do not neccesarily have to be identical among sites. # --------------------------------------------------------------------------------------------------------------- # # Code: # ===== # Import required Python3 modules import os, sys, subprocess from pathlib import Path # Create VLAN-interfaces on trunk interface (e.g. 'eth1.100', 'eth1.105', ...) def trunk_vlan_add(): # Initialize our trunk interface, if it is not up yet p = subprocess.Popen("ip link set dev " + trunk_interface + " up", shell=True) p.communicate() # Create VLAN interfaces on trunk_interface for filename in os.listdir(vlan_dir): p = subprocess.Popen("ip link add link " + trunk_interface + " name " + trunk_interface + "." + filename + " type vlan id " + filename +" ; " + "ip link set " + trunk_interface + "." + filename + " up", shell=True) p.communicate() continue return # Function to remove VLAN interfaces from trunk interface def trunk_vlan_del(): # Remove VLAN interfaces on trunk_interface for filename in os.listdir(vlan_dir): p = subprocess.Popen("ip link set dev " + trunk_interface + "." + filename + " down" + " ; " + "ip link delete " + trunk_interface + "." + filename, shell=True) p.communicate() continue return # Function to create main bridge (no VLAN ID - May be used to attach a VLAN/network to provide network to devices without VLAN support (VLAN0 - untagged)) def bridge_add(): p = subprocess.Popen("ip link add name " + bridge_name + " type bridge" + " ; " + "ip link set " + bridge_name + " up" + " ; " + "ip link set " + trunk_interface + " master " + bridge_name, shell=True) p.communicate() return # Function to remove bridge def bridge_del(): p = subprocess.Popen("ip link set " + bridge_name + " down" + " ; " + "ip link delete " + bridge_name + " type bridge", shell=True) p.communicate() return # Creates bridges to be used for VLAN bridging (e.g. 'trunk0.100', 'trunk0.105', ..) - illustration: eth1.105 <-> Bridge: trunk0.105 <-> tap0.105 def bridge_vlan_add(): for filename in os.listdir(vlan_dir): p = subprocess.Popen("ip link add name " + bridge_name + "." + filename + " type bridge" + " ; " + "ip link set " + bridge_name + "." + filename + " up", shell=True) p.communicate() continue return # Function to remove VLAN interfaces from the bridge def bridge_vlan_del(): for filename in os.listdir(vlan_dir): p = subprocess.Popen("ip link set dev " + bridge_name + "." + filename + " down" + " ; " + "ip link delete " + bridge_name + "." + filename, shell=True) p.communicate() continue return # Function to bridge the VLANs of the physical interface with the VLANs of the bridge def bridge(): for filename in os.listdir(vlan_dir): p = subprocess.Popen("ip link set " + trunk_interface + "." + filename + " master " + bridge_name + "." + filename, shell=True) p.communicate() continue return # Create VLAN-interfaces on tap interface def tap_vlan_add(): # Initialize the tap interface, if it is not up yet p = subprocess.Popen("ip link set dev " + tap_interface + " up", shell=True) p.communicate() # Create VLAN interfaces on tap interface for filename in os.listdir(vlan_dir): p = subprocess.Popen("ip link add link " + tap_interface + " name " + tap_interface + "." + filename + " type vlan id " + filename + " ; " + "ip link set dev " + tap_interface + "." + filename + " up", shell=True) p.communicate() continue return # Function to bridge the VLANs of the physical interface with the VLANs of the bridge def tap_bridge(): for filename in os.listdir(vlan_dir): p = subprocess.Popen("ip link set " + tap_interface + "." + filename + " master " + bridge_name + "." + filename, shell=True) p.communicate() continue return # Function to enable ("up") the tap interface def tap_if_up(): p = subprocess.Popen("ip link set dev " + tap_interface + " down", shell=True) p.communicate(); return # Function to disable ("down") the tap interface def tap_if_down(): p = subprocess.Popen("ip link set dev " + tap_interface + " down", shell=True) p.communicate(); return # Function to remove VLAN interfaces from tap interface def tap_vlan_del(): # Remove VLAN interfaces on tinc_interface for filename in os.listdir(vlan_dir): p = subprocess.Popen("ip link set dev " + tap_interface + "." + filename + " down" + " ; " + "ip link delete " + tap_interface + "." + filename, shell=True) p.communicate() continue return # Function to remove members attached by the tap_bridge() function def tap_unbridge(): for filename in os.listdir(vlan_dir): p = subprocess.Popen("ip link set " + tap_interface + "." + filename + " nomaster", shell=True) p.communicate() continue return # Function to remove members attached by the bridge() function def unbridge(): for filename in os.listdir(vlan_dir): p = subprocess.Popen("ip link set " + trunk_interface + "." + filename + " nomaster", shell=True) p.communicate() continue return # ------------------------ # Note: Order of execution # ------------------------ # Start: # ------ # trunk_vlan_add() # bridge_add() # bridge_vlan_add() # bridge() # tap_if_up() # tap_vlan_add() # tap_bridge() # Stop: # ----- # tap_unbridge() # tap_vlan_del() # tap_if_down() # unbridge() # bridge_vlan_del() # bridge_del() # trunk_vlan_del() # Start function - Used to execute all other functions def start(no_tap): trunk_vlan_add() bridge_add() bridge_vlan_add() bridge() # Don't do anything with the TAP interface if --no_tap was specified if not no_tap: tap_if_up() tap_vlan_add() tap_bridge() return # Stop function - reverses the actions performed by start() def stop(no_tap): # Don't do anything with the TAP interface if --no_tap was specified if not no_tap: tap_unbridge() tap_vlan_del() tap_if_down() unbridge() bridge_vlan_del() bridge_del() trunk_vlan_del() return # # # # # # # # # # Main function # # # # # # # # # # def main(): # If no arguments are specified, quit. if len(sys.argv) == 1: print("Error: No arguments specified. Enter ./trunktap.py --help for more information.") quit() # If arguments are given, parse them and run script. import argparse parser = argparse.ArgumentParser() # Add arguments parser.add_argument("-start", dest="is_start", action="store_true", help="Creates all interfaces and establishes VLAN bridges") parser.add_argument("-stop", dest="is_stop", action="store_true", help="Reverses -start: Removes the previously created interfaces") parser.add_argument("-i", "--interface", dest="trunk_interface", help="Specify the trunk interface on the host that will provide the VLANs to the network (e.g. eth1)") parser.add_argument("-t", "--tap-interface", dest="tap_interface", help="Specify the TAP interface on the host that will be used by TINC/OpenVPN (e.g. $INTERFACE, tap0)") parser.add_argument("-v", "--vlan-dir", dest="vlan_dir", help="The path to the folder that contains the files that represent the VLANs that will be created. - Default: ./vlans/ ", default="./vlans/") parser.add_argument("-b", "--bridge", dest="bridge_name", help="Name of the bridge that will be created. (e.g. trunk0, br0)") parser.add_argument("--no-tap", dest="no_tap", help="Only for special use: If used, the VLANs will be created locally (e.g. trunk0.105 <-> eth1.105), but the TAP interface won't be used.", default=False, action="store_true") # Parse arguments arguments = parser.parse_args() # Create local variables because the functions use these global trunk_interface, tap_interface, vlan_dir, bridge_name trunk_interface = arguments.trunk_interface tap_interface = arguments.tap_interface vlan_dir = arguments.vlan_dir bridge_name = arguments.bridge_name # Make sure that either start or stop was specified (NOT XOR) if not arguments.is_start ^ arguments.is_stop: print("Error: You have to specify either -start or -stop. Only one option is valid.") quit() # Make sure that arguments are not empty if not (trunk_interface and tap_interface and vlan_dir and bridge_name): print("Error: You have to specify -i, -t, -b and -v.") quit() # Execute either function start() or stop() and pass the no_tap-variable if arguments.is_start: start(arguments.no_tap) if arguments.is_stop: stop(arguments.no_tap) quit() # Only run main if the script is explicitly executed (e.g. './trunktap.py') if __name__ == "__main__": main()

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7bc353399a2502106befa0365666e5d586522d04

4,404

py

Python

tests/common/mock_cgroup_commands.py

rbgithuub/WALinuxAgent

c0462f33bb5e3a33430fe3d172676d85cefa6227

[ "Apache-2.0" ]

null

tests/common/mock_cgroup_commands.py

rbgithuub/WALinuxAgent

c0462f33bb5e3a33430fe3d172676d85cefa6227

[ "Apache-2.0" ]

null

tests/common/mock_cgroup_commands.py

rbgithuub/WALinuxAgent

c0462f33bb5e3a33430fe3d172676d85cefa6227

[ "Apache-2.0" ]

null

# Copyright 2020 Microsoft Corporation # # 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. # # Requires Python 2.6+ and Openssl 1.0+ # import contextlib import os import re import subprocess from azurelinuxagent.common.utils import fileutil from tests.tools import patch, data_dir # # Default values for the mocked commands. # # The output comes from an Ubuntu 18 system # _default_commands = [ (r"systemctl --version", '''systemd 237 +PAM +AUDIT +SELINUX +IMA +APPARMOR +SMACK +SYSVINIT +UTMP +LIBCRYPTSETUP +GCRYPT +GNUTLS +ACL +XZ +LZ4 +SECCOMP +BLKID +ELFUTILS +KMOD -IDN2 +IDN -PCRE2 default-hierarchy=hybrid '''), (r"mount -t cgroup", '''cgroup on /sys/fs/cgroup/systemd type cgroup (rw,nosuid,nodev,noexec,relatime,xattr,name=systemd) cgroup on /sys/fs/cgroup/rdma type cgroup (rw,nosuid,nodev,noexec,relatime,rdma) cgroup on /sys/fs/cgroup/cpuset type cgroup (rw,nosuid,nodev,noexec,relatime,cpuset) cgroup on /sys/fs/cgroup/net_cls,net_prio type cgroup (rw,nosuid,nodev,noexec,relatime,net_cls,net_prio) cgroup on /sys/fs/cgroup/perf_event type cgroup (rw,nosuid,nodev,noexec,relatime,perf_event) cgroup on /sys/fs/cgroup/hugetlb type cgroup (rw,nosuid,nodev,noexec,relatime,hugetlb) cgroup on /sys/fs/cgroup/freezer type cgroup (rw,nosuid,nodev,noexec,relatime,freezer) cgroup on /sys/fs/cgroup/memory type cgroup (rw,nosuid,nodev,noexec,relatime,memory) cgroup on /sys/fs/cgroup/pids type cgroup (rw,nosuid,nodev,noexec,relatime,pids) cgroup on /sys/fs/cgroup/devices type cgroup (rw,nosuid,nodev,noexec,relatime,devices) cgroup on /sys/fs/cgroup/cpu,cpuacct type cgroup (rw,nosuid,nodev,noexec,relatime,cpu,cpuacct) cgroup on /sys/fs/cgroup/blkio type cgroup (rw,nosuid,nodev,noexec,relatime,blkio) '''), (r"mount -t cgroup2", '''cgroup on /sys/fs/cgroup/unified type cgroup2 (rw,nosuid,nodev,noexec,relatime) '''), (r"systemctl show walinuxagent\.service --property CPUAccounting", '''CPUAccounting=no '''), (r"systemctl show walinuxagent\.service --property MemoryAccounting", '''MemoryAccounting=no '''), (r"systemd-run --unit=([^\s]+) --scope ([^\s]+)", ''' Running scope as unit: TEST_UNIT.scope Thu 28 May 2020 07:25:55 AM PDT '''), ] _default_files = ( (r"/proc/self/cgroup", os.path.join(data_dir, 'cgroups', 'proc_self_cgroup')), (r"/proc/[0-9]+/cgroup", os.path.join(data_dir, 'cgroups', 'proc_pid_cgroup')), (r"/sys/fs/cgroup/unified/cgroup.controllers", os.path.join(data_dir, 'cgroups', 'sys_fs_cgroup_unified_cgroup.controllers')), ) @contextlib.contextmanager def mock_cgroup_commands(): original_popen = subprocess.Popen original_read_file = fileutil.read_file original_path_exists = os.path.exists def mock_popen(command, *args, **kwargs): if isinstance(command, list): command_string = " ".join(command) else: command_string = command for cmd in _default_commands: match = re.match(cmd[0], command_string) if match is not None: command = ["echo", cmd[1]] return original_popen(command, *args, **kwargs) def mock_read_file(filepath, **kwargs): for file in _default_files: match = re.match(file[0], filepath) if match is not None: filepath = file[1] return original_read_file(filepath, **kwargs) def mock_path_exists(path): for file in _default_files: match = re.match(file[0], path) if match is not None: return True return original_path_exists(path) with patch("azurelinuxagent.common.cgroupapi.subprocess.Popen", side_effect=mock_popen) as patcher: with patch("azurelinuxagent.common.cgroupapi.os.path.exists", side_effect=mock_path_exists): with patch("azurelinuxagent.common.cgroupapi.fileutil.read_file", side_effect=mock_read_file): yield patcher

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7bc78e4dfebfc4162a535f0855d380aa68aa6df8

1,474

py

Python

main.py

saiamphora/XOR-NEATpy

091b6d6fc3b662491c8216227f5305841521e0ed

[ "Unlicense" ]

1

2021-11-29T03:30:49.000Z

main.py

saiamphora/XOR-NEATpy

091b6d6fc3b662491c8216227f5305841521e0ed

[ "Unlicense" ]

1

2021-11-29T15:28:09.000Z

main.py

saiamphora/XOR-NEATpy

091b6d6fc3b662491c8216227f5305841521e0ed

[ "Unlicense" ]

null

from __future__ import print_function import os import neat # 2-input XOR inputs and expected outputs. xor_inputs = [(0.0, 0.0), (0.0, 1.0), (1.0, 0.0), (1.0, 1.0)] xor_outputs = [(0.0,),(1.0,),(1.0,),(0.0,)] def eval_genomes(genomes, config): for genome_id, genome in genomes: genome.fitness = 4.0 net = neat.nn.FeedForwardNetwork.create(genome, config) for xi, xo in zip(xor_inputs, xor_outputs): output = net.activate(xi) genome.fitness -= (output[0] - xo[0]) ** 4 def run(config_file): config = neat.Config(neat.DefaultGenome, neat.DefaultReproduction, neat.DefaultSpeciesSet, neat.DefaultStagnation, config_file) p = neat.Population(config) p.add_reporter(neat.StdOutReporter(True)) stats = neat.StatisticsReporter() p.add_reporter(stats) p.add_reporter(neat.Checkpointer(5)) winner = p.run(eval_genomes, 500) print('\nBest genome:\n{!s}'.format(winner)) print('\nOutput:') winner_net = neat.nn.FeedForwardNetwork.create(winner, config) for xi, xo in zip(xor_inputs, xor_outputs): output = winner_net.activate(xi) print("input {!r}, expected output {!r}, got {!r}".format(xi, xo, output)) p = neat.Checkpointer.restore_checkpoint('neat-checkpoint-4') p.run(eval_genomes, 10) local_dir = os.path.dirname(__file__) config_path = os.path.join(local_dir, 'config-feedforward') run(config_path)

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7bc9519279bbaea50bce0ecf16967333a0bd62b5

319

py

Python

Autre/Internet.py

Yaya-Cout/Python

500a2bc18cbb0b9bf1470943def8fd8e8e76d36d

[ "Unlicense" ]

5

2020-12-05T14:00:39.000Z

2021-12-02T11:44:54.000Z

Autre/Internet.py

Yaya-Cout/Python

500a2bc18cbb0b9bf1470943def8fd8e8e76d36d

[ "Unlicense" ]

11

2021-03-15T17:51:43.000Z

2021-11-24T13:24:39.000Z

Autre/Internet.py

Yaya-Cout/Python

500a2bc18cbb0b9bf1470943def8fd8e8e76d36d

[ "Unlicense" ]

1

2021-01-02T14:15:10.000Z

def main(): import webbrowser recherche = 0 while True: if recherche >= 2: print("Vous avez fait " + str(recherche) + " recherches.") recherche += 1 adresse = input("Quel adresse veut-tu ouvrir") webbrowser.open(adresse) if __name__ == "__main__": main()

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null

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7bc96e1706c4c4494a902bdb9aa51a33d9269620

6,502

py

Python

older/rc-qradar-search/query_runner/components/ariel_query.py

nickpartner-goahead/resilient-community-apps

097c0dbefddbd221b31149d82af9809420498134

[ "MIT" ]

65

2017-12-04T13:58:32.000Z

2022-03-24T18:33:17.000Z

older/rc-qradar-search/query_runner/components/ariel_query.py

nickpartner-goahead/resilient-community-apps

097c0dbefddbd221b31149d82af9809420498134

[ "MIT" ]

48

2018-03-02T19:17:14.000Z

2022-03-09T22:00:38.000Z

older/rc-qradar-search/query_runner/components/ariel_query.py

nickpartner-goahead/resilient-community-apps

097c0dbefddbd221b31149d82af9809420498134

[ "MIT" ]

95

2018-01-11T16:23:39.000Z

2022-03-21T11:34:29.000Z

"""Action Module circuits component to update incidents from QRadar Ariel queries""" import logging from datetime import datetime import time import copy import json from string import Template from pkg_resources import Requirement, resource_filename import resilient_circuits.template_functions as template_functions from query_runner.lib.query_action import QueryRunner from query_runner.lib.qradar_rest_client import QRadarClient from query_runner.lib.misc import SearchTimeout, SearchFailure try: basestring except NameError: basestring = str LOG = logging.getLogger(__name__) CONFIG_DATA_SECTION = 'ariel' def config_section_data(): """sample config data for use in app.config""" section_config_fn = resource_filename(Requirement("rc-qradar-search"), "query_runner/data/app.config.qradar") query_dir = resource_filename(Requirement("rc-qradar-search"), "query_runner/data/queries_ariel") with open(section_config_fn, 'r') as section_config_file: section_config = Template(section_config_file.read()) return section_config.safe_substitute(directory=query_dir) class AQLIncidentUpdate(QueryRunner): """ Acknowledges and fires off new query requests """ def __init__(self, opts): query_options = opts.get(CONFIG_DATA_SECTION, {}) jinja_filters = template_functions.JINJA_FILTERS jinja_filters["datetime"] = self._datetime_filter template_functions.ENV.filters.update(jinja_filters) super(AQLIncidentUpdate, self).__init__(opts, query_options, run_search) def _datetime_filter(self, val): """ JINJA filter to convert ms to YYYY-MM-DD HH:mm:ss """ dt = datetime.fromtimestamp(val/1000.0) return dt.strftime("%Y-%m-%d %H:%M:%S") ############################# # Functions for running Query ############################# def _wait_for_query_to_complete(search_id, qradar_client, timeout, polling_interval): """ Poll QRadar until search execution finishes """ start_time = time.time() search_status = qradar_client.get_search_status(search_id) if not search_status: # Sometimes it takes a little while to be able to query a search id time.sleep(4) search_status = qradar_client.get_search_status(search_id) while search_status.get("status", "") in ("WAIT", "EXECUTE", "SORTING"): if timeout != 0: if time.time() - start_time > timeout: raise SearchTimeout(search_id, search_status.get("status", "")) time.sleep(polling_interval) search_status = qradar_client.get_search_status(search_id) if search_status.get("status", "") != "COMPLETED": LOG.error(search_status) raise SearchFailure(search_id, search_status.get("status", "")) # end _wait_for_query_to_complete def _get_query_results(search_id, qradar_client, item_range): """ Get results from a complete QRadar query """ if item_range: headers = {"Range": item_range} else: headers = None url = "ariel/searches/{0}/results".format(search_id, headers=headers) response = qradar_client.get(url) LOG.debug(response) # Replace "NULL" with "" response = remove_nulls(response) return response # end _get_query_results def remove_nulls(d): """ recursively replace 'NULL' with '' in dictionary """ if isinstance(d, basestring): if d == u'NULL': return u'' else: return d new = {} LOG.debug("d={d} ".format(d=d)) LOG.debug("type of d is {t}".format(t=type(d))) for k, v in d.items(): if isinstance(v, dict): v = remove_nulls(v) elif isinstance(v, list): v = [remove_nulls(v1) for v1 in v] elif isinstance(v, basestring) and v == u'NULL': v = u'' new[k] = v LOG.info("Returning: {n}".format(n=new)) return new def run_search(options, query_definition, event_message): """ Run Ariel search and return result """ # Read the options and construct a QRadar client qradar_url = options.get("qradar_url", "") qradar_token = options.get("qradar_service_token", "") timeout = int(options.get("query_timeout", 600)) polling_interval = int(options.get("polling_interval", 5)) if not all((qradar_url, qradar_token, timeout, polling_interval)): LOG.error("Configuration file missing required values!") raise Exception("Missing Configuration Values") verify = options.get("qradar_verify", "") if verify[:1].lower() in ("0", "f", "n"): verify = False else: verify = True qradar_client = QRadarClient(qradar_url, qradar_token, verify=verify) error = None response = None try: params = {'query_expression': query_definition.query} url = "ariel/searches" response = qradar_client.post(url, params=params) LOG.debug(response) search_id = response.get('search_id', '') if not search_id: error = "Query Failed: " + response.get("message", "No Error Message Found") else: LOG.info("Queued Search %s", search_id) _wait_for_query_to_complete(search_id, qradar_client, timeout, polling_interval) # Query Execution Finished, Get Results response = _get_query_results(search_id, qradar_client, query_definition.range) except Exception as exc: if not query_definition.onerror: raise LOG.error(exc) error = u"{}".format(exc) if error: mapdata = copy.deepcopy(event_message) mapdata.update(query_definition.vars) mapdata.update({"query": query_definition.query}) mapdata.update({"error": error}) error_template = json.dumps({"events": [query_definition.onerror]}, indent=2) error_rendered = template_functions.render_json(error_template, mapdata) response = error_rendered if not response or len(response["events"]) == 0: LOG.warn("No data returned from query") if query_definition.default: mapdata = copy.deepcopy(event_message) mapdata.update(query_definition.vars) mapdata.update({"query": query_definition.query}) default_template = json.dumps({"events": [query_definition.default]}, indent=2) default_rendered = template_functions.render_json(default_template, mapdata) response = default_rendered return response # end run_search

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null

0

1

0

7bcaa605df103e994b12588df4d84741fe74b87f

2,371

py

Python

first/sendmail-practice.py

bujige/Python-practice

c1eb76b0caaada628f23a477303f07d6be3f707c

[ "Apache-2.0" ]

null

first/sendmail-practice.py

bujige/Python-practice

c1eb76b0caaada628f23a477303f07d6be3f707c

[ "Apache-2.0" ]

null

first/sendmail-practice.py

bujige/Python-practice

c1eb76b0caaada628f23a477303f07d6be3f707c

[ "Apache-2.0" ]

null

#! /usr/bin/env python # -*- coding: utf-8 -*- from email import encoders from email.header import Header from email.mime.multipart import MIMEBase, MIMEMultipart from email.mime.text import MIMEText from email.utils import parseaddr, formataddr import smtplib # 格式化一个邮件地址 def _format_addr(s): # parseaddr：解析字符串中的email地址 name, addr = parseaddr(s) # name中包含中文，需要通过Header对象进行编码 # formataddr:parseaddr函数的逆函数 return formataddr((Header(name, 'utf-8').encode(), addr)) # 登录账户和口令 from_addr = input('From：') password = input('Password：') # 目标地址 to_addr = input('To：') # 目标服务器 smtp_server = input('SMTP server：') # 封装邮件 # 内容 msg = MIMEText('Hello,send by Python...', 'plain', 'utf-8') # HTML邮件 msg = MIMEText('<html><body><h1>Hello</h1>' + '<p>send by <a href="http://www.python.org">Python</a>...</p>' + '</body></html>', 'html', 'utf-8') # 发件人 msg['From'] = _format_addr('Python爱好者<%s>' % from_addr) # 收件人 msg['To'] = _format_addr('管理员<%s>' % to_addr) # 主题 msg['Subject'] = Header('来自SMTP的问候...', 'utf-8').encode() # 邮件对象 msg = MIMEMultipart() msg = MIMEMultipart('alternative') msg['From'] = _format_addr('Python爱好者<%s>' % from_addr) msg['To'] = _format_addr('管理员<%s>' % to_addr) msg['Subject'] = Header('来自SMTP的问候。。。', 'utf-8').encode() # 邮件正文是MIMEText: msg.attach(MIMEText('send with file...', 'plain', 'utf-8')) msg.attach(MIMEText('<html><body><h1>Hello</h1>' + '<p><img src="cid:0"></p>' + '</body></html>', 'html', 'utf-8')) with open('/Users/doc88/Desktop/banner.png', 'rb') as f: # 设置附件和MIME，从本地读取一个图片 mime = MIMEBase('image', 'jpeg', filename='banner.png') # 加上必要的头信息： mime.add_header('Content-Disposition', 'attachment', filename='banner.png') mime.add_header('Content-ID', '<0>') mime.add_header('X-Attachment-Id', '0') # 把附件的内容读进来： mime.set_payload(f.read()) # 用Base64编码： encoders.encode_base64(mime) # 添加到MIMEMultipart: msg.attach(mime) try: # 发送邮件 # 创建服务器 server = smtplib.SMTP_SSL(smtp_server, 465) # 打印出和SMTP服务器所有的交互信息 server.set_debuglevel(1) # 登录服务器 server.login(from_addr, password) # 发送邮件 # 发件账户，收件账户，内容 server.sendmail(from_addr, [to_addr], msg.as_string()) # 退出服务器 server.quit() print('Success!') except smtplib.SMTPException as e: print('Fail,%s' % e)

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0.02168

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2,371

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0.043478

0

null

0

null

0

1

0

7bcea7388e12344b8c218c07128ff9fb1cd5ed79

1,519

py

Python

yat-master/pymodule/common_sql/plain_parser/reader.py

opengauss-mirror/Yat

aef107a8304b94e5d99b4f1f36eb46755eb8919e

[ "MulanPSL-1.0" ]

null

yat-master/pymodule/common_sql/plain_parser/reader.py

opengauss-mirror/Yat

aef107a8304b94e5d99b4f1f36eb46755eb8919e

[ "MulanPSL-1.0" ]

null

yat-master/pymodule/common_sql/plain_parser/reader.py

opengauss-mirror/Yat

aef107a8304b94e5d99b4f1f36eb46755eb8919e

[ "MulanPSL-1.0" ]

null

#!/usr/bin/env python # encoding=utf-8 """ Copyright (c) 2021 Huawei Technologies Co.,Ltd. openGauss is licensed under Mulan PSL v2. You can use this software according to the terms and conditions of the Mulan PSL v2. You may obtain a copy of Mulan PSL v2 at: http://license.coscl.org.cn/MulanPSL2 THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE. See the Mulan PSL v2 for more details. """ class PlainReader: def __init__(self, content): if isinstance(content, (str, )): self.content = content.splitlines(keepends=False) else: self.content = content self.content_iter = iter(self.content) self._cache = None def next_line(self): if self._cache is None: return next(self.content_iter) else: swap = self._cache self._cache = None return swap def top_line(self): if self._cache is None: self._cache = next(self.content_iter) return self._cache def skip_line(self): if self._cache is None: next(self.content_iter) else: self._cache = None def has_next(self): try: if self._cache is None: self._cache = next(self.content_iter) return True except StopIteration: return False

27.125

84

0.623436

202

1,519

4.569307

0.485149

0.107259

0.081257

0.056338

0.223185

0.173348

0.110509

0

0.009434

0.302172

1,519

55

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27.618182

0.861321

0.35813

0

0.387097

0

1

0.16129

false

0

0.354839

0

null

0

null

0

1

0

c8730231294cec0e238e9725d099edb7ac1ec02d

7,359

py

Python

compecon/basisSpline.py

daniel-schaefer/CompEcon-python

d3f66e04a7e02be648fc5a68065806ec7cc6ffd6

[ "MIT" ]

null

compecon/basisSpline.py

daniel-schaefer/CompEcon-python

d3f66e04a7e02be648fc5a68065806ec7cc6ffd6

[ "MIT" ]

null

compecon/basisSpline.py

daniel-schaefer/CompEcon-python

d3f66e04a7e02be648fc5a68065806ec7cc6ffd6

[ "MIT" ]

1

2021-06-01T03:47:35.000Z

import numpy as np from scipy.sparse import csc_matrix, diags, tril from .basis import Basis __author__ = 'Randall' # TODO: complete this class # todo: compare performance of csr_matrix and csc_matrix to deal with sparse interpolation operators # fixme: interpolation is 25 slower than in matlab when 2 dimensions!! 2x slower with only one class BasisSpline(Basis): def __init__(self, *args, k=3, **kwargs): nargs = len(args) if nargs == 1: if isinstance(args[0], tuple): breaks = [np.sort(br) for br in args[0]] n = np.array([br.size + k - 1 for br in breaks]) a = np.array([br[0] for br in breaks]) b = np.array([br[-1] for br in breaks]) kwargs['nodetype'] = 'user' else: raise ValueError("If only 1 positional argument is provided, it must be a tuple of 'd' array-like, " + "each of them containing the breaks for one dimension.") elif nargs == 3: n, a, b = np.broadcast_arrays(*np.atleast_1d(*args)) breaks = [np.linspace(aa, bb, nn + 1 - k) for aa, bb, nn in zip(a, b, n)] kwargs['nodetype'] = 'canonical' else: txt = 'Either 1 or 3 positional arguments must be provided\n' txt += '\t1 argument -> break points\n' txt += '\t3 argument -> n, a, b' raise ValueError(txt) ''' Check inputs ''' assert ((k > 0) and type(k) is int), 'k must be a positive integer' assert np.all(n > k), 'number of nodes must exceed order of spline' assert np.all([(br.size > 1) for br in breaks]), 'breakpoint sequence must contain at least two elements' ''' Make instance ''' kwargs['basistype'] = 'spline' super().__init__(n, a, b, **kwargs) self.k = k self.breaks = breaks self._set_nodes() def _set_nodes(self): """ Sets the basis nodes :return: None """ n = self.n k = self.k self._nodes = list() for i in range(self.d): x = np.cumsum(self._augbreaks(i, k)) x = (x[k : n[i] + k] - x[:n[i]]) / k x[0] = self.a[i] x[-1] = self.b[i] self._nodes.append(x) self._expand_nodes() def _augbreaks(self, i, m,): aa = np.repeat(self.a[i], m) bb = np.repeat(self.b[i], m) return np.concatenate((aa, self.breaks[i], bb)) def _update_diff_operators(self, i, order): """ Updates the list _D of differentiation operators :param order: order of required derivative :return: None """ keys = set(self._diff_operators[i].keys()) if (order in keys) or (order == 0): return # Use previously stored values if available n = self.n[i] a = self.a[i] b = self.b[i] k = self.k assert order <= k, 'order must be less or equal to k' kk = k - 1 - min(order, 0) augbreaks = self._augbreaks(i, kk) if order > 0: def sptemp(j): temp = np.atleast_2d((k + 1 - j) / (augbreaks[k:(n + k - j)] - augbreaks[(j - 1):(n - 1)])) return diags((-temp, temp), [0, 1], (n - j, n + 1 - j)) missing_keys = set(range(1, order + 1)) - keys if 1 in missing_keys: self._diff_operators[i][1] = sptemp(1) missing_keys -= {1} missing_keys = list(missing_keys) missing_keys.sort(reverse=True) while missing_keys: j = missing_keys.pop() self._diff_operators[i][j] = np.dot(sptemp(j), self._diff_operators[i][j - 1]) else: def sptemp(j): temp = (augbreaks[(kk + 1):(kk + n - j)] - augbreaks[(kk - k + j + 1):(kk + n - k)]) / (k - j) return tril(np.tile(temp, (n - j, 1)), -1) missing_keys = set(range(order, 0)) - keys if -1 in missing_keys: self._diff_operators[i][-1] = sptemp(-1) missing_keys -= {-1} missing_keys = list(missing_keys) missing_keys.sort(reverse=False) while missing_keys: j = missing_keys.pop() self._diff_operators[i][j] = sptemp(j) * self._diff_operators[i][j + 1] """ Interpolation methods """ def _phi1d(self, i, x=None, order=0): """ Computes interpolation matrices for given data x and order of differentiation 'order' (integration if negative) :param x: evaluation points (defaults to nodes) :param order: a list of orders for differentiation (+) / integration (-) :return a: dictionary with interpolation matrices, keys given by unique elements of order. Example: Create a basis with 5 nodes, get the interpolation matrix evaluated at 20 points:: n, a, b = 5, 0, 4 x = numpy.linspace(a,b, 20) Phi = BasisSpline(n, a, b) Phi.Phi(x) Phi(x) Calling an instance directly (as in the last line) is equivalent to calling the interpolation method. """ n = self.n[i] k = self.k if order is None: order = 0 order = np.atleast_1d(order).flatten() assert np.max(order) < k, 'Derivatives defined for order less than k' nn = n + np.maximum(0, -np.min(order)) # todo review why nn is not used, weird # Check for x argument xIsProvided = (x is not None) x = x.flatten() if xIsProvided else self._nodes[i] nx = x.size minorder = np.min(order) kaug = k - minorder augbreaks = self._augbreaks(i, kaug) ind = self._lookup(augbreaks, x) # Recursively determine the values of a k-order basis matrix. # This is placed in an (m x k+1-order) matrix bas = np.zeros((kaug + 1, nx)) bas[0] = 1 Phidict = dict() for j in range(1, kaug + 1): for jj in range(j, 0, -1): b0 = augbreaks[ind + jj - j] b1 = augbreaks[ind + jj] temp = bas[jj - 1] / (b1 - b0) bas[jj] = (x - b0) * temp + bas[jj] bas[jj - 1] = (b1 - x) * temp # as now contains the order j spline basis ii = np.where((k - j) == order)[0] if ii.size > 0: ii = ii[0] oi = order[ii] # Put values in appropriate columns of a sparse matrix r = np.tile(np.arange(nx), k - oi + 1) c = np.atleast_2d(np.arange(oi - k, 1)).T + np.atleast_2d(ind) c = (c - (oi - minorder)).flatten() data = bas[:k - oi + 1].flatten() Phidict[oi] = csc_matrix((data, (r, c)), (nx, n-oi)) if oi: # If needed compute derivative or anti-derivative Phidict[oi] = Phidict[oi] * self._diff(i, oi) # todo: review, i think this will return only unique values Phi = np.array([Phidict[k] for k in order]) return Phi

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null

0

null

0

1

0

c873b44db1fbe52cb97100b99eb41550c409cc9f

2,279

py

Python

vendors/rez-2.23.1-py2.7/rez/backport/shutilwhich.py

ColinKennedy/tk-config-default2-respawn

855fb8033daa549b92615792442f19a7f9c4f55c

[ "Linux-OpenIB" ]

4

2019-01-11T03:41:28.000Z

2019-09-12T06:57:17.000Z

vendors/rez-2.23.1-py2.7/rez/backport/shutilwhich.py

ColinKennedy/tk-config-default2-respawn

855fb8033daa549b92615792442f19a7f9c4f55c

[ "Linux-OpenIB" ]

null

vendors/rez-2.23.1-py2.7/rez/backport/shutilwhich.py

ColinKennedy/tk-config-default2-respawn

855fb8033daa549b92615792442f19a7f9c4f55c

[ "Linux-OpenIB" ]

2

2019-01-10T05:00:18.000Z

2020-02-15T16:32:56.000Z

import os import os.path import sys # Modified version from Python-3.3. 'env' environ dict override has been added. def which(cmd, mode=os.F_OK | os.X_OK, env=None): """Given a command, mode, and a PATH string, return the path which conforms to the given mode on the PATH, or None if there is no such file. `mode` defaults to os.F_OK | os.X_OK. `env` defaults to os.environ, if not supplied. """ # Check that a given file can be accessed with the correct mode. # Additionally check that `file` is not a directory, as on Windows # directories pass the os.access check. def _access_check(fn, mode): return (os.path.exists(fn) and os.access(fn, mode) and not os.path.isdir(fn)) # Short circuit. If we're given a full path which matches the mode # and it exists, we're done here. if _access_check(cmd, mode): return cmd if env is None: env = os.environ path = env.get("PATH", os.defpath).split(os.pathsep) if sys.platform == "win32": # The current directory takes precedence on Windows. if not os.curdir in path: path.insert(0, os.curdir) # PATHEXT is necessary to check on Windows. default_pathext = \ '.COM;.EXE;.BAT;.CMD;.VBS;.VBE;.JS;.JSE;.WSF;.WSH;.MSC' pathext = env.get("PATHEXT", default_pathext).split(os.pathsep) # See if the given file matches any of the expected path extensions. # This will allow us to short circuit when given "python.exe". matches = [cmd for ext in pathext if cmd.lower().endswith(ext.lower())] # If it does match, only test that one, otherwise we have to try # others. files = [cmd] if matches else [cmd + ext.lower() for ext in pathext] else: # On other platforms you don't have things like PATHEXT to tell you # what file suffixes are executable, so just pass on cmd as-is. files = [cmd] seen = set() for dir in path: dir = os.path.normcase(dir) if not dir in seen: seen.add(dir) for thefile in files: name = os.path.join(dir, thefile) if _access_check(name, mode): return name return None

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0.289162

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null

0

null

0

1

0

c87b5c6d8dff26ac4e6274273976c58563c8553b

13,380

py

Python

clustering/runner.py

kburnik/naps-clustering

8ceaad61e7f1c3d76ad9e7c7491b705b936a6f19

[ "MIT" ]

null

clustering/runner.py

kburnik/naps-clustering

8ceaad61e7f1c3d76ad9e7c7491b705b936a6f19

[ "MIT" ]

null

clustering/runner.py

kburnik/naps-clustering

8ceaad61e7f1c3d76ad9e7c7491b705b936a6f19

[ "MIT" ]

null

"""Class with high-level methods for processing NAPS and NAPS BE datasets.""" from config import DATA_NAPS_BE_ALL from lib import partition_naps from lib import plot from lib import plot_clusters from lib import plot_clusters_with_probability from lib import plot_setup from lib import read_naps from lib import read_naps_be from lib import reindex_partitions import json import matplotlib.pyplot as plt import numpy as np import os import scipy import sklearn class Runner: """Provides methods for processing NAPS with the clustering algorithm.""" def __init__(self, input_data, config): self.input_data = input_data self.config = config def compute_raw_partitions(self): """Compute the k-means and returns the cluster index for each sample.""" kmeans = partition_naps( samples=self.input_data.samples, n_clusters=self.config.n_clusters) return kmeans.labels_ def compute_stable_partitions(self): """Same as compute_raw_partition, but with stable index coloring.""" return reindex_partitions( samples=self.input_data.samples, indices=self.compute_raw_partitions()) def compute_average_partitions(self): """ Repeats the stable colored k-means and computes the average membership of each input sample. For each sample, return the percentage of membership to a cluster, as an array of size n_clusters (Monte-Carlo simulation). """ cluster_hist = np.zeros( (self.input_data.size, self.config.n_clusters)) for k in range(self.config.n_iterations): indices = self.compute_stable_partitions() for i, cluster in enumerate(indices): cluster_hist[i][cluster] += 1 return np.divide(cluster_hist, self.config.n_iterations) def compute_stable_argmax_partitions(self): """Computes the stable partitions using the Monte-Carlo simulation, and selects the most frequent cluster based on the probability (argmax).""" indices_prob = self.compute_average_partitions() self._display_undecided_index_count(indices_prob) return np.argmax(indices_prob, axis=1) def compute_naps_results(self, num_samples=5, prefix_dir='naps-clustering'): """Saves the clustering results and plots the NAPS clusters.""" with self.config.fork() as config: p = config.n_iterations for k in range(*config.n_clusters_range): config.n_clusters = k # Partition with caching. indices = np.array(self.cached( func=self.compute_stable_argmax_partitions, prefix_dir=prefix_dir, name='naps-clustering-k=%d-p=%d' % (k, p))) # Split the input data. partitioned_data = self.partition_input_data(indices) # Save the separated datasets. partitions_filename = self.join_path( prefix_dir, 'naps-clustering-partitioned-full-k=%d-p=%d.csv' % (k, p)) with open(partitions_filename, "w") as f: for cluster, data in partitioned_data.items(): f.write(data.serialize() + "\n") # Save the chosen samples. samples_filename = self.join_path( prefix_dir, 'naps-clustering-partitioned-samples-k=%d-p=%d.csv' % (k, p)) with open(samples_filename, "w") as f: for cluster, data in partitioned_data.items(): chunk = data.reduce_to_samples(num_samples) f.write(chunk.serialize(";", use_quotes=False) + "\n") self.plot( indices=indices, filename=self.join_path( prefix_dir, 'naps-clustering-k=%d-p=%d.png' % (k, p)), output_action='save') def compute_naps_be_results( self, x_axis, y_axis, num_samples=5, prefix_dir='naps-be-clustering'): """Saves the clustering results and plots the NAPS BE clusters.""" p = self.config.n_iterations k = self.config.n_clusters # Partition with caching. indices = np.array(self.cached( func=self.compute_stable_argmax_partitions, prefix_dir=prefix_dir, name='naps-be-clustering-%s-%s-k=%d-p=%d' % (x_axis, y_axis, k, p))) # Split the input data. partitioned_data = self.partition_input_data(indices) # Save the separated datasets. partitions_filename = self.join_path( prefix_dir, 'naps-be-clustering-partitioned-full-%s-%s-k=%d-p=%d.csv' % ( x_axis, y_axis, k, p)) with open(partitions_filename, "w") as f: for cluster, data in partitioned_data.items(): f.write(data.serialize() + "\n") # Save the chosen samples. samples_filename = self.join_path( prefix_dir, 'naps-be-clustering-partitioned-samples-%s-%s-k=%d-p=%d.csv' % ( x_axis, y_axis, k, p)) with open(samples_filename, "w") as f: for cluster, data in partitioned_data.items(): chunk = data.reduce_to_samples(num_samples) f.write(chunk.serialize(";", use_quotes=False) + "\n") self.plot( indices=indices, filename=self.join_path( prefix_dir, 'naps-be-clustering-%s-%s-k=%d-p=%d.png' % (x_axis, y_axis, k, p)), output_action='save') def compute_stability_error_of_iterations(self): """Computes the stability error curve as a function of number of iterations.""" with self.config.fork() as config: return [ self._compute_stability_error_point(config.n_iterations) for config.n_iterations in range(*config.n_iterations_range) ] def compute_stability_error_of_partition_count(self): """Computes the stability error curve as a function of number of clusters.""" with self.config.fork() as config: return [ self._compute_stability_error_point(config.n_clusters) for config.n_clusters in range(*config.n_clusters_range) ] def partition_input_data(self, indices): """Splits the input data to partitions as defined by the indices.""" return self.input_data.split_on_key(lambda i, row: indices[i]) def plot(self, indices, output_action='save', filename=None): """Plots the clusters.""" if filename is None: # TODO: Add date? filename = self.join_path('out-single-run.png') plot_clusters( indices=indices, input_data=self.input_data, n_clusters=self.config.n_clusters, output_action=output_action, filename=filename) def plot_repeated( self, partition_factory, n_plots=10, name='out', prefix_dir='.'): """ Runs the partition_factory requested number of times, plots and saves the images. """ for i in range(n_plots): self.plot( indices=partition_factory(), output_action='save', filename=self.join_path(prefix_dir, '%s-%02d.png' % (name, i))) def plot_fuzzy(self, prefix_dir='.', name='out-fuzzy-simple'): """Plots the undecidable points.""" indices_prob = np.array(self.cached( func=self.compute_average_partitions, name=name, prefix_dir=prefix_dir)) plot_clusters_with_probability( indices_prob=indices_prob, input_data=self.input_data, plot_fuzzy_simple=True, output_action='save', filename=self.join_path(prefix_dir, '%s.png' % name)) def plot_cluster_number_evaluation_curve( self, evaluate, title, name, score_label, prefix_dir='.'): """Plots the evaluation curve as a function of number of clusters K.""" samples = self.input_data.samples k_range = range(*self.config.n_clusters_range) score = [evaluate(samples, k) for k in k_range] self.save_csv( data=zip(k_range, score), columns=['partition count', score_label], prefix_dir=prefix_dir, name=name) plt.figure(num=None, figsize=(16, 9), dpi=300) plt.title(title) plt.xlabel('partition count') plt.ylabel(score_label) plt.xticks(np.arange(*self.config.n_clusters_range, 2.0)) plt.plot(k_range, score) plt.grid() plt.savefig(self.join_path(prefix_dir, '%s.png' % name)) return plt def plot_stability_error_curve( self, results, title, name, xlabel, ylabel, xticks=200, yticks=5, figsize=(16, 6), dpi=300, prefix_dir='.'): plt.figure(num=None, figsize=figsize, dpi=dpi) plt.title(title) plt.xlabel(xlabel) plt.ylabel(ylabel) plt.xticks(np.arange(0, 1 + max([x for x, y in results]), xticks)) plt.yticks(np.arange(0, 1 + max([y for x, y in results]), yticks)) plt.plot(*zip(*results)) plt.grid() plt.savefig( self.join_path(prefix_dir, '%s.png' % name), bbox_inches='tight') return plt def plot_multiple_cluster_number_evaluation_curves( self, input_data_list, evaluate, n_clusters_range, title, name, score_label, prefix_dir='.'): """Plots the evaluation curve for a given range of K.""" fig, ax = plot_setup() plt.title(title) plt.xlabel('partition count') plt.ylabel(score_label) plt.xticks(np.arange(*n_clusters_range, 2.0)) color = plt.cm.rainbow(np.linspace(0, 1, len(input_data_list))) k_range = range(*n_clusters_range) score_vectors = [] for i, input_data in enumerate(input_data_list): score = [evaluate(input_data.samples, k) for k in k_range] ax.plot(k_range, score, color=color[i], label=input_data.label_name) score_vectors.append(score) score_average = np.average(score_vectors, axis=0) ax.plot(k_range, score_average, color=(0, 0, 0, 1), label="Average") plt.grid() plt.legend() plt.savefig(self.join_path(prefix_dir, '%s.png' % name)) def _compute_stability_error_point(self, variable): """Computes one error point though the given number of evaluation simulations.""" cluster_hist = np.zeros( (self.input_data.size, self.config.n_clusters)) for i in range(self.config.n_evaluations): indices = self.compute_stable_argmax_partitions() for j, cluster in enumerate(indices): cluster_hist[j][cluster] += 1 total_error = self._compute_total_histogram_error( cluster_hist, self.config.n_evaluations) error_point = (variable, total_error) print(error_point) return error_point def cached(self, func, name, prefix_dir='.'): """Runs the provided method using a caching mechanism.""" filename = self.join_path(prefix_dir, '%s.cached-result.json' % name) if os.path.exists(filename): with open(filename, 'r') as f: results = json.load(f) else: results = func() with open(filename, 'w') as f: try: results = results.tolist() except: pass json.dump(results, f) return results def save_csv( self, data, columns, name, delimiter=';', prefix_dir='.', extension='.csv'): """Saves data into a CSV file.""" filename = self.join_path(prefix_dir, name + extension); def encode(item): return str(item) with open(filename, 'w') as f: f.write(delimiter.join(['"%s"' % column for column in columns]) + '\n') for row in data: f.write(delimiter.join([encode(item) for item in row]) + '\n') def join_path(self, *args): """Joins a path for an output file and creates directories if they don't exist.""" filename = os.path.join(self.config.out_dir, *args) dirname = os.path.dirname(filename) if not os.path.exists(dirname): os.makedirs(dirname, 0o755) print("I/O path:", os.path.abspath(filename)) return filename def _compute_total_histogram_error(self, hist, n_evaluations): """Computes the total error from the histogram of point cluster membership.""" hist[hist == n_evaluations] = 0 sums_per_row = (hist != 0).sum(1) return sums_per_row.sum() - np.count_nonzero(sums_per_row) def _display_undecided_index_count(self, indices_prob): """Counts and prints out how many points have appeared at the edges of clusters (the undecidability region).""" print("Undecided count:", len(list(filter( lambda row: np.max(row) == 0.5, indices_prob)))) @staticmethod def compute_silhouette_score(samples, n_clusters): """Computes the silhouette score for a provided clustering result.""" kmeans = partition_naps(samples, n_clusters) return sklearn.metrics.silhouette_score( samples, kmeans.labels_, metric='euclidean') @staticmethod def stream_naps_be( config, x_dimensions, y_dimensions, x_dimension_names, y_dimension_names): """Generates datasets for chosen pairs of dimensions.""" for i in range(len(x_dimensions)): for j in range(len(y_dimensions)): if len(x_dimensions) == len(y_dimensions) and j <= i: continue x_axis, y_axis = x_dimensions[i], y_dimensions[j] x_name, y_name = x_dimension_names[i], y_dimension_names[j] input_data = read_naps_be( DATA_NAPS_BE_ALL, label_field="label", x_axis=x_axis, y_axis=y_axis, label_name="Label", x_name=x_name, y_name=y_name) yield Runner(input_data=input_data, config=config), x_axis, y_axis

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c87d1cba2782a99d03e9fe56c04a83d537ce2a1a

2,936

py

Python

Algorithms_medium/1618. Maximum Font to Fit a Sentence in a Screen.py

VinceW0/Leetcode_Python_solutions

09e9720afce21632372431606ebec4129eb79734

[ "Xnet", "X11" ]

4

2020-08-11T20:45:15.000Z

2021-03-12T00:33:34.000Z

Algorithms_medium/1618. Maximum Font to Fit a Sentence in a Screen.py

VinceW0/Leetcode_Python_solutions

09e9720afce21632372431606ebec4129eb79734

[ "Xnet", "X11" ]

null

Algorithms_medium/1618. Maximum Font to Fit a Sentence in a Screen.py

VinceW0/Leetcode_Python_solutions

09e9720afce21632372431606ebec4129eb79734

[ "Xnet", "X11" ]

null

""" 1618. Maximum Font to Fit a Sentence in a Screen Medium You are given a string text. We want to display text on a screen of width w and height h. You can choose any font size from array fonts, which contains the available font sizes in ascending order. You can use the FontInfo interface to get the width and height of any character at any available font size. The FontInfo interface is defined as such: interface FontInfo { // Returns the width of character ch on the screen using font size fontSize. // O(1) per call public int getWidth(int fontSize, char ch); // Returns the height of any character on the screen using font size fontSize. // O(1) per call public int getHeight(int fontSize); } The calculated width of text for some fontSize is the sum of every getWidth(fontSize, text[i]) call for each 0 <= i < text.length (0-indexed). The calculated height of text for some fontSize is getHeight(fontSize). Note that text is displayed on a single line. It is guaranteed that FontInfo will return the same value if you call getHeight or getWidth with the same parameters. It is also guaranteed that for any font size fontSize and any character ch: getHeight(fontSize) <= getHeight(fontSize+1) getWidth(fontSize, ch) <= getWidth(fontSize+1, ch) Return the maximum font size you can use to display text on the screen. If text cannot fit on the display with any font size, return -1. Example 1: Input: text = "helloworld", w = 80, h = 20, fonts = [6,8,10,12,14,16,18,24,36] Output: 6 Example 2: Input: text = "leetcode", w = 1000, h = 50, fonts = [1,2,4] Output: 4 Example 3: Input: text = "easyquestion", w = 100, h = 100, fonts = [10,15,20,25] Output: -1 Constraints: 1 <= text.length <= 50000 text contains only lowercase English letters. 1 <= w <= 107 1 <= h <= 104 1 <= fonts.length <= 105 1 <= fonts[i] <= 105 fonts is sorted in ascending order and does not contain duplicates. """ # """ # This is FontInfo's API interface. # You should not implement it, or speculate about its implementation # """ #class FontInfo(object): # Return the width of char ch when fontSize is used. # def getWidth(self, fontSize, ch): # """ # :type fontSize: int # :type ch: char # :rtype int # """ # # def getHeight(self, fontSize): # """ # :type fontSize: int # :rtype int # """ class Solution: def maxFont(self, text: str, w: int, h: int, fonts: List[int], fontInfo : 'FontInfo') -> int: def check(fs): if fontInfo.getHeight(fs) > h: return False if sum(fontInfo.getWidth(fs, c) for c in text) > w: return False return True l, r = -1, len(fonts) - 1 while l < r: m = r - (r - l) // 2 if check(fonts[m]): l = m else: r = m - 1 return fonts[l] if l > -1 else -1

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c880853878e1cff80cb76bcab65d294bfff7d0f4

6,407

py

Python

climateeconomics/sos_wrapping/sos_wrapping_dice/tempchange/tempchange_discipline.py

os-climate/witness-core

3ef9a44d86804c5ad57deec3c9916348cb3bfbb8

[ "MIT", "Apache-2.0", "BSD-3-Clause" ]

1

2022-01-14T06:37:42.000Z

climateeconomics/sos_wrapping/sos_wrapping_dice/tempchange/tempchange_discipline.py

os-climate/witness-core

3ef9a44d86804c5ad57deec3c9916348cb3bfbb8

[ "MIT", "Apache-2.0", "BSD-3-Clause" ]

null

climateeconomics/sos_wrapping/sos_wrapping_dice/tempchange/tempchange_discipline.py

os-climate/witness-core

3ef9a44d86804c5ad57deec3c9916348cb3bfbb8

[ "MIT", "Apache-2.0", "BSD-3-Clause" ]

null

''' Copyright 2022 Airbus SAS 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 sos_trades_core.execution_engine.sos_discipline import SoSDiscipline from climateeconomics.core.core_dice.tempchange_model import TempChange from sos_trades_core.tools.post_processing.charts.two_axes_instanciated_chart import InstanciatedSeries, TwoAxesInstanciatedChart from sos_trades_core.tools.post_processing.charts.chart_filter import ChartFilter import pandas as pd class TempChangeDiscipline(SoSDiscipline): " Temperature evolution" # ontology information _ontology_data = { 'label': 'Temperature Change DICE Model', 'type': 'Research', 'source': 'SoSTrades Project', 'validated': '', 'validated_by': 'SoSTrades Project', 'last_modification_date': '', 'category': '', 'definition': '', 'icon': 'fas fa-thermometer-three-quarters fa-fw', 'version': '', } DESC_IN = { 'year_start': {'type': 'int', 'visibility': 'Shared', 'namespace': 'ns_dice'}, 'year_end': {'type': 'int', 'visibility': 'Shared', 'namespace': 'ns_dice'}, 'time_step': {'type': 'int', 'visibility': 'Shared', 'namespace': 'ns_dice'}, 'init_temp_ocean': {'type': 'float', 'default': 0.00687}, 'init_temp_atmo': {'type': 'float', 'default': 0.85}, 'eq_temp_impact': {'type': 'float', 'default': 3.1}, 'init_forcing_nonco': {'type': 'float', 'default': 0.5}, 'hundred_forcing_nonco': {'type': 'float', 'default': 1 }, 'climate_upper': {'type': 'float', 'default': 0.1005}, 'transfer_upper': {'type': 'float', 'default': 0.088}, 'transfer_lower': {'type': 'float', 'default': 0.025}, 'forcing_eq_co2': {'type': 'float', 'default': 3.6813}, 'lo_tocean': {'type': 'float', 'default': -1}, 'up_tatmo': {'type': 'float', 'default': 12}, 'up_tocean': {'type': 'float', 'default' : 20}, 'carboncycle_df': {'type': 'dataframe', 'visibility': 'Shared', 'namespace': 'ns_scenario'}} DESC_OUT = { 'temperature_df': {'type': 'dataframe', 'visibility': 'Shared', 'namespace': 'ns_scenario'}} _maturity = 'Research' def run(self): ''' model execution ''' # get inputs in_dict = self.get_sosdisc_inputs() # carboncycle_df = in_dict.pop('carboncycle_df') # model execution model = TempChange() temperature_df = model.compute(in_dict) # store output data out_dict = {"temperature_df": temperature_df} self.store_sos_outputs_values(out_dict) def get_chart_filter_list(self): # For the outputs, making a graph for tco vs year for each range and for specific # value of ToT with a shift of five year between then chart_filters = [] chart_list = ['temperature evolution'] # First filter to deal with the view : program or actor chart_filters.append(ChartFilter( 'Charts', chart_list, chart_list, 'charts')) return chart_filters def get_post_processing_list(self, chart_filters=None): # For the outputs, making a graph for tco vs year for each range and for specific # value of ToT with a shift of five year between then instanciated_charts = [] # Overload default value with chart filter if chart_filters is not None: for chart_filter in chart_filters: if chart_filter.filter_key == 'charts': chart_list = chart_filter.selected_values if 'temperature evolution' in chart_list: to_plot = ['temp_atmo', 'temp_ocean'] temperature_df = self.get_sosdisc_outputs('temperature_df') temperature_df = resize_df(temperature_df) legend = {'temp_atmo': 'atmosphere temperature', 'temp_ocean': 'ocean temperature'} years = list(temperature_df.index) year_start = years[0] year_end = years[len(years) - 1] max_value = 0 min_value = 0 for key in to_plot: max_value = max(temperature_df[key].values.max(), max_value) min_value = min(temperature_df[key].values.min(), min_value) chart_name = 'temperature evolution over the years' new_chart = TwoAxesInstanciatedChart('years', 'temperature evolution (degrees Celsius above preindustrial)', [year_start - 5, year_end + 5], [ min_value * 0.9, max_value * 1.1], chart_name) for key in to_plot: visible_line = True ordonate_data = list(temperature_df[key]) new_series = InstanciatedSeries( years, ordonate_data, legend[key], 'lines', visible_line) new_chart.series.append(new_series) instanciated_charts.append(new_chart) return instanciated_charts def resize_df(df): index = df.index i = len(index) - 1 key = df.keys() to_check = df.loc[index[i], key[0]] while to_check == 0: i = i - 1 to_check = df.loc[index[i], key[0]] size_diff = len(index) - i new_df = pd.DataFrame() if size_diff == 0: new_df = df else: for element in key: new_df[element] = df[element][0:i + 1] new_df.index = index[0: i + 1] return new_df def resize_array(array): i = len(array) - 1 to_check = array[i] while to_check == 0: i = i - 1 to_check = to_check = array[i] size_diff = len(array) - i new_array = array[0:i] return new_array def resize_index(index, array): l = len(array) new_index = index[0:l] return new_index

