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beba7761e3c9f91219f06b74b9cbab4211c96b1e | 4148260054c2cf4605dacb8bdef3605c82eca470 | /temboo/Library/YouTube/GetVideoData.py | 251023437876e0043e127d825dfedaf39bd70dca | [] | no_license | wimsy/actuarize-web | 0f23d5f00afe3d36d430621cdb497d2e64998416 | 5f43af3019da6fb08cafeec9ff0a89df5196b864 | refs/heads/master | 2021-03-12T19:38:21.887681 | 2012-12-19T01:13:50 | 2012-12-19T01:13:50 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,892 | py | # -*- coding: utf-8 -*-
###############################################################################
#
# GetVideoData
# Retrieve information about a single video using its ID.
#
# Python version 2.6
#
###############################################################################
from temboo.core.choreography import Choreography
from temboo.core.choreography import InputSet
from temboo.core.choreography import ResultSet
from temboo.core.choreography import ChoreographyExecution
class GetVideoData(Choreography):
"""
Create a new instance of the GetVideoData Choreography. A TembooSession object, containing a valid
set of Temboo credentials, must be supplied.
"""
def __init__(self, temboo_session):
Choreography.__init__(self, temboo_session, '/Library/YouTube/GetVideoData')
def new_input_set(self):
return GetVideoDataInputSet()
def _make_result_set(self, result, path):
return GetVideoDataResultSet(result, path)
def _make_execution(self, session, exec_id, path):
return GetVideoDataChoreographyExecution(session, exec_id, path)
"""
An InputSet with methods appropriate for specifying the inputs to the GetVideoData
choreography. The InputSet object is used to specify input parameters when executing this choreo.
"""
class GetVideoDataInputSet(InputSet):
"""
Set the value of the Callback input for this choreography. ((optional, string) Value to identify the callback function to which the API response will be sent. Only necessary when ResponseFormat is jason-in-script.)
"""
def set_Callback(self, value):
InputSet._set_input(self, 'Callback', value)
"""
Set the value of the ResponseFormat input for this choreography. ((optional, string) The format of the response from YouTube. Accepts atom, rss, json, json-in-script, and jsonc. Defaults to atom.)
"""
def set_ResponseFormat(self, value):
InputSet._set_input(self, 'ResponseFormat', value)
"""
Set the value of the VideoID input for this choreography. ((required, string) The unique ID given to a video by YouTube.)
"""
def set_VideoID(self, value):
InputSet._set_input(self, 'VideoID', value)
"""
A ResultSet with methods tailored to the values returned by the GetVideoData choreography.
The ResultSet object is used to retrieve the results of a choreography execution.
"""
class GetVideoDataResultSet(ResultSet):
"""
Retrieve the value for the "Response" output from this choreography execution. (The response from YouTube.)
"""
def get_Response(self):
return self._output.get('Response', None)
class GetVideoDataChoreographyExecution(ChoreographyExecution):
def _make_result_set(self, response, path):
return GetVideoDataResultSet(response, path)
| [
"[email protected]"
] | |
87b1dfad526d13c87d36c656f69dc17a42896b7a | 966280ab617298a3ced79bc60189b301c795067a | /Arrays/849_Maximize_Distance_to_Closest_Person.py | a0995f69ead8954fb648b5097c491a25f7386fab | [] | no_license | Rishabhh/LeetCode-Solutions | c0382e5ba5b77832322c992418f697f42213620f | 2536744423ee9dc7da30e739eb0bca521c216f00 | refs/heads/master | 2020-06-10T02:37:42.103289 | 2019-05-29T06:38:02 | 2019-05-29T06:38:02 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 920 | py | class Solution:
def maxDistToClosest(self, seats):
"""
:type seats: List[int]
:rtype: int
"""
temp = []
MIN_ = -sys.maxsize
MAX_ = sys.maxsize
left_closest, right_closest = -1, -1
for ii, seat in enumerate(seats):
if seat == 1:
left_closest = ii
temp.append(-1)
else:
if left_closest >= 0:
temp.append(ii - left_closest)
else:
temp.append(MAX_)
res = MIN_
temp = temp[::-1]
for ii, seat in enumerate(seats[::-1], 0):
if seat == 1:
right_closest = ii
else:
if right_closest >= 0:
res = max(res, min(temp[ii], ii - right_closest))
else:
res = max(res, temp[ii])
return res
| [
"[email protected]"
] | |
c945d1ab3b59d516b883b0730daa41751640c90e | 95fcf7ebe0fa31d93a5792ec2970096840be2fa0 | /synthesize_forcing.py | 52c7e92d9cc3cc5c9b704f2dfc79ab793e35eef9 | [] | no_license | alphaparrot/rossby-blocking | d40036ff46a590cff115bca5cd65dae20b3872ca | a04beb0ff406f92002cd572387bc1ab23ccf5c48 | refs/heads/master | 2020-03-20T02:28:15.805933 | 2018-07-10T21:29:32 | 2018-07-10T21:29:32 | 137,114,260 | 0 | 0 | null | 2018-06-15T20:32:15 | 2018-06-12T18:48:57 | Jupyter Notebook | UTF-8 | Python | false | false | 2,663 | py | import numpy as np
import matplotlib.pyplot as plt
# Block Shape: (time, xwidth, twidth, phase, (xcoord,tcoord)) (the last only for onsets)
if __name__=="__main__":
nblocks = np.zeros((5,10,5,20))
starts = np.zeros((5,10,5,20,2),dtype=object)
avgdelays = np.zeros((5,10,5,20))
stddelays = np.zeros((5,10,5,20))
phase = np.zeros(20)
foundphase = False
t1 = 0.5
t2 = 10.0
x1 = 50.0
x2 = 5000.0
p1 = 0.5
p2 = 10.0
peaks = np.logspace(np.log10(p1),np.log10(p2),num=5)
xwidth = np.logspace(np.log10(x1),np.log10(x2),num=10)
twidth = np.logspace(np.log10(t1),np.log10(t2),num=5)
nt=0
for t in twidth:
nx=0
for x in xwidth:
ip=0
for p in peaks:
name = "block%02.1f_%04.1f_%02.1f.npy"%(t,x,p)
try:
output = np.load(name)
except:
print(name)
raise
for nphase in range(0,20):
nblocks[nt,nx,ip,nphase] = output[nphase]["nblocks"]
starts[nt,nx,ip,nphase,0] = output[nphase]["onset"][0]
starts[nt,nx,ip,nphase,1] = output[nphase]["onset"][1]
dels = np.array(output[nphase]["delay"][1])
if len(dels)>0:
avgdelays[nt,nx,ip,nphase] = np.mean(dels)
stddelays[nt,nx,ip,nphase] = np.std(dels)
else:
avgdelays[nt,nx,ip,nphase] = np.nan
stddelays[nt,nx,ip,nphase] = np.nan
if not foundphase:
phase[nphase] = output[nphase]["forcing phase"]
foundphase=True
ip+=1
nx+=1
print("Finished Time %d of %d"%(nt+1,len(twidth)))
nt+=1
nblkstats = np.zeros((5,10,5,2))
ndelstats = np.zeros((5,10,5,2))
nblkstats[:,:,:,0] = np.mean(nblocks,axis=3)
nblkstats[:,:,:,1] = np.std(nblocks,axis=3)
ndelstats[:,:,:,0] = np.nanmean(avgdelays,axis=3)
ndelstats[:,:,:,1] = np.sqrt(np.nansum((stddelays*avgdelays)**2,axis=3))
output = {"raw blocks":nblocks,
"onset coords":starts,
"onset delays":(avgdelays,stddelays),
"block stats":nblkstats,
"delay stats":ndelstats,
"forcing peak":peaks,
"forcing xwidth":xwidth,
"forcing twidth":twidth,
"phase":phase,
"shape":"(peak,xwidth,twidth,phase or (mean,std))"}
np.save("forcingsweep.npy",output)
| [
"[email protected]"
] | |
52261d342e614435f55e2603a9afba68ca74ec2a | 9f760efe9f29e1a6275897491a26717066009366 | /raet/test/test_resend.py | fff4d770daf03816762c6fd548e94c28b5f7eca8 | [
"Apache-2.0"
] | permissive | KennethWilke/raet | ba2e79dfcbd831bff8e34d9d69f9c4490c233d03 | 9e0aa33e1c75c9ff4c3843094c62806a22ea98bc | refs/heads/master | 2020-12-26T03:33:14.493683 | 2014-04-25T00:56:41 | 2014-04-25T00:56:41 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 7,454 | py | # -*- coding: utf-8 -*-
'''
Tests to try out stacking. Potentially ephemeral
'''
# pylint: skip-file
import os
import time
from ioflo.base.odicting import odict
from ioflo.base.aiding import Timer, StoreTimer
from ioflo.base import storing
from ioflo.base.consoling import getConsole
console = getConsole()
from salt.transport.road.raet import (raeting, nacling, packeting, keeping,
estating, yarding, transacting, stacking)
def test():
'''
initially
master on port 7530 with eid of 1
minion on port 7531 with eid of 0
eventually
master eid of 1
minion eid of 2
'''
console.reinit(verbosity=console.Wordage.concise)
store = storing.Store(stamp=0.0)
#master stack
masterName = "master"
signer = nacling.Signer()
masterSignKeyHex = signer.keyhex
privateer = nacling.Privateer()
masterPriKeyHex = privateer.keyhex
masterDirpath = os.path.join(os.getcwd(), 'keep', masterName)
#minion0 stack
minionName0 = "minion0"
signer = nacling.Signer()
minionSignKeyHex = signer.keyhex
privateer = nacling.Privateer()
minionPriKeyHex = privateer.keyhex
m0Dirpath = os.path.join(os.getcwd(), 'keep', minionName0)
keeping.clearAllKeepSafe(masterDirpath)
keeping.clearAllKeepSafe(m0Dirpath)
estate = estating.LocalEstate( eid=1,
name=masterName,
sigkey=masterSignKeyHex,
prikey=masterPriKeyHex,)
stack0 = stacking.RoadStack(name=masterName,
estate=estate,
store=store,
main=True,
dirpath=masterDirpath)
estate = estating.LocalEstate( eid=0,
name=minionName0,
ha=("", raeting.RAET_TEST_PORT),
sigkey=minionSignKeyHex,
prikey=minionPriKeyHex,)
stack1 = stacking.RoadStack(name=minionName0,
estate=estate,
store=store,
dirpath=m0Dirpath)
print "\n********* Join Transaction **********"
stack1.join()
#timer = StoreTimer(store=store, duration=3.0)
while store.stamp < 2.0:
stack1.serviceAll()
stack0.serviceAll()
if store.stamp >= 0.3:
for estate in stack0.estates.values():
if estate.acceptance == raeting.acceptances.pending:
stack0.safe.acceptRemote(estate)
store.advanceStamp(0.1)
time.sleep(0.1)
for estate in stack0.estates.values():
print "Remote Estate {0} joined= {1}".format(estate.eid, estate.joined)
for estate in stack1.estates.values():
print "Remote Estate {0} joined= {1}".format(estate.eid, estate.joined)
print "{0} eid={1}".format(stack0.name, stack0.estate.uid)
print "{0} estates=\n{1}".format(stack0.name, stack0.estates)
print "{0} transactions=\n{1}".format(stack0.name, stack0.transactions)
print "{0} eid={1}".format(stack1.name, stack1.estate.uid)
print "{0} estates=\n{1}".format(stack1.name, stack1.estates)
print "{0} transactions=\n{1}".format(stack1.name, stack1.transactions)
print "Road {0}".format(stack0.name)
print stack0.road.loadLocalData()
print stack0.road.loadAllRemoteData()
print "Safe {0}".format(stack0.name)
print stack0.safe.loadLocalData()
print stack0.safe.loadAllRemoteData()
print
print "Road {0}".format(stack1.name)
print stack1.road.loadLocalData()
print stack1.road.loadAllRemoteData()
print "Safe {0}".format(stack1.name)
print stack1.safe.loadLocalData()
print stack1.safe.loadAllRemoteData()
print
print "\n********* Allow Transaction **********"
if not stack1.estates.values()[0].joined:
return
stack1.allow()
#timer = StoreTimer(store=store, duration=3.0)
while store.stamp < 4.0:
stack1.serviceAll()
stack0.serviceAll()
store.advanceStamp(0.1)
time.sleep(0.1)
for estate in stack0.estates.values():
print "Remote Estate {0} allowed= {1}".format(estate.eid, estate.allowed)
for estate in stack1.estates.values():
print "Remote Estate {0} allowed= {1}".format(estate.eid, estate.allowed)
print "{0} eid={1}".format(stack0.name, stack0.estate.uid)
print "{0} estates=\n{1}".format(stack0.name, stack0.estates)
print "{0} transactions=\n{1}".format(stack0.name, stack0.transactions)
print "{0} eid={1}".format(stack1.name, stack1.estate.uid)
print "{0} estates=\n{1}".format(stack1.name, stack1.estates)
print "{0} transactions=\n{1}".format(stack1.name, stack1.transactions)
#while stack1.transactions or stack0.transactions:
#stack1.serviceAll()
#stack0.serviceAll()
#store.advanceStamp(0.1)
print "{0} Stats".format(stack0.name)
for key, val in stack0.stats.items():
print " {0}={1}".format(key, val)
print
print "{0} Stats".format(stack1.name)
for key, val in stack1.stats.items():
print " {0}={1}".format(key, val)
print
print "\n********* Message Transactions Both Ways Again **********"
#stack1.transmit(odict(house="Oh Boy1", queue="Nice"))
#stack1.transmit(odict(house="Oh Boy2", queue="Mean"))
#stack1.transmit(odict(house="Oh Boy3", queue="Ugly"))
#stack1.transmit(odict(house="Oh Boy4", queue="Pretty"))
#stack0.transmit(odict(house="Yeah Baby1", queue="Good"))
#stack0.transmit(odict(house="Yeah Baby2", queue="Bad"))
#stack0.transmit(odict(house="Yeah Baby3", queue="Fast"))
#stack0.transmit(odict(house="Yeah Baby4", queue="Slow"))
#segmented packets
stuff = []
for i in range(300):
stuff.append(str(i).rjust(10, " "))
stuff = "".join(stuff)
stack1.transmit(odict(house="Snake eyes", queue="near stuff", stuff=stuff))
stack0.transmit(odict(house="Craps", queue="far stuff", stuff=stuff))
#timer.restart(duration=3)
while store.stamp < 8.0: #not timer.expired
stack1.serviceAll()
stack0.serviceAll()
store.advanceStamp(0.1)
time.sleep(0.1)
print "{0} eid={1}".format(stack0.name, stack0.estate.uid)
print "{0} estates=\n{1}".format(stack0.name, stack0.estates)
print "{0} transactions=\n{1}".format(stack0.name, stack0.transactions)
print "{0} Received Messages".format(stack0.name)
for msg in stack0.rxMsgs:
print msg
print "{0} Stats".format(stack0.name)
for key, val in stack0.stats.items():
print " {0}={1}".format(key, val)
print
print "{0} eid={1}".format(stack1.name, stack1.estate.uid)
print "{0} estates=\n{1}".format(stack1.name, stack1.estates)
print "{0} transactions=\n{1}".format(stack1.name, stack1.transactions)
print "{0} Received Messages".format(stack1.name)
for msg in stack1.rxMsgs:
print msg
print "{0} Stats".format(stack1.name)
for key, val in stack1.stats.items():
print " {0}={1}".format(key, val)
print
stack0.server.close()
stack1.server.close()
stack0.clearLocal()
stack0.clearRemoteKeeps()
stack1.clearLocal()
stack1.clearRemoteKeeps()
if __name__ == "__main__":
test()
| [
"[email protected]"
] | |
8bc4198785572801be0e33bd3d272e583af48ee1 | 1f5dc6ce24fdf4557456e3cd6775142338043d9c | /active_reward_learning/envs/reward_model_mean_wrapper.py | 35a2121067a6bf1dea06a214f4b3b56acc0f21ba | [
"MIT"
] | permissive | david-lindner/idrl | 7c747211fc38e0478b26d254206ddef70d3dac64 | 54cfad330b0598ad4f6621796f2411644e50a6ba | refs/heads/main | 2023-09-02T11:20:23.120737 | 2021-11-16T17:31:33 | 2021-11-16T17:31:33 | 421,450,567 | 13 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,208 | py | from types import SimpleNamespace
from typing import Dict, Tuple, Union
import gym
import numpy as np
class RewardModelMeanWrapper(gym.RewardWrapper):
def __init__(self, env: gym.Env, reward_model, debug=False, normalize=False):
self.reward_model = reward_model
self.debug = debug
self.normalize = normalize
# import outside leads to circular import
from active_reward_learning.reward_models.kernels.linear import LinearKernel
self.is_linear = isinstance(self.reward_model.gp_model.kernel, LinearKernel)
super().__init__(env)
def step(self, action: int) -> Tuple[Union[int, np.ndarray], float, bool, Dict]:
obs, reward, done, info = self.env.step(action)
orig_reward = reward
info["true_reward"] = reward
if self.debug:
print()
print("gp_repr", info["gp_repr"])
print("reward true", reward)
if self.is_linear:
weight = self.reward_model.gp_model.linear_predictive_mean
if self.normalize:
weight /= np.linalg.norm(weight) + 1e-3
# DL: This is necessary for performance reasons in the Mujoco environments
reward = np.dot(info["gp_repr"], weight)
else:
if self.normalize:
raise NotImplementedError()
reward, _ = self.reward_model.gp_model.predict([info["gp_repr"]])
if isinstance(reward, np.ndarray):
assert reward.shape == (1,)
reward = reward[0]
info["inferred_reward"] = reward
if self.debug:
print("reward new", reward)
print()
return obs, reward, done, info
@classmethod
def load_from_model(cls, env, filename, debug=False):
# importing here prevents some circular dependencies
from active_reward_learning.reward_models.gaussian_process_linear import (
LinearObservationGP,
)
gp_model = LinearObservationGP.load(filename)
print(f"Loaded mode from {filename}")
reward_model = SimpleNamespace()
setattr(reward_model, "gp_model", gp_model)
return cls(env, reward_model, debug=debug)
| [
"[email protected]"
] | |
98635d4fc5f53515fc9a84f85e75df945ff982b5 | 9d0d3b7de45213b285856b85c0045166524826aa | /pymorphy2/tokenizers.py | 57806fb35350349c44c49a7111887a34b1e6c623 | [] | no_license | albedium/pymorphy2 | b9b41eacef6c3d12464a513f76e71e64ece125ee | 174662e229535cba7ae06360d14d41c943175b3a | refs/heads/master | 2021-01-14T11:49:19.806721 | 2015-05-02T21:06:20 | 2015-05-02T21:06:20 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 290 | py | # -*- coding: utf-8 -*-
import re
GROUPING_SPACE_REGEX = re.compile('([^\w_-]|[+])', re.UNICODE)
def simple_word_tokenize(text, _split=GROUPING_SPACE_REGEX.split):
""" Split text into tokens. Don't split by a hyphen. """
return [t for t in _split(text) if t and not t.isspace()]
| [
"[email protected]"
] | |
9e364fe7c9378298374f78d0f5533f5be9285466 | ecab049c0784e61b05fd9c76320cc7b330a63a73 | /Adversarial Games/connect4.py | 95224c8b6814908093c5bdfca9641f0cfa79dc41 | [] | no_license | Praj390/CPSC6420_Artificial_Intelligence | 50ea2440c3e46b1baed3a6d0706e70b41eb61255 | 8257a50fab73a909a9791521bf097a7a11291e2f | refs/heads/main | 2023-01-29T18:53:32.057094 | 2020-12-12T15:26:53 | 2020-12-12T15:26:53 | 306,147,373 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 14,476 | py | # connect4.py
# ---------
# Licensing Information: You are free to use or extend these projects for
# educational purposes provided that (1) you do not distribute or publish
# solutions, (2) you retain this notice, and (3) you provide clear
# attribution to Clemson University and the authors.
#
# Authors: Pei Xu ([email protected]) and Ioannis Karamouzas ([email protected])
#
"""
In this assignment, the task is to implement the minimax algorithm with depth
limit for a Connect-4 game.
To complete the assignment, you must finish these functions:
minimax (line 196), alphabeta (line 237), and expectimax (line 280)
in this file.
In the Connect-4 game, two players place discs in a 6-by-7 board one by one.
The discs will fall straight down and occupy the lowest available space of
the chosen column. The player wins if four of his or her discs are connected
in a line horizontally, vertically or diagonally.
See https://en.wikipedia.org/wiki/Connect_Four for more details about the game.
A Board() class is provided to simulate the game board.
It has the following properties:
b.rows # number of rows of the game board
b.cols # number of columns of the game board
b.PLAYER1 # an integer flag to represent the player 1
b.PLAYER2 # an integer flag to represent the player 2
b.EMPTY_SLOT # an integer flag to represent an empty slot in the board;
and the following methods:
b.terminal() # check if the game is terminal
# terminal means draw or someone wins
b.has_draw() # check if the game is a draw
w = b.who_wins() # return the winner of the game or None if there
# is no winner yet
# w should be in [b.PLAYER1,b.PLAYER2, None]
b.occupied(row, col) # check if the slot at the specific location is
# occupied
x = b.get(row, col) # get the player occupying the given slot
# x should be in [b.PLAYER1, b.PLAYER2, b.EMPTY_SLOT]
row = b.row(r) # get the specific row of the game described using
# b.PLAYER1, b.PLAYER2 and b.EMPTY_SLOT
col = b.column(r) # get a specific column of the game board
b.placeable(col) # check if a disc can be placed at the specific
# column
b.place(player, col) # place a disc at the specific column for player
# raise ValueError if the specific column does not have available space
new_board = b.clone() # return a new board instance having the same
# disc placement with b
str = b.dump() # a string to describe the game board using
# b.PLAYER1, b.PLAYER2 and b.EMPTY_SLOT
Hints:
1. Depth-limited Search
We use depth-limited search in the current code. That is we
stop the search forcefully, and perform evaluation directly not only when a
terminal state is reached but also when the search reaches the specified
depth.
2. Game State
Three elements decide the game state. The current board state, the
player that needs to take an action (place a disc), and the current search
depth (remaining depth).
3. Evaluation Target
The minimax algorithm always considers that the adversary tries to
minimize the score of the max player, for whom the algorithm is called
initially. The adversary never considers its own score at all during this
process. Therefore, when evaluating nodes, the target should always be
the max player.
4. Search Result
The pesudo code provided in the slides only returns the best utility value.
However, in practice, we need to select the action that is associated with this
value. Here, such action is specified as the column in which a disc should be
placed for the max player. Therefore, for each search algorithm, you should
consider all valid actions for the max player, and return the one that leads
to the best value.
"""
# use math library if needed
import math
def get_child_boards(player, board):
"""
Generate a list of succesor boards obtained by placing a disc
at the given board for a given player
Parameters
----------
player: board.PLAYER1 or board.PLAYER2
the player that will place a disc on the board
board: the current board instance
Returns
-------
a list of (col, new_board) tuples,
where col is the column in which a new disc is placed (left column has a 0 index),
and new_board is the resulting board instance
"""
res = []
for c in range(board.cols):
if board.placeable(c):
tmp_board = board.clone()
tmp_board.place(player, c)
res.append((c, tmp_board))
return res
def evaluate(player, board):
"""
This is a function to evaluate the advantage of the specific player at the
given game board.
Parameters
----------
player: board.PLAYER1 or board.PLAYER2
the specific player
board: the board instance
Returns
-------
score: float
a scalar to evaluate the advantage of the specific player at the given
game board
"""
adversary = board.PLAYER2 if player == board.PLAYER1 else board.PLAYER1
# Initialize the value of scores
# [s0, s1, s2, s3, --s4--]
# s0 for the case where all slots are empty in a 4-slot segment
# s1 for the case where the player occupies one slot in a 4-slot line, the rest are empty
# s2 for two slots occupied
# s3 for three
# s4 for four
score = [0]*5
adv_score = [0]*5
# Initialize the weights
# [w0, w1, w2, w3, --w4--]
# w0 for s0, w1 for s1, w2 for s2, w3 for s3
# w4 for s4
weights = [0, 1, 4, 16, 1000]
# Obtain all 4-slot segments on the board
seg = []
invalid_slot = -1
left_revolved = [
[invalid_slot]*r + board.row(r) + \
[invalid_slot]*(board.rows-1-r) for r in range(board.rows)
]
right_revolved = [
[invalid_slot]*(board.rows-1-r) + board.row(r) + \
[invalid_slot]*r for r in range(board.rows)
]
for r in range(board.rows):
# row
row = board.row(r)
for c in range(board.cols-3):
seg.append(row[c:c+4])
for c in range(board.cols):
# col
col = board.col(c)
for r in range(board.rows-3):
seg.append(col[r:r+4])
for c in zip(*left_revolved):
# slash
for r in range(board.rows-3):
seg.append(c[r:r+4])
for c in zip(*right_revolved):
# backslash
for r in range(board.rows-3):
seg.append(c[r:r+4])
# compute score
for s in seg:
if invalid_slot in s:
continue
if adversary not in s:
score[s.count(player)] += 1
if player not in s:
adv_score[s.count(adversary)] += 1
reward = sum([s*w for s, w in zip(score, weights)])
penalty = sum([s*w for s, w in zip(adv_score, weights)])
return reward - penalty
def minimax(player, board, depth_limit):
"""
Minimax algorithm with limited search depth.
Parameters
----------
player: board.PLAYER1 or board.PLAYER2
the player that needs to take an action (place a disc in the game)
board: the current game board instance
depth_limit: int
the tree depth that the search algorithm needs to go further before stopping
max_player: boolean
Returns
-------
placement: int or None
the column in which a disc should be placed for the specific player
(counted from the most left as 0)
None to give up the game
"""
max_player = player
placement = None
### Please finish the code below ##############################################
###############################################################################
def value(player, board, depth_limit):
if board.terminal() == True or depth_limit == 0 :
return evaluate(max_player,board), None
if player == max_player:
val,act = max_value(max_player,board,depth_limit)
return val,act
else:
val, act = min_value(next_player,board,depth_limit)
return val, act
def max_value(player, board, depth_limit):
score = -math.inf
successors = get_child_boards(player,board)
for act,successor in successors:
new_score, _ = value(next_player,successor,depth_limit-1)
if new_score > score:
score = new_score
action = act
return score , action
def min_value(player, board, depth_limit):
score = math.inf
successors = get_child_boards(player, board)
for act, successor in successors:
new_score, _ = value(max_player,successor,depth_limit-1)
if new_score < score:
score = new_score
action = act
return score,action
next_player = board.PLAYER2 if player == board.PLAYER1 else board.PLAYER1
score = -math.inf
score,placement = value(max_player,board,depth_limit)
return placement
def alphabeta(player, board, depth_limit):
"""
Minimax algorithm with alpha-beta pruning.
Parameters
----------
player: board.PLAYER1 or board.PLAYER2
the player that needs to take an action (place a disc in the game)
board: the current game board instance
depth_limit: int
the tree depth that the search algorithm needs to go further before stopping
alpha: float
beta: float
max_player: boolean
Returns
-------
placement: int or None
the column in which a disc should be placed for the specific player
(counted from the most left as 0)
None to give up the game
"""
max_player = player
placement = None
### Please finish the code below ##############################################
###############################################################################
def value(player, board,alpha,beta,depth_limit):
if board.terminal() == True or depth_limit == 0 :
return evaluate(max_player,board), None
if player == max_player:
val,act = max_value(max_player,board,alpha,beta,depth_limit)
return val,act
else:
val, act = min_value(next_player,board,alpha,beta,depth_limit)
return val, act
def max_value(player, board,alpha,beta, depth_limit):
score = -math.inf
successors = get_child_boards(player,board)
for act,successor in successors:
new_score, _ = value(next_player,successor,alpha,beta,depth_limit-1)
if new_score > score:
score = new_score
action = act
if new_score > alpha:
alpha = new_score
if beta <= alpha:
break
return score , action
def min_value(player, board,alpha,beta, depth_limit):
score = math.inf
successors = get_child_boards(player,board)
for act,successor in successors:
new_score, _ = value(max_player,successor,alpha,beta,depth_limit-1)
if new_score < score:
score = new_score
action = act
if new_score < alpha:
alpha = new_score
if beta <= alpha:
break
return score , action
next_player = board.PLAYER2 if player == board.PLAYER1 else board.PLAYER1
score = -math.inf
score,placement = value(max_player,board,-math.inf,+math.inf,depth_limit)
return placement
def expectimax(player, board, depth_limit):
"""
Expectimax algorithm.
We assume that the adversary of the initial player chooses actions
uniformly at random.
Say that it is the turn for Player 1 when the function is called initially,
then, during search, Player 2 is assumed to pick actions uniformly at
random.
