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paulo037
/
starcoder

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insights/parsers/tests/test_zipl_conf.py
lhuett/insights-core
121
14053
<filename>insights/parsers/tests/test_zipl_conf.py from insights.parsers.zipl_conf import ZiplConf from insights.tests import context_wrap from insights.parsers import ParseException import pytest ZIPL_CONF = """ [defaultboot] defaultauto prompt=1 timeout=5 default=linux target=/boot [linux] image=/boot/vmlinuz-3.10.0-693.el7.s390x ramdisk=/boot/initramfs-3.10.0-693.el7.s390x.img parameters="root=/dev/mapper/rhel_gss5-root crashkernel=auto rd.dasd=0.0.0100 rd.dasd=0.0.0101 rd.dasd=0.0.0102 rd.lvm.lv=rhel_gss5/root rd.lvm.lv=rhel_gss5/swap net.ifnames=0 rd.znet=qeth,0.0.0600,0.0.0601,0.0.0602,layer2=0,portname=gss5,portno=0 LANG=en_US.UTF-8" [linux-0-rescue-a27932c8d57248e390cee3798bbd3709] image=/boot/vmlinuz-0-rescue-a27932c8d57248e390cee3798bbd3709 ramdisk=/boot/initramfs-0-rescue-a27932c8d57248e390cee3798bbd3709.img parameters="root=/dev/mapper/rhel_gss5-root crashkernel=auto rd.dasd=0.0.0100 rd.dasd=0.0.0101 rd.dasd=0.0.0102 rd.lvm.lv=rhel_gss5/root rd.lvm.lv=rhel_gss5/swap net.ifnames=0 rd.znet=qeth,0.0.0600,0.0.0601,0.0.0602,layer2=0,portname=gss5,portno=0" [other] image=/boot/vmlinuz ramdisk=/boot/initramfs.img parameters="root=/dev/mapper/rhel_gss5-root crashkernel=auto rd.dasd=0.0.0100 # Configuration for dumping to SCSI disk # Separate IPL and dump partitions [dumpscsi] target=/boot dumptofs=/dev/sda2 parameters="dump_dir=/mydumps dump_compress=none dump_mode=auto" # Menu containing two DASD boot configurations :menu1 1=linux 2=linux-0-rescue-a27932c8d57248e390cee3798bbd3709 default=1 prompt=1 timeout=30 """.strip() ZIPL_CONF_INVALID = """ prompt=1 timeout=5 default=linux [linux] image=/boot/vmlinuz-3.10.0-693.el7.s390x ramdisk=/boot/initramfs-3.10.0-693.el7.s390x.img parameters="root=/dev/mapper/rhel_gss5-root crashkernel=auto rd.dasd=0.0.0100 rd.dasd=0.0.0101 rd.dasd=0.0.0102 rd.lvm.lv=rhel_gss5/root rd.lvm.lv=rhel_gss5/swap net.ifnames=0 rd.znet=qeth,0.0.0600,0.0.0601,0.0.0602,layer2=0,portname=gss5,portno=0 LANG=en_US.UTF-8" """.strip() def test_zipl_conf(): res = ZiplConf(context_wrap(ZIPL_CONF)) assert res.get('linux').get('image') == "/boot/vmlinuz-3.10.0-693.el7.s390x" assert res['linux']['image'] == "/boot/vmlinuz-3.10.0-693.el7.s390x" assert res[':menu1']['1'] == 'linux' assert 'defaultauto' in res['defaultboot'] assert res['defaultboot']['defaultauto'] is True assert res['other']['parameters'] == '"root=/dev/mapper/rhel_gss5-root crashkernel=auto rd.dasd=0.0.0100' assert res.images == { 'linux': '/boot/vmlinuz-3.10.0-693.el7.s390x', 'linux-0-rescue-a27932c8d57248e390cee3798bbd3709': '/boot/vmlinuz-0-rescue-a27932c8d57248e390cee3798bbd3709', 'other': '/boot/vmlinuz' } assert res.dumptofses == {'dumpscsi': '/dev/sda2'} def test_zipl_conf_invalid(): with pytest.raises(ParseException) as pe: ZiplConf(context_wrap(ZIPL_CONF_INVALID)) assert "Invalid zipl configuration file is found." in str(pe)
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server.py
jalqueza/CMPUT404-assignment-webserver
0
75600
<reponame>jalqueza/CMPUT404-assignment-webserver<filename>server.py # coding: utf-8 import socketserver, os # Copyright 2013 <NAME>, <NAME> # Copyright 2021 <NAME> # # 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. # # # Furthermore it is derived from the Python documentation examples thus # some of the code is Copyright © 2001-2013 Python Software # Foundation; All Rights Reserved # # http://docs.python.org/2/library/socketserver.html # # run: python freetests.py # try: curl -v -X GET http://127.0.0.1:8080/ class MyWebServer(socketserver.BaseRequestHandler): def handle(self): self.data = self.request.recv(1024).strip() print ("Got a request of: %s\n" % self.data) request = self.data.decode('utf-8').split() if len(request) < 2: self.error_404() method = request[0] url = request[1] if method != "GET": self.error_405() else: self.GET(url) def GET(self, url): if "../" in url: return self.error_404() url = "www" + url if url[-1] == "/": url = url + "index.html" if os.path.isdir(url): return self.error_301(url) elif os.path.exists(url): return self.success_200(url) else: return self.error_404() def success_200(self, url): resource = open(url).read() content_type = "text/html; encoding=uft-8" if url.endswith('.html') else "text/css; encoding=utf-8" response = "HTTP/1.1 200 OK\r\n" + f'Content-Type: {content_type}\r\n\r\n' + f'{resource}\r\n' self.request.sendall(response.encode('utf-8')) def error_405(self): response = "HTTP/1.1 405 Method Not Allowed\r\n" self.request.sendall(response.encode('utf-8')) def error_404(self): response = "HTTP/1.1 404 Not Found\r\n" self.request.sendall(response.encode('utf-8')) def error_301(self, url): response = f'HTTP/1.1 301 Moved Permanently \r\nLocation: {url[3:]}/\r\n' self.request.sendall(response.encode('utf-8')) if __name__ == "__main__": HOST, PORT = "localhost", 8080 socketserver.TCPServer.allow_reuse_address = True # Create the server, binding to localhost on port 8080 server = socketserver.TCPServer((HOST, PORT), MyWebServer) # Activate the server; this will keep running until you # interrupt the program with Ctrl-C server.serve_forever()
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gatsby/urls.py
Simple2B/askew12
0
1606590
from django.contrib import admin from django.conf import settings from django.conf.urls.static import static from django.urls import path, include from django.views.generic import TemplateView from rest_framework import routers from api import views router = routers.DefaultRouter() router.register(r'samples', views.CodingSampleView, 'sample') urlpatterns = [ path('admin/', admin.site.urls), path('api/', include(router.urls)), path('', TemplateView.as_view(template_name='index.html')), ] if settings.DEBUG: urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)
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tripoli/resource_validators/manifest_validator.py
DDMAL/tripoli
18
80355
# Copyright (c) 2016 <NAME>, <NAME> # 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. from collections import OrderedDict from .base_validator import BaseValidator class ManifestValidator(BaseValidator): VIEW_DIRS = ['left-to-right', 'right-to-left', 'top-to-bottom', 'bottom-to-top'] VIEW_HINTS = ['individuals', 'paged', 'continuous'] KNOWN_FIELDS = BaseValidator.COMMON_FIELDS | {"viewingDirection", "navDate", "sequences", "structures", "@context"} FORBIDDEN_FIELDS = {"format", "height", "width", "startCanvas", "first", "last", "total", "next", "prev", "startIndex", "collections", "manifests", "members", "canvases", "resources", "otherContent", "images", "ranges"} REQUIRED_FIELDS = {"label", "@context", "@id", "@type", "sequences"} RECOMMENDED_FIELDS = {"metadata", "description", "thumbnail"} def __init__(self, iiif_validator): super().__init__(iiif_validator) self.ManifestSchema = OrderedDict(( ('@context', self.context_field), ('structures', self.structures_field), ('sequences', self.sequences_field), ('viewingDirection', self.viewing_dir_field), )) self.setup() def _run_validation(self, **kwargs): self._check_all_key_constraints("manifest", self._json) return self._compare_dicts(self.ManifestSchema, self._json) def type_field(self, value): """Assert that ``@type`` == ``sc:Manifest``. """ if not value == 'sc:Manifest': self.log_error("@type", "@type must be 'sc:Manifest'.") return value def context_field(self, value): """Assert that ``@context`` is the IIIF 2.0 presentation API.""" if isinstance(value, str): if not value == self.PRESENTATION_API_URI: self.log_error("@context", "'@context' must be set to '{}'".format(self.PRESENTATION_API_URI)) if isinstance(value, list): if self.PRESENTATION_API_URI not in value: self.log_error("@context", "'@context' must be set to '{}'".format(self.PRESENTATION_API_URI)) return value def structures_field(self, value): """Validate the ``structures`` field.""" return value def sequences_field(self, value): """Validate ``sequences`` list for Manifest. Checks that at least 1 sequence is embedded. """ if not isinstance(value, list): self.log_error("sequences", "'sequences' MUST be a list") return value if len(value) == 0: self.log_error("sequences", "Manifest requires at least one sequence") return value results = [] path = self._path + ("sequences",) for i, seq in enumerate(value): temp_path = path + i if i == 0: results.append(self._sub_validate(self.SequenceValidator, seq, temp_path, emb=True)) else: results.append(self._sub_validate(self.SequenceValidator, seq, temp_path, emb=False)) return results
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chopsticks/bubble.py
gitter-badger/chopsticks
0
184457
from __future__ import print_function import sys sys.path = [p for p in sys.path if p.startswith('/')] __name__ = '__bubble__' sys.modules[__name__] = sys.modules.pop('__main__') def debug(msg): print(msg, file=sys.stderr) # Reshuffle fds so that we can't break our transport by printing to stdout import os infd = os.dup(0) outfd = os.dup(1) inpipe = os.fdopen(infd, 'rb') outpipe = os.fdopen(outfd, 'wb', 0) sys.stdin.close() sys.stdin = open(os.devnull, 'rb') sys.stdout.close() sys.stdout = open(os.devnull, 'wb') PY2 = sys.version_info < (3,) PY3 = not PY2 import threading if PY2: __metaclass__ = type from Queue import Queue import cPickle as pickle def exec_(_code_, _globs_=None, _locs_=None): """Execute code in a namespace.""" if _globs_ is None: frame = sys._getframe(1) _globs_ = frame.f_globals if _locs_ is None: _locs_ = frame.f_locals del frame elif _locs_ is None: _locs_ = _globs_ exec("""exec _code_ in _globs_, _locs_""") else: from queue import Queue import pickle exec_ = getattr(__builtins__, 'exec') import json import struct import imp from collections import namedtuple import signal from hashlib import sha1 import traceback outqueue = Queue(maxsize=10) tasks = Queue() done = object() running = True Imp = namedtuple('Imp', 'exists is_pkg file source') PREFIX = 'controller://' class Loader: cache = {} lock = threading.RLock() ev = threading.Condition(lock) def __init__(self, path): if not path.startswith(PREFIX): raise ImportError() self.path = path @classmethod def on_receive(cls, mod, imp): with cls.lock: cls.cache[mod] = imp cls.ev.notifyAll() def get(self, fullname): with self.lock: if fullname in self.cache: return self.cache[fullname] send_msg(OP_IMP, 0, {'imp': fullname}) while True: self.ev.wait() try: imp = self.cache[fullname] except KeyError: continue else: break if not imp.exists: raise ImportError() return imp def find_module(self, fullname, path=None): try: self.get(fullname) except ImportError: return None return self def load_module(self, fullname): m = self.get(fullname) modname = fullname if fullname == '__main__': # Special-case __main__ so as not to execute # if __name__ == '__main__' blocks modname = '__chopsticks_main__' mod = sys.modules.setdefault(modname, imp.new_module(modname)) mod.__file__ = PREFIX + m.file mod.__loader__ = self if m.is_pkg: mod.__path__ = [PREFIX + m.file] mod.__package__ = modname else: mod.__package__ = modname.rpartition('.')[0] code = compile(m.source, mod.__file__, 'exec', dont_inherit=True) exec(code, mod.__dict__) if fullname == '__main__': mod.__name__ == '__main__' sys.modules[fullname] = mod return mod def is_package(self, fullname): return self.get(fullname).is_pkg def get_source(self, fullname): return self.get(fullname).source sys.path.append(PREFIX) sys.path_hooks.append(Loader) def transmit_errors(func): def wrapper(req_id, *args, **kwargs): try: return func(req_id, *args, **kwargs) except: send_msg(OP_EXC, req_id, {'tb': traceback.format_exc()}) return wrapper @transmit_errors def handle_call_threaded(req_id, data): threading.Thread(target=handle_call_thread, args=(req_id, data)).start() @transmit_errors def handle_call_thread(req_id, data): callable, args, kwargs = pickle.loads(data) do_call(req_id, callable, args, kwargs) @transmit_errors def handle_call_queued(req_id, data): callable, args, kwargs = pickle.loads(data) tasks.put((req_id, callable, args, kwargs)) OP_CALL = 0 OP_RET = 1 OP_EXC = 2 OP_IMP = 3 OP_FETCH_BEGIN = 4 OP_FETCH_DATA = 5 OP_FETCH_END = 6 OP_PUT_BEGIN = 7 OP_PUT_DATA = 8 OP_PUT_END = 9 # FIXME: handle_call_queued seems to deadlock! handle_call = handle_call_threaded @transmit_errors def handle_fetch(req_id, path): """Fetch a file by path.""" tasks.put((req_id, do_fetch, (req_id, path,))) def do_fetch(req_id, path): """Send chunks of a file to the orchestration host.""" h = sha1() with open(path, 'rb') as f: while True: chunk = f.read(10240) if not chunk: break h.update(chunk) send_msg(OP_FETCH_DATA, req_id, chunk) send_msg( OP_RET, req_id, {'ret': { 'remote_path': os.path.abspath(path), 'sha1sum': h.hexdigest(), }} ) @transmit_errors def do_call(req_id, callable, args=(), kwargs={}): ret = callable(*args, **kwargs) send_msg(OP_RET, req_id, {'ret': ret}) def handle_imp(req_id, mod, exists, is_pkg, file, source): Loader.on_receive(mod, Imp(exists, is_pkg, file, source)) active_puts = {} @transmit_errors def handle_begin_put(req_id, path, mode): prev_umask = os.umask(0o077) try: if path is None: import tempfile f = tempfile.NamedTemporaryFile(delete=False) path = wpath = f.name else: if os.path.isdir(path): raise IOError('%s is a directory' % path) wpath = path + '~chopsticks-tmp' f = open(wpath, 'wb') finally: os.umask(prev_umask) os.fchmod(f.fileno(), mode) active_puts[req_id] = (f, wpath, path, sha1()) @transmit_errors def handle_put_data(req_id, data): f, wpath, path, cksum = active_puts[req_id] try: cksum.update(data) f.write(data) except: try: os.unlink(wpath) f.close() except OSError: pass raise class ChecksumMismatch(Exception): pass @transmit_errors def handle_end_put(req_id, sha1sum): f, wpath, path, cksum = active_puts.pop(req_id) received = f.tell() f.close() digest = cksum.hexdigest() if digest != sha1sum: try: os.unlink(wpath) except OSError: pass raise ChecksumMismatch('Checksum failed for transfer %s' % path) if wpath != path: os.rename(wpath, path) send_msg( OP_RET, req_id, {'ret': { 'remote_path': os.path.abspath(path), 'sha1sum': digest, 'size': received }} ) HEADER = struct.Struct('!LLbb') MSG_JSON = 0 MSG_BYTES = 1 def send_msg(op, req_id, data): """Send a message to the orchestration host. We can send either bytes or JSON-encoded structured data; the opcode will determine which. """ if isinstance(data, dict): data = json.dumps(data) if not PY2: data = data.encode('ascii') fmt = MSG_JSON else: fmt = MSG_BYTES chunk = HEADER.pack(len(data), req_id, op, fmt) + data outqueue.put(chunk) def read_msg(): buf = inpipe.read(HEADER.size) if not buf: return (size, req_id, op, fmt) = HEADER.unpack(buf) data = inpipe.read(size) if fmt == MSG_JSON: if PY3: data = data.decode('ascii') obj = json.loads(data) if PY2: obj = dict((str(k), v) for k, v in obj.iteritems()) else: obj = {'data': data} return (req_id, op, obj) HANDLERS = { OP_CALL: handle_call, OP_IMP: handle_imp, OP_FETCH_BEGIN: handle_fetch, OP_PUT_BEGIN: handle_begin_put, OP_PUT_DATA: handle_put_data, OP_PUT_END: handle_end_put } def reader(): try: while True: msg = read_msg() if msg is None: break req_id, op, params = msg HANDLERS[op](req_id, **params) finally: outqueue.put(done) tasks.put(done) def writer(): while True: msg = outqueue.get() if msg is done: break outpipe.write(msg) for func in (reader, writer): threading.Thread(target=func).start() while True: task = tasks.get() if task is done: break do_call(*task)
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waterspout_api/models.py
Water-Systems-Management-UCM/Waterspout
1
1606815
import logging import traceback import decimal import json import numpy import django from django.db import models # we're going to geodjango this one - might not need it, but could make some things nicer from django.db.models import Q from django.contrib.auth.models import Group from django.contrib.auth import get_user_model User = get_user_model() # define user by this method rather than direct import - safer for the future from guardian.shortcuts import assign_perm from django.db.models.signals import post_save from django.dispatch import receiver from Waterspout import settings import pandas from Dapper import scenarios, get_version as get_dapper_version, worst_case log = logging.getLogger("waterspout.models") class SimpleJSONField(models.TextField): """ converts dicts to JSON strings on save and converts JSON to dicts on load """ def get_prep_value(self, value): return json.dumps(value) def from_db_value(self, value, expression, connection): return json.loads(value) class UserProfile(models.Model): """ Basically just for user settings """ user = models.OneToOneField(User, related_name="profile", on_delete=models.CASCADE) _serializer_fields = ["id", "user", "show_organization_model_runs", "show_organization_model_runs_tooltip", "dense_tables", "dense_tables_tooltip"] # basic settings show_organization_model_runs = models.BooleanField(default=True) show_organization_model_runs_tooltip = "By default, the application shows all model runs from within your organization" \ " and gives you the option to temporarily show only your model runs." \ " This setting changes that behavior so that, by default, you only see model runs" \ " that you created yourself and then you can temporarily change the listing to" \ " see all model runs in your organization." dense_tables = models.BooleanField(default=False) dense_tables_tooltip = "Use less spacing in tables to see more data on screen at the same time" # set up the signal receivers that get triggered after a user is created so that everyone has a userprofile @receiver(post_save, sender=User) def create_user_profile(sender, instance, created, **kwargs): if created: new_profile = UserProfile.objects.create(user=instance) assign_perm("change_userprofile", instance, new_profile) @receiver(post_save, sender=User) def save_user_profile(sender, instance, **kwargs): instance.profile.save() class Organization(models.Model): """ Since this application is designed to support multiple models, possibly in the same instance, make most things be ties to an "organization" of some kind - we'll include users in the organization and arrange permissions around users within the organization. We could have made this a subclass of the Group object, but then reverse relationships may not work correctly. Instead we'll just """ name = models.CharField(max_length=255, null=False, blank=False) # TODO: This shouldn't allow nulls or blanks in the future group = models.OneToOneField(Group, on_delete=models.DO_NOTHING, null=True, blank=True) def has_member(self, user): return self.group in user.groups.all() # True if this group is in that set, otherwise, False def add_member(self, user): self.group.user_set.add(user) self.group.save() def __str__(self): return f"Organization: {self.name}" class ModelArea(models.Model): """ This would be something like "The Delta", or "Washington" - mostly, this will be one to one with organizations, but just in case we want to be able to deploy multiple models for an organization in the future, we'll store it this way. """ organization = models.ForeignKey(Organization, null=True, blank=True, on_delete=models.SET_NULL, related_name="model_areas") name = models.CharField(max_length=255, unique=True) data_folder = models.CharField(max_length=255, default="dap") # which folder did the data for this come from during loading? Can # help us if we want to update some data later description = models.TextField(null=True, blank=True) # preferences reverse 1 to 1 map_center_latitude = models.DecimalField(max_digits=4, decimal_places=2) map_center_longitude = models.DecimalField(max_digits=5, decimal_places=2) map_default_zoom = models.SmallIntegerField() # these values define the ranges available when creating a model run in this region min_water = models.PositiveSmallIntegerField(default=50) max_water = models.PositiveSmallIntegerField(default=120) min_rainfall = models.PositiveSmallIntegerField(default=10) max_rainfall = models.PositiveSmallIntegerField(default=200) min_land = models.PositiveSmallIntegerField(default=50) max_land = models.PositiveSmallIntegerField(default=100) min_price = models.PositiveSmallIntegerField(default=80) max_price = models.PositiveSmallIntegerField(default=120) min_yield = models.PositiveSmallIntegerField(default=80) max_yield = models.PositiveSmallIntegerField(default=120) min_crop_area = models.PositiveSmallIntegerField(default=0) max_crop_area = models.PositiveSmallIntegerField(default=200) main_help_page_content = models.TextField(null=True, blank=True) feature_package_name = models.CharField(max_length=100, default="DEFAULT") def __str__(self): return self.name @property def model_defaults(self): # Just making it a dict so that it comes out of the serializer grouped return { "min_water": self.min_water, "max_water": self.max_water, "min_rainfall": self.min_rainfall, "max_rainfall": self.max_rainfall, "min_land": self.min_land, "max_land": self.max_land, "min_price": self.min_price, "max_price": self.max_price, "min_yield": self.min_yield, "max_yield": self.max_yield, "min_crop_area": self.min_crop_area, "max_crop_area": self.max_crop_area, } # elasticities code commented out because we don't run calibration # ourselves right now #@property #def elasticities_as_dict(self): # return {item.crop.crop_code: float(item.value) for item in self.elasticities} @property def supports_rainfall(self): return self.region_set.filter(supports_rainfall=True).exists() @property def supports_irrigation(self): return self.region_set.filter(supports_irrigation=True).exists() @property def background_code(self): # this is a bit of a hack, but we're using the folder # names as a class to set the backgrounds temporarily return self.data_folder class ModelAreaPreferences(models.Model): """ This model is so that we can group preferences and features for model areas and keep them organized """ # should all users of this model area be able to see the model runs of all other users? shared_model_runs = models.BooleanField(default=True) # prevent users from reducing price/yield below the value that would make profits negative # basically forces stormchaser to create cards for crops when All Crops # goes to negative profits for the crop enforce_price_yield_constraints = models.BooleanField(default=True) # if True, and the user makes an adjustment that would create # negative profits, it forces the slider they weren't using upward as they move the other # downward. If False, it's purely advisory lock_price_yield_ratio = models.BooleanField(default=False) # Should visualizations and downloads include net revenues for this model area? By default we # don't want to - in most cases, it won't be something we want people to see - but we'll want # it to be able to be accessed for debugging purposes include_net_revenue = models.BooleanField(default=False) # should region-linking of crops be enabled? region_linked_crops = models.BooleanField(default=False) # whether or not to show the ability to view model run creation code allow_model_run_creation_code_view = models.BooleanField(default=False) # flags to indicate an entire set of features in data visualization # where they can choose additional model runs for comparison, # can normalize to a base run, and can see worst case outcomes allow_viz_multiple_comparisons = models.BooleanField(default=False) allow_viz_normalization = models.BooleanField(default=False) allow_viz_region_filter = models.BooleanField(default=False) allow_viz_worst_case = models.BooleanField(default=False) allow_static_regions = models.BooleanField(default=False) allow_removed_regions = models.BooleanField(default=False) allow_linear_scaled_regions = models.BooleanField(default=False) use_default_region_behaviors = models.BooleanField(default=True) model_area = models.OneToOneField(ModelArea, on_delete=models.CASCADE, related_name="preferences" ) # set up the signal receivers that get triggered after a model area is created so that everyone has a userprofile @receiver(post_save, sender=ModelArea) def create_model_area_preferences(sender, instance, created, **kwargs): if created: ModelAreaPreferences.objects.create(model_area=instance) @receiver(post_save, sender=ModelArea) def save_model_area(sender, instance, **kwargs): instance.preferences.save() #class Elasticity(models.Model): # elasticities code commented out because we don't run calibration # ourselves right now # """ # We store elasticities for Dapper as individual records here, # but we'll access them on the ModelArea object to send to Dapper # """ # model_area = models.ForeignKey(ModelArea, on_delete=models.CASCADE, related_name="elasticities") # crop = models.ForeignKey("Crop", on_delete=models.CASCADE, related_name="elasticities") # value = models.DecimalField(max_digits=6, decimal_places=4) class RegionGroup(models.Model): name = models.CharField(max_length=255, null=False, blank=False) internal_id = models.CharField(max_length=100, null=False, blank=False) # typically we have some kind of known ID to feed to a model that means something to people model_area = models.ForeignKey(ModelArea, on_delete=models.CASCADE) geometry = models.JSONField(null=True, blank=True) # this will just store GeoJSON and then we'll combine into collections manually class Region(models.Model): MODELED = 0 REMOVED = 1 FIXED = 2 LINEAR_SCALED = 3 REGION_DEFAULT_MODELING_CHOICES = ( (MODELED, "Modeled"), (REMOVED, "Removed"), (FIXED, "Fixed"), (LINEAR_SCALED, "Linear Scaled"), ) class Meta: unique_together = ['name', 'model_area'] indexes = [ models.Index(fields=("internal_id",)), models.Index(fields=("model_area_id", "supports_rainfall",)), # we'll use these to set an attribute whenever someone loads a model area models.Index(fields=("model_area_id", "supports_irrigation",)) ] name = models.CharField(max_length=255, null=False, blank=False) internal_id = models.CharField(max_length=100, null=False, blank=False) # typically we have some kind of known ID to feed to a model that means something to people external_id = models.CharField(max_length=100, null=True, blank=True) # a common external identifier of some kind description = models.TextField(null=True, blank=True) # .extra_attributes reverse lookup # .modifications reverse lookup default_behavior = models.SmallIntegerField(default=MODELED, choices=REGION_DEFAULT_MODELING_CHOICES) geometry = models.JSONField(null=True, blank=True) # this will just store GeoJSON and then we'll combine into collections manually model_area = models.ForeignKey(ModelArea, on_delete=models.CASCADE) group = models.ForeignKey(RegionGroup, null=True, blank=True, on_delete=models.CASCADE) # there could be a reason to make it a many to many instead, but # I can't think of a use case right now, and it'd require some PITA # logic to tease apart specifications for regions in overlapping groups supports_rainfall = models.BooleanField(default=False) # whether or not this region has any rainfall/dryland components supports_irrigation = models.BooleanField(default=True) # whether or not this region has any irrigated components serializer_fields = ("id", "name", "internal_id", "description", "geometry", "model_area", "group", "supports_rainfall", "supports_irrigation", "multipliers", "default_behavior", "MODELED", "FIXED", "REMOVED", "LINEAR_SCALED") def __str__(self): return "Area {}: Region {}".format(self.model_area.name, self.name) class RegionMultipliers(models.Model): region = models.OneToOneField(Region, on_delete=models.CASCADE, related_name="multipliers") total_revenue = models.DecimalField(max_digits=10, decimal_places=8, null=True, blank=True) direct_value_add = models.DecimalField(max_digits=10, decimal_places=8, null=True, blank=True) total_value_add = models.DecimalField(max_digits=10, decimal_places=8, null=True, blank=True) direct_jobs = models.DecimalField(max_digits=10, decimal_places=8, null=True, blank=True) total_jobs = models.DecimalField(max_digits=10, decimal_places=8, null=True, blank=True) class RegionExtra(models.Model): """ Extra custom attributes that can be set per region instance, available by filtering `region.extra_attributes.filter(name == "{attribute_name}")`, for example. """ region = models.ForeignKey(Region, on_delete=models.CASCADE, related_name="extra_attributes") name = models.CharField(max_length=255, null=False, blank=False) value = models.TextField(null=True, blank=True) data_type = models.CharField(max_length=5) # indicates the Python data type to cast it to if it's not a string class Crop(models.Model): """ A single unit for individual crops - note that we need to pull crops by organization - the same crop could exist for multiple organizations. We don't want to load them in for all organizations because then we have to worry about if it means the same exact thing across organizations, and what do changes mean to each group, etc, etc. Let's keep it a known, manageable level of complex and assign crops to organizations even if it means duplicating crops between organizations. """ class Meta: unique_together = ['crop_code', 'model_area'] indexes = [ models.Index(fields=("name",)) ] name = models.CharField(max_length=255, null=False, blank=False) # human readable crop name crop_code = models.CharField(max_length=30, null=False, blank=False) # code used in the models (like ALFAL for Alfalfa) model_area = models.ForeignKey(ModelArea, on_delete=models.CASCADE) # clear any crops for an org when deleted def __str__(self): return self.name class CropGroup(models.Model): """ A unit to group individual crops together - note that we need to crop groups are also by organization and the same crop group exist for multiple organizations """ name = models.CharField(max_length=255, null=False, blank=False) crops = models.ManyToManyField(Crop) # Each crop can be in many groups class RecordSet(models.Model): class Meta: abstract = True """ For storing the results of calibration parameters that come out of phase 1 of the model - we'll load a few sets of calibrated parameters initially, but then provide a black box version of the calibration parameters. We can then have behavior that tries a lookup for a calibration set, and if it doesn't exist, runs the calibration. """ record_model_name = "ModelItem" years = models.TextField() # yes, text. We'll concatenate text as a year lookup # prices = model def as_data_frame(self, exclude_regions=None): """ Returns the data frame that needs to be run through the model itself :param exclude_regions: list of region internal ids - regions in this list will not have their data included in the output data frame :return: """ # .values() makes a queryset into an iterable of dicts. Coerce that to a list of dicts # and pass it to the pandas constructor. # Django has a handy method to transform a queryset into a list of dicts, # but we can't really use it because we need to retrieve foreign keys, and it # can only retrieve either bare attributes or the ones we specify, so we'd # need to either hardcode everything or run it twice and merge it. Easier # to write our own, unfortunately. It's quite a bit slower than the Django though # so maybe we can speed it up somehow. Maybe doing it as a raw SQL query and then # dropping any excess fields would be better. Going to leave that for another # day right now. foreign_keys = ["region", "crop", "rainfall_set"] record_model = globals()[self.record_model_name] fields = [f.name for f in record_model._meta.get_fields()] # get all the fields for calibrated parameters basic_fields = list(set(fields) - set(foreign_keys)) # remove the foreign keys - we'll process those separately # reverse_name will exist for subclasses data = getattr(self, self.reverse_name).all() # get all the records for this set output = [] for record in data: # if we were told to skip this region, don't add it - could also do this in pandas after conversion if exclude_regions and record.region.internal_id in exclude_regions: continue output_dict = {} for field in basic_fields: # apply the basic fields directly into a dictionary and coerce to floats field_value = getattr(record, field) output_dict[field] = float(field_value) if type(field_value) is decimal.Decimal else field_value output_dict["i"] = record.crop.crop_code # but then grab the specific attributes of the foreign keys we wawnt output_dict["g"] = record.region.internal_id output.append(output_dict) # put the dict into the list so we can make a DF of it return pandas.DataFrame(output) # construct a data frame and send it back def to_csv(self, *args, **kwargs): """ Saves the set to a CSV file - all args are passed through to Pandas.to_csv, so it's possible to have it return a CSV as a string by just calling to_csv() :param args: waterspout_sort_columns is not compatible with waterspout_limited :param kwargs: :return: """ df = self.as_data_frame().sort_values(axis=0, by=["year", "g", "i"]) df = df.rename(columns={"g": "region", "i": "crop"}) if kwargs.pop("waterspout_sort_columns", False) is True: # match the column output to how Spencer has it so we can compare column_order = ("region", "crop", "year", "omegaland", "omegasupply", "omegalabor", "omegaestablish", "omegacash", "omeganoncash", "omegatotal", "xwater", "p", "y", "xland", "omegawater", "sigma", "theta", "pimarginal", "rho", "betaland", "betawater", "betasupply", "betalabor", "tau", "gamma", "delta", "xlandsc", "xwatersc", "xdiffland", "xdifftotalland", "xdiffwater", "resource_flag") df = df.reindex(columns=column_order) if kwargs.pop("waterspout_limited", False) is True: columns = settings.LIMITED_RESULTS_FIELDS df = df[columns] if "index" not in kwargs: # if the caller doesn't specify an option for pandas' index, set it to False explicitly so it doesn't export the index kwargs["index"] = False return df.to_csv(*args, **kwargs) class InputDataSet(RecordSet): model_area = models.ForeignKey(ModelArea, on_delete=models.CASCADE, related_name="input_data") reverse_name = "input_data_set" class CalibrationSet(RecordSet): model_area = models.ForeignKey(ModelArea, on_delete=models.CASCADE, related_name="calibration_data") record_model_name = "CalibratedParameter" reverse_name = "calibration_set" class RainfallSet(RecordSet): model_area = models.ForeignKey(ModelArea, on_delete=models.CASCADE, related_name="rainfall_data") reverse_name = "rainfall_set" record_model_name = "RainfallParameter" class ResultSet(RecordSet): reverse_name = "result_set" record_model_name = "Result" model_run = models.ForeignKey("ModelRun", null=True, blank=True, on_delete=models.CASCADE, related_name="results") date_run = models.DateTimeField(default=django.utils.timezone.now, null=True, blank=True) # store the dapper version that the model ran with - that way we can detect if an updated version might provide different results dapper_version = models.CharField(max_length=20) # in_calibration indicates whether or not the results have any negative profits # or have otherwise fallen outside of calibrated values. in_calibration = models.BooleanField(default=True) # not using in_calibration_text for now. We'll have the web app figure out # which records are negative profits and show a table if in_calibration is false #in_calibration_text = models.TextField(null=True, blank=True) infeasibilities_text = models.TextField(null=True, blank=True) # infeasibilities reverse relation def __str__(self): return f"Results for Model Run {self.model_run.name} from Dapper {self.dapper_version} at {self.date_run}" class ModelItem(models.Model): """ Fields that are allowed to be null are ones that aren't part of calibration, but instead may be set for final results """ class Meta: abstract = True indexes = [ models.Index(fields=("year",)) ] crop = models.ForeignKey(Crop, on_delete=models.CASCADE) region = models.ForeignKey(Region, on_delete=models.CASCADE) year = models.IntegerField(null=True, blank=True) # inputs will have this, but calibrated items and results may not p = models.DecimalField(max_digits=18, decimal_places=10) y = models.DecimalField(max_digits=13, decimal_places=5) xland = models.DecimalField(max_digits=18, decimal_places=10) class InputModelItem(ModelItem): class Meta: abstract = True omegaland = models.DecimalField(max_digits=10, decimal_places=1) omegasupply = models.DecimalField(max_digits=10, decimal_places=1) omegalabor = models.DecimalField(max_digits=10, decimal_places=1) omegaestablish = models.DecimalField(max_digits=10, decimal_places=1, null=True, blank=True) omegacash = models.DecimalField(max_digits=10, decimal_places=1, null=True, blank=True) omeganoncash = models.DecimalField(max_digits=10, decimal_places=1, null=True, blank=True) omegatotal = models.DecimalField(max_digits=10, decimal_places=1, null=True, blank=True) xwater = models.DecimalField(max_digits=18, decimal_places=10) class InputDataItem(InputModelItem): class Meta: unique_together = ['crop', 'region', 'year'] dataset = models.ForeignKey(InputDataSet, on_delete=models.CASCADE, related_name="input_data_set") serializer_fields = ["crop", "region", "year", "omegaland", "omegasupply", "omegalabor", "omegaestablish", "omegacash", "omeganoncash", "omegatotal", "p", "y", "xland", "xwater"] class CalibratedParameter(InputModelItem): """ Note that it's of class InputDataItem, which is of class ModelItem - ModelItems define the various input parameters and results that we use for calibration inputs, calibrated parameters, and model results """ class Meta: unique_together = ['crop', 'region', 'year'] omegawater = models.DecimalField(max_digits=10, decimal_places=2) pc = models.DecimalField(max_digits=10, decimal_places=3) sigma = models.DecimalField(max_digits=5, decimal_places=4) theta = models.DecimalField(max_digits=5, decimal_places=4) pimarginal = models.DecimalField(max_digits=18, decimal_places=10) #alphaland = models.DecimalField(max_digits=11, decimal_places=10, null=True, blank=True) # alphawater = models.DecimalField(max_digits=11, decimal_places=10, null=True, blank=True) # alphasupply = models.DecimalField(max_digits=11, decimal_places=10, null=True, blank=True) # alphalabor = models.DecimalField(max_digits=11, decimal_places=10, null=True, blank=True) rho = models.DecimalField(max_digits=15, decimal_places=10) # lambdaland = models.DecimalField(max_digits=15, decimal_places=10, null=True, blank=True) #lambdacrop = models.DecimalField(max_digits=15, decimal_places=10, null=True, blank=True) betaland = models.DecimalField(max_digits=18, decimal_places=10) betawater = models.DecimalField(max_digits=18, decimal_places=10) betasupply = models.DecimalField(max_digits=18, decimal_places=10) betalabor = models.DecimalField(max_digits=18, decimal_places=10) tau = models.DecimalField(max_digits=18, decimal_places=10) gamma = models.DecimalField(max_digits=18, decimal_places=10) delta = models.DecimalField(max_digits=18, decimal_places=10) #xlandcalib = models.DecimalField(max_digits=18, decimal_places=10, null=True, blank=True) #xwatercalib = models.DecimalField(max_digits=18, decimal_places=10, null=True, blank=True) #difflandpct = models.DecimalField(max_digits=12, decimal_places=10, null=True, blank=True) #diffwaterpct = models.DecimalField(max_digits=12, decimal_places=10, null=True, blank=True) # this item helps us check on the front end whether or not modifications will send profits negative. # we'll store it here since it comes out of the calibration and will be per region/crop combination price_yield_correction_factor = models.DecimalField(max_digits=6, decimal_places=3, default=1) calibration_set = models.ForeignKey(CalibrationSet, on_delete=models.CASCADE, related_name="calibration_set") serializer_fields = ["crop", "region", "price_yield_correction_factor"] #["crop", "region", "year", "omegaland", "omegawater", # "omegasupply", "omegalabor", "omegaestablish", "omegacash", # "omeganoncash", "omegatotal", "p", "y", "price_yeild_correction_factor"] class RainfallParameter(ModelItem): class Meta: unique_together = ['crop', 'region', 'year'] rainfall_set = models.ForeignKey(RainfallSet, on_delete=models.CASCADE, related_name="rainfall_set") coef_intercept = models.DecimalField(max_digits=10, decimal_places=5) twin = models.DecimalField(max_digits=10, decimal_places=5) tspr = models.DecimalField(max_digits=10, decimal_places=5) tsum = models.DecimalField(max_digits=10, decimal_places=5) coef_tsum = models.DecimalField(max_digits=10, decimal_places=5) coef_tspr = models.DecimalField(max_digits=10, decimal_places=5) coef_twin = models.DecimalField(max_digits=10, decimal_places=5) ptspr = models.DecimalField(max_digits=10, decimal_places=5) ptwin = models.DecimalField(max_digits=10, decimal_places=5) ptsum = models.DecimalField(max_digits=10, decimal_places=5) coef_ptspr = models.DecimalField(max_digits=10, decimal_places=5) coef_ptwin = models.DecimalField(max_digits=10, decimal_places=5) coef_ptsum = models.DecimalField(max_digits=10, decimal_places=5) coef_crop = models.DecimalField(max_digits=10, decimal_places=5) class Result(ModelItem): """ Holds the results for a single region/crop """ omegawater = models.DecimalField(max_digits=10, decimal_places=2) resource_flag = models.CharField(max_length=5, null=True, blank=True) # we may be able to drop these fields later, but they help us while we're comparing to the original DAP and our validation xlandsc = models.DecimalField(max_digits=18, decimal_places=10, null=True, blank=True) xwatersc = models.DecimalField(max_digits=18, decimal_places=10, null=True, blank=True) xdiffland = models.DecimalField(max_digits=18, decimal_places=10, null=True, blank=True) xdifftotalland = models.DecimalField(max_digits=18, decimal_places=10, null=True, blank=True) xdiffwater = models.DecimalField(max_digits=18, decimal_places=10, null=True, blank=True) result_set = models.ForeignKey(ResultSet, on_delete=models.CASCADE, related_name="result_set") net_revenue = models.DecimalField(max_digits=18, decimal_places=3, null=True, blank=True) gross_revenue = models.DecimalField(max_digits=18, decimal_places=3, null=True, blank=True) water_per_acre = models.DecimalField(max_digits=18, decimal_places=5, null=True, blank=True) class RainfallResult(models.Model): serializer_fields = ["region", "crop", "calc_yield", "gross_revenue", "xlandsc", "xwatersc"] result_set = models.ForeignKey(ResultSet, on_delete=models.CASCADE, related_name="rainfall_result_set") region = models.ForeignKey(Region, on_delete=models.CASCADE, related_name="rainfall_results") crop = models.ForeignKey(Crop, on_delete=models.CASCADE, related_name="rainfall_results") # yield is the only real result from the rainfall statistical model, so that's what we're storing, but we'll also # want to store some of the same main results as the Result items so we can merge them calc_yield = models.DecimalField(max_digits=18, decimal_places=3, null=True, blank=True) gross_revenue = models.DecimalField(max_digits=18, decimal_places=3, null=True, blank=True) # these aren't yet really outputs, but we need to include them for the viz to work correctly xlandsc = models.DecimalField(max_digits=18, decimal_places=10, null=True, blank=True) xwatersc = models.DecimalField(max_digits=18, decimal_places=10, null=True, blank=True) class ModelRun(models.Model): """ The central object for configuring an individual run of the model - is related to modification objects from the modification side. """ class Meta: indexes = [ models.Index(fields=("ready", "running", "complete")), models.Index(fields=("date_submitted",)) ] name = models.CharField(max_length=255) description = models.TextField(null=True, blank=True) ready = models.BooleanField(default=False, null=False) # marked after the web interface adds all modifications running = models.BooleanField(default=False, null=False) # marked while in processing complete = models.BooleanField(default=False, null=False) # tracks if the model has actually been run for this result yet status_message = models.CharField(max_length=2048, default="", null=True, blank=True) # for status info or error messages result_values = models.TextField(default="", null=True, blank=True) log_data = models.TextField(null=True, blank=True) # we'll store log outputs from the model run here. date_submitted = models.DateTimeField(default=django.utils.timezone.now, null=True, blank=True) date_completed = models.DateTimeField(null=True, blank=True) # which model run is the base, unmodified version? Useful for data viz base_model_run = models.ForeignKey("ModelRun", null=True, blank=True, on_delete=models.DO_NOTHING) is_base = models.BooleanField(default=False) # is this a base model run (True), or a normal model run (False) # model_area relation is through calibration_set calibration_set = models.ForeignKey(CalibrationSet, on_delete=models.CASCADE, related_name="model_runs") calibrated_parameters_text = models.TextField(null=True, blank=True) # we'll put a snapshot of the calibration parameters in here, probably # as a CSV. This way, if people eidt the calibration data for future runs, # we still know what inputs ran this version of the model. rainfall_set = models.ForeignKey(RainfallSet, on_delete=models.CASCADE, related_name="model_runs", null=True, blank=True) user = models.ForeignKey(User, on_delete=models.DO_NOTHING, related_name="model_runs") organization = models.ForeignKey(Organization, on_delete=models.CASCADE, related_name="model_runs") # region_modifications - back-reference from related content # crop_modifications - back-reference from related content serializer_fields = ['id', 'name', 'description', 'ready', 'running', 'complete', 'status_message', 'date_submitted', 'date_completed', "calibration_set", "rainfall_set", "user_id", "organization", "base_model_run_id", "is_base", "land_modifications_average", "water_modifications_average", "price_modifications_average", "yield_modifications_average"] def __str__(self): return f"Model Run: {self.name}" def as_dict(self): return {field: getattr(self, field) for field in self.serializer_fields} def as_json(self): # using the Django serializer class handles datetimes, etc properly return json.dumps(self.as_dict(), cls=django.core.serializers.json.DjangoJSONEncoder) def _get_modification_average(self, queryset_name, property_name): mods = list(getattr(self, queryset_name).all()) num_items = len(mods) if num_items == 0: return 0 return float(sum([getattr(mod, property_name) for mod in mods]))/num_items @property def land_modifications_average(self): return self._get_modification_average("region_modifications", "land_proportion") @property def water_modifications_average(self): return self._get_modification_average("region_modifications", "water_proportion") @property def price_modifications_average(self): return self._get_modification_average("crop_modifications", "price_proportion") @property def yield_modifications_average(self): return self._get_modification_average("crop_modifications", "yield_proportion") @property def scenario_df(self): df = self.get_df(self.calibration_set) # save the calibration data with any modifications as a text string to the DB - will exclude scenario # level constraints though! # saving the text version hasn't helped us and slows things down. Stop doing that for now #self.calibrated_parameters_text = df.to_csv() # if we don't provide a path to to_csv, it returns a string #self.save() return df @property def rainfall_df(self): if self.rainfall_set: return self.get_df(self.rainfall_set) else: return None def get_df(self, base_model): """ Given the currently attached modifications, etc, returns a complete calibration DF :return: """ # we'll not send static or removed regions through the model, so drop them here # for removed regions, this is all we need to do - for static regions, we'll # pull their base case results later when we process results exclude_ids = self.get_regions_for_behaviors([Region.FIXED, Region.REMOVED]) # pull initial calibration dataset as it is df = base_model.as_data_frame(exclude_regions=exclude_ids) # do any overrides or changes from the modifications return df def get_regions_for_behaviors(self, behaviors): """ Given one or more behaviors in an interable, returns the region IDs that the behaviors apply to as a list. This isn't straightforward, because we need to see if the region modification applies a behavior, then also check all the regions that *didn't* have a modification in the current model run (which will have the behavior specified there) for their default behaviors :param behavior: :return: """ # compose the query portions for each section here in one loop region_filter_includes = Q() default_behavior_includes = Q() for behavior in behaviors: region_filter_includes = region_filter_includes | Q(modeled_type=behavior) default_behavior_includes = default_behavior_includes | Q(default_behavior=behavior) # get the regions to explicitly include from modifications modifications = self.region_modifications.filter(region_filter_includes) if modifications: ids = [mod.region.internal_id for mod in modifications] else: ids = [] if self.calibration_set.model_area.preferences.use_default_region_behaviors: # figure out which regions didn't have modifications in the current model run - we'll pull the defaults for these regions_to_use_defaults_from = self.calibration_set.model_area.region_set.filter(default_behavior_includes)\ .difference(Region.objects.filter(modifications__in=self.region_modifications.all())) if regions_to_use_defaults_from: ids.extend([region.internal_id for region in regions_to_use_defaults_from]) return ids def attach_modifications(self, scenario): land_modifications = {} water_modifications = {} rainfall_modifications = {} region_modifications = self.region_modifications.filter(region__isnull=False) for modification in region_modifications: # get all the nondefault modifications land_modifications[modification.region.internal_id] = float(modification.land_proportion) if modification.region.supports_irrigation: water_modifications[modification.region.internal_id] = float(modification.water_proportion) if modification.region.supports_rainfall: rainfall_modifications[modification.region.internal_id] = float(modification.rainfall_proportion) default_region_modification = self.region_modifications.get(region__isnull=True) scenario.perform_adjustment("land", land_modifications, default=float(default_region_modification.land_proportion)) # attach modifications for irrigation and rainfall depending on what the model area supports if self.calibration_set.model_area.supports_irrigation: scenario.perform_adjustment("water", water_modifications, default=float(default_region_modification.water_proportion)) if self.calibration_set.model_area.supports_rainfall: scenario.perform_adjustment("rainfall", rainfall_modifications, default=float(default_region_modification.rainfall_proportion)) region_linked_key = "region_linked" # now attach the crop modifications - start by loading the data into a dict price_modifications = {region_linked_key: []} yield_modifications = {region_linked_key: []} crop_modifications = self.crop_modifications.filter(crop__isnull=False) for modification in crop_modifications: # get all the nondefault modifications crop_code = modification.crop.crop_code if modification.region is None: # if it's not region-linked price_modifications[crop_code] = float(modification.price_proportion) yield_modifications[crop_code] = float(modification.yield_proportion) region_id = None else: # if it is region-linked region_id = modification.region.internal_id # create dictionaries for both price and yield constraints that include # the region ID, the crop code, and the value price_modifications["region_linked"].append({ "region": region_id, "crop": crop_code, "value": float(modification.price_proportion), }) yield_modifications["region_linked"].append({ "region": region_id, "crop": crop_code, "value": float(modification.yield_proportion), }) max_crop_area_constraint = modification.max_land_area_proportion if max_crop_area_constraint and modification.min_land_area_proportion and not max_crop_area_constraint > modification.min_land_area_proportion: # if the max and the min are both defined and the max is less than the min, skip adding the max - # this is because stormchaser uses a value of -1 as its max value to indicate no upper limit max_crop_area_constraint = None # we can always add it, and it's OK if they're both None - that'll get checked later scenario.add_crop_area_constraint(crop_code=modification.crop.crop_code, min_proportion=modification.min_land_area_proportion, max_proportion=max_crop_area_constraint, region=region_id) default_crop_modification = self.crop_modifications.get(crop__isnull=True) # then pass those dicts to the scenario code regardless if there are items (so defaults get set) scenario.perform_adjustment("price", price_modifications, default=float(default_crop_modification.price_proportion)) scenario.perform_adjustment("yield", yield_modifications, default=float(default_crop_modification.yield_proportion)) def run(self, csv_output=None, worst_case_csv_output=None): # initially, we won't support calibrating the data here - we'll # just use an initial calibration set and then make our modifications # before running the scenarios #calib = calibration.ModelCalibration(run.calbration_df) #calib.calibrate() self.running = True self.save() # mark it as running and save it so the API updates the status try: scenario_runner = scenarios.Scenario(calibration_df=self.scenario_df, rainfall_df=self.rainfall_df) self.attach_modifications(scenario=scenario_runner) results = scenario_runner.run() if csv_output is not None: results.to_csv(csv_output) # add the worst case scenario values to the same data frame results = self.add_worst_case_and_linear_scaled_values(results) if worst_case_csv_output is not None: results.to_csv(worst_case_csv_output) # before loading results, make sure we weren't deleted in the app between starting the run and now if not ModelRun.objects.filter(id=self.id).exists(): return # now we need to load the resulting df back into the DB result_set = self.load_records(results_df=results, rainfall_df=scenario_runner.rainfall_df) self.load_infeasibilities(scenario_runner, result_set) self.complete = True log.info("Model run complete") finally: # make sure to mark it as not running regardless of its status or if it crashes self.running = False self.save() def add_worst_case_and_linear_scaled_values(self, results): results = worst_case.default_worst_case_scaling_function(results) linear_scaled_regions = self.get_regions_for_behaviors([Region.LINEAR_SCALED,]) # just straight up overwrite the values for revenue, land, and water for linear scaled regions using the worst case values (linear scaled) for region in linear_scaled_regions: results.loc[(results.g == region), "gross_revenue"] = results.loc[(results.g == region), "worst_case_gross_revenue"] results.loc[(results.g == region), "xlandsc"] = results.loc[(results.g == region), "worst_case_land"] results.loc[(results.g == region), "xwatersc"] = results.loc[(results.g == region), "worst_case_water"] results.loc[(results.g == region), "net_revenue"] = 0 # null out the net revenue field since it's invalid now. return results def load_records(self, results_df, rainfall_df=None): """ Given a set of model results, loads the results to the database :param results_df: :param model_run: :return: """ log.info(f"Loading results for model run {self.id}") result_set = ResultSet(model_run=self, dapper_version=get_dapper_version()) result_set.save() # load the PMP results first self._load_df(results_df=results_df, result_set=result_set, record_model=Result) # now load the rainfall data if it applies if rainfall_df is not None: self._load_df(results_df=rainfall_df, result_set=result_set, record_model=RainfallResult) return result_set def _load_df(self, results_df, result_set, record_model=Result): try: for record in results_df.itertuples(): # get the first tuple's fields, then exit the loop # this should be faster than retrieving it every time in the next loop, but we need to do it once fields = list(set(record._fields) - set(["id", "g", "i", "calibration_set"])) break for record in results_df.itertuples(): # returns named tuples result = record_model(result_set=result_set) result.crop = Crop.objects.get(crop_code=record.i, model_area=self.calibration_set.model_area) result.region = Region.objects.get(internal_id=record.g, model_area=self.calibration_set.model_area) for column in fields: # _fields isn't private - it's just preventing conflicts - see namedtuple docs value = getattr(record, column) if value is not None and type(value) is not str and numpy.isnan(value): # if we got NaN (such as with an infeasibility) - filter out None values because they make numpy.isnan fail value = None # then we need to skip it or it'll bork the whole table (seriously) setattr(result, column, value) if not result.net_revenue or result.net_revenue < 0: # if any record has negative net revenues, we're out of bounds on calibration - mark it result_set.in_calibration = False result.save() if result_set.in_calibration is False: # if we changed the in_calibration value, save the result set - we check this here so we only trigger the save once result_set.save() return result_set except: result_set.delete() # clean up if we fail while loading data raise def load_infeasibilities(self, scenario, result_set): log.debug("Assessing infeasibilities") for infeasibility in scenario.infeasibilities: Infeasibility.objects.create(result_set=result_set, region=Region.objects.get(internal_id=infeasibility.region, model_area=self.calibration_set.model_area), year=infeasibility.timeframe, description=infeasibility.description ) total_infeasibilities = len(scenario.infeasibilities) crops = {} # now let's see if there are any crops that are common to the infeasibilities #if total_infeasibilities > 2: # if we have more than a handful, we'll assess which crops are common for infeasibility in scenario.infeasibilities: infeasible_region_results = Result.objects.filter(result_set=result_set, region__internal_id=infeasibility.region, year=infeasibility.timeframe) for result in infeasible_region_results: crop = result.crop.crop_code if crop in crops: crops[crop] += 1 else: crops[crop] = 1 # ideally we'd want to key this by name so that we can show the name here if len(crops.keys()) > 0: sorted_by_appearance = {k:v for k,v in sorted(crops.items(), key=lambda item: item[1], reverse=True)} infeasibility_items = ["{} ({})".format(crop, value) for crop, value in sorted_by_appearance.items()] self.infeasibilities_text = ", ".join(infeasibility_items) class Infeasibility(models.Model): result_set = models.ForeignKey(ResultSet, on_delete=models.CASCADE, related_name="infeasibilities") year = models.SmallIntegerField() region = models.ForeignKey(Region, null=True, on_delete=models.SET_NULL, related_name="infeasibilities") description = models.TextField() class RegionModification(models.Model): """ A modification on a region to use in a model run. We'll need to have the model run build a new pandas data frame from these based on the code inputs and the model adjustments """ class Meta: unique_together = ['model_run', 'region'] # we have two nullable foreign keys here. If both are new, then the rule applies to the whole model area as the default. # if only one is active, then it applies to either the region, or the group (and then we need something that applies # it to the individual regions). It's possible that the group->individual application will occur in the JS because # we'll want to display it all in a map, but we might want to do it here so that API calls can use the groups too region = models.ForeignKey(Region, on_delete=models.CASCADE, related_name="modifications", null=True, blank=True) region_group = models.ForeignKey(RegionGroup, on_delete=models.CASCADE, related_name="modifications", null=True, blank=True) water_proportion = models.FloatField(default=1.0, blank=True) # the amount, relative to base values, to provide rainfall_proportion = models.FloatField(default=1.0, blank=True) # the amount, relative to base values, to provide land_proportion = models.FloatField(default=1.0, blank=True) # extra flags on regions modeled_type = models.SmallIntegerField(default=Region.MODELED, choices=Region.REGION_DEFAULT_MODELING_CHOICES) model_run = models.ForeignKey(ModelRun, blank=True, on_delete=models.CASCADE, related_name="region_modifications") serializer_fields = ["id", "region", "region_group", "water_proportion", "land_proportion", "rainfall_proportion", "modeled_type"] class CropModification(models.Model): """ A modification on a crop to use in a model run. We'll need to have the model run build a new pandas data frame from these based on the code inputs and the model adjustments """ class Meta: unique_together = ['model_run', 'crop', 'region'] serializer_fields = ["id", "crop", "crop_group", "price_proportion", "yield_proportion", "min_land_area_proportion", "max_land_area_proportion", "region"] crop = models.ForeignKey(Crop, on_delete=models.CASCADE, related_name="modifications", null=True, blank=True) region = models.ForeignKey(Region, on_delete=models.CASCADE, related_name="crop_modifications", null=True, blank=True) # we'll be allowing region-tied crop modifications in a future update - as of 2021/March/02, there is no logic for handling this value yet - it'll all be null and won't go into the solver anywhere crop_group = models.ForeignKey(CropGroup, on_delete=models.CASCADE, related_name="modifications", null=True, blank=True) price_proportion = models.FloatField(default=1.0, blank=True) # the amount, relative to base values, to provide yield_proportion = models.FloatField(default=1.0, blank=True) # the amount, relative to base values, to provide min_land_area_proportion = models.FloatField(null=True, blank=True) # the amount, relative to base values, to provide max_land_area_proportion = models.FloatField(null=True, blank=True) # the amount, relative to base values, to provide model_run = models.ForeignKey(ModelRun, null=True, blank=True, on_delete=models.CASCADE, related_name="crop_modifications") class HelpDocument(models.Model): """ We'll store help documents in the DB as well so that they can be pulled into the app via the API - we can also publish them elswhere if we'd like - this is ultimately just a simple article system. We'll plan to load these from specific articles in the Sphinx documentation """ title = models.CharField(max_length=2048) author = models.ForeignKey(User, on_delete=models.DO_NOTHING) slug = models.CharField(max_length=64) summary = models.TextField() body = models.TextField()
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ui/app/startup.py
BenjaminPelletier/streamtagger
2
183133
<filename>ui/app/startup.py<gh_stars>1-10 import logging from .lib import dbinit logging.basicConfig(format='%(levelname)s:%(message)s', level=logging.INFO) log = logging.getLogger('streamtagger') log.info('Initializing database...') dbinit.init_db() log.info('Startup complete; system ready.')