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null

0

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0

c8813251417f083ef4764a6d0d80104c34d5a26a

56,368

py

Python

pymkm/pymkm_app.py

Guibod/pymkm

58ac805c8072979f3059c7faafc264386ae98141

[ "MIT" ]

null

pymkm/pymkm_app.py

Guibod/pymkm

58ac805c8072979f3059c7faafc264386ae98141

[ "MIT" ]

null

pymkm/pymkm_app.py

Guibod/pymkm

58ac805c8072979f3059c7faafc264386ae98141

[ "MIT" ]

null

#!/usr/bin/env python3 """ The PyMKM example app. """ __author__ = "Andreas Ehrlund" __version__ = "2.0.4" __license__ = "MIT" import os import csv import json import shelve import logging import logging.handlers import pprint import uuid import sys from datetime import datetime import micromenu import progressbar import requests import tabulate as tb from pkg_resources import parse_version from .pymkm_helper import PyMkmHelper from .pymkmapi import PyMkmApi, CardmarketError class PyMkmApp: logger = None def __init__(self, config=None): self.logger = logging.getLogger(__name__) # self.logger.setLevel(logging.INFO) formatter = logging.Formatter( "%(asctime)s - %(name)s - %(levelname)s - %(message)s" ) fh = logging.handlers.RotatingFileHandler( f"log_pymkm.log", maxBytes=500000, backupCount=2 ) fh.setLevel(logging.WARNING) fh.setFormatter(formatter) self.logger.addHandler(fh) sh = logging.StreamHandler() sh.setLevel(logging.ERROR) # This gets outputted to stdout sh.setFormatter(formatter) self.logger.addHandler(sh) if config is None: self.logger.debug(">> Loading config file") try: self.config = json.load(open("config.json")) # Sync missing attributes to active config template_config = json.load(open("config_template.json")) template_config.update(self.config) self.config = template_config except FileNotFoundError: self.logger.error( "You must copy config_template.json to config.json and populate the fields." ) sys.exit(0) # if no UUID is present, generate one and add it to the file if "uuid" not in self.config: self.config["uuid"] = str(uuid.uuid4()) with open("config.json", "w") as json_config_file: json.dump(self.config, json_config_file, indent=2) else: self.config = config self.DEV_MODE = False try: self.DEV_MODE = self.config["dev_mode"] except Exception as err: pass fh.setLevel(self.config["log_level"]) self.logger.setLevel(self.config["log_level"]) self.api = PyMkmApi(config=self.config) def report(self, command): uuid = self.config["uuid"] # if self.config["reporting"] and not self.DEV_MODE: # try: # r = requests.post( # "https://andli-stats-server.herokuapp.com/pymkm", # json={"command": command, "uuid": uuid, "version": __version__}, # ) # except Exception as err: # self.logger.error("Connection error to stats server.") # pass pass def check_latest_version(self): latest_version = None try: r = requests.get("https://api.github.com/repos/andli/pymkm/releases/latest") latest_version = r.json()["tag_name"] except Exception as err: self.logger.error("Connection error with github.com") if parse_version(__version__) < parse_version(latest_version): return f"Go to Github and download version {latest_version}! It's better!" else: return None def start(self, args=None): if not len(sys.argv) > 1: # if args have been passed while True: top_message = self.check_latest_version() if hasattr(self, "DEV_MODE") and self.DEV_MODE: top_message = "dev mode" menu = micromenu.Menu( f"PyMKM {__version__}", top_message, f"API calls used today: {self.api.requests_count}/{self.api.requests_max}", cycle=False, ) menu.add_function_item( "Update stock prices", self.update_stock_prices_to_trend, {"api": self.api, "cli_called": False}, ) menu.add_function_item( "Update price for a product", self.update_product_to_trend, {"api": self.api}, ) menu.add_function_item( "List competition for a product", self.list_competition_for_product, {"api": self.api}, ) menu.add_function_item( "Find deals from a user", self.find_deals_from_user, {"api": self.api}, ) menu.add_function_item( f"Show top {self.config['show_top_x_expensive_items']} expensive items in stock", self.show_top_expensive_articles_in_stock, { "num_articles": self.config["show_top_x_expensive_items"], "api": self.api, }, ) menu.add_function_item( "Wantslists cleanup suggestions", self.clean_purchased_from_wantslists, {"api": self.api}, ) menu.add_function_item( "Show account info", self.show_account_info, {"api": self.api} ) menu.add_function_item( "Clear entire stock (WARNING)", self.clear_entire_stock, {"api": self.api}, ) menu.add_function_item( f"Import stock from {self.config['csv_import_filename']}", self.import_from_csv, {"api": self.api}, ) menu.add_function_item( f"Track price data to {self.config['csv_prices_filename']}", self.track_prices_to_csv, {"api": self.api}, ) if self.DEV_MODE: menu.add_function_item( f"⚠ Check product id", self.check_product_id, {"api": self.api}, ) menu.add_function_item( f"⚠ Add fake stock", self.add_fake_stock, {"api": self.api}, ) if self.api.requests_count < self.api.requests_max: break_signal = menu.show() else: menu.print_menu() self.logger.error("Out of quota, exiting app.") sys.exit(0) if break_signal: break else: # command line interface if args.price_check_wantslist: self.track_prices_to_csv( self.api, args.price_check_wantslist, args.cached ) if args.update_stock: self.update_stock_prices_to_trend( self.api, args.update_stock, args.cached, args.partial ) def check_product_id(self, api): """ Dev function check on a product id. """ pid = int(PyMkmHelper.prompt_string("pid")) product_json = api.get_product(pid) del product_json["product"]["reprint"] del product_json["product"]["links"] pp = pprint.PrettyPrinter() pp.pprint(product_json) def add_fake_stock(self, api): """ Dev function to add fake stock. """ range_start = int(PyMkmHelper.prompt_string("Range pid start")) range_end = int(PyMkmHelper.prompt_string("Range pid end")) if PyMkmHelper.prompt_bool("Sure?"): print("Adding fake stock...") product_list = [] for product_no in range(range_start, range_end): product_list.append( { "idProduct": product_no, "idLanguage": 1, "count": 1, "price": 1, "comments": "TEST ARTICLE DO NOT BUY", "condition": "PO", "isFoil": "false", } ) api.add_stock(product_list) def clean_json_for_upload(self, not_uploadable_json): for entry in not_uploadable_json: del entry["price_diff"] del entry["old_price"] del entry["name"] return not_uploadable_json def update_stock_prices_to_trend(self, api, cli_called, cached=None, partial=0): """ This function updates all prices in the user's stock to TREND. """ self.report("update stock price to trend") stock_list = self.get_stock_as_array(self.api, cli_called, cached) already_checked_articles = PyMkmHelper.read_from_cache( self.config["local_cache_filename"], "partial_updated" ) if already_checked_articles: print( f"{len(already_checked_articles)} articles found in previous updates, ignoring those." ) partial_stock_update_size = 0 if partial > 0: partial_stock_update_size = partial elif not cli_called: partial_status_string = "" if already_checked_articles: partial_status_string = ( f"({len(already_checked_articles)}/{len(stock_list)} done)" ) partial_stock_update_size = PyMkmHelper.prompt_string( f"Partial update? {partial_status_string} \n" + "If so, enter number of cards (or press Enter to update all remaining stock)" ) if partial_stock_update_size != "": partial_stock_update_size = int(partial_stock_update_size) else: partial_stock_update_size = 0 if cli_called or self.config["never_undercut_local_market"]: undercut_local_market = False else: undercut_local_market = PyMkmHelper.prompt_bool( "Try to undercut local market? (slower, more requests)" ) uploadable_json, checked_articles = self.calculate_new_prices_for_stock( stock_list, undercut_local_market, partial_stock_update_size, already_checked_articles, api=self.api, ) cache_size = 0 if checked_articles: cache_size = PyMkmHelper.append_to_cache( self.config["local_cache_filename"], "partial_updated", checked_articles, ) if cache_size == len(stock_list): PyMkmHelper.clear_cache( self.config["local_cache_filename"], "partial_updated" ) print( f"Entire stock updated in partial updates. Partial update data cleared." ) if len(uploadable_json) > 0: self.display_price_changes_table(uploadable_json) if cli_called or PyMkmHelper.prompt_bool( "Do you want to update these prices?" ): print("Updating prices...") api.set_stock(uploadable_json) print("Prices updated.") else: print("Prices not updated.") else: print("No prices to update.") self.logger.debug("-> update_stock_prices_to_trend: Done") def __filter(self, article_list): sticky_price_char = self.config["sticky_price_char"] # if we find the sticky price marker, filter out articles def filtered(stock_item): if stock_item.get("comments"): return stock_item.get("comments").startswith(sticky_price_char) else: return False filtered_articles = [x for x in article_list if not filtered(x)] return filtered_articles def update_product_to_trend(self, api): """ This function updates one product in the user's stock to TREND. """ self.report("update product price to trend") search_string = PyMkmHelper.prompt_string("Search product name") try: articles = api.find_stock_article(search_string, 1) except Exception as err: print(err) filtered_articles = self.__filter(articles) ### --- refactor? if not filtered_articles: print(f"{len(articles)} articles found, no editable prices.") else: if len(filtered_articles) > 1: article = self.select_from_list_of_articles(filtered_articles) else: article = filtered_articles[0] found_string = f"Found: {article['product']['enName']}" if article["product"].get("expansion"): found_string += f"[{article['product'].get('expansion')}] " if article["isFoil"]: found_string += f"[foil: {article['isFoil']}] " if article["comments"]: found_string += f"[comment: {article['comments']}] " else: found_string += "." print(found_string) undercut_local_market = PyMkmHelper.prompt_bool( "Try to undercut local market? (slower, more requests)" ) product = self.api.get_product(article["idProduct"]) r = self.update_price_for_article( article, product, undercut_local_market, api=self.api ) if r: self.draw_price_changes_table([r]) print( "\nTotal price difference: {}.".format( str( round( sum(item["price_diff"] * item["count"] for item in [r]), 2, ) ) ) ) if PyMkmHelper.prompt_bool("Do you want to update these prices?"): # Update articles on MKM print("Updating prices...") api.set_stock(self.clean_json_for_upload([r])) print("Price updated.") else: print("Prices not updated.") else: print("No prices to update.") self.logger.debug("-> update_product_to_trend: Done") def list_competition_for_product(self, api): self.report("list competition for product") print("Note: does not support playsets, booster displays etc (yet).") search_string = PyMkmHelper.prompt_string("Search product name") is_foil = PyMkmHelper.prompt_bool("Foil?") try: result = api.find_product( search_string, **{ # 'exact ': 'true', "idGame": 1, "idLanguage": 1, # TODO: Add language support }, ) except CardmarketError as err: self.logger.error(err.mkm_msg()) print(err.mkm_msg()) else: if result: products = result stock_list_products = [ x["idProduct"] for x in self.get_stock_as_array(api=self.api) ] products = [ x for x in products if x["idProduct"] in stock_list_products ] if len(products) == 0: print("No matching cards in stock.") else: if len(products) > 1: product = self.select_from_list_of_products( [i for i in products if i["categoryName"] == "Magic Single"] ) elif len(products) == 1: product = products[0] self.show_competition_for_product( product["idProduct"], product["enName"], is_foil, api=self.api ) else: print("No results found.") self.logger.debug("-> list_competition_for_product: Done") def find_deals_from_user(self, api): self.report("find deals from user") search_string = PyMkmHelper.prompt_string("Enter username") try: result = api.find_user_articles(search_string) except CardmarketError as err: self.logger.error(err.mkm_msg()) print(err.mkm_msg()) else: filtered_articles = [x for x in result if x.get("price") > 1] # language from configured filter language_filter_string = self.config["search_filters"]["language"] if language_filter_string: language_filter_code = api.get_language_code_from_string( language_filter_string ) if language_filter_code: filtered_articles = [ x for x in filtered_articles if x.get("language").get("idLanguage") == language_filter_code ] sorted_articles = sorted( filtered_articles, key=lambda x: x["price"], reverse=True ) print( f"User '{search_string}' has {len(sorted_articles)} articles that meet the criteria." ) num_searches = int( PyMkmHelper.prompt_string( f"Searching top X expensive cards for deals, choose X (1-{len(sorted_articles)})" ) ) if 1 <= num_searches <= len(sorted_articles): table_data = [] products_to_get = [] index = 0 bar = progressbar.ProgressBar(max_value=num_searches) bar.update(index) products_to_get = [ x["idProduct"] for x in sorted_articles[:num_searches] ] products = api.get_items_async("products", products_to_get) for article in sorted_articles[:num_searches]: try: p = next( x for x in products if x["product"]["idProduct"] == article["idProduct"] ) except StopIteration: # Stock item not found in update batch, continuing continue name = p["product"]["enName"] expansion = p["product"].get("expansion") price = float(article["price"]) if expansion: expansion_name = expansion.get("enName") else: expansion_name = "N/A" if article.get("isFoil"): market_price = p["product"]["priceGuide"]["TRENDFOIL"] else: market_price = p["product"]["priceGuide"]["TREND"] if market_price > 0: price_diff = price - market_price percent_deal = round(-100 * (price_diff / market_price)) if price_diff < -1 or percent_deal >= 10: table_data.append( [ name, expansion_name, article.get("condition"), article.get("language").get("languageName"), "\u2713" if article.get("isFoil") else "", "\u2713" if article.get("isPlayset") else "", price, market_price, price_diff, percent_deal, ] ) index += 1 bar.update(index) bar.finish() if table_data: print("Found some interesting prices:") print( tb.tabulate( sorted(table_data, key=lambda x: x[9], reverse=True), headers=[ "Name", "Expansion", "Condition", "Language", "Foil", "Playset", "Price", "Market price", "Market diff", "Deal %", ], tablefmt="simple", ) ) else: print("Found no deals. :(") else: print("Invalid number.") self.logger.debug("-> find_deals_from_user: Done") def show_top_expensive_articles_in_stock(self, num_articles, api): self.report("show top expensive in stock") stock_list = self.get_stock_as_array(api=self.api) table_data = [] total_price = 0 for article in stock_list: name = article["product"]["enName"] expansion = article.get("product").get("expansion") foil = article.get("isFoil") playset = article.get("isPlayset") condition = article.get("condition") language_code = article.get("language") language_name = language_code.get("languageName") price = article.get("price") table_data.append( [ name, expansion, "\u2713" if foil else "", "\u2713" if playset else "", language_name, condition, price, ] ) total_price += price if len(table_data) > 0: print( f"Top {str(num_articles)} most expensive articles in stock (total {len(stock_list)} items):\n" ) print( tb.tabulate( sorted(table_data, key=lambda x: x[6], reverse=True)[:num_articles], headers=[ "Name", "Expansion", "Foil", "Playset", "Language", "Condition", "Price", ], tablefmt="simple", ) ) print("\nTotal stock value: {}".format(str(total_price))) return None def track_prices_to_csv(self, api, wantslist_name=None, cached=False): self.report("track prices") wantslists, wantslists_lists = self.get_wantslists_data(api, cached) if wantslist_name is None: selected_list = self.select_from_list_of_wantslists(wantslists) selected_list_id = selected_list["idWantslist"] else: selected_list_id = next( x["idWantslist"] for x in wantslists if x["name"] == wantslist_name ) # TODO: fails for metaproduct products_to_get = [ x["idProduct"] for x in wantslists_lists[selected_list_id] if x["type"] == "product" ] for x in wantslists_lists[selected_list_id]: if x["type"] == "metaproduct": self.logger.warning( f"Wantslist contains metaproduct ({x['metaproduct']['enName']}) which cannot be used to get prices." ) updated_products = [] try: updated_products = api.get_items_async("products", products_to_get) except Exception as err: pass # Write to CSV: if len(updated_products) > 0: # if blank, then header: datetime, productid, priceguide labels example_priceguide = updated_products[0]["product"]["priceGuide"] priceguide_header_items = [k for k in example_priceguide.keys()] header_list = [ "datetime", "product id", "name", "expansion", ] header_list.extend(priceguide_header_items) data_array = [] for product in updated_products: price_data_exploded = [ k for k in product["product"]["priceGuide"].values() ] data_row = [ datetime.now().isoformat(" "), product["product"]["idProduct"], product["product"]["enName"], product["product"]["expansion"]["enName"], ] data_row.extend(price_data_exploded) data_array.append(data_row) self.write_to_csv(header_list, data_array) def write_to_csv(self, header_list, data_array): if len(data_array) > 0: try: with open( self.config["csv_prices_filename"], "a", newline="", encoding="utf-8", ) as csv_a, open(self.config["csv_prices_filename"], "r",) as csv_r: csv_reader = csv.reader(csv_r) row_count = sum(1 for row in csv_reader) csv_writer = csv.writer(csv_a, delimiter=";") if row_count == 0: csv_writer.writerow(header_list) csv_writer.writerows(data_array) self.logger.debug( f"write_to_csv:: {len(data_array)} lines written to {self.config['csv_prices_filename']}." ) print( f"Wrote {len(data_array)} price updates to {self.config['csv_prices_filename']}." ) except Exception as err: print(err.value) def clean_purchased_from_wantslists(self, api): self.report("clean wantslists") print("This will show items in your wantslists you have already received.") wantslists, wantslists_lists = self.get_wantslists_data(api) try: print("Gettings received orders from Cardmarket...") received_orders = api.get_orders("buyer", "received", start=1) except Exception as err: print(err) if wantslists_lists and received_orders: purchased_product_ids = [] purchased_products = [] for ( order ) in received_orders: # TODO: foil in purchase removes non-foil in wants purchased_product_ids.extend( [i["idProduct"] for i in order.get("article")] ) purchased_products.extend( { "id": i["idProduct"], "foil": i.get("isFoil"), "count": i["count"], "date": order["state"]["dateReceived"], } for i in order.get("article") ) purchased_products = sorted( purchased_products, key=lambda t: t["date"], reverse=True ) total_number_of_items = sum([len(x) for x in wantslists_lists.values()]) index = 0 print("Matching received purchases with wantslists...") bar = progressbar.ProgressBar(max_value=total_number_of_items) matches = [] for key, articles in wantslists_lists.items(): metaproducts_article_list = [ x for x in articles if x.get("type") == "metaproduct" ] metaproducts_to_get = [ x["idMetaproduct"] for x in metaproducts_article_list ] metaproduct_list = api.get_items_async( "metaproducts", metaproducts_to_get ) for article in articles: a_type = article.get("type") a_foil = article.get("isFoil") == True product_matches = [] if a_type == "metaproduct": try: metaproduct = next( x for x in metaproduct_list if x["metaproduct"]["idMetaproduct"] == article["idMetaproduct"] ) except StopIteration: # Stock item not found in update batch, continuing continue metaproduct_product_ids = [ i["idProduct"] for i in metaproduct["product"] ] product_matches = [ i for i in purchased_products if i["id"] in metaproduct_product_ids and i["foil"] == a_foil ] else: a_product_id = article.get("idProduct") product_matches = [ i for i in purchased_products if i["id"] == a_product_id and i["foil"] == a_foil ] if product_matches: match = { "wantlist_id": key, "wantlist_name": wantslists[key], "date": product_matches[0]["date"], "is_foil": a_foil, "count": sum([x.get("count") for x in product_matches]), } if a_type == "product": match.update( { "product_id": a_product_id, "product_name": article.get("product").get( "enName" ), "expansion_name": article.get("product").get( "expansionName" ), } ) elif a_type == "metaproduct": match.update( { "metaproduct_id": article.get("idMetaproduct"), "product_name": article.get("metaproduct").get( "enName" ), "expansion_name": article.get("metaproduct").get( "expansionName" ), } ) matches.append(match) index += 1 bar.update(index) bar.finish() if matches: print( tb.tabulate( [ [ item["wantlist_name"], item["count"], "\u2713" if item["is_foil"] else "", item["product_name"], item["expansion_name"], item["date"], ] for item in matches ], headers=[ "Wantlist", "# bought", "Foil", "Name", "Expansion", "Date (last) received", ], tablefmt="simple", ) ) else: print("No cleanup needed.") else: print("No wantslists or received orders.") def show_account_info(self, api): self.report("show account info") pp = pprint.PrettyPrinter() pp.pprint(api.get_account()) self.logger.debug("-> show_account_info: Done") def clear_entire_stock(self, api): self.report("clear entire stock") stock_list = self.get_stock_as_array(api=self.api) if PyMkmHelper.prompt_bool( "Do you REALLY want to clear your entire stock ({} items)?".format( len(stock_list) ) ): # for article in stock_list: # article['count'] = 0 delete_list = [ {"count": x["count"], "idArticle": x["idArticle"]} for x in stock_list ] print("Clearing stock...") api.delete_stock(delete_list) self.logger.debug("-> clear_entire_stock: done") print("Stock cleared.") PyMkmHelper.clear_cache(self.config["local_cache_filename"], "stock") else: print("Aborted.") def import_from_csv(self, api): self.report("import from csv") print( "Note the required format: Card, Set name, Quantity, Foil, Language (with header row)." ) problem_cards = [] with open(self.config["csv_import_filename"], newline="") as csvfile: csv_reader = csvfile.readlines() index = 0 card_rows = (sum(1 for row in csv_reader)) - 1 bar = progressbar.ProgressBar(max_value=card_rows) self.logger.debug(f"-> import_from_csv: {card_rows} cards in csv file.") csvfile.seek(0) for row in csv_reader: row = row.rstrip() row_array = row.split(",") if index > 0: row_array = [x.strip('"') for x in row_array] try: (name, set_name, count, foil, language, *other) = row_array except Exception as err: problem_cards.append(row_array) else: foil = True if foil.lower() == "foil" else False if not self.match_card_and_add_stock( api, name, set_name, count, foil, language, *other ): problem_cards.append(row_array) bar.update(index) index += 1 bar.finish() if len(problem_cards) > 0: try: with open( "failed_imports.csv", "w", newline="", encoding="utf-8" ) as csvfile: csv_writer = csv.writer(csvfile) csv_writer.writerows(problem_cards) self.logger.debug( f"import_from_csv:: {len(problem_cards)} failed imports." ) print( f"Wrote {len(problem_cards)} failed imports to failed_imports.csv" ) print("Report failures as an issue in the pymkm GitHub repo, please!") except Exception as err: print(err.value) else: print("All cards added successfully") # End of menu item functions ============================================ def get_wantslists_data(self, api, cached=False): # Check for cached wantslists local_wantslists_cache = None PyMkmHelper.read_from_cache(self.config["local_cache_filename"], "wantslists") local_wantslists_lists_cache = None PyMkmHelper.read_from_cache( self.config["local_cache_filename"], "wantslists_lists" ) if local_wantslists_cache: if cached or PyMkmHelper.prompt_bool( f"Cached wantslists ({len(local_wantslists_cache)} items) found, use it? (if not, then it will be cleared)" ): return local_wantslists_cache, local_wantslists_lists_cache else: PyMkmHelper.clear_cache( self.config["local_cache_filename"], "wantslists" ) PyMkmHelper.clear_cache( self.config["local_cache_filename"], "wantslists_lists" ) self.get_wantslists_data(api) else: # no local cache wantslists = [] wantslists_lists = {} try: print("Gettings wantslists from Cardmarket...") wantslists = api.get_wantslists() wantslists_lists = { item["idWantslist"]: api.get_wantslist_items(item["idWantslist"])[ "item" ] for item in wantslists } except Exception as err: print(err) PyMkmHelper.store_to_cache( self.config["local_cache_filename"], "wantslists", wantslists ) PyMkmHelper.store_to_cache( self.config["local_cache_filename"], "wantslists_lists", wantslists_lists, ) return wantslists, wantslists_lists def match_card_and_add_stock( self, api, name, set_name, count, foil, language, *other ): if all(v != "" for v in [name, set_name, count]): try: possible_products = api.find_product(name, idGame="1") # ["product"] except CardmarketError as err: self.logger.error(err.mkm_msg()) print(err.mkm_msg()) except Exception as err: return False else: if len(possible_products) == 0: # no viable match return False else: product_match = [ x for x in possible_products if x["categoryName"] == "Magic Single" and self.card_equals( x["enName"], x["expansionName"], name, set_name ) ] if len(product_match) == 1: language_id = ( 1 if language == "" else api.languages.index(language) + 1 ) product = api.get_product(product_match[0]["idProduct"]) price = self.get_price_for_product( product, product_match[0]["rarity"], self.config["csv_import_condition"], foil, False, language_id=language_id, api=self.api, ) card = { "idProduct": product_match[0]["idProduct"], "idLanguage": language_id, "count": count, "price": str(price), "condition": self.config["csv_import_condition"], "isFoil": ("true" if foil else "false"), } api.add_stock([card]) return True else: # no single matching card return False else: # incomplete data from card scanner return False def card_equals(self, db_cardname, db_setname, local_cardname, local_setname): # TODO: add some sort of string distance like Levenshtein filtered_db_cardname = db_cardname.replace(",", "") filtered_db_cardname = filtered_db_cardname.replace("Æ", "Ae") if db_setname != local_setname: return False else: # filter for flip card / split card names if filtered_db_cardname == local_cardname or ( "/" in filtered_db_cardname and filtered_db_cardname.startswith(local_cardname) ): return True else: return False def select_from_list_of_wantslists(self, wantslists): index = 1 for wantlist in wantslists: print(f"{index}: {wantlist['name']} ({wantlist['game']['abbreviation']})") index += 1 choice = int(input("Choose wantslist: ")) return wantslists[choice - 1] def select_from_list_of_products(self, products): index = 1 for product in products: print( "{}: {} [{}] {}".format( index, product["enName"], product["expansionName"], product["rarity"], ) ) index += 1 choice = "" while not isinstance(choice, int) or choice > len(products): try: choice = int(input("Choose card: ")) except ValueError as err: print("Not a number.") return products[choice - 1] def select_from_list_of_articles(self, articles): index = 1 for article in articles: product = article["product"] print( f'{index}: {product["enName"]}[{product["expansion"]}], foil: {article["isFoil"]}, comment: {article["comments"]}' ) index += 1 choice = int(input("Choose card: ")) return articles[choice - 1] def show_competition_for_product(self, product_id, product_name, is_foil, api): print("Selected product: {}".format(product_name)) table_data_local, table_data = self.get_competition(api, product_id, is_foil) if table_data_local: self.print_product_top_list("Local competition:", table_data_local, 4, 20) if table_data: self.print_product_top_list("Top 20 cheapest:", table_data, 4, 20) else: print("No prices found.") def get_competition(self, api, product_id, is_foil): # TODO: Add support for playsets # TODO: Add support for card condition self.account = api.get_account()["account"] country_code = self.account["country"] config = self.config is_altered = config["search_filters"]["isAltered"] is_signed = config["search_filters"]["isSigned"] min_condition = config["search_filters"]["minCondition"] user_type = config["search_filters"]["userType"] id_language = config["search_filters"]["idLanguage"] articles = api.get_articles( product_id, **{ "isFoil": str(is_foil).lower(), "isAltered": is_altered, "isSigned": is_signed, "minCondition": min_condition, "country": country_code, "userType": user_type, "idLanguage": id_language, }, ) table_data = [] table_data_local = [] for article in articles: username = article["seller"]["username"] if article["seller"]["username"] == self.account["username"]: username = "-> " + username item = [ username, article["seller"]["address"]["country"], article["condition"], article["language"]["languageName"], article["count"], article["price"], ] if article["seller"]["address"]["country"] == country_code: table_data_local.append(item) table_data.append(item) return table_data_local, table_data def print_product_top_list(self, title_string, table_data, sort_column, rows): print(70 * "-") print("{} \n".format(title_string)) print( tb.tabulate( sorted(table_data, key=lambda x: x[sort_column], reverse=False)[:rows], headers=[ "Username", "Country", "Condition", "Language", "Count", "Price", ], tablefmt="simple", ) ) print(70 * "-") print( "Total average price: {}, Total median price: {}, Total # of articles: {}\n".format( str(PyMkmHelper.calculate_average(table_data, 4, 5)), str(PyMkmHelper.calculate_median(table_data, 4, 5)), str(len(table_data)), ) ) def calculate_new_prices_for_stock( self, stock_list, undercut_local_market, partial_stock_update_size, already_checked_articles, api, ): filtered_stock_list = self.__filter(stock_list) sticky_count = len(stock_list) - len(filtered_stock_list) # articles_in_shoppingcarts = api.get_articles_in_shoppingcarts() if already_checked_articles: filtered_stock_list = [ x for x in filtered_stock_list if x["idArticle"] not in already_checked_articles ] if len(filtered_stock_list) == 0: PyMkmHelper.clear_cache( self.config["local_cache_filename"], "partial_updated" ) print( f"Entire stock updated in partial updates. Partial update data cleared." ) return [], [] if partial_stock_update_size: filtered_stock_list = filtered_stock_list[:partial_stock_update_size] result_json = [] checked_articles = [] total_price = 0 index = 0 bar = progressbar.ProgressBar(max_value=len(filtered_stock_list)) bar.update(index) products_to_get = [x["idProduct"] for x in filtered_stock_list] product_list = api.get_items_async("products", products_to_get) product_list = [x for x in product_list if x] for article in filtered_stock_list: try: product = next( x for x in product_list if x["product"]["idProduct"] == article["idProduct"] ) except StopIteration: # Stock item not found in update batch, continuing self.logger.error( f"aid {article['idArticle']} pid {article['idProduct']} - {article['product']['enName']} {article['product']['expansion']} failed to find a product" ) continue checked_articles.append(article.get("idArticle")) updated_article = self.update_price_for_article( article, product, undercut_local_market, api=self.api ) if updated_article: result_json.append(updated_article) total_price += updated_article.get("price") else: total_price += article.get("price") index += 1 bar.update(index) bar.finish() print("Value in this update: {}".format(str(round(total_price, 2)))) if len(stock_list) != len(filtered_stock_list): print(f"Note: {sticky_count} items filtered out because of sticky prices.") return result_json, checked_articles def update_price_for_article( self, article, product, undercut_local_market=False, api=None ): new_price = self.get_price_for_product( product, article["product"].get("rarity"), article.get("condition"), article.get("isFoil", False), article.get("isPlayset", False), language_id=article["language"]["idLanguage"], undercut_local_market=undercut_local_market, api=self.api, ) if new_price: price_diff = new_price - article["price"] if price_diff != 0: return { "name": article["product"]["enName"], "isFoil": article.get("isFoil", False), "isPlayset": article.get("isPlayset", False), "language": article["language"]["languageName"], "condition": article["condition"], "old_price": article["price"], "price": new_price, "price_diff": price_diff, "idArticle": article["idArticle"], "count": article["count"], } def get_rounding_limit_for_rarity(self, rarity, product_id): rounding_limit = float(self.config["price_limit_by_rarity"]["default"]) try: rounding_limit = float(self.config["price_limit_by_rarity"][rarity.lower()]) except KeyError as err: print( f"ERROR: Unknown rarity '{rarity}' (pid: {product_id}). Using default rounding." ) return rounding_limit def get_discount_for_condition(self, condition): try: discount = float(self.config["discount_by_condition"][condition]) except KeyError as err: print(f"ERROR: Unknown condition '{condition}'.") raise err else: return discount def get_price_for_product( self, product, rarity, condition, is_foil, is_playset, language_id=1, undercut_local_market=False, api=None, ): rounding_limit = self.get_rounding_limit_for_rarity( rarity, product["product"]["idProduct"] ) if not is_foil: trend_price = product["product"]["priceGuide"]["TREND"] else: trend_price = product["product"]["priceGuide"]["TRENDFOIL"] # Set competitive price for region if undercut_local_market: table_data_local, table_data = self.get_competition( api, product["product"]["idProduct"], is_foil ) if len(table_data_local) > 0: # Undercut if there is local competition lowest_in_country = PyMkmHelper.get_lowest_price_from_table( table_data_local, 4 ) new_price = max( rounding_limit, min(trend_price, lowest_in_country - rounding_limit), ) else: # No competition in our country, set price a bit higher. new_price = trend_price * 1.2 else: # don't try to undercut local market new_price = trend_price if new_price is None: raise ValueError("No price found!") else: if is_playset: new_price = 4 * new_price old_price = new_price # Apply condition discount if condition: new_price = new_price * self.get_discount_for_condition(condition) # Round new_price = PyMkmHelper.round_up_to_multiple_of_lower_limit( rounding_limit, new_price ) return new_price def display_price_changes_table(self, changes_json): num_items = self.config["show_num_best_worst_items"] print("\nBest diffs:\n") sorted_best = sorted(changes_json, key=lambda x: x["price_diff"], reverse=True)[ :num_items ] self.draw_price_changes_table(i for i in sorted_best if i["price_diff"] > 0) print("\nWorst diffs:\n") sorted_worst = sorted(changes_json, key=lambda x: x["price_diff"])[:num_items] self.draw_price_changes_table(i for i in sorted_worst if i["price_diff"] < 0) print( "\nTotal price difference: {}.".format( # TODO: fix bug where summary is wrong str( round( sum(item["price_diff"] * item["count"] for item in sorted_best), 2, ) ) ) ) def draw_price_changes_table(self, sorted_best): print( tb.tabulate( [ [ item["count"], item["name"], "\u2713" if item["isFoil"] else "", "\u2713" if item["isPlayset"] else "", item["condition"], item["language"], item["old_price"], item["price"], item["price_diff"], ] for item in sorted_best ], headers=[ "Count", "Name", "Foil", "Playset", "Condition", "Language", "Old price", "New price", "Diff", ], tablefmt="simple", ) ) def get_stock_as_array(self, api, cli_called=False, cached=None): # Check for cached stock local_stock_cache = None local_stock_cache = PyMkmHelper.read_from_cache( self.config["local_cache_filename"], "stock" ) if local_stock_cache: if not cli_called: if PyMkmHelper.prompt_bool( f"Cached stock ({len(local_stock_cache)} items) found, use it? Note that prices may be outdated." ): return local_stock_cache else: if cached: return local_stock_cache PyMkmHelper.clear_cache(self.config["local_cache_filename"], "stock") print( "Getting your stock from Cardmarket (the API can be slow for large stock)..." ) try: d = api.get_stock() except CardmarketError as err: self.logger.error(err.mkm_msg()) print(err.mkm_msg()) sys.exit(0) # except Exception as err: # msg = f"No response from API. Error: {err}" # print(msg) # self.logger.error(msg) # sys.exit(0) else: keys = [ "idArticle", "idProduct", "product", "count", "comments", "price", "condition", "isFoil", "isPlayset", "isSigned", "language", ] stock_list = [ {x: y for x, y in article.items() if x in keys} for article in d ] print("Stock fetched.") PyMkmHelper.store_to_cache( self.config["local_cache_filename"], "stock", stock_list ) return stock_list

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0.482064

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0.105303

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0.163731

0.127269

0.098923

0

0.004573

0.425809

56,368

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169

38.345578

0.798678

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0.00236

0.02203

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0.063729

0

null

0

null

0

1

0

c8829aec3d5b9877236b2115916c5ca2a14ab73b

333

py

Python

Datasets/Terrain/us_ned_physio_diversity.py

monocilindro/qgis-earthengine-examples

82aea8926d34ed3f4ad4a4a345ddbd225819d28f

[ "MIT" ]

646

2019-12-03T06:09:03.000Z

2022-03-28T03:37:08.000Z

Datasets/Terrain/us_ned_physio_diversity.py

csaybar/qgis-earthengine-examples

ba8942683834d2847ff3246bdd1859b36e50fe44

[ "MIT" ]

10

2019-12-30T03:42:44.000Z

2021-05-22T07:34:07.000Z

Datasets/Terrain/us_ned_physio_diversity.py

csaybar/qgis-earthengine-examples

ba8942683834d2847ff3246bdd1859b36e50fe44

[ "MIT" ]

219

2019-12-06T02:20:53.000Z

2022-03-30T15:14:27.000Z

import ee from ee_plugin import Map dataset = ee.Image('CSP/ERGo/1_0/US/physioDiversity') physiographicDiversity = dataset.select('b1') physiographicDiversityVis = { 'min': 0.0, 'max': 1.0, } Map.setCenter(-94.625, 39.825, 7) Map.addLayer( physiographicDiversity, physiographicDiversityVis, 'Physiographic Diversity')

23.785714

54

0.738739

40

333

6.1

0.7

0.016393

0

0.062069

0.129129

333

13

55

25.615385

0.77931

0

0.186186

0.093093

0

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0

0.166667

0

null

0

null

0

1

0

c8845f1c14219b145ec8b7fa1bba57f5b2418dfb

497

py

Python

bin/base64util.py

SnowleopardXI/stash

a14f016e5b568095af8d1e78addedc562e3cde70

[ "MIT" ]

null

bin/base64util.py

SnowleopardXI/stash

a14f016e5b568095af8d1e78addedc562e3cde70

[ "MIT" ]

null

bin/base64util.py

SnowleopardXI/stash

a14f016e5b568095af8d1e78addedc562e3cde70

[ "MIT" ]

null

# -*- coding: utf-8 -*- import base64 print('Choose your choice:') n=''' 1:Encode string to base64 2:Decode base64 to string ''' c=int(eval(input(n))) #定义菜单变量 if c == 1: #进入菜单1的判断 print('Type string to be encoded:') inp=input() out = str(base64.encodebytes(inp.encode("utf-8")), "utf-8") print(out) # 去掉编码结果前的 b if c == 2: print('Type string to be decoded:') inp2=bytes(input(),('utf-8')) dec = base64.decodebytes(inp2) print(dec.decode())

24.85

63

0.593561

73

497

4.041096

0.506849

0.054237

0.101695

0.115254

0.128814

0

0.054545

0.225352

497

19

64

26.157895

0.711688

0.092555

0

0.365471

0

1

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false

0

0.058824

0

0.058824

0.294118

0

null

0

null

0

1

0

c88551ac723dd08106aa9434592b74d5d60bf757

2,614

py

Python

linefinder/job_scripts/linefinder_sightlines.py

zhafen/linefinder

0f4f36a83246f1b833d0c281e635d86be3d1eb95

[ "MIT" ]

null

linefinder/job_scripts/linefinder_sightlines.py

zhafen/linefinder

0f4f36a83246f1b833d0c281e635d86be3d1eb95

[ "MIT" ]

12

2018-08-26T14:10:18.000Z

2021-04-15T21:48:58.000Z

linefinder/job_scripts/linefinder_sightlines.py

zhafen/linefinder

0f4f36a83246f1b833d0c281e635d86be3d1eb95

[ "MIT" ]

1

2021-05-19T16:45:21.000Z

import linefinder.linefinder as linefinder import linefinder.config as linefinder_config import linefinder.utils.file_management as file_management ######################################################################## sim_name = 'm12i' '''The simulation to run tracking on.''' tag = '{}_sightline'.format( sim_name ) '''Identifying tag used as part of the filenames. E.g. the IDs file will have the format `ids_{}.hdf5.format( tag )`. ''' # Tracking Parameters tracker_kwargs = { # What particle types to track. Typically just stars and gas. 'p_types': [ 0, 4,], # What snapshots to compile the particle tracks for. 'snum_start': 1, 'snum_end': 600, 'snum_step': 1, } file_manager = file_management.FileManager() sampler_kwargs = { 'ignore_duplicates': True, 'p_types': [ 0, 4 ], 'snapshot_kwargs': { 'sdir': file_manager.get_sim_dir( sim_name ), 'halo_data_dir': file_manager.get_halo_dir( sim_name ), 'main_halo_id': linefinder_config.MAIN_MT_HALO_ID[sim_name], 'ahf_index': 600, 'length_scale_used': 'R_vir', } } visualization_kwargs = { 'install_firefly': True, 'export_to_firefly_kwargs': { 'firefly_dir': '/work/03057/zhafen/firefly_repos/sightline', 'classifications': [ 'is_in_CGM', 'is_CGM_IGM_accretion', 'is_CGM_wind', 'is_CGM_satellite_wind', 'is_CGM_satellite_ISM', ], 'classification_ui_labels': [ 'All', 'IGMAcc', 'Wind', 'SatWind', 'Sat' ], 'tracked_properties': [ 'logT', 'logZ', 'logDen', 'vr_div_v_cool', 'logvr_div_v_cool_offset', ], 'tracked_filter_flags': [ True, ] * 5, 'tracked_colormap_flags': [ True, ] * 5, 'snum': 465, }, } # This is the actual function that runs linefinder. # In general you don't need to touch this function but if you want to, # for example, turn off one of the steps because you're rerunning and you # already did that step, you can do so below. linefinder.run_linefinder_jug( sim_name = sim_name, tag = tag, galdef = '_galdefv3', # The galdef is a set of parameters used for the galaxy linking and # classification steps. Don't touch this unless you know what you're doing. tracker_kwargs = tracker_kwargs, sampler_kwargs = sampler_kwargs, visualization_kwargs = visualization_kwargs, run_id_selecting = False, run_id_sampling = False, run_tracking = False, run_galaxy_linking = False, run_classifying = False, )

30.045977

82

0.630451

326

2,614

4.773006

0.484663

0.031491

0.008997

0.010283

0

0.013046

0.237567

2,614

86

83

30.395349

0.767687

0.193191

0

0.033333

0

0.271704

0.083601

0

1

0

false

0

0.05

0

0.05

0

null

0

null

0

1

0

c8864bea2e2f25d967c38986aef9fb5517d5143b

285

py

Python

SwordToOffer/SwordToOffer-PythonSolution/47_Sum_Solution.py

dingchaofan/AlgorithmSolution

46198e3f0dbda867e7b75f0d0e52be5f0181238a

[ "MIT" ]

1

2020-06-23T02:18:39.000Z

SwordToOffer/SwordToOffer-PythonSolution/47_Sum_Solution.py

dingchaofan/AlgorithmSolution

46198e3f0dbda867e7b75f0d0e52be5f0181238a

[ "MIT" ]

null

SwordToOffer/SwordToOffer-PythonSolution/47_Sum_Solution.py

dingchaofan/AlgorithmSolution

46198e3f0dbda867e7b75f0d0e52be5f0181238a

[ "MIT" ]

1

2021-01-11T12:07:03.000Z

# 47. 求1+2+3+...+n # 求1+2+3+...+n，要求不能使用乘除法、for、while、if、else、switch、case等关键字及条件判断语句（A?B:C）。 # -*- coding:utf-8 -*- class Solution: def Sum_Solution(self, n): # write code here res = n if(res): res += self.Sum_Solution(n-1) return res

21.923077

73

0.540351

44

285

3.454545

0.659091

0.039474

0.052632

0.065789

0

0.048077

0.270175

285

13

74

21.923077

0.682692

0.438596

0

1

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false

0

0.5

0

null

0

null

0

1

0

c889096998408750f88d5b4c179ee06539614ee4

48,562

py

Python

hawc_hal/HAL.py

torresramiro350/hawc_hal

048536df22bdfa3ace2925e60d802beb76775849

[ "BSD-3-Clause" ]

null

hawc_hal/HAL.py

torresramiro350/hawc_hal

048536df22bdfa3ace2925e60d802beb76775849

[ "BSD-3-Clause" ]

null

hawc_hal/HAL.py

torresramiro350/hawc_hal

048536df22bdfa3ace2925e60d802beb76775849

[ "BSD-3-Clause" ]

null

from __future__ import division from builtins import str from builtins import range from astropy.utils.misc import isiterable from past.utils import old_div import copy import collections import numpy as np import healpy as hp import astropy.units as u import matplotlib.pyplot as plt import matplotlib as mpl from scipy.stats import poisson from astropy.convolution import Gaussian2DKernel from astropy.convolution import convolve_fft as convolve from astropy.coordinates import Angle from threeML.plugin_prototype import PluginPrototype from threeML.utils.statistics.gammaln import logfactorial from threeML.parallel import parallel_client from threeML.io.logging import setup_logger log = setup_logger(__name__) log.propagate = False from tqdm.auto import tqdm from astromodels import Parameter from hawc_hal.maptree import map_tree_factory from hawc_hal.maptree.map_tree import MapTree from hawc_hal.maptree.data_analysis_bin import DataAnalysisBin from hawc_hal.response import hawc_response_factory from hawc_hal.convolved_source import ConvolvedPointSource, \ ConvolvedExtendedSource3D, ConvolvedExtendedSource2D, ConvolvedSourcesContainer from hawc_hal.healpix_handling import FlatSkyToHealpixTransform from hawc_hal.healpix_handling import SparseHealpix from hawc_hal.healpix_handling import get_gnomonic_projection from hawc_hal.psf_fast import PSFConvolutor from hawc_hal.log_likelihood import log_likelihood from hawc_hal.util import ra_to_longitude class HAL(PluginPrototype): """ The HAWC Accelerated Likelihood plugin for 3ML. :param name: name for the plugin :param maptree: Map Tree (either ROOT or hdf5 format) :param response: Response of HAWC (either ROOT or hd5 format) :param roi: a ROI instance describing the Region Of Interest :param flat_sky_pixels_size: size of the pixel for the flat sky projection (Hammer Aitoff) """ def __init__(self, name, maptree, response_file, roi, flat_sky_pixels_size=0.17): # Store ROI self._roi = roi # Set up the flat-sky projection self.flat_sky_pixels_size=flat_sky_pixels_size self._flat_sky_projection = self._roi.get_flat_sky_projection(self.flat_sky_pixels_size) # Read map tree (data) self._maptree = map_tree_factory(maptree, roi=self._roi) # Read detector response_file self._response = hawc_response_factory(response_file) # Use a renormalization of the background as nuisance parameter # NOTE: it is fixed to 1.0 unless the user explicitly sets it free (experimental) self._nuisance_parameters = collections.OrderedDict() #self._nuisance_parameters['%s_bkg_renorm' % name] = Parameter('%s_bkg_renorm' % name, 1.0, self._nuisance_parameters[f'{name}_bkg_renorm'] = Parameter(f'{name}_bkg_renorm', 1.0, min_value=0.5, max_value=1.5, delta=0.01, desc="Renormalization for background map", free=False, is_normalization=False) # Instance parent class super(HAL, self).__init__(name, self._nuisance_parameters) self._likelihood_model = None # These lists will contain the maps for the point sources self._convolved_point_sources = ConvolvedSourcesContainer() # and this one for extended sources self._convolved_ext_sources = ConvolvedSourcesContainer() # All energy/nHit bins are loaded in memory self._all_planes = list(self._maptree.analysis_bins_labels) # The active planes list always contains the list of *indexes* of the active planes self._active_planes = None # Set up the transformations from the flat-sky projection to Healpix, as well as the list of active pixels # (one for each energy/nHit bin). We make a separate transformation because different energy bins might have # different nsides self._active_pixels = collections.OrderedDict() self._flat_sky_to_healpix_transform = collections.OrderedDict() for bin_id in self._maptree: this_maptree = self._maptree[bin_id] this_nside = this_maptree.nside this_active_pixels = roi.active_pixels(this_nside) this_flat_sky_to_hpx_transform = FlatSkyToHealpixTransform(self._flat_sky_projection.wcs, 'icrs', this_nside, this_active_pixels, (self._flat_sky_projection.npix_width, self._flat_sky_projection.npix_height), order='bilinear') self._active_pixels[bin_id] = this_active_pixels self._flat_sky_to_healpix_transform[bin_id] = this_flat_sky_to_hpx_transform # This will contain a list of PSF convolutors for extended sources, if there is any in the model self._psf_convolutors = None # Pre-compute the log-factorial factor in the likelihood, so we do not keep to computing it over and over # again. self._log_factorials = collections.OrderedDict() # We also apply a bias so that the numerical value of the log-likelihood stays small. This helps when # fitting with algorithms like MINUIT because the convergence criterium involves the difference between # two likelihood values, which would be affected by numerical precision errors if the two values are # too large self._saturated_model_like_per_maptree = collections.OrderedDict() # The actual computation is in a method so we can recall it on clone (see the get_simulated_dataset method) self._compute_likelihood_biases() # This will save a clone of self for simulations self._clone = None # Integration method for the PSF (see psf_integration_method) self._psf_integration_method = "exact" @property def psf_integration_method(self): """ Get or set the method for the integration of the PSF. * "exact" is more accurate but slow, if the position is free to vary it adds a lot of time to the fit. This is the default, to be used when the position of point sources are fixed. The computation in that case happens only once so the impact on the run time is negligible. * "fast" is less accurate (up to an error of few percent in flux) but a lot faster. This should be used when the position of the point source is free, because in that case the integration of the PSF happens every time the position changes, so several times during the fit. If you have a fit with a free position, use "fast". When the position is found, you can fix it, switch to "exact" and redo the fit to obtain the most accurate measurement of the flux. For normal sources the difference will be small, but for very bright sources it might be up to a few percent (most of the time < 1%). If you are interested in the localization contour there is no need to rerun with "exact". :param mode: either "exact" or "fast" :return: None """ return self._psf_integration_method @psf_integration_method.setter def psf_integration_method(self, mode): assert mode.lower() in ["exact", "fast"], ( "PSF integration method must be either 'exact' or 'fast'" ) self._psf_integration_method = mode.lower() def _setup_psf_convolutors(self): central_response_bins = self._response.get_response_dec_bin(self._roi.ra_dec_center[1]) self._psf_convolutors = collections.OrderedDict() for bin_id in central_response_bins: #Only set up PSF convolutors for active bins. if bin_id in self._active_planes: self._psf_convolutors[bin_id] = PSFConvolutor(central_response_bins[bin_id].psf, self._flat_sky_projection) def _compute_likelihood_biases(self): for bin_label in self._maptree: data_analysis_bin = self._maptree[bin_label] this_log_factorial = np.sum(logfactorial(data_analysis_bin.observation_map.as_partial().astype(int))) self._log_factorials[bin_label] = this_log_factorial # As bias we use the likelihood value for the saturated model obs = data_analysis_bin.observation_map.as_partial() bkg = data_analysis_bin.background_map.as_partial() sat_model = np.clip(obs - bkg, 1e-50, None).astype(np.float64) self._saturated_model_like_per_maptree[bin_label] = log_likelihood(obs, bkg, sat_model) - this_log_factorial def get_saturated_model_likelihood(self): """ Returns the likelihood for the saturated model (i.e. a model exactly equal to observation - background). :return: """ return sum(self._saturated_model_like_per_maptree.values()) def set_active_measurements(self, bin_id_min=None, bin_id_max=None, bin_list=None): """ Set the active analysis bins to use during the analysis. It can be used in two ways: - Specifying a range: if the response and the maptree allows it, you can specify a minimum id and a maximum id number. This only works if the analysis bins are numerical, like in the normal fHit analysis. For example: > set_active_measurement(bin_id_min=1, bin_id_max=9) - Specifying a list of bins as strings. This is more powerful, as allows to select any bins, even non-contiguous bins. For example: > set_active_measurement(bin_list=[list]) :param bin_id_min: minimum bin (only works for fHit analysis. For the others, use bin_list) :param bin_id_max: maximum bin (only works for fHit analysis. For the others, use bin_list) :param bin_list: a list of analysis bins to use :return: None """ # Check for legal input if bin_id_min is not None: assert bin_id_max is not None, ( "If you provide a minimum bin, you also need to provide a maximum bin." ) # Make sure they are integers bin_id_min = int(bin_id_min) bin_id_max = int(bin_id_max) self._active_planes = [] for this_bin in range(bin_id_min, bin_id_max + 1): this_bin = str(this_bin) if this_bin not in self._all_planes: raise ValueError(f"Bin {this_bin} is not contained in this maptree.") self._active_planes.append(this_bin) else: assert bin_id_max is None, ( "If you provie a maximum bin, you also need to provide a minimum bin." ) assert bin_list is not None self._active_planes = [] for this_bin in bin_list: if not this_bin in self._all_planes: raise ValueError(f"Bin {this_bin} is not contained in this maptree.") self._active_planes.append(this_bin) if self._likelihood_model: self.set_model( self._likelihood_model ) def display(self, verbose=False): """ Prints summary of the current object content. """ log.info("Region of Interest: ") log.info("-------------------") self._roi.display() log.info("") log.info("Flat sky projection: ") log.info("--------------------") log.info( f"Width x height {self._flat_sky_projection.npix_width} x {self._flat_sky_projection.npix_height} px" ) #log.info("Width x height: %s x %s px" % (self._flat_sky_projection.npix_width, # self._flat_sky_projection.npix_height)) log.info(f"Pixel sizes: {self._flat_sky_projection.pixel_size} deg") #log.info("Pixel sizes: %s deg" % self._flat_sky_projection.pixel_size) log.info("") log.info("Response: ") log.info("---------") self._response.display(verbose) log.info("") log.info("Map Tree: ") log.info("----------") self._maptree.display() log.info("") #log.info("Active energy/nHit planes ({}):".format(len(self._active_planes))) log.info(f"Active energy/nHit planes ({len(self._active_planes)}):") log.info("-------------------------------") log.info(self._active_planes) def set_model(self, likelihood_model_instance): """ Set the model to be used in the joint minimization. Must be a LikelihoodModel instance. """ self._likelihood_model = likelihood_model_instance # Reset self._convolved_point_sources.reset() self._convolved_ext_sources.reset() # For each point source in the model, build the convolution class for source in list(self._likelihood_model.point_sources.values()): this_convolved_point_source = ConvolvedPointSource(source, self._response, self._flat_sky_projection) self._convolved_point_sources.append(this_convolved_point_source) # Samewise for extended sources ext_sources = list(self._likelihood_model.extended_sources.values()) # NOTE: ext_sources evaluate to False if empty if ext_sources: # We will need to convolve self._setup_psf_convolutors() for source in ext_sources: if source.spatial_shape.n_dim == 2: this_convolved_ext_source = ConvolvedExtendedSource2D(source, self._response, self._flat_sky_projection) else: this_convolved_ext_source = ConvolvedExtendedSource3D(source, self._response, self._flat_sky_projection) self._convolved_ext_sources.append(this_convolved_ext_source) def get_excess_background(self, ra, dec, radius): """ Calculates area, excess (data - background) and model counts of source at different distance from the source. :param: radius: radial distance away from the center (degrees). :returns: tuple of numpy.ndarrays for areas, excess, model, and background this information is used in the get_radial_profile function. """ radius_radians = np.deg2rad(radius) total_counts = np.zeros(len(self._active_planes), dtype=float) background = np.zeros_like(total_counts) observation = np.zeros_like(total_counts) model = np.zeros_like(total_counts) signal = np.zeros_like(total_counts) area = np.zeros_like(total_counts) n_point_sources = self._likelihood_model.get_number_of_point_sources() n_ext_sources = self._likelihood_model.get_number_of_extended_sources() longitude = ra_to_longitude(ra) latitude = dec center = hp.ang2vec(longitude, latitude, lonlat=True) for i, energy_id in enumerate(self._active_planes): data_analysis_bin = self._maptree[energy_id] this_nside = data_analysis_bin.observation_map.nside pixels_at_radius = hp.query_disc( this_nside, center, radius_radians, inclusive=False, ) # calculate the areas per bin by the product # of pixel area by the number of pixels at each radial bin area[i] = hp.nside2pixarea(this_nside)*pixels_at_radius.shape[0] # NOTE: select active pixels according to each radial bin bin_active_pixel_indexes = np.searchsorted(self._active_pixels[energy_id], pixels_at_radius) # obtain the excess, background, and expected excess at each radial bin data = data_analysis_bin.observation_map.as_partial() bkg = data_analysis_bin.background_map.as_partial() mdl = self._get_model_map(energy_id, n_point_sources, n_ext_sources).as_partial() bin_data = np.array([data[i] for i in bin_active_pixel_indexes]) bin_bkg = np.array([bkg[i] for i in bin_active_pixel_indexes]) bin_model = np.array([mdl[i] for i in bin_active_pixel_indexes]) this_data_tot = np.sum(bin_data) this_bkg_tot = np.sum(bin_bkg) this_model_tot = np.sum(bin_model) background[i] = this_bkg_tot observation[i] = this_data_tot model[i] = this_model_tot signal[i] = this_data_tot - this_bkg_tot return area, signal, model, background def get_radial_profile( self, ra, dec, active_planes=None, max_radius=3.0, n_radial_bins=30, model_to_subtract=None, subtract_model_from_model=False, ): """ Calculates radial profiles of data - background & model. :param ra: R.A. of origin for radial profile. :param dec: Declination of origin of radial profile. :param active_planes: List of analysis over which to average; if None, use HAWC default (bins 1-9). :param: max_radius: Radius up to which the radial profile is evaluated; for the disk to calculate the gamma/hadron weights (Default: 3.0). :param n_radial_bins: Number of bins for the radial profile (Default: 30). :param model_to_subtract: Another model that is to be subtracted from the data excess (Default: None). :param subtract_model_from_model: If True and model_to_subtract is not None, subtract model from model too (Defalt: False). :return: np.arrays with the radii, model profile, data profile, data uncertainty, and list of analysis bins used. """ # default is to use all active bins if active_planes is None: active_planes = self._active_planes # Make sure we use bins with data good_planes = [plane_id in active_planes for plane_id in self._active_planes] plane_ids = set(active_planes) & set(self._active_planes) delta_r = 1.0*max_radius/n_radial_bins radii = np.array([delta_r*(r + 0.5) for r in range(0, n_radial_bins)]) # Get area of all pixels in a given circle # The area of each ring is then given by the difference between two # subsequent circe areas. area = np.array( [self.get_excess_background(ra, dec, r + 0.5*delta_r)[0] for r in radii ] ) temp = area[1:] - area[:-1] area[1:] = temp # model # convert 'top hat' excess into 'ring' excesses. model = np.array( [self.get_excess_background(ra, dec, r + 0.5*delta_r)[2] for r in radii] ) temp = model[1:] - model[:-1] model[1:] = temp # signals signal = np.array( [self.get_excess_background(ra, dec, r + 0.5*delta_r)[1] for r in radii] ) temp = signal[1:] - signal[:-1] signal[1:] = temp # backgrounds bkg = np.array( [self.get_excess_background(ra, dec, r + 0.5*delta_r)[3] for r in radii] ) temp = bkg[1:] - bkg[:-1] bkg[1:] = temp counts = signal + bkg if model_to_subtract is not None: this_model = copy.deepcopy(self._likelihood_model) self.set_model(model_to_subtract) model_subtract = np.array( [self.get_excess_background(ra, dec, r + 0.5*delta_r)[2] for r in radii] ) temp = model_subtract[1:] - model_subtract[:-1] model_subtract[1:] = temp signal -= model_subtract if subtract_model_from_model: model -= model_subtract self.set_model(this_model) # NOTE: weights are calculated as expected number of gamma-rays/number of background counts. # here, use max_radius to evaluate the number of gamma-rays/bkg counts. # The weights do not depend on the radius, but fill a matrix anyway so # there's no confusion when multiplying them to the data later. # Weight is normalized (sum of weights over the bins = 1). total_excess = np.array( self.get_excess_background(ra, dec, max_radius)[1] )[good_planes] total_model = np.array( self.get_excess_background(ra, dec, max_radius)[2] )[good_planes] total_bkg = np.array( self.get_excess_background(ra, dec, max_radius)[3] )[good_planes] w = np.divide(total_model, total_bkg) weight = np.array([w/np.sum(w) for r in radii]) # restric profiles to the user-specified analysis bins area = area[:, good_planes] signal = signal[:, good_planes] model = model[:, good_planes] counts = counts[:, good_planes] bkg = bkg[:, good_planes] # average over the analysis bins excess_data = np.average(signal/area, weights=weight, axis=1) excess_error = np.sqrt(np.sum(counts*weight*weight/(area*area), axis=1)) excess_model = np.average(model/area, weights=weight, axis=1) return radii, excess_model, excess_data, excess_error, sorted(plane_ids) def plot_radial_profile( self, ra, dec, active_planes=None, max_radius=3.0, n_radial_bins=30, model_to_subtract=None, subtract_model_from_model=False ): """ Plots radial profiles of data - background & model. :param ra: R.A. of origin for radial profile. :param dec: Declination of origin of radial profile. :param active_planes: List of analysis bins over which to average; if None, use HAWC default (bins 1-9). :param max_radius: Radius up to which the radial profile is evaluated; also used as the radius for the disk to calculate the gamma/hadron weights. Default: 3.0 :param model_to_subtract: Another model that is to be subtracted from the data excess (Default: None). :param subtract_model_from_model: If True and model_to_subtract is not None, subtract from model too (Default: False). :return: plot of data - background vs model radial profiles. """ ( radii, excess_model, excess_data, excess_error, plane_ids, ) = self.get_radial_profile( ra, dec, active_planes, max_radius, n_radial_bins, model_to_subtract, subtract_model_from_model, ) #font = { # "family":"serif", # "weight":"regular", # "size":12 #} #mpl.rc("font", **font) fig, ax = plt.subplots(figsize=(10,8)) plt.errorbar( radii, excess_data, yerr=excess_error, capsize=0, color="black", label="Excess (data-bkg)", fmt=".", ) plt.plot(radii, excess_model, color="red", label="Model") plt.legend(bbox_to_anchor=(1.0, 1.0), loc="upper right", numpoints=1) plt.axhline(0, color="deepskyblue", linestyle="--") x_limits=[0, max_radius] plt.xlim(x_limits) plt.ylabel(r"Apparent Radial Excess [sr$^{-1}$]") plt.xlabel( f"Distance from source at ({ra:0.2f} $^{{\circ}}$, {dec:0.2f} $^{{\circ}}$)" ) if len(plane_ids) == 1: title = f"Radial Profile, bin {plane_ids[0]}" else: tmptitle=f"Radial Profile, bins \n{plane_ids}" width=70 title="\n".join( tmptitle[i:i+width] for i in range(0, len(tmptitle), width) ) title=tmptitle plt.title(title) ax.grid(True) try: plt.tight_layout() except: pass return fig def display_spectrum(self): """ Make a plot of the current spectrum and its residuals (integrated over space) :return: a matplotlib.Figure """ n_point_sources = self._likelihood_model.get_number_of_point_sources() n_ext_sources = self._likelihood_model.get_number_of_extended_sources() total_counts = np.zeros(len(self._active_planes), dtype=float) total_model = np.zeros_like(total_counts) model_only = np.zeros_like(total_counts) net_counts = np.zeros_like(total_counts) yerr_low = np.zeros_like(total_counts) yerr_high = np.zeros_like(total_counts) for i, energy_id in enumerate(self._active_planes): data_analysis_bin = self._maptree[energy_id] this_model_map_hpx = self._get_expectation(data_analysis_bin, energy_id, n_point_sources, n_ext_sources) this_model_tot = np.sum(this_model_map_hpx) this_data_tot = np.sum(data_analysis_bin.observation_map.as_partial()) this_bkg_tot = np.sum(data_analysis_bin.background_map.as_partial()) total_counts[i] = this_data_tot net_counts[i] = this_data_tot - this_bkg_tot model_only[i] = this_model_tot this_wh_model = this_model_tot + this_bkg_tot total_model[i] = this_wh_model if this_data_tot >= 50.0: # Gaussian limit # Under the null hypothesis the data are distributed as a Gaussian with mu = model # and sigma = sqrt(model) # NOTE: since we neglect the background uncertainty, the background is part of the # model yerr_low[i] = np.sqrt(this_data_tot) yerr_high[i] = np.sqrt(this_data_tot) else: # Low-counts # Under the null hypothesis the data are distributed as a Poisson distribution with # mean = model, plot the 68% confidence interval (quantile=[0.16,1-0.16]). # NOTE: since we neglect the background uncertainty, the background is part of the # model quantile = 0.16 mean = this_wh_model y_low = poisson.isf(1-quantile, mu=mean) y_high = poisson.isf(quantile, mu=mean) yerr_low[i] = mean-y_low yerr_high[i] = y_high-mean residuals = old_div((total_counts - total_model), np.sqrt(total_model)) residuals_err = [old_div(yerr_high, np.sqrt(total_model)), old_div(yerr_low, np.sqrt(total_model))] yerr = [yerr_high, yerr_low] return self._plot_spectrum(net_counts, yerr, model_only, residuals, residuals_err) def _plot_spectrum(self, net_counts, yerr, model_only, residuals, residuals_err): fig, subs = plt.subplots(2, 1, gridspec_kw={'height_ratios': [2, 1], 'hspace': 0}, figsize=(12,6)) planes = np.array(self._active_planes) subs[0].errorbar(planes, net_counts, yerr=yerr, capsize=0, color='black', label='Net counts', fmt='.') subs[0].plot(planes, model_only, label='Convolved model') subs[0].legend(bbox_to_anchor=(1.0, 1.0), loc="upper right", numpoints=1) # Residuals subs[1].axhline(0, linestyle='--') subs[1].errorbar( planes, residuals, yerr=residuals_err, capsize=0, fmt='.' ) y_limits = [min(net_counts[net_counts > 0]) / 2., max(net_counts) * 2.] subs[0].set_yscale("log", nonpositive='clip') subs[0].set_ylabel("Counts per bin") subs[0].set_xticks([]) subs[1].set_xlabel("Analysis bin") subs[1].set_ylabel(r"$\frac{{cts - mod - bkg}}{\sqrt{mod + bkg}}$") subs[1].set_xticks(planes) subs[1].set_xticklabels(self._active_planes) subs[0].set_ylim(y_limits) return fig def get_log_like(self): """ Return the value of the log-likelihood with the current values for the parameters """ n_point_sources = self._likelihood_model.get_number_of_point_sources() n_ext_sources = self._likelihood_model.get_number_of_extended_sources() # Make sure that no source has been added since we filled the cache assert (n_point_sources == self._convolved_point_sources.n_sources_in_cache and n_ext_sources == self._convolved_ext_sources.n_sources_in_cache), ( "The number of sources has changed. Please re-assign the model to the plugin." ) #assert n_point_sources == self._convolved_point_sources.n_sources_in_cache and \ # n_ext_sources == self._convolved_ext_sources.n_sources_in_cache, \ # "The number of sources has changed. Please re-assign the model to the plugin." # This will hold the total log-likelihood total_log_like = 0 for bin_id in self._active_planes: data_analysis_bin = self._maptree[bin_id] this_model_map_hpx = self._get_expectation(data_analysis_bin, bin_id, n_point_sources, n_ext_sources) # Now compare with observation bkg_renorm = list(self._nuisance_parameters.values())[0].value obs = data_analysis_bin.observation_map.as_partial() # type: np.array bkg = data_analysis_bin.background_map.as_partial() * bkg_renorm # type: np.array this_pseudo_log_like = log_likelihood(obs, bkg, this_model_map_hpx) total_log_like += this_pseudo_log_like - self._log_factorials[bin_id] \ - self._saturated_model_like_per_maptree[bin_id] return total_log_like def write(self, response_file_name, map_tree_file_name): """ Write this dataset to disk in HDF format. :param response_file_name: filename for the response :param map_tree_file_name: filename for the map tree :return: None """ self._maptree.write(map_tree_file_name) self._response.write(response_file_name) def get_simulated_dataset(self, name): """ Return a simulation of this dataset using the current model with current parameters. :param name: new name for the new plugin instance :return: a HAL instance """ # First get expectation under the current model and store them, if we didn't do it yet if self._clone is None: n_point_sources = self._likelihood_model.get_number_of_point_sources() n_ext_sources = self._likelihood_model.get_number_of_extended_sources() expectations = collections.OrderedDict() for bin_id in self._maptree: data_analysis_bin = self._maptree[bin_id] if bin_id not in self._active_planes: expectations[bin_id] = None else: expectations[bin_id] = self._get_expectation(data_analysis_bin, bin_id, n_point_sources, n_ext_sources) + \ data_analysis_bin.background_map.as_partial() if parallel_client.is_parallel_computation_active(): # Do not clone, as the parallel environment already makes clones clone = self else: clone = copy.deepcopy(self) self._clone = (clone, expectations) # Substitute the observation and background for each data analysis bin for bin_id in self._clone[0]._maptree: data_analysis_bin = self._clone[0]._maptree[bin_id] if bin_id not in self._active_planes: continue else: # Active plane. Generate new data expectation = self._clone[1][bin_id] new_data = np.random.poisson(expectation, size=(1, expectation.shape[0])).flatten() # Substitute data data_analysis_bin.observation_map.set_new_values(new_data) # Now change name and return self._clone[0]._name = name # Adjust the name of the nuisance parameter old_name = list(self._clone[0]._nuisance_parameters.keys())[0] new_name = old_name.replace(self.name, name) self._clone[0]._nuisance_parameters[new_name] = self._clone[0]._nuisance_parameters.pop(old_name) # Recompute biases self._clone[0]._compute_likelihood_biases() return self._clone[0] def _get_expectation(self, data_analysis_bin, energy_bin_id, n_point_sources, n_ext_sources): # Compute the expectation from the model this_model_map = None for pts_id in range(n_point_sources): this_conv_src = self._convolved_point_sources[pts_id] expectation_per_transit = this_conv_src.get_source_map(energy_bin_id, tag=None, psf_integration_method=self._psf_integration_method) expectation_from_this_source = expectation_per_transit * data_analysis_bin.n_transits if this_model_map is None: # First addition this_model_map = expectation_from_this_source else: this_model_map += expectation_from_this_source # Now process extended sources if n_ext_sources > 0: this_ext_model_map = None for ext_id in range(n_ext_sources): this_conv_src = self._convolved_ext_sources[ext_id] expectation_per_transit = this_conv_src.get_source_map(energy_bin_id) if this_ext_model_map is None: # First addition this_ext_model_map = expectation_per_transit else: this_ext_model_map += expectation_per_transit # Now convolve with the PSF if this_model_map is None: # Only extended sources this_model_map = (self._psf_convolutors[energy_bin_id].extended_source_image(this_ext_model_map) * data_analysis_bin.n_transits) else: this_model_map += (self._psf_convolutors[energy_bin_id].extended_source_image(this_ext_model_map) * data_analysis_bin.n_transits) # Now transform from the flat sky projection to HEALPiX if this_model_map is not None: # First divide for the pixel area because we need to interpolate brightness #this_model_map = old_div(this_model_map, self._flat_sky_projection.project_plane_pixel_area) this_model_map = this_model_map/self._flat_sky_projection.project_plane_pixel_area this_model_map_hpx = self._flat_sky_to_healpix_transform[energy_bin_id](this_model_map, fill_value=0.0) # Now multiply by the pixel area of the new map to go back to flux this_model_map_hpx *= hp.nside2pixarea(data_analysis_bin.nside, degrees=True) else: # No sources this_model_map_hpx = 0.0 return this_model_map_hpx @staticmethod def _represent_healpix_map(fig, hpx_map, longitude, latitude, xsize, resolution, smoothing_kernel_sigma): proj = get_gnomonic_projection(fig, hpx_map, rot=(longitude, latitude, 0.0), xsize=xsize, reso=resolution) if smoothing_kernel_sigma is not None: # Get the sigma in pixels sigma = old_div(smoothing_kernel_sigma * 60, resolution) proj = convolve(list(proj), Gaussian2DKernel(sigma), nan_treatment='fill', preserve_nan=True) return proj def display_fit(self, smoothing_kernel_sigma=0.1, display_colorbar=False): """ Make a figure containing 4 maps for each active analysis bins with respectively model, data, background and residuals. The model, data and residual maps are smoothed, the background map is not. :param smoothing_kernel_sigma: sigma for the Gaussian smoothing kernel, for all but background maps :param display_colorbar: whether or not to display the colorbar in the residuals :return: a matplotlib.Figure """ n_point_sources = self._likelihood_model.get_number_of_point_sources() n_ext_sources = self._likelihood_model.get_number_of_extended_sources() # This is the resolution (i.e., the size of one pixel) of the image resolution = 3.0 # arcmin # The image is going to cover the diameter plus 20% padding xsize = self._get_optimal_xsize(resolution) n_active_planes = len(self._active_planes) n_columns = 4 fig, subs = plt.subplots(n_active_planes, n_columns, figsize=(2.7 * n_columns, n_active_planes * 2), squeeze=False) prog_bar = tqdm(total = len(self._active_planes), desc="Smoothing planes") images = ['None'] * n_columns for i, plane_id in enumerate(self._active_planes): data_analysis_bin = self._maptree[plane_id] # Get the center of the projection for this plane this_ra, this_dec = self._roi.ra_dec_center # Make a full healpix map for a second whole_map = self._get_model_map(plane_id, n_point_sources, n_ext_sources).as_dense() # Healpix uses longitude between -180 and 180, while R.A. is between 0 and 360. We need to fix that: longitude = ra_to_longitude(this_ra) # Declination is already between -90 and 90 latitude = this_dec # Background and excess maps bkg_subtracted, _, background_map = self._get_excess(data_analysis_bin, all_maps=True) # Make all the projections: model, excess, background, residuals proj_model = self._represent_healpix_map(fig, whole_map, longitude, latitude, xsize, resolution, smoothing_kernel_sigma) # Here we removed the background otherwise nothing is visible # Get background (which is in a way "part of the model" since the uncertainties are neglected) proj_data = self._represent_healpix_map(fig, bkg_subtracted, longitude, latitude, xsize, resolution, smoothing_kernel_sigma) # No smoothing for this one (because a goal is to check it is smooth). proj_bkg = self._represent_healpix_map(fig, background_map, longitude, latitude, xsize, resolution, None) proj_residuals = proj_data - proj_model # Common color scale range for model and excess maps vmin = min(np.nanmin(proj_model), np.nanmin(proj_data)) vmax = max(np.nanmax(proj_model), np.nanmax(proj_data)) # Plot model images[0] = subs[i][0].imshow(proj_model, origin='lower', vmin=vmin, vmax=vmax) subs[i][0].set_title('model, bin {}'.format(data_analysis_bin.name)) # Plot data map images[1] = subs[i][1].imshow(proj_data, origin='lower', vmin=vmin, vmax=vmax) subs[i][1].set_title('excess, bin {}'.format(data_analysis_bin.name)) # Plot background map. images[2] = subs[i][2].imshow(proj_bkg, origin='lower') subs[i][2].set_title('background, bin {}'.format(data_analysis_bin.name)) # Now residuals images[3] = subs[i][3].imshow(proj_residuals, origin='lower') subs[i][3].set_title('residuals, bin {}'.format(data_analysis_bin.name)) # Remove numbers from axis for j in range(n_columns): subs[i][j].axis('off') if display_colorbar: for j, image in enumerate(images): plt.colorbar(image, ax=subs[i][j]) prog_bar.update(1) fig.set_tight_layout(True) return fig def _get_optimal_xsize(self, resolution): return 2.2 * self._roi.data_radius.to("deg").value / (resolution / 60.0) def display_stacked_image(self, smoothing_kernel_sigma=0.5): """ Display a map with all active analysis bins stacked together. :param smoothing_kernel_sigma: sigma for the Gaussian smoothing kernel to apply :return: a matplotlib.Figure instance """ # This is the resolution (i.e., the size of one pixel) of the image in arcmin resolution = 3.0 # The image is going to cover the diameter plus 20% padding xsize = self._get_optimal_xsize(resolution) active_planes_bins = [self._maptree[x] for x in self._active_planes] # Get the center of the projection for this plane this_ra, this_dec = self._roi.ra_dec_center # Healpix uses longitude between -180 and 180, while R.A. is between 0 and 360. We need to fix that: longitude = ra_to_longitude(this_ra) # Declination is already between -90 and 90 latitude = this_dec total = None for i, data_analysis_bin in enumerate(active_planes_bins): # Plot data background_map = data_analysis_bin.background_map.as_dense() this_data = data_analysis_bin.observation_map.as_dense() - background_map idx = np.isnan(this_data) # this_data[idx] = hp.UNSEEN if i == 0: total = this_data else: # Sum only when there is no UNSEEN, so that the UNSEEN pixels will stay UNSEEN total[~idx] += this_data[~idx] delta_coord = (self._roi.data_radius.to("deg").value * 2.0) / 15.0 fig, sub = plt.subplots(1, 1) proj = self._represent_healpix_map(fig, total, longitude, latitude, xsize, resolution, smoothing_kernel_sigma) cax = sub.imshow(proj, origin='lower') fig.colorbar(cax) sub.axis('off') hp.graticule(delta_coord, delta_coord) return fig def inner_fit(self): """ This is used for the profile likelihood. Keeping fixed all parameters in the LikelihoodModel, this method minimize the logLike over the remaining nuisance parameters, i.e., the parameters belonging only to the model for this particular detector. If there are no nuisance parameters, simply return the logLike value. """ return self.get_log_like() def get_number_of_data_points(self): """ Return the number of active bins across all active analysis bins :return: number of active bins """ n_points = 0 for bin_id in self._maptree: n_points += self._maptree[bin_id].observation_map.as_partial().shape[0] return n_points def _get_model_map(self, plane_id, n_pt_src, n_ext_src): """ This function returns a model map for a particular bin """ if plane_id not in self._active_planes: raise ValueError( f"{plane_id} not a plane in the current model" ) model_map = SparseHealpix(self._get_expectation(self._maptree[plane_id], plane_id, n_pt_src, n_ext_src), self._active_pixels[plane_id], self._maptree[plane_id].observation_map.nside) return model_map def _get_excess(self, data_analysis_bin, all_maps=True): """ This function returns the excess counts for a particular bin if all_maps=True, also returns the data and background maps """ data_map = data_analysis_bin.observation_map.as_dense() bkg_map = data_analysis_bin.background_map.as_dense() excess = data_map - bkg_map if all_maps: return excess, data_map, bkg_map return excess def _write_a_map(self, file_name, which, fluctuate=False, return_map=False): """ This writes either a model map or a residual map, depending on which one is preferred """ which = which.lower() assert which in ['model', 'residual'] n_pt = self._likelihood_model.get_number_of_point_sources() n_ext = self._likelihood_model.get_number_of_extended_sources() map_analysis_bins = collections.OrderedDict() if fluctuate: poisson_set = self.get_simulated_dataset("model map") for plane_id in self._active_planes: data_analysis_bin = self._maptree[plane_id] bkg = data_analysis_bin.background_map obs = data_analysis_bin.observation_map if fluctuate: model_excess = poisson_set._maptree[plane_id].observation_map \ - poisson_set._maptree[plane_id].background_map else: model_excess = self._get_model_map(plane_id, n_pt, n_ext) if which == 'residual': bkg += model_excess if which == 'model': obs = model_excess + bkg this_bin = DataAnalysisBin(plane_id, observation_hpx_map=obs, background_hpx_map=bkg, active_pixels_ids=self._active_pixels[plane_id], n_transits=data_analysis_bin.n_transits, scheme='RING') map_analysis_bins[plane_id] = this_bin # save the file new_map_tree = MapTree(map_analysis_bins, self._roi) new_map_tree.write(file_name) if return_map: return new_map_tree def write_model_map(self, file_name, poisson_fluctuate=False, test_return_map=False): """ This function writes the model map to a file. The interface is based off of HAWCLike for consistency """ if test_return_map: log.warning("test_return_map=True should only be used for testing purposes!") return self._write_a_map(file_name, 'model', poisson_fluctuate, test_return_map) def write_residual_map(self, file_name, test_return_map=False): """ This function writes the residual map to a file. The interface is based off of HAWCLike for consistency """ if test_return_map: log.warning("test_return_map=True should only be used for testing purposes!") return self._write_a_map(file_name, 'residual', False, test_return_map)