Parameters
----------
player: board.PLAYER1 or board.PLAYER2
the player that needs to take an action (place a disc in the game)
board: the current game board instance
depth_limit: int
the tree depth that the search algorithm needs to go before stopping
max_player: boolean
Returns
-------
placement: int or None
the column in which a disc should be placed for the specific player
(counted from the most left as 0)
None to give up the game
"""
max_player = player
placement = None
### Please finish the code below ##############################################
###############################################################################
def value(player, board, depth_limit):
if board.terminal() == True or depth_limit == 0 :
return evaluate(max_player,board), None
if player == max_player:
val,act = max_value(max_player,board,depth_limit)
return val,act
else:
val,act = exp_value(next_player,board,depth_limit)
return val,act
def max_value(player, board, depth_limit):
score = -math.inf
successors = get_child_boards(player,board)
for act,successor in successors:
new_score, _ = value(next_player,successor,depth_limit-1)
if new_score > score:
score = new_score
action = act
return score , action
def exp_value(player, board, depth_limit):
score = 0
successors = get_child_boards(player,board)
for act,successor in successors:
new_score, _ = value(max_player,successor,depth_limit-1)
score += new_score/(len(successors))
return score , None
next_player = board.PLAYER2 if player == board.PLAYER1 else board.PLAYER1
score = -math.inf
score, placement = value(max_player,board,depth_limit)
###############################################################################
return placement
if __name__ == "__main__":
from utils.app import App
import tkinter
algs = {
"Minimax": minimax,
"Alpha-beta pruning": alphabeta,
"Expectimax": expectimax
}
root = tkinter.Tk()
App(algs, root)
root.mainloop()
| [
"[email protected]"
] | |
9970bdc6d139a4de2ffe6edfe933ebe791804b02 | 08024f21c1319d54ea3e524edf08e614eb0960bc | /old code/DoubanClient/douban_top250_demoV2.py | f8981b23e5671add044a5d515fc708ec6da8dc8e | [] | no_license | beforeuwait/webCrawl | 08dd5375da93f511672d746c64ef42cf5b25449c | dc30ed7ef00f077894cfc0c2555f9dddb22c3537 | refs/heads/master | 2020-06-23T02:16:40.828657 | 2017-06-15T02:59:05 | 2017-06-15T02:59:05 | 74,671,539 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 2,672 | py | # -*- coding:utf8 -*-
import requests
from lxml import etree
import pymongo
class DoubanClient():
def __init__(self):
object.__init__(self)
self.url = 'http://movie.douban.com/top250'
self.headers = {
'User-Agent':
'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.100 Safari/537.36',
'Host': 'movie.douban.com'
}
self.session = requests.session()
self.session.headers.update(self.headers)
self.connection = pymongo.MongoClient()
def setupsession(self):
r = self.session.get(self.url)
#获取response的cookies作为后续请求的cookies
self.cookies = r.cookies
self.session.cookies.update(self.cookies)
dbName =self.connection.Douban
self.post_info = dbName.DoubanMovieTop250
#创建链接时即创建数据库
return self.get_data(r.content)
def get_data(self, content):
selector = etree.HTML(content)
input_data = {}
Movies = selector.xpath('//div[@class="info"]')
for eachMovie in Movies:
title = eachMovie.xpath('div[@class="hd"]/a/span/text()')
full_title = ''
for each in title:
full_title += each
input_data['title'] = full_title
input_data['movieInfo'] = eachMovie.xpath('div[@class="bd"]/p/text()')[0].replace(' ','')
input_data['star'] = eachMovie.xpath('div[@class="bd"]/div[@class="star"]/span[@class="rating_num"]/text()')[0]
#测试过程中发现有的电影没有quote，这里需要对他做一个判断，没有quote的则赋空值
quote = eachMovie.xpath('div[@class="bd"]/p[@class="quote"]/span/text()')
if quote:
input_data['quote'] = quote[0]
else:
input_data['quote'] = ''
# 因为数据插入是一条一条以字典的格式，并不是插入一个字典，因此每次插入后，应该重新定义字典
self.post_info.insert(input_data)
input_data = {}
Paginator = selector.xpath('//span[@class="next"]/a/@href')
#到最后一页没有数据，则对列表做一个判断
if Paginator:
paginator_url = 'http://movie.douban.com/top250'+Paginator[0]
n = self.session.get(paginator_url)
return self.nextPage(n.content)
print 'it\'done'
#接收数据翻页数据，返回给get_data
def nextPage(self, content):
return self.get_data(content)
if __name__ == '__main__':
c = DoubanClient()
c.setupsession() | [
"[email protected]"
] | |
57c88811564948b7722125c6c0d4fb97522586aa | 7c964cd93343ac704ac3d9c82c977a0cd0a672e7 | /listing/migrations/0001_initial.py | de9a37a83efcd5555bca0d1c47a762b2a37abb1c | [] | no_license | praekelt/jmbo-listing | be32bd43225ccf6de2c530e46c54dcac6b3c6a46 | 91a9e369a67cccef38d125e16272e01187c0ef1c | refs/heads/master | 2020-04-19T10:31:51.345676 | 2017-08-30T12:20:30 | 2017-08-30T12:20:30 | 66,532,006 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,099 | py | # -*- coding: utf-8 -*-
# Generated by Django 1.9.6 on 2016-08-25 07:21
from __future__ import unicode_literals
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
('jmbo', '0003_auto_20160530_1247'),
('contenttypes', '0002_remove_content_type_name'),
('sites', '0002_alter_domain_unique'),
('category', '0001_initial'),
]
operations = [
migrations.CreateModel(
name='Listing',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('title', models.CharField(help_text=b'A short descriptive title.', max_length=256)),
('subtitle', models.CharField(blank=True, help_text=b'Some titles may be the same. A subtitle makes a distinction. It is not displayed on the site.', max_length=256, null=True)),
('slug', models.SlugField(max_length=32)),
('count', models.IntegerField(default=0, help_text=b'Number of items to display (excludes any pinned items).\nSet to zero to display all items.')),
('style', models.CharField(choices=[(b'Horizontal', b'Horizontal'), (b'Vertical', b'Vertical'), (b'Promo', b'Promo'), (b'VerticalThumbnail', b'VerticalThumbnail'), (b'Widget', b'Widget'), (b'CustomFive', b'CustomFive'), (b'CustomFour', b'CustomFour'), (b'CustomOne', b'CustomOne'), (b'CustomThree', b'CustomThree'), (b'CustomTwo', b'CustomTwo'), (b'Horizontal', b'Horizontal'), (b'Promo', b'Promo'), (b'Vertical', b'Vertical'), (b'VerticalThumbnail', b'VerticalThumbnail'), (b'Widget', b'Widget'), (b'Widget', b'Widget'), (b'CustomOne', b'CustomOne'), (b'CustomTwo', b'CustomTwo'), (b'CustomThree', b'CustomThree'), (b'CustomFour', b'CustomFour'), (b'CustomFive', b'CustomFive')], max_length=64)),
('items_per_page', models.PositiveIntegerField(default=0, help_text=b'Number of items displayed on a page (excludes any pinned items). Set to zero to disable paging.')),
('categories', models.ManyToManyField(blank=True, help_text=b'Categories for which to collect items.', null=True, related_name='listing_categories', to='category.Category')),
],
options={
'ordering': ('title', 'subtitle'),
},
),
migrations.CreateModel(
name='ListingContent',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('position', models.PositiveIntegerField(default=0)),
('listing', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='content_link_to_listing', to='listing.Listing')),
('modelbase_obj', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='jmbo.ModelBase')),
],
),
migrations.CreateModel(
name='ListingPinned',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('position', models.PositiveIntegerField(default=0)),
('listing', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='pinned_link_to_listing', to='listing.Listing')),
('modelbase_obj', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='jmbo.ModelBase')),
],
),
migrations.AddField(
model_name='listing',
name='content',
field=models.ManyToManyField(blank=True, help_text=b'Individual items to display. Setting this will ignore any setting for <i>Content Type</i>, <i>Categories</i> and <i>Tags</i>.', null=True, related_name='listing_content', through='listing.ListingContent', to='jmbo.ModelBase'),
),
migrations.AddField(
model_name='listing',
name='content_types',
field=models.ManyToManyField(blank=True, help_text=b'Content types to display, eg. post or gallery.', null=True, to='contenttypes.ContentType'),
),
migrations.AddField(
model_name='listing',
name='pinned',
field=models.ManyToManyField(blank=True, help_text=b'Individual items to pin to the top of the listing. These\nitems are visible across all pages when navigating the listing.', null=True, related_name='listing_pinned', through='listing.ListingPinned', to='jmbo.ModelBase'),
),
migrations.AddField(
model_name='listing',
name='sites',
field=models.ManyToManyField(blank=True, help_text=b'Sites that this listing will appear on.', null=True, to='sites.Site'),
),
migrations.AddField(
model_name='listing',
name='tags',
field=models.ManyToManyField(blank=True, help_text=b'Tags for which to collect items.', null=True, related_name='listing_tags', to='category.Tag'),
),
]
| [
"[email protected]"
] | |
339abc4f028a0a65d15b39f546ab2a0fa4beafd3 | 58e5e46b7e213e55f0f6e2a003b0a4ecfb261673 | /filtered_contenttypes/fields.py | 9bf1d2a7db8be61de7e3fa94d65a104ae86a4352 | [
"MIT"
] | permissive | pombredanne/djorm-ext-filtered-contenttypes | 3b334d89c08c74b898b8e27ae82581cbbd59013d | ad15e35d0242259182d72db8ae61455b2bc833fa | refs/heads/master | 2021-01-17T22:59:42.608934 | 2014-10-26T09:02:37 | 2014-10-26T09:02:37 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 7,677 | py | # -*- encoding: utf-8 -*-
from django.contrib.contenttypes.fields import GenericForeignKey
from django.db.models import Lookup
from django.contrib.contenttypes.models import ContentType
from django.db.models.lookups import RegisterLookupMixin
from django.db.models.query import QuerySet
from django.utils.itercompat import is_iterable
from django.db import models
class FilteredGenericForeignKeyFilteringException(Exception):
pass
class FilteredGenericForeignKey(RegisterLookupMixin, GenericForeignKey):
"""This is a GenericForeignKeyField, that can be used to perform
filtering in Django ORM.
"""
def __init__(self, *args, **kw):
# The line below is needed to bypass this
# https://github.com/django/django/commit/572885729e028eae2f2b823ef87543b7c66bdb10
# thanks to MarkusH @ freenode for help
self.attname = self.related = '(this is a hack)'
# this is needed when filtering(this__contains=x, this__not_contains=y)
self.null = False
GenericForeignKey.__init__(self, *args, **kw)
def get_prep_lookup(self, lookup_name, rhs):
"""
Perform preliminary non-db specific lookup checks and conversions
"""
if lookup_name == 'exact':
if not isinstance(rhs, models.Model):
raise FilteredGenericForeignKeyFilteringException(
"For exact lookup, please pass a single Model instance.")
elif lookup_name in ['in', 'in_raw']:
if type(rhs) == QuerySet:
return rhs, None
if not is_iterable(rhs):
raise FilteredGenericForeignKeyFilteringException(
"For 'in' lookup, please pass an iterable or a QuerySet.")
else:
raise FilteredGenericForeignKeyFilteringException(
"Lookup %s not supported." % lookup_name)
return rhs, None
def get_db_prep_lookup(self, lookup_name, param, db, prepared, **kw):
rhs, _ignore = param
if lookup_name == 'exact':
ct_id = ContentType.objects.get_for_model(rhs).pk
return "(%s, %s)", (ct_id, rhs.pk)
elif lookup_name == 'in':
if isinstance(rhs, QuerySet):
# QuerSet was passed. Don't fetch its items. Use server-side
# subselect, which will be way faster. Get the content_type_id
# from django_content_type table.
compiler = rhs.query.get_compiler(connection=db)
compiled_query, compiled_args = compiler.as_sql()
query = """
SELECT
%(django_content_type_db_table)s.id AS content_type_id,
U0.id AS object_id
FROM
%(django_content_type_db_table)s,
(%(compiled_query)s) U0
WHERE
%(django_content_type_db_table)s.model = '%(model)s' AND
%(django_content_type_db_table)s.app_label = '%(app_label)s'
""" % dict(
django_content_type_db_table=ContentType._meta.db_table,
compiled_query=compiled_query,
model=rhs.model._meta.model_name,
app_label=rhs.model._meta.app_label)
return query, compiled_args
if is_iterable(rhs):
buf = []
for elem in rhs:
if isinstance(elem, models.Model):
buf.append((ContentType.objects.get_for_model(elem).pk, elem.pk))
else:
raise FilteredGenericForeignKeyFilteringException(
"Unknown type: %r" % type(elem))
query = ",".join(["%s"] * len(buf))
return query, buf
raise NotImplementedError("You passed %r and I don't know what to do with it" % rhs)
elif lookup_name == 'in_raw':
if isinstance(rhs, QuerySet):
# Use the passed QuerSet as a 'raw' one - it selects 2 fields
# first is content_type_id, second is object_id
compiler = rhs.query.get_compiler(connection=db)
compiled_query, compiled_args = compiler.as_sql()
# XXX: HACK AHEAD. Perhaps there is a better way to change
# select, preferably by using extra. I need to have the proper
# order of columns AND the proper count of columns, which
# is no more, than two.
#
# Currently, even if I use "only", I have no control over
# the order of columns. And, if I use
# .extra(select=SortedDict([...]), I get the proper order
# of columns and the primary key and other two columns even
# if I did not specify them in the query.
#
# So, for now, let's split the query on first "FROM" and change
# the beginning part with my own SELECT:
compiled_query = "SELECT content_type_id, object_id FROM " + \
compiled_query.split("FROM", 1)[1]
return compiled_query, compiled_args
if is_iterable(rhs):
buf = []
for elem in rhs:
if isinstance(elem, tuple) and type(elem[0]) == int and type(elem[1]) == int and len(elem)==2:
buf.append(elem)
else:
raise FilteredGenericForeignKeyFilteringException(
"If you pass a list of tuples as an argument, every tuple "
"must have exeactly 2 elements and they must be integers")
query = ",".join(["%s"] * len(buf))
return query, buf
raise NotImplementedError("You passed %r and I don't know what to do with it" % rhs)
else:
raise FilteredGenericForeignKeyFilteringException(
"Unsupported lookup_name: %r" % lookup_name)
pass
class FilteredGenericForeignKeyLookup(Lookup):
def as_sql(self, qn, connection):
ct_attname = self.lhs.output_field.model._meta.get_field(
self.lhs.output_field.ct_field).get_attname()
lhs = '(%s."%s", %s."%s")' % (
self.lhs.alias,
self.lhs.output_field.ct_field + "_id",
self.lhs.alias,
self.lhs.output_field.fk_field)
rhs, rhs_params = self.process_rhs(qn, connection)
# in
subquery, args = rhs_params
return "%s %s (%s)" % (lhs, self.operator, subquery), args
class FilteredGenericForeignKeyLookup_Exact(FilteredGenericForeignKeyLookup):
lookup_name = 'exact'
operator = '='
class FilteredGenericForeignKeyLookup_In(FilteredGenericForeignKeyLookup):
lookup_name = 'in'
operator = 'in'
class FilteredGenericForeignKeyLookup_In_Raw(FilteredGenericForeignKeyLookup):
"""
in_raw lookup will not try to get the content_type_id of the right hand
side QuerySet of the lookup, but instead it will re-write the query, so
it selects columns named 'content_type_id' and 'object_id' from the right-
hand side QuerySet. See comments in
FilteredGenericForeignKeyLookup.get_db_prep
"""
lookup_name = 'in_raw'
operator = 'in'
FilteredGenericForeignKey.register_lookup(
FilteredGenericForeignKeyLookup_Exact)
FilteredGenericForeignKey.register_lookup(
FilteredGenericForeignKeyLookup_In)
FilteredGenericForeignKey.register_lookup(
FilteredGenericForeignKeyLookup_In_Raw)
| [
"[email protected]"
] | |
ff6af04706daacf32c78ad33e94466af43186202 | 61cb083d5d3c70d3ea1c66e4bae341c20c8e38b4 | /accounts/migrations/0007_auto_20190827_0050.py | 89703acd856462b1ddf5729e73c3da0661535cb8 | [] | no_license | Fabricourt/villacare | 1c60aab2f76096d1ae4b773508fe6eb437763555 | 983e512eace01b4dee23c98cc54fcb7fdbd90987 | refs/heads/master | 2022-11-28T11:15:18.530739 | 2019-09-05T03:30:03 | 2019-09-05T03:30:03 | 205,774,903 | 0 | 0 | null | 2022-11-22T03:16:03 | 2019-09-02T04:09:48 | CSS | UTF-8 | Python | false | false | 1,017 | py | # Generated by Django 2.1.5 on 2019-08-26 21:50
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('accounts', '0006_auto_20190827_0035'),
]
operations = [
migrations.RenameField(
model_name='account',
old_name='total_payments_made',
new_name='propertys_payments_made',
),
migrations.RenameField(
model_name='property_payment',
old_name='balance',
new_name='total_balance',
),
migrations.AlterField(
model_name='buyer',
name='property_bought',
field=models.ManyToManyField(help_text='all properties bought by buyer', to='accounts.Property_id'),
),
migrations.AlterField(
model_name='property_payment',
name='payment_expected',
field=models.IntegerField(help_text='total payment expected from all properties bought', null=True),
),
]
| [
"[email protected]"
] | |
3b57edfe545881fccfea9a871d223f56cffa25bd | 0263e511b29dde94d833bec1e5a2a95b8c29f8b0 | /TIMBER/Utilities/TrigTester.py | dc4383688da44e30736433d761ac15ba56d772b0 | [] | no_license | jialin-guo1/TIMBER | f4907466e39281385c153be5372d266543908a0d | 448b6063234632ef0239e4cd42c15a98ab497501 | refs/heads/master | 2023-01-08T18:03:54.917039 | 2020-11-05T14:22:32 | 2020-11-05T14:22:32 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 11,272 | py | #####################################################################################################
# Name: TrigTester.py #
# Author: Lucas Corcodilos #
# Date: 1/2/2020 #
# Description: Tests the efficiency of triggers that are NOT included in a specified trigger #
# selection. Will print the following for each trigger: #
# pass(cuts & tested HLT & !(standard HLTs))/pass(cuts & (standard HLTs)) #
# The numerical values will be printed and plotted in a histogram for each HLT considered. #
# It's recommended to run the JHUanalyzer to slim and skim before using this tool as it will #
# simplify the evaluation (for speed and your sanity) #
#####################################################################################################
import ROOT,pprint,sys
from optparse import OptionParser
pp = pprint.PrettyPrinter(indent=4)
ROOT.gStyle.SetOptStat(0)
parser = OptionParser(usage="usage: %prog [options]")
parser.add_option('-i', '--input', metavar='FILE', type='string', action='store',
default = '',
dest = 'input',
help = 'A root file or text file with multiple root file locations to analyze')
parser.add_option('-t', '--tree', metavar='TTREE', type='string', action='store',
default = 'Events',
dest = 'tree',
help = 'Name of the tree in the input file with the HLT branches')
parser.add_option('-c', '--cuts', type='string', action='store',
default = '',
dest = 'cuts',
help = 'C++ boolean evaluation of branches to select on (excluding triggers)')
parser.add_option('--not', type='string', action='store',
default = '',
dest = 'Not',
help = 'C++ boolean evaluation of HLTs to veto that would otherwise be in your selection')
parser.add_option('--ignore', metavar='LIST', type='string', action='store',
default = '',
dest = 'ignore',
help = 'Comma separated list of strings. Ignore any triggers containing one of these strings in the name (case insensitive).')
parser.add_option('--threshold', type='float', action='store',
default = 0.01,
dest = 'threshold',
help = 'Threshold of (pass events)/(total events) of HLTs tested to determine whether info should be recorded (default to > 0.01)')
parser.add_option('--manual', type='string', action='store',
default = '',
dest = 'manual',
help = 'Rather than looping over all triggers, supply comma separated list of those to consider.')
parser.add_option('--vs', type='string', action='store',
default = '',
dest = 'vs',
help = 'Branch name to compare HLTs against. Plots together the distribution of the provided variable for events that pass the top 9 most efficient triggers (vetoing those events that pass the triggers in the `not` option).')
parser.add_option('--noTrig', action='store_true',
default = False,
dest = 'noTrig',
help = 'Branch name to compare HLTs against. Plots together the distribution of the provided variable for events that pass the top 9 most efficient triggers (vetoing those events that pass the triggers in the `not` option).')
parser.add_option('-o', '--output', metavar='FILE', type='string', action='store',
default = '',
dest = 'output',
help = 'Output file name (no extension - will be pdf). Defaults to variation of input file name.')
(options, args) = parser.parse_args()
# Quick function for drawing trigger bits vs a variable
def drawHere(name,tree,var,cuts,histWbinning=None):
# base_hist = ROOT.TH1F(name,name,14,array.array('d',[700,800,900,1000,1100,1200,1300,1400,1500,1700,1900,2100,2500,3000,3500]))
if histWbinning != None:
base_hist = histWbinning.Clone(name)
base_hist.Reset()
tree.Draw('%s>>%s'%(var,name),cuts)
outhist = ROOT.gDirectory.Get(name)
outhist.GetYaxis().SetTitle("Gain/Current")
outhist.GetXaxis().SetTitle(var)
return outhist
# Open file/tree
f = ROOT.TFile.Open(options.input)
tree = f.Get(options.tree)
possible_trigs = {}
# If just checking if there are no trigger bits for any events...
if options.noTrig:
all_trigs = []
for branchObj in tree.GetListOfBranches():
if 'HLT_' in branchObj.GetName():
all_trigs.append(branchObj.GetName())
nEntries= tree.GetEntries()
for i in range(0, nEntries):
tree.GetEntry(i)
found_trig = False
sys.stdout.write("\r%d/%s" % (i,nEntries))
sys.stdout.flush()
for trig in all_trigs:
trig_bit = getattr(tree,trig)
if trig_bit != 0:
found_trig = True
break
if not found_trig: print('\nEvent %s has no trigger bits that are non-zero!' %(i))
quit()
# Otherwise, establish what we're looking for
fullSelection_string = '(%s) && (%s)'%(options.cuts,options.Not)
print('Full selection will be evaluated as '+fullSelection_string)
if options.vs == '':
fullSelection = tree.GetEntries(fullSelection_string)
print('Selected %s events with standard triggers' %fullSelection)
else:
fullSelection = drawHere('fullSelection',tree,options.vs,fullSelection_string)
print('Selected %s events with standard triggers' %fullSelection.Integral())
# Automatically scan all triggers
if options.manual == '':
for branchObj in tree.GetListOfBranches():
if 'HLT' in branchObj.GetName():
# Ignore trigger if requested
ignore = False
for ign in options.ignore.split(','):
if ign.lower() in branchObj.GetName().lower():
print('Ignoring '+branchObj.GetName())
ignore = True
if ignore: continue
# Say what's being processed
print(branchObj.GetName()+'...')
# If no comparison against another branch, just count
if options.vs == '':
thisTrigPassCount = float(tree.GetEntries('%s==1 && %s==1 && !%s'%(options.cuts,branchObj.GetName(),options.Not)))
if thisTrigPassCount/(fullSelection) > options.threshold: possible_trigs[branchObj.GetName()] = '%s/%s = %.2f' % (int(thisTrigPassCount),int(fullSelection),thisTrigPassCount/fullSelection)
# If comparing against another branch, draw
else:
thisTrigPassCount = drawHere('pass_'+branchObj.GetName(),tree,options.vs,'%s==1 && %s==1 && !%s'%(options.cuts,branchObj.GetName(),options.Not),histWbinning=fullSelection)
ratio = thisTrigPassCount.Clone('ratio_'+branchObj.GetName())
ratio.Divide(fullSelection)
# ratio.Draw('hist')
# raw_input(ratio.GetName())
possible_trigs[branchObj.GetName()] = ratio
# Only consider those triggers manually specified
else:
for trig in options.manual.split(','):
# If no comparison against another branch, just count
if options.vs == '':
thisTrigPassCount = float(tree.GetEntries('%s==1 && %s==1 && !%s'%(options.cuts,branchObj.GetName(),options.Not)))
if thisTrigPassCount/(fullSelection) > options.threshold: possible_trigs[branchObj.GetName()] = '%s/%s = %.2f' % (int(thisTrigPassCount),int(fullSelection),thisTrigPassCount/fullSelection)
# If comparing against another branch, draw
else:
thisTrigPassCount = drawHere('pass_'+branchObj.GetName(),tree,options.vs,'%s==1 && %s==1 && !%s'%(options.cuts,branchObj.GetName(),options.Not),histWbinning=fullSelection)
ratio = thisTrigPassCount.Clone('ratio_'+branchObj.GetName())
ratio.Divide(fullSelection)
possible_trigs[branchObj.GetName()] = ratio
# pp.pprint(possible_trigs)
# Print out results if just counting
if options.vs == '':
pp.pprint(possible_trigs)
# Book histogram
out = ROOT.TH1F('out','out',len(possible_trigs.keys()),0,len(possible_trigs.keys()))
out.GetYaxis().SetTitle("Gain/Current")
out.GetXaxis().SetTitle("")
# Loop over HLTs/bins and set bin label and content
bincount = 1
for k in possible_trigs.keys():
out.GetXaxis().SetBinLabel(bincount,k.replace('HLT_','')[:25]) # truncate file name so that it fits in bin label
out.SetBinContent(bincount,float(possible_trigs[k].split(' = ')[-1]))
bincount+=1
# Save histogram
c = ROOT.TCanvas('c','c',1400,700)
c.SetBottomMargin(0.35)
out.Draw('hist')
if options.output == '':
c.Print('TrigTester_'+options.input.split('.root')[0]+'.pdf','pdf')
else:
c.Print(options.output+'.pdf','pdf')
# Draw results as function of options.vs variable
else:
# Get number of triggers to start
ntrigs = len(possible_trigs.keys())
# As long as we have > 9 triggers... (9 chosen for the sake of simplicity in coloring later on)
while ntrigs>9:
# Initialize minimum and mintrig name
minval = 100
mintrig = ''
# Loop over all triggers and find minimum
for k in possible_trigs.keys():
if possible_trigs[k].Integral() < minval:
print('Replacing %s(%s) with %s(%s) as min' %(mintrig,minval,k,possible_trigs[k].Integral()))
minval = possible_trigs[k].Integral()
mintrig = k
# Drop the found minimum and reflect this in the ntrigs count
if mintrig in possible_trigs.keys():
print('Removing min %s(%s)' %(mintrig,minval))
del possible_trigs[mintrig]
ntrigs = ntrigs-1
# Draw with legend
c = ROOT.TCanvas('c','c',1400,700)
color = 1
l = ROOT.TLegend(0.5,0.4,0.9,0.9)
# Find maximum
trig_max = -1
for k in possible_trigs.keys():
if possible_trigs[k].GetMaximum()>trig_max:
trig_max = possible_trigs[k].GetMaximum()
first = True
for k in possible_trigs.keys():
print('%s: %s' %(k,possible_trigs[k].Integral()))
this_hist = possible_trigs[k]
if first: this_hist.SetMaximum(trig_max*1.1)
this_hist.SetLineColor(color)
this_hist.SetTitle('Trigger test as a function of %s'%options.vs)
color+=1
if first: this_hist.Draw('hist')
else: this_hist.Draw('histsame')
first = False
l.AddEntry(this_hist,k,'l')
l.Draw()
if options.output == '':
c.Print('TrigTester_'+sys.argv[1].split('.')[0]+'_vs_'+options.vs+'.pdf','pdf')
else:
c.Print(options.output+'.pdf','pdf') | [
"[email protected]"
] | |
3659bd20dff05e3843f0e513a207c6e3ef09f4c6 | e26a5d55a219da7a5fb2e691a7fe2adb1683543d | /tensortrade/features/feature_pipeline.py | e158740f1584f1ea90839b7c4e01681617605b4d | [
"Apache-2.0"
] | permissive | MildlyOffensive/tensortrade | e02dbcbec07597292de91eaa375b8af9ae305194 | 9fe223c1ff0d58428e9e88d4d287570e24ee1ae4 | refs/heads/master | 2022-04-06T21:48:35.380231 | 2019-10-24T18:17:02 | 2019-10-24T18:17:02 | 282,754,798 | 1 | 1 | Apache-2.0 | 2020-07-27T00:05:44 | 2020-07-27T00:05:43 | null | UTF-8 | Python | false | false | 3,842 | py | # Copyright 2019 The TensorTrade Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import pandas as pd
import numpy as np
from gym import Space
from typing import List, Union, Callable
from .feature_transformer import FeatureTransformer
DTypeString = Union[type, str]
class FeaturePipeline(object):
"""An pipeline for transforming observation data frames into features for learning."""
def __init__(self, steps: List[FeatureTransformer], **kwargs):
"""
Arguments:
dtype: The `dtype` elements in the pipeline should be cast to.
"""
self._steps = steps
self._dtype: DTypeString = kwargs.get('dtype', np.float16)
@property
def steps(self) -> List[FeatureTransformer]:
"""A list of feature transformations to apply to observations."""
return self._steps
@steps.setter
def steps(self, steps: List[FeatureTransformer]):
self._steps = steps
@property
def dtype(self) -> DTypeString:
"""The `dtype` that elements in the pipeline should be input and output as."""
return self._dtype
@dtype.setter
def dtype(self, dtype: DTypeString):
self._dtype = dtype
def reset(self):
"""Reset all transformers within the feature pipeline."""
for transformer in self._steps:
transformer.reset()
def transform_space(self, input_space: Space, column_names: List[str]) -> Space:
"""Get the transformed output space for a given input space.
Args:
input_space: A `gym.Space` matching the shape of the pipeline's input.
column_names: A list of all column names in the input data frame.
Returns:
A `gym.Space` matching the shape of the pipeline's output.
"""
output_space = input_space
for transformer in self._steps:
output_space = transformer.transform_space(output_space, column_names)
return output_space
def _transform(self, observations: pd.DataFrame, input_space: Space) -> pd.DataFrame:
"""Utility method for transforming observations via a list of `FeatureTransformer` objects."""
for transformer in self._steps:
observations = transformer.transform(observations, input_space)
return observations
def transform(self, observation: pd.DataFrame, input_space: Space) -> pd.DataFrame:
"""Apply the pipeline of feature transformations to an observation frame.