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download_scp_stream/download_ftp.py
ahmadsalimi/UtilityScripts
0
99676
<filename>download_scp_stream/download_ftp.py import ftplib from ftplib import FTP from sys import argv import warnings import functions as F from tqdm import tqdm import os warnings.filterwarnings('ignore') not_to_download = [ "LIDC-IDRI-0001", "LIDC-IDRI-0004", "LIDC-IDRI-0007", "LIDC-IDRI-0010", "LIDC-IDRI-0013", "LIDC-IDRI-0016", "LIDC-IDRI-0002", "LIDC-IDRI-0005", "LIDC-IDRI-0008", "LIDC-IDRI-0011", "LIDC-IDRI-0014", "LIDC-IDRI-0017", "LIDC-IDRI-0003", "LIDC-IDRI-0006", "LIDC-IDRI-0009", "LIDC-IDRI-0012", "LIDC-IDRI-0015" ] def _is_ftp_dir(ftp, name, guess_by_extension=True): """ simply determines if an item listed on the ftp server is a valid directory or not """ # if the name has a "." in the fourth to last position, its probably a file extension # this is MUCH faster than trying to set every file to a working directory, and will work 99% of time. if guess_by_extension is True: if len(name) >= 4: if name[-4] == '.': return False original_cwd = ftp.pwd() # remember the current working directory try: ftp.cwd(name) # try to set directory to new name ftp.cwd(original_cwd) # set it back to what it was return True except ftplib.error_perm as e: print(e) return False except Exception as e: print(e) return False def generate_sftp_write(file, bar): def sftp_write(data): file.write(data) bar.update(len(data)) return sftp_write def _make_parent_dir(sftp, sftp_path): dirname = os.path.dirname(sftp_path) if not sftp.exists(dirname): _make_parent_dir(sftp, dirname) sftp.mkdir(dirname) def _download_ftp_file(ftp, ftp_path, sftp, sftp_path): print("downloading " + ftp_path + " to " + sftp_path) _make_parent_dir(sftp, sftp_path) with sftp.open(sftp_path, 'w') as file: with tqdm(unit='B', unit_scale=True, unit_divisor=1024) as bar: ftp.retrbinary("RETR {0}".format(ftp_path), generate_sftp_write(file, bar)) def _mirror_ftp_dir(ftp, ftp_path, sftp, sftp_path): if ftp_path.split('/')[-1] in not_to_download: print('skipping ' + ftp_path) return for item in ftp.nlst(ftp_path): destination = sftp_path + '/' + item.split('/')[-1] if _is_ftp_dir(ftp, item, False): _mirror_ftp_dir(ftp, item, sftp, destination) else: _download_ftp_file(ftp, item, sftp, destination) def download_ftp_tree(ftp, ftp_path, sftp, sftp_path): ftp_path = ftp_path.lstrip('/') sftp_path = sftp_path.rstrip('/') _mirror_ftp_dir(ftp, ftp_path, sftp, sftp_path) if __name__ == "__main__": if len(argv) is not 7: print("Usage: python download_ftp.py ftp_server ftp_path ssh_server ssh_username ssh_password ssh_path") else: ftp_server = argv[1] ftp_path = argv[2] ssh_server = argv[3] ssh_username = argv[4] ssh_password = argv[5] ssh_path = argv[6] ftp = FTP(ftp_server) ftp.login("", "") sftp = F.sftp_connect(ssh_server, ssh_username, ssh_password) download_ftp_tree(ftp, ftp_path, sftp, ssh_path)
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tests/util_test.py
novaksam/elastalert2
1
133460
# -*- coding: utf-8 -*- import logging import os import pytest from datetime import datetime from datetime import timedelta from dateutil.parser import parse as dt from dateutil.tz import tzutc from unittest import mock from elastalert.util import add_raw_postfix from elastalert.util import build_es_conn_config from elastalert.util import dt_to_int from elastalert.util import dt_to_ts from elastalert.util import dt_to_ts_with_format from elastalert.util import EAException from elastalert.util import elasticsearch_client from elastalert.util import flatten_dict from elastalert.util import format_index from elastalert.util import get_module from elastalert.util import inc_ts from elastalert.util import lookup_es_key from elastalert.util import parse_deadline from elastalert.util import parse_duration from elastalert.util import pytzfy from elastalert.util import replace_dots_in_field_names from elastalert.util import resolve_string from elastalert.util import set_es_key from elastalert.util import should_scrolling_continue from elastalert.util import total_seconds from elastalert.util import ts_to_dt_with_format from elastalert.util import ts_utc_to_tz from elastalert.util import expand_string_into_dict from elastalert.util import unixms_to_dt from elastalert.util import format_string from elastalert.util import pretty_ts @pytest.mark.parametrize('spec, expected_delta', [ ('hours=2', timedelta(hours=2)), ('minutes=30', timedelta(minutes=30)), ('seconds=45', timedelta(seconds=45)), ]) def test_parse_duration(spec, expected_delta): """``unit=num`` specs can be translated into ``timedelta`` instances.""" assert parse_duration(spec) == expected_delta @pytest.mark.parametrize('spec, expected_deadline', [ ('hours=2', dt('2017-07-07T12:00:00.000Z')), ('minutes=30', dt('2017-07-07T10:30:00.000Z')), ('seconds=45', dt('2017-07-07T10:00:45.000Z')), ]) def test_parse_deadline(spec, expected_deadline): """``unit=num`` specs can be translated into ``datetime`` instances.""" # Note: Can't mock ``utcnow`` directly because ``datetime`` is a built-in. class MockDatetime(datetime): @staticmethod def utcnow(): return dt('2017-07-07T10:00:00.000Z') with mock.patch('datetime.datetime', MockDatetime): assert parse_deadline(spec) == expected_deadline def test_setting_keys(ea): expected = 12467267 record = { 'Message': '12345', 'Fields': { 'ts': 'fail', 'severity': 'large', 'user': 'jimmay' } } # Set the value assert set_es_key(record, 'Fields.ts', expected) # Get the value again assert lookup_es_key(record, 'Fields.ts') == expected def test_looking_up_missing_keys(ea): record = { 'Message': '12345', 'Fields': { 'severity': 'large', 'user': 'jimmay', 'null': None } } assert lookup_es_key(record, 'Fields.ts') is None assert lookup_es_key(record, 'Fields.null.foo') is None def test_looking_up_nested_keys(ea): expected = 12467267 record = { 'Message': '12345', 'Fields': { 'ts': expected, 'severity': 'large', 'user': 'jimmay' } } assert lookup_es_key(record, 'Fields.ts') == expected def test_looking_up_nested_composite_keys(ea): expected = 12467267 record = { 'Message': '12345', 'Fields': { 'ts.value': expected, 'severity': 'large', 'user': 'jimmay' } } assert lookup_es_key(record, 'Fields.ts.value') == expected def test_looking_up_arrays(ea): record = { 'flags': [1, 2, 3], 'objects': [ {'foo': 'bar'}, {'foo': [{'bar': 'baz'}]}, {'foo': {'bar': 'baz'}} ] } assert lookup_es_key(record, 'flags[0]') == 1 assert lookup_es_key(record, 'flags[1]') == 2 assert lookup_es_key(record, 'objects[0]foo') == 'bar' assert lookup_es_key(record, 'objects[1]foo[0]bar') == 'baz' assert lookup_es_key(record, 'objects[2]foo.bar') == 'baz' assert lookup_es_key(record, 'objects[1]foo[1]bar') is None assert lookup_es_key(record, 'objects[1]foo[0]baz') is None def test_add_raw_postfix(ea): expected = 'foo.raw' assert add_raw_postfix('foo', False) == expected assert add_raw_postfix('foo.raw', False) == expected expected = 'foo.keyword' assert add_raw_postfix('foo', True) == expected assert add_raw_postfix('foo.keyword', True) == expected def test_replace_dots_in_field_names(ea): actual = { 'a': { 'b.c': 'd', 'e': { 'f': { 'g.h': 0 } } }, 'i.j.k': 1, 'l': { 'm': 2 } } expected = { 'a': { 'b_c': 'd', 'e': { 'f': { 'g_h': 0 } } }, 'i_j_k': 1, 'l': { 'm': 2 } } assert replace_dots_in_field_names(actual) == expected assert replace_dots_in_field_names({'a': 0, 1: 2}) == {'a': 0, 1: 2} def test_resolve_string(ea): match = { 'name': 'mySystem', 'temperature': 45, 'humidity': 80.56, 'sensors': ['outsideSensor', 'insideSensor'], 'foo': {'bar': 'baz'} } expected_outputs = [ "mySystem is online <MISSING VALUE>", "Sensors ['outsideSensor', 'insideSensor'] in the <MISSING VALUE> have temp 45 and 80.56 humidity", "Actuator <MISSING VALUE> in the <MISSING VALUE> has temp <MISSING VALUE>", 'Something baz'] old_style_strings = [ "%(name)s is online %(noKey)s", "Sensors %(sensors)s in the %(noPlace)s have temp %(temperature)s and %(humidity)s humidity", "Actuator %(noKey)s in the %(noPlace)s has temp %(noKey)s", 'Something %(foo.bar)s'] assert resolve_string(old_style_strings[0], match) == expected_outputs[0] assert resolve_string(old_style_strings[1], match) == expected_outputs[1] assert resolve_string(old_style_strings[2], match) == expected_outputs[2] assert resolve_string(old_style_strings[3], match) == expected_outputs[3] new_style_strings = [ "{name} is online {noKey}", "Sensors {sensors} in the {noPlace} have temp {temperature} and {humidity} humidity", "Actuator {noKey} in the {noPlace} has temp {noKey}", "Something {foo[bar]}"] assert resolve_string(new_style_strings[0], match) == expected_outputs[0] assert resolve_string(new_style_strings[1], match) == expected_outputs[1] assert resolve_string(new_style_strings[2], match) == expected_outputs[2] assert resolve_string(new_style_strings[3], match) == expected_outputs[3] def test_format_index(): pattern = 'logstash-%Y.%m.%d' pattern2 = 'logstash-%Y.%W' date = dt('2018-06-25T12:00:00Z') date2 = dt('2018-06-26T12:00:00Z') assert sorted(format_index(pattern, date, date).split(',')) == ['logstash-2018.06.25'] assert sorted(format_index(pattern, date, date2).split(',')) == ['logstash-2018.06.25', 'logstash-2018.06.26'] assert sorted(format_index(pattern, date, date2, True).split(',')) == ['logstash-2018.06.24', 'logstash-2018.06.25', 'logstash-2018.06.26'] assert sorted(format_index(pattern2, date, date2, True).split(',')) == ['logstash-2018.25', 'logstash-2018.26'] def test_should_scrolling_continue(): rule_no_max_scrolling = {'max_scrolling_count': 0, 'scrolling_cycle': 1} rule_reached_max_scrolling = {'max_scrolling_count': 2, 'scrolling_cycle': 2} rule_before_first_run = {'max_scrolling_count': 0, 'scrolling_cycle': 0} rule_before_max_scrolling = {'max_scrolling_count': 2, 'scrolling_cycle': 1} rule_over_max_scrolling = {'max_scrolling_count': 2, 'scrolling_cycle': 3} assert should_scrolling_continue(rule_no_max_scrolling) is True assert should_scrolling_continue(rule_reached_max_scrolling) is False assert should_scrolling_continue(rule_before_first_run) is True assert should_scrolling_continue(rule_before_max_scrolling) is True assert should_scrolling_continue(rule_over_max_scrolling) is False def test_ts_to_dt_with_format1(): assert ts_to_dt_with_format('2021/02/01 12:30:00', '%Y/%m/%d %H:%M:%S') == dt('2021-02-01 12:30:00+00:00') def test_ts_to_dt_with_format2(): assert ts_to_dt_with_format('01/02/2021 12:30:00', '%d/%m/%Y %H:%M:%S') == dt('2021-02-01 12:30:00+00:00') def test_ts_to_dt_with_format3(): date = datetime(2021, 7, 6, hour=0, minute=0, second=0) assert ts_to_dt_with_format(date, '') == dt('2021-7-6 00:00') def test_ts_to_dt_with_format4(): assert ts_to_dt_with_format('01/02/2021 12:30:00 +0900', '%d/%m/%Y %H:%M:%S %z') == dt('2021-02-01 12:30:00+09:00') def test_dt_to_ts_with_format1(): assert dt_to_ts_with_format(dt('2021-02-01 12:30:00+00:00'), '%Y/%m/%d %H:%M:%S') == '2021/02/01 12:30:00' def test_dt_to_ts_with_format2(): assert dt_to_ts_with_format(dt('2021-02-01 12:30:00+00:00'), '%d/%m/%Y %H:%M:%S') == '01/02/2021 12:30:00' def test_dt_to_ts_with_format3(): assert dt_to_ts_with_format('2021-02-01 12:30:00+00:00', '%d/%m/%Y %H:%M:%S') == '2021-02-01 12:30:00+00:00' def test_flatten_dict(): assert flatten_dict({'test': 'value1', 'test2': 'value2'}) == {'test': 'value1', 'test2': 'value2'} def test_pytzfy1(): assert pytzfy(dt('2021-02-01 12:30:00+00:00')) == dt('2021-02-01 12:30:00+00:00') def test_pytzfy2(): assert pytzfy(datetime(2018, 12, 31, 5, 0, 30, 1000)) == dt('2018-12-31 05:00:30.001000') def test_get_module(): with pytest.raises(EAException) as ea: get_module('test') assert 'Could not import module' in str(ea) def test_dt_to_ts(caplog): caplog.set_level(logging.WARNING) dt_to_ts('a') user, level, message = caplog.record_tuples[0] assert 'elastalert' == user assert logging.WARNING == level assert 'Expected datetime, got' in message def test_ts_utc_to_tz(): date = datetime(2021, 7, 6, hour=0, minute=0, second=0) actual_data = ts_utc_to_tz(date, 'Europe/Istanbul') assert '2021-07-06 03:00:00+03:00' == str(actual_data) test_build_es_conn_config_param = 'es_host, es_port, es_conn_timeout, es_send_get_body_as, ssl_show_warn, es_username, ' test_build_es_conn_config_param += 'es_password, es_api_key, es_bearer, aws_region, profile, use_ssl, verify_certs, ' test_build_es_conn_config_param += 'ca_certs, client_cert,client_key,es_url_prefix, expected_data' @pytest.mark.parametrize(test_build_es_conn_config_param, [ ('', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', True), ('localhost', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', True), ('localhost', 9200, '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', { 'use_ssl': False, 'verify_certs': True, 'ca_certs': None, 'client_cert': None, 'client_key': None, 'http_auth': None, 'es_username': None, 'es_password': None, 'es_api_key': None, 'es_bearer': None, 'aws_region': None, 'profile': None, 'headers': None, 'es_host': 'localhost', 'es_port': 9200, 'es_url_prefix': '', 'es_conn_timeout': 20, 'send_get_body_as': 'GET', 'ssl_show_warn': True }), ('localhost', 9200, 30, 'POST', False, 'user', '<PASSWORD>', 'key', 'bearer', 'us-east-1', 'default', True, False, '/path/to/cacert.pem', '/path/to/client_cert.pem', '/path/to/client_key.key', 'elasticsearch', { 'use_ssl': True, 'verify_certs': False, 'ca_certs': '/path/to/cacert.pem', 'client_cert': '/path/to/client_cert.pem', 'client_key': '/path/to/client_key.key', 'http_auth': None, 'es_username': 'user', 'es_password': '<PASSWORD>', 'es_api_key': 'key', 'es_bearer': 'bearer', 'aws_region': 'us-east-1', 'profile': 'default', 'headers': None, 'es_host': 'localhost', 'es_port': 9200, 'es_url_prefix': 'elasticsearch', 'es_conn_timeout': 30, 'send_get_body_as': 'POST', 'ssl_show_warn': False }), ]) def test_build_es_conn_config(es_host, es_port, es_conn_timeout, es_send_get_body_as, ssl_show_warn, es_username, es_password, es_api_key, es_bearer, aws_region, profile, use_ssl, verify_certs, ca_certs, client_cert, client_key, es_url_prefix, expected_data): try: conf = {} if es_host: conf['es_host'] = es_host if es_port: conf['es_port'] = es_port if es_conn_timeout: conf['es_conn_timeout'] = es_conn_timeout if es_send_get_body_as: conf['es_send_get_body_as'] = es_send_get_body_as if ssl_show_warn != '': conf['ssl_show_warn'] = ssl_show_warn if es_username: conf['es_username'] = es_username if es_password: conf['es_password'] = es_password if es_api_key: conf['es_api_key'] = es_api_key if es_bearer: conf['es_bearer'] = es_bearer if aws_region: conf['aws_region'] = aws_region if profile: conf['profile'] = profile if use_ssl != '': conf['use_ssl'] = use_ssl if verify_certs != '': conf['verify_certs'] = verify_certs if ca_certs: conf['ca_certs'] = ca_certs if client_cert: conf['client_cert'] = client_cert if client_key: conf['client_key'] = client_key if es_url_prefix: conf['es_url_prefix'] = es_url_prefix actual = build_es_conn_config(conf) assert expected_data == actual except KeyError: assert expected_data @mock.patch.dict(os.environ, {'ES_USERNAME': 'USER', 'ES_PASSWORD': '<PASSWORD>', 'ES_API_KEY': 'KEY', 'ES_BEARER': 'BEARE'}) def test_build_es_conn_config2(): conf = {} conf['es_host'] = 'localhost' conf['es_port'] = 9200 expected = { 'use_ssl': False, 'verify_certs': True, 'ca_certs': None, 'client_cert': None, 'client_key': None, 'http_auth': None, 'es_username': 'USER', 'es_password': '<PASSWORD>', 'es_api_key': 'KEY', 'es_bearer': 'BEARE', 'aws_region': None, 'profile': None, 'headers': None, 'es_host': 'localhost', 'es_port': 9200, 'es_url_prefix': '', 'es_conn_timeout': 20, 'send_get_body_as': 'GET', 'ssl_show_warn': True } actual = build_es_conn_config(conf) assert expected == actual @pytest.mark.parametrize('es_host, es_port, es_bearer, es_api_key', [ ('localhost', 9200, '', ''), ('localhost', 9200, 'bearer', 'bearer') ]) @mock.patch.dict(os.environ, {'AWS_DEFAULT_REGION': ''}) def test_elasticsearch_client(es_host, es_port, es_bearer, es_api_key): conf = {} conf['es_host'] = es_host conf['es_port'] = es_port if es_bearer: conf['es_bearer'] = es_bearer if es_api_key: conf['es_api_key'] = es_api_key acutual = elasticsearch_client(conf) assert None is not acutual def test_expand_string_into_dict(): dictionnary = {'@timestamp': '2021-07-06 01:00:00', 'metric_netfilter.ipv4_dst_cardinality': 401} string = 'metadata.source.ip' value = '0.0.0.0' expand_string_into_dict(dictionnary, string, value) assert dictionnary['metadata']['source']['ip'] == value def test_inc_ts(): dt = datetime(2021, 7, 6, hour=0, minute=0, second=0) actual = inc_ts(dt) expected = '2021-07-06T00:00:00.001000Z' assert expected == actual @pytest.mark.parametrize('dt, expected', [ (None, 0), ( timedelta( days=50, seconds=27, microseconds=10, milliseconds=29000, minutes=5, hours=8, weeks=2), 5558756.00001 ) ]) def test_total_seconds(dt, expected): actual = total_seconds(dt) assert expected == actual def test_unixms_to_dt(): ts = 1626707067 actual = unixms_to_dt(ts) expected = datetime(1970, 1, 19, 19, 51, 47, 67000, tzinfo=tzutc()) assert expected == actual def test_dt_to_int(): dt = datetime(2021, 7, 6, hour=0, minute=0, second=0) actual = dt_to_int(dt) expected = 1625529600000 assert expected == actual def test_format_string(): target = 0.966666667 expected_percent_formatting = '0.97' assert format_string('%.2f', target) == expected_percent_formatting expected_str_formatting = '96.67%' assert format_string('{:.2%}', target) == expected_str_formatting def test_pretty_ts(): ts = datetime(year=2021, month=8, day=16, hour=16, minute=35, second=5) assert '2021-08-16 16:35 UTC' == pretty_ts(ts) assert '2021-08-16 16:35 ' == pretty_ts(ts, False) assert '2021-08-16 16:35 +0000' == pretty_ts(ts, ts_format='%Y-%m-%d %H:%M %z')
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setup.py
mudox/pytav
0
41608
# -*- coding: utf-8 -*- """setup.py: setuptools control.""" import re from setuptools import find_packages, setup version = re.search(r"^__version__\s*=\s*'(.*)'", open('src/tav/cmd.py').read(), re.M).group(1) setup( name='tav', version=version, description='TBD', long_description='TBD', author='Mudox', author_email='<EMAIL>', url='https://github.com/mudox/tav', package_dir={'': 'src'}, packages=find_packages('src'), install_requires=[ 'libtmux', 'ruamel.yaml', ], package_data={ '': ['resources/*'], }, scripts=[ 'asset/script/tav', ], entry_points={ "console_scripts": ['tav-core = tav.cmd:run'] })
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src/utils/regex_utils/regex_utils.py
BichengWang/python-notebook
0
23069
import re def find_indices(): return [m.start(0) for m in re.finditer(reg, content)] def find_content(): return re.findall(reg, content) if __name__ == "__main__": content = 'an example word:cat and word:dog' reg = r'word:\w' print(find_indices()) print(find_content())
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webapp/run.py
128technology/blaster
0
121805
from blaster import factory import blaster if __name__ == "__main__": app = factory.create_app(celery=blaster.celery) app.jinja_env.add_extension('jinja2.ext.do') app.run(host='0.0.0.0', port=80)
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Exam_Exercises/Sase.py
Konstantin-Bogdanoski/VI
2
68530
<reponame>Konstantin-Bogdanoski/VI from Python_neinformirano_prebaruvanje_final import * # Check if the white Rook is in a valid position (not in the line of fire of all the black Rooks) def validityOfWhite(gun, A): #print("TESTING WHITE VALIDITY") # print(gun) location = gun #print(location[0] != A[4][0] and location[0] != A[5][0] and location[0] != A[6][0] and location[0] != A[7][0] and # location[1] != A[4][1] and location[1] != A[5][1] and location[1] != A[6][1] and location[1] != A[7][1] and # location[0] < 6 and location[0] > 0 and location[1] > 0 and location[1] < 8) return (location[0] != A[4][0] and location[0] != A[5][0] and location[0] != A[6][0] and location[0] != A[7][0] and location[1] != A[4][1] and location[1] != A[5][1] and location[1] != A[6][1] and location[1] != A[7][1] and location[0] < 6 and location[0] > 0 and location[1] > 0 and location[1] < 8) # Check if the black Rook is in a valid position (not in the line of fire of all the white Rooks) def validityOfBlack(gun, A): #print("TESTING BLACK VALIDITY") # print(gun) location = gun #print(location[0] != A[0][0] and location[0] != A[1][0] and location[0] != A[2][0] and location[0] != A[3][0] and # location[1] != A[0][1] and location[1] != A[1][1] and location[1] != A[2][1] and location[1] != A[3][1] and # location[0] < 6 and location[0] > 0 and location[1] > 0 and location[1] < 8) return (location[0] != A[0][0] and location[0] != A[1][0] and location[0] != A[2][0] and location[0] != A[3][0] and location[1] != A[0][1] and location[1] != A[1][1] and location[1] != A[2][1] and location[1] != A[3][1] and location[0] < 6 and location[0] > 0 and location[1] > 0 and location[1] < 8) # Check if the white rooks are not on top of each other def validityWhiteOnWhite(A): #print("TESTING WHITEonWHITE") #print(A[0][0] != A[1][0] and A[0][0] != A[2][0] and A[0][0] != A[3][0] and # A[1][0] != A[2][0] and A[1][0] != A[3][0] and # A[2][0] != A[3][0] and # A[0][1] != A[1][1] and A[0][1] != A[2][1] and A[0][1] != A[3][1] and # A[1][1] != A[2][1] and A[1][1] != A[3][1] and # A[2][1] != A[3][1]) return (A[0][0] != A[1][0] and A[0][0] != A[2][0] and A[0][0] != A[3][0] and A[1][0] != A[2][0] and A[1][0] != A[3][0] and A[2][0] != A[3][0] and A[0][1] != A[1][1] and A[0][1] != A[2][1] and A[0][1] != A[3][1] and A[1][1] != A[2][1] and A[1][1] != A[3][1] and A[2][1] != A[3][1]) # Check if the black rooks are not on top of each other def validityBlackOnBlack(A): #print("TESTING BLACKonBLACK") #print(A[0][0] != A[1][0] and A[0][0] != A[2][0] and A[0][0] != A[3][0] and # A[1][0] != A[2][0] and A[1][0] != A[3][0] and # A[2][0] != A[3][0] and # A[0][1] != A[1][1] and A[0][1] != A[2][1] and A[0][1] != A[3][1] and # A[1][1] != A[2][1] and A[1][1] != A[3][1] and # A[2][1] != A[3][1]) return (A[4][0] != A[5][0] and A[4][0] != A[6][0] and A[4][0] != A[7][0] and A[5][0] != A[6][0] and A[5][0] != A[7][0] and A[6][0] != A[7][0] and A[4][1] != A[5][1] and A[4][1] != A[6][1] and A[4][1] != A[7][1] and A[5][1] != A[6][1] and A[5][1] != A[7][1] and A[6][1] != A[7][1]) class Rooks(Problem): def __init__(self, initial): self.initial = initial def goal_test(self, state): return ((state[0] == (5,8) or state[0] == (5,7) or state[0] == (5,6) or state[0] == (5,5)) and (state[1] == (5, 8) or state[1] == (5, 7) or state[1] == (5, 6) or state[1] == (5, 5)) and (state[2] == (5, 8) or state[2] == (5, 7) or state[2] == (5, 6) or state[2] == (5, 5)) and (state[3] == (5, 8) or state[3] == (5, 7) or state[3] == (5, 6) or state[3] == (5, 5)) and (state[4] == (1, 1) or state[4] == (1, 2) or state[4] == (1, 3) or state[4] == (1, 4)) and (state[5] == (1, 1) or state[5] == (1, 2) or state[5] == (1, 3) or state[5] == (1, 4)) and (state[6] == (1, 1) or state[6] == (1, 2) or state[6] == (1, 3) or state[6] == (1, 4)) and (state[7] == (1, 1) or state[7] == (1, 2) or state[7] == (1, 3) or state[7] == (1, 4))) def actions(self, state): return self.successor(state).keys() def result(self, state, action): possible = self.successor(state) return possible[action] def successor(self, state): successors = dict() WhiteRook1 = state[0] WhiteRook2 = state[1] WhiteRook3 = state[2] WhiteRook4 = state[3] BlackRook1 = state[4] BlackRook2 = state[5] BlackRook3 = state[6] BlackRook4 = state[7] # # # # WHITE ROOK 1 # # # # WhiteRook1 UP newWhiteRook1 = WhiteRook1 moves=0 tempState = (newWhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, BlackRook2, BlackRook3, BlackRook4) while(not(validityOfWhite(newWhiteRook1, tempState)) and validityWhiteOnWhite(tempState) and validityBlackOnBlack(tempState)): newWhiteRook1 = (newWhiteRook1[0], newWhiteRook1[1] - 1) moves+=1 tempState = (newWhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, BlackRook2, BlackRook3, BlackRook4) newWhiteRook1 = (newWhiteRook1[0], newWhiteRook1[1] + 1) stateNew = (newWhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, BlackRook2, BlackRook3, BlackRook4) successors['WhiteRook1 - UP: ' + str(moves)] = stateNew # WhiteRook1 DOWN newWhiteRook1 = WhiteRook1 moves = 0 tempState = (newWhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, BlackRook2, BlackRook3, BlackRook4) while (not(validityOfWhite(newWhiteRook1, tempState)) and validityWhiteOnWhite(tempState) and validityBlackOnBlack(tempState)): newWhiteRook1 = (newWhiteRook1[0], newWhiteRook1[1] + 1) moves += 1 tempState = (newWhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, BlackRook2, BlackRook3, BlackRook4) newWhiteRook1 = (newWhiteRook1[0], newWhiteRook1[1] - 1) stateNew = (newWhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, BlackRook2, BlackRook3, BlackRook4) successors['WhiteRook1 - DOWN: ' + str(moves)] = stateNew # WhiteRook1 LEFT newWhiteRook1 = WhiteRook1 moves = 0 tempState = (newWhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, BlackRook2, BlackRook3, BlackRook4) while (not(validityOfWhite(newWhiteRook1, tempState)) and validityWhiteOnWhite(tempState) and validityBlackOnBlack(tempState)): newWhiteRook1 = (newWhiteRook1[0] - 1, newWhiteRook1[1]) moves += 1 tempState = (newWhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, BlackRook2, BlackRook3, BlackRook4) newWhiteRook1 = (newWhiteRook1[0] + 1, newWhiteRook1[1]) stateNew = (newWhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, BlackRook2, BlackRook3, BlackRook4) successors['WhiteRook1 - LEFT: ' + str(moves)] = stateNew # WhiteRook1 RIGHT newWhiteRook1 = WhiteRook1 moves = 0 tempState = (newWhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, BlackRook2, BlackRook3, BlackRook4) while (not(validityOfWhite(newWhiteRook1, tempState)) and validityWhiteOnWhite(tempState) and validityBlackOnBlack(tempState)): newWhiteRook1 = (newWhiteRook1[0] + 1, newWhiteRook1[1]) moves += 1 tempState = ( newWhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, BlackRook2, BlackRook3, BlackRook4) newWhiteRook1 = (newWhiteRook1[0] - 1, newWhiteRook1[1]) stateNew = (newWhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, BlackRook2, BlackRook3, BlackRook4) successors['WhiteRook1 - RIGHT: ' + str(moves)] = stateNew # # # # # WHITE ROOK 2 # # # # WhiteRook2 UP newWhiteRook2 = WhiteRook2 moves = 0 tempState = (WhiteRook1, newWhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, BlackRook2, BlackRook3, BlackRook4) while (not(validityOfWhite(newWhiteRook2, tempState)) and validityWhiteOnWhite(tempState) and validityBlackOnBlack(tempState)): newWhiteRook2 = (newWhiteRook2[0], newWhiteRook2[1] - 1) moves += 1 tempState = (WhiteRook1, newWhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, BlackRook2, BlackRook3, BlackRook4) newWhiteRook2 = (newWhiteRook2[0], newWhiteRook2[1] + 1) stateNew = (WhiteRook1, newWhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, BlackRook2, BlackRook3, BlackRook4) successors['WhiteRook2 - UP: ' + str(moves)] = stateNew # WhiteRook2 DOWN newWhiteRook2 = WhiteRook2 moves = 0 tempState = (WhiteRook1, newWhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, BlackRook2, BlackRook3, BlackRook4) while (not(validityOfWhite(newWhiteRook2, tempState)) and validityWhiteOnWhite(tempState) and validityBlackOnBlack(tempState)): newWhiteRook2 = (newWhiteRook2[0], newWhiteRook2[1] + 1) moves += 1 tempState = (WhiteRook1, newWhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, BlackRook2, BlackRook3, BlackRook4) newWhiteRook2 = (newWhiteRook2[0], newWhiteRook2[1] - 1) stateNew = (WhiteRook1, newWhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, BlackRook2, BlackRook3, BlackRook4) successors['WhiteRook2 - DOWN: ' + str(moves)] = stateNew # WhiteRook2 LEFT newWhiteRook2 = WhiteRook2 moves = 0 tempState = (WhiteRook1, newWhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, BlackRook2, BlackRook3, BlackRook4) while (not(validityOfWhite(newWhiteRook2, tempState)) and validityWhiteOnWhite(tempState) and validityBlackOnBlack(tempState)): newWhiteRook2 = (newWhiteRook2[0] - 1, newWhiteRook2[1]) moves += 1 tempState = (WhiteRook1, newWhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, BlackRook2, BlackRook3, BlackRook4) newWhiteRook2 = (newWhiteRook2[0] + 1, newWhiteRook2[1]) stateNew = (WhiteRook1, newWhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, BlackRook2, BlackRook3, BlackRook4) successors['WhiteRook2 - LEFT: ' + str(moves)] = stateNew # WhiteRook2 RIGHT newWhiteRook2 = WhiteRook2 moves = 0 tempState = (WhiteRook1, newWhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, BlackRook2, BlackRook3, BlackRook4) while (not(validityOfWhite(newWhiteRook2, tempState)) and validityWhiteOnWhite(tempState) and validityBlackOnBlack(tempState)): newWhiteRook2 = (newWhiteRook2[0] + 1, newWhiteRook2[1]) moves += 1 tempState = (WhiteRook1, newWhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, BlackRook2, BlackRook3, BlackRook4) newWhiteRook2 = (newWhiteRook2[0] - 1, newWhiteRook2[1]) stateNew = (WhiteRook1, newWhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, BlackRook2, BlackRook3, BlackRook4) successors['WhiteRook2 - RIGHT: ' + str(moves)] = stateNew # # # # # WHITE ROOK 3 # # # # WhiteRook3 UP newWhiteRook3 = WhiteRook3 moves = 0 tempState = (WhiteRook1, WhiteRook2, newWhiteRook3, WhiteRook4, BlackRook1, BlackRook2, BlackRook3, BlackRook4) while (not(validityOfWhite(newWhiteRook3, tempState)) and validityWhiteOnWhite(tempState) and validityBlackOnBlack(tempState)): newWhiteRook3 = (newWhiteRook3[0], newWhiteRook3[1] - 1) moves += 1 tempState = (WhiteRook1, WhiteRook2, newWhiteRook3, WhiteRook4, BlackRook1, BlackRook2, BlackRook3, BlackRook4) newWhiteRook3 = (newWhiteRook3[0], newWhiteRook3[1] + 1) stateNew = (WhiteRook1, WhiteRook2, newWhiteRook3, WhiteRook4, BlackRook1, BlackRook2, BlackRook3, BlackRook4) successors['WhiteRook3 - UP: ' + str(moves)] = stateNew # WhiteRook3 DOWN newWhiteRook3 = WhiteRook3 moves = 0 tempState = (WhiteRook1, WhiteRook2, newWhiteRook3, WhiteRook4, BlackRook1, BlackRook2, BlackRook3, BlackRook4) while (not(validityOfWhite(newWhiteRook3, tempState)) and validityWhiteOnWhite(tempState) and validityBlackOnBlack( tempState)): newWhiteRook3 = (newWhiteRook3[0], newWhiteRook3[1] + 1) moves += 1 tempState = ( WhiteRook1, WhiteRook2, newWhiteRook3, WhiteRook4, BlackRook1, BlackRook2, BlackRook3, BlackRook4) newWhiteRook3 = (newWhiteRook3[0], newWhiteRook3[1] - 1) stateNew = (WhiteRook1, WhiteRook2, newWhiteRook3, WhiteRook4, BlackRook1, BlackRook2, BlackRook3, BlackRook4) successors['WhiteRook3 - DOWN: ' + str(moves)] = stateNew # WhiteRook3 LEFT newWhiteRook3 = WhiteRook3 moves = 0 tempState = (WhiteRook1, WhiteRook2, newWhiteRook3, WhiteRook4, BlackRook1, BlackRook2, BlackRook3, BlackRook4) while (not(validityOfWhite(newWhiteRook3, tempState)) and validityWhiteOnWhite(tempState) and validityBlackOnBlack( tempState)): newWhiteRook3 = (newWhiteRook3[0] - 1, newWhiteRook3[1]) moves += 1 tempState = ( WhiteRook1, WhiteRook2, newWhiteRook3, WhiteRook4, BlackRook1, BlackRook2, BlackRook3, BlackRook4) newWhiteRook3 = (newWhiteRook3[0] + 1, newWhiteRook3[1]) stateNew = (WhiteRook1, WhiteRook2, newWhiteRook3, WhiteRook4, BlackRook1, BlackRook2, BlackRook3, BlackRook4) successors['WhiteRook3 - LEFT: ' + str(moves)] = stateNew # WhiteRook3 RIGHT newWhiteRook3 = WhiteRook3 moves = 0 tempState = (WhiteRook1, WhiteRook2, newWhiteRook3, WhiteRook4, BlackRook1, BlackRook2, BlackRook3, BlackRook4) while (not(validityOfWhite(newWhiteRook3, tempState)) and validityWhiteOnWhite(tempState) and validityBlackOnBlack( tempState)): newWhiteRook3 = (newWhiteRook3[0] + 1, newWhiteRook3[1]) moves += 1 tempState = (WhiteRook1, WhiteRook2, newWhiteRook3, WhiteRook4, BlackRook1, BlackRook2, BlackRook3, BlackRook4) newWhiteRook3 = (newWhiteRook3[0] - 1, newWhiteRook3[1]) stateNew = (WhiteRook1, WhiteRook2, newWhiteRook3, WhiteRook4, BlackRook1, BlackRook2, BlackRook3, BlackRook4) successors['WhiteRook3 - RIGHT: ' + str(moves)] = stateNew # # # # # WHITE ROOK 4 # # # # WhiteRook4 UP newWhiteRook4 = WhiteRook4 moves = 0 tempState = (WhiteRook1, WhiteRook2, WhiteRook3, newWhiteRook4, BlackRook1, BlackRook2, BlackRook3, BlackRook4) while (not(validityOfWhite(newWhiteRook4, tempState)) and validityWhiteOnWhite(tempState) and validityBlackOnBlack( tempState)): newWhiteRook4 = (newWhiteRook4[0], newWhiteRook4[1] - 1) moves += 1 tempState = ( WhiteRook1, WhiteRook2, WhiteRook3, newWhiteRook4, BlackRook1, BlackRook2, BlackRook3, BlackRook4) newWhiteRook4 = (newWhiteRook4[0], newWhiteRook4[1] + 1) stateNew = (WhiteRook1, WhiteRook2, WhiteRook3, newWhiteRook4, BlackRook1, BlackRook2, BlackRook3, BlackRook4) successors['WhiteRook4 - UP: ' + str(moves)] = stateNew # WhiteRook4 DOWN newWhiteRook4 = WhiteRook4 moves = 0 tempState = (WhiteRook1, WhiteRook2, WhiteRook3, newWhiteRook4, BlackRook1, BlackRook2, BlackRook3, BlackRook4) while (not(validityOfWhite(newWhiteRook4, tempState)) and validityWhiteOnWhite(tempState) and validityBlackOnBlack( tempState)): newWhiteRook4 = (newWhiteRook4[0], newWhiteRook4[1] + 1) moves += 1 tempState = ( WhiteRook1, WhiteRook2, WhiteRook3, newWhiteRook4, BlackRook1, BlackRook2, BlackRook3, BlackRook4) newWhiteRook4 = (newWhiteRook4[0], newWhiteRook4[1] - 1) stateNew = (WhiteRook1, WhiteRook2, WhiteRook3, newWhiteRook4, BlackRook1, BlackRook2, BlackRook3, BlackRook4) successors['WhiteRook4 - DOWN: ' + str(moves)] = stateNew # WhiteRook4 LEFT newWhiteRook4 = WhiteRook4 moves = 0 tempState = (WhiteRook1, WhiteRook2, WhiteRook3, newWhiteRook4, BlackRook1, BlackRook2, BlackRook3, BlackRook4) while (not(validityOfWhite(newWhiteRook4, tempState)) and validityWhiteOnWhite(tempState) and validityBlackOnBlack( tempState)): newWhiteRook4 = (newWhiteRook4[0] - 1, newWhiteRook4[1]) moves += 1 tempState = ( WhiteRook1, WhiteRook2, WhiteRook3, newWhiteRook4, BlackRook1, BlackRook2, BlackRook3, BlackRook4) newWhiteRook4 = (newWhiteRook4[0] + 1, newWhiteRook4[1]) stateNew = (WhiteRook1, WhiteRook2, WhiteRook3, newWhiteRook4, BlackRook1, BlackRook2, BlackRook3, BlackRook4) successors['WhiteRook4 - LEFT: ' + str(moves)] = stateNew # WhiteRook4 RIGHT newWhiteRook4 = WhiteRook4 moves = 0 tempState = (WhiteRook1, WhiteRook2, WhiteRook3, newWhiteRook4, BlackRook1, BlackRook2, BlackRook3, BlackRook4) while (not(validityOfWhite(newWhiteRook4, tempState)) and validityWhiteOnWhite(tempState) and validityBlackOnBlack( tempState)): newWhiteRook4 = (newWhiteRook4[0] + 1, newWhiteRook4[1]) moves += 1 tempState = (WhiteRook1, WhiteRook2, WhiteRook3, newWhiteRook4, BlackRook1, BlackRook2, BlackRook3, BlackRook4) newWhiteRook4 = (newWhiteRook4[0] - 1, newWhiteRook4[1]) stateNew = (WhiteRook1, WhiteRook2, WhiteRook3, newWhiteRook4, BlackRook1, BlackRook2, BlackRook3, BlackRook4) successors['WhiteRook4 - RIGHT: ' + str(moves)] = stateNew # # # # BLACK ROOK 1 # # # # BlackRook1 UP newBlackRook1 = BlackRook1 moves = 0 tempState = (WhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, newBlackRook1, BlackRook2, BlackRook3, BlackRook4) while (not(validityOfBlack(newBlackRook1, tempState)) and validityWhiteOnWhite(tempState) and validityBlackOnBlack( tempState)): newBlackRook1 = (newBlackRook1[0], newBlackRook1[1] - 1) moves += 1 tempState = ( WhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, newBlackRook1, BlackRook2, BlackRook3, BlackRook4) newBlackRook1 = (newBlackRook1[0], newBlackRook1[1] + 1) stateNew = (WhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, newBlackRook1, BlackRook2, BlackRook3, BlackRook4) successors['BlackRook1 - UP: ' + str(moves)] = stateNew # BlackRook1 DOWN newBlackRook1 = WhiteRook1 moves = 0 tempState = (WhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, newBlackRook1, BlackRook2, BlackRook3, BlackRook4) while (not(validityOfBlack(newBlackRook1, tempState)) and validityWhiteOnWhite(tempState) and validityBlackOnBlack( tempState)): newBlackRook1 = (newBlackRook1[0], newBlackRook1[1] + 1) moves += 1 tempState = (WhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, newBlackRook1, BlackRook2, BlackRook3, BlackRook4) newBlackRook1 = (newBlackRook1[0], newBlackRook1[1] - 1) stateNew = (WhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, newBlackRook1, BlackRook2, BlackRook3, BlackRook4) successors['BlackRook1 - DOWN: ' + str(moves)] = stateNew # BlackRook1 LEFT newBlackRook1 = WhiteRook1 moves = 0 tempState = (WhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, newBlackRook1, BlackRook2, BlackRook3, BlackRook4) while (not(validityOfBlack(newBlackRook1, tempState)) and validityWhiteOnWhite(tempState) and validityBlackOnBlack( tempState)): newBlackRook1 = (newBlackRook1[0] - 1, newBlackRook1[1]) moves += 1 tempState = (WhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, newBlackRook1, BlackRook2, BlackRook3, BlackRook4) newBlackRook1 = (newBlackRook1[0] + 1, newBlackRook1[1]) stateNew = (WhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, newBlackRook1, BlackRook2, BlackRook3, BlackRook4) successors['BlackRook1 - LEFT: ' + str(moves)] = stateNew # BlackRook1 RIGHT newBlackRook1 = WhiteRook1 moves = 0 tempState = (WhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, newBlackRook1, BlackRook2, BlackRook3, BlackRook4) while (not(validityOfBlack(newBlackRook1, tempState)) and validityWhiteOnWhite(tempState) and validityBlackOnBlack( tempState)): newBlackRook1 = (newBlackRook1[0] + 1, newBlackRook1[1]) moves += 1 tempState = (WhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, newBlackRook1, BlackRook2, BlackRook3, BlackRook4) newBlackRook1 = (newBlackRook1[0] - 1, newBlackRook1[1]) stateNew = (WhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, newBlackRook1, BlackRook2, BlackRook3, BlackRook4) successors['BlackRook1 - RIGHT: ' + str(moves)] = stateNew # # # # # BLACK ROOK 2 # # # # BlackRook2 UP newBlackRook2 = BlackRook2 moves = 0 tempState = (WhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, newBlackRook2, BlackRook3, BlackRook4) while (not(validityOfBlack(newBlackRook2, tempState)) and validityWhiteOnWhite(tempState) and validityBlackOnBlack( tempState)): newBlackRook2 = (newBlackRook2[0], newBlackRook2[1] - 1) moves += 1 tempState = ( WhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, newBlackRook2, BlackRook3, BlackRook4) newBlackRook2 = (newBlackRook2[0], newBlackRook2[1] + 1) stateNew = (WhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, newBlackRook2, BlackRook3, BlackRook4) successors['BlackRook2 - UP: ' + str(moves)] = stateNew # BlackRook2 DOWN newBlackRook2 = BlackRook2 moves = 0 tempState = (WhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, newBlackRook2, BlackRook3, BlackRook4) while (not(validityOfBlack(newBlackRook2, tempState)) and validityWhiteOnWhite(tempState) and validityBlackOnBlack( tempState)): newBlackRook2 = (newBlackRook2[0], newBlackRook2[1] + 1) moves += 1 tempState = ( WhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, newBlackRook2, BlackRook3, BlackRook4) newBlackRook2 = (newBlackRook2[0], newBlackRook2[1] - 1) stateNew = (WhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, newBlackRook2, BlackRook3, BlackRook4) successors['BlackRook2 - DOWN: ' + str(moves)] = stateNew # BlackRook2 LEFT newBlackRook2 = BlackRook2 moves = 0 tempState = (WhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, newBlackRook2, BlackRook3, BlackRook4) while (not(validityOfBlack(newBlackRook2, tempState)) and validityWhiteOnWhite(tempState) and validityBlackOnBlack( tempState)): newBlackRook2 = (newBlackRook2[0] - 1, newBlackRook2[1]) moves += 1 tempState = ( WhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, newBlackRook2, BlackRook3, BlackRook4) newBlackRook2 = (newBlackRook2[0] + 1, newBlackRook2[1]) stateNew = (WhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, newBlackRook2, BlackRook3, BlackRook4) successors['BlackRook2 - LEFT: ' + str(moves)] = stateNew # BlackRook2 RIGHT newBlackRook2 = BlackRook2 moves = 0 tempState = (WhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, newBlackRook2, BlackRook3, BlackRook4) while (not(validityOfBlack(newBlackRook2, tempState)) and validityWhiteOnWhite(tempState) and validityBlackOnBlack( tempState)): newBlackRook2 = (newBlackRook2[0] + 1, newBlackRook2[1]) moves += 1 tempState = ( WhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, newBlackRook2, BlackRook3, BlackRook4) newBlackRook2 = (newBlackRook2[0] - 1, newBlackRook2[1]) stateNew = (WhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, newBlackRook2, BlackRook3, BlackRook4) successors['BlackRook2 - RIGHT: ' + str(moves)] = stateNew # # # # # BLACK ROOK 3 # # # # BlackRook3 UP newBlackRook3 = BlackRook3 moves = 0 tempState = (WhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, BlackRook2, newBlackRook3, BlackRook4) while (not(validityOfBlack(newBlackRook3, tempState)) and validityWhiteOnWhite(tempState) and validityBlackOnBlack( tempState)): newBlackRook3 = (newBlackRook3[0], newBlackRook3[1] - 1) moves += 1 tempState = ( WhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, BlackRook2, newBlackRook3, BlackRook4) newBlackRook3 = (newBlackRook3[0], newBlackRook3[1] + 1) stateNew = (WhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, BlackRook2, newBlackRook3, BlackRook4) successors['BlackRook3 - UP: ' + str(moves)] = stateNew # BlackRook3 DOWN newBlackRook3 = BlackRook3 moves = 0 tempState = (WhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, BlackRook2, newBlackRook3, BlackRook4) while (not(validityOfBlack(newBlackRook3, tempState)) and validityWhiteOnWhite(tempState) and validityBlackOnBlack( tempState)): newBlackRook3 = (newBlackRook3[0], newBlackRook3[1] + 1) moves += 1 tempState = ( WhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, BlackRook2, newBlackRook3, BlackRook4) newBlackRook3 = (newBlackRook3[0], newBlackRook3[1] - 1) stateNew = (WhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, BlackRook2, newBlackRook3, BlackRook4) successors['BlackRook3 - DOWN: ' + str(moves)] = stateNew # BlackRook3 LEFT newBlackRook3 = BlackRook3 moves = 0 tempState = (WhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, BlackRook2, newBlackRook3, BlackRook4) while (not(validityOfBlack(newBlackRook3, tempState)) and validityWhiteOnWhite(tempState) and validityBlackOnBlack( tempState)): newBlackRook3 = (newBlackRook3[0] - 1, newBlackRook3[1]) moves += 1 tempState = ( WhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, BlackRook2, newBlackRook3, BlackRook4) newBlackRook3 = (newBlackRook3[0] + 1, newBlackRook3[1] - 1) stateNew = (WhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, BlackRook2, newBlackRook3, BlackRook4) successors['BlackRook3 - LEFT: ' + str(moves)] = stateNew # BlackRook3 RIGHT newBlackRook3 = BlackRook3 moves = 0 tempState = (WhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, BlackRook2, newBlackRook3, BlackRook4) while (not(validityOfBlack(newBlackRook3, tempState)) and validityWhiteOnWhite(tempState) and validityBlackOnBlack( tempState)): newBlackRook3 = (newBlackRook3[0] + 1, newBlackRook3[1]) moves += 1 tempState = ( WhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, BlackRook2, newBlackRook3, BlackRook4) newBlackRook3 = (newBlackRook3[0] - 1, newBlackRook3[1]) stateNew = (WhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, BlackRook2, newBlackRook3, BlackRook4) successors['BlackRook3 - RIGHT: ' + str(moves)] = stateNew # # # # # BLACK ROOK 4 # # # # BlackRook4 UP newBlackRook4 = BlackRook4 moves = 0 tempState = (WhiteRook1, WhiteRook2, WhiteRook3, newWhiteRook4, BlackRook1, BlackRook2, BlackRook3, BlackRook4) while (not(validityOfBlack(newBlackRook4, tempState)) and validityWhiteOnWhite(tempState) and validityBlackOnBlack( tempState)): newBlackRook4 = (newBlackRook4[0], newBlackRook4[1] - 1) moves += 1 tempState = ( WhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, BlackRook2, BlackRook3, newBlackRook4) newBlackRook4 = (newBlackRook4[0], newBlackRook4[1] + 1) stateNew = (WhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, BlackRook2, BlackRook3, newBlackRook4) successors['BlackRook4 - UP: ' + str(moves)] = stateNew # BlackRook4 DOWN newBlackRook4 = BlackRook4 moves = 0 tempState = (WhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, BlackRook2, BlackRook3, newBlackRook4) while (not(validityOfBlack(newBlackRook4, tempState)) and validityWhiteOnWhite(tempState) and validityBlackOnBlack( tempState)): newBlackRook4 = (newBlackRook4[0], newBlackRook4[1] + 1) moves += 1 tempState = ( WhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, BlackRook2, BlackRook3, newBlackRook4) newBlackRook4 = (newBlackRook4[0], newBlackRook4[1] - 1) stateNew = (WhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, BlackRook2, BlackRook3, newBlackRook4) successors['BlackRook4 - DOWN: ' + str(moves)] = stateNew # BlackRook4 LEFT newBlackRook4 = BlackRook4 moves = 0 tempState = (WhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, BlackRook2, BlackRook3, newBlackRook4) while (not(validityOfBlack(newBlackRook4, tempState)) and validityWhiteOnWhite(tempState) and validityBlackOnBlack( tempState)): newBlackRook4 = (newBlackRook4[0] - 1, newBlackRook4[1]) moves += 1 tempState = ( WhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, BlackRook2, BlackRook3, newBlackRook4) newBlackRook4 = (newBlackRook4[0] + 1, newBlackRook4[1]) stateNew = (WhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, BlackRook2, BlackRook3, newBlackRook4) successors['BlackRook4 - LEFT: ' + str(moves)] = stateNew # BlackRook4 RIGHT newBlackRook4 = BlackRook4 moves = 0 tempState = (WhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, BlackRook2, BlackRook3, newBlackRook4) while (not(validityOfBlack(newBlackRook4, tempState)) and validityWhiteOnWhite(tempState) and validityBlackOnBlack( tempState)): newBlackRook4 = (newBlackRook4[0] + 1, newBlackRook4[1]) moves += 1 tempState = ( WhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, BlackRook2, BlackRook3, newBlackRook4) newBlackRook4 = (newBlackRook4[0] - 1, newBlackRook4[1]) stateNew = (WhiteRook1, WhiteRook2, WhiteRook3, WhiteRook4, BlackRook1, BlackRook2, BlackRook3, newBlackRook4) successors['BlackRook4 - RIGHT: ' + str(moves)] = stateNew return successors Testing = Rooks(((1, 1), (1, 2), (1, 3), (1, 4), (5, 5), (5, 6), (5, 7), (5, 8))) answer = breadth_first_tree_search(Testing) print (answer.solution())
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Content/Networking/loadbalancers/AzureELB/WorkloadManager/src/azurelb/common.py
saikirangurijal/cloudcentersuite
8
127598
<reponame>saikirangurijal/cloudcentersuite<gh_stars>1-10 #!/usr/bin/env python def construct_fip_id(subscription_id, group_name, lb_name, fip_name): """Build the future FrontEndId based on components name. """ return ('/subscriptions/{}' '/resourceGroups/{}' '/providers/Microsoft.Network' '/loadBalancers/{}' '/frontendIPConfigurations/{}').format( subscription_id, group_name, lb_name, fip_name ) def construct_bap_id(subscription_id, group_name, lb_name, address_pool_name): """Build the future BackEndId based on components name. """ return ('/subscriptions/{}' '/resourceGroups/{}' '/providers/Microsoft.Network' '/loadBalancers/{}' '/backendAddressPools/{}').format( subscription_id, group_name, lb_name, address_pool_name ) def construct_probe_id(subscription_id, group_name, lb_name, probe_name): """Build the future ProbeId based on components name. """ return ('/subscriptions/{}' '/resourceGroups/{}' '/providers/Microsoft.Network' '/loadBalancers/{}' '/probes/{}').format( subscription_id, group_name, lb_name, probe_name ) def update_nic_paramaters(address_pool_id, location, nic_info, params): """Update the NIC parameters structure. """ nic_params = params['nicParams'] nic_params['location'] = location nic_params['ip_configurations'][0]['name'] = nic_info.ip_configurations[0].name nic_params['ip_configurations'][0]['subnet']['id'] = nic_info.ip_configurations[0].subnet.id nic_params['ip_configurations'][0]['load_balancer_backend_address_pools'] = [{ "id": address_pool_id }] return nic_params #nic_params['ip_configurations'][0]['load_balancer_inbound_nat_rules'][0]['id'] = natrule_id def create_vm_parameters(nic_id, is_nic_primary, location, vm_info, params): """Create the VM parameters structure. """ vm_params = params['vmParams'] vm_params['location'] = location vm_params['network_profile']['network_interfaces'][0]['id'] = nic_id vm_params['network_profile']['network_interfaces'][0]['primary'] = is_nic_primary return vm_params
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Core/chekhabar.py
hossshakiba/Che-Khabar-Bot
3
101719
from telegram.ext import Updater, CommandHandler, callbackcontext from telegram import Update, ReplyKeyboardMarkup from telegram.chataction import ChatAction from telegram.ext import MessageHandler from telegram.ext.filters import Filters from currency import * from coin_gold import * from crypto import * from stock import * from data import date from decouple import config token = config("TOKEN") date = date() def main_menu_handler(update: Update, context: callbackcontext): dollar_emoji = '\U0001F4B5' coin_emoji = '\U0001F4B0' stock_emoji = '\U0001F4B9' crypto_emoji = '\U0001F4C8' buttons = [ [f"{coin_emoji} قیمت طلا و سکه", f"{dollar_emoji} نرخ ارز"], [f"{crypto_emoji} ارزهای دیجیتال", f"{stock_emoji} بورس"], ["تبدیل ارز"] ] update.message.reply_text(text="منو اصلی", reply_markup= ReplyKeyboardMarkup(buttons, resize_keyboard=True)) def start_handler(update: Update, context: callbackcontext): chat_id = update.message.chat_id first_name = update.message.chat.first_name context.bot.send_chat_action(chat_id, ChatAction.TYPING) update.message.reply_text(f"سلام {first_name} عزیز.\nبه 'چ‌خبر' خوش اومدی!") main_menu_handler(update, context) def sekke_gold(update: Update, context: callbackcontext): buttons = [ ["طلای ۲۴ عیار","طلای ۱۸ عیار"], ["سکه امامی","سکه بهار آزادی"], ["ربع سکه", "نیم سکه"], ["بازگشت"] ] update.message.reply_text(text=f"قیمت طلا و سکه در تاریخ {date}", reply_markup= ReplyKeyboardMarkup(buttons, resize_keyboard=True)) def exchange(update: Update, context: callbackcontext): buttons = [ ["بازگشت \U0001F519"] ] text = ''' برای تبدیل ارز از الگوهای زیر استفاده کنید : مثال : ۱۵ دلار به ریال مثال : ۱۵۰۰۰۰ ریال به دلار *فعلا تبدیل ارزهای دلار و ریال امکان‌پذیر است. *اعداد اعشاری موجب بروز خطای محاسباتی می‌شوند. برای مثال : ۳۴.۶ دلار به ریال''' update.message.reply_text(text=f"{text}", reply_markup= ReplyKeyboardMarkup(buttons, resize_keyboard=True)) def arz(update: Update, context: callbackcontext): buttons = [ ["پوند انگلیس","یورو", "دلار آمریکا"], ["لیر ترکیه", "درهم امارات","فرانک سوئیس"], ["دلار کانادا", "ین ژاپن","یوان چین"], ["بازگشت \U0001F519"] ] update.message.reply_text(text=f"نرخ ارز در تاریخ {date}", reply_markup= ReplyKeyboardMarkup(buttons, resize_keyboard=True)) def crypto(update: Update, context: callbackcontext): buttons = [ ["تتر","اتریوم", "بیت‌کوین"], ["دش", "ریپل","لایت‌کوین"], ["بازگشت \U0001F519"] ] update.message.reply_text(text=f"نرخ ارزهای دیجیتال در تاریخ {date}", reply_markup= ReplyKeyboardMarkup(buttons, resize_keyboard=True)) def return_handler(update: Updater, context: callbackcontext): main_menu_handler(update, context) def main(): updater = Updater(token, use_context=True) updater.dispatcher.add_handler(CommandHandler("start", start_handler)) updater.dispatcher.add_handler(MessageHandler(Filters.regex("قیمت طلا و سکه"), sekke_gold)) updater.dispatcher.add_handler(MessageHandler(Filters.regex("نرخ ارز"), arz)) updater.dispatcher.add_handler(MessageHandler(Filters.regex("ارزهای دیجیتال"), crypto)) updater.dispatcher.add_handler(MessageHandler(Filters.regex("تبدیل ارز"), exchange)) updater.dispatcher.add_handler(MessageHandler(Filters.regex("بازگشت"), return_handler)) updater.dispatcher.add_handler(MessageHandler(Filters.regex("سکه بهار آزادی"), bahar)) updater.dispatcher.add_handler(MessageHandler(Filters.regex("سکه امامی"), emami)) updater.dispatcher.add_handler(MessageHandler(Filters.regex("نیم سکه"), nim)) updater.dispatcher.add_handler(MessageHandler(Filters.regex("ربع سکه"), rob)) updater.dispatcher.add_handler(MessageHandler(Filters.regex("طلای ۱۸ عیار"), gold_18)) updater.dispatcher.add_handler(MessageHandler(Filters.regex("طلای ۲۴ عیار"), gold_24)) updater.dispatcher.add_handler(MessageHandler(Filters.regex("دلار آمریکا"),dollar_usa)) updater.dispatcher.add_handler(MessageHandler(Filters.regex("یورو"), euro)) updater.dispatcher.add_handler(MessageHandler(Filters.regex("پوند انگلیس"), pond)) updater.dispatcher.add_handler(MessageHandler(Filters.regex("فرانک سوئیس"), frank)) updater.dispatcher.add_handler(MessageHandler(Filters.regex("درهم امارات"), derham)) updater.dispatcher.add_handler(MessageHandler(Filters.regex("لیر ترکیه"), lir)) updater.dispatcher.add_handler(MessageHandler(Filters.regex("یوان چین"), youan)) updater.dispatcher.add_handler(MessageHandler(Filters.regex("ین ژاپن"), yen)) updater.dispatcher.add_handler(MessageHandler(Filters.regex("دلار کانادا"), dollar_canada)) updater.dispatcher.add_handler(MessageHandler(Filters.regex("بورس"), stock_price)) updater.dispatcher.add_handler(MessageHandler(Filters.regex("بیت‌کوین"), bitcoin)) updater.dispatcher.add_handler(MessageHandler(Filters.regex("اتریوم"), etherium)) updater.dispatcher.add_handler(MessageHandler(Filters.regex("تتر"), tether)) updater.dispatcher.add_handler(MessageHandler(Filters.regex("دش"), dash)) updater.dispatcher.add_handler(MessageHandler(Filters.regex("ریپل"), riple)) updater.dispatcher.add_handler(MessageHandler(Filters.regex("لایت‌کوین"), litecoin)) updater.dispatcher.add_handler(MessageHandler(Filters.regex("\d دلار به ریال"), dollar_to_rial)) updater.dispatcher.add_handler(MessageHandler(Filters.regex("\d ریال به دلار"), rial_to_dollar)) updater.start_polling() updater.idle() main()
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saas/backend/apps/group/views.py
Canway-shiisa/bk-iam-saas
0
5428