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c88aff50b9e6ce0d5c309be594a03b1f208a90db

15,227

py

Python

sshcustodian/sshcustodian.py

jkglasbrenner/sshcustodian

870d1088f27e1528e27f94f55f2efad7dad32d5d

[ "MIT" ]

null

sshcustodian/sshcustodian.py

jkglasbrenner/sshcustodian

870d1088f27e1528e27f94f55f2efad7dad32d5d

[ "MIT" ]

null

sshcustodian/sshcustodian.py

jkglasbrenner/sshcustodian

870d1088f27e1528e27f94f55f2efad7dad32d5d

[ "MIT" ]

null

# File: sshcustodian/sshcustodian.py # -*- coding: utf-8 -*- # Python 2/3 Compatibility from __future__ import (unicode_literals, division, absolute_import, print_function) from six.moves import filterfalse """ This module creates a subclass of the main Custodian class in the Custodian project (github.com/materialsproject/custodian), which is a wrapper that manages jobs running on computing clusters. The Custodian module is part of The Materials Project (materialsproject.org/). This subclass adds the functionality to copy the temporary directory created via monty to the scratch partitions on slave compute nodes, provided that the cluster's filesystem is configured in this way. The implementation invokes a subprocess to utilize the ssh executable installed on the cluster, so it is not particularly elegant or platform independent, nor is this solution likely to be general to all clusters. This is why this modification has not been submitted as a pull request to the main Custodian project. """ # Import modules import logging import subprocess import sys import datetime import time import os import re from itertools import islice, groupby from socket import gethostname from monty.tempfile import ScratchDir from monty.shutil import gzip_dir from monty.json import MontyEncoder from monty.serialization import dumpfn from custodian.custodian import Custodian from custodian.custodian import CustodianError # Module-level logger logger = logging.getLogger(__name__) class SSHCustodian(Custodian): """ The SSHCustodian class modifies the Custodian class from the custodian module to be able to handle clusters that have separate scratch partitions for each node. When scratch_dir_node_only is enabled, the temp_dir that monty creates will be copied to all other compute nodes used in the calculation and subsequently removed when the job is finished. """ __doc__ += Custodian.__doc__ def __init__(self, handlers, jobs, validators=None, max_errors=1, polling_time_step=10, monitor_freq=30, skip_over_errors=False, scratch_dir=None, gzipped_output=False, checkpoint=False, scratch_dir_node_only=False, pbs_nodefile=None): """ scratch_dir_node_only (bool): If set to True, custodian will grab the list of nodes in the file path provided to pbs_nodefile and use copy the temp_dir to the scratch_dir on each node over ssh. This is necessary on cluster setups where each node has its own independent scratch partition. pbs_nodefile (str): The filepath to the list of nodes to be used in a calculation. If this path does not point to a valid file, then scratch_dir_node_only will be automatically set to False. """ super(SSHCustodian, self).__init__(handlers, jobs, validators, max_errors, polling_time_step, monitor_freq, skip_over_errors, scratch_dir, gzipped_output, checkpoint) self.hostname = gethostname() if pbs_nodefile is None: self.scratch_dir_node_only = False self.slave_compute_node_list = None elif os.path.exists(pbs_nodefile): self.scratch_dir_node_only = scratch_dir_node_only self.pbs_nodefile = pbs_nodefile self.slave_compute_node_list = ( self._process_pbs_nodefile(self.pbs_nodefile, self.hostname)) else: self.scratch_dir_node_only = False self.pbs_nodefile = None self.slave_compute_node_list = None @staticmethod def _process_pbs_nodefile(pbs_nodefile, hostname): with open(pbs_nodefile) as in_file: nodelist = in_file.read().splitlines() slave_compute_node_list = [ node for node, _ in groupby(filterfalse(lambda x: x == hostname, nodelist)) ] return slave_compute_node_list def _copy_to_slave_node_dirs(self, temp_dir_path): """ Copy temporary scratch directory from master node to other nodes. Args: temp_dir_path (str): The path to the temporary scratch directory. It is assumed here that the root path of the scratch directory is the same on all nodes. """ process_list = [] for node in self.slave_compute_node_list: command = ['rsync', '-azhq', temp_dir_path, '{0}:{1}'.format(node, os.path.abspath(self.scratch_dir))] p = subprocess.Popen(command, shell=False) process_list.append(p) # Wait for syncing to finish before moving on for process in process_list: process.wait() def _update_slave_node_vasp_input_files(self, temp_dir_path): """ Update VASP input files in the scratch partition on the slave compute nodes. Args: temp_dir_path (str): The path to the temporary scratch directory. It is assumed here that the root path of the scratch directory is the same on all nodes. """ VASP_INPUT_FILES = [x for x in ["{0}/CHGCAR".format(temp_dir_path), "{0}/WAVECAR".format(temp_dir_path), "{0}/INCAR".format(temp_dir_path), "{0}/POSCAR".format(temp_dir_path), "{0}/POTCAR".format(temp_dir_path), "{0}/KPOINTS".format(temp_dir_path)] if os.path.exists(x)] process_list = [] for node in self.slave_compute_node_list: for filepath in VASP_INPUT_FILES: command = 'scp {0} {1}:{2}/'.format(filepath, node, temp_dir_path) p = subprocess.Popen(command, shell=True) process_list.append(p) # Wait for syncing to finish before moving on for process in process_list: process.wait() def _delete_slave_node_dirs(self, temp_dir_path): """ Delete the temporary scratch directory on the slave nodes. Args: temp_dir_path (str): The path to the temporary scratch directory. It is assumed here that the root path of the scratch directory is the same on all nodes. """ process_list = [] for node in self.slave_compute_node_list: command = 'ssh {0} "rm -rf {1}"'.format(node, temp_dir_path) p = subprocess.Popen(command, shell=True) process_list.append(p) # Wait for deletion to finish before moving on for process in process_list: process.wait() def _manage_node_scratch(self, temp_dir_path, job_start): """ Checks whether the user wants to make use of scratch partitions on each compute node, and if True, either copies the temporary directory to or deletes the temporary directory from each slave compute node. If the user does not specify to use node-specific scratch partitions, then the function does nothing. Args: temp_dir_path (str): The path to the temporary scratch directory. job_start (bool): If True, then the job has started and the temporary directory will be copied to the slave compute nodes. If False, then the temporary directories will be deleted from the slave compute nodes. """ if self.scratch_dir_node_only: if job_start: self._copy_to_slave_node_dirs(temp_dir_path) else: self._delete_slave_node_dirs(temp_dir_path) else: pass def _update_node_scratch(self, temp_dir_path, job): """ Method to update the scratch partitions on the slave compute nodes if they exist and are running a VASP job. Args: temp_dir_path (str): The path to the temporary scratch directory. job (object): The job object you intend to run. Currently supports VASP jobs. """ vasp_re = re.compile(r'vasp') if self.scratch_dir is not None: try: jobtype = job.get_jobtype() if self.scratch_dir_node_only: if vasp_re.match(jobtype): self._update_slave_node_vasp_input_files(temp_dir_path) else: pass else: pass except: pass else: pass def run(self): """ Override of Custodian.run() to include instructions to copy the temp_dir to the scratch partition on slave compute nodes if requested. """ cwd = os.getcwd() with ScratchDir(self.scratch_dir, create_symbolic_link=True, copy_to_current_on_exit=True, copy_from_current_on_enter=True) as temp_dir: self._manage_node_scratch(temp_dir_path=temp_dir, job_start=True) self.total_errors = 0 start = datetime.datetime.now() logger.info("Run started at {} in {}.".format( start, temp_dir)) v = sys.version.replace("\n", " ") logger.info("Custodian running on Python version {}".format(v)) try: # skip jobs until the restart for job_n, job in islice(enumerate(self.jobs, 1), self.restart, None): self._run_job(job_n, job, temp_dir) # Checkpoint after each job so that we can recover from # last point and remove old checkpoints if self.checkpoint: super(SSHCustodian, self)._save_checkpoint(cwd, job_n) except CustodianError as ex: logger.error(ex.message) if ex.raises: raise RuntimeError("{} errors reached: {}. Exited..." .format(self.total_errors, ex)) finally: # Log the corrections to a json file. logger.info("Logging to {}...".format(super(SSHCustodian, self).LOG_FILE)) dumpfn(self.run_log, super(SSHCustodian, self).LOG_FILE, cls=MontyEncoder, indent=4) end = datetime.datetime.now() logger.info("Run ended at {}.".format(end)) run_time = end - start logger.info("Run completed. Total time taken = {}." .format(run_time)) # Remove duplicate copy of log file, provided it ends with # ".log" for x in ([x for x in os.listdir(temp_dir) if re.match(r'\w*\.log', x)]): os.remove(os.path.join(temp_dir, x)) self._manage_node_scratch(temp_dir_path=temp_dir, job_start=False) if self.gzipped_output: gzip_dir(".") # Cleanup checkpoint files (if any) if run is successful. super(SSHCustodian, self)._delete_checkpoints(cwd) return self.run_log def _run_job(self, job_n, job, temp_dir): """ Overrides custodian.custodian._run_job() to propagate changes to input files on different scratch partitions on compute nodes, if needed. """ self.run_log.append({"job": job.as_dict(), "corrections": []}) job.setup() for attempt in range(1, self.max_errors - self.total_errors + 1): # Propagate updated input files, if needed self._update_node_scratch(temp_dir, job) logger.info( "Starting job no. {} ({}) attempt no. {}. Errors " "thus far = {}.".format( job_n, job.name, attempt, self.total_errors)) p = job.run() # Check for errors using the error handlers and perform # corrections. has_error = False # While the job is running, we use the handlers that are # monitors to monitor the job. if isinstance(p, subprocess.Popen): if self.monitors: n = 0 while True: n += 1 time.sleep(self.polling_time_step) if p.poll() is not None: break if n % self.monitor_freq == 0: has_error = self._do_check(self.monitors, p.terminate) else: p.wait() logger.info("{}.run has completed. " "Checking remaining handlers".format(job.name)) # Check for errors again, since in some cases non-monitor # handlers fix the problems detected by monitors # if an error has been found, not all handlers need to run if has_error: self._do_check([h for h in self.handlers if not h.is_monitor]) else: has_error = self._do_check(self.handlers) # If there are no errors detected, perform # postprocessing and exit. if not has_error: for v in self.validators: if v.check(): s = "Validation failed: {}".format(v) raise CustodianError(s, True, v) job.postprocess() return # check that all errors could be handled for x in self.run_log[-1]["corrections"]: if not x["actions"] and x["handler"].raises_runtime_error: s = "Unrecoverable error for handler: {}. " \ "Raising RuntimeError".format(x["handler"]) raise CustodianError(s, True, x["handler"]) for x in self.run_log[-1]["corrections"]: if not x["actions"]: s = "Unrecoverable error for handler: %s" % x["handler"] raise CustodianError(s, False, x["handler"]) logger.info("Max errors reached.") raise CustodianError("MaxErrors", True) # Inherit Custodian docstrings __init__.__doc__ = Custodian.__init__.__doc__ + __init__.__doc__ run.__doc__ = Custodian.run.__doc__ _run_job.__doc__ = Custodian._run_job.__doc__

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c88ca1454e3c43e792033b4722a580761e424d90

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Python

sherlock/__init__.py

akudelka/sherlock

9e85f36c01e0cb1d495283f024423bc60c3f7a4e

[ "MIT" ]

165

2015-01-12T09:09:19.000Z

2022-03-14T11:26:23.000Z

sherlock/__init__.py

akudelka/sherlock

9e85f36c01e0cb1d495283f024423bc60c3f7a4e

[ "MIT" ]

35

2015-01-07T14:57:24.000Z

2022-03-24T17:43:28.000Z

sherlock/__init__.py

akudelka/sherlock

9e85f36c01e0cb1d495283f024423bc60c3f7a4e

[ "MIT" ]

38

2015-03-11T09:10:05.000Z

2022-01-17T11:29:38.000Z

''' Sherlock: Distributed Locks with a choice of backend ==================================================== :mod:`sherlock` is a library that provides easy-to-use distributed inter-process locks and also allows you to choose a backend of your choice for lock synchronization. |Build Status| |Coverage Status| .. |Build Status| image:: https://travis-ci.org/vaidik/sherlock.png :target: https://travis-ci.org/vaidik/sherlock/ .. |Coverage Status| image:: https://coveralls.io/repos/vaidik/incoming/badge.png :target: https://coveralls.io/r/vaidik/incoming Overview -------- When you are working with resources which are accessed by multiple services or distributed services, more than often you need some kind of locking mechanism to make it possible to access some resources at a time. Distributed Locks or Mutexes can help you with this. :mod:`sherlock` provides the exact same facility, with some extra goodies. It provides an easy-to-use API that resembles standard library's `threading.Lock` semantics. Apart from this, :mod:`sherlock` gives you the flexibilty of using a backend of your choice for managing locks. :mod:`sherlock` also makes it simple for you to extend :mod:`sherlock` to use backends that are not supported. Features ++++++++ * API similar to standard library's `threading.Lock`. * Support for With statement, to cleanly acquire and release locks. * Backend agnostic: supports `Redis`_, `Memcached`_ and `Etcd`_ as choice of backends. * Extendable: can be easily extended to work with any other of backend of choice by extending base lock class. Read :ref:`extending`. .. _Redis: http://redis.io .. _Memcached: http://memcached.org .. _Etcd: http://github.com/coreos/etcd Supported Backends and Client Libraries +++++++++++++++++++++++++++++++++++++++ Following client libraries are supported for every supported backend: * Redis: `redis-py`_ * Memcached: `pylibmc`_ * Etcd: `python-etcd`_ .. _redis-py: http://github.com .. _pylibmc: http://github.com .. _python-etcd: https://github.com/jplana/python-etcd As of now, only the above mentioned libraries are supported. Although :mod:`sherlock` takes custom client objects so that you can easily provide settings that you want to use for that backend store, but :mod:`sherlock` also checks if the provided client object is an instance of the supported clients and accepts client objects which pass this check, even if the APIs are the same. :mod:`sherlock` might get rid of this issue later, if need be and if there is a demand for that. Installation ------------ Installation is simple. .. code:: bash pip install sherlock .. note:: :mod:`sherlock` will install all the client libraries for all the supported backends. Basic Usage ----------- :mod:`sherlock` is simple to use as at the API and semantics level, it tries to conform to standard library's :mod:`threading.Lock` APIs. .. code-block:: python import sherlock from sherlock import Lock # Configure :mod:`sherlock`'s locks to use Redis as the backend, # never expire locks and retry acquiring an acquired lock after an # interval of 0.1 second. sherlock.configure(backend=sherlock.backends.REDIS, expire=None, retry_interval=0.1) # Note: configuring sherlock to use a backend does not limit you # another backend at the same time. You can import backend specific locks # like RedisLock, MCLock and EtcdLock and use them just the same way you # use a generic lock (see below). In fact, the generic Lock provided by # sherlock is just a proxy that uses these specific locks under the hood. # acquire a lock called my_lock lock = Lock('my_lock') # acquire a blocking lock lock.acquire() # check if the lock has been acquired or not lock.locked() == True # release the lock lock.release() Support for ``with`` statement ++++++++++++++++++++++++++++++ .. code-block:: python # using with statement with Lock('my_lock'): # do something constructive with your locked resource here pass Blocking and Non-blocking API +++++++++++++++++++++++++++++ .. code-block:: python # acquire non-blocking lock lock1 = Lock('my_lock') lock2 = Lock('my_lock') # successfully acquire lock1 lock1.acquire() # try to acquire lock in a non-blocking way lock2.acquire(False) == True # returns False # try to acquire lock in a blocking way lock2.acquire() # blocks until lock is acquired to timeout happens Using two backends at the same time +++++++++++++++++++++++++++++++++++ Configuring :mod:`sherlock` to use a backend does not limit you from using another backend at the same time. You can import backend specific locks like RedisLock, MCLock and EtcdLock and use them just the same way you use a generic lock (see below). In fact, the generic Lock provided by :mod:`sherlock` is just a proxy that uses these specific locks under the hood. .. code-block:: python import sherlock from sherlock import Lock # Configure :mod:`sherlock`'s locks to use Redis as the backend sherlock.configure(backend=sherlock.backends.REDIS) # Acquire a lock called my_lock, this lock uses Redis lock = Lock('my_lock') # Now acquire locks in Memcached from sherlock import MCLock mclock = MCLock('my_mc_lock') mclock.acquire() Tests ----- To run all the tests (including integration), you have to make sure that all the databases are running. Make sure all the services are running: .. code:: bash # memcached memcached # redis-server redis-server # etcd (etcd is probably not available as package, here is the simplest way # to run it). wget https://github.com/coreos/etcd/releases/download/<version>/etcd-<version>-<platform>.tar.gz tar -zxvf etcd-<version>-<platform>.gz ./etcd-<version>-<platform>/etcd Run tests like so: .. code:: bash python setup.py test Documentation ------------- Available `here`_. .. _here: http://sher-lock.readthedocs.org Roadmap ------- * Support for `Zookeeper`_ as backend. * Support for `Gevent`_, `Multithreading`_ and `Multiprocessing`_. .. _Zookeeper: http://zookeeper.apache.org/ .. _Gevent: http://www.gevent.org/ .. _Multithreading: http://docs.python.org/2/library/multithreading.html .. _Multiprocessing: http://docs.python.org/2/library/multiprocessing.html License ------- See `LICENSE`_. **In short**: This is an open-source project and exists in the public domain for anyone to modify and use it. Just be nice and attribute the credits wherever you can. :) .. _LICENSE: http://github.com/vaidik/sherlock/blob/master/LICENSE.rst Distributed Locking in Other Languages -------------------------------------- * NodeJS - https://github.com/thedeveloper/warlock ''' import etcd import pylibmc import redis class _Backends(object): ''' A simple object that provides a list of available backends. ''' REDIS = { 'name': 'REDIS', 'library': 'redis', 'client_class': redis.StrictRedis, 'lock_class': 'RedisLock', 'default_args': (), 'default_kwargs': {}, } ETCD = { 'name': 'ETCD', 'library': 'etcd', 'client_class': etcd.Client, 'lock_class': 'EtcdLock', 'default_args': (), 'default_kwargs': {}, } MEMCACHED = { 'name': 'MEMCACHED', 'library': 'pylibmc', 'client_class': pylibmc.Client, 'lock_class': 'MCLock', 'default_args': ( ['localhost'], ), 'default_kwargs': { 'binary': True, }, } _valid_backends = ( REDIS, ETCD, MEMCACHED, ) def register(self, name, lock_class, library, client_class, default_args=(), default_kwargs={}): ''' Register a custom backend. :param str name: Name of the backend by which you would want to refer this backend in your code. :param class lock_class: the sub-class of :class:`sherlock.lock.BaseLock` that you have implemented. The reference to your implemented lock class will be used by :class:`sherlock.Lock` proxy to use your implemented class when you globally set that the choice of backend is the one that has been implemented by you. :param str library: dependent client library that this implementation makes use of. :param client_class: the client class or valid type which you use to connect the datastore. This is used by the :func:`configure` function to validate that the object provided for the `client` parameter is actually an instance of this class. :param tuple default_args: default arguments that need to passed to create an instance of the callable passed to `client_class` parameter. :param dict default_kwargs: default keyword arguments that need to passed to create an instance of the callable passed to `client_class` parameter. Usage: >>> import some_db_client >>> class MyLock(sherlock.lock.BaseLock): ... # your implementation comes here ... pass >>> >>> sherlock.configure(name='Mylock', ... lock_class=MyLock, ... library='some_db_client', ... client_class=some_db_client.Client, ... default_args=('localhost:1234'), ... default_kwargs=dict(connection_pool=6)) ''' if not issubclass(lock_class, lock.BaseLock): raise ValueError('lock_class parameter must be a sub-class of ' 'sherlock.lock.BaseLock') setattr(self, name, { 'name': name, 'lock_class': lock_class, 'library': library, 'client_class': client_class, 'default_args': default_args, 'default_kwargs': default_kwargs, }) valid_backends = list(self._valid_backends) valid_backends.append(getattr(self, name)) self._valid_backends = tuple(valid_backends) @property def valid_backends(self): ''' Return a tuple of valid backends. :returns: a list of valid supported backends :rtype: tuple ''' return self._valid_backends def configure(**kwargs): ''' Set basic global configuration for :mod:`sherlock`. :param backend: global choice of backend. This backend will be used for managing locks by :class:`sherlock.Lock` class objects. :param client: global client object to use to connect with backend store. This client object will be used to connect to the backend store by :class:`sherlock.Lock` class instances. The client object must be a valid object of the client library. If the backend has been configured using the `backend` parameter, the custom client object must belong to the same library that is supported for that backend. If the backend has not been set, then the custom client object must be an instance of a valid supported client. In that case, :mod:`sherlock` will set the backend by introspecting the type of provided client object. :param str namespace: provide global namespace :param float expire: provide global expiration time. If expicitly set to `None`, lock will not expire. :param float timeout: provide global timeout period :param float retry_interval: provide global retry interval Basic Usage: >>> import sherlock >>> from sherlock import Lock >>> >>> # Configure sherlock to use Redis as the backend and the timeout for >>> # acquiring locks equal to 20 seconds. >>> sherlock.configure(timeout=20, backend=sherlock.backends.REDIS) >>> >>> import redis >>> redis_client = redis.StrictRedis(host='X.X.X.X', port=6379, db=1) >>> sherlock.configure(client=redis_client) ''' _configuration.update(**kwargs) class _Configuration(object): def __init__(self): # Choice of backend self._backend = None # Client object to connect with the backend store self._client = None # Namespace to use for setting lock keys in the backend store self.namespace = None # Lock expiration time. If explicitly set to `None`, lock will not # expire. self.expire = 60 # Timeout to acquire lock self.timeout = 10 # Retry interval to retry acquiring a lock if previous attempts failed self.retry_interval = 0.1 @property def backend(self): return self._backend @backend.setter def backend(self, val): if val not in backends.valid_backends: backend_names = list(map( lambda x: 'sherlock.backends.%s' % x['name'], backends.valid_backends)) error_str = ', '.join(backend_names[:-1]) backend_names = '%s and %s' % (error_str, backend_names[-1]) raise ValueError('Invalid backend. Valid backends are: ' '%s.' % backend_names) self._backend = val @property def client(self): if self._client is not None: return self._client else: if self.backend is None: raise ValueError('Cannot create a default client object when ' 'backend is not configured.') for backend in backends.valid_backends: if self.backend == backend: self.client = self.backend['client_class']( *self.backend['default_args'], **self.backend['default_kwargs']) return self._client @client.setter def client(self, val): # When backend is set, check client type if self.backend is not None: exc_msg = ('Only a client of the %s library can be used ' 'when using %s as the backend store option.') if isinstance(val, self.backend['client_class']): self._client = val else: raise ValueError(exc_msg % (self.backend['library'], self.backend['name'])) else: for backend in backends.valid_backends: if isinstance(val, backend['client_class']): self._client = val self.backend = backend if self._client is None: raise ValueError('The provided object is not a valid client' 'object. Client objects can only be ' 'instances of redis library\'s client class, ' 'python-etcd library\'s client class or ' 'pylibmc library\'s client class.') def update(self, **kwargs): ''' Update configuration. Provide keyword arguments where the keyword parameter is the configuration and its value (the argument) is the value you intend to set. :param backend: global choice of backend. This backend will be used for managing locks. :param client: global client object to use to connect with backend store. :param str namespace: optional global namespace to namespace lock keys for your application in order to avoid conflicts. :param float expire: set lock expiry time. If explicitly set to `None`, lock will not expire. :param float timeout: global timeout for acquiring a lock. :param float retry_interval: global timeout for retrying to acquire the lock if previous attempts failed. ''' for key, val in kwargs.items(): if key not in dir(self): raise AttributeError('Invalid configuration. No such ' 'configuration as %s.' % key) setattr(self, key, val) # Create a backends singleton backends = _Backends() # Create a configuration singleton _configuration = _Configuration() # Import important Lock classes from . import lock from .lock import *

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c88d252547df6d3f79fae0aefc72512a6ebb61d4

7,199

py

Python

misc.py

ChristophReich1996/Semantic_Pyramid_for_Image_Generation

00e6e7787a5d90b9c09f50a5d7039cb9b5cd4509

[ "MIT" ]

46

2020-04-13T07:54:49.000Z

2022-03-01T06:29:15.000Z

misc.py

ChristophReich1996/Semantic_Pyramid_for_Image_Generation

00e6e7787a5d90b9c09f50a5d7039cb9b5cd4509

[ "MIT" ]

2

2020-07-27T15:11:09.000Z

2021-04-04T10:58:03.000Z

misc.py

ChristophReich1996/Semantic_Pyramid_for_Image_Generation

00e6e7787a5d90b9c09f50a5d7039cb9b5cd4509

[ "MIT" ]

5

2020-06-22T01:56:30.000Z

2021-12-22T04:34:49.000Z

from typing import List, Tuple, Union import torch import torch.nn as nn import torch.nn.functional as F import numpy as np import random from skimage.draw import random_shapes import os import json def get_masks_for_training( mask_shapes: List[Tuple] = [(1, 128, 128), (1, 64, 64), (1, 32, 32), (1, 16, 16), (1, 8, 8), (4096,), (365,)], device: str = 'cpu', add_batch_size: bool = False, p_random_mask: float = 0.3) -> List[torch.Tensor]: ''' Method returns random masks similar to 3.2. of the paper :param mask_shapes: (List[Tuple]) Shapes of the features generated by the vgg16 model :param device: (str) Device to store tensor masks :param add_batch_size: (bool) If true a batch size is added to each mask :param p_random_mask: (float) Probability that a random mask is generated else no mask is utilized :return: (List[torch.Tensor]) Generated masks for each feature tensor ''' # Select layer where no masking is used. Every output from the deeper layers get mapped out. Every higher layer gets # masked by a random shape selected_stage = random.choice(list(range(len(mask_shapes))) + [0, 1]) # Make masks masks = [] # Apply spatial varying masks spatial_varying_masks = (np.random.rand() < p_random_mask) \ and (selected_stage < (len(mask_shapes) - 1)) \ and (selected_stage > 0) # Init random mask if spatial_varying_masks: random_mask = random_shapes(tuple(reversed(mask_shapes))[selected_stage + 1][1:], min_shapes=1, max_shapes=4, min_size=min(8, tuple(reversed(mask_shapes))[selected_stage + 1][1] // 2), allow_overlap=True)[0][:, :, 0] # Random mask to torch tensor random_mask = torch.tensor(random_mask, dtype=torch.float32, device=device)[None, :, :] # Change range of mask to [0, 1] random_mask = (random_mask == 255.0).float() # Loop over all shapes for index, mask_shape in enumerate(reversed(mask_shapes)): # Case if spatial varying masks are applied after selected stage if spatial_varying_masks: if index == selected_stage: masks.append(torch.ones(mask_shape, dtype=torch.float32, device=device)) elif index < selected_stage: masks.append(torch.zeros(mask_shape, dtype=torch.float32, device=device)) else: masks.append(F.interpolate(random_mask[None], size=mask_shape[1:], mode='nearest')[0]) # Case if only one stage is selected else: if index == selected_stage: masks.append(torch.ones(mask_shape, dtype=torch.float32, device=device)) else: masks.append(torch.zeros(mask_shape, dtype=torch.float32, device=device)) # Add batch size dimension if add_batch_size: for index in range(len(masks)): masks[index] = masks[index].unsqueeze(dim=0) # Reverse order of masks to match the features of the vgg16 model masks.reverse() return masks def get_masks_for_validation(mask_shapes: Tuple[Tuple[int, int, int], ...] = ((1, 128, 128), (1, 64, 64), (1, 32, 32), (1, 16, 16), (1, 8, 8), (4096,), (365,)), device: str = 'cpu', add_batch_size: bool = False) -> List[torch.Tensor]: return get_masks_for_inference(stage_index_to_choose=random.choice(range(len(mask_shapes))), mask_shapes=mask_shapes, device=device, add_batch_size=add_batch_size) def get_masks_for_inference(stage_index_to_choose: int, mask_shapes: Tuple[Tuple[int, int, int], ...] = ( (1, 128, 128), (1, 64, 64), (1, 32, 32), (1, 16, 16), (1, 8, 8), (4096,), (365,)), device: str = 'cpu', add_batch_size: bool = False) -> List[torch.Tensor]: # Init list for masks masks = [] # Loop over all shapes for index, mask_shape in enumerate(reversed(mask_shapes)): if index == stage_index_to_choose: masks.append(torch.ones(mask_shape, dtype=torch.float32, device=device)) else: masks.append(torch.zeros(mask_shape, dtype=torch.float32, device=device)) # Add batch size dimension if add_batch_size: for index in range(len(masks)): masks[index] = masks[index].unsqueeze(dim=0) # Reverse order of masks to match the features of the vgg16 model masks.reverse() return masks def normalize_0_1_batch(input: torch.tensor) -> torch.tensor: ''' Normalize a given tensor to a range of [-1, 1] :param input: (Torch tensor) Input tensor :return: (Torch tensor) Normalized output tensor ''' input_flatten = input.view(input.shape[0], -1) return ((input - torch.min(input_flatten, dim=1)[0][:, None, None, None]) / ( torch.max(input_flatten, dim=1)[0][:, None, None, None] - torch.min(input_flatten, dim=1)[0][:, None, None, None])) def normalize_m1_1_batch(input: torch.tensor) -> torch.tensor: ''' Normalize a given tensor to a range of [-1, 1] :param input: (Torch tensor) Input tensor :return: (Torch tensor) Normalized output tensor ''' input_flatten = input.view(input.shape[0], -1) return 2 * ((input - torch.min(input_flatten, dim=1)[0][:, None, None, None]) / ( torch.max(input_flatten, dim=1)[0][:, None, None, None] - torch.min(input_flatten, dim=1)[0][:, None, None, None])) - 1 class Logger(object): """ Class to log different metrics """ def __init__(self) -> None: self.metrics = dict() self.hyperparameter = dict() def log(self, metric_name: str, value: float) -> None: """ Method writes a given metric value into a dict including list for every metric :param metric_name: (str) Name of the metric :param value: (float) Value of the metric """ if metric_name in self.metrics: self.metrics[metric_name].append(value) else: self.metrics[metric_name] = [value] def save_metrics(self, path: str) -> None: """ Static method to save dict of metrics :param metrics: (Dict[str, List[float]]) Dict including metrics :param path: (str) Path to save metrics :param add_time_to_file_name: (bool) True if time has to be added to filename of every metric """ # Save dict of hyperparameter as json file with open(os.path.join(path, 'hyperparameter.txt'), 'w') as json_file: json.dump(self.hyperparameter, json_file) # Iterate items in metrics dict for metric_name, values in self.metrics.items(): # Convert list of values to torch tensor to use build in save method from torch values = torch.tensor(values) # Save values torch.save(values, os.path.join(path, '{}.pt'.format(metric_name)))

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c88f24e0c4f56b49a1514bbc5fcfcc00efd5e15c

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py

Python

EasyMCDM/models/Irmo.py

qanastek/EasyMCDM

7fa2e2dfe9397834ca9f50211ea2717a16785394

[ "MIT" ]

4

2022-03-05T20:51:38.000Z

2022-03-15T17:10:22.000Z

EasyMCDM/models/Irmo.py

qanastek/EasyMCDM

7fa2e2dfe9397834ca9f50211ea2717a16785394

[ "MIT" ]

null

EasyMCDM/models/Irmo.py

qanastek/EasyMCDM

7fa2e2dfe9397834ca9f50211ea2717a16785394

[ "MIT" ]

1

2022-03-08T13:45:22.000Z

import math from typing import Dict, List, Tuple, Union from EasyMCDM.models.MCDM import MCDM # Instant-Runoff Multicriteria Optimization (IRMO) class Irmo(MCDM): # Memory allocation __slots__ = ['verbose', 'matrix', 'names', 'indexes', 'preferences', 'matrix'] # Constructor def __init__(self, data : Union[str, dict], col_sep=',', row_sep='\n', verbose=True): super().__init__(data, col_sep=col_sep, row_sep=row_sep, verbose=verbose) # Read the lines of indexes def get_indexes(self, path) -> List: f = open(path,"r") content = f.read() f.close() return [[int(i) for i in w.split(self.col_sep)] for w in content.split(self.row_sep) if len(w) > 0] def __getVector(self, i, idx, banned, nbr_rounds): items_lst = [] for s in self.matrix.keys(): # Check if already banned if s not in banned: insert_value = self.matrix[s][i] else: # Best item value if (idx == nbr_rounds - 1 and self.preferences[idx] == "min") or (idx != nbr_rounds - 1 and self.preferences[idx] == "max"): insert_value = math.inf else: insert_value = -math.inf items_lst.append(insert_value) return items_lst # Compute def __compute(self) -> Tuple[float, float]: banned = [] # Check if the number of criteria is higher than the number of subjects else reduce the number of rounds nbr_rounds = len(self.indexes) if len(self.indexes) <= len(self.matrix.keys()) else len(self.matrix.keys()) # For each criteria for idx, i in enumerate(self.indexes): # Values for the subjects left items_vec = self.__getVector(i, idx, banned, nbr_rounds) # Best item value if (idx == nbr_rounds - 1 and self.preferences[idx] == "min") or (idx != nbr_rounds - 1 and self.preferences[idx] == "max"): value = min(items_vec) else: value = max(items_vec) # Worst item index item_idx = items_vec.index(value) item_name = list(self.matrix.keys())[item_idx] # Ban Worst item banned.append(item_name) # Reverse the rank banned.reverse() return { "best": banned[0], # Return best "eleminated": banned } # Solve the problem def solve( self, indexes : Union[str, list], prefs : Union[str, List[str]], indexes_idx = 0 ) -> Dict: # Define the indexes of the attributes if type(indexes) == str: self.indexes = self.get_indexes(indexes)[indexes_idx] elif type(indexes) == list: self.indexes = indexes # Check if the lengths matches togethers assert len(self.indexes) <= self.constraints_length, '\033[91m' + "The number of indexes as a variable length, please give a consistent length with the matrix constraints !" + '\033[0m' # Check variable types assert all(isinstance(e, (int)) for e in self.indexes), '\033[91m' + "The indexes as variable types, please give only integers !" + '\033[0m' # Get preferences if type(prefs) == str: self.preferences = self.get_preferences(prefs) elif type(prefs) == list: self.preferences = prefs # Check if has preferences other than max and min assert all([a in ['max', 'min'] for a in sorted(list(set(self.preferences)))]), '\033[91m' + "The preferences need to containt only min and max. Found : " + str(sorted(list(set(self.preferences)))) + '\033[0m' # Check if the lengths matches togethers assert len(self.preferences) == len(self.indexes), '\033[91m' + "The number of preferences as a variable length, please give a consistent length with the indexes !" + '\033[0m' return self.__compute()

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1

0

c8919966f9b0c8cb69e17d80a649cb9b3d0b7138

2,046

py

Python

ramp/estimators/r.py

kvh/ramp

8618ce673e49b95f40c9659319c3cb72281dacac

[ "MIT" ]

214

2015-01-01T07:42:25.000Z

2022-03-08T08:57:49.000Z

ramp/estimators/r.py

Marigold/ramp

f9ddea84bc3b5097c0ddb8a3f71a0fce1775ba76

[ "MIT" ]

8

2020-05-19T20:15:40.000Z

2020-05-19T20:15:41.000Z

ramp/estimators/r.py

Marigold/ramp

f9ddea84bc3b5097c0ddb8a3f71a0fce1775ba76

[ "MIT" ]

87

2015-01-13T19:25:15.000Z

2021-05-16T10:40:05.000Z

import numpy as np from rpy2.robjects import FloatVector from rpy2.robjects.packages import importr from rpy2 import robjects stats = importr('stats') base = importr('base') def matrix_to_r_dataframe(x): rx = FloatVector(np.ravel(x)) rx = robjects.r['matrix'](rx, nrow = len(x), byrow=True) return robjects.r["data.frame"](rx) class REstimator(object): def __init__(self, r_estimator, **kwargs): self.estimator = r_estimator self.kwargs = kwargs def fit(self, x, y): rx = matrix_to_r_dataframe(x) ry = FloatVector(y) robjects.globalenv["y"] = ry self.estimator_fit = self.estimator("y ~ .", data=rx, **self.kwargs) def predict(self, x): rx = matrix_to_r_dataframe(x) return np.array(stats.predict(self.estimator_fit, rx)[0]) class OrderedLogit(object): def fit(self, x, y): ordinal = importr('ordinal') rx = matrix_to_r_dataframe(x) self.levels = range(int(round(min(y))), int(round(max(y)))+1) ry = base.factor(FloatVector(y), levels=self.levels, ordered=True) robjects.globalenv["y"] = ry self.clmfit = ordinal.clm("y ~ .", data=rx) #print base.summary(self.clmfit) def predict(self, x): rx = matrix_to_r_dataframe(x) rfac = stats.predict(self.clmfit, rx, type="class")[0] rvec = [self.levels[v - 1] for v in rfac] return rvec class WeightedLM(object): def fit(self, x, y, weights): rx = matrix_to_r_dataframe(x) ry = FloatVector(y) rw = FloatVector(weights) robjects.globalenv["score"] = ry self.lmfit = stats.lm("score ~ .", data=rx, weights=rw) #print base.summary(self.clmfit) def predict(self, x): rx = matrix_to_r_dataframe(x) rvec = stats.predict(self.lmfit, rx)[0] return np.array(rvec) class GBM(REstimator): def __init__(self, **kwargs): gbm = importr('gbm') super(GBM, self).__init__(gbm.gbm, **kwargs)

29.652174

74

0.610948

278

2,046

4.363309

0.251799

0.046167

0.051937

0.103875

0.301731

0.236603

0.189613

0.131904

0

0.005215

0.250244

2,046

68

75

30.088235

0.785528

0.030303

0

0.294118

0

0.033317

0

1

0.176471

false

0

0.156863

0

0.490196

0

null

0

null

0

1

0

c89234777cdd2b2357d8a397dcec12fefab43a56

1,138

py

Python

tests/decorators/test_timer.py

ShaneMicro/azure-functions-python-library

f56564effbf291a27e1bd5751a38484af387bb7f

[ "MIT" ]

null

tests/decorators/test_timer.py

ShaneMicro/azure-functions-python-library

f56564effbf291a27e1bd5751a38484af387bb7f

[ "MIT" ]

1

2022-03-02T11:49:02.000Z

tests/decorators/test_timer.py

ShaneMicro/azure-functions-python-library

f56564effbf291a27e1bd5751a38484af387bb7f

[ "MIT" ]

null

# Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. import unittest from azure.functions.decorators.constants import TIMER_TRIGGER from azure.functions.decorators.core import BindingDirection, DataType from azure.functions.decorators.timer import TimerTrigger class TestTimer(unittest.TestCase): def test_timer_trigger_valid_creation(self): trigger = TimerTrigger(name="req", schedule="dummy_schedule", data_type=DataType.UNDEFINED, run_on_startup=False, use_monitor=False, dummy_field="dummy") self.assertEqual(trigger.get_binding_name(), "timerTrigger") self.assertEqual(trigger.get_dict_repr(), { "type": TIMER_TRIGGER, "direction": BindingDirection.IN, 'dummyField': 'dummy', "name": "req", "dataType": DataType.UNDEFINED, "schedule": "dummy_schedule", "runOnStartup": False, "useMonitor": False })

37.933333

70

0.598418

102

1,138

6.509804

0.558824

0.040663

0.081325

0.126506

0

0.311951

1,138

29

71

39.241379

0.84802

0.079086

0

0.1159

0

0.086957

1

0.043478

false

0

0.173913

0

0.26087

0

null

0

null

0

1

0

c895e6b35498811fbcaa8204ceff2eff7744a4b3

8,368

py

Python

src/client.py

Da3dalu2/SimpleNetworkSimulator

447bc099b35720ab8d6e8a9703bb2354f1f01cae

[ "MIT" ]

null

src/client.py

Da3dalu2/SimpleNetworkSimulator

447bc099b35720ab8d6e8a9703bb2354f1f01cae

[ "MIT" ]

null

src/client.py

Da3dalu2/SimpleNetworkSimulator

447bc099b35720ab8d6e8a9703bb2354f1f01cae

[ "MIT" ]

null

import socket import threading import time from threading import Thread import utilities as utils import error_handling as check BUFFER_SIZE = 1024 BROADCAST_MAC = "FF:FF:FF:FF:FF:FF" class ClientThread(threading.Thread): """ Initializes the client. The event synchronization primitive, among the initialization parameters given, is used to guarantee that the client will send its message when the router is actually listening. """ def __init__(self, init_params): self.connected = False self.clients_threads = init_params["clients_threads"] self.arp_table_mac = init_params["arp_table_mac"] self.client_data = init_params["client_data"] self.client_id = init_params["client_id"] self.router_thread = init_params["router_thread"] self.router_id = init_params["router_id"] self.sync_event_message = init_params["sync_event_message"] self.sync_event_connection = init_params["sync_event_connection"] self.stop_event = threading.Event() self.sleep_time = 1.0 port = self.client_data["port"] address = ("localhost", port) self.client_connection = check.socket_create( address, backlog = 0, timeout = 3, reuse_address = True ) threading.Thread.__init__(self, name=self.client_id) """ Client main loop. Listens for messages from the network. """ def run(self): utils.show_status(self.getName(), "starting") connected = self.go_online() if(connected is True): utils.show_status(self.client_id, "listening for incoming packets") while not self.stop_event.isSet(): self.listen_packets() # exit procedure utils.show_status(self.client_id, "going offline") utils.show_status(self.client_id, "closing connection") self.client_connection.close() self.stop_event.clear() del self.clients_threads[self.client_id] """ Tells the client to exit from its main loop. It goes offline thus closing its connection to the network. """ def join(self, timeout=None): self.stop_event.set() threading.Thread.join(self, timeout) """ Tells the router of its network to start listening for a message from this client. """ def notify_incoming_message(self): msg = " ".join(["notifying", self.router_id, "of an incoming message"]) utils.show_status(self.client_id, msg) my_ip_address = self.client_data["ip_address"] listen_task = threading.Thread( target=self.router_thread.listen_client_side, args=[my_ip_address], daemon=True ) listen_task.start() """ Tells the router of its network to start listening for a connection from this client. """ def notify_incoming_connection(self): msg = " ".join(["notifying",self.router_id, \ "of an incoming connection"]) utils.show_status(self.client_id, msg) listen_task = threading.Thread( target=self.router_thread.listen_connections_client_side, daemon=True ) listen_task.start() """ Sends packets to other clients. """ def send_message(self, recipient_ip, message): gateway_ip = self.client_data["gateway_ip"] packet = utils.write_packet( self.client_data["ip_address"], recipient_ip, self.client_data.get("mac_address"), self.arp_table_mac[gateway_ip], message ) utils.show_status( self.client_id, "waiting for router listening messages" ) self.notify_incoming_message() # waiting for router approving message sending self.sync_event_message.wait() sent = check.socket_send(self.client_connection, packet, self.router_id) if(sent is True): msg = " ".join(["message sent to", gateway_ip]) utils.show_status(self.client_id, msg) self.sync_event_message.clear() """ Sends a special packet to notify the server it is currently online. Returns false if the connection was not established or the packet could not be sent (in the latter case the server will not recognize the client as online, hence the action go_online is considered failed even if a connection has been created) """ def go_online(self): utils.show_status(self.client_id, "connecting to the network") server_ip = self.client_data["server_ip"] router_port = self.client_data["gateway_port"] router_address = ("localhost", router_port) gateway_ip = self.client_data["gateway_ip"] self.notify_incoming_connection() # waiting for router approving connection self.sync_event_connection.wait() self.sync_event_connection.clear() # ready for reuse connected = check.socket_connect( self.client_connection, router_address, self.client_id ) if(connected is True): utils.show_status(self.client_id, "going online") # waiting for router completing connection procedure self.sync_event_connection.wait() self.sync_event_connection.clear() # ready for reuse greeting_packet = utils.write_packet( self.client_data.get("ip_address"), server_ip, self.client_data.get("mac_address"), self.arp_table_mac[gateway_ip], "{going online}" ) utils.show_status( self.client_id, "waiting for router accepting message" ) self.notify_incoming_message() # waiting for router approving message sending self.sync_event_message.wait() self.sync_event_message.clear() check.socket_send( self.client_connection, greeting_packet, self.client_id, "Greeting packet could not be sent" ) return connected """ Sends a special packet to notify the server it is currently offline. Then closes its connection to the network. """ def go_offline(self): utils.show_status(self.client_id, "going offline") gateway_ip = self.client_data["gateway_ip"] server_ip = self.client_data["server_ip"] leave_packet = utils.write_packet( self.client_data.get("ip_address"), server_ip, self.client_data.get("mac_address"), self.arp_table_mac[gateway_ip], "{going offline}" ) self.notify_incoming_message() self.sync_event_message.wait() # wait for router approval self.sync_event_message.clear() check.socket_send( self.client_connection, leave_packet, self.client_id, "Leave packet could not be sent" ) self.join() """ Listens for packets from the server. """ def listen_packets(self): received_message = check.socket_recv( self.client_connection, self.client_id ) if(received_message is not None and len(received_message) > 0): parsed_message = utils.read_packet(received_message) time.sleep(2) # give time to router to show its status msg = " ".join(["message received from:", parsed_message["source_ip"]]) utils.show_status(self.client_id, msg) utils.report( self.client_id, parsed_message, "reading received packet" ) if(parsed_message["destination_mac"] == BROADCAST_MAC): msg = " ".join(["received an ARP request from", parsed_message["source_ip"]]) utils.show_status(self.client_id, msg) self.send_message( parsed_message.get("source_ip"), "{ARP reply}" )

32.30888

80

0.603848

963

8,368

5.011423

0.186916

0.089101

0.052217

0.055118

0.456486

0.410485

0.373187

0.30978

0.293618

0.245545

0

0.001735

0.311066

8,368

258

81

32.434109

0.835386

0.059154

0

0.357143

0

0.113235

0.003064

0

1

0.053571

false

0

0.035714

0

0.10119

0

null

0

null

0

1

0

c8a19d3ee1214101499b5145f53a93867a82f056

675

py

Python

dl/src/CookieManager.py

PatrykCholewa/PI_Stored

4ff4d72fe56281b76ddf7b759c19aabbce3c9899

[ "MIT" ]

null

dl/src/CookieManager.py

PatrykCholewa/PI_Stored

4ff4d72fe56281b76ddf7b759c19aabbce3c9899

[ "MIT" ]

null

dl/src/CookieManager.py

PatrykCholewa/PI_Stored

4ff4d72fe56281b76ddf7b759c19aabbce3c9899

[ "MIT" ]

null

from datetime import datetime import jwt from src import ConfigManager secret = ConfigManager.get_config("DL_COOKIE_SECRET_KEY") secure = ConfigManager.get_config("APP_SECURE") def validate_user_jwt(token, username): token = jwt.decode(token, secret, "HS256") expire = token['exp'] if username != token['user']: return False return datetime.now() < datetime.fromtimestamp(expire) def validate_file_by_jwt(token, file_id): token = jwt.decode(token, secret, "HS256") expire = token['exp'] file_ids = token['file_list'] if file_id not in file_ids: return False return datetime.now() < datetime.fromtimestamp(expire)