Arguments:
observation: A `pandas.DataFrame` corresponding to an observation within a `TradingEnvironment`.
input_space: A `gym.Space` matching the shape of the pipeline's input.
Returns:
A `pandas.DataFrame` of features corresponding to an input oversvation.
Raises:
ValueError: In the case that an invalid observation frame has been input.
"""
features = self._transform(observation, input_space)
if not isinstance(features, pd.DataFrame):
raise ValueError("A FeaturePipeline must transform a pandas.DataFrame into another pandas.DataFrame.\n \
Expected return type: {} `\n \
Actual return type: {}.".format(type(pd.DataFrame([])), type(features)))
return features
| [
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] | |
1f48ce3aa62ece3db3e57b1134cb2ab81a563424 | 1c655b13e159ec064968d74297fb3e994a1fa3f7 | /0x00-python_variable_annotations/main/100-main.py | 3163850ec41e7e25a085e95403fb340a6a58ad05 | [] | no_license | Faith-qa/alx-backend-python | 886672663a8206afa47ce877331deef97802512c | 1eb5ad2cbc2d2bfa4dc911901417797d160d3645 | refs/heads/master | 2023-08-16T07:21:55.127802 | 2021-09-21T09:50:49 | 2021-09-21T09:50:49 | 388,657,895 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 146 | py | #!/usr/bin/env python3
safe_first_element = __import__('100-safe_first_element').safe_first_element
print(safe_first_element.__annotations__)
| [
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] | |
a943d4d0152eac60671d88862ad057df96206c55 | 9c63f6d39a6085674ab42d1488476d0299f39ec9 | /Python/LC_Minesweeper.py | 7fce4305e14c974fcd536afc78f77ee3cbc38f62 | [] | no_license | vijayjag-repo/LeetCode | 2237e3117e7e902f5ac5c02bfb5fbe45af7242d4 | 0a5f47e272f6ba31e3f0ff4d78bf6e3f4063c789 | refs/heads/master | 2022-11-14T17:46:10.847858 | 2022-11-08T10:28:30 | 2022-11-08T10:28:30 | 163,639,628 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,589 | py | class Solution(object):
def updateBoard(self, board, click):
"""
:type board: List[List[str]]
:type click: List[int]
:rtype: List[List[str]]
Approach:
You're given click, which corresponds to a particular coordinate within the board.
If this point is a mine, you change the board to 'X' and return the board.
Else,
This point could be an unrevealed square.
The first thing that you do is, check if this point has any mines nearby.
If it does not have any mines nearby, you gotta call its neighbors and set the current point to 'B'.
Else,
you set the current point to whatever the mine value is and do not call dfs.
Finally after first dfs calls ends, you return the state of the board.
"""
neighbors = [(-1,-1),(-1,0),(-1,1),(0,-1),(0,1),(1,-1),(1,0),(1,1)]
def dfs(x,y):
mine = 0
for (i,j) in neighbors:
if(0<=x+i<len(board) and 0<=y+j<len(board[0]) and board[x+i][y+j]=='M'):
mine+=1
if(mine>0):
board[x][y] = str(mine)
else:
board[x][y] = 'B'
for (i,j) in neighbors:
if(0<=x+i<len(board) and 0<=y+j<len(board[0]) and board[x+i][y+j]=='E'):
dfs(x+i,y+j)
x,y = click
if(board[x][y]=='M'):
board[x][y] = 'X'
else:
dfs(x,y)
return board
| [
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] | |
6c32e35bd711b0391725f3959ab4d3aa654ee111 | 7117862ecbccf811b8c9f476d328c0c91db15b48 | /Config/local_settings.py | 6e9e9080547256775543884280c4888d7dfb2d77 | [] | no_license | turamant/Profile | 330e968ffe797fd7759269a3c804b82388a5838e | a7c32f22b11e93f1264aaa9d7b72aaf497acff65 | refs/heads/main | 2023-04-08T15:50:40.052101 | 2021-04-26T19:50:55 | 2021-04-26T19:50:55 | 361,712,435 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 251 | py | # SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = 'django-insecure-gy^s)i8g=3i46ypvnrk-j_x+@s4^y&1*fd%$-2$dnc=#4(vxek'
# SECURITY WARNING: don't run with debug turned on in production!
DEBUG = True
ALLOWED_HOSTS = ['*'] | [
"[email protected]"
] | |
ec22a156d3c9287499019a6a37d7ad52401e6b2f | cb4902406f4271394c352bfc49ac4a0e8931d5dc | /backend/home/migrations/0002_load_initial_data.py | 3c22b9d08248db69bd7856d4286b1ed72cc5dd2c | [] | no_license | crowdbotics-apps/yourtb-21307 | 492e2eec85e8a562d71f67a7c99ed64ba915ce5e | ce93c74f6bb21bceca6abdca6bb4c4e46bd5e7cb | refs/heads/master | 2022-12-29T23:02:15.003469 | 2020-10-09T19:27:13 | 2020-10-09T19:27:13 | 302,735,020 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,278 | py | from django.db import migrations
def create_customtext(apps, schema_editor):
CustomText = apps.get_model("home", "CustomText")
customtext_title = "Yourtb"
CustomText.objects.create(title=customtext_title)
def create_homepage(apps, schema_editor):
HomePage = apps.get_model("home", "HomePage")
homepage_body = """
<h1 class="display-4 text-center">Yourtb</h1>
<p class="lead">
This is the sample application created and deployed from the Crowdbotics app.
You can view list of packages selected for this application below.
</p>"""
HomePage.objects.create(body=homepage_body)
def create_site(apps, schema_editor):
Site = apps.get_model("sites", "Site")
custom_domain = "yourtb-21307.botics.co"
site_params = {
"name": "Yourtb",
}
if custom_domain:
site_params["domain"] = custom_domain
Site.objects.update_or_create(defaults=site_params, id=1)
class Migration(migrations.Migration):
dependencies = [
("home", "0001_initial"),
("sites", "0002_alter_domain_unique"),
]
operations = [
migrations.RunPython(create_customtext),
migrations.RunPython(create_homepage),
migrations.RunPython(create_site),
]
| [
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] | |
f4342daeb939ca4561554ef1dbb83de6b0ace017 | f6c6085c34ac6e1b494ff039cd0173b47cc9c6c4 | /byceps/services/user_badge/transfer/models.py | 42e439d39bb717540a0ec59220198242e23a10c4 | [
"BSD-3-Clause",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | FakoorCo/byceps | 971042f598a12c00465b0c3a714f4ac7dbdc9d5b | 11290b154b83f5ac3a9530fb6cd752b3d7a3989b | refs/heads/main | 2023-01-23T18:53:59.930267 | 2020-11-30T01:09:20 | 2020-11-30T01:09:50 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 822 | py | """
byceps.services.user_badge.transfer.models
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
:Copyright: 2006-2020 Jochen Kupperschmidt
:License: Revised BSD (see `LICENSE` file for details)
"""
from dataclasses import dataclass
from datetime import datetime
from typing import NewType
from uuid import UUID
from ....typing import BrandID, UserID
BadgeID = NewType('BadgeID', UUID)
@dataclass(frozen=True)
class Badge:
id: BadgeID
slug: str
label: str
description: str
image_filename: str
image_url_path: str
brand_id: BrandID
featured: bool
@dataclass(frozen=True)
class BadgeAwarding:
id: UUID
badge_id: BadgeID
user_id: UserID
awarded_at: datetime
@dataclass(frozen=True)
class QuantifiedBadgeAwarding:
badge_id: BadgeID
user_id: UserID
quantity: int
| [
"[email protected]"
] | |
348940e392a4bfa8cbd62fb6a434869f9e9f56d9 | feba0822906bc306aacd1ba07254f9d85f7195db | /week 5/MoneyChange.py | 391778b6abe4d8c365e88c4d8f9df1e2c14a983f | [] | no_license | Pratyaksh7/Algorithmic-Toolbox | 0d535d652a5619c8d1c1e8b602ab8b1cf4e12eb7 | 2153a37426a510e9232d3b3cdd01a2d624eee83f | refs/heads/master | 2022-12-03T21:57:53.699507 | 2020-08-24T07:49:59 | 2020-08-24T07:49:59 | 281,179,714 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 361 | py | # using python3
def minCoins(n, Coins):
maxCoin = max(Coins)
dic = {1: 1, 2: 2, 3: 1, 4: 1}
if n > 4:
if n % maxCoin == 0:
print(n // 4)
elif n % maxCoin == 1 or n % maxCoin == 2 or n % maxCoin == 3:
print((n // 4) + 1)
else:
print(dic[n])
n = int(input())
Coins = [1, 3, 4]
minCoins(n, Coins)
| [
"[email protected]"
] | |
d8ac7dbd544e75aa6e93408cab924c49ff306ac1 | d491c11dc87a955c95a4e14a2feea19fe1fa859e | /python/Arcade/Python/P15FeebackReview.py | 77f7ef9557afa7896637dc067567d08a73208df9 | [] | no_license | Vagacoder/Codesignal | 0f6ea791b25716cad7c46ab7df73679fb18a9882 | 87eaf44555603dd5b8cf221fbcbae5421ae20727 | refs/heads/master | 2023-07-16T04:18:44.780821 | 2021-08-15T18:41:16 | 2021-08-15T18:41:16 | 294,745,195 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,975 | py | #
# * Python 15, Feedback Review
# * Easy
# * You've launched a revolutionary service not long ago, and were busy improving
# * it for the last couple of months. When you finally decided that the service
# * is perfect, you remembered that you created a feedbacks page long time ago,
# * which you never checked out since then. Now that you have nothing left to do,
# * you would like to have a look at what the community thinks of your service.
# * Unfortunately it looks like the feedbacks page is far from perfect: each
# * feedback is displayed as a one-line string, and if it's too long there's no
# * way to see what it is about. Naturally, this horrible bug should be fixed.
# * Implement a function that, given a feedback and the size of the screen, splits
# * the feedback into lines so that:
# each token (i.e. sequence of non-whitespace characters) belongs to one of
# the lines entirely;
# each line is at most size characters long;
# no line has trailing or leading spaces;
# each line should have the maximum possible length, assuming that all lines
# before it were also the longest possible.
# * Example
# For feedback = "This is an example feedback" and size = 8,
# the output should be
# feedbackReview(feedback, size) = ["This is",
# "an",
# "example",
# "feedback"]
# * Input/Output
# [execution time limit] 4 seconds (py3)
# [input] string feedback
# A string containing a feedback. Each feedback is guaranteed to contain only letters, punctuation marks and whitespace characters (' ').
# Guaranteed constraints:
# 0 ≤ feedback.length ≤ 100.
# [input] integer size
# The size of the screen. It is guaranteed that it is not smaller than the longest token in the feedback.
# Guaranteed constraints:
# 1 ≤ size ≤ 100.
# [output] array.string
# Lines from the feedback, split as described above.
#%%
# * Solution 1
# ! Hard to use regex, NOT solved yet
import re
def feedbackReview1(feedback:str, size:int)->list:
pattern = '(?<=(.{{{}}}))\w+'.format(4)
print(pattern)
return re.split(pattern, feedback)
# * Solution 2
# ! Easy one using textwrap
import textwrap
def feedbackReview2(feedback:str, size: int)->list:
return textwrap.wrap(feedback, size)
# * Solution 3
# !! Awesome
def feedbackReview3(feedback:str, size:int)->list:
return re.findall('(?:\s|^)(\S(?:.{0,%d}\S)?)(?=\s|$)' % (size-2),feedback)
# * Solution 4
# !! Awesome too
def feedbackReview4(feedback:str, size:int)->list:
return [feedback[x:y].strip() for x,y in [(m.start(),m.end()) for m in re.finditer('(.{1,%d}$)|(.{1,%d} )'%(size,size), feedback)]]
a1 = 'This is an example feedback'
a2 = 8
e1 = ["This is", "an", "example", "feedback"]
r1 = feedbackReview3(a1, a2)
# print('Expected:')
# print(e1)
print('Result:')
print(r1)
# %%
| [
"[email protected]"
] | |
147027e78c585ffbc62132cb0116bc6522c71e71 | 8a5669848a675c367e6a58d57dd25f15bf8bb2a5 | /examples/classification/01_functional_style_metric.py | 5a90f22f4be00cb4891772d18054d28865e4af4d | [
"GPL-1.0-or-later",
"GPL-3.0-only"
] | permissive | thieu1995/permetrics | a6c74150e6f03d2e62ad762dc7fe1fa7d03c5d84 | 85b0f3a750bdebc5ade0422e6be7343d90d3cb98 | refs/heads/master | 2023-08-17T14:00:03.705206 | 2023-08-12T15:23:53 | 2023-08-12T15:23:53 | 280,617,738 | 30 | 8 | Apache-2.0 | 2022-06-27T04:03:30 | 2020-07-18T08:47:17 | Python | UTF-8 | Python | false | false | 1,692 | py | #!/usr/bin/env python
# Created by "Thieu" at 11:36, 25/03/2022 ----------%
# Email: [email protected] %
# Github: https://github.com/thieu1995 %
# --------------------------------------------------%
## This is traditional way to call a specific metric you want to use.
## Everytime, you want to use a function, you need to pass y_true and y_pred
## 1. Import packages, classes
## 2. Create object
## 3. From object call function and use
import numpy as np
from permetrics.classification import ClassificationMetric
y_true = [0, 1, 0, 0, 1, 0]
y_pred = [0, 1, 0, 0, 0, 1]
evaluator = ClassificationMetric()
## 3.1 Call specific function inside object, each function has 2 names like below
ps1 = evaluator.precision_score(y_true, y_pred, decimal=5)
ps2 = evaluator.PS(y_true, y_pred)
print(f"Precision: {ps1}, {ps2}")
recall = evaluator.recall_score(y_true, y_pred)
accuracy = evaluator.accuracy_score(y_true, y_pred)
print(f"recall: {recall}, accuracy: {accuracy}")
# CM = confusion_matrix
# PS = precision_score
# NPV = negative_predictive_value
# RS = recall_score
# AS = accuracy_score
# F1S = f1_score
# F2S = f2_score
# FBS = fbeta_score
# SS = specificity_score
# MCC = matthews_correlation_coefficient
# HS = hamming_score
# LS = lift_score
# CKS = cohen_kappa_score
# JSI = JSC = jaccard_similarity_coefficient = jaccard_similarity_index
# GMS = g_mean_score
# GINI = gini_index
# ROC = AUC = RAS = roc_auc_score
| [
"[email protected]"
] | |
8bfae8d09d2c6cb4ff1a5cce566dad3e7cbe52ed | e23a4f57ce5474d468258e5e63b9e23fb6011188 | /125_algorithms/_exercises/templates/_algorithms_challenges/pybites/intermediate/259_v2/test_reverse_complement.py | 8306e1e9cc6d840d6832bd8d3e4d29db696d1bb5 | [] | no_license | syurskyi/Python_Topics | 52851ecce000cb751a3b986408efe32f0b4c0835 | be331826b490b73f0a176e6abed86ef68ff2dd2b | refs/heads/master | 2023-06-08T19:29:16.214395 | 2023-05-29T17:09:11 | 2023-05-29T17:09:11 | 220,583,118 | 3 | 2 | null | 2023-02-16T03:08:10 | 2019-11-09T02:58:47 | Python | UTF-8 | Python | false | false | 7,946 | py | # _______ p__
#
# _______ ?
#
# # Table copied from
# # http://arep.med.harvard.edu/labgc/adnan/projects/Utilities/revcomp.html
# # Note that this table is different from the simple table in the template
# # This table includes additional rules which are used in more advanced
# # reverse complement generators. Please ensure that your functions work
# # with both tables (complementary base always in last column)
#
# COMPLEMENTS_STR """# Full table with ambigous bases
# Base Name Bases Represented Complementary Base
# A Adenine A T
# T Thymidine T A
# U Uridine(RNA only) U A
# G Guanidine G C
# C Cytidine C G
# Y pYrimidine C T R
# R puRine A G Y
# S Strong(3Hbonds) G C S
# W Weak(2Hbonds) A T W
# K Keto T/U G M
# M aMino A C K
# B not A C G T V
# D not C A G T H
# H not G A C T D
# V not T/U A C G B
# N Unknown A C G T N
# """
#
# # ############################################################################
# # Use default table from bite template and test functions
# # ############################################################################
#
# ACGT_BASES_ONLY
# "ACGT",
# "TTTAAAGGGCCC",
# ("TACTGGTACTAATGCCTAAGTGACCGGCAGCAAAATGTTGCAGCACTGACCCTTTTGGGACCGCAATGGGT"
# "TGAATTAGCGGAACGTCGTGTAGGGGGAAAGCGGTCGACCGCATTATCGCTTCTCCGGGCGTGGCTAGCGG"
# "GAAGGGTTGTCAACGCGTCGGACTTACCGCTTACCGCGAAACGGACCAAAGGCCGTGGTCTTCGCCACGGC"
# "CTTTCGACCGACCTCACGCTAGAAGGA"),
#
# MIXED_CASE_DNA
# "AcgT",
# "TTTaaaGGGCCc",
# ("TACtGGTACTAATGCCtAAGtGaccggcagCAAAATGTTGCAGCACTGACCCTTTTGGGACCGCAATGGGT"
# "TGAATTAGCGGAACGTCGTGTAGGGGGAAAgcgGTCGACCGCATTATCGCTTCTCCGGGCGTGGCTAGCGG"
# "GAAGGGTTGTCAACGCGTCGGACTTACCGCttaCCGCGAAACGGAccAAAGGCCGTGGTCTTCGCCACGGC"
# "CTTtcGACCGACCTCACGCTAGAAGGA"),
#
# DIRTY_DNA
# "335>\nA c g T",
# ">\nT-TT-AAA- GGGCCC!!!",
# ("TAC TGG TAC TAA TGC CTA AGT GAC CGG CAG CAA AAT GTT GCA GCA CTG ACC CTT"
# " TTG GGA CCG CAA TGG GTT GAA TTA GCG GAA CGT CGT GTA GGG GGA AAG CGG TC"
# "G ACC GCA TTA TCG CTT CTC CGG GCG TGG CTA GCG GGA AGG GTT GTC AAC GCG T"
# "CG GAC TTA CCG CTT ACC GCG AAA CGG ACC AAA GGC CGT GGT CTT CGC CAC GGC "
# "CTT TCG ACC GAC CTC ACG CTA GAA GGA"),
#
#
# CORRECT_ANSWERS_COMPLEMENTED
# "TGCA",
# "AAATTTCCCGGG",
# ("ATGACCATGATTACGGATTCACTGGCCGTCGTTTTACAACGTCGTGACTGGGAAAACCCTGGCGTTACCCA"
# "ACTTAATCGCCTTGCAGCACATCCCCCTTTCGCCAGCTGGCGTAATAGCGAAGAGGCCCGCACCGATCGCC"
# "CTTCCCAACAGTTGCGCAGCCTGAATGGCGAATGGCGCTTTGCCTGGTTTCCGGCACCAGAAGCGGTGCCG"
# "GAAAGCTGGCTGGAGTGCGATCTTCCT"),
#
# CORRECT_ANSWERS_REVERSE
# "TGCA",
# "CCCGGGAAATTT",
# ("AGGAAGATCGCACTCCAGCCAGCTTTCCGGCACCGCTTCTGGTGCCGGAAACCAGGCAAAGCGCCATTCGC"
# "CATTCAGGCTGCGCAACTGTTGGGAAGGGCGATCGGTGCGGGCCTCTTCGCTATTACGCCAGCTGGCGAAA"
# "GGGGGATGTGCTGCAAGGCGATTAAGTTGGGTAACGCCAGGGTTTTCCCAGTCACGACGTTGTAAAACGAC"
# "GGCCAGTGAATCCGTAATCATGGTCAT"),
#
# CORRECT_ANSWERS_REVERSE_COMPLEMENT
# "ACGT",
# "GGGCCCTTTAAA",
# ("TCCTTCTAGCGTGAGGTCGGTCGAAAGGCCGTGGCGAAGACCACGGCCTTTGGTCCGTTTCGCGGTAAGCG"
# "GTAAGTCCGACGCGTTGACAACCCTTCCCGCTAGCCACGCCCGGAGAAGCGATAATGCGGTCGACCGCTTT"
# "CCCCCTACACGACGTTCCGCTAATTCAACCCATTGCGGTCCCAAAAGGGTCAGTGCTGCAACATTTTGCTG"
# "CCGGTCACTTAGGCATTAGTACCAGTA"),
#
#
# # ############################################################################
# # Test complement function
# # ############################################################################
#
#
# ?p__.m__.p.
# "input_sequence,expected",
# z.. A.. C..
#
# ___ test_acgt_complement input_sequence e..
# ... r__.c.. ?.u.. __ e..
#
#
# ?p__.m__.p.
# "input_sequence,expected",
# z.. M.. C..
#
# ___ test_mixed_case_complement input_sequence e..
# ... r__.c.. ?.u.. __ e..
#
#
# ?p__.m__.p.
# "input_sequence,expected", z.. D.. C..
#
# ___ test_dirty_complement input_sequence e..
# ... r__.c.. ?.u.. __ e..
#
#
# # ############################################################################
# # Test reverse function
# # ############################################################################
#
#
# ?p__.m__.p.
# "input_sequence,expected", z.. A.. C..
#
# ___ test_acgt_reverse input_sequence e..
# ... r__.r.. ?.u.. __ e..
#
#
# ?p__.m__.p.
# "input_sequence,expected", z.. M.. C..
#
# ___ test_mixed_case_reverse input_sequence e..
# ... r__.r.. ?.u.. __ e..
#
#
# ?p__.m__.p.
# "input_sequence,expected", z.. D.. C..
#
# ___ test_dirty_reverse input_sequence e..
# ... r__.r.. ?.u.. __ e..
#
#
# # ############################################################################
# # Test reverse complement function
# # ############################################################################
#
#
# ?p__.m__.p.
# "input_sequence,expected",
# z.. A.. C..
#
# ___ test_acgt_reverse_complement input_sequence e..
# ...
# r__.r.. ?.u..
# __ e..
#
#
#
# ?p__.m__.p.
# "input_sequence,expected",
# z.. M.. C..
#
# ___ test_mixed_case_reverse_complement input_sequence e..
# ...
# r__.r.. ?.u..
# __ e..
#
#
#
# ?p__.m__.p.
# "input_sequence,expected",
# z.. D.. C..
#
# ___ test_dirty_reverse_complement input_sequence e..
# ...
# r__.r.. ?.u..
# __ e..
#
#
# # ############################################################################
# # Use more complex complement table
# # ############################################################################
#
#
# AMBIGOUS_DIRTY_DNA
# "AGB Vnc gRy Tvv V",
# ">\nT-TT-AAA-BDNNSSRYMNXXXX GGGCCC!!!",
# ("TAC WSA YBG KGK DVN YRS TGG TAC TAA TGC CTA AGT GAC CGG CAG CAA AAT GTT"
# " GCA GCA CTG ACC CTT TTG GGA CCG CAA TGG GTT GAA TTA GCG GAA CGT CGT GT"
# "A GGG GGA AAG CGG TCG ACC GCA TTA TCG CTT CTC CGG GCG TGG CTA GCG GGA A"
# "GG GTT GTC AAC GCG TCG GAC TTA CCG CTT ACC GCG AAA CGG ACC AAA GGC CGT "
# "GGT CTT CGC CAC GGC CTT TCG ACC GAC CTC ACG CTA GAA GGA"),
#
# CORRECT_ANSWER_AMBIGOUS_DNA_COMPLEMENT
# "TCVBNGCYRABBB",
# "AAATTTVHNNSSYRKNCCCGGG",
# ("ATGWSTRVCMCMHBNRYSACCATGATTACGGATTCACTGGCCGTCGTTTTACAACGTCGTGACTGGGAAAA"
# "CCCTGGCGTTACCCAACTTAATCGCCTTGCAGCACATCCCCCTTTCGCCAGCTGGCGTAATAGCGAAGAGG"
# "CCCGCACCGATCGCCCTTCCCAACAGTTGCGCAGCCTGAATGGCGAATGGCGCTTTGCCTGGTTTCCGGCA"
# "CCAGAAGCGGTGCCGGAAAGCTGGCTGGAGTGCGATCTTCCT"),
#
# CORRECT_ANSWER_AMBIGOUS_DNA_REVERSE
# "VVVTYRGCNVBGA",
# "CCCGGGNMYRSSNNDBAAATTT",
# ("AGGAAGATCGCACTCCAGCCAGCTTTCCGGCACCGCTTCTGGTGCCGGAAACCAGGCAAAGCGCCATTCGC"
# "CATTCAGGCTGCGCAACTGTTGGGAAGGGCGATCGGTGCGGGCCTCTTCGCTATTACGCCAGCTGGCGAAA"
# "GGGGGATGTGCTGCAAGGCGATTAAGTTGGGTAACGCCAGGGTTTTCCCAGTCACGACGTTGTAAAACGAC"
# "GGCCAGTGAATCCGTAATCATGGTSRYNVDKGKGBYASWCAT"),
#
# CORRECT_ANSWER_AMBIGOUS_DNA_REVERSE_COMPLEMENT
# "BBBARYCGNBVCT",
# "GGGCCCNKRYSSNNHVTTTAAA",
# ("TCCTTCTAGCGTGAGGTCGGTCGAAAGGCCGTGGCGAAGACCACGGCCTTTGGTCCGTTTCGCGGTAAGCGG"
# "TAAGTCCGACGCGTTGACAACCCTTCCCGCTAGCCACGCCCGGAGAAGCGATAATGCGGTCGACCGCTTTCC"
# "CCCTACACGACGTTCCGCTAATTCAACCCATTGCGGTCCCAAAAGGGTCAGTGCTGCAACATTTTGCTGCCG"
# "GTCACTTAGGCATTAGTACCASYRNBHMCMCVRTSWGTA"),
#
#
#
# # ############################################################################
# # Test reverse, complement and rev comp. function with new table
# # ############################################################################
#
#
# ?p__.m__.p.
# "input_sequence,expected",
# z.. A.. C..
#
# ___ test_acgt_complement_new_table input_sequence e..
# ...
# ? ? C__ .u..
# __ e..
#
#
#
# ?p__.m__.p.
# "input_sequence,expected",
# z..A.., C..
#
# ___ test_mixed_case_reverse_new_table input_sequence e..
# ...
# ? ? C...u..
# __ e..
#
#
#
# ?p__.m__.p.
# "input_sequence,expected",
# z.. A.., C..
#
# ___ test_dirty_reverse_complement_new_table input_sequence e..
# ...
# ?.r..
# ? C..
# .u..
# __ e..
#
| [
"[email protected]"
] | |
1098a789c29b45f0d9d74b610f2bd525c611d6ba | e70b678712a355a0b51632728c7781b0bdcf29f4 | /Algorithms/Python/House-Robber-III.py | a74ac277aba3199f40ac9f6b7b313a6b34906408 | [] | no_license | keyi/Leetcode_Solutions | b3e3c6835ed335d7d4ad53a1b37e59ac15fcf3af | 69e4e969b435ff2796bd7c4b5dad9284a853ab54 | refs/heads/master | 2020-05-21T23:36:20.450053 | 2018-11-11T03:45:28 | 2018-11-11T03:45:28 | 33,714,612 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 577 | py | # Definition for a binary tree node.
# class TreeNode(object):
# def __init__(self, x):
# self.val = x
# self.left = None
# self.right = None
class Solution(object):
def rob(self, root):
"""
:type root: TreeNode
:rtype: int
"""
def dfs(node):
if not node:
return 0, 0
robL, no_robL = dfs(node.left)
robR, no_robR = dfs(node.right)
return node.val + no_robL + no_robR, max(robL, no_robL) + max(robR, no_robR)
return max(dfs(root))
| [
"[email protected]"
] | |
372a51ab5a1ba04386d600954a4b8dfd5297b211 | e61e725d9a962837e2b56f84e3934b0fb52dd0b1 | /eoxserver/backends/middleware.py | fcd2756c0adad6b32c04637ceb2a4f2e2ccd6006 | [
"MIT",
"LicenseRef-scancode-warranty-disclaimer"
] | permissive | ESA-VirES/eoxserver | 719731172c2e5778186a4b144a201f602c07ce7e | d7b65adf9317538b267d5cbb1281acb72bc0de2c | refs/heads/master | 2021-01-21T20:06:22.164030 | 2014-10-14T12:21:13 | 2014-10-14T12:21:13 | 25,151,203 | 1 | 0 | null | 2014-12-04T09:46:54 | 2014-10-13T09:00:54 | Python | UTF-8 | Python | false | false | 2,089 | py | #-------------------------------------------------------------------------------
# $Id$
#
# Project: EOxServer <http://eoxserver.org>
# Authors: Fabian Schindler <[email protected]>
#
#-------------------------------------------------------------------------------
# Copyright (C) 2013 EOX IT Services GmbH
#
# 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 of this Software or works derived from this 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.