# -*- coding: utf-8 -*- """ TencentBlueKing is pleased to support the open source community by making 蓝鲸智云-权限中心(BlueKing-IAM) available. Copyright (C) 2017-2021 THL A29 Limited, a Tencent company. All rights reserved. Licensed under the MIT License (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://opensource.org/licenses/MIT Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ import logging from functools import wraps from typing import List from django.shortcuts import get_object_or_404 from django.utils.translation import gettext as _ from drf_yasg.utils import swagger_auto_schema from pydantic.tools import parse_obj_as from rest_framework import serializers, status, views from rest_framework.pagination import LimitOffsetPagination from rest_framework.response import Response from rest_framework.viewsets import GenericViewSet, mixins from backend.account.permissions import RolePermission, role_perm_class from backend.apps.application.serializers import ConditionCompareSLZ, ConditionTagSLZ from backend.apps.group import tasks # noqa from backend.apps.group.models import Group from backend.apps.policy.serializers import PolicyDeleteSLZ, PolicySLZ, PolicySystemSLZ from backend.apps.template.models import PermTemplatePolicyAuthorized from backend.audit.audit import audit_context_setter, view_audit_decorator from backend.biz.group import GroupBiz, GroupCheckBiz, GroupMemberExpiredAtBean from backend.biz.policy import PolicyBean, PolicyOperationBiz, PolicyQueryBiz from backend.biz.policy_tag import ConditionTagBean, ConditionTagBiz from backend.biz.role import RoleBiz, RoleListQuery, RoleObjectRelationChecker from backend.biz.template import TemplateBiz from backend.common.error_codes import error_codes from backend.common.filters import NoCheckModelFilterBackend from backend.common.serializers import SystemQuerySLZ from backend.common.time import PERMANENT_SECONDS from backend.service.constants import PermissionCodeEnum, RoleType, SubjectType from backend.service.models import Subject from backend.trans.group import GroupTrans from .audit import ( GroupCreateAuditProvider, GroupDeleteAuditProvider, GroupMemberCreateAuditProvider, GroupMemberDeleteAuditProvider, GroupMemberRenewAuditProvider, GroupPolicyDeleteAuditProvider, GroupPolicyUpdateAuditProvider, GroupTemplateCreateAuditProvider, GroupTransferAuditProvider, GroupUpdateAuditProvider, ) from .constants import OperateEnum from .filters import GroupFilter, GroupTemplateSystemFilter from .serializers import ( GroupAddMemberSLZ, GroupAuthoriedConditionSLZ, GroupAuthorizationSLZ, GroupCreateSLZ, GroupDeleteMemberSLZ, GroupIdSLZ, GroupMemberUpdateExpiredAtSLZ, GroupPolicyUpdateSLZ, GroupSLZ, GroupTemplateDetailSchemaSLZ, GroupTemplateDetailSLZ, GroupTemplateSchemaSLZ, GroupTemplateSLZ, GroupTransferSLZ, GroupUpdateSLZ, MemberSLZ, SearchMemberSLZ, ) permission_logger = logging.getLogger("permission") def check_readonly_group(operation): """用户组可读检测""" def decorate(func): @wraps(func) def wrapper(view, request, *args, **kwargs): group = view.get_object() readonly = group.readonly if readonly: raise error_codes.FORBIDDEN.format( message=_("只读用户组({})无法进行({})操作！").format(group.id, operation), replace=True ) response = func(view, request, *args, **kwargs) return response return wrapper return decorate class GroupQueryMixin: def get_queryset(self): request = self.request return RoleListQuery(request.role, request.user).query_group() class GroupPermissionMixin: def check_object_permissions(self, request, obj): if not RoleObjectRelationChecker(request.role).check_group(obj): self.permission_denied(request, message=f"{request.role.type} role can not access group {obj.id}") class GroupViewSet(mixins.RetrieveModelMixin, mixins.ListModelMixin, GenericViewSet): permission_classes = [RolePermission] action_permission = { "create": PermissionCodeEnum.MANAGE_GROUP.value, "update": PermissionCodeEnum.MANAGE_GROUP.value, "destroy": PermissionCodeEnum.MANAGE_GROUP.value, } queryset = Group.objects.all() serializer_class = GroupSLZ filterset_class = GroupFilter lookup_field = "id" group_biz = GroupBiz() group_check_biz = GroupCheckBiz() role_biz = RoleBiz() group_trans = GroupTrans() @swagger_auto_schema( operation_description="创建用户组", request_body=GroupCreateSLZ(label="用户组"), responses={status.HTTP_201_CREATED: GroupIdSLZ(label="用户组ID")}, tags=["group"], ) @view_audit_decorator(GroupCreateAuditProvider) def create(self, request, *args, **kwargs): """ 创建用户组 """ serializer = GroupCreateSLZ(data=request.data) serializer.is_valid(raise_exception=True) user_id = request.user.username data = serializer.validated_data # 用户组名称在角色内唯一 self.group_check_biz.check_role_group_name_unique(request.role.id, data["name"]) # 用户组数量在角色内是否超限 number_of_new_group = 1 # 接口只支持创建一个用户组，不支持批量，所以新增用户组数量为1 self.group_check_biz.check_role_group_limit(request.role, number_of_new_group) # 检测成员是否满足管理的授权范围 members = parse_obj_as(List[Subject], data["members"]) self.group_check_biz.check_role_subject_scope(request.role, members) group = self.group_biz.create_and_add_members( request.role.id, data["name"], data["description"], user_id, members, data["expired_at"] ) # 使用长时任务触发多个模板同时授权 if data["templates"]: templates = self.group_trans.from_group_grant_data(data["templates"]) self.group_biz.grant(request.role, group, templates) # 写入审计上下文 audit_context_setter(group=group) return Response({"id": group.id}, status=status.HTTP_201_CREATED) def get_queryset(self): request = self.request role = request.role username = request.user.username filter_role_id = request.query_params.get("role_id") # 如果当前角色是staff 并且 存在筛选的role_id if role.type == RoleType.STAFF.value and filter_role_id: # 检查用户是否在角色的授权范围内 filter_role = self.role_biz.get_role_scope_include_user(filter_role_id, username) if not filter_role: return Group.objects.none() # 返回角色的用户组列表 return RoleListQuery(filter_role, request.user).query_group() return RoleListQuery(role, request.user).query_group() @swagger_auto_schema( operation_description="用户组列表", responses={status.HTTP_200_OK: GroupSLZ(label="用户组", many=True)}, tags=["group"], ) def list(self, request, *args, **kwargs): return super().list(request, *args, **kwargs) @swagger_auto_schema( operation_description="用户组详情", responses={status.HTTP_200_OK: GroupSLZ(label="用户组")}, tags=["group"], ) def retrieve(self, request, *args, **kwargs): return super().retrieve(request, *args, **kwargs) @swagger_auto_schema( operation_description="修改用户组", request_body=GroupUpdateSLZ(label="用户组"), responses={status.HTTP_200_OK: GroupUpdateSLZ(label="用户组")}, tags=["group"], ) @view_audit_decorator(GroupUpdateAuditProvider) @check_readonly_group(operation=OperateEnum.GROUP_UPDATE.label) def update(self, request, *args, **kwargs): group = self.get_object() serializer = GroupUpdateSLZ(group, data=request.data) serializer.is_valid(raise_exception=True) user_id = request.user.username data = serializer.validated_data # 用户组名称在角色内唯一 self.group_check_biz.check_role_group_name_unique(request.role.id, data["name"], group.id) group = self.group_biz.update(group, data["name"], data["description"], user_id) # 写入审计上下文 audit_context_setter(group=group) return Response(serializer.data) @swagger_auto_schema( operation_description="删除用户组", responses={status.HTTP_200_OK: serializers.Serializer()}, tags=["group"], ) @view_audit_decorator(GroupDeleteAuditProvider) @check_readonly_group(operation=OperateEnum.GROUP_DELETE.label) def destroy(self, request, *args, **kwargs): group = self.get_object() self.group_biz.delete(group.id) # 写入审计上下文 audit_context_setter(group=group) return Response({}) class GroupMemberViewSet(GroupPermissionMixin, GenericViewSet): permission_classes = [RolePermission] action_permission = { "list": PermissionCodeEnum.MANAGE_GROUP.value, "create": PermissionCodeEnum.MANAGE_GROUP.value, "destroy": PermissionCodeEnum.MANAGE_GROUP.value, } queryset = Group.objects.all() lookup_field = "id" biz = GroupBiz() group_check_biz = GroupCheckBiz() @swagger_auto_schema( operation_description="用户组成员列表", query_serializer=SearchMemberSLZ(label="keyword"), responses={status.HTTP_200_OK: MemberSLZ(label="成员")}, tags=["group"], ) def list(self, request, *args, **kwargs): group = get_object_or_404(self.queryset, pk=kwargs["id"]) # 校验权限 checker = RoleObjectRelationChecker(request.role) if not checker.check_group(group): raise error_codes.FORBIDDEN.format(message=_("用户组({})不在当前用户身份可访问的范围内").format(group.id), replace=True) if request.query_params.get("keyword"): slz = SearchMemberSLZ(data=request.query_params) slz.is_valid(raise_exception=True) keyword = slz.validated_data["keyword"].lower() group_members = self.biz.search_member_by_keyword(group.id, keyword) return Response({"results": [one.dict() for one in group_members]}) pagination = LimitOffsetPagination() limit = pagination.get_limit(request) offset = pagination.get_offset(request) count, group_members = self.biz.list_paging_group_member(group.id, limit, offset) return Response({"count": count, "results": [one.dict() for one in group_members]}) @swagger_auto_schema( operation_description="用户组添加成员", request_body=GroupAddMemberSLZ(label="成员"), responses={status.HTTP_200_OK: serializers.Serializer()}, tags=["group"], ) @view_audit_decorator(GroupMemberCreateAuditProvider) @check_readonly_group(operation=OperateEnum.GROUP_MEMBER_CREATE.label) def create(self, request, *args, **kwargs): serializer = GroupAddMemberSLZ(data=request.data) serializer.is_valid(raise_exception=True) group = self.get_object() data = serializer.validated_data members_data = data["members"] expired_at = data["expired_at"] # 成员Dict结构转换为Subject结构，并去重 members = list(set(parse_obj_as(List[Subject], members_data))) # 检测成员是否满足管理的授权范围 self.group_check_biz.check_role_subject_scope(request.role, members) self.group_check_biz.check_member_count(group.id, len(members)) permission_logger.info("group %s add members %s by user %s", group.id, members, request.user.username) # 添加成员 self.biz.add_members(group.id, members, expired_at) # 写入审计上下文 audit_context_setter(group=group, members=[m.dict() for m in members]) return Response({}, status=status.HTTP_201_CREATED) @swagger_auto_schema( operation_description="用户组删除成员", request_body=GroupDeleteMemberSLZ(label="成员"), responses={status.HTTP_200_OK: serializers.Serializer()}, tags=["group"], ) @view_audit_decorator(GroupMemberDeleteAuditProvider) @check_readonly_group(operation=OperateEnum.GROUP_MEMBER_DELETE.label) def destroy(self, request, *args, **kwargs): serializer = GroupDeleteMemberSLZ(data=request.data) serializer.is_valid(raise_exception=True) group = self.get_object() data = serializer.validated_data permission_logger.info( "group %s delete members %s by user %s", group.id, data["members"], request.user.username ) self.biz.remove_members(str(group.id), parse_obj_as(List[Subject], data["members"])) # 写入审计上下文 audit_context_setter(group=group, members=data["members"]) return Response({}) class GroupMemberUpdateExpiredAtViewSet(GroupPermissionMixin, GenericViewSet): permission_classes = [role_perm_class(PermissionCodeEnum.MANAGE_GROUP.value)] queryset = Group.objects.all() lookup_field = "id" # service group_biz = GroupBiz() @swagger_auto_schema( operation_description="用户组成员续期", request_body=GroupMemberUpdateExpiredAtSLZ(label="成员"), responses={status.HTTP_200_OK: serializers.Serializer()}, tags=["group"], ) @view_audit_decorator(GroupMemberRenewAuditProvider) @check_readonly_group(operation=OperateEnum.GROUP_MEMBER_RENEW.label) def create(self, request, *args, **kwargs): serializer = GroupMemberUpdateExpiredAtSLZ(data=request.data) serializer.is_valid(raise_exception=True) group = self.get_object() data = serializer.validated_data permission_logger.info( "group %s update members %s expired_at by user %s", group.id, data["members"], request.user.username ) for m in data["members"]: m["policy_expired_at"] = m.pop("expired_at") self.group_biz.update_members_expired_at( group.id, parse_obj_as(List[GroupMemberExpiredAtBean], data["members"]) ) # 写入审计上下文 audit_context_setter(group=group, members=data["members"]) return Response({}) class GroupTemplateViewSet(GroupPermissionMixin, GenericViewSet): permission_classes = [RolePermission] action_permission = {"create": PermissionCodeEnum.MANAGE_GROUP.value} pagination_class = None # 去掉swagger中的limit offset参数 queryset = Group.objects.all() filterset_class = GroupTemplateSystemFilter filter_backends = [NoCheckModelFilterBackend] lookup_field = "id" template_biz = TemplateBiz() @swagger_auto_schema( operation_description="用户组拥有的权限模板列表", responses={status.HTTP_200_OK: GroupTemplateSchemaSLZ(label="权限模板", many=True)}, tags=["group"], ) def list(self, request, *args, **kwargs): group = get_object_or_404(self.queryset, pk=kwargs["id"]) subject = Subject(type=SubjectType.GROUP.value, id=str(group.id)) queryset = PermTemplatePolicyAuthorized.objects.filter_by_subject(subject).defer("_data") queryset = self.filter_queryset(queryset) return Response(GroupTemplateSLZ(queryset, many=True).data) @swagger_auto_schema( operation_description="用户组权限模板授权信息", responses={status.HTTP_200_OK: GroupTemplateDetailSchemaSLZ(label="授权信息")}, tags=["group"], ) def retrieve(self, request, *args, **kwargs): group = get_object_or_404(self.queryset, pk=kwargs["id"]) template_id = kwargs["template_id"] subject = Subject(type=SubjectType.GROUP.value, id=str(group.id)) authorized_template = PermTemplatePolicyAuthorized.objects.get_by_subject_template(subject, int(template_id)) return Response(GroupTemplateDetailSLZ(authorized_template).data) class GroupPolicyViewSet(GroupPermissionMixin, GenericViewSet): permission_classes = [RolePermission] action_permission = { "create": PermissionCodeEnum.MANAGE_GROUP.value, "destroy": PermissionCodeEnum.MANAGE_GROUP.value, "update": PermissionCodeEnum.MANAGE_GROUP.value, } pagination_class = None # 去掉swagger中的limit offset参数 queryset = Group.objects.all() lookup_field = "id" policy_query_biz = PolicyQueryBiz() policy_operation_biz = PolicyOperationBiz() group_biz = GroupBiz() group_trans = GroupTrans() @swagger_auto_schema( operation_description="用户组添加权限", request_body=GroupAuthorizationSLZ(label="授权信息"), responses={status.HTTP_201_CREATED: serializers.Serializer()}, tags=["group"], ) @view_audit_decorator(GroupTemplateCreateAuditProvider) @check_readonly_group(operation=OperateEnum.GROUP_POLICY_CREATE.label) def create(self, request, *args, **kwargs): serializer = GroupAuthorizationSLZ(data=request.data) serializer.is_valid(raise_exception=True) group = self.get_object() data = serializer.validated_data templates = self.group_trans.from_group_grant_data(data["templates"]) self.group_biz.grant(request.role, group, templates) # 写入审计上下文 audit_context_setter( group=group, templates=[{"system_id": t["system_id"], "template_id": t["template_id"]} for t in data["templates"]], ) return Response({}, status=status.HTTP_201_CREATED) @swagger_auto_schema( operation_description="用户组自定义权限列表", query_serializer=SystemQuerySLZ, responses={status.HTTP_200_OK: PolicySLZ(label="策略", many=True)}, tags=["group"], ) def list(self, request, *args, **kwargs): slz = SystemQuerySLZ(data=request.query_params) slz.is_valid(raise_exception=True) system_id = slz.validated_data["system_id"] group = get_object_or_404(self.queryset, pk=kwargs["id"]) subject = Subject(type=SubjectType.GROUP.value, id=str(group.id)) policies = self.policy_query_biz.list_by_subject(system_id, subject) # ResourceNameAutoUpdate updated_policies = self.policy_operation_biz.update_due_to_renamed_resource(system_id, subject, policies) return Response([p.dict() for p in updated_policies]) @swagger_auto_schema( operation_description="用户组删除自定义权限", request_body=PolicyDeleteSLZ(label="ids"), responses={status.HTTP_200_OK: serializers.Serializer()}, tags=["group"], ) @view_audit_decorator(GroupPolicyDeleteAuditProvider) @check_readonly_group(operation=OperateEnum.GROUP_POLICY_DELETE.label) def destroy(self, request, *args, **kwargs): slz = PolicyDeleteSLZ(data=request.data) slz.is_valid(raise_exception=True) system_id = slz.validated_data["system_id"] ids = slz.validated_data["ids"] group = self.get_object() subject = Subject(type=SubjectType.GROUP.value, id=str(group.id)) permission_logger.info( "subject type=%s, id=%s policy deleted by user %s", subject.type, subject.id, request.user.username ) policy_list = self.policy_query_biz.query_policy_list_by_policy_ids(system_id, subject, ids) # 删除权限 self.policy_operation_biz.delete_by_ids(system_id, subject, ids) # 写入审计上下文 audit_context_setter(group=group, system_id=system_id, policies=policy_list.policies) return Response() @swagger_auto_schema( operation_description="用户组权限修改", request_body=GroupPolicyUpdateSLZ(label="修改策略"), responses={status.HTTP_200_OK: serializers.Serializer()}, tags=["group"], ) @view_audit_decorator(GroupPolicyUpdateAuditProvider) @check_readonly_group(operation=OperateEnum.GROUP_POLICY_UPDATE.label) def update(self, request, *args, **kwargs): group = self.get_object() slz = GroupPolicyUpdateSLZ(data=request.data) slz.is_valid(raise_exception=True) data = slz.validated_data system_id = data["system_id"] template_id = data["template_id"] policies = [PolicyBean(expired_at=PERMANENT_SECONDS, **action) for action in data["actions"]] self.group_biz.update_policies(request.role, group.id, system_id, template_id, policies) # 写入审计上下文 audit_context_setter(group=group, system_id=system_id, template_id=template_id, policies=policies) return Response({}) class GroupSystemViewSet(GenericViewSet): pagination_class = None # 去掉swagger中的limit offset参数 queryset = Group.objects.all() lookup_field = "id" biz = GroupBiz() @swagger_auto_schema( operation_description="用户组有权限的所有系统列表", responses={status.HTTP_200_OK: PolicySystemSLZ(label="系统", many=True)}, tags=["group"], ) def list(self, request, *args, **kwargs): group = self.get_object() data = self.biz.list_system_counter(group.id) return Response([one.dict() for one in data]) class GroupTransferView(views.APIView): """ 用户组转出 """ permission_classes = [role_perm_class(PermissionCodeEnum.TRANSFER_GROUP.value)] role_biz = RoleBiz() @swagger_auto_schema( operation_description="用户组批量转出", request_body=GroupTransferSLZ(label="用户转移"), responses={status.HTTP_200_OK: serializers.Serializer()}, tags=["group"], ) @view_audit_decorator(GroupTransferAuditProvider) def post(self, request, *args, **kwargs): slz = GroupTransferSLZ(data=request.data, context={"role": request.role}) slz.is_valid(raise_exception=True) group_ids = slz.validated_data["group_ids"] role_id = slz.validated_data["role_id"] self.role_biz.transfer_groups_role(group_ids, role_id) audit_context_setter(group_ids=group_ids, role_id=role_id) return Response({}) class GroupTemplateConditionCompareView(GroupPermissionMixin, GenericViewSet): condition_biz = ConditionTagBiz() template_biz = TemplateBiz() queryset = Group.objects.all() lookup_field = "id" @swagger_auto_schema( operation_description="权限模板操作条件对比", request_body=GroupAuthoriedConditionSLZ(label="操作条件"), responses={status.HTTP_200_OK: ConditionTagSLZ(label="条件差异", many=True)}, tags=["group"], ) def create(self, request, *args, **kwargs): serializer = GroupAuthoriedConditionSLZ(data=request.data) serializer.is_valid(raise_exception=True) data = serializer.validated_data group = self.get_object() action_id = data["action_id"] resource_group_id = data["resource_group_id"] related_resource_type = data["related_resource_type"] new_condition = parse_obj_as(List[ConditionTagBean], related_resource_type["condition"]) # 从模板数据中查找匹配的操作, 资源类型的条件 template_id = kwargs["template_id"] subject = Subject(type=SubjectType.GROUP.value, id=str(group.id)) authorized_template = PermTemplatePolicyAuthorized.objects.get_by_subject_template(subject, int(template_id)) for action in authorized_template.data["actions"]: policy = PolicyBean.parse_obj(action) # 查询对应的操作 if policy.action_id == action_id: # 操作操作中对应于资源类型的操作 related_resource_type = policy.get_related_resource_type( resource_group_id, related_resource_type["system_id"], related_resource_type["type"] ) old_condition = related_resource_type.condition if related_resource_type else [] # 对比用户组已有的条件与用户提交的条件 conditions = self.condition_biz.compare_and_tag( new_condition, parse_obj_as(List[ConditionTagBean], old_condition), is_template=True ) return Response([c.dict() for c in conditions]) raise error_codes.VALIDATE_ERROR.format(_("模板: {} 没有操作: {} 的权限").format(template_id, action_id)) class GroupCustomPolicyConditionCompareView(GroupPermissionMixin, GenericViewSet): policy_biz = PolicyQueryBiz() condition_biz = ConditionTagBiz() queryset = Group.objects.all() lookup_field = "id" @swagger_auto_schema( operation_description="条件差异对比", request_body=ConditionCompareSLZ(label="资源条件"), responses={status.HTTP_200_OK: ConditionTagSLZ(label="条件差异", many=True)}, tags=["group"], ) def create(self, request, *args, **kwargs): serializer = ConditionCompareSLZ(data=request.data) serializer.is_valid(raise_exception=True) data = serializer.validated_data group = self.get_object() subject = Subject(type=SubjectType.GROUP.value, id=str(group.id)) # 1. 查询policy的condition related_resource_type = data["related_resource_type"] old_condition = self.policy_biz.get_policy_resource_type_conditions( subject, data["policy_id"], data["resource_group_id"], related_resource_type["system_id"], related_resource_type["type"], ) # 2. 对比合并差异 conditions = self.condition_biz.compare_and_tag( parse_obj_as(List[ConditionTagBean], related_resource_type["condition"]), parse_obj_as(List[ConditionTagBean], old_condition), is_template=True, ) return Response([c.dict() for c in conditions])
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app/realoem.py
ballon3/electricdisc
0
51335
import httpx import requests from bs4 import BeautifulSoup as bso class BMW: BASE_URL = 'https://www.realoem.com/bmw/' LANGUAGE = 'enUS' URL = f"{BASE_URL}{LANGUAGE}" 'https://www.realoem.com/bmw/enUS/partgrp?id=0573-USA-12-1991-K569-BMW-K_75_RT_0565,0573_&mg=46' 'https://www.realoem.com/bmw/enUS/showparts?id=0573-USA-12-1991-K569-BMW-K_75_RT_0565,0573_&diagId=34_1906' 'https://www.realoem.com/bmw/enUS/showparts?id=0573-USA-12-1991-K569-BMW-K_75_RT_0565,0573_&diagId=34_1905' def get_main_group(id): params = {'id':id} r = httpx.get(f'{URL}/partgrp', params=params) print(r) print(r.url) return r.text def get_sub_group(url, filter): pass def get_part(diagId): pass def extract_link(soupy): soup = bso(soupy, "html.parser") for links in soup.find_all("a"): href = links. print(href,title) def test_main_group(id): BASE_URL = 'https://www.realoem.com/bmw/' LANGUAGE = 'enUS' URL = f"{BASE_URL}{LANGUAGE}" params = {'id':id} r = httpx.get(f'{URL}/partgrp', params=params) soup = bso(r.text, "html.parser") for links in soup.find_all("div", {"class": "mg-thumb"}): href = links. print(href,title) K75 = '0573-USA-12-1991-K569-BMW-K_75_RT_0565%2C0573_' test_main_group(K75)
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catalogo/views/empleado_view.py
lp2-20172/polleria-serve
0
113709
from ..models_raiz import Empleado from rest_framework import serializers, viewsets from rest_framework import permissions from django.db.models import Q from operator import __or__ as OR from functools import reduce class EmpleadoSerializer(serializers.ModelSerializer): class Meta: model = Empleado fields = '__all__' #fields = ('id', 'username', 'email', 'is_staff') class EmpleadoViewSet(viewsets.ModelViewSet): queryset = Empleado.objects.all() serializer_class = EmpleadoSerializer
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O.py
duongnguyenkt11/data-realtime
0
19835
<reponame>duongnguyenkt11/data-realtime from functools import reduce from bokeh.plotting import figure, output_file, show from bokeh.io import output_notebook from CONSTANTS import * from utilities import * from bokeh.plotting import figure, output_file, show import pandas as pd, numpy as np ENVIRON = C.LOCAL def mmap(*args): return list(map(*args)) class O: ################################################################################ # Init # ################################################################################ def __init__(self, cleansed, df=None): self.cleansed = cleansed self.n = len(cleansed[C.CTIME]) self.hoseData = {cleansed[C.CTIME][i]: cleansed[C.CHOSE][i] for i in range(len(cleansed[C.CTIME]))} self.hoseArr = cleansed[C.CHOSE] self.stocks = sorted(list(cleansed[C.CHOSE][0].keys())) self.times = cleansed[C.CTIME] self.errorDataPoints = [] self.hours = mmap(numerizeTime, self.times) self.seconds = mmap(toSeconds, self.times) self.Xs = mmap(numerizeTime, self.times) self.p = figure(plot_width=1400, plot_height=400) self.df = pd.DataFrame.from_dict(self.cleansed) self.nnBuyVolumes = [-1] * self.n self.nnSellVolumes = [-1] * self.n self.nnBuyValues = [-1] * self.n self.nnSellValues = [-1] * self.n self.numpied = False self.allPlots = [] self.intervals = [] self.totalLiquidity = [] def numpyItUp(self): if self.numpied: return self.numpied = True a = [self.nnBuyVolumes, self.nnSellVolumes, self.nnBuyValues, self.nnSellValues, self.hours, self.seconds, self.Xs, self.times] for x in a: x = np.array(x) ################################################################################ # Utilities - Misc # ################################################################################ def timeRange(self, start, end): resStart, resEnd = -1, -1 for i in range(1, len(self.Xs)): if self.Xs[i] > start: resStart = i - 1 break for i in range(len(self.Xs) - 1, 0, -1): if self.Xs[i] < end: resEnd = i + 1 break return resStart, resEnd def setInterval(self, STEP=60): return ############################################################################### # Computation # ############################################################################### def _calculateNNVolumes_i(self, i): time = self.times[i] print(f"\r{i}: {time}", end="") hose = self.hoseData[time] buys, sells = [], [] try: buys = mmap(lambda stock: hose[stock][C.NN_BUY], self.stocks) sells = mmap(lambda stock: hose[stock][C.NN_SELL], self.stocks) except: self.errorDataPoints.append(time) finally: if len(buys) > 0 and len(sells) > 0: self.nnBuyVolumes[i] = buys self.nnSellVolumes[i] = sells def calculateNNVolumes(self): mmap(self._calculateNNVolumes_i, range(self.n)) def _calculateNNValues_i(self, i): time = self.times[i] hose = self.hoseData[time] print(f"\r{i}: {time}", end="") self.nnBuyValues[i] = (reduce(lambda a, b: a + b, map(lambda stock: hose[stock][C.NN_BUY] * hose[stock][C.COL_AVG_PRICE], self.stocks))) self.nnSellValues[i] = (reduce(lambda a, b: a + b, map(lambda stock: hose[stock][C.NN_SELL] * hose[stock][C.COL_AVG_PRICE], self.stocks))) def calculateNNValues(self): mmap(self._calculateNNValues_i, range(self.n)) def applyPricingConventions(self): self.nnBuyValues = [x / 100000 for x in self.nnBuyValues] self.nnSellValues = [x / 100000 for x in self.nnSellValues] def calculateTradedValues(self): def valAtSnapShot(time): hose = self.hoseData[time] stockTradedValue = lambda stock: hose[stock][C.COL_TOTAL_VOL]*hose[stock][C.COL_AVG_PRICE] vals = mmap(stockTradedValue, self.stocks) return reduce(lambda a, b: a + b, vals) self.totalLiquidity = mmap(valAtSnapShot, self.times) ################################################################################ # Plotting # ################################################################################ def plot_Liquidity_Bar(self): return def initializePlot(self, file,p, ENVIRON=ENVIRON, title="HOSE"): if ENVIRON == C.LOCAL: output_file(file) else: output_notebook() if p is None: p = figure(plot_width=PC.WIDTH, plot_height=PC.HEIGHT, title=title) self.allPlots.append(p) return p return p def plot_BS_Pressure(self, p=None, file=FN.PLOT_BUY_SELL_PRESSURE_FILE): p = self.initializePlot(file, p, ENVIRON=ENVIRON, title="Ap luc mua, apluc ban") p.line(self.Xs, self.cleansed[C.CBUYP], line_width=2, color="green") p.line(self.Xs, self.cleansed[C.CSELLP], line_width=2, color="red") show(p) def plot_NN_Liquidity_Bar(self, file="/home/sotola/foo.html", p=None): def prep(seconds, vals, STEP=20, REVERSED=False): # Prepare Data bins2 = []; x = [];bins, xs = intervalize(seconds, vals, STEP=STEP) for i in range(len(bins)): bins2.append(bins[i]); bins2.append(bins[i]) for i in range(len(xs)): x.append(xs[i][0]); x.append(xs[i][1]) if REVERSED: return x, [bin * -1 for bin in bins2] else: return x, bins2 p = self.initializePlot(file, p, ENVIRON=ENVIRON, title="Thanh khoan khoi nuoc ngoai") xbuy, topbuy = prep(self.seconds, self.nnBuyValues) p.vbar(x=xbuy, top=toDelta(topbuy), width=0.01, color="green") xsell, topsell = prep(self.seconds, self.nnSellValues, REVERSED=True) p.vbar(x=xsell, top=toDelta(topsell), width=0.01, color="red") show(p) def plot__Liquidity_Bar(self, file="/home/sotola/Hose-MarketLiquidity.html", p=None): def prep(seconds, vals, STEP=20, REVERSED=False): # Prepare Data bins2 = []; x = [];bins, xs = intervalize(seconds, vals, STEP=STEP) for i in range(len(bins)): bins2.append(bins[i]); bins2.append(bins[i]) for i in range(len(xs)): x.append(xs[i][0]); x.append(xs[i][1]) if REVERSED: return x, [bin * -1 for bin in bins2] else: return x, bins2 p = self.initializePlot(file, p, ENVIRON=ENVIRON, title="Thanh Khoan Thi Truong") xbuy, topbuy = prep(self.seconds, self.totalLiquidity) p.vbar(x=xbuy, top=toDelta(topbuy), width=0.01, color="green") show(p) def plot_NN_Accumulated_Values(self, p=None, file=FN.PLOT_NN_VALUES_FILE): p = self.initializePlot(file, p, ENVIRON=ENVIRON, title="Tong Thanh Khoan (NN)") p.line(self.Xs, np.array(self.nnBuyValues ) / 100000, line_width=2, color="green") p.line(self.Xs, np.array(self.nnSellValues) / 100000, line_width=2, color="red") show(p) def plot_NN_Velocity_Values(self, p=None, start=None, end=None, file=FN.PLOT_NN_VELOCITY_FILE): p = self.initializePlot(file, p, ENVIRON=ENVIRON) if not(start is None): start_i, end_i = self.timeRange(start, end) else: start_i = 0; end_i = -1 buys = [x / 100000 for x in toDelta(self.nnBuyValues)[start_i:end_i]] sells = [x / 100000 for x in toDelta(self.nnSellValues)[start_i:end_i]] diffs = [buys[i] - sells[i] for i in range(len(sells))][start_i:end_i] p.line(self.Xs[start_i:end_i], buys, line_width=2, color="green") p.line(self.Xs[start_i:end_i], sells, line_width=2, color="red") show(p)
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stats/management/commands/cachewarmup.py
OlexandrTopuzov/Data_converter
0
1614950
<filename>stats/management/commands/cachewarmup.py from django.core.management.base import BaseCommand from stats.tasks import cache_warm_up class Command(BaseCommand): help = 'Cache warming up for stats for now.' def handle(self, *args, **options): cache_warm_up()
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tests/integration/test_map_failure.py
elin1231/htmap
21
52556
# Copyright 2018 HTCondor Team, Computer Sciences Department, # University of Wisconsin-Madison, WI. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pytest from htmap import mapping def test_exception_inside_submit_removes_map_dir(mocker, doubler): class Marker(Exception): pass def bad_execute_submit(*args, **kwargs): raise Marker() mocker.patch("htmap.mapping.execute_submit", bad_execute_submit) with pytest.raises(Marker): mapping.map(doubler, range(10)) assert len(list(mapping.maps_dir_path().iterdir())) == 0
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multi_agent.py
liuruoze/HierNet-SC2
2
190654
# Copyright 2017 Google Inc. 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. """A random agent for starcraft.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import tensorflow as tf from pysc2.agents import base_agent from pysc2.lib import actions as sc2_actions import lib.utils as U from new_network import HierNetwork from lib import config as C from lib import transform_pos as T from lib import human_expert_statis as HES from lib import option as M from lib import environment from lib import my_sc2_env as sc2_env from lib.replay_buffer import Buffer, Cnn_Buffer from logging import warning as logging import param as P class MultiAgent(base_agent.BaseAgent): """My first agent for starcraft.""" def __init__(self, index=0, rl_training=False, restore_model=False, global_buffer=None, net=None): super(MultiAgent, self).__init__() self.net = net self.index = index self.global_buffer = global_buffer self.restore_model = restore_model # count num self.step = 0 self.controller_wait_secs = 8 self.base_wait_secs = 1 self.battle_wait_secs = 1 self.controller_flag = False self.base_flag = True self.battle_flag = True self.env = None self.obs = None self.use_fight = False self.restore_fight = False # buffer self.controller_buffer = Buffer() self.base_buffer = Buffer() self.tech_buffer = Buffer() self.pop_buffer = Buffer() self.battle_buffer = Buffer() self.fight_buffer = Cnn_Buffer() self.fight_temp = None self.num_players = 2 self.on_select = None self._result = None self.is_end = False self.rl_training = rl_training self.update_counter = 0 self.use_triple_reward = False self.use_only_result_reward = True self.use_battle_net = True self.use_trick_1 = False self.use_trick_2 = False self.use_trick_3 = False def reset(self): super(MultiAgent, self).reset() self.step = 0 self.obs = None self._result = None self.is_end = False self.controller_flag = False self.base_flag = True self.battle_flag = True self.controller_buffer.reset() self.base_buffer.reset() self.tech_buffer.reset() self.pop_buffer.reset() self.battle_buffer.reset() self.fight_buffer.reset() self.fight_temp = None def set_env(self, env): self.env = env def init_network(self): self.net.initialize() if self.restore_model: self.net.restore_controller() self.net.restore_base() self.net.restore_tech() self.net.restore_pop() self.net.restore_battle() if self.restore_fight: self.net.restore_fight() def reset_old_network(self): self.net.reset_old_network() def save_model(self): self.net.save_controller() self.net.save_base() self.net.save_tech() self.net.save_pop() self.net.save_battle() if self.use_fight: self.net.save_fight() def update_network(self, result_list): self.update_counter += 1 if self.use_trick_1: pass if P.use_alternative_update: logging('use_alternative_update, update_counter: %d', int(self.update_counter)) if self.update_counter % 20 < 10: logging('only update controller') self.net.Update_controller(self.global_buffer.controller_buffer) else: logging('update all') self.net.Update_controller(self.global_buffer.controller_buffer) self.net.Update_base_net(self.global_buffer.base_buffer) self.net.Update_tech_net(self.global_buffer.tech_buffer) self.net.Update_pop_net(self.global_buffer.pop_buffer) self.net.Update_battle_net(self.global_buffer.battle_buffer) if self.use_fight: self.net.Update_fight(self.global_buffer.fight_buffer) else: self.net.Update_controller(self.global_buffer.controller_buffer) self.net.Update_base_net(self.global_buffer.base_buffer) self.net.Update_tech_net(self.global_buffer.tech_buffer) self.net.Update_pop_net(self.global_buffer.pop_buffer) self.net.Update_battle_net(self.global_buffer.battle_buffer) if self.use_fight: self.net.Update_fight(self.global_buffer.fight_buffer) self.net.Update_result(result_list) def update_summary(self, counter): return self.net.Update_summary(counter) def get_controller_input(self): controller_input, _, _ = U.get_input(self.obs) return controller_input def get_base_input(self): high_input, tech_cost, pop_num = U.get_input(self.obs) base_input = np.concatenate([high_input, tech_cost, pop_num], axis=0) return base_input def get_tech_input(self): high_input, tech_cost, _ = U.get_input(self.obs) tech_obs = np.concatenate([high_input, tech_cost], axis=0) return tech_obs def get_pop_input(self): high_input, _, pop_num = U.get_input(self.obs) pop_obs = np.concatenate([high_input, pop_num], axis=0) return pop_obs def get_battle_input(self): high_input, tech_cost, pop_num = U.get_input(self.obs) battle_input = np.concatenate([high_input, tech_cost, pop_num], axis=0) return battle_input def tech_step(self, tech_action): # to execute a tech_action # [pylon, gas1, gas2, gateway, cyber] if tech_action == 0: # pylon no_unit_index = U.get_unit_mask_screen(self.obs, size=2) pos = U.get_pos(no_unit_index) M.build_by_idle_worker(self, C._BUILD_PYLON_S, pos) elif tech_action == 1 and not U.find_gas(self.obs, 1): # gas_1 gas_1 = U.find_gas_pos(self.obs, 1) gas_1_pos = T.world_to_screen_pos(self.env.game_info, gas_1.pos, self.obs) M.build_by_idle_worker(self, C._BUILD_ASSIMILATOR_S, gas_1_pos) elif tech_action == 1 and not U.find_gas(self.obs, 2): # gas_2 gas_2 = U.find_gas_pos(self.obs, 2) gas_2_pos = T.world_to_screen_pos(self.env.game_info, gas_2.pos, self.obs) M.build_by_idle_worker(self, C._BUILD_ASSIMILATOR_S, gas_2_pos) elif tech_action == 2: # gateway power_index = U.get_power_mask_screen(self.obs, size=5) pos = U.get_pos(power_index) M.build_by_idle_worker(self, C._BUILD_GATEWAY_S, pos) elif tech_action == 3: # cyber power_index = U.get_power_mask_screen(self.obs, size=3) pos = U.get_pos(power_index) M.build_by_idle_worker(self, C._BUILD_CYBER_S, pos) else: self.safe_action(C._NO_OP, 0, []) def pop_step(self, pop_action): # to execute a pop_action # [ mineral_probe, zealot, stalker] if pop_action == 0: # mineral_probe M.mineral_worker(self) elif pop_action == 1: # zealot M.train_army(self, C._TRAIN_ZEALOT) elif pop_action == 2: # stalker M.train_army(self, C._TRAIN_STALKER) else: self.safe_action(C._NO_OP, 0, []) def fight_step(self, fight_action, fight_pos): army, army_pos = U.get_best_army(self.env.game_info, self.obs) if army: self.safe_action(C._MOVE_CAMERA, 0, [army_pos]) if fight_action == 1: # attack M.attack_step(self, fight_pos) elif fight_action == 2: # retreat M.retreat_step(self, fight_pos) else: self.safe_action(C._NO_OP, 0, []) def play(self): # M.set_source(self) self.safe_action(C._NO_OP, 0, []) while True: controller_input = self.get_controller_input() _, controller_pred, controller_v_pred = self.net.controller.act(controller_input, verbose=False) controller_reward = 0 # enemy_num = controller_input[18] # army_count = controller_input[10] self.controller_flag = False if controller_pred == 0: # base net self.safe_action(C._MOVE_CAMERA, 0, [C.base_camera_pos]) while (not self.controller_flag) and (not self.is_end): self.safe_action(C._NO_OP, 0, []) if self.base_flag: base_input = self.get_base_input() _, base_pred, base_v_pred = self.net.base_net.act(base_input, verbose=False) base_reward = 0 self.base_flag = False if base_pred == 0: base_reward += self.tech_action() elif base_pred == 1: base_reward += self.pop_action() else: self.safe_action(C._NO_OP, 0, []) now_base_input = self.get_base_input() base_v_pred_next = self.net.base_net.get_values(now_base_input) base_v_pred_next = self.get_values(base_v_pred_next) if self.use_only_result_reward: base_reward = 0 self.base_buffer.append(base_input, base_pred, base_reward, base_v_pred, base_v_pred_next) controller_reward += base_reward else: # battle net while (not self.controller_flag) and (not self.is_end): self.safe_action(C._NO_OP, 0, []) if self.battle_flag: if self.use_battle_net: battle_input = self.get_battle_input() _, battle_pred, battle_v_pred = self.net.battle_net.act(battle_input, verbose=False) battle_reward = 0 self.battle_flag = False if battle_pred == 0: battle_reward += self.pop_action() elif battle_pred == 1: battle_reward += self.fight_action() else: self.safe_action(C._NO_OP, 0, []) now_battle_input = self.get_battle_input() battle_v_pred_next = self.net.battle_net.get_values(now_battle_input) battle_v_pred_next = self.get_values(battle_v_pred_next) if self.use_only_result_reward: battle_reward = 0 self.battle_buffer.append(battle_input, battle_pred, battle_reward, battle_v_pred, battle_v_pred_next) controller_reward += battle_reward else: battle_reward = 0 self.battle_flag = False battle_reward += self.fight_action() if self.use_trick_2: self.pop_action() # TODO: remove controller_reward += battle_reward now_controller_input = self.get_controller_input() controller_v_pred_next = self.net.controller.get_values(now_controller_input) controller_v_pred_next = self.get_values(controller_v_pred_next) if self.use_only_result_reward: controller_reward = 0 self.controller_buffer.append(controller_input, controller_pred, controller_reward, controller_v_pred, controller_v_pred_next) if self.is_end: logging('self.is_end') if self.rl_training: logging('self.rl_training') if not self.use_only_result_reward: logging('self.use_only_result_reward %d', int(self.use_only_result_reward)) self.controller_buffer.rewards[-1] -= 10 * self.obs.raw_observation.observation.game_loop // (22.4 * 60) self.controller_buffer.rewards[-1] += 50 * self.result['win'] self.base_buffer.rewards[-1] -= 10 * self.obs.raw_observation.observation.game_loop // (22.4 * 60) self.base_buffer.rewards[-1] += 50 * self.result['win'] self.battle_buffer.rewards[-1] -= 10 * self.obs.raw_observation.observation.game_loop // (22.4 * 60) self.battle_buffer.rewards[-1] += 50 * self.result['win'] self.tech_buffer.rewards[-1] += 50 * self.result['win'] self.pop_buffer.rewards[-1] += 50 * self.result['win'] else: logging('self.use_only_result_reward %d', int(self.use_only_result_reward)) self.controller_buffer.rewards[-1] += P.reward_weight[0] * self.result['reward'] logging('self.controller_buffer.rewards' + str(self.controller_buffer.rewards)[1:-1]) self.base_buffer.rewards[-1] += P.reward_weight[1] * self.result['reward'] self.battle_buffer.rewards[-1] += P.reward_weight[4] * self.result['reward'] self.tech_buffer.rewards[-1] += P.reward_weight[2] * self.result['reward'] self.pop_buffer.rewards[-1] += P.reward_weight[3] * self.result['reward'] self.global_buffer.append(self.controller_buffer, self.base_buffer, self.tech_buffer, self.pop_buffer, self.battle_buffer) break def tech_action(self): # act tech_obs = self.get_tech_input() act_array, act, v_pred = self.net.tech_net.act(tech_obs, verbose=False) self.tech_step(act) # TODO: next state should be the action call next once # get next state and next values now_tech_obs = self.get_tech_input() v_pred_next = self.net.tech_net.get_values(now_tech_obs) v_pred_next = self.get_values(v_pred_next) reward = 0 if self.rl_training and self.obs.raw_observation.observation.game_loop < 10000: # get reward pylon_num_idx = 14 gas_num_idx = 15 gateway_num_idx = 16 cyber_num_idx = 17 last_building_count = tech_obs[[pylon_num_idx, gas_num_idx, gateway_num_idx, cyber_num_idx]] last_building_count += tech_obs[-4:] now_building_count = now_tech_obs[[pylon_num_idx, gas_num_idx, gateway_num_idx, cyber_num_idx]] now_building_count += now_tech_obs[-4:] if now_building_count[0] <= HES.pylon: reward += now_building_count[0] - last_building_count[0] else: reward -= now_building_count[0] - last_building_count[0] if now_building_count[1] <= HES.gas: reward += now_building_count[1] - last_building_count[1] else: reward -= now_building_count[1] - last_building_count[1] if now_building_count[2] <= HES.gateway: reward += now_building_count[2] - last_building_count[2] else: reward -= now_building_count[2] - last_building_count[2] if now_building_count[3] <= HES.cyber: reward += (now_building_count[3] - last_building_count[3]) * 1 else: reward -= (now_building_count[3] - last_building_count[3]) * 1 if self.use_only_result_reward: reward = 0 self.tech_buffer.append(tech_obs, act, reward, v_pred, v_pred_next) return reward def pop_action(self): # act pop_obs = self.get_pop_input() act_array, act, v_pred = self.net.pop_net.act(pop_obs, verbose=False) self.pop_step(act) # get next state and next values now_pop_obs = self.get_pop_input() v_pred_next = self.net.pop_net.get_values(now_pop_obs) v_pred_next = self.get_values(v_pred_next) reward = 0 if self.rl_training: worker_index = C._SIZE_HIGH_NET_INPUT army_index = 10 last_worker_count = pop_obs[worker_index] + pop_obs[worker_index + 9] now_worker_count = now_pop_obs[worker_index] + now_pop_obs[worker_index + 9] ideal_worker_count = now_pop_obs[worker_index + 1] last_army_count = pop_obs[army_index] + pop_obs[worker_index + 10] + pop_obs[worker_index + 11] now_army_count = now_pop_obs[army_index] + now_pop_obs[worker_index + 10] + now_pop_obs[worker_index + 11] if now_worker_count <= ideal_worker_count: reward += now_worker_count - last_worker_count elif act == 0: reward -= now_worker_count - last_worker_count if now_army_count <= HES.army: reward += now_army_count - last_army_count if self.use_only_result_reward: reward = 0 self.pop_buffer.append(pop_obs, act, reward, v_pred, v_pred_next) return reward def fight_action(self): reward = 0 # move camera army, army_pos = U.get_best_army(self.env.game_info, self.obs) if army: self.safe_action(C._MOVE_CAMERA, 0, [army_pos]) self.select(C._SELECT_ARMY, C._ARMY_INDEX, [[0]]) if not self.use_fight: M.attack_step(self) if self.use_trick_3: if self.obs.raw_observation.observation.player_common.army_count >= 8: # M.control_step(self) # M.attack_step(self) reward = 1 return reward def set_flag(self): if self.step % C.time_wait(self.controller_wait_secs) == 1: self.controller_flag = True if self.step % C.time_wait(self.base_wait_secs) == 1: self.base_flag = True if self.step % C.time_wait(self.battle_wait_secs) == 1: self.battle_flag = True def safe_action(self, action, unit_type, args): if M.check_params(self, action, unit_type, args, 1): obs = self.env.step([sc2_actions.FunctionCall(action, args)])[0] self.obs = obs self.step += 1 self.update_result() self.set_flag() def select(self, action, unit_type, args): # safe select if M.check_params(self, action, unit_type, args, 0): self.obs = self.env.step([sc2_actions.FunctionCall(action, args)])[0] self.on_select = unit_type self.update_result() self.step += 1 self.set_flag() # else: # print('Unavailable_actions id:', action, ' and type:', unit_type, ' and args:', args) @property def result(self): return self._result def update_result(self): if self.obs is None: return if self.obs.last() or self.env.state == environment.StepType.LAST: self.is_end = True outcome = 0 o = self.obs.raw_observation player_id = o.observation.player_common.player_id for r in o.player_result: if r.player_id == player_id: outcome = sc2_env._possible_results.get(r.result, 0) frames = o.observation.game_loop result = dict() result['outcome'] = outcome result['reward'] = self.obs.reward result['frames'] = frames if not self.use_triple_reward: result['win'] = -1 # TODO, change it if result['reward'] == 1: result['win'] = 1 else: result['win'] = self.obs.reward self._result = result print('play end, total return', self.obs.reward) self.step = 0 def get_values(self, values): # check if the game is end if self.is_end: return 0 else: return values
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whisk/template/{{ cookiecutter.repo_name }}/setup.py
BookletAI/whisk
8
83958
<filename>whisk/template/{{ cookiecutter.repo_name }}/setup.py from setuptools import find_packages, setup setup( name='{{cookiecutter.project_name}}', package_dir={'': 'src'}, packages=find_packages(where='src'), version='0.1.0', include_package_data=True, # By default only whisk is added as a dependency. whisk is required for # accessing the artifacts_dir. # # You likely need to list more dependencies for your model package. # For example: your model framework (Scikit, Torch, etc), numpy, Pandas, etc. # # See https://docs.whisk-ml.org/en/latest/packaging.html for help on packaging your model. install_requires=[ '{{cookiecutter.whisk_install_requires}}' ], entry_points={ 'console_scripts': [ '{{cookiecutter.project_name}}={{cookiecutter.project_name}}.cli.main:cli', ], }, description='A short description of the project.', author='<NAME> (or your organization/company/team)', author_email='<EMAIL>', python_requires='>=3.6', url="https://ADD THE URL TO YOUR PROJECT GITHUB REPO OR DOCS" )
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2016/python/day20.py
astonshane/AdventOfCode
0
47724
MAX = 4294967295 blacklist = [] with open("inputs/day20.txt") as f: for line in f: line = line.strip().split('-') blacklist.append([int(x) for x in line]) blacklist.sort() def part1(): ip = 0 for i in range(0, len(blacklist)): bl = blacklist[i] if ip < bl[0]: break if bl[1] > ip: ip = bl[1]+1 print "(part1):", ip def part2(): ip = 0 good_ips = 0 for i in range(0, len(blacklist)): bl = blacklist[i] if ip < bl[0]: good_ips += bl[0]-ip ip = bl[1]+1 elif bl[1] > ip: ip = bl[1]+1 print "(part2):", good_ips part1() part2()
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readqueue.py
YousefAllam221b/QuickStor
0
1602795
#!/usr/bin/python3.6 import os,sys, datetime from subprocess import run from subprocess import PIPE cmdline='sh getqueue.sh' x=run(cmdline.split(),stdout=PIPE).stdout y=x.decode('utf-8') z=y.split('\n') stopfinish=[] allstopfinish=[] other=[] countfinish=0 for x in z: if len(x) < 4: continue st=x.split() if 'finish' in st[3] or 'stop' in st[3]: stopfinish.append((st[2],st[5])) if countfinish < 2: other.append(x) countfinish+=1 else: allstopfinish.append(x) elif (st[2],st[5]) in stopfinish: stopfinish.remove((st[2],st[5])) allstopfinish.append(x) else: other.append(x) other.reverse() print(other)
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synestia-book/_build/jupyter_execute/docs/syndef/PowerLawFits.py
ststewart/synestiabook2
0
174367
#!/usr/bin/env python # coding: utf-8 # In[1]: #move this notebook to folder above syndef to run from syndef import synfits #import synestia snapshot (impact database) import numpy as np import matplotlib.pyplot as plt test_rxy=np.linspace(7e6,60e6,100) #m test_z=np.linspace(0.001e6,30e6,50) #m rxy=np.log10(test_rxy/1e6) #Mm log10 z=np.log10(test_z/1e6) #Mm log10 TESTRXY,TESTZ=np.meshgrid(test_rxy,test_z) #2-D grid of rxy, z for color plot #y=np.zeros(np.shape(rxy)) #array of zeros for residual fit #rho1=synfits.resfuncspl(synfits.SNAP_Canup.rhomidfit[1],rxy,y) #rho2=synfits.resfuncspl(synfits.SNAP_CukStewart.rhomidfit[1],rxy,y) #rho3=synfits.resfuncspl(synfits.SNAP_Quintana.rhomidfit[1],rxy,y) snaprho1=np.log10(synfits.SNAP_Canup.rho[synfits.SNAP_Canup.ind_outer]) snaprho2=np.log10(synfits.SNAP_CukStewart.rho[synfits.SNAP_CukStewart.ind_outer]) snaprho3=np.log10(synfits.SNAP_Quintana.rho[synfits.SNAP_Quintana.ind_outer]) snaprhomid1=np.log10(synfits.SNAP_Canup.rho[synfits.SNAP_Canup.ind_outer_mid]) snaprhomid2=np.log10(synfits.SNAP_CukStewart.rho[synfits.SNAP_CukStewart.ind_outer_mid]) snaprhomid3=np.log10(synfits.SNAP_Quintana.rho[synfits.SNAP_Quintana.ind_outer_mid]) snaprxy1=np.log10(synfits.SNAP_Canup.rxy[synfits.SNAP_Canup.ind_outer]/1e6) snaprxy2=np.log10(synfits.SNAP_CukStewart.rxy[synfits.SNAP_CukStewart.ind_outer]/1e6) snaprxy3=np.log10(synfits.SNAP_Quintana.rxy[synfits.SNAP_Quintana.ind_outer]/1e6) snaprxymid1=np.log10(synfits.SNAP_Canup.rxy[synfits.SNAP_Canup.ind_outer_mid]/1e6) snaprxymid2=np.log10(synfits.SNAP_CukStewart.rxy[synfits.SNAP_CukStewart.ind_outer_mid]/1e6) snaprxymid3=np.log10(synfits.SNAP_Quintana.rxy[synfits.SNAP_Quintana.ind_outer_mid]/1e6) snapz1=np.log10(synfits.SNAP_Canup.z[synfits.SNAP_Canup.ind_outer]/1e6) snapz2=np.log10(synfits.SNAP_CukStewart.z[synfits.SNAP_CukStewart.ind_outer]/1e6) snapz3=np.log10(synfits.SNAP_Quintana.z[synfits.SNAP_Quintana.ind_outer]/1e6) const1=10.5#10 to 11; 10.55 (fiducial) const2=0.86#0.85 to 0.9; 0.86 (fiducial) const3=1e38 #0.9e35 (fiducial) / 1.5e33 (underestimate) / 1.1e37 (cross) / 1e38 (overestimate) const4=-5.1 #-4.7 (fiducial) / -4.5 (underestimate) / -5 (cross) / -5.1 (overestimate) test_z_s=const1*np.power(TESTRXY,const2) #scale height fit in m test_rho_g=const3*np.power(TESTRXY,const4)*np.exp(-np.power(TESTZ/test_z_s,2)) test_rho_gmid=const3*np.power(test_rxy,const4) plt.figure(figsize=(16,5)) plt.subplot(131) #plt.plot(rxy,rho1,'b') plt.plot(snaprxymid1,snaprhomid1,'r.') plt.plot(np.log10(test_rxy/1e6),np.log10(test_rho_gmid),'k') plt.xlabel('log r$_{xy}$ (Mm)') plt.ylabel('log midplane density (kg/m$^3$)') plt.title('Canup') plt.xlim([.8,2]) plt.ylim([-2,3]) plt.subplot(132) #plt.plot(rxy,rho2,'b') plt.plot(snaprxymid2,snaprhomid2,'r.') plt.plot(np.log10(test_rxy/1e6),np.log10(test_rho_gmid),'k') plt.xlabel('log r$_{xy}$ (Mm)') plt.ylabel('log midplane density (kg/m$^3$)') plt.title('Cuk and Stewart') plt.xlim([.8,2]) plt.ylim([-2,3]) plt.subplot(133) #plt.plot(rxy,rho3,'b') plt.plot(snaprxymid3,snaprhomid3,'r.') plt.plot(np.log10(test_rxy/1e6),np.log10(test_rho_gmid),'k') plt.xlabel('log r$_{xy}$ (Mm)') plt.ylabel('log midplane density (kg/m$^3$)') plt.title('Quintana') plt.xlim([.8,2]) plt.ylim([-2,3]) plt.show() plt.close() plt.figure(figsize=(16,5)) plt.subplot(131) plt.pcolor(np.log10(TESTRXY/1e6),np.log10(TESTZ/1e6),np.log10(test_rho_g)) plt.scatter(snaprxy1,snapz1,c=snaprho1) plt.xlabel('log r$_{xy}$ (Mm)') plt.ylabel('log z (Mm)') plt.colorbar(label='log density (kg/m$^3$)') plt.xlim([.8,2]) plt.subplot(132) plt.pcolor(np.log10(TESTRXY/1e6),np.log10(TESTZ/1e6),np.log10(test_rho_g)) plt.scatter(snaprxy2,snapz2,c=snaprho2) plt.xlabel('log r$_{xy}$ (Mm)') plt.ylabel('log z (Mm)') plt.colorbar(label='log density (kg/m$^3$)') plt.xlim([.8,2]) plt.subplot(133) plt.pcolor(np.log10(TESTRXY/1e6),np.log10(TESTZ/1e6),np.log10(test_rho_g)) plt.scatter(snaprxy3,snapz3,c=snaprho3) plt.xlabel('log r$_{xy}$ (Mm)') plt.ylabel('log z (Mm)') plt.colorbar(label='log density (kg/m$^3$)') plt.xlim([.8,2]) plt.show() plt.close() # In[ ]:
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covid/stream.py
yiliayliu/twitter-streaming
1
150038
<filename>covid/stream.py<gh_stars>1-10 import boto3 import json from datetime import datetime import calendar import random import time import sys # from tweepy.streaming import StreamListener from tweepy import OAuthHandler from tweepy import Stream from configparser import SafeConfigParser #Variables that contains the user credentials to access Twitter API parser = SafeConfigParser() parser.read('../stream.cfg') #This is the super secret information access_token = parser.get('stream', 'access_token') access_token_secret = parser.get('stream', 'access_token_secret') consumer_key = parser.get('stream', 'consumer_key') consumer_secret = parser.get('stream', 'consumer_secret') aws_key_id = parser.get('stream', 'aws_key_id') aws_key = parser.get('stream', 'aws_key') stream_name = 'twitter-covid' # fill the name of Kinesis data stream you created # create kinesis client connection kinesis_client = boto3.client('firehose', region_name='us-east-1', # enter the region aws_access_key_id=aws_key_id, # fill your AWS access key id aws_secret_access_key=aws_key) # fill you aws secret access key class TweetStream(Stream): # on success def on_data(self, data): tweet = json.loads(data) try: if 'text' in tweet.keys(): #print (tweet['text']) # message = str(tweet)+',\n' message = json.dumps(tweet) message = message + ",\n" print(message+"=====new version\n") kinesis_client.put_record( DeliveryStreamName=stream_name, Record={ 'Data': message } ) except (AttributeError, Exception) as e: print (e) return True # on failure def on_error(self, status): print(status) if __name__ == '__main__': # create instance of the tweepy tweet stream listener stream = TweetStream(consumer_key,consumer_secret, access_token,access_token_secret) # set twitter keys/tokens auth = OAuthHandler(consumer_key, consumer_secret) auth.set_access_token(access_token, access_token_secret) # create instance of the tweepy stream # stream = Stream(auth, listener) # search twitter for tags or keywords from cli parameters query = sys.argv[1:] # list of CLI arguments query_fname = ' '.join(query) # string stream.filter(track=query)
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src/udemy_dl/udemy_dl.py
krishnaramg3/hello1
17
82797
<reponame>krishnaramg3/hello1<gh_stars>10-100 #!/usr/bin/env python # -*- coding: utf8 -*- import requests import requests.sessions import argparse import getpass import sys import re import os import json from .download import download, DLException from ._version import __version__ class Session: headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.10; rv:39.0) Gecko/20100101 Firefox/39.0', 'X-Requested-With': 'XMLHttpRequest', 'Host': 'www.udemy.com', 'Referer': 'https://www.udemy.com/join/login-popup'} def __init__(self): self.session = requests.sessions.Session() def set_auth_headers(self, access_token, client_id): self.headers['X-Udemy-Bearer-Token'] = access_token self.headers['X-Udemy-Client-Id'] = client_id def get(self, url): return self.session.get(url, headers=self.headers) def post(self, url, data): return self.session.post(url, data, headers=self.headers) session = Session() def get_csrf_token(): response = session.get('https://www.udemy.com/join/login-popup') match = re.search("name=\'csrfmiddlewaretoken\' value=\'(.*)\'", response.text) return match.group(1) def login(username, password): login_url = 'https://www.udemy.com/join/login-popup/?displayType=ajax&display_type=popup&showSkipButton=1&returnUrlAfterLogin=https%3A%2F%2Fwww.udemy.com%2F&next=https%3A%2F%2Fwww.udemy.com%2F&locale=en_US' csrf_token = get_csrf_token() payload = {'isSubmitted': 1, 'email': username, 'password': password, 'displayType': 'ajax', 'csrfmiddlewaretoken': csrf_token} response = session.post(login_url, payload) access_token = response.cookies.get('access_token') client_id = response.cookies.get('client_id') session.set_auth_headers(access_token, client_id) response = response.text if 'error' in response: print(response) sys.exit(1) def get_course_id(course_link): response = session.get(course_link) matches = re.search('data-courseid="(\d+)"', response.text, re.IGNORECASE) return matches.groups()[0] if matches else None def parse_video_url(lecture_id, hd=False): '''A hacky way to find the json used to initalize the swf object player''' embed_url = 'https://www.udemy.com/embed/{0}'.format(lecture_id) html = session.get(embed_url).text data = re.search(r'\$\("#player"\).jwplayer\((.*?)\);.*</script>', html, re.MULTILINE | re.DOTALL).group(1) video = json.loads(data) if 'playlist' in video and 'sources' in video['playlist'][0]: if hd: for source in video['playlist'][0]['sources']: if '720' in source['label'] or 'HD' in source['label']: return source['file'] # The 360p case and fallback if no HD version source = video['playlist'][0]['sources'][0] return source['file'] else: print("Failed to parse video url") return None def get_video_links(course_id, lecture_start, lecture_end, hd=False): course_url = 'https://www.udemy.com/api-1.1/courses/{0}/curriculum?fields[lecture]=@min,completionRatio,progressStatus&fields[quiz]=@min,completionRatio'.format(course_id) course_data = session.get(course_url).json() chapter = None video_list = [] lecture_number = 1 chapter_number = 0 # A udemy course has chapters, each having one or more lectures for item in course_data: if item['__class'] == 'chapter': chapter = item['title'] chapter_number += 1 elif item['__class'] == 'lecture' and item['assetType'] == 'Video': lecture = item['title'] if valid_lecture(lecture_number, lecture_start, lecture_end): try: lecture_id = item['id'] video_url = parse_video_url(lecture_id, hd) video_list.append({'chapter': chapter, 'lecture': lecture, 'video_url': video_url, 'lecture_number': lecture_number, 'chapter_number': chapter_number}) except: print('Cannot download lecture "%s"' % (lecture)) lecture_number += 1 return video_list def valid_lecture(lecture_number, lecture_start, lecture_end): if lecture_start and lecture_end: return lecture_start <= lecture_number <= lecture_end elif lecture_start: return lecture_start <= lecture_number else: return lecture_number <= lecture_end def sanitize_path(s): return "".join([c for c in s if c.isalpha() or c.isdigit() or c in ' .-_,']).rstrip() def mkdir(directory): if not os.path.exists(directory): os.makedirs(directory) def get_video(directory, filename, link): print('Downloading %s ' % (filename.encode('utf-8'))) previous_dir = os.getcwd() mkdir(directory) os.chdir(directory) try: download(link, filename) except DLException as e: print('Couldn\'t download this lecture: {0}'.format(e)) os.chdir(previous_dir) print('\n'), def udemy_dl(username, password, course_link, lecture_start, lecture_end, dest_dir=""): login(username, password) course_id = get_course_id(course_link) if not course_id: print('Failed to get course ID') return for video in get_video_links(course_id, lecture_start, lecture_end, hd=True): directory = '%02d %s' % (video['chapter_number'], video['chapter']) directory = sanitize_path(directory) if dest_dir: directory = os.path.join(dest_dir, directory) filename = '%03d %s.mp4' % (video['lecture_number'], video['lecture']) filename = sanitize_path(filename) get_video(directory, filename, video['video_url']) session.get('http://www.udemy.com/user/logout') def is_integer(p): try: int(p) return True except ValueError: return False def main(): parser = argparse.ArgumentParser(description='Fetch all the videos for a udemy course') parser.add_argument('link', help='Link for udemy course', action='store') parser.add_argument('-u', '--username', help='Username/Email', default=None, action='store') parser.add_argument('-p', '--password', help='Password', default=None, action='store') parser.add_argument('--lecture-start', help='Lecture to start at (default is 1)', default=1, action='store') parser.add_argument('--lecture-end', help='Lecture to end at (default is last)', default=None, action='store') parser.add_argument('-o', '--output-dir', help='Output directory', default=None, action='store') parser.add_argument('-v', '--version', help='Display the version of udemy-dl and exit', action='version', version='%(prog)s {version}'.format(version=__version__)) args = vars(parser.parse_args()) username = args['username'] password = args['password'] link = args['link'].rstrip('/') lecture_start = args['lecture_start'] lecture_end = args['lecture_end'] if lecture_start is not None: if not is_integer(lecture_start) or int(lecture_start) <= 0: print('--lecture_start requires natural number argument') sys.exit(1) lecture_start = int(lecture_start) if lecture_end is not None: if not is_integer(lecture_end) or int(lecture_end) <= 0: print('--lecture_end requires natural number argument') sys.exit(1) lecture_end = int(lecture_end) if args['output_dir']: # Normalize the output path if specified output_dir = os.path.normpath(args['output_dir']) else: # Get output dir name from the URL output_dir = os.path.join(".", link.rsplit('/', 1)[1]) if not username: try: username = raw_input("Username/Email: ") # Python 2 except NameError: username = input("Username/Email: ") # Python 3 if not password: password = <PASSWORD>(prompt='Password: ') print('Downloading to: %s\n' % (os.path.abspath(output_dir))) udemy_dl(username, password, link, lecture_start, lecture_end, output_dir) if __name__ == '__main__': main()
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lib_exec/StereoPipeline/libexec/asp_image_utils.py
sebasmurphy/iarpa
20
4190
#!/usr/bin/env python # -*- coding: utf-8 -*- # __BEGIN_LICENSE__ # Copyright (c) 2009-2013, United States Government as represented by the # Administrator of the National Aeronautics and Space Administration. All # rights reserved. # # The NGT platform is 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. # __END_LICENSE__ """ Basic functions for working with images on disk. """ import sys, os, re, subprocess, string, time, errno import asp_string_utils def stripRgbImageAlphaChannel(inputPath, outputPath): """Makes an RGB copy of an RBGA image""" cmd = 'gdal_translate ' + inputPath + ' ' + outputPath + ' -b 1 -b 2 -b 3 -co "COMPRESS=LZW" -co "TILED=YES" -co "BLOCKXSIZE=256" -co "BLOCKYSIZE=256"' print cmd os.system(cmd) def getImageSize(imagePath): """Returns the size [samples, lines] in an image""" # Make sure the input file exists if not os.path.exists(imagePath): raise Exception('Image file ' + imagePath + ' not found!') # Use subprocess to suppress the command output cmd = ['gdalinfo', imagePath] p = subprocess.Popen(cmd, stdout=subprocess.PIPE) textOutput, err = p.communicate() # Extract the size from the text sizePos = textOutput.find('Size is') endPos = textOutput.find('\n', sizePos+7) sizeStr = textOutput[sizePos+7:endPos] sizeStrs = sizeStr.strip().split(',') numSamples = int(sizeStrs[0]) numLines = int(sizeStrs[1]) size = [numSamples, numLines] return size def isIsisFile(filePath): """Returns True if the file is an ISIS file, False otherwise.""" # Currently we treat all files with .cub extension as ISIS files extension = os.path.splitext(filePath)[1] return (extension == '.cub') def getImageStats(imagePath): """Obtains some image statistics from gdalinfo""" if not os.path.exists(imagePath): raise Exception('Image file ' + imagePath + ' not found!') # Call command line tool silently cmd = ['gdalinfo', imagePath, '-stats'] p = subprocess.Popen(cmd, stdout=subprocess.PIPE) textOutput, err = p.communicate() # Statistics are computed seperately for each band bandStats = [] band = 0 while (True): # Loop until we run out of bands # Look for the stats line for this band bandString = 'Band ' + str(band+1) + ' Block=' bandLoc = textOutput.find(bandString) if bandLoc < 0: return bandStats # Quit if we did not find it # Now parse out the statistics for this band bandMaxStart = textOutput.find('STATISTICS_MAXIMUM=', bandLoc) bandMeanStart = textOutput.find('STATISTICS_MEAN=', bandLoc) bandMinStart = textOutput.find('STATISTICS_MINIMUM=', bandLoc) bandStdStart = textOutput.find('STATISTICS_STDDEV=', bandLoc) bandMax = asp_string_utils.getNumberAfterEqualSign(textOutput, bandMaxStart) bandMean = asp_string_utils.getNumberAfterEqualSign(textOutput, bandMeanStart) bandMin = asp_string_utils.getNumberAfterEqualSign(textOutput, bandMinStart) bandStd = asp_string_utils.getNumberAfterEqualSign(textOutput, bandStdStart) # Add results to the output list bandStats.append( (bandMin, bandMax, bandMean, bandStd) ) band = band + 1 # Move to the next band
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graphdg/parse/distances.py
gncs/graphdg
22
116456