23.275862

58

0.708148

88

675

5.25

0.397727

0.060606

0.095238

0.082251

0.428571

0.190476

0

0.010889

0.183704

675

28

59

24.107143

0.827586

0

0.444444

0

0.087407

0

1

0.111111

false

0

0.166667

0

0.5

0

null

0

null

0

1

0

c8a47ee8db41845109ebaa2bf272e65a01b66623

2,683

py

Python

argos/countdown.9s.py

solettitiger/countdown

c5df89c7d67984171de08508ef4433ea9d6fbbd1

[ "MIT" ]

null

argos/countdown.9s.py

solettitiger/countdown

c5df89c7d67984171de08508ef4433ea9d6fbbd1

[ "MIT" ]

null

argos/countdown.9s.py

solettitiger/countdown

c5df89c7d67984171de08508ef4433ea9d6fbbd1

[ "MIT" ]

null

#!/usr/bin/python3 # -*- coding: utf-8 -*- import datetime import sys import subprocess import os from playsound import playsound # ****************************************************************** # Definitionen # ****************************************************************** filename = 'countdown.txt' audiofile = 'ringing.mp3' settimer = 'add.py' stoptimer = 'stop.py' overlay = 'overlay.py' title = "⏰" zeit = "" command = "" path = "" diff = 0 # ****************************************************************** # Funktionen # ****************************************************************** def readdata(): global title, zeit, command, path full_path = os.path.realpath(__file__) path, thisfile = os.path.split(full_path) ff = open(path+"/countdown/"+filename,"r") ll = ff.readlines() if(len(ll) == 3): title = ll[0].strip() zeit = ll[1].strip() command = ll[2].strip() ff.close() def gettimediff(): global zeit now = datetime.datetime.now() day = datetime.datetime(now.year, now.month, now.day) endtime = datetime.datetime.strptime(now.strftime("%Y-%m-%d ") + zeit, "%Y-%m-%d %H:%M") diff = int((endtime-now).seconds/60) if(diff < 0): diff = diff + 1440 if(diff < 1 and diff >= -1): runDone() else: zeit = convertTime(diff) def runDone(): global zeit # Command ausführen if(command != ""): cmdlist = command.split() subprocess.Popen(cmdlist, stdout=subprocess.PIPE, stderr=subprocess.PIPE, stdin=subprocess.PIPE) # Overlay anzeigen subprocess.Popen([path+"/countdown/"+overlay, beautifyTimestring(zeit), title], stdout=subprocess.PIPE, stderr=subprocess.PIPE, stdin=subprocess.PIPE) zeit = "" # Sound abspielen playsound(path+"/countdown/"+audiofile) # Countdown beenden - dauert die Zeit von Argos stopCountdown() def stopCountdown(): ff = open(path+"/countdown/"+filename,"w") ff.close() def convertTime(minutes): hours = int(minutes/60) minutes = minutes - hours*60 str_hours = "0" + str(hours) str_minutes = "0" + str(minutes) return (str_hours[-2:] + ":" + str_minutes[-2:]) def beautifyTimestring(timestring): times = timestring.split(":") str_hours = "0" + times[0] str_minutes = "0" + times[1] return (str_hours[-2:] + ":" + str_minutes[-2:]) # ****************************************************************** # Main # ****************************************************************** def main(): readdata() if(zeit != ""): gettimediff() print (title + " " + zeit) print ("---") print ("Set Timer | bash='"+ path+"/countdown/"+settimer +"' terminal=false") print ("Stopp Timer | bash='"+ path+"/countdown/"+stoptimer +"' terminal=false") if __name__ == "__main__": main()

26.83

151

0.561685

295

2,683

5.037288

0.362712

0.05249

0.020188

0.025572

0.15074

0.114401

0.079408

0

0.013339

0.133805

2,683

99

152

27.10101

0.625645

0.211331

0

0.112676

0

0.10852

0

1

0.098592

false

0

0.070423

0

0.197183

0.056338

0

null

0

null

0

1

0

c8a8f855a2d0fbd314903aae2f023f9e8c19884d

5,043

py

Python

multimodal_models/StackGAN_V2_PyTorch/models.py

kumayu0108/model-zoo

4285779f6ff51fa1efb0625d67b428e90c343c0c

[ "MIT" ]

43

2020-05-16T21:05:34.000Z

2022-02-08T11:33:29.000Z

multimodal_models/StackGAN_V2_PyTorch/models.py

kumayu0108/model-zoo

4285779f6ff51fa1efb0625d67b428e90c343c0c

[ "MIT" ]

52

2020-05-14T16:18:08.000Z

2021-11-02T19:13:47.000Z

multimodal_models/StackGAN_V2_PyTorch/models.py

kumayu0108/model-zoo

4285779f6ff51fa1efb0625d67b428e90c343c0c

[ "MIT" ]

69

2020-05-14T13:39:23.000Z

2021-07-30T00:51:27.000Z

import torch import torch.nn as nn from generator_model import G1, G2 from helper_functions.Blocks import downBlock, Block3x3_leakRelu from helper_functions.ret_image import Interpolate, condAugmentation from helper_functions.initial_weights import weights_init from helper_functions.losses import KLloss, custom_loss from helper_functions.Blocks import upScale, normalBlock, Residual import helper_functions.config as cfg class GET_IMAGE_G(nn.Module): def __init__(self, ngf): super(GET_IMAGE_G, self).__init__() self.gf_dim = ngf self.img = nn.Sequential( nn.Conv2d(ngf, 3, kernel_size=3, stride=1, padding=1, bias=False), nn.Tanh()) def forward(self, h_code): out_img = self.img(h_code) return out_img class G_NET(nn.Module): def __init__(self, StageNum, zDim = 100): super(G_NET, self).__init__() self.zDim = zDim self.StageNum = StageNum self.gf_dim = cfg.generatorDim self.define_module() def define_module(self): self.ca_net = condAugmentation() if self.StageNum == 1: self.h_net1 = G1(self.gf_dim * 16, self.zDim) self.img_net1 = GET_IMAGE_G(self.gf_dim) elif self.StageNum == 2: self.h_net1 = G1(self.gf_dim * 16, self.zDim) self.img_net1 = GET_IMAGE_G(self.gf_dim) self.h_net2 = G2(self.gf_dim) self.img_net2 = GET_IMAGE_G(self.gf_dim // 2) elif self.StageNum == 3: self.h_net1 = G1(self.gf_dim * 16, self.zDim) self.img_net1 = GET_IMAGE_G(self.gf_dim) self.h_net2 = G2(self.gf_dim) self.img_net2 = GET_IMAGE_G(self.gf_dim // 2) self.h_net3 = G2(self.gf_dim // 2) self.img_net3 = GET_IMAGE_G(self.gf_dim // 4) elif self.StageNum == 4: self.h_net1 = G1(self.gf_dim * 16, self.zDim) self.img_net1 = GET_IMAGE_G(self.gf_dim) self.h_net2 = G2(self.gf_dim) self.img_net2 = GET_IMAGE_G(self.gf_dim // 2) self.h_net3 = G2(self.gf_dim // 2) self.img_net3 = GET_IMAGE_G(self.gf_dim // 4) self.h_net4 = G2(self.gf_dim // 4, num_residual=1) self.img_net4 = GET_IMAGE_G(self.gf_dim // 8) def forward(self, z_code, text_embedding=None): c_code, mu, logvar = self.ca_net(text_embedding) fake_imgs = [] if self.StageNum == 1: h_code1 = self.h_net1(z_code, c_code) fake_img1 = self.img_net1(h_code1) fake_imgs.append(fake_img1) elif self.StageNum == 2: h_code1 = self.h_net1(z_code, c_code) fake_img1 = self.img_net1(h_code1) fake_imgs.append(fake_img1) h_code2 = self.h_net2(h_code1, c_code) fake_img2 = self.img_net2(h_code2) fake_imgs.append(fake_img2) elif self.StageNum == 3: h_code1 = self.h_net1(z_code, c_code) fake_img1 = self.img_net1(h_code1) fake_imgs.append(fake_img1) h_code2 = self.h_net2(h_code1, c_code) fake_img2 = self.img_net2(h_code2) fake_imgs.append(fake_img2) h_code3 = self.h_net3(h_code2, c_code) fake_img3 = self.img_net3(h_code3) fake_imgs.append(fake_img3) elif self.StageNum == 4: h_code1 = self.h_net1(z_code, c_code) fake_img1 = self.img_net1(h_code1) fake_imgs.append(fake_img1) h_code2 = self.h_net2(h_code1, c_code) fake_img2 = self.img_net2(h_code2) fake_imgs.append(fake_img2) h_code3 = self.h_net3(h_code2, c_code) fake_img3 = self.img_net3(h_code3) fake_imgs.append(fake_img3) h_code4 = self.h_net4(h_code3, c_code) fake_img4 = self.img_net4(h_code4) fake_imgs.append(fake_img4) return fake_imgs, mu, logvar class eval256(nn.Module): def __init__(self): super(eval256, self).__init__() self.df_dim = cfg.discriminatorDim self.ef_dim = cfg.embeddingsDim self.define_module() def define_module(self): ndf = self.df_dim efg = self.ef_dim self.img_code_s16 = encode_image_by_16times(ndf) self.img_code_s32 = downBlock(ndf * 8, ndf * 16) self.img_code_s64 = downBlock(ndf * 16, ndf * 32) self.img_code_s64_1 = Block3x3_leakRelu(ndf * 32, ndf * 16) self.img_code_s64_2 = Block3x3_leakRelu(ndf * 16, ndf * 8) self.logits = nn.Sequential( nn.Conv2d(ndf * 8, 1, kernel_size=4, stride=4), nn.Sigmoid()) def forward(self, x_var, c_code=None): x_code = self.img_code_s16(x_var) x_code = self.img_code_s32(x_code) x_code = self.img_code_s64(x_code) x_code = self.img_code_s64_1(x_code) x_code = self.img_code_s64_2(x_code) h_c_code = x_code output = self.logits(h_c_code) return output.view(-1)

39.093023

78

0.615507

766

5,043

3.708877

0.148825

0.078845

0.069694

0.050334

0.530799

0.471313

0.451602

0.426962

0.401619

0

0.052632

0.284156

5,043

129

79

39.093023

0.734349

0

0.491379

0

1

0.068966

false

0

0.077586

0

0.198276

0

null

0

null

0

1

0

c8a9475637b6493e4ff65f91b1c3dca0e1d6f885

382

py

Python

utils/agro_utils.py

TiagoMarta/data_fusion_Vineyard-Segmentation

de54e149d36027bb314b5890ea4a1e71ba472d17

[ "Unlicense", "MIT" ]

3

2021-08-04T08:03:50.000Z

2022-03-25T11:22:09.000Z

utils/agro_utils.py

TiagoMarta/data_fusion_Vineyard-Segmentation

de54e149d36027bb314b5890ea4a1e71ba472d17

[ "Unlicense", "MIT" ]

null

utils/agro_utils.py

TiagoMarta/data_fusion_Vineyard-Segmentation

de54e149d36027bb314b5890ea4a1e71ba472d17

[ "Unlicense", "MIT" ]

null

import numpy as np def NDVI(nir,red): ''' # https://eos.com/make-an-analysis/ndvi/ Inputs: nxm numpy arrays NIR – reflection in the near-infrared spectrum RED – reflection in the red range of the spectrum ''' num = nir-red dom = nir+red ndvi = np.divide(num,dom) ndvi[np.isnan(ndvi)]=0 # Clean array with nan return(ndvi)

25.466667

57

0.609948

59

382

3.983051

0.610169

0.076596

0.110638

0.13617

0

0.003636

0.280105

382

15

58

25.466667

0.843636

0.505236

0

1

0.142857

false

0

0.142857

0

0.285714

0

null

0

null

0

1

0

c8abec201704fed99560906ddf5c95d5088bad9f

840

py

Python

heap/maxSlidingWindow.py

saai/LeetcodePythonSolutions

201f2054dda3f303ae6a376b40cbc7f98688322c

[ "MIT" ]

null

heap/maxSlidingWindow.py

saai/LeetcodePythonSolutions

201f2054dda3f303ae6a376b40cbc7f98688322c

[ "MIT" ]

null

heap/maxSlidingWindow.py

saai/LeetcodePythonSolutions

201f2054dda3f303ae6a376b40cbc7f98688322c

[ "MIT" ]

null

class Solution(object): def maxSlidingWindow(self, nums, k): """ :type nums: List[int] :type k: int :rtype: List[int] """ res = [] tmp = [] # tmp[0] always save the current windows max for i in xrange(len(nums)): if i < k-1: # first k-1 numbers while tmp and nums[tmp[-1]]<nums[i]: # keep tmp[0] the max tmp.pop() tmp.append(i) continue while tmp and nums[tmp[-1]] < nums[i]: # find proper location for nums[i] tmp.pop() tmp.append(i) while tmp and tmp[0]<= i-k: #pop the old max values tmp.pop(0) res.append(nums[tmp[0]]) return res

31.111111

85

0.42381

102

840

3.490196

0.411765

0.044944

0.092697

0.08427

0.224719

0.134831

0

0.019912

0.461905

840

27

86

31.111111

0.767699

0.22619

0

0.352941

0

1

0.058824

false

0

0.176471

0

null

0

null

0

1

0

c8adae8d9f3f33704f82f32bb3e323260ea0ba97

29,151

py

Python

tccli/services/tsf/v20180326/help.py

zyh911/tencentcloud-cli

dfc5dbd660d4c60d265921c4edc630091478fc41

[ "Apache-2.0" ]

null

tccli/services/tsf/v20180326/help.py

zyh911/tencentcloud-cli

dfc5dbd660d4c60d265921c4edc630091478fc41

[ "Apache-2.0" ]

null

tccli/services/tsf/v20180326/help.py

zyh911/tencentcloud-cli

dfc5dbd660d4c60d265921c4edc630091478fc41

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- DESC = "tsf-2018-03-26" INFO = { "DeletePublicConfig": { "params": [ { "name": "ConfigId", "desc": "配置项ID" } ], "desc": "删除公共配置项" }, "DescribeSimpleGroups": { "params": [ { "name": "GroupIdList", "desc": "部署组ID列表，不填写时查询全量" }, { "name": "ApplicationId", "desc": "应用ID，不填写时查询全量" }, { "name": "ClusterId", "desc": "集群ID，不填写时查询全量" }, { "name": "NamespaceId", "desc": "命名空间ID，不填写时查询全量" }, { "name": "Limit", "desc": "每页条数" }, { "name": "Offset", "desc": "起始偏移量" }, { "name": "GroupId", "desc": "部署组ID，不填写时查询全量" }, { "name": "SearchWord", "desc": "模糊查询，部署组名称，不填写时查询全量" }, { "name": "AppMicroServiceType", "desc": "部署组类型，精确过滤字段，M：service mesh, P：原生应用， M：网关应用" } ], "desc": "查询简单部署组列表" }, "CreateGroup": { "params": [ { "name": "ApplicationId", "desc": "部署组所属的应用ID" }, { "name": "NamespaceId", "desc": "部署组所属命名空间ID" }, { "name": "GroupName", "desc": "部署组名称" }, { "name": "ClusterId", "desc": "集群ID" }, { "name": "GroupDesc", "desc": "部署组描述" } ], "desc": "创建容器部署组" }, "CreateCluster": { "params": [ { "name": "ClusterName", "desc": "集群名称" }, { "name": "ClusterType", "desc": "集群类型" }, { "name": "VpcId", "desc": "私有网络ID" }, { "name": "ClusterCIDR", "desc": "分配给集群容器和服务IP的CIDR" }, { "name": "ClusterDesc", "desc": "集群备注" }, { "name": "TsfRegionId", "desc": "集群所属TSF地域" }, { "name": "TsfZoneId", "desc": "集群所属TSF可用区" }, { "name": "SubnetId", "desc": "私有网络子网ID" } ], "desc": "创建集群" }, "DescribePkgs": { "params": [ { "name": "ApplicationId", "desc": "应用ID（只传入应用ID，返回该应用下所有软件包信息）" }, { "name": "SearchWord", "desc": "查询关键字（支持根据包ID，包名，包版本号搜索）" }, { "name": "OrderBy", "desc": "排序关键字（默认为\"UploadTime\"：上传时间）" }, { "name": "OrderType", "desc": "升序：0/降序：1（默认降序）" }, { "name": "Offset", "desc": "查询起始偏移" }, { "name": "Limit", "desc": "返回数量限制" } ], "desc": "无" }, "ModifyContainerReplicas": { "params": [ { "name": "GroupId", "desc": "部署组ID，部署组唯一标识" }, { "name": "InstanceNum", "desc": "实例数量" } ], "desc": "修改容器部署组实例数" }, "DescribeConfigSummary": { "params": [ { "name": "ApplicationId", "desc": "应用ID，不传入时查询全量" }, { "name": "SearchWord", "desc": "查询关键字，模糊查询：应用名称，配置项名称，不传入时查询全量" }, { "name": "Offset", "desc": "偏移量，默认为0" }, { "name": "Limit", "desc": "每页条数，默认为20" } ], "desc": "查询配置汇总列表" }, "DeployContainerGroup": { "params": [ { "name": "GroupId", "desc": "部署组ID，分组唯一标识" }, { "name": "Server", "desc": "镜像server" }, { "name": "TagName", "desc": "镜像版本名称,如v1" }, { "name": "InstanceNum", "desc": "实例数量" }, { "name": "Reponame", "desc": "旧版镜像名，如/tsf/nginx" }, { "name": "CpuLimit", "desc": "最大的 CPU 核数，对应 K8S 的 limit；不填时默认为 request 的 2 倍" }, { "name": "MemLimit", "desc": "最大的内存 MiB 数，对应 K8S 的 limit；不填时默认为 request 的 2 倍" }, { "name": "JvmOpts", "desc": "jvm参数" }, { "name": "CpuRequest", "desc": "分配的 CPU 核数，对应 K8S 的 request" }, { "name": "MemRequest", "desc": "分配的内存 MiB 数，对应 K8S 的 request" }, { "name": "DoNotStart", "desc": "是否不立即启动" }, { "name": "RepoName", "desc": "（优先使用）新版镜像名，如/tsf/nginx" }, { "name": "UpdateType", "desc": "更新方式：0:快速更新 1:滚动更新" }, { "name": "UpdateIvl", "desc": "滚动更新必填，更新间隔" } ], "desc": "部署容器应用" }, "AddClusterInstances": { "params": [ { "name": "ClusterId", "desc": "集群ID" }, { "name": "InstanceIdList", "desc": "云主机ID列表" }, { "name": "OsName", "desc": "操作系统名称" }, { "name": "ImageId", "desc": "操作系统镜像ID" }, { "name": "Password", "desc": "重装系统密码设置" }, { "name": "KeyId", "desc": "重装系统，关联密钥设置" }, { "name": "SgId", "desc": "安全组设置" }, { "name": "InstanceImportMode", "desc": "云主机导入方式，虚拟机集群必填，容器集群不填写此字段，R：重装TSF系统镜像，M：手动安装agent" } ], "desc": "添加云主机节点至TSF集群" }, "DescribePodInstances": { "params": [ { "name": "GroupId", "desc": "实例所属groupId" }, { "name": "Offset", "desc": "偏移量，取值从0开始" }, { "name": "Limit", "desc": "分页个数，默认为20，取值应为1~50" } ], "desc": "获取部署组实例列表" }, "DescribeServerlessGroups": { "params": [ { "name": "SearchWord", "desc": "搜索字段，模糊搜索groupName字段" }, { "name": "ApplicationId", "desc": "分组所属应用ID" }, { "name": "OrderBy", "desc": "排序字段，默认为 createTime字段，支持id， name， createTime" }, { "name": "OrderType", "desc": "排序方式，默认为1：倒序排序，0：正序，1：倒序" }, { "name": "Offset", "desc": "偏移量，取值从0开始" }, { "name": "Limit", "desc": "分页个数，默认为20，取值应为1~50" }, { "name": "NamespaceId", "desc": "分组所属名字空间ID" }, { "name": "ClusterId", "desc": "分组所属集群ID" } ], "desc": "查询Serverless部署组列表" }, "CreateNamespace": { "params": [ { "name": "NamespaceName", "desc": "命名空间名称" }, { "name": "ClusterId", "desc": "集群ID" }, { "name": "NamespaceDesc", "desc": "命名空间描述" }, { "name": "NamespaceResourceType", "desc": "命名空间资源类型(默认值为DEF)" }, { "name": "NamespaceType", "desc": "是否是全局命名空间(默认是DEF，表示普通命名空间；GLOBAL表示全局命名空间)" }, { "name": "NamespaceId", "desc": "命名空间ID" } ], "desc": "创建命名空间" }, "DeleteApplication": { "params": [ { "name": "ApplicationId", "desc": "应用ID" } ], "desc": "删除应用" }, "DeleteMicroservice": { "params": [ { "name": "MicroserviceId", "desc": "微服务ID" } ], "desc": "删除微服务" }, "StartGroup": { "params": [ { "name": "GroupId", "desc": "部署组ID" } ], "desc": "启动分组" }, "DeleteNamespace": { "params": [ { "name": "NamespaceId", "desc": "命名空间ID" }, { "name": "ClusterId", "desc": "集群ID" } ], "desc": "删除命名空间" }, "DescribeGroupInstances": { "params": [ { "name": "GroupId", "desc": "部署组ID" }, { "name": "SearchWord", "desc": "搜索字段" }, { "name": "OrderBy", "desc": "排序字段" }, { "name": "OrderType", "desc": "排序类型" }, { "name": "Offset", "desc": "偏移量" }, { "name": "Limit", "desc": "分页个数" } ], "desc": "查询虚拟机部署组云主机列表" }, "DeleteConfig": { "params": [ { "name": "ConfigId", "desc": "配置项ID" } ], "desc": "删除配置项" }, "DescribePublicConfigSummary": { "params": [ { "name": "SearchWord", "desc": "查询关键字，模糊查询：配置项名称，不传入时查询全量" }, { "name": "Offset", "desc": "偏移量，默认为0" }, { "name": "Limit", "desc": "每页条数，默认为20" } ], "desc": "查询公共配置汇总列表" }, "DeletePkgs": { "params": [ { "name": "ApplicationId", "desc": "应用ID" }, { "name": "PkgIds", "desc": "需要删除的程序包ID列表" } ], "desc": "从软件仓库批量删除程序包。\n一次最多支持删除1000个包，数量超过1000，返回UpperDeleteLimit错误。" }, "RevocationPublicConfig": { "params": [ { "name": "ConfigReleaseId", "desc": "配置项发布ID" } ], "desc": "撤回已发布的公共配置" }, "DescribePublicConfigs": { "params": [ { "name": "ConfigId", "desc": "配置项ID，不传入时查询全量，高优先级" }, { "name": "Offset", "desc": "偏移量，默认为0" }, { "name": "Limit", "desc": "每页条数，默认为20" }, { "name": "ConfigIdList", "desc": "配置项ID列表，不传入时查询全量，低优先级" }, { "name": "ConfigName", "desc": "配置项名称，精确查询，不传入时查询全量" }, { "name": "ConfigVersion", "desc": "配置项版本，精确查询，不传入时查询全量" } ], "desc": "查询公共配置项列表" }, "DescribeSimpleClusters": { "params": [ { "name": "ClusterIdList", "desc": "需要查询的集群ID列表，不填或不传入时查询所有内容" }, { "name": "ClusterType", "desc": "需要查询的集群类型，不填或不传入时查询所有内容" }, { "name": "Offset", "desc": "查询偏移量，默认为0" }, { "name": "Limit", "desc": "分页个数，默认为20，取值应为1~50" }, { "name": "SearchWord", "desc": "对id和name进行关键词过滤" } ], "desc": "查询简单集群列表" }, "CreateServerlessGroup": { "params": [ { "name": "ApplicationId", "desc": "分组所属应用ID" }, { "name": "GroupName", "desc": "分组名称字段，长度1~60，字母或下划线开头，可包含字母数字下划线" }, { "name": "NamespaceId", "desc": "分组所属名字空间ID" }, { "name": "ClusterId", "desc": "分组所属集群ID" } ], "desc": "创建Serverless部署组" }, "DescribeConfigs": { "params": [ { "name": "ApplicationId", "desc": "应用ID，不传入时查询全量" }, { "name": "ConfigId", "desc": "配置项ID，不传入时查询全量，高优先级" }, { "name": "Offset", "desc": "偏移量" }, { "name": "Limit", "desc": "每页条数" }, { "name": "ConfigIdList", "desc": "配置项ID列表，不传入时查询全量，低优先级" }, { "name": "ConfigName", "desc": "配置项名称，精确查询，不传入时查询全量" }, { "name": "ConfigVersion", "desc": "配置项版本，精确查询，不传入时查询全量" } ], "desc": "查询配置项列表" }, "DescribeConfig": { "params": [ { "name": "ConfigId", "desc": "配置项ID" } ], "desc": "查询配置" }, "DescribeMicroservices": { "params": [ { "name": "NamespaceId", "desc": "命名空间ID" }, { "name": "SearchWord", "desc": "搜索字段" }, { "name": "OrderBy", "desc": "排序字段" }, { "name": "OrderType", "desc": "排序类型" }, { "name": "Offset", "desc": "偏移量" }, { "name": "Limit", "desc": "分页个数" } ], "desc": "获取微服务列表" }, "StartContainerGroup": { "params": [ { "name": "GroupId", "desc": "部署组ID" } ], "desc": "启动容器部署组" }, "RemoveInstances": { "params": [ { "name": "ClusterId", "desc": "集群 ID" }, { "name": "InstanceIdList", "desc": "云主机 ID 列表" } ], "desc": "从 TSF 集群中批量移除云主机节点" }, "ExpandGroup": { "params": [ { "name": "GroupId", "desc": "部署组ID" }, { "name": "InstanceIdList", "desc": "扩容的机器实例ID列表" } ], "desc": "虚拟机部署组添加实例" }, "DeleteGroup": { "params": [ { "name": "GroupId", "desc": "部署组ID" } ], "desc": "删除容器部署组" }, "DescribeContainerGroupDetail": { "params": [ { "name": "GroupId", "desc": "分组ID" } ], "desc": " 容器部署组详情" }, "DeleteContainerGroup": { "params": [ { "name": "GroupId", "desc": "部署组ID，分组唯一标识" } ], "desc": "删除容器部署组" }, "RollbackConfig": { "params": [ { "name": "ConfigReleaseLogId", "desc": "配置项发布历史ID" }, { "name": "ReleaseDesc", "desc": "回滚描述" } ], "desc": "回滚配置" }, "ModifyMicroservice": { "params": [ { "name": "MicroserviceId", "desc": "微服务 ID" }, { "name": "MicroserviceDesc", "desc": "微服务备注信息" } ], "desc": "修改微服务详情" }, "CreatePublicConfig": { "params": [ { "name": "ConfigName", "desc": "配置项名称" }, { "name": "ConfigVersion", "desc": "配置项版本" }, { "name": "ConfigValue", "desc": "配置项值，总是接收yaml格式的内容" }, { "name": "ConfigVersionDesc", "desc": "配置项版本描述" }, { "name": "ConfigType", "desc": "配置项类型" } ], "desc": "创建公共配置项" }, "DescribeImageTags": { "params": [ { "name": "ApplicationId", "desc": "应用Id" }, { "name": "Offset", "desc": "偏移量，取值从0开始" }, { "name": "Limit", "desc": "分页个数，默认为20，取值应为1~100" }, { "name": "QueryImageIdFlag", "desc": "不填和0:查询 1:不查询" }, { "name": "SearchWord", "desc": "可用于搜索的 tag 名字" } ], "desc": "镜像版本列表" }, "DescribeServerlessGroup": { "params": [ { "name": "GroupId", "desc": "部署组ID" } ], "desc": "查询Serverless部署组明细" }, "DescribeMicroservice": { "params": [ { "name": "MicroserviceId", "desc": "微服务ID" }, { "name": "Offset", "desc": "偏移量" }, { "name": "Limit", "desc": "分页个数" } ], "desc": "查询微服务详情" }, "DescribePublicConfigReleaseLogs": { "params": [ { "name": "NamespaceId", "desc": "命名空间ID，不传入时查询全量" }, { "name": "Offset", "desc": "偏移量，默认为0" }, { "name": "Limit", "desc": "每页条数，默认为20" } ], "desc": "查询公共配置发布历史" }, "DescribeApplicationAttribute": { "params": [ { "name": "ApplicationId", "desc": "应用ID" } ], "desc": "获取应用列表其它字段，如实例数量信息等" }, "RevocationConfig": { "params": [ { "name": "ConfigReleaseId", "desc": "配置项发布ID" } ], "desc": "撤回已发布的配置" }, "ReleasePublicConfig": { "params": [ { "name": "ConfigId", "desc": "配置ID" }, { "name": "NamespaceId", "desc": "命名空间ID" }, { "name": "ReleaseDesc", "desc": "发布描述" } ], "desc": "发布公共配置" }, "ReleaseConfig": { "params": [ { "name": "ConfigId", "desc": "配置ID" }, { "name": "GroupId", "desc": "部署组ID" }, { "name": "ReleaseDesc", "desc": "发布描述" } ], "desc": "发布配置" }, "DescribeReleasedConfig": { "params": [ { "name": "GroupId", "desc": "部署组ID" } ], "desc": "查询group发布的配置" }, "CreateContainGroup": { "params": [ { "name": "ApplicationId", "desc": "分组所属应用ID" }, { "name": "NamespaceId", "desc": "分组所属命名空间ID" }, { "name": "GroupName", "desc": "分组名称字段，长度1~60，字母或下划线开头，可包含字母数字下划线" }, { "name": "InstanceNum", "desc": "实例数量" }, { "name": "AccessType", "desc": "0:公网 1:集群内访问 2：NodePort" }, { "name": "ProtocolPorts", "desc": "数组对象，见下方定义" }, { "name": "ClusterId", "desc": "集群ID" }, { "name": "CpuLimit", "desc": "最大分配 CPU 核数，对应 K8S limit" }, { "name": "MemLimit", "desc": "最大分配内存 MiB 数，对应 K8S limit" }, { "name": "GroupComment", "desc": "分组备注字段，长度应不大于200字符" }, { "name": "UpdateType", "desc": "更新方式：0:快速更新 1:滚动更新" }, { "name": "UpdateIvl", "desc": "滚动更新必填，更新间隔" }, { "name": "CpuRequest", "desc": "初始分配的 CPU 核数，对应 K8S request" }, { "name": "MemRequest", "desc": "初始分配的内存 MiB 数，对应 K8S request" } ], "desc": "创建容器部署组" }, "DescribePublicConfigReleases": { "params": [ { "name": "ConfigName", "desc": "配置项名称，不传入时查询全量" }, { "name": "NamespaceId", "desc": "命名空间ID，不传入时查询全量" }, { "name": "Limit", "desc": "每页条数" }, { "name": "Offset", "desc": "偏移量" }, { "name": "ConfigId", "desc": "配置项ID，不传入时查询全量" } ], "desc": "查询公共配置发布信息" }, "DescribeGroups": { "params": [ { "name": "SearchWord", "desc": "搜索字段" }, { "name": "ApplicationId", "desc": "应用ID" }, { "name": "OrderBy", "desc": "排序字段" }, { "name": "OrderType", "desc": "排序方式" }, { "name": "Offset", "desc": "偏移量" }, { "name": "Limit", "desc": "分页个数" }, { "name": "NamespaceId", "desc": "命名空间ID" }, { "name": "ClusterId", "desc": "集群ID" }, { "name": "GroupResourceTypeList", "desc": "部署组资源类型列表" } ], "desc": "获取虚拟机部署组列表" }, "DescribeSimpleNamespaces": { "params": [ { "name": "NamespaceIdList", "desc": "命名空间ID列表，不传入时查询全量" }, { "name": "ClusterId", "desc": "集群ID，不传入时查询全量" }, { "name": "Limit", "desc": "每页条数" }, { "name": "Offset", "desc": "起始偏移量" }, { "name": "NamespaceId", "desc": "命名空间ID，不传入时查询全量" }, { "name": "NamespaceResourceTypeList", "desc": "查询资源类型列表" }, { "name": "SearchWord", "desc": "通过id和name进行过滤" }, { "name": "NamespaceTypeList", "desc": "查询的命名空间类型列表" }, { "name": "NamespaceName", "desc": "通过命名空间名精确过滤" }, { "name": "IsDefault", "desc": "通过是否是默认命名空间过滤，不传表示拉取全部命名空间。0：默认，命名空间。1：非默认命名空间" } ], "desc": "查询简单命名空间列表 " }, "DescribeConfigReleaseLogs": { "params": [ { "name": "GroupId", "desc": "部署组ID，不传入时查询全量" }, { "name": "Offset", "desc": "偏移量，默认为0" }, { "name": "Limit", "desc": "每页条数，默认为20" }, { "name": "NamespaceId", "desc": "命名空间ID，不传入时查询全量" }, { "name": "ClusterId", "desc": "集群ID，不传入时查询全量" }, { "name": "ApplicationId", "desc": "应用ID，不传入时查询全量" } ], "desc": "查询配置发布历史" }, "CreateMicroservice": { "params": [ { "name": "NamespaceId", "desc": "命名空间ID" }, { "name": "MicroserviceName", "desc": "微服务名称" }, { "name": "MicroserviceDesc", "desc": "微服务描述信息" } ], "desc": "新增微服务" }, "DescribeDownloadInfo": { "params": [ { "name": "ApplicationId", "desc": "应用ID" }, { "name": "PkgId", "desc": "程序包ID" } ], "desc": "TSF上传的程序包存放在腾讯云对象存储（COS）中，通过该API可以获取从COS下载程序包需要的信息，包括包所在的桶、存储路径、鉴权信息等，之后使用COS API（或SDK）进行下载。\nCOS相关文档请查阅：https://cloud.tencent.com/document/product/436" }, "DeployServerlessGroup": { "params": [ { "name": "GroupId", "desc": "部署组ID" }, { "name": "PkgId", "desc": "程序包ID" }, { "name": "Memory", "desc": "所需实例内存大小，取值为 1Gi 2Gi 4Gi 8Gi 16Gi，缺省为 1Gi，不传表示维持原态" }, { "name": "InstanceRequest", "desc": "要求最小实例数，取值范围 [1, 4]，缺省为 1，不传表示维持原态" }, { "name": "StartupParameters", "desc": "部署组启动参数，不传表示维持原态" } ], "desc": "部署Serverless应用" }, "DescribeGroup": { "params": [ { "name": "GroupId", "desc": "部署组ID" } ], "desc": "查询虚拟机部署组详情" }, "CreateConfig": { "params": [ { "name": "ConfigName", "desc": "配置项名称" }, { "name": "ConfigVersion", "desc": "配置项版本" }, { "name": "ConfigValue", "desc": "配置项值" }, { "name": "ApplicationId", "desc": "应用ID" }, { "name": "ConfigVersionDesc", "desc": "配置项版本描述" }, { "name": "ConfigType", "desc": "配置项值类型" } ], "desc": "创建配置项" }, "DescribeContainerGroups": { "params": [ { "name": "SearchWord", "desc": "搜索字段，模糊搜索groupName字段" }, { "name": "ApplicationId", "desc": "分组所属应用ID" }, { "name": "OrderBy", "desc": "排序字段，默认为 createTime字段，支持id， name， createTime" }, { "name": "OrderType", "desc": "排序方式，默认为1：倒序排序，0：正序，1：倒序" }, { "name": "Offset", "desc": "偏移量，取值从0开始" }, { "name": "Limit", "desc": "分页个数，默认为20，取值应为1~50" }, { "name": "ClusterId", "desc": "集群ID" }, { "name": "NamespaceId", "desc": "命名空间 ID" } ], "desc": "容器部署组列表" }, "DeleteImageTags": { "params": [ { "name": "ImageTags", "desc": "镜像版本数组" } ], "desc": "批量删除镜像版本" }, "DescribeClusterInstances": { "params": [ { "name": "ClusterId", "desc": "集群ID" }, { "name": "SearchWord", "desc": "搜索字段" }, { "name": "OrderBy", "desc": "排序字段" }, { "name": "OrderType", "desc": "排序类型" }, { "name": "Offset", "desc": "偏移量" }, { "name": "Limit", "desc": "分页个数" } ], "desc": "查询集群实例" }, "CreateApplication": { "params": [ { "name": "ApplicationName", "desc": "应用名称" }, { "name": "ApplicationType", "desc": "应用类型，V：虚拟机应用；C：容器应用；S：serverless应用" }, { "name": "MicroserviceType", "desc": "应用微服务类型，M：service mesh应用；N：普通应用；G：网关应用" }, { "name": "ApplicationDesc", "desc": "应用描述" }, { "name": "ApplicationLogConfig", "desc": "应用日志配置项，废弃参数" }, { "name": "ApplicationResourceType", "desc": "应用资源类型，废弃参数" }, { "name": "ApplicationRuntimeType", "desc": "应用runtime类型" } ], "desc": "创建应用" }, "StopGroup": { "params": [ { "name": "GroupId", "desc": "部署组ID" } ], "desc": "停止虚拟机部署组" }, "ShrinkGroup": { "params": [ { "name": "GroupId", "desc": "部署组ID" } ], "desc": "下线部署组所有机器实例" }, "DeployGroup": { "params": [ { "name": "GroupId", "desc": "部署组ID" }, { "name": "PkgId", "desc": "程序包ID" }, { "name": "StartupParameters", "desc": "部署组启动参数" } ], "desc": "部署虚拟机部署组应用" }, "DescribeApplications": { "params": [ { "name": "SearchWord", "desc": "搜索字段" }, { "name": "OrderBy", "desc": "排序字段" }, { "name": "OrderType", "desc": "排序类型" }, { "name": "Offset", "desc": "偏移量" }, { "name": "Limit", "desc": "分页个数" }, { "name": "ApplicationType", "desc": "应用类型" }, { "name": "MicroserviceType", "desc": "应用的微服务类型" }, { "name": "ApplicationResourceTypeList", "desc": "应用资源类型数组" } ], "desc": "获取应用列表" }, "DeleteServerlessGroup": { "params": [ { "name": "GroupId", "desc": "groupId，分组唯一标识" } ], "desc": "删除Serverless部署组" }, "DescribeUploadInfo": { "params": [ { "name": "ApplicationId", "desc": "应用ID" }, { "name": "PkgName", "desc": "程序包名" }, { "name": "PkgVersion", "desc": "程序包版本" }, { "name": "PkgType", "desc": "程序包类型" }, { "name": "PkgDesc", "desc": "程序包介绍" } ], "desc": "TSF会将软件包上传到腾讯云对象存储（COS）。调用此接口获取上传信息，如目标地域，桶，包Id，存储路径，鉴权信息等，之后请使用COS API（或SDK）进行上传。\nCOS相关文档请查阅：https://cloud.tencent.com/document/product/436" }, "DescribeConfigReleases": { "params": [ { "name": "ConfigName", "desc": "配置项名称，不传入时查询全量" }, { "name": "GroupId", "desc": "部署组ID，不传入时查询全量" }, { "name": "NamespaceId", "desc": "命名空间ID，不传入时查询全量" }, { "name": "ClusterId", "desc": "集群ID，不传入时查询全量" }, { "name": "Limit", "desc": "每页条数" }, { "name": "Offset", "desc": "偏移量" }, { "name": "ConfigId", "desc": "配置ID，不传入时查询全量" }, { "name": "ApplicationId", "desc": "应用ID，不传入时查询全量" } ], "desc": "查询配置发布信息" }, "StopContainerGroup": { "params": [ { "name": "GroupId", "desc": "部署组ID" } ], "desc": "停止容器部署组" }, "DescribeSimpleApplications": { "params": [ { "name": "ApplicationIdList", "desc": "应用ID列表" }, { "name": "ApplicationType", "desc": "应用类型" }, { "name": "Limit", "desc": "每页条数" }, { "name": "Offset", "desc": "起始偏移量" }, { "name": "MicroserviceType", "desc": "微服务类型" }, { "name": "ApplicationResourceTypeList", "desc": "资源类型数组" }, { "name": "SearchWord", "desc": "通过id和name进行关键词过滤" } ], "desc": "查询简单应用列表" }, "DescribePublicConfig": { "params": [ { "name": "ConfigId", "desc": "需要查询的配置项ID" } ], "desc": "查询公共配置（单条）" }, "ModifyContainerGroup": { "params": [ { "name": "GroupId", "desc": "部署组ID" }, { "name": "AccessType", "desc": "0:公网 1:集群内访问 2：NodePort" }, { "name": "ProtocolPorts", "desc": "ProtocolPorts数组" }, { "name": "UpdateType", "desc": "更新方式：0:快速更新 1:滚动更新" }, { "name": "UpdateIvl", "desc": "更新间隔,单位秒" } ], "desc": "修改容器部署组" }, "DescribeApplication": { "params": [ { "name": "ApplicationId", "desc": "应用ID" } ], "desc": "获取应用详情" }, "ShrinkInstances": { "params": [ { "name": "GroupId", "desc": "部署组ID" }, { "name": "InstanceIdList", "desc": "下线机器实例ID列表" } ], "desc": "虚拟机部署组下线实例" }, "ModifyUploadInfo": { "params": [ { "name": "ApplicationId", "desc": "应用ID" }, { "name": "PkgId", "desc": "调用DescribeUploadInfo接口时返回的软件包ID" }, { "name": "Result", "desc": "COS返回上传结果（默认为0：成功，其他值表示失败）" }, { "name": "Md5", "desc": "程序包MD5" }, { "name": "Size", "desc": "程序包大小（单位字节）" } ], "desc": "调用该接口和COS的上传接口后，需要调用此接口更新TSF中保存的程序包状态。\n调用此接口完成后，才标志上传包流程结束。" }, "AddInstances": { "params": [ { "name": "ClusterId", "desc": "集群ID" }, { "name": "InstanceIdList", "desc": "云主机ID列表" }, { "name": "OsName", "desc": "操作系统名称" }, { "name": "ImageId", "desc": "操作系统镜像ID" }, { "name": "Password", "desc": "重装系统密码设置" }, { "name": "KeyId", "desc": "重装系统，关联密钥设置" }, { "name": "SgId", "desc": "安全组设置" }, { "name": "InstanceImportMode", "desc": "云主机导入方式，虚拟机集群必填，容器集群不填写此字段，R：重装TSF系统镜像，M：手动安装agent" } ], "desc": "添加云主机节点至TSF集群" } }

18.567516

165

0.392028

1,844

29,151

6.197397

0.284707

0.064753

0.032814

0.038502

0.510851

0.45861

0.35973

0.322979

0.287714

0.254725

0

0.007365

0.41309

29,151

1,570

166

18.567516

0.660588

0.00072

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0.404511

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0

1

0

false

0.001275

0

0.001275

0

null

0

null

0

1

0

c8b067f63a4c14a9b78ac5bf7aace3e8420c7a16

1,729

py

Python

workflow_scripts/test_models.py

jcwchen/models

2fd86acdd51037570e1daefa03873237b76bd5a6

[ "MIT" ]

1

2020-12-19T14:46:23.000Z

workflow_scripts/test_models.py

sumit6597/models

2fd86acdd51037570e1daefa03873237b76bd5a6

[ "MIT" ]

null

workflow_scripts/test_models.py

sumit6597/models

2fd86acdd51037570e1daefa03873237b76bd5a6

[ "MIT" ]

1

2021-08-08T11:47:35.000Z

import onnx from pathlib import Path import subprocess import sys def run_lfs_install(): result = subprocess.run(['git', 'lfs', 'install'], cwd=cwd_path, stdout=subprocess.PIPE, stderr=subprocess.PIPE) print("Git LFS install completed with return code=" + str(result.returncode)) def pull_lfs_file(file_name): result = subprocess.run(['git', 'lfs', 'pull', '--include', file_name, '--exclude', '\"\"'], cwd=cwd_path, stdout=subprocess.PIPE, stderr=subprocess.PIPE) print("LFS pull completed with return code=" + str(result.returncode)) cwd_path = Path.cwd() # obtain list of added or modified files in this PR obtain_diff = subprocess.Popen(['git', 'diff', '--name-only', '--diff-filter=AM', 'origin/master', 'HEAD'], cwd=cwd_path, stdout=subprocess.PIPE, stderr=subprocess.PIPE) stdoutput, stderroutput = obtain_diff.communicate() diff_list = stdoutput.split() # identify list of changed onnx models in model Zoo model_list = [str(model).replace("b'","").replace("'", "") for model in diff_list if ".onnx" in str(model)] # run lfs install before starting the tests run_lfs_install() print("\n=== Running ONNX Checker on added models ===\n") # run checker on each model failed_models = [] for model_path in model_list: model_name = model_path.split('/')[-1] print("Testing:", model_name) try: pull_lfs_file(model_path) model = onnx.load(model_path) onnx.checker.check_model(model) print("Model", model_name, "has been successfully checked!") except Exception as e: print(e) failed_models.append(model_path) if len(failed_models) != 0: print(str(len(failed_models)) +" models failed onnx checker.") sys.exit(1) print(len(model_list), "model(s) checked.")

35.285714

156

0.707924

248

1,729

4.798387

0.354839

0.070588

0.032773

0.040336

0.247059

0.205042

0.134454

0.092437

0

0.002024

0.142857

1,729

48

157

36.020833

0.800945

0.096588

0

0.205523

0

1

0.058824

false

0

0.117647

0

0.176471

0.235294

0

null

0

null

0

1

0

c8b5d127b254896268904720f95e3739d411d338

1,374

py

Python

src/classifier/utils/create_data.py

maxscheijen/dutch-sentiment-classifier

6b3149d906710fadc0b104a9f79ca389a7f5cba3

[ "Apache-2.0" ]

null

src/classifier/utils/create_data.py

maxscheijen/dutch-sentiment-classifier

6b3149d906710fadc0b104a9f79ca389a7f5cba3

[ "Apache-2.0" ]

null

src/classifier/utils/create_data.py

maxscheijen/dutch-sentiment-classifier

6b3149d906710fadc0b104a9f79ca389a7f5cba3

[ "Apache-2.0" ]

null

import glob import pandas as pd from tqdm import tqdm from classifier import config class Dataset: """Create dataset class""" def __init__(self): # Get all txt files self.paths = sorted(glob.glob("data/*/*/*.txt")) self.dataframe = None def load_data(self): dfs = [] # initialize list for dataframes # Loop over all txt files for filepath in tqdm(self.paths): # Read text files with open(filepath, "r") as f: text = f.read() # Create label from path if "pos" in filepath: sentiment = "positief" else: sentiment = "negatief" # Append dataframe to list dfs.append(pd.DataFrame({"text": [text], "sentiment": [sentiment]})) # Concat DataFrames self.dataframe = pd.concat(dfs).reset_index(drop=True) def save_data(self): # Create train and test split train_data = self.dataframe.sample(frac=config.SPLIT_SIZE, random_state=config.SEED) test_data = self.dataframe.iloc[train_data.index] # Save data train_data.to_csv(config.TRAIN_DATA, index=None) test_data.to_csv(config.TEST_DATA, index=None)

28.625

68

0.54294

154

1,374

4.727273

0.441558

0.071429

0.03022

0.041209

0

0.365357

1,374

47

69

29.234043

0.834862

0.15575

0

0.041012

0

1

0.115385

false

0

0.153846

0

0.307692

0

null

0

null

0

1

0

c8b68cb341dae475cc25f2d74d8dcd06d0f58623

1,682

py

Python

algorithms/intervals.py

calebperkins/algorithms

9f4a029261160e6b12b8bedd53f0a0ebf541237a

[ "MIT" ]

null

algorithms/intervals.py

calebperkins/algorithms

9f4a029261160e6b12b8bedd53f0a0ebf541237a

[ "MIT" ]

null

algorithms/intervals.py

calebperkins/algorithms

9f4a029261160e6b12b8bedd53f0a0ebf541237a

[ "MIT" ]

null

import collections Interval = collections.namedtuple("Interval", "start, end") class AugmentedTree: """ An augmented tree for querying intervals. The nodes are ordered by the start interval. The high attribute is the maximum end interval of the node and any of its children. This tree could become imbalanced. More advanced augmented trees should be a based on a self-balancing BST. """ def __init__(self, interval): self.interval = interval self.high = interval.end self.left = None self.right = None def overlaps(self, interval): i = self.interval return i.end >= interval.start and i.start <= interval.end def intersecting(self, interval): s = [self] while s: n = s.pop() if n.high < interval.start: continue if n.overlaps(interval): yield n.interval if n.right and n.right.interval.start <= interval.end: s.append(n.right) if n.left: s.append(n.left) def __lt__(self, other): return self.interval.start < other.interval.start def add(self, interval): # Create a new node and add it to a leaf m = AugmentedTree(interval) n = self while True: n.high = max(n.high, m.high) if m < n: if n.left: n = n.left else: n.left = m return else: if n.right: n = n.right else: n.right = m return

29

116

0.521998

202

1,682

4.306931

0.361386

0.096552

0.036782

0

0.399524

1,682

57

117

29.508772

0.861386

0.189655

0

0.166667

0

0.013443

0

1

0.119048

false

0

0.02381

0.261905

0

null

0

null

0

1

0

c8bd12730bd20c4875906f949b15caeb99026f0f

4,874

py

Python

utils/visualization.py

yigitozgumus/Polimi_Thesis

711c1edcf1fdb92fc6c15bf5ab1be141c13995c3

[ "MIT" ]

3

2019-07-27T14:00:42.000Z

2020-01-17T17:07:51.000Z

utils/visualization.py

yigitozgumus/Polimi_Thesis

711c1edcf1fdb92fc6c15bf5ab1be141c13995c3

[ "MIT" ]

null

utils/visualization.py

yigitozgumus/Polimi_Thesis

711c1edcf1fdb92fc6c15bf5ab1be141c13995c3

[ "MIT" ]

4

2019-10-22T02:58:26.000Z

2020-10-06T09:59:26.000Z

import numpy as np import matplotlib.pyplot as plt def show_anomalies(patch_array): num_figs = len(patch_array) fig = plt.figure(figsize=(num_figs * 30, 30)) plt.tight_layout() for i in range(len(patch_array)): plt.subplot(num_figs, 1, i + 1) plt.imshow(patch_array[i]) plt.axis("off") def make_3_channel(image): return np.array([[[s, s, s] for s in r] for r in image], dtype="u1") def add_color_red_2d(image): #return np.array([[[0.7, s, s] for s in r] for r in image], dtype="u1") return np.array([[[s, 0, 0] for s in r] for r in image], dtype="u1") def add_color_green_2d(image): #return np.array([[[0.4, s, 0.9] for s in r] for r in image], dtype="u1") return np.array([[[0, s, 0] for s in r] for r in image], dtype="u1") def add_color_blue_2d(image): #return np.array([[[s, 0.3, 0.3] for s in r] for r in image], dtype="u1") return np.array([[[0, 0, s] for s in r] for r in image], dtype="u1") def paint_image_anomalies(image_list, true_labels, pred_labels): imgs = [] h_turns = 21 w_turns = 32 for img in image_list: image = make_3_channel(img) top = 0 left = 0 h, w = image.shape[:2] for adv_h in range(h_turns): for adv_w in range(w_turns): tag = img_tag[adv_h * 32 : (adv_h + 1) * 32, adv_w * 32 : (adv_w + 1) * 32] anomaly = np.sum(tag) if anomaly: mask = np.array(tag == 255) image[adv_h * 32 : (adv_h + 1) * 32, adv_w * 32 : (adv_w + 1) * 32, 0][ mask ] = 255 imgs.append(image) return imgs def connect_imgs(imgs): patch = np.squeeze(imgs[0]) for i in range(1, len(imgs)): patch = np.vstack((patch, np.squeeze(imgs[i]))) return patch def paint_anomalies(num, patches, scores_pred, tl_bool, statistics=False, show=False): patch_image = np.zeros(2064384, dtype=int) patch_image = patch_image.reshape(672, 1024, 3) # plt.imshow(patch_image) tests = patches[672 * num : 672 * (num + 1)] preds = scores_pred[672 * num : 672 * (num + 1)] tl_bool = tl_bool.astype(bool) real = tl_bool[672 * num : 672 * (num + 1)] height = 21 width = 32 trues = 0 fps = 0 fns = 0 for i in range(height): for j in range(width): index = j + (width * i) if preds[index] and real[index]: # make it green, correct_guess add = add_color_green_2d(tests[index] * 255) trues += 1 elif preds[index]: # false positive add = add_color_red_2d(tests[index] * 255) fps += 1 elif real[index]: # False Negative add = add_color_blue_2d(tests[index] * 255) fns += 1 else: add = make_3_channel(tests[index] * 255) patch_image[i * 32 : (i + 1) * 32, j * 32 : (j + 1) * 32] += add if statistics: print("true predictions: {}".format(trues)) print("False Positives: {}".format(fps)) print("False Negatives: {}".format(fns)) if show: plt.figure(figsize=(15, 15)) plt.imshow(patch_image) return return patch_image def paint_anomalies_pixelwise(num, patches, scores_pred, true_scores, statistics=False, show=False): patch_image = np.zeros(1972098, dtype=int) patch_image = patch_image.reshape(662, 993, 3) tests = patches[660345 * num : 660345 * (num + 1)] preds = scores_pred[660345 * num : 660345 * (num + 1)] true_scores = true_scores.astype(bool) real = true_scores[660345 * num : 660345 * (num + 1)] height = 662 width = 993 trues, fps, fns = 0, 0, 0 for h in range(height): for w in range(width): index = w + (width * h) if preds[index] and real[index]: add = add_color_green_2d(tests[index][15:16, 16:17] * 255) trues += 1 elif preds[index]: add = add_color_red_2d(tests[index][15:16, 16:17] * 255) fps += 1 elif real[index]: add = add_color_blue_2d(tests[index][15:16, 16:17] * 255) fns += 1 else: add = make_3_channel(tests[index][15:16, 16:17] * 255) patch_image[h : (h + 1), w : (w + 1)] += add if statistics: print("true predictions: {}".format(trues)) print("False Positives: {}".format(fps)) print("False Negatives: {}".format(fns)) if show: plt.figure(figsize=(15, 15)) plt.imshow(patch_image) return return patch_image def compute_predictions(scores, percentile): per = np.percentile(scores, percentile) predictions = scores >= per return predictions

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c8c0726d584812a525a610e545b5c0960badaf74

18,223

py

Python

tests/unit/core/tensorrt_loaders.py

ParikhKadam/NeMo

ee11f7c4666d410d91f9da33c61f4819ea625013

[ "Apache-2.0" ]

10

2020-03-17T08:32:06.000Z

2021-04-19T19:03:50.000Z

tests/unit/core/tensorrt_loaders.py

dcmartin/NeMo

d2120a40bf23d3e38ff5677c2685c712f297e6b1

[ "Apache-2.0" ]

1

2020-06-11T00:54:42.000Z

tests/unit/core/tensorrt_loaders.py

dcmartin/NeMo

d2120a40bf23d3e38ff5677c2685c712f297e6b1

[ "Apache-2.0" ]

3

2020-03-10T05:10:07.000Z

2020-12-08T01:33:35.000Z

# ! /usr/bin/python # -*- coding: utf-8 -*- # ============================================================================= # Copyright 2020 NVIDIA. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================= import time import warnings from collections import OrderedDict import numpy as np import onnx import tensorrt as trt from .tensorrt_format import FormatManager from .tensorrt_runner import ( DEFAULT_SHAPE_VALUE, TRT_LOGGER, TensorRTRunnerV2, default_value, find_in_dict, get_input_metadata_from_profile, is_dimension_dynamic, is_shape_dynamic, is_valid_shape_override, send_on_queue, write_timestamped, ) from nemo import logging, logging_mode def set_onnx_logging_level(sev): if sev >= logging.INFO: warnings.filterwarnings("ignore") class BaseDataLoader(object): """ Responsible for fetching or generting input data for runners. """ def __call__(self, index, input_metadata, input_example=None): """ Fetches or generates inputs. Args: index (int): The index of inputs to fetch. For any given index, the inputs should always be the same. input_metadata (OrderedDict[str, Tuple[np.dtype, Tuple[int]]]): Mapping of input names to their data types and shapes. Returns: OrderedDict[str, np.ndarray]: Mapping of input names to numpy buffers containing data. """ raise NotImplementedError("BaseDataLoader is an abstract class") class DefaultDataLoader(BaseDataLoader): def __init__( self, seed=None, default_shape_value=None, default_shapes=None, int_min=None, int_max=None, float_min=None, float_max=None, ): """ Optional Args: seed (int): The seed to use when generating random inputs. default_shape_value (int): The default value to use when a dimension is dynamic. default_shapes (Dict[str, Tuple[int]]): A mapping of input names to their corresponding shapes. """ self.seed = default_value(seed, int(time.time())) self.default_shapes = default_value(default_shapes, {}) self.default_shape_value = default_value(default_shape_value, DEFAULT_SHAPE_VALUE) self.int_min = default_value(int_min, 1) self.int_max = default_value(int_max, 25) self.float_min = default_value(float_min, -1.0) self.float_max = default_value(float_max, 1.0) def __call__(self, index, input_metadata, input_example=None): logging.debug("Updating seed to: {:}".format(self.seed + index)) rng = np.random.RandomState(self.seed + index) buffers = OrderedDict() i = 0 for name, (dtype, shape) in input_metadata.items(): if input_example is not None and (not isinstance(input_example, tuple) or i < len(input_example)): if isinstance(input_example, tuple): static_shape = input_example[i].shape elif isinstance(input_example, OrderedDict): static_shape = tuple(input_example.values())[i].shape else: static_shape = [tuple(input_example.shape)] elif is_shape_dynamic(shape): if name in self.default_shapes: static_shape = self.default_shapes[name] else: static_shape = [self.default_shape_value if is_dimension_dynamic(elem) else elem for elem in shape] if static_shape != shape: if not is_valid_shape_override(static_shape, shape): logging.critical( "Cannot override original shape: {:}, for input: {:} to {:}".format( shape, name, static_shape ) ) logging.warning( "Input: {:}: Adjusted dynamic shape: {:} to: {:}".format(name, shape, static_shape), mode=logging_mode.ONCE, ) else: if name in self.default_shapes: logging.warning( "Will not override static shape: {:}, for input: {:}".format(shape, name), mode=logging_mode.ONCE, ) static_shape = shape if input_example is not None and (not isinstance(input_example, tuple) or i < len(input_example)): if isinstance(input_example, OrderedDict): buffers[name] = list(input_example.values())[i].cpu() else: buffers[name] = input_example[i].cpu() if isinstance(input_example, tuple) else input_example.cpu() elif np.issubdtype(dtype, np.integer): buffers[name] = rng.randint(low=self.int_min, high=self.int_max, size=static_shape, dtype=dtype) elif np.issubdtype(dtype, np.bool_): buffers[name] = rng.randint(low=0, high=2, size=static_shape).astype(dtype) else: buffers[name] = ( rng.random_sample(size=static_shape) * (self.float_max - self.float_min) + self.float_min ).astype(dtype) buffers[name] = np.array( buffers[name] ) # To handle scalars. The above functions return a float if shape is (). # If the shape is 1D, and has a length equal to the rank of the provided default shape, it is # likely to be a TRT shape tensor, and so should be overriden such that it's value (not shape) is the default shape. is_shape_tensor = ( (not is_shape_dynamic(shape)) and (name in self.default_shapes) and (len(shape) == 1) and (shape[0] == len(self.default_shapes[name])) ) if is_shape_tensor: buffers[name] = np.array(self.default_shapes[name], dtype=dtype) logging.warning( "Assuming {:} is a shape tensor. Setting to: {:}".format(name, buffers[name]), mode=logging_mode.ONCE, ) i = i + 1 return buffers # Caches data loaded by a DataLoader for use across multiple runners. class DataLoaderCache(object): def __init__(self, data_loader): self.data_loader = data_loader self.cache = {} # Dict[int, OrderedDict[str, np.ndarray]] def load(self, iteration, input_metadata, input_example=None): """ Load the specified iteration from the cache if present, or generate using the data loader. Args: iteration (int): The iteration whose data to retrieve. input_metadata (OrderedDict[str, Tuple[np.dtype, Tuple[int]]]): Input Metadata, including shape and type information. The loader may attempt to match input_metadata when data in the cache does not exactly match a new set of input_metadata. """ if iteration not in self.cache: logging.debug("Iteration {:} not found in cache, generating new buffers for all inputs".format(iteration)) self.cache[iteration] = self.data_loader(iteration, input_metadata, input_example) if self.cache[iteration] is None: logging.critical( "Received no data from data_loader(iteration, input_metadata) for input_metadata: {:}".format( input_metadata ) ) else: logging.info("Found iteration {:} in cache".format(iteration)) feed_dict = OrderedDict() for index, (name, (dtype, shape)) in enumerate(input_metadata.items()): cached_name = find_in_dict(name, self.cache[iteration], index) if cached_name is None: logging.warning("Could not find input: {:} in cache, regenerating buffers".format(name)) self.cache[iteration] = self.data_loader(iteration, input_metadata, input_example) cached_name = name buffer = self.cache[iteration][cached_name] if dtype != buffer.dtype: logging.warning( "Cached buffer data type does not match data type for input: {:}. Note: Cached type: {:}, input type: {:}. Attempting to cast".format( name, buffer.dtype, dtype ) ) buffer = buffer.astype(dtype) if not is_valid_shape_override(buffer.shape, shape): logging.warning( "Cached buffer shape does not match shape for input. Note: Cached shape: {:}, input shape: {:}.".format( buffer.shape, shape ) ) # Try to permute the shape to match try: perm = FormatManager.permutation( FormatManager.deduce_format(buffer.shape), FormatManager.deduce_format(shape) ) new_shape = FormatManager.convert(tuple(buffer.shape), FormatManager.deduce_format(shape)) logging.warning( "Attempting to permute shape: {:} using permutation {:}. New shape: {:}".format( buffer.shape, perm, new_shape ) ) buffer = np.transpose(buffer, perm) except NotImplementedError as err: # If the FormatManager does not recognize the format, skip permutation. logging.info("Skipping permutation due to {:}".format(err)) except KeyError as err: # If the FormatManager cannot generate the permutation for the format combination, skip permutation. logging.info("Skipping permutation due to {:}".format(err)) feed_dict[name] = buffer return feed_dict class BaseModelLoader(object): """ Loads a model for a runner. """ def __call__(self): """ Load the model. Returns: A model usable by the runner. The return type is dependent on the runner the loader has been implemented for. """ raise NotImplementedError("BaseModelLoader is an abstract class") class BaseOnnxModelLoader(BaseModelLoader): def check(self, model): try: onnx.checker.check_model(model) logging.debug("ONNX Checker Passed") except onnx.checker.ValidationError as err: logging.warning("ONNX Checker exited with an error: {:}".format(err)) return model # ONNX loaders return ONNX models in memory. class OnnxFileLoader(BaseOnnxModelLoader): def __init__(self, path): """ Loads an ONNX model from a file. Args: path (str): The path from which to load the model. """ self.path = path def __call__(self): logging.info("Loading {:}".format(self.path)) return self.check(onnx.load(self.path)) def __str__(self): return "ONNX Model Loader: {:}".format(self.path) def __repr__(self): return self.__str__() class OnnxNetworkLoader(BaseModelLoader): def __init__(self, onnx_loader, explicit_precision=None): """ Parses an ONNX model to create an engine. Args: onnx_loader (Callable() -> onnx.ModelProto): A loader that can supply an ONNX model. Optional Args: explicit_precision (bool): Whether to create the network with explicit precision enabled. """ self.onnx_loader = onnx_loader self.explicit_precision = default_value(explicit_precision, False) def __call__(self): network = TensorRTRunnerV2.create_network(explicit_precision=self.explicit_precision) parser = trt.OnnxParser(network, TRT_LOGGER) success = parser.parse(self.onnx_loader().SerializeToString()) if not success: for index in range(parser.num_errors): logging.error(parser.get_error(index)) logging.critical("Could not parse ONNX correctly") return network, parser class BuildEngineLoader(BaseModelLoader): def __init__( self, network_loader, max_workspace_size=None, fp16_mode=None, int8_mode=None, profile_shapes=None, write_engine=None, calibrator=None, preprocess_network=None, layerwise=None, ): """ Uses a TensorRT INetworkDefinition to build an engine Args: network_loader (Callable()->trt.INetworkDefinition): A callable capable of returning an TensorRT INetworkDefinition. The returned network is owned by the BuildEngineLoader and should not be freed manually. The callable may have at most 2 return values if another object needs to be kept alive for the duration of the network, e.g., in the case of a parser. BuildEngineLoader will take ownership of the second return value, and, like the network, it should not be freed by the callable. The first return value must always be the network. Optional Args: max_workspace_size (int): The maximum workspace size, in bytes, when building the engine. fp16_mode (bool): Whether to build the engine with fp16 mode enabled. int8_mode (bool): Whether to build the engine with int8 mode enabled. profile_shapes (Dict[str, List[shape, shape, shape]]): A mapping of binding name to min/opt/max shapes. Only needed for networks with dynamic input shapes. write_engine (str): A directory in which to save the engine. calibrator (trt_smeagol.runners.tensorrt_runner_v2.Calibrator): An int8 calibrator. Only required in int8 mode when the network does not have explicit precision. preprocess_network (Callable(trt.INetworkDefinition)): Preprocessing function for the network definition. May be used to modify the network after parsing. This is called before enabling layerwise outputs. layerwise (bool): Whether to treat the output of every layer as an output of the network. Defaults to False. """ self.network_loader = network_loader self.max_workspace_size = default_value(max_workspace_size, 1 << 24) self.fp16_mode = default_value(fp16_mode, False) self.int8_mode = default_value(int8_mode, False) self.profile_shapes = default_value(profile_shapes, OrderedDict()) self.write_engine = write_engine self.written_engine_path = None self.calibrator = calibrator self.preprocess_network = default_value(preprocess_network, None) self.layerwise = default_value(layerwise, False) def __call__(self): class DummyContextManager(object): def __enter__(self): return None def __exit__(self, exc_type, exc_value, traceback): return None network_parser = self.network_loader() try: network, parser = network_parser assert isinstance(network, trt.INetworkDefinition) except (ValueError, AssertionError): network = network_parser parser = DummyContextManager() with trt.Builder(TRT_LOGGER) as builder, network, parser: if self.preprocess_network: logging.debug("Applying network preprocessing: {:}".format(self.preprocess_network)) self.preprocess_network(network) if self.layerwise: TensorRTRunnerV2.mark_layerwise(network) if logging.getEffectiveLevel() <= logging.DEBUG: TensorRTRunnerV2.log_network(network) config = builder.create_builder_config() profile = TensorRTRunnerV2.build_profile(builder, network, self.profile_shapes) config.add_optimization_profile(profile) config.max_workspace_size = int(self.max_workspace_size) if self.fp16_mode: config.flags = 1 << int(trt.BuilderFlag.FP16) if self.int8_mode: config.flags = config.flags | 1 << int(trt.BuilderFlag.INT8) if not network.has_explicit_precision: if not self.calibrator: logging.critical( "Network does not have explicit precision. A calibrator must be provided in order to use int8 mode." ) self.calibrator.set_input_metadata(get_input_metadata_from_profile(profile, network)) config.int8_calibrator = self.calibrator logging.debug("Using builder configuration flags: {:}".format(config.flags)) logging.info( "Building engine: max workspace size={:} bytes, fp16={:}, int8={:}, layerwise={:}".format( self.max_workspace_size, self.fp16_mode, self.int8_mode, self.layerwise ) ) engine = builder.build_engine(network, config) self.written_engine_path = write_timestamped( contents=lambda: engine.serialize(), dir=self.write_engine, name="tensorrt_runner_v2.engine" ) return engine def get_engine_path(self): """ Returns the path at which the engine was written, or None if write_engine was not specified. """ return self.written_engine_path