#-------------------------------------------------------------------------------
import logging
from eoxserver.backends.cache import setup_cache_session, shutdown_cache_session
logger = logging.getLogger(__name__)
class BackendsCacheMiddleware(object):
""" A request middleware f
"""
def process_request(self, request):
setup_cache_session()
def process_response(self, request, response):
shutdown_cache_session()
return response
def process_template_response(self, request, response):
shutdown_cache_session()
return response
def process_exception(self, request, exception):
shutdown_cache_session()
return None
| [
"[email protected]"
] | |
451fca20992f85b6ab2bdff565ff25314a62df06 | 5a72f4ad3dee9c93e907e5db6ae073a0f6173557 | /web/actions/log_handler.py | 8eb2c9e92525a051d778d3ec66e77de9aa6b3fba | [
"Apache-2.0"
] | permissive | avikowy/machinaris | 170117fa8857942d90b33b15a727674924da1d66 | 23eead3c30e5d4a75b13c142638c61bcd0af4bfe | refs/heads/main | 2023-06-17T15:54:08.795622 | 2021-07-16T04:19:37 | 2021-07-16T04:19:37 | 386,497,979 | 0 | 0 | Apache-2.0 | 2021-07-16T03:35:17 | 2021-07-16T03:35:17 | null | UTF-8 | Python | false | false | 822 | py | #
# Actions around access to logs on distributed workers.
#
import datetime
import os
import psutil
import re
import signal
import shutil
import socket
import time
import traceback
import yaml
from flask import Flask, jsonify, abort, request, flash
from web import app, db, utils
def get_log_lines(worker, log_type, log_id, blockchain):
try:
payload = {"type": log_type }
if log_id != 'undefined':
payload['log_id'] = log_id
if blockchain != 'undefined':
payload['blockchain'] = blockchain
response = utils.send_get(worker, "/logs/{0}".format(log_type), payload, debug=False)
return response.content.decode('utf-8')
except:
app.logger.info(traceback.format_exc())
return 'Failed to load log file from {0}'.format(worker.hostname)
| [
"[email protected]"
] | |
1e748bcd33d6fe1bf140ccdb45cb963a56a7eaaa | 55c552b03a07dcfa2d621b198aa8664d6ba76b9a | /Algorithm/BOJ/13300_방 배정_b2/13300.py | 1f760d8e2112fd870232bb7889e148e0582076d3 | [] | no_license | LastCow9000/Algorithms | 5874f1523202c10864bdd8bb26960953e80bb5c0 | 738d7e1b37f95c6a1b88c99eaf2bc663b5f1cf71 | refs/heads/master | 2023-08-31T12:18:45.533380 | 2021-11-07T13:24:32 | 2021-11-07T13:24:32 | 338,107,899 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 723 | py | # boj 13300 방 배정 b2
# https://www.acmicpc.net/problem/13300
import math
N, K = map(int, input().split())
count_studentMen = [0] * 7 # 6학년까지 ex. 1학년은 1번인덱스를 사용
count_studentWomen = [0] * 7
res = 0
for i in range(N):
S, Y = map(int, input().split()) # S성별 Y학년
if S == 0: # 여자라면
count_studentWomen[Y] += 1 # 해당하는 학년의 여자수 증가
else:
count_studentMen[Y] += 1
for i in range(1, 7): # 1~6학년까지 돌면서
# 학년당 인원수 / 한방에 들어갈 수 있는 인원수 올림처리 ex. 2.3 일겨우 3개
res += math.ceil(count_studentMen[i] / K)
res += math.ceil(count_studentWomen[i] / K)
print(res)
| [
"[email protected]"
] | |
36e25d40975cb3f2bbb9ca251092b2dcf81d78b0 | de24f83a5e3768a2638ebcf13cbe717e75740168 | /moodledata/vpl_data/303/usersdata/298/68638/submittedfiles/testes.py | 7bb6c9f0c7507804399fccf4e2c1e9aca7ac8f50 | [] | no_license | rafaelperazzo/programacao-web | 95643423a35c44613b0f64bed05bd34780fe2436 | 170dd5440afb9ee68a973f3de13a99aa4c735d79 | refs/heads/master | 2021-01-12T14:06:25.773146 | 2017-12-22T16:05:45 | 2017-12-22T16:05:45 | 69,566,344 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,370 | py | print('Sejam a, b e c os coeficientes de uma equação do segundo grau')
a = float(input('Digite a: '))
b = float(input('Digite b: '))
c = float(input('Digite c: '))
delta = (b**2 - 4*a*c)
if (delta>=0):
raizdelta = float((delta)**(1/2))
else:
raizdelta = float((-(delta)**(1/2))*float(j))
x1 = float(-b/2 + raizdelta/2)
x2 = float(-b/2 - raizdelta/2)
print('A variável x assume os valores x1=%f e x2+%f' % (x1, x2))
n1 = float(input('Digite um valor N1: '))
n2 = float(input('Digite um valor N2: '))
n3 = float(input('Digite um valor N3: '))
total = n1 + n2 + n3
print('TOTAL = %f' % total)
print('\n')
raio = float(input('Insira o raio do círculo, em centímetros: '))
pi = 3.141592
area = float(pi*((raio)**2))
print('A área do círculo é, aproximadamente, A=%.3f cm^2' % area)
print('\n')
altura = float(input('Qual a sua altura, em metros?: '))
peso = float((72.7*altura) - 58)
print('Seu peso ideal é %.2f kg' % peso)
print('\n')
metro = float(input('Insira a medida f, em metros: '))
cent = float((metro)*100)
print('A medida f é f=%.2f cm' % cent)
print('\n')
nota1 = float(input('Qual sua primeira nota?: '))
nota2 = float(input('Qual sua segunda nota?: '))
nota3 = float(input('Qual sua terceira nota?: '))
nota4 = float(input('Qual sua quarta nota?: '))
media = float((nota1 + nota2 + nota3 + nota4)/4)
print('Sua média é %.1f' % media) | [
"[email protected]"
] | |
ff33b0745544823f2ef412e0af480151dc88739a | 8ef477149fdd8cd8c0ad88f160e2b8a445550a1e | /ebay_message_mgnt_v13/models/sale.py | 640d56cdf61c3f2595d74646f9d71dd5e8ca8e3f | [] | no_license | cokotracy/ebay_connector_v13 | 97f3e23ba951f14457514b71b408b389a7c16dc7 | 3c08603875d464e5bee818091fa704f5f7192499 | refs/heads/master | 2022-07-19T21:15:23.065880 | 2020-05-20T15:20:23 | 2020-05-20T15:20:23 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 15,814 | py | from odoo import api, fields, models, _
import time
import datetime
# from odoo import SUPERUSER_ID, api
import logging
logger = logging.getLogger('sale')
class sale_shop(models.Model):
_inherit = "sale.shop"
last_ebay_messages_import = fields.Datetime('Last Ebay Messages Import')
def import_ebay_customer_messages(self):
'''
This function is used to Import Ebay customer messages
parameters:
No Parameter
'''
print ("ibnmmmmmmmmmmmmmmm innnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnn")
connection_obj = self.env['ebayerp.osv']
mail_obj =self.env['mail.thread']
mail_msg_obj = self.env['mail.message']
partner_obj = self.env['res.partner']
sale_shop_obj = self.env['sale.shop']
message_obj = self.env['ebay.messages']
# shop_obj = self
inst_lnk = self.instance_id
#
# for id in ids:
# shop_data = self.browse(cr,uid,id)
inst_lnk = self.instance_id
currentTimeTo = datetime.datetime.utcnow()
currentTimeTo = time.strptime(str(currentTimeTo), "%Y-%m-%d %H:%M:%S.%f")
currentTimeTo = time.strftime("%Y-%m-%dT%H:%M:%S.000Z",currentTimeTo)
currentTimeFrom = self.last_ebay_messages_import
currentTime = datetime.datetime.strptime(currentTimeTo, "%Y-%m-%dT%H:%M:%S.000Z")
if not currentTimeFrom:
now = currentTime - datetime.timedelta(days=100)
currentTimeFrom = now.strftime("%Y-%m-%dT%H:%M:%S.000Z")
else:
currentTimeFrom = time.strptime(currentTimeFrom, "%Y-%m-%d %H:%M:%S")
now = currentTime - datetime.timedelta(days=5)
currentTimeFrom = now.strftime("%Y-%m-%dT%H:%M:%S.000Z")
pageNo = 1
data = True
while data:
results = connection_obj.call(inst_lnk, 'GetMemberMessages',currentTimeFrom,currentTimeTo,pageNo)
if not results:
data = False
continue
pageNo = pageNo + 1
# for testing
# results.append({
# 'Subject' : "Test For Available sender Id Type of MESSAGES, states",
# 'MessageID' : "1124165313010244433",
# 'SenderID' : "karamveer26",
# 'SenderEmail' : "[email protected]",
# 'RecipientID' : "powerincloud.store2",
# 'Body' : "Test For Available sender Id Type of MESSAGES",
# 'ItemID' : "261698035031"
# })
if results:
for msg in results:
if msg:
if msg.get('RecipientID'):
part_ids_recep = partner_obj.search([('name','=', msg.get('RecipientID'))])
if not len(part_ids_recep):
part_id_recp = partner_obj.create({'name' : msg.get('RecipientID')})
else:
part_id_recp = part_ids_recep[0]
if msg.get('SenderID'):
part_ids_sender = partner_obj.search([('name','=', msg.get('SenderID'))])
if not len(part_ids_sender):
part_id_sender = partner_obj.create({'name' : msg.get('SenderID')})
else:
part_id_sender = part_ids_sender[0]
if not msg.get('RecipientID') or not msg.get('SenderID'):
continue
msg_vals = {
'name' : msg.get('Subject',False),
'message_id' : msg.get('MessageID',False),
'sender' : part_id_sender.id,
'sender_email' : msg.get('SenderEmail',False),
'recipient_user_id' : part_id_recp.id,
'body' : msg.get('Body',False),
'item_id' : msg.get('ItemID',False),
'shop_id' : self.id
}
# m_ids = message_obj.search(cr, uid, [('message_id','=', msg['MessageID'])])
m_ids = message_obj.search([('message_id','=',msg.get('MessageID'))])
if not m_ids:
# self._context.update({'ebay' : True})
m_id = message_obj.create(msg_vals)
self._cr.commit()
else:
msg_vals = {
'res_id' : m_ids[0].id,
'model' : 'ebay.messages',
'record_name' : msg.get('SenderEmail') or '',
'body' : msg.get('Body',False),
'email_from' : msg.get('SenderEmail') or '',
'message_id_log' : msg.get('MessageID',False),
'shop_id': self._context.get('active_id')
}
log_msg_ids = mail_msg_obj.search([('message_id_log','=', msg.get('MessageID'))])
if not len(log_msg_ids):
# self._context.update({'ebay' : True})
mail_id = mail_msg_obj.create(msg_vals)
now_data = m_ids[0]
if now_data.state != 'unassigned':
message_obj.write(m_ids[0].id, {'state' : 'pending'})
self._cr.commit()
else:
mail_id = log_msg_ids[0]
self._cr.commit()
self.write({'last_ebay_messages_import' : currentTimeTo})
return True
def import_ebay_customer_messages_jinal(self):
'''
This function is used to Import Ebay customer messages
parameters:
No Parameter
'''
connection_obj = self.env['ebayerp.osv']
mail_obj = self.env['mail.thread']
mail_msg_obj = self.env['mail.message']
partner_obj = self.env['res.partner']
sale_shop_obj = self.env['sale.shop']
message_obj = self.env['ebay.messages']
# inst_lnk = s.instance_id
#
# for id in ids:
# shop_data = self.browse(cr,uid,id)
inst_lnk = self.instance_id
currentTimeTo = datetime.datetime.utcnow()
currentTimeTo = time.strptime(str(currentTimeTo), "%Y-%m-%d %H:%M:%S.%f")
currentTimeTo = time.strftime("%Y-%m-%dT%H:%M:%S.000Z",currentTimeTo)
currentTimeFrom = self.last_ebay_messages_import
currentTime = datetime.datetime.strptime(currentTimeTo, "%Y-%m-%dT%H:%M:%S.000Z")
if not currentTimeFrom:
now = currentTime - datetime.timedelta(days=100)
currentTimeFrom = now.strftime("%Y-%m-%dT%H:%M:%S.000Z")
else:
currentTimeFrom = time.strptime(currentTimeFrom, "%Y-%m-%d %H:%M:%S")
now = currentTime - datetime.timedelta(days=5)
currentTimeFrom = now.strftime("%Y-%m-%dT%H:%M:%S.000Z")
results = connection_obj.call(inst_lnk, 'GetMyMessages', currentTimeFrom, currentTimeTo, False)
if results:
datas = connection_obj.call(inst_lnk, 'GetMyMessages', currentTimeFrom, currentTimeTo, results)
if datas:
for msg in datas:
if msg:
dd = False
if msg['ExpirationDate']:
d = msg['ExpirationDate']
dd = datetime.datetime.strptime(d[:19], '%Y-%m-%dT%H:%M:%S')
msg_vals = {
'name' : msg.get('Subject',False),
'message_id' : msg.get('MessageID',False),
'external_msg_id' : msg.get('ExternalMessageID',False),
'sender' : msg.get('Sender',False),
'recipient_user_id' : msg.get('RecipientUserID',False),
'expiry_on_date' : dd,
'body' : msg.get('Text',False),
'item_id' : msg.get('ItemID',False)
}
m_ids = message_obj.search([('message_id','=', msg['MessageID'])])
if not m_ids:
self._cr.commit()
self.write({'last_ebay_messages_import' : currentTimeTo})
return True
sale_shop()
class mail_message(models.Model):
_inherit = "mail.message"
message_id_log = fields.Char('MessageID',size=256)
is_reply = fields.Boolean('Reply')
@api.model
def _message_read_dict_postprocess(self, messages, message_tree):
""" Post-processing on values given by message_read. This method will
handle partners in batch to avoid doing numerous queries.
:param list messages: list of message, as get_dict result
:param dict message_tree: {[msg.id]: msg browse record as super user}
"""
# 1. Aggregate partners (author_id and partner_ids), attachments and tracking values
partners = self.env['res.partner'].sudo()
attachments = self.env['ir.attachment']
trackings = self.env['mail.tracking.value']
# for key, message in message_tree.iteritems():
for key, message in message_tree.items():
if message.author_id:
partners |= message.author_id
if message.subtype_id and message.partner_ids: # take notified people of message with a subtype
partners |= message.partner_ids
elif not message.subtype_id and message.partner_ids: # take specified people of message without a subtype (log)
partners |= message.partner_ids
if message.needaction_partner_ids: # notified
partners |= message.needaction_partner_ids
if message.attachment_ids:
attachments |= message.attachment_ids
if message.tracking_value_ids:
trackings |= message.tracking_value_ids
# Read partners as SUPERUSER -> message being browsed as SUPERUSER it is already the case
partners_names = partners.name_get()
partner_tree = dict((partner[0], partner) for partner in partners_names)
# 2. Attachments as SUPERUSER, because could receive msg and attachments for doc uid cannot see
attachments_data = attachments.sudo().read(['id', 'name', 'mimetype'])
attachments_tree = dict((attachment['id'], {
'id': attachment['id'],
'filename': attachment['name'],
'name': attachment['name'],
'mimetype': attachment['mimetype'],
}) for attachment in attachments_data)
# 3. Tracking values
tracking_tree = dict((tracking.id, {
'id': tracking.id,
'changed_field': tracking.field_desc,
'old_value': tracking.get_old_display_value()[0],
'new_value': tracking.get_new_display_value()[0],
'field_type': tracking.field_type,
}) for tracking in trackings)
# 4. Update message dictionaries
for message_dict in messages:
message_id = message_dict.get('id')
message = message_tree[message_id]
if message.author_id:
author = partner_tree[message.author_id.id]
else:
author = (0, message.email_from)
partner_ids = []
if message.subtype_id:
partner_ids = [partner_tree[partner.id] for partner in message.partner_ids
if partner.id in partner_tree]
else:
partner_ids = [partner_tree[partner.id] for partner in message.partner_ids
if partner.id in partner_tree]
customer_email_data = []
for notification in message.notification_ids.filtered(lambda notif: notif.res_partner_id.partner_share):
customer_email_data.append((partner_tree[notification.res_partner_id.id][0],
partner_tree[notification.res_partner_id.id][1], notification.email_status))
attachment_ids = []
for attachment in message.attachment_ids:
if attachment.id in attachments_tree:
attachment_ids.append(attachments_tree[attachment.id])
tracking_value_ids = []
for tracking_value in message.tracking_value_ids:
if tracking_value.id in tracking_tree:
tracking_value_ids.append(tracking_tree[tracking_value.id])
if self._context.get('default_model') == 'ebay.messages' and self._context.get('default_res_id') and not self._context.get(
'mail_post_autofollow') == True:
message_obj = self.env['ebay.messages']
obj = message_obj.browse(self._context.get('default_res_id'))
mail_obj = self.env['mail.message']
mail_data = mail_obj.browse(message_id)
if mail_data.message_id_log == False or mail_data.is_reply == True:
partner_ids = [(obj.sender.id, obj.sender.name)]
else:
partner_ids = [(obj.recipient_user_id.id, obj.recipient_user_id.name)]
if self._context.get('mail_post_autofollow') == True and self._context.get(
'default_model') == 'ebay.messages' and self._context.get('default_res_id'):
message_obj = self.env['ebay.messages']
obj = message_obj.browse(self._context.get('default_res_id'))
partner_ids = [(obj.sender.id, obj.sender.name)]
# partner_ids = obj.sender.id
message_dict.update({
'author_id': author,
'partner_ids': partner_ids,
'customer_email_status': (all(d[2] == 'sent' for d in customer_email_data) and 'sent') or
(any(d[2] == 'exception' for d in customer_email_data) and 'exception') or
(any(d[2] == 'bounce' for d in customer_email_data) and 'bounce') or 'ready',
'customer_email_data': customer_email_data,
'attachment_ids': attachment_ids,
'tracking_value_ids': tracking_value_ids,
})
return True
@api.model
def create(self,values):
message_obj = self.env['ebay.messages']
if self._context.get('ebay'):
if values.get('res_id'):
msg_data = message_obj.browse( values.get('res_id'))
values.update({'author_id' : msg_data.sender.id})
if self._context.get('default_model') == 'ebay.messages' and self._context.get('default_res_id') and self._context.get('mail_post_autofollow') == True:
msg_data = message_obj.browse( self._context.get('default_res_id'))
values.update({'author_id' : msg_data.recipient_user_id.id})
if self._context.get('ebay_reply') and self._context.get('active_id'):
msg_data = message_obj.browse( self._context.get('active_id'))
values.update({'author_id' : msg_data.recipient_user_id.id})
return super(mail_message, self).create(values) | [
"https://[email protected]"
] | https://[email protected] |
6e3dfb3631b760423c667b2beb46878bf90acc3d | 79d9637df98bc89387963fc0173f375a690f8922 | /coffee-time-challenges/01-two-bases/main.py | edbf0a927a8c53a494d1f2bc839ea9be5f6798ff | [] | no_license | schickling/challenges | 979248b916a24d232dd8adce89355b9eaf767b34 | 1af54711f0530a46395ccb18eb51fc25bf385f22 | refs/heads/master | 2020-04-06T04:00:37.007054 | 2014-11-06T22:50:33 | 2014-11-06T22:50:33 | 21,869,648 | 2 | 3 | null | 2014-08-06T21:44:20 | 2014-07-15T17:46:46 | Python | UTF-8 | Python | false | false | 369 | py | #!/usr/bin/env python3
from itertools import combinations_with_replacement
def check(x, y, z):
return x*100+y*10+z == x+y*9+z*9**2
def main():
retults = []
for x, y, z in combinations_with_replacement(list(range(10)), 3):
if check(x, y, z):
retults.append((x, y, z))
return retults
if __name__ == '__main__':
print(main())
| [
"[email protected]"
] | |
05dfe07de9ba0591de1500ea636fa068095f821c | 6fa7f99d3d3d9b177ef01ebf9a9da4982813b7d4 | /ZAYqnMhmqT5K3JWu8_23.py | c5aa2fd02e9faf3824d7d3e58878054a3bc3506e | [] | no_license | daniel-reich/ubiquitous-fiesta | 26e80f0082f8589e51d359ce7953117a3da7d38c | 9af2700dbe59284f5697e612491499841a6c126f | refs/heads/master | 2023-04-05T06:40:37.328213 | 2021-04-06T20:17:44 | 2021-04-06T20:17:44 | 355,318,759 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 84 | py |
def calculate_fuel(n):
if n * 10 <100:
return 100
else:
return n * 10
| [
"[email protected]"
] | |
8c475f1e44e2c48ce766a5e3e2fdc0acbf79ef4b | 50b9e93fb40e368c73d8d22680171f22e0200c65 | /tuesday-api/first-meet-jwt.py | a45f78edc5bc526f05c65977e80d5700d79089cc | [] | no_license | bopopescu/python-cgi-monitor | cd87a2a07a41547a19222dd7bef7190eec221ed1 | c79e81e129a5f1da3bffae6d69ec8ba2c0e2a8a6 | refs/heads/master | 2022-11-19T23:44:39.484833 | 2019-01-22T11:36:55 | 2019-01-22T11:36:55 | 281,807,428 | 0 | 0 | null | 2020-07-22T23:55:00 | 2020-07-22T23:54:59 | null | UTF-8 | Python | false | false | 2,432 | py | from datetime import datetime, timedelta
import jwt
JWT_SECRET = 'secret'
JWT_ALGORITHM = 'HS256'
JWT_ISS = 'http://192.168.254.31'
JWT_AUD = 'http://192.168.254.31'
JWT_EXP_DELTA_SECONDS = 60
def encode_jwt(user):
JWT_IAT=JWT_NBF = datetime.utcnow()
playload = {
'user_iid': user,
'iss': JWT_ISS,
'aud': JWT_AUD,
'iat': JWT_IAT,
'nbf': JWT_NBF,
'exp': datetime.utcnow() + timedelta(seconds=JWT_EXP_DELTA_SECONDS)
}
jwt_token = jwt.encode(playload, JWT_SECRET, JWT_ALGORITHM)
return jwt_token
def decode_jwt(str, user):
zoo = jwt.decode(str, 'plain', JWT_ALGORITHM, audience=user)
print zoo
print type(zoo)
print dir(zoo)
code = encode_jwt('admin')
print code
# decode_jwt('eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJhdWQiOiJodHRwOi8vMTkyLjE2OC4yNTQuMzEiLCJ1c2VyX2lpZCI6ImFkbWluIiwiaXNzIjoiaHR0cDovLzE5Mi4xNjguMjU0LjMxIiwiZXhwIjoxNTQwNTQ2MjM4LCJpYXQiOjE1NDA0NTk4MzgsIm5iZiI6MTU0MDQ1OTgzOH0.vZsPWmHUd_zcdHHQau5rzRyXdL-sw2NDymVXrSKpkUE')
# decode_jwt('eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJ1c2VyX2lkIjoid2FydXQua2QiLCJleHAiOjE1NDA0NTYwMjB9.zoWIgEN8z0X9IyxDUTi2iIPtpNfSzPREMtkEVEKV4kw')
# decode_jwt('eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJhdWQiOiJodHRwOi8vMTkyLjE2OC4yNTQuMzEiLCJ1c2VyX2lpZCI6ImFkbWluIiwiaXNzIjoiaHR0cDovLzE5Mi4xNjguMjU0LjMxIiwiZXhwIjoxNTQwNDYwNTQzLCJpYXQiOjE1NDA0NjA0ODMsIm5iZiI6MTU0MDQ2MDQ4M30.3_orBKIxZYfRq-BwlRQF__8SrFxbrAbK_OFMMgoMA0k', 'http://192.168.254.31')
# decode_jwt(code, JWT_AUD)
# decode_jwt('eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9')
def python_sha256():
import hashlib
print hashlib.sha256("playload = {'user_id': 'warut.kd','exp': datetime.utcnow() + timedelta(seconds=JWT_EXP_DELTA_SECONDS)}").hexdigest()
# python_sha256()
# async def login(request):
# post_data = await request.post()
#
# try:
# user = User.objects.get(email=post_data['email'])
# user.match_password(post_data['password'])
# except (User.DoesNotExist, User.PasswordDoesNotMatch):
# return json_response({'message': 'Wrong credentials'}, status=400)
#
# payload = {
# 'user_id': user.id,
# 'exp': datetime.utcnow() + timedelta(seconds=JWT_EXP_DELTA_SECONDS)
# }
# jwt_token = jwt.encode(payload, JWT_SECRET, JWT_ALGORITHM)
# return json_response({'token': jwt_token.decode('utf-8')})
#
# app = web.Application()
# app.router.add_route('POST', '/login', login) | [
"[email protected]"
] | |
f4cd681d7022d51d6c996a82e8b22f278d3d8f88 | 34548104ae25886505a681971a7a55e11046a213 | /facenet/train_softmax.py | 67fb73713b8236a209cc17fad3e3b13c1fead756 | [] | no_license | classnalytic/classnalytic-ML | 37b5b20c9ad57dab7a3d3128bc42bcb4d21dea15 | e78e87883978e7c415f03931136e40346da6cabc | refs/heads/master | 2020-03-22T19:44:10.523444 | 2019-02-08T14:05:45 | 2019-02-08T14:05:45 | 140,547,359 | 2 | 1 | null | null | null | null | UTF-8 | Python | false | false | 32,919 | py | """Training a face recognizer with TensorFlow using softmax cross entropy loss"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from datetime import datetime
import os.path
import time
import sys
import random
import tensorflow as tf
import numpy as np
import importlib
import argparse
import facenet
import facenet.lfw
import h5py
import math
import tensorflow.contrib.slim as slim
from tensorflow.python.ops import data_flow_ops
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
def main(args):
network = importlib.import_module(args.model_def)
image_size = (args.image_size, args.image_size)
subdir = datetime.strftime(datetime.now(), '%Y%m%d-%H%M%S')
log_dir = os.path.join(os.path.expanduser(args.logs_base_dir), subdir)
if not os.path.isdir(log_dir): # Create the log directory if it doesn't exist
os.makedirs(log_dir)
model_dir = os.path.join(os.path.expanduser(args.models_base_dir), subdir)
if not os.path.isdir(model_dir): # Create the model directory if it doesn't exist
os.makedirs(model_dir)
stat_file_name = os.path.join(log_dir, 'stat.h5')
# Write arguments to a text file
facenet.write_arguments_to_file(
args, os.path.join(log_dir, 'arguments.txt'))
# Store some git revision info in a text file in the log directory
src_path, _ = os.path.split(os.path.realpath(__file__))
facenet.store_revision_info(src_path, log_dir, ' '.join(sys.argv))
np.random.seed(seed=args.seed)
random.seed(args.seed)
dataset = facenet.get_dataset(args.data_dir)
if args.filter_filename:
dataset = filter_dataset(dataset, os.path.expanduser(args.filter_filename),
args.filter_percentile, args.filter_min_nrof_images_per_class)
if args.validation_set_split_ratio > 0.0:
train_set, val_set = facenet.split_dataset(
dataset, args.validation_set_split_ratio, args.min_nrof_val_images_per_class, 'SPLIT_IMAGES')
else:
train_set, val_set = dataset, []
nrof_classes = len(train_set)
print('Model directory: %s' % model_dir)
print('Log directory: %s' % log_dir)
pretrained_model = None
if args.pretrained_model:
pretrained_model = os.path.expanduser(args.pretrained_model)
print('Pre-trained model: %s' % pretrained_model)
if args.lfw_dir:
print('LFW directory: %s' % args.lfw_dir)
# Read the file containing the pairs used for testing
pairs = lfw.read_pairs(os.path.expanduser(args.lfw_pairs))
# Get the paths for the corresponding images
lfw_paths, actual_issame = lfw.get_paths(
os.path.expanduser(args.lfw_dir), pairs)
with tf.Graph().as_default():
tf.set_random_seed(args.seed)
global_step = tf.Variable(0, trainable=False)
# Get a list of image paths and their labels
image_list, label_list = facenet.get_image_paths_and_labels(train_set)
assert len(image_list) > 0, 'The training set should not be empty'
val_image_list, val_label_list = facenet.get_image_paths_and_labels(
val_set)
# Create a queue that produces indices into the image_list and label_list
labels = ops.convert_to_tensor(label_list, dtype=tf.int32)
range_size = array_ops.shape(labels)[0]
index_queue = tf.train.range_input_producer(range_size, num_epochs=None,
shuffle=True, seed=None, capacity=32)
index_dequeue_op = index_queue.dequeue_many(
args.batch_size*args.epoch_size, 'index_dequeue')
learning_rate_placeholder = tf.placeholder(
tf.float32, name='learning_rate')
batch_size_placeholder = tf.placeholder(tf.int32, name='batch_size')
phase_train_placeholder = tf.placeholder(tf.bool, name='phase_train')
image_paths_placeholder = tf.placeholder(
tf.string, shape=(None, 1), name='image_paths')
labels_placeholder = tf.placeholder(
tf.int32, shape=(None, 1), name='labels')
control_placeholder = tf.placeholder(
tf.int32, shape=(None, 1), name='control')
nrof_preprocess_threads = 4
input_queue = data_flow_ops.FIFOQueue(capacity=2000000,
dtypes=[tf.string,
tf.int32, tf.int32],
shapes=[(1,), (1,), (1,)],
shared_name=None, name=None)
enqueue_op = input_queue.enqueue_many(
[image_paths_placeholder, labels_placeholder, control_placeholder], name='enqueue_op')
image_batch, label_batch = facenet.create_input_pipeline(
input_queue, image_size, nrof_preprocess_threads, batch_size_placeholder)
image_batch = tf.identity(image_batch, 'image_batch')
image_batch = tf.identity(image_batch, 'input')
label_batch = tf.identity(label_batch, 'label_batch')
print('Number of classes in training set: %d' % nrof_classes)
print('Number of examples in training set: %d' % len(image_list))
print('Number of classes in validation set: %d' % len(val_set))
print('Number of examples in validation set: %d' % len(val_image_list))
print('Building training graph')
# Build the inference graph
prelogits, _ = network.inference(image_batch, args.keep_probability,
phase_train=phase_train_placeholder, bottleneck_layer_size=args.embedding_size,
weight_decay=args.weight_decay)
logits = slim.fully_connected(prelogits, len(train_set), activation_fn=None,
weights_initializer=slim.initializers.xavier_initializer(),
weights_regularizer=slim.l2_regularizer(
args.weight_decay),
scope='Logits', reuse=False)
embeddings = tf.nn.l2_normalize(prelogits, 1, 1e-10, name='embeddings')
# Norm for the prelogits
eps = 1e-4
prelogits_norm = tf.reduce_mean(
tf.norm(tf.abs(prelogits)+eps, ord=args.prelogits_norm_p, axis=1))
tf.add_to_collection(tf.GraphKeys.REGULARIZATION_LOSSES,
prelogits_norm * args.prelogits_norm_loss_factor)
# Add center loss
prelogits_center_loss, _ = facenet.center_loss(
prelogits, label_batch, args.center_loss_alfa, nrof_classes)
tf.add_to_collection(tf.GraphKeys.REGULARIZATION_LOSSES,
prelogits_center_loss * args.center_loss_factor)
learning_rate = tf.train.exponential_decay(learning_rate_placeholder, global_step,
args.learning_rate_decay_epochs*args.epoch_size, args.learning_rate_decay_factor, staircase=True)
tf.summary.scalar('learning_rate', learning_rate)
# Calculate the average cross entropy loss across the batch
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=label_batch, logits=logits, name='cross_entropy_per_example')
cross_entropy_mean = tf.reduce_mean(
cross_entropy, name='cross_entropy')
tf.add_to_collection('losses', cross_entropy_mean)
correct_prediction = tf.cast(
tf.equal(tf.argmax(logits, 1), tf.cast(label_batch, tf.int64)), tf.float32)
accuracy = tf.reduce_mean(correct_prediction)
# Calculate the total losses
regularization_losses = tf.get_collection(
tf.GraphKeys.REGULARIZATION_LOSSES)
total_loss = tf.add_n([cross_entropy_mean] +
regularization_losses, name='total_loss')
# Build a Graph that trains the model with one batch of examples and updates the model parameters
train_op = facenet.train(total_loss, global_step, args.optimizer,
learning_rate, args.moving_average_decay, tf.global_variables(), args.log_histograms)
# Create a saver
saver = tf.train.Saver(tf.trainable_variables(), max_to_keep=3)
# Build the summary operation based on the TF collection of Summaries.
summary_op = tf.summary.merge_all()