from dataclasses import dataclass from typing import Tuple, List import ase.data import numpy as np from networkx import Graph from rdkit import Chem import graphdg.parse.tools as tools from graphdg.parse.extended_graph import mol_to_extended_graph from graphdg.tools import GLOBALS, NODES, EDGES, SENDERS, RECEIVERS, to_one_hot # Elements SYMBOLS = ase.data.chemical_symbols[1:10] SYMBOL_TO_OH = {symbol: to_one_hot(index=index, num_classes=len(SYMBOLS)) for index, symbol in enumerate(SYMBOLS)} # Rings MAX_RING_SIZE = 9 RING_SIZES = range(3, MAX_RING_SIZE + 1) NOT_IN_RING = tuple(0 for _ in RING_SIZES) # Edges EDGE_KINDS = [1, 2, 3] EDGE_KIND_TO_OH = { edge_kind: to_one_hot(index=index, num_classes=len(EDGE_KINDS)) for index, edge_kind in enumerate(EDGE_KINDS) } # Bond type BOND_TYPES = [ Chem.BondType.ZERO, Chem.BondType.SINGLE, Chem.BondType.DOUBLE, Chem.BondType.TRIPLE, Chem.BondType.AROMATIC ] BOND_TYPE_TO_OH = { bond_type: to_one_hot(index=index, num_classes=len(BOND_TYPES)) for index, bond_type in enumerate(BOND_TYPES) } # Stereo STEREO_TYPES = [Chem.BondStereo.STEREONONE, Chem.BondStereo.STEREOANY, Chem.BondStereo.STEREOE, Chem.BondStereo.STEREOZ] STEREO_TYPE_TO_OH = { stereo_type: to_one_hot(index=index, num_classes=len(STEREO_TYPES)) for index, stereo_type in enumerate(STEREO_TYPES) } # Chirality CHI_TAGS = [Chem.CHI_UNSPECIFIED, Chem.CHI_TETRAHEDRAL_CW, Chem.CHI_TETRAHEDRAL_CCW] CHI_TAGS_TO_OH = {chi_tag: to_one_hot(index=index, num_classes=len(CHI_TAGS)) for index, chi_tag in enumerate(CHI_TAGS)} @dataclass class NodeInfo: symbol: str chiral_tag: int def to_features(self) -> List: return SYMBOL_TO_OH[self.symbol] + CHI_TAGS_TO_OH[self.chiral_tag] @dataclass class EdgeInfo: distance: float atom_ids: Tuple[int, int] kind: int stereo: int = Chem.BondStereo.STEREONONE bond_type: int = Chem.BondType.ZERO is_aromatic: bool = False is_conjugated: bool = False is_in_ring_size: Tuple[int, ...] = NOT_IN_RING def to_features(self) -> Tuple[Tuple, List[int], float]: feats = EDGE_KIND_TO_OH[self.kind] + STEREO_TYPE_TO_OH[self.stereo] + BOND_TYPE_TO_OH[self.bond_type] + [ int(self.is_aromatic), int(self.is_conjugated) ] + list(self.is_in_ring_size) return self.atom_ids, feats, self.distance def get_node_infos(molecule: Chem.Mol) -> List[NodeInfo]: return [ NodeInfo( symbol=ase.data.chemical_symbols[atom.GetAtomicNum()], chiral_tag=atom.GetChiralTag(), ) for atom in molecule.GetAtoms() ] def get_edge_infos(molecule: Chem.Mol, graph: Graph): edge_infos = [] for (source, sink) in graph.edges: kind = graph.edges[(source, sink)]['kind'] if kind == 1: bond = molecule.GetBondBetweenAtoms(source, sink) edge_info = EdgeInfo( distance=tools.get_atom_distance(molecule, source, sink), atom_ids=(source, sink), kind=kind, stereo=bond.GetStereo(), bond_type=bond.GetBondType(), is_aromatic=bond.GetIsAromatic(), is_conjugated=bond.GetIsConjugated(), is_in_ring_size=tuple(int(bond.IsInRingSize(size)) for size in RING_SIZES), ) else: edge_info = EdgeInfo( distance=tools.get_atom_distance(molecule, source, sink), atom_ids=(source, sink), kind=kind, ) edge_infos.append(edge_info) return edge_infos def get_feats_and_targets(node_infos: List[NodeInfo], edge_infos: List[EdgeInfo]) -> Tuple[dict, np.ndarray]: nodes = [node_info.to_features() for node_info in node_infos] edges = [] senders = [] receivers = [] targets = [] for edge_info in edge_infos: (sender, receiver), edge_feats, distance = edge_info.to_features() # Forward edges.append(edge_feats) senders.append(sender) receivers.append(receiver) targets.append([distance]) # Reverse edges.append(edge_feats) senders.append(receiver) receivers.append(sender) targets.append([distance]) assert (len(edges) == len(senders) == len(receivers) == len(targets)) feats = { GLOBALS: np.array([], dtype=np.float), NODES: np.array(nodes, dtype=np.float), EDGES: np.array(edges, dtype=np.float), SENDERS: np.array(senders, dtype=np.int), RECEIVERS: np.array(receivers, dtype=np.int), } targets = np.array(targets, dtype=np.float) return feats, targets def get_info_tuple(molecule: Chem.Mol, seed: int) -> Tuple[List[NodeInfo], List[EdgeInfo]]: atom_infos = get_node_infos(molecule) graph = mol_to_extended_graph(molecule, seed=seed) edge_infos = get_edge_infos(molecule=molecule, graph=graph) return atom_infos, edge_infos def get_dataset(molecules: List[Chem.Mol], seed: int, count: int) -> List[Tuple[dict, np.ndarray]]: dataset = [] for mol_id, molecule in enumerate(molecules): for index in range(count): node_infos, edge_infos = get_info_tuple(molecule, seed=seed + mol_id + index) feats_targets = get_feats_and_targets(node_infos=node_infos, edge_infos=edge_infos) dataset.append(feats_targets) return dataset
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pandasbikeshed/plot.py
sholderbach/pandasbikeshed
1
135763
<gh_stars>1-10 import numpy as np import pandas as pd import matplotlib as mpl import matplotlib.pyplot as plt import seaborn as sns from scipy.stats import pearsonr, spearmanr def robust_hist(x, ax=None, **kwargs): """ Wrapper function to `plt.hist` dropping values that are not finite Returns: Axes """ mask = np.isfinite(x) ax = ax or plt.gca() ax.hist(x[mask], **kwargs) return ax def robust_scatter(x, y, size=0.5, alpha=0.3, **kwargs): """ Wrapper function to `sns.scatterplot` dropping value pairs that are not finite Sets default `size` of 0.5 and `alpha` to 0.3 Returns: Axes """ mask = np.isfinite(x) & np.isfinite(y) return sns.scatterplot(x[mask], y[mask], size=size, alpha=alpha, **kwargs) def robust_kde(x, y=None, **kwargs): """ Wrapper function to `sns.kdeplot` dropping values or value pairs that are not finite Returns: Axes """ if y is not None: mask = np.isfinite(x) & np.isfinite(y) return sns.kdeplot(x[mask], y[mask], **kwargs) else: mask = np.isfinite(x) return sns.kdeplot(x[mask], **kwargs) def robust_info(x, y, ax=None, **kwargs): """ Prints correlation information of two arrays into axis. Useful for `sns.PairGrid` (see `pb.plot.robust_pairplot`). As non finite value pairs are dropped prints number of comparisons. Pearson and Spearman correlation values with associated p-value. Returns: Axes """ mask = np.isfinite(x) & np.isfinite(y) n = np.sum(mask) pear_r, pear_p = pearsonr(x[mask], y[mask]) spea_r, spea_p = spearmanr(x[mask], y[mask]) ax = ax or plt.gca() ax.annotate( f'N = {n} \n' f'Pearson:\nr={pear_r:.3f} p={pear_p:.2E}\n' f'Spearman:\nr={spea_r:.3f} p={spea_p:.2E}', xy=(0.5, 0.5), xycoords='axes fraction', va='center', ha='center', wrap=True, **kwargs) return ax def robust_pairplot(df, lower_kind='scatter', upper_kind='info', diag_kind='hist', **kwargs): """ Similar function to `sns.pairplot` that drops non-finite value pairs instead of all rows containing NaNs Args: lower_kind: {'scatter', 'kde', 'info'} upper_kind: {'scatter', 'kde', 'info'} diag_kind: {'hist', 'kde'} **kwargs: passed to sns.PairGrid Returns: sns.PairGrid """ diag_methods = {'hist': robust_hist, 'kde': robust_kde} tria_methods = {'scatter': robust_scatter, 'kde': robust_kde, 'info': robust_info} g = sns.PairGrid(df, **kwargs) g.map_diag(diag_methods[diag_kind]) g.map_upper(tria_methods[upper_kind]) g.map_lower(tria_methods[lower_kind]) return g def corr_heatmap(df, method='pearson', triangle_only=True, ax=None, cmap='RdBu_r', linewidths=0.1, **heat_map_kwargs): """ Plot a correlation heatmap directly from a dataframe Args: df: pd.DataFrame method: {'pearson', 'spearman'} triangle_only: bool Hide the diagonal and upper triangle. default=True Returns: Axes """ corrmat = df.corr(method=method) mask = (~np.tri(corrmat.shape[0], dtype=np.bool)) if triangle_only else None locator = mpl.ticker.MultipleLocator(0.25) return sns.heatmap(corrmat, ax=ax, mask=mask, vmin=-1., vmax=1., center=0, cbar_kws={'ticks': locator}, linewidths=linewidths, cmap=cmap, **heat_map_kwargs) def dist_catplot(data=None, x=None, kind='hist', dist_columns=None, col=None, row=None, hue=None, col_wrap=None, **facet_kwargs): """ Make faceted histograms or kdeplots either from tidy longform data or columns of a wide DataFrame Args: data: pd.DataFrame x: str, None Defines a column in tidy data, from which to take the values for the histogram. If unspecified, numeric columns are considered for the histograms kind: {'hist', 'kde'} dist_columns: Iterable column names, optional If x unspecified, defines the columns to be considered for histograms col, row, hue: str, optional Column names to facet the data. col_wrap: int, optional breaks cols, if no row specified Returns: sns.FacetGrid """ if x is None: if dist_columns is not None: numeric_columns = dist_columns else: numeric_columns = [name for name, dtype in data.dtypes.items() if pd.api.types.is_numeric_dtype(dtype)] non_numeric_columns = data.columns.difference(numeric_columns) cols_name = data.columns.name or 'columns' hist_col = 'value' if 'value' not in data.columns else 'histogram_value' dat = data.melt(id_vars=non_numeric_columns, value_vars=numeric_columns, var_name=cols_name, value_name=hist_col) if col is None: col = cols_name elif row is None and col_wrap is None: row = cols_name elif hue is None: hue = cols_name else: raise ValueError('No dimension available to unpack the numeric columns.') else: dat = data hist_col = x plt_funcs = {'hist': plt.hist, 'kde': sns.kdeplot} g = sns.FacetGrid(dat, col=col, row=row, hue=hue, col_wrap=col_wrap, **facet_kwargs) g.map(plt_funcs[kind], hist_col) return g
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vpnmon.py
clambin/vpnmon
0
155941
<gh_stars>0 from vpnmon.vpnmon import vpnmon from vpnmon.configuration import get_configuration if __name__ == '__main__': vpnmon(get_configuration())
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hproxy/scheduler/valid_proxy.py
yejianxin2015/hproxy
0
80218
#!/usr/bin/env python """ Created by howie.hu at 08/04/2018. """ import asyncio import sys import time sys.path.append('../../') from hproxy.database import DatabaseSetting from hproxy.utils import logger from hproxy.spider.proxy_tools import get_proxy_info db_client = DatabaseSetting() async def valid_proxies(): all_res = await db_client.get_all() start = time.time() tasks = [] if all_res: for each in all_res.keys(): tasks.append(asyncio.ensure_future(valid_proxy(each, nums=1))) done_list, pending_list = await asyncio.wait(tasks) good_nums = 0 for task in done_list: if task.result(): good_nums += 1 logger.info(type="Authentication finished", message="Authenticating finished ，total proxy num : {0} - valid proxy num : {1} ,Time costs : {2}}".format( len(tasks), good_nums, time.time() - start)) async def valid_proxy(proxy, nums=1): if nums > 5: await db_client.delete(proxy) logger.error(type='Invalid proxy', message="{0} had been abandoned".format(proxy)) return False else: ip, port = proxy.split(':') isOk = await get_proxy_info(ip, port) if not isOk: logger.error(type='Invalid proxy', message="{0}：retry times = {1}".format(proxy, nums)) res = await valid_proxy(proxy, nums=nums + 1) return res else: logger.info(type='Valid proxy', message="{0} is valid".format(proxy)) return True def refresh_proxy(): asyncio.get_event_loop().run_until_complete(valid_proxies()) if __name__ == '__main__': refresh_proxy()
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webservice/gh_api.py
maoo/github_app_boilerplate
0
175357
from cryptography.hazmat.backends import default_backend from requests.models import HTTPBasicAuth import jwt import requests import time import os gh_pat = os.environ.get("GH_PAT") gh_user = "maoo" gh_app_id = os.environ.get("GH_APP_ID") fname = os.environ.get("GH_PRIVATE_KEY") cert_str = open(fname, 'r').read() cert_bytes = cert_str.encode() private_key = default_backend().load_pem_private_key(cert_bytes, None) def app_headers(): time_since_epoch_in_seconds = int(time.time()) payload = { 'iat': time_since_epoch_in_seconds, 'exp': time_since_epoch_in_seconds + (10 * 60), 'iss': gh_app_id } actual_jwt = jwt.encode(payload, private_key, algorithm='RS256') headers = {"Authorization": f"Bearer {actual_jwt}", "Accept": "application/vnd.github.machine-man-preview+json"} return headers def call_gh_api(url, page=1): prefix = "https://api.github.com" ret = requests.get( prefix + url + f"?per_page=100&page={page}", auth=HTTPBasicAuth(gh_user, gh_pat), headers=app_headers()).json() if (len(ret) == 100): return ret + call_gh_api(url, page=page+1) else: return ret def get_members_and_invites(org, skip_failed=True): resp = call_gh_api(f'/orgs/{org}/members') members = [i['login'] for i in resp] resp = call_gh_api(f'/orgs/{org}/invitations') invitations = [i['login'] for i in resp] resp = call_gh_api(f'/orgs/{org}/failed_invitations') failed_invitations = [i['login'] for i in resp] print('Members: ', len(members)) print('Pending Invitations: ', len(invitations)) print('Failed Invitations: ', len(failed_invitations)) if skip_failed: return members + invitations else: return members + invitations + failed_invitations def invite_user(user,org): resp = requests.put( f'https://api.github.com/orgs/{org}/memberships/{user}', auth=HTTPBasicAuth(user, os.environ.get("GH_PAT")), headers=app_headers()) print(f'Invited {user} to {org} - response {resp.status_code}')
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prototorch/functions/__init__.py
codacy-badger/prototorch
0
177899
<reponame>codacy-badger/prototorch<filename>prototorch/functions/__init__.py<gh_stars>0 """ProtoTorch functions.""" from .activations import identity, sigmoid_beta, swish_beta from .competitions import knnc, wtac __all__ = [ 'identity', 'sigmoid_beta', 'swish_beta', 'knnc', 'wtac', ]
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src/utils.py
dsilvalo28/AIVA-DAIA
1
185823
import cv2 import os, sys import numpy as np save_path = 'images/train/' # Helpful functions # def save_image(name, img): if not os.path.exists(save_path): os.makedirs(save_path) cv2.imwrite(save_path+name+'.tif', np.array(img, dtype=np.uint8)) def get_api_key(): if len(sys.argv) is 2: print('Reading API key from input argument') return sys.argv.pop() else: try: from src import credentials if hasattr(credentials, 'GOOGLE_MAPS_API_KEY'): print('Reading API key from credentials.py') return credentials.GOOGLE_MAPS_API_KEY except: if 'GOOGLE_MAPS_API_KEY' in os.environ: print('Reading API key from environment') return os.environ['GOOGLE_MAPS_API_KEY'] else: print('API Key not found.') sys.exit(1)
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bupt_ncov_report/program_utils/program_utils.py
llllxq/bupt-ncov-report
8
110027
<gh_stars>1-10 __all__ = ( 'ProgramUtils', ) import json import logging from typing import Any, Dict, List, Tuple, cast from ..constant import * from ..predef import * from ..pure_utils import * logger = logging.getLogger(__name__) class ProgramUtils: """关系到疫情上报网站的具体逻辑的工具函数。""" def __init__( self, pure_utils: PureUtils, ): self.pure_util = pure_utils def extract_post_data(self, html: str) -> Dict[str, Any]: """ 从上报页面的 HTML 中，提取出上报 API 所需要填写的参数。 该函数获取页面上 def 与 oldInfo 变量的值，将其正确混合后返回。 :param html: 上报页 HTML :return: dict 类型，可用于最终上报时提交的参数 """ old_dict, new_dict = self.extract_old_new_data(html) # 需要从 new dict 中提取如下数据 PICK_PROPS = ( 'id', 'uid', 'date', 'created', ) for prop in PICK_PROPS: val = new_dict.get(prop, ...) if val is ...: raise RuntimeError(f'从网页上提取的 new data 中缺少属性 {prop}，可能网页已经改版。') old_dict[prop] = val return old_dict def extract_old_new_data(self, html: str) -> Tuple[Dict[str, Any], Dict[str, Any]]: """ 从页面 HTML 中提取 def 变量与 oldInfo 变量的值（分别叫做 new 与 old data），并用 Python dict 的形式返回。 当过短时，抛出异常。 :param html: 上报页面的 HTML :return: 元组，(old_dict, new_dict) """ new_data = self.pure_util.match_re_group1(r'var def = (\{.+\});', html) old_data = self.pure_util.match_re_group1(r'oldInfo: (\{.+\}),', html) # 检查数据是否足够长 if len(old_data) < REASONABLE_LENGTH or len(new_data) < REASONABLE_LENGTH: raise ValueError('获取到的数据过短。请阅读脚本文档的「使用前提」部分。') old_dict, new_dict = json.loads(old_data), json.loads(new_data) return old_dict, new_dict def is_data_broken(self, data: Dict[str, Any]) -> bool: """ 检查最终上报数据内容是否破损（指出现不可能出现的值） （如果破损，可能是上报页面改版。） :param data: 最终上报的数据 :return: True/False 表示是/否破损 """ # self.pure_util 太长了 util = self.pure_util MUST_EXIST_PROPERTIES = ( 'tw', 'jcjgqr', 'remark', 'sfsfbh', 'ismoved', ) for prop in MUST_EXIST_PROPERTIES: if prop not in data: return True # `tw` 数据必须在 1 到 9 之间 if not util.is_number_data_in_range(data.get('tw'), (1, 10)): return True # `jcjgqr` 必须在 0 到 3 之间 if not util.is_number_data_in_range(data.get('jcjgqr'), (0, 4)): return True # 值为 0 或 1 的属性 BINARY_PROPERTIES = ( 'zgfxdq', 'sfcxtz', 'sfjcbh', 'mjry', 'csmjry', 'sfcyglq', 'szsqsfybl', 'sfcxzysx', ) for prop in BINARY_PROPERTIES: if not util.is_number_data_in_range(data.get(prop), (0, 2)): return True return False def verify_data(self, data: Dict[str, Any]) -> VerifiedData: """ 验证 data 是否是破损的；如果 data 未破损，则原样返回。此处使用类型系统保证正确性。 :param data: 最终上报数据 :return: data 本身 """ if self.is_data_broken(data): raise RuntimeError('要上报的数据似乎是破损的，可能网页已经改版。' '此时无法使用 STOP_WHEN_SICK 功能；上报失败，请手动上报。') # 仅用于类型检查器的类型转换 return cast(VerifiedData, data) def data_sick_report(self, data: VerifiedData) -> List[str]: """ 检测当前数据中表示生病的项，生成「生病项报告」。 如：体温 38.5°C，「其他信息」不为空，接触过疑似确诊人群等。 :param data: 最终上报的数据，必须经过检查确定其未破损 :return: str 数组，其中每一条为一个异常项。无异常则为空 list """ abnormal_items = [] # 体温：>= 37°C 则为异常 body_temp = int(data['tw']) if body_temp > 3: abnormal_items.append('您上一次填报了高于 37 度的体温') current_situation = int(data['jcjgqr']) if current_situation != 0: abnormal_items.append('您当前状态为疑似感染/确诊感染/其他') if isinstance(data['remark'], str) and data['remark'].strip() != '': abnormal_items.append('您的「其他信息」一栏不为空') if self.pure_util.looks_truthy(data['sfsfbh']) or self.pure_util.looks_truthy(data['ismoved']): abnormal_items.append('您可能昨天或今天去了别的地方，导致位置有变化；现在提交将导致数据异常，请您今天手动提交') BINARY_PROPERTIES = { 'zgfxdq': '是否在中高风险地区', 'sfcxtz': '是否出现症状', 'sfjcbh': '是否接触感染人群', 'mjry': '是否接触‘密切接触’人员', 'csmjry': '是否去过疫情场所', 'sfcyglq': '是否处于观察期', 'szsqsfybl': '所在社区是否有确诊病例', 'sfcxzysx': '是否有值得注意的情况', } for prop, desc in BINARY_PROPERTIES.items(): if self.pure_util.looks_truthy(data[prop]): abnormal_items.append(f'「{desc}」，您填了「是」') return abnormal_items def check_data_sick(self, data: VerifiedData) -> None: """ 如果提交的数据表明用户生病，则抛出异常。 :param data: 最终提交数据，必须经过检查确定其未破损 :return: None """ # 获取生病列表 sick_list = self.data_sick_report(data) if len(sick_list) == 0: return # 格式化生病列表 err_msg: List[str] = ['\n您上一次填报的数据中，以下数据异常：\n'] for i in sick_list: err_msg.append('· ' + i) err_msg.append('\n由于您开启了 STOP_WHEN_SICK 配置，且检测到您数据为异常数据，自动上报失败；' '请您今天手动填报正常数据，从明天开始再自动填报。') raise RuntimeError('\n'.join(err_msg))
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rogerthat/utils/parsing_numbers.py
TheHolyRoger/RogerThat
9
1610470
<reponame>TheHolyRoger/RogerThat from decimal import Decimal as Dec def decimal_or_none(num_string): return Dec(str(num_string)) if num_string and str(num_string).replace('.', '').isdigit() else None def decimal_or_zero(num_string): return Dec(str(num_string)) if num_string and str(num_string).replace('.', '').isdigit() else Dec("0") def int_or_none(num_string): return int(str(num_string)) if num_string and str(num_string).isdigit() else None
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service/app.py
alceballosa/BikeDemandEACD
0
130377
<reponame>alceballosa/BikeDemandEACD import sys import typing as t from datetime import datetime from functools import lru_cache import joblib import numpy as np import pandas as pd from fastapi import Body # type: ignore # noqa: E402 from fastapi import Depends from fastapi import FastAPI from pydantic import BaseSettings from pydantic import PositiveFloat from entities import ModelInput app = FastAPI(title="API for bike demand inference", version="0.0.1") class Settings(BaseSettings): SERIALIZED_MODEL_PATH: str MODEL_LIB_DIR: str @lru_cache(None) def get_settings(): return Settings() @lru_cache(None) def load_estimator(): sys.path.append(get_settings().MODEL_LIB_DIR) estimator = joblib.load(get_settings().SERIALIZED_MODEL_PATH) return estimator # @app.post("/") # async def make_prediction(input_: str = Body(...), estimator=Depends(load_estimator)): # """ # Call this using something like # YrSold=2010,YearBuilt=1970,YearRemodAdd=1999,GarageYrBlt=1980,LotArea=24,Neighborhood=Blmngtn,HouseStyle=SFoyer # """ # input_dict = {} # for var in input_.split(","): # name, value = var.split("=") # if value.isnumeric(): # value = int(value) # input_dict[name] = [value] # X = pd.DataFrame(input_dict) # prediction = estimator.predict(X).tolist() # return prediction # @app.post("/") # async def make_prediction( # inputs: t.List[ModelInput] = Body(...), # estimator=Depends(load_estimator), # ): # X = pd.DataFrame([row.dict() for row in inputs]) # prediction = estimator.predict(X).tolist() # return prediction # @app.post("/", response_model=t.List[float]) # async def make_prediction( # inputs: t.List[ModelInput] = Body(...), # estimator=Depends(load_estimator), # ): # X = pd.DataFrame([row.dict() for row in inputs]) # prediction = estimator.predict(X).tolist() # return prediction class Logger: def __init__(self, filepath="log.log"): self.filepath = filepath def log(self, inputs: t.List[ModelInput], predictions): file = open(self.filepath, "a") for row, pred in zip(inputs, predictions): record = {"datetime": datetime.now(), "input": row.dict(), "pred": pred} print(record, file=file) file.close() def get_logger(): return Logger() @app.post("/predict", response_model=t.List[float]) async def make_prediction( inputs: t.List[ModelInput] = Body(...), estimator=Depends(load_estimator), logger=Depends(get_logger), ): X = pd.DataFrame([row.dict() for row in inputs]) prediction = estimator.predict(X).astype(np.uint32).tolist() logger.log(inputs, prediction) return prediction @app.get("/get") async def service_status(): """Check the status of the service""" return {"status": "ok"} if __name__ == "__main__": import uvicorn uvicorn.run(app)
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scripts/Figures and Videos/Figure 2B/produce_fig_2B.py
jfaccioni/clovars
0
133688
<reponame>jfaccioni/clovars<gh_stars>0 import random import sys from pathlib import Path import numpy as np from clovars.main import main as clovars_main RANDOMNESS_SEED = 31 def main(): """Main function of this script.""" random.seed(RANDOMNESS_SEED) np.random.seed(RANDOMNESS_SEED) for scenario in ['i', 'ii', 'iii']: sys.argv = ['', 'run', 'Fig_2B_run.toml', f'Fig_2B_colonies_{scenario}.toml'] clovars_main() sys.argv = ['', 'view', 'Fig_2B_view.toml'] clovars_main() remove_tree(path=Path('output')) def remove_tree(path: Path): """Recursively deletes files and folders starting from path.""" # Source: https://stackoverflow.com/a/57892171/11161432 for child in path.iterdir(): if child.is_file(): child.unlink() else: remove_tree(child) path.rmdir() if __name__ == '__main__': main()
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losses/msloss.py
hiimmuc/Speaker-verification
2
46182
<filename>losses/msloss.py<gh_stars>1-10 # Copyright (c) Malong Technologies Co., Ltd. # All rights reserved. # # Contact: <EMAIL> # # This source code is licensed under the LICENSE file in the root directory of this source tree. import torch import torch.nn as nn import torch.nn.functional as F class LossFunction(nn.Module): def __init__(self, margin=0.1, scale_neg=50.0, scale_pos=2.0, ** kwargs): super(LossFunction, self).__init__() self.test_normalize = True self.thresh = 0.5 # lambda self.margin = margin self.scale_pos = scale_pos # alpha self.scale_neg = scale_neg # beta def forward(self, feats, labels): assert feats.size(0) == labels.size(0), \ f"feats.size(0): {feats.size(0)} is not equal to labels.size(0): {labels.size(0)}" # batchxn -> batch.n n: nPerSpeaker ; size [64] -> [640], repeat by number of utterances labels = labels.repeat_interleave(feats.size()[1]) # batchxnxd -> batch.n x d; [64, 5, 128] -> [640, 128] feats = feats.reshape(-1, feats.size()[-1]) batch_size = feats.size(0) batch_size = feats.size(0) # feat: batch_size x outdim sim_mat = torch.matmul(feats, torch.t(feats)) # test: normalize sim mat so all values is below 1 sim_mat = F.normalize(sim_mat) epsilon = 1e-5 losses = list() c = 0 for i in range(batch_size): # pair mining step # implement same as hard mining loss https://github.com/bnu-wangxun/Deep_Metric/blob/master/losses/HardMining.py pos_pair_ = torch.masked_select(sim_mat[i], labels == labels[i]) # move itself pos_pair_ = torch.masked_select(pos_pair_, pos_pair_ < 1 - epsilon) neg_pair_ = torch.masked_select(sim_mat[i], labels != labels[i]) neg_pair = torch.masked_select( neg_pair_, neg_pair_ > min(pos_pair_) - self.margin) pos_pair = torch.masked_select( pos_pair_, pos_pair_ < max(neg_pair_) + self.margin) if len(neg_pair) < 1: c += 1 continue if len(pos_pair) < 1: continue # pair weighting stage pos_loss = (1.0 / self.scale_pos) * torch.log( 1 + torch.sum(torch.exp(-self.scale_pos * (pos_pair - self.thresh)))) neg_loss = (1.0 / self.scale_neg) * torch.log( 1 + torch.sum(torch.exp(self.scale_neg * (neg_pair - self.thresh)))) loss_ms = pos_loss + neg_loss losses.append(loss_ms) if len(losses) == 0: return torch.zeros([], requires_grad=True), 0 loss = sum(losses) / batch_size prec1 = float(c) *100 / batch_size return loss, prec1 if __name__ == '__main__': loss = LossFunction() feats = torch.rand([64, 512]) labels = torch.rand(64) loss, pred = loss(feats, labels) print(loss) print(pred)
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loldib/getratings/models/NA/na_khazix/na_khazix_top.py
koliupy/loldib
0
19217
from getratings.models.ratings import Ratings class NA_Khazix_Top_Aatrox(Ratings): pass class NA_Khazix_Top_Ahri(Ratings): pass class NA_Khazix_Top_Akali(Ratings): pass class NA_Khazix_Top_Alistar(Ratings): pass class NA_Khazix_Top_Amumu(Ratings): pass class NA_Khazix_Top_Anivia(Ratings): pass class NA_Khazix_Top_Annie(Ratings): pass class NA_Khazix_Top_Ashe(Ratings): pass class NA_Khazix_Top_AurelionSol(Ratings): pass class NA_Khazix_Top_Azir(Ratings): pass class NA_Khazix_Top_Bard(Ratings): pass class NA_Khazix_Top_Blitzcrank(Ratings): pass class NA_Khazix_Top_Brand(Ratings): pass class NA_Khazix_Top_Braum(Ratings): pass class NA_Khazix_Top_Caitlyn(Ratings): pass class NA_Khazix_Top_Camille(Ratings): pass class NA_Khazix_Top_Cassiopeia(Ratings): pass class NA_Khazix_Top_Chogath(Ratings): pass class NA_Khazix_Top_Corki(Ratings): pass class NA_Khazix_Top_Darius(Ratings): pass class NA_Khazix_Top_Diana(Ratings): pass class NA_Khazix_Top_Draven(Ratings): pass class NA_Khazix_Top_DrMundo(Ratings): pass class NA_Khazix_Top_Ekko(Ratings): pass class NA_Khazix_Top_Elise(Ratings): pass class NA_Khazix_Top_Evelynn(Ratings): pass class NA_Khazix_Top_Ezreal(Ratings): pass class NA_Khazix_Top_Fiddlesticks(Ratings): pass class NA_Khazix_Top_Fiora(Ratings): pass class NA_Khazix_Top_Fizz(Ratings): pass class NA_Khazix_Top_Galio(Ratings): pass class NA_Khazix_Top_Gangplank(Ratings): pass class NA_Khazix_Top_Garen(Ratings): pass class NA_Khazix_Top_Gnar(Ratings): pass class NA_Khazix_Top_Gragas(Ratings): pass class NA_Khazix_Top_Graves(Ratings): pass class NA_Khazix_Top_Hecarim(Ratings): pass class NA_Khazix_Top_Heimerdinger(Ratings): pass class NA_Khazix_Top_Illaoi(Ratings): pass class NA_Khazix_Top_Irelia(Ratings): pass class NA_Khazix_Top_Ivern(Ratings): pass class NA_Khazix_Top_Janna(Ratings): pass class NA_Khazix_Top_JarvanIV(Ratings): pass class NA_Khazix_Top_Jax(Ratings): pass class NA_Khazix_Top_Jayce(Ratings): pass class NA_Khazix_Top_Jhin(Ratings): pass class NA_Khazix_Top_Jinx(Ratings): pass class NA_Khazix_Top_Kalista(Ratings): pass class NA_Khazix_Top_Karma(Ratings): pass class NA_Khazix_Top_Karthus(Ratings): pass class NA_Khazix_Top_Kassadin(Ratings): pass class NA_Khazix_Top_Katarina(Ratings): pass class NA_Khazix_Top_Kayle(Ratings): pass class NA_Khazix_Top_Kayn(Ratings): pass class NA_Khazix_Top_Kennen(Ratings): pass class NA_Khazix_Top_Khazix(Ratings): pass class NA_Khazix_Top_Kindred(Ratings): pass class NA_Khazix_Top_Kled(Ratings): pass class NA_Khazix_Top_KogMaw(Ratings): pass class NA_Khazix_Top_Leblanc(Ratings): pass class NA_Khazix_Top_LeeSin(Ratings): pass class NA_Khazix_Top_Leona(Ratings): pass class NA_Khazix_Top_Lissandra(Ratings): pass class NA_Khazix_Top_Lucian(Ratings): pass class NA_Khazix_Top_Lulu(Ratings): pass class NA_Khazix_Top_Lux(Ratings): pass class NA_Khazix_Top_Malphite(Ratings): pass class NA_Khazix_Top_Malzahar(Ratings): pass class NA_Khazix_Top_Maokai(Ratings): pass class NA_Khazix_Top_MasterYi(Ratings): pass class NA_Khazix_Top_MissFortune(Ratings): pass class NA_Khazix_Top_MonkeyKing(Ratings): pass class NA_Khazix_Top_Mordekaiser(Ratings): pass class NA_Khazix_Top_Morgana(Ratings): pass class NA_Khazix_Top_Nami(Ratings): pass class NA_Khazix_Top_Nasus(Ratings): pass class NA_Khazix_Top_Nautilus(Ratings): pass class NA_Khazix_Top_Nidalee(Ratings): pass class NA_Khazix_Top_Nocturne(Ratings): pass class NA_Khazix_Top_Nunu(Ratings): pass class NA_Khazix_Top_Olaf(Ratings): pass class NA_Khazix_Top_Orianna(Ratings): pass class NA_Khazix_Top_Ornn(Ratings): pass class NA_Khazix_Top_Pantheon(Ratings): pass class NA_Khazix_Top_Poppy(Ratings): pass class NA_Khazix_Top_Quinn(Ratings): pass class NA_Khazix_Top_Rakan(Ratings): pass class NA_Khazix_Top_Rammus(Ratings): pass class NA_Khazix_Top_RekSai(Ratings): pass class NA_Khazix_Top_Renekton(Ratings): pass class NA_Khazix_Top_Rengar(Ratings): pass class NA_Khazix_Top_Riven(Ratings): pass class NA_Khazix_Top_Rumble(Ratings): pass class NA_Khazix_Top_Ryze(Ratings): pass class NA_Khazix_Top_Sejuani(Ratings): pass class NA_Khazix_Top_Shaco(Ratings): pass class NA_Khazix_Top_Shen(Ratings): pass class NA_Khazix_Top_Shyvana(Ratings): pass class NA_Khazix_Top_Singed(Ratings): pass class NA_Khazix_Top_Sion(Ratings): pass class NA_Khazix_Top_Sivir(Ratings): pass class NA_Khazix_Top_Skarner(Ratings): pass class NA_Khazix_Top_Sona(Ratings): pass class NA_Khazix_Top_Soraka(Ratings): pass class NA_Khazix_Top_Swain(Ratings): pass class NA_Khazix_Top_Syndra(Ratings): pass class NA_Khazix_Top_TahmKench(Ratings): pass class NA_Khazix_Top_Taliyah(Ratings): pass class NA_Khazix_Top_Talon(Ratings): pass class NA_Khazix_Top_Taric(Ratings): pass class NA_Khazix_Top_Teemo(Ratings): pass class NA_Khazix_Top_Thresh(Ratings): pass class NA_Khazix_Top_Tristana(Ratings): pass class NA_Khazix_Top_Trundle(Ratings): pass class NA_Khazix_Top_Tryndamere(Ratings): pass class NA_Khazix_Top_TwistedFate(Ratings): pass class NA_Khazix_Top_Twitch(Ratings): pass class NA_Khazix_Top_Udyr(Ratings): pass class NA_Khazix_Top_Urgot(Ratings): pass class NA_Khazix_Top_Varus(Ratings): pass class NA_Khazix_Top_Vayne(Ratings): pass class NA_Khazix_Top_Veigar(Ratings): pass class NA_Khazix_Top_Velkoz(Ratings): pass class NA_Khazix_Top_Vi(Ratings): pass class NA_Khazix_Top_Viktor(Ratings): pass class NA_Khazix_Top_Vladimir(Ratings): pass class NA_Khazix_Top_Volibear(Ratings): pass class NA_Khazix_Top_Warwick(Ratings): pass class NA_Khazix_Top_Xayah(Ratings): pass class NA_Khazix_Top_Xerath(Ratings): pass class NA_Khazix_Top_XinZhao(Ratings): pass class NA_Khazix_Top_Yasuo(Ratings): pass class NA_Khazix_Top_Yorick(Ratings): pass class NA_Khazix_Top_Zac(Ratings): pass class NA_Khazix_Top_Zed(Ratings): pass class NA_Khazix_Top_Ziggs(Ratings): pass class NA_Khazix_Top_Zilean(Ratings): pass class NA_Khazix_Top_Zyra(Ratings): pass
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dislib/model_selection/_search.py
alexbarcelo/dislib
36
9659
<gh_stars>10-100 from abc import ABC, abstractmethod from collections import defaultdict from collections.abc import Sequence from functools import partial from itertools import product import numpy as np from pycompss.api.api import compss_wait_on from scipy.stats import rankdata from sklearn import clone from sklearn.model_selection import ParameterGrid, ParameterSampler from numpy.ma import MaskedArray from dislib.model_selection._split import infer_cv from dislib.model_selection._validation import check_scorer, fit_and_score, \ validate_score, aggregate_score_dicts class BaseSearchCV(ABC): """Abstract base class for hyper parameter search with cross-validation.""" def __init__(self, estimator, scoring=None, cv=None, refit=True): self.estimator = estimator self.scoring = scoring self.cv = cv self.refit = refit @abstractmethod def _run_search(self, evaluate_candidates): """Abstract method to perform the search. The parameter `evaluate_candidates` is a function that evaluates a ParameterGrid at a time """ pass def fit(self, x, y=None, **fit_params): """Run fit with all sets of parameters. Parameters ---------- x : ds-array Training data samples. y : ds-array, optional (default = None) Training data labels or values. **fit_params : dict of string -> object Parameters passed to the ``fit`` method of the estimator """ estimator = self.estimator cv = infer_cv(self.cv) scorers, refit_metric = self._infer_scorers() base_estimator = clone(estimator) n_splits = None all_candidate_params = [] all_out = [] def evaluate_candidates(candidate_params): """Evaluate some parameters""" candidate_params = list(candidate_params) out = [fit_and_score(clone(base_estimator), train, validation, scorer=scorers, parameters=parameters, fit_params=fit_params) for parameters, (train, validation) in product(candidate_params, cv.split(x, y))] nonlocal n_splits n_splits = cv.get_n_splits() all_candidate_params.extend(candidate_params) all_out.extend(out) self._run_search(evaluate_candidates) for params_result in all_out: scores = params_result[0] for scorer_name, score in scores.items(): score = compss_wait_on(score) scores[scorer_name] = validate_score(score, scorer_name) results = self._format_results(all_candidate_params, scorers, n_splits, all_out) # For multi-metric evaluation, store the best_index_, best_params_ and # best_score_ iff refit is one of the scorer names # In single metric evaluation, refit_metric is "score" if self.refit or not self.multimetric_: # If callable, refit is expected to return the index of the best # parameter set. if callable(self.refit): self.best_index_ = self.refit(results) if not isinstance(self.best_index_, (int, np.integer)): raise TypeError('best_index_ returned is not an integer') if (self.best_index_ < 0 or self.best_index_ >= len(results["params"])): raise IndexError('best_index_ index out of range') else: self.best_index_ = results["rank_test_%s" % refit_metric].argmin() self.best_score_ = results["mean_test_%s" % refit_metric][ self.best_index_] self.best_params_ = results["params"][self.best_index_] if self.refit: self.best_estimator_ = clone(base_estimator).set_params( **self.best_params_) self.best_estimator_.fit(x, y, **fit_params) # Store the only scorer not as a dict for single metric evaluation self.scorer_ = scorers if self.multimetric_ else scorers['score'] self.cv_results_ = results self.n_splits_ = n_splits return self @staticmethod def _format_results(candidate_params, scorers, n_splits, out): n_candidates = len(candidate_params) (test_score_dicts,) = zip(*out) test_scores = aggregate_score_dicts(test_score_dicts) results = {} def _store(key_name, array, splits=False, rank=False): """A small helper to store the scores/times to the cv_results_""" array = np.array(array, dtype=np.float64).reshape(n_candidates, n_splits) if splits: for split_i in range(n_splits): # Uses closure to alter the results results["split%d_%s" % (split_i, key_name)] = array[:, split_i] array_means = np.mean(array, axis=1) results['mean_%s' % key_name] = array_means array_stds = np.std(array, axis=1) results['std_%s' % key_name] = array_stds if rank: results["rank_%s" % key_name] = np.asarray( rankdata(-array_means, method='min'), dtype=np.int32) # Use one MaskedArray and mask all the places where the param is not # applicable for that candidate. Use defaultdict as each candidate may # not contain all the params param_results = defaultdict(partial(MaskedArray, np.empty(n_candidates, ), mask=True, dtype=object)) for cand_i, params in enumerate(candidate_params): for name, value in params.items(): # An all masked empty array gets created for the key # `"param_%s" % name` at the first occurrence of `name`. # Setting the value at an index also unmasks that index param_results["param_%s" % name][cand_i] = value results.update(param_results) # Store a list of param dicts at the key 'params' results['params'] = candidate_params for scorer_name in scorers.keys(): _store('test_%s' % scorer_name, test_scores[scorer_name], splits=True, rank=True) return results def _infer_scorers(self): estimator = self.estimator scoring = self.scoring refit = self.refit if scoring is None or callable(scoring): scorers = {"score": check_scorer(estimator, scoring)} refit_metric = 'score' self.multimetric_ = False elif isinstance(scoring, dict): scorers = {key: check_scorer(estimator, scorer) for key, scorer in scoring.items()} if refit is not False and ( not isinstance(refit, str) or refit not in scorers) and not callable(refit): raise ValueError("For multi-metric scoring, the parameter " "refit must be set to a scorer key or a " "callable to refit an estimator with the " "best parameter setting on the whole " "data and make the best_* attributes " "available for that metric. If this is " "not needed, refit should be set to " "False explicitly. %r was passed." % refit) refit_metric = refit self.multimetric_ = True else: raise ValueError('scoring is not valid') return scorers, refit_metric class GridSearchCV(BaseSearchCV): """Exhaustive search over specified parameter values for an estimator. GridSearchCV implements a "fit" and a "score" method. The parameters of the estimator used to apply these methods are optimized by cross-validated grid-search over a parameter grid. Parameters ---------- estimator : estimator object. This is assumed to implement the scikit-learn estimator interface. Either estimator needs to provide a ``score`` function, or ``scoring`` must be passed. param_grid : dict or list of dictionaries Dictionary with parameters names (string) as keys and lists of parameter settings to try as values, or a list of such dictionaries, in which case the grids spanned by each dictionary in the list are explored. This enables searching over any sequence of parameter settings. scoring : callable, dict or None, optional (default=None) A callable to evaluate the predictions on the test set. It should take 3 parameters, estimator, x and y, and return a score (higher meaning better). For evaluating multiple metrics, give a dict with names as keys and callables as values. If None, the estimator's score method is used. cv : int or cv generator, optional (default=None) Determines the cross-validation splitting strategy. Possible inputs for cv are: - None, to use the default 5-fold cross validation, - integer, to specify the number of folds in a `KFold`, - custom cv generator. refit : boolean, string, or callable, optional (default=True) Refit an estimator using the best found parameters on the whole dataset. For multiple metric evaluation, this needs to be a string denoting the scorer that would be used to find the best parameters for refitting the estimator at the end. Where there are considerations other than maximum score in choosing a best estimator, ``refit`` can be set to a function which returns the selected ``best_index_`` given ``cv_results_``. The refitted estimator is made available at the ``best_estimator_`` attribute and permits using ``predict`` directly on this ``GridSearchCV`` instance. Also for multiple metric evaluation, the attributes ``best_index_``, ``best_score_`` and ``best_params_`` will only be available if ``refit`` is set and all of them will be determined w.r.t this specific scorer. ``best_score_`` is not returned if refit is callable. See ``scoring`` parameter to know more about multiple metric evaluation. Examples -------- >>> import dislib as ds >>> from dislib.model_selection import GridSearchCV >>> from dislib.classification import RandomForestClassifier >>> import numpy as np >>> from sklearn import datasets >>> >>> >>> if __name__ == '__main__': >>> x_np, y_np = datasets.load_iris(return_X_y=True) >>> x = ds.array(x_np, (30, 4)) >>> y = ds.array(y_np[:, np.newaxis], (30, 1)) >>> param_grid = {'n_estimators': (2, 4), 'max_depth': range(3, 5)} >>> rf = RandomForestClassifier() >>> searcher = GridSearchCV(rf, param_grid) >>> searcher.fit(x, y) >>> searcher.cv_results_ Attributes ---------- cv_results_ : dict of numpy (masked) ndarrays A dict with keys as column headers and values as columns, that can be imported into a pandas ``DataFrame``. For instance the below given table: +------------+------------+-----------------+---+---------+ |param_kernel|param_degree|split0_test_score|...|rank_t...| +============+============+=================+===+=========+ | 'poly' | 2 | 0.80 |...| 2 | +------------+------------+-----------------+---+---------+ | 'poly' | 3 | 0.70 |...| 4 | +------------+------------+-----------------+---+---------+ | 'rbf' | -- | 0.80 |...| 3 | +------------+------------+-----------------+---+---------+ | 'rbf' | -- | 0.93 |...| 1 | +------------+------------+-----------------+---+---------+ will be represented by a ``cv_results_`` dict of:: { 'param_kernel': masked_array(data = ['poly', 'poly', 'rbf', 'rbf'], mask = [False False False False]...), 'param_degree': masked_array(data = [2.0 3.0 -- --], mask = [False False True True]...), 'split0_test_score' : [0.80, 0.70, 0.80, 0.93], 'split1_test_score' : [0.82, 0.50, 0.68, 0.78], 'split2_test_score' : [0.79, 0.55, 0.71, 0.93], ... 'mean_test_score' : [0.81, 0.60, 0.75, 0.85], 'std_test_score' : [0.01, 0.10, 0.05, 0.08], 'rank_test_score' : [2, 4, 3, 1], 'params' : [{'kernel': 'poly', 'degree': 2}, ...], } NOTES: The key ``'params'`` is used to store a list of parameter settings dicts for all the parameter candidates. The ``mean_fit_time``, ``std_fit_time``, ``mean_score_time`` and ``std_score_time`` are all in seconds. For multi-metric evaluation, the scores for all the scorers are available in the ``cv_results_`` dict at the keys ending with that scorer's name (``'_<scorer_name>'``) instead of ``'_score'`` shown above ('split0_test_precision', 'mean_train_precision' etc.). best_estimator_ : estimator or dict Estimator that was chosen by the search, i.e. estimator which gave highest score (or smallest loss if specified) on the left out data. Not available if ``refit=False``. See ``refit`` parameter for more information on allowed values. best_score_ : float Mean cross-validated score of the best_estimator For multi-metric evaluation, this is present only if ``refit`` is specified. best_params_ : dict Parameter setting that gave the best results on the hold out data. For multi-metric evaluation, this is present only if ``refit`` is specified. best_index_ : int The index (of the ``cv_results_`` arrays) which corresponds to the best candidate parameter setting. The dict at ``search.cv_results_['params'][search.best_index_]`` gives the parameter setting for the best model, that gives the highest mean score (``search.best_score_``). For multi-metric evaluation, this is present only if ``refit`` is specified. scorer_ : function or a dict Scorer function used on the held out data to choose the best parameters for the model. For multi-metric evaluation, this attribute holds the validated ``scoring`` dict which maps the scorer key to the scorer callable. n_splits_ : int The number of cross-validation splits (folds/iterations). """ def __init__(self, estimator, param_grid, scoring=None, cv=None, refit=True): super().__init__(estimator=estimator, scoring=scoring, cv=cv, refit=refit) self.param_grid = param_grid self._check_param_grid(param_grid) def _run_search(self, evaluate_candidates): evaluate_candidates(ParameterGrid(self.param_grid)) @staticmethod def _check_param_grid(param_grid): if hasattr(param_grid, 'items'): param_grid = [param_grid] for p in param_grid: for name, v in p.items(): if isinstance(v, np.ndarray) and v.ndim > 1: raise ValueError("Parameter array should be " "one-dimensional.") if (isinstance(v, str) or not isinstance(v, (np.ndarray, Sequence))): raise ValueError( "Parameter values for parameter ({0}) need " "to be a sequence (but not a string) or" " np.ndarray.".format(name)) if len(v) == 0: raise ValueError( "Parameter values for parameter ({0}) need " "to be a non-empty sequence.".format(name)) class RandomizedSearchCV(BaseSearchCV): """Randomized search on hyper parameters. RandomizedSearchCV implements a "fit" and a "score" method. The parameters of the estimator used to apply these methods are optimized by cross-validated search over parameter settings. In contrast to GridSearchCV, not all parameter values are tried out, but rather a fixed number of parameter settings is sampled from the specified distributions. The number of parameter settings that are tried is given by n_iter. If all parameters are presented as a list, sampling without replacement is performed. If at least one parameter is given as a distribution, sampling with replacement is used. Parameters ---------- estimator : estimator object. This is assumed to implement the scikit-learn estimator interface. Either estimator needs to provide a ``score`` function, or ``scoring`` must be passed. param_distributions : dict Dictionary with parameters names (string) as keys and distributions or lists of parameters to try. Distributions must provide a ``rvs`` method for sampling (such as those from scipy.stats.distributions). If a list is given, it is sampled uniformly. n_iter : int, optional (default=10) Number of parameter settings that are sampled. scoring : callable, dict or None, optional (default=None) A callable to evaluate the predictions on the test set. It should take 3 parameters, estimator, x and y, and return a score (higher meaning better). For evaluating multiple metrics, give a dict with names as keys and callables as values. If None, the estimator's score method is used. cv : int or cv generator, optional (default=None) Determines the cross-validation splitting strategy. Possible inputs for cv are: - None, to use the default 5-fold cross validation, - integer, to specify the number of folds in a `KFold`, - custom cv generator. refit : boolean, string, or callable, optional (default=True) Refit an estimator using the best found parameters on the whole dataset. For multiple metric evaluation, this needs to be a string denoting the scorer that would be used to find the best parameters for refitting the estimator at the end. Where there are considerations other than maximum score in choosing a best estimator, ``refit`` can be set to a function which returns the selected ``best_index_`` given ``cv_results_``. The refitted estimator is made available at the ``best_estimator_`` attribute and permits using ``predict`` directly on this ``GridSearchCV`` instance. Also for multiple metric evaluation, the attributes ``best_index_``, ``best_score_`` and ``best_params_`` will only be available if ``refit`` is set and all of them will be determined w.r.t this specific scorer. ``best_score_`` is not returned if refit is callable. See ``scoring`` parameter to know more about multiple metric evaluation. random_state : int, RandomState instance or None, optional, default=None Pseudo random number generator state used for random sampling of params in param_distributions. This is not passed to each estimator. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. Examples -------- >>> import dislib as ds >>> from dislib.model_selection import RandomizedSearchCV >>> from dislib.classification import CascadeSVM >>> import numpy as np >>> import scipy.stats as stats >>> from sklearn import datasets >>> >>> >>> if __name__ == '__main__': >>> x_np, y_np = datasets.load_iris(return_X_y=True) >>> # Pre-shuffling required for CSVM >>> p = np.random.permutation(len(x_np)) >>> x = ds.array(x_np[p], (30, 4)) >>> y = ds.array((y_np[p] == 0)[:, np.newaxis], (30, 1)) >>> param_distributions = {'c': stats.expon(scale=0.5), >>> 'gamma': stats.expon(scale=10)} >>> csvm = CascadeSVM() >>> searcher = RandomizedSearchCV(csvm, param_distributions, n_iter=10) >>> searcher.fit(x, y) >>> searcher.cv_results_ Attributes ---------- cv_results_ : dict of numpy (masked) ndarrays A dict with keys as column headers and values as columns, that can be imported into a pandas ``DataFrame``. For instance the below given table +---------+-------------+-------------------+---+---------------+ | param_c | param_gamma | split0_test_score |...|rank_test_score| +=========+=============+===================+===+===============+ | 0.193 | 1.883 | 0.82 |...| 3 | +---------+-------------+-------------------+---+---------------+ | 1.452 | 0.327 | 0.81 |...| 2 | +---------+-------------+-------------------+---+---------------+ | 0.926 | 3.452 | 0.94 |...| 1 | +---------+-------------+-------------------+---+---------------+ will be represented by a ``cv_results_`` dict of:: { 'param_kernel' : masked_array(data = ['rbf', 'rbf', 'rbf'], mask = False), 'param_gamma' : masked_array(data = [0.1 0.2 0.3], mask = False), 'split0_test_score' : [0.82, 0.81, 0.94], 'split1_test_score' : [0.66, 0.75, 0.79], 'split2_test_score' : [0.82, 0.87, 0.84], ... 'mean_test_score' : [0.76, 0.84, 0.86], 'std_test_score' : [0.01, 0.20, 0.04], 'rank_test_score' : [3, 2, 1], 'params' : [{'c' : 0.193, 'gamma' : 1.883}, ...], } NOTE The key ``'params'`` is used to store a list of parameter settings dicts for all the parameter candidates. The ``mean_fit_time``, ``std_fit_time``, ``mean_score_time`` and ``std_score_time`` are all in seconds. For multi-metric evaluation, the scores for all the scorers are available in the ``cv_results_`` dict at the keys ending with that scorer's name (``'_<scorer_name>'``) instead of ``'_score'`` shown above. ('split0_test_precision', 'mean_train_precision' etc.) best_estimator_ : estimator or dict Estimator that was chosen by the search, i.e. estimator which gave highest score (or smallest loss if specified) on the left out data. Not available if ``refit=False``. For multi-metric evaluation, this attribute is present only if ``refit`` is specified. See ``refit`` parameter for more information on allowed values. best_score_ : float Mean cross-validated score of the best_estimator. For multi-metric evaluation, this is not available if ``refit`` is ``False``. See ``refit`` parameter for more information. best_params_ : dict Parameter setting that gave the best results on the hold out data. For multi-metric evaluation, this is not available if ``refit`` is ``False``. See ``refit`` parameter for more information. best_index_ : int The index (of the ``cv_results_`` arrays) which corresponds to the best candidate parameter setting. The dict at ``search.cv_results_['params'][search.best_index_]`` gives the parameter setting for the best model, that gives the highest mean score (``search.best_score_``). For multi-metric evaluation, this is not available if ``refit`` is ``False``. See ``refit`` parameter for more information. scorer_ : function or a dict Scorer function used on the held out data to choose the best parameters for the model. For multi-metric evaluation, this attribute holds the validated ``scoring`` dict which maps the scorer key to the scorer callable. n_splits_ : int The number of cross-validation splits (folds/iterations). """ def __init__(self, estimator, param_distributions, n_iter=10, scoring=None, cv=None, refit=True, random_state=None): super().__init__(estimator=estimator, scoring=scoring, cv=cv, refit=refit) self.param_distributions = param_distributions self.n_iter = n_iter self.random_state = random_state def _run_search(self, evaluate_candidates): """Search n_iter candidates from param_distributions""" ps = ParameterSampler(self.param_distributions, self.n_iter, random_state=self.random_state) evaluate_candidates(ps)
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remove.py
LaureoleSAS/RaspberryPi
0
109569
<reponame>LaureoleSAS/RaspberryPi import os, sys path = "/home/pi/Documents/Serveur/Videos/copy/" dirs = os.listdir(path) for file in dirs: if '.mp4' in file: nom_split = file.split('-') os.system('sudo cp '+ path + file + ' /home/pi/Documents/Serveur/Videos/'+nom_split[0]+'_'+nom_split[1]+'/') os.system('sudo rm ' + file) if '.h264' in file: os.system('sudo rm ' + file)
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cdd/cst.py
SamuelMarks/docstring2class
0
69780
""" Concrete Syntax Tree for Python 3.6+ source code """ from cdd.cst_utils import cst_parser, cst_scanner def cst_parse(source): """ Parse Python source lines into a Concrete Syntax Tree :param source: Python source code :type source: ```str``` :return: List of `namedtuple`s with at least ("line_no_start", "line_no_end", "value") attributes :rtype: ```List[Any]``` """ scanned = cst_scanner(source) parsed = cst_parser(scanned) return parsed __all__ = ["cst_parse"]
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tests/main.py
tdegeus/enstat
0
46535
import unittest from collections import defaultdict import numpy as np import enstat.mean class Test_mean(unittest.TestCase): """ tests """ def test_scalar(self): """ Basic test of "mean" and "std" using a random sample. """ average = enstat.scalar() average.add_sample(np.array(1.0)) self.assertFalse(np.isnan(average.mean())) self.assertTrue(np.isnan(average.std())) average.add_sample(np.array(1.0)) self.assertFalse(np.isnan(average.mean())) self.assertFalse(np.isnan(average.std())) def test_scalar_division(self): """ Check for zero division. """ average = enstat.scalar() a = np.random.random(50 * 20).reshape(50, 20) for i in range(a.shape[0]): average.add_sample(a[i, :]) self.assertTrue(np.isclose(average.mean(), np.mean(a))) self.assertTrue(np.isclose(average.std(), np.std(a), rtol=1e-3)) def test_static(self): """ Basic test of "mean" and "std" using a random sample. """ average = enstat.static() a = np.random.random(35 * 50 * 20).reshape(35, 50, 20) for i in range(a.shape[0]): average.add_sample(a[i, :, :]) self.assertTrue(np.allclose(average.mean(), np.mean(a, axis=0))) self.assertTrue(np.allclose(average.std(), np.std(a, axis=0), rtol=5e-1, atol=1e-3)) self.assertTrue(average.shape() == a.shape[1:]) self.assertTrue(average.size() == np.prod(a.shape[1:])) def test_static_ravel(self): """ Like :py:func:`test_static` but with a test of `ravel`. """ arraylike = enstat.static() scalar = enstat.scalar() a = np.random.random(35 * 50 * 20).reshape(35, 50, 20) for i in range(a.shape[0]): arraylike.add_sample(a[i, :, :]) scalar.add_sample(a[i, :, :]) flat = arraylike.ravel() self.assertTrue(np.allclose(flat.mean(), np.mean(a))) self.assertTrue(np.allclose(flat.std(), np.std(a), rtol=5e-1, atol=1e-3)) self.assertTrue(np.allclose(flat.mean(), scalar.mean())) self.assertTrue(np.allclose(flat.std(), scalar.std(), rtol=5e-1, atol=1e-3)) def test_static_division(self): """ Check for zero division. """ average = enstat.static() average.add_sample(np.array([1.0])) self.assertFalse(np.isnan(average.mean())) self.assertTrue(np.isnan(average.std())) average.add_sample(np.array([1.0])) self.assertFalse(np.isnan(average.mean())) self.assertFalse(np.isnan(average.std())) def test_static_mask(self): average = enstat.static() a = np.random.random(35 * 50 * 20).reshape(35, 50, 20) m = np.random.random(35 * 50 * 20).reshape(35, 50, 20) > 0.8 for i in range(a.shape[0]): average.add_sample(a[i, :, :], m[i, :, :]) self.assertTrue( np.isclose( np.sum(average.first()) / np.sum(average.norm()), np.mean(a[np.logical_not(m)]), ) ) self.assertTrue( np.isclose( np.sum(average.first()) / np.sum(average.norm()), np.mean(a[np.logical_not(m)]), ) ) self.assertTrue(np.all(np.equal(average.norm(), np.sum(np.logical_not(m), axis=0)))) def test_dynamic1d(self): average = enstat.dynamic1d() average.add_sample(np.array([1, 2, 3])) average.add_sample(np.array([1, 2, 3])) average.add_sample(np.array([1, 2])) average.add_sample(np.array([1])) self.assertTrue(np.allclose(average.mean(), np.array([1, 2, 3]))) self.assertTrue(np.allclose(average.std(), np.array([0, 0, 0]))) self.assertEqual(average.shape(), (3,)) self.assertEqual(average.size(), 3) class Test_defaultdict(unittest.TestCase): """ functionality """ def test_scalar(self): average = defaultdict(enstat.scalar) a = np.random.random(50 * 20).reshape(50, 20) b = np.random.random(52 * 21).reshape(52, 21) for i in range(a.shape[0]): average["a"].add_sample(a[i, :]) for i in range(b.shape[0]): average["b"].add_sample(b[i, :]) self.assertTrue(np.isclose(average["a"].mean(), np.mean(a))) self.assertTrue(np.isclose(average["b"].mean(), np.mean(b))) def test_static(self): average = defaultdict(enstat.static) a = np.random.random(35 * 50 * 20).reshape(35, 50, 20) b = np.random.random(37 * 52 * 21).reshape(37, 52, 21) for i in range(a.shape[0]): average["a"].add_sample(a[i, :, :]) for i in range(b.shape[0]): average["b"].add_sample(b[i, :, :]) self.assertTrue(np.allclose(average["a"].mean(), np.mean(a, axis=0))) self.assertTrue(np.allclose(average["b"].mean(), np.mean(b, axis=0))) self.assertTrue(average["a"].shape() == a.shape[1:]) self.assertTrue(average["b"].shape() == b.shape[1:]) if __name__ == "__main__": unittest.main()
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Clients/PythonCatalyst/Testing/package_test/__init__.py
xj361685640/ParaView
815
82042
<gh_stars>100-1000 # filename: __init__.py # used to test that Catalyst can load Packages # correctly. from paraview.simple import * from paraview import print_info # print start marker print_info("begin '%s'", __name__) tracker = {} def count(f): def wrapper(*args, **kwargs): global tracker c = tracker.get(f.__name__, 0) tracker[f.__name__] = c+1 return f(*args, **kwargs) return wrapper @count def catalyst_initialize(): print_info("in '%s::catalyst_initialize'", __name__) @count def catalyst_execute(info): print_info("in '%s::catalyst_execute'", __name__) @count def catalyst_finalize(): print_info("in '%s::catalyst_finalize'", __name__) global tracker assert tracker["catalyst_initialize"] == 1 assert tracker["catalyst_finalize"] == 1 assert tracker["catalyst_execute"] >= 1 print_info("All's ok") # print end marker print_info("end '%s'", __name__)
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tests/test_condition.py
richshaw2015/behavior3py
0
64830
import b3 import unittest class TestCondition(unittest.TestCase): def test_category(self): self.assertEqual(b3.Condition.category, b3.CONDITION) if __name__ == '__main__': unittest.main()
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main.py
Kingtous/pyLex
1
94374
<gh_stars>1-10 ''' @Author: Kingtous @Date: 2019-11-01 18:40:29 @LastEditors: Kingtous @LastEditTime: 2019-11-06 19:47:42 @Description: Kingtous' Code ''' """ Author: Kingtous Date : 2019/09/24 """ import nfa2dfa as DFATool import reg2nfa as REGTool import sys,os,networkx as nx import tools,constDef # 因为字符中(,),*跟词法里面的(,),*有冲突，我们需要先将源代码里的 # ()分别替换为{} # * 替换为 ^ rule_sub_str='(a|b|c|d|e|f|g|h|i|j|k|l|m|n|o|p|q|r|s|t|o|p|q|r|s|t|u|v|w|x|y|z|A|B|C|D|E|F|G|H|I|J|K|L|M|N|O|P|Q|R|S|T|U|V|W|X|Y|Z)*(a|b|c|d|e|f|g|h|i|j|k|l|m|n|o|p|q|r|s|t|o|p|q|r|s|t|u|v|w|x|y|z|A|B|C|D|E|F|G|H|I|J|K|L|M|N|O|P|Q|R|S|T|U|V|W|X|Y|Z)' rule_str = rule_sub_str + '(a|b|c|d|e|f|g|h|i|j|k|l|m|n|o|p|q|r|s|t|o|p|q|r|s|t|u|v|w|x|y|z|A|B|C|D|E|F|G|H|I|J|K|L|M|N|O|P|Q|R|S|T|U|V|W|X|Y|Z|0|1|2|3|4|5|6|7|8|9)*' rule_bound=',|.|;|{|}' rule_cal = '+|-|^|/|:=|<|<=|>|>=|#|=' rule_num = '(0|1|2|3|4|5|6|7|8|9)*(0|1|2|3|4|5|6|7|8|9)' rule_critical = 'begin|call|const|do|end|if|odd|procedure|read|then|var|while|write' # [标识符，无符号整数，关键字，运算符，分隔符] rules = [rule_str,rule_num,rule_critical,rule_cal,rule_bound] if __name__ == "__main__": g = [] for i in range(len(rules)): mg = DFATool.convert2NFA(REGTool.genNFA(rules[i])) name = tools.findSpeNodeInGraph(mg,constDef.color_startNode) mg.node[name]['label'] = str(i)+'_'+ name g.append(mg) totalGraph = nx.compose_all(g) tools.writeGraph2File(totalGraph,"pl0/dfa.dot") # dot图生成时如果有,会出现问题，应该把,改为"," f = open('pl0/dfa.dot') content = f.read() f.close() f=open('pl0/dfa.dot','w') content = content.replace("=,",'=","') f.write(content) f.close()
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setup.py
ev3dev-python-tools/ev3devlogging
0
175774
from setuptools import setup import os.path import sys setup( name="ev3devlogging", version="1.0.1", description="easy logging library for ev3dev", long_description=""" easy logging library for ev3dev For more info: https://github.com/ev3dev-python-tools/ev3devlogging """, url="https://github.com/ev3dev-python-tools/ev3devlogging", author="<NAME>", author_email="<EMAIL>", license="MIT", classifiers=[ "Environment :: MacOS X", "Environment :: Win32 (MS Windows)", "Environment :: X11 Applications", "Intended Audience :: Developers", "Intended Audience :: Education", "Intended Audience :: End Users/Desktop", "License :: Freeware", "License :: OSI Approved :: MIT License", "Natural Language :: English", "Operating System :: MacOS", "Operating System :: Microsoft :: Windows", "Operating System :: POSIX", "Operating System :: POSIX :: Linux", "Programming Language :: Python", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3 :: Only", "Programming Language :: Python :: 3.4", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Topic :: Education", "Topic :: Software Development", ], keywords="IDE education programming EV3 mindstorms lego", platforms=["Windows", "macOS", "Linux"], python_requires=">=3.6", # no packages required; 'logging' already in standard distribution py_modules=["ev3devlogging"] )