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0

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false

0.00369

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0.177122

0

null

0

null

0

1

0

c8c174e66db5ae93829e5da36ac5e18a48241662

15,382

py

Python

server/services/wiki/pages/overview_service.py

hotosm/oeg-reporter

f0c3da80ba380df907a818db224e9ca2ae0018b3

[ "BSD-2-Clause" ]

1

2021-02-03T13:37:48.000Z

server/services/wiki/pages/overview_service.py

hotosm/oeg-reporter

f0c3da80ba380df907a818db224e9ca2ae0018b3

[ "BSD-2-Clause" ]

8

2020-07-16T23:17:51.000Z

2020-10-14T20:40:00.000Z

server/services/wiki/pages/overview_service.py

hotosm/oeg-reporter

f0c3da80ba380df907a818db224e9ca2ae0018b3

[ "BSD-2-Clause" ]

null

from server.services.wiki.pages.templates import OverviewPageTemplates from server.services.wiki.pages.page_service import PageService from server.services.wiki.mediawiki_service import MediaWikiService from server.services.wiki.wiki_text_service import WikiTextService from server.services.wiki.wiki_table_service import WikiTableService from server.services.wiki.wiki_section_service import WikiSectionService from server.models.serializers.document import OverviewPageSchema class OverviewPageService(PageService): def __init__(self): self.templates = OverviewPageTemplates() self.page_fields = [ "organisation.name", "organisation.url", "platform.name", "platform.url", ] def filter_page_data(self, document_data: dict) -> dict: """ Filter required data for the overview page from document data Keyword arguments: document_data -- All required data for a project using Organised Editing Guidelines Returns: overview_page_data -- Dict containing only the required data for the overview page """ overview_page_data = { "organisation": { "name": document_data["organisation"]["name"], "url": document_data["organisation"]["url"], }, "platform": { "name": document_data["platform"]["name"], "url": document_data["platform"]["url"], }, } return overview_page_data def generate_page_sections_dict(self, overview_page_data: dict) -> dict: """ Generate dict containing the document content parsed to wikitext for all sections present in the overview page Keyword arguments: overview_page_data -- Dictionary containing the required data for the overview page sections Returns: overview_page_sections -- Dictionary with the document content parsed to wikitext for the overview page sections """ new_row = self.generate_activities_list_table_row(overview_page_data) activities_list_section = self.templates.activities_list_section_title overview_page_sections = {activities_list_section: new_row} return overview_page_sections def generate_activities_list_table_row(self, overview_page_data: dict) -> str: """ Generates a new table row for activities list table overview_page_data -- Dict containing only the required data for the overview page Returns: new_row -- String in wikitext format for a new table row """ wikitext = WikiTextService() organisation_name = overview_page_data["organisation"]["name"].capitalize() organisation_page_title = f"{self.templates.oeg_page}/" f"{organisation_name}" organisation_link = wikitext.hyperlink_wiki_page( organisation_page_title, organisation_name ) platform_link = wikitext.hyperlink_external_link( overview_page_data["platform"]["name"], overview_page_data["platform"]["url"], ) new_row = f"\n| {organisation_link}\n| {platform_link}\n|-" return new_row def create_page(self, document_data: dict) -> None: """ Creates a wiki page Keyword arguments: document_data -- All required data for a project using Organised Editing Guidelines """ mediawiki = MediaWikiService() wikitext = WikiTextService() token = mediawiki.get_token() page_title = self.templates.oeg_page overview_page_sections = self.document_to_page_sections(document_data) sections_text = wikitext.generate_text_from_dict( self.templates.page_template, f"=={self.templates.page_initial_section}==", overview_page_sections, ) updated_text = WikiTableService().add_table_row( page_text=sections_text, new_row=self.generate_activities_list_table_row(document_data), table_section_title=self.templates.activities_list_section_title, table_template=self.templates.table_template, ) if mediawiki.is_existing_page(page_title): page_text = MediaWikiService().get_page_text(self.templates.oeg_page) overview_page_table = ( WikiSectionService() .get_section_table( page_text, self.templates.activities_list_section_title ) .string ) updated_text = WikiTableService().add_table_row( page_text=page_text, new_row=self.generate_activities_list_table_row(document_data), table_section_title=self.templates.activities_list_section_title, table_template=overview_page_table, ) mediawiki.edit_page(token, self.templates.oeg_page, updated_text) else: mediawiki.create_page(token, page_title, updated_text) def enabled_to_report(self, document_data: dict): if MediaWikiService().is_existing_page(self.templates.oeg_page): overview_dictionary = self.wikitext_to_dict(self.templates.oeg_page) serialized_overview_page = self.parse_page_to_serializer( overview_dictionary ) organisation_names = [ organisation_data["name"] for organisation_data in serialized_overview_page["organisation"] ] platform_names = [ platform_data["name"] for platform_data in serialized_overview_page["platform"] ] if ( document_data["organisation"]["name"].capitalize() in organisation_names and document_data["platform"]["name"] in platform_names ): return False else: return True else: return True def edit_page_text( self, update_fields: dict, overview_page_data: dict, document_data: dict ): page_text = MediaWikiService().get_page_text(self.templates.oeg_page) updated_table_fields = self.get_update_table_fields( update_fields, overview_page_data ) if updated_table_fields: overview_page_table = WikiSectionService().get_section_table( page_text, self.templates.activities_list_section_title ) project_list_section_title = ( f"\n=={self.templates.page_initial_section}==\n" f"==={self.templates.activities_list_section_title}===\n" ) updated_text = WikiTableService().edit_table( overview_page_table.string, project_list_section_title, updated_table_fields, ) return updated_text else: return page_text def edit_page( self, document_data: dict, update_fields: dict, overview_page_data: dict ): mediawiki = MediaWikiService() token = mediawiki.get_token() updated_text = self.edit_page_text( update_fields, overview_page_data, document_data ) mediawiki.edit_page(token, self.templates.oeg_page, updated_text) def table_field_updated(self, update_fields: dict, overview_page_data: dict): if "platform" in update_fields.keys(): return WikiTextService().hyperlink_external_link( overview_page_data["platform"]["name"], overview_page_data["platform"]["url"], ) elif "organisation" in update_fields.keys(): organisation_page_title = ( f"{self.templates.oeg_page}/" f"{overview_page_data['organisation']['name'].capitalize()}" ) return WikiTextService().hyperlink_wiki_page( organisation_page_title, overview_page_data["organisation"]["name"].capitalize(), ) else: return False def get_update_table_fields(self, update_fields, overview_page_data): current_organisation_page_title = ( "Organised_Editing/Activities/Auto_report/" f"{overview_page_data['organisation']['name'].capitalize()}" ) current_row_data = { "organisation": WikiTextService().hyperlink_wiki_page( current_organisation_page_title, overview_page_data["organisation"]["name"].capitalize(), ), "platform": WikiTextService().hyperlink_external_link( overview_page_data["platform"]["name"], overview_page_data["platform"]["url"], ), } if ( "platform" in update_fields.keys() and "organisation" in update_fields.keys() ): update_platform_name = ( update_fields["platform"]["name"] if "name" in update_fields["platform"].keys() else overview_page_data["platform"]["name"] ) update_platform_url = ( update_fields["platform"]["url"] if "url" in update_fields["platform"].keys() else overview_page_data["platform"]["url"] ) update_organisation_name = ( update_fields["organisation"]["name"].capitalize() if "name" in update_fields["organisation"].keys() else overview_page_data["organisation"]["name"].capitalize() ) update_organisation_page_title = ( "Organised_Editing/Activities/Auto_report/" f"{update_organisation_name.capitalize()}" ) update_fields = { self.templates.overview_list_organisation_name_column: { "current": current_row_data["organisation"], "update": WikiTextService().hyperlink_wiki_page( update_organisation_page_title, update_organisation_name.capitalize(), ), }, self.templates.overview_list_platform_name_column: { "current": current_row_data["platform"], "update": WikiTextService().hyperlink_external_link( update_platform_name, update_platform_url ), }, } return update_fields elif "platform" in update_fields.keys(): update_platform_name = ( update_fields["platform"]["name"] if "name" in update_fields["platform"].keys() else overview_page_data["platform"]["name"] ) update_platform_url = ( update_fields["platform"]["url"] if "url" in update_fields["platform"].keys() else overview_page_data["platform"]["url"] ) update_fields = { self.templates.overview_list_organisation_name_column: { "current": current_row_data["organisation"], "update": current_row_data["organisation"], }, self.templates.overview_list_platform_name_column: { "current": current_row_data["platform"], "update": WikiTextService().hyperlink_external_link( update_platform_name, update_platform_url ), }, } return update_fields elif "organisation" in update_fields.keys(): update_organisation_name = ( update_fields["organisation"]["name"].capitalize() if "name" in update_fields["organisation"].keys() else overview_page_data["organisation"]["name"].capitalize() ) update_organisation_page_title = ( "Organised_Editing/Activities/Auto_report/" f"{update_organisation_name.capitalize()}" ) update_fields = { self.templates.overview_list_organisation_name_column: { "current": current_row_data["organisation"], "update": WikiTextService().hyperlink_wiki_page( update_organisation_page_title, update_organisation_name.capitalize(), ), }, self.templates.overview_list_platform_name_column: { "current": current_row_data["platform"], "update": current_row_data["platform"], }, } return update_fields else: return False def parse_page_to_serializer(self, page_dictionary: dict): overview_page_data = {"organisation": [], "platform": []} overview_page_table_text = page_dictionary[self.templates.page_initial_section][ self.templates.activities_list_section_title ] ( platform_list, organisation_list, ) = self.get_overview_page_platforms_and_organisations(overview_page_table_text) overview_page_data["organisation"] = organisation_list overview_page_data["platform"] = platform_list # Validate overview_page_schema = OverviewPageSchema(partial=True) overview_page_schema.load(overview_page_data) return overview_page_data def get_overview_page_platforms_and_organisations( self, overview_page_table_text: str ): overview_page_table = WikiTableService().get_text_table( overview_page_table_text ) overview_page_table_data = overview_page_table.data(span=False) organisation_list = [] platform_list = [] wikitext = WikiTextService() for table_row_number, table_row_data in enumerate( overview_page_table_data[1:], start=1 ): hyperlinked_organisation_url = overview_page_table.cells( row=table_row_number, column=self.templates.overview_list_organisation_name_column, ).value hyperlinked_platform_url = overview_page_table.cells( row=table_row_number, column=self.templates.overview_list_platform_name_column, ).value organisation_list.append( { "name": wikitext.get_page_link_and_text_from_wiki_page_hyperlink( hyperlinked_organisation_url )[1] } ) ( platform_url, platform_name, ) = wikitext.get_page_link_and_text_from_external_hyperlink( hyperlinked_platform_url ) platform_list.append({"name": platform_name, "url": platform_url}) return platform_list, organisation_list

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c8c574de241b0c8199ec3be2586cfc5532691047

5,253

py

Python

xmuda/eval_sem_pcd.py

anhquancao/xmuda-extend

4b670ec2f6766e3a624e81dbe5d97b209c1c4f76

[ "Apache-2.0" ]

null

xmuda/eval_sem_pcd.py

anhquancao/xmuda-extend

4b670ec2f6766e3a624e81dbe5d97b209c1c4f76

[ "Apache-2.0" ]

null

xmuda/eval_sem_pcd.py

anhquancao/xmuda-extend

4b670ec2f6766e3a624e81dbe5d97b209c1c4f76

[ "Apache-2.0" ]

null

from xmuda.models.SSC2d_proj3d2d import SSC2dProj3d2d from xmuda.data.NYU.nyu_dm import NYUDataModule from xmuda.data.semantic_kitti.kitti_dm import KittiDataModule from xmuda.common.utils.sscMetrics import SSCMetrics from xmuda.data.NYU.params import class_relation_freqs as NYU_class_relation_freqs, class_freq_1_4 as NYU_class_freq_1_4, class_freq_1_8 as NYU_class_freq_1_8, class_freq_1_16 as NYU_class_freq_1_16 import numpy as np import torch import torch.nn.functional as F from xmuda.models.ssc_loss import get_class_weights from tqdm import tqdm import pickle import os #model_path = "/gpfsscratch/rech/kvd/uyl37fq/logs/no_mask_255/v12_removeCPThreshold_KLnonzeros_LRDecay30_NYU_1_0.0001_0.0001_CPThreshold0.0_CEssc_MCAssc_ProportionLoss_CERel_CRCP_Proj_2_4_8/checkpoints/epoch=030-val/mIoU=0.26983.ckpt" model_path = "/gpfsscratch/rech/kvd/uyl37fq/logs/kitti/v12_ProjectScale2_CPAt1_8_1divlog_LargerFOV_kitti_1_FrusSize_4_WD0_lr0.0001_CEssc_MCAssc_ProportionLoss_CERel_CRCP_Proj_2_4_8/checkpoints/epoch=037-val/mIoU=0.11056.ckpt" class_weights = { '1_4': get_class_weights(NYU_class_freq_1_4).cuda(), '1_8': get_class_weights(NYU_class_freq_1_8).cuda(), '1_16': get_class_weights(NYU_class_freq_1_16).cuda(), } #dataset = "NYU" dataset = "kitti" if dataset == "NYU": NYU_root = "/gpfswork/rech/kvd/uyl37fq/data/NYU/depthbin" NYU_preprocess_dir = "/gpfsscratch/rech/kvd/uyl37fq/precompute_data/NYU" kitti_root = "/gpfswork/rech/kvd/uyl37fq/data/semantic_kitti" full_scene_size = (240, 144, 240) output_scene_size = (60, 36, 60) NYUdm = NYUDataModule(NYU_root, NYU_preprocess_dir, batch_size=4, num_workers=3) NYUdm.setup() _C = 12 data_loader = NYUdm.val_dataloader() else: kitti_root = "/gpfswork/rech/kvd/uyl37fq/data/semantic_kitti" kitti_depth_root = "/gpfsscratch/rech/kvd/uyl37fq/Adabin/KITTI/" kitti_logdir = '/gpfsscratch/rech/kvd/uyl37fq/logs/kitti' kitti_tsdf_root = "/gpfsscratch/rech/kvd/uyl37fq/sketch_dataset/TSDF_pred_depth_adabin/kitti" kitti_label_root = "/gpfsscratch/rech/kvd/uyl37fq/sketch_dataset/labels/kitti" kitti_occ_root = "/gpfsscratch/rech/kvd/uyl37fq/sketch_dataset/occupancy_adabin/kitti" kitti_sketch_root = "/gpfsscratch/rech/kvd/uyl37fq/sketch_dataset/sketch_3D/kitti" kitti_mapping_root = "/gpfsscratch/rech/kvd/uyl37fq/sketch_dataset/mapping_adabin/kitti" full_scene_size = (256, 256, 32) KITTIdm = KittiDataModule(root=kitti_root, data_aug=True, TSDF_root=kitti_tsdf_root, label_root=kitti_label_root, mapping_root=kitti_mapping_root, occ_root=kitti_occ_root, depth_root=kitti_depth_root, sketch_root=kitti_sketch_root, batch_size=1, num_workers=3) KITTIdm.setup() _C = 20 data_loader = KITTIdm.val_dataloader() class_relation_weights = get_class_weights(NYU_class_relation_freqs) model = SSC2dProj3d2d.load_from_checkpoint(model_path) model.cuda() model.eval() count = 0 out_dict = {} count = 0 write_path = "/gpfsscratch/rech/kvd/uyl37fq/temp/draw_output/kitti" with torch.no_grad(): for batch in tqdm(data_loader): if dataset == "NYU": y_true = batch['ssc_label_1_4'].detach().cpu().numpy() valid_pix_4 = batch['valid_pix_4'] else: y_true = batch['ssc_label_1_1'].detach().cpu().numpy() # valid_pix_1 = batch['valid_pix_1'] valid_pix_1 = batch['valid_pix_double'] batch['img'] = batch['img'].cuda() pred = model(batch) y_pred = torch.softmax(pred['ssc'], dim=1).detach().cpu().numpy() y_pred = np.argmax(y_pred, axis=1) for i in range(y_true.shape[0]): out_dict = { "y_pred": y_pred[i].astype(np.uint16), "y_true": y_true[i].astype(np.uint16), } if dataset == "NYU": filepath = os.path.join(write_path, batch['name'][i] + ".pkl") out_dict["cam_pose"] = batch['cam_pose'][i].detach().cpu().numpy() out_dict["vox_origin"] = batch['vox_origin'][i].detach().cpu().numpy() elif dataset == "kitti": filepath = os.path.join(write_path, batch['sequence'][i], batch['frame_id'][i] + ".pkl") out_dict['valid_pix_1'] = valid_pix_1[i].detach().cpu().numpy() out_dict['cam_k'] = batch['cam_k'][i].detach().cpu().numpy() out_dict['T_velo_2_cam'] = batch['T_velo_2_cam'][i].detach().cpu().numpy() os.makedirs(os.path.join(write_path, batch['sequence'][i]), exist_ok=True) with open(filepath, 'wb') as handle: pickle.dump(out_dict, handle) print("wrote to", filepath) count += 1 # if count == 4: # break # write_path = "/gpfsscratch/rech/kvd/uyl37fq/temp/output" # filepath = os.path.join(write_path, "output.pkl") # with open(filepath, 'wb') as handle: # pickle.dump(out_dict, handle) # print("wrote to", filepath)

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c8c6f7ca2165cf621b2f2448c66168d6e16e7af2

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Python

hnn/src/apps/dataparallel.py

anlewy/mt-dnn

eeb6f01ce0630e61a52b8c9c6f7537cd34978e45

[ "MIT" ]

2,075

2019-02-25T08:54:38.000Z

2022-03-31T10:44:50.000Z

hnn/src/apps/dataparallel.py

anlewy/mt-dnn

eeb6f01ce0630e61a52b8c9c6f7537cd34978e45

[ "MIT" ]

176

2019-03-12T02:58:42.000Z

2022-03-22T20:17:23.000Z

hnn/src/apps/dataparallel.py

anlewy/mt-dnn

eeb6f01ce0630e61a52b8c9c6f7537cd34978e45

[ "MIT" ]

437

2019-03-11T21:36:21.000Z

2022-03-29T02:40:53.000Z

# Author: penhe@microsoft.com # Date: 05/30/2019 # """ Data parallel module """ from collections import OrderedDict import numpy as np import torch from torch.cuda.comm import broadcast_coalesced from torch.cuda.comm import reduce_add_coalesced from torch.nn.parallel import parallel_apply from torch.nn.parallel.scatter_gather import scatter_kwargs,gather import torch.cuda.comm as comm import pdb from bert.optimization import BertAdam def replicate(network, devices): devices = tuple(devices) num_replicas = len(devices) params = list(network.parameters()) param_indices = {param: idx for idx, param in enumerate(params)} param_copies = broadcast_coalesced(params, devices) buffers = list(network._all_buffers()) buffer_indices = {buf: idx for idx, buf in enumerate(buffers)} buffer_copies = broadcast_coalesced(buffers, devices) modules = list(network.modules()) module_copies = [[] for device in devices] module_indices = {} for i, module in enumerate(modules): module_indices[module] = i for j in range(num_replicas): replica = module.__new__(type(module)) replica.__dict__ = module.__dict__.copy() replica._parameters = replica._parameters.copy() replica._buffers = replica._buffers.copy() replica._modules = replica._modules.copy() module_copies[j].append(replica) for i, module in enumerate(modules): for key, child in module._modules.items(): if child is None: for j in range(num_replicas): replica = module_copies[j][i] replica._modules[key] = None else: module_idx = module_indices[child] for j in range(num_replicas): replica = module_copies[j][i] replica._modules[key] = module_copies[j][module_idx] for key, param in module._parameters.items(): if param is None: for j in range(num_replicas): replica = module_copies[j][i] replica._parameters[key] = None else: param_idx = param_indices[param] for j in range(num_replicas): replica = module_copies[j][i] replica._parameters[key] = param_copies[j][param_idx] replica._parameters[key].requires_grad = param.requires_grad for key, buf in module._buffers.items(): if buf is None: for j in range(num_replicas): replica = module_copies[j][i] replica._buffers[key] = None else: buffer_idx = buffer_indices[buf] for j in range(num_replicas): replica = module_copies[j][i] replica._buffers[key] = buffer_copies[j][buffer_idx] return [module_copies[j][0] for j in range(num_replicas)] class XDataParallel(torch.nn.Module): def __init__(self, module): super().__init__() self.device_ids = [i for i in range(torch.cuda.device_count())] module = module.cuda(self.device_ids[0]) self.replicas = replicate(module, self.device_ids) self.output_device = self.device_ids[0] self.dim = 0 self.module = module def forward(self, *inputs, **kwargs): #if not self.device_ids: # return self.module(*inputs, **kwargs) inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids) #if len(self.device_ids) == 1: # return self.module(*inputs[0], **kwargs[0]) #replicas = self.replicate(self.module, self.device_ids[:len(inputs)]) outputs = self.parallel_apply(self.replicas[:len(inputs)], inputs, kwargs) return self.gather(outputs, self.output_device) def state_dict(self, destination=None, prefix='', keep_vars=False): sd = self.replicas[0].state_dict(destination, prefix, keep_vars) return sd def eval(self): for m in self.replicas: m.eval() return self def train(self, mode=True): for m in self.replicas: m.train(mode) return self def zero_grad(self): for m in self.replicas: for p in m.parameters(): p.grad = None def scatter(self, inputs, kwargs, device_ids): return scatter_kwargs(inputs, kwargs, device_ids, dim=self.dim) def parallel_apply(self, replicas, inputs, kwargs): return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)]) def gather(self, outputs, output_device): return gather(outputs, output_device, dim=self.dim) class XParallelOptimizer(): def __init__(self, model, optimizer_fn, grad_clip_norm=1.0): self.replicas = [model] if hasattr(model, 'replicas'): self.replicas = model.replicas dcnt = torch.cuda.device_count() total_size = sum([np.prod(p.size()) for p in self.replicas[0].parameters()]) quota = {i:0 for i in range(dcnt)} #quota[0] = total_size//dcnt param_groups = {i: [] for i in range(dcnt)} self.named_parameters=[] for i,(n, param) in enumerate(self.replicas[0].named_parameters()): ps = np.prod(param.size()) index = list(sorted(quota.items(), key=lambda x: x[1]))[0][0] quota[index] += ps if param.dtype==torch.half: cp = param.clone().type(torch.cuda.FloatTensor).detach().to('cuda:{}'.format(index)).requires_grad_() else: cp = dict(self.replicas[index].named_parameters())[n] name = n[len('module.'):] if n.startswith('module.') else n param_groups[index].append((name, cp)) self.named_parameters.append((name, cp)) self.param_groups = param_groups self.sub_optimizers = [DeviceOptimizer(self.replicas, p, i, optimizer_fn(p, max_grad_norm=0)) for i,p in self.param_groups.items()] self.grad_clip_norm = grad_clip_norm def parameters(self): return OrderedDict(self.named_parameters) def step(self, grad_scale=1): def bk(g): return g.backward() l2norm_square = parallel_apply([bk for _ in self.sub_optimizers], self.sub_optimizers, devices=[g.device for g in self.sub_optimizers]) l2norm = sum(l2norm_square)**0.5 if str(l2norm) in ['inf', 'nan']: return False if grad_scale != 1: l2norm *= grad_scale coef = self.grad_clip_norm/(l2norm+1e-6) if coef<1: grad_scale = grad_scale*coef if grad_scale != 1: for n,p in self.named_parameters: if p.grad is not None: p.grad.mul_(grad_scale) def st(g): return g.step(l2norm) parallel_apply([st for _ in self.sub_optimizers], self.sub_optimizers, devices=[g.device for g in self.sub_optimizers]) return True def zero_grad(self): for m in self.replicas: for p in m.parameters(): p.grad = None for g in self.sub_optimizers: g.zero_grad() class DeviceOptimizer(): def __init__(self, replicas, param_group, device, optimizer): self.param_group = param_group self.device = device self.optimizer = optimizer self.replicas = replicas self.named_params = [dict(m.named_parameters()) for m in replicas] def backward(self): group_params = [[(n,m[n]) for n,p in self.param_group if m[n].grad is not None] for m in self.named_params] grad_params = [g for g in group_params if len(g)>0] assert all([len(g)==len(grad_params[0]) for g in grad_params]), [len(g) for g in grad_params] grad = [[p.grad for n,p in g] for g in grad_params] reduced_grad = reduce_add_coalesced(grad, self.device) grads = dict([(n,g) for ((n,p),g) in zip(grad_params[0], reduced_grad)]) l2norm = 0 for n,p in self.param_group: if n in grads: p.grad = grads[n].float() if grads[n].dtype==torch.half else grads[n] l2norm += p.grad.norm().item()**2 else: assert p.grad is None, n return l2norm def step(self, l2norm): self.optimizer.step() group_params = [(i, [(n,m[n]) for n,p in self.param_group]) for i,m in enumerate(self.named_params)] group_params = sorted(group_params, key=lambda x:x[0] if x[0]!=self.device else -1) params = dict(self.param_group) for n,p in group_params[0][1]: if p.data.dtype == torch.half: p.data.copy_(params[n].data) else: p.data = params[n].data param_list = [[p for n,p in g] for i,g in group_params] device_list =[i for i,g in group_params] outputs = broadcast_coalesced(param_list[0], device_list) for o,p in zip(outputs, param_list): for x,y in zip(o, p): y.data.copy_(x.data) def zero_grad(self): for n,p in self.param_group: p.grad = None self.optimizer.zero_grad() def optimizer_factory(args, training_steps=None, init_spec=None, no_decay=['bias', 'LayerNorm.weight']): def optimizer_fn(param_group, max_grad_norm=None): group0 = dict(params=[], weight_decay_rate=args.weight_decay, names=[]) group1 = dict(params=[], weight_decay_rate=0.00, names=[]) for (n,p) in param_group: if not any(nd in n for nd in no_decay): group0['params'].append(p) group0['names'].append(n) else: group1['params'].append(p) group1['names'].append(n) optimizer_grouped_parameters = [group0, group1] optimizer = BertAdam(optimizer_grouped_parameters, lr=args.learning_rate, b1=args.adam_beta1, b2=args.adam_beta2, v1=args.qhadam_v1, v2=args.qhadam_v2, lr_ends=args.lr_schedule_ends, warmup=args.warmup_proportion if args.warmup_proportion<1 else args.warmup_proportion/training_steps, t_total=training_steps, schedule=args.lr_schedule, max_grad_norm = args.max_grad_norm if max_grad_norm is None else max_grad_norm, global_grad_norm = args.global_grad_norm, init_spec = init_spec, weight_decay_rate = args.weight_decay) return optimizer return optimizer_fn

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null

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0

c8ca44f18c6c1244335778442d0b31143cb496f7

811

py

Python

ch02/multiSinal_button.py

you-know-who-2017/pythonQT

a713bfacbb53c5f23e9d7f61dc44592335a24423

[ "MIT" ]

null

ch02/multiSinal_button.py

you-know-who-2017/pythonQT

a713bfacbb53c5f23e9d7f61dc44592335a24423

[ "MIT" ]

null

ch02/multiSinal_button.py

you-know-who-2017/pythonQT

a713bfacbb53c5f23e9d7f61dc44592335a24423

[ "MIT" ]

null

''' Author: geekli Date: 2020-12-27 10:38:46 LastEditTime: 2020-12-27 10:40:44 LastEditors: your name Description: FilePath: \pythonQT\ch02\multiSinal_button.py ''' import sys from PyQt5.QtWidgets import QApplication, QWidget, QPushButton class Demo(QWidget): def __init__(self): super(Demo, self).__init__() self.button = QPushButton('Start', self) self.button.pressed.connect(self.change_text) # 1 self.button.released.connect(self.change_text) # 2 #插槽 def change_text(self): if self.button.text() == 'Start': # 3 self.button.setText('Stop') else: self.button.setText('Start') if __name__ == '__main__': app = QApplication(sys.argv) demo = Demo() demo.show() sys.exit(app.exec_())

25.34375

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0.630086

101

811

4.851485

0.554455

0.122449

0.032653

0.040816

0

0.054927

0.236745

811

32

63

25.34375

0.736672

0.204686

0

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0.111111

false

0

0.111111

0

0.277778

0

null

0

null

0

1

0

c8ccf268808a95f71f44af0d1f8a0dcac8ac8aa6

835

py

Python

record_voice.py

y1255018/voice-printer

cea33ae978a0709346bdbaf009f4fa07a97c7463

[ "MIT" ]

null

record_voice.py

y1255018/voice-printer

cea33ae978a0709346bdbaf009f4fa07a97c7463

[ "MIT" ]

1

2020-05-10T12:57:46.000Z

2020-05-10T12:59:27.000Z

record_voice.py

y1255018/voice-printer

cea33ae978a0709346bdbaf009f4fa07a97c7463

[ "MIT" ]

null

# -*- coding: utf-8 -*- import sys, select, termios,tty import os def getKey(): fd = sys.stdin.fileno() old = termios.tcgetattr(fd) new = termios.tcgetattr(fd) new[3] &= ~termios.ICANON new[3] &= ~termios.ECHO try: termios.tcsetattr(fd, termios.TCSANOW, new) ch = sys.stdin.read(1) finally: termios.tcsetattr(fd, termios.TCSANOW, old) print(ch) return ch def main(): try: while 1: key = getKey() if key == 'r': # record sound os.system("arecord -d 5 -f cd 'test.wav'") print("finish recording") elif key == 'p': #play sound os.system("aplay 'test.wav'") elif key == 'q': break elif key: print(key) except( KeyboardInterrupt, SystemExit): print( "SIGINTを検知" ) if __name__ == "__main__": main()

19.880952

50

0.568862

106

835

4.40566

0.537736

0.044968

0.077088

0.089936

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0

0.009983

0.28024

835

42

51

19.880952

0.767055

0.052695

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0.0625

0

0.102792

0

1

0.0625

false

0

0.0625

0

0.15625

0.125

0

null

0

null

0

1

0

c8cd1764a3562bbf6dce2fed67c34407e35a1349

1,516

py

Python

findpeak.py

BartMassey/pdx-cs-sound

52f671f155f71eb75a635d9b125f9324889dd329

[ "MIT" ]

null

findpeak.py

BartMassey/pdx-cs-sound

52f671f155f71eb75a635d9b125f9324889dd329

[ "MIT" ]

null

findpeak.py

BartMassey/pdx-cs-sound

52f671f155f71eb75a635d9b125f9324889dd329

[ "MIT" ]

null

#!/usr/bin/python3 # Copyright (c) 2019 Bart Massey # [This program is licensed under the "MIT License"] # Please see the file LICENSE in the source # distribution of this software for license terms. # Find maximum and minimum sample in an audio file. import sys import wave as wav # Get the signal file. wavfile = wav.open(sys.argv[1], 'rb') # Channels per frame. channels = wavfile.getnchannels() # Bytes per sample. width = wavfile.getsampwidth() # Sample rate rate = wavfile.getframerate() # Number of frames. frames = wavfile.getnframes() # Length of a frame frame_width = width * channels # Get the signal and check it. max_sample = None min_sample = None wave_bytes = wavfile.readframes(frames) # Iterate over frames. for f in range(0, len(wave_bytes), frame_width): frame = wave_bytes[f : f + frame_width] # Iterate over channels. for c in range(0, len(frame), width): # Build a sample. sample_bytes = frame[c : c + width] # XXX Eight-bit samples are unsigned sample = int.from_bytes(sample_bytes, byteorder='little', signed=(width>1)) # Check extrema. if max_sample == None: max_sample = sample if min_sample == None: min_sample = sample if sample > max_sample: max_sample = sample if sample < min_sample: min_sample = sample wavfile.close() print("min: {} max: {}".format(min_sample, max_sample))

25.694915

56

0.638522

202

1,516

4.688119

0.435644

0.057022

0.044351

0.040127

0

0.008108

0.26781

1,516

58

57

26.137931

0.845045

0.32058

0

0.142857

0

0.023715

0

1

0

false

0

0.071429

0

0.071429

0.035714

0

null

0

null

0

1

0

c8d1af14aa978ccc8ecf4f4ebec0ffa36d951d1c

345

py

Python

test/test_report.py

aymatveev/testing_framework

3e522d23b46ddb27b3b389210c244aaee5c3370e

[ "MIT" ]

null

test/test_report.py

aymatveev/testing_framework

3e522d23b46ddb27b3b389210c244aaee5c3370e

[ "MIT" ]

null

test/test_report.py

aymatveev/testing_framework

3e522d23b46ddb27b3b389210c244aaee5c3370e

[ "MIT" ]

null

from testing_framework.report import report from typing import Tuple import html def test_report(): result = report(("test_report", "second line")) expected_result = f""" <!DOCTYPE html> <html> <body> <div>test_report</div><div>second line</div> </body> </html> """ assert html.escape(expected_result) == html.escape(result)

23

62

0.695652

46

345

5.086957

0.434783

0.128205

0

0.153623

345

15

62

23

0.80137

0

0.352601

0.095376

0

0.071429

1

0.071429

false

0

0.214286

0

0.285714

0

null

0

null

0

1

0

c8d23bd00fcfedf98199c38fb1e64ea94cbde637

4,480

py

Python

qr_rover_lost_comms/src/qr_rover_lost_comms/qr_rover_lost_comms.py

QuantumRoboticsURC/qrteam

bb28f4ad82eab6fb0706be13f8571e0b3261641e

[ "MIT" ]

null

qr_rover_lost_comms/src/qr_rover_lost_comms/qr_rover_lost_comms.py

QuantumRoboticsURC/qrteam

bb28f4ad82eab6fb0706be13f8571e0b3261641e

[ "MIT" ]

null

qr_rover_lost_comms/src/qr_rover_lost_comms/qr_rover_lost_comms.py

QuantumRoboticsURC/qrteam

bb28f4ad82eab6fb0706be13f8571e0b3261641e

[ "MIT" ]

null

#!/usr/bin/env python import sys import time import rospy import subprocess import actionlib from std_msgs.msg import Float32 from sensor_msgs.msg import Joy from geometry_msgs.msg import Twist, PoseWithCovarianceStamped from actionlib_msgs.msg import GoalStatus, GoalStatusArray from move_base_msgs.msg import MoveBaseAction, MoveBaseGoal def ping_host(host): ping_fail_count = rospy.get_param('~ping_fail_count', 2) ping_command = "ping -c %s -n -W 1 %s" % (ping_fail_count, host) # TODO: don't shell out, use a more secure python library p = subprocess.Popen(ping_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True) (output, error) = p.communicate() returncode = p.returncode return output, error, returncode class RecorveryController(): def __init__(self): self.cmd_vel = rospy.Publisher('cmd_vel', Twist, queue_size=10) self.joy_drive = rospy.Publisher('joy_drive', Joy, queue_size=10) self.joy_arm = rospy.Publisher('joy_arm', Joy, queue_size=10) self.vel_limit_lost_comms = rospy.Publisher('vel_limit_lost_comms', Float32, queue_size=10) self.cmd_vel_sub = rospy.Subscriber('cmd_vel', Twist, self.cmd_vel_callback) self.cmd_vel_twist = Twist() def cmd_vel_callback(self, msg): self.cmd_vel_twist = msg def working_comms(self): working_comms = False if (self.ips != "no"): for ip in self.ips.split(','): (output, error, returncode) = ping_host(ip) if returncode == 0: #ping = int(output.split('/')[-1].split('.')[0]) ping = float(output.split('time=')[1].split(' ')[0]) rospy.loginfo("ping %s: %s" % (ip, ping)) twist = Twist() if ping > 1000: self.vel_limit_lost_comms.publish(0.3) twist.linear.x = self.cmd_vel_twist.linear.x/4 twist.angular.z = self.cmd_vel_twist.angular.z/4 self.cmd_vel.publish(twist) elif ping > 500: self.vel_limit_lost_comms.publish(0.6) twist.linear.x = self.cmd_vel_twist.linear.x/2 twist.angular.z = self.cmd_vel_twist.angular.z/2 self.cmd_vel.publish(twist) elif ping < 500: self.vel_limit_lost_comms.publish(1) twist.linear.x = self.cmd_vel_twist.linear.x twist.angular.z = self.cmd_vel_twist.angular.z self.cmd_vel.publish(twist) working_comms = True else: working_comms = True return working_comms def zero_joystick(self): joyDrive = Joy() joyArm = Joy() if (self.joy_drive_model == 'xbox'): joyDrive.axes = [0] * 8 joyDrive.buttons = [0] * 11 elif (self.joy_drive_model == 'ec'): joyDrive.axes = [0] * 8 joyDrive.buttons = [0] * 15 elif (self.joy_drive_model == 'ps5'): joyDrive.axes = [0] * 12 joyDrive.buttons = [0] * 12 joyArm.axes = [0] * 3 joyArm.buttons = [0] * 11 self.joy_drive.publish(joyDrive) self.joy_arm.publish(joyArm) def do_recovery(self): if rospy.is_shutdown(): return rospy.logerr('No connection to base station.') #if self.connect_to_move_base(): #if self.goal_in_progress(): #rospy.loginfo("Navigation in progress, not recovering until finished...") #return #self.navigation_goal_to(self.recovery_pose) self.zero_joystick() self.stop_motors() def stop_motors(self): twist = Twist() # zero motion self.cmd_vel.publish(twist) def main_loop(self): while not rospy.is_shutdown(): if not self.working_comms(): self.do_recovery() time.sleep(1) def main(): rospy.init_node("qr_rover_lost_comms") qr_rover_lost_comms = RecorveryController() qr_rover_lost_comms.ips = rospy.get_param('~ips_to_monitor') qr_rover_lost_comms.joy_drive_model = rospy.get_param('~joy_drive_model') rospy.loginfo('Monitoring base station on IP(s): %s.' % qr_rover_lost_comms.ips) qr_rover_lost_comms.main_loop() # start monitoring

39.646018

99

0.59375

568

4,480

4.450704

0.262324

0.042722

0.059335

0.047468

0.245253

0.170886

0.16693

0.131329

0.048259

0

0.019396

0.297991

4,480

113

100

39.646018

0.78442

0.074107

0

0.106383

0

0.056294

0

0.00885

0

1

0.095745

false

0

0.106383

0

0.234043

0

null

0

null

0

1

0

c8d5d6f27303f0d53ce075025843560499c32f81

508

py

Python

backend/swagger_server/helpers/_add_audit_entry.py

Lend88/libresign

9537f39a696fa5f3433052406329d77d528b6cf9

[ "MIT" ]

6

2019-01-29T05:58:37.000Z

2021-11-02T22:47:02.000Z

backend/swagger_server/helpers/_add_audit_entry.py

Lend88/libresign

9537f39a696fa5f3433052406329d77d528b6cf9

[ "MIT" ]

9

2020-09-09T04:53:01.000Z

2022-03-08T22:52:18.000Z

backend/swagger_server/helpers/_add_audit_entry.py

Lend88/libresign

9537f39a696fa5f3433052406329d77d528b6cf9

[ "MIT" ]

4

2019-01-29T07:38:55.000Z

2021-10-16T21:06:42.000Z

from uuid import UUID import json from ..mappings import * def add_doc_audit_entry(session, doc_id, status, data): """"Add an audit entry, requires that a commit be run on the session afterwards """ if not isinstance(doc_id, UUID): raise ValueError("Expecting UUID") if not isinstance(data, dict): raise ValueError("Expecting dict") session.add(FileUsage( document_id=doc_id.bytes, fileusage_type=status, data=json.dumps(data) ))

22.086957

55

0.65748

67

508

4.865672

0.537313

0.046012

0.092025

0

0.25

508

22

56

23.090909

0.855643

0.149606

0

0.067961

0

1

0.076923

false

0

0.230769

0

0.307692

0

null

0

null

0

1

0

c8da9080a11e6c113c5b2a18202d6e7d74fba286

4,942

py

Python

bioinfo/assembly/overlap.py

sohyongsheng/sequence_assembly

f2dea763da447f09f49de8fbf3ceaad8ed3e0559

[ "MIT" ]

1

2022-02-02T07:49:58.000Z

bioinfo/assembly/overlap.py

sohyongsheng/sequence_assembly

f2dea763da447f09f49de8fbf3ceaad8ed3e0559

[ "MIT" ]

null

bioinfo/assembly/overlap.py

sohyongsheng/sequence_assembly

f2dea763da447f09f49de8fbf3ceaad8ed3e0559

[ "MIT" ]

null

import numpy as np from bioinfo.assembly.errors import InvalidPair from bioinfo.molecules.sequence import Sequence class LargestOverlapFinder: def __init__(self): pass # Get indices a, b, c, d of longest substrings first, # such that substring == first[a: b] == second[c: d]. # Also returns length of substring. def get_substrings(self, counter): while not np.all(counter == 0): i, j = np.unravel_index(counter.argmax(), counter.shape) length = counter[i, j] for k in range(length): counter[i - k, j - k] = 0 b, d = i + 1, j + 1 a, c = b - length, d - length indices = a, b, c, d yield indices, length def is_overlap(self, indices, first, second): a, b, c, d = indices # First overlaps with second, e.g. # 0123 # 1234 # ^^^ if b == len(first) and c == 0: return True # Second overlaps with first, e.g. # 1234 # 0123 # ^^^ elif a == 0 and d == len(second): return True # First is within second, e.g. # 123 # 01234 # ^^^ elif a == 0 and b == len(first): return True # Second is within first, e.g. # 01234 # 123 # ^^^ elif c == 0 and d == len(second): return True else: return False # Taken from longest common substring problem. See # following for tutorial on dynamic programming solution: # https://www.youtube.com/watch?v=BysNXJHzCEs def tally_counter(self, first, second): num_rows = len(first) + 1 num_cols = len(second) + 1 counter = np.zeros((num_rows, num_cols), dtype = int) for i, m in enumerate(first, start = 1): for j, n in enumerate(second, start = 1): if m == n: counter[i, j] = counter[i - 1, j - 1] + 1 counter = self.remove_first_row_first_col(counter) return counter def find(self, first, second): counter = self.tally_counter(first, second) for indices, length in self.get_substrings(counter): a, b, c, d = indices assert first[a: b] == second[c: d] if self.is_overlap(indices, first, second): return indices, length else: indices, length = None, 0 return indices, length def remove_first_row_first_col(self, x): return x[1:, 1:] class Pair: finder = LargestOverlapFinder() def __init__(self, first, second): self.first = first self.second = second if self.first.is_dna != self.second.is_dna: raise InvalidPair( "Cannot compare DNA with RNA sequences." ) self.indices, self.overlap_length = self.finder.find( self.first.seq_str, self.second.seq_str, ) def combine(self): first = self.first.seq_str second = self.second.seq_str # No overlap, so just concatenate. if self.overlap_length == 0: combined = first + second return Sequence( combined, is_dna = self.first.is_dna, ) else: a, b, c, d = self.indices # First overlaps with second, e.g. # 0123 # 1234 # ^^^ if b == len(self.first) and c == 0: prefix = first[:a] assert first[a: b] == second[c: d] overlap = first[a: b] suffix = second[d:] combined = prefix + overlap + suffix return Sequence( combined, is_dna = self.first.is_dna, ) # Second overlaps with first, e.g. # 1234 # 0123 # ^^^ elif a == 0 and d == len(self.second): prefix = second[:c] assert second[c: d] == first[a: b] overlap = second[c: d] suffix = first[b:] combined = prefix + overlap + suffix return Sequence( combined, is_dna = self.first.is_dna, ) # First is within second, e.g. # 123 # 01234 # ^^^ elif a == 0 and b == len(self.first): return Sequence( second, is_dna = self.second.is_dna, ) # Second is within first, e.g. # 01234 # 123 # ^^^ elif c == 0 and d == len(self.second): return Sequence( first, is_dna = self.first.is_dna, )

31.679487

68

0.474909

561

4,942

4.103387

0.219251

0.054735

0.026064

0.008688

0.337967

0.30278

0.261946

0.2298

0.21199

0

0.030753

0.42756

4,942

155

69

31.883871

0.782962

0.140834

0

0.259615

0

0.009039

0

0.028846

1

0.076923

false

0.009615

0.028846

0.009615

0.269231

0

null

0

null

0

1

0

c8dab9e9589a6e0d7ec3775c63cd68cd42f91ee4

857

py

Python

models/operations.py

NikolayXHD/tinkoff-api-python

4a4b71f7af1d752b8566299c058b712b513fa92f

[ "MIT" ]

null

models/operations.py

NikolayXHD/tinkoff-api-python

4a4b71f7af1d752b8566299c058b712b513fa92f

[ "MIT" ]

null

models/operations.py

NikolayXHD/tinkoff-api-python

4a4b71f7af1d752b8566299c058b712b513fa92f

[ "MIT" ]

null

from __future__ import annotations from . import _base class Operations(_base.Model): swagger_types: dict[str, str] = {'operations': 'list[Operation]'} attribute_map: dict[str, str] = {'operations': 'operations'} def __init__(self, operations=None): self._operations = None self.discriminator = None self.operations = operations @property def operations(self): """ :rtype: list[clients.tinkoff.models.Operation] """ return self._operations @operations.setter def operations(self, operations): """ :param list[clients.tinkoff.models.Operation] operations: """ if operations is None: raise ValueError( 'Invalid value for `operations`, must not be `None`' ) self._operations = operations

25.205882

69

0.611435

83

857

6.13253

0.46988

0.165029

0.10609

0.078585

0.129666

0

0.28238

857

33

70

25.969697

0.827642

0.121354

0

0.134561

0

1

0.157895

false

0

0.105263

0

0.473684

0

null

0

null

0

1

0

c8dbf09302e48945dea0b1250add3f9a59269652

827

py

Python

app/api/utils/remoteImageMapper.py

nurely/lxdui

8cb31dc1117719b140f440f8a705282781db7b35

[ "Apache-2.0" ]

589

2017-10-22T04:11:08.000Z

2022-03-26T22:50:30.000Z

app/api/utils/remoteImageMapper.py

nurely/lxdui

8cb31dc1117719b140f440f8a705282781db7b35

[ "Apache-2.0" ]

134

2017-11-14T02:52:03.000Z

2022-03-22T12:51:09.000Z

app/api/utils/remoteImageMapper.py

nurely/lxdui

8cb31dc1117719b140f440f8a705282781db7b35

[ "Apache-2.0" ]

170

2017-10-06T06:22:43.000Z

2022-03-15T02:12:34.000Z

def remoteImagesList(images): response = [] aliasesProcessed = [] aliases = [alias[20:] for alias in images['metadata']] for alias in aliases: strippedAlias = alias.replace('/default','') if strippedAlias not in aliasesProcessed: aliasesDetails = alias.split('/') if len(aliasesDetails) > 2: image = prepRemoteImageObject(strippedAlias, aliasesDetails) if image not in response: response.append(image) aliasesProcessed.append(strippedAlias) return response def prepRemoteImageObject(alias, aliasesDetails): image = { 'name': aliasesDetails[0].__str__(), 'distribution': aliasesDetails[1].__str__(), 'architecture': aliasesDetails[2].__str__(), 'image': alias } return image

30.62963

76

0.628779

71

827

7.15493

0.422535

0.031496

0.03937

0

0.009836

0.262394

827

26

77

31.807692

0.822951

0

0.060606

0

1

0.095238

false

0

0.190476

0

null

0

null

0

1

0

c8e2a3f8d1524fcc6efb93afc74fa20ef2432c75

2,049

py

Python

gemd/entity/template/has_property_templates.py

CitrineInformatics/gemd-python

4f80045c1b481269c7451f6a205755c22093eb74

[ "Apache-2.0" ]

7

2020-04-02T11:11:09.000Z

2022-02-05T23:19:51.000Z

gemd/entity/template/has_property_templates.py

CitrineInformatics/gemd-python

4f80045c1b481269c7451f6a205755c22093eb74

[ "Apache-2.0" ]

24

2020-04-22T16:55:09.000Z

2022-03-30T20:44:39.000Z

gemd/entity/template/has_property_templates.py

CitrineInformatics/gemd-python

4f80045c1b481269c7451f6a205755c22093eb74

[ "Apache-2.0" ]

3

2020-05-08T00:50:02.000Z

2020-12-19T00:48:56.000Z

"""For entities that have a property template.""" from gemd.entity.link_by_uid import LinkByUID from gemd.entity.setters import validate_list from gemd.entity.template.base_template import BaseTemplate from gemd.entity.template.property_template import PropertyTemplate from gemd.entity.bounds.base_bounds import BaseBounds from typing import Iterable class HasPropertyTemplates(object): """ Mixin-trait for entities that include property templates. Parameters ---------- properties: List[(PropertyTemplate, BaseBounds)] A list of tuples containing this entity's property templates as well as any restrictions on those templates' bounds. """ def __init__(self, properties): self._properties = None self.properties = properties @property def properties(self): """ Get the list of property template/bounds tuples. Returns ------- List[(PropertyTemplate, bounds)] List of this entity's property template/bounds pairs """ return self._properties @properties.setter def properties(self, properties): """ Set the list of parameter templates. Parameters ---------- properties: List[(PropertyTemplate, bounds)] A list of tuples containing this entity's property templates as well as any restrictions on those templates' bounds. Returns ------- List[(PropertyTemplate, bounds)] List of this entity's property template/bounds pairs """ if isinstance(properties, Iterable): if any(isinstance(x, BaseBounds) for x in properties): properties = [properties] # It's a template/bounds tuple (probably) self._properties = validate_list(properties, (PropertyTemplate, LinkByUID, list, tuple), trigger=BaseTemplate._homogenize_ranges )

32.52381

84

0.625671

207

2,049

6.115942

0.323672

0.028436

0.055292

0.060032

0.350711

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0

0.5

0

null

0

null

0

1

0

c8e80bc7bd958f10a7a1f279ed0d99283b77f722

1,184

py

Python

preprocessing.py

Alloooshe/facelib_modular_face_recognition_pipline

0313214b6f919e49e84235c1a6a4a4838b813e73

[ "MIT" ]

10

2019-12-29T13:38:56.000Z

2021-03-15T07:21:52.000Z

preprocessing.py

Alloooshe/facelib_modular_face_recognition_pipline

0313214b6f919e49e84235c1a6a4a4838b813e73

[ "MIT" ]

1

2021-03-15T07:45:45.000Z

2021-03-17T11:10:53.000Z

preprocessing.py

Alloooshe/facelib_modular_face_recognition_pipline

0313214b6f919e49e84235c1a6a4a4838b813e73

[ "MIT" ]

2

2020-05-03T08:33:39.000Z

2021-02-06T16:49:54.000Z

import cv2 import numpy as np class preprocessing: def process_image(self,image, rescale, recolor): if rescale['req']: image= self.rescale(image,rescale['width'], rescale['height']) if recolor['req']: image = self.rgb2gray(image) return image def rescale (self,image,width,height): image= cv2.resize(image,(width,height)) return image def rgb2gray(self,image): image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY) return image def crop (self,image,boxes ): faces = [] for box in boxes : x=int( round (box[0])) y=int( round (box[1])) w=int (round (box[2]) ) h=int (round ( box[3])) cropped = image[y:h+y,x : w+x,:] faces.append(cropped) return faces def resize2square (self,image,x,y): resized= cv2.resize(image,(x,y),interpolation=cv2.INTER_AREA) return resized def preprocess_facenet(self, images): ret = np.zeros([len(images),160,160,3]) for image in images : resized = self.resize2square(image,160,160) np.append(ret,resized) return ret

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72

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0.441176

0

null

0

null

0

1

0

c8ebd9a417dcbfc90f2665cef2e143f107c15986

497

py

Python

covid_19_stat.py

pavelkalinchuk/api

3b2eccbb09b012ac2c841dd30c44a285a8f5bdc2

[ "Apache-2.0" ]

null

covid_19_stat.py

pavelkalinchuk/api

3b2eccbb09b012ac2c841dd30c44a285a8f5bdc2

[ "Apache-2.0" ]

null

covid_19_stat.py

pavelkalinchuk/api

3b2eccbb09b012ac2c841dd30c44a285a8f5bdc2

[ "Apache-2.0" ]

null

import requests from datetime import date, timedelta today = date.today() yesterday = today - timedelta(days=1) country = "Russia" endpoint = f"https://api.covid19api.com/country/{country}/status/confirmed" params = {"from": str(yesterday), "to": str(today)} response = requests.get(endpoint, params=params).json() total_confirmed = 0 for day in response: cases = day.get("Cases", 0) total_confirmed += cases print("\n"f"Total Confirmed Covid-19 cases in {country}: {total_confirmed}")

29.235294

76

0.724346

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497

5.25

0.529412

0.156863

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0.016129

0.126761

497

16

77

31.0625

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0.285714

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1

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false

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0

0.153846

0.076923

0

null

0

null

0

1

0

c8ee532a04ed15373dc8d2091c28d0c7dca10643

2,834

py

Python

MPI/py/plot_mpi_timing.py

mlxd/myscripts

b8b7d6b270ef24b06028e21f066c2bb587f94cef

[ "MIT" ]

null

MPI/py/plot_mpi_timing.py

mlxd/myscripts

b8b7d6b270ef24b06028e21f066c2bb587f94cef

[ "MIT" ]

null

MPI/py/plot_mpi_timing.py

mlxd/myscripts

b8b7d6b270ef24b06028e21f066c2bb587f94cef

[ "MIT" ]

null

#This file plots the results from the MPI timing runs import sys import numpy as np from mpl_toolkits.mplot3d import Axes3D import matplotlib.pyplot as plt import matplotlib.markers as mkr plt_style='ggplot' plt.rcParams['font.size'] = 11 plt.rcParams['font.family'] = 'serif' plt.rcParams['axes.labelsize'] = 11 plt.rcParams['axes.labelweight'] = 'bold' plt.rcParams['xtick.labelsize'] = 9 plt.rcParams['ytick.labelsize'] = 9 plt.rcParams['figure.titlesize'] = 12 #We begin by loading the CSV file of rank pairings and times into the appropriate format StartStr = str(sys.argv[1]) EndStr = str(sys.argv[2]) start = np.loadtxt(open(StartStr), delimiter=',', dtype={'names': ('A','B','t'), 'formats':('i4','i4','f8')}) end = np.loadtxt(open(EndStr), delimiter=',', dtype={'names': ('A','B','t'), 'formats':('i4','i4','f8')}) ds=[{'%s:%s'%(a,b): (a,b,t) for a,b,t in zip(start['A'],start['B'],start['t']) }] de=[{'%s:%s'%(a,b): (a,b,t) for a,b,t in zip(end['A'],end['B'],end['t']) }] #We take note of the starting time over all ranks as a 0 offset t0 = np.min(start['t']) #3D Rank A:B vs time diagram fig = plt.figure() plt.style.use(plt_style) fig.clf() ax = fig.add_subplot(111, projection='3d') ax.set_zlabel('time [s]') ax.set_ylabel('Rank To Merge') ax.set_xlabel('Rank Base') #Plot the recorded times and connect ranks that have been merged toegther for a in ds[0].keys(): ax.scatter( ds[0][a][0], ds[0][a][1], ds[0][a][2]-t0, c='r', marker='o') #Plot start ax.scatter( de[0][a][0], de[0][a][1], de[0][a][2]-t0, c='b', marker='x') #Plot end ax.plot( [ ds[0][a][0], de[0][a][0] ], [ ds[0][a][1], de[0][a][1] ], [ ds[0][a][2] - t0, de[0][a][2] - t0 ], c='k') #Draw line between start and finish ax.set_zlim3d([ 0, np.max(end['t']) - t0 ]) ax.set_ylim3d([ np.min([end['A'], end['B']]), np.max([end['A'],end['B']]) ]) ax.set_xlim3d([ np.min([end['A'], end['B']]), np.max([end['A'],end['B']]) ]) plt.show() #Save the 3D plot output plt.savefig('3d_%s_%s.pdf'%(StartStr, EndStr)) plt.clf() plt.style.use( plt_style ) #2D connections diagram #Draw lines to mark the MPI ranks for ii in xrange(np.max([start['A'],start['B']])): plt.axhline(ii, xmin=0, xmax=1, linewidth=0.5) #Draw lines between the start and end for reducing 2 data sets for a in ds[0].keys(): plt.plot( [ ds[0][a][2] - t0, de[0][a][2] - t0] , [ds[0][a][1], de[0][a][0]], linestyle='-', linewidth=0.5, c='k', alpha=0.8) plt.scatter( start['t'] - t0, start['B'], marker='x', c='r', alpha=0.8) plt.scatter( end['t'] - t0, end['A'], marker='o', c='b', alpha=0.8) plt.xlabel('time [s]') plt.ylabel('MPI rank') plt.title('%s_%s'%(StartStr, EndStr)) plt.xlim([ 0, np.max(end['t']) - t0 ]) plt.ylim([ np.min([end['A'], end['B']]), np.max([end['A'],end['B']]) ]) plt.show() #Save the 2D plot output plt.savefig('2d_%s_%s.pdf'%(StartStr, EndStr))