# Start running operations on the Graph.
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=args.gpu_memory_fraction)
sess = tf.Session(config=tf.ConfigProto(
gpu_options=gpu_options, log_device_placement=False))
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
summary_writer = tf.summary.FileWriter(log_dir, sess.graph)
coord = tf.train.Coordinator()
tf.train.start_queue_runners(coord=coord, sess=sess)
with sess.as_default():
if pretrained_model:
print('Restoring pretrained model: %s' % pretrained_model)
saver.restore(sess, pretrained_model)
# Training and validation loop
print('Running training')
nrof_steps = args.max_nrof_epochs*args.epoch_size
# Validate every validate_every_n_epochs as well as in the last epoch
nrof_val_samples = int(
math.ceil(args.max_nrof_epochs / args.validate_every_n_epochs))
stat = {
'loss': np.zeros((nrof_steps,), np.float32),
'center_loss': np.zeros((nrof_steps,), np.float32),
'reg_loss': np.zeros((nrof_steps,), np.float32),
'xent_loss': np.zeros((nrof_steps,), np.float32),
'prelogits_norm': np.zeros((nrof_steps,), np.float32),
'accuracy': np.zeros((nrof_steps,), np.float32),
'val_loss': np.zeros((nrof_val_samples,), np.float32),
'val_xent_loss': np.zeros((nrof_val_samples,), np.float32),
'val_accuracy': np.zeros((nrof_val_samples,), np.float32),
'lfw_accuracy': np.zeros((args.max_nrof_epochs,), np.float32),
'lfw_valrate': np.zeros((args.max_nrof_epochs,), np.float32),
'learning_rate': np.zeros((args.max_nrof_epochs,), np.float32),
'time_train': np.zeros((args.max_nrof_epochs,), np.float32),
'time_validate': np.zeros((args.max_nrof_epochs,), np.float32),
'time_evaluate': np.zeros((args.max_nrof_epochs,), np.float32),
'prelogits_hist': np.zeros((args.max_nrof_epochs, 1000), np.float32),
}
for epoch in range(1, args.max_nrof_epochs+1):
step = sess.run(global_step, feed_dict=None)
# Train for one epoch
t = time.time()
cont = train(args, sess, epoch, image_list, label_list, index_dequeue_op, enqueue_op, image_paths_placeholder, labels_placeholder,
learning_rate_placeholder, phase_train_placeholder, batch_size_placeholder, control_placeholder, global_step,
total_loss, train_op, summary_op, summary_writer, regularization_losses, args.learning_rate_schedule_file,
stat, cross_entropy_mean, accuracy, learning_rate,
prelogits, prelogits_center_loss, args.random_rotate, args.random_crop, args.random_flip, prelogits_norm, args.prelogits_hist_max, args.use_fixed_image_standardization)
stat['time_train'][epoch-1] = time.time() - t
if not cont:
break
t = time.time()
if len(val_image_list) > 0 and ((epoch-1) % args.validate_every_n_epochs == args.validate_every_n_epochs-1 or epoch == args.max_nrof_epochs):
validate(args, sess, epoch, val_image_list, val_label_list, enqueue_op, image_paths_placeholder, labels_placeholder, control_placeholder,
phase_train_placeholder, batch_size_placeholder,
stat, total_loss, regularization_losses, cross_entropy_mean, accuracy, args.validate_every_n_epochs, args.use_fixed_image_standardization)
stat['time_validate'][epoch-1] = time.time() - t
# Save variables and the metagraph if it doesn't exist already
save_variables_and_metagraph(
sess, saver, summary_writer, model_dir, subdir, epoch)
# Evaluate on LFW
t = time.time()
if args.lfw_dir:
evaluate(sess, enqueue_op, image_paths_placeholder, labels_placeholder, phase_train_placeholder, batch_size_placeholder, control_placeholder,
embeddings, label_batch, lfw_paths, actual_issame, args.lfw_batch_size, args.lfw_nrof_folds, log_dir, step, summary_writer, stat, epoch,
args.lfw_distance_metric, args.lfw_subtract_mean, args.lfw_use_flipped_images, args.use_fixed_image_standardization)
stat['time_evaluate'][epoch-1] = time.time() - t
print('Saving statistics')
with h5py.File(stat_file_name, 'w') as f:
for key, value in stat.items():
f.create_dataset(key, data=value)
return model_dir
def find_threshold(var, percentile):
hist, bin_edges = np.histogram(var, 100)
cdf = np.float32(np.cumsum(hist)) / np.sum(hist)
bin_centers = (bin_edges[:-1]+bin_edges[1:])/2
#plt.plot(bin_centers, cdf)
threshold = np.interp(percentile*0.01, cdf, bin_centers)
return threshold
def filter_dataset(dataset, data_filename, percentile, min_nrof_images_per_class):
with h5py.File(data_filename, 'r') as f:
distance_to_center = np.array(f.get('distance_to_center'))
label_list = np.array(f.get('label_list'))
image_list = np.array(f.get('image_list'))
distance_to_center_threshold = find_threshold(
distance_to_center, percentile)
indices = np.where(distance_to_center >=
distance_to_center_threshold)[0]
filtered_dataset = dataset
removelist = []
for i in indices:
label = label_list[i]
image = image_list[i]
if image in filtered_dataset[label].image_paths:
filtered_dataset[label].image_paths.remove(image)
if len(filtered_dataset[label].image_paths) < min_nrof_images_per_class:
removelist.append(label)
ix = sorted(list(set(removelist)), reverse=True)
for i in ix:
del(filtered_dataset[i])
return filtered_dataset
def train(args, sess, epoch, image_list, label_list, index_dequeue_op, enqueue_op, image_paths_placeholder, labels_placeholder,
learning_rate_placeholder, phase_train_placeholder, batch_size_placeholder, control_placeholder, step,
loss, train_op, summary_op, summary_writer, reg_losses, learning_rate_schedule_file,
stat, cross_entropy_mean, accuracy,
learning_rate, prelogits, prelogits_center_loss, random_rotate, random_crop, random_flip, prelogits_norm, prelogits_hist_max, use_fixed_image_standardization):
batch_number = 0
if args.learning_rate > 0.0:
lr = args.learning_rate
else:
lr = facenet.get_learning_rate_from_file(
learning_rate_schedule_file, epoch)
if lr <= 0:
return False
index_epoch = sess.run(index_dequeue_op)
label_epoch = np.array(label_list)[index_epoch]
image_epoch = np.array(image_list)[index_epoch]
# Enqueue one epoch of image paths and labels
labels_array = np.expand_dims(np.array(label_epoch), 1)
image_paths_array = np.expand_dims(np.array(image_epoch), 1)
control_value = facenet.RANDOM_ROTATE * random_rotate + facenet.RANDOM_CROP * random_crop + \
facenet.RANDOM_FLIP * random_flip + \
facenet.FIXED_STANDARDIZATION * use_fixed_image_standardization
control_array = np.ones_like(labels_array) * control_value
sess.run(enqueue_op, {image_paths_placeholder: image_paths_array,
labels_placeholder: labels_array, control_placeholder: control_array})
# Training loop
train_time = 0
while batch_number < args.epoch_size:
start_time = time.time()
feed_dict = {learning_rate_placeholder: lr,
phase_train_placeholder: True, batch_size_placeholder: args.batch_size}
tensor_list = [loss, train_op, step, reg_losses, prelogits, cross_entropy_mean,
learning_rate, prelogits_norm, accuracy, prelogits_center_loss]
if batch_number % 100 == 0:
loss_, _, step_, reg_losses_, prelogits_, cross_entropy_mean_, lr_, prelogits_norm_, accuracy_, center_loss_, summary_str = sess.run(
tensor_list + [summary_op], feed_dict=feed_dict)
summary_writer.add_summary(summary_str, global_step=step_)
else:
loss_, _, step_, reg_losses_, prelogits_, cross_entropy_mean_, lr_, prelogits_norm_, accuracy_, center_loss_ = sess.run(
tensor_list, feed_dict=feed_dict)
duration = time.time() - start_time
stat['loss'][step_-1] = loss_
stat['center_loss'][step_-1] = center_loss_
stat['reg_loss'][step_-1] = np.sum(reg_losses_)
stat['xent_loss'][step_-1] = cross_entropy_mean_
stat['prelogits_norm'][step_-1] = prelogits_norm_
stat['learning_rate'][epoch-1] = lr_
stat['accuracy'][step_-1] = accuracy_
stat['prelogits_hist'][epoch-1, :] += np.histogram(np.minimum(np.abs(
prelogits_), prelogits_hist_max), bins=1000, range=(0.0, prelogits_hist_max))[0]
duration = time.time() - start_time
print('Epoch: [%d][%d/%d]\tTime %.3f\tLoss %2.3f\tXent %2.3f\tRegLoss %2.3f\tAccuracy %2.3f\tLr %2.5f\tCl %2.3f' %
(epoch, batch_number+1, args.epoch_size, duration, loss_, cross_entropy_mean_, np.sum(reg_losses_), accuracy_, lr_, center_loss_))
batch_number += 1
train_time += duration
# Add validation loss and accuracy to summary
summary = tf.Summary()
#pylint: disable=maybe-no-member
summary.value.add(tag='time/total', simple_value=train_time)
summary_writer.add_summary(summary, global_step=step_)
return True
def validate(args, sess, epoch, image_list, label_list, enqueue_op, image_paths_placeholder, labels_placeholder, control_placeholder,
phase_train_placeholder, batch_size_placeholder,
stat, loss, regularization_losses, cross_entropy_mean, accuracy, validate_every_n_epochs, use_fixed_image_standardization):
print('Running forward pass on validation set')
nrof_batches = len(label_list) // args.lfw_batch_size
nrof_images = nrof_batches * args.lfw_batch_size
# Enqueue one epoch of image paths and labels
labels_array = np.expand_dims(np.array(label_list[:nrof_images]), 1)
image_paths_array = np.expand_dims(np.array(image_list[:nrof_images]), 1)
control_array = np.ones_like(
labels_array, np.int32)*facenet.FIXED_STANDARDIZATION * use_fixed_image_standardization
sess.run(enqueue_op, {image_paths_placeholder: image_paths_array,
labels_placeholder: labels_array, control_placeholder: control_array})
loss_array = np.zeros((nrof_batches,), np.float32)
xent_array = np.zeros((nrof_batches,), np.float32)
accuracy_array = np.zeros((nrof_batches,), np.float32)
# Training loop
start_time = time.time()
for i in range(nrof_batches):
feed_dict = {phase_train_placeholder: False,
batch_size_placeholder: args.lfw_batch_size}
loss_, cross_entropy_mean_, accuracy_ = sess.run(
[loss, cross_entropy_mean, accuracy], feed_dict=feed_dict)
loss_array[i], xent_array[i], accuracy_array[i] = (
loss_, cross_entropy_mean_, accuracy_)
if i % 10 == 9:
print('.', end='')
sys.stdout.flush()
print('')
duration = time.time() - start_time
val_index = (epoch-1)//validate_every_n_epochs
stat['val_loss'][val_index] = np.mean(loss_array)
stat['val_xent_loss'][val_index] = np.mean(xent_array)
stat['val_accuracy'][val_index] = np.mean(accuracy_array)
print('Validation Epoch: %d\tTime %.3f\tLoss %2.3f\tXent %2.3f\tAccuracy %2.3f' %
(epoch, duration, np.mean(loss_array), np.mean(xent_array), np.mean(accuracy_array)))
def evaluate(sess, enqueue_op, image_paths_placeholder, labels_placeholder, phase_train_placeholder, batch_size_placeholder, control_placeholder,
embeddings, labels, image_paths, actual_issame, batch_size, nrof_folds, log_dir, step, summary_writer, stat, epoch, distance_metric, subtract_mean, use_flipped_images, use_fixed_image_standardization):
start_time = time.time()
# Run forward pass to calculate embeddings
print('Runnning forward pass on LFW images')
# Enqueue one epoch of image paths and labels
nrof_embeddings = len(actual_issame)*2 # nrof_pairs * nrof_images_per_pair
nrof_flips = 2 if use_flipped_images else 1
nrof_images = nrof_embeddings * nrof_flips
labels_array = np.expand_dims(np.arange(0, nrof_images), 1)
image_paths_array = np.expand_dims(
np.repeat(np.array(image_paths), nrof_flips), 1)
control_array = np.zeros_like(labels_array, np.int32)
if use_fixed_image_standardization:
control_array += np.ones_like(labels_array) * \
facenet.FIXED_STANDARDIZATION
if use_flipped_images:
# Flip every second image
control_array += (labels_array % 2)*facenet.FLIP
sess.run(enqueue_op, {image_paths_placeholder: image_paths_array,
labels_placeholder: labels_array, control_placeholder: control_array})
embedding_size = int(embeddings.get_shape()[1])
assert nrof_images % batch_size == 0, 'The number of LFW images must be an integer multiple of the LFW batch size'
nrof_batches = nrof_images // batch_size
emb_array = np.zeros((nrof_images, embedding_size))
lab_array = np.zeros((nrof_images,))
for i in range(nrof_batches):
feed_dict = {phase_train_placeholder: False,
batch_size_placeholder: batch_size}
emb, lab = sess.run([embeddings, labels], feed_dict=feed_dict)
lab_array[lab] = lab
emb_array[lab, :] = emb
if i % 10 == 9:
print('.', end='')
sys.stdout.flush()
print('')
embeddings = np.zeros((nrof_embeddings, embedding_size*nrof_flips))
if use_flipped_images:
# Concatenate embeddings for flipped and non flipped version of the images
embeddings[:, :embedding_size] = emb_array[0::2, :]
embeddings[:, embedding_size:] = emb_array[1::2, :]
else:
embeddings = emb_array
assert np.array_equal(lab_array, np.arange(
nrof_images)) == True, 'Wrong labels used for evaluation, possibly caused by training examples left in the input pipeline'
_, _, accuracy, val, val_std, far = lfw.evaluate(
embeddings, actual_issame, nrof_folds=nrof_folds, distance_metric=distance_metric, subtract_mean=subtract_mean)
print('Accuracy: %2.5f+-%2.5f' % (np.mean(accuracy), np.std(accuracy)))
print('Validation rate: %2.5f+-%2.5f @ FAR=%2.5f' % (val, val_std, far))
lfw_time = time.time() - start_time
# Add validation loss and accuracy to summary
summary = tf.Summary()
#pylint: disable=maybe-no-member
summary.value.add(tag='lfw/accuracy', simple_value=np.mean(accuracy))
summary.value.add(tag='lfw/val_rate', simple_value=val)
summary.value.add(tag='time/lfw', simple_value=lfw_time)
summary_writer.add_summary(summary, step)
with open(os.path.join(log_dir, 'lfw_result.txt'), 'at') as f:
f.write('%d\t%.5f\t%.5f\n' % (step, np.mean(accuracy), val))
stat['lfw_accuracy'][epoch-1] = np.mean(accuracy)
stat['lfw_valrate'][epoch-1] = val
def save_variables_and_metagraph(sess, saver, summary_writer, model_dir, model_name, step):
# Save the model checkpoint
print('Saving variables')
start_time = time.time()
checkpoint_path = os.path.join(model_dir, 'model-%s.ckpt' % model_name)
saver.save(sess, checkpoint_path, global_step=step, write_meta_graph=False)
save_time_variables = time.time() - start_time
print('Variables saved in %.2f seconds' % save_time_variables)
metagraph_filename = os.path.join(model_dir, 'model-%s.meta' % model_name)
save_time_metagraph = 0
if not os.path.exists(metagraph_filename):
print('Saving metagraph')
start_time = time.time()
saver.export_meta_graph(metagraph_filename)
save_time_metagraph = time.time() - start_time
print('Metagraph saved in %.2f seconds' % save_time_metagraph)
summary = tf.Summary()
#pylint: disable=maybe-no-member
summary.value.add(tag='time/save_variables',
simple_value=save_time_variables)
summary.value.add(tag='time/save_metagraph',
simple_value=save_time_metagraph)
summary_writer.add_summary(summary, step)
def parse_arguments(argv):
parser = argparse.ArgumentParser()
parser.add_argument('--logs_base_dir', type=str,
help='Directory where to write event logs.', default='~/logs/facenet')
parser.add_argument('--models_base_dir', type=str,
help='Directory where to write trained models and checkpoints.', default='~/models/facenet')
parser.add_argument('--gpu_memory_fraction', type=float,
help='Upper bound on the amount of GPU memory that will be used by the process.', default=1.0)
parser.add_argument('--pretrained_model', type=str,
help='Load a pretrained model before training starts.')
parser.add_argument('--data_dir', type=str,
help='Path to the data directory containing aligned face patches.',
default='~/datasets/casia/casia_maxpy_mtcnnalign_182_160')
parser.add_argument('--model_def', type=str,
help='Model definition. Points to a module containing the definition of the inference graph.', default='models.inception_resnet_v1')
parser.add_argument('--max_nrof_epochs', type=int,
help='Number of epochs to run.', default=500)
parser.add_argument('--batch_size', type=int,
help='Number of images to process in a batch.', default=90)
parser.add_argument('--image_size', type=int,
help='Image size (height, width) in pixels.', default=160)
parser.add_argument('--epoch_size', type=int,
help='Number of batches per epoch.', default=1000)
parser.add_argument('--embedding_size', type=int,
help='Dimensionality of the embedding.', default=128)
parser.add_argument('--random_crop',
help='Performs random cropping of training images. If false, the center image_size pixels from the training images are used. ' +
'If the size of the images in the data directory is equal to image_size no cropping is performed', action='store_true')
parser.add_argument('--random_flip',
help='Performs random horizontal flipping of training images.', action='store_true')
parser.add_argument('--random_rotate',
help='Performs random rotations of training images.', action='store_true')
parser.add_argument('--use_fixed_image_standardization',
help='Performs fixed standardization of images.', action='store_true')
parser.add_argument('--keep_probability', type=float,
help='Keep probability of dropout for the fully connected layer(s).', default=1.0)
parser.add_argument('--weight_decay', type=float,
help='L2 weight regularization.', default=0.0)
parser.add_argument('--center_loss_factor', type=float,
help='Center loss factor.', default=0.0)
parser.add_argument('--center_loss_alfa', type=float,
help='Center update rate for center loss.', default=0.95)
parser.add_argument('--prelogits_norm_loss_factor', type=float,
help='Loss based on the norm of the activations in the prelogits layer.', default=0.0)
parser.add_argument('--prelogits_norm_p', type=float,
help='Norm to use for prelogits norm loss.', default=1.0)
parser.add_argument('--prelogits_hist_max', type=float,
help='The max value for the prelogits histogram.', default=10.0)
parser.add_argument('--optimizer', type=str, choices=['ADAGRAD', 'ADADELTA', 'ADAM', 'RMSPROP', 'MOM'],
help='The optimization algorithm to use', default='ADAGRAD')
parser.add_argument('--learning_rate', type=float,
help='Initial learning rate. If set to a negative value a learning rate ' +
'schedule can be specified in the file "learning_rate_schedule.txt"', default=0.1)
parser.add_argument('--learning_rate_decay_epochs', type=int,
help='Number of epochs between learning rate decay.', default=100)
parser.add_argument('--learning_rate_decay_factor', type=float,
help='Learning rate decay factor.', default=1.0)
parser.add_argument('--moving_average_decay', type=float,
help='Exponential decay for tracking of training parameters.', default=0.9999)
parser.add_argument('--seed', type=int,
help='Random seed.', default=666)
parser.add_argument('--nrof_preprocess_threads', type=int,
help='Number of preprocessing (data loading and augmentation) threads.', default=4)
parser.add_argument('--log_histograms',
help='Enables logging of weight/bias histograms in tensorboard.', action='store_true')
parser.add_argument('--learning_rate_schedule_file', type=str,
help='File containing the learning rate schedule that is used when learning_rate is set to to -1.', default='data/learning_rate_schedule.txt')
parser.add_argument('--filter_filename', type=str,
help='File containing image data used for dataset filtering', default='')
parser.add_argument('--filter_percentile', type=float,
help='Keep only the percentile images closed to its class center', default=100.0)
parser.add_argument('--filter_min_nrof_images_per_class', type=int,
help='Keep only the classes with this number of examples or more', default=0)
parser.add_argument('--validate_every_n_epochs', type=int,
help='Number of epoch between validation', default=5)
parser.add_argument('--validation_set_split_ratio', type=float,
help='The ratio of the total dataset to use for validation', default=0.0)
parser.add_argument('--min_nrof_val_images_per_class', type=float,
help='Classes with fewer images will be removed from the validation set', default=0)
# Parameters for validation on LFW
parser.add_argument('--lfw_pairs', type=str,
help='The file containing the pairs to use for validation.', default='data/pairs.txt')
parser.add_argument('--lfw_dir', type=str,
help='Path to the data directory containing aligned face patches.', default='')
parser.add_argument('--lfw_batch_size', type=int,
help='Number of images to process in a batch in the LFW test set.', default=100)
parser.add_argument('--lfw_nrof_folds', type=int,
help='Number of folds to use for cross validation. Mainly used for testing.', default=10)
parser.add_argument('--lfw_distance_metric', type=int,
help='Type of distance metric to use. 0: Euclidian, 1:Cosine similarity distance.', default=0)
parser.add_argument('--lfw_use_flipped_images',
help='Concatenates embeddings for the image and its horizontally flipped counterpart.', action='store_true')
parser.add_argument('--lfw_subtract_mean',
help='Subtract feature mean before calculating distance.', action='store_true')
return parser.parse_args(argv)
if __name__ == '__main__':
main(parse_arguments(sys.argv[1:]))
| [
"[email protected]"
] | |
1a604200841cdb5e426f73eaa13a5b46d175c696 | e85e846960750dd498431ac8412d9967646ff98d | /cms/urls/admin.py | a20ef9f30b42d704db803611f070617109c8e0d0 | [] | no_license | onosaburo/clublink_django | 19368b4a59b3aed3632883ceffe3326bfc7a61a6 | d2f6024b6224ea7f47595481b3382b8d0670584f | refs/heads/master | 2022-03-30T05:30:12.288354 | 2020-01-27T18:09:11 | 2020-01-27T18:09:11 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 404 | py | from django.conf.urls import include, url
urlpatterns = [
url(r'^club-sites/', include('clublink.cms.modules.club_sites.urls')),
url(r'^corp-site/', include('clublink.cms.modules.corp_site.urls')),
url(r'^assets/', include('clublink.cms.modules.assets.urls')),
url(r'^users/', include('clublink.cms.modules.users.urls')),
url(r'', include('clublink.cms.modules.dashboard.urls')),
]
| [
"[email protected]"
] | |
d52c94cca0b3de9681b5b345b45606a67bb54bd9 | 329664fce59d25d6e88c125c1105bc6b4989a3b8 | /_exercice_version_prof.py | 6fa3252521c4ec7bf995ba41442cfd66c1597a72 | [] | no_license | INF1007-2021A/2021a-c01-ch6-supp-3-exercices-LucasBouchard1 | a8e7d13d8c582ebc44fe10f4be5e98ab65609bcb | f46ea10010a823cad46bbc93dba8d7691568d330 | refs/heads/master | 2023-08-13T20:49:54.742681 | 2021-09-30T18:05:38 | 2021-09-30T18:05:38 | 410,077,221 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,700 | py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
def check_brackets(text, brackets):
# TODO: Associer les ouvrantes et fermantes (à l'aide d'un dict)
opening_brackets = dict(zip(brackets[0::2], brackets[1::2])) # Ouvrants à fermants
closing_brackets = dict(zip(brackets[1::2], brackets[0::2])) # Fermants à ouvrants
# TODO: Vérifier les ouvertures/fermetures
bracket_stack = []
# Pour chaque char de la string
for chr in text:
# Si ouvrant:
if chr in opening_brackets:
# On empile
bracket_stack.append(chr)
# Si fermant
elif chr in closing_brackets:
# Si la pile est vide ou on n'a pas l'ouvrant associé au top de la pile
if len(bracket_stack) == 0 or bracket_stack[-1] != closing_brackets[chr]:
# Pas bon
return False
# On dépile
bracket_stack.pop()
# On vérifie que la pile est vide à la fin (au cas où il y aurait des ouvrants de trop)
return len(bracket_stack) == 0
def remove_comments(full_text, comment_start, comment_end):
# Cette ligne sert à rien, on ne modifie pas la variable originale de toute façon
text = full_text
while True:
# Trouver le prochain début de commentaire
start = text.find(comment_start)
# Trouver la prochaine fin de commentaire
end = text.find(comment_end)
# Si aucun des deux trouvés
if start == -1 and end == -1:
return text
# Si fermeture précède ouverture ou j'en ai un mais pas l'autre
if end < start or (start == -1) != (end == -1):
# Pas bon
return None
# Enlever le commentaire de la string
text = text[:start] + text[end + len(comment_end):]
def get_tag_prefix(text, opening_tags, closing_tags):
for t in zip(opening_tags, closing_tags):
if text.startswith(t[0]):
return (t[0], None)
elif text.startswith(t[1]):
return (None, t[1])
return (None, None)
def check_tags(full_text, tag_names, comment_tags):
text = remove_comments(full_text, *comment_tags)
if text is None:
return False
# On construit nos balises à la HTML ("head" donne "<head>" et "</head>")
otags = {f"<{name}>": f"</{name}>" for name in tag_names}
ctags = dict((v, k) for k, v in otags.items())
# Même algo qu'au numéro 1, mais adapté aux balises de plusieurs caractères
tag_stack = []
while len(text) != 0:
tag = get_tag_prefix(text, otags.keys(), ctags.keys())
# Si ouvrant:
if tag[0] is not None:
# On empile et on avance
tag_stack.append(tag[0])
text = text[len(tag[0]):]
# Si fermant:
elif tag[1] is not None:
# Si pile vide OU match pas le haut de la pile:
if len(tag_stack) == 0 or tag_stack[-1] != ctags[tag[1]]:
# Pas bon
return False
# On dépile et on avance
tag_stack.pop()
text = text[len(tag[1]):]
# Sinon:
else:
# On avance jusqu'à la prochaine balise.
text = text[1:]
# On vérifie que la pile est vide à la fin (au cas où il y aurait des balises ouvrantes de trop)
return len(tag_stack) == 0
if __name__ == "__main__":
brackets = ("(", ")", "{", "}", "[", "]")
yeet = "(yeet){yeet}"
yeeet = "({yeet})"
yeeeet = "({yeet)}"
yeeeeet = "(yeet"
print(check_brackets(yeet, brackets))
print(check_brackets(yeeet, brackets))
print(check_brackets(yeeeet, brackets))
print(check_brackets(yeeeeet, brackets))
print()
spam = "Hello, world!"