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Project1/toFrames.py
vinitkumawat/uta-machine-learning
0
183634
import cv2 import numpy as np import imutils import shutil,os shutil.rmtree('frames') try: os.makedirs('frames') except OSError as e: pass video='2.mp4' cap = cv2.VideoCapture(video) cnt=0 if (cap.isOpened()== False): print("Error opening video stream or file") ret,first_frame = cap.read() while(cap.isOpened()): ret, frame = cap.read() if ret == True: cv2.imshow("image",frame) if cv2.waitKey(25) & 0xFF == ord('q'): break cv2.imwrite('frames/'+str(cnt)+'.png',frame) cnt=cnt+1 else: break cv2.destroyAllWindows()
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backend/termination_request/models.py
adabutch/account_tracker
0
111360
from django.db import models from django.contrib.auth.models import User class TerminationRequest(models.Model): """ When an employee leaves the organization remove access to the different services that were previously requested """ requester = models.ForeignKey( User, related_name='requested_terminations', on_delete=models.SET_NULL, blank=True, null=True) # who is leaving? user = models.ForeignKey(User, related_name='terminations', on_delete=models.SET_NULL, blank=True, null=True) # is it new? approved? completed? denied? request_status = models.CharField(max_length=50) # will the employee be coming back at a later date? i.e. seasonal? returning = models.BooleanField(default=False) return_date = models.DateField(blank=True, null=True) requested = models.DateTimeField(auto_now_add=True) # any associated logged actions could trigger an update to updated updated = models.DateTimeField(auto_now=True) # when the account was closed -- job's finished terminated = models.DateTimeField(blank=True, null=True)
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utils/rendering_layer.py
CSuppan/two-shot-brdf-shape
0
186625
<reponame>CSuppan/two-shot-brdf-shape # ----------------------------------------------------------------------- # Copyright (c) 2020, NVIDIA Corporation. All rights reserved. # # This work is made available # under the Nvidia Source Code License (1-way Commercial). # # Official Implementation of the CVPR2020 Paper # Two-shot Spatially-varying BRDF and Shape Estimation # <NAME>, <NAME>, <NAME>, <NAME>, <NAME> # ----------------------------------------------------------------------- import os import sys from typing import Any, List, Tuple import numpy as np import tensorflow as tf from tensorflow.python.framework import tensor_shape import utils.common_layers as cl import utils.layer_helper as layer_helper from utils.common_layers import ( gamma_to_linear, isclose, linear_to_gamma, mix, safe_sqrt, saturate, srgb_to_linear, ) from utils.dataflow_utils import apply_mask, chwToHwc, ensureSingleChannel, hwcToChw EPS = 1e-7 class RenderingLayer: def __init__( self, fov: int, distanceToZero: float, output_shape: tensor_shape.TensorShape, data_format: str = "channels_last", ): self.distanceToZero = distanceToZero self.fov = fov self.data_format = layer_helper.normalize_data_format(data_format) self.build(output_shape) def build(self, output_shape): with tf.variable_scope("Prep"): if self.data_format == "channels_first": channel_axis = 1 height_axis = 2 width_axis = 3 else: channel_axis = -1 height_axis = 1 width_axis = 2 if ( output_shape.dims[height_axis].value is None or output_shape.dims[width_axis].value is None ): raise ValueError( "The width and height dimension of the inputs " "should be defined. Found `None`." ) height = int(output_shape[height_axis]) width = int(output_shape[width_axis]) yRange = xRange = self.distanceToZero * np.tan((self.fov * np.pi / 180) / 2) x, y = np.meshgrid( np.linspace(-xRange, xRange, height), np.linspace(-yRange, yRange, width), ) y = np.flipud(y) x = np.fliplr(x) z = np.ones((height, width), dtype=np.float32) coord = np.stack([x, y, z]).astype(np.float32) if self.data_format == "channels_last": coord = chwToHwc(coord) self.base_mesh = tf.convert_to_tensor( np.expand_dims(coord, 0), dtype=tf.float32 ) def call( self, diffuse: tf.Tensor, specular: tf.Tensor, roughness: tf.Tensor, normal: tf.Tensor, depth: tf.Tensor, mask: tf.Tensor, camera_pos: tf.Tensor, light_pos: tf.Tensor, light_color: tf.Tensor, sgs: tf.Tensor, ) -> tf.Tensor: """ Evaluate the rendering equation """ with tf.variable_scope("Setup"): assert ( sgs.shape[self._get_channel_axis()] == 7 and len(sgs.shape) == 3 ) # n, sgs, c assert ( diffuse.shape[self._get_channel_axis()] == 3 and len(diffuse.shape) == 4 ) assert ( specular.shape[self._get_channel_axis()] == 3 and len(specular.shape) == 4 ) assert ( roughness.shape[self._get_channel_axis()] == 1 and len(roughness.shape) == 4 ) assert ( normal.shape[self._get_channel_axis()] == 3 and len(normal.shape) == 4 ) assert mask.shape[self._get_channel_axis()] == 1 and len(mask.shape) == 4 assert ( camera_pos.shape[self._get_channel_axis()] == 3 and len(camera_pos.shape) == 2 ) assert ( light_pos.shape[self._get_channel_axis()] == 3 and len(light_pos.shape) == 2 ) assert ( light_color.shape[self._get_channel_axis()] == 3 and len(light_color.shape) == 2 ) realDepth = self._uncompressDepth(depth) perturbed_mesh = self.base_mesh * realDepth if self.data_format == "channels_first": reshapeShape = [-1, 3, 1, 1] else: reshapeShape = [-1, 1, 1, 3] lp = tf.reshape(light_pos, reshapeShape) vp = tf.reshape(camera_pos, reshapeShape) lc = tf.reshape(light_color, reshapeShape) l_vec = lp - perturbed_mesh v = self._normalize(vp - perturbed_mesh) l = self._normalize(l_vec) h = self._normalize(l + v) axis_flip = tf.constant([-1, 1, -1], dtype=tf.float32) axis_flip = tf.reshape(axis_flip, reshapeShape) n = self._normalize(normal * 2.0 - 1.0) * axis_flip ndl = saturate(self._dot(n, l)) ndv = saturate(self._dot(n, v), 1e-5) ndh = saturate(self._dot(n, h)) ldh = saturate(self._dot(l, h)) vdh = saturate(self._dot(v, h)) sqrLightDistance = self._dot(l_vec, l_vec) light = tf.div_no_nan(lc, sqrLightDistance) diff = srgb_to_linear(diffuse) spec = srgb_to_linear(specular) directSpecular = self.spec(ndl, ndv, ndh, ldh, vdh, spec, roughness) with tf.variable_scope("Diffuse"): directDiffuse = diff * (1.0 / np.pi) * ndv * (1.0 - self.F(spec, ldh)) with tf.variable_scope("Direct_light"): brdf = directSpecular + directDiffuse direct = brdf * light direct = tf.where( tf.math.less(self._to_vec3(ndl), EPS), tf.zeros_like(direct), direct ) direct = tf.where( tf.math.less(self._to_vec3(ndv), EPS), tf.zeros_like(direct), direct ) with tf.variable_scope("SG_light"): sg_stack_axis = 2 if self.data_format == "channels_first" else 1 number_of_sgs = sgs.shape[sg_stack_axis] env_direct = tf.zeros_like(direct) for i in range(number_of_sgs): if self.data_format == "channels_first": sg = sgs[:, :, i] else: sg = sgs[:, i] evaled = self.sg_eval( sg, diffuse, specular, roughness, n, mask, perturbed_mesh, vp ) env_direct = env_direct + evaled with tf.variable_scope("Blending"): return direct + env_direct def F(self, F0: tf.Tensor, ldh: tf.Tensor) -> tf.Tensor: with tf.variable_scope("Fresnel"): ct = 1 - ldh ctsq = ct * ct ct5 = ctsq * ctsq * ct return F0 + (1 - F0) * ct5 def _G(self, a2: tf.Tensor, ndx: tf.Tensor) -> tf.Tensor: return tf.div_no_nan(2 * ndx, ndx + safe_sqrt(a2 + (1 - a2) * ndx * ndx)) def G(self, alpha: tf.Tensor, ndl: tf.Tensor, ndv: tf.Tensor) -> tf.Tensor: with tf.variable_scope("Geometry"): a2 = alpha * alpha return self._G(a2, ndl) * self._G(a2, ndv) def D(self, alpha: tf.Tensor, ndh: tf.Tensor) -> tf.Tensor: with tf.variable_scope("Distribution"): a2 = alpha * alpha denom = (ndh * ndh) * (a2 - 1) + 1.0 denom2 = denom * denom return tf.div_no_nan(a2, np.pi * denom2) def spec( self, ndl: tf.Tensor, ndv: tf.Tensor, ndh: tf.Tensor, ldh: tf.Tensor, vdh: tf.Tensor, F0: tf.Tensor, roughness: tf.Tensor, ) -> tf.Tensor: with tf.variable_scope("Specular"): alpha = saturate(roughness * roughness, 1e-3) F = self.F(F0, ldh) G = self.G(alpha, ndl, ndv) D = self.D(alpha, ndh) ret = tf.div_no_nan(F * G * D, 4.0 * ndl) ret = tf.where( tf.math.less(self._to_vec3(ndh), EPS), tf.zeros_like(ret), ret ) ret = tf.where( tf.math.less(self._to_vec3(ldh * ndl), EPS), tf.zeros_like(ret), ret ) ret = tf.where( tf.math.less(self._to_vec3(vdh * ndv), EPS), tf.zeros_like(ret), ret ) return ret def _sg_integral(self, sg: tf.Tensor) -> tf.Tensor: with tf.variable_scope("Integral"): assert sg.shape[self._get_channel_axis()] == 7 and len(sg.shape) == 4 s_amplitude, s_axis, s_sharpness = self._extract_sg_components(sg) expTerm = 1.0 - tf.exp(-2.0 * s_sharpness) return 2 * np.pi * tf.div_no_nan(s_amplitude, s_sharpness) * expTerm def _sg_evaluate(self, sg: tf.Tensor, d: tf.Tensor) -> tf.Tensor: with tf.variable_scope("Evaluate"): assert sg.shape[self._get_channel_axis()] == 7 and len(sg.shape) == 4 assert d.shape[self._get_channel_axis()] == 3 and len(d.shape) == 4 s_amplitude, s_axis, s_sharpness = self._extract_sg_components(sg) cosAngle = self._dot(d, s_axis) return s_amplitude * tf.exp(s_sharpness * (cosAngle - 1.0)) def _sg_inner_product(self, sg1: tf.Tensor, sg2: tf.Tensor) -> tf.Tensor: with tf.variable_scope("InnerProd"): assert sg1.shape[self._get_channel_axis()] == 7 and len(sg1.shape) == 4 assert sg2.shape[self._get_channel_axis()] == 7 and len(sg2.shape) == 4 s1_amplitude, s1_axis, s1_sharpness = self._extract_sg_components(sg1) s2_amplitude, s2_axis, s2_sharpness = self._extract_sg_components(sg2) umLength = self._magnitude(s1_sharpness * s1_axis + s2_sharpness * s2_axis) expo = ( tf.exp(umLength - s1_sharpness - s2_sharpness) * s1_amplitude * s2_amplitude ) other = 1.0 - tf.exp(-2.0 * umLength) return tf.div_no_nan(2.0 * np.pi * expo * other, umLength) def _sg_evaluate_diffuse( self, sg: tf.Tensor, diffuse: tf.Tensor, normal: tf.Tensor ) -> tf.Tensor: with tf.variable_scope("Diffuse"): assert ( sg.shape[self._get_channel_axis()] == 7 and len(sg.shape) == 4 ) # b, h, w, c | b, c, h, w assert ( diffuse.shape[self._get_channel_axis()] == 3 and len(diffuse.shape) == 4 ) assert ( normal.shape[self._get_channel_axis()] == 3 and len(normal.shape) == 4 ) diff = tf.div_no_nan(diffuse, np.pi) s_amplitude, s_axis, s_sharpness = self._extract_sg_components(sg) mudn = saturate(self._dot(s_axis, normal)) c0 = 0.36 c1 = 1.0 / (4.0 * c0) eml = tf.exp(-s_sharpness) em2l = eml * eml rl = tf.div_no_nan(1.0, s_sharpness) scale = 1.0 + 2.0 * em2l - rl bias = (eml - em2l) * rl - em2l x = safe_sqrt(1.0 - scale) x0 = c0 * mudn x1 = c1 * x n = x0 + x1 y = tf.where(tf.less_equal(tf.abs(x0), x1), n * tf.div_no_nan(n, x), mudn) res = scale * y + bias res = res * self._sg_integral(sg) * diff return res def _sg_distribution_term(self, d: tf.Tensor, roughness: tf.Tensor) -> tf.Tensor: with tf.variable_scope("Distribution"): assert d.shape[self._get_channel_axis()] == 3 and len(d.shape) == 4 assert ( roughness.shape[self._get_channel_axis()] == 1 and len(roughness.shape) == 4 ) a2 = saturate(roughness * roughness, 1e-3) ret = tf.concat( [ self._to_vec3(tf.div_no_nan(1.0, np.pi * a2)), d, tf.div_no_nan(2.0, a2), ], self._get_channel_axis(), ) return ret def _sg_warp_distribution(self, ndfs: tf.Tensor, v: tf.Tensor) -> tf.Tensor: with tf.variable_scope("WarpDistribution"): assert ndfs.shape[self._get_channel_axis()] == 7 and len(ndfs.shape) == 4 assert v.shape[self._get_channel_axis()] == 3 and len(v.shape) == 4 ndf_amplitude, ndf_axis, ndf_sharpness = self._extract_sg_components(ndfs) ret = tf.concat( [ ndf_amplitude, self._reflect(-v, ndf_axis), tf.div_no_nan( ndf_sharpness, (4.0 * saturate(self._dot(ndf_axis, v), 1e-4)) ), ], self._get_channel_axis(), ) return ret def _sg_ggx(self, a2: tf.Tensor, ndx: tf.Tensor) -> tf.Tensor: with tf.variable_scope("Geometric"): return tf.div_no_nan(1.0, (ndx + safe_sqrt(a2 + (1 - a2) * ndx * ndx))) def _sg_evaluate_specular( self, sg: tf.Tensor, specular: tf.Tensor, roughness: tf.Tensor, warped_ndf: tf.Tensor, ndl: tf.Tensor, ndv: tf.Tensor, ldh: tf.Tensor, vdh: tf.Tensor, ndh: tf.Tensor, ) -> tf.Tensor: with tf.variable_scope("Specular"): assert sg.shape[self._get_channel_axis()] == 7 and len(sg.shape) == 4 assert ( warped_ndf.shape[self._get_channel_axis()] == 7 and len(warped_ndf.shape) == 4 ) assert ( specular.shape[self._get_channel_axis()] == 3 and len(specular.shape) == 4 ) assert ( roughness.shape[self._get_channel_axis()] == 1 and len(roughness.shape) == 4 ) assert ndl.shape[self._get_channel_axis()] == 1 and len(ndl.shape) == 4 assert ndv.shape[self._get_channel_axis()] == 1 and len(ndv.shape) == 4 assert ldh.shape[self._get_channel_axis()] == 1 and len(ldh.shape) == 4 assert vdh.shape[self._get_channel_axis()] == 1 and len(vdh.shape) == 4 assert ndh.shape[self._get_channel_axis()] == 1 and len(ndh.shape) == 4 a2 = saturate(roughness * roughness, 1e-3) with tf.variable_scope("Distribution"): D = self._sg_inner_product(warped_ndf, sg) G = self._sg_ggx(a2, ndl) * self._sg_ggx(a2, ndv) with tf.variable_scope("Fresnel"): powTerm = tf.pow(1.0 - ldh, 5) F = specular + (1.0 - specular) * powTerm output = D * G * F * ndl shadowed = tf.zeros_like(output) zero_vec = tf.zeros_like(ndh) output = tf.where(self._to_vec3(isclose(ndh, zero_vec)), shadowed, output) output = tf.where( self._to_vec3(isclose(ldh * ndl, zero_vec)), shadowed, output ) output = tf.where( self._to_vec3(isclose(vdh * ndv, zero_vec)), shadowed, output ) return tf.maximum(output, 0.0) def sg_eval( self, sg: tf.Tensor, diffuse: tf.Tensor, specular: tf.Tensor, roughness: tf.Tensor, normal: tf.Tensor, mask: tf.Tensor, perturbed_mesh: tf.Tensor, camera_pos: tf.Tensor, ) -> tf.Tensor: with tf.variable_scope("SG"): assert sg.shape[self._get_channel_axis()] == 7 and len(sg.shape) == 2 assert ( diffuse.shape[self._get_channel_axis()] == 3 and len(diffuse.shape) == 4 ) assert ( specular.shape[self._get_channel_axis()] == 3 and len(specular.shape) == 4 ) assert ( roughness.shape[self._get_channel_axis()] == 1 and len(roughness.shape) == 4 ) assert ( normal.shape[self._get_channel_axis()] == 3 and len(normal.shape) == 4 ) assert ( normal.shape[self._get_channel_axis()] == 3 and len(normal.shape) == 4 ) assert mask.shape[self._get_channel_axis()] == 1 and len(mask.shape) == 4 assert ( camera_pos.shape[self._get_channel_axis()] == 3 and len(camera_pos.shape) == 4 ) if self.data_format == "channels_first": sgShape = [-1, 7, 1, 1] else: sgShape = [-1, 1, 1, 7] sg = tf.reshape(sg, sgShape) v = self._normalize(camera_pos - perturbed_mesh) diff = srgb_to_linear(diffuse) spec = srgb_to_linear(specular) norm = normal rogh = roughness ndf = self._sg_distribution_term(norm, rogh) warped_ndf = self._sg_warp_distribution(ndf, v) _, wndf_axis, _ = self._extract_sg_components(warped_ndf) warpDir = wndf_axis ndl = saturate(self._dot(norm, warpDir)) ndv = saturate(self._dot(norm, v), 1e-5) h = self._normalize(warpDir + v) ndh = saturate(self._dot(norm, h)) ldh = saturate(self._dot(warpDir, h)) vdh = saturate(self._dot(v, h)) diffuse_eval = self._sg_evaluate_diffuse(sg, diff, norm) * ndl specular_eval = self._sg_evaluate_specular( sg, spec, rogh, warped_ndf, ndl, ndv, ldh, vdh, ndh ) shadowed = tf.zeros_like(diffuse_eval) zero_vec = tf.zeros_like(ndl) diffuse_eval = tf.where( self._to_vec3(isclose(ndl, zero_vec)), shadowed, diffuse_eval ) diffuse_eval = tf.where( self._to_vec3(isclose(ndv, zero_vec)), shadowed, diffuse_eval ) specular_eval = tf.where( self._to_vec3(isclose(ndl, zero_vec)), shadowed, specular_eval ) specular_eval = tf.where( self._to_vec3(isclose(ndv, zero_vec)), shadowed, specular_eval ) brdf_eval = diffuse_eval + specular_eval return tf.where( self._to_vec3(tf.equal(mask, zero_vec)), shadowed, brdf_eval ) def _extract_sg_components( self, sg: tf.Tensor ) -> Tuple[tf.Tensor, tf.Tensor, tf.Tensor]: if self.data_format == "channels_first": s_amplitude = sg[:, 0:3] s_axis = sg[:, 3:6] s_sharpness = sg[:, 6:7] else: s_amplitude = sg[..., 0:3] s_axis = sg[..., 3:6] s_sharpness = sg[..., 6:7] return (s_amplitude, s_axis, s_sharpness) def visualize_sgs(self, sgs: tf.Tensor, output: tf.Tensor, name: str = "sgs"): with tf.variable_scope("Visualize"): us, vs = tf.meshgrid( tf.linspace(0.0, 1.0, output.shape[2]), tf.linspace(0.0, 1.0, output.shape[1]), ) # OK uvs = tf.stack([us, vs], -1) # q f theta = 2.0 * np.pi * uvs[..., 0] - (np.pi / 2) phi = np.pi * uvs[..., 1] d = tf.expand_dims( tf.stack( [ tf.cos(theta) * tf.sin(phi), tf.cos(phi), tf.sin(theta) * tf.sin(phi), ], -1, ), 0, ) for i in range(sgs.shape[1]): output = output + self._sg_evaluate( tf.reshape(sgs[:, i], [-1, 1, 1, 7]), d ) tf.summary.image(name, output, max_outputs=10) def _magnitude(self, x: tf.Tensor) -> tf.Tensor: return cl.magnitude(x, data_format=self.data_format) def _normalize(self, x: tf.Tensor) -> tf.Tensor: return cl.normalize(x, data_format=self.data_format) def _dot(self, x: tf.Tensor, y: tf.Tensor) -> tf.Tensor: return cl.dot(x, y, data_format=self.data_format) def _reflect(self, d: tf.Tensor, n: tf.Tensor) -> tf.Tensor: return d - 2 * self._dot(d, n) * n def _to_vec3(self, x: tf.Tensor) -> tf.Tensor: return cl.to_vec3(x, data_format=self.data_format) def _get_channel_axis(self) -> int: return cl.get_channel_axis(data_format=self.data_format) def _uncompressDepth( self, d: tf.Tensor, sigma: float = 2.5, epsilon: float = 0.7 ) -> tf.Tensor: """From 0-1 values to full depth range. The possible depth range is modelled by sigma and epsilon and with sigma=2.5 and epsilon=0.7 it is between 0.17 and 1.4. """ return tf.div_no_nan(1.0, 2.0 * sigma * d + epsilon)
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genes/models.py
xueyanmu/django-genes
4
106011
<reponame>xueyanmu/django-genes<gh_stars>1-10 import re from django.db import models from django.core.exceptions import FieldError from organisms.models import Organism # Regular expressions to be matched in the methods of 'Gene'. nonalpha = re.compile(r'[^a-zA-Z0-9]') num = re.compile(r'[0-9]') class Gene(models.Model): """ The class 'Gene' extends the Model class in Django. For more information, see: https://docs.djangoproject.com/en/dev/topics/db/models/ Genes will be added from an online database like 'Entrez', using the Django management commands (see management/commands/genes_load_geneinfo.py). """ entrezid = models.IntegerField(null=True, db_index=True, unique=True) # Used for organisms like yeast. systematic_name = models.CharField(max_length=32, db_index=True) standard_name = models.CharField(max_length=32, null=True, db_index=True) # Description/full name of gene. description = models.TextField() # "organism" field contains the primary key of whichever organism # instance the gene belongs to. For more info, see: # https://docs.djangoproject.com/en/dev/ref/models/fields/#django.db.models.ForeignKey organism = models.ForeignKey(Organism, null=False) # "aliases" stores a space-separated list of aliases. aliases = models.TextField() obsolete = models.BooleanField(default=False) # Weight used for search results, needed now that we allow genes with # identical symbols. weight = models.FloatField(default=1) # __unicode__ in django, explained at: # https://docs.djangoproject.com/en/dev/ref/models/instances/#unicode def __unicode__(self): if self.standard_name: return self.standard_name return self.systematic_name def wall_of_name(self): ''' Appends identifiers for the different databases (such as Entrez id's) and returns them. Uses the CrossRef class below. ''' names = [] if self.standard_name: names.append(self.standard_name) if self.systematic_name: names.append(self.systematic_name) names.extend([xref.xrid for xref in self.crossref_set.all()]) for i in range(len(names)): names[i] = re.sub(nonalpha, '', names[i]) names_string = ' '.join(names) if self.standard_name: names_string += ' ' + re.sub(num, '', self.standard_name) return names_string def save(self, *args, **kwargs): """ Override save() method to make sure that standard_name and systematic_name won't be null or empty, or consist of only space characters (such as space, tab, new line, etc). """ empty_std_name = False if not self.standard_name or self.standard_name.isspace(): empty_std_name = True empty_sys_name = False if not self.systematic_name or self.systematic_name.isspace(): empty_sys_name = True if empty_std_name and empty_sys_name: raise ValueError( "Both standard_name and systematic_name are empty") super(Gene, self).save(*args, **kwargs) # Call the "real" save(). class CrossRefDB(models.Model): name = models.CharField(max_length=64, unique=True, db_index=True, blank=False) url = models.URLField() def save(self, *args, **kwargs): """ Extends save() method of Django models to check that the database name is not left blank. Note: 'blank=False' is only checked at a form-validation-stage. A test using Fixtureless that tries to randomly create a CrossRefDB with an empty string name would unintentionally break the test. """ if self.name == '': raise FieldError else: return super(CrossRefDB, self).save(*args, **kwargs) def __unicode__(self): return self.name class Meta: verbose_name = "Cross Reference Database" class CrossRef(models.Model): crossrefdb = models.ForeignKey(CrossRefDB, null=False) gene = models.ForeignKey(Gene, null=False) xrid = models.CharField(max_length=32, null=False, db_index=True) def __unicode__(self): return self.xrid # Return the url for this entry def _get_url(self): url = self.crossrefdb.url if "_SPEC_" in url: species_name = self.gene.organism.scientific_name url = url.replace("_SPEC_", species_name.replace(" ", "+")) url = url.replace("_REPL_", self.xrid) return url specific_url = property(_get_url)
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src/utils/pytorch_evaluation.py
owkin/idash-2020
5
89801
#!/usr/bin/env python # Copyright 2021 Owkin, inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import torch import numpy as np def predict(model, X, threshold=0.5): """Generate NumPy output predictions on a dataset using a given model. Args: model (torch model): A Pytroch model X (dataloader): A dataframe-based gene dataset to predict on """ X_tensor, _ = convert_dataframe_to_tensor(X, []) model.eval() with torch.no_grad(): y_pred = (model(X_tensor) >= threshold).int().numpy() return y_pred def convert_dataframe_to_tensor(X, y): tensor_x = torch.Tensor(X.to_numpy().astype(np.float32)) tensor_y = torch.Tensor(y) return tensor_x, tensor_y
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Tests/Methods/Mesh/Interpolation/test_interpolation.py
harshasunder-1/pyleecan
2
8222
<filename>Tests/Methods/Mesh/Interpolation/test_interpolation.py<gh_stars>1-10 # -*- coding: utf-8 -*- import pytest import numpy as np from unittest import TestCase from pyleecan.Classes.CellMat import CellMat from pyleecan.Classes.MeshSolution import MeshSolution from pyleecan.Classes.PointMat import PointMat from pyleecan.Classes.MeshMat import MeshMat from pyleecan.Classes.ScalarProductL2 import ScalarProductL2 from pyleecan.Classes.Interpolation import Interpolation from pyleecan.Classes.RefSegmentP1 import RefSegmentP1 from pyleecan.Classes.FPGNSeg import FPGNSeg @pytest.mark.MeshSol class unittest_real_points(TestCase): """ Tests for interpolation method""" def test_line(self): DELTA = 1e-10 mesh = MeshMat() mesh.cell["line"] = CellMat(nb_pt_per_cell=2) mesh.point = PointMat() mesh.point.add_point(np.array([0, 0])) mesh.point.add_point(np.array([1, 0])) mesh.point.add_point(np.array([0, 1])) mesh.point.add_point(np.array([2, 3])) mesh.point.add_point(np.array([3, 3])) mesh.add_cell(np.array([0, 1]), "line") mesh.add_cell(np.array([0, 2]), "line") mesh.add_cell(np.array([1, 2]), "line") c_line = mesh.cell["line"] c_line.interpolation = Interpolation() c_line.interpolation.ref_cell = RefSegmentP1() c_line.interpolation.scalar_product = ScalarProductL2() c_line.interpolation.gauss_point = FPGNSeg() meshsol = MeshSolution() meshsol.mesh = [mesh] vert = mesh.get_vertice(0)["line"] test_pt = np.array([0.7, 0]) test_field = np.array([1, 1]) sol = [1] func = c_line.interpolation.ref_cell.interpolation(test_pt, vert, test_field) testA = np.sum(abs(func - sol)) msg = "Wrong result: returned " + str(func) + ", expected: " + str(test_field) self.assertAlmostEqual(testA, 0, msg=msg, delta=DELTA) vert = mesh.get_vertice(0)["line"] test_pt = np.array([0.7, 0]) test_field = np.ones( (2, 120, 3) ) # Simulate a 3D vector field for 120 time step func = c_line.interpolation.ref_cell.interpolation(test_pt, vert, test_field) sol = np.ones((120, 3)) testA = np.sum(abs(func - sol)) msg = "Wrong result: returned " + str(func) + ", expected: " + str(sol) self.assertAlmostEqual(testA, 0, msg=msg, delta=DELTA) vert = mesh.get_vertice(2)["line"] test_pt = np.array([0.6, 0.4]) test_field = np.zeros((2, 120, 3)) test_field[0, :] = np.ones( (1, 120, 3) ) # Simulate a 3D vector field for 120 time step func = c_line.interpolation.ref_cell.interpolation(test_pt, vert, test_field) sol = 0.6 * np.ones((120, 3)) testA = np.sum(abs(sol - func)) msg = "Wrong result: returned " + str(func) + ", expected: " + str(sol) self.assertAlmostEqual(testA, 0, msg=msg, delta=DELTA) vert = mesh.get_vertice(1)["line"] test_pt = np.array([0, 0.4]) test_field = np.zeros((2, 120, 3)) test_field[1, :] = np.ones( (1, 120, 3) ) # Simulate a 3D vector field for 120 time step func = c_line.interpolation.ref_cell.interpolation(test_pt, vert, test_field) sol = 0.4 * np.ones((120, 3)) testA = np.sum(abs(sol - func)) msg = "Wrong result: returned " + str(func) + ", expected: " + str(sol) self.assertAlmostEqual(testA, 0, msg=msg, delta=DELTA)
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dashboard/source/dashboard/__init__.py
mueller-stephan/car-classification
2
190011
from .layout import attempt, finish_page, main_layout, result, start_page __all__ = ['main_layout', 'attempt', 'result', 'start_page', 'finish_page']
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LogRecorder.py
yangshouguo/Graph-based_Bug_Search
9
1613237
import logging class CLogRecoder: def __init__(self, logfile = 'log.log', format = '%(asctime)s : %(message)s', level = logging.DEBUG): logging.basicConfig(filename= logfile, level= level , format= format) self._ft = format def addStreamHandler(self): console = logging.StreamHandler() console.setLevel(logging.INFO) formater = logging.Formatter(self._ft) console.setFormatter(formater) logging.getLogger('').addHandler(console) return self def INFO(self, message): logging.info(message) return self if __name__ == '__main__': lr = CLogRecoder().addStreamHandler() lr.INFO("test")
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percona-nagios-plugins/nagios/bin/pmp-check-aws-rds.py
waja/pkg-nagios-plugins-contrib
0
143366
<gh_stars>0 #!/usr/bin/env python """Nagios plugin for Amazon RDS monitoring. This program is part of $PROJECT_NAME$ License: GPL License (see COPYING) Author <NAME> Copyright 2014-2015 Percona LLC and/or its affiliates """ import datetime import optparse import pprint import sys import boto import boto.rds import boto.ec2.cloudwatch # Nagios status codes OK = 0 WARNING = 1 CRITICAL = 2 UNKNOWN = 3 class RDS(object): """RDS connection class""" def __init__(self, region, profile=None, identifier=None): """Get RDS instance details""" self.region = region self.profile = profile self.identifier = identifier if self.region == 'all': self.regions_list = [reg.name for reg in boto.rds.regions()] else: self.regions_list = [self.region] self.info = None if self.identifier: for reg in self.regions_list: try: rds = boto.rds.connect_to_region(reg, profile_name=self.profile) self.info = rds.get_all_dbinstances(self.identifier) except (boto.provider.ProfileNotFoundError, boto.exception.BotoServerError) as msg: debug(msg) else: # Exit on the first region and identifier match self.region = reg break def get_info(self): """Get RDS instance info""" if self.info: return self.info[0] else: return None def get_list(self): """Get list of available instances by region(s)""" result = dict() for reg in self.regions_list: try: rds = boto.rds.connect_to_region(reg, profile_name=self.profile) result[reg] = rds.get_all_dbinstances() except (boto.provider.ProfileNotFoundError, boto.exception.BotoServerError) as msg: debug(msg) return result def get_metric(self, metric, start_time, end_time, step): """Get RDS metric from CloudWatch""" cw_conn = boto.ec2.cloudwatch.connect_to_region(self.region, profile_name=self.profile) result = cw_conn.get_metric_statistics( step, start_time, end_time, metric, 'AWS/RDS', 'Average', dimensions={'DBInstanceIdentifier': [self.identifier]} ) if result: if len(result) > 1: # Get the last point result = sorted(result, key=lambda k: k['Timestamp']) result.reverse() result = float('%.2f' % result[0]['Average']) return result def debug(val): """Debugging output""" global options if options.debug: print 'DEBUG: %s' % val def main(): """Main function""" global options short_status = { OK: 'OK', WARNING: 'WARN', CRITICAL: 'CRIT', UNKNOWN: 'UNK' } # DB instance classes as listed on # http://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/Concepts.DBInstanceClass.html db_classes = { 'db.t1.micro': 0.615, 'db.m1.small': 1.7, 'db.m1.medium': 3.75, 'db.m1.large': 7.5, 'db.m1.xlarge': 15, 'db.m4.large': 8, 'db.m4.xlarge': 16, 'db.m4.2xlarge': 32, 'db.m4.4xlarge': 64, 'db.m4.10xlarge': 160, 'db.r3.large': 15, 'db.r3.xlarge': 30.5, 'db.r3.2xlarge': 61, 'db.r3.4xlarge': 122, 'db.r3.8xlarge': 244, 'db.t2.micro': 1, 'db.t2.small': 2, 'db.t2.medium': 4, 'db.t2.large': 8, 'db.m3.medium': 3.75, 'db.m3.large': 7.5, 'db.m3.xlarge': 15, 'db.m3.2xlarge': 30, 'db.m2.xlarge': 17.1, 'db.m2.2xlarge': 34.2, 'db.m2.4xlarge': 68.4, 'db.cr1.8xlarge': 244, } # RDS metrics http://docs.aws.amazon.com/AmazonCloudWatch/latest/DeveloperGuide/rds-metricscollected.html metrics = { 'status': 'RDS availability', 'load': 'CPUUtilization', 'memory': 'FreeableMemory', 'storage': 'FreeStorageSpace' } units = ('percent', 'GB') # Parse options parser = optparse.OptionParser() parser.add_option('-l', '--list', help='list of all DB instances', action='store_true', default=False, dest='db_list') parser.add_option('-n', '--profile', default=None, help='AWS profile from ~/.boto or /etc/boto.cfg. Default: None, fallbacks to "[Credentials]".') parser.add_option('-r', '--region', default='us-east-1', help='AWS region. Default: us-east-1. If set to "all", we try to detect the instance region ' 'across all of them, note this will be slower than if you specify the region explicitly.') parser.add_option('-i', '--ident', help='DB instance identifier') parser.add_option('-p', '--print', help='print status and other details for a given DB instance', action='store_true', default=False, dest='printinfo') parser.add_option('-m', '--metric', help='metric to check: [%s]' % ', '.join(metrics.keys())) parser.add_option('-w', '--warn', help='warning threshold') parser.add_option('-c', '--crit', help='critical threshold') parser.add_option('-u', '--unit', help='unit of thresholds for "storage" and "memory" metrics: [%s].' 'Default: percent' % ', '.join(units), default='percent') parser.add_option('-t', '--time', help='time period in minutes to query. Default: 5', type='int', default=5) parser.add_option('-a', '--avg', help='time average in minutes to request. Default: 1', type='int', default=1) parser.add_option('-f', '--forceunknown', help='force alerts on unknown status. This prevents issues related to ' 'AWS Cloudwatch throttling limits Default: False', action='store_true', default=False) parser.add_option('-d', '--debug', help='enable debug output', action='store_true', default=False) options, _ = parser.parse_args() if options.debug: boto.set_stream_logger('boto') rds = RDS(region=options.region, profile=options.profile, identifier=options.ident) # Check args if len(sys.argv) == 1: parser.print_help() sys.exit() elif options.db_list: info = rds.get_list() print 'List of all DB instances in %s region(s):' % (options.region,) pprint.pprint(info) sys.exit() elif not options.ident: parser.print_help() parser.error('DB identifier is not set.') elif options.printinfo: info = rds.get_info() if info: pprint.pprint(vars(info)) else: print 'No DB instance "%s" found on your AWS account and %s region(s).' % (options.ident, options.region) sys.exit() elif not options.metric or options.metric not in metrics.keys(): parser.print_help() parser.error('Metric is not set or not valid.') elif not options.warn and options.metric != 'status': parser.print_help() parser.error('Warning threshold is not set.') elif not options.crit and options.metric != 'status': parser.print_help() parser.error('Critical threshold is not set.') elif options.avg <= 0 and options.metric != 'status': parser.print_help() parser.error('Average must be greater than zero.') elif options.time <= 0 and options.metric != 'status': parser.print_help() parser.error('Time must be greater than zero.') now = datetime.datetime.utcnow() status = None note = '' perf_data = None # RDS Status if options.metric == 'status': info = rds.get_info() if not info: status = UNKNOWN note = 'Unable to get RDS instance' else: status = OK try: version = info.EngineVersion except: version = info.engine_version note = '%s %s. Status: %s' % (info.engine, version, info.status) # RDS Load Average elif options.metric == 'load': # Check thresholds try: warns = [float(x) for x in options.warn.split(',')] crits = [float(x) for x in options.crit.split(',')] fail = len(warns) + len(crits) except: fail = 0 if fail != 6: parser.error('Warning and critical thresholds should be 3 comma separated numbers, e.g. 20,15,10') loads = [] fail = False j = 0 perf_data = [] for i in [1, 5, 15]: if i == 1: # Some stats are delaying to update on CloudWatch. # Let's pick a few points for 1-min load avg and get the last point. points = 5 else: points = i load = rds.get_metric(metrics[options.metric], now - datetime.timedelta(seconds=points * 60), now, i * 60) if not load: status = UNKNOWN note = 'Unable to get RDS statistics' perf_data = None break loads.append(str(load)) perf_data.append('load%s=%s;%s;%s;0;100' % (i, load, warns[j], crits[j])) # Compare thresholds if not fail: if warns[j] > crits[j]: parser.error('Parameter inconsistency: warning threshold is greater than critical.') elif load >= crits[j]: status = CRITICAL fail = True elif load >= warns[j]: status = WARNING j = j + 1 if status != UNKNOWN: if status is None: status = OK note = 'Load average: %s%%' % '%, '.join(loads) perf_data = ' '.join(perf_data) # RDS Free Storage # RDS Free Memory elif options.metric in ['storage', 'memory']: # Check thresholds try: warn = float(options.warn) crit = float(options.crit) except: parser.error('Warning and critical thresholds should be integers.') if crit > warn: parser.error('Parameter inconsistency: critical threshold is greater than warning.') if options.unit not in units: parser.print_help() parser.error('Unit is not valid.') info = rds.get_info() free = rds.get_metric(metrics[options.metric], now - datetime.timedelta(seconds=options.time * 60), now, options.avg * 60) if not info or not free: status = UNKNOWN note = 'Unable to get RDS details and statistics' else: if options.metric == 'storage': storage = float(info.allocated_storage) elif options.metric == 'memory': try: storage = db_classes[info.instance_class] except: print 'Unknown DB instance class "%s"' % info.instance_class sys.exit(CRITICAL) free = '%.2f' % (free / 1024 ** 3) free_pct = '%.2f' % (float(free) / storage * 100) if options.unit == 'percent': val = float(free_pct) val_max = 100 elif options.unit == 'GB': val = float(free) val_max = storage # Compare thresholds if val <= crit: status = CRITICAL elif val <= warn: status = WARNING if status is None: status = OK note = 'Free %s: %s GB (%.0f%%) of %s GB' % (options.metric, free, float(free_pct), storage) perf_data = 'free_%s=%s;%s;%s;0;%s' % (options.metric, val, warn, crit, val_max) # Final output if status != UNKNOWN and perf_data: print '%s %s | %s' % (short_status[status], note, perf_data) elif status == UNKNOWN and not options.forceunknown: print '%s %s | null' % ('OK', note) sys.exit(0) else: print '%s %s' % (short_status[status], note) sys.exit(status) if __name__ == '__main__': main() # ############################################################################ # Documentation # ############################################################################ """ =pod =head1 NAME pmp-check-aws-rds.py - Check Amazon RDS metrics. =head1 SYNOPSIS Usage: pmp-check-aws-rds.py [options] Options: -h, --help show this help message and exit -l, --list list of all DB instances -n PROFILE, --profile-name=PROFILE AWS profile from ~/.boto or /etc/boto.cfg. Default: None, fallbacks to "[Credentials]". -r REGION, --region=REGION AWS region. Default: us-east-1. If set to "all", we try to detect the instance region across all of them, note this will be slower than you specify the region. -i IDENT, --ident=IDENT DB instance identifier -p, --print print status and other details for a given DB instance -m METRIC, --metric=METRIC metric to check: [status, load, storage, memory] -w WARN, --warn=WARN warning threshold -c CRIT, --crit=CRIT critical threshold -u UNIT, --unit=UNIT unit of thresholds for "storage" and "memory" metrics: [percent, GB]. Default: percent -t TIME, --time=TIME time period in minutes to query. Default: 5 -a AVG, --avg=AVG time average in minutes to request. Default: 1 -f, --forceunknown force alerts on unknown status. This prevents issues related to AWS Cloudwatch throttling limits Default: False -d, --debug enable debug output =head1 REQUIREMENTS This plugin is written on Python and utilizes the module C<boto> (Python interface to Amazon Web Services) to get various RDS metrics from CloudWatch and compare them against the thresholds. * Install the package: C<yum install python-boto> or C<apt-get install python-boto> * Create a config /etc/boto.cfg or ~nagios/.boto with your AWS API credentials. See http://code.google.com/p/boto/wiki/BotoConfig This plugin that is supposed to be run by Nagios, i.e. under ``nagios`` user, should have permissions to read the config /etc/boto.cfg or ~nagios/.boto. Example: [root@centos6 ~]# cat /etc/boto.cfg [Credentials] aws_access_key_id = THISISATESTKEY aws_secret_access_key = thisisatestawssecretaccesskey If you do not use this config with other tools such as our Cacti script, you can secure this file the following way: [root@centos6 ~]# chown nagios /etc/boto.cfg [root@centos6 ~]# chmod 600 /etc/boto.cfg =head1 DESCRIPTION The plugin provides 4 checks and some options to list and print RDS details: * RDS Status * RDS Load Average * RDS Free Storage * RDS Free Memory To get the list of all RDS instances under AWS account: # ./pmp-check-aws-rds.py -l To get the detailed status of RDS instance identified as C<blackbox>: # ./pmp-check-aws-rds.py -i blackbox -p Nagios check for the overall status. Useful if you want to set the rest of the checks dependent from this one: # ./pmp-check-aws-rds.py -i blackbox -m status OK mysql 5.1.63. Status: available Nagios check for CPU utilization, specify thresholds as percentage of 1-min., 5-min., 15-min. average accordingly: # ./pmp-check-aws-rds.py -i blackbox -m load -w 90,85,80 -c 98,95,90 OK Load average: 18.36%, 18.51%, 15.95% | load1=18.36;90.0;98.0;0;100 load5=18.51;85.0;95.0;0;100 load15=15.95;80.0;90.0;0;100 Nagios check for the free memory, specify thresholds as percentage: # ./pmp-check-aws-rds.py -i blackbox -m memory -w 5 -c 2 OK Free memory: 5.90 GB (9%) of 68 GB | free_memory=8.68;5.0;2.0;0;100 # ./pmp-check-aws-rds.py -i blackbox -m memory -u GB -w 4 -c 2 OK Free memory: 5.90 GB (9%) of 68 GB | free_memory=5.9;4.0;2.0;0;68 Nagios check for the free storage space, specify thresholds as percentage or GB: # ./pmp-check-aws-rds.py -i blackbox -m storage -w 10 -c 5 OK Free storage: 162.55 GB (33%) of 500.0 GB | free_storage=32.51;10.0;5.0;0;100 # ./pmp-check-aws-rds.py -i blackbox -m storage -u GB -w 10 -c 5 OK Free storage: 162.55 GB (33%) of 500.0 GB | free_storage=162.55;10.0;5.0;0;500.0 By default, the region is set to ``us-east-1``. You can re-define it globally in boto config or specify with -r option. The following command will list all instances across all regions under your AWS account: # ./pmp-check-aws-rds.py -r all -l The following command will show the status for the first instance identified as ``blackbox`` in all regions: # ./pmp-check-aws-rds.py -r all -i blackbox -p Remember, scanning regions are slower operation than specifying it explicitly. =head1 CONFIGURATION Here is the excerpt of potential Nagios config: define host{ use mysql-host host_name blackbox alias blackbox address blackbox.abcdefgh.us-east-1.rds.amazonaws.com } define servicedependency{ host_name blackbox service_description RDS Status dependent_service_description RDS Load Average, RDS Free Storage, RDS Free Memory execution_failure_criteria w,c,u,p notification_failure_criteria w,c,u,p } define service{ use active-service host_name blackbox service_description RDS Status check_command check_rds!status!0!0 } define service{ use active-service host_name blackbox service_description RDS Load Average check_command check_rds!load!90,85,80!98,95,90 } define service{ use active-service host_name blackbox service_description RDS Free Storage check_command check_rds!storage!10!5 } define service{ use active-service host_name blackbox service_description RDS Free Memory check_command check_rds!memory!5!2 } define command{ command_name check_rds command_line $USER1$/pmp-check-aws-rds.py -i $HOSTALIAS$ -m $ARG1$ -w $ARG2$ -c $ARG3$ } =head1 COPYRIGHT, LICENSE, AND WARRANTY This program is copyright 2014 Percona LLC and/or its affiliates. Feedback and improvements are welcome. THIS PROGRAM IS PROVIDED "AS IS" AND WITHOUT ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, WITHOUT LIMITATION, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, version 2. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA. =head1 VERSION $PROJECT_NAME$ pmp-check-aws-rds.py $VERSION$ =cut """
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10467, 29899, 5499, 29879, 29889, 2272, 395, 16358, 29938, 13, 13, 29922, 7582, 13, 13, 15945, 29908, 13, 2 ]
Models/__init__.py
Sijiu/Xbill
14
81756
<reponame>Sijiu/Xbill from .BaseModel import database, BillModel from .AlipayBill import AlipayBill from .WeChatBill import WeChatBill from .ICBCBill import ICBCBill from .XBill import XBill def create_table(): database.create_tables([AlipayBill, WeChatBill, ICBCBill, XBill], safe=True)
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TwoDPCA.py
UtkarshPandey55557/Face_detcetion
0
131938
<reponame>UtkarshPandey55557/Face_detcetion import numpy as np import cv2 import scipy.linalg as s_linalg class two_d_pca_class: #function used for finding value of p for covering 95% of image information def give_p(self, d): #sum of all eigen values sum = np.sum(d) sum_95 = 0.95 * sum temp = 0 p = 0 while temp < sum_95: temp += d[p] p += 1 return p def reduce_dim(self): no_of_images = self.images.shape[0] mat_height = self.images.shape[1] #creating emptymatrix for find covarience matrix g_t = np.zeros((mat_height, mat_height)) for i in range(no_of_images): #multiplying net subtracted image with its transpose and adding in gt temp = np.dot(self.images_mean_subtracted[i].T, self.images_mean_subtracted[i]) g_t += temp #dividing by total number of images g_t /= no_of_images #finding eigen values and eigen vectors d_mat, p_mat = np.linalg.eig(g_t) #finding first p important vectors p = self.give_p(d_mat) self.new_bases = p_mat[:, 0:p] #finding new coordinates using dot product new bases self.new_coordinates = np.dot(self.images, self.new_bases) #returning new coordinates matrix return self.new_coordinates def __init__(self, images, y, target_names): self.images = np.asarray(images) self.y = y self.target_names = target_names #finding means of image self.mean_face = np.mean(self.images, 0) #subtracting mean face from images self.images_mean_subtracted = self.images - self.mean_face def original_data(self, new_coordinates): return (np.dot(new_coordinates, self.new_bases.T)) def new_cord(self, name, img_height, img_width): img = cv2.imread(name, 0) cv2.imshow("Recognize Image", img) cv2.waitKey() gray = cv2.resize(img, (img_height, img_width)) return np.dot(gray, self.new_bases) def new_cord_for_image(self, image): return np.dot(image, self.new_bases) def recognize_face(self, new_cord): no_of_images = len(self.y) distances = [] for i in range(no_of_images): temp_imgs = self.new_coordinates[i] dist = np.linalg.norm(new_cord - temp_imgs) distances += [dist] print("Distances", distances) min = np.argmin(distances) per = self.y[min] per_name = self.target_names[per] print("Person", per, ":", min, self.target_names[per], "Dist:", distances[min]) return per_name
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graphbar/graphbar.py
manuelgilm/graphbar
0
170253
import matplotlib.pyplot as plt import numpy as np def plot_bars_by_group(grouped_data,colors,edgecolor=None,property_name="Data by group",ylabel="Value",figsize=(16,6),annotations = True): """Make bar graph by group. Params: ------ grouped_data: (dict) Each key represents a group, each group is a dictionary, where each key is a characteristic with its value. colors: (list) List with the colors of each characteristic edgecolor: (str) Color of the border of the rectangle, if it is None the border will be equeal to the color of the rectangle property_name: (str) Chart title ylabel: (str) Y axis name figsize: (tuple) Chart dimensions annotations (boolean) If each bar shows the value Output: ------ Bar graph by group """ groups = list(grouped_data.keys()) labels = [list(grouped_data[k].keys()) for k in grouped_data.keys()][0] n_groups = len(groups) n_labels = len(labels) width = 1/(n_groups+1) # the label locations main_x = np.arange(n_labels) if n_groups % 2 == 0: pos = "edge" else: pos = "center" X = [] k = [k if i == 0 else -1*k for k in range(1,n_groups) for i in range(2)] for n in range(n_groups-1): X.append([r+k[n]*width for r in main_x]) fig, ax = plt.subplots() fig.set_size_inches(figsize) rects = [] for g in range(n_groups): if g == 0: rects.append(ax.bar(main_x,grouped_data[groups[g]].values(),width=width,label=groups[g],color=colors[g],edgecolor = edgecolor,align=pos)) else: rects.append(ax.bar(X[g-1],grouped_data[groups[g]].values(),width=width,label=groups[g],color=colors[g],edgecolor = edgecolor,align=pos)) # Add some text for labels, title and custom x-axis tick labels, etc. ax.set_ylabel(ylabel) ax.set_title(f'{property_name}') if pos == "center": ax.set_xticks(main_x) else: ax.set_xticks(X[0]) ax.set_xticklabels(labels) ax.grid(False) ax.legend() #add annotations if annotations: heights = [] for rect in rects: for r in rect: height = r.get_height() ax.annotate('{} '.format(height), xy=(r.get_x() + r.get_width() / 2, height), xytext=(0, 3), # 3 points vertical offset textcoords="offset points", ha='center', va='bottom')
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RACE_for_thesis/src/nn_layers.py
l11x0m7/master_thesis_public
1
199101
<gh_stars>1-10 import theano.tensor as T import lasagne import lasagne.layers as L # my addition class SelfAttention(L.MergeLayer): # incomings[0]: B * P * 2D # B * P def __init__(self, incomings, num_units, nonlinearity=lasagne.nonlinearities.tanh, mask_input=None, name='', init=lasagne.init.Uniform(), **kwargs): if len(incomings) != 2: raise NotImplementedError if mask_input is not None: incomings.append(mask_input) super(SelfAttention, self).__init__(incomings, **kwargs) self.nonlinearity = nonlinearity self.num_units = num_units self.W0 = self.add_param(init, (self.num_units, self.num_units), name='W0_sa_{}'.format(name)) self.Wb = self.add_param(init, (self.num_units, ), name='Wb_sa_{}'.format(name)) # inputs[0]: B * P * 2D # inputs[1]: B * P def get_output_for(self, inputs, **kwargs): B, P, D = inputs[0].shape # B * P alphas = T.dot(self.nonlinearity(T.dot(inputs[0], self.W0)), self.Wb) alphas = T.nnet.softmax(alphas) * inputs[1] alphas = alphas / (alphas.sum(axis=1, keepdims=True) + 1e-8) * inputs[1] att = T.sum(inputs[0] * alphas.dimshuffle(0, 1, 'x'), axis=1) return att def get_output_shape_for(self, input_shapes): # outputs: B * 2D return (input_shapes[0][0], input_shapes[0][2]) class SqueezeLayer(lasagne.layers.Layer): def get_output_for(self, input, **kwargs): return T.cast(input.sum(axis=-1) > 0, 'int32') def get_output_shape_for(self, input_shape): return input_shape[:-1] class CombineLayer(L.MergeLayer): # inputs[0]: B * N # inputs[1]: B * N def get_output_for(self, inputs, **kwargs): return (inputs[0] + inputs[1]) / 2. def get_output_shape_for(self, input_shapes): return input_shapes[0] class OptionGateLayer(L.MergeLayer): # inputs[0]: B * 4 * N # inputs[1]: B * 4 def get_output_for(self, inputs, **kwargs): return inputs[0] * inputs[1].dimshuffle(0, 1, 'x') def get_output_shape_for(self, input_shapes): return input_shapes[0] # -----------------------------origin------------------------------ class GatedAttentionLayerWithQueryAttention(L.MergeLayer): # inputs[0]: B * N * 2D # inputs[1]: B * Q * 2D # inputs[2]: B * Q (l_m_q) def get_output_for(self, inputs, **kwargs): M = T.batched_dot(inputs[0], inputs[1].dimshuffle((0,2,1))) # B x N x Q B, N, Q = M.shape alphas = T.nnet.softmax(T.reshape(M, (B*N, Q))) alphas_r = T.reshape(alphas, (B,N,Q)) * inputs[2].dimshuffle(0, 'x', 1) # B x N x Q alphas_r = alphas_r / alphas_r.sum(axis=2, keepdims=True) # B x N x Q q_rep = T.batched_dot(alphas_r, inputs[1]) # B x N x 2D d_gated = inputs[0] * q_rep return d_gated def get_output_shape_for(self, input_shapes): return input_shapes[0] class GateWithQuery(L.MergeLayer): # inputs[0]: B * N * 2D # inputs[1]: B * 2D def get_output_for(self, inputs, **kwargs): M = T.batched_dot(inputs[0], inputs[1]) M = T.nnet.sigmoid(M) d_gated = inputs[0] * M.dimshuffle((0, 1, 'x')) return d_gated def get_output_shape_for(self, input_shapes): return input_shapes[0] class QuerySliceLayer(L.MergeLayer): # inputs[0]: B * Q * 2D (q) # inputs[1]: B (q_var) def get_output_for(self, inputs, **kwargs): q_slice = inputs[0][T.arange(inputs[0].shape[0]), inputs[1]-1, :] # B x 2D return q_slice def get_output_shape_for(self, input_shapes): return (input_shapes[0][0], input_shapes[0][2]) class GatedAttentionLayer(L.MergeLayer): # inputs[0]: B * N * 2D # inputs[1]: N * 2D def get_output_for(self, inputs, **kwargs): return inputs[0] * inputs[1].dimshuffle(0, 'x', 1) def get_output_shape_for(self, input_shapes): return input_shapes[0] class AttentionSumLayer(L.MergeLayer): # inputs[0]: batch * len * h (d) # inputs[1]: batch * h (q_slice) # inputs[2]: batch * len * num_cand (c_var) # inputs[3]: batch * len (m_c_var) def get_output_for(self, inputs, **kwargs): dq = T.batched_dot(inputs[0], inputs[1]) # B x len attention = T.nnet.softmax(dq) * inputs[3] # B x len attention = attention / attention.sum(axis=1, keepdims=True) probs = T.batched_dot(attention, inputs[2]) # B x num_cand probs = probs / probs.sum(axis=1, keepdims=True) return probs def get_output_shape_for(self, input_shapes): return (input_shapes[2][0], input_shapes[2][2]) def stack_rnn(l_emb, l_mask, num_layers, num_units, grad_clipping=10, dropout_rate=0., bidir=True, only_return_final=False, name='', rnn_layer=lasagne.layers.LSTMLayer): """ Stack multiple RNN layers. """ def _rnn(backwards=True, name=''): network = l_emb for layer in range(num_layers): if dropout_rate > 0: network = lasagne.layers.DropoutLayer(network, p=dropout_rate) c_only_return_final = only_return_final and (layer == num_layers - 1) network = rnn_layer(network, num_units, grad_clipping=grad_clipping, mask_input=l_mask, only_return_final=c_only_return_final, backwards=backwards, name=name + '_layer' + str(layer + 1)) return network network = _rnn(True, name) if bidir: network = lasagne.layers.ConcatLayer([network, _rnn(False, name + '_back')], axis=-1) return network class AveragePoolingLayer(lasagne.layers.MergeLayer): """ Average pooling. incoming: batch x len x h """ def __init__(self, incoming, mask_input=None, **kwargs): incomings = [incoming] if mask_input is not None: incomings.append(mask_input) super(AveragePoolingLayer, self).__init__(incomings, **kwargs) if len(self.input_shapes[0]) != 3: raise ValueError('the shape of incoming must be a 3-element tuple') def get_output_shape_for(self, input_shapes): return input_shapes[0][:-2] + input_shapes[0][-1:] def get_output_for(self, inputs, **kwargs): if len(inputs) == 1: # mask_input is None return T.mean(inputs[0], axis=1) else: # inputs[0]: batch x len x h # inputs[1] = mask_input: batch x len return (T.sum(inputs[0] * inputs[1].dimshuffle(0, 1, 'x'), axis=1) / T.sum(inputs[1], axis=1).dimshuffle(0, 'x')) class MLPAttentionLayer(lasagne.layers.MergeLayer): """ An MLP attention layer. incomings[0]: batch x len x h incomings[1]: batch x h Reference: http://arxiv.org/abs/1506.03340 """ def __init__(self, incomings, num_units, nonlinearity=lasagne.nonlinearities.tanh, mask_input=None, init=lasagne.init.Uniform(), **kwargs): if len(incomings) != 2: raise NotImplementedError if mask_input is not None: incomings.append(mask_input) super(MLPAttentionLayer, self).__init__(incomings, **kwargs) self.nonlinearity = nonlinearity self.num_units = num_units self.W0 = self.add_param(init, (self.num_units, self.num_units), name='W0_mlp') self.W1 = self.add_param(init, (self.num_units, self.num_units), name='W1_mlp') self.Wb = self.add_param(init, (self.num_units, ), name='Wb_mlp') def get_output_shape_for(self, input_shapes): return input_shapes[1] def get_output_for(self, inputs, **kwargs): M = T.dot(inputs[0], self.W0) + T.dot(inputs[1], self.W1).dimshuffle(0, 'x', 1) M = self.nonlinearity(M) alpha = T.nnet.softmax(T.dot(M, self.Wb)) if len(inputs) == 3: alpha = alpha * inputs[2] alpha = alpha / alpha.sum(axis=1).reshape((alpha.shape[0], 1)) return T.sum(inputs[0] * alpha.dimshuffle(0, 1, 'x'), axis=1) class LengthLayer(lasagne.layers.Layer): def get_output_for(self, input, **kwargs): return T.cast(input.sum(axis=-1), 'int32') def get_output_shape_for(self, input_shape): return input_shape[:-1] class QuerySliceLayer(L.MergeLayer): # inputs[0]: B * Q * 2D (q) # inputs[1]: B (q_var) def get_output_for(self, inputs, **kwargs): q_slice = inputs[0][T.arange(inputs[0].shape[0]), inputs[1] - 1, :] # B x 2D return q_slice def get_output_shape_for(self, input_shapes): return (input_shapes[0][0], input_shapes[0][2]) class MultiplyLayer(L.MergeLayer): # inputs[0]: B * P * 2D # inputs[1]: B * P def get_output_for(self, inputs, **kwargs): return T.sum(inputs[0] * inputs[1].dimshuffle(0, 1, 'x'), axis=1) def get_output_shape_for(self, input_shapes): return input_shapes[0] class BilinearAttentionLayer(lasagne.layers.MergeLayer): """ A bilinear attention layer. incomings[0]: batch x len x h incomings[1]: batch x h """ def __init__(self, incomings, num_units, mask_input=None, init=lasagne.init.Uniform(), **kwargs): if len(incomings) != 2: raise NotImplementedError if mask_input is not None: incomings.append(mask_input) super(BilinearAttentionLayer, self).__init__(incomings, **kwargs) self.num_units = num_units if 'name' not in kwargs: self.W = self.add_param(init, (self.num_units, self.num_units), name='W_bilinear') else: self.W = self.add_param(init, (self.num_units, self.num_units), name='W_bilinear_{}'.format(kwargs['name'])) def get_output_shape_for(self, input_shapes): return input_shapes[1] def get_output_for(self, inputs, **kwargs): # inputs[0]: batch * len * h # inputs[1]: batch * h # W: h * h M = T.dot(inputs[1], self.W).dimshuffle(0, 'x', 1) alpha = T.nnet.softmax(T.sum(inputs[0] * M, axis=2)) if len(inputs) == 3: alpha = alpha * inputs[2] alpha = alpha / (alpha.sum(axis=1).reshape((alpha.shape[0], 1)) + 1e-8) * inputs[2] return T.sum(inputs[0] * alpha.dimshuffle(0, 1, 'x'), axis=1) class BilinearAttentionMatLayer(lasagne.layers.MergeLayer): """ A bilinear attention layer. incomings[0]: batch x len x h incomings[1]: batch x h """ def __init__(self, incomings, num_units, mask_input=None, init=lasagne.init.Uniform(), **kwargs): if len(incomings) != 2: raise NotImplementedError if mask_input is not None: incomings.append(mask_input) super(BilinearAttentionMatLayer, self).__init__(incomings, **kwargs) self.num_units = num_units if 'name' not in kwargs: self.W = self.add_param(init, (self.num_units, self.num_units), name='W_bilinear') else: self.W = self.add_param(init, (self.num_units, self.num_units), name='W_bilinear_{}'.format(kwargs['name'])) def get_output_shape_for(self, input_shapes): return input_shapes[0][:2] def get_output_for(self, inputs, **kwargs): # inputs[0]: batch * len * h # inputs[1]: batch * h # W: h * h M = T.dot(inputs[1], self.W).dimshuffle(0, 'x', 1) alpha = T.nnet.softmax(T.sum(inputs[0] * M, axis=2)) if len(inputs) == 3: alpha = alpha * inputs[2] alpha = alpha / (alpha.sum(axis=1).reshape((alpha.shape[0], 1)) + 1e-8) * inputs[2] return alpha class BilinearDotLayer(lasagne.layers.MergeLayer): """ A bilinear attention layer. incomings[0]: batch x len x h incomings[1]: batch x h """ def __init__(self, incomings, num_units, mask_input=None, init=lasagne.init.Uniform(), **kwargs): if len(incomings) != 2: raise NotImplementedError if mask_input is not None: incomings.append(mask_input) super(BilinearDotLayer, self).__init__(incomings, **kwargs) self.num_units = num_units if 'name' not in kwargs: self.W = self.add_param(init, (self.num_units, self.num_units), name='W_bilinear') else: self.W = self.add_param(init, (self.num_units, self.num_units), name='W_bilinear_{}'.format(kwargs['name'])) def get_output_shape_for(self, input_shapes): return input_shapes[0][:2] def get_output_for(self, inputs, **kwargs): # inputs[0]: batch * len * h # inputs[1]: batch * h # inputs[2]: batch * len # W: h * h M = T.dot(inputs[1], self.W).dimshuffle(0, 'x', 1) #batch * 1 * h alpha = T.nnet.softmax(T.sum(inputs[0] * M, axis=2)) #batch * len if len(inputs) == 3: alpha = alpha * inputs[2] alpha = alpha / (alpha.sum(axis=1).reshape((alpha.shape[0], 1)) + 1e-8) * inputs[2] return alpha class BilinearDotLayerTensor(lasagne.layers.MergeLayer): """ A bilinear attention layer. incomings[0]: batch x len x h incomings[1]: batch x len x h """ def __init__(self, incomings, num_units, mask_input=None, init=lasagne.init.Uniform(), **kwargs): if len(incomings) != 2: raise NotImplementedError if mask_input is not None: incomings.append(mask_input) super(BilinearDotLayerTensor, self).__init__(incomings, **kwargs) self.num_units = num_units def get_output_shape_for(self, input_shapes): return input_shapes[0][:2] def get_output_for(self, inputs, **kwargs): alpha = T.nnet.softmax(T.sum(inputs[0] * inputs[1], axis=2)) return alpha class DotProductAttentionLayer(lasagne.layers.MergeLayer): """ A bilinear attention layer. incomings[0]: batch x len x h incomings[1]: batch x h """ def __init__(self, incomings, mask_input=None, **kwargs): if len(incomings) != 2: raise NotImplementedError if mask_input is not None: incomings.append(mask_input) super(DotProductAttentionLayer, self).__init__(incomings, **kwargs) def get_output_shape_for(self, input_shapes): return input_shapes[1] def get_output_for(self, inputs, **kwargs): # inputs[0]: batch * len * h # inputs[1]: batch * h # mask_input (if any): batch * len alpha = T.nnet.softmax(T.sum(inputs[0] * inputs[1].dimshuffle(0, 'x', 1), axis=2)) if len(inputs) == 3: alpha = alpha * inputs[2] alpha = alpha / alpha.sum(axis=1).reshape((alpha.shape[0], 1)) return T.sum(inputs[0] * alpha.dimshuffle(0, 1, 'x'), axis=1)
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tmach/tmach_def.py
cbchoi/tmach
0
120132