38.821918

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0.61856

538

2,834

3.228625

0.291822

0.018423

0.032239

0.264824

0.200921

0.15947

0.145653

0.138169

0

0.036977

0.122089

2,834

72

156

39.361111

0.661174

0.181016

0

0.117647

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0.121317

0

1

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false

0

0.098039

0

0.098039

0

null

0

null

0

1

0

c8effc674c65f81f1f4c9fdac1c750120b3d16ef

716

py

Python

octavia-cli/unit_tests/test_entrypoint.py

pluralsh/airbyte

9b1ed03fe482f5154f6c1843b1be76de87f3605d

[ "MIT" ]

1

2022-01-27T22:29:38.000Z

octavia-cli/unit_tests/test_entrypoint.py

pluralsh/airbyte

9b1ed03fe482f5154f6c1843b1be76de87f3605d

[ "MIT" ]

null

octavia-cli/unit_tests/test_entrypoint.py

pluralsh/airbyte

9b1ed03fe482f5154f6c1843b1be76de87f3605d

[ "MIT" ]

null

# # Copyright (c) 2021 Airbyte, Inc., all rights reserved. # import pytest from click.testing import CliRunner from octavia_cli import entrypoint def test_octavia(): runner = CliRunner() result = runner.invoke(entrypoint.octavia) assert result.exit_code == 0 assert result.output.startswith("Usage: octavia [OPTIONS] COMMAND [ARGS]...") @pytest.mark.parametrize( "command", [entrypoint.init, entrypoint.apply, entrypoint.create, entrypoint.delete, entrypoint._list, entrypoint._import], ) def test_not_implemented_commands(command): runner = CliRunner() result = runner.invoke(command) assert result.exit_code == 1 assert result.output.endswith("not yet implemented.\n")

27.538462

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0.152235

716

25

117

28.64

0.841845

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0.117647

false

0

0.235294

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0.352941

0

null

0

null

0

1

0

c8f2a4e3254c600092c6d8f19d958953e7b804a3

5,261

py

Python

src/device/eltako/fsr61_actor.py

rosenloecher-it/enocean-mqtt-bridge

d56e41a1a67e70bdeb1aa46d10f48ed5a12ca59c

[ "MIT" ]

1

2020-12-01T17:10:14.000Z

src/device/eltako/fsr61_actor.py

rosenloecher-it/enocean-mqtt-bridge

d56e41a1a67e70bdeb1aa46d10f48ed5a12ca59c

[ "MIT" ]

1

2021-09-19T13:38:02.000Z

src/device/eltako/fsr61_actor.py

rosenloecher-it/enocean-mqtt-bridge

d56e41a1a67e70bdeb1aa46d10f48ed5a12ca59c

[ "MIT" ]

null

import json import logging import random from datetime import datetime from typing import Optional from paho.mqtt.client import MQTTMessage from enocean.protocol.constants import PACKET from enocean.protocol.packet import RadioPacket from src.command.switch_command import SwitchCommand from src.common.json_attributes import JsonAttributes from src.common.switch_state import SwitchState from src.device.base.cyclic_device import CheckCyclicTask from src.device.base.scene_actor import SceneActor from src.device.eltako.fsr61_eep import Fsr61Eep, Fsr61Action, Fsr61Command from src.device.misc.rocker_switch_tools import RockerSwitchTools, RockerAction, RockerButton from src.enocean_connector import EnoceanMessage from src.tools.enocean_tools import EnoceanTools from src.tools.pickle_tools import PickleTools class Fsr61Actor(SceneActor, CheckCyclicTask): """ Specialized for: Eltako FSR61-230V (an ON/OFF relay switch) """ DEFAULT_REFRESH_RATE = 300 # in seconds def __init__(self, name): SceneActor.__init__(self, name) CheckCyclicTask.__init__(self) self._current_switch_state = None # type: Optional[SwitchState] self._last_status_request = None # type: Optional[datetime] def process_enocean_message(self, message: EnoceanMessage): packet = message.payload # type: RadioPacket if packet.packet_type != PACKET.RADIO: self._logger.debug("skipped packet with packet_type=%s", EnoceanTools.packet_type_to_string(packet.rorg)) return if packet.rorg == RockerSwitchTools.DEFAULT_EEP.rorg: props = RockerSwitchTools.extract_props(packet) self._logger.debug("proceed_enocean - got=%s", props) action = RockerSwitchTools.extract_action(props) # type: RockerAction if action.button == RockerButton.ROCK3: self._current_switch_state = SwitchState.ON elif action.button == RockerButton.ROCK2: self._current_switch_state = SwitchState.OFF else: self._current_switch_state = SwitchState.ERROR else: self._current_switch_state = SwitchState.ERROR if self._current_switch_state not in [SwitchState.ON, SwitchState.OFF]: if self._logger.isEnabledFor(logging.DEBUG): self._logger.debug("proceed_enocean - pickled error packet:\n%s", PickleTools.pickle_packet(packet)) self._logger.debug("proceed_enocean - switch_state=%s", self._current_switch_state) self._last_status_request = self._now() self._reset_offline_refresh_timer() message = self._create_json_message(self._current_switch_state) self._publish_mqtt(message) def _create_json_message(self, switch_state: SwitchState): data = { JsonAttributes.DEVICE: self.name, JsonAttributes.STATE: switch_state.value, JsonAttributes.TIMESTAMP: self._now().isoformat(), } json_text = json.dumps(data) return json_text def process_mqtt_message(self, message: MQTTMessage): try: self._logger.debug('process_mqtt_message: "%s"', message.payload) command = SwitchCommand.parse(message.payload) self._logger.debug("mqtt command: '{}'".format(repr(command))) self._execute_actor_command(command) except ValueError: self._logger.error("cannot execute command! message: {}".format(message.payload)) def _execute_actor_command(self, command: SwitchCommand): if command.is_toggle: command = SwitchCommand.OFF if self._current_switch_state == SwitchState.ON else SwitchCommand.ON if command.is_on_or_off: action = Fsr61Action( command=Fsr61Command.SWITCHING, switch_state=SwitchState.ON if command.is_on else SwitchState.OFF, ) elif command.is_update: action = Fsr61Action(command=Fsr61Command.STATUS_REQUEST) elif command.is_learn: action = Fsr61Action(command=Fsr61Command.SWITCHING, switch_state=SwitchState.ON, learn=True) else: raise ValueError("SwitchCommand ({}) not supported!".format(command)) action.sender = self._enocean_sender action.destination = self._enocean_target or 0xffffffff props, packet = Fsr61Eep.create_props_and_packet(action) self._logger.debug("sending '{}' => {}".format(action, props)) self._send_enocean_packet(packet) def check_cyclic_tasks(self): self._check_and_send_offline() self._request_update() def _request_update(self): diff_seconds = None now = self._now() refresh_rate = self._randomized_refresh_rate if self._last_status_request is not None: diff_seconds = (now - self._last_status_request).total_seconds() if diff_seconds is None or diff_seconds >= refresh_rate: self._last_status_request = now self._execute_actor_command(SwitchCommand.UPDATE) @property def _randomized_refresh_rate(self) -> int: return self.DEFAULT_REFRESH_RATE + random.randint(0, self.DEFAULT_REFRESH_RATE * 0.1)

40.469231

117

0.698536

599

5,261

5.851419

0.250417

0.047076

0.043652

0.056491

0.151213

0.103281

0.063338

0.039372

0

0.009248

0.21897

5,261

129

118

40.782946

0.843758

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0

0.05

0

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0.004134

0

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1

0.08

false

0

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0.01

0.31

0

null

0

null

0

1

0

c8f361858524234ea8e385c43bd790d28e9507fd

1,960

py

Python

neuroml/arraymorph_load_time_benchmark.py

NeuralEnsemble/libNeuroML

75d1630a0c6354a3997c4068dc8cdc447491b6f8

[ "BSD-3-Clause" ]

20

2015-03-11T11:21:32.000Z

2021-10-11T16:03:27.000Z

neuroml/arraymorph_load_time_benchmark.py

NeuralEnsemble/libNeuroML

75d1630a0c6354a3997c4068dc8cdc447491b6f8

[ "BSD-3-Clause" ]

48

2015-01-15T18:41:01.000Z

2022-01-05T13:53:58.000Z

neuroml/arraymorph_load_time_benchmark.py

NeuralEnsemble/libNeuroML

75d1630a0c6354a3997c4068dc8cdc447491b6f8

[ "BSD-3-Clause" ]

16

2015-01-14T21:53:46.000Z

2019-09-04T23:05:27.000Z

import numpy as np import neuroml import neuroml.arraymorph as am class Benchmark: def __init__(self, num_segments): self.num_segments = num_segments def set_up(self): num_segments = int(1e4) # Per cell num_vertices = num_segments + 1 x = np.linspace(0, 10, num_vertices) y = np.zeros(num_vertices) z = np.zeros(num_vertices) d = np.linspace(1, 0.01, num_vertices) vertices = np.array([x, y, z, d]).T connectivity = range(-1, num_segments) big_arraymorph = am.ArrayMorphology( vertices=vertices, connectivity=connectivity ) transposed_x = x + 10 transposed_vertices = np.array([transposed_x, y, z, d]).T transposed_arraymorph = am.ArrayMorphology( vertices=transposed_vertices, connectivity=connectivity ) bigger_d = d + 0.5 fatter_vertices = np.array([x, y, z, bigger_d]).T fatter_arraymorph = am.ArrayMorphology( vertices=fatter_vertices, connectivity=connectivity ) neuroml_cell = neuroml.Cell(id="cell_4") neuroml_morphology = neuroml.Morphology(id="my_morph") neuroml_cell.morphology = neuroml_morphology self.transposed_arraymorph = transposed_arraymorph self.fatter_arraymorph = fatter_arraymorph self.big_arraymorph = big_arraymorph self.cell_1 = neuroml.Cell(id="cell_1") self.cell_2 = neuroml.Cell(id="cell_2") self.cell_3 = neuroml.Cell(id="cell_3") self.cell_1.morphology = transposed_arraymorph self.cell_2.morphology = fatter_arraymorph self.cell_3.morphology = big_arraymorph self.test_doc = neuroml.NeuroMLDocument(id="TestDocument") self.test_doc.cells.append(self.cell_1) self.test_doc.cells.append(self.cell_2) self.test_doc.cells.append(self.cell_3) self.test_doc.cells.append(neuroml_cell)

31.612903

67

0.656122

245

1,960

5.004082

0.220408

0.058728

0.044861

0.055465

0.126427

0.102773

0.073409

0

0.019035

0.24949

1,960

61

68

32.131148

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0

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0

0.066667

0

0.133333

0

null

0

null

0

1

0

c8f61ba84ff26314734e24f05cd833da5e3ee801

2,813

py

Python

pymtl/tools/translation/verilog_bug_test.py

belang/pymtl

4a96738724b007cbd684753aed0ac3de5b5dbebb

[ "BSD-3-Clause" ]

206

2015-01-05T21:53:56.000Z

2022-03-14T08:04:49.000Z

pymtl/tools/translation/verilog_bug_test.py

belang/pymtl

4a96738724b007cbd684753aed0ac3de5b5dbebb

[ "BSD-3-Clause" ]

84

2015-01-25T19:57:33.000Z

2021-05-11T15:46:56.000Z

pymtl/tools/translation/verilog_bug_test.py

belang/pymtl

4a96738724b007cbd684753aed0ac3de5b5dbebb

[ "BSD-3-Clause" ]

99

2015-02-17T17:43:44.000Z

2022-02-14T17:58:18.000Z

#======================================================================= # verilog_bug_test.py #======================================================================= import pytest from pymtl import * from exceptions import VerilatorCompileError pytestmark = requires_verilator #----------------------------------------------------------------------- # Point BitStruct #----------------------------------------------------------------------- class Point( BitStructDefinition ): def __init__( s ): s.x = BitField(4) s.y = BitField(4) #----------------------------------------------------------------------- # setup_sim #----------------------------------------------------------------------- def setup_sim( model ): model = TranslationTool( model ) model.elaborate() sim = SimulationTool( model ) return model, sim #----------------------------------------------------------------------- # test_bitstruct_tick_reg #----------------------------------------------------------------------- @pytest.mark.parametrize( 'config', ['Tick','TickFields','Comb','CombFields'] ) def test_bitstruct_reg( config ): class AssignBitStruct( Model ): def __init__( s, config=None ): s.in_ = InPort ( Point() ) s.out = OutPort( Point() ) if config == 'Tick': @s.tick_rtl def block(): s.out.next = s.in_ elif config == 'TickFields': @s.tick_rtl def block(): s.out.x.next = s.in_.x s.out.y.next = s.in_.y elif config == 'Comb': @s.combinational def block(): s.out.value = s.in_ elif config == 'CombFields': @s.combinational def block(): s.out.x.value = s.in_.x s.out.y.value = s.in_.y else: raise Exception( 'Invalid config =', config ) # verify verilator simulation model, sim = setup_sim( AssignBitStruct( config ) ) for i in range( 10 ): input_value = concat( *2*[Bits(4,i)] ) model.in_.value = input_value sim.cycle() assert model.out == input_value # read verilog to verify our output signal is being declared as a reg # (required by Synopsys design compiler) with open( model.__class__.__name__+'.v', 'r' ) as fp: assert 'output reg' in fp.read() #----------------------------------------------------------------------- # test_verilator_compile_error #----------------------------------------------------------------------- def test_verilator_compile_error( ): class TestVerilatorCompileError( Model ): def __init__( s ): s.in_ = InPort(8) s.out = OutPort(8) @s.combinational def logic(): s.in_.value = s.out with pytest.raises( VerilatorCompileError ): model = TestVerilatorCompileError() model, sim = setup_sim( model )

29

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0.460363

265

2,813

4.686792

0.358491

0.021739

0.028986

0.038647

0.090177

0.088567

0.032206

0

0.00354

0.196587

2,813

96

73

29.302083

0.546018

0.335585

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0.033898

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0.186441

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0.305085

0

null

0

null

0

1

0

c8f667d55a6083981558407ab139318c270d5ca3

436

py

Python

library/TraverseDirectory-M2.py

remytanx/python3-created-in-github

83b3dd0f36da6fc4df7c1cc37cac12f178f985a3

[ "MIT" ]

null

library/TraverseDirectory-M2.py

remytanx/python3-created-in-github

83b3dd0f36da6fc4df7c1cc37cac12f178f985a3

[ "MIT" ]

null

library/TraverseDirectory-M2.py

remytanx/python3-created-in-github

83b3dd0f36da6fc4df7c1cc37cac12f178f985a3

[ "MIT" ]

null

import os # Get the list of all files with a specific extension # In this example, we will take a path of a directory and try to # list all the files, with a specific extension .py here, # in the directory and its sub-directories recursively. path = r'C:\Users\10900225\Documents\Witch\BTX\Workspaces\Library' for root, dirs, files in os.walk(path): for file in files: if(file.endswith(".py")): print(os.path.join(root,file))

33.538462

66

0.733945

76

436

4.210526

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0.05625

0.0625

0.1125

0.16875

0

0.021978

0.165138

436

13

67

33.538462

0.857143

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0

0.286408

0.271845

0

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false

0

0.166667

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0.166667

0

null

0

null

0

1

0

c8f71840564fdc1ff2e1787b21b4d5173407d801

1,509

py

Python

Modules/carlosma7/wizard/create_appointment.py

Carlosma7/Odoo

c234fcc18d15d4d8369e237286bee610fd76ceee

[ "CC0-1.0" ]

null

Modules/carlosma7/wizard/create_appointment.py

Carlosma7/Odoo

c234fcc18d15d4d8369e237286bee610fd76ceee

[ "CC0-1.0" ]

null

Modules/carlosma7/wizard/create_appointment.py

Carlosma7/Odoo

c234fcc18d15d4d8369e237286bee610fd76ceee

[ "CC0-1.0" ]

null

#-*- coding: utf-8-*- from odoo import api, fields, models, _ # Wizard class class CreateAppointmentWizard(models.TransientModel): _name = "create.appointment.wizard" _description = "Create Appointment Wizard" date_appointment = fields.Date(string='Date', required=False) patient_id = fields.Many2one('hospital.patient', string="Patient", required=True) # Wizard function def action_create_appointment(self): print("Wizard button is clicked") vals = { 'patient_id': self.patient_id.id, 'date_appointment': self.date_appointment } # Create a new record appointment_rec = self.env['hospital.appointment'].create(vals) return { 'name': _('Appointment'), 'type': 'ir.actions.act_window', 'view_mode': 'form', 'res_model': 'hospital.appointment', 'res_id': appointment_rec.id, } # View appointment def action_view_appointment(self): # Method 1 # action = self.env.ref('carlosma7.action_hospital_appointment').read()[0] # action['domain'] = [('patient_id', '=', self.patient_id.id)] # return action # Method 2 # action = self.env.['ir.actions.actions']._for_xml_id('carlosma7.action_hospital_appointment') # action['domain'] = [('patient_id', '=', self.patient_id.id)] # return action # Method 3 return { 'type': 'ir.actions.act_window', 'name': 'Appointments', 'res_model': 'hospital.appointment', 'view_type': 'form', 'domain': [('patient_id', '=', self.patient_id.id)], 'view_mode': 'tree,form', 'target': 'current', }

29.019231

97

0.686547

181

1,509

5.519337

0.364641

0.081081

0.052052

0.08008

0.206206

0.162162

0.138138

0.108108

0

0.006216

0.147117

1,509

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false

0

0.033333

0.333333

0.033333

0

null

0

null

0

1

0

c8f838e818d81e237d9d5d8fa11595a921a6fae3

4,731

py

Python

groups.py

davidmehren/udm_group_matrix

ae71feef4bf299588aa473c95e9073c7d2f5f23e

[ "MIT" ]

null

groups.py

davidmehren/udm_group_matrix

ae71feef4bf299588aa473c95e9073c7d2f5f23e

[ "MIT" ]

null

groups.py

davidmehren/udm_group_matrix

ae71feef4bf299588aa473c95e9073c7d2f5f23e

[ "MIT" ]

1

2019-12-06T14:59:39.000Z

#!/bin/env python3 import re from typing import List import numpy as np import matplotlib.pyplot as plt filtered_users = ["join-backup", "join-slave", "ucs-sso"] filtered_groups = ["computers", "dc backup hosts", "dc slave hosts"] class LDAPUser: name: str def __init__(self, name): self.name = name def __eq__(self, o: 'LDAPUser') -> bool: return self.name == o.name def __lt__(self, o: 'LDAPUser') -> bool: return self.name < o.name def __hash__(self) -> int: return self.name.__hash__() class LDAPGroupList: content: List['LDAPGroup'] def __init__(self): self.content = [] def add(self, group): if group.name not in filtered_groups: self.content.append(group) def get_by_name(self, name): for _group in self.content: if _group.name == name: return _group return None def get_user_list(self): user_list = set() for group in self.content: user_list.update(group.members) return list(user_list) def tidy(self): new_content = [] for group in self.content: if group.samba_rid < 0: continue if len(group.members) > 0: new_content.append(group) self.content = sorted(new_content) class LDAPGroup: name: str samba_rid: int subgroups: List[str] members: List[LDAPUser] def __str__(self) -> str: _repr = f"{self.name}\n Mitglieder:\n" for member in self.members: _repr = _repr + f" {member.name}\n" _repr = _repr + " Untergruppen:\n" for _group in self.subgroups: _repr = _repr + f" {_group}\n" return _repr def __lt__(self, o: 'LDAPGroup') -> bool: return self.name < o.name def __init__(self, name: str): self.name = name.lower() self.subgroups = [] self.members = [] def add_subgroup(self, group: str): self.subgroups.append(group.lower()) def parse_subgroups(self, global_groups: LDAPGroupList): for group_name in self.subgroups: ldap_group = global_groups.get_by_name(group_name) if ldap_group is None: print(f"can't find group '{group_name}'") else: for member in ldap_group.members: if member not in self.members: self.members.append(member) def add_member(self, member): if member.name not in filtered_users: self.members.append(member) def read_groupdump(): _group_list = LDAPGroupList() with open("groupdump.txt", "r") as file: current_group = None for line in file: if line == "\n": continue if line.startswith("DN"): current_group = LDAPGroup(re.findall(r"cn=(.*?),", line)[0]) _group_list.add(current_group) # print(current_user) if current_group.name.startswith("dns-") or current_group.name.startswith( "ucs-") or current_group.name.startswith("join-"): continue if line.startswith(" users"): user = LDAPUser(re.findall(r"uid=(.*?),", line)[0]) # print(" ", group) current_group.add_member(user) if line.startswith(" nestedGroup"): subgroup = re.findall(r"cn=(.*?),", line)[0] # print(" ", group) current_group.add_subgroup(subgroup) if line.startswith(" sambaRID:"): rid = re.findall(r"([0-9]{1,4})", line)[0] current_group.samba_rid = int(rid) return _group_list def paint_matrix(groups: LDAPGroupList): user_list = sorted(groups.get_user_list(), reverse=True) x_count = len(groups.content) y_count = len(user_list) matrix = np.zeros((x_count, y_count)) for g_index, group in enumerate(groups.content): for user in group.members: matrix[g_index][user_list.index(user)] = 1 plt.pcolor(matrix.T, edgecolors='k', cmap="Greys", vmin=0, vmax=1) x_locations = [x + 0.5 for x in range(x_count)] y_locations = [x + 0.5 for x in range(y_count)] plt.xticks(x_locations, [group.name for group in groups.content], rotation=45, fontsize=4, ha="right") plt.yticks(y_locations, [user.name for user in user_list], fontsize=2) plt.tight_layout() plt.savefig("groups.png", dpi=600) if __name__ == '__main__': groups = read_groupdump() for group in groups.content: group.parse_subgroups(groups) groups.tidy() paint_matrix(groups)

31.125

106

0.581484

595

4,731

4.406723

0.233613

0.030511

0.022883

0.021358

0.179252

0.11251

0.101449

0.047292

0.029748

0

0.007541

0.299302

4,731

151

107

31.331126

0.783409

0.015853

0

0.09322

0

0.073087

0

1

0.144068

false

0

0.033898

0.330508

0.008475

0

null

0

null

0

1

0

c8f8f117d6dace7d4b6c578a60f491f9e6393f0d

1,836

py

Python

common_tools/report_dialog.py

jamiecook/AequilibraE

b1013d59cbeaf6fc4e1a944cf31f20460a2a4156

[ "MIT" ]

null

common_tools/report_dialog.py

jamiecook/AequilibraE

b1013d59cbeaf6fc4e1a944cf31f20460a2a4156

[ "MIT" ]

null

common_tools/report_dialog.py

jamiecook/AequilibraE

b1013d59cbeaf6fc4e1a944cf31f20460a2a4156

[ "MIT" ]

null

""" ----------------------------------------------------------------------------------------------------------- Package: AequilibraE Name: Report dialog Purpose: Dialog for showing the report from algorithm runs Original Author: Pedro Camargo (c@margo.co) Contributors: Last edited by: Pedro Camargo Website: www.AequilibraE.com Repository: https://github.com/AequilibraE/AequilibraE Created: 2014-03-19 Updated: 30/09/2016 Copyright: (c) AequilibraE authors Licence: See LICENSE.TXT ----------------------------------------------------------------------------------------------------------- """ from qgis.core import * from PyQt4 import QtGui, uic from PyQt4.QtGui import * import sys import os from auxiliary_functions import standard_path FORM_CLASS, _ = uic.loadUiType(os.path.join(os.path.dirname(__file__), 'forms/ui_report.ui')) class ReportDialog(QtGui.QDialog, FORM_CLASS): def __init__(self, iface, reporting): QDialog.__init__(self) self.iface = iface self.setupUi(self) self.path = standard_path() self.reporting = reporting for t in reporting: self.all_data.append(t) self.but_save_log.clicked.connect(self.save_log) self.but_close.clicked.connect(self.exit_procedure) def save_log(self): file_types = "Text files(*.txt)" new_name = QFileDialog.getSaveFileName(None, 'Save log', self.path, file_types) if len(new_name) > 0: if new_name[-3].upper() != 'TXT': new_name = new_name + '.txt' outp = open(new_name, 'w') for t in self.reporting: print >> outp, t outp.flush() outp.close() self.exit_procedure() def exit_procedure(self): self.close()

30.6

108

0.56427

206

1,836

4.854369

0.490291

0.042

0.033

0.04

0

0.014114

0.228214

1,836

59

109

31.118644

0.691602

0.337691

0

0.042749

0

1

0.09375

false

0

0.1875

0

0.3125

0.03125

0

null

0

null

0

1

0

c8f9b47386e455dd9e70d1f591e4c141b1b8e828

21,580

py

Python

gui/robot_data_visualizer.py

wh1210/robot-data-visualizer

ebb59687233a8d09c8ed327c66ed1d69c4623136

[ "MIT" ]

null

gui/robot_data_visualizer.py

wh1210/robot-data-visualizer

ebb59687233a8d09c8ed327c66ed1d69c4623136

[ "MIT" ]

13

2018-11-20T22:55:39.000Z

2022-03-11T23:36:18.000Z

gui/robot_data_visualizer.py

wh1210/robot-data-visualizer

ebb59687233a8d09c8ed327c66ed1d69c4623136

[ "MIT" ]

2

2018-11-09T01:48:07.000Z

2018-12-29T23:10:53.000Z

import os import sys sys.path.append('.') sys.path.append('..') import warnings warnings.filterwarnings("ignore") from datetime import datetime import matplotlib matplotlib.use("TkAgg") import matplotlib.lines as lines import matplotlib.image as mpimg from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg from matplotlib.figure import Figure import tkinter as tk from tools.get_dates_umich import get_dates_umich from tools.staticmap_for_gps import map_for_gps from tools.data_manager import DataManager from tools.view_lidar import hokuyo_plot from tools.view_lidar import threshold_lidar_pts class VisualizerFrame(tk.Frame): """ This is the main window where the robot data is seen by the user. """ def __init__(self, parent): tk.Frame.__init__(self, parent) self.parent = parent self.label = None self.ax_map = None self.ax_gps = None self.ax_lidar = None self.map_plot = None self.gps_plot = None self.lidar_plot = None self.canvas = None self.data_manager = None self.gps_data = None self.lidar_data = None self.gps_on = False self.map_on = False self.lidar_on = False self.map_image = None self.widgets() def widgets(self): """ Set up widgets for the frame. :return: None """ self.label = tk.Label(self, text="Viewer") self.label.pack(side=tk.TOP) self.fig = Figure(figsize=(5, 4), dpi=100) self.ax_map = self.fig.add_subplot(111) self.ax_gps = self.fig.add_subplot(111) self.ax_lidar = self.fig.add_subplot(111) self.canvas = FigureCanvasTkAgg(self.fig, master=self.master) self.canvas.draw() self.canvas.get_tk_widget().pack(fill=tk.BOTH, expand=True) def callback_initialize_data_manager(self): """ This callback responds to the *Load Data* button. :return: None """ date = self.parent.toolbar.date.get() if self.data_manager is None: self.setup_data(date) else: if self.data_manager.date is not date: os.chdir('../..') # TODO patched here - add this to end of load_gps() / load_lidar() functions self.setup_data(date) else: pass def setup_data(self, date): """ This function sets up all of the data (except lidar) needed by the application. :param date: Determines which date from the robotics dataset to use. :type date: str. :return: None """ if self.data_manager is not None: os.chdir(self.data_manager.owd) self.ax_gps.clear() self.ax_map.clear() self.ax_lidar.clear() self.canvas.draw() self.gps_on = False self.map_on = False self.lidar_on = False self.parent.set_status('DM_START', hold=True) self.data_manager = DataManager(date) self.data_manager.setup_data_files('sensor_data') self.data_manager.load_gps() x_coords, y_coords = map_for_gps(self.data_manager.data_dict, self.data_manager.data_dir) self.lidar_data = None self.gps_data = [x_coords, y_coords] # in image coords self.map_image = mpimg.imread(os.path.join(self.data_manager.data_dir, 'map.png')) self.label.config(text='Viewer') self.parent.set_status('DM_READY') def callback_gps_on(self): """ This callback responds to the *On* button under the *GPS Control* menu. :return: None """ if not self.lidar_on: if not self.gps_on: self.gps_on = True self.parent.set_status('GPS_START') idx = self.get_idx_for_gps_update() self.update_timestamp(idx) self.gps_plot = self.ax_gps.plot(self.gps_data[0][:idx], self.gps_data[1][:idx], 'r')[0] self.canvas.show() self.parent.set_status('GPS_READY') else: pass else: self.callback_lidar_off() self.callback_gps_on() def callback_gps_off(self): """ This callback responds to the *Off* button under the *GPS Control* menu. :return: None """ if self.gps_on: self.gps_on = False self.update_gps(0) self.label.config(text='Viewer') self.parent.set_status('GPS_REMOVE') else: pass def callback_gps_slider_changed(self, event): """ This callback responds to the scale position changing under the *GPS Control* menu. :return: None """ self.gps_on = True idx = self.get_idx_for_gps_update() self.update_gps(idx) self.update_timestamp(idx) self.parent.set_status('GPS_UPDATE') def update_gps(self, idx): """ This function updates the GPS data that is displayed in the main viewing window. :param idx: Index into the array of GPS data that is to be displayed. :type idx: int. :return: None """ if self.gps_data is not None: self.gps_plot.set_xdata(self.gps_data[0][:idx]) self.gps_plot.set_ydata(self.gps_data[1][:idx]) self.canvas.draw() else: pass def update_timestamp(self, idx): """ This function updates the timestamp in the main viewing window. :param idx: Index into the array of GPS data to be used for retrieval of the time stamp. :type idx: int. :return: None """ curr_tstamp = self.get_timestamp_for_gps_update(idx) self.label.config(text=str('time stamp: ' + curr_tstamp)) def get_idx_for_gps_update(self): """ This function returns the index to be used for updating the GPS data. :return: int -- the index to be used for the GPS update """ slider_val = self.parent.control.gps_control.selection_scale.get() idx_ratio = len(self.gps_data[0]) / 100 return int(slider_val * idx_ratio) def get_timestamp_for_gps_update(self, gps_data_idx): """ This function returns the timestamp in a readable format for the given GPS data index. :param gps_data_idx: Index into the array of GPS data to be used for retrieval of the time stamp. :return: str -- the timestamp """ idx_ratio = len(self.data_manager.data_dict['gps']['tstamp']) / len(self.gps_data[0]) idx = int(gps_data_idx * idx_ratio) - 1 ts = int(self.data_manager.data_dict['gps']['tstamp'][idx] / 1000000) return datetime.fromtimestamp(ts).strftime('%Y-%m-%d %H:%M:%S') def callback_map_on(self): """ This callback responds to the *On* button under the *Map Control* menu. :return: None """ if not self.lidar_on: if not self.map_on: self.map_on = True if self.map_image is not None: self.ax_map.imshow(self.map_image) # draw scale on the map map_scale = self.get_map_scale() line = lines.Line2D([0, 200], [0, 0], linewidth=4, color='b') self.ax_map.add_line(line) distance = map_scale * 200 if distance > 1000: scale_str = "scale = " + str(float("%.2f" % (distance / 1000))) + " kilometers" else: scale_str = "scale = " + str(float("%.2f" % (distance))) + " meters" self.ax_map.text(0, -10, scale_str, fontsize=8) self.canvas.draw() self.parent.set_status('MAP_READY') else: self.parent.set_status('MAP_ERROR') else: pass else: self.callback_lidar_off() self.callback_map_on() def callback_map_off(self): """ This callback responds to the *Off* button under the *Map Control* menu. :return: None """ if self.map_on: self.map_on = False self.ax_map.clear() if self.gps_on: self.gps_on = False self.callback_gps_on() # because the previous line clears both map and gps self.canvas.draw() else: pass def callback_date_changed(self): """ This callback responds to a change in the date selection menu in the toolbar. :return: None """ new_date = self.parent.toolbar.date.get() # Need to call get() because this is a StringVar object if self.parent.toolbar.date is not new_date: self.parent.toolbar.date.set(new_date) else: pass def get_map_scale(self): """ This function calculates the map scale in units of meters per pixel. :return: float64 -- map scale (m/px) """ k = 111000 # meters per degree of latitude (approx.) lat_range = self.data_manager.data_dict['gps_range'][0] d_lat_range = abs(lat_range[0] - lat_range[1]) d_x_pixels = abs(max(self.gps_data[0]) - min(self.gps_data[0])) map_scale = d_lat_range * k / d_x_pixels return map_scale # units of meters per pixel def callback_lidar_slider_changed(self, event): """ This callback responds to the scale position changing under the *Lidar Control* menu. :return: None """ self.lidar_on = True idx = self.get_idx_for_lidar_update() self.update_lidar(idx) # self.update_timestamp(idx) self.parent.set_status('Lidar updated') def get_idx_for_lidar_update(self): """ This function returns the index to be used for updating the Lidar data. :return: int -- the index to be used for the Lidar update """ slider_val = self.parent.control.lidar_control.selection_scale.get() idx_ratio = len(self.lidar_data) / 100 return max(int(slider_val * idx_ratio) - 1, 0) def update_lidar(self, idx): """ This function updates the Lidar data that is displayed in the main viewing window. :param idx: Index into the array of Lidar data that is to be displayed. :type idx: int. :return: None """ if self.lidar_data is not None: yt, xt, _ = threshold_lidar_pts(self.lidar_data[idx]) self.lidar_plot.set_xdata(xt) self.lidar_plot.set_ydata(yt) self.canvas.draw() else: pass def callback_lidar_on(self): """ This callback responds to the *On* button under the *Lidar Control* menu. :return: None """ if not self.lidar_on: self.lidar_on = True self.callback_map_off() self.callback_gps_off() if self.data_manager is None: self.callback_initialize_data_manager() if not 'lidar' in self.data_manager.data_dict.keys(): self.data_manager.setup_data_files('hokuyo') pickled = True delete_pickle = False self.data_manager.load_lidar(4000, pickled, delete_pickle) # TODO - global constant for lidar samples self.lidar_data = self.data_manager.data_dict['lidar'] xlimits, ylimits = [-32, 32], [-32, 32] self.ax_lidar.set_xlim(xlimits) self.ax_lidar.set_ylim(ylimits) hokuyo_plot(self.ax_lidar) yt, xt, _ = threshold_lidar_pts(self.lidar_data[0]) self.lidar_plot = self.ax_lidar.plot(xt, yt, 'r.')[0] self.canvas.show() else: pass def callback_lidar_off(self): """ This callback responds to the *Off* button under the *Lidar Control* menu. :return: None """ if self.lidar_on: self.lidar_on = False self.ax_lidar.clear() self.canvas.draw() else: pass class ToolbarFrame(tk.Frame): """ This class represents the toolbar at the top of the window. """ def __init__(self, parent): tk.Frame.__init__(self, parent) self.parent = parent self.date = None self.dates = get_dates_umich() self.load_button = None self.option_menu = None self.widgets() def widgets(self): """ Set up widgets for the frame. :return: None """ self.dates = get_dates_umich() self.load_button = tk.Button(self, text="Load Data") self.load_button.pack(side=tk.LEFT, padx=2, pady=2) self.date = tk.StringVar(self) self.date.set(self.dates[24]) self.option_menu = tk.OptionMenu(self, self.date, *self.dates, command=self.callback_date_changed) self.option_menu.pack(side=tk.LEFT, padx=2, pady=2) def bind_widgets(self): """ Bind widgets to their callback functions. :return: None """ self.load_button.config(command=self.parent.window.callback_initialize_data_manager) def callback_date_changed(self, event): self.parent.window.callback_date_changed() class ControlFrame(tk.Frame): """ This class represents the controls on the right hand side of the main window. There are two nested classes for the slam and map controls. """ def __init__(self, parent): tk.Frame.__init__(self, parent, width=400) self.parent = parent self.root = parent self.slam_control = None self.map_control = None self.lidar_control = None self.widgets() class GpsControlFrame(tk.Frame): def __init__(self, parent, root): tk.Frame.__init__(self, parent, width=400) self.parent = parent self.root = root self.selection_scale = None self.scale_val = None self.on_button = None self.off_button = None self.widgets() def widgets(self): """ Set up widgets for the frame. :return: None """ label = tk.Label(self, text="GPS Control", bg="blue", fg="white") label.pack(side=tk.TOP, fill=tk.X) self.selection_scale = tk.Scale(self, orient=tk.HORIZONTAL, to=100, variable=self.scale_val) self.selection_scale.set(100) self.selection_scale.pack(side=tk.TOP) self.on_button = tk.Button(self, text="On", bg="green", fg="white") self.on_button.pack(side=tk.LEFT) self.off_button = tk.Button(self, text="Off", bg="red", fg="white") self.off_button.pack(side=tk.RIGHT) def bind_widgets(self): """ Bind widgets to their callback functions. :return: None """ self.on_button.config(command=self.root.window.callback_gps_on) self.off_button.config(command=self.root.window.callback_gps_off) self.selection_scale.bind("<ButtonRelease-1>", self.root.window.callback_gps_slider_changed) class MapControlFrame(tk.Frame): def __init__(self, parent, root): tk.Frame.__init__(self, parent, width=400) self.parent = parent self.root = root self.on_button = None self.off_button = None self.widgets() def widgets(self): """ Set up widgets for the frame. :return: None """ label = tk.Label(self, text="Map Control", bg="blue", fg="white") label.pack(fill=tk.X) self.on_button = tk.Button(self, text="On", bg="green", fg="white") self.on_button.pack(side=tk.LEFT) self.off_button = tk.Button(self, text="Off", bg="red", fg="white") self.off_button.pack(side=tk.RIGHT) def bind_widgets(self): """ Bind widgets to their callback functions. :return: None """ self.on_button.config(command=self.root.window.callback_map_on) self.off_button.config(command=self.root.window.callback_map_off) class LidarControlFrame(tk.Frame): def __init__(self, parent, root): tk.Frame.__init__(self, parent, width=400) self.parent = parent self.root = root self.scale_val = None self.on_button = None self.off_button = None self.widgets() def widgets(self): """ Set up widgets for the frame. :return: None """ label = tk.Label(self, text="Lidar Control", bg="blue", fg="white") label.pack(side=tk.TOP, fill=tk.X) self.selection_scale = tk.Scale(self, orient=tk.HORIZONTAL, to=100, variable=self.scale_val) self.selection_scale.set(100) self.selection_scale.pack(side=tk.TOP) self.on_button = tk.Button(self, text="On", bg="green", fg="white") self.on_button.pack(side=tk.LEFT) self.off_button = tk.Button(self, text="Off", bg="red", fg="white") self.off_button.pack(side=tk.RIGHT) def bind_widgets(self): """ Bind widgets to their callback functions. :return: None """ self.on_button.config(command=self.root.window.callback_lidar_on) self.off_button.config(command=self.root.window.callback_lidar_off) self.selection_scale.bind("<ButtonRelease-1>", self.root.window.callback_lidar_slider_changed) def widgets(self): """ Set up widgets for the frame. :return: None """ self.gps_control = self.GpsControlFrame(self, self.root) self.gps_control.pack(fill=tk.X) self.map_control = self.MapControlFrame(self, self.root) self.map_control.pack(fill=tk.X) self.lidar_control = self.LidarControlFrame(self, self.root) self.lidar_control.pack(fill=tk.X) def bind_widgets(self): """ Bind widgets to their callback functions. :return: None """ self.gps_control.bind_widgets() self.map_control.bind_widgets() self.lidar_control.bind_widgets() class MainWindow(tk.Tk): """ This is the main window for the application. Here the main layout is established using a combination of the above classes and individual tkinter widgets. """ def __init__(self, parent): tk.Tk.__init__(self, parent) self.parent = parent self.status_text = dict(READY="Ready", DM_START="Initializing data manager ...", DM_READY="Data is ready", DM_NOT_READY="Data not loaded", GPS_START="GPS loading ...", GPS_READY="GPS is ready", GPS_REMOVE="GPS removed", GPS_UPDATE="GPS updated", MAP_START="Map loading ...", MAP_READY="Map is ready", MAP_REMOVE="Map removed", MAP_ERROR="Must load data before map can be displayed") self.STATUS_DELAY = 2000 # (ms) delay between status changes self.title("Robot Data Visualizer") self.mainWidgets() def mainWidgets(self): """ Set up widgets for the main window frame. :return: None """ # Toolbar self.toolbar = ToolbarFrame(self) self.toolbar.pack(side=tk.TOP, fill=tk.X) # Status bar self.status = tk.Label(self, text=self.status_text['READY'], bd=1, relief=tk.SUNKEN, anchor=tk.W) self.status.pack(side=tk.BOTTOM, fill=tk.X) # Controls - GPS and Map self.control = ControlFrame(self) self.control.pack(side=tk.RIGHT, fill=tk.Y) # Main viewing window self.window = VisualizerFrame(self) self.window.pack(side=tk.LEFT, padx=2, pady=2) # Bind widgets to their callback functions self.toolbar.bind_widgets() self.control.bind_widgets() def set_status(self, status, hold=False): """ This function sets the status bar at the bottom of the window (with a time delay). :param status: Key to look up status message in the status_text dictionary. :type status: str. :param hold: When *hold=True*, the status update will not time out. :type hold: bool. :return: None """ if status in self.status_text.keys(): self.status.config(text=self.status_text[status]) if not hold: self.status.after(self.STATUS_DELAY, lambda: self.status.config(text=self.status_text['READY'])) else: self.status.config(text=str(status)) if not hold: self.status.after(self.STATUS_DELAY, lambda: self.status.config(text=self.status_text['READY'])) if __name__ == '__main__': app = MainWindow(None) app.mainloop()

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null

0

null

0

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0

c8fa3bb594a67f398ad5e9f8e305ca9da2fda5ed

1,780

py

Python

day10/day10.py

BroderickCarlin/AdventOfCode

52d12d16f3d291a51984e6d85dbe97e604abc005

[ "MIT" ]

null

day10/day10.py

BroderickCarlin/AdventOfCode

52d12d16f3d291a51984e6d85dbe97e604abc005

[ "MIT" ]

null

day10/day10.py

BroderickCarlin/AdventOfCode

52d12d16f3d291a51984e6d85dbe97e604abc005

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- lengths = "187,254,0,81,169,219,1,190,19,102,255,56,46,32,2,216" suffix = [17, 31, 73, 47, 23] num_rounds = 64 def puzzle1(): knot = range(256) skip_size = 0 idx1 = 0 for l in [int(a) for a in lengths.split(",")]: idx2 = idx1 + l k = [] if idx2 >= len(knot): k = knot[idx1:] + knot[:idx2 - len(knot)] else: k = knot[idx1:idx2] k = list(reversed(k)) if idx2 >= len(knot): knot[idx1:] = k[:len(knot) - idx1] knot[:idx2 - len(knot)] = k[len(knot) - idx1:] else: knot[idx1:idx2] = k idx1 += skip_size + l while idx1 >= len(knot): idx1 -= len(knot) skip_size += 1 return knot[0] * knot[1] def puzzle2(): knot = range(256) hash_knot = "" skip_size = 0 idx1 = 0 for _ in range(num_rounds): for l in list(bytearray(lengths)) + suffix: idx2 = idx1 + l k = [] if idx2 >= len(knot): k = knot[idx1:] + knot[:idx2 - len(knot)] else: k = knot[idx1:idx2] k = list(reversed(k)) if idx2 >= len(knot): knot[idx1:] = k[:len(knot) - idx1] knot[:idx2 - len(knot)] = k[len(knot) - idx1:] else: knot[idx1:idx2] = k idx1 += skip_size + l while idx1 >= len(knot): idx1 -= len(knot) skip_size += 1 for x in range(16): s = 0 for y in range(16): s ^= knot[x * 16 + y] hash_knot += "%0.2X" % s return hash_knot if __name__ == "__main__": print("1: {}".format(puzzle1())) print("2: {}".format(puzzle2()))

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null

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null

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1

0

c8fc7cc35ebc665797970c840fc5d039b1988b5c

1,914

py

Python

17tensorflow/tf2/2my_model.py

cheerfulwang/python-tutorial

d0f7348e1da4ff954e3add66e1aae55d599283ee

[ "Apache-2.0" ]

2

2021-01-04T10:44:44.000Z

2022-02-13T07:53:41.000Z

17tensorflow/tf2/2my_model.py

zm79287/python-tutorial

d0f7348e1da4ff954e3add66e1aae55d599283ee

[ "Apache-2.0" ]

null

17tensorflow/tf2/2my_model.py

zm79287/python-tutorial

d0f7348e1da4ff954e3add66e1aae55d599283ee

[ "Apache-2.0" ]

2

2020-11-23T08:58:51.000Z

2022-02-13T07:53:42.000Z

# -*- coding: utf-8 -*- """ @author:XuMing（xuming624@qq.com) @description: """ import tensorflow as tf import tensorflow.keras as keras import tensorflow.keras.layers as layers # 超参 num_words = 2000 num_tags = 12 num_departments = 4 # 输入 body_input = keras.Input(shape=(None,), name='body') title_input = keras.Input(shape=(None,), name='title') tag_input = keras.Input(shape=(num_tags,), name='tag') # 嵌入层 body_feat = layers.Embedding(num_words, 64)(body_input) title_feat = layers.Embedding(num_words, 64)(title_input) # 特征提取层 body_feat = layers.LSTM(32)(body_feat) title_feat = layers.LSTM(128)(title_feat) features = layers.concatenate([title_feat,body_feat, tag_input]) # 分类层 priority_pred = layers.Dense(1, activation='sigmoid', name='priority')(features) department_pred = layers.Dense(num_departments, activation='softmax', name='department')(features) # 构建模型 model = keras.Model(inputs=[body_input, title_input, tag_input], outputs=[priority_pred, department_pred]) model.summary() keras.utils.plot_model(model, 'multi_model.png', show_shapes=True) model.compile(optimizer=keras.optimizers.RMSprop(1e-3), loss={'priority': 'binary_crossentropy', 'department': 'categorical_crossentropy'}, loss_weights=[1., 0.2]) import numpy as np # 载入输入数据 title_data = np.random.randint(num_words, size=(1280, 10)) body_data = np.random.randint(num_words, size=(1280, 100)) tag_data = np.random.randint(2, size=(1280, num_tags)).astype('float32') # 标签 priority_label = np.random.random(size=(1280, 1)) department_label = np.random.randint(2, size=(1280, num_departments)) # 训练 history = model.fit( {'title': title_data, 'body':body_data, 'tag':tag_data}, {'priority':priority_label, 'department':department_label}, batch_size=32, epochs=5 ) model.save('model_save.h5') del model model = keras.models.load_model('model_save.h5')

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0.102564

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null

0

null

0

1

0

c8ffacba13563fc63e94eff5bc851a3e548d81b6

4,566

py

Python

rain/cloud/system/system.py

SuPerCxyz/rain

578b6d125f535414d3ea3fcfee4015b70fed560c

[ "Apache-2.0" ]

2

2018-12-20T01:38:56.000Z

2018-12-29T14:49:36.000Z

rain/cloud/system/system.py

SuPerCxyz/rain

578b6d125f535414d3ea3fcfee4015b70fed560c

[ "Apache-2.0" ]

null

rain/cloud/system/system.py

SuPerCxyz/rain

578b6d125f535414d3ea3fcfee4015b70fed560c

[ "Apache-2.0" ]

null

#!/usr/bin/env python # -*- coding:utf-8 -*- import json import platform import time from getdevinfo import getdevinfo import psutil from rain.common import rain_log from rain.common import utils from rain.common.utils import async_call logger = rain_log.logg(__name__) class SystemInfo(object): """system information. Collect system information, including cpu, memory, hostname, boot time, login information... """ def __init__(self): self.thread = {} def _load_stat(self): """Collecting system load. """ cpu_count = psutil.cpu_count() with open("/proc/loadavg") as f: con = f.read().split() load_1 = con[0] load_5 = con[1] load_15 = con[2] sys_load_1 = round(float(load_1)/cpu_count * 100, 2) sys_load_5 = round(float(load_5)/cpu_count * 100, 2) sys_load_15 = round(float(load_15)/cpu_count * 100, 2) system_load = { 'sys_load_1': sys_load_1, 'sys_load_5': sys_load_5, 'sys_load_15': sys_load_15, 'load_1': load_1, 'load_5': load_5, 'load_15': load_15 } logger.info('Collect system load.') return system_load @async_call def _cpu_percent(self): tmp = psutil.cpu_percent(interval=1, percpu=True) self.thread['cpu_percent'] = tmp @async_call def _cpus_times_percent(self): tmp = psutil.cpu_times_percent(interval=1, percpu=True) self.thread['cpus_times_percent'] = tmp def get_cpuinfo_info(self): """Collect the number of cpu and usage information and return the dictionary type. """ cpu_count = psutil.cpu_count() self._cpu_percent() self._cpus_times_percent() while True: if len(self.thread.keys()) == 2: break time.sleep(0.1) cpu_percent_info = [] for cpu in self.thread['cpus_times_percent']: percent_info = { 'user': cpu.user, 'system': cpu.system, 'idle': cpu.idle, 'iowait': cpu.iowait } cpu_percent_info.append(percent_info) system_load = self._load_stat() cpu_info_dict = { 'cpu_count': cpu_count, 'cpu_percent': self.thread['cpu_percent'], 'cpu_percent_info': cpu_percent_info, 'system_load': system_load } logger.info('Collect cpu related information.') return cpu_info_dict def get_memcache_info(self): """Collect memory and swap information and return dictionary type. """ memcache_info = psutil.virtual_memory() memcache_total = memcache_info.total / 1024 ** 2 memcache_used = memcache_info.used / 1024 ** 2 memcache_available = memcache_info.available / 1024 ** 2 memcache_buff = memcache_info.cached / 1024 ** 2 memcache_cached = memcache_info.cached / 1024 ** 2 memcache_percent = memcache_info.percent memcache_info_dict = { 'memcache_total_MB': memcache_total, 'memcache_used_MB': memcache_used, 'memcache_available_MB': memcache_available, 'memcache_buff_MB': memcache_buff, 'memcache_cached_MB': memcache_cached, 'memcache_percent': memcache_percent } logger.info('Collect memory related information.') return memcache_info_dict def _get_user(self): """Collect login user information. """ user_info_list = [] user_list = psutil.users() for user in user_list: user_dict = {} user_dict['name'] = user.name user_dict['host'] = user.host user_dict['conn_time'] = utils.str_time(user.started) user_info_list.append(user_dict) return user_info_list def get_system_info(self): """Collect system information. """ system_info = {} system_info['python_version'] = platform.python_version() system_info['hostname'] = platform.node() system_info['system_info'] = platform.platform() system_info['boot_time'] = utils.str_time(psutil.boot_time()) system_info['time'] = time.asctime(time.localtime(time.time())) system_info['user'] = self._get_user() logger.info('Collect user login information.') return system_info

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0

null

0

null

0

1

0

c8ffe69de767e55075d5f9e090d7f69a2c93dd80

7,517

py

Python

models.py

rudrasohan/Trust-Region-Policy-Optimization

bbaadf37aa3ea4ccc35907038eea4add9e5e050c

[ "MIT" ]

3

2019-11-16T15:40:14.000Z

2021-12-28T14:26:36.000Z

models.py

rudrasohan/Trust-Region-Policy-Optimization

bbaadf37aa3ea4ccc35907038eea4add9e5e050c

[ "MIT" ]

null

models.py

rudrasohan/Trust-Region-Policy-Optimization

bbaadf37aa3ea4ccc35907038eea4add9e5e050c

[ "MIT" ]

null

"""Model Definations for trpo.""" import gym import numpy as np import torch import time import scipy.optimize import torch.nn as nn import torch.nn.functional as F from torch.autograd import Variable from distributions import DiagonalGaussian from helpers import get_flat_params, set_flat_params, get_flat_grads #from helpers import sample_trajectories, compute_advantage_returns, get_flat_params class Model(object): """Generic Model Template""" def __init__(self, observation_space, action_space, **kwargs): #super(Model).__init__(**kwargs) self.observation_space = observation_space self.action_space = action_space self.obs_dim = None self.act_dim = None if isinstance(self.observation_space, gym.spaces.Box): self.obs_dim = np.prod(self.observation_space.shape) else: self.obs_dim = self.observation_space.n if isinstance(self.action_space, gym.spaces.Box): self.act_dim = np.prod(self.action_space.shape) else: self.act_dim = self.action_space.n class MLP_Policy(nn.Module): """MLP model fo the network""" def __init__(self, input_dim, output_dim, name, **kwargs): super(MLP_Policy, self).__init__() self.name = name self.use_new_head = False self.fc1 = nn.Linear(input_dim, 128) self.fc2 = nn.Linear(128, 64) self.fc3 = nn.Linear(64, output_dim) self.fc3.weight.data.mul_(0.1) self.fc3.bias.data.mul_(0.0) if bool(kwargs): self.use_new_head = kwargs["use_new_head"] self.fc4 = nn.Linear(64, output_dim) else: self.log_std = nn.Parameter(torch.zeros(output_dim)) #print(self.log_std.size()) #self.bn1 = nn.BatchNorm1d(64) #self.bn2 = nn.BatchNorm1d(64) def forward(self, x): #print(self.fc1(x)) x = torch.tanh(self.fc1(x)) x = torch.tanh(self.fc2(x)) mean = self.fc3(x) if self.use_new_head: std = self.fc4(x) else: std = self.log_std.expand(mean.size()) #print(mean) return mean, std class MLP_Value(nn.Module): """MLP model fo the network""" def __init__(self, input_dim, output_dim, name, **kwargs): super(MLP_Value, self).__init__() self.name = name self.fc1 = nn.Linear(input_dim, 128) self.fc2 = nn.Linear(128, 64) self.fc3 = nn.Linear(64, output_dim) self.fc3.weight.data.mul_(0.1) self.fc3.bias.data.mul_(0.0) def forward(self, x): #print(self.fc1(x)) x = torch.tanh(self.fc1(x)) x = torch.tanh(self.fc2(x)) out = self.fc3(x) return out class GaussianMLPPolicy(Model): """Gaussian MLP Policy""" def __init__(self, observation_space, action_space, **kwargs): Model.__init__(self, observation_space, action_space, **kwargs) #self.mean_network = MLP(self.obs_dim, self.act_dim, "mean").type(torch.float64) self.std_net = None #self.std_network = None #print(kwargs) if bool(kwargs): self.std_net = kwargs["use_std_net"] if self.std_net: self.network = MLP_Policy(self.obs_dim, self.act_dim, "MLP_policy", use_new_head=True)#.type(torch.float64) else: self.network = MLP_Policy(self.obs_dim, self.act_dim, "MLP_policy")#.type(torch.float64) def actions(self, obs): obs = torch.from_numpy(obs) mean, log_std = self.network(obs) dist = DiagonalGaussian(mean, log_std) sample = dist.sample() return sample, dist.logli(sample) def get_dists(self, obs): obs = torch.from_numpy(obs) mean, log_std = self.network(obs) dist = DiagonalGaussian(mean, log_std) return dist def clear_grads(self): self.network.zero_grad() class MLPBaseline(Model): """"MLP Baseline""" def __init__(self, observation_space, action_space, **kwargs): Model.__init__(self, observation_space, action_space, **kwargs) self.value = MLP_Value(self.obs_dim, 1, "MLP_baseline") #self.criterion = nn.MSELoss() #self.optimizer = torch.optim.LBFGS(self.value.parameters()) def predict(self, obs): obs = torch.tensor(obs) with torch.no_grad(): val = self.value(obs) return val def compute_baseline(self, obs): obs = Variable(torch.tensor(obs)) return self.value(obs) def clear_grads(self): self.value.zero_grad() def update(self, trajs): obs = np.asarray(trajs["state"]) #obs = torch.from_numpy(obs) returns = trajs["returns"] baselines = trajs["baselines"] targets = returns * 0.9 + 0.1 * baselines #returns = #targets = Variable(returns) #print(targets) ''' def closure(): self.clear_grads() values = self.value(torch.from_numpy(obs)) self.optimizer.zero_grad() loss = self.criterion(values, targets) print("LBFGS_LOSS:{}".format(loss)) loss.backward() return loss ''' #self.optimizer.step(closure) #curr_params = get_flat_params(self.value.parameters()).data.detach().double().numpy() curr_flat_params = get_flat_params(self.value).detach().double().numpy() def val_loss_grad(x): set_flat_params(self.value, torch.tensor(x)) self.clear_grads() #for param in self.value.parameters(): #if param.grad is not None: #print("HHAHAHAHAHHA") #param.grad.data.fill_(0) #values_ = #self.value(torch.from_numpy(obs)) values_ = self.compute_baseline(obs) #print("VALUES",values_.size()) #print("TARGETS",targets.size()) #print((values_-targets).size()) #time1 = time.time() vf_loss = (values_ - targets).pow(2).mean() #print("LBFGS_LOSS:{}".format(vf_loss)) #time2 = time.time() #print("TIME:{}".format(time2-time1)) #for param in self.value.parameters(): # vf_loss += param.pow(2).sum() * 1e-2 vf_loss.backward() flat_grad = get_flat_grads(self.value) return (vf_loss.data.double().numpy(), flat_grad.data.double().numpy()) new_params, _, opt_info = scipy.optimize.fmin_l_bfgs_b(val_loss_grad, curr_flat_params, maxiter=25) set_flat_params(self.value, torch.tensor(new_params)) print(opt_info) def test_policy_value(): env = gym.make("MountainCarContinuous-v0") policy = GaussianMLPPolicy(env.observation_space, env.action_space, use_std_net=True) paths = sample_trajectories(env, policy, 1000) print(len(paths["rewards"])) baseline = MLPBaseline(env.observation_space, env.action_space) compute_advantage_returns(paths, baseline, 0.9, 0.1) print(paths.keys()) baseline.update(paths) print(paths['dist'].keys()) flat_params_mean = get_flat_params(policy.mean_network.parameters()) flat_params_std = get_flat_params(policy.std_network.parameters()) print(flat_params) #test_policy_value()

33.261062

119

0.600905

947

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4.543823

0.182682

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null

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0

7405313149ad1d453f1faa1ff9ea0b0aec012d46

3,572

py

Python

keeper/v2api/projects.py

lsst-sqre/ltd-keeper

c658bcce726764e7416a8a386b418e83912b0f32

[ "Apache-2.0", "MIT" ]

5

2016-05-16T18:46:26.000Z

2019-07-08T15:16:41.000Z

keeper/v2api/projects.py

lsst-sqre/ltd-keeper

c658bcce726764e7416a8a386b418e83912b0f32

[ "Apache-2.0", "MIT" ]

46

2016-02-18T16:54:36.000Z

2022-03-25T19:43:45.000Z

keeper/v2api/projects.py

lsst-sqre/ltd-keeper

c658bcce726764e7416a8a386b418e83912b0f32

[ "Apache-2.0", "MIT" ]

4

2016-08-20T23:10:07.000Z

2022-03-25T19:52:09.000Z

"""Handlers for project-related APIs.""" from __future__ import annotations from typing import Dict, Tuple from flask import request from flask_accept import accept_fallback from keeper.auth import token_auth from keeper.logutils import log_route from keeper.models import Organization, Product, db from keeper.services.createproduct import create_product from keeper.services.updateproduct import update_product from keeper.taskrunner import launch_tasks from keeper.v2api import v2api from ._models import ( ProjectPatchRequest, ProjectPostRequest, ProjectResponse, ProjectsResponse, ) from ._urls import url_for_project __all__ = ["get_projects", "get_project", "create_project", "update_project"] @v2api.route("/orgs/<org>/projects", methods=["GET"]) @accept_fallback @log_route() @token_auth.login_required def get_projects(org: str) -> str: products = ( Product.query.join( Organization, Organization.id == Product.organization_id ) .filter(Organization.slug == org) .all() ) response = ProjectsResponse.from_products(products) return response.json() @v2api.route("/orgs/<org>/projects/<slug>", methods=["GET"]) @accept_fallback @log_route() @token_auth.login_required def get_project(org: str, slug: str) -> str: product = ( Product.query.join( Organization, Organization.id == Product.organization_id ) .filter(Organization.slug == org) .filter(Product.slug == slug) .first_or_404() ) response = ProjectResponse.from_product(product) return response.json() @v2api.route("/orgs/<org>/projects", methods=["POST"]) @accept_fallback @log_route() @token_auth.login_required def create_project(org: str) -> Tuple[str, int, Dict[str, str]]: request_data = ProjectPostRequest.parse_obj(request.json) organization = Organization.query.filter( Organization.slug == org ).first_or_404() try: product, default_edition = create_product( org=organization, slug=request_data.slug, doc_repo=request_data.source_repo_url, title=request_data.title, default_edition_mode=( request_data.default_edition_mode if request_data.default_edition_mode is not None else None ), ) except Exception: db.session.rollback() raise task = launch_tasks() response = ProjectResponse.from_product(product, task=task) project_url = url_for_project(product) return response.json(), 201, {"Location": project_url} @v2api.route("/orgs/<org>/projects/<slug>", methods=["PATCH"]) @accept_fallback @log_route() @token_auth.login_required def update_project(org: str, slug: str) -> Tuple[str, int, Dict[str, str]]: request_data = ProjectPatchRequest.parse_obj(request.json) product = ( Product.query.join( Organization, Organization.id == Product.organization_id ) .filter(Organization.slug == org) .filter(Product.slug == slug) .first_or_404() ) try: product = update_product( product=product, new_doc_repo=request_data.source_repo_url, new_title=request_data.title, ) except Exception: db.session.rollback() raise task = launch_tasks() response = ProjectResponse.from_product(product, task=task) project_url = url_for_project(product) return response.json(), 200, {"Location": project_url}