eggs = "Hello, /* OOGAH BOOGAH world!"
parrot = "Hello, OOGAH BOOGAH*/ world!"
print(remove_comments(spam, "/*", "*/"))
print(remove_comments(eggs, "/*", "*/"))
print(remove_comments(parrot, "/*", "*/"))
print()
otags = ("<head>", "<body>", "<h1>")
ctags = ("</head>", "</body>", "</h1>")
print(get_tag_prefix("<body><h1>Hello!</h1></body>", otags, ctags))
print(get_tag_prefix("<h1>Hello!</h1></body>", otags, ctags))
print(get_tag_prefix("Hello!</h1></body>", otags, ctags))
print(get_tag_prefix("</h1></body>", otags, ctags))
print(get_tag_prefix("</body>", otags, ctags))
print()
spam = (
"<html>"
" <head>"
" <title>"
" <!-- Ici j'ai écrit qqch -->"
" Example"
" </title>"
" </head>"
" <body>"
" <h1>Hello, world</h1>"
" <!-- Les tags vides sont ignorés -->"
" <br>"
" <h1/>"
" </body>"
"</html>"
)
eggs = (
"<html>"
" <head>"
" <title>"
" <!-- Ici j'ai écrit qqch -->"
" Example"
" <!-- Il manque un end tag"
" </title>-->"
" </head>"
"</html>"
)
parrot = (
"<html>"
" <head>"
" <title>"
" Commentaire mal formé -->"
" Example"
" </title>"
" </head>"
"</html>"
)
tags = ("html", "head", "title", "body", "h1")
comment_tags = ("<!--", "-->")
print(check_tags(spam, tags, comment_tags))
print(check_tags(eggs, tags, comment_tags))
print(check_tags(parrot, tags, comment_tags))
print()
| [
"66690702+github-classroom[bot]@users.noreply.github.com"
] | 66690702+github-classroom[bot]@users.noreply.github.com |
ad894d4f56aba4cac738231d9e972e40bc62b0a9 | 3d7e1a506d65c23c84b7430fa46623cb98de8c64 | /moreifelse.py | d00d9e4937067bacab561253371fcb13cdce1fc3 | [] | no_license | crakama/UdacityIntrotoComputerScience | cb6ac8a9084f078eaf245a52adc43541c35dc3f4 | 416b82b85ff70c48eabae6bb9d7b43354a158d9a | refs/heads/master | 2021-01-09T20:39:15.974791 | 2016-07-18T20:59:09 | 2016-07-18T20:59:09 | 60,571,882 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 396 | py | # Define a procedure, is_friend, that takes
# a string as its input, and returns a
# Boolean indicating if the input string
# is the name of a friend. Assume
# I am friends with everyone whose name
# starts with either 'D' or 'N', but no one
# else. You do not need to check for
# lower case 'd' or 'n
def is_friend(string):
if string[0] == 'D' or string[0] == 'N':
return True
return False
| [
"[email protected]"
] | |
8231022b260cc49ceca99cdc566df4b0860f21eb | fb468eee3a5a6467d299373c9632802f903c2ea8 | /CIL/rastersystem.py | 852c637a35a6a47c79a18a505dbba2b8e199b57a | [] | no_license | fish2000/cython-imaging | ebcd51789692de756fd8cd1030636364d1c248ba | a3f0d1784275a3d4e3ad509b77c7563856be96d4 | refs/heads/master | 2021-01-20T11:23:00.414850 | 2012-07-14T20:17:29 | 2012-07-14T20:17:29 | 4,513,759 | 1 | 1 | null | null | null | null | UTF-8 | Python | false | false | 16,504 | py | #!/usr/bin/env python
#
# NB: see warning regarding matrix row/column ordering in raytracer.cpp file header
#
# The matrix is stored in sparse format.
# For each row we have an (index,value) pair.
import numpy as np
from os.path import isdir, join, basename, dirname, abspath, splitext
from os import getcwd, chdir
from scipy.integrate import quadrature
from scipy import special
from h5py import File
from datetime import datetime
from glob import glob
from optparse import OptionParser
import sys
ipython = hasattr(sys, 'ipcompleter')
if ipython:
from IPython.Debugger import Tracer
dbg = Tracer()
else:
def dbg():
print("Run inside ipython to activate debugging")
def log_verbose(msg, force=True):
print( "{} {}".format(datetime.now(),msg) )
def log_quiet(msg, force=False):
if force:
log_verbose( msg )
pass
def logerr(msg):
log_verbose( "ERROR: " + msg )
def logwarn(msg):
log_verbose( "WARNING: " + msg )
log = log_quiet
#############################################################################################
# C++ interface glue
#############################################################################################
import ctypes
from ctypes import c_void_p as cvoidp, c_int as cint
#from ctypes import c_float as cfloat
#from ctypes import c_char_p as ccharp
#from ctypes import c_bool as cbool
from ctypes import c_uint64 as cuint64
from ctypes import c_uint8 as cuint8
from ctypes import c_double as cdouble
from ctypes import POINTER
from functools import partial
ptrUInt8_3D = np.ctypeslib.ndpointer(dtype=np.uint8, ndim=3, flags='CONTIGUOUS')
ptrUInt8_2D = np.ctypeslib.ndpointer(dtype=np.uint8, ndim=2, flags='CONTIGUOUS')
ptrUInt8 = np.ctypeslib.ndpointer(dtype=np.uint8, ndim=1, flags='CONTIGUOUS')
ptrInt32_3D = np.ctypeslib.ndpointer(dtype=np.int32, ndim=3, flags='CONTIGUOUS')
ptrInt32_2D = np.ctypeslib.ndpointer(dtype=np.int32, ndim=2, flags='CONTIGUOUS')
ptrInt32 = np.ctypeslib.ndpointer(dtype=np.int32, ndim=1, flags='CONTIGUOUS')
ptrFloat_3D = np.ctypeslib.ndpointer(dtype=np.float32, ndim=3, flags='CONTIGUOUS')
ptrFloat_2D = np.ctypeslib.ndpointer(dtype=np.float32, ndim=2, flags='CONTIGUOUS')
ptrFloat = np.ctypeslib.ndpointer(dtype=np.float32, ndim=1, flags='CONTIGUOUS')
ptrUInt64_3D = np.ctypeslib.ndpointer(dtype=np.uint64, ndim=3, flags='CONTIGUOUS')
ptrUInt64_2D = np.ctypeslib.ndpointer(dtype=np.uint64, ndim=2, flags='CONTIGUOUS')
ptrUInt64 = np.ctypeslib.ndpointer(dtype=np.uint64, ndim=1, flags='CONTIGUOUS')
class CPlusPlusInterface(object):
def bindfunc(self, name, restype=None, argtypes=[]):
mangledName = self.__class__.__name__ + '_' + name
self._dll.__setattr__(mangledName, self._dll.__getattr__(mangledName))
func = self._dll.__getattr__(mangledName)
func.restype = restype
func.argtypes = [cvoidp] + argtypes
self.__setattr__(name, partial(func, self.obj))
self._name = {
'name': name,
'mangledName': mangledName}
#############################################################################################
# Example external C++ library interface
#############################################################################################
'''
class ExampleWrapper(CPlusPlusInterface):
def __init__(self):
self._dll = ctypes.cdll.LoadLibrary('./build/libexample.dylib')
self._dll.Example_New.restype = cvoidp
self.obj = self._dll.Example_New()
self.bindfunc('Release')
self.bindfunc('Foo', None, [ccharp])
self.bindfunc('Sum_Array', cfloat, [ptrFloat_3D, cint, cint, cint])
self.bindfunc('Get_Cached_Sum', cfloat)
self._Sum_Array = self.Sum_Array
self.Sum_Array = lambda X: self._Sum_Array(np.ascontiguousarray(X), *X.shape)
'''
try:
if __file__:
pass
except:
from os import environ
__file__ = environ.get('PWD')
#############################################################################################
# External C++ library interface
#############################################################################################
RS = None
class RasterSystem(CPlusPlusInterface):
class Digest(ctypes.Structure):
_fields_ = [("id", POINTER(cint)),
("coeffs", POINTER(cuint8)),
("size", cint)]
def __init__(self, dataDir):
# currently need to be in the raytracer dir for this to work
chdir(abspath(dirname(__file__)))
self._dll = ctypes.cdll.LoadLibrary(join(getcwd(), 'ext/librastersystem.so'))
self._dll.RasterSystem_Get_Instance.restype = cvoidp
self.obj = self._dll.RasterSystem_Get_Instance()
"""
# setters
self.bindfunc( 'Bind_Path', None,
[ptrFloat_2D, cint] )
self.bindfunc( 'Bind_Ray', None,
[ptrFloat] )
self.bindfunc( 'Bind_Exit_Dir', None,
[ptrFloat] )
self.bindfunc( 'Bind_Row_Indices', None,
[ptrInt32, cint] )
self.bindfunc( 'Bind_Row_Weights', None,
[ptrFloat, cint] )
self.bindfunc( 'Bind_RBF_LUT', None,
[ptrFloat, cint] )
self.bindfunc( 'Bind_RBF_Integral_LUT', None,
[ptrFloat, cint] )
self.bindfunc( 'Set_RBF_Radius', None,
[cfloat] )
# main interaction functions
self.bindfunc( 'Init_HDF5', None,
[ccharp] )
self.bindfunc( 'Close_HDF5', None,
[] )
self.bindfunc( 'Insert_Points', None,
[ptrFloat_2D, cint, ptrFloat, ptrFloat_2D] )
self.bindfunc( 'Solve_ODE', cint,
[] )
"""
# MY SHIT
self.bindfunc( 'Calculate_PHash_Digest', None,
[ptrUInt8_3D, cdouble, cdouble, self.Digest, cint] )
self.bindfunc( 'Calculate_PHash_DCT', None,
[ptrUInt8_3D, cuint64] )
"""
# for debugging
self.bindfunc( 'Build_Matrix_Row', cint,
[ptrFloat_2D, cint] )
self.bindfunc( 'Range_Search', cint,
[cfloat, cfloat, cfloat] )
self.bindfunc( 'Interpolate', cfloat,
[cfloat, cfloat, cfloat, ptrFloat] )
self.bindfunc( 'Get_RBF_Radius', cfloat,
[] )
self.bindfunc( 'Get_HMin', cfloat,
[] )
self.bindfunc( 'Get_HMax', cfloat,
[] )
"""
def old_init(self, dataDir):
# init ray in/out buffers
self.ray = np.zeros( 6, dtype=np.float32 )
self.Bind_Ray( self.ray )
self.exitDir = np.zeros( 3, dtype=np.float32 )
self.Bind_Exit_Dir( self.exitDir )
# init path
# bad things will happen if these buffers overflow in the C++ code
BUFSIZE = 4096
self.path = np.zeros( (BUFSIZE,3), dtype=np.float32 )
self.Bind_Path( self.path, self.path.shape[0] )
self.rowIndices = np.zeros( BUFSIZE, dtype=np.int32 )
self.Bind_Row_Indices( self.rowIndices, self.rowIndices.shape[0] )
self.rowWeights = np.zeros( BUFSIZE, dtype=np.float32 )
self.Bind_Row_Weights( self.rowWeights, self.rowWeights.shape[0] )
# init voxels and rays
assert isdir( dataDir ), "Invalid data directory"
self.dataDir = dataDir
voxelFile = join( dataDir, 'voxels.h5' )
#rayFile = join( dataDir, 'rays.h5' )
# get interVoxelSpace of finest resolution grid
log( "Loading " + voxelFile )
with File( voxelFile, 'r' ) as dat:
self.interVoxelSpace = sorted( map(float,dat.keys()) )[0]
group = dat[str(self.interVoxelSpace)]
try:
pos = group['pos'][:]
rindex = group['rindex'][:]
gradients = group['gradients'][:]
if pos.shape != gradients.shape or len(pos) != len(rindex):
logerr( "Mismatching voxel dataset sizes" )
return
except KeyError, err:
logerr( "{} in group '{}'".format(err.message,group.name) )
return
log( "Initialising voxels for raytracer" )
self.insert_points( pos, rindex, gradients )
# init RBF (radial basis function / filter kernel)
nBins = 256
self.radius = self.select_rbf_radius()
(self.rbf_lut, self.rbf_integral_lut) = self.get_rbf( nBins )
self.Bind_RBF_LUT( self.rbf_lut, nBins )
self.Bind_RBF_Integral_LUT( self.rbf_integral_lut, nBins )
self.Set_RBF_Radius( self.radius )
def init_traced_file(self, cam):
assert '{:03d}'.format(int(cam)) == cam
self.tracedFile = join( self.dataDir, 'traced_{}.h5'.format(cam) )
self.Init_HDF5( self.tracedFile )
def kaiser_bessel_filter(self, resolution, alpha=2.0):
"""Tabled 1D function from 1 to [slightly above] 0 in 'resolution' steps"""
# let radius = 1 unit
samples = np.linspace( 0, 1, resolution )
def kb(d):
"""Value of KaiserBessel filter of radius 1 at distance d from centre"""
bessel = lambda y: special.iv(0,y)
return bessel( np.pi*alpha*np.sqrt(1-d**2) ) / bessel( np.pi*alpha )
rbf_lut = kb( samples ).astype(np.float32)
def integrand(d, y):
"""kb filter value at distance y away from midpoint of a chord
d world units away (orthogonally) from basis function centre"""
hypot = np.sqrt( d**2 + y**2 )
return kb( hypot )
rbf_integral_lut = np.empty( resolution, dtype=np.float32 )
for (i,d) in enumerate( samples ):
ymax = np.sqrt( 1 - d**2 )
f = lambda y: integrand( d, y )
rbf_integral_lut[i] = quadrature( f, -ymax,ymax, vec_func=False )[0]
return (rbf_lut, rbf_integral_lut/rbf_integral_lut[0])
def get_rbf(self, resolution, alpha=2.0):
"""Load existing RBF kernel from disk, otherwise generate it"""
lutFilename = join( self.dataDir, 'rbf.h5' )
try:
# load existing kernel from disk
with File( lutFilename, 'r' ) as dat:
log( "Loading RBF kernel" )
rbf_lut = dat['rbf_lut'][:]
rbf_integral_lut = dat['rbf_integral_lut'][:]
if rbf_lut.shape != rbf_integral_lut.shape:
raise ValueError( "LUT resolution mismatch" )
if rbf_lut.size != resolution:
raise ValueError( "LUT resolution mismatch" )
except (IOError, ValueError):
# if it didn't already exist in correct size, create data and save
(rbf_lut, rbf_integral_lut) = self.kaiser_bessel_filter( resolution, alpha )
with File( lutFilename, 'w' ) as dat:
log( "Precomputing RBF kernel" )
def insert(name, data):
dat.create_dataset( name, data=data, compression=3, shuffle=True )
insert( 'rbf_lut', rbf_lut )
insert( 'rbf_integral_lut', rbf_integral_lut )
dat.attrs['timestamp'] = str( datetime.now() )
return (rbf_lut, rbf_integral_lut)
def select_rbf_radius(self):
"""Find reasonable size for RBF (width in inches)"""
# want to reach the voxel diagonally adjacent to a voxel, but not overlap
# the one two voxels off along a single dimension
minFactor = np.sqrt(3)
maxFactor = 2
factor = (minFactor + maxFactor) / 2
log( "InterVoxel:{}, RBF_Radius:{}".format(self.interVoxelSpace, self.interVoxelSpace*factor) )
return self.interVoxelSpace * factor
def insert_points(self, points, rindices, gradients):
"""points = Nx3, will be labelled sequentially"""
ascont = lambda arr: np.ascontiguousarray( arr, dtype=np.float32 )
self.Insert_Points( ascont(points), points.shape[0],
ascont(rindices), ascont(gradients) )
def solve_ode(self, ray):
"""ray = 1x6 vector (x,y,z,dx,dy,dz), unit length dir"""
# passing ctypes arrays to C++ funcs incurs some nontrivial overhead
# so rather than passing ray as an argument, write its values directly
# to memory shared by Python/C++ and then call Solve_ODE without args
self.ray[:] = ray[:]
pair = self.Solve_ODE()
# decode the 'tuple' of two 16bit ints packed into single 32bit int
pathLength = pair >> 16
#nnz = pair - (pathLength << 16)
if pathLength > 0:
# return angle difference between ingoing and exiting directions (radians)
dot = np.dot( ray[3:], self.exitDir )
return np.arccos( dot ) if abs(dot)<=1 else 0
# NOTE there may be something wrong with computed delta.
# they are always zero or occassionally 0.0197823 (homogeneous voxels)
else:
return 0
def build_matrix_row(self, path):
"""path = mx3 matrix (x,y,z)
m = num steps inside bbox after resampling
After this, self.path and self.indices are sparse vector of points"""
path = np.ascontiguousarray( path, dtype=np.float32 )
return self.Build_Matrix_Row( path, path.shape[0] )
# for debugging
def range_search(self, x, y, z):
return self.Range_Search( x, y, z )
# for debugging
def interpolate(self, x, y, z):
gradient = np.empty( 3, dtype=np.float32 )
rindex = self.Interpolate( x,y,z, gradient )
return (rindex, gradient)
def main(cam):
"""Trace and output results for all rays belonging to this camera"""
# note that we intentionally output traced rays to multiple files rather
# then keeping them more neatly as groups within a single HDF5 file. This
# is because raytracing is the slowest operation and we may want to
# perform it on multiple computers in parallel, so they cannot all be
# writing to the same file simultaneously.
RS.init_traced_file(cam)
# now trace each camera
rayFile = join( RS.dataDir, 'rays.h5' )
log( "Loading " + rayFile )
with File( rayFile, 'r' ) as dat:
if cam not in dat.keys():
logerr( "Missing '{}' in '{}'".format(cam,dat.name) )
return
group = dat[cam]
log( "Tracing camera " + cam )
origin = group['origin']
direction = group['direction']
rays = np.hstack( [origin, direction] )
delta = np.rad2deg( np.array(map(RS.solve_ode,rays),dtype=np.float32) )
# now write output to disk
# don't open file with 'w' otherwise you clobber the existing data
RS.Close_HDF5()
log( "Saving data to " + RS.tracedFile )
with File( RS.tracedFile, 'r+' ) as dat:
dat.attrs['timestamp'] = str( datetime.now() )
def insert(name, data):
dat.create_dataset( name, data=data, compression=3, shuffle=True )
insert( 'degreesTraced', delta )
log( "Done tracing " + cam )
if __name__ == '__main__':
cmd = OptionParser()
cmd.add_option("--dataDir", default=None,
type="string", metavar="DIR",
help="location of data files to process [%default]")
cmd.add_option("--camera", default=-1,
type="int", metavar="N",
help="camera angle to trace (-1 for all) [%default]")
cmd.add_option("-v", "--verbose", default=False,
action="store_true",
help="output additional progress information [%default]")
(opt,args) = cmd.parse_args()
if opt.verbose:
log = log_verbose
RS = RasterSystem( opt.dataDir )
if opt.camera == -1:
cameras = [splitext( basename(c) )[0].split( '_' )[-1]
for c in glob( join(opt.dataDir,'data_far_???.h5') )]
for cam in cameras:
main( cam )
else:
cam = '{:03d}'.format( opt.camera )
main( cam )
| [
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] | |
97ee66ff1a3f90a62d9c62d68d7867612770f666 | eee6dd18897d3118f41cb5e6f93f830e06fbfe2f | /venv/lib/python3.6/site-packages/sklearn/gaussian_process/tests/test_kernels.py | 291458c12d1df9af9acbc79e933cb0ac4a84c03a | [] | no_license | georgeosodo/ml | 2148ecd192ce3d9750951715c9f2bfe041df056a | 48fba92263e9295e9e14697ec00dca35c94d0af0 | refs/heads/master | 2020-03-14T11:39:58.475364 | 2018-04-30T13:13:01 | 2018-04-30T13:13:01 | 131,595,044 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 12,539 | py | """Testing for kernels for Gaussian processes."""
# Author: Jan Hendrik Metzen <[email protected]>
# License: BSD 3 clause
from sklearn.externals.funcsigs import signature
import numpy as np
from sklearn.gaussian_process.kernels import _approx_fprime
from sklearn.metrics.pairwise \
import PAIRWISE_KERNEL_FUNCTIONS, euclidean_distances, pairwise_kernels
from sklearn.gaussian_process.kernels \
import (RBF, Matern, RationalQuadratic, ExpSineSquared, DotProduct,
ConstantKernel, WhiteKernel, PairwiseKernel, KernelOperator,
Exponentiation)
from sklearn.base import clone
from sklearn.utils.testing import (assert_equal, assert_almost_equal,
assert_not_equal, assert_array_equal,
assert_array_almost_equal)
X = np.random.RandomState(0).normal(0, 1, (5, 2))
Y = np.random.RandomState(0).normal(0, 1, (6, 2))
kernel_white = RBF(length_scale=2.0) + WhiteKernel(noise_level=3.0)
kernels = [RBF(length_scale=2.0), RBF(length_scale_bounds=(0.5, 2.0)),
ConstantKernel(constant_value=10.0),
2.0 * RBF(length_scale=0.33, length_scale_bounds="fixed"),
2.0 * RBF(length_scale=0.5), kernel_white,
2.0 * RBF(length_scale=[0.5, 2.0]),
2.0 * Matern(length_scale=0.33, length_scale_bounds="fixed"),
2.0 * Matern(length_scale=0.5, nu=0.5),
2.0 * Matern(length_scale=1.5, nu=1.5),
2.0 * Matern(length_scale=2.5, nu=2.5),
2.0 * Matern(length_scale=[0.5, 2.0], nu=0.5),
3.0 * Matern(length_scale=[2.0, 0.5], nu=1.5),
4.0 * Matern(length_scale=[0.5, 0.5], nu=2.5),
RationalQuadratic(length_scale=0.5, alpha=1.5),
ExpSineSquared(length_scale=0.5, periodicity=1.5),
DotProduct(sigma_0=2.0), DotProduct(sigma_0=2.0) ** 2,
RBF(length_scale=[2.0]), Matern(length_scale=[2.0])]
for metric in PAIRWISE_KERNEL_FUNCTIONS:
if metric in ["additive_chi2", "chi2"]:
continue
kernels.append(PairwiseKernel(gamma=1.0, metric=metric))
def test_kernel_gradient():
# Compare analytic and numeric gradient of kernels.
for kernel in kernels:
K, K_gradient = kernel(X, eval_gradient=True)
assert_equal(K_gradient.shape[0], X.shape[0])
assert_equal(K_gradient.shape[1], X.shape[0])
assert_equal(K_gradient.shape[2], kernel.theta.shape[0])
def eval_kernel_for_theta(theta):
kernel_clone = kernel.clone_with_theta(theta)
K = kernel_clone(X, eval_gradient=False)
return K
K_gradient_approx = \
_approx_fprime(kernel.theta, eval_kernel_for_theta, 1e-10)
assert_almost_equal(K_gradient, K_gradient_approx, 4)
def test_kernel_theta():
# Check that parameter vector theta of kernel is set correctly.
for kernel in kernels:
if isinstance(kernel, KernelOperator) \
or isinstance(kernel, Exponentiation): # skip non-basic kernels
continue
theta = kernel.theta
_, K_gradient = kernel(X, eval_gradient=True)
# Determine kernel parameters that contribute to theta
init_sign = list(signature(kernel.__class__.__init__).parameters.values())
args = [p.name for p in init_sign if p.name != 'self']
theta_vars = [s.rstrip("_bounds") for s in [s for s in args if s.endswith("_bounds")]]
assert_equal(
set(hyperparameter.name
for hyperparameter in kernel.hyperparameters),
set(theta_vars))
# Check that values returned in theta are consistent with
# hyperparameter values (being their logarithms)
for i, hyperparameter in enumerate(kernel.hyperparameters):
assert_equal(theta[i],
np.log(getattr(kernel, hyperparameter.name)))
# Fixed kernel parameters must be excluded from theta and gradient.
for i, hyperparameter in enumerate(kernel.hyperparameters):
# create copy with certain hyperparameter fixed
params = kernel.get_params()
params[hyperparameter.name + "_bounds"] = "fixed"
kernel_class = kernel.__class__
new_kernel = kernel_class(**params)
# Check that theta and K_gradient are identical with the fixed
# dimension left out
_, K_gradient_new = new_kernel(X, eval_gradient=True)
assert_equal(theta.shape[0], new_kernel.theta.shape[0] + 1)
assert_equal(K_gradient.shape[2], K_gradient_new.shape[2] + 1)
if i > 0:
assert_equal(theta[:i], new_kernel.theta[:i])
assert_array_equal(K_gradient[..., :i],
K_gradient_new[..., :i])
if i + 1 < len(kernel.hyperparameters):
assert_equal(theta[i + 1:], new_kernel.theta[i:])
assert_array_equal(K_gradient[..., i + 1:],
K_gradient_new[..., i:])
# Check that values of theta are modified correctly
for i, hyperparameter in enumerate(kernel.hyperparameters):
theta[i] = np.log(42)
kernel.theta = theta
assert_almost_equal(getattr(kernel, hyperparameter.name), 42)
setattr(kernel, hyperparameter.name, 43)
assert_almost_equal(kernel.theta[i], np.log(43))
def test_auto_vs_cross():
# Auto-correlation and cross-correlation should be consistent.
for kernel in kernels:
if kernel == kernel_white:
continue # Identity is not satisfied on diagonal
K_auto = kernel(X)
K_cross = kernel(X, X)
assert_almost_equal(K_auto, K_cross, 5)
def test_kernel_diag():
# Test that diag method of kernel returns consistent results.
for kernel in kernels:
K_call_diag = np.diag(kernel(X))
K_diag = kernel.diag(X)
assert_almost_equal(K_call_diag, K_diag, 5)
def test_kernel_operator_commutative():
# Adding kernels and multiplying kernels should be commutative.
# Check addition
assert_almost_equal((RBF(2.0) + 1.0)(X),
(1.0 + RBF(2.0))(X))
# Check multiplication
assert_almost_equal((3.0 * RBF(2.0))(X),
(RBF(2.0) * 3.0)(X))
def test_kernel_anisotropic():
# Anisotropic kernel should be consistent with isotropic kernels.
kernel = 3.0 * RBF([0.5, 2.0])
K = kernel(X)
X1 = np.array(X)
X1[:, 0] *= 4
K1 = 3.0 * RBF(2.0)(X1)
assert_almost_equal(K, K1)
X2 = np.array(X)
X2[:, 1] /= 4
K2 = 3.0 * RBF(0.5)(X2)
assert_almost_equal(K, K2)
# Check getting and setting via theta
kernel.theta = kernel.theta + np.log(2)
assert_array_equal(kernel.theta, np.log([6.0, 1.0, 4.0]))
assert_array_equal(kernel.k2.length_scale, [1.0, 4.0])
def test_kernel_stationary():
# Test stationarity of kernels.
for kernel in kernels:
if not kernel.is_stationary():
continue
K = kernel(X, X + 1)
assert_almost_equal(K[0, 0], np.diag(K))
def check_hyperparameters_equal(kernel1, kernel2):
# Check that hyperparameters of two kernels are equal
for attr in set(dir(kernel1) + dir(kernel2)):
if attr.startswith("hyperparameter_"):
attr_value1 = getattr(kernel1, attr)
attr_value2 = getattr(kernel2, attr)
assert_equal(attr_value1, attr_value2)
def test_kernel_clone():
# Test that sklearn's clone works correctly on kernels.
bounds = (1e-5, 1e5)
for kernel in kernels:
kernel_cloned = clone(kernel)
# XXX: Should this be fixed?
# This differs from the sklearn's estimators equality check.
assert_equal(kernel, kernel_cloned)
assert_not_equal(id(kernel), id(kernel_cloned))
# Check that all constructor parameters are equal.
assert_equal(kernel.get_params(), kernel_cloned.get_params())
# Check that all hyperparameters are equal.
yield check_hyperparameters_equal, kernel, kernel_cloned
# This test is to verify that using set_params does not
# break clone on kernels.
# This used to break because in kernels such as the RBF, non-trivial
# logic that modified the length scale used to be in the constructor
# See https://github.com/scikit-learn/scikit-learn/issues/6961
# for more details.
params = kernel.get_params()
# RationalQuadratic kernel is isotropic.
isotropic_kernels = (ExpSineSquared, RationalQuadratic)
if 'length_scale' in params and not isinstance(kernel,
isotropic_kernels):
length_scale = params['length_scale']
if np.iterable(length_scale):
params['length_scale'] = length_scale[0]
params['length_scale_bounds'] = bounds
else:
params['length_scale'] = [length_scale] * 2
params['length_scale_bounds'] = bounds * 2
kernel_cloned.set_params(**params)
kernel_cloned_clone = clone(kernel_cloned)
assert_equal(kernel_cloned_clone.get_params(),
kernel_cloned.get_params())
assert_not_equal(id(kernel_cloned_clone), id(kernel_cloned))
yield (check_hyperparameters_equal, kernel_cloned,
kernel_cloned_clone)
def test_matern_kernel():
# Test consistency of Matern kernel for special values of nu.
K = Matern(nu=1.5, length_scale=1.0)(X)
# the diagonal elements of a matern kernel are 1
assert_array_almost_equal(np.diag(K), np.ones(X.shape[0]))
# matern kernel for coef0==0.5 is equal to absolute exponential kernel
K_absexp = np.exp(-euclidean_distances(X, X, squared=False))
K = Matern(nu=0.5, length_scale=1.0)(X)
assert_array_almost_equal(K, K_absexp)
# test that special cases of matern kernel (coef0 in [0.5, 1.5, 2.5])
# result in nearly identical results as the general case for coef0 in
# [0.5 + tiny, 1.5 + tiny, 2.5 + tiny]
tiny = 1e-10
for nu in [0.5, 1.5, 2.5]:
K1 = Matern(nu=nu, length_scale=1.0)(X)
K2 = Matern(nu=nu + tiny, length_scale=1.0)(X)
assert_array_almost_equal(K1, K2)
def test_kernel_versus_pairwise():
# Check that GP kernels can also be used as pairwise kernels.
for kernel in kernels:
# Test auto-kernel
if kernel != kernel_white:
# For WhiteKernel: k(X) != k(X,X). This is assumed by
# pairwise_kernels
K1 = kernel(X)
K2 = pairwise_kernels(X, metric=kernel)
assert_array_almost_equal(K1, K2)
# Test cross-kernel
K1 = kernel(X, Y)
K2 = pairwise_kernels(X, Y, metric=kernel)
assert_array_almost_equal(K1, K2)
def test_set_get_params():
# Check that set_params()/get_params() is consistent with kernel.theta.
for kernel in kernels:
# Test get_params()
index = 0
params = kernel.get_params()
for hyperparameter in kernel.hyperparameters:
if hyperparameter.bounds == "fixed":
continue
size = hyperparameter.n_elements
if size > 1: # anisotropic kernels
assert_almost_equal(np.exp(kernel.theta[index:index + size]),
params[hyperparameter.name])
index += size
else:
assert_almost_equal(np.exp(kernel.theta[index]),
params[hyperparameter.name])
index += 1
# Test set_params()
index = 0
value = 10 # arbitrary value
for hyperparameter in kernel.hyperparameters:
if hyperparameter.bounds == "fixed":
continue
size = hyperparameter.n_elements
if size > 1: # anisotropic kernels
kernel.set_params(**{hyperparameter.name: [value] * size})
assert_almost_equal(np.exp(kernel.theta[index:index + size]),
[value] * size)
index += size
else:
kernel.set_params(**{hyperparameter.name: value})
assert_almost_equal(np.exp(kernel.theta[index]), value)
index += 1
def test_repr_kernels():
# Smoke-test for repr in kernels.
for kernel in kernels:
repr(kernel)
| [
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] | |
2090d13db2c206218556a5ebdfd8c9f99b604499 | d0ddcccea697ddcb3aad37c51e11f57a5fc6573c | /tests/test_data/recursive_hello.py | a492ccaec19505a36f3f0177320bd88dd6a487c6 | [
"MIT"
] | permissive | kiok46/webkivy | 0c0d8fcd1b1e596ec45833501e92d659159f5b1c | 8f3522e61cd5951f825f5f1eef5482aa1bc436a5 | refs/heads/master | 2021-01-22T01:38:04.007487 | 2016-03-13T06:46:11 | 2016-03-13T06:46:11 | 53,776,897 | 1 | 0 | null | 2016-03-13T09:23:20 | 2016-03-13T09:23:19 | null | UTF-8 | Python | false | false | 74 | py | from subfolder.subtest import subhello
def hello():
return subhello() | [
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] | |
ece46492b31fb46f106f1115a52f482853cf4f21 | b167407960a3b69b16752590def1a62b297a4b0c | /tools/project-creator/Python2.6.6/Lib/bsddb/test/test_get_none.py | 9c08a61b1e86fd2f0f11cfd0919ea35f308fe7de | [
"MIT"
] | permissive | xcode1986/nineck.ca | 543d1be2066e88a7db3745b483f61daedf5f378a | 637dfec24407d220bb745beacebea4a375bfd78f | refs/heads/master | 2020-04-15T14:48:08.551821 | 2019-01-15T07:36:06 | 2019-01-15T07:36:06 | 164,768,581 | 1 | 1 | MIT | 2019-01-15T08:30:27 | 2019-01-09T02:09:21 | C++ | UTF-8 | Python | false | false | 2,330 | py | """
TestCases for checking set_get_returns_none.