from enum import Enum, auto class StrEnum(Enum): def _generate_next_value(name, start, count, last_values): return name def __repr__(self): return self.name def __str__(self): return self.name class RegRegOpcode(StrEnum): HALT = auto() IN = auto() OUT = auto() ADD = auto() SUB = auto() MUL = auto() DIV = auto() UNK = auto() class RegMemOpcode(StrEnum): LD = auto() ST = auto() class RegValOpcode(StrEnum): LDA = auto() LDC = auto() JLT = auto() JLE = auto() JGT = auto() JGE = auto() JEQ = auto() JNE = auto() class Instruction(object): def __init__(self, opcode = RegRegOpcode.UNK, arg1=-1, arg2=-1, arg3=-1): self.opcode = opcode self.arg1 = arg1 self.arg2 = arg2 self.arg3 = arg3 class StepResult(StrEnum): OKAY = auto() HALT = auto() IMEM_ERR = auto() DMEM_ERR = auto() DIV_ZERO = auto() INPUT_ERR = auto()
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tests/transformers/test_uniqueness.py
vishalbelsare/tsfuse
23
72718
import numpy as np from tsfuse.transformers.uniqueness import * from tsfuse.data import Collection def test_has_duplicate_true(): x = Collection.from_array([1, 2, 3, 3]) actual = HasDuplicate().transform(x).values np.testing.assert_equal(actual, True) def test_has_duplicate_false(): x = Collection.from_array([1, 2, 3, 4]) actual = HasDuplicate().transform(x).values np.testing.assert_equal(actual, False) def test_has_duplicate_min_true(): x = Collection.from_array([1, 1, 2, 3]) actual = HasDuplicateMin().transform(x).values np.testing.assert_equal(actual, True) def test_has_duplicate_min_false(): x = Collection.from_array([2, 3, 4, 4]) actual = HasDuplicateMin().transform(x).values np.testing.assert_equal(actual, False) def test_has_duplicate_max_true(): x = Collection.from_array([2, 3, 4, 4]) actual = HasDuplicateMax().transform(x).values np.testing.assert_equal(actual, True) def test_has_duplicate_max_false(): x = Collection.from_array([1, 1, 2, 3]) actual = HasDuplicateMax().transform(x).values np.testing.assert_equal(actual, False) def test_has_duplicate_empty(): x = Collection.from_array([np.nan]) actual = HasDuplicate().transform(x).values np.testing.assert_equal(actual, False) def test_number_of_unique_values_rel(): x = Collection.from_array([1, 2, 3, 4]) actual = NumberUniqueValues(rel=True).transform(x).values np.testing.assert_equal(actual, 1.0) def test_number_of_unique_values_abs(): x = Collection.from_array([1, 2, 3, 4]) actual = NumberUniqueValues(rel=False).transform(x).values np.testing.assert_equal(actual, 4) def test_number_of_unique_values_1_rel(): x = Collection.from_array([2, 2, 2, 2]) actual = NumberUniqueValues(rel=True).transform(x).values np.testing.assert_equal(actual, 0.25) def test_number_of_unique_values_1_abs(): x = Collection.from_array([2, 2, 2, 2]) actual = NumberUniqueValues(rel=False).transform(x).values np.testing.assert_equal(actual, 1) def test_number_of_unique_values_0_rel(): x = Collection.from_array([np.nan]) actual = NumberUniqueValues(rel=True).transform(x).values np.testing.assert_equal(actual, np.nan) def test_number_of_unique_values_0_abs(): x = Collection.from_array([np.nan]) actual = NumberUniqueValues(rel=False).transform(x).values np.testing.assert_equal(actual, np.nan) def test_sum_of_reoccurring_data_poins(): x = Collection.from_array([1, 1, 2, 3, 3, 4]) actual = SumReoccurringDataPoints().transform(x).values np.testing.assert_equal(actual, 8) def test_sum_of_reoccurring_data_points_0(): x = Collection.from_array([1, 2, 3, 4]) actual = SumReoccurringDataPoints().transform(x).values np.testing.assert_equal(actual, 0) def test_sum_of_reoccurring_values(): x = Collection.from_array([1, 1, 2, 3, 3, 4]) actual = SumReoccurringValues().transform(x).values np.testing.assert_equal(actual, 4) def test_sum_of_reoccurring_values_0(): x = Collection.from_array([1, 2, 3, 4]) actual = SumReoccurringValues().transform(x).values np.testing.assert_equal(actual, 0)
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app.py
victorcesc/sistema-gerador-avaliacoes
0
133028
<reponame>victorcesc/sistema-gerador-avaliacoes import random from flask import render_template, flash, request from flask_bootstrap import Bootstrap from flask_nav import Nav from flask_nav.elements import Navbar, View, Subgroup, Link from werkzeug.utils import redirect from forms import * from models import * boostrap = Bootstrap(app) # isso habilita o template bootstrap/base.html nav = Nav() nav.init_app(app) # isso habilita a criação de menus de navegação do pacote Flask-Nav @nav.navigation() def meunavbar(): menu = Navbar('Minha aplicação') menu.items = [View('Home', 'inicio')] if session.get('logged_in') is True: menu.items.append(Subgroup('Disciplina', View('Cadastro de Disciplina', 'cadastroDisciplina'), View('Listar Disciplina', 'listarDisciplina'))) menu.items.append(Subgroup('Assunto', View('Cadastro de Assuntos', 'cadastroAssunto'), View('Listar Assuntos', 'listarAssunto'))) menu.items.append(Subgroup('Questoes', View('Cadastro de Questoes', 'cadastroQuestao'), View('Listar Questoes', 'listarQuestao'))) menu.items.append(Subgroup('Alternativas', View('Cadastro de Alternativas', 'cadastroAlternativa'), View('Listar Alternativas', 'listarAlternativa'))) menu.items.append(Subgroup('Avaliacoes', View('Cadastro de Avaliacoes', 'cadastroAvaliacao'), View('Listar Avaliacoes', 'listarAvaliacao'))) menu.items.append(View('Sair','sair')) else: menu.items.append(View('Login', 'autenticar')) menu.items.append(View('Registrar','cadastro')) menu.items.append(Link('Ajuda','https://www.google.com')) return menu @app.route('/registro', methods=['GET', 'POST']) def cadastro(): form = CadastroForm() if form.validate_on_submit(): usuarioLogin = form.username.data usuarioPassword = form.password.data if Usuario.query.filter_by(login=form.username.data).first() != None: flash("O username {} já existe, digite outro".format(usuarioLogin), 'error') else: novo_usuario = Usuario(login=usuarioLogin, senha=usuarioPassword) db.session.add(novo_usuario) db.session.commit() flash('Usuário {} criado com sucesso'.format(usuarioLogin)) return render_template('index.html', title="Usuário registrado") else: return render_template('registro.html', title='Cadastro de usuário', form=form) @app.route('/login', methods=['GET', 'POST']) def autenticar(): form = LoginForm() if form.validate_on_submit(): # Veja mais em: http://flask-sqlalchemy.pocoo.org/2.3/queries/#querying-records usuario = Usuario.query.filter_by(login=form.username.data).first_or_404() if (usuario.check_password(form.password.data)): session['logged_in'] = True session['idUsuario'] = usuario.get_idUsuario() flash('Bem vindo {}'.format(usuario.login)) return render_template('autenticado.html', title="Usuário autenticado",usuario=usuario.get_idUsuario()) else: flash('Usuário ou senha inválidos') return render_template('login.html', title='Autenticação de usuários', form=form) else: return render_template('login.html', title='Autenticação de usuários', form=form) #<EMAIL>('/disciplina') #def cadastrarDisciplina(): @app.route('/cadastroDisciplina',methods=['GET','POST']) def cadastroDisciplina(): form = CadastroDisciplinaForm() if form.validate_on_submit(): nomeDisciplina = form.nomeDisciplina.data if Disciplina.query.filter_by(nomeDisciplina=nomeDisciplina,idUsuario=session.get('idUsuario'),statusDisc='ativo').first() != None: flash('A disciplina {} ja existe, digite outra'.format(nomeDisciplina),'error') return render_template('registro.html',title='Cadastro Disciplina',form=form) else: nova_disciplina = Disciplina(nomeDisciplina=nomeDisciplina) db.session.add(nova_disciplina) db.session.commit() flash('Disciplina {} criada com sucesso'.format(nomeDisciplina)) return render_template('registro.html',title='Cadastro Disciplina',form=form) return render_template('registro.html', title='Cadastro de disciplina', form=form) @app.route('/listarDisciplina',methods=['GET','POST']) def listarDisciplina(): listaD = Disciplina.query.filter_by(idUsuario=session.get('idUsuario'), statusDisc='ativo').all() listadF = [(Disciplina.idDisciplina, Disciplina.nomeDisciplina) for Disciplina in listaD] form = ListarDisciplinasForm() form.disciplinas.choices = listadF if form.disciplinas.data != None: if form.submitExcluir.data: idDisciplina = form.disciplinas.data disciplina = Disciplina.query.filter_by(idDisciplina=idDisciplina, statusDisc='ativo').first() disciplina.desativarDisciplina() db.session.commit() flash('Disciplina excluida com sucesso') return render_template('listarDisciplina.html', title='Listar Disciplinas', form=form) if form.submitEditar.data: idDisciplina = form.disciplinas.data return redirect(url_for('editarDisciplina', idDisciplina=idDisciplina)) return render_template('listarDisciplina.html', title='Listar Disciplinas', form=form) @app.route('/editarDisciplina', methods=['GET', 'POST']) def editarDisciplina(): if session.get('logged_in') is False: return inicio() idDisciplina = str(request.args.get('idDisciplina')) disciplina = Disciplina.query.filter_by(idDisciplina=idDisciplina).first() if disciplina is None: flash('Selecione uma disciplina') return redirect(url_for('listarDisciplina')) form = EditarDisciplinaForm(nomeDisciplina=disciplina.get_nomeDisciplina()) if request.method == 'GET': # pegar o id da pessoa via GET (parâmetro id na URL) if int(disciplina.get_idUsuario()) != session['idUsuario']: return inicio() if disciplina is None: return redirect(url_for('listarDisciplinas')) return render_template('editar.html', title='Editar disciplina', form=form, disciplina=disciplina) else: novaDisciplina = form.novaDisciplina.data if novaDisciplina == disciplina.get_nomeDisciplina(): flash("Digite um nome diferente pra disciplina", 'error') return render_template('editar.html', title='Editar disciplina', form=form, disciplina=disciplina) else: if Disciplina.query.filter_by(nomeDisciplina=novaDisciplina,statusDisc='ativo', idUsuario=session.get('idUsuario')).first() != None: flash("A disciplina {} já existe".format(novaDisciplina), 'error') return render_template('editar.html', title='Editar disciplina', form=form, disciplina=disciplina) disciplina.set_nomeDisciplina(novaDisciplina) db.session.commit() flash("Disciplina alterada com sucesso!") return render_template('editar.html', title='Editar disciplina', form=form, disciplina=disciplina) @app.route('/cadastroAssunto',methods=['GET', 'POST']) def cadastroAssunto(): listaD = Disciplina.query.filter_by(idUsuario=session.get('idUsuario'), statusDisc='ativo').all() listadF = [(Disciplina.idDisciplina , Disciplina.nomeDisciplina) for Disciplina in listaD] form = CadastroAssuntoForm() form.disciplinas.choices = listadF if form.validate_on_submit(): nomeAssunto = form.nomeAssunto.data idDisciplina = form.disciplinas.data if Assunto.query.filter_by(nomeAssunto=nomeAssunto,statusAss='ativo',idDisciplina=idDisciplina).first()!=None: flash('O assunto {} para a disciplina selecionada ja existe, digite outro'.format(nomeAssunto), 'error') return render_template('cadastroAssunto.html',title='Cadastro de Assunto',disciplinas=listaD,form=form) else: novo_assunto = Assunto(nomeAssunto=nomeAssunto,idDisciplina=idDisciplina) db.session.add(novo_assunto) db.session.commit() flash('Assunto {} criado com sucesso'.format(form.nomeAssunto.data)) return render_template('cadastroAssunto.html', title='Cadastro de Assunto', disciplinas=listaD, form=form) return render_template('cadastroAssunto.html', title='Cadastro de Assunto', disciplinas=listaD,form=form) @app.route('/listarAssunto',methods=['GET','POST']) def listarAssunto(): listaD = Disciplina.query.filter_by(idUsuario=session.get('idUsuario'), statusDisc='ativo').all() listadF = [(Disciplina.idDisciplina, Disciplina.nomeDisciplina) for Disciplina in listaD] form = ListarAssuntoForm() form.disciplinas.choices = listadF idDisciplina = form.disciplinas.data listaA = Assunto.query.filter_by(idDisciplina=idDisciplina, statusAss='ativo').all() listaAf = [(Assunto.idAssunto, Assunto.nomeAssunto) for Assunto in listaA] form.assuntos.choices = listaAf if form.validate_on_submit(): if form.submit2.data and form.assuntos.choices!=None: idAssunto = form.assuntos.data assunto = Assunto.query.filter_by(idAssunto=idAssunto,statusAss='ativo').first() assunto.desativarAssunto() db.session.commit() flash("Assunto excluído com sucesso!") return redirect(url_for('listarAssunto')) if form.submit3.data and form.assuntos.choices!=None: idAssunto = form.assuntos.data return redirect(url_for('editarAssunto',idAssunto=idAssunto)) return render_template('listarAssunto.html',nomeColuna= 'Assuntos',title='Listar Assuntos',form=form) @app.route('/editarAssunto',methods=['GET','POST']) def editarAssunto(): if session.get('logged_in') is False: return inicio() idAssunto = str(request.args.get('idAssunto')) assunto = Assunto.query.filter_by(idAssunto=idAssunto).first() form = EditarAssuntoForm(nomeAssunto=assunto.get_nomeAssunto()) if request.method == 'GET': # pegar o id da pessoa via GET (parâmetro id na URL) if assunto is None: return redirect(url_for('listarAssuntos')) return render_template('editarAssunto.html', title='Editar assunto', form=form) else: novoAssunto = form.novoAssunto.data if novoAssunto == assunto.get_nomeAssunto(): flash("Digite um nome diferente pro Assunto", 'error') return render_template('editarAssunto.html',title='Editar Assunto',form=form) else: if Assunto.query.filter_by(nomeAssunto=novoAssunto,statusAss='ativo',idDisciplina=assunto.idDisciplina).first() != None: flash("O assunto {} já existe".format(novoAssunto), 'error') return render_template('editarAssunto.html', title='Editar Assunto', form=form) assunto.set_nomeAssunto(novoAssunto) db.session.commit() flash("Assunto alterado com sucesso!") return render_template('editarAssunto.html', title='Editar Assunto', form=form) @app.route('/cadastroQuestao',methods=['GET','POST']) def cadastroQuestao(): listaD = Disciplina.query.filter_by(idUsuario=session.get('idUsuario'), statusDisc='ativo').all() listadF = [(Disciplina.idDisciplina, Disciplina.nomeDisciplina) for Disciplina in listaD] form = CadastroQuestaoForm() form.disciplinas.choices = listadF idDisciplina = form.disciplinas.data listaA = Assunto.query.filter_by(idDisciplina=idDisciplina, statusAss='ativo').all() listaAf = [(Assunto.idAssunto, Assunto.nomeAssunto) for Assunto in listaA] form.assuntos.choices = listaAf idAssunto = form.assuntos.data if form.submitCadastro.data: if form.pergunta.data!='' and form.resposta.data!='' and idAssunto!=None: pergunta = form.pergunta.data resposta = form.resposta.data tipo = form.tipo.data if Questao.query.filter_by(idAssunto=idAssunto,pergunta=pergunta,resposta=resposta,tipo=tipo,statusQ='ativo').first()!=None: flash('A questao {} ja existe'.format(pergunta)) return render_template('cadastroQuestao.html',title='Cadastro de Questao',form=form) else: nova_questao = Questao(idAssunto=idAssunto,pergunta=pergunta,resposta=resposta,tipo=tipo,vezesUsada=0,statusQ='ativo') db.session.add(nova_questao) db.session.commit() flash('Questao " {} " cadastrada com sucesso'.format(nova_questao.get_pergunta())) return render_template('cadastroQuestao.html',title='Cadastro de Questao',form=form) else: flash('Preencha todos os campos!') return render_template('cadastroQuestao.html',title='Cadastro de Questao',form=form) return render_template('cadastroQuestao.html',title='Cadastro de Questao',form=form) @app.route('/listarQuestao',methods=['GET','POST']) def listarQuestao(): listaD = Disciplina.query.filter_by(idUsuario=session.get('idUsuario'), statusDisc='ativo').all() listadF = [(Disciplina.idDisciplina, Disciplina.nomeDisciplina) for Disciplina in listaD] form = ListarQuestaoForm() form.disciplinas.choices = listadF idDisciplina = form.disciplinas.data listaA = Assunto.query.filter_by(idDisciplina=idDisciplina, statusAss='ativo').all() listaAf = [(Assunto.idAssunto, Assunto.nomeAssunto) for Assunto in listaA] form.assuntos.choices = listaAf idAssunto = form.assuntos.data listaQ = Questao.query.filter_by(idAssunto=idAssunto,statusQ='ativo').all() listaQf = [(Questao.idQuestao,Questao.pergunta) for Questao in listaQ] form.questoes.choices = listaQf if form.questoes.data!=None: if form.submitExcluir.data: idQuestao = form.questoes.data questao = Questao.query.filter_by(idQuestao=idQuestao, statusQ='ativo').first() questao.desativarQuestao() db.session.commit() flash('Questao excluida com sucesso') return render_template('listarQuestao.html', title='Listar Questoes', form=form) if form.submitEditar.data: idQuestao = form.questoes.data return redirect(url_for('editarQuestao', idQuestao=idQuestao)) return render_template('listarQuestao.html', title='Listar Questoes', form=form) @app.route('/editarQuestao',methods=['GET','POST']) def editarQuestao(): idQuestao = str(request.args.get('idQuestao')) questao = Questao.query.filter_by(idQuestao=idQuestao).first() form = EditarQuestaoForm(pergunta= questao.get_pergunta(),tipo=questao.get_tipo()) if request.method == 'GET': if questao is None: return redirect(url_for('listarQuestao')) return render_template('editarQuestao.html', title='Editar Questao', form=form) else: novaPergunta = form.nova_pergunta.data novaResposta = form.nova_resposta.data tipo = form.novo_tipo.data if novaPergunta == questao.get_pergunta() and novaResposta == questao.get_resposta(): flash("Digite um nome diferente para a Questao", 'error') return render_template('editarQuestao.html', title='Editar Questao', form=form) else: if Questao.query.filter_by(pergunta=novaPergunta,resposta=novaResposta, statusQ='ativo', idAssunto=questao.idAssunto).first() != None: flash('A questao "{}" já existe'.format(novaPergunta), 'error') return render_template('editarQuestao.html', title='Editar Questao', form=form) questao.set_pergunta(novaPergunta) questao.set_resposta(novaResposta) questao.set_tipo(tipo) db.session.commit() flash("Questao alterada com sucesso!") return render_template('editarQuestao.html',form=form) @app.route('/cadastroAlternativa',methods=['GET','POST']) def cadastroAlternativa(): listaD = Disciplina.query.filter_by(idUsuario=session.get('idUsuario'), statusDisc='ativo').all() listadF = [(Disciplina.idDisciplina, Disciplina.nomeDisciplina) for Disciplina in listaD] form = CadastroAlternativaForm() form.disciplinas.choices = listadF idDisciplina = form.disciplinas.data listaA = Assunto.query.filter_by(idDisciplina=idDisciplina, statusAss='ativo').all() listaAf = [(Assunto.idAssunto, Assunto.nomeAssunto) for Assunto in listaA] form.assuntos.choices = listaAf idAssunto = form.assuntos.data listaQ = Questao.query.filter_by(idAssunto=idAssunto,tipo='Multipla Escolha', statusQ='ativo').all() listaQf = [(Questao.idQuestao, Questao.pergunta) for Questao in listaQ] form.questoes.choices = listaQf idQuestao = form.questoes.data if form.submitCadastro.data: if form.disciplinas.data!= None and form.questoes.data != None: alternativa = form.alternativa.data if Alternativas.query.filter_by(idAssunto=idAssunto,idQuestao=idQuestao, alternativa=alternativa, statusAl='ativo').first() != None: flash('A alternativa {} ja existe'.format(alternativa)) return render_template('cadastroAlternativa.html', title='Cadastro de Alternativas', form=form) else: nova_alternativa = Alternativas(idAssunto=idAssunto,idQuestao=idQuestao,alternativa=alternativa, statusAl='ativo') db.session.add(nova_alternativa) db.session.commit() flash('Alternativa " {} " cadastrada com sucesso'.format(nova_alternativa.get_alternativa())) return render_template('cadastroAlternativa.html', title='Cadastro de Alternativas', form=form) else: flash('Preencha todos os campos!') return render_template('cadastroAlternativa.html', title='Cadastro de Alternativas', form=form) return render_template('cadastroAlternativa.html',title='Cadastro de Alternativas',form=form) @app.route('/listarAlternativa', methods=['GET','POST']) def listarAlternativa(): listaD = Disciplina.query.filter_by(idUsuario=session.get('idUsuario'), statusDisc='ativo').all() listadF = [(Disciplina.idDisciplina, Disciplina.nomeDisciplina) for Disciplina in listaD] form = ListarAlternativaForm() form.disciplinas.choices = listadF idDisciplina = form.disciplinas.data listaA = Assunto.query.filter_by(idDisciplina=idDisciplina, statusAss='ativo').all() listaAf = [(Assunto.idAssunto, Assunto.nomeAssunto) for Assunto in listaA] form.assuntos.choices = listaAf idAssunto = form.assuntos.data listaQ = Questao.query.filter_by(idAssunto=idAssunto, tipo='Multipla Escolha', statusQ='ativo').all() listaQf = [(Questao.idQuestao, Questao.pergunta) for Questao in listaQ] form.questoes.choices = listaQf idQuestao = form.questoes.data listaAl = Alternativas.query.filter_by(idAssunto=idAssunto,idQuestao=idQuestao,statusAl='ativo').all() listaAlf = [(Alternativas.idAlternativas, Alternativas.alternativa) for Alternativas in listaAl] form.alternativas.choices = listaAlf if form.alternativas.data != None: if form.submitExcluir.data: idAlternativas = form.alternativas.data alternativa = Alternativas.query.filter_by(idAlternativas=idAlternativas, statusAl='ativo').first() alternativa.desativarAlternativa() db.session.commit() flash('Alternativa excluida com sucesso') return render_template('listarAlternativa.html', title='Listar Alternativas', form=form) if form.submitEditar.data: idAlternativas = form.alternativas.data return redirect(url_for('editarAlternativa', idAlternativas=idAlternativas)) return render_template('listarAlternativa.html', title='Listar Alternativa', form=form) @app.route('/editarAlternativa',methods=['GET','POST']) def editarAlternativa(): idAlternativas = str(request.args.get('idAlternativas')) alternativa = Alternativas.query.filter_by(idAlternativas=idAlternativas,statusAl='ativo').first() form = EditarAlternativaForm(alternativa=alternativa.get_alternativa()) if request.method == 'GET': if alternativa is None: return redirect(url_for('listarAlternativa')) return render_template('editarAlternativa.html', title='Editar Alternativa', form=form) else: novaAlternativa = form.nova_alternativa.data if novaAlternativa== alternativa.get_alternativa(): flash("Digite um nome diferente pra Alternativa", 'error') return render_template('editarAlternativa.html', title='Editar Alternativa', form=form) else: if Alternativas.query.filter_by(alternativa=novaAlternativa, idQuestao=str(alternativa.get_idQuestao), idAssunto=str(alternativa.get_idAssunto), statusAl='ativo').first() != None: flash('A alternativa "{}" já existe'.format(novaAlternativa), 'error') return render_template('editarAlternativa.html', title='Editar Alternativa', form=form) alternativa.set_alternativa(novaAlternativa) db.session.commit() flash("Alternativa alterada com sucesso!") return redirect(url_for('listarAlternativa')) @app.route('/cadastroAvaliacao',methods=['GET','POST']) def cadastroAvaliacao(): listaD = Disciplina.query.filter_by(idUsuario=session.get('idUsuario'), statusDisc='ativo').all() listadF = [(Disciplina.idDisciplina, Disciplina.nomeDisciplina) for Disciplina in listaD] form = CadastroAvaliacaoForm() form.disciplinas.choices = listadF idDisciplina = form.disciplinas.data listaA = Assunto.query.filter_by(idDisciplina=idDisciplina,statusAss='ativo').all() listaAf = [(Assunto.idAssunto) for Assunto in listaA] if form.is_submitted(): if form.nomeAvaliacao.data!='' and form.semestre.data!='' and form.ano.data!='' and form.numerodeQuestoes!='' and form.vezesUsadas.data != '' and form.disciplinas.data!=None and len(listaAf)!=0: semestre = form.semestre.data ano = form.ano.data numerodeQuestoes = form.numerodeQuestoes.data vezesUsadas = form.vezesUsadas.data nova_avaliacao = form.nomeAvaliacao.data if Avaliacao.query.filter_by(idDisciplina=idDisciplina, nomeAvaliacao=nova_avaliacao,statusAv='ativo',semestre=semestre,ano=ano).first() != None: flash('A avaliacao "{}" ja existe.'.format(nova_avaliacao),'error') return render_template('cadastroAvaliacao.html', title='Cadastro de Avaliacoes', form=form) else: vetorL = list() for idAssunto in listaAf: listaQ = Questao.query.filter_by(idAssunto=idAssunto,vezesUsada=int(vezesUsadas),statusQ='ativo').all() listaQf = [(Questao.idQuestao) for Questao in listaQ] for idQuestao in listaQf: vetorL.append(idQuestao) if(len(vetorL)!=0): new_av = Avaliacao(nomeAvaliacao=nova_avaliacao,statusAv='ativo',idDisciplina=idDisciplina,semestre=semestre,ano=ano,numerodeQuestoes=numerodeQuestoes) db.session.add(new_av) db.session.commit() if int(numerodeQuestoes)>len(vetorL) or int(numerodeQuestoes)==len(vetorL): for idQuestao in vetorL: q_add_vezes = Questao.query.filter_by(idQuestao=idQuestao,statusQ='ativo').first() q_add_vezes.plusvezesUsada() nova_av_q = AvaliacoesQuestao(idAvaliacao=new_av.idAvaliacao,idQuestao=idQuestao) db.session.add(nova_av_q) db.session.commit() flash('Foram adicionadas {} Questoes que respeitam os requesitos a Avaliacao'.format(len(vetorL))) return render_template('cadastroAvaliacao.html', title='Cadastro de Avaliacoes', form=form) if int(numerodeQuestoes)<len(vetorL): i=0 naoRepetir = list() while(i<int(numerodeQuestoes)): idQuestao = random.choice(vetorL) if idQuestao not in naoRepetir: naoRepetir.append(idQuestao) q_add_vezes = Questao.query.filter_by(idQuestao=idQuestao, statusQ='ativo').first() q_add_vezes.plusvezesUsada() nova_av_q = AvaliacoesQuestao(idAvaliacao=new_av.idAvaliacao,idQuestao=idQuestao) db.session.add(nova_av_q) db.session.commit() i+=1 flash('Foram adicionadas {} Questoes a Avaliacao'.format(numerodeQuestoes)) return render_template('cadastroAvaliacao.html', title='Cadastro de Avaliacoes', form=form) else: flash('Nao há questoes cadastradas ou nao saciam os requisitos para adicionar em uma avaliacao') return render_template('cadastroAvaliacao.html', title='Cadastro de Avaliacoes', form=form) else: flash('É necessario preencher todos os campos') return render_template('cadastroAvaliacao.html', title='Cadastro de Avaliacoes', form=form) return render_template('cadastroAvaliacao.html',title = 'Cadastro de Avaliacoes',form=form) @app.route('/listarAvaliacao',methods=['GET','POST']) def listarAvaliacao(): form = ListarAvaliacaoForm() listaD = Disciplina.query.filter_by(idUsuario=session.get('idUsuario'), statusDisc='ativo').all() listadF = [(Disciplina.idDisciplina , Disciplina.nomeDisciplina) for Disciplina in listaD] form.disciplinas.choices = listadF idDisciplina = form.disciplinas.data semestre = form.semestre.data ano = form.ano.data listaAv = Avaliacao.query.filter_by(idDisciplina=idDisciplina,semestre=semestre,ano=ano,statusAv='ativo') listaAvf = [(Avaliacao.idAvaliacao, Avaliacao.nomeAvaliacao) for Avaliacao in listaAv] form.avaliacoes.choices = listaAvf print('idAvaliacao = {}'.format(form.avaliacoes.data)) if form.avaliacoes.data != None: if form.submitExcluir.data: idAvaliacao = form.avaliacoes.data avaliacao = Avaliacao.query.filter_by(idAvaliacao=idAvaliacao, statusAv='ativo').first() avaliacao.desativarAvaliacao() db.session.commit() flash('Avaliacao excluida com sucesso') return render_template('listarAvaliacao.html', title='Listar Avaliacoes', form=form) if form.submitEditar.data: idAvaliacao = form.avaliacoes.data return redirect(url_for('editarAvaliacao', idAvaliacao=idAvaliacao)) else: flash('Nao há avaliacao selecionada') return render_template('listarAvaliacao.html', title='Listar Avaliacoes', form=form) return render_template('listarAvaliacao.html', title='Listar Avaliacoes', form=form) @app.route('/editarAvaliacao',methods=['GET','POST']) def editarAvaliacao(): idAvaliacao = str(request.args.get('idAvaliacao')) avaliacao = Avaliacao.query.filter_by(idAvaliacao=idAvaliacao,statusAv='ativo').first() if avaliacao is None: flash('Selecione uma avaliacao') return redirect(url_for('listarAvaliacao')) form = EditarAvaliacaoForm(avaliacao=avaliacao.get_nomeAvaliacao(),semestre=avaliacao.get_semestre(),ano=avaliacao.get_ano()) if request.method == 'GET': if avaliacao is None: return redirect(url_for('listarAvaliacao')) return render_template('editarAlternativa.html', title='Editar Alternativa', form=form) else: nomeAvaliacao = form.novaAvaliacao.data novoSemestre = form.novoSemestre.data novoAno = form.novoAno.data if nomeAvaliacao == avaliacao.get_nomeAvaliacao() and novoSemestre == avaliacao.get_semestre() and novoAno == avaliacao.get_ano() : flash("Digite valores diferentes pra Avaliacao", 'error') return render_template('editarAvaliacao.html', title='Editar Avaliacao', form=form) else: if Avaliacao.query.filter_by(nomeAvaliacao=nomeAvaliacao, idDisciplina=avaliacao.idDisciplina, semestre=novoSemestre,ano=novoAno, statusAv='ativo').first() != None: flash('A avaliacao "{}" já existe'.format(nomeAvaliacao), 'error') return render_template('editarAvaliacao.html', title='Editar Avaliacao', form=form) avaliacao.set_nomeAvaliacao(nomeAvaliacao) avaliacao.set_ano(novoAno) avaliacao.set_semestre(novoSemestre) db.session.commit() flash("Avaliacao alterada com sucesso!") return render_template('editarAvaliacao.html', title='Editar Avaliacao', form=form) @app.route('/') def inicio(): return render_template('index.html') @app.route('/aluno') def aluno(): ''' Ilustra um exemplo de como exibir tabelas. Também mostrado um exemplo do flask-bootstrap-table As estuturas de dados 'data'e 'columns' estão no script dadostabela.py :return: ''' return render_template('alunos.html',data=data,columns=columns) @app.errorhandler(404) def page_not_found(e): ''' Para tratar erros de páginas não encontradas - HTTP 404 :param e: :return: ''' return render_template('404.html'), 404 @app.route('/logout') def sair(): session['logged_in'] = False return redirect(url_for('inicio')) if __name__ == '__main__': app.run(host='0.0.0.0', debug=True)
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backend/app/utils.py
dashdashforce/int20h-test-photo-viewer
0
23276
from functools import reduce from itertools import groupby from operator import add, itemgetter def merge_records_by(key, combine): return lambda first, second: { k: first[k] if k == key else combine(first[k], second[k]) for k in first } def merge_list_of_records_by(key, combine): keyprop = itemgetter(key) return lambda lst: [ reduce(merge_records_by(key, combine), records) for _, records in groupby(sorted(lst, key=keyprop), keyprop) ]
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arviz/plots/backends/matplotlib/separationplot.py
sudojarvis/arviz
1,159
31540
<reponame>sudojarvis/arviz """Matplotlib separation plot.""" import matplotlib.pyplot as plt import numpy as np from ...plot_utils import _scale_fig_size from . import backend_kwarg_defaults, backend_show, create_axes_grid def plot_separation( y, y_hat, y_hat_line, label_y_hat, expected_events, figsize, textsize, color, legend, locs, width, ax, plot_kwargs, y_hat_line_kwargs, exp_events_kwargs, backend_kwargs, show, ): """Matplotlib separation plot.""" if backend_kwargs is None: backend_kwargs = {} if plot_kwargs is None: plot_kwargs = {} # plot_kwargs.setdefault("color", "C0") # if color: plot_kwargs["color"] = color if y_hat_line_kwargs is None: y_hat_line_kwargs = {} y_hat_line_kwargs.setdefault("color", "k") if exp_events_kwargs is None: exp_events_kwargs = {} exp_events_kwargs.setdefault("color", "k") exp_events_kwargs.setdefault("marker", "^") exp_events_kwargs.setdefault("s", 100) exp_events_kwargs.setdefault("zorder", 2) backend_kwargs = { **backend_kwarg_defaults(), **backend_kwargs, } (figsize, *_) = _scale_fig_size(figsize, textsize, 1, 1) backend_kwargs.setdefault("figsize", figsize) backend_kwargs["squeeze"] = True if ax is None: _, ax = create_axes_grid(1, backend_kwargs=backend_kwargs) idx = np.argsort(y_hat) for i, loc in enumerate(locs): positive = not y[idx][i] == 0 alpha = 1 if positive else 0.3 ax.bar(loc, 1, width=width, alpha=alpha, **plot_kwargs) if y_hat_line: ax.plot(np.linspace(0, 1, len(y_hat)), y_hat[idx], label=label_y_hat, **y_hat_line_kwargs) if expected_events: expected_events = int(np.round(np.sum(y_hat))) ax.scatter( y_hat[idx][len(y_hat) - expected_events - 1], 0, label="Expected events", **exp_events_kwargs ) if legend and (expected_events or y_hat_line): handles, labels = ax.get_legend_handles_labels() labels_dict = dict(zip(labels, handles)) ax.legend(labels_dict.values(), labels_dict.keys()) ax.set_xticks([]) ax.set_yticks([]) ax.set_xlim(0, 1) ax.set_ylim(0, 1) if backend_show(show): plt.show() return ax
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src/python/turicreate/toolkits/object_detector/_tf_image_augmenter.py
cookingcodewithme/turicreate
11,356
194278
# -*- coding: utf-8 -*- # Copyright © 2019 Apple Inc. All rights reserved. # # Use of this source code is governed by a BSD-3-clause license that can # be found in the LICENSE.txt file or at https://opensource.org/licenses/BSD-3-Clause from __future__ import print_function as _ from __future__ import division as _ from __future__ import absolute_import as _ import numpy as np from PIL import Image import turicreate.toolkits._tf_utils as _utils import turicreate as tc from turicreate._deps.minimal_package import _minimal_package_import_check # in conjunction with minimal package def _lazy_import_tensorflow(): from turicreate._deps.minimal_package import _minimal_package_import_check _tf = _minimal_package_import_check("tensorflow.compat.v1") return _tf _DEFAULT_AUG_PARAMS = { "max_hue_adjust": 0.05, "max_brightness": 0.05, "max_contrast": 1.25, "max_saturation": 1.25, "skip_probability_flip": 0.5, "min_aspect_ratio": 0.8, "max_aspect_ratio": 1.25, "min_area_fraction_crop": 0.15, "max_area_fraction_crop": 1.0, "min_area_fraction_pad": 1.0, "max_area_fraction_pad": 2.0, "max_attempts": 50, "skip_probability_pad": 0.1, "skip_probability_crop": 0.1, "min_object_covered": 0.0, "min_eject_coverage": 0.5, "resize_method": "turicreate", } def interpolate(alpha_tf, x, y): tf = _lazy_import_tensorflow() x = tf.constant(x, dtype=tf.float32) y = tf.constant(y, dtype=tf.float32) range = tf.math.subtract(y, x) delta = tf.math.multiply(alpha_tf, range) return tf.math.add(x, delta) def hue_augmenter( image, annotation, random_alpha_tf, max_hue_adjust=_DEFAULT_AUG_PARAMS["max_hue_adjust"], ): tf = _lazy_import_tensorflow() # Apply the random rotation around the color wheel. hue_delta = interpolate(random_alpha_tf[0], -max_hue_adjust, max_hue_adjust) image = tf.image.adjust_hue(image, hue_delta) image = tf.clip_by_value(image, 0, 1) # No geometry changes, so just copy the annotations. return image, annotation def color_augmenter( image, annotation, random_alpha_tf, max_brightness=_DEFAULT_AUG_PARAMS["max_brightness"], max_contrast=_DEFAULT_AUG_PARAMS["max_contrast"], max_saturation=_DEFAULT_AUG_PARAMS["max_saturation"], ): tf = _lazy_import_tensorflow() # Apply the random adjustment to brightness. brightness_delta = interpolate(random_alpha_tf[0], -max_brightness, max_brightness) image = tf.image.adjust_brightness(image, brightness_delta) # Apply the random adjustment to contrast. saturation_delta = interpolate(random_alpha_tf[1], 1/max_saturation, max_saturation) image = tf.image.adjust_saturation(image, saturation_delta) # Apply random adjustment to saturation. contrast_delta = interpolate(random_alpha_tf[2], 1/max_contrast, max_contrast) image = tf.image.adjust_contrast(image, contrast_delta) image = tf.clip_by_value(image, 0, 1) # No geometry changes, so just copy the annotations. return image, annotation def resize_augmenter(image, annotation, output_shape): resize_method = _DEFAULT_AUG_PARAMS["resize_method"] def resize_PIL_image(image, output_shape): image *= 255.0 image = image.astype("uint8") pil_img = Image.fromarray(image) resize_img = pil_img.resize( (output_shape[1], output_shape[0]), resample=Image.BILINEAR ) np_img = np.array(resize_img) np_img = np_img.astype(np.float32) np_img /= 255.0 return np_img def resize_turicreate_image(image, output_shape): image *= 255.0 image = image.astype("uint8") FORMAT_RAW = 2 tc_image = tc.Image( _image_data=image.tobytes(), _width=image.shape[1], _height=image.shape[0], _channels=image.shape[2], _format_enum=FORMAT_RAW, _image_data_size=image.size, ) tc_image = tc.image_analysis.resize( tc_image, output_shape[1], output_shape[0], resample="bilinear" ) image = tc_image.pixel_data image = image.astype(np.float32) image /= 255.0 return image if resize_method == "tensorflow": tf = _lazy_import_tensorflow() new_height = tf.cast(output_shape[0], dtype=tf.int32) new_width = tf.cast(output_shape[1], dtype=tf.int32) # Determine the affine transform to apply and apply to the image itself. image_scaled = tf.squeeze( tf.image.resize_bilinear(tf.expand_dims(image, 0), [new_height, new_width]), [0], ) elif resize_method == "PIL": image_scaled = tf.numpy_function( func=resize_PIL_image, inp=[image, output_shape], Tout=[tf.float32] ) elif resize_method == "turicreate": tf = _lazy_import_tensorflow() image_scaled = tf.numpy_function( func=resize_turicreate_image, inp=[image, output_shape], Tout=[tf.float32] ) else: raise Exception("Non-supported resize method.") image_clipped = tf.clip_by_value(image_scaled, 0.0, 1.0) # No geometry changes (because of relative co-ordinate system) return image_clipped, annotation def horizontal_flip_augmenter( image, annotation, random, skip_probability=_DEFAULT_AUG_PARAMS["skip_probability_flip"], ): if random.uniform(0.0, 1.0) < skip_probability: return image, annotation image_height, image_width, _ = image.shape flipped_image = np.flip(image, 1) # One image can have more than one annotation. so loop through annotations and flip across the horizontal axis for i in range(len(annotation)): # Only flip if the annotation is not an empty annotation. if np.any(annotation[i][1:5]): annotation[i][1] = 1 - annotation[i][1] - annotation[i][3] return flipped_image, annotation def padding_augmenter( image, annotation, random, skip_probability=_DEFAULT_AUG_PARAMS["skip_probability_pad"], min_aspect_ratio=_DEFAULT_AUG_PARAMS["min_aspect_ratio"], max_aspect_ratio=_DEFAULT_AUG_PARAMS["max_aspect_ratio"], min_area_fraction=_DEFAULT_AUG_PARAMS["min_area_fraction_pad"], max_area_fraction=_DEFAULT_AUG_PARAMS["max_area_fraction_pad"], max_attempts=_DEFAULT_AUG_PARAMS["max_attempts"], ): if random.uniform(0.0, 1.0) < skip_probability: return np.array(image), annotation image_height, image_width, _ = image.shape # Randomly sample aspect ratios until one derives a non-empty range of # compatible heights, or until reaching the upper limit on attempts. for i in range(max_attempts): # Randomly sample an aspect ratio. aspect_ratio = random.uniform(min_aspect_ratio, max_aspect_ratio) # The padded height must be at least as large as the original height. # h' >= h min_height = float(image_height) # The padded width must be at least as large as the original width. # w' >= w IMPLIES ah' >= w IMPLIES h' >= w / a min_height_from_width = float(image_width) / aspect_ratio if min_height < min_height_from_width: min_height = min_height_from_width # The padded area must attain the minimum area fraction. # w'h' >= fhw IMPLIES ah'h' >= fhw IMPLIES h' >= sqrt(fhw/a) min_height_from_area = np.sqrt( image_height * image_width * min_area_fraction / aspect_ratio ) if min_height < min_height_from_area: min_height = min_height_from_area # The padded area must not exceed the maximum area fraction. max_height = np.sqrt( image_height * image_width * max_area_fraction / aspect_ratio ) if min_height >= max_height: break # We did not find a compatible aspect ratio. Just return the original data. if min_height > max_height: return np.array(image), annotation # Sample a final size, given the sampled aspect ratio and range of heights. padded_height = random.uniform(min_height, max_height) padded_width = padded_height * aspect_ratio # Sample the offset of the source image inside the padded image. x_offset = random.uniform(0.0, (padded_width - image_width)) y_offset = random.uniform(0.0, (padded_height - image_height)) # Compute padding needed on the image after_padding_width = padded_width - image_width - x_offset after_padding_height = padded_height - image_height - y_offset # Pad the image npad = ( (int(y_offset), int(after_padding_height)), (int(x_offset), int(after_padding_width)), (0, 0), ) padded_image = np.pad(image, pad_width=npad, mode="constant", constant_values=0.5) ty = float(y_offset) tx = float(x_offset) # Transformation matrix for the annotations transformation_matrix = np.array([[1.0, 0.0, ty], [0.0, 1.0, tx], [0.0, 0.0, 1.0]]) # Use transformation matrix to augment annotations formatted_annotation = [] for aug in annotation: identifier = aug[0:1] bounds = aug[1:5] confidence = aug[5:6] if not np.any(bounds): formatted_annotation.append( np.concatenate([identifier, np.array([0, 0, 0, 0]), confidence]) ) continue width = bounds[2] height = bounds[3] x1 = bounds[0] * image_width y1 = bounds[1] * image_height x2 = (bounds[0] + width) * image_width y2 = (bounds[1] + height) * image_height augmentation_coordinates = np.array([y1, x1, y2, x2], dtype=np.float32) v = np.concatenate( [ augmentation_coordinates.reshape((2, 2)), np.ones((2, 1), dtype=np.float32), ], axis=1, ) transposed_v = np.dot(v, np.transpose(transformation_matrix)) t_intersection = np.squeeze(transposed_v[:, :2].reshape(-1, 4)) # Sort the points top, left, bottom, right if t_intersection[0] > t_intersection[2]: t_intersection[0], t_intersection[2] = t_intersection[2], t_intersection[0] if t_intersection[1] > t_intersection[3]: t_intersection[1], t_intersection[3] = t_intersection[3], t_intersection[1] # Normalize the elements to the cropped width and height ele_1 = t_intersection[1] / padded_width ele_2 = t_intersection[0] / padded_height ele_3 = (t_intersection[3] - t_intersection[1]) / padded_width ele_4 = (t_intersection[2] - t_intersection[0]) / padded_height formatted_annotation.append( np.concatenate( [identifier, np.array([ele_1, ele_2, ele_3, ele_4]), confidence] ) ) return np.array(padded_image), np.array(formatted_annotation, dtype=np.float32) def crop_augmenter( image, annotation, random, skip_probability=_DEFAULT_AUG_PARAMS["skip_probability_crop"], min_aspect_ratio=_DEFAULT_AUG_PARAMS["min_aspect_ratio"], max_aspect_ratio=_DEFAULT_AUG_PARAMS["max_aspect_ratio"], min_area_fraction=_DEFAULT_AUG_PARAMS["min_area_fraction_crop"], max_area_fraction=_DEFAULT_AUG_PARAMS["max_area_fraction_crop"], min_object_covered=_DEFAULT_AUG_PARAMS["min_object_covered"], max_attempts=_DEFAULT_AUG_PARAMS["max_attempts"], min_eject_coverage=_DEFAULT_AUG_PARAMS["min_eject_coverage"], ): if random.uniform(0.0, 1.0) < skip_probability: return np.array(image), annotation image_height, image_width, _ = image.shape # Sample crop rects until one satisfies our constraints (by yielding a valid # list of cropped annotations), or reaching the limit on attempts. for i in range(max_attempts): # Randomly sample an aspect ratio. aspect_ratio = random.uniform(min_aspect_ratio, max_aspect_ratio) # Next we'll sample a height (which combined with the now known aspect # ratio, determines the size and area). But first we must compute the range # of valid heights. The crop cannot be taller than the original image, # cannot be wider than the original image, and must have an area in the # specified range. # The cropped height must be no larger the original height. # h' <= h max_height = float(image_height) # The cropped width must be no larger than the original width. # w' <= w IMPLIES ah' <= w IMPLIES h' <= w / a max_height_from_width = float(image_width) / aspect_ratio if max_height > max_height_from_width: max_height = max_height_from_width # The cropped area must not exceed the maximum area fraction. max_height_from_area = np.sqrt( image_height * image_width * max_area_fraction / aspect_ratio ) if max_height > max_height_from_area: max_height = max_height_from_area # The padded area must attain the minimum area fraction. min_height = np.sqrt( image_height * image_width * min_area_fraction / aspect_ratio ) # If the range is empty, then crops with the sampled aspect ratio cannot # satisfy the area constraint. if min_height > max_height: continue # Sample a position for the crop, constrained to lie within the image. cropped_height = random.uniform(min_height, max_height) cropped_width = cropped_height * aspect_ratio x_offset = random.uniform(0.0, (image_width - cropped_width)) y_offset = random.uniform(0.0, (image_height - cropped_height)) crop_bounds_x1 = x_offset crop_bounds_y1 = y_offset crop_bounds_x2 = x_offset + cropped_width crop_bounds_y2 = y_offset + cropped_height formatted_annotation = [] is_min_object_covered = True for aug in annotation: identifier = aug[0:1] bounds = aug[1:5] confidence = aug[5:6] width = bounds[2] height = bounds[3] x1 = bounds[0] * image_width y1 = bounds[1] * image_height x2 = (bounds[0] + width) * image_width y2 = (bounds[1] + height) * image_height # This tests whether the crop bounds are out of the annotated bounds, if not it returns an empty annotation if ( crop_bounds_x1 < x2 and crop_bounds_y1 < y2 and x1 < crop_bounds_x2 and y1 < crop_bounds_y2 ): x_bounds = [x1, x2, x_offset, x_offset + cropped_width] y_bounds = [y1, y2, y_offset, y_offset + cropped_height] x_bounds.sort() y_bounds.sort() x_pairs = x_bounds[1:3] y_pairs = y_bounds[1:3] intersection = np.array( [y_pairs[0], x_pairs[0], y_pairs[1], x_pairs[1]] ) intersection_area = (intersection[3] - intersection[1]) * ( intersection[2] - intersection[0] ) annotation_area = (y2 - y1) * (x2 - x1) area_coverage = intersection_area / annotation_area # Invalidate the crop if it did not sufficiently overlap each annotation and try again. if area_coverage < min_object_covered: is_min_object_covered = False break # If the area coverage is greater the min_eject_coverage, then actually keep the annotation if area_coverage >= min_eject_coverage: # Transformation matrix for the annotations transformation_matrix = np.array( [[1.0, 0.0, -y_offset], [0.0, 1.0, -x_offset], [0.0, 0.0, 1.0]] ) v = np.concatenate( [ intersection.reshape((2, 2)), np.ones((2, 1), dtype=np.float32), ], axis=1, ) transposed_v = np.dot(v, np.transpose(transformation_matrix)) t_intersection = np.squeeze(transposed_v[:, :2].reshape(-1, 4)) # Sort the points top, left, bottom, right if t_intersection[0] > t_intersection[2]: t_intersection[0], t_intersection[2] = ( t_intersection[2], t_intersection[0], ) if t_intersection[1] > t_intersection[3]: t_intersection[1], t_intersection[3] = ( t_intersection[3], t_intersection[1], ) # Normalize the elements to the cropped width and height ele_1 = t_intersection[1] / cropped_width ele_2 = t_intersection[0] / cropped_height ele_3 = (t_intersection[3] - t_intersection[1]) / cropped_width ele_4 = (t_intersection[2] - t_intersection[0]) / cropped_height formatted_annotation.append( np.concatenate( [ identifier, np.array([ele_1, ele_2, ele_3, ele_4]), confidence, ] ) ) else: formatted_annotation.append( np.concatenate( [identifier, np.array([0.0, 0.0, 0.0, 0.0]), confidence] ) ) else: formatted_annotation.append( np.concatenate( [identifier, np.array([0.0, 0.0, 0.0, 0.0]), confidence] ) ) if not is_min_object_covered: continue y_offset = int(y_offset) x_offset = int(x_offset) end_y = int(cropped_height + y_offset) end_x = int(cropped_width + x_offset) image_cropped = image[y_offset:end_y, x_offset:end_x] return np.array(image_cropped), np.array(formatted_annotation, dtype=np.float32) return np.array(image), annotation def numpy_augmenter(img, ann, seed): random = np.random.RandomState(seed=seed) img, ann = crop_augmenter(img, ann, random) img, ann = padding_augmenter(img, ann, random) img, ann = horizontal_flip_augmenter(img, ann, random) return img, ann def complete_augmenter(img_tf, ann_tf, seed_tf, alpha_tf, output_height, output_width): tf = _lazy_import_tensorflow() img_tf, ann_tf = tf.numpy_function( func=numpy_augmenter, inp=[img_tf, ann_tf, seed_tf], Tout=[tf.float32, tf.float32], ) img_tf, ann_tf = color_augmenter(img_tf, ann_tf, alpha_tf[0:3]) img_tf, ann_tf = hue_augmenter(img_tf, ann_tf, alpha_tf[3:4]) img_tf, ann_tf = resize_augmenter(img_tf, ann_tf, (output_height, output_width)) return img_tf, ann_tf class DataAugmenter(object): def __init__(self, output_height, output_width, batch_size, resize_only): tf = _lazy_import_tensorflow() self.batch_size = batch_size self.graph = tf.Graph() self.resize_only = resize_only with self.graph.as_default(): self.img_tf = [ tf.placeholder(tf.float32, [None, None, 3]) for x in range(0, self.batch_size) ] self.ann_tf = [ tf.placeholder(tf.float32, [None, 6]) for x in range(0, self.batch_size) ] self.alpha_tf = tf.placeholder(tf.float32, [self.batch_size, 4]) self.random_seed_tf = tf.placeholder(tf.uint32) self.resize_op_batch = [] for i in range(0, self.batch_size): if resize_only: aug_img_tf, aug_ann_tf = resize_augmenter( self.img_tf[i], self.ann_tf[i], (output_height, output_width) ) self.resize_op_batch.append([aug_img_tf, aug_ann_tf]) else: aug_img_tf, aug_ann_tf = complete_augmenter( self.img_tf[i], self.ann_tf[i], self.random_seed_tf[i], self.alpha_tf[i], output_height, output_width, ) self.resize_op_batch.append([aug_img_tf, aug_ann_tf]) def get_augmented_data(self, images, annotations, random_seed): tf = _lazy_import_tensorflow() with tf.Session(graph=self.graph) as session: feed_dict = dict() # Populate feed_dict with images and annotations graph_op = self.resize_op_batch[0 : len(images)] for i in range(len(images)): feed_dict[self.img_tf[i]] = _utils.convert_shared_float_array_to_numpy( images[i] ) feed_dict[self.ann_tf[i]] = _utils.convert_shared_float_array_to_numpy( annotations[i] ) # Populate feed_dict with random seed and random alpha values, used # to sample image perturbations. We don't use TensorFlow's built-in # support for random number generation, since we want to effectively # reset the seed for each session (batch). random = np.random.RandomState(seed=random_seed) feed_dict[self.alpha_tf] = random.rand(*self.alpha_tf.shape) feed_dict[self.random_seed_tf] = random.randint( 0, 2 ** 32, size=self.batch_size ) aug_output = session.run(graph_op, feed_dict=feed_dict) processed_images = [] processed_annotations = [] for o in aug_output: processed_images.append(o[0]) processed_annotations.append( np.ascontiguousarray(o[1], dtype=np.float32) ) processed_images = np.array(processed_images, dtype=np.float32) processed_images = np.ascontiguousarray(processed_images, dtype=np.float32) return (processed_images, processed_annotations)
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tests/__init__.py
zhangyiming07/QT4C
53
6345
# -*- coding: utf-8 -*- # # Tencent is pleased to support the open source community by making QT4C available. # Copyright (C) 2020 THL A29 Limited, a Tencent company. All rights reserved. # QT4C is licensed under the BSD 3-Clause License, except for the third-party components listed below. # A copy of the BSD 3-Clause License is included in this file. # '''单元测试 ''' import unittest import os import sys test_dir = os.path.dirname(os.path.abspath(__file__)) sys.path.insert(0, os.path.dirname(test_dir)) def main(): runner = unittest.TextTestRunner(verbosity=10 + sys.argv.count('-v')) suite = unittest.TestLoader().discover(test_dir, pattern='test_*.py') raise SystemExit(not runner.run(suite).wasSuccessful()) if __name__ == '__main__': main()
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ver_colores.py
maltenzo/surrendeador
0
34878
<gh_stars>0 import mouse import keyboard from PIL import Image import pyscreenshot from time import sleep import os def ver(): while True: img = pyscreenshot.grab() width, height = img.size pixel_values = list(img.getdata()) print (pixel_values[width*mouse.get_position()[1]+mouse.get_position()[0]]) def dame_colores(): img = pyscreenshot.grab() width, height = img.size pixel_values = list(img.getdata()) return (pixel_values[width*mouse.get_position()[1]+mouse.get_position()[0]]) while True: # print(mouse.get_position()) print( dame_colores())
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bounded_multi_processing.py
zinob/python_tutorials
0
117953
#!/usr/bin/python ## """ ## How do i parallelize a retriever function and a sender/saver function when they ## 1: require mpthreads ## 2: The dataammount is significant enough that I don't want the receiver to get to far ahead of the sender. ## """ ## This code uses a semaphore to bound the amount of in-flight data. ## The general rule of thumb is that you should use twice the number of threads that you have CPUs, ## so this one uses one pool for senders and one pool for receivers, each with equal workers as there are CPUs in the system. ## The semaphore size is set to the twice the number of possible in-flight sending requests in order to avoid thread starvation ## (if it is less than cpu*chunk it will stall, if its less than twice that performance might suffer) import multiprocessing from multiprocessing import Pool import time try: from tqdm import tqdm except: tqdm=lambda x,*_,**__:x import random def ini(y): global semaphore semaphore=y def receive(i): semaphore.acquire() time.sleep(1+random.randrange(-4,4)/10) return i+1 def send(x): time.sleep(2) semaphore.release() return(f"sent:{x}") ITERS=99 CHUNK=4 CPUs=multiprocessing.cpu_count() m = multiprocessing.Manager() s = m.BoundedSemaphore(CHUNK*CPUs*2) t=time.time() with Pool(CPUs,initializer=ini, initargs=[s] ) as receiver_p: with Pool(CPUs,initializer=ini, initargs=[s]) as sender_p: indata = range(ITERS) received=receiver_p.imap_unordered(receive, indata,chunksize=CHUNK) sent=sender_p.imap_unordered(send, tqdm(received, desc="received", total=ITERS,miniters=1),chunksize=CHUNK) for i in tqdm(sent,desc="sent",total=ITERS,miniters=1): pass time.sleep(1) print() print(time.time()-t) print(time.time()-t)
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__init__.py
dedalodaelus/hass-http_legacy_auth
1
135155
<reponame>dedalodaelus/hass-http_legacy_auth import asyncio from aiohttp.web import middleware from aiohttp.frozenlist import FrozenList import logging from homeassistant.components.http import HomeAssistantHTTP from homeassistant.components.http.const import KEY_AUTHENTICATED # The domain of your component. Should be equal to the name of your component. DOMAIN = "http_legacy_auth" _LOGGER = logging.getLogger(__name__) KEY_API_PASSWORD = '<PASSWORD>' @asyncio.coroutine def async_setup(hass, config): api_password = config[DOMAIN][KEY_API_PASSWORD] @middleware async def auth_middleware(request, handler): if KEY_API_PASSWORD in request.query and \ request.query[KEY_API_PASSWORD] == api_password: auth_type = 'api_password' _LOGGER.debug( "Authenticated %s for %s using %s", request, request.path, auth_type, ) request[KEY_AUTHENTICATED] = True return await handler(request) # NOTE: This hack is an accident waiting to happen, however HASS # doesn't seem to offer a way to add a middleware to the http # component. aiohttp "freezes" app state after start so it's # impossible to use public API to add a middleware there directly. hass.http.app._middlewares = FrozenList([*hass.http.app.middlewares, auth_middleware]) hass.http.app._middlewares_handlers = tuple(hass.http.app._prepare_middleware()) return True
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src/zojax/security/utils.py
Zojax/zojax.security
0
43279
############################################################################## # # Copyright (c) 2007 Zope Corporation and Contributors. # All Rights Reserved. # # This software is subject to the provisions of the Zope Public License, # Version 2.1 (ZPL). A copy of the ZPL should accompany this distribution. # THIS SOFTWARE IS PROVIDED "AS IS" AND ANY AND ALL EXPRESS OR IMPLIED # WARRANTIES ARE DISCLAIMED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED # WARRANTIES OF TITLE, MERCHANTABILITY, AGAINST INFRINGEMENT, AND FITNESS # FOR A PARTICULAR PURPOSE. # ############################################################################## """ $Id$ """ from zope.component import getUtility from zope.security.proxy import removeSecurityProxy from zope.security.management import queryInteraction from zope.app.security.interfaces import IAuthentication, PrincipalLookupError from zope.app.security.principalregistry import Principal from interfaces import IZojaxSecurityPolicy, _ def getPrincipal(id=None): """ get current interaction principal """ if id is None: interaction = queryInteraction() if interaction is not None: for participation in interaction.participations: if participation.principal is not None: return participation.principal else: try: return getUtility(IAuthentication).getPrincipal(id) except PrincipalLookupError: return Principal('unknown', _('Unknown'), _('Unknown'), 'unknown', 'unknown') def getPrincipals(ids): auth = getUtility(IAuthentication) for pid in ids: try: principal = auth.getPrincipal(pid) except PrincipalLookupError: continue yield principal def checkPermissionForPrincipal(principal, permission, object): interaction = queryInteraction() if IZojaxSecurityPolicy.providedBy(interaction): return interaction.cached_decision( removeSecurityProxy(object), principal.id, interaction._groupsFor(principal), permission) return False def invalidateSecurityCache(): queryInteraction().invalidate_cache()
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simulation_fluid.py
danielpm153/fluid2D
1
82469