28.349206

77

0.676932

406

3,572

5.724138

0.226601

0.042599

0.024096

0.02926

0.562823

0.491394

0.477625

0.433735

0.39673

0.326162

0

0.007505

0.216685

3,572

125

78

28.576

0.823088

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0.038835

false

0

0.126214

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0.203884

0

null

0

null

0

1

0

7405685566287cf4e859fe85e98cb0c021c50b86

2,237

py

Python

plugins/markdown_extensions/katex.py

raabrp/rraabblog

a1d47ede918f4838ac3bbcff9ef4e7c67f851c32

[ "MIT" ]

null

plugins/markdown_extensions/katex.py

raabrp/rraabblog

a1d47ede918f4838ac3bbcff9ef4e7c67f851c32

[ "MIT" ]

null

plugins/markdown_extensions/katex.py

raabrp/rraabblog

a1d47ede918f4838ac3bbcff9ef4e7c67f851c32

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Allow server-side KaTeX rendering for Markdown through node.js The markdown extension adds regex patterns for `$` and `$$` in the source `.md` file, and applies KaTeX to the intermediate text with a `python-bond` call to node.js requires * node * npm * katex (npm install katex) * python-bond (pip3 install --user python-bond) KaTeX: https://github.com/Khan/KaTeX """ import markdown from markdown.util import etree import bond JS = bond.make_bond('JavaScript') JS.eval_block( r''' katex = require('katex'); function render(s, is_block) { return katex.renderToString(s, { displayMode: is_block, throwOnError: false }); } ''' ) katex = JS.callable('render') memoise = {} ############################################################################### class MathPattern(markdown.inlinepatterns.Pattern): def __init__(self, tag, pattern): super().__init__(pattern) self.tag = tag def handleMatch(self, m): global memoise node = markdown.util.etree.Element(self.tag) node.set('class', 'math') orig = m.group('math') entry = (orig, self.tag == 'div') if entry in memoise: result = memoise[entry] else: result = katex(orig, self.tag == 'div') memoise[entry] = result node.text = result return node class Katex(markdown.Extension): def extendMarkdown(self, md, md_globals): # Regex to detect math delimiters math_inline_regex = \ r'(?P<prefix>\$)(?P<math>.+?)(?P<suffix>(?<!\s)\2)' math_block_regex = \ r'(?P<prefix>\$\$|\\begin\{(.+?)\}|\\\[)(?P<math>.+?)(?P<suffix>\2|\\end\{\3\}|\\\])' # Process math before escapes are processed since escape processing # will interfere. The order in which the displayed and inlined math # is registered below matters md.inlinePatterns.add( 'math_block', MathPattern('div', math_block_regex), '<escape' ) md.inlinePatterns.add( 'math_inline', MathPattern('span', math_inline_regex), '<escape' )

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97

0.565042

252

2,237

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0.456349

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0.017728

0.022562

0

0.003041

0.265087

2,237

92

98

24.315217

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0.150765

0.094683

0

1

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false

0

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0

0.214286

0

null

0

null

0

1

0

7408452dfdbed6f56d0e2243de45d1e90b286cdf

1,490

py

Python

simpleclassroom/urls.py

cbetheridge/simpleclassroom

9e99262ffdb4efc0e27566855866dfc26244bf26

[ "MIT" ]

null

simpleclassroom/urls.py

cbetheridge/simpleclassroom

9e99262ffdb4efc0e27566855866dfc26244bf26

[ "MIT" ]

null

simpleclassroom/urls.py

cbetheridge/simpleclassroom

9e99262ffdb4efc0e27566855866dfc26244bf26

[ "MIT" ]

null

"""simpleclassroom URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/1.9/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: url(r'^$', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.conf.urls import url, include 2. Add a URL to urlpatterns: url(r'^blog/', include('blog.urls')) """ from django.conf.urls import url from django.contrib import admin from views import views from views import io urlpatterns = [ url(r'^$', views.display_classrooms, name='index'), url(r'^classrooms/', views.display_classrooms, name='classrooms'), url(r'^student_list/', views.display_students, name='student list'), url(r'^student_details/', views.display_student_details, name='student view'), url(r'^io/add_class/', io.add_classroom, name='add class'), url(r'^io/del_class/', io.delete_classroom, name='delete class'), url(r'^io/add_student/', io.add_student, name='add student'), url(r'^io/del_student/', io.delete_student, name='delete student'), url(r'^io/enroll/', io.enroll_student, name='enroll student'), url(r'^io/unenroll/', io.unenroll_student, name='unenroll student'), url(r'^admin/', admin.site.urls), ]

42.571429

80

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1,490

4.574561

0.280702

0.053691

0.034516

0.023011

0.16395

0.071908

0

0.006178

0.130872

1,490

34

81

43.823529

0.799228

0.42953

0

0.297746

0

1

0

false

0

0.235294

0

0.235294

0

null

0

null

0

1

0

cd9ec9af338573f552a9119ee09d53bff7f7cebd

4,939

py

Python

simplereg/data_writer.py

gift-surg/SimpleReg

9d9a774f5b7823c2256844c9d0260395604fb396

[ "BSD-3-Clause" ]

18

2017-11-10T15:09:41.000Z

2021-01-12T07:48:46.000Z

simplereg/data_writer.py

gift-surg/SimpleReg

9d9a774f5b7823c2256844c9d0260395604fb396

[ "BSD-3-Clause" ]

null

simplereg/data_writer.py

gift-surg/SimpleReg

9d9a774f5b7823c2256844c9d0260395604fb396

[ "BSD-3-Clause" ]

3

2019-03-20T14:13:03.000Z

2020-01-15T01:32:51.000Z

# \file DataWriter.py # \brief Class to read data # # \author Michael Ebner (michael.ebner.14@ucl.ac.uk) # \date June 2018 import os import sys import numpy as np import nibabel as nib import SimpleITK as sitk import pysitk.python_helper as ph import pysitk.simple_itk_helper as sitkh from simplereg.definitions import ALLOWED_IMAGES from simplereg.definitions import ALLOWED_LANDMARKS from simplereg.definitions import ALLOWED_TRANSFORMS from simplereg.definitions import ALLOWED_TRANSFORMS_DISPLACEMENTS class DataWriter(object): @staticmethod def write_image(image_sitk, path_to_file, verbose=0): extension = ph.strip_filename_extension(path_to_file)[1] if extension not in ALLOWED_IMAGES: raise IOError("Image file extension must be of type %s " % ", or ".join(ALLOWED_IMAGES)) if isinstance(image_sitk, sitk.Image): sitkh.write_nifti_image_sitk( image_sitk=image_sitk, path_to_file=path_to_file, verbose=verbose) else: sitkh.write_nifti_image_itk( image_itk=image_sitk, path_to_file=path_to_file, verbose=verbose) @staticmethod def write_vector_image(vector_image_sitk, path_to_file, verbose=0): extension = ph.strip_filename_extension(path_to_file)[1] if extension not in ALLOWED_IMAGES: raise IOError("Image file extension must be of type %s " % ", or ".join(ALLOWED_IMAGES)) if isinstance(vector_image_sitk, sitk.Image): sitkh.write_sitk_vector_image( vector_image_sitk, path_to_file, verbose=verbose, ) else: raise ValueError("Only implemented for SimpleITK images") @staticmethod def write_landmarks(landmarks_nda, path_to_file, verbose=0): extension = ph.strip_filename_extension(path_to_file)[1] if extension not in ALLOWED_LANDMARKS: raise IOError("Landmark file extension must be of type %s " % ", or ".join(ALLOWED_LANDMARKS)) ph.write_array_to_file( path_to_file, landmarks_nda, delimiter=" ", access_mode="w", verbose=verbose) @staticmethod def write_transform(transform_sitk, path_to_file, verbose=0): extension = ph.strip_filename_extension(path_to_file)[1] if extension not in ALLOWED_TRANSFORMS and \ extension not in ALLOWED_TRANSFORMS_DISPLACEMENTS: raise IOError("Transform file extension must be of type " "%s (transformation) or %s (displacements)" % ( ", ".join(ALLOWED_TRANSFORMS), ", ".join(ALLOWED_TRANSFORMS_DISPLACEMENTS))) if extension in ALLOWED_TRANSFORMS: if isinstance(transform_sitk, sitk.Image): raise IOError("Cannot convert displacement field (%s) to " "transform (%s)" % ( ", ".join(ALLOWED_TRANSFORMS_DISPLACEMENTS), ", ".join(ALLOWED_TRANSFORMS), )) if isinstance(transform_sitk, sitk.Transform): ph.create_directory(os.path.dirname(path_to_file)) sitk.WriteTransform(transform_sitk, path_to_file) if verbose: ph.print_info("Transform written to '%s'" % path_to_file) elif isinstance(transform_sitk, np.ndarray): ph.write_array_to_file( path_to_file, transform_sitk, delimiter=" ", access_mode="w", verbose=verbose) else: raise IOError("Transform must be of type " "sitk.Transform or np.ndarray") else: if isinstance(transform_sitk, sitk.Transform): raise IOError("Cannot convert transform (%s) to " "displacement field (%s)" % ( ", ".join(ALLOWED_TRANSFORMS), ", ".join(ALLOWED_TRANSFORMS_DISPLACEMENTS), )) elif isinstance(transform_sitk, sitk.Image): sitkh.write_nifti_image_sitk( image_sitk=transform_sitk, path_to_file=path_to_file, verbose=verbose) elif isinstance(transform_sitk, nib.nifti1.Nifti1Image): ph.create_directory(os.path.dirname(path_to_file)) nib.save(transform_sitk, path_to_file) else: raise IOError("Transform must be of type " "sitk.Image or nibabel.nifti1.Nifti1Image")

40.154472

78

0.578255

519

4,939

5.246628

0.194605

0.052883

0.080793

0.046272

0.654793

0.565553

0.471172

0.38744

0.366875

0.271392

0

0.00559

0.348046

4,939

122

79

40.483607

0.840062

0.023284

0

0.46

0

0.110004

0.005396

0

1

0.04

false

0

0.11

0

0.16

0.01

0

null

0

null

0

1

0

cd9f005c2266883ac0727dd4f11b65c0cc61acbf

3,881

py

Python

configman/datetime_util.py

peterbe/configman

724d80b25a0ebbb2e75ad69e92a6611494cd68b4

[ "BSD-3-Clause" ]

null

configman/datetime_util.py

peterbe/configman

724d80b25a0ebbb2e75ad69e92a6611494cd68b4

[ "BSD-3-Clause" ]

null

configman/datetime_util.py

peterbe/configman

724d80b25a0ebbb2e75ad69e92a6611494cd68b4

[ "BSD-3-Clause" ]

null

# ***** BEGIN LICENSE BLOCK ***** # Version: MPL 1.1/GPL 2.0/LGPL 2.1 # # The contents of this file are subject to the Mozilla Public License Version # 1.1 (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.mozilla.org/MPL/ # # Software distributed under the License is distributed on an "AS IS" basis, # WITHOUT WARRANTY OF ANY KIND, either express or implied. See the License # for the specific language governing rights and limitations under the # License. # # The Original Code is configman # # The Initial Developer of the Original Code is # Mozilla Foundation # Portions created by the Initial Developer are Copyright (C) 2011 # the Initial Developer. All Rights Reserved. # # Contributor(s): # K Lars Lohn, lars@mozilla.com # Peter Bengtsson, peterbe@mozilla.com # # Alternatively, the contents of this file may be used under the terms of # either the GNU General Public License Version 2 or later (the "GPL"), or # the GNU Lesser General Public License Version 2.1 or later (the "LGPL"), # in which case the provisions of the GPL or the LGPL are applicable instead # of those above. If you wish to allow use of your version of this file only # under the terms of either the GPL or the LGPL, and not to allow others to # use your version of this file under the terms of the MPL, indicate your # decision by deleting the provisions above and replace them with the notice # and other provisions required by the GPL or the LGPL. If you do not delete # the provisions above, a recipient may use your version of this file under # the terms of any one of the MPL, the GPL or the LGPL. # # ***** END LICENSE BLOCK ***** import datetime def datetime_from_ISO_string(s): """ Take an ISO date string of the form YYYY-MM-DDTHH:MM:SS.S and convert it into an instance of datetime.datetime """ try: return datetime.datetime.strptime(s, '%Y-%m-%dT%H:%M:%S') except ValueError: try: return datetime.datetime.strptime(s, '%Y-%m-%d') except ValueError: return datetime.datetime.strptime(s, '%Y-%m-%dT%H:%M:%S.%f') def date_from_ISO_string(s): """ Take an ISO date string of the form YYYY-MM-DD and convert it into an instance of datetime.date """ return datetime.datetime.strptime(s, '%Y-%m-%d').date() def datetime_to_ISO_string(aDate): """ Take a datetime and convert to string of the form YYYY-MM-DDTHH:MM:SS.S """ return aDate.isoformat() def date_to_ISO_string(aDate): """ Take a datetime and convert to string of the form YYYY-MM-DD """ return aDate.strftime('%Y-%m-%d') def hours_str_to_timedelta(hoursAsString): return datetime.timedelta(hours=int(hoursAsString)) def timedelta_to_seconds(td): return td.days * 24 * 60 * 60 + td.seconds def str_to_timedelta(input_str): """ a string conversion function for timedelta for strings in the format DD:HH:MM:SS """ days, hours, minutes, seconds = 0, 0, 0, 0 details = input_str.split(':') if len(details) >= 4: days = int(details[-4]) if len(details) >= 3: hours = int(details[-3]) if len(details) >= 2: minutes = int(details[-2]) if len(details) >= 1: seconds = int(details[-1]) return datetime.timedelta(days=days, hours=hours, minutes=minutes, seconds=seconds) def timedelta_to_str(aTimedelta): """ a conversion function for time deltas to string in the form DD:HH:MM:SS """ days = aTimedelta.days temp_seconds = aTimedelta.seconds hours = temp_seconds / 3600 minutes = (temp_seconds - hours * 3600) / 60 seconds = temp_seconds - hours * 3600 - minutes * 60 return '%d:%d:%d:%d' % (days, hours, minutes, seconds)

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null

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null

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cda678a982b6a913bc586a56ae657d42e29745b5

508

py

Python

main.py

ki-ljl/Scaffold-Federated-Learning

12e04217df3af2c326ea90fef6cff47beaaec485

[ "MIT" ]

9

2022-03-02T13:58:29.000Z

2022-03-31T06:45:40.000Z

main.py

ki-ljl/Scaffold-Federated-Learning

12e04217df3af2c326ea90fef6cff47beaaec485

[ "MIT" ]

null

main.py

ki-ljl/Scaffold-Federated-Learning

12e04217df3af2c326ea90fef6cff47beaaec485

[ "MIT" ]

null

# -*- coding:utf-8 -*- """ @Time：2022/05/05 12:57 @Author：KI @File：main.py @Motto：Hungry And Humble """ from data_process import clients_wind from server import Scaffold def main(): K, C, E, B, r = 10, 0.5, 30, 50, 10 input_dim = 28 lr = 0.08 options = {'K': K, 'C': C, 'E': E, 'B': B, 'r': r, 'clients': clients_wind, 'input_dim': input_dim, 'lr': lr} scaffold = Scaffold(options) scaffold.server() scaffold.global_test() if __name__ == '__main__': main()

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cda9eb07b967369dac4f17bb21af05cd80acf296

1,472

py

Python

Data Analysis with Pandas Intermediate/Pandas Internals_ Series-145.py

vipmunot/Data-Analysis-using-Python

34586d8cbbc336508c4a7a68abe14944f1096252

[ "MIT" ]

null

Data Analysis with Pandas Intermediate/Pandas Internals_ Series-145.py

vipmunot/Data-Analysis-using-Python

34586d8cbbc336508c4a7a68abe14944f1096252

[ "MIT" ]

null

Data Analysis with Pandas Intermediate/Pandas Internals_ Series-145.py

vipmunot/Data-Analysis-using-Python

34586d8cbbc336508c4a7a68abe14944f1096252

[ "MIT" ]

null

## 1. Data Structures ## import pandas as pd fandango = pd.read_csv('fandango_score_comparison.csv') print(fandango.head(2)) ## 2. Integer Indexes ## fandango = pd.read_csv('fandango_score_comparison.csv') series_film = fandango['FILM'] series_rt = fandango['RottenTomatoes'] print(series_film[:5]) print(series_rt[:5]) ## 3. Custom Indexes ## # Import the Series object from pandas from pandas import Series film_names = series_film.values rt_scores = series_rt.values series_custom=pd.Series(index = film_names, data = rt_scores) ## 4. Integer Index Preservation ## series_custom = Series(rt_scores , index=film_names) series_custom[['Minions (2015)', 'Leviathan (2014)']] fiveten = series_custom[5:10] print(fiveten) ## 5. Reindexing ## original_index = series_custom.index.tolist() sorted_by_index = series_custom.reindex(index = sorted(original_index)) ## 6. Sorting ## sc2 = series_custom.sort_index() sc3 = series_custom.sort_values() print(sc2.head(10)) print(sc3.head(10)) ## 7. Transforming Columns With Vectorized Operations ## series_normalized = series_custom/20 ## 8. Comparing and Filtering ## criteria_one = series_custom > 50 criteria_two = series_custom < 75 both_criteria = series_custom[criteria_one & criteria_two] ## 9. Alignment ## rt_critics = Series(fandango['RottenTomatoes'].values, index=fandango['FILM']) rt_users = Series(fandango['RottenTomatoes_User'].values, index=fandango['FILM']) rt_mean =(rt_users + rt_critics) / 2

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null

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cdae861a30ba2bb3bd941147a704995ddbb3e7b8

4,894

py

Python

pytest_ipynb/plugin.py

kevingerman/pytest-ipynb

04b5fed4f280983f64254b01e3b24b7733e99224

[ "BSD-3-Clause" ]

104

2015-01-21T16:10:46.000Z

2021-05-31T06:53:35.000Z

pytest_ipynb/plugin.py

kevingerman/pytest-ipynb

04b5fed4f280983f64254b01e3b24b7733e99224

[ "BSD-3-Clause" ]

26

2015-04-09T04:12:48.000Z

2018-12-22T18:41:33.000Z

pytest_ipynb/plugin.py

kevingerman/pytest-ipynb

04b5fed4f280983f64254b01e3b24b7733e99224

[ "BSD-3-Clause" ]

21

2015-02-06T10:07:28.000Z

2021-04-19T21:31:48.000Z

import pytest import os,sys import warnings try: from exceptions import Exception, TypeError, ImportError except: pass from runipy.notebook_runner import NotebookRunner wrapped_stdin = sys.stdin sys.stdin = sys.__stdin__ sys.stdin = wrapped_stdin try: from Queue import Empty except: from queue import Empty # code copied from runipy main.py with warnings.catch_warnings(): try: from IPython.utils.shimmodule import ShimWarning warnings.filterwarnings('error', '', ShimWarning) except ImportError: class ShimWarning(Warning): """Warning issued by iPython 4.x regarding deprecated API.""" pass try: # IPython 3 from IPython.nbformat import reads, NBFormatError except ShimWarning: # IPython 4 from nbformat import reads, NBFormatError except ImportError: # IPython 2 from IPython.nbformat.current import reads, NBFormatError finally: warnings.resetwarnings() class IPyNbException(Exception): """ custom exception for error reporting. """ def pytest_collect_file(path, parent): if path.fnmatch("test*.ipynb"): return IPyNbFile(path, parent) def get_cell_description(cell_input): """Gets cell description Cell description is the first line of a cell, in one of this formats: * single line docstring * single line comment * function definition """ try: first_line = cell_input.split("\n")[0] if first_line.startswith(('"', '#', 'def')): return first_line.replace('"','').replace("#",'').replace('def ', '').replace("_", " ").strip() except: pass return "no description" class IPyNbFile(pytest.File): def collect(self): with self.fspath.open() as f: payload = f.read() self.notebook_folder = self.fspath.dirname try: # Ipython 3 self.nb = reads(payload, 3) except (TypeError, NBFormatError): # Ipython 2 self.nb = reads(payload, 'json') self.runner = NotebookRunner(self.nb) cell_num = 1 for cell in self.runner.iter_code_cells(): yield IPyNbCell(self.name, self, cell_num, cell) cell_num += 1 def setup(self): self.fixture_cell = None def teardown(self): self.runner.shutdown_kernel() class IPyNbCell(pytest.Item): def __init__(self, name, parent, cell_num, cell): super(IPyNbCell, self).__init__(name, parent) self.cell_num = cell_num self.cell = cell self.cell_description = get_cell_description(self.cell.input) def runtest(self): self.parent.runner.km.restart_kernel() if self.parent.notebook_folder: self.parent.runner.kc.execute( """import os os.chdir("%s")""" % self.parent.notebook_folder) if ("SKIPCI" in self.cell_description) and ("CI" in os.environ): pass else: if self.parent.fixture_cell: self.parent.runner.kc.execute(self.parent.fixture_cell.input, allow_stdin=False) msg_id = self.parent.runner.kc.execute(self.cell.input, allow_stdin=False) if self.cell_description.lower().startswith("fixture") or self.cell_description.lower().startswith("setup"): self.parent.fixture_cell = self.cell timeout = 20 while True: try: msg = self.parent.runner.kc.get_shell_msg(block=True, timeout=timeout) if msg.get("parent_header", None) and msg["parent_header"].get("msg_id", None) == msg_id: break except Empty: raise IPyNbException(self.cell_num, self.cell_description, self.cell.input, "Timeout of %d seconds exceeded executing cell: %s" % (timeout, self.cell.input)) reply = msg['content'] if reply['status'] == 'error': raise IPyNbException(self.cell_num, self.cell_description, self.cell.input, '\n'.join(reply['traceback'])) def repr_failure(self, excinfo): """ called when self.runtest() raises an exception. """ if isinstance(excinfo.value, IPyNbException): return "\n".join([ "Notebook execution failed", "Cell %d: %s\n\n" "Input:\n%s\n\n" "Traceback:\n%s\n" % excinfo.value.args, ]) else: return "pytest plugin exception: %s" % str(excinfo.value) def _makeid(self): description = self.parent.nodeid + "::" + self.name description += "::" + "cell %d" % self.cell_num if self.cell_description: description += ", " + self.cell_description return description

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cdb4d928fe81a97440ce0c56dea2317a5512f228

2,258

py

Python

setup.py

vbrinnel/ztflc

b1ccab67e5e0e385d8406f179c1ad0c346afa129

[ "Apache-2.0" ]

1

2020-04-07T14:36:49.000Z

setup.py

vbrinnel/ztflc

b1ccab67e5e0e385d8406f179c1ad0c346afa129

[ "Apache-2.0" ]

3

2020-01-16T18:25:46.000Z

2021-05-19T20:51:52.000Z

setup.py

vbrinnel/ztflc

b1ccab67e5e0e385d8406f179c1ad0c346afa129

[ "Apache-2.0" ]

1

2021-03-31T19:47:33.000Z

#! /usr/bin/env python # DESCRIPTION = "ztflc: Force photometry lc fitter" LONG_DESCRIPTION = """ Force photometry lc fitter""" DISTNAME = "ztflc" AUTHOR = "Mickael Rigault" MAINTAINER = "Mickael Rigault" MAINTAINER_EMAIL = "m.rigault@ipnl.in2p3.fr" URL = "https://github.com/MickaelRigault/ztflc/" LICENSE = "BSD (3-clause)" DOWNLOAD_URL = "https://github.com/MickaelRigault/ztflc/tarball/0.2" VERSION = "0.2.3" try: from setuptools import setup, find_packages _has_setuptools = True except ImportError: from distutils.core import setup _has_setuptools = False def check_dependencies(): install_requires = [] # Just make sure dependencies exist, I haven't rigorously # tested what the minimal versions that will work are # (help on that would be awesome) try: import ztfquery except ImportError: install_requires.append("ztfquery") try: import pandas except ImportError: install_requires.append("pandas") return install_requires if __name__ == "__main__": install_requires = check_dependencies() if _has_setuptools: packages = find_packages() print(packages) else: # This should be updated if new submodules are added packages = ["ztflc"] setup( name=DISTNAME, author=AUTHOR, author_email=MAINTAINER_EMAIL, maintainer=MAINTAINER, maintainer_email=MAINTAINER_EMAIL, description=DESCRIPTION, long_description=LONG_DESCRIPTION, license=LICENSE, url=URL, version=VERSION, download_url=DOWNLOAD_URL, install_requires=install_requires, scripts=["bin/forcephoto.py"], packages=packages, include_package_data=True, # package_data={'pysedm': ['data/*.*']}, classifiers=[ "Intended Audience :: Science/Research", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3.5", "License :: OSI Approved :: BSD License", "Topic :: Scientific/Engineering :: Astronomy", "Operating System :: POSIX", "Operating System :: Unix", "Operating System :: MacOS", ], )

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null

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null

0

1

0

cdb6e8d6090040ad0dc31239d89e99153192bd44

1,927

py

Python

wordfinds/raw.py

GrandMoff100/WordFinds

4b56532f399178e5f2b18b246084644061c5bfc2

[ "MIT" ]

2

2021-05-22T19:19:56.000Z

2021-08-16T11:34:11.000Z

wordfinds/raw.py

GrandMoff100/WordFinds

4b56532f399178e5f2b18b246084644061c5bfc2

[ "MIT" ]

null

wordfinds/raw.py

GrandMoff100/WordFinds

4b56532f399178e5f2b18b246084644061c5bfc2

[ "MIT" ]

1

2021-11-09T13:55:43.000Z

import random from .utils import filler from .array import RawWordFindArray, WordArray class RawWordFind(RawWordFindArray): def __init__(self, size, wordbank): super().__init__(size, wordbank) for word in wordbank.words: if not self.valid_word_length(word): raise ValueError( 'The word "{}" cannot fit into a {}x{} array.' .format(word, *self.size) + 'Try using less words or shorter ones.') total = sum([len(word) for word in wordbank.words]) w,h = size if total > w * h: raise ValueError(f'Cannot fit {total} characters in a {w}x{h} array. Try using less words or shorter ones.') self.letter_array = self.generate() def directions(self, x, y, word): return [ (x-len(word), y-len(word)), (x-len(word), y), (x-len(word),y+len(word)), (x, y-len(word)), (x, y), (x,y+len(word)), (x+len(word), y-len(word)), (x+len(word), y), (x+len(word),y+len(word)), ] def find_spots(self, grid, word): w, h = self.size for x in range(w): for y in range(h): for end in self.directions(x,y,word): try: grid.place_word(word,(x,y),end,True) yield (x,y), end except (ValueError, IndexError): pass def generate(self): w, h = self.size grid = WordArray([['.' for _ in range(w)] for _ in range(h)]) for word in self.wordbank.words: start, end = random.choice(list(self.find_spots(grid, word))) grid.place_word(word, start, end) return WordArray([[x if x != '.' else filler() for x in row] for row in grid]) class WordFind(RawWordFind): pass

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null

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null

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0

cdb7047c417fa314c5e02129e1672265cc3318ba

2,969

py

Python

src/neon/frontend/aeon_shim.py

MUTTERSCHIFF/ngraph-neon

762e8ea639cdc671311ee4929bd1ee8cdf83e8bb

[ "Apache-2.0" ]

13

2018-03-17T00:27:18.000Z

2020-06-18T01:36:34.000Z

src/neon/frontend/aeon_shim.py

MUTTERSCHIFF/ngraph-neon

762e8ea639cdc671311ee4929bd1ee8cdf83e8bb

[ "Apache-2.0" ]

20

2018-03-17T14:49:04.000Z

2018-04-19T17:47:38.000Z

src/neon/frontend/aeon_shim.py

NervanaSystems/ngraph-neon

8988ab90ee81c8b219ea5c374702e56d7f383302

[ "Apache-2.0" ]

5

2018-03-23T22:47:17.000Z

2020-10-21T16:15:02.000Z

# ****************************************************************************** # Copyright 2017-2018 Intel Corporation # # 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 __future__ import print_function, absolute_import import logging from builtins import object import neon as ng logger = logging.getLogger(__name__) try: from aeon import DataLoader except ImportError: msg = "\n".join(["", "Unable to import Aeon module.", "Please see installation instructions at:", "*****************", "https://github.com/NervanaSystems/aeon/blob/rc1-master/README.md", "*****************", ""]) logger.error(msg) raise ImportError(msg) NAME_MAP = {"channels": "C", "height": "H", "width": "W", "frames": "D"} """Converts aeon axis names to canonical ngraph axis types.""" class AeonDataLoader(object): def __init__(self, config, *args, **kwargs): self.config = config self._dataloader = DataLoader(config) self.ndata = self._dataloader.ndata if self.ndata < self._dataloader.batch_size: raise ValueError('Number of examples is smaller than the batch size') def __next__(self): bufs = next(self._dataloader) bufs_dict = dict((key, val) for key, val in bufs) if 'label' in bufs_dict: bufs_dict['label'] = bufs_dict['label'].flatten() return bufs_dict def __iter__(self): return self def make_placeholders(self, include_iteration=False): placeholders = {} batch_axis = ng.make_axis(self._dataloader.batch_size, name="N") for placeholder_name, axis_info in self._dataloader.axes_info: p_axes = ng.make_axes([batch_axis]) for nm, sz in axis_info: if placeholder_name == 'label': continue if nm in NAME_MAP: nm = NAME_MAP[nm] p_axes += ng.make_axis(name=nm, length=sz) placeholders[placeholder_name] = ng.placeholder(p_axes) if include_iteration: placeholders['iteration'] = ng.placeholder(axes=()) return placeholders def reset(self): self._dataloader.reset() def ndata(self): self._dataloader.ndata

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null

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null

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1

0

cdb91795db8c176b9e6d1d2b0ffc0bc2b063adbd

857

py

Python

Lessons/Chapter9Exercise1.py

Luderio/Scientific-Computing-with-Python

c7eebcc3b46b68b3d5c08ad25fb802ae9ff42f7f

[ "MIT" ]

null

Lessons/Chapter9Exercise1.py

Luderio/Scientific-Computing-with-Python

c7eebcc3b46b68b3d5c08ad25fb802ae9ff42f7f

[ "MIT" ]

null

Lessons/Chapter9Exercise1.py

Luderio/Scientific-Computing-with-Python

c7eebcc3b46b68b3d5c08ad25fb802ae9ff42f7f

[ "MIT" ]

null

wordCounter = dict() while True : inputFile = input('Enter a file: ') try : fileName = open(inputFile) except : fileName = 'invalid' if fileName == 'invalid' : if inputFile == 'done' : break else : print('Invalid Input') continue for lines in fileName : lines = lines.rstrip() words = lines.split() for wordItems in words : wordCounter[wordItems] = wordCounter.get(wordItems, 0) + 1 largestWordCount = None largestWord = None for word,count in wordCounter.items() : if largestWordCount is None or count > largestWordCount : largestWord = word largestWordCount = count print('Largest Word:', largestWord, 'Count:', largestWordCount) print(wordCounter) continue

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cdb9f5699b06eaa0f164fb54a701bb1fdb951c1f

3,321

py

Python

src/Featurizers/DateTimeFeaturizerData/Tools/JsonGenerator.py

Bhaskers-Blu-Org2/FeaturizersLibrary

229ae38ea233bfb02a6ff92ec3a67c1751c58005

[ "MIT" ]

15

2019-12-14T07:54:18.000Z

2021-03-14T14:53:28.000Z

src/Featurizers/DateTimeFeaturizerData/Tools/JsonGenerator.py

Lisiczka27/FeaturizersLibrary

dc7b42abd39589af0668c896666affb4abe8a622

[ "MIT" ]

30

2019-12-03T20:58:56.000Z

2020-04-21T23:34:39.000Z

src/Featurizers/DateTimeFeaturizerData/Tools/JsonGenerator.py

Lisiczka27/FeaturizersLibrary

dc7b42abd39589af0668c896666affb4abe8a622

[ "MIT" ]

13

2020-01-23T00:18:47.000Z

2021-10-04T17:46:45.000Z

# ---------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License # ---------------------------------------------------------------------- """Generates JSON files based on data previously pickled""" import lzma import os import pickle import sys import json # Note that this isn't used directly, but is required by the picked python content import pandas as pd import CommonEnvironment from CommonEnvironment import CommandLine from CommonEnvironment import FileSystem from CommonEnvironment.StreamDecorator import StreamDecorator # ---------------------------------------------------------------------- _script_fullpath = CommonEnvironment.ThisFullpath() _script_dir, _script_name = os.path.split(_script_fullpath) # ---------------------------------------------------------------------- @CommandLine.EntryPoint( ) @CommandLine.Constraints( zipped_input_filename=CommandLine.FilenameTypeInfo(), output_stream=None, ) def EntryPoint( zipped_input_filename, output_stream=sys.stdout, ): """Generates JSON files based on data previously pickled""" with StreamDecorator(output_stream).DoneManager( line_prefix="", prefix="\nResults: ", suffix="\n", ) as dm: output_dir = os.path.join(_script_dir, "..", "GeneratedCode") FileSystem.RemoveTree(output_dir) FileSystem.MakeDirs(output_dir) df = _holiday_data_loader(zipped_input_filename) #with open('holidays.json', 'w') as f: #f.write(df.to_json(orient='records', lines=True)) allCountryNames = list(set((df['countryOrRegion']))) for countryName in allCountryNames: dfByCountry = df.loc[df['countryOrRegion'] == countryName] date = [int(x.timestamp()) for x in list(dfByCountry['date'])] name = list(dfByCountry['normalizeHolidayName']) date_dict = {"Date" : date} name_dict = {"Holiday" : name} out = {} out.update(date_dict) out.update(name_dict) jsonPath = os.path.join(output_dir, "{}.json".format(countryName)) with open(jsonPath, 'w') as f: json.dump(out, f) return dm.result # ---------------------------------------------------------------------- # ---------------------------------------------------------------------- # ---------------------------------------------------------------------- def _holiday_data_loader(_path): """Load holiday data as a static initializer.""" with lzma.open(_path, "rb") as fr: df = pickle.loads(fr.read()) df['countryRegionCode'] = df['countryRegionCode'] \ .apply(lambda x: x if type(x) == str else None) df['isPaidTimeOff'] = df['isPaidTimeOff'] \ .apply(lambda x: x if type(x) == bool else None) return df # ---------------------------------------------------------------------- # ---------------------------------------------------------------------- # ---------------------------------------------------------------------- if __name__ == "__main__": try: sys.exit(CommandLine.Main()) except KeyboardInterrupt: pass

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null

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null

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cdba82790169d516d43e4d1c83b7c0a26c10e1fe

7,152

py

Python

fer.py

MahmoudSabra1/Emotion-recognition-song-recommendation

5cad8413b6c98cee12798334009fe8942a420527

[ "MIT" ]

11

2020-11-11T14:52:05.000Z

2022-03-11T11:37:42.000Z

fer.py

MahmoudSabra1/Emotion-recognition-song-recommendation

5cad8413b6c98cee12798334009fe8942a420527

[ "MIT" ]

1

2021-06-21T06:42:59.000Z

fer.py

MahmoudSabra1/Emotion-recognition-song-recommendation

5cad8413b6c98cee12798334009fe8942a420527

[ "MIT" ]

7

2021-01-26T03:40:12.000Z

2021-12-20T12:24:34.000Z

# Two lines that remove tensorflow GPU logs # import os # os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' import numpy as np import pandas as pd import matplotlib.pyplot as plt from keras.optimizers import Adam from keras.models import Sequential, model_from_json from keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Dropout, BatchNormalization, Activation from keras.preprocessing.image import ImageDataGenerator from sklearn import model_selection from math import ceil # Loads csv files and appends pixels to X and labels to y def preprocess_data(): data = pd.read_csv('fer2013.csv') labels = pd.read_csv('fer2013new.csv') orig_class_names = ['neutral', 'happiness', 'surprise', 'sadness', 'anger', 'disgust', 'fear', 'contempt', 'unknown', 'NF'] n_samples = len(data) w = 48 h = 48 y = np.array(labels[orig_class_names]) X = np.zeros((n_samples, w, h, 1)) for i in range(n_samples): X[i] = np.fromstring(data['pixels'][i], dtype=int, sep=' ').reshape((h, w, 1)) return X, y def clean_data_and_normalize(X, y): orig_class_names = ['neutral', 'happiness', 'surprise', 'sadness', 'anger', 'disgust', 'fear', 'contempt', 'unknown', 'NF'] # Using mask to remove unknown or NF images y_mask = y.argmax(axis=-1) mask = y_mask < orig_class_names.index('unknown') X = X[mask] y = y[mask] # Convert to probabilities between 0 and 1 y = y[:, :-2] * 0.1 # Add contempt to neutral and remove it y[:, 0] += y[:, 7] y = y[:, :7] # Normalize image vectors X = X / 255.0 return X, y def split_data(X, y): test_size = ceil(len(X) * 0.1) # Split Data x_train, x_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=test_size, random_state=42) x_train, x_val, y_train, y_val = model_selection.train_test_split(x_train, y_train, test_size=test_size, random_state=42) return x_train, y_train, x_val, y_val, x_test, y_test def data_augmentation(x_train): shift = 0.1 datagen = ImageDataGenerator( rotation_range=20, horizontal_flip=True, height_shift_range=shift, width_shift_range=shift) datagen.fit(x_train) return datagen def show_augmented_images(datagen, x_train, y_train): it = datagen.flow(x_train, y_train, batch_size=1) plt.figure(figsize=(10, 7)) for i in range(25): plt.subplot(5, 5, i + 1) plt.xticks([]) plt.yticks([]) plt.grid(False) plt.imshow(it.next()[0][0], cmap='gray') # plt.xlabel(class_names[y_train[i]]) plt.show() def define_model(input_shape=(48, 48, 1), classes=7): num_features = 64 model = Sequential() # 1st stage model.add(Conv2D(num_features, kernel_size=(3, 3), input_shape=input_shape)) model.add(BatchNormalization()) model.add(Activation(activation='relu')) model.add(Conv2D(num_features, kernel_size=(3, 3))) model.add(BatchNormalization()) model.add(Activation(activation='relu')) model.add(Dropout(0.5)) # 2nd stage model.add(Conv2D(num_features, (3, 3), activation='relu')) model.add(Conv2D(num_features, (3, 3), activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2))) # 3rd stage model.add(Conv2D(2 * num_features, kernel_size=(3, 3))) model.add(BatchNormalization()) model.add(Activation(activation='relu')) model.add(Conv2D(2 * num_features, kernel_size=(3, 3))) model.add(BatchNormalization()) model.add(Activation(activation='relu')) # 4th stage model.add(Conv2D(2 * num_features, (3, 3), activation='relu')) model.add(Conv2D(2 * num_features, (3, 3), activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2))) # 5th stage model.add(Conv2D(4 * num_features, kernel_size=(3, 3))) model.add(BatchNormalization()) model.add(Activation(activation='relu')) model.add(Conv2D(4 * num_features, kernel_size=(3, 3))) model.add(BatchNormalization()) model.add(Activation(activation='relu')) model.add(Flatten()) # Fully connected neural networks model.add(Dense(1024, activation='relu')) model.add(Dropout(0.2)) model.add(Dense(1024, activation='relu')) model.add(Dropout(0.2)) model.add(Dense(classes, activation='softmax')) return model def plot_acc_loss(history): # Plot accuracy graph plt.plot(history.history['accuracy'], label='accuracy') plt.plot(history.history['val_accuracy'], label='val_accuracy') plt.xlabel('Epoch') plt.ylabel('accuracy') plt.ylim([0, 1.0]) plt.legend(loc='upper left') plt.show() # Plot loss graph plt.plot(history.history['loss'], label='loss') plt.plot(history.history['val_loss'], label='val_loss') plt.xlabel('Epoch') plt.ylabel('Loss') # plt.ylim([0, 3.5]) plt.legend(loc='upper right') plt.show() def save_model_and_weights(model, test_acc): # Serialize and save model to JSON test_acc = int(test_acc * 10000) model_json = model.to_json() with open('Saved-Models\\model' + str(test_acc) + '.json', 'w') as json_file: json_file.write(model_json) # Serialize and save weights to JSON model.save_weights('Saved-Models\\model' + str(test_acc) + '.h5') print('Model and weights are saved in separate files.') def load_model_and_weights(model_path, weights_path): # Loading JSON model json_file = open(model_path, 'r') loaded_model_json = json_file.read() json_file.close() model = model_from_json(loaded_model_json) # Loading weights model.load_weights(weights_path) model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) print('Model and weights are loaded and compiled.') def run_model(): fer_classes = ['neutral', 'happiness', 'surprise', 'sadness', 'anger', 'disgust', 'fear'] X, y = preprocess_data() X, y = clean_data_and_normalize(X, y) x_train, y_train, x_val, y_val, x_test, y_test = split_data(X, y) datagen = data_augmentation(x_train) epochs = 100 batch_size = 64 print("X_train shape: " + str(x_train.shape)) print("Y_train shape: " + str(y_train.shape)) print("X_test shape: " + str(x_test.shape)) print("Y_test shape: " + str(y_test.shape)) print("X_val shape: " + str(x_val.shape)) print("Y_val shape: " + str(y_val.shape)) # Training model from scratch model = define_model(input_shape=x_train[0].shape, classes=len(fer_classes)) model.summary() model.compile(optimizer=Adam(lr=0.0001), loss='binary_crossentropy', metrics=['accuracy']) history = model.fit(datagen.flow(x_train, y_train, batch_size=batch_size), epochs=epochs, steps_per_epoch=len(x_train) // batch_size, validation_data=(x_val, y_val), verbose=2) test_loss, test_acc = model.evaluate(x_test, y_test, batch_size=batch_size) plot_acc_loss(history) save_model_and_weights(model, test_acc) run_model()

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0

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7,152

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0

null

0

null

0

1

0

cdbd2bded66eee36ec46ada4de75a010512f317b

2,962

py

Python

app/requests.py

gabrielcoder247/News-Highlight-v2

595f4ee9739b173142d1012bdda63526818930e4

[ "Unlicense" ]

null

app/requests.py

gabrielcoder247/News-Highlight-v2

595f4ee9739b173142d1012bdda63526818930e4

[ "Unlicense" ]

null

app/requests.py

gabrielcoder247/News-Highlight-v2

595f4ee9739b173142d1012bdda63526818930e4

[ "Unlicense" ]

null

import urllib.request,json from .models import Source,Article from . import main # Getting Api Key api_Key = None #Getting the base urls sources_base_url = None articles_base_url = None def configure_request(app): ''' Function to acquire the api key and base urls ''' global api_Key,sources_base_url,articles_base_url api_Key = app.config['NEWS_API_KEY'] sources_base_url = app.config['NEWS_SOURCES_BASE_URL'] articles_base_url = app.config['NEWS_ARTICLES_BASE_URL'] def get_sources(category): ''' Function that gets the json response to our url request ''' get_sources_url = sources_base_url.format(category) with urllib.request.urlopen(get_sources_url,data=None) as url: get_sources_data = url.read() get_sources_response = json.loads(get_sources_data) sources_results = None if get_sources_response['sources']: sources_results_list = get_sources_response['sources'] sources_results = process_sources(sources_results_list) # print(sources_results) return sources_results def process_sources(sources_results): ''' Function that processes the sources result and transform them to a list of Objects Args: sources_results: A list of dictionaries that contain sources details Returns : sources_list: A list of sources objects ''' sources_list = [] for source_item in sources_results: id = source_item.get('id') name = source_item.get('name') description = source_item.get('description') url = source_item.get('url') category = source_item.get('category') source_object = Source(id,name,description,url,category) sources_list.append(source_object) return sources_list def get_articles(source): ''' Function that gets the json response to our url request ''' get_articles_url = articles_base_url.format(source,api_Key) with urllib.request.urlopen(get_articles_url,data=None) as url: get_articles_data = url.read() get_articles_response = json.loads(get_articles_data) articles_results = None if get_articles_response['articles']: articles_results_list = get_articles_response['articles'] articles_results = process_articles(articles_results_list) return articles_results def process_articles(articles_results): ''' Function that processes the articles result and transform them to a list of Objects Args: articles_results: A list of dictionaries that contain articles details Returns : articles_list: A list of articles objects ''' articles_list = [] for article_item in articles_results: name = article_item.get('name') author = article_item.get('author') title = article_item.get('title') description = article_item.get('description') url = article_item.get('url') urlToImage = article_item.get('urlToImage') publishedAt = article_item.get('publishedAt') if publishedAt and author and urlToImage: article_object = Article(name,author,title,description,url,urlToImage,publishedAt) articles_list.append(article_object) return articles_list

30.854167

85

0.778528

416

2,962

5.278846

0.173077

0.038251

0.044627

0.02459

0.310565

0.234062

0.116576

0.082878

0

0.135044

2,962

96

86

30.854167

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0

0.072188

0.019043

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1

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false

0

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0

0.214286

0

null

0

null

0

1

0

cdc3ceae4eb0b0fc7a29f9482fb7047dcfef58b4

727

py

Python

main.py

csmyth93/solo_scoring

6c1a32a3430058aa7d51be604dcc02d11ce85edd

[ "MIT" ]

null

main.py

csmyth93/solo_scoring

6c1a32a3430058aa7d51be604dcc02d11ce85edd

[ "MIT" ]

null

main.py

csmyth93/solo_scoring

6c1a32a3430058aa7d51be604dcc02d11ce85edd

[ "MIT" ]

null

def get_names(): names = [] while True: name = input("Enter players name: ") if name != 'done': print(f'{name} added to the list of players') names.append(name) continue else: break return names def get_player_scores(players): for player in players: scores = [] while True: score = input(f"What are {player}'s final cards? ") if score != 'end': scores.append(score) continue else: break return scores if __name__ == '__main__': players = get_names() print(players) scores = get_player_scores(players) print(scores)

22.71875

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0.515818

78

727

4.628205

0.448718

0.033241

0.094183

0.127424

0

0.389271

727

31

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1

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false

0

0.148148

0.111111

0

null

0

null

0

1

0

cdc442d6b9ce4b9876165256e71bc1dbffd0f620

760

py

Python

python/twisted/web_echo.py

y2ghost/work

b7f5b02db9dc0df6157bc799ddb4a1ac9d574cf3

[ "MIT" ]

null

python/twisted/web_echo.py

y2ghost/work

b7f5b02db9dc0df6157bc799ddb4a1ac9d574cf3

[ "MIT" ]

null

python/twisted/web_echo.py

y2ghost/work

b7f5b02db9dc0df6157bc799ddb4a1ac9d574cf3

[ "MIT" ]

null

from twisted.protocols import basic from twisted.internet import protocol, reactor class HTTPEchoProtocol(basic.LineReceiver): def __init__(self): self.lines = [] def lineReceived(self, line): self.lines.append(line.decode()) if not line: self.sendResponse() def sendResponse(self): self.sendLine(b"HTTP/1.1 200 OK") self.sendLine(b"") responseBody = "You said:\r\n\r\n" + "\r\n".join(self.lines) data = responseBody.encode() self.transport.write(data) self.transport.loseConnection() class HTTPEchoFactory(protocol.ServerFactory): def buildProtocol(self, addr): return HTTPEchoProtocol() reactor.listenTCP(8000, HTTPEchoFactory()) reactor.run()

27.142857

68

0.661842

86

760

5.802326

0.546512

0.054108

0.052104

0.016032

0

0.015126

0.217105

760

27

69

28.148148

0.823529

0

0.047431

0

0.047619

0

1

0.190476

false

0

0.095238

0.047619

0.428571

0

null

0

null

0

1

0

cdc5fa09b3e8bd5d035d3ebb8b21feb4b7b64279

2,183

py

Python

core/thirdparty/load_openpose.py

jshuhnow/OddEyeCam

ed76cd1c29701b7b49f20bcd61e7e72d3140fda8

[ "MIT" ]

8

2020-10-08T13:32:33.000Z

2021-12-08T10:59:03.000Z

core/thirdparty/load_openpose.py

jshuhnow/OddEyeCam

ed76cd1c29701b7b49f20bcd61e7e72d3140fda8

[ "MIT" ]

null

core/thirdparty/load_openpose.py

jshuhnow/OddEyeCam

ed76cd1c29701b7b49f20bcd61e7e72d3140fda8

[ "MIT" ]

1

2021-04-15T23:50:13.000Z

import os import sys this_dir = os.path.dirname(__file__) import numpy as np openpose_path = os.path.join(this_dir, 'openpose') op_release_path = os.path.join(openpose_path, 'Release') model_path = os.path.join(openpose_path, 'models') print(op_release_path) sys.path.append(op_release_path); os.environ['PATH'] = os.environ['PATH'] + ';' + openpose_path + '/x64/Release;' + openpose_path + '/bin;' import pyopenpose as op opWrapper = op.WrapperPython() params = dict() params["model_folder"] = model_path params["number_people_max"] = 1 params["net_resolution"]="-1x160" params["body"] = 1 params["output_resolution"] = "-1x-1" params["disable_multi_thread"] = True opWrapper.configure(params) opWrapper.start() class PoseEstimator(): def __init__(self): self.RShColor = (0, 140, 255) self.LShColor = (0, 255, 215) self.NeckColor = (0, 0, 215) self.NoseColor = (215, 0, 215) def _keypoint_to_index(self,keypoints): v = keypoints[:,1] u = keypoints[:,0] idx = np.array([v,u]).astype(np.int).transpose() return idx def find_body_on_2D(self, src_img, verts): datum = op.Datum() datum.cvInputData = src_img opWrapper.emplaceAndPop([datum]) self.op_img = datum.cvOutputData #print(datum.poseKeypoints) # Check validity if not str(datum.poseKeypoints.shape) == '(1, 25, 3)': return np.zeros((25, 3)).astype(np.int) data = datum.poseKeypoints # self.RShoulder2D = np.array([data[0,2,0], data[0,2,1]]) # self.LShoulder2D = np.array([data[0,5,0], data[0,5,1]]) # self.Neck2D = np.array([data[0,1,0], data[0,1,1]]) # keypoint = np.array([self.RShoulder2D, self.LShoulder2D, self.Neck2D]).astype(np.int) # return keypoint keypoints = data[0] # switch (u,v) -> (v,u) idx = self._keypoint_to_index(keypoints) return idx def just_find_body_on_2D(self, src_img): datum = op.Datum() datum.cvInputData = src_img opWrapper.emplaceAndPop([datum]) self.op_img = datum.cvOutputData return datum.cvOutputData, datum.poseKeypoints

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2,183

4.566102

0.328814

0.025984

0.022272

0.03118

0.200445

0.161841

0.129176

0

0.041812

0.211177

2,183

62

108

35.209677

0.740418

0.150252

0

0.204082

0

0.082792

0

1

0.081633

false

0

0.081633

0

0.265306

0.020408

0

null

0

null

0

1

0

cdc72216af29eaceb6c114484063fc2831f99596

420

py

Python

ABC127C/resolve.py

staguchi0703/problems_easy

82804b99b3ce8104762c3f6f5cc60b009a17bdc8

[ "MIT" ]

null

ABC127C/resolve.py

staguchi0703/problems_easy

82804b99b3ce8104762c3f6f5cc60b009a17bdc8

[ "MIT" ]

null

ABC127C/resolve.py

staguchi0703/problems_easy

82804b99b3ce8104762c3f6f5cc60b009a17bdc8

[ "MIT" ]

null

def resolve(): ''' code here ''' N , M = [int(item) for item in input().split()] LRs = [[int(item) for item in input().split()] for _ in range(M)] L_max = 0 R_min = N for L, R in LRs: L_max = max(L_max, L) R_min = min(R_min, R) delta = R_min - L_max if delta >= 0: print(delta + 1) else: print(0) if __name__ == "__main__": resolve()

16.8

69

0.490476

66

420

2.863636

0.393939

0.084656

0.10582

0.148148

0.275132

0

0.014815

0.357143

420

24

70

17.5

0.685185

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0.020253

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0.066667

false

0

0.066667

0.133333

0

null

0

null

0

1

0

cdc9ffbc19062cc077e25fb215d33c0447db75e0

7,109

py

Python

om10/plotting.py

drphilmarshall/OM10

009c16f0ef4e1c5f8f78c78df3c7711b7be24938

[ "MIT" ]

5

2017-02-17T19:43:54.000Z

2021-05-19T09:30:53.000Z

om10/plotting.py

drphilmarshall/OM10

009c16f0ef4e1c5f8f78c78df3c7711b7be24938

[ "MIT" ]

55

2015-02-06T19:25:58.000Z

2021-03-09T07:57:04.000Z

om10/plotting.py

drphilmarshall/OM10

009c16f0ef4e1c5f8f78c78df3c7711b7be24938

[ "MIT" ]

16

2015-01-29T23:55:45.000Z

2021-04-16T03:06:38.000Z

# ====================================================================== # Globally useful modules, imported here and then accessible by all # functions in this file: from __future__ import print_function # Fonts, latex: import matplotlib matplotlib.rc('font',**{'family':'serif', 'serif':['TimesNewRoman']}) matplotlib.rc('text', usetex=True) import corner import pylab, sys, numpy as np # ====================================================================== def plot_sample(sample, saveImg=False, fig=None, color='black', parameters=('MAGI','IMSEP','VELDISP','ZLENS','ZSRC')): """ Given an OM10 sample, make a corner plot of the required quantities. Parameters ---------- parameters : str, tuple Names of the lens parameters to plot saveImg : bool If true, save image with standardized name. IQ : float Image quality, for reference. fig : matplotlib figure object Overlay plot on an existing figure Returns ------- fig : matplotlib figure object New or updated figure """ features, labels = extract_features(sample, parameters) if fig is None: fig = corner.corner(features, labels=labels, color=color, smooth=1.0) else: _ = corner.corner(features, labels=labels, color=color, smooth=1.0, fig=fig) for ax in fig.axes: for item in ([ax.xaxis.label, ax.yaxis.label]): item.set_fontsize(20) for item in (ax.get_xticklabels() + ax.get_yticklabels()): item.set_fontsize(16) if saveImg: pngfile = "om10_sample.png" pylab.savefig(pngfile) print("OM10: Sample plot saved to file:", pngfile) return fig # ====================================================================== def extract_features(x, names): """ Given an OM10 table of lenses, extract the required parameters and provide labels for them. Parameters ---------- x : Table OM10 lens sample. names : str, tuple Names of features required. Returns ------- features : float, ndarray Values of requested features, for each lens in the Table labels : str, list Corresponding axis labels """ features = np.array([]) labels = [] p = len(names) n = len(x) for name in names: features = np.append(features, x[name]) labels.append(axis_labels[name]) return features.reshape(p,n).transpose(), labels # ====================================================================== def plot_lens(lens, saveImg=False, IQ=0.7): """ Given an OM10 lens, compute some basic quantities and use them to plot a cartoon visualization of the lens. Parameters ---------- saveImg : bool If true, save image with standardized name. IQ : float Image quality, for reference. """ # # Force matplotlib to not use any Xwindows backend: # if saveImg: # try: matplotlib.use('Agg') # except: pass # else: # try: matplotlib.use('TkAgg') # except: pass # Pull out data for ease of use: id = lens['LENSID'][0] xi = lens['XIMG'][0] yi = lens['YIMG'][0] nim = lens['NIMG'][0] mui = lens['MAG'][0] md = lens['APMAG_I'][0] ms = lens['MAGI_IN'][0] xs = lens['XSRC'][0] ys = lens['YSRC'][0] xd = 0.0 yd = 0.0 zd = lens['ZLENS'][0] zs = lens['ZSRC'][0] q = 1.0 - lens['ELLIP'][0] phi = lens['PHIE'][0] print("OM10: Plotting image configuration of lens ID ",id) # Compute image magnitudes: mi = np.zeros(nim) lfi = np.zeros(nim) for i in range(nim): mi[i] = ms - 2.5*np.log10(np.abs(mui[i])) lfi[i] = 0.4*(24-mi[i]) print("OM10: lens, image magnitudes:",md,mi) lfd = 0.4*(24-md) # print("om10.plot_lens: lens, image log fluxes:",lfd,lfi) # ------------------------------------------------------------------ # Compute caustics and critical curves: # ------------------------------------------------------------------ # Start figure: fig = pylab.figure(figsize=(8,8)) # ,aspect='equal') # Axes limits, useful sizes: xmax = 1.99 dm = 1.0/10 # Plot command sets its own axes. 'bp' = blue pentagons # pylab.plot(xi, yi, 'bp') pylab.plot(xi, yi, color='blue', \ marker='+', markersize=10, markeredgewidth=2, \ linestyle='') pylab.plot(xs, ys, color='lightblue', \ marker='+', markersize=10, markeredgewidth=2, \ linestyle='') pylab.plot(xd, yd, color='orange', \ marker='+', markersize=10, markeredgewidth=2, \ linestyle='') # Ellipse to represent lens brightness: ell = matplotlib.patches.Ellipse((xd,yd), width=2*dm*lfd, height=2*q*dm*lfd, angle=phi, alpha=0.2, fc='orange') pylab.gca().add_patch(ell) # Circles to represent image brightness: for i in range(nim): cir = pylab.Circle((xi[i],yi[i]), radius=dm*lfi[i], alpha=0.2, fc='blue') pylab.gca().add_patch(cir) # Circle to represent seeing: cir = pylab.Circle((1.5,-1.5), radius=IQ/2.0, alpha=0.1, fc='grey') pylab.gca().add_patch(cir) text = '{:3.1f}" seeing'.format(IQ) pylab.annotate(text, (370,5), xytext=None, fontsize=14, \ xycoords='axes points',textcoords='axes points') # Legend giving lens, source redshift: text1 = "$z_d$ = %5.2f" % zd text2 = "$z_s$ = %5.2f" % zs pylab.annotate(text1, (10,430), xytext=None, fontsize=14, \ xycoords='axes points',textcoords='axes points') pylab.annotate(text2, (10,410), xytext=None, fontsize=14, \ xycoords='axes points',textcoords='axes points') # Plot title: title = "OM10 lensed QSO, ID="+str(id) pylab.title(title,fontsize=20) # Set axes labels: pylab.xlabel("x / arcsec",fontsize=20) pylab.ylabel("y / arcsec",fontsize=20) # Set axis limits: pylab.axis([-xmax,xmax,-xmax,xmax]) # Add a grid: pylab.grid(color='grey', linestyle='--', linewidth=0.5) # Plot graph to file: if saveImg: pngfile = "om10_qso_ID="+str(id)+".png" pylab.savefig(pngfile) print("OM10: Lens plot saved to file:",pngfile) # ====================================================================== axis_labels = {} axis_labels['ZLENS'] = '$z_{\\rm d}$' axis_labels['VELDISP'] = '$\sigma_{\\rm d}$ / km/s' axis_labels['ELLIP'] = '$\epsilon_{\\rm d}$' axis_labels['PHIE'] = '$\phi_{\\rm d}$ / km/s' axis_labels['GAMMA'] = '$\gamma$' axis_labels['PHIG'] = '$\phi_{\gamma}$' axis_labels['ZSRC'] = '$z_{\\rm s}$' axis_labels['MAGI'] = '$i_3$' axis_labels['MAGI_IN'] = '$i_{\\rm s}$' axis_labels['IMSEP'] = '$\Delta \\theta$ / arcsec' axis_labels['i_SDSS_lens'] = '$i_{\\rm d}$ (AB mag)' axis_labels['i_SDSS_quasar'] = '$i_{\\rm s}$ (AB mag)' axis_labels['ug'] = '$u-g$ color' axis_labels['gr'] = '$g-r$ color' axis_labels['ri'] = '$r-i$ color' axis_labels['iz'] = '$i-z$ color' axis_labels['ug'] = '$u-g$ color'

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7,109

4.253319

0.320796

0.052016

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0.111313

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7,109

234

116

30.380342

0.678558

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1

0.026786

false

0

0.035714

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0.080357

0.044643

0

null

0

null

0

1

0

cdccadfab450a4e9a57ce9f5439e430bde2038d3

527

py

Python

tfutils/losses/losses.py

njchiang/tf-keras-utils

6ea5e51ef3ca5729fbc71bf3cffecf4faec033dd

[ "MIT" ]

null

tfutils/losses/losses.py

njchiang/tf-keras-utils

6ea5e51ef3ca5729fbc71bf3cffecf4faec033dd

[ "MIT" ]

null

tfutils/losses/losses.py

njchiang/tf-keras-utils

6ea5e51ef3ca5729fbc71bf3cffecf4faec033dd

[ "MIT" ]

null

# this actually won't work with keras... not exactly a keras utility import tensorflow as tf def ae_loss_fn(model, x, y, training=None): pred = model(x, training) mse = tf.keras.losses.MSE(y, pred) return tf.reduce_mean(mse), pred # function is untested def vae_loss_fn(model, x, y, training=None): z, m, v = model.encoder(x, training) pred = model.decoder(z) mse = tf.reduce_sum(tf.keras.losses.MSE(y, pred)) kld = -0.5 * tf.reduce_sum(1 + v - tf.pow(m, 2) - tf.exp(v)) return mse + kld, pred