"""
import os, string
import unittest
from test_all import db, verbose, get_new_database_path
#----------------------------------------------------------------------
class GetReturnsNoneTestCase(unittest.TestCase):
def setUp(self):
self.filename = get_new_database_path()
def tearDown(self):
try:
os.remove(self.filename)
except os.error:
pass
def test01_get_returns_none(self):
d = db.DB()
d.open(self.filename, db.DB_BTREE, db.DB_CREATE)
d.set_get_returns_none(1)
for x in string.letters:
d.put(x, x * 40)
data = d.get('bad key')
self.assertEqual(data, None)
data = d.get(string.letters[0])
self.assertEqual(data, string.letters[0]*40)
count = 0
c = d.cursor()
rec = c.first()
while rec:
count = count + 1
rec = c.next()
self.assertEqual(rec, None)
self.assertEqual(count, len(string.letters))
c.close()
d.close()
def test02_get_raises_exception(self):
d = db.DB()
d.open(self.filename, db.DB_BTREE, db.DB_CREATE)
d.set_get_returns_none(0)
for x in string.letters:
d.put(x, x * 40)
self.assertRaises(db.DBNotFoundError, d.get, 'bad key')
self.assertRaises(KeyError, d.get, 'bad key')
data = d.get(string.letters[0])
self.assertEqual(data, string.letters[0]*40)
count = 0
exceptionHappened = 0
c = d.cursor()
rec = c.first()
while rec:
count = count + 1
try:
rec = c.next()
except db.DBNotFoundError: # end of the records
exceptionHappened = 1
break
self.assertNotEqual(rec, None)
self.assert_(exceptionHappened)
self.assertEqual(count, len(string.letters))
c.close()
d.close()
#----------------------------------------------------------------------
def test_suite():
return unittest.makeSuite(GetReturnsNoneTestCase)
if __name__ == '__main__':
unittest.main(defaultTest='test_suite')
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59d056538621fac227eb382e34ac693ad942fe9a | 5189a2e2e1fbf04edb2e212652b268361bc54de6 | /dephell/commands/jail_show.py | 0dea75396705ef81137ac8537f6baff5868bde42 | [
"MIT"
] | permissive | Brishen/dephell | 50f74a4d9c94fc81c3ae5bc3b8472b4d89e23fd7 | 1a09de4b9204466b4c9b833fd880542fb6b73d52 | refs/heads/master | 2021-03-30T09:19:05.518468 | 2020-03-17T17:54:22 | 2020-03-17T17:54:22 | 248,037,267 | 0 | 0 | MIT | 2020-03-17T17:53:53 | 2020-03-17T17:53:52 | null | UTF-8 | Python | false | false | 2,249 | py | # built-in
from argparse import ArgumentParser
from pathlib import Path
# external
from dephell_venvs import VEnvs
from packaging.utils import canonicalize_name
# app
from ..actions import format_size, get_path_size, make_json
from ..config import builders
from ..converters import InstalledConverter
from .base import BaseCommand
class JailShowCommand(BaseCommand):
"""Show info about the package isolated environment.
"""
@staticmethod
def build_parser(parser) -> ArgumentParser:
builders.build_config(parser)
builders.build_venv(parser)
builders.build_output(parser)
builders.build_other(parser)
parser.add_argument('name', help='jail name')
return parser
def __call__(self) -> bool:
venvs = VEnvs(path=self.config['venv'])
name = canonicalize_name(self.args.name)
venv = venvs.get_by_name(name)
if not venv.exists():
self.logger.error('jail does not exist', extra=dict(package=name))
return False
# get list of exposed entrypoints
entrypoints_names = []
for entrypoint in venv.bin_path.iterdir():
global_entrypoint = Path(self.config['bin']) / entrypoint.name
if not global_entrypoint.exists():
continue
if not global_entrypoint.resolve().samefile(entrypoint):
continue
entrypoints_names.append(entrypoint.name)
root = InstalledConverter().load(paths=[venv.lib_path], names={name})
version = None
for subdep in root.dependencies:
if subdep.name != name:
continue
version = str(subdep.constraint).replace('=', '')
data = dict(
name=name,
path=str(venv.path),
entrypoints=entrypoints_names,
version=version,
size=dict(
lib=format_size(get_path_size(venv.lib_path)),
total=format_size(get_path_size(venv.path)),
),
)
print(make_json(
data=data,
key=self.config.get('filter'),
colors=not self.config['nocolors'],
table=self.config['table'],
))
return True
| [
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5f761522ce7ca247bfbcbff7daf927ba4f28a87b | bf902add6952d7f7decdb2296bb136eea55bf441 | /YOLO/.history/pytorch-yolo-v3/video_demo_v1_20201106013212.py | 68f68f8e2ac4d0205b1ba8899014a62bf1fab713 | [
"MIT"
] | permissive | jphacks/D_2003 | c78fb2b4d05739dbd60eb9224845eb78579afa6f | 60a5684d549862e85bdf758069518702d9925a48 | refs/heads/master | 2023-01-08T16:17:54.977088 | 2020-11-07T06:41:33 | 2020-11-07T06:41:33 | 304,576,949 | 1 | 4 | null | null | null | null | UTF-8 | Python | false | false | 16,516 | py | from __future__ import division
import time
import torch
import torch.nn as nn
from torch.autograd import Variable
import numpy as np
import cv2
from util import *
from darknet import Darknet
from preprocess import prep_image, inp_to_image
import pandas as pd
import random
import argparse
import pickle as pkl
import requests
from requests.auth import HTTPDigestAuth
import io
from PIL import Image, ImageDraw, ImageFilter
import play
import csv
import pprint
with open('csv/Lidar.csv', 'r', encoding="utf-8_sig", newline = '') as f:
LiDAR = csv.reader(f)
l = [row for row in LiDAR]
# for row in LiDAR:
# print(row)
print(l)
print(LiDAR[0])
def prep_image(img, inp_dim):
# CNNに通すために画像を加工する
orig_im = img
dim = orig_im.shape[1], orig_im.shape[0]
img = cv2.resize(orig_im, (inp_dim, inp_dim))
img_ = img[:,:,::-1].transpose((2,0,1)).copy()
img_ = torch.from_numpy(img_).float().div(255.0).unsqueeze(0)
return img_, orig_im, dim
def count(x, img, count):
# 画像に結果を描画
c1 = tuple(x[1:3].int())
c2 = tuple(x[3:5].int())
cls = int(x[-1])
label = "{0}".format(classes[cls])
print("label:\n", label)
# 人数カウント
if(label=='no-mask'):
count+=1
print(count)
return count
def write(x, img,camId):
global count
global point
p = [0,0]
# 画像に結果を描画
c1 = tuple(x[1:3].int())
c2 = tuple(x[3:5].int())
cls = int(x[-1])
print(camId, "_c0:",c1)
print(camId, "_c1:",c2)
label = "{0}".format(classes[cls])
print("label:", label)
# 人数カウント
if(label=='no-mask'):
count+=1
print(count)
p[0] = (c2[0]+c1[0])/2
p[1] = (c2[1]+c1[1])/2
point[camId].append(p)
color = random.choice(colors)
cv2.rectangle(img, c1, c2,color, 1)
t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_PLAIN, 1 , 1)[0]
c2 = c1[0] + t_size[0] + 3, c1[1] + t_size[1] + 4
cv2.rectangle(img, c1, c2,color, -1)
cv2.putText(img, label, (c1[0], c1[1] + t_size[1] + 4), cv2.FONT_HERSHEY_PLAIN, 1, [225,255,255], 1);
return img
def arg_parse():
# モジュールの引数を作成
parser = argparse.ArgumentParser(description='YOLO v3 Cam Demo') # ArgumentParserで引数を設定する
parser.add_argument("--confidence", dest = "confidence", help = "Object Confidence to filter predictions", default = 0.25)
# confidenceは信頼性
parser.add_argument("--nms_thresh", dest = "nms_thresh", help = "NMS Threshhold", default = 0.4)
# nms_threshは閾値
parser.add_argument("--reso", dest = 'reso', help =
"Input resolution of the network. Increase to increase accuracy. Decrease to increase speed",
default = "160", type = str)
# resoはCNNの入力解像度で、増加させると精度が上がるが、速度が低下する。
return parser.parse_args() # 引数を解析し、返す
def cvpaste(img, imgback, x, y, angle, scale):
# x and y are the distance from the center of the background image
r = img.shape[0]
c = img.shape[1]
rb = imgback.shape[0]
cb = imgback.shape[1]
hrb=round(rb/2)
hcb=round(cb/2)
hr=round(r/2)
hc=round(c/2)
# Copy the forward image and move to the center of the background image
imgrot = np.zeros((rb,cb,3),np.uint8)
imgrot[hrb-hr:hrb+hr,hcb-hc:hcb+hc,:] = img[:hr*2,:hc*2,:]
# Rotation and scaling
M = cv2.getRotationMatrix2D((hcb,hrb),angle,scale)
imgrot = cv2.warpAffine(imgrot,M,(cb,rb))
# Translation
M = np.float32([[1,0,x],[0,1,y]])
imgrot = cv2.warpAffine(imgrot,M,(cb,rb))
# Makeing mask
imggray = cv2.cvtColor(imgrot,cv2.COLOR_BGR2GRAY)
ret, mask = cv2.threshold(imggray, 10, 255, cv2.THRESH_BINARY)
mask_inv = cv2.bitwise_not(mask)
# Now black-out the area of the forward image in the background image
img1_bg = cv2.bitwise_and(imgback,imgback,mask = mask_inv)
# Take only region of the forward image.
img2_fg = cv2.bitwise_and(imgrot,imgrot,mask = mask)
# Paste the forward image on the background image
imgpaste = cv2.add(img1_bg,img2_fg)
return imgpaste
def cosineTheorem(Lidar, radian1, radian2):
theta = abs(radian1-radian2)
distance = Lidar[radian1][1] ** 2 + Lidar[radian2][1] ** 2 - 2 * Lidar[radian1][1] * Lidar[radian2][1] * math.cos(abs(radian2 - radian1))
return distance
def combinations_count(n, r):
return math.factorial(n) // (math.factorial(n - r) * math.factorial(r))
# def beep(freq, dur=100):
# winsound.Beep(freq, dur)
if __name__ == '__main__':
#学習前YOLO
# cfgfile = "cfg/yolov3.cfg" # 設定ファイル
# weightsfile = "weight/yolov3.weights" # 重みファイル
# classes = load_classes('data/coco.names') # 識別クラスのリスト
#マスク学習後YOLO
cfgfile = "cfg/mask.cfg" # 設定ファイル
weightsfile = "weight/mask_1500.weights" # 重みファイル
classes = load_classes('data/mask.names') # 識別クラスのリスト
num_classes = 80 # クラスの数
args = arg_parse() # 引数を取得
confidence = float(args.confidence) # 信頼性の設定値を取得
nms_thesh = float(args.nms_thresh) # 閾値を取得
start = 0
CUDA = torch.cuda.is_available() # CUDAが使用可能かどうか
num_classes = 80 # クラスの数
bbox_attrs = 5 + num_classes
max = 0 #限界人数
num_camera = 1 #camera数
model = [[] for i in range(num_camera)]
inp_dim = [[] for i in range(num_camera)]
cap = [[] for i in range(num_camera)]
ret = [[] for i in range(num_camera)]
frame = [[] for i in range(num_camera)]
img = [[] for i in range(num_camera)]
orig_im = [[] for i in range(num_camera)]
dim = [[] for i in range(num_camera)]
# output = [[] for i in range(num_camera)]
# output = torch.tensor(output)
# print("output_shape\n", output.shape)
for i in range(num_camera):
model[i] = Darknet(cfgfile) #model1の作成
model[i].load_weights(weightsfile) # model1に重みを読み込む
model[i].net_info["height"] = args.reso
inp_dim[i] = int(model[i].net_info["height"])
assert inp_dim[i] % 32 == 0
assert inp_dim[i] > 32
#mixer.init() #初期化
if CUDA:
for i in range(num_camera):
model[i].cuda() #CUDAが使用可能であればcudaを起動
for i in range(num_camera):
model[i].eval()
cap[0] = cv2.VideoCapture(1) #カメラを指定（USB接続）
# cap[1] = cv2.VideoCapture(1) #カメラを指定（USB接続）
# cap = cv2.VideoCapture("movies/sample.mp4")
#cap = cv2.VideoCapture("movies/one_v2.avi")
# Use the next line if your camera has a username and password
# cap = cv2.VideoCapture('protocol://username:password@IP:port/1')
#cap = cv2.VideoCapture('rtsp://admin:[email protected]/1') #（ネットワーク接続）
#cap = cv2.VideoCapture('rtsp://admin:[email protected]/80')
#cap = cv2.VideoCapture('http://admin:[email protected]:80/video')
#cap = cv2.VideoCapture('http://admin:[email protected]/camera-cgi/admin/recorder.cgi?action=start&id=samba')
#cap = cv2.VideoCapture('http://admin:[email protected]/recorder.cgi?action=start&id=samba')
#cap = cv2.VideoCapture('http://admin:[email protected]:80/snapshot.jpg?user=admin&pwd=admin&strm=0')
print('-1')
#assert cap.isOpened(), 'Cannot capture source' #カメラが起動できたか確認
img1 = cv2.imread("images/phase_1.jpg")
img2 = cv2.imread("images/phase_2.jpg")
img3 = cv2.imread("images/phase_2_red.jpg")
img4 = cv2.imread("images/phase_3.jpg")
#mixer.music.load("voice/voice_3.m4a")
#print(img1)
frames = 0
count_frame = 0 #フレーム数カウント
flag = 0 #密状態（0：疎密，1：密入り）
start = time.time()
print('-1')
while (cap[i].isOpened() for i in range(num_camera)): #カメラが起動している間
count=0 #人数をカウント
point = [[] for i in range(num_camera)]
for i in range(num_camera):
ret[i], frame[i] = cap[i].read() #キャプチャ画像を取得
if (ret[i] for i in range(num_camera)):
# 解析準備としてキャプチャ画像を加工
for i in range(num_camera):
img[i], orig_im[i], dim[i] = prep_image(frame[i], inp_dim[i])
if CUDA:
for i in range(num_camera):
im_dim[i] = im_dim[i].cuda()
img[i] = img[i].cuda()
for i in range(num_camera):
# output[i] = model[i](Variable(img[i]), CUDA)
output = model[i](Variable(img[i]), CUDA)
#print("output:\n", output)
# output[i] = write_results(output[i], confidence, num_classes, nms = True, nms_conf = nms_thesh)
output = write_results(output, confidence, num_classes, nms = True, nms_conf = nms_thesh)
# print("output", i, ":\n", output[i])
print(output.shape)
"""
# FPSの表示
if (type(output[i]) == int for i in range(num_camera)):
print("表示")
frames += 1
print("FPS of the video is {:5.2f}".format( frames / (time.time() - start)))
# qキーを押すとFPS表示の終了
key = cv2.waitKey(1)
if key & 0xFF == ord('q'):
break
continue
for i in range(num_camera):
output[i][:,1:5] = torch.clamp(output[i][:,1:5], 0.0, float(inp_dim[i]))/inp_dim[i]
output[i][:,[1,3]] *= frame[i].shape[1]
output[i][:,[2,4]] *= frame[i].shape[0]
"""
# FPSの表示
if type(output) == int:
print("表示")
frames += 1
print("FPS of the video is {:5.2f}".format( frames / (time.time() - start)))
# qキーを押すとFPS表示の終了
key = cv2.waitKey(1)
if key & 0xFF == ord('q'):
break
continue
for i in range(num_camera):
output[:,1:5] = torch.clamp(output[:,1:5], 0.0, float(inp_dim[i]))/inp_dim[i]
output[:,[1,3]] *= frame[i].shape[1]
output[:,[2,4]] *= frame[i].shape[0]
colors = pkl.load(open("pallete", "rb"))
#count = lambda x: count(x, orig_im, count) #人数をカウント
"""
for i in range(num_camera):
list(map(lambda x: write(x, orig_im[i]), output[i]))
print("count:\n",count)
"""
for i in range(num_camera):
list(map(lambda x: write(x, orig_im[i], i), output))
print("count:\n",count)
print("count_frame", count_frame)
print("framex", frame[0].shape[1])
print("framey", frame[0].shape[0])
print("point0",point[0])
#LiDARの情報の人識別
num_person = 0
radian_lists = []
for count, (radian, length) in enumerate(LiDAR):
radian_cam = [[] for i in range(point)]
if count % 90 == 0:
radian_list = []
if count < 90:
for num, p in enumerate(point[0]):
radian_cam[num] = p / frame[0].shape[1] * 100
for dif in range(10):
if int(radian)+dif-5 == int(radian_cam):
num_person += 1
radian_list.append(radian)
elif count < 180:
for num, p in enumerate(point[0]):
radian_cam[num] = p / frame[0].shape[1] * 100
for dif in range(10):
if int(radian)+dif-5 == int(radian_cam):
num_person += 1
radian_list.append(radian)
elif count < 270:
for num, p in enumerate(point[0]):
radian_cam[num] = p / frame[0].shape[1] * 100
for dif in range(10):
if int(radian)+dif-5 == int(radian_cam):
num_person += 1
radian_list.append(radian)
else:
for num, p in enumerate(point[0]):
radian_cam[num] = p / frame[0].shape[1] * 100
for dif in range(10):
if int(radian)+dif-5 == int(radian_cam):
num_person += 1
radian_list.append(radian)
radian_lists.append(radian_list)
dis_list = []
for direction in range(4):
if len(radian_lists[direction]) > 1:
# n = combinations_count(len(radian_lists[direction]), 2)
dis_combination = itertools.combinations(radian_lists[direction], 2)
distance = [[] for i in range(len(dis_combination))]
for num_dis, com_list in enumerate(dis_combination):
distance[num_dis] = cosineTheorem(Lidar,com_list[0], com_list[1])
dis_list.append(distance)
#密集判定
close_list = [0] * 4
dense_list = [0] * 4
for direction in range(4):
close = 0 #密接数
dense = 0 #密集数
for dis in distance[distance]:
if dis < 2:
close += 1
close_list[direction] = 1
if close > 1:
dense_list[direction] = 1
print("close_list", close_list)
print("dense_list", dense_list)
# print("point1",point[1])
if count > max:
count_frame += 1
#print("-1")
if count_frame <= 50:
x=0
y=0
angle=20
scale=1.5
for i in range(num_camera):
imgpaste = cvpaste(img1, orig_im[i], x, y, angle, scale)
if flag == 1:
play.googlehome()
flag += 1
#mixer.music.play(1)
elif count_frame <= 100:
x=-30
y=10
angle=20
scale=1.1
if count_frame%2==1:
for i in range(num_camera):
imgpaste = cvpaste(img2, orig_im[i], x, y, angle, scale)
else:
for i in range(num_camera):
imgpaste = cvpaste(img3, orig_im[i], x, y, angle, scale)
if flag == 2:
play.googlehome()
flag += 1
else:
x=-30
y=0
angle=20
scale=1.5
for i in range(num_camera):
imgpaste = cvpaste(img4, orig_im[i], x, y, angle, scale)
if count_frame > 101: #<--2フレームずらす
print("\007") #警告音
time.sleep(3)
if flag == 3:
play.googlehome()
flag += 1
cv2.imshow("frame", imgpaste)
else:
count_frame = 0
flag = 0
#print("-2")
for i in range(num_camera):
cv2.imshow("frame", orig_im[i])
# play.googlehome()
key = cv2.waitKey(1)
# qキーを押すと動画表示の終了
if key & 0xFF == ord('q'):
break
frames += 1
print("count_frame:\n", count_frame)
print("FPS of the video is {:5.2f}".format( frames / (time.time() - start)))
else:
break
| [
"[email protected]"
] | |
b62c2124b3745f705af3028c3d40ff02d6de01eb | a6f8aae8f552a06b82fe018246e8dcd65c27e632 | /pr064/pr064.py | 118db0003bd02932d1a6e1bd3e0df29b4e8e386a | [] | no_license | P4SSER8Y/ProjectEuler | 2339ee7676f15866ceb38cad35e21ead0dad57e9 | 15d1b681e22133fc562a08b4e8e41e582ca8e625 | refs/heads/master | 2021-06-01T09:22:11.165235 | 2016-05-06T14:02:40 | 2016-05-06T14:02:40 | 46,722,844 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 916 | py | from math import sqrt, floor
from itertools import count
def get(n):
def gcd(a, b):
if b == 0:
return a
return gcd(b, a%b)
if n == (int(sqrt(n)))**2:
return 0
an = lambda k, c, d: int(floor(((k*sqrt(n)-c)/d)))
pn = lambda k, c, d: [d*k,
-d*(c+d*an(k,c,d)),
k*k*n-(c+d*an(k,c,d))**2]
a = [0]
p = [[1, 0, 1]]
a.append(an(*p[-1]))
t = pn(*p[-1])
g = gcd(gcd(t[0], t[1]), t[2])
p.append([t[0]//g, t[1]//g, t[2]//g])
for _ in count(2):
a.append(an(*p[-1]))
t = pn(*p[-1])
g = gcd(gcd(t[0], t[1]), t[2])
p.append([t[0]//g, t[1]//g, t[2]//g])
if p[-1] == p[1]:
return _ - 1
def run():
ret = 0
for n in range(1, 10001):
if get(n) % 2 == 1:
ret += 1
return ret
if __name__ == "__main__":
print(run())
| [
"[email protected]"
] | |
ecac4bdb1c8034948e2a47fc06bb722bf9c7a990 | c1960138a37d9b87bbc6ebd225ec54e09ede4a33 | /adafruit-circuitpython-bundle-py-20210402/lib/adafruit_mcp230xx/digital_inout.py | 877d588cc07250f952b5661bf7a59f8ccf8b843d | [] | no_license | apalileo/ACCD_PHCR_SP21 | 76d0e27c4203a2e90270cb2d84a75169f5db5240 | 37923f70f4c5536b18f0353470bedab200c67bad | refs/heads/main | 2023-04-07T00:01:35.922061 | 2021-04-15T18:02:22 | 2021-04-15T18:02:22 | 332,101,844 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,166 | py | # SPDX-FileCopyrightText: 2017 Tony DiCola for Adafruit Industries
# SPDX-FileCopyrightText: 2019 Carter Nelson
#
# SPDX-License-Identifier: MIT
"""
`digital_inout`
====================================================
Digital input/output of the MCP230xx.
* Author(s): Tony DiCola
"""
import digitalio
__version__ = "2.4.5"
__repo__ = "https://github.com/adafruit/Adafruit_CircuitPython_MCP230xx.git"
# Internal helpers to simplify setting and getting a bit inside an integer.
def _get_bit(val, bit):
return val & (1 << bit) > 0
def _enable_bit(val, bit):
return val | (1 << bit)
def _clear_bit(val, bit):
return val & ~(1 << bit)
class DigitalInOut:
"""Digital input/output of the MCP230xx. The interface is exactly the
same as the digitalio.DigitalInOut class, however:
* MCP230xx family does not support pull-down resistors;
* MCP23016 does not support pull-up resistors.
Exceptions will be thrown when attempting to set unsupported pull
configurations.
"""
def __init__(self, pin_number, mcp230xx):
"""Specify the pin number of the MCP230xx (0...7 for MCP23008, or 0...15
for MCP23017) and MCP23008 instance.
"""
self._pin = pin_number
self._mcp = mcp230xx
# kwargs in switch functions below are _necessary_ for compatibility
# with DigitalInout class (which allows specifying pull, etc. which
# is unused by this class). Do not remove them, instead turn off pylint
# in this case.
# pylint: disable=unused-argument
def switch_to_output(self, value=False, **kwargs):
"""Switch the pin state to a digital output with the provided starting
value (True/False for high or low, default is False/low).
"""
self.direction = digitalio.Direction.OUTPUT
self.value = value
def switch_to_input(self, pull=None, invert_polarity=False, **kwargs):
"""Switch the pin state to a digital input with the provided starting
pull-up resistor state (optional, no pull-up by default) and input polarity. Note that
pull-down resistors are NOT supported!
"""
self.direction = digitalio.Direction.INPUT
self.pull = pull
self.invert_polarity = invert_polarity
# pylint: enable=unused-argument
@property
def value(self):
"""The value of the pin, either True for high or False for
low. Note you must configure as an output or input appropriately
before reading and writing this value.
"""
return _get_bit(self._mcp.gpio, self._pin)
@value.setter
def value(self, val):
if val:
self._mcp.gpio = _enable_bit(self._mcp.gpio, self._pin)
else:
self._mcp.gpio = _clear_bit(self._mcp.gpio, self._pin)
@property
def direction(self):
"""The direction of the pin, either True for an input or
False for an output.
"""
if _get_bit(self._mcp.iodir, self._pin):
return digitalio.Direction.INPUT
return digitalio.Direction.OUTPUT
@direction.setter
def direction(self, val):
if val == digitalio.Direction.INPUT:
self._mcp.iodir = _enable_bit(self._mcp.iodir, self._pin)
elif val == digitalio.Direction.OUTPUT:
self._mcp.iodir = _clear_bit(self._mcp.iodir, self._pin)
else:
raise ValueError("Expected INPUT or OUTPUT direction!")
@property
def pull(self):
"""Enable or disable internal pull-up resistors for this pin. A
value of digitalio.Pull.UP will enable a pull-up resistor, and None will
disable it. Pull-down resistors are NOT supported!
"""
try:
if _get_bit(self._mcp.gppu, self._pin):
return digitalio.Pull.UP
except AttributeError as error:
# MCP23016 doesn't have a `gppu` register.
raise ValueError("Pull-up/pull-down resistors not supported.") from error
return None
@pull.setter
def pull(self, val):
try:
if val is None:
self._mcp.gppu = _clear_bit(self._mcp.gppu, self._pin)
elif val == digitalio.Pull.UP:
self._mcp.gppu = _enable_bit(self._mcp.gppu, self._pin)
elif val == digitalio.Pull.DOWN:
raise ValueError("Pull-down resistors are not supported!")
else:
raise ValueError("Expected UP, DOWN, or None for pull state!")
except AttributeError as error:
# MCP23016 doesn't have a `gppu` register.
raise ValueError("Pull-up/pull-down resistors not supported.") from error
@property
def invert_polarity(self):
"""The polarity of the pin, either True for an Inverted or
False for an normal.
"""
if _get_bit(self._mcp.ipol, self._pin):
return True
return False
@invert_polarity.setter
def invert_polarity(self, val):
if val:
self._mcp.ipol = _enable_bit(self._mcp.ipol, self._pin)
else:
self._mcp.ipol = _clear_bit(self._mcp.ipol, self._pin)
| [
"[email protected]"
] | |
3effc29166723a048756a4a499fd6f88ece2cb63 | aba00d6272765b71397cd3eba105fc79b3a346e0 | /Digisig/digsigvenv/lib/python3.6/site-packages/ufl/algorithms/formtransformations.py | 1d4fe6041eeb9b87781c111e6617b423b24ab219 | [] | no_license | JosteinGj/School | a2c7cc090571b867637003fe6c647898ba9d8d24 | 3b5f29846e443b97f042241237dbda3208b20831 | refs/heads/master | 2023-05-02T11:07:29.517669 | 2021-04-26T09:04:57 | 2021-04-26T09:04:57 | 295,340,194 | 3 | 1 | null | null | null | null | UTF-8 | Python | false | false | 16,674 | py | # -*- coding: utf-8 -*-
"""This module defines utilities for transforming
complete Forms into new related Forms."""
# Copyright (C) 2008-2016 Martin Sandve Alnæs
#
# This file is part of UFL.
#
# UFL is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# UFL is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with UFL. If not, see <http://www.gnu.org/licenses/>.
#
# Modified by Anders Logg, 2008-2009.
# Modified by Garth N. Wells, 2010.
# Modified by Marie E. Rognes, 2010.
from ufl.log import error, warning, debug
# All classes:
from ufl.core.expr import ufl_err_str
from ufl.argument import Argument
from ufl.coefficient import Coefficient
from ufl.constantvalue import Zero
from ufl.algebra import Conj
# Other algorithms:
from ufl.algorithms.map_integrands import map_integrands
from ufl.algorithms.transformer import Transformer
from ufl.algorithms.replace import replace
# FIXME: Don't use this below, it makes partextracter more expensive than necessary
def _expr_has_terminal_types(expr, ufl_types):
input = [expr]
while input:
e = input.pop()
ops = e.ufl_operands
if ops:
input.extend(ops)
elif isinstance(e, ufl_types):
return True
return False
def zero_expr(e):
return Zero(e.ufl_shape, e.ufl_free_indices, e.ufl_index_dimensions)
class PartExtracter(Transformer):
"""
PartExtracter extracts those parts of a form that contain the
given argument(s).
"""
def __init__(self, arguments):
Transformer.__init__(self)
self._want = set(arguments)
def expr(self, x):
"""The default is a nonlinear operator not accepting any
Arguments among its children."""
if _expr_has_terminal_types(x, Argument):
error("Found Argument in %s, this is an invalid expression." % ufl_err_str(x))
return (x, set())
# Terminals that are not Variables or Arguments behave as default
# expr-s.
terminal = expr
def variable(self, x):
"Return relevant parts of this variable."
# Extract parts/provides from this variable's expression
expression, label = x.ufl_operands
part, provides = self.visit(expression)
# If the extracted part is zero or we provide more than we
# want, return zero
if isinstance(part, Zero) or (provides - self._want):
return (zero_expr(x), set())
# Reuse varible if possible (or reconstruct from part)
x = self.reuse_if_possible(x, part, label)
return (x, provides)
def argument(self, x):
"Return itself unless itself provides too much."
# An argument provides itself
provides = {x}
# If we provide more than we want, return zero
if provides - self._want:
return (zero_expr(x), set())
return (x, provides)
def sum(self, x):
"""
Return the terms that might eventually yield the correct
parts(!)
The logic required for sums is a bit elaborate:
A sum may contain terms providing different arguments. We
should return (a sum of) a suitable subset of these
terms. Those should all provide the same arguments.
For each term in a sum, there are 2 simple possibilities:
1a) The relevant part of the term is zero -> skip.
1b) The term provides more arguments than we want -> skip
2) If all terms fall into the above category, we can just
return zero.
Any remaining terms may provide exactly the arguments we want,
or fewer. This is where things start getting interesting.
3) Bottom-line: if there are terms with providing different
arguments -- provide terms that contain the most arguments. If
there are terms providing different sets of same size -> throw
error (e.g. Argument(-1) + Argument(-2))
"""
parts_that_provide = {}
# 1. Skip terms that provide too much
original_terms = x.ufl_operands
assert len(original_terms) == 2
for term in original_terms:
# Visit this term in the sum
part, term_provides = self.visit(term)
# If this part is zero or it provides more than we want,
# skip it
if isinstance(part, Zero) or (term_provides - self._want):
continue
# Add the contributions from this part to temporary list
term_provides = frozenset(term_provides)
if term_provides in parts_that_provide:
parts_that_provide[term_provides] += [part]
else:
parts_that_provide[term_provides] = [part]
# 2. If there are no remaining terms, return zero
if not parts_that_provide:
return (zero_expr(x), set())
# 3. Return the terms that provide the biggest set
most_provided = frozenset()
for (provideds, parts) in parts_that_provide.items(): # TODO: Just sort instead?
# Throw error if size of sets are equal (and not zero)
if len(provideds) == len(most_provided) and len(most_provided):
error("Don't know what to do with sums with different Arguments.")
if provideds > most_provided:
most_provided = provideds
terms = parts_that_provide[most_provided]
if len(terms) == 2:
x = self.reuse_if_possible(x, *terms)
else:
x, = terms
return (x, most_provided)
def product(self, x, *ops):
""" Note: Product is a visit-children-first handler. ops are
the visited factors."""
provides = set()
factors = []
for factor, factor_provides in ops:
# If any factor is zero, return
if isinstance(factor, Zero):
return (zero_expr(x), set())
# Add factor to factors and extend provides
factors.append(factor)
provides = provides | factor_provides
# If we provide more than we want, return zero
if provides - self._want:
return (zero_expr(x), provides)
# Reuse product if possible (or reconstruct from factors)
x = self.reuse_if_possible(x, *factors)
return (x, provides)
# inner, outer and dot all behave as product
inner = product
outer = product
dot = product
def division(self, x):
"Return parts_of_numerator/denominator."
# Get numerator and denominator
numerator, denominator = x.ufl_operands
# Check for Arguments in the denominator
if _expr_has_terminal_types(denominator, Argument):
error("Found Argument in denominator of %s , this is an invalid expression." % ufl_err_str(x))
# Visit numerator
numerator_parts, provides = self.visit(numerator)
# If numerator is zero, return zero. (No need to check whether
# it provides too much, already checked by visit.)
if isinstance(numerator_parts, Zero):
return (zero_expr(x), set())
# Reuse x if possible, otherwise reconstruct from (parts of)
# numerator and denominator
x = self.reuse_if_possible(x, numerator_parts, denominator)
return (x, provides)
def linear_operator(self, x, arg):
"""A linear operator with a single operand accepting arity > 0,
providing whatever Argument its operand does."""
# linear_operator is a visit-children-first handler. Handled
# arguments are in arg.
part, provides = arg
# Discard if part is zero. (No need to check whether we
# provide too much, already checked by children.)
if isinstance(part, Zero):
return (zero_expr(x), set())
x = self.reuse_if_possible(x, part)
return (x, provides)
# Positive and negative restrictions behave as linear operators
positive_restricted = linear_operator
negative_restricted = linear_operator
# Cell and facet average are linear operators
cell_avg = linear_operator
facet_avg = linear_operator
# Grad is a linear operator
grad = linear_operator
# Conj, Real, Imag are linear operators
conj = linear_operator
real = linear_operator
imag = linear_operator
def linear_indexed_type(self, x):
"""Return parts of expression belonging to this indexed
expression."""
expression, index = x.ufl_operands
part, provides = self.visit(expression)
# Return zero if extracted part is zero. (The expression
# should already have checked if it provides too much.)
if isinstance(part, Zero):
return (zero_expr(x), set())
# Reuse x if possible (or reconstruct by indexing part)
x = self.reuse_if_possible(x, part, index)
return (x, provides)
# All of these indexed thingies behave as a linear_indexed_type
indexed = linear_indexed_type
index_sum = linear_indexed_type
component_tensor = linear_indexed_type
def list_tensor(self, x, *ops):
# list_tensor is a visit-children-first handler. ops contains
# the visited operands with their provides. (It follows that
# none of the visited operands provide more than wanted.)
# Extract the most arguments provided by any of the components
most_provides = ops[0][1]
for (component, provides) in ops:
if (provides - most_provides):
most_provides = provides
# Check that all components either provide the same arguments
# or vanish. (This check is here b/c it is not obvious what to
# return if the components provide different arguments, at
# least with the current transformer design.)
for (component, provides) in ops:
if (provides != most_provides and not isinstance(component, Zero)):
error("PartExtracter does not know how to handle list_tensors with non-zero components providing fewer arguments")
# Return components
components = [op[0] for op in ops]
x = self.reuse_if_possible(x, *components)
return (x, most_provides)
def compute_form_with_arity(form, arity, arguments=None):
"""Compute parts of form of given arity."""
# Extract all arguments in form
if arguments is None:
arguments = form.arguments()
parts = [arg.part() for arg in arguments]
if set(parts) - {None}:
error("compute_form_with_arity cannot handle parts.")
if len(arguments) < arity:
warning("Form has no parts with arity %d." % arity)
return 0 * form
# Assuming that the form is not a sum of terms
# that depend on different arguments, e.g. (u+v)*dx
# would result in just v*dx. But that doesn't make
# any sense anyway.
sub_arguments = set(arguments[:arity])
pe = PartExtracter(sub_arguments)
def _transform(e):
e, provides = pe.visit(e)
if provides == sub_arguments:
return e
return Zero()
return map_integrands(_transform, form)
def compute_form_arities(form):
"""Return set of arities of terms present in form."""
# Extract all arguments present in form
arguments = form.arguments()
parts = [arg.part() for arg in arguments]
if set(parts) - {None}:
error("compute_form_arities cannot handle parts.")
arities = set()
for arity in range(len(arguments) + 1):
# Compute parts with arity "arity"
parts = compute_form_with_arity(form, arity, arguments)
# Register arity if "parts" does not vanish
if parts and parts.integrals():
arities.add(arity)
return arities
def compute_form_lhs(form):
"""Compute the left hand side of a form.
Example:
a = u*v*dx + f*v*dx
a = lhs(a) -> u*v*dx
"""
return compute_form_with_arity(form, 2)
def compute_form_rhs(form):
"""Compute the right hand side of a form.
Example:
a = u*v*dx + f*v*dx
L = rhs(a) -> -f*v*dx
"""
return -compute_form_with_arity(form, 1)
def compute_form_functional(form):
"""Compute the functional part of a form, that
is the terms independent of Arguments.
(Used for testing, not sure if it's useful for anything?)"""
return compute_form_with_arity(form, 0)
def compute_form_action(form, coefficient):
"""Compute the action of a form on a Coefficient.
This works simply by replacing the last Argument
with a Coefficient on the same function space (element).
The form returned will thus have one Argument less
and one additional Coefficient at the end if no
Coefficient has been provided.
"""
# TODO: Check whatever makes sense for coefficient
# Extract all arguments
arguments = form.arguments()
parts = [arg.part() for arg in arguments]
if set(parts) - {None}:
error("compute_form_action cannot handle parts.")
# Pick last argument (will be replaced)
u = arguments[-1]
fs = u.ufl_function_space()
if coefficient is None:
coefficient = Coefficient(fs)
elif coefficient.ufl_function_space() != fs:
debug("Computing action of form on a coefficient in a different function space.")
return replace(form, {u: coefficient})
def compute_energy_norm(form, coefficient):
"""Compute the a-norm of a Coefficient given a form a.
This works simply by replacing the two Arguments
with a Coefficient on the same function space (element).
The Form returned will thus be a functional with no
Arguments, and one additional Coefficient at the
end if no coefficient has been provided.
"""
arguments = form.arguments()
parts = [arg.part() for arg in arguments]
if set(parts) - {None}:
error("compute_energy_norm cannot handle parts.")
if len(arguments) != 2:
error("Expecting bilinear form.")
v, u = arguments
U = u.ufl_function_space()
V = v.ufl_function_space()
if U != V:
error("Expecting equal finite elements for test and trial functions, got '%s' and '%s'." % (U, V))
if coefficient is None:
coefficient = Coefficient(V)
else:
if coefficient.ufl_function_space() != U:
error("Trying to compute action of form on a "
"coefficient in an incompatible element space.")
return replace(form, {u: coefficient, v: coefficient})
def compute_form_adjoint(form, reordered_arguments=None):
"""Compute the adjoint of a bilinear form.
This works simply by swapping the number and part of the two arguments,
but keeping their elements and places in the integrand expressions.
"""
arguments = form.arguments()
parts = [arg.part() for arg in arguments]
if set(parts) - {None}:
error("compute_form_adjoint cannot handle parts.")
if len(arguments) != 2:
error("Expecting bilinear form.")
v, u = arguments
if v.number() >= u.number():
error("Mistaken assumption in code!")
if reordered_arguments is None:
reordered_u = Argument(u.ufl_function_space(), number=v.number(),
part=v.part())
reordered_v = Argument(v.ufl_function_space(), number=u.number(),
part=u.part())
else:
reordered_u, reordered_v = reordered_arguments
if reordered_u.number() >= reordered_v.number():
error("Ordering of new arguments is the same as the old arguments!")
if reordered_u.part() != v.part():
error("Ordering of new arguments is the same as the old arguments!")
if reordered_v.part() != u.part():
error("Ordering of new arguments is the same as the old arguments!")
if reordered_u.ufl_function_space() != u.ufl_function_space():
error("Element mismatch between new and old arguments (trial functions).")
if reordered_v.ufl_function_space() != v.ufl_function_space():
error("Element mismatch between new and old arguments (test functions).")
return map_integrands(Conj, replace(form, {v: reordered_v, u: reordered_u}))
| [
"[email protected]"
] | |
751478a95c3542713d69302bb04e829266b80014 | 67b62f49ff89982ef62ce5d5c68bc7655ededcee | /chap6/basic_evaluator.py | 7fd1b1bcb0e15607838e88ad314e8711d2e2c8e8 | [] | no_license | planset/stone-py | 0614737e59a14745019754c453f9b49e44f125cf | 467617e201a3f4028f2fb23b2bc6ce3aaa371125 | refs/heads/master | 2016-09-06T18:36:49.564450 | 2012-12-31T12:57:06 | 2012-12-31T12:57:06 | 6,389,161 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,955 | py | # -*- coding: utf-8 -*-
from stone.util import reviser, super
from stone.myast import ast, literal, expression, statement
from stone import exception
#class BasicEvaluator(object):
# def __init__(self):
# pass
TRUE = 1
FALSE = 0
@reviser
class ASTreeEx(ast.ASTree):
def eval(self, env):
raise NotImplementedError()
@reviser
class ASTListEx(ast.ASTList):
def __init__(self, list_of_astree):
super(ASTListEx, self).__init__(list_of_astree)
def eval(self, env):
raise exception.StoneException("cannot eval: " + self.to_string(), self)
@reviser
class ASTLeafEx(ast.ASTLeaf):
def __init__(self, token):
super(ASTLeafEx, self).__init__(token)
def eval(self, env):
raise exception.StoneException("cannot eval: " + self.to_string(), self)
@reviser
class NumberEx(literal.NumberLiteral):
def __init__(self, token):
super(NumberEx, self).__init__(token)
def eval(self, env):
return self.value()
@reviser
class StringEx(literal.StringLiteral):
def __init__(self, token):
super(StringEx, self).__init__(token)
def eval(self, env):
return self.value()
@reviser
class NameEx(literal.Name):
def __init__(self, token):
super(NameEx, self).__init__(token)
def eval(self, env):
value = env.get(self.name())
if value is None:
raise exception.StoneException('undefined name: ' + self.name(), self)
else:
return value
@reviser
class NegativeEx(expression.NegativeExpr):
def __init__(self, list_of_astree):
super(NegativeEx, self).__init__(list_of_astree)
def eval(self, env):
v = self.operand().eval(env)
if isinstance(v, int):
return -v
else:
raise exception.StoneException('bad type for -', self)
@reviser
class BinaryEx(expression.BinaryExpr):
def __init__(self, list_of_astree):
super(BinaryEx, self).__init__(list_of_astree)
def eval(self, env):
op = self.operator()
if op == '=':
right = self.right().eval(env)
return self.compute_assign(env, right)
else:
left = self.left().eval(env)
right = self.right().eval(env)
return self.compute_op(left, op, right)
def compute_assign(self, env, rvalue):
l = self.left()
if isinstance(l, literal.Name):
env.put(l.name(), rvalue)
return rvalue
else:
raise expression.StoneException('bad assignment', self)
def compute_op(self, left, op, right):
if isinstance(left, int) and isinstance(right, int):
return self.compute_number(left, op, right)
else:
if op == '+':
return str(left) + str(right)
elif op == '==':
if left is None:
return TRUE if right else FALSE
else:
return TRUE if left == right else FALSE
else:
raise exception.StoneException('bad type', self)
def compute_number(self, left, op, right):
a = int(left)
b = int(right)
if op == '+':
return a + b
elif op == '-':
return a - b
elif op == '*':
return a * b
elif op == '/':
return a / b
elif op == '%':
return a % b
elif op == '==':
return TRUE if a == b else FALSE
elif op == '>':
return TRUE if a > b else FALSE
elif op == '<':
return TRUE if a < b else FALSE
else:
return exception.StoneException('bad operator', self)
@reviser
class BlockEx(statement.BlockStmnt):
def __init__(self, list_of_astree):
super(BlockEx, self).__init__(list_of_astree)
def eval(self, env):
result = 0
for token in self.children():
if not isinstance(token, statement.NullStmnt):
result = token.eval(env)
return result
@reviser
class IfEx(statement.IfStmnt):
def __init__(self, list_of_astree):
super(IfEx, self).__init__(list_of_astree)
def eval(self, env):
c = self.condition().eval(env)
if isinstance(c, int) and int(c) != FALSE:
return self.then_block().eval(env)
else:
b = self.else_block()
if b is None:
return 0
else:
return b.eval(env)
@reviser
class WhileEx(statement.WhileStmnt):
def __init__(self, list_of_astree):
super(WhileEx, self).__init__(list_of_astree)
def eval(self, env):
result = 0
while True:
c = self.condition().eval(env)
if isinstance(c, int) and int(c) == FALSE:
return result
else:
result = self.body().eval(env)
# vim: tabstop=8 expandtab shiftwidth=4 softtabstop=4
| [
"[email protected]"
] | |
8de737368f805d8d9c748a04dc6b26df83141c3e | f55623e3aea5b8ae3d2c37bcfa8a853150a0b376 | /a10_horizon/_enabled_hooks/_91000_a10admin_panel.py | bbfda541cbf0467babfe76f4b091824b63db24dd | [
"Apache-2.0"
] | permissive | isaaczurn/a10-horizon | 0fab288d321516de72ea276d54b415a1c2e4d7d6 | a96f9e95dfcda619f08a19a9057b061bdba12487 | refs/heads/development | 2020-12-26T04:49:33.131115 | 2016-08-11T16:47:09 | 2016-08-11T16:47:09 | 64,965,205 | 1 | 0 | null | 2016-08-04T20:31:28 | 2016-08-04T20:31:28 | null | UTF-8 | Python | false | false | 846 | py | # Copyright (C) 2016, A10 Networks 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.
PANEL_GROUP = 'a10admin'
PANEL_GROUP_NAME = 'A10 Networks'
PANEL_DASHBOARD = "admin"
PANEL_GROUP_DASHBOARD = PANEL_DASHBOARD
ADD_INSTALLED_APPS = ['a10_horizon.dashboard.admin']
AUTO_DISCOVER_STATIC_FILES = True
| [
"[email protected]"
] | |
7151eacc02e7268941d808551b82a302f388b83a | a24d889cc6fd5cd3fc180bcf5cf7dcd688eb691a | /yggdrasil/metaschema/properties/__init__.py | e26481182221d47dd0902bcc594398be467a91c2 | [
"BSD-3-Clause"
] | permissive | ghanashyamchalla/yggdrasil | f5346bad5d1f4ed8a53e7b570c19eefb58a8a921 | 7b59439276eacb66f1f6ea4177d3a85cc061eed5 | refs/heads/master | 2022-04-20T06:08:49.110242 | 2020-04-15T02:44:51 | 2020-04-15T02:44:51 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,579 | py | import os
import glob
import importlib
from collections import OrderedDict
_metaschema_properties = OrderedDict()
def register_metaschema_property(prop_class):
r"""Register a schema property.
Args:
prop_class (class): Class to be registered.
Raises:
ValueError: If a property class has already been registered under the
same name.
ValueError: If the base validator already has a validator function for
the property and the new property class has a schema defined.
ValueError: If the base validator already has a validator function for
the property and the validate method on the new property class is
not disabled.
ValueError: If the property class does not have an entry in the existing
metaschema.
"""
from yggdrasil.metaschema import _metaschema, _base_validator
global _metaschema_properties
prop_name = prop_class.name
if prop_name in _metaschema_properties:
raise ValueError("Property '%s' already registered." % prop_name)
if prop_name in _base_validator.VALIDATORS:
if (prop_class.schema is not None):
raise ValueError("Replacement property '%s' modifies the default schema."
% prop_name)
if (((prop_class._validate not in [None, False])
or ('validate' in prop_class.__dict__))):
raise ValueError("Replacement property '%s' modifies the default validator."
% prop_name)
prop_class._validate = False
# prop_class.types = [] # To ensure base class not modified by all
# prop_class.python_types = []
# Check metaschema if it exists
if _metaschema is not None:
if prop_name not in _metaschema['properties']:
raise ValueError("Property '%s' not in pre-loaded metaschema." % prop_name)
_metaschema_properties[prop_name] = prop_class
return prop_class
class MetaschemaPropertyMeta(type):
r"""Meta class for registering properties."""
def __new__(meta, name, bases, class_dict):
cls = type.__new__(meta, name, bases, class_dict)
if not (name.endswith('Base') or (cls.name in ['base'])
or cls._dont_register):
cls = register_metaschema_property(cls)
return cls
def get_registered_properties():
r"""Return a dictionary of registered properties.
Returns:
dict: Registered property/class pairs.
"""
return _metaschema_properties
def get_metaschema_property(property_name, skip_generic=False):
r"""Get the property class associated with a metaschema property.
Args:
property_name (str): Name of property to get class for.
skip_generic (bool, optional): If True and the property dosn't have a
class, None is returned. Defaults to False.
Returns:
MetaschemaProperty: Associated property class.
"""
from yggdrasil.metaschema.properties import MetaschemaProperty
if property_name in _metaschema_properties:
return _metaschema_properties[property_name]
else:
if skip_generic:
return None
else:
return MetaschemaProperty.MetaschemaProperty
def import_all_properties():
r"""Import all types to ensure they are registered."""
for x in glob.glob(os.path.join(os.path.dirname(__file__), '*.py')):
mod = os.path.basename(x)[:-3]
if not mod.startswith('__'):
importlib.import_module('yggdrasil.metaschema.properties.%s' % mod)
| [
"[email protected]"
] | |
d8263e73a475526bf1e265cb8fa6a400a8694500 | 33b7a63d0866f9aabfdfdc342236191bebd1c1e6 | /django_learning/django_03_url介绍/path_converter_demo/path_converter_demo/settings.py | cb32c9dcd3e6eb052f719db998531d3478ac4df0 | [] | no_license | leolvcl/leo_python | 21f61bb898f8c755d1ff405f90864887e18a317e | 5be0d897eeee34d50d835707112fb610de69b4c8 | refs/heads/master | 2020-08-02T21:00:01.808704 | 2019-12-06T17:04:59 | 2019-12-06T17:04:59 | 211,505,290 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,292 | py | """
Django settings for path_converter_demo project.
Generated by 'django-admin startproject' using Django 2.0.2.
For more information on this file, see
https://docs.djangoproject.com/en/2.0/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/2.0/ref/settings/
"""
import os
# Build paths inside the project like this: os.path.join(BASE_DIR, ...)
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
# Quick-start development settings - unsuitable for production
# See https://docs.djangoproject.com/en/2.0/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = 'cub82$820^fngq+)z=17p^92f!nch=e+j3ldvu)d#)4k(%*k88'
# SECURITY WARNING: don't run with debug turned on in production!
DEBUG = True
ALLOWED_HOSTS = []
# Application definition
INSTALLED_APPS = [
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
]
MIDDLEWARE = [
'django.middleware.security.SecurityMiddleware',
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
]
ROOT_URLCONF = 'path_converter_demo.urls'
TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'DIRS': [os.path.join(BASE_DIR, 'templates')]
,
'APP_DIRS': True,
'OPTIONS': {
'context_processors': [
'django.template.context_processors.debug',
'django.template.context_processors.request',
'django.contrib.auth.context_processors.auth',
'django.contrib.messages.context_processors.messages',
],
},
},
]
WSGI_APPLICATION = 'path_converter_demo.wsgi.application'
# Database
# https://docs.djangoproject.com/en/2.0/ref/settings/#databases
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': os.path.join(BASE_DIR, 'db.sqlite3'),
}
}
# Password validation
# https://docs.djangoproject.com/en/2.0/ref/settings/#auth-password-validators
AUTH_PASSWORD_VALIDATORS = [
{
'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',
},
]
# Internationalization
# https://docs.djangoproject.com/en/2.0/topics/i18n/
LANGUAGE_CODE = 'en-us'
TIME_ZONE = 'UTC'
USE_I18N = True
USE_L10N = True
USE_TZ = True
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/2.0/howto/static-files/
STATIC_URL = '/static/'
| [
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] | |
11783965c99a7fec6aa8653159a599e25ce3ebfa | 491d2fd36f2ca26975b3eb302a3d5600415bf7c4 | /central/multi_node_utils.py | 8fbeb6444b67d9aab6cdff45669e13032d27f208 | [] | no_license | kmanchella-habana/Model-References | 9fa42654d57a867d82f417e9fff668946f9105f6 | 460d3b23ce75f30561e8f725ebcb21298897d163 | refs/heads/master | 2023-08-28T17:42:48.866251 | 2021-09-18T21:38:04 | 2021-09-18T21:38:04 | 411,371,667 | 0 | 0 | null | 2021-09-28T17:08:13 | 2021-09-28T17:08:13 | null | UTF-8 | Python | false | false | 6,092 | py | ###############################################################################
# Copyright (C) 2020-2021 Habana Labs, Ltd. an Intel Company
###############################################################################
"""Utilities for multi-card and scaleout training"""
import os
import socket
import subprocess
import sys
from functools import lru_cache
from central.habana_model_runner_utils import (get_canonical_path,
get_multi_node_config_nodes,
is_valid_multi_node_config)
def run_cmd_as_subprocess(cmd=str, use_devnull=False):
print(cmd)
sys.stdout.flush()
sys.stderr.flush()
if use_devnull:
with subprocess.Popen(cmd, shell=True, executable='/bin/bash', stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) as proc:
proc.wait()
else:
with subprocess.Popen(cmd, shell=True, executable='/bin/bash') as proc:
proc.wait()
# -------------------------------------------------------------------------------
# For scaleout, depends on MULTI_HLS_IPS environment variable being set to contain
# ",' separated list of host IPs.
# If MULTI_HLS_IPS is not set, the command "cmd" will be run on the local host.
# Make sure that the command "cmd" being run on the remote host does not depend on
# any other environment variables being available on the remote host besides the ones
# in the "env_vars_for_mpi" list that this function will export to the remote IPs.
# -------------------------------------------------------------------------------
def run_per_ip(cmd, env_vars_for_mpi=None, use_devnull=False, kubernetes_run=False):
if kubernetes_run:
print("************************* Kubernetes mode *************************")
run_cmd_as_subprocess(cmd, use_devnull)
return
if os.environ.get('OMPI_COMM_WORLD_SIZE') is not None:
raise RuntimeError(
"Function run_per_ip is not meant to be run from within an OpenMPI context. It is intended to invoke mpirun by itelf.")
if not is_valid_multi_node_config():
print("************************* Single-HLS mode *************************")
run_cmd_as_subprocess(cmd, use_devnull)
else:
if os.environ.get('DOCKER_SSHD_PORT'):
portnum = os.environ.get('DOCKER_SSHD_PORT')
else:
portnum = 3022
scmd = f"mpirun --allow-run-as-root --mca plm_rsh_args -p{portnum} --tag-output --merge-stderr-to-stdout --prefix {os.environ.get('MPI_ROOT')} -H {os.environ.get('MULTI_HLS_IPS')} "
if env_vars_for_mpi is not None:
for env_var in env_vars_for_mpi:
scmd += f"-x {env_var} "