<reponame>danielpm153/fluid2D import numpy import cv2 class MeshImage: def __init__(self, image_path: str) -> None: self.__image = cv2.imread(image_path) self.__image_gray = cv2.cvtColor(self.__image, cv2.COLOR_RGB2GRAY) self.__height = self.__image_gray.shape[0] self.__width = self.__image_gray.shape[1] self.gray_tone = 250 def shape(self) -> tuple: ''' return: height and width of the image ''' return self.__height, self.__width def Mesh(self) -> numpy.ndarray: ''' return: numpy array ''' self.__meshObject = self.__gray_tone >= self.__image_gray return self.__meshObject @property def gray_tone(self) -> int: ''' return: int ''' return self.__gray_tone @gray_tone.setter def gray_tone(self, value: int) -> None: if 255 >= value >= 0: self.__gray_tone = value else: raise ValueError("The value of is between 0 and 255") class FluidSimulation2D: def __init__(self, obj_mesh2D: MeshImage): self.obj_mesh2D = obj_mesh2D self.__height, self.__width = obj_mesh2D.shape() self.obj = self.obj_mesh2D.Mesh() self.__viscosity = 0.005 self.__u0 = 0.12 # speed in x direction self.omega = 1 / (3*self.__viscosity + 0.5) # lattice-Boltzmann weight factors self.__w0 = 4/9 self.__w1_4 = 1/9 self.__w5_8 = 1/36 # referencia mais à cima-direita(padrao da tela) self.x_ref, self.y_ref = 0, 0 self.count = 0 def reshape(self, add_up=0, add_down=0, add_right=0, add_left=0): ''' :param add_up: concatenate layers of zero above the matrix :param add_down: concatenate zero layers below the matrix :param add_right: concatenate zero layers to the right of the matrix :param add_left: concatenate zero layers to the left of the matrix :return: ''' if add_up > 0: zeros_width_up = numpy.zeros( (add_up, self.obj.shape[1]), dtype=bool) self.obj = numpy.concatenate((zeros_width_up, self.obj), axis=0) elif add_up < 0: self.obj = self.obj[-add_up:] if add_down > 0: zeros_width_down = numpy.zeros( (add_down, self.obj.shape[1]), dtype=bool) self.obj = numpy.concatenate((self.obj, zeros_width_down), axis=0) elif add_down < 0: self.obj = self.obj[:add_down] if add_right > 0: zeros_height_right = numpy.zeros( (self.obj.shape[0], add_right), dtype=bool) self.obj = numpy.concatenate( (self.obj, zeros_height_right), axis=1) elif add_right < 0: self.obj = self.obj[:, -add_right:] if add_left > 0: zeros_height_left = numpy.zeros( (self.obj.shape[0], add_left), dtype=bool) self.obj = numpy.concatenate((zeros_height_left, self.obj), axis=1) elif add_left < 0: self.obj = self.obj[:, :add_left] self.__height, self.__width = self.obj.shape if self.x_ref+add_left >= 0: self.x_ref += add_left if self.y_ref+add_up >= 0: self.y_ref += add_up def shape(self) -> tuple: ''' return: height and width of the mesh ''' return self.__height, self.__width def initialize(self): ''' Initialize all arrays to stabilize the flow to the right particle densities over 9 directions ''' self.Fx, self.Fy = 0, 0 self.n0 = self.__w0 * \ (numpy.ones((self.__height, self.__width)) - 1.5*self.__u0**2) self.nN = self.__w1_4 * \ (numpy.ones((self.__height, self.__width)) - 1.5*self.__u0**2) self.nS = self.__w1_4 * \ (numpy.ones((self.__height, self.__width)) - 1.5*self.__u0**2) self.nE = self.__w1_4 * (numpy.ones((self.__height, self.__width)) + 3*self.__u0 + 4.5*self.__u0**2 - 1.5*self.__u0**2) self.nW = self.__w1_4 * (numpy.ones((self.__height, self.__width)) - 3*self.__u0 + 4.5*self.__u0**2 - 1.5*self.__u0**2) self.nNE = self.__w5_8 * (numpy.ones((self.__height, self.__width)) + 3*self.__u0 + 4.5*self.__u0**2 - 1.5*self.__u0**2) self.nSE = self.__w5_8 * (numpy.ones((self.__height, self.__width)) + 3*self.__u0 + 4.5*self.__u0**2 - 1.5*self.__u0**2) self.nNW = self.__w5_8 * (numpy.ones((self.__height, self.__width)) - 3*self.__u0 + 4.5*self.__u0**2 - 1.5*self.__u0**2) self.nSW = self.__w5_8 * (numpy.ones((self.__height, self.__width)) - 3*self.__u0 + 4.5*self.__u0**2 - 1.5*self.__u0**2) # macroscopic properties self.rho = self.n0 + self.nN + self.nS + self.nE + \ self.nW + self.nNE + self.nSE + self.nNW + self.nSW self.ux = (self.nE + self.nNE + self.nSE - self.nW - self.nNW - self.nSW) / self.rho self.uy = (self.nN + self.nNE + self.nNW - self.nS - self.nSE - self.nSW) / self.rho # object initialization, true where there is part of the object #self.obj = self.obj_mesh2D.Mesh() self.objN = numpy.roll(self.obj, 1, axis=0) self.objS = numpy.roll(self.obj, -1, axis=0) self.objE = numpy.roll(self.obj, 1, axis=1) self.objW = numpy.roll(self.obj, -1, axis=1) self.objNE = numpy.roll(self.objN, 1, axis=1) self.objNW = numpy.roll(self.objN, -1, axis=1) self.objSE = numpy.roll(self.objS, 1, axis=1) self.objSW = numpy.roll(self.objS, -1, axis=1) @property def viscosity(self): return self.__viscosity @viscosity.setter def viscosity(self, value: float): if value > 0: self.__viscosity = value self.omega = 1 / (3*self.__viscosity + 0.5) else: raise ValueError @property def u0(self): return self.__u0 @u0.setter def u0(self, value: float): self.__u0 = value def stream(self): # axis 0 is north-south; + direction is north self.nN = numpy.roll(self.nN, 1, axis=0) self.nNE = numpy.roll(self.nNE, 1, axis=0) self.nNW = numpy.roll(self.nNW, 1, axis=0) self.nS = numpy.roll(self.nS, -1, axis=0) self.nSE = numpy.roll(self.nSE, -1, axis=0) self.nSW = numpy.roll(self.nSW, -1, axis=0) # axis 1 is east-west; + direction is east self.nE = numpy.roll(self.nE, 1, axis=1) self.nNE = numpy.roll(self.nNE, 1, axis=1) self.nSE = numpy.roll(self.nSE, 1, axis=1) self.nW = numpy.roll(self.nW, -1, axis=1) self.nNW = numpy.roll(self.nNW, -1, axis=1) self.nSW = numpy.roll(self.nSW, -1, axis=1) # Use tricky boolean arrays to handle obj collisions (bounce-back): self.nN[self.objN] = self.nS[self.obj] self.nS[self.objS] = self.nN[self.obj] self.nE[self.objE] = self.nW[self.obj] self.nW[self.objW] = self.nE[self.obj] self.nNE[self.objNE] = self.nSW[self.obj] self.nNW[self.objNW] = self.nSE[self.obj] self.nSE[self.objSE] = self.nNW[self.obj] self.nSW[self.objSW] = self.nNE[self.obj] #height_origin, width_origin = self.obj_mesh2D.Mesh self.Fx = sum(sum((self.nE + self.nNE + self.nSE - self.nW - self.nNW - self.nSW)*self.obj)) self.Fy = sum(sum((self.nN + self.nNE + self.nNW - self.nS - self.nSE - self.nSW)*self.obj)) def collide(self): self.rho = self.n0 + self.nN + self.nS + self.nE + \ self.nW + self.nNE + self.nSE + self.nNW + self.nSW self.ux = (self.nE + self.nNE + self.nSE - self.nW - self.nNW - self.nSW) / self.rho self.uy = (self.nN + self.nNE + self.nNW - self.nS - self.nSE - self.nSW) / self.rho ux2 = self.ux**2 uy2 = self.uy**2 u2 = ux2 + uy2 omu215 = 1 - 1.5*u2 uxuy = self.ux * self.uy self.n0 = (1-self.omega)*self.n0 + self.omega * \ self.__w0 * self.rho * omu215 self.nN = (1-self.omega)*self.nN + self.omega * \ self.__w1_4 * self.rho * (omu215 + 3*self.uy + 4.5*uy2) self.nS = (1-self.omega)*self.nS + self.omega * \ self.__w1_4 * self.rho * (omu215 - 3*self.uy + 4.5*uy2) self.nE = (1-self.omega)*self.nE + self.omega * \ self.__w1_4 * self.rho * (omu215 + 3*self.ux + 4.5*ux2) self.nW = (1-self.omega)*self.nW + self.omega * \ self.__w1_4 * self.rho * (omu215 - 3*self.ux + 4.5*ux2) self.nNE = (1-self.omega)*self.nNE + self.omega * self.__w5_8 * \ self.rho * (omu215 + 3*(self.ux+self.uy) + 4.5*(u2+2*uxuy)) self.nNW = (1-self.omega)*self.nNW + self.omega * self.__w5_8 * \ self.rho * (omu215 + 3*(-self.ux+self.uy) + 4.5*(u2-2*uxuy)) self.nSE = (1-self.omega)*self.nSE + self.omega * self.__w5_8 * \ self.rho * (omu215 + 3*(self.ux-self.uy) + 4.5*(u2-2*uxuy)) self.nSW = (1-self.omega)*self.nSW + self.omega * self.__w5_8 * \ self.rho * (omu215 + 3*(-self.ux-self.uy) + 4.5*(u2+2*uxuy)) # Force steady rightward flow at ends (no need to set 0, N, and S components): self.nE[:, 0] = self.__w1_4 * \ (1 + 3*self.__u0 + 4.5*self.__u0**2 - 1.5*self.__u0**2) self.nW[:, 0] = self.__w1_4 * \ (1 - 3*self.__u0 + 4.5*self.__u0**2 - 1.5*self.__u0**2) self.nNE[:, 0] = self.__w5_8 * \ (1 + 3*self.__u0 + 4.5*self.__u0**2 - 1.5*self.__u0**2) self.nSE[:, 0] = self.__w5_8 * \ (1 + 3*self.__u0 + 4.5*self.__u0**2 - 1.5*self.__u0**2) self.nNW[:, 0] = self.__w5_8 * \ (1 - 3*self.__u0 + 4.5*self.__u0**2 - 1.5*self.__u0**2) self.nSW[:, 0] = self.__w5_8 * \ (1 - 3*self.__u0 + 4.5*self.__u0**2 - 1.5*self.__u0**2) def curl(self): return numpy.roll(self.uy, -1, axis=1) - numpy.roll(self.uy, 1, axis=1) - numpy.roll(self.ux, -1, axis=0) + numpy.roll(self.ux, 1, axis=0) if __name__ == "__main__": obj = MeshImage( '/image/circulo3.png') print(type(obj.shape())) simu = FluidSimulation2D(obj) simu.initialize() for i in range(50): simu.stream() simu.collide() ux2 = simu.ux uy2 = simu.uy
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script/download_pretrained.py
cttsai1985/google-quest-qa-labeling-pipeline
2
22816
<filename>script/download_pretrained.py """ fork THIS excellent downloader https://www.kaggle.com/maroberti/transformers-model-downloader-pytorch-tf2-0 """ from typing import Union from pathlib import Path import os import transformers from transformers import AutoConfig, AutoTokenizer, TFAutoModel def transformers_model_dowloader(pretrained_model_name: str, working_dir: Union[str, Path], is_tf: bool = True) -> bool: model_class = None if is_tf: model_class = TFAutoModel NEW_DIR = working_dir / pretrained_model_name try: os.mkdir(NEW_DIR) print(f"Successfully created directory {NEW_DIR}") except OSError: print(f"Creation of directory {NEW_DIR} failed") print(f"Download model and tokenizer {pretrained_model_name}") transformer_model = model_class.from_pretrained(pretrained_model_name) transformer_tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name) try: transformer_model.save_pretrained(NEW_DIR) transformer_tokenizer.save_pretrained(NEW_DIR) print(f"Save model and tokenizer {pretrained_model_name} in directory {NEW_DIR}") except: print(f"Save model and tokenizer {pretrained_model_name} in directory {NEW_DIR}: Failed") return False return True def main(): pretrained_model_name_list = [ 'bert-base-uncased', 'bert-base-cased', 'bert-large-cased', 'distilbert-base-uncased', 'albert-xxlarge-v2', 'albert-xlarge-v2', 'albert-large-v2', 'roberta-base', 'roberta-large', 'roberta-large-mnli', 'distilroberta-base', 'distilbert-base-uncased', ] print(f'Transformers version {transformers.__version__}') # Current version: 2.3.0 WORKING_DIR = Path("../input/hugging_face_pretrained") try: os.mkdir(WORKING_DIR) except: pass for i, pretrained_model_name in enumerate(pretrained_model_name_list, start=1): print(i, '/', len(pretrained_model_name_list)) transformers_model_dowloader(pretrained_model_name, WORKING_DIR, is_tf=True) return if "__main__" == __name__: main()
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drf_signed_auth/compat.py
marcgibbons/drf_signed_auth
11
125249
# pylint: disable=W0611 try: from django.urls import reverse except ImportError: from django.core.urlresolvers import reverse try: from unittest import mock except ImportError: import mock try: from importlib import reload except ImportError: reload = reload
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codewars/range-extraction.py
austinmccalley/challenge-sites
0
104252
<reponame>austinmccalley/challenge-sites def check_next_num(inp, j): for i in range(j, len(inp)): elm = inp[i] if len(inp) > i+1: if inp[i+1] == (elm+1): check_next_num(inp, i+1) else: if j == 0: return i return i else: return len(inp)-1 def solution(inp): """ 1. Sort inp 2. Check to see if next in inp slist is the next value """ inp.sort() ranges = [] res = [] for i in range(len(inp)): l = check_next_num(inp, i) ranges.append([i, l]) print([i, l]) seen = [] for r in ranges: si = r[0] ei = r[1] sv = inp[si] ev = inp[ei] if ei not in seen: if ev == sv: res.append(str(ev)) seen.append(ei) if (ev - sv) >= 2: s = str(sv)+'-'+str(ev) res.append(s) seen.append(ei) elif (ev-sv) == 1: res.append(str(sv)) return (",".join(res)) print(ranges) #Indexs 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 solution([-6,-3,-2,-1,0,1,3,4,5,7,8, 9, 10,11,14,15,17,18,19,20]) """ [0,0], [1,5], [6,13], [14,15], [17, 19] [[0, 0], [1, 5], [2, 5], [3, 5], [4, 5], [5, 5], [6, 8], [7, 8], [8, 8], [9, 13], [10, 13], [11, 13], [12, 13], [13, 13], [14, 15], [15, 15], [16, 19] -6,-3-1,3-5,7-11,14,15,17-20 -6,-3-1,3-5,7-11,14,15,17-20 -6,-3-1,3-5,7-11,14,15,17-20 """ #Indexes 0 1 2 3 4 5 6 7 8 9 # solution([-3,-2,-1,2,10,15,16,18,19,20]) """ [0, 2], [3, 3], [4, 4], [5, 5], [6, 6], [7, 9] [[0, 2], [3, 3], [4, 4], [5, 6], [6, 6], [7, 9]] [[0, 0], [1, 2], [2, 2], [3, 3], [4, 4], [5, 6], [6, 6], [7, 9], [8, 9], [9, 9]] -3--1,2,10,15,16,18-20 -3--1,2,10,15,16,18-20 -3,-2,-1,2,10,15,16,18-20 """
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src/core/schemas/base.py
nefarius/portfolio-backend
6
1617046
<filename>src/core/schemas/base.py import json import logging from marshmallow import Schema, post_load from django.utils.translation import get_language from ..skosmos import get_preflabel logger = logging.getLogger(__name__) class GenericModel: def __init__(self, schema, **kwargs): self.__schema__ = schema self.metadata = {} for attr in schema.declared_fields: self.metadata[attr] = schema.declared_fields[attr].metadata if hasattr(schema.declared_fields[attr], 'many'): setattr(self, attr, []) else: setattr(self, attr, None) for k, v in kwargs.items(): if k in schema.declared_fields: setattr(self, k, v) def __repr__(self): return self.to_json() def to_dict(self): out = {} for key in self.__schema__.declared_fields: v = getattr(self, key) if isinstance(v, GenericModel): out[key] = v.to_dict() elif isinstance(v, list): out[key] = [x.to_dict() for x in v] if v and isinstance(v[0], GenericModel) else v elif isinstance(v, (int, float)) or v is None: out[key] = v else: out[key] = str(v) return out def to_json(self): return json.dumps(self.to_dict()) def to_display(self): out = [] for key in self.__schema__.declared_fields: v = getattr(self, key) if v: metadata = getattr(self, 'metadata').get(key) label = metadata.get('title') if isinstance(v, GenericModel): value = v.to_display() elif isinstance(v, list): kwargs = {'roles': True} if key == 'contributors' else {} value = [x.to_display(**kwargs) for x in v] if v and isinstance(v[0], GenericModel) else v elif isinstance(v, (int, float)): value = v else: value = str(v) if value: out.append({'label': label, 'value': value}) return out class BaseSchema(Schema): __model__ = GenericModel def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.contributors_fields = [] self.date_fields = [] self.locations_fields = [] for fld in self.declared_fields: if ( fld == 'contributors' or self.declared_fields[fld].metadata.get('x-attrs', {}).get('equivalent') == 'contributors' ): self.contributors_fields.append(fld) else: if 'location' in fld: self.locations_fields.append(fld) if 'date' in fld: self.date_fields.append(fld) @post_load def create_object(self, data): return self.__model__(self, **data) def get_model(self, data): return self.load(data).data def data_display(self, data): m = self.get_model(data) return m.to_display() def role_display(self, data, user_source): lang = get_language() or 'en' roles = [] for fld in self.contributors_fields: if data.get(fld): for c in data[fld]: if c.get('source') == user_source and c.get('roles'): for r in c['roles']: try: roles.append(r.get('label').get(lang)) except AttributeError: logger.warning(f'Missing label for role {r}') label = get_preflabel(r.get('source').split('/')[-1]) if label: roles.append(label) if roles: return ', '.join(sorted(set(roles))) def location_display(self, data): locations = [] for fld in self.locations_fields: if data.get(fld): if fld == 'location': for loc in data[fld]: if loc.get('label'): locations.append(loc['label']) else: for o in data[fld]: if o.get('location'): for loc in o['location']: if loc.get('label'): locations.append(loc['label']) if locations: return ', '.join(sorted(set(locations))) def year_display(self, data): return None
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manage.py
sergiosuarez/MapTweets
0
165924
#!/usr/bin/env python import os import sys from twit.wsgi import application WSGI_APPLICATION = 'twit.wsgi.application' if __name__ == "__main__": os.environ.setdefault("DJANGO_SETTINGS_MODULE", "twit.settings") from django.core.management import execute_from_command_line execute_from_command_line(sys.argv)
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namePlotter.py
partofthething/nameAnalysis
0
54742
<reponame>partofthething/nameAnalysis """ Scans text for first names and prints out most common ones as document goes on Useful for analyzing journals or whatever. """ # controls NUM_FILE_SEGMENTS = 25 NUM_TOP_NAMES = 8 # consts FEMALE = 0 MALE = 1 EITHER = 2 def read_names(filename): """ read potential first names from data library Get names from, for example, http://www.outpost9.com/files/WordLists.html See http://stackoverflow.com/questions/1803628/raw-list-of-person-names for other options. """ names = [] with open(filename) as f: for name in f: name = name.strip().lower() if len(name) > 2: names.append(name) return names def scan_for_names(data_file_name, names): """ look through text file for names """ with open(data_file_name) as f: num_words = 0 names_found = [] for line in f: for word in line.split(): num_words += 1 word = process(word) if word in names: names_found.append((word, num_words)) # print word # hack to prevent ann from "ann arbor" from showing up if word == 'arbor' and names_found[-1][0] == 'ann': names_found.pop() return names_found def process(word): """ Mangle a word and gets rid of contractions so "John's" shows up as "john" """ word = word.lower() if "'" in word and word not in ["don't", "haven't"]: word = word.split("'")[0] return word def rank(name_results): """ process the names found and print out the top few. """ for names in chunk(name_results, NUM_FILE_SEGMENTS): names_here, _word_num_here = zip(*names) counts = [] for name in uniquify(names_here): counts.append((names_here.count(name), name)) counts.sort() counts.reverse() print ''.join(['{0:10s}'.format(name) for _count, name in counts[:NUM_TOP_NAMES]]) def uniquify(iterable): """ Make unique list while preserving order. """ unique = [] for entry in iterable: if entry not in unique: unique.append(entry) return unique def chunk(iterable, num_chunks, fillvalue=(None, None)): """break list into num_chunks chunks. Ugly. Stolen from the web somewhere. """ num = float(len(iterable)) / num_chunks chunks = [iterable[i:i + int(num)] for i in range(0, (num_chunks - 1) * int(num), int(num))] chunks.append(iterable[(num_chunks - 1) * int(num):]) return chunks def make_name_data(chicks, dudes): """ make convenient data structure about names. It separates out men and women in case future development wants to plot them in different colors or something. """ names = {} for name in chicks: names[name] = FEMALE for name in dudes: if name in names: names[name] = EITHER else: names[name] = MALE return names if __name__ == '__main__': chicks = read_names('female-names') dudes = read_names('male-names') name_lib = make_name_data(chicks, dudes) results = scan_for_names('data.txt', name_lib) rank(results)
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sagas/tests/test_trans_2.py
samlet/stack
3
199146
#! /usr/bin/env python # -*- coding:utf-8 -*- import urllib.request import urllib.parse import json import execjs import re import time import random from sagas.nlu.translator import display_translations class GoogleTrans(object): def __init__(self): self.url = 'https://translate.google.cn/translate_a/single' self.TKK = "434674.96463358" # 随时都有可能需要更新的TKK值 self.header = { "accept": "*/*", "accept-language": "zh-CN,zh;q=0.9", "cookie": "NID=188=<KEY>", "referer": "https://translate.google.cn/", "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.142 Safari/537.36", "x-client-data": "CJK2yQEIpLbJAQjEtskBCKmdygEIqKPKAQi5pcoBCLGnygEI4qjKAQjxqcoBCJetygEIza3KAQ==", } self.data = { "client": "webapp", # 基于网页访问服务器 "sl": "auto", # 源语言,auto表示由谷歌自动识别 "tl": "en", # 翻译的目标语言 "hl": "zh-CN", # 界面语言选中文，毕竟URL都是cn后缀了，就不装美国人了 "dt": ["at", "bd", "ex", "ld", "md", "qca", "rw", "rm", "ss", "t"], # dt表示要求服务器返回的数据类型 "otf": "2", "ssel": "0", "tsel": "0", "kc": "1", "tk": "", # 谷歌服务器会核对的token "q": "" # 待翻译的字符串 } with open('token.js', 'r', encoding='utf-8') as f: self.js_fun = execjs.compile(f.read()) def update_TKK(self): url = "https://translate.google.cn/" req = urllib.request.Request(url=url, headers=self.header) page_source = urllib.request.urlopen(req).read().decode("utf-8") self.TKK = re.findall(r"tkk:'([0-9]+\.[0-9]+)'", page_source)[0] def construct_url(self): base = self.url + '?' for key in self.data: if isinstance(self.data[key], list): base = base + "dt=" + "&dt=".join(self.data[key]) + "&" else: base = base + key + '=' + self.data[key] + '&' base = base[:-1] return base def query(self, q, lang_from='', lang_to=''): self.data['q'] = urllib.parse.quote(q) self.data['tk'] = self.js_fun.call('wo', q, self.TKK) if lang_from: self.data['sl'] = lang_from if lang_to: self.data['tl'] = lang_to url = self.construct_url() req = urllib.request.Request(url=url, headers=self.header) response = json.loads(urllib.request.urlopen(req).read().decode("utf-8")) print(response[0][0][0]) if response[1] is not None: print('.. translations for word') # print(r[1]) display_translations(response[1]) class TransCli(object): def tests(self): """ $ ./test_trans_2.py tests :return: """ googletrans = GoogleTrans() googletrans.update_TKK() # 构建完对象以后要同步更新一下TKK值 time.sleep(random.uniform(0.05, 0.20)) text = 'يىغلىمايۋاتىدۇ' googletrans.query(text, 'ug', 'en') # 默认的目标语言是英语，如果要翻译到其他语言请自行修改相应参数 time.sleep(random.uniform(0.05, 0.20)) # googletrans = GoogleTrans() googletrans.update_TKK() time.sleep(random.uniform(0.05, 0.20)) googletrans.query('你好', 'zh', 'en') def trans(self, sents, lang): """ $ ./test_trans_2.py trans 'Ontem nós fizemos almoço na minha padaria.' pt $ ./test_trans_2.py trans 'يىغلىمايۋاتىدۇ' ug :param sents: :param lang: :return: """ from urllib.error import URLError for n in range(3): try: googletrans = GoogleTrans() googletrans.update_TKK() # time.sleep(random.uniform(0.05, 0.20)) googletrans.query(sents, lang, 'en') break except URLError: print('.. wait to retry') time.sleep(random.uniform(0.05, 0.20)) continue if __name__ == '__main__': import fire fire.Fire(TransCli)
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installer/plugins/apt.py
ssbanerje/dotfiles
5
182808
<gh_stars>1-10 #!/usr/bin/env python # -*- coding: utf-8 -*- import os import subprocess import dotbot class Apt(dotbot.Plugin): _updateDirective = 'aptupdate' _aptDirective = 'apt' def __init__(self, context): super(Apt, self).__init__(context) self._sup_stdout = False if context.options().verbose > 0 else True self._sup_stderr = False def can_handle(self, directive): return directive in [self._aptDirective, self._updateDirective] def handle(self, directive, data): if directive == self._updateDirective: return self._run_update() elif directive == self._aptDirective: return self._install_packages(data) else: raise ValueError('Cannot handle this directive %s' % directive) def _run_update(self): self._log.info('Updating list of packages') return self._run_command('apt update', True, False) == 0 def _install(self, p): self._log.info('Installing %s' % p) res = self._run_command('apt install --yes ' + p, self._sup_stdout, self._sup_stderr) if res != 0: self._log.warning('Could not install %s' % p) return False else: return True def _install_packages(self, data): pre_commands = [] post_commands = [] packages = [] for p in data: if isinstance(p, str): packages.append(p) elif isinstance(p, dict): n, o = list(p.items())[0] packages.append(n) try: for c in o['shell_before']: pre_commands.append(c) except KeyError: pass try: for c in o['shell_after']: post_commands.append(c) except KeyError: pass pkgs = ' '.join(packages) self._log.info('Running pre-installation commands') res = True for c in pre_commands: self._log.debug('Running pre-installation command: ' + c) res = res and (self._run_command( c, self._sup_stdout, self._sup_stderr) == 0) if not res: self._log.error('Could not run command: ' + c) return False self._log.debug('Installing packages: ' + pkgs) res = res and self._install(pkgs) if not res: self._log.error('Could not install packages: ' + pkgs) return False for c in post_commands: self._log.debug('Running post-installation command: ' + c) res = res and (self._run_command( c, self._sup_stdout, self._sup_stderr) == 0) if not res: self._log.error('Could not run command: ' + c) return False return res def _run_command(self, cmd, sup_stdout=True, sup_stderr=False): cmd_prefix = 'sudo -E ' if os.geteuid() != 0 else '' stdout = subprocess.DEVNULL if sup_stdout else None stderr = subprocess.DEVNULL if sup_stderr else None cwd = self._context.base_directory() self._log.debug('Running command: ' + cmd) return subprocess.call(cmd_prefix + cmd, stdout=stdout, stderr=stderr, shell=True, cwd=cwd)
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second_neural_network/generator_second_neural_network.py
ADCenterNetwork/discern-fmk
0
137753
import numpy import os import torch as torch import torch.nn as nn import torch.nn.functional as F from utils.node_object_creator import * from layers.coding_layer import Coding_layer from layers.convolutional_layer import Convolutional_layer from layers.pooling_layer import Pooling_layer from layers.dynamic_pooling import Max_pooling_layer, Dynamic_pooling_layer from layers.hidden_layer import Hidden_layer from second_neural_network.second_neural_network import SecondNeuralNetwork from utils.utils import writer, plot_confusion_matrix, conf_matrix, accuracy, bad_predicted_files class Generator_second_neural_network(SecondNeuralNetwork): def __init__(self, device, pattern, n = 20, m = 4, pooling = 'one-way pooling'): super().__init__(device, n, m) self.pooling = pooling self.pattern = pattern def matrices_and_layers_initialization(self): # Initialize the layers self.conv = Convolutional_layer(self.vector_size, self.device, features_size = self.feature_size) self.hidden = Hidden_layer(self.feature_size) if self.pooling == 'three-way pooling': self.dynamic = Dynamic_pooling_layer() self.max_pool = Max_pooling_layer() else: self.pooling = Pooling_layer() # Initialize matrices and bias self.w_t, self.w_l, self.w_r, self.b_conv = self.conv.initialize_matrices_and_bias() if self.pooling == 'three-way pooling': self.w_hidden, self.b_hidden = self.hidden.initialize_matrices_and_bias() else: self.w_hidden, self.b_hidden = self.hidden.initialize_matrices_and_bias() params = [self.w_t, self.w_l, self.w_r, self.b_conv, self.w_hidden, self.b_hidden] return params def layers(self, vector_representation_params): # Parameters of the first neural network ls_nodes, w_l_code, w_r_code, b_code = vector_representation_params del w_l_code del w_r_code del b_code ls_nodes = self.conv.convolutional_layer(ls_nodes) if self.pooling == 'three-way pooling': dict_sibling = None self.max_pool.max_pooling(ls_nodes) vector = self.dynamic.three_way_pooling(ls_nodes, dict_sibling) else: vector = self.pooling.pooling_layer(ls_nodes) del ls_nodes output = self.hidden.hidden_layer(vector) del vector return output def save(self): '''Save all the trained parameters into a csv file''' directory = os.path.join('params', self.pattern) if not os.path.exists(directory): os.mkdir(directory) # save w_t into csv file w_t = self.w_t.detach().numpy() numpy.savetxt(os.path.join(directory, "w_t.csv"), w_t, delimiter = ",") # save w_l into csv file w_l = self.w_l.detach().numpy() numpy.savetxt(os.path.join(directory, "w_l.csv"), w_l, delimiter = ",") # save w_r into csv file w_r = self.w_r.detach().numpy() numpy.savetxt(os.path.join(directory, "w_r.csv"), w_r, delimiter = ",") # save b_conv into csv file b_conv = self.b_conv.detach().numpy() numpy.savetxt(os.path.join(directory, "b_conv.csv"), b_conv, delimiter = ",") # save w_hidden into csv file w_hidden = self.w_hidden.detach().numpy() numpy.savetxt(os.path.join(directory, "w_hidden.csv"), w_hidden, delimiter = ",") # save b_conv into csv file b_hidden = self.b_hidden.detach().numpy() numpy.savetxt(os.path.join(directory, "b_hidden.csv"), b_hidden, delimiter = ",")
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tests/examples/minlplib/waterno2_03.py
ouyang-w-19/decogo
2
23511
<reponame>ouyang-w-19/decogo # MINLP written by GAMS Convert at 04/21/18 13:55:18 # # Equation counts # Total E G L N X C B # 617 367 103 147 0 0 0 0 # # Variable counts # x b i s1s s2s sc si # Total cont binary integer sos1 sos2 scont sint # 499 472 27 0 0 0 0 0 # FX 6 6 0 0 0 0 0 0 # # Nonzero counts # Total const NL DLL # 1636 1333 303 0 # # Reformulation has removed 1 variable and 1 equation from pyomo.environ import * model = m = ConcreteModel() m.b2 = Var(within=Binary,bounds=(0,1),initialize=0) m.b3 = Var(within=Binary,bounds=(0,1),initialize=0) m.b4 = Var(within=Binary,bounds=(0,1),initialize=0) m.b5 = Var(within=Binary,bounds=(0,1),initialize=0) m.b6 = Var(within=Binary,bounds=(0,1),initialize=0) m.b7 = Var(within=Binary,bounds=(0,1),initialize=0) m.b8 = Var(within=Binary,bounds=(0,1),initialize=0) m.b9 = Var(within=Binary,bounds=(0,1),initialize=0) m.b10 = Var(within=Binary,bounds=(0,1),initialize=0) m.b11 = Var(within=Binary,bounds=(0,1),initialize=0) m.b12 = Var(within=Binary,bounds=(0,1),initialize=0) m.b13 = Var(within=Binary,bounds=(0,1),initialize=0) m.b14 = Var(within=Binary,bounds=(0,1),initialize=0) m.b15 = Var(within=Binary,bounds=(0,1),initialize=0) m.b16 = Var(within=Binary,bounds=(0,1),initialize=0) m.b17 = Var(within=Binary,bounds=(0,1),initialize=0) m.b18 = Var(within=Binary,bounds=(0,1),initialize=0) m.b19 = Var(within=Binary,bounds=(0,1),initialize=0) m.b20 = Var(within=Binary,bounds=(0,1),initialize=0) m.b21 = Var(within=Binary,bounds=(0,1),initialize=0) m.b22 = Var(within=Binary,bounds=(0,1),initialize=0) m.b23 = Var(within=Binary,bounds=(0,1),initialize=0) m.b24 = Var(within=Binary,bounds=(0,1),initialize=0) m.b25 = Var(within=Binary,bounds=(0,1),initialize=0) m.b26 = Var(within=Binary,bounds=(0,1),initialize=0) m.b27 = Var(within=Binary,bounds=(0,1),initialize=0) m.b28 = Var(within=Binary,bounds=(0,1),initialize=0) m.x29 = Var(within=Reals,bounds=(-1000,1000),initialize=0) m.x30 = Var(within=Reals,bounds=(None,None),initialize=0) m.x31 = Var(within=Reals,bounds=(None,None),initialize=0) m.x32 = Var(within=Reals,bounds=(-1000,1000),initialize=0) m.x33 = Var(within=Reals,bounds=(None,None),initialize=0) m.x34 = Var(within=Reals,bounds=(None,None),initialize=0) m.x35 = Var(within=Reals,bounds=(None,None),initialize=0) m.x36 = Var(within=Reals,bounds=(-1000,1000),initialize=0) m.x37 = Var(within=Reals,bounds=(None,None),initialize=0) m.x38 = Var(within=Reals,bounds=(None,None),initialize=0) m.x39 = Var(within=Reals,bounds=(None,None),initialize=0) m.x40 = Var(within=Reals,bounds=(-1000,1000),initialize=0) m.x41 = Var(within=Reals,bounds=(None,None),initialize=0) m.x42 = Var(within=Reals,bounds=(None,None),initialize=0) m.x43 = Var(within=Reals,bounds=(None,None),initialize=0) m.x44 = Var(within=Reals,bounds=(-1000,1000),initialize=0) m.x45 = Var(within=Reals,bounds=(None,None),initialize=0) m.x46 = Var(within=Reals,bounds=(None,None),initialize=0) m.x47 = Var(within=Reals,bounds=(-1000,1000),initialize=0) m.x48 = Var(within=Reals,bounds=(None,None),initialize=0) m.x49 = Var(within=Reals,bounds=(None,None),initialize=0) m.x50 = Var(within=Reals,bounds=(-1000,1000),initialize=0) m.x51 = Var(within=Reals,bounds=(None,None),initialize=0) m.x52 = Var(within=Reals,bounds=(None,None),initialize=0) m.x53 = Var(within=Reals,bounds=(-1000,1000),initialize=0) m.x54 = Var(within=Reals,bounds=(None,None),initialize=0) m.x55 = Var(within=Reals,bounds=(None,None),initialize=0) m.x56 = Var(within=Reals,bounds=(None,None),initialize=0) m.x57 = Var(within=Reals,bounds=(-1000,1000),initialize=0) m.x58 = Var(within=Reals,bounds=(None,None),initialize=0) m.x59 = Var(within=Reals,bounds=(None,None),initialize=0) m.x60 = Var(within=Reals,bounds=(None,None),initialize=0) m.x61 = Var(within=Reals,bounds=(-1000,1000),initialize=0) m.x62 = Var(within=Reals,bounds=(None,None),initialize=0) m.x63 = Var(within=Reals,bounds=(None,None),initialize=0) m.x64 = Var(within=Reals,bounds=(None,None),initialize=0) m.x65 = Var(within=Reals,bounds=(-1000,1000),initialize=0) m.x66 = Var(within=Reals,bounds=(None,None),initialize=0) m.x67 = Var(within=Reals,bounds=(None,None),initialize=0) m.x68 = Var(within=Reals,bounds=(-1000,1000),initialize=0) m.x69 = Var(within=Reals,bounds=(None,None),initialize=0) m.x70 = Var(within=Reals,bounds=(None,None),initialize=0) m.x71 = Var(within=Reals,bounds=(-1000,1000),initialize=0) m.x72 = Var(within=Reals,bounds=(None,None),initialize=0) m.x73 = Var(within=Reals,bounds=(None,None),initialize=0) m.x74 = Var(within=Reals,bounds=(-1000,1000),initialize=0) m.x75 = Var(within=Reals,bounds=(None,None),initialize=0) m.x76 = Var(within=Reals,bounds=(None,None),initialize=0) m.x77 = Var(within=Reals,bounds=(None,None),initialize=0) m.x78 = Var(within=Reals,bounds=(-1000,1000),initialize=0) m.x79 = Var(within=Reals,bounds=(None,None),initialize=0) m.x80 = Var(within=Reals,bounds=(None,None),initialize=0) m.x81 = Var(within=Reals,bounds=(None,None),initialize=0) m.x82 = Var(within=Reals,bounds=(-1000,1000),initialize=0) m.x83 = Var(within=Reals,bounds=(None,None),initialize=0) m.x84 = Var(within=Reals,bounds=(None,None),initialize=0) m.x85 = Var(within=Reals,bounds=(None,None),initialize=0) m.x86 = Var(within=Reals,bounds=(-1000,1000),initialize=0) m.x87 = Var(within=Reals,bounds=(None,None),initialize=0) m.x88 = Var(within=Reals,bounds=(None,None),initialize=0) m.x89 = Var(within=Reals,bounds=(-1000,1000),initialize=0) m.x90 = Var(within=Reals,bounds=(None,None),initialize=0) m.x91 = Var(within=Reals,bounds=(None,None),initialize=0) m.x92 = Var(within=Reals,bounds=(-1000,1000),initialize=0) m.x93 = Var(within=Reals,bounds=(None,None),initialize=0) m.x94 = Var(within=Reals,bounds=(None,None),initialize=0) m.x95 = Var(within=Reals,bounds=(0,5),initialize=0) m.x96 = Var(within=Reals,bounds=(0,5),initialize=0) m.x97 = Var(within=Reals,bounds=(0,5),initialize=0) m.x98 = Var(within=Reals,bounds=(0,5),initialize=0) m.x99 = Var(within=Reals,bounds=(0,2.4),initialize=0) m.x100 = Var(within=Reals,bounds=(0,5),initialize=0) m.x101 = Var(within=Reals,bounds=(0,2.4),initialize=0) m.x102 = Var(within=Reals,bounds=(0,5),initialize=0) m.x103 = Var(within=Reals,bounds=(0,2.4),initialize=0) m.x104 = Var(within=Reals,bounds=(0,5),initialize=0) m.x105 = Var(within=Reals,bounds=(0,2.4),initialize=0) m.x106 = Var(within=Reals,bounds=(0,5),initialize=0) m.x107 = Var(within=Reals,bounds=(0,2.4),initialize=0) m.x108 = Var(within=Reals,bounds=(0,5),initialize=0) m.x109 = Var(within=Reals,bounds=(0,2.4),initialize=0) m.x110 = Var(within=Reals,bounds=(0,5),initialize=0) m.x111 = Var(within=Reals,bounds=(0,5),initialize=0) m.x112 = Var(within=Reals,bounds=(0,5),initialize=0) m.x113 = Var(within=Reals,bounds=(0,5),initialize=0) m.x114 = Var(within=Reals,bounds=(0,1.16),initialize=0) m.x115 = Var(within=Reals,bounds=(0,5),initialize=0) m.x116 = Var(within=Reals,bounds=(0,1.16),initialize=0) m.x117 = Var(within=Reals,bounds=(0,5),initialize=0) m.x118 = Var(within=Reals,bounds=(0,1.16),initialize=0) m.x119 = Var(within=Reals,bounds=(0,5),initialize=0) m.x120 = Var(within=Reals,bounds=(0,5),initialize=0) m.x121 = Var(within=Reals,bounds=(0,5),initialize=0) m.x122 = Var(within=Reals,bounds=(3.5,3.5),initialize=3.5) m.x123 = Var(within=Reals,bounds=(2,5),initialize=2) m.x124 = Var(within=Reals,bounds=(2,5),initialize=2) m.x125 = Var(within=Reals,bounds=(2,5),initialize=2) m.x126 = Var(within=Reals,bounds=(2,5),initialize=2) m.x127 = Var(within=Reals,bounds=(2,5),initialize=2) m.x128 = Var(within=Reals,bounds=(4.1,4.1),initialize=4.1) m.x129 = Var(within=Reals,bounds=(2.5,5),initialize=2.5) m.x130 = Var(within=Reals,bounds=(2.5,5),initialize=2.5) m.x131 = Var(within=Reals,bounds=(2.5,5),initialize=2.5) m.x132 = Var(within=Reals,bounds=(2.5,5),initialize=2.5) m.x133 = Var(within=Reals,bounds=(2.5,5),initialize=2.5) m.x134 = Var(within=Reals,bounds=(4,4),initialize=4) m.x135 = Var(within=Reals,bounds=(2,6),initialize=2) m.x136 = Var(within=Reals,bounds=(2,6),initialize=2) m.x137 = Var(within=Reals,bounds=(2,6),initialize=2) m.x138 = Var(within=Reals,bounds=(2,6),initialize=2) m.x139 = Var(within=Reals,bounds=(2,6),initialize=2) m.x140 = Var(within=Reals,bounds=(0,0.8),initialize=0) m.x141 = Var(within=Reals,bounds=(-1000,1000),initialize=0) m.x142 = Var(within=Reals,bounds=(0,0.8),initialize=0) m.x143 = Var(within=Reals,bounds=(None,None),initialize=0) m.x144 = Var(within=Reals,bounds=(0,0.8),initialize=0) m.x145 = Var(within=Reals,bounds=(None,None),initialize=0) m.x146 = Var(within=Reals,bounds=(0,0.8),initialize=0) m.x147 = Var(within=Reals,bounds=(None,None),initialize=0) m.x148 = Var(within=Reals,bounds=(0,0.8),initialize=0) m.x149 = Var(within=Reals,bounds=(-1000,1000),initialize=0) m.x150 = Var(within=Reals,bounds=(0,0.8),initialize=0) m.x151 = Var(within=Reals,bounds=(None,None),initialize=0) m.x152 = Var(within=Reals,bounds=(0,0.8),initialize=0) m.x153 = Var(within=Reals,bounds=(None,None),initialize=0) m.x154 = Var(within=Reals,bounds=(0,0.8),initialize=0) m.x155 = Var(within=Reals,bounds=(None,None),initialize=0) m.x156 = Var(within=Reals,bounds=(0,0.8),initialize=0) m.x157 = Var(within=Reals,bounds=(-1000,1000),initialize=0) m.x158 = Var(within=Reals,bounds=(0,0.5),initialize=0) m.x159 = Var(within=Reals,bounds=(None,None),initialize=0) m.x160 = Var(within=Reals,bounds=(0,0.5),initialize=0) m.x161 = Var(within=Reals,bounds=(None,None),initialize=0) m.x162 = Var(within=Reals,bounds=(0,0.5),initialize=0) m.x163 = Var(within=Reals,bounds=(None,None),initialize=0) m.x164 = Var(within=Reals,bounds=(0,0.5),initialize=0) m.x165 = Var(within=Reals,bounds=(-1000,1000),initialize=0) m.x166 = Var(within=Reals,bounds=(0,0.5),initialize=0) m.x167 = Var(within=Reals,bounds=(None,None),initialize=0) m.x168 = Var(within=Reals,bounds=(0,0.5),initialize=0) m.x169 = Var(within=Reals,bounds=(None,None),initialize=0) m.x170 = Var(within=Reals,bounds=(0,0.7),initialize=0) m.x171 = Var(within=Reals,bounds=(-1000,1000),initialize=0) m.x172 = Var(within=Reals,bounds=(0,0.7),initialize=0) m.x173 = Var(within=Reals,bounds=(None,None),initialize=0) m.x174 = Var(within=Reals,bounds=(0,0.7),initialize=0) m.x175 = Var(within=Reals,bounds=(None,None),initialize=0) m.x176 = Var(within=Reals,bounds=(0,0.7),initialize=0) m.x177 = Var(within=Reals,bounds=(-1000,1000),initialize=0) m.x178 = Var(within=Reals,bounds=(0,0.7),initialize=0) m.x179 = Var(within=Reals,bounds=(None,None),initialize=0) m.x180 = Var(within=Reals,bounds=(0,0.7),initialize=0) m.x181 = Var(within=Reals,bounds=(None,None),initialize=0) m.x182 = Var(within=Reals,bounds=(0,0.58),initialize=0) m.x183 = Var(within=Reals,bounds=(-1000,1000),initialize=0) m.x184 = Var(within=Reals,bounds=(0,0.58),initialize=0) m.x185 = Var(within=Reals,bounds=(None,None),initialize=0) m.x186 = Var(within=Reals,bounds=(0,0.58),initialize=0) m.x187 = Var(within=Reals,bounds=(None,None),initialize=0) m.x188 = Var(within=Reals,bounds=(0,0.58),initialize=0) m.x189 = Var(within=Reals,bounds=(-1000,1000),initialize=0) m.x190 = Var(within=Reals,bounds=(0,0.58),initialize=0) m.x191 = Var(within=Reals,bounds=(None,None),initialize=0) m.x192 = Var(within=Reals,bounds=(0,0.58),initialize=0) m.x193 = Var(within=Reals,bounds=(None,None),initialize=0) m.x194 = Var(within=Reals,bounds=(62,65),initialize=62) m.x195 = Var(within=Reals,bounds=(62,65),initialize=62) m.x196 = Var(within=Reals,bounds=(62,65),initialize=62) m.x197 = Var(within=Reals,bounds=(92.5,95),initialize=92.5) m.x198 = Var(within=Reals,bounds=(92.5,95),initialize=92.5) m.x199 = Var(within=Reals,bounds=(92.5,95),initialize=92.5) m.x200 = Var(within=Reals,bounds=(105,109),initialize=105) m.x201 = Var(within=Reals,bounds=(105,109),initialize=105) m.x202 = Var(within=Reals,bounds=(105,109),initialize=105) m.x203 = Var(within=Reals,bounds=(0,1000),initialize=0) m.x204 = Var(within=Reals,bounds=(-125,125),initialize=0) m.x205 = Var(within=Reals,bounds=(0,1000),initialize=0) m.x206 = Var(within=Reals,bounds=(-125,125),initialize=0) m.x207 = Var(within=Reals,bounds=(0,1000),initialize=0) m.x208 = Var(within=Reals,bounds=(-125,125),initialize=0) m.x209 = Var(within=Reals,bounds=(0,1000),initialize=0) m.x210 = Var(within=Reals,bounds=(0,1000),initialize=0) m.x211 = Var(within=Reals,bounds=(-100,100),initialize=0) m.x212 = Var(within=Reals,bounds=(0,1000),initialize=0) m.x213 = Var(within=Reals,bounds=(0,1000),initialize=0) m.x214 = Var(within=Reals,bounds=(-100,100),initialize=0) m.x215 = Var(within=Reals,bounds=(0,1000),initialize=0) m.x216 = Var(within=Reals,bounds=(0,1000),initialize=0) m.x217 = Var(within=Reals,bounds=(-100,100),initialize=0) m.x218 = Var(within=Reals,bounds=(0,1000),initialize=0) m.x219 = Var(within=Reals,bounds=(-125,125),initialize=0) m.x220 = Var(within=Reals,bounds=(0,1000),initialize=0) m.x221 = Var(within=Reals,bounds=(-125,125),initialize=0) m.x222 = Var(within=Reals,bounds=(0,1000),initialize=0) m.x223 = Var(within=Reals,bounds=(-125,125),initialize=0) m.x224 = Var(within=Reals,bounds=(49,49),initialize=49) m.x225 = Var(within=Reals,bounds=(-49,1000),initialize=0) m.x226 = Var(within=Reals,bounds=(49,49),initialize=49) m.x227 = Var(within=Reals,bounds=(-49,1000),initialize=0) m.x228 = Var(within=Reals,bounds=(49,49),initialize=49) m.x229 = Var(within=Reals,bounds=(-49,1000),initialize=0) m.x230 = Var(within=Reals,bounds=(-65,1000),initialize=0) m.x231 = Var(within=Reals,bounds=(-65,1000),initialize=0) m.x232 = Var(within=Reals,bounds=(-65,1000),initialize=0) m.x233 = Var(within=Reals,bounds=(-95,1000),initialize=0) m.x234 = Var(within=Reals,bounds=(-95,1000),initialize=0) m.x235 = Var(within=Reals,bounds=(-95,1000),initialize=0) m.x236 = Var(within=Reals,bounds=(0.2,0.8),initialize=0.2) m.x237 = Var(within=Reals,bounds=(0.2,0.8),initialize=0.2) m.x238 = Var(within=Reals,bounds=(0.2,0.8),initialize=0.2) m.x239 = Var(within=Reals,bounds=(0.2,0.8),initialize=0.2) m.x240 = Var(within=Reals,bounds=(0.2,0.8),initialize=0.2) m.x241 = Var(within=Reals,bounds=(0.2,0.8),initialize=0.2) m.x242 = Var(within=Reals,bounds=(0.2,0.8),initialize=0.2) m.x243 = Var(within=Reals,bounds=(0.2,0.8),initialize=0.2) m.x244 = Var(within=Reals,bounds=(0.2,0.8),initialize=0.2) m.x245 = Var(within=Reals,bounds=(0.25,0.5),initialize=0.25) m.x246 = Var(within=Reals,bounds=(0.25,0.5),initialize=0.25) m.x247 = Var(within=Reals,bounds=(0.25,0.5),initialize=0.25) m.x248 = Var(within=Reals,bounds=(0.25,0.5),initialize=0.25) m.x249 = Var(within=Reals,bounds=(0.25,0.5),initialize=0.25) m.x250 = Var(within=Reals,bounds=(0.25,0.5),initialize=0.25) m.x251 = Var(within=Reals,bounds=(0.4,0.7),initialize=0.4) m.x252 = Var(within=Reals,bounds=(0.4,0.7),initialize=0.4) m.x253 = Var(within=Reals,bounds=(0.4,0.7),initialize=0.4) m.x254 = Var(within=Reals,bounds=(0.4,0.7),initialize=0.4) m.x255 = Var(within=Reals,bounds=(0.4,0.7),initialize=0.4) m.x256 = Var(within=Reals,bounds=(0.4,0.7),initialize=0.4) m.x257 = Var(within=Reals,bounds=(0.24,0.58),initialize=0.24) m.x258 = Var(within=Reals,bounds=(0.24,0.58),initialize=0.24) m.x259 = Var(within=Reals,bounds=(0.24,0.58),initialize=0.24) m.x260 = Var(within=Reals,bounds=(0.24,0.58),initialize=0.24) m.x261 = Var(within=Reals,bounds=(0.24,0.58),initialize=0.24) m.x262 = Var(within=Reals,bounds=(0.24,0.58),initialize=0.24) m.x263 = Var(within=Reals,bounds=(0.6,1),initialize=0.6) m.x264 = Var(within=Reals,bounds=(0.6,1),initialize=0.6) m.x265 = Var(within=Reals,bounds=(0.6,1),initialize=0.6) m.x266 = Var(within=Reals,bounds=(0.8,1),initialize=0.8) m.x267 = Var(within=Reals,bounds=(0.8,1),initialize=0.8) m.x268 = Var(within=Reals,bounds=(0.8,1),initialize=0.8) m.x269 = Var(within=Reals,bounds=(0.85,1),initialize=0.85) m.x270 = Var(within=Reals,bounds=(0.85,1),initialize=0.85) m.x271 = Var(within=Reals,bounds=(0.85,1),initialize=0.85) m.x272 = Var(within=Reals,bounds=(0.7,1),initialize=0.7) m.x273 = Var(within=Reals,bounds=(0.7,1),initialize=0.7) m.x274 = Var(within=Reals,bounds=(0.7,1),initialize=0.7) m.x275 = Var(within=Reals,bounds=(100,1000),initialize=100) m.x276 = Var(within=Reals,bounds=(100,1000),initialize=100) m.x277 = Var(within=Reals,bounds=(100,1000),initialize=100) m.x278 = Var(within=Reals,bounds=(0,54.1717996137183),initialize=0) m.x279 = Var(within=Reals,bounds=(None,None),initialize=0) m.x280 = Var(within=Reals,bounds=(None,None),initialize=0) m.x281 = Var(within=Reals,bounds=(None,None),initialize=0) m.x282 = Var(within=Reals,bounds=(None,None),initialize=0) m.x283 = Var(within=Reals,bounds=(0,54.1717996137183),initialize=0) m.x284 = Var(within=Reals,bounds=(None,None),initialize=0) m.x285 = Var(within=Reals,bounds=(None,None),initialize=0) m.x286 = Var(within=Reals,bounds=(None,None),initialize=0) m.x287 = Var(within=Reals,bounds=(None,None),initialize=0) m.x288 = Var(within=Reals,bounds=(0,54.1717996137183),initialize=0) m.x289 = Var(within=Reals,bounds=(None,None),initialize=0) m.x290 = Var(within=Reals,bounds=(None,None),initialize=0) m.x291 = Var(within=Reals,bounds=(None,None),initialize=0) m.x292 = Var(within=Reals,bounds=(None,None),initialize=0) m.x293 = Var(within=Reals,bounds=(0,54.1717996137183),initialize=0) m.x294 = Var(within=Reals,bounds=(None,None),initialize=0) m.x295 = Var(within=Reals,bounds=(None,None),initialize=0) m.x296 = Var(within=Reals,bounds=(None,None),initialize=0) m.x297 = Var(within=Reals,bounds=(None,None),initialize=0) m.x298 = Var(within=Reals,bounds=(0,54.1717996137183),initialize=0) m.x299 = Var(within=Reals,bounds=(None,None),initialize=0) m.x300 = Var(within=Reals,bounds=(None,None),initialize=0) m.x301 = Var(within=Reals,bounds=(None,None),initialize=0) m.x302 = Var(within=Reals,bounds=(None,None),initialize=0) m.x303 = Var(within=Reals,bounds=(0,54.1717996137183),initialize=0) m.x304 = Var(within=Reals,bounds=(None,None),initialize=0) m.x305 = Var(within=Reals,bounds=(None,None),initialize=0) m.x306 = Var(within=Reals,bounds=(None,None),initialize=0) m.x307 = Var(within=Reals,bounds=(None,None),initialize=0) m.x308 = Var(within=Reals,bounds=(0,54.1717996137183),initialize=0) m.x309 = Var(within=Reals,bounds=(None,None),initialize=0) m.x310 = Var(within=Reals,bounds=(None,None),initialize=0) m.x311 = Var(within=Reals,bounds=(None,None),initialize=0) m.x312 = Var(within=Reals,bounds=(None,None),initialize=0) m.x313 = Var(within=Reals,bounds=(0,54.1717996137183),initialize=0) m.x314 = Var(within=Reals,bounds=(None,None),initialize=0) m.x315 = Var(within=Reals,bounds=(None,None),initialize=0) m.x316 = Var(within=Reals,bounds=(None,None),initialize=0) m.x317 = Var(within=Reals,bounds=(None,None),initialize=0) m.x318 = Var(within=Reals,bounds=(0,54.1717996137183),initialize=0) m.x319 = Var(within=Reals,bounds=(None,None),initialize=0) m.x320 = Var(within=Reals,bounds=(None,None),initialize=0) m.x321 = Var(within=Reals,bounds=(None,None),initialize=0) m.x322 = Var(within=Reals,bounds=(None,None),initialize=0) m.x323 = Var(within=Reals,bounds=(0,93.045051789432),initialize=0) m.x324 = Var(within=Reals,bounds=(None,None),initialize=0) m.x325 = Var(within=Reals,bounds=(None,None),initialize=0) m.x326 = Var(within=Reals,bounds=(None,None),initialize=0) m.x327 = Var(within=Reals,bounds=(None,None),initialize=0) m.x328 = Var(within=Reals,bounds=(0,93.045051789432),initialize=0) m.x329 = Var(within=Reals,bounds=(None,None),initialize=0) m.x330 = Var(within=Reals,bounds=(None,None),initialize=0) m.x331 = Var(within=Reals,bounds=(None,None),initialize=0) m.x332 = Var(within=Reals,bounds=(None,None),initialize=0) m.x333 = Var(within=Reals,bounds=(0,93.045051789432),initialize=0) m.x334 = Var(within=Reals,bounds=(None,None),initialize=0) m.x335 = Var(within=Reals,bounds=(None,None),initialize=0) m.x336 = Var(within=Reals,bounds=(None,None),initialize=0) m.x337 = Var(within=Reals,bounds=(None,None),initialize=0) m.x338 = Var(within=Reals,bounds=(0,93.045051789432),initialize=0) m.x339 = Var(within=Reals,bounds=(None,None),initialize=0) m.x340 = Var(within=Reals,bounds=(None,None),initialize=0) m.x341 = Var(within=Reals,bounds=(None,None),initialize=0) m.x342 = Var(within=Reals,bounds=(None,None),initialize=0) m.x343 = Var(within=Reals,bounds=(None,None),initialize=0) m.x344 = Var(within=Reals,bounds=(None,None),initialize=0) m.x345 = Var(within=Reals,bounds=(0,93.045051789432),initialize=0) m.x346 = Var(within=Reals,bounds=(None,None),initialize=0) m.x347 = Var(within=Reals,bounds=(None,None),initialize=0) m.x348 = Var(within=Reals,bounds=(0,93.045051789432),initialize=0) m.x349 = Var(within=Reals,bounds=(None,None),initialize=0) m.x350 = Var(within=Reals,bounds=(None,None),initialize=0) m.x351 = Var(within=Reals,bounds=(None,None),initialize=0) m.x352 = Var(within=Reals,bounds=(None,None),initialize=0) m.x353 = Var(within=Reals,bounds=(0,112.384987749469),initialize=0) m.x354 = Var(within=Reals,bounds=(None,None),initialize=0) m.x355 = Var(within=Reals,bounds=(None,None),initialize=0) m.x356 = Var(within=Reals,bounds=(None,None),initialize=0) m.x357 = Var(within=Reals,bounds=(None,None),initialize=0) m.x358 = Var(within=Reals,bounds=(0,112.384987749469),initialize=0) m.x359 = Var(within=Reals,bounds=(None,None),initialize=0) m.x360 = Var(within=Reals,bounds=(None,None),initialize=0) m.x361 = Var(within=Reals,bounds=(None,None),initialize=0) m.x362 = Var(within=Reals,bounds=(None,None),initialize=0) m.x363 = Var(within=Reals,bounds=(0,112.384987749469),initialize=0) m.x364 = Var(within=Reals,bounds=(None,None),initialize=0) m.x365 = Var(within=Reals,bounds=(None,None),initialize=0) m.x366 = Var(within=Reals,bounds=(None,None),initialize=0) m.x367 = Var(within=Reals,bounds=(None,None),initialize=0) m.x368 = Var(within=Reals,bounds=(0,112.384987749469),initialize=0) m.x369 = Var(within=Reals,bounds=(None,None),initialize=0) m.x370 = Var(within=Reals,bounds=(None,None),initialize=0) m.x371 = Var(within=Reals,bounds=(None,None),initialize=0) m.x372 = Var(within=Reals,bounds=(None,None),initialize=0) m.x373 = Var(within=Reals,bounds=(0,112.384987749469),initialize=0) m.x374 = Var(within=Reals,bounds=(None,None),initialize=0) m.x375 = Var(within=Reals,bounds=(None,None),initialize=0) m.x376 = Var(within=Reals,bounds=(None,None),initialize=0) m.x377 = Var(within=Reals,bounds=(None,None),initialize=0) m.x378 = Var(within=Reals,bounds=(0,112.384987749469),initialize=0) m.x379 = Var(within=Reals,bounds=(None,None),initialize=0) m.x380 = Var(within=Reals,bounds=(None,None),initialize=0) m.x381 = Var(within=Reals,bounds=(None,None),initialize=0) m.x382 = Var(within=Reals,bounds=(None,None),initialize=0) m.x383 = Var(within=Reals,bounds=(0,42.066542469172),initialize=0) m.x384 = Var(within=Reals,bounds=(None,None),initialize=0) m.x385 = Var(within=Reals,bounds=(None,None),initialize=0) m.x386 = Var(within=Reals,bounds=(None,None),initialize=0) m.x387 = Var(within=Reals,bounds=(None,None),initialize=0) m.x388 = Var(within=Reals,bounds=(0,42.066542469172),initialize=0) m.x389 = Var(within=Reals,bounds=(None,None),initialize=0) m.x390 = Var(within=Reals,bounds=(None,None),initialize=0) m.x391 = Var(within=Reals,bounds=(None,None),initialize=0) m.x392 = Var(within=Reals,bounds=(None,None),initialize=0) m.x393 = Var(within=Reals,bounds=(0,42.066542469172),initialize=0) m.x394 = Var(within=Reals,bounds=(None,None),initialize=0) m.x395 = Var(within=Reals,bounds=(None,None),initialize=0) m.x396 = Var(within=Reals,bounds=(None,None),initialize=0) m.x397 = Var(within=Reals,bounds=(None,None),initialize=0) m.x398 = Var(within=Reals,bounds=(0,42.066542469172),initialize=0) m.x399 = Var(within=Reals,bounds=(None,None),initialize=0) m.x400 = Var(within=Reals,bounds=(None,None),initialize=0) m.x401 = Var(within=Reals,bounds=(None,None),initialize=0) m.x402 = Var(within=Reals,bounds=(None,None),initialize=0) m.x403 = Var(within=Reals,bounds=(0,42.066542469172),initialize=0) m.x404 = Var(within=Reals,bounds=(None,None),initialize=0) m.x405 = Var(within=Reals,bounds=(None,None),initialize=0) m.x406 = Var(within=Reals,bounds=(None,None),initialize=0) m.x407 = Var(within=Reals,bounds=(None,None),initialize=0) m.x408 = Var(within=Reals,bounds=(0,42.066542469172),initialize=0) m.x409 = Var(within=Reals,bounds=(None,None),initialize=0) m.x410 = Var(within=Reals,bounds=(None,None),initialize=0) m.x411 = Var(within=Reals,bounds=(None,None),initialize=0) m.x412 = Var(within=Reals,bounds=(None,None),initialize=0) m.x413 = Var(within=Reals,bounds=(0,25),initialize=0) m.x414 = Var(within=Reals,bounds=(0,25),initialize=0) m.x415 = Var(within=Reals,bounds=(0,25),initialize=0) m.x416 = Var(within=Reals,bounds=(0,25),initialize=0) m.x417 = Var(within=Reals,bounds=(0,25),initialize=0) m.x418 = Var(within=Reals,bounds=(0,25),initialize=0) m.x419 = Var(within=Reals,bounds=(0,25),initialize=0) m.x420 = Var(within=Reals,bounds=(0,25),initialize=0) m.x421 = Var(within=Reals,bounds=(0,25),initialize=0) m.x422 = Var(within=Reals,bounds=(0,0.64),initialize=0) m.x423 = Var(within=Reals,bounds=(0,0.512),initialize=0) m.x424 = Var(within=Reals,bounds=(0,0.64),initialize=0) m.x425 = Var(within=Reals,bounds=(0,0.512),initialize=0) m.x426 = Var(within=Reals,bounds=(0,0.64),initialize=0) m.x427 = Var(within=Reals,bounds=(0,0.512),initialize=0) m.x428 = Var(within=Reals,bounds=(0,0.64),initialize=0) m.x429 = Var(within=Reals,bounds=(0,0.512),initialize=0) m.x430 = Var(within=Reals,bounds=(0,0.64),initialize=0) m.x431 = Var(within=Reals,bounds=(0,0.512),initialize=0) m.x432 = Var(within=Reals,bounds=(0,0.64),initialize=0) m.x433 = Var(within=Reals,bounds=(0,0.512),initialize=0) m.x434 = Var(within=Reals,bounds=(0,0.64),initialize=0) m.x435 = Var(within=Reals,bounds=(0,0.512),initialize=0) m.x436 = Var(within=Reals,bounds=(0,0.64),initialize=0) m.x437 = Var(within=Reals,bounds=(0,0.512),initialize=0) m.x438 = Var(within=Reals,bounds=(0,0.64),initialize=0) m.x439 = Var(within=Reals,bounds=(0,0.512),initialize=0) m.x440 = Var(within=Reals,bounds=(0,0.25),initialize=0) m.x441 = Var(within=Reals,bounds=(0,0.125),initialize=0) m.x442 = Var(within=Reals,bounds=(0,0.25),initialize=0) m.x443 = Var(within=Reals,bounds=(0,0.125),initialize=0) m.x444 = Var(within=Reals,bounds=(0,0.25),initialize=0) m.x445 = Var(within=Reals,bounds=(0,0.125),initialize=0) m.x446 = Var(within=Reals,bounds=(0,0.25),initialize=0) m.x447 = Var(within=Reals,bounds=(0,0.125),initialize=0) m.x448 = Var(within=Reals,bounds=(0,0.25),initialize=0) m.x449 = Var(within=Reals,bounds=(0,0.125),initialize=0) m.x450 = Var(within=Reals,bounds=(0,0.25),initialize=0) m.x451 = Var(within=Reals,bounds=(0,0.125),initialize=0) m.x452 = Var(within=Reals,bounds=(0,0.49),initialize=0) m.x453 = Var(within=Reals,bounds=(0,0.343),initialize=0) m.x454 = Var(within=Reals,bounds=(0,0.49),initialize=0) m.x455 = Var(within=Reals,bounds=(0,0.343),initialize=0) m.x456 = Var(within=Reals,bounds=(0,0.49),initialize=0) m.x457 = Var(within=Reals,bounds=(0,0.343),initialize=0) m.x458 = Var(within=Reals,bounds=(0,0.49),initialize=0) m.x459 = Var(within=Reals,bounds=(0,0.343),initialize=0) m.x460 = Var(within=Reals,bounds=(0,0.49),initialize=0) m.x461 = Var(within=Reals,bounds=(0,0.343),initialize=0) m.x462 = Var(within=Reals,bounds=(0,0.49),initialize=0) m.x463 = Var(within=Reals,bounds=(0,0.343),initialize=0) m.x464 = Var(within=Reals,bounds=(0,0.3364),initialize=0) m.x465 = Var(within=Reals,bounds=(0,0.195112),initialize=0) m.x466 = Var(within=Reals,bounds=(0,0.3364),initialize=0) m.x467 = Var(within=Reals,bounds=(0,0.195112),initialize=0) m.x468 = Var(within=Reals,bounds=(0,0.3364),initialize=0) m.x469 = Var(within=Reals,bounds=(0,0.195112),initialize=0) m.x470 = Var(within=Reals,bounds=(0,0.3364),initialize=0) m.x471 = Var(within=Reals,bounds=(0,0.195112),initialize=0) m.x472 = Var(within=Reals,bounds=(0,0.3364),initialize=0) m.x473 = Var(within=Reals,bounds=(0,0.195112),initialize=0) m.x474 = Var(within=Reals,bounds=(0,0.3364),initialize=0) m.x475 = Var(within=Reals,bounds=(0,0.195112),initialize=0) m.x476 = Var(within=Reals,bounds=(0.36,1),initialize=0.36) m.x477 = Var(within=Reals,bounds=(0.216,1),initialize=0.216) m.x478 = Var(within=Reals,bounds=(0.36,1),initialize=0.36) m.x479 = Var(within=Reals,bounds=(0.216,1),initialize=0.216) m.x480 = Var(within=Reals,bounds=(0.36,1),initialize=0.36) m.x481 = Var(within=Reals,bounds=(0.216,1),initialize=0.216) m.x482 = Var(within=Reals,bounds=(0.64,1),initialize=0.64) m.x483 = Var(within=Reals,bounds=(0.512,1),initialize=0.512) m.x484 = Var(within=Reals,bounds=(0.64,1),initialize=0.64) m.x485 = Var(within=Reals,bounds=(0.512,1),initialize=0.512) m.x486 = Var(within=Reals,bounds=(0.64,1),initialize=0.64) m.x487 = Var(within=Reals,bounds=(0.512,1),initialize=0.512) m.x488 = Var(within=Reals,bounds=(0.7225,1),initialize=0.7225) m.x489 = Var(within=Reals,bounds=(0.614125,1),initialize=0.614125) m.x490 = Var(within=Reals,bounds=(0.7225,1),initialize=0.7225) m.x491 = Var(within=Reals,bounds=(0.614125,1),initialize=0.614125) m.x492 = Var(within=Reals,bounds=(0.7225,1),initialize=0.7225) m.x493 = Var(within=Reals,bounds=(0.614125,1),initialize=0.614125) m.x494 = Var(within=Reals,bounds=(0.49,1),initialize=0.49) m.x495 = Var(within=Reals,bounds=(0.343,1),initialize=0.343) m.x496 = Var(within=Reals,bounds=(0.49,1),initialize=0.49) m.x497 = Var(within=Reals,bounds=(0.343,1),initialize=0.343) m.x498 = Var(within=Reals,bounds=(0.49,1),initialize=0.49) m.x499 = Var(within=Reals,bounds=(0.343,1),initialize=0.343) m.obj = Objective(expr= m.x278 + m.x283 + m.x288 + m.x293 + m.x298 + m.x303 + m.x308 + m.x313 + m.x318 + m.x323 + m.x328 + m.x333 + m.x338 + m.x345 + m.x348 + m.x353 + m.x358 + m.x363 + m.x368 + m.x373 + m.x378 + m.x383 + m.x388 + m.x393 + m.x398 + m.x403 + m.x408, sense=minimize) m.c2 = Constraint(expr= m.x141 + 27.42831624*m.x143 + 37.5407324*m.x145 - 57.2814121*m.x147 == 0) m.c3 = Constraint(expr= m.x149 + 27.42831624*m.x151 - 57.2814121*m.x153 + 37.5407324*m.x155 == 0) m.c4 = Constraint(expr= m.x157 + 27.42831624*m.x159 - 57.2814121*m.x161 + 37.5407324*m.x163 == 0) m.c5 = Constraint(expr= - 57.2814121*m.x147 + m.x165 + 27.42831624*m.x167 + 37.5407324*m.x169 == 0) m.c6 = Constraint(expr= - 57.2814121*m.x153 + m.x171 + 37.5407324*m.x173 + 27.42831624*m.x175 == 0) m.c7 = Constraint(expr= - 57.2814121*m.x161 + m.x177 + 37.5407324*m.x179 + 27.42831624*m.x181 == 0) m.c8 = Constraint(expr= - 57.2814121*m.x147 + m.x183 + 37.5407324*m.x185 + 27.42831624*m.x187 == 0) m.c9 = Constraint(expr= - 57.2814121*m.x153 + m.x189 + 27.42831624*m.x191 + 37.5407324*m.x193 == 0) m.c10 = Constraint(expr= m.x29 + 27.42831624*m.x30 + 37.5407324*m.x31 - 57.2814121*m.x161 == 0) m.c11 = Constraint(expr= m.x32 - 76.45219958*m.x33 + 43.14087708*m.x34 + 50.37356589*m.x35 == 0) m.c12 = Constraint(expr= m.x36 + 50.37356589*m.x37 - 76.45219958*m.x38 + 43.14087708*m.x39 == 0) m.c13 = Constraint(expr= m.x40 + 43.14087708*m.x41 + 50.37356589*m.x42 - 76.45219958*m.x43 == 0) m.c14 = Constraint(expr= - 76.45219958*m.x33 + m.x44 + 43.14087708*m.x45 + 50.37356589*m.x46 == 0) m.c15 = Constraint(expr= - 76.45219958*m.x38 + m.x47 + 50.37356589*m.x48 + 43.14087708*m.x49 == 0) m.c16 = Constraint(expr= - 76.45219958*m.x43 + m.x50 + 43.14087708*m.x51 + 50.37356589*m.x52 == 0) m.c17 = Constraint(expr= m.x53 + 58.31011875*m.x54 - 69.39622571*m.x55 - 25.39911174*m.x56 == 0) m.c18 = Constraint(expr= m.x57 - 25.39911174*m.x58 + 58.31011875*m.x59 - 69.39622571*m.x60 == 0) m.c19 = Constraint(expr= m.x61 - 69.39622571*m.x62 + 58.31011875*m.x63 - 25.39911174*m.x64 == 0) m.c20 = Constraint(expr= - 69.39622571*m.x55 + m.x65 + 58.31011875*m.x66 - 25.39911174*m.x67 == 0) m.c21 = Constraint(expr= - 69.39622571*m.x60 + m.x68 - 25.39911174*m.x69 + 58.31011875*m.x70 == 0) m.c22 = Constraint(expr= - 69.39622571*m.x62 + m.x71 + 58.31011875*m.x72 - 25.39911174*m.x73 == 0) m.c23 = Constraint(expr= m.x74 - 2.03724124*m.x75 + 63.61644904*m.x76 - 34.92732674*m.x77 == 0) m.c24 = Constraint(expr= m.x78 - 2.03724124*m.x79 - 34.92732674*m.x80 + 63.61644904*m.x81 == 0) m.c25 = Constraint(expr= m.x82 - 2.03724124*m.x83 - 34.92732674*m.x84 + 63.61644904*m.x85 == 0) m.c26 = Constraint(expr= - 34.92732674*m.x77 + m.x86 + 63.61644904*m.x87 - 2.03724124*m.x88 == 0) m.c27 = Constraint(expr= - 34.92732674*m.x80 + m.x89 + 63.61644904*m.x90 - 2.03724124*m.x91 == 0) m.c28 = Constraint(expr= - 34.92732674*m.x84 + m.x92 - 2.03724124*m.x93 + 63.61644904*m.x94 == 0) m.c29 = Constraint(expr= m.x95 + m.x96 + m.x97 >= 0.875) m.c30 = Constraint(expr= - m.x98 + m.x99 == 0) m.c31 = Constraint(expr= - m.x100 + m.x101 == 0) m.c32 = Constraint(expr= - m.x102 + m.x103 == 0) m.c33 = Constraint(expr= - m.x104 + m.x105 == 0) m.c34 = Constraint(expr= - m.x106 + m.x107 == 0) m.c35 = Constraint(expr= - m.x108 + m.x109 == 0) m.c36 = Constraint(expr= m.x104 - m.x110 == 0) m.c37 = Constraint(expr= m.x106 - m.x111 == 0) m.c38 = Constraint(expr= m.x108 - m.x112 == 0) m.c39 = Constraint(expr= - m.x113 + m.x114 == 0) m.c40 = Constraint(expr= - m.x115 + m.x116 == 0) m.c41 = Constraint(expr= - m.x117 + m.x118 == 0) m.c42 = Constraint(expr= m.x119 == 0.296666667) m.c43 = Constraint(expr= m.x120 == 0.294444444) m.c44 = Constraint(expr= m.x121 == 0.283888889) m.c45 = Constraint(expr= m.x95 - m.x99 == 0) m.c46 = Constraint(expr= m.x96 - m.x101 == 0) m.c47 = Constraint(expr= m.x97 - m.x103 == 0) m.c48 = Constraint(expr= 3600*m.x98 - 3600*m.x105 + 1800*m.x122 - 1800*m.x123 == 0) m.c49 = Constraint(expr= 3600*m.x100 - 3600*m.x107 + 1800*m.x124 - 1800*m.x125 == 0) m.c50 = Constraint(expr= 3600*m.x102 - 3600*m.x109 + 1800*m.x126 - 1800*m.x127 == 0) m.c51 = Constraint(expr= 3600*m.x110 - 3600*m.x114 + 720*m.x128 - 720*m.x129 == 0) m.c52 = Constraint(expr= 3600*m.x111 - 3600*m.x116 + 720*m.x130 - 720*m.x131 == 0) m.c53 = Constraint(expr= 3600*m.x112 - 3600*m.x118 + 720*m.x132 - 720*m.x133 == 0) m.c54 = Constraint(expr= 3600*m.x113 - 3600*m.x119 + 1600*m.x134 - 1600*m.x135 == 0) m.c55 = Constraint(expr= 3600*m.x115 - 3600*m.x120 + 1600*m.x136 - 1600*m.x137 == 0) m.c56 = Constraint(expr= 3600*m.x117 - 3600*m.x121 + 1600*m.x138 - 1600*m.x139 == 0) m.c57 = Constraint(expr= - m.x123 + m.x124 == 0) m.c58 = Constraint(expr= - m.x125 + m.x126 == 0) m.c59 = Constraint(expr= - m.x129 + m.x130 == 0) m.c60 = Constraint(expr= - m.x131 + m.x132 == 0) m.c61 = Constraint(expr= - m.x135 + m.x136 == 0) m.c62 = Constraint(expr= - m.x137 + m.x138 == 0) m.c63 = Constraint(expr= - 0.2*m.b2 + m.x140 >= 0) m.c64 = Constraint(expr= - 0.2*m.b3 + m.x142 >= 0) m.c65 = Constraint(expr= - 0.2*m.b4 + m.x144 >= 0) m.c66 = Constraint(expr= - 0.2*m.b5 + m.x146 >= 0) m.c67 = Constraint(expr= - 0.2*m.b6 + m.x148 >= 0) m.c68 = Constraint(expr= - 0.2*m.b7 + m.x150 >= 0) m.c69 = Constraint(expr= - 0.2*m.b8 + m.x152 >= 0) m.c70 = Constraint(expr= - 0.2*m.b9 + m.x154 >= 0) m.c71 = Constraint(expr= - 0.2*m.b10 + m.x156 >= 0) m.c72 = Constraint(expr= - 0.25*m.b11 + m.x158 >= 0) m.c73 = Constraint(expr= - 0.25*m.b12 + m.x160 >= 0) m.c74 = Constraint(expr= - 0.25*m.b13 + m.x162 >= 0) m.c75 = Constraint(expr= - 0.25*m.b14 + m.x164 >= 0) m.c76 = Constraint(expr= - 0.25*m.b15 + m.x166 >= 0) m.c77 = Constraint(expr= - 0.25*m.b16 + m.x168 >= 0) m.c78 = Constraint(expr= - 0.4*m.b17 + m.x170 >= 0) m.c79 = Constraint(expr= - 0.4*m.b18 + m.x172 >= 0) m.c80 = Constraint(expr= - 0.4*m.b19 + m.x174 >= 0) m.c81 = Constraint(expr= - 0.4*m.b20 + m.x176 >= 0) m.c82 = Constraint(expr= - 0.4*m.b21 + m.x178 >= 0) m.c83 = Constraint(expr= - 0.4*m.b22 + m.x180 >= 0) m.c84 = Constraint(expr= - 0.24*m.b23 + m.x182 >= 0) m.c85 = Constraint(expr= - 0.24*m.b24 + m.x184 >= 0) m.c86 = Constraint(expr= - 0.24*m.b25 + m.x186 >= 0) m.c87 = Constraint(expr= - 0.24*m.b26 + m.x188 >= 0) m.c88 = Constraint(expr= - 0.24*m.b27 + m.x190 >= 0) m.c89 = Constraint(expr= - 0.24*m.b28 + m.x192 >= 0) m.c90 = Constraint(expr= - 0.8*m.b2 + m.x140 <= 0) m.c91 = Constraint(expr= - 0.8*m.b3 + m.x142 <= 0) m.c92 = Constraint(expr= - 0.8*m.b4 + m.x144 <= 0) m.c93 = Constraint(expr= - 0.8*m.b5 + m.x146 <= 0) m.c94 = Constraint(expr= - 0.8*m.b6 + m.x148 <= 0) m.c95 = Constraint(expr= - 0.8*m.b7 + m.x150 <= 0) m.c96 = Constraint(expr= - 0.8*m.b8 + m.x152 <= 0) m.c97 = Constraint(expr= - 0.8*m.b9 + m.x154 <= 0) m.c98 = Constraint(expr= - 0.8*m.b10 + m.x156 <= 0) m.c99 = Constraint(expr= - 0.5*m.b11 + m.x158 <= 0) m.c100 = Constraint(expr= - 0.5*m.b12 + m.x160 <= 0) m.c101 = Constraint(expr= - 0.5*m.b13 + m.x162 <= 0) m.c102 = Constraint(expr= - 0.5*m.b14 + m.x164 <= 0) m.c103 = Constraint(expr= - 0.5*m.b15 + m.x166 <= 0) m.c104 = Constraint(expr= - 0.5*m.b16 + m.x168 <= 0) m.c105 = Constraint(expr= - 0.7*m.b17 + m.x170 <= 0) m.c106 = Constraint(expr= - 0.7*m.b18 + m.x172 <= 0) m.c107 = Constraint(expr= - 0.7*m.b19 + m.x174 <= 0) m.c108 = Constraint(expr= - 0.7*m.b20 + m.x176 <= 0) m.c109 = Constraint(expr= - 0.7*m.b21 + m.x178 <= 0) m.c110 = Constraint(expr= - 0.7*m.b22 + m.x180 <= 0) m.c111 = Constraint(expr= - 0.58*m.b23 + m.x182 <= 0) m.c112 = Constraint(expr= - 0.58*m.b24 + m.x184 <= 0) m.c113 = Constraint(expr= - 0.58*m.b25 + m.x186 <= 0) m.c114 = Constraint(expr= - 0.58*m.b26 + m.x188 <= 0) m.c115 = Constraint(expr= - 0.58*m.b27 + m.x190 <= 0) m.c116 = Constraint(expr= - 0.58*m.b28 + m.x192 <= 0) m.c117 = Constraint(expr= - m.x122 + m.x194 == 60) m.c118 = Constraint(expr= - m.x124 + m.x195 == 60) m.c119 = Constraint(expr= - m.x126 + m.x196 == 60) m.c120 = Constraint(expr= - m.x128 + m.x197 == 90) m.c121 = Constraint(expr= - m.x130 + m.x198 == 90) m.c122 = Constraint(expr= - m.x132 + m.x199 == 90) m.c123 = Constraint(expr= - m.x134 + m.x200 == 103) m.c124 = Constraint(expr= - m.x136 + m.x201 == 103) m.c125 = Constraint(expr= - m.x138 + m.x202 == 103) m.c126 = Constraint(expr= - m.x194 + m.x203 - m.x204 == 0) m.c127 = Constraint(expr= - m.x195 + m.x205 - m.x206 == 0) m.c128 = Constraint(expr= - m.x196 + m.x207 - m.x208 == 0) m.c129 = Constraint(expr= m.x209 - m.x210 - m.x211 == 0) m.c130 = Constraint(expr= m.x212 - m.x213 - m.x214 == 0) m.c131 = Constraint(expr= m.x215 - m.x216 - m.x217 == 0) m.c132 = Constraint(expr= - m.x200 + m.x218 - m.x219 == 0) m.c133 = Constraint(expr= - m.x201 + m.x220 - m.x221 == 0) m.c134 = Constraint(expr= - m.x202 + m.x222 - m.x223 == 0) m.c135 = Constraint(expr= m.x203 - m.x224 - m.x225 == 0) m.c136 = Constraint(expr= m.x205 - m.x226 - m.x227 == 0) m.c137 = Constraint(expr= m.x207 - m.x228 - m.x229 == 0) m.c138 = Constraint(expr= - m.x194 + m.x209 - m.x230 == 0) m.c139 = Constraint(expr= - m.x195 + m.x212 - m.x231 == 0) m.c140 = Constraint(expr= - m.x196 + m.x215 - m.x232 == 0) m.c141 = Constraint(expr= - m.x197 + m.x218 - m.x233 == 0) m.c142 = Constraint(expr= - m.x198 + m.x220 - m.x234 == 0) m.c143 = Constraint(expr= - m.x199 + m.x222 - m.x235 == 0) m.c144 = Constraint(expr= 0.2*m.b2 - m.x140 + m.x236 <= 0.2) m.c145 = Constraint(expr= 0.2*m.b3 - m.x142 + m.x237 <= 0.2) m.c146 = Constraint(expr= 0.2*m.b4 - m.x144 + m.x238 <= 0.2) m.c147 = Constraint(expr= 0.2*m.b5 - m.x146 + m.x239 <= 0.2) m.c148 = Constraint(expr= 0.2*m.b6 - m.x148 + m.x240 <= 0.2) m.c149 = Constraint(expr= 0.2*m.b7 - m.x150 + m.x241 <= 0.2) m.c150 = Constraint(expr= 0.2*m.b8 - m.x152 + m.x242 <= 0.2) m.c151 = Constraint(expr= 0.2*m.b9 - m.x154 + m.x243 <= 0.2) m.c152 = Constraint(expr= 0.2*m.b10 - m.x156 + m.x244 <= 0.2) m.c153 = Constraint(expr= 0.25*m.b11 - m.x158 + m.x245 <= 0.25) m.c154 = Constraint(expr= 0.25*m.b12 - m.x160 + m.x246 <= 0.25) m.c155 = Constraint(expr= 0.25*m.b13 - m.x162 + m.x247 <= 0.25) m.c156 = Constraint(expr= 0.25*m.b14 - m.x164 + m.x248 <= 0.25) m.c157 = Constraint(expr= 0.25*m.b15 - m.x166 + m.x249 <= 0.25) m.c158 = Constraint(expr= 0.25*m.b16 - m.x168 + m.x250 <= 0.25) m.c159 = Constraint(expr= 0.4*m.b17 - m.x170 + m.x251 <= 0.4) m.c160 = Constraint(expr= 0.4*m.b18 - m.x172 + m.x252 <= 0.4) m.c161 = Constraint(expr= 0.4*m.b19 - m.x174 + m.x253 <= 0.4) m.c162 = Constraint(expr= 0.4*m.b20 - m.x176 + m.x254 <= 0.4) m.c163 = Constraint(expr= 0.4*m.b21 - m.x178 + m.x255 <= 0.4) m.c164 = Constraint(expr= 0.4*m.b22 - m.x180 + m.x256 <= 0.4) m.c165 = Constraint(expr= 0.24*m.b23 - m.x182 + m.x257 <= 0.24) m.c166 = Constraint(expr= 0.24*m.b24 - m.x184 + m.x258 <= 0.24) m.c167 = Constraint(expr= 0.24*m.b25 - m.x186 + m.x259 <= 0.24) m.c168 = Constraint(expr= 0.24*m.b26 - m.x188 + m.x260 <= 0.24) m.c169 = Constraint(expr= 0.24*m.b27 - m.x190 + m.x261 <= 0.24) m.c170 = Constraint(expr= 0.24*m.b28 - m.x192 + m.x262 <= 0.24) m.c171 = Constraint(expr= - m.x140 + m.x236 >= 0) m.c172 = Constraint(expr= - m.x142 + m.x237 >= 0) m.c173 = Constraint(expr= - m.x144 + m.x238 >= 0) m.c174 = Constraint(expr= - m.x146 + m.x239 >= 0) m.c175 = Constraint(expr= - m.x148 + m.x240 >= 0) m.c176 = Constraint(expr= - m.x150 + m.x241 >= 0) m.c177 = Constraint(expr= - m.x152 + m.x242 >= 0) m.c178 = Constraint(expr= - m.x154 + m.x243 >= 0) m.c179 = Constraint(expr= - m.x156 + m.x244 >= 0) m.c180 = Constraint(expr= - m.x158 + m.x245 >= 0) m.c181 = Constraint(expr= - m.x160 + m.x246 >= 0) m.c182 = Constraint(expr= - m.x162 + m.x247 >= 0) m.c183 = Constraint(expr= - m.x164 + m.x248 >= 0) m.c184 = Constraint(expr= - m.x166 + m.x249 >= 0) m.c185 = Constraint(expr= - m.x168 + m.x250 >= 0) m.c186 = Constraint(expr= - m.x170 + m.x251 >= 0) m.c187 = Constraint(expr= - m.x172 + m.x252 >= 0) m.c188 = Constraint(expr= - m.x174 + m.x253 >= 0) m.c189 = Constraint(expr= - m.x176 + m.x254 >= 0) m.c190 = Constraint(expr= - m.x178 + m.x255 >= 0) m.c191 = Constraint(expr= - m.x180 + m.x256 >= 0) m.c192 = Constraint(expr= - m.x182 + m.x257 >= 0) m.c193 = Constraint(expr= - m.x184 + m.x258 >= 0) m.c194 = Constraint(expr= - m.x186 + m.x259 >= 0) m.c195 = Constraint(expr= - m.x188 + m.x260 >= 0) m.c196 = Constraint(expr= - m.x190 + m.x261 >= 0) m.c197 = Constraint(expr= - m.x192 + m.x262 >= 0) m.c198 = Constraint(expr= - 0.6*m.b2 + m.x236 <= 0.2) m.c199 = Constraint(expr= - 0.6*m.b3 + m.x237 <= 0.2) m.c200 = Constraint(expr= - 0.6*m.b4 + m.x238 <= 0.2) m.c201 = Constraint(expr= - 0.6*m.b5 + m.x239 <= 0.2) m.c202 = Constraint(expr= - 0.6*m.b6 + m.x240 <= 0.2) m.c203 = Constraint(expr= - 0.6*m.b7 + m.x241 <= 0.2) m.c204 = Constraint(expr= - 0.6*m.b8 + m.x242 <= 0.2) m.c205 = Constraint(expr= - 0.6*m.b9 + m.x243 <= 0.2) m.c206 = Constraint(expr= - 0.6*m.b10 + m.x244 <= 0.2) m.c207 = Constraint(expr= - 0.25*m.b11 + m.x245 <= 0.25) m.c208 = Constraint(expr= - 0.25*m.b12 + m.x246 <= 0.25) m.c209 = Constraint(expr= - 0.25*m.b13 + m.x247 <= 0.25) m.c210 = Constraint(expr= - 0.25*m.b14 + m.x248 <= 0.25) m.c211 = Constraint(expr= - 0.25*m.b15 + m.x249 <= 0.25) m.c212 = Constraint(expr= - 0.25*m.b16 + m.x250 <= 0.25) m.c213 = Constraint(expr= - 0.3*m.b17 + m.x251 <= 0.4) m.c214 = Constraint(expr= - 0.3*m.b18 + m.x252 <= 0.4) m.c215 = Constraint(expr= - 0.3*m.b19 + m.x253 <= 0.4) m.c216 = Constraint(expr= - 0.3*m.b20 + m.x254 <= 0.4) m.c217 = Constraint(expr= - 0.3*m.b21 + m.x255 <= 0.4) m.c218 = Constraint(expr= - 0.3*m.b22 + m.x256 <= 0.4) m.c219 = Constraint(expr= - 0.34*m.b23 + m.x257 <= 0.24) m.c220 = Constraint(expr= - 0.34*m.b24 + m.x258 <= 0.24) m.c221 = Constraint(expr= - 0.34*m.b25 + m.x259 <= 0.24) m.c222 = Constraint(expr= - 0.34*m.b26 + m.x260 <= 0.24) m.c223 = Constraint(expr= - 0.34*m.b27 + m.x261 <= 0.24) m.c224 = Constraint(expr= - 0.34*m.b28 + m.x262 <= 0.24) m.c225 = Constraint(expr= - 0.4*m.b2 + m.x263 <= 0.6) m.c226 = Constraint(expr= - 0.4*m.b3 + m.x264 <= 0.6) m.c227 = Constraint(expr= - 0.4*m.b4 + m.x265 <= 0.6) m.c228 = Constraint(expr= - 0.2*m.b11 + m.x266 <= 0.8) m.c229 = Constraint(expr= - 0.2*m.b12 + m.x267 <= 0.8) m.c230 = Constraint(expr= - 0.2*m.b13 + m.x268 <= 0.8) m.c231 = Constraint(expr= - 0.15*m.b17 + m.x269 <= 0.85) m.c232 = Constraint(expr= - 0.15*m.b18 + m.x270 <= 0.85) m.c233 = Constraint(expr= - 0.15*m.b19 + m.x271 <= 0.85) m.c234 = Constraint(expr= - 0.3*m.b23 + m.x272 <= 0.7) m.c235 = Constraint(expr= - 0.3*m.b24 + m.x273 <= 0.7) m.c236 = Constraint(expr= - 0.3*m.b25 + m.x274 <= 0.7) m.c237 = Constraint(expr= m.b2 - m.b5 >= 0) m.c238 = Constraint(expr= m.b3 - m.b6 >= 0) m.c239 = Constraint(expr= m.b4 - m.b7 >= 0) m.c240 = Constraint(expr= m.b5 - m.b8 >= 0) m.c241 = Constraint(expr= m.b6 - m.b9 >= 0) m.c242 = Constraint(expr= m.b7 - m.b10 >= 0) m.c243 = Constraint(expr= m.b11 - m.b14 >= 0) m.c244 = Constraint(expr= m.b12 - m.b15 >= 0) m.c245 = Constraint(expr= m.b13 - m.b16 >= 0) m.c246 = Constraint(expr= m.b17 - m.b20 >= 0) m.c247 = Constraint(expr= m.b18 - m.b21 >= 0) m.c248 = Constraint(expr= m.b19 - m.b22 >= 0) m.c249 = Constraint(expr= m.b23 - m.b26 >= 0) m.c250 = Constraint(expr= m.b24 - m.b27 >= 0) m.c251 = Constraint(expr= m.b25 - m.b28 >= 0) m.c252 = Constraint(expr= m.x99 - m.x140 - m.x146 - m.x152 == 0) m.c253 = Constraint(expr= m.x101 - m.x142 - m.x148 - m.x154 == 0) m.c254 = Constraint(expr= m.x103 - m.x144 - m.x150 - m.x156 == 0) m.c255 = Constraint(expr= m.x105 - m.x158 - m.x164 - m.x170 - m.x176 == 0) m.c256 = Constraint(expr= m.x107 - m.x160 - m.x166 - m.x172 - m.x178 == 0) m.c257 = Constraint(expr= m.x109 - m.x162 - m.x168 - m.x174 - m.x180 == 0) m.c258 = Constraint(expr= m.x114 - m.x182 - m.x188 == 0) m.c259 = Constraint(expr= m.x116 - m.x184 - m.x190 == 0) m.c260 = Constraint(expr= m.x118 - m.x186 - m.x192 == 0) m.c261 = Constraint(expr= - 2000*m.b2 + m.x141 - m.x225 >= -2000) m.c262 = Constraint(expr= - 2000*m.b3 + m.x149 - m.x227 >= -2000) m.c263 = Constraint(expr= - 2000*m.b4 + m.x157 - m.x229 >= -2000) m.c264 = Constraint(expr= - 2000*m.b5 + m.x165 - m.x225 >= -2000) m.c265 = Constraint(expr= - 2000*m.b6 + m.x171 - m.x227 >= -2000) m.c266 = Constraint(expr= - 2000*m.b7 + m.x177 - m.x229 >= -2000) m.c267 = Constraint(expr= - 2000*m.b8 + m.x183 - m.x225 >= -2000) m.c268 = Constraint(expr= - 2000*m.b9 + m.x189 - m.x227 >= -2000) m.c269 = Constraint(expr= - 2000*m.b10 + m.x29 - m.x229 >= -2000) m.c270 = Constraint(expr= - 2000*m.b11 + m.x32 - m.x230 >= -2000) m.c271 = Constraint(expr= - 2000*m.b12 + m.x36 - m.x231 >= -2000) m.c272 = Constraint(expr= - 2000*m.b13 + m.x40 - m.x232 >= -2000) m.c273 = Constraint(expr= - 2000*m.b14 + m.x44 - m.x230 >= -2000) m.c274 = Constraint(expr= - 2000*m.b15 + m.x47 - m.x231 >= -2000) m.c275 = Constraint(expr= - 2000*m.b16 + m.x50 - m.x232 >= -2000) m.c276 = Constraint(expr= - 2000*m.b17 + m.x53 - m.x230 >= -2000) m.c277 = Constraint(expr= - 2000*m.b18 + m.x57 - m.x231 >= -2000) m.c278 = Constraint(expr= - 2000*m.b19 + m.x61 - m.x232 >= -2000) m.c279 = Constraint(expr= - 2000*m.b20 + m.x65 - m.x230 >= -2000) m.c280 = Constraint(expr= - 2000*m.b21 + m.x68 - m.x231 >= -2000) m.c281 = Constraint(expr= - 2000*m.b22 + m.x71 - m.x232 >= -2000) m.c282 = Constraint(expr= - 2000*m.b23 + m.x74 - m.x233 >= -2000) m.c283 = Constraint(expr= - 2000*m.b24 + m.x78 - m.x234 >= -2000) m.c284 = Constraint(expr= - 2000*m.b25 + m.x82 - m.x235 >= -2000) m.c285 = Constraint(expr= - 2000*m.b26 + m.x86 - m.x233 >= -2000) m.c286 = Constraint(expr= - 2000*m.b27 + m.x89 - m.x234 >= -2000) m.c287 = Constraint(expr= - 2000*m.b28 + m.x92 - m.x235 >= -2000) m.c288 = Constraint(expr= 1049*m.b2 + m.x141 - m.x225 <= 1049) m.c289 = Constraint(expr= 1049*m.b3 + m.x149 - m.x227 <= 1049) m.c290 = Constraint(expr= 1049*m.b4 + m.x157 - m.x229 <= 1049) m.c291 = Constraint(expr= 1049*m.b5 + m.x165 - m.x225 <= 1049) m.c292 = Constraint(expr= 1049*m.b6 + m.x171 - m.x227 <= 1049) m.c293 = Constraint(expr= 1049*m.b7 + m.x177 - m.x229 <= 1049) m.c294 = Constraint(expr= 1049*m.b8 + m.x183 - m.x225 <= 1049) m.c295 = Constraint(expr= 1049*m.b9 + m.x189 - m.x227 <= 1049) m.c296 = Constraint(expr= 1049*m.b10 + m.x29 - m.x229 <= 1049) m.c297 = Constraint(expr= 1065*m.b11 + m.x32 - m.x230 <= 1065) m.c298 = Constraint(expr= 1065*m.b12 + m.x36 - m.x231 <= 1065) m.c299 = Constraint(expr= 1065*m.b13 + m.x40 - m.x232 <= 1065) m.c300 = Constraint(expr= 1065*m.b14 + m.x44 - m.x230 <= 1065) m.c301 = Constraint(expr= 1065*m.b15 + m.x47 - m.x231 <= 1065) m.c302 = Constraint(expr= 1065*m.b16 + m.x50 - m.x232 <= 1065) m.c303 = Constraint(expr= 1065*m.b17 + m.x53 - m.x230 <= 1065) m.c304 = Constraint(expr= 1065*m.b18 + m.x57 - m.x231 <= 1065) m.c305 = Constraint(expr= 1065*m.b19 + m.x61 - m.x232 <= 1065) m.c306 = Constraint(expr= 1065*m.b20 + m.x65 - m.x230 <= 1065) m.c307 = Constraint(expr= 1065*m.b21 + m.x68 - m.x231 <= 1065) m.c308 = Constraint(expr= 1065*m.b22 + m.x71 - m.x232 <= 1065) m.c309 = Constraint(expr= 1095*m.b23 + m.x74 - m.x233 <= 1095) m.c310 = Constraint(expr= 1095*m.b24 + m.x78 - m.x234 <= 1095) m.c311 = Constraint(expr= 1095*m.b25 + m.x82 - m.x235 <= 1095) m.c312 = Constraint(expr= 1095*m.b26 + m.x86 - m.x233 <= 1095) m.c313 = Constraint(expr= 1095*m.b27 + m.x89 - m.x234 <= 1095) m.c314 = Constraint(expr= 1095*m.b28 + m.x92 - m.x235 <= 1095) m.c315 = Constraint(expr= - m.x197 + m.x210 >= 0) m.c316 = Constraint(expr= - m.x198 + m.x213 >= 0) m.c317 = Constraint(expr= - m.x199 + m.x216 >= 0) m.c318 = Constraint(expr= m.x200 - m.x275 >= 0) m.c319 = Constraint(expr= m.x201 - m.x276 >= 0) m.c320 = Constraint(expr= m.x202 - m.x277 >= 0) m.c321 = Constraint(expr= - 0.309838295393634*m.x278 + 13.94696158*m.x279 + 24.46510819*m.x280 - 7.28623839*m.x281 - 23.57687014*m.x282 <= 0) m.c322 = Constraint(expr= - 0.309838295393634*m.x283 + 13.94696158*m.x284 + 24.46510819*m.x285 - 7.28623839*m.x286 - 23.57687014*m.x287 <= 0) m.c323 = Constraint(expr= - 0.309838295393634*m.x288 + 13.94696158*m.x289 + 24.46510819*m.x290 - 7.28623839*m.x291 - 23.57687014*m.x292 <= 0) m.c324 = Constraint(expr= - 0.309838295393634*m.x293 + 13.94696158*m.x294 + 24.46510819*m.x295 - 7.28623839*m.x296 - 23.57687014*m.x297 <= 0) m.c325 = Constraint(expr= - 0.309838295393634*m.x298 + 13.94696158*m.x299 + 24.46510819*m.x300 - 7.28623839*m.x301 - 23.57687014*m.x302 <= 0) m.c326 = Constraint(expr= - 0.309838295393634*m.x303 + 13.94696158*m.x304 + 24.46510819*m.x305 - 7.28623839*m.x306 - 23.57687014*m.x307 <= 0) m.c327 = Constraint(expr= - 0.309838295393634*m.x308 + 13.94696158*m.x309 + 24.46510819*m.x310 - 7.28623839*m.x311 - 23.57687014*m.x312 <= 0) m.c328 = Constraint(expr= - 0.309838295393634*m.x313 + 13.94696158*m.x314 + 24.46510819*m.x315 - 7.28623839*m.x316 - 23.57687014*m.x317 <= 0) m.c329 = Constraint(expr= - 0.309838295393634*m.x318 + 13.94696158*m.x319 + 24.46510819*m.x320 - 7.28623839*m.x321 - 23.57687014*m.x322 <= 0) m.c330 = Constraint(expr= - 0.309838295393634*m.x323 + 29.29404529*m.x324 - 108.39408287*m.x325 + 442.21990639*m.x326 - 454.58448169*m.x327 <= 0) m.c331 = Constraint(expr= - 0.309838295393634*m.x328 + 29.29404529*m.x329 - 108.39408287*m.x330 + 442.21990639*m.x331 - 454.58448169*m.x332 <= 0) m.c332 = Constraint(expr= - 0.309838295393634*m.x333 + 29.29404529*m.x334 - 108.39408287*m.x335 + 442.21990639*m.x336 - 454.58448169*m.x337 <= 0) m.c333 = Constraint(expr= - 0.309838295393634*m.x338 + 29.29404529*m.x339 - 108.39408287*m.x340 + 442.21990639*m.x341 - 454.58448169*m.x342 <= 0) m.c334 = Constraint(expr= 442.21990639*m.x343 - 454.58448169*m.x344 - 0.309838295393634*m.x345 + 29.29404529*m.x346 - 108.39408287*m.x347 <= 0) m.c335 = Constraint(expr= - 0.309838295393634*m.x348 + 29.29404529*m.x349 - 108.39408287*m.x350 + 442.21990639*m.x351 - 454.58448169*m.x352 <= 0) m.c336 = Constraint(expr= - 0.309838295393634*m.x353 + 25.92674585*m.x354 + 18.13482123*m.x355 + 22.12766012*m.x356 - 42.68950769*m.x357 <= 0) m.c337 = Constraint(expr= - 0.309838295393634*m.x358 + 25.92674585*m.x359 + 18.13482123*m.x360 + 22.12766012*m.x361 - 42.68950769*m.x362 <= 0) m.c338 = Constraint(expr= - 0.309838295393634*m.x363 + 25.92674585*m.x364 + 18.13482123*m.x365 + 22.12766012*m.x366 - 42.68950769*m.x367 <= 0) m.c339 = Constraint(expr= - 0.309838295393634*m.x368 + 25.92674585*m.x369 + 18.13482123*m.x370 + 22.12766012*m.x371 - 42.68950769*m.x372 <= 0) m.c340 = Constraint(expr= - 0.309838295393634*m.x373 + 25.92674585*m.x374 + 18.13482123*m.x375 + 22.12766012*m.x376 - 42.68950769*m.x377 <= 0) m.c341 = Constraint(expr= - 0.309838295393634*m.x378 + 25.92674585*m.x379 + 18.13482123*m.x380 + 22.12766012*m.x381 - 42.68950769*m.x382 <= 0) m.c342 = Constraint(expr= - 0.309838295393634*m.x383 + 17.4714791*m.x384 - 39.98407808*m.x385 + 134.55943082*m.x386 - 135.88441782*m.x387 <= 0) m.c343 = Constraint(expr= - 0.309838295393634*m.x388 + 17.4714791*m.x389 - 39.98407808*m.x390 + 134.55943082*m.x391 - 135.88441782*m.x392 <= 0) m.c344 = Constraint(expr= - 0.309838295393634*m.x393 + 17.4714791*m.x394 - 39.98407808*m.x395 + 134.55943082*m.x396 - 135.88441782*m.x397 <= 0) m.c345 = Constraint(expr= - 0.309838295393634*m.x398 + 17.4714791*m.x399 - 39.98407808*m.x400 + 134.55943082*m.x401 - 135.88441782*m.x402 <= 0) m.c346 = Constraint(expr= - 0.309838295393634*m.x403 + 17.4714791*m.x404 - 39.98407808*m.x405 + 134.55943082*m.x406 - 135.88441782*m.x407 <= 0) m.c347 = Constraint(expr= - 0.309838295393634*m.x408 + 17.4714791*m.x409 - 39.98407808*m.x410 + 134.55943082*m.x411 - 135.88441782*m.x412 <= 0) m.c348 = Constraint(expr=m.x98**2 - m.x413 == 0) m.c349 = Constraint(expr= m.x204 - 5*m.x413 == 0) m.c350 = Constraint(expr=m.x100**2 - m.x414 == 0) m.c351 = Constraint(expr= m.x206 - 5*m.x414 == 0) m.c352 = Constraint(expr=m.x102**2 - m.x415 == 0) m.c353 = Constraint(expr= m.x208 - 5*m.x415 == 0) m.c354 = Constraint(expr=m.x104**2 - m.x416 == 0) m.c355 = Constraint(expr= m.x211 - 4*m.x416 == 0) m.c356 = Constraint(expr=m.x106**2 - m.x417 == 0) m.c357 = Constraint(expr= m.x214 - 4*m.x417 == 0) m.c358 = Constraint(expr=m.x108**2 - m.x418 == 0) m.c359 = Constraint(expr= m.x217 - 4*m.x418 == 0) m.c360 = Constraint(expr=m.x113**2 - m.x419 == 0) m.c361 = Constraint(expr= m.x219 - 5*m.x419 == 0) m.c362 = Constraint(expr=m.x115**2 - m.x420 == 0) m.c363 = Constraint(expr= m.x221 - 5*m.x420 == 0) m.c364 = Constraint(expr=m.x117**2 - m.x421 == 0) m.c365 = Constraint(expr= m.x223 - 5*m.x421 == 0) m.c366 = Constraint(expr=m.x140**2 - m.x422 == 0) m.c367 = Constraint(expr= m.x143 - m.x422 == 0) m.c368 = Constraint(expr=m.x140**3 - m.x423 == 0) m.c369 = Constraint(expr= m.x282 - m.x423 == 0) m.c370 = Constraint(expr=m.x142**2 - m.x424 == 0) m.c371 = Constraint(expr= m.x151 - m.x424 == 0) m.c372 = Constraint(expr=m.x142**3 - m.x425 == 0) m.c373 = Constraint(expr= m.x287 - m.x425 == 0) m.c374 = Constraint(expr=m.x144**2 - m.x426 == 0) m.c375 = Constraint(expr= m.x159 - m.x426 == 0) m.c376 = Constraint(expr=m.x144**3 - m.x427 == 0) m.c377 = Constraint(expr= m.x292 - m.x427 == 0) m.c378 = Constraint(expr=m.x146**2 - m.x428 == 0) m.c379 = Constraint(expr= m.x167 - m.x428 == 0) m.c380 = Constraint(expr=m.x146**3 - m.x429 == 0) m.c381 = Constraint(expr= m.x297 - m.x429 == 0) m.c382 = Constraint(expr=m.x148**2 - m.x430 == 0) m.c383 = Constraint(expr= m.x175 - m.x430 == 0) m.c384 = Constraint(expr=m.x148**3 - m.x431 == 0) m.c385 = Constraint(expr= m.x302 - m.x431 == 0) m.c386 = Constraint(expr=m.x150**2 - m.x432 == 0) m.c387 = Constraint(expr= m.x181 - m.x432 == 0) m.c388 = Constraint(expr=m.x150**3 - m.x433 == 0) m.c389 = Constraint(expr= m.x307 - m.x433 == 0) m.c390 = Constraint(expr=m.x152**2 - m.x434 == 0) m.c391 = Constraint(expr= m.x187 - m.x434 == 0) m.c392 = Constraint(expr=m.x152**3 - m.x435 == 0) m.c393 = Constraint(expr= m.x312 - m.x435 == 0) m.c394 = Constraint(expr=m.x154**2 - m.x436 == 0) m.c395 = Constraint(expr= m.x191 - m.x436 == 0) m.c396 = Constraint(expr=m.x154**3 - m.x437 == 0) m.c397 = Constraint(expr= m.x317 - m.x437 == 0) m.c398 = Constraint(expr=m.x156**2 - m.x438 == 0) m.c399 = Constraint(expr= m.x30 - m.x438 == 0) m.c400 = Constraint(expr=m.x156**3 - m.x439 == 0) m.c401 = Constraint(expr= m.x322 - m.x439 == 0) m.c402 = Constraint(expr=m.x158**2 - m.x440 == 0) m.c403 = Constraint(expr= m.x35 - m.x440 == 0) m.c404 = Constraint(expr=m.x158**3 - m.x441 == 0) m.c405 = Constraint(expr= m.x327 - m.x441 == 0) m.c406 = Constraint(expr=m.x160**2 - m.x442 == 0) m.c407 = Constraint(expr= m.x37 - m.x442 == 0) m.c408 = Constraint(expr=m.x160**3 - m.x443 == 0) m.c409 = Constraint(expr= m.x332 - m.x443 == 0) m.c410 = Constraint(expr=m.x162**2 - m.x444 == 0) m.c411 = Constraint(expr= m.x42 - m.x444 == 0) m.c412 = Constraint(expr=m.x162**3 - m.x445 == 0) m.c413 = Constraint(expr= m.x337 - m.x445 == 0) m.c414 = Constraint(expr=m.x164**2 - m.x446 == 0) m.c415 = Constraint(expr= m.x46 - m.x446 == 0) m.c416 = Constraint(expr=m.x164**3 - m.x447 == 0) m.c417 = Constraint(expr= m.x342 - m.x447 == 0) m.c418 = Constraint(expr=m.x166**2 - m.x448 == 0) m.c419 = Constraint(expr= m.x48 - m.x448 == 0) m.c420 = Constraint(expr=m.x166**3 - m.x449 == 0) m.c421 = Constraint(expr= m.x344 - m.x449 == 0) m.c422 = Constraint(expr=m.x168**2 - m.x450 == 0) m.c423 = Constraint(expr= m.x52 - m.x450 == 0) m.c424 = Constraint(expr=m.x168**3 - m.x451 == 0) m.c425 = Constraint(expr= m.x352 - m.x451 == 0) m.c426 = Constraint(expr=m.x170**2 - m.x452 == 0) m.c427 = Constraint(expr= m.x56 - m.x452 == 0) m.c428 = Constraint(expr=m.x170**3 - m.x453 == 0) m.c429 = Constraint(expr= m.x357 - m.x453 == 0) m.c430 = Constraint(expr=m.x172**2 - m.x454 == 0) m.c431 = Constraint(expr= m.x58 - m.x454 == 0) m.c432 = Constraint(expr=m.x172**3 - m.x455 == 0) m.c433 = Constraint(expr= m.x362 - m.x455 == 0) m.c434 = Constraint(expr=m.x174**2 - m.x456 == 0) m.c435 = Constraint(expr= m.x64 - m.x456 == 0) m.c436 = Constraint(expr=m.x174**3 - m.x457 == 0) m.c437 = Constraint(expr= m.x367 - m.x457 == 0) m.c438 = Constraint(expr=m.x176**2 - m.x458 == 0) m.c439 = Constraint(expr= m.x67 - m.x458 == 0) m.c440 = Constraint(expr=m.x176**3 - m.x459 == 0) m.c441 = Constraint(expr= m.x372 - m.x459 == 0) m.c442 = Constraint(expr=m.x178**2 - m.x460 == 0) m.c443 = Constraint(expr= m.x69 - m.x460 == 0) m.c444 = Constraint(expr=m.x178**3 - m.x461 == 0) m.c445 = Constraint(expr= m.x377 - m.x461 == 0) m.c446 = Constraint(expr=m.x180**2 - m.x462 == 0) m.c447 = Constraint(expr= m.x73 - m.x462 == 0) m.c448 = Constraint(expr=m.x180**3 - m.x463 == 0) m.c449 = Constraint(expr= m.x382 - m.x463 == 0) m.c450 = Constraint(expr=m.x182**2 - m.x464 == 0) m.c451 = Constraint(expr= m.x76 - m.x464 == 0) m.c452 = Constraint(expr=m.x182**3 - m.x465 == 0) m.c453 = Constraint(expr= m.x387 - m.x465 == 0) m.c454 = Constraint(expr=m.x184**2 - m.x466 == 0) m.c455 = Constraint(expr= m.x81 - m.x466 == 0) m.c456 = Constraint(expr=m.x184**3 - m.x467 == 0) m.c457 = Constraint(expr= m.x392 - m.x467 == 0) m.c458 = Constraint(expr=m.x186**2 - m.x468 == 0) m.c459 = Constraint(expr= m.x85 - m.x468 == 0) m.c460 = Constraint(expr=m.x186**3 - m.x469 == 0) m.c461 = Constraint(expr= m.x397 - m.x469 == 0) m.c462 = Constraint(expr=m.x188**2 - m.x470 == 0) m.c463 = Constraint(expr= m.x87 - m.x470 == 0) m.c464 = Constraint(expr=m.x188**3 - m.x471 == 0) m.c465 = Constraint(expr= m.x402 - m.x471 == 0) m.c466 = Constraint(expr=m.x190**2 - m.x472 == 0) m.c467 = Constraint(expr= m.x90 - m.x472 == 0) m.c468 = Constraint(expr=m.x190**3 - m.x473 == 0) m.c469 = Constraint(expr= m.x407 - m.x473 == 0) m.c470 = Constraint(expr=m.x192**2 - m.x474 == 0) m.c471 = Constraint(expr= m.x94 - m.x474 == 0) m.c472 = Constraint(expr=m.x192**3 - m.x475 == 0) m.c473 = Constraint(expr= m.x412 - m.x475 == 0) m.c474 = Constraint(expr=m.x140*m.x263 - m.x145 == 0) m.c475 = Constraint(expr=m.x263*m.x422 - m.x281 == 0) m.c476 = Constraint(expr=m.x146*m.x263 - m.x169 == 0) m.c477 = Constraint(expr=m.x263*m.x428 - m.x296 == 0) m.c478 = Constraint(expr=m.x152*m.x263 - m.x185 == 0) m.c479 = Constraint(expr=m.x263*m.x434 - m.x311 == 0) m.c480 = Constraint(expr=m.x263**2 - m.x476 == 0) m.c481 = Constraint(expr= m.x147 - m.x476 == 0) m.c482 = Constraint(expr=m.x140*m.x476 - m.x280 == 0) m.c483 = Constraint(expr=m.x146*m.x476 - m.x295 == 0) m.c484 = Constraint(expr=m.x152*m.x476 - m.x310 == 0) m.c485 = Constraint(expr=m.x263**3 - m.x477 == 0) m.c486 = Constraint(expr=m.b2*m.x477 - m.x279 == 0) m.c487 = Constraint(expr=m.b5*m.x477 - m.x294 == 0) m.c488 = Constraint(expr=m.b8*m.x477 - m.x309 == 0) m.c489 = Constraint(expr=m.x142*m.x264 - m.x155 == 0) m.c490 = Constraint(expr=m.x264*m.x424 - m.x286 == 0) m.c491 = Constraint(expr=m.x148*m.x264 - m.x173 == 0) m.c492 = Constraint(expr=m.x264*m.x430 - m.x301 == 0) m.c493 = Constraint(expr=m.x154*m.x264 - m.x193 == 0) m.c494 = Constraint(expr=m.x264*m.x436 - m.x316 == 0) m.c495 = Constraint(expr=m.x264**2 - m.x478 == 0) m.c496 = Constraint(expr= m.x153 - m.x478 == 0) m.c497 = Constraint(expr=m.x142*m.x478 - m.x285 == 0) m.c498 = Constraint(expr=m.x148*m.x478 - m.x300 == 0) m.c499 = Constraint(expr=m.x154*m.x478 - m.x315 == 0) m.c500 = Constraint(expr=m.x264**3 - m.x479 == 0) m.c501 = Constraint(expr=m.b3*m.x479 - m.x284 == 0) m.c502 = Constraint(expr=m.b6*m.x479 - m.x299 == 0) m.c503 = Constraint(expr=m.b9*m.x479 - m.x314 == 0) m.c504 = Constraint(expr=m.x144*m.x265 - m.x163 == 0) m.c505 = Constraint(expr=m.x265*m.x426 - m.x291 == 0) m.c506 = Constraint(expr=m.x150*m.x265 - m.x179 == 0) m.c507 = Constraint(expr=m.x265*m.x432 - m.x306 == 0) m.c508 = Constraint(expr=m.x156*m.x265 - m.x31 == 0) m.c509 = Constraint(expr=m.x265*m.x438 - m.x321 == 0) m.c510 = Constraint(expr=m.x265**2 - m.x480 == 0) m.c511 = Constraint(expr= m.x161 - m.x480 == 0) m.c512 = Constraint(expr=m.x144*m.x480 - m.x290 == 0) m.c513 = Constraint(expr=m.x150*m.x480 - m.x305 == 0) m.c514 = Constraint(expr=m.x156*m.x480 - m.x320 == 0) m.c515 = Constraint(expr=m.x265**3 - m.x481 == 0) m.c516 = Constraint(expr=m.b4*m.x481 - m.x289 == 0) m.c517 = Constraint(expr=m.b7*m.x481 - m.x304 == 0) m.c518 = Constraint(expr=m.b10*m.x481 - m.x319 == 0) m.c519 = Constraint(expr=m.x158*m.x266 - m.x34 == 0) m.c520 = Constraint(expr=m.x266*m.x440 - m.x326 == 0) m.c521 = Constraint(expr=m.x164*m.x266 - m.x45 == 0) m.c522 = Constraint(expr=m.x266*m.x446 - m.x341 == 0) m.c523 = Constraint(expr=m.x266**2 - m.x482 == 0) m.c524 = Constraint(expr= m.x33 - m.x482 == 0) m.c525 = Constraint(expr=m.x158*m.x482 - m.x325 == 0) m.c526 = Constraint(expr=m.x164*m.x482 - m.x340 == 0) m.c527 = Constraint(expr=m.x266**3 - m.x483 == 0) m.c528 = Constraint(expr=m.b11*m.x483 - m.x324 == 0) m.c529 = Constraint(expr=m.b14*m.x483 - m.x339 == 0) m.c530 = Constraint(expr=m.x160*m.x267 - m.x39 == 0) m.c531 = Constraint(expr=m.x267*m.x442 - m.x331 == 0) m.c532 = Constraint(expr=m.x166*m.x267 - m.x49 == 0) m.c533 = Constraint(expr=m.x267*m.x448 - m.x343 == 0) m.c534 = Constraint(expr=m.x267**2 - m.x484 == 0) m.c535 = Constraint(expr= m.x38 - m.x484 == 0) m.c536 = Constraint(expr=m.x160*m.x484 - m.x330 == 0) m.c537 = Constraint(expr=m.x166*m.x484 - m.x347 == 0) m.c538 = Constraint(expr=m.x267**3 - m.x485 == 0) m.c539 = Constraint(expr=m.b12*m.x485 - m.x329 == 0) m.c540 = Constraint(expr=m.b15*m.x485 - m.x346 == 0) m.c541 = Constraint(expr=m.x162*m.x268 - m.x41 == 0) m.c542 = Constraint(expr=m.x268*m.x444 - m.x336 == 0) m.c543 = Constraint(expr=m.x168*m.x268 - m.x51 == 0) m.c544 = Constraint(expr=m.x268*m.x450 - m.x351 == 0) m.c545 = Constraint(expr=m.x268**2 - m.x486 == 0) m.c546 = Constraint(expr= m.x43 - m.x486 == 0) m.c547 = Constraint(expr=m.x162*m.x486 - m.x335 == 0) m.c548 = Constraint(expr=m.x168*m.x486 - m.x350 == 0) m.c549 = Constraint(expr=m.x268**3 - m.x487 == 0) m.c550 = Constraint(expr=m.b13*m.x487 - m.x334 == 0) m.c551 = Constraint(expr=m.b16*m.x487 - m.x349 == 0) m.c552 = Constraint(expr=m.x170*m.x269 - m.x54 == 0) m.c553 = Constraint(expr=m.x269*m.x452 - m.x356 == 0) m.c554 = Constraint(expr=m.x176*m.x269 - m.x66 == 0) m.c555 = Constraint(expr=m.x269*m.x458 - m.x371 == 0) m.c556 = Constraint(expr=m.x269**2 - m.x488 == 0) m.c557 = Constraint(expr= m.x55 - m.x488 == 0) m.c558 = Constraint(expr=m.x170*m.x488 - m.x355 == 0) m.c559 = Constraint(expr=m.x176*m.x488 - m.x370 == 0) m.c560 = Constraint(expr=m.x269**3 - m.x489 == 0) m.c561 = Constraint(expr=m.b17*m.x489 - m.x354 == 0) m.c562 = Constraint(expr=m.b20*m.x489 - m.x369 == 0) m.c563 = Constraint(expr=m.x172*m.x270 - m.x59 == 0) m.c564 = Constraint(expr=m.x270*m.x454 - m.x361 == 0) m.c565 = Constraint(expr=m.x178*m.x270 - m.x70 == 0) m.c566 = Constraint(expr=m.x270*m.x460 - m.x376 == 0) m.c567 = Constraint(expr=m.x270**2 - m.x490 == 0) m.c568 = Constraint(expr= m.x60 - m.x490 == 0) m.c569 = Constraint(expr=m.x172*m.x490 - m.x360 == 0) m.c570 = Constraint(expr=m.x178*m.x490 - m.x375 == 0) m.c571 = Constraint(expr=m.x270**3 - m.x491 == 0) m.c572 = Constraint(expr=m.b18*m.x491 - m.x359 == 0) m.c573 = Constraint(expr=m.b21*m.x491 - m.x374 == 0) m.c574 = Constraint(expr=m.x174*m.x271 - m.x63 == 0) m.c575 = Constraint(expr=m.x271*m.x456 - m.x366 == 0) m.c576 = Constraint(expr=m.x180*m.x271 - m.x72 == 0) m.c577 = Constraint(expr=m.x271*m.x462 - m.x381 == 0) m.c578 = Constraint(expr=m.x271**2 - m.x492 == 0) m.c579 = Constraint(expr= m.x62 - m.x492 == 0) m.c580 = Constraint(expr=m.x174*m.x492 - m.x365 == 0) m.c581 = Constraint(expr=m.x180*m.x492 - m.x380 == 0) m.c582 = Constraint(expr=m.x271**3 - m.x493 == 0) m.c583 = Constraint(expr=m.b19*m.x493 - m.x364 == 0) m.c584 = Constraint(expr=m.b22*m.x493 - m.x379 == 0) m.c585 = Constraint(expr=m.x182*m.x272 - m.x75 == 0) m.c586 = Constraint(expr=m.x272*m.x464 - m.x386 == 0) m.c587 = Constraint(expr=m.x188*m.x272 - m.x88 == 0) m.c588 = Constraint(expr=m.x272*m.x470 - m.x401 == 0) m.c589 = Constraint(expr=m.x272**2 - m.x494 == 0) m.c590 = Constraint(expr= m.x77 - m.x494 == 0) m.c591 = Constraint(expr=m.x182*m.x494 - m.x385 == 0) m.c592 = Constraint(expr=m.x188*m.x494 - m.x400 == 0) m.c593 = Constraint(expr=m.x272**3 - m.x495 == 0) m.c594 = Constraint(expr=m.b23*m.x495 - m.x384 == 0) m.c595 = Constraint(expr=m.b26*m.x495 - m.x399 == 0) m.c596 = Constraint(expr=m.x184*m.x273 - m.x79 == 0) m.c597 = Constraint(expr=m.x273*m.x466 - m.x391 == 0) m.c598 = Constraint(expr=m.x190*m.x273 - m.x91 == 0) m.c599 = Constraint(expr=m.x273*m.x472 - m.x406 == 0) m.c600 = Constraint(expr=m.x273**2 - m.x496 == 0) m.c601 = Constraint(expr= m.x80 - m.x496 == 0) m.c602 = Constraint(expr=m.x184*m.x496 - m.x390 == 0) m.c603 = Constraint(expr=m.x190*m.x496 - m.x405 == 0) m.c604 = Constraint(expr=m.x273**3 - m.x497 == 0) m.c605 = Constraint(expr=m.b24*m.x497 - m.x389 == 0) m.c606 = Constraint(expr=m.b27*m.x497 - m.x404 == 0) m.c607 = Constraint(expr=m.x186*m.x274 - m.x83 == 0) m.c608 = Constraint(expr=m.x274*m.x468 - m.x396 == 0) m.c609 = Constraint(expr=m.x192*m.x274 - m.x93 == 0) m.c610 = Constraint(expr=m.x274*m.x474 - m.x411 == 0) m.c611 = Constraint(expr=m.x274**2 - m.x498 == 0) m.c612 = Constraint(expr= m.x84 - m.x498 == 0) m.c613 = Constraint(expr=m.x186*m.x498 - m.x395 == 0) m.c614 = Constraint(expr=m.x192*m.x498 - m.x410 == 0) m.c615 = Constraint(expr=m.x274**3 - m.x499 == 0) m.c616 = Constraint(expr=m.b25*m.x499 - m.x394 == 0) m.c617 = Constraint(expr=m.b28*m.x499 - m.x409 == 0)
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project/project/basicOpencv.py
LamLauChiu/DjangoReact-Boilerplate
1
169927
<reponame>LamLauChiu/DjangoReact-Boilerplate import numpy as np import cv2 from matplotlib import pyplot as plt # sources: https://blog.gtwang.org/programming/opencv-basic-image-read-and-write-tutorial/ # 讀取圖檔 img = cv2.imread('/Users/Ivan.lam/Projects/DjangoReact-Boilerplate/project/project/human-20180303-01-1024x683.jpg') # 查看資料型態 #type(img) #img.shape # 以灰階的方式讀取圖檔 #img_gray = cv2.imread('image.jpg', cv2.IMREAD_GRAYSCALE) # 顯示圖片 #cv2.imshow('My Image', img) # 按下任意鍵則關閉所有視窗 #cv2.waitKey(0) #cv2.destroyAllWindows() # 關閉 'My Image' 視窗 #cv2.destroyWindow('My Image') # 讓視窗可以自由縮放大小 #cv2.namedWindow('My Image', cv2.WINDOW_NORMAL) #cv2.imshow('My Image', img) #cv2.waitKey(0) #cv2.destroyAllWindows() # 寫入圖檔 #cv2.imwrite('output.jpg', img) # 寫入不同圖檔格式 #cv2.imwrite('output.png', img) #cv2.imwrite('output.tiff', img) # 設定 JPEG 圖片品質為 90（可用值為 0 ~ 100） #cv2.imwrite('output.jpg', img, [cv2.IMWRITE_JPEG_QUALITY, 90]) # 設定 PNG 壓縮層級為 5（可用值為 0 ~ 9） #cv2.imwrite('output.png', img, [cv2.IMWRITE_PNG_COMPRESSION, 5]) # 使用 OpenCV 讀取圖檔 #img_bgr = cv2.imread('/Users/Ivan.lam/Projects/DjangoReact-Boilerplate/project/project/human-20180303-01-1024x683.jpg') # 將 BGR 圖片轉為 RGB 圖片 #img_rgb = img_bgr[:,:,::-1] # 或是這樣亦可 # img_rgb = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB) # 使用 Matplotlib 顯示圖片 #plt.imshow(img_rgb) #plt.show() # 使用 OpenCV 讀取灰階圖檔 img_gray = cv2.imread('/Users/Ivan.lam/Projects/DjangoReact-Boilerplate/project/project/human-20180303-01-1024x683.jpg', cv2.IMREAD_GRAYSCALE) # 使用 Matplotlib 顯示圖片 plt.imshow(img_gray, cmap = 'gray') plt.show()
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rl/utils/return_utils.py
jrobine/smaller-world-models
0
134943
<filename>rl/utils/return_utils.py import torch from torch import Tensor __all__ = ['compute_return', 'compute_gae_return'] def compute_return( rewards: Tensor, terminals: Tensor, bootstrap_value: Tensor, discount_rate: float, batch_first: bool = False) -> Tensor: """Computes the return based on sequences of rewards, terminals and the bootstrapped values of the next observation. Arguments: rewards (Tensor): Float tensor of shape `(time, batch)` that contains the reward sequence. terminals (Tensor): Boolean tensor of shape `(time, batch)` that contains the terminal sequence. bootstrap_value (Tensor): Float tensor of shape `(batch)` that contains the bootstrapped values of the next obsrvation. discount_rate (float): The discount rate that is used to compute the return. batch_first (bool, optional): Whether the rewards and terminals are of shape `(batch, time)` instead. The returns will also be of that shape. Defaults to ``False``. Returns: Tensor of shape `(time, batch)` containing the computed returns (or of shape `(batch, time)`, if `batch_first` is ``True``). """ return_ = torch.zeros_like(rewards) next_return = bootstrap_value num_steps = rewards.shape[1 if batch_first else 0] for t in reversed(range(num_steps)): index_t = (slice(None), t) if batch_first else (t, slice(None)) non_terminal = ~terminals[index_t] return_[index_t] = rewards[index_t] return_[index_t][non_terminal] += discount_rate * next_return[non_terminal] next_return = return_[index_t] return return_ def compute_gae_return( rewards: Tensor, terminals: Tensor, values: Tensor, bootstrap_value: Tensor, discount_rate: float, gae_lambda: float, batch_first: bool = False) -> Tensor: """TODO docstring Implementation adopted from [1]. References: [1] https://stable-baselines.readthedocs.io/en/master/_modules/stable_baselines/ppo2/ppo2.html#PPO2 """ return_ = torch.zeros_like(rewards) next_value = bootstrap_value old_gae = torch.zeros_like(next_value) num_steps = rewards.shape[1 if batch_first else 0] for t in reversed(range(num_steps)): index_t = (slice(None), t) if batch_first else (t, slice(None)) non_terminal = ~terminals[index_t] value_t = values[index_t] delta = rewards[index_t] - value_t delta[non_terminal] += discount_rate * next_value[non_terminal] gae = delta.clone() gae[non_terminal] += discount_rate * gae_lambda * old_gae[non_terminal] old_gae = gae return_[index_t] = gae + value_t next_value = value_t return return_
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pyqt_drawer/drawer.py
yjg30737/pyqt-drawer
7
164698
<reponame>yjg30737/pyqt-drawer from PyQt5.QtWidgets import QWidget, QGridLayout, QGraphicsOpacityEffect, qApp, QMainWindow from PyQt5.QtCore import Qt, QPropertyAnimation, QAbstractAnimation, QParallelAnimationGroup from PyQt5.QtGui import QColor, QPalette from pyqt_svg_icon_pushbutton import SvgIconPushButton class Drawer(QWidget): def __init__(self, parent, widget, orientation=Qt.Horizontal): super().__init__() self.__initUi(parent, widget=widget, orientation=orientation) def __initUi(self, parent, widget, orientation): self.__parent = parent self.__parent.installEventFilter(self) self.__btn = SvgIconPushButton() self.__btn.setCheckable(True) self.__btn.toggled.connect(self.__drawerToggled) self.__btn.setIcon('ico/drawer.svg') self.__btn.setPadding(5) self.__widget = widget align = '' # init size animation self.__sizeAnimation = '' if orientation == Qt.Horizontal: self.__widget.setFixedWidth(0) align = Qt.AlignLeft self.__sizeAnimation = QPropertyAnimation(self, b"width") self.__sizeAnimation.valueChanged.connect(self.__widget.setFixedWidth) else: self.__widget.setFixedHeight(0) align = Qt.AlignTop self.__sizeAnimation = QPropertyAnimation(self, b"height") self.__sizeAnimation.valueChanged.connect(self.__widget.setFixedHeight) self.__sizeAnimation.setStartValue(0) self.__sizeAnimation.setDuration(200) # default duration self.__sizeAnimation.setEndValue(200) # default end value # init opacity animation self.__opacityAnimation = QPropertyAnimation(self, b"opacity") self.__widget.setGraphicsEffect(QGraphicsOpacityEffect(opacity=0.0)) self.__opacityAnimation.valueChanged.connect(self.__setOpacity) self.__opacityAnimation.setStartValue(0.0) self.__opacityAnimation.setDuration(200) self.__opacityAnimation.setEndValue(1.0) self.__parentOpacityAnimation = QPropertyAnimation(self, b"parentopacity") self.__parentOpacityAnimation.valueChanged.connect(self.__setParentOpacity) self.__parentOpacityAnimation.setStartValue(255) self.__parentOpacityAnimation.setDuration(200) self.__parentOpacityAnimation.setEndValue(127) self.__animationGroup = QParallelAnimationGroup() self.__animationGroup.addAnimation(self.__sizeAnimation) self.__animationGroup.addAnimation(self.__opacityAnimation) self.__animationGroup.addAnimation(self.__parentOpacityAnimation) self.__animationGroup.stateChanged.connect(self.__animationStateChanged) lay = QGridLayout() lay.addWidget(self.__btn, 0, 0, 1, 1, Qt.AlignTop | Qt.AlignLeft) lay.addWidget(self.__widget, 0, 0, 1, 2, align) lay.setSpacing(0) lay.setContentsMargins(0, 0, 0, 0) self.setLayout(lay) def __drawerToggled(self, f): if f: self.__animationGroup.setDirection(QAbstractAnimation.Forward) self.__btn.hide() else: self.__animationGroup.setDirection(QAbstractAnimation.Backward) self.__btn.show() self.__animationGroup.start() def __setOpacity(self, opacity): opacity_effect = QGraphicsOpacityEffect(opacity=opacity) self.__widget.setGraphicsEffect(opacity_effect) def __setParentOpacity(self, opacity): palette = self.__parent.palette() palette.setColor(QPalette.Window, QColor(255, 255, 255, opacity)) self.__parent.setPalette(palette) def setDuration(self, msecs): self.__sizeAnimation.setDuration(msecs) self.__opacityAnimation.setDuration(msecs) self.__parentOpacityAnimation.setDuration(msecs) def setEndValue(self, value): self.__sizeAnimation.setEndValue(value) def setIcon(self, icon): self.__btn.setIcon(icon) def eventFilter(self, obj, e): if isinstance(obj, type(self.__parent)): if e.type() == 3: if self.__sizeAnimation.currentValue() == self.__sizeAnimation.endValue(): self.__btn.toggle() return super().eventFilter(obj, e) def __animationStateChanged(self, new_state, old_state): if new_state == 2: self.__widget.setAttribute(Qt.WA_TransparentForMouseEvents, True) self.__parentOpacityAnimation.start() elif new_state == 0: self.__widget.setAttribute(Qt.WA_TransparentForMouseEvents, False)
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vows/blacklist_vows.py
2947721120/thumbor
0
1608274
<gh_stars>0 #!/usr/bin/python # -*- coding: utf-8 -*- # thumbor imaging service # https://github.com/thumbor/thumbor/wiki # Licensed under the MIT license: # http://www.opensource.org/licenses/mit-license # Copyright (c) 2011 globo.com <EMAIL> from pyvows import Vows, expect from tornado_pyvows.context import TornadoHTTPContext from thumbor.app import ThumborServiceApp from thumbor.importer import Importer from thumbor.config import Config from thumbor.context import Context, ServerParameters from os.path import abspath, join, dirname, exists from shutil import rmtree class BaseContext(TornadoHTTPContext): def get_app(self): file_storage_root_path = '/tmp/thumbor-vows/storage' if exists(file_storage_root_path): rmtree(file_storage_root_path) cfg = Config() cfg.USE_BLACKLIST = True cfg.LOADER = "thumbor.loaders.file_loader" cfg.FILE_LOADER_ROOT_PATH = abspath(join(dirname(__file__), 'fixtures/')) cfg.STORAGE = 'thumbor.storages.file_storage' cfg.FILE_STORAGE_ROOT_PATH = file_storage_root_path importer = Importer(cfg) importer.import_modules() server = ServerParameters(8889, 'localhost', 'thumbor.conf', None, 'debug', None) ctx = Context(server, cfg, importer) application = ThumborServiceApp(ctx) return application @Vows.batch class Blacklist(BaseContext): class BaseBlacklist(TornadoHTTPContext): def topic(self): response = self.get('/blacklist') return (response.code, response.body) def should_return_blank(self, topic): expect(topic[1]).to_equal("") def should_return_200(self, topic): expect(topic[0]).to_equal(200) class AddingToBlacklist(TornadoHTTPContext): def topic(self): response = self.fetch('/blacklist?blocked.jpg', method='PUT', body='') return (response.code, response.body) def should_return_200(self, topic): expect(topic[0]).to_equal(200) class ReadingUpdatedBlacklist(TornadoHTTPContext): def topic(self): response = self.get('/blacklist') return (response.code, response.body) def should_return_200(self, topic): expect(topic[0]).to_equal(200) def should_contain_blacklisted_file(self, topic): expect("blocked.jpg\n" in topic[1]).to_equal(True) @Vows.batch class BlacklistIntegration(BaseContext): class NormalGetImage(TornadoHTTPContext): def topic(self): response = self.get('/unsafe/image.jpg') return response.code def should_return_200(self, topic): expect(topic).to_equal(200) class BlacklistedGetImage(TornadoHTTPContext): def topic(self): self.fetch('/blacklist?image.jpg', method='PUT', body='') response = self.get('/unsafe/image.jpg') return response.code def should_return_bad_request(self, topic): expect(topic).to_equal(400)
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lab03/gabriel-teston.py
Desnord/lab-mc102
7
85579
# Rescebe os valores de entrada. P1 = float(input()) P2 = float(input()) Ml = float(input()) # Calculo da media parcial. Mp = (2 * P1 + 3 * P2) / 5.0 # Regras para aprovação. if Ml < 5.0 or Mp < 5.0: M = min(Mp, 4.9) else: M = (3 * Mp + 2 * Ml) / 5.0 if 5.0 > M >= 2.5: E = float(input()) F = (M + E) / 2.0 else: F = M # Imprime os resultados. print("%.1f" % Mp) print("%.1f" % M) print("%.1f" % F)
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Dense_mnist.py
YuanshengZhao/adiabaticbinary
0
123895
import tensorflow as tf import numpy as np import layers import mnist import trainer import sys class DenseNet(tf.keras.Model): def __init__(self,binW,binA): super(DenseNet, self).__init__() self.binW=binW self.binA=binA self.flat= tf.keras.layers.Flatten() self.dense1 = layers.BinaryDense(128) if binW else tf.keras.layers.Dense(128) self.dense2 = layers.BinaryDense(10) if binW else tf.keras.layers.Dense(10) self.actv1=layers.BinaryActivationRL() if binA else tf.keras.layers.Activation("relu") self.actv2=tf.keras.layers.Activation("softmax") self.drop=tf.keras.layers.Dropout(.2) def set_kk(self, kka): if not self.binW: raise NotImplementedError self.dense1.set_kk(kka) self.dense2.set_kk(kka) def set_ka(self,kka): if not self.binA: raise NotImplementedError self.actv1.set_kk(kka) def get_kk(self): if not self.binW: raise NotImplementedError return self.dense1.kk def get_ka(self): if not self.binA: raise NotImplementedError return self.actv1.kk def call(self, inputs, training=False): x=self.drop(self.actv1(self.dense1(self.flat(inputs))),training=training) return self.actv2(self.dense2(x)) bw=int(sys.argv[1]) ba=int(sys.argv[2]) if(bw and not ba): mode="w" elif(ba and not bw): mode="a" else: mode="b" print("VGG Small, mode "+mode) model=DenseNet(bw,ba) optz=tf.keras.optimizers.Adam() model(mnist.x_train[:2]) model.compile(optimizer=optz, loss='sparse_categorical_crossentropy', metrics=['accuracy']) model.summary() trr=trainer.Trainer(1000,mode) trr.val_bs=32 trr.lr_power=.2 model.evaluate(mnist.x_val,mnist.y_val,batch_size=trr.val_bs,verbose=2) if(mode=="a"): trr.sw_epc=0 trr.lr_base=1.5 trr.lr_power=.3 model.set_ka(trr.lr_base) trr.train(model,None,mnist.x_train,mnist.y_train,mnist.x_val,mnist.y_val,8,1e-3,"Dse_",True,mnist.x_test,mnist.y_test) model.set_ka(6) trr.train(model,None,mnist.x_train,mnist.y_train,mnist.x_val,mnist.y_val,3,1e-3,"Dse_",True,mnist.x_test,mnist.y_test) model.set_ka(1001) trr.train(model,None,mnist.x_train,mnist.y_train,mnist.x_val,mnist.y_val,2,1e-3,"Dse_",True,mnist.x_test,mnist.y_test) if(mode=="w"): trr.sw_epc=0 trr.lr_base=3.0 model.set_kk(trr.lr_base) trr.train(model,None,mnist.x_train,mnist.y_train,mnist.x_val,mnist.y_val,8,1e-3,"Dse_",True,mnist.x_test,mnist.y_test) for kkz in [10, 20, 50, 100, 300, 500, 999]: model.set_kk(kkz) trr.train(model,None,mnist.x_train,mnist.y_train,mnist.x_val,mnist.y_val,3,1e-3,"Dse_",True,mnist.x_test,mnist.y_test) if(mode=="b"): trr.sw_epc=0 trr.lr_base=3.0 model.set_kk(trr.lr_base) model.set_ka(trr.lr_base) trr.train(model,None,mnist.x_train,mnist.y_train,mnist.x_val,mnist.y_val,8,1e-3,"Dse_",True,mnist.x_test,mnist.y_test) for kkz in [10, 20, 50, 100, 300, 500, 999]: model.set_ka(kkz) model.set_kk(kkz) trr.train(model,None,mnist.x_train,mnist.y_train,mnist.x_val,mnist.y_val,3,1e-3,"Dse_",True,mnist.x_test,mnist.y_test)
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bulma/apps.py
makiolo/django-bulma
330
137759
from django.apps import AppConfig class BulmaConfig(AppConfig): name = 'bulma'
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bnn_mcmc_examples/datasets/pima/data1/constants.py
papamarkou/bnn_mcmc_examples
1
197212
<reponame>papamarkou/bnn_mcmc_examples # %% Import packages from bnn_mcmc_examples.datasets import data_paths # %% Define constants data_base_name = 'data1' data_path = data_paths['pima'].joinpath(data_base_name)
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TrainHelper/callbacks/__init__.py
MaLiN2223/TrainHelper
1
113493
<reponame>MaLiN2223/TrainHelper from .repeater import Repeater
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src/api/controllers/connection/CheckConnectionDatabaseResource.py
PythonDataIntegrator/pythondataintegrator
14
49370
from injector import inject from domain.connection.CheckDatabaseConnection.CheckDatabaseConnectionCommand import CheckDatabaseConnectionCommand from domain.connection.CheckDatabaseConnection.CheckDatabaseConnectionRequest import CheckDatabaseConnectionRequest from infrastructure.api.ResourceBase import ResourceBase from infrastructure.api.decorators.Controller import controller from infrastructure.cqrs.Dispatcher import Dispatcher @controller() class CheckConnectionDatabaseResource(ResourceBase): @inject def __init__(self, dispatcher: Dispatcher, *args, **kwargs): super().__init__(*args, **kwargs) self.dispatcher = dispatcher def post(self, req: CheckDatabaseConnectionRequest): """ Check Database Connection """ command = CheckDatabaseConnectionCommand(request=req) self.dispatcher.dispatch(command)
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depsolver/solver/policy.py
teoliphant/depsolver
1
149791
<filename>depsolver/solver/policy.py<gh_stars>1-10 import collections import six from depsolver.errors \ import \ DepSolverError from depsolver.version \ import \ MaxVersion class DefaultPolicy(object): """A Policy class that implements 'reasonable' defaults. Its behavior is: 1. when multiple candidates are available, pick up the highest version first 2. if a package is already installed, it takes precendence over higher version (over-ruling 1.) """ def _compute_prefered_packages_installed_first(self, pool, installed_map, package_ids): """Returns a package name -> package queue mapping, with each queue being priority-sorted. It puts packages already installed first Arguments --------- pool: Pool Pool instance used to resolve packages, provides, etc... Returns ------- package_queue: dict package name -> sorted queue of package ids. """ package_name_to_package_ids = collections.defaultdict(collections.deque) for package_id in package_ids: package = pool.package_by_id(package_id) if package_id in installed_map: package_name_to_package_ids[package.name].appendleft(package_id) else: package_name_to_package_ids[package.name].append(package_id) return package_name_to_package_ids def prefered_package_ids(self, pool, installed_map, decision_queue): """Return a list of preferred package ids to install for the given package ids list, sorted by priority (from highest to lowest).""" package_queues = \ self._compute_prefered_packages_installed_first(pool, installed_map, decision_queue) def package_id_to_version(package_id): if package_id in installed_map: return MaxVersion() else: package = pool.package_by_id(package_id) return package.version for package_name, package_queue in package_queues.items(): sorted_package_queue = sorted(package_queue, key=package_id_to_version)[::-1] package_queues[package_name] = sorted_package_queue for package_name, package_queue in package_queues.items(): package_queues[package_name] = prune_to_best_version(pool, package_queue) if len(package_queues) > 1: raise NotImplementedError("More than one package name in select " \ "and install not supported yet") else: try: candidates = six.next(iter(package_queues.values())) except StopIteration: raise DepSolverError("No candidate in package_queues ?") return collections.deque(candidates) def prune_to_best_version(pool, package_ids): # Assume package_ids is already sorted (from max to min) if len(package_ids) < 1: return [] else: best_package = pool.package_by_id(package_ids[0]) best_version_only = [package_ids[0]] for package_id in package_ids[1:]: package = pool.package_by_id(package_id) if package.version < best_package.version: break else: best_version_only.append(package_id) return best_version_only
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acceptance_tests/features/pages/inbox_internal.py
ONSdigital/ras-integration-tests
2
160501
from acceptance_tests import browser from common.browser_utilities import wait_for_url_matches from config import Config def go_to_using_context(context, conversation_tab='open'): tab = conversation_tab.replace(' ', '+') target_url = f"{Config.RESPONSE_OPERATIONS_UI}/messages/{context.short_name}?conversation_tab={tab}" browser.visit(target_url) wait_for_url_matches(target_url, timeout=3, retry=0.5, post_change_delay=0.1) def go_to_select_survey(): target_url = f"{Config.RESPONSE_OPERATIONS_UI}/messages/select-survey" browser.visit(target_url) wait_for_url_matches(target_url, timeout=3, retry=0.5, post_change_delay=0.1) def get_page_title(): return browser.title def get_messages(): messages = [] table = browser.find_by_id('tbl-messages') rows = table.find_by_tag('tbody').find_by_tag('tr') for row in rows: messages.append({ 'ru_ref': row.find_by_name('tbl-messages-RU_Ref').value, 'business_name': row.find_by_name('tbl-messages-business').value, 'subject': row.find_by_name('tbl-messages-subject').value, 'from': row.find_by_name('tbl-messages-from').value, 'to': row.find_by_name('tbl-messages-to').value, 'received': row.find_by_name('tbl-messages-received').value }) return messages def get_table_heading(): table = browser.find_by_id('tbl-messages') headings = table.find_by_tag('thead').find_by_tag('tr') return headings[0].value def get_no_messages_text(): return browser.find_by_text('No new conversations') def get_no_closed_conversations_text(): return browser.find_by_text('No closed conversations') def get_no_my_conversations_text(): return browser.find_by_text('There are currently no messages') def get_dropdown_list(): return browser.driver.find_element_by_id('survey-list') def get_filter_page_title(): return browser.find_by_text('Filter messages by survey') def get_unread_messages(): return browser.find_by_name('message-unread') def get_pagination_previous_link(): return browser.driver.find_element_by_class_name('previous') def get_pagination(): return browser.driver.find_element_by_class_name('pagination') def get_pagination_next_link(): return browser.driver.find_element_by_class_name('next') def get_message_link_index(number_of_messages): return browser.find_by_id(f"message-link-{number_of_messages}") def closed_tab_present(): return browser.driver.find_element_by_link_text('Closed')
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deepinesStore/cardg.py
xoascf/store_deepines
6
33873
<filename>deepinesStore/cardg.py<gh_stars>1-10 # -*- coding: utf-8 -*- # Form implementation generated from reading ui file 'guis/card.ui' # # Created by: PyQt5 UI code generator 5.13.0 # # WARNING! All changes made in this file will be lost! from PyQt5 import QtCore, QtGui, QtWidgets class QLabelClickable(QtWidgets.QLabel): clicked = QtCore.pyqtSignal() def __init__(self, *args): QtWidgets.QLabel.__init__(self, *args) def mouseReleaseEvent(self, ev): self.clicked.emit() class Ui_Frame(object): def setupUi(self, Frame): Frame.setObjectName("Frame") Frame.resize(230, 249) Frame.setMinimumSize(QtCore.QSize(226, 249)) Frame.setMaximumSize(QtCore.QSize(230, 16777215)) self.verticalLayout = QtWidgets.QVBoxLayout(Frame) self.verticalLayout.setContentsMargins(0, 0, 0, 0) self.verticalLayout.setSpacing(0) self.verticalLayout.setObjectName("verticalLayout") self.image_app = QLabelClickable(Frame) self.image_app.setText("") self.image_app.setScaledContents(True) self.image_app.setAlignment(QtCore.Qt.AlignCenter) self.image_app.setCursor(QtGui.QCursor(QtCore.Qt.PointingHandCursor)) self.image_app.setObjectName("image_app") self.image_app.setStyleSheet("#image_app{margin-top: 10px;}") self.verticalLayout.addWidget(self.image_app) self.lbl_name_app = QLabelClickable(Frame) self.lbl_name_app.setStyleSheet("background-color: transparent;" "margin-top:5px;") self.lbl_name_app.setText("") self.lbl_name_app.setAlignment(QtCore.Qt.AlignCenter) self.lbl_name_app.setCursor(QtGui.QCursor(QtCore.Qt.PointingHandCursor)) font = QtGui.QFont() font.setFamily("Segoe UI Semibold") font.setPointSize(11) font.setItalic(False) self.lbl_name_app.setFont(font) self.lbl_name_app.setWordWrap(True) self.lbl_name_app.setObjectName("lbl_name_app") self.verticalLayout.addWidget(self.lbl_name_app) self.btn_select_app = QLabelClickable(Frame) font = QtGui.QFont() font.setFamily("Segoe UI Semibold") font.setPointSize(9) font.setItalic(False) self.btn_select_app.setFont(font) self.btn_select_app.setWordWrap(True) self.btn_select_app.setAlignment(QtCore.Qt.AlignCenter) self.btn_select_app.setCursor(QtGui.QCursor(QtCore.Qt.PointingHandCursor)) self.btn_select_app.setObjectName("btn_select_app") self.verticalLayout.addWidget(self.btn_select_app) self.retranslateUi(Frame) QtCore.QMetaObject.connectSlotsByName(Frame) def retranslateUi(self, Frame): _translate = QtCore.QCoreApplication.translate Frame.setWindowTitle(_translate("Frame", "Card")) self.btn_select_app.setText(_translate("Frame", "Instalar"))
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setup.py
rabernat/xarray
0
196445
#!/usr/bin/env python import os import re import sys import warnings from setuptools import setup, find_packages from setuptools import Command MAJOR = 0 MINOR = 10 MICRO = 0 ISRELEASED = False VERSION = '%d.%d.%d' % (MAJOR, MINOR, MICRO) QUALIFIER = 'rc2' DISTNAME = 'xarray' LICENSE = 'Apache' AUTHOR = 'xarray Developers' AUTHOR_EMAIL = '<EMAIL>' URL = 'https://github.com/pydata/xarray' CLASSIFIERS = [ 'Development Status :: 4 - Beta', 'License :: OSI Approved :: Apache Software License', 'Operating System :: OS Independent', 'Intended Audience :: Science/Research', 'Programming Language :: Python', 'Programming Language :: Python :: 2', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.4', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', 'Topic :: Scientific/Engineering', ] INSTALL_REQUIRES = ['numpy >= 1.11', 'pandas >= 0.18.0'] TESTS_REQUIRE = ['pytest >= 2.7.1'] if sys.version_info[0] < 3: TESTS_REQUIRE.append('mock') DESCRIPTION = "N-D labeled arrays and datasets in Python" LONG_DESCRIPTION = """ **xarray** (formerly **xray**) is an open source project and Python package that aims to bring the labeled data power of pandas_ to the physical sciences, by providing N-dimensional variants of the core pandas data structures. Our goal is to provide a pandas-like and pandas-compatible toolkit for analytics on multi-dimensional arrays, rather than the tabular data for which pandas excels. Our approach adopts the `Common Data Model`_ for self- describing scientific data in widespread use in the Earth sciences: ``xarray.Dataset`` is an in-memory representation of a netCDF file. .. _pandas: http://pandas.pydata.org .. _Common Data Model: http://www.unidata.ucar.edu/software/thredds/current/netcdf-java/CDM .. _netCDF: http://www.unidata.ucar.edu/software/netcdf .. _OPeNDAP: http://www.opendap.org/ Important links --------------- - HTML documentation: http://xarray.pydata.org - Issue tracker: http://github.com/pydata/xarray/issues - Source code: http://github.com/pydata/xarray - SciPy2015 talk: https://www.youtube.com/watch?v=X0pAhJgySxk """ # Code to extract and write the version copied from pandas. # Used under the terms of pandas's license, see licenses/PANDAS_LICENSE. FULLVERSION = VERSION write_version = True if not ISRELEASED: import subprocess FULLVERSION += '.dev' pipe = None for cmd in ['git', 'git.cmd']: try: pipe = subprocess.Popen( [cmd, "describe", "--always", "--match", "v[0-9]*"], stdout=subprocess.PIPE) (so, serr) = pipe.communicate() if pipe.returncode == 0: break except: pass if pipe is None or pipe.returncode != 0: # no git, or not in git dir if os.path.exists('xarray/version.py'): warnings.warn("WARNING: Couldn't get git revision, using existing xarray/version.py") write_version = False else: warnings.warn("WARNING: Couldn't get git revision, using generic version string") else: # have git, in git dir, but may have used a shallow clone (travis does this) rev = so.strip() # makes distutils blow up on Python 2.7 if sys.version_info[0] >= 3: rev = rev.decode('ascii') if not rev.startswith('v') and re.match("[a-zA-Z0-9]{7,9}", rev): # partial clone, manually construct version string # this is the format before we started using git-describe # to get an ordering on dev version strings. rev = "v%s.dev-%s" % (VERSION, rev) # Strip leading v from tags format "vx.y.z" to get th version string FULLVERSION = rev.lstrip('v') else: FULLVERSION += QUALIFIER def write_version_py(filename=None): cnt = """\ version = '%s' short_version = '%s' """ if not filename: filename = os.path.join( os.path.dirname(__file__), 'xarray', 'version.py') a = open(filename, 'w') try: a.write(cnt % (FULLVERSION, VERSION)) finally: a.close() if write_version: write_version_py() setup(name=DISTNAME, version=FULLVERSION, license=LICENSE, author=AUTHOR, author_email=AUTHOR_EMAIL, classifiers=CLASSIFIERS, description=DESCRIPTION, long_description=LONG_DESCRIPTION, install_requires=INSTALL_REQUIRES, tests_require=TESTS_REQUIRE, url=URL, packages=find_packages(), package_data={'xarray': ['tests/data/*', 'plot/default_colormap.csv']})
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models/Conditionners/CouplingConditioner.py
jonathandumas/generative-models-power-systems
11
1600329
<filename>models/Conditionners/CouplingConditioner.py from .Conditioner import Conditioner import torch import torch.nn as nn class CouplingMLP(nn.Module): def __init__(self, in_size, hidden, out_size, cond_in = 0): super(CouplingMLP, self).__init__() l1 = [in_size - int(in_size/2) + cond_in] + hidden l2 = hidden + [out_size * int(in_size/2)] layers = [] for h1, h2 in zip(l1, l2): layers += [nn.Linear(h1, h2), nn.ReLU()] layers.pop() self.net = nn.Sequential(*layers) def forward(self, x): return self.net(x) class CouplingConditioner(Conditioner): def __init__(self, in_size, hidden, out_size, cond_in=0): super(CouplingConditioner, self).__init__() self.in_size = in_size self.out_size = out_size self.cond_size = int(in_size/2) self.indep_size = in_size - self.cond_size self.embeding_net = CouplingMLP(in_size, hidden, out_size, cond_in) self.constants = nn.Parameter(torch.randn(self.indep_size, out_size)) self.register_buffer("A", torch.cat((torch.zeros(self.cond_size, in_size), torch.cat((torch.ones(self.indep_size, self.cond_size), torch.zeros(self.indep_size, self.indep_size)), 1)), 0)) def forward(self, x, context=None): if context is not None: x = torch.cat((x, context), 1) h1 = self.constants.unsqueeze(0).expand(x.shape[0], -1, -1) h2 = self.embeding_net(x[:, :self.indep_size]).view(x.shape[0], self.cond_size, self.out_size) return torch.cat((h1, h2), 1) def depth(self): return 1
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