35.133333

68

0.660342

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527

3.62766

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0

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0.197343

527

15

69

35.133333

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0

1

0.181818

false

0

0.090909

0

0.454545

0

null

0

null

0

1

0

cdd5f8ad7b2f42d4bfe80a22a6bf9fc481e565ca

2,750

py

Python

U-NET/utils.py

HarshZ26/Object-Detection

1d73f6aeb7452b0b26bd2713e69f340d129a5ba5

[ "MIT" ]

1

2022-03-23T15:49:02.000Z

U-NET/utils.py

HarshZ26/Object-Detection

1d73f6aeb7452b0b26bd2713e69f340d129a5ba5

[ "MIT" ]

null

U-NET/utils.py

HarshZ26/Object-Detection

1d73f6aeb7452b0b26bd2713e69f340d129a5ba5

[ "MIT" ]

null

from init import * VOC_CLASSES = [ "background", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "potted plant", "sheep", "sofa", "train", "tv/monitor", ] VOC_COLORMAP = [ [0, 0, 0], [128, 0, 0], [0, 128, 0], [128, 128, 0], [0, 0, 128], [128, 0, 128], [0, 128, 128], [128, 128, 128], [64, 0, 0], [192, 0, 0], [64, 128, 0], [192, 128, 0], [64, 0, 128], [192, 0, 128], [64, 128, 128], [192, 128, 128], [0, 64, 0], [128, 64, 0], [0, 192, 0], [128, 192, 0], [0, 64, 128], ] palette = np.array(VOC_COLORMAP) custom_transforms = [transforms.Normalize(mean=[-0.485, -0.456,-0.406], std=[1/0.229, 1/0.224,1/0.225])] inv_trans = torchvision.transforms.Compose(custom_transforms) transform = A.Compose([A.Resize(512,512), A.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),ToTensorV2() ]) def calculate_weight(loader): weight_map = torch.zeros(21) for i,(_,mask) in enumerate(loader): mask = mask.permute(0,3,1,2) index,counts = torch.unique(torch.argmax(mask,axis = 1),sorted = True,return_counts=True) for i in range(len(index)): weight_map[index[i]] = counts[i] weight_map = (mask.size(2)*mask.size(3)*len(loader))/(weight_map) return weight_map/21 def calculate_acc(grnd,predicted): grnd = torch.argmax(grnd,axis = 1) predicted = torch.argmax(predicted,axis = 1) x = torch.eq(grnd,predicted).int() acc= torch.sum(x)/(grnd.size(1)*grnd.size(1)) return acc def collate_fn(batch): data = [] #filled with 64 elements thorugh for loops target = [] for item in batch: #batch = 64 items list one item = [image,label] im = item[0] open_cv_image = np.array(im) open_cv_image = open_cv_image.copy() transformed = transform(image=open_cv_image,mask = item[1]) im = transformed['image'] mask = transformed['mask'] data.append(im) target.append(mask) target = torch.stack(target,dim =0) data = torch.stack(data,dim=0) return [data, target] def test_img(loader): tes_img = iter(loader) images,masks = tes_img.next() print("images",images.size()) print("labels",masks.size()) print(np.shape(images)) img = images[0].squeeze() img = inv_trans(img) img = img.numpy() im2display = img.transpose((1,2,0)) grnd_mask = masks.numpy().transpose[0] a1 = np.argmax(grnd_mask,axis = 2) g_mask = palette[a1] plt.imshow(im2display, interpolation='nearest') plt.imshow(g_mask)

25.229358

105

0.577818

394

2,750

3.951777

0.34264

0.012845

0.028259

0.011561

0.094412

0.050096

0.035967

0

0.105036

0.241818

2,750

108

106

25.462963

0.641727

0.032

0

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0

1

0.040816

false

0

0.010204

0

0.081633

0.030612

0

null

0

null

0

1

0

cdd78b4b371ac658a03d1638d8afdbda0805a759

24,528

py

Python

datawinners/accountmanagement/admin.py

ICT4H/dcs-web

fb0f53fad4401cfac1c1789ff28b9d5bda40c975

[ "Apache-2.0" ]

1

2015-11-02T09:11:12.000Z

datawinners/accountmanagement/admin.py

ICT4H/dcs-web

fb0f53fad4401cfac1c1789ff28b9d5bda40c975

[ "Apache-2.0" ]

null

datawinners/accountmanagement/admin.py

ICT4H/dcs-web

fb0f53fad4401cfac1c1789ff28b9d5bda40c975

[ "Apache-2.0" ]

null

# vim: ai ts=4 sts=4 et sw=4 encoding=utf-8 import datetime import logging from django.contrib.auth.admin import UserAdmin from django.contrib.auth.forms import UserChangeForm from django.core.exceptions import ValidationError from django.forms import CharField from django.utils.translation import ugettext_lazy as _ from django.contrib import admin from django.contrib.auth.models import User, Group from django_digest.models import PartialDigest from django.contrib import messages from django.utils.safestring import mark_safe from django.contrib.admin.views.main import ChangeList from datawinners.common.admin.utils import get_text_search_filter, get_admin_panel_filter from datawinners.project.submission.export import create_excel_response from datawinners.search.index_utils import get_elasticsearch_handle from forms import forms from datawinners.accountmanagement.models import OrganizationSetting, SMSC, PaymentDetails, MessageTracker, Organization, NGOUserProfile, OutgoingNumberSetting from mangrove.form_model.field import ExcelDate from mangrove.utils.types import is_empty, is_not_empty from datawinners.countrytotrialnumbermapping.models import Country, Network from datawinners.utils import get_database_manager_for_org from datawinners.feeds.database import feeds_db_for from django.db.models import Q admin.site.disable_action('delete_selected') class DatawinnerAdmin(admin.ModelAdmin): def has_delete_permission(self, request, obj=None): return False def has_add_permission(self, request): return False class OrganizationSettingAdmin(DatawinnerAdmin): list_display = ('organization_name', 'organization_id', 'type', 'payment_details', 'activation_date', 'admin_email') fields = ('sms_tel_number', 'outgoing_number') search_fields = ['organization__name','organization__org_id'] ordering = ('-organization__active_date',) def organization_name(self, obj): return obj.organization.name organization_name.admin_order_field = "organization__name" def _get_ngo_admin(self, organization_setting): user_profiles = NGOUserProfile.objects.filter(org_id=organization_setting.organization.org_id) admin_users = [x.user for x in user_profiles if x.user.groups.filter(name="NGO Admins")] #right now there is only one ngo admin return admin_users[0] if is_not_empty(admin_users) else NullAdmin() def admin_email(self, obj): return self._get_ngo_admin(obj).email def organization_id(self, obj): return obj.organization.org_id organization_id.admin_order_field = "organization__org_id" def payment_details(self, obj): organization = obj.organization payment_details = PaymentDetails.objects.filter(organization=organization) if not is_empty(payment_details): return payment_details[0].preferred_payment return "--" def type(self, obj): return obj.organization.account_type type.admin_order_field = 'organization__account_type' def activation_date(self, obj): return obj.organization.active_date if obj.organization.active_date is not None else '--' activation_date.admin_order_field = "organization__active_date" activation_date.short_description = "Created on" class MessageTrackerAdmin(DatawinnerAdmin): list_display = ("organization_name", "organization_id","type", "month", "combined_total_incoming", "total_incoming_per_month", "total_messages", "total_outgoing_messages", "outgoing_sms_count","outgoing_sms_charged_count", "sent_reminders_count","sent_reminders_charged_count", "send_message_count","send_message_charged_count", "sms_api_usage_count","sms_api_usage_charged_count", "sms_submission", "incoming_sp_count", "incoming_web_count", "sms_registration_count") search_fields = ['organization__name', 'organization__org_id', 'month'] ordering = ('-month',) def __init__(self, *args, **kwargs): super(MessageTrackerAdmin, self).__init__(*args, **kwargs) self.list_display_links = (None,) def organization_name(self, obj): return obj.organization.name organization_name.short_description = mark_safe('Organisation<br/>name') def type(self,obj): return obj.organization.account_type def organization_id(self, obj): return obj.organization.org_id organization_id.short_description = mark_safe('Organisation<br/>ID') def combined_total_incoming(self, obj): return obj.total_incoming_in_total() combined_total_incoming.short_description = mark_safe('Total<br/>incoming<br/>Submissions<br/>(In total)') def total_incoming_per_month(self, obj): return obj.total_monthly_incoming_messages() total_incoming_per_month.short_description = mark_safe('Total<br/>Incoming<br/>Submissions<br/>') def current_month(self, obj): return datetime.datetime.strftime(obj.month, "%m-%Y") current_month.short_description = "Month" def total_outgoing_messages(self, obj): return obj.outgoing_message_count() total_outgoing_messages.short_description = mark_safe('Outgoing Charged SMS:<br/>Total') def total_messages(self, obj): return obj.total_messages() total_messages.short_description = mark_safe('Total SMS<br/>(incoming<br/>and<br/>outgoing)') def combined_total_messages(self, obj): return obj.combined_total_messages() combined_total_messages.short_description = mark_safe('Total SMS<br/>(in total)') def sms_submission(self, obj): return obj.incoming_sms_count - obj.sms_registration_count sms_submission.short_description = mark_safe('SMS<br/>Submissions') def export_message_tracker_details_to_excel(modeladmin, request, query_set): headers = ["Organization Name", "Organization Id","Type", "Month", "Total Incoming Submissions (In total)", "Total Incoming Submissions", "Total SMS (incoming and outgoing)", "Outgoing Charged SMS: Total", "Outgoing SMS: Auto Reply", "Outgoing Charged SMS: Auto Reply", "Outgoing SMS: Reminders", "Outgoing Charged SMS: Reminders", "Outgoing SMS: Send Message", "Outgoing Charged SMS: Send Message", "Outgoing SMS: API", "Outgoing Charged SMS: API", "SMS Submissions", "SP Submissions", "Web Submissions", "SMS Subject Registration"] list = [] textSearchFilter = get_text_search_filter(request.GET,MessageTrackerAdmin.search_fields) adminPanelFilter = get_admin_panel_filter(request.GET) filteredSms = MessageTracker.objects.all().filter(Q(**adminPanelFilter) & (textSearchFilter)) for messageTracker in filteredSms: sms_tracker_month = ExcelDate(datetime.datetime.combine(messageTracker.month, datetime.datetime.min.time()), 'dd.mm.yyyy') if messageTracker.month else None list.append([modeladmin.organization_name(messageTracker), modeladmin.organization_id(messageTracker), modeladmin.type(messageTracker), sms_tracker_month, messageTracker.total_incoming_in_total(), messageTracker.total_monthly_incoming_messages(), messageTracker.total_messages(), messageTracker.outgoing_message_count(), messageTracker.outgoing_sms_count, messageTracker.outgoing_sms_charged_count, messageTracker.sent_reminders_count, messageTracker.sent_reminders_charged_count, messageTracker.send_message_count, messageTracker.send_message_charged_count, messageTracker.sms_api_usage_count, messageTracker.sms_api_usage_charged_count, modeladmin.sms_submission(messageTracker), messageTracker.incoming_sp_count, messageTracker.incoming_web_count, messageTracker.sms_registration_count]) response = create_excel_response(headers, list, 'tracker_list') return response actions = [export_message_tracker_details_to_excel] class OrganizationChangeList(ChangeList): def get_query_set(self): if not self.params.get("q", ""): return super(OrganizationChangeList, self).get_query_set() from django.db import connection cursor = connection.cursor() query = """Select array_agg(DISTINCT o.org_id) from accountmanagement_organization o inner join accountmanagement_ngouserprofile p on p.org_id = o.org_id inner join auth_user u on u.id = p.user_id inner join auth_user_groups ug on ug.user_id = u.id inner join auth_group g on ug.group_id = g.id and g.name = %s """ params = ["NGO Admins"] for index, keyword in enumerate(self.params.get("q").split()): from django_countries.countries import COUNTRIES codes = ["'" + code + "'" for code, name in COUNTRIES if unicode(name).lower().find(keyword.lower()) != -1 ] country_codes = ', '.join(codes) if len(codes) else "''" query += "and " if index else "where" query += " (o.country in (%s) " % country_codes query += """OR u.email ilike %s OR u.first_name||u.last_name ilike %s OR o.name ilike %s OR p.mobile_phone ilike %s OR o.address||o.addressline2||o.city||o.zipcode||o.state ilike %s OR o.office_phone ilike %s OR o.website ilike %s OR o.org_id ilike %s OR to_char(o.active_date, 'YYYY-MM-DD HH:MI:SS') ilike %s) """ params.extend(["%" + keyword + "%"] * 9) cursor.execute(query, params) org_ids = cursor.fetchone()[0] qs = Organization.objects.filter(org_id__in=org_ids or []) if self.order_field: qs = qs.order_by('%s%s' % ((self.order_type == 'desc' and '-' or ''), self.order_field)) else: qs = qs.order_by('-active_date') return qs class OrganizationChangeList(ChangeList): def get_query_set(self): if not self.params.get("q", ""): return super(OrganizationChangeList, self).get_query_set() from django.db import connection cursor = connection.cursor() query = """Select array_agg(DISTINCT o.org_id) from accountmanagement_organization o inner join accountmanagement_ngouserprofile p on p.org_id = o.org_id inner join auth_user u on u.id = p.user_id inner join auth_user_groups ug on ug.user_id = u.id inner join auth_group g on ug.group_id = g.id and g.name = %s """ params = ["NGO Admins"] for index, keyword in enumerate(self.params.get("q").split()): from django_countries.countries import COUNTRIES codes = ["'" + code + "'" for code, name in COUNTRIES if unicode(name).lower().find(keyword.lower()) != -1 ] country_codes = ', '.join(codes) if len(codes) else "''" query += "and " if index else "where" query += " (o.country in (%s) " % country_codes query += """OR u.email ilike %s OR u.first_name||u.last_name ilike %s OR o.name ilike %s OR p.mobile_phone ilike %s OR o.address||o.addressline2||o.city||o.zipcode||o.state ilike %s OR o.office_phone ilike %s OR o.website ilike %s OR o.org_id ilike %s OR to_char(o.active_date, 'YYYY-MM-DD HH:MI:SS') ilike %s) """ params.extend(["%" + keyword + "%"] * 9) cursor.execute(query, params) org_ids = cursor.fetchone()[0] qs = Organization.objects.filter(org_id__in=org_ids or []) if self.order_field: qs = qs.order_by('%s%s' % ((self.order_type == 'desc' and '-' or ''), self.order_field)) else: qs = qs.order_by('-active_date') return qs class OrganizationAdmin(DatawinnerAdmin): list_display = ( 'name', 'org_id', 'complete_address', 'office_phone', 'website', 'paid', 'active_date', 'admin_name', 'admin_email', 'admin_mobile_number', 'sms_api_users', 'status') actions = ['deactivate_organizations', 'activate_organizations', 'delete_organizations'] search_fields = ['name', 'address', 'addressline2', 'city', 'zipcode', 'state', 'office_phone', 'website'] ordering = ('-active_date',) def get_changelist(self, request, **kwargs): return OrganizationChangeList def get_query_set(self, request, queryset, search_term): queryset, use_distinct = super(OrganizationAdmin, self).get_search_results(request, queryset, search_term) if search_term: queryset = queryset.filter(ngouserprofile__title__icontains=search_term) return queryset, use_distinct def deactivate_organizations(modeladmin, request, queryset): queryset.exclude(status__in=['Deactivated','Pending Activation']).update(status='Deactivated', status_changed_datetime=datetime.datetime.now()) messages.success(request, _('The accounts selected have been deactivated successfully.')) selected = request.POST.getlist(admin.ACTION_CHECKBOX_NAME) orgs_id = Organization.objects.filter(org_id__in=selected).exclude(status='Pending Activation').\ values_list('org_id', flat=True) User.objects.filter(ngouserprofile__org_id__in=orgs_id).update(is_active=False) deactivate_organizations.short_description = "Deactivate accounts" def activate_organizations(modeladmin, request, queryset): queryset.exclude(status__in=['Activated','Pending Activation']).update(status='Activated', status_changed_datetime=datetime.datetime.now()) messages.success(request, _('The accounts selected have been activated successfully.')) selected = request.POST.getlist(admin.ACTION_CHECKBOX_NAME) orgs_id = Organization.objects.filter(org_id__in=selected).exclude(status='Pending Activation').\ values_list('org_id', flat=True) User.objects.filter(ngouserprofile__org_id__in=orgs_id).update(is_active=True) activate_organizations.short_description = "Activate accounts" def delete_organizations(modeladmin, request, queryset): orgs = queryset.filter(status__in=['Deactivated', "Pending Activation"]) for organization in orgs: dbm = get_database_manager_for_org(organization) organization.purge_all_data() del dbm.server[dbm.database_name] feed_database_name = "feed_" + dbm.database_name feed_dbm = feeds_db_for(feed_database_name) del feed_dbm.server[feed_database_name] es = get_elasticsearch_handle() try: es.delete_index(dbm.database_name) except Exception as e: logging.info("Could not delete index " + str(e.message)) delete_organizations.short_description = "Delete accounts" class Media: css = {"all": ("/media/css/plugins/jqueryUI/jquery-ui-1.8.13.custom.css",)} js = ("/media/javascript/jquery.js", "/media/javascript/jqueryUI/jquery-ui-1.8.13.custom.min.js",) def sms_api_users(self, organization): user_profiles = NGOUserProfile.objects.filter(org_id=organization.org_id) return " , ".join([x.user.username for x in user_profiles if x.user.groups.filter(name="SMS API Users")]) def paid(self, obj): return "No" if obj.in_trial_mode else "Yes" def _get_ngo_admin(self, organization): user_profiles = NGOUserProfile.objects.filter(org_id=organization.org_id) admin_users = [x.user for x in user_profiles if x.user.groups.filter(name="NGO Admins")] #right now there is only one ngo admin return admin_users[0] if is_not_empty(admin_users) else NullAdmin() def admin_email(self, obj): return self._get_ngo_admin(obj).email def admin_office_phone(self, obj): admin_user = self._get_ngo_admin(obj) return admin_user.get_profile().office_phone def admin_mobile_number(self, obj): admin_user = self._get_ngo_admin(obj) return admin_user.get_profile().mobile_phone def admin_name(self, obj): admin_user = self._get_ngo_admin(obj) return admin_user.first_name def complete_address(self, obj): complete_address = [obj.address, obj.addressline2, obj.city, obj.zipcode, obj.state, obj.country_name()] return ", ".join([element for element in complete_address if is_not_empty(element)]) def get_readonly_fields(self, request, obj=None): if obj: return self.readonly_fields + ('status',) return self.readonly_fields class NullAdmin: def __init__(self): self.email = '' self.mobile_phone = '' self.office_phone = '' self.first_name = '' def get_profile(self): return self class CountryAdmin(admin.ModelAdmin): ordering = ('country_name_en',) list_display = ('country_name_en', 'country_code') class NetworkAdmin(admin.ModelAdmin): ordering = ('network_name',) list_display = ('network_name', 'trial_sms_number', 'country_name') filter_horizontal = ['country'] def country_name(self, obj): return ' ,'.join([country.country_name for country in obj.country.all()]) class UserAdminForm(forms.ModelForm): class Meta: model = User def clean(self): cleaned_data = self.cleaned_data if 'email' in cleaned_data: username = cleaned_data.get('email').strip() if not len(username): raise forms.ValidationError("This email address is required") existing_users_with_username = User.objects.filter(username=username) if existing_users_with_username.count() > 0 and existing_users_with_username[0] != self.instance: raise forms.ValidationError( "This email address is already in use. Please supply a different email address") cleaned_data['email'] = username return cleaned_data class NgoUserAdmin(DatawinnerAdmin): list_display = ('organization_name', 'country', 'organization_id', 'admin_name', 'admin_email') fields = ('email', ) form = UserAdminForm def organization_name(self, obj): profile = obj.get_profile() return Organization.objects.get(org_id=profile.org_id).name def country(self, obj): return (Organization.objects.get(org_id=obj.get_profile().org_id)).country_name() def organization_id(self, obj): return obj.get_profile().org_id def admin_name(self, obj): return obj.first_name def admin_email(self, obj): return obj.email def queryset(self, request): qs = super(NgoUserAdmin, self).queryset(request) return qs.filter(groups=Group.objects.filter(name="NGO Admins")) def save_model(self, request, obj, form, change): username = form.cleaned_data['email'] obj.username = username obj.email = username obj.save() class DWUserChangeForm(UserChangeForm): organization_id = CharField(label="Organization ID") def __init__(self, *args, **kwargs): super(DWUserChangeForm, self).__init__(*args, **kwargs) self.fields['organization_id'] = CharField(label="Organization ID") if self.instance: self.organization_id_field() self.fields['password'].widget.attrs['readonly'] = 'readonly' self.fields['first_name'].label = "Name" class Meta: model = User def organization_id_field(self): org_id = '' try: user_profile = NGOUserProfile.objects.get(user=self.instance) org_id = user_profile.org_id except: pass self.fields['organization_id'] = CharField(label="Organization ID", initial=org_id) def clean_organization_id(self): org_id = self.cleaned_data.get('organization_id', '') try: org = Organization.objects.get(org_id__iexact=org_id) return org.org_id except Organization.DoesNotExist: raise ValidationError('Organization with id : %s does not exist.Please enter a valid id' % org_id) def _remove_default_name_fields(): user_display_fields = list(UserAdmin.list_display) user_display_fields.remove('first_name') user_display_fields.remove('last_name') return tuple(user_display_fields) def export_user_list_to_excel(a,b,c): #Custom Method to export user details. def is_required(user): return True if user.groups.filter(name="NGO Admins").count() or user.groups.filter(name="Project Managers").count() else False def user_role(user): if user.groups.filter(name='NGO Admins').count(): return 'Admin' elif user.groups.filter(name='Project Managers').count(): return 'User' list = [] for ngo_user in NGOUserProfile.objects.all(): try: user = User.objects.get(id=ngo_user.user_id) if is_required(user) and not user.is_superuser: details = [] details.append(user.first_name + ' ' + user.last_name) details.append(user.username) org_id = ngo_user.org_id organization = Organization.objects.get(org_id = org_id) details.append(organization.name) details.append(organization.status) details.append(organization.language) details.append(user_role(user)) list.append(details) except Exception: continue headers = ['Name', 'email', 'Organization Name', 'Status', 'Account language','User Role'] response = create_excel_response(headers,list,'user_list') return response class DWUserAdmin(UserAdmin): list_filter = ('groups__name',) UserAdmin.fieldsets = ( (None, {'fields': ('username', 'password')}), (_('Personal info'), {'fields': ('first_name', 'email')}), (_('Permissions'), {'fields': ('is_active', 'is_staff', 'is_superuser')}), (_('Important dates'), {'fields': ('last_login', 'date_joined')}), (_('Membership'), {'fields': ('groups', 'organization_id')}), ) readonly_fields = ('last_login', 'date_joined') list_display = _remove_default_name_fields() + ('name','organization_name', 'organization_id') form = DWUserChangeForm actions = [export_user_list_to_excel] def name(self,obj): return obj.first_name def organization_name(self, obj): org_id = NGOUserProfile.objects.get(user=obj).org_id return Organization.objects.get(org_id=org_id).name def organization_id(self, obj): return NGOUserProfile.objects.get(user=obj).org_id def save_model(self, request, obj, form, change): super(DWUserAdmin, self).save_model(request, obj, form, change) if change: if 'email' in form.changed_data or 'username' in form.changed_data: try: existing_digests = PartialDigest.objects.filter(user=obj) if existing_digests: for existing_digest in existing_digests: existing_digest.delete() except PartialDigest.DoesNotExist: pass if form.cleaned_data.get('organization_id') is not None: try: user_profile = NGOUserProfile.objects.get(user=obj) user_profile.org_id = form.cleaned_data['organization_id'] user_profile.save() except NGOUserProfile.DoesNotExist: user_profile = NGOUserProfile() user_profile.org_id = form.cleaned_data['organization_id'] user_profile.title = 'Title' user_profile.user = obj user_profile.save() admin.site.unregister(Group) admin.site.unregister(User) admin.site.register(OrganizationSetting, OrganizationSettingAdmin) admin.site.register(OutgoingNumberSetting, admin.ModelAdmin) admin.site.register(SMSC, admin.ModelAdmin) admin.site.register(MessageTracker, MessageTrackerAdmin) admin.site.register(Organization, OrganizationAdmin) admin.site.register(Country, CountryAdmin) admin.site.register(Network, NetworkAdmin) admin.site.register(User, DWUserAdmin)

44.194595

218

0.677471

2,949

24,528

5.391319

0.127501

0.016668

0.019624

0.017108

0.428392

0.376565

0.341657

0.303981

0.265614

0.253915

0

0.001405

0.216569

24,528

554

219

44.274368

0.825978

0.006197

0

0.294393

0

0.028037

0.192647

0.037996

0

1

0.13785

false

0.009346

0.067757

0.432243

0

null

0

null

0

1

0

cddc0485c396754b68315d1f0f82db760ff25dc5

2,580

py

Python

floodfill_pathfinding.py

mnursey/Battlesnake-2021

884b9cf1b40c9b03cc49bd1594135e7caf41ee82

[ "MIT" ]

null

floodfill_pathfinding.py

mnursey/Battlesnake-2021

884b9cf1b40c9b03cc49bd1594135e7caf41ee82

[ "MIT" ]

null

floodfill_pathfinding.py

mnursey/Battlesnake-2021

884b9cf1b40c9b03cc49bd1594135e7caf41ee82

[ "MIT" ]

null

import board class Floodfill: frontier = [] grid = None board = None def __init__(self, game_board, start_cord): self.board = game_board self.grid = [[None for i in range(self.board.width)] for j in range(self.board.width)] start_node = self.create_node(start_cord["x"], start_cord["y"], False, None) self.frontier_add(start_node) self.grid[start_cord['x']][start_cord['y']] = start_node self.solve() return def solve(self): while len(self.frontier) > 0: current = self.frontier_pop() if not current["blocked"]: for n in self.board.neighbours(current["x"], current["y"]): # Add to frontier if we haven't seen it if self.grid[n['x']][n['y']] == None: unseen_node = self.create_node(n['x'], n['y'], self.board.isBlocked(n['x'], n['y']), current) self.grid[n['x']][n['y']] = unseen_node self.frontier_add(unseen_node) return def path(self, target_cord): node = self.grid[target_cord['x']][target_cord['y']] path = [] while node: path.append({"x" : node["x"], "y" : node["y"]}) node = node["from"] path.reverse() return path def frontier_add(self, node): self.frontier.append(node) return def frontier_pop(self): return self.frontier.pop(0) def create_node(self, x, y, blocked, prev): return {"x" : x, "y" : y, "blocked" : blocked, "from" : prev} def print(self): output = "Grid:\n" for y in range(self.board.width): line = "\n" for x in range(self.board.width): node = self.grid[x][self.board.width - y - 1] value = "-" if node: if node["from"] == None: value = "s" elif node["blocked"]: value = "x" else: if node["from"]["x"] < node["x"]: value = "<" if node["from"]["x"] > node["x"]: value = ">" if node["from"]["y"] < node["y"]: value = "v" if node["from"]["y"] > node["y"]: value = "^" line = line + value output += line print(output) return

27.446809

117

0.445736

293

2,580

3.829352

0.204778

0.064171

0.062389

0.057041

0.206774

0.131907

0.074866

0.046346

0

0.001963

0.407752

2,580

93

118

27.741935

0.73233

0.014341

0

0.063492

0

0.037387

0

1

0.111111

false

0

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0.031746

0.301587

0.031746

0

null

0

null

0

1

0

cddc0ce80665ce382edeabc67713697083130041

3,736

py

Python

Gobot-Omni/robot.py

FRC1076/2019-Parade

3824449ed10e33b401efb646fd2e6470c3941c8b

[ "MIT" ]

null

Gobot-Omni/robot.py

FRC1076/2019-Parade

3824449ed10e33b401efb646fd2e6470c3941c8b

[ "MIT" ]

2

2019-06-17T23:38:23.000Z

2019-06-17T23:39:43.000Z

Gobot-Omni/robot.py

FRC1076/2019-Parade

3824449ed10e33b401efb646fd2e6470c3941c8b

[ "MIT" ]

null

import wpilib import ctre from wpilib.drive import DifferentialDrive from wpilib.interfaces import GenericHID #MOTOR PORTS LEFT = 1 RIGHT = 3 CENTER1 = 2 CENTER2 = 4 #BALL MANIPULATOR BALL_MANIP_ID = 5 GATHER_SPEED = 1.0 SPIT_SPEED = -1.0 STOP_SPEED = 0.0 LEFT_HAND = GenericHID.Hand.kLeft RIGHT_HAND = GenericHID.Hand.kRight class MyRobot(wpilib.TimedRobot): def robotInit(self): """Robot initialization function""" # object that handles basic drive operations self.leftVictor = ctre.WPI_VictorSPX(LEFT) self.rightVictor = ctre.WPI_VictorSPX(RIGHT) self.centerVictor1 = ctre.WPI_VictorSPX(CENTER1) self.centerVictor2 = ctre.WPI_VictorSPX(CENTER2) self.left = wpilib.SpeedControllerGroup(self.leftVictor) self.right = wpilib.SpeedControllerGroup(self.rightVictor) self.center1 = wpilib.SpeedControllerGroup(self.centerVictor1) self.center2 = wpilib.SpeedControllerGroup(self.centerVictor2) self.myRobot = DifferentialDrive(self.left, self.right) self.myRobot.setExpiration(0.1) # joysticks 1 & 2 on the driver station # self.leftStick = wpilib.Joystick(0) # self.rightStick = wpilib.Joystick(1) self.DEADZONE = 0.4 self.LEFT = GenericHID.Hand.kLeft self.RIGHT = GenericHID.Hand.kRight self.driver = wpilib.XboxController(0) self.ballManipulator = BallManipulator(ctre.WPI_VictorSPX(BALL_MANIP_ID)) def autonomousInit(self): self.myRobot.tankDrive(0.8, 0.8) def autonomousPeriodic(self): self.myRobot.tankDrive(1, 0.5) def teleopInit(self): """Executed at the start of teleop mode""" self.myRobot.setSafetyEnabled(True) def setCenters(self, speed_value): self.center1.set(-speed_value) self.center2.set(speed_value) def deadzone(self, val, deadzone): if abs(val) < deadzone: return 0 return val def teleopPeriodic(self): ballMotorSetPoint = 0 if self.driver.getBumper(self.LEFT): ballMotorSetPoint = 1.0 elif self.driver.getBumper(self.RIGHT): ballMotorSetPoint = -1.0 else: ballMotorSetPoint = 0.0 self.ballManipulator.set(ballMotorSetPoint) """Runs the motors with tank steering""" #right = self.driver.getY(self.RIGHT) #left = self.driver.getY(self.LEFT) #self.myRobot.tankDrive(right, left) forward = -self.driver.getRawAxis(5) rotation_value = rotation_value = self.driver.getX(LEFT_HAND) forward = deadzone(forward, 0.2) self.myRobot.arcadeDrive(forward, rotation_value) center_speed = self.driver.getX(self.RIGHT) self.setCenters(self.deadzone(center_speed, self.DEADZONE)) class BallManipulator: """ Manipulator wraps a motor controller that gathers and spits out the cargo balls. """ def __init__(self, motor): self.motor = motor def gather(self, speed = GATHER_SPEED): self.motor.set(speed) def spit(self, speed = SPIT_SPEED): self.motor.set(speed) def stop(self): self.motor.set(STOP_SPEED) def set(self, setValue): """ Direct control to be used with a controller that puts out f, 0, and -f for gather, stop, and spit, respectively. """ self.motor.set(setValue) def deadzone(val, deadzone): if abs(val) < deadzone: return 0 elif val < (0): x = ((abs(val) - deadzone)/(1-deadzone)) return (-x) else: x = ((val - deadzone)/(1-deadzone)) return (x) if __name__ == "__main__": wpilib.run(MyRobot)

27.470588

81

0.646413

442

3,736

5.377828

0.282805

0.033656

0.020194

0.07236

0.028607

0

0.019671

0.251606

3,736

136

82

27.470588

0.830472

0.146413

0

0.098765

0

0.002603

0

1

0.160494

false

0

0.049383

0

0.296296

0

null

0

null

0

1

0

cddea9a721eee8e3cc13555afb08ee013159480b

2,158

py

Python

integration/emulator/device.py

cvlabmiet/master-programming-example

8a4a231ba2b72a93ae14da2c04e17b2ae3fc6651

[ "MIT" ]

null

integration/emulator/device.py

cvlabmiet/master-programming-example

8a4a231ba2b72a93ae14da2c04e17b2ae3fc6651

[ "MIT" ]

null

integration/emulator/device.py

cvlabmiet/master-programming-example

8a4a231ba2b72a93ae14da2c04e17b2ae3fc6651

[ "MIT" ]

null

import re, operator, array from collections import namedtuple class Argument(object): def __init__(self, viewtype, begin, end=None): self.type = viewtype self.begin = int(begin) self.end = None if end is not None: self.end = int(end) class Lram(bytearray): pass class Pram(Lram): def __init__(self): # grammar: [<output>]<operation>(<input0>[, <input1>, ...]) # <output>, <input0>, ... - <type>:<begin>[:<end>] (begin, end - bytes offsets) # <type> - u8, i8, u16, i16, ... # <operation> - add, sub, div, mod, mul, ... # <begin>, <end> - int # example: [s16:200:400]add(u8:0, u8:100) self.instruction = re.compile(r'\[(?P<out>[^\]]+)\](?P<op>\w+)$(?P<in>[^$]+)\)') self.operation = dict(add=operator.add, mul=operator.mul, mod=operator.mod, sub=operator.sub, div=operator.truediv) self.type = dict(i8='b', u8='B', i16='h', u16='H', i32='l', u32='L', f32='f') def _parse_arguments(self, op, lram): arguments = [Argument(*x.split(':')) for x in op.split(',')] return [memoryview(lram)[x.begin:x.end].cast(self.type[x.type]) for x in arguments] def _vectorize(self, op, output, inputs): for x in zip(range(len(output)), *inputs): output[x[0]] = op(*x[1:]) def run(self, lram): operations = self.instruction.findall(str(self).replace(' ', '')) for op in operations: outputs = self._parse_arguments(op[0], lram) inputs = self._parse_arguments(op[2], lram) self._vectorize(self.operation[op[1]], outputs[0], inputs) class Unit(object): def __init__(self): self.lram = Lram() self.pram = Pram() class Ctrl(list): def __init__(self, units): self.units = units def wait(self): if len(self) == 0: return [] number = self.pop(0); unit = self.units[number] unit.pram.run(unit.lram) return [number] class Device(object): def __init__(self, units): self.units = [Unit() for _ in range(units)]; self.ctrl = Ctrl(self.units)

33.2

123

0.561631

284

2,158

4.165493

0.330986

0.045647

0.046492

0.043111

0.042265

0

0.026758

0.255329

2,158

64

124

33.71875

0.709396

0.125116

0

0.086957

0

0.030835

0.025518

0

1

0.195652

false

0.021739

0.043478

0

0.434783

0

null

0

null

0

1

0

cde6ca9c0b5b99aea51fe8a0efe3ed98163008e0

17,570

py

Python

win/pywinauto/findbestmatch.py

sk8darr/BrowserRefresh-Sublime

daee0eda6480c07f8636ed24e5c555d24e088886

[ "MIT", "Unlicense" ]

191

2015-01-02T12:17:07.000Z

2021-05-26T09:26:05.000Z

win/pywinauto/findbestmatch.py

sk8darr/BrowserRefresh-Sublime

daee0eda6480c07f8636ed24e5c555d24e088886

[ "MIT", "Unlicense" ]

48

2015-01-14T00:57:36.000Z

2021-04-06T21:45:42.000Z

win/pywinauto/findbestmatch.py

sk8darr/BrowserRefresh-Sublime

daee0eda6480c07f8636ed24e5c555d24e088886

[ "MIT", "Unlicense" ]

36

2015-01-14T18:54:25.000Z

2021-07-18T10:54:42.000Z

# GUI Application automation and testing library # Copyright (C) 2006 Mark Mc Mahon # # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Lesser General Public License # as published by the Free Software Foundation; either version 2.1 # of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with this library; if not, write to the # Free Software Foundation, Inc., # 59 Temple Place, # Suite 330, # Boston, MA 02111-1307 USA "Module to find the closest match of a string in a list" __revision__ = "$Revision: 679 $" import re import difflib from . import fuzzydict #import ctypes #import ldistance #levenshtein_distance = ctypes.cdll.levenshtein.levenshtein_distance #levenshtein_distance = ldistance.distance # need to use sets.Set for python 2.3 compatability # but 2.6 raises a deprecation warning about sets module try: set except NameError: import sets set = sets.Set find_best_control_match_cutoff = .6 #==================================================================== class MatchError(IndexError): "A suitable match could not be found" def __init__(self, items = None, tofind = ''): "Init the parent with the message" self.tofind = tofind self.items = items if self.items is None: self.items = [] IndexError.__init__(self, "Could not find '%s' in '%s'"% (tofind, self.items)) _cache = {} # given a list of texts return the match score for each # and the best score and text with best score #==================================================================== def _get_match_ratios(texts, match_against): "Get the match ratio of how each item in texts compared to match_against" # now time to figre out the matching ratio_calc = difflib.SequenceMatcher() ratio_calc.set_seq1(match_against) ratios = {} best_ratio = 0 best_text = '' global cache for text in texts: if 0: pass if (text, match_against) in _cache: ratios[text] = _cache[(text, match_against)] elif(match_against, text) in _cache: ratios[text] = _cache[(match_against, text)] else: # set up the SequenceMatcher with other text ratio_calc.set_seq2(text) # try using the levenshtein distance instead #lev_dist = levenshtein_distance(unicode(match_against), unicode(text)) #ratio = 1 - lev_dist / 10.0 #ratios[text] = ratio # calculate ratio and store it ratios[text] = ratio_calc.ratio() _cache[(match_against, text)] = ratios[text] # if this is the best so far then update best stats if ratios[text] > best_ratio: best_ratio = ratios[text] best_text = text return ratios, best_ratio, best_text #==================================================================== def find_best_match(search_text, item_texts, items, limit_ratio = .5): """Return the item that best matches the search_text * **search_text** The text to search for * **item_texts** The list of texts to search through * **items** The list of items corresponding (1 to 1) to the list of texts to search through. * **limit_ratio** How well the text has to match the best match. If the best match matches lower then this then it is not considered a match and a MatchError is raised, (default = .5) """ search_text = _cut_at_tab(search_text) text_item_map = UniqueDict() # Clean each item, make it unique and map to # to the item index for text, item in zip(item_texts, items): text_item_map[_cut_at_tab(text)] = item ratios, best_ratio, best_text = \ _get_match_ratios(list(text_item_map.keys()), search_text) if best_ratio < limit_ratio: raise MatchError(items = list(text_item_map.keys()), tofind = search_text) return text_item_map[best_text] #==================================================================== _after_tab = re.compile(r"\t.*", re.UNICODE) _non_word_chars = re.compile(r"\W", re.UNICODE) def _cut_at_tab(text): "Clean out non characters from the string and return it" # remove anything after the first tab return _after_tab.sub("", text) def _clean_non_chars(text): "Remove non word characters" # should this also remove everything after the first tab? # remove non alphanumeric characters return _non_word_chars.sub("", text) def IsAboveOrToLeft(ref_control, other_ctrl): "Return true if the other_ctrl is above or to the left of ref_control" text_r = other_ctrl.Rectangle() ctrl_r = ref_control.Rectangle() # skip controls where text win is to the right of ctrl if text_r.left >= ctrl_r.right: return False # skip controls where text win is below ctrl if text_r.top >= ctrl_r.bottom: return False # text control top left corner is below control # top left corner - so not to the above or left :) if text_r.top >= ctrl_r.top and text_r.left >= ctrl_r.left: return False return True #==================================================================== distance_cuttoff = 999 def GetNonTextControlName(ctrl, controls): """return the name for this control by finding the closest text control above and to its left""" names = [] ctrl_index = controls.index(ctrl) if ctrl_index != 0: prev_ctrl = controls[ctrl_index-1] if prev_ctrl.FriendlyClassName() == "Static" and \ prev_ctrl.IsVisible() and prev_ctrl.WindowText() and \ IsAboveOrToLeft(ctrl, prev_ctrl): names.append( prev_ctrl.WindowText() + ctrl.FriendlyClassName()) # get the visible text controls so that we can get # the closest text if the control has no text text_ctrls = [ctrl_ for ctrl_ in controls if ctrl_.IsVisible() and ctrl_.WindowText() and ctrl_.can_be_label] best_name = '' closest = distance_cuttoff # now for each of the visible text controls for text_ctrl in text_ctrls: # get aliases to the control rectangles text_r = text_ctrl.Rectangle() ctrl_r = ctrl.Rectangle() # skip controls where text win is to the right of ctrl if text_r.left >= ctrl_r.right: continue # skip controls where text win is below ctrl if text_r.top >= ctrl_r.bottom: continue # calculate the distance between the controls # at first I just calculated the distance from the top let # corner of one control to the top left corner of the other control # but this was not best, so as a text control should either be above # or to the left of the control I get the distance between # the top left of the non text control against the # Top-Right of the text control (text control to the left) # Bottom-Left of the text control (text control above) # then I get the min of these two # We do not actually need to calculate the difference here as we # only need a comparative number. As long as we find the closest one # the actual distance is not all that important to us. # this reduced the unit tests run on my by about 1 second # (from 61 ->60 s) # (x^2 + y^2)^.5 #distance = ( # (text_r.left - ctrl_r.left) ** 2 + # (x^2 + y^2) # (text_r.bottom - ctrl_r.top) ** 2) \ # ** .5 # ^.5 #distance2 = ( # (text_r.right - ctrl_r.left) ** 2 + # (x^2 + y^2) # (text_r.top - ctrl_r.top) ** 2) \ # ** .5 # ^.5 distance = abs(text_r.left - ctrl_r.left) + abs(text_r.bottom - ctrl_r.top) distance2 = abs(text_r.right - ctrl_r.left) + abs(text_r.top - ctrl_r.top) distance = min(distance, distance2) # if this distance was closer then the last one if distance < closest: closest = distance best_name = text_ctrl.WindowText() + ctrl.FriendlyClassName() names.append(best_name) return names #==================================================================== def get_control_names(control, allcontrols): "Returns a list of names for this control" names = [] # if it has a reference control - then use that #if hasattr(control, 'ref') and control.ref: # control = control.ref # Add the control based on it's friendly class name names.append(control.FriendlyClassName()) # if it has some character text then add it base on that # and based on that with friendly class name appended cleaned = control.WindowText() # Todo - I don't like the hardcoded classnames here! if cleaned and control.has_title: names.append(cleaned) names.append(cleaned + control.FriendlyClassName()) # it didn't have visible text else: # so find the text of the nearest text visible control non_text_names = GetNonTextControlName(control, allcontrols) # and if one was found - add it if non_text_names: names.extend(non_text_names) # return the names - and make sure there are no duplicates return set(names) #==================================================================== class UniqueDict(dict): "A dictionary subclass that handles making it's keys unique" def __setitem__(self, text, item): "Set an item of the dictionary" # this text is already in the map # so we need to make it unique if text in self: # find next unique text after text1 unique_text = text counter = 2 while unique_text in self: unique_text = text + str(counter) counter += 1 # now we also need to make sure the original item # is under text0 and text1 also! if text + '0' not in self: dict.__setitem__(self, text+'0', self[text]) dict.__setitem__(self, text+'1', self[text]) # now that we don't need original 'text' anymore # replace it with the uniq text text = unique_text # add our current item dict.__setitem__(self, text, item) def FindBestMatches( self, search_text, clean = False, ignore_case = False): """Return the best matches for search_text in the items * **search_text** the text to look for * **clean** whether to clean non text characters out of the strings * **ignore_case** compare strings case insensitively """ # now time to figure out the matching ratio_calc = difflib.SequenceMatcher() if ignore_case: search_text = search_text.lower() ratio_calc.set_seq1(search_text) ratios = {} best_ratio = 0 best_texts = [] ratio_offset = 1 if clean: ratio_offset *= .9 if ignore_case: ratio_offset *= .9 for text_ in self: # make a copy of the text as we need the original later text = text_ if clean: text = _clean_non_chars(text) if ignore_case: text = text.lower() # check if this item is in the cache - if yes, then retrieve it if (text, search_text) in _cache: ratios[text_] = _cache[(text, search_text)] elif(search_text, text) in _cache: ratios[text_] = _cache[(search_text, text)] # not in the cache - calculate it and add it to the cache else: # set up the SequenceMatcher with other text ratio_calc.set_seq2(text) # if a very quick check reveals that this is not going # to match then ratio = ratio_calc.real_quick_ratio() * ratio_offset if ratio >= find_best_control_match_cutoff: ratio = ratio_calc.quick_ratio() * ratio_offset if ratio >= find_best_control_match_cutoff: ratio = ratio_calc.ratio() * ratio_offset # save the match we got and store it in the cache ratios[text_] = ratio _cache[(text, search_text)] = ratio # try using the levenshtein distance instead #lev_dist = levenshtein_distance(unicode(search_text), unicode(text)) #ratio = 1 - lev_dist / 10.0 #ratios[text_] = ratio #print "%5s" %("%0.2f"% ratio), search_text, `text` # if this is the best so far then update best stats if ratios[text_] > best_ratio and \ ratios[text_] >= find_best_control_match_cutoff: best_ratio = ratios[text_] best_texts = [text_] elif ratios[text_] == best_ratio: best_texts.append(text_) #best_ratio *= ratio_offset return best_ratio, best_texts #==================================================================== def build_unique_dict(controls): """Build the disambiguated list of controls Separated out to a different function so that we can get the control identifiers for printing. """ name_control_map = UniqueDict() # collect all the possible names for all controls # and build a list of them for ctrl in controls: ctrl_names = get_control_names(ctrl, controls) # for each of the names for name in ctrl_names: name_control_map[name] = ctrl return name_control_map #==================================================================== def find_best_control_matches(search_text, controls): """Returns the control that is the the best match to search_text This is slightly differnt from find_best_match in that it builds up the list of text items to search through using information from each control. So for example for there is an OK, Button then the following are all added to the search list: "OK", "Button", "OKButton" But if there is a ListView (which do not have visible 'text') then it will just add "ListView". """ name_control_map = build_unique_dict(controls) # # collect all the possible names for all controls # # and build a list of them # for ctrl in controls: # ctrl_names = get_control_names(ctrl, controls) # # # for each of the names # for name in ctrl_names: # name_control_map[name] = ctrl search_text = str(search_text) best_ratio, best_texts = name_control_map.FindBestMatches(search_text) best_ratio_ci, best_texts_ci = \ name_control_map.FindBestMatches(search_text, ignore_case = True) best_ratio_clean, best_texts_clean = \ name_control_map.FindBestMatches(search_text, clean = True) best_ratio_clean_ci, best_texts_clean_ci = \ name_control_map.FindBestMatches( search_text, clean = True, ignore_case = True) if best_ratio_ci > best_ratio: best_ratio = best_ratio_ci best_texts = best_texts_ci if best_ratio_clean > best_ratio: best_ratio = best_ratio_clean best_texts = best_texts_clean if best_ratio_clean_ci > best_ratio: best_ratio = best_ratio_clean_ci best_texts = best_texts_clean_ci if best_ratio < find_best_control_match_cutoff: raise MatchError(items = list(name_control_map.keys()), tofind = search_text) return [name_control_map[best_text] for best_text in best_texts] # #def GetControlMatchRatio(text, ctrl): # # get the texts for the control # ctrl_names = get_control_names(ctrl) # # #get the best match for these # matcher = UniqueDict() # for name in ctrl_names: # matcher[name] = ctrl # # best_ratio, unused = matcher.FindBestMatches(text) # # return best_ratio # # # #def get_controls_ratios(search_text, controls): # name_control_map = UniqueDict() # # # collect all the possible names for all controls # # and build a list of them # for ctrl in controls: # ctrl_names = get_control_names(ctrl) # # # for each of the names # for name in ctrl_names: # name_control_map[name] = ctrl # # match_ratios, best_ratio, best_text = \ # _get_match_ratios(name_control_map.keys(), search_text) # # return match_ratios, best_ratio, best_text,

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null

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cde9443d5f9dce44149feca0d10e665a2fbcf090

1,074

py

Python

setup.py

boichee/fabricator

33ad4fa615c153817b014d6b7fe9807f1752db25

[ "MIT" ]

11

2018-07-09T07:08:16.000Z

2018-07-13T14:05:46.000Z

setup.py

boichee/fabricator

33ad4fa615c153817b014d6b7fe9807f1752db25

[ "MIT" ]

3

2020-03-24T17:37:47.000Z

2021-02-02T22:18:59.000Z

setup.py

boichee/fabricator

33ad4fa615c153817b014d6b7fe9807f1752db25

[ "MIT" ]

null

from setuptools import setup, find_packages exclude_dirs = ['ez_setup', 'examples', 'tests', 'venv'] # Runtime requirements reqs = [ 'requests', 'six', 'future', 'aenum' ] # Requirements for testing test_reqs = ['pytest', 'hypothesis', 'requests_mock'] # Requirements for setup setup_reqs = ['flake8', 'pep8', 'pytest-runner'] setup( name='fabricate-it', version='1.1.0', author='Brett Levenson', author_email='blevenson@apple.com', description='A library that makes creating API clients simple and declarative', url='https://github.com/boichee/fabricator', packages=find_packages(exclude=exclude_dirs), install_requires=reqs, tests_require=test_reqs, setup_requires=setup_reqs, classifiers=[ 'Development Status :: 4 - Beta', 'Operating System :: MacOS :: MacOS X', 'Operating System :: POSIX', 'Topic :: Software Development', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3.6', 'Intended Audience :: Developers' ] )

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0

cde9dfcf27b3e92945a09440ebd5cd1eb09e8452

12,607

py

Python

src/gan/ccgan/ccGAN.py

matkir/Master_programs

70c4c399f9c9fc3e1643e78694223b24d7b94b18

[ "MIT" ]

null

src/gan/ccgan/ccGAN.py

matkir/Master_programs

70c4c399f9c9fc3e1643e78694223b24d7b94b18

[ "MIT" ]

null

src/gan/ccgan/ccGAN.py

matkir/Master_programs

70c4c399f9c9fc3e1643e78694223b24d7b94b18

[ "MIT" ]

null

from __future__ import print_function, division if __name__=='__main__': from cc_weights import Weight_model else: from . import Weight_model from keras.models import load_model import keras.backend as K import plotload import sys from selector import Selector #from masker import mask_from_template,mask_randomly_square,mask_green_corner,combine_imgs_with_mask import masker as ms import matplotlib.pyplot as plt import numpy as np from tqdm import tqdm import cutter import masker class CCgan(): def __init__(self,img_cols,img_rows): """ Initializes the autoencoder. """ self.set_training_info() globals().update(self.info) self.threshold=threshold self.img_cols = img_cols # Original is ~576 self.img_rows = img_rows # Original is ~720 self.channels = 3 # RGB self.img_shape=(self.img_cols,self.img_rows,self.channels) if not mask: dummy=plotload.load_polyp_batch(self.img_shape,20,data_type='med/stool-inclusions',crop=False) self.dims =cutter.find_square_coords(dummy) self.combined=None self.discriminator=None self.generator=None self.pretrained=False def load_model(self): """ loads a model to the object instead of creating one. :param adress: string of adress to the file of type h5. """ if self.combined!=None: print("Warning: overriding a loaded model") self.generator=load_model(f"models/CCgan-gen-{self.img_shape[0]}-{self.img_shape[1]}-{'c' if corner else 'n'}.h5") self.discriminator=load_model(f"models/CCgan-dic-{self.img_shape[0]}-{self.img_shape[1]}-{'c' if corner else 'n'}.h5") self.combined=load_model(f"models/CCgan-com-{self.img_shape[0]}-{self.img_shape[1]}-{'c' if corner else 'n'}.h5") def load_model_weights(self): if self.combined==None: print("Error: no model in object") else: try: self.combined.load_weights(f"models/CCgan-{self.img_shape[0]}-{self.img_shape[1]}-{'c' if corner else 'n'}-w-com.h5") self.discriminator.load_weights(f"models/CCgan-{self.img_shape[0]}-{self.img_shape[1]}-{'c' if corner else 'n'}-w-dis.h5") self.generator.load_weights(f"models/CCgan-{self.img_shape[0]}-{self.img_shape[1]}-{'c' if corner else 'n'}-w-gen.h5") self.pretrained=True except e: print("Error: weights could not be loaded") print(e) def build_model(self): """ builds a model to the object instead of loading one. Uses AE_weights.py as model """ if self.combined!=None: print("Warning: overriding a loaded model") wm=Weight_model(self.img_cols,self.img_rows) self.discriminator,self.generator,self.combined=wm.build_model() def set_training_info(self): self.info={} import sys try: if len(sys.argv)==1: choise=2 else: choise=int(input("press 1 for last run or 2 for info.txt ")) except: choise=False if choise==1: self.info=np.load("temp_info.npy").item() return elif choise==2: with open("info.txt") as f: for line in f: (key, val) = line.split() try: self.info[key] = int(val) except: self.info[key] = float(val) np.save("temp_info.npy", self.info) return else: self.info["mask"]=int(input("Mask [1] or corner [0]? ")) if self.info['mask']==1: tmp=input("Mask adress? (default: /masks) ") self.info["mask_folder"]=tmp if isinstance(tmp, str) else "/masks" self.info["epochs"]=int(input("Number of epochs? ")) self.info["batch_size"]=int(input("Batch size? ")) self.info["save_interval"]=int(input("save interval? ")) np.save("temp_info.npy", self.info) def train_model(self): def t(m,bol): for layer in m.layers: layer.trainable=bol if self.info==None: print("Warning no info found, prompting for info") self.set_training_info() globals().update(self.info) if self.combined==None: print("Error: no model loaded") return if self.pretrained==True: print("Warning: model has pretrained weights") half_batch = batch_size for epoch in tqdm(range(epochs)): X_train = plotload.load_polyp_batch(self.img_shape, batch_size, data_type='med/none',crop=False) if corner: masked_imgs, missing, mask = ms.mask_green_corner(X_train) m=np.zeros(shape=X_train.shape) for i in range(X_train.shape[0]): m[i,mask[0]:mask[1],mask[2]:mask[3]]=missing[i] missing=m else: masked_imgs, missing, mask = ms.mask_from_template(X_train) if soft: valid = 0.2*np.random.random_sample((half_batch,1))+0.9 fake = 0.1*np.random.random_sample((half_batch,1)) else: valid = np.ones((half_batch, 1)) fake = np.zeros((half_batch, 1)) # --------------------- # Train Generator # --------------------- valid = np.ones((batch_size, 1)) # Train the generator t(self.discriminator,False) g_loss = self.combined.train_on_batch(masked_imgs, [X_train, valid]) t(self.discriminator,True) # --------------------- # Train discriminator # --------------------- gen_fake = self.generator.predict(masked_imgs) gen_fake = ms.combine_imgs_with_mask(gen_fake, X_train, mask) if epoch%120==0 and epoch!=0: #small shakeup to get out of local minimas fake, valid = valid , fake # Train the discriminator d_loss_real = self.discriminator.train_on_batch(X_train, valid) d_loss_fake = self.discriminator.train_on_batch(gen_fake, fake) d_loss = 0.5 * np.add(d_loss_real, d_loss_fake) # Plot the progress print ("[D: %f G: %f, mse: %f]" % (d_loss[0], g_loss[0], g_loss[1])) if g_loss[1]<self.threshold: self.threshold=g_loss[1] self.generator.save(f"models/CCgan-gen-{self.img_shape[0]}-{self.img_shape[1]}-{'c' if corner else 'n'}.h5") self.discriminator.save(f"models/CCgan-dic-{self.img_shape[0]}-{self.img_shape[1]}-{'c' if corner else 'n'}.h5") self.combined.save(f"models/CCgan-com-{self.img_shape[0]}-{self.img_shape[1]}-{'c' if corner else 'n'}.h5") self.combined.save_weights(f"models/CCgan-{self.img_shape[0]}-{self.img_shape[1]}-{'c' if corner else 'n'}-w-com.h5") self.discriminator.save_weights(f"models/CCgan-{self.img_shape[0]}-{self.img_shape[1]}-{'c' if corner else 'n'}-w-dis.h5") self.generator.save_weights(f"models/CCgan-{self.img_shape[0]}-{self.img_shape[1]}-{'c' if corner else 'n'}-w-gen.h5") if g_loss[1]<self.threshold: self.threshold=g_loss[1] self.generator.save(f"models/CCgan-gen-{self.img_shape[0]}-{self.img_shape[1]}-{'c' if corner else 'n'}_fin.h5") self.discriminator.save(f"models/CCgan-dic-{self.img_shape[0]}-{self.img_shape[1]}-{'c' if corner else 'n'}_fin.h5") self.combined.save(f"models/CCgan-com-{self.img_shape[0]}-{self.img_shape[1]}-{'c' if corner else 'n'}_fin.h5") self.combined.save_weights(f"models/CCgan-{self.img_shape[0]}-{self.img_shape[1]}-{'c' if corner else 'n'}-w-com_fin.h5") self.discriminator.save_weights(f"models/CCgan-{self.img_shape[0]}-{self.img_shape[1]}-{'c' if corner else 'n'}-w-dis_fin.h5") self.generator.save_weights(f"models/CCgan-{self.img_shape[0]}-{self.img_shape[1]}-{'c' if corner else 'n'}-w-gen_fin.h5") def build_wrapper(self): """ Returns a func that works as a complete preprocsess tool """ if mask==1: def ret(input_img,mask=None): """ Without a corner, a mask must be added """ if not cutter.is_green(input_img): return input_img if mask is None: mask=plotload.load_single_template(input_img.shape,dest='med/green') img=input_img.copy() if len(img.shape)==3: img=np.expand_dims(img, 0) prediced=np.squeeze(self.generator.predict(img),0) img=masker.combine_imgs_with_mask(prediced, img, mask) return np.expand_dims(img,0) else: def ret(input_img): if not cutter.is_green(input_img): return input_img img=input_img.copy() if len(img.shape)==3: img=np.expand_dims(img, 0) y1,y2,x1,x2=self.dims img, _, _ = ms.mask_green_corner(img) prediced=np.squeeze(self.generator.predict(img),0) img=np.squeeze(img,0) img[y1:y2,x1:x2]=prediced[y1:y2,x1:x2] return np.expand_dims(img,0) return ret def sample_images(self, epoch, imgs): r, c = 3, 6 masked_imgs, missing_parts, m = mask_from_template(imgs) gen_fake1 = self.generator.predict(missing_parts) gen_fake = combine_imgs_with_mask(gen_fake1, imgs, m) imgs = 0.5 * imgs + 0.5 masked_imgs = 0.5 * masked_imgs + 0.5 gen_fake = 0.5 * gen_fake + 0.5 gen_fake1 = 0.5 * gen_fake1 + 0.5 fig, axs = plt.subplots(r, c) for i in range(c): axs[0,i].imshow(imgs[i, :,:]) axs[0,i].axis('off') axs[1,i].imshow(gen_fake[i, :,:]) axs[1,i].axis('off') axs[2,i].imshow(gen_fake1[i,:,:]) axs[2,i].axis('off') fig.savefig("images/cc_%d.png" % epoch) plt.close() def sort_folder(self,w,path=None): import os import cv2 from tqdm import tqdm from shutil import copyfile import sys if path is not None: dirs_i=[] dirs_o=[] d=next(os.walk(path))[1] for i in d: if i =='none' or i=='green' or i=='preprocessed': continue dirs_o.append(path+'preprocessed/'+i) dirs_i.append(path+i) for i in dirs_o: if not os.path.exists(i): os.makedirs(i) else: polyps='polyps' ulcerative_colitis='ulcerative-colitis' dirs=[polyps,ulcerative_colitis] if not os.path.exists(polyps_prep): os.makedirs(polyps_prep) if not os.path.exists(ulcerative_colitis_prep): os.makedirs(ulcerative_colitis_prep) for i,o in tqdm(zip(dirs_i,dirs_o)): for img_name in os.listdir(i): path=os.path.join(i,img_name) img=plotload.load_one_img((self.img_cols,self.img_rows), dest=path, extra_dim=True) if cutter.is_green(img): tmp=cv2.imwrite(os.path.join(o,img_name), cv2.cvtColor(127.5*w(img)[0]+127.5,cv2.COLOR_RGB2BGR)) else: tmp=cv2.imwrite(os.path.join(o,img_name), cv2.cvtColor(127.5*img[0]+127.5,cv2.COLOR_RGB2BGR)) if __name__ == '__main__': cc = CCgan(256,256) #cc.build_model() #cc.train_model() cc.load_model() #cc.load_model_weights() w=cc.build_wrapper() root='/home/mathias/Documents/kvasir-dataset-v2/med/' cc.sort_folder(w,path=root) cc.sort_folder(w,path='/media/mathias/A_New_Hope/medico_test/')

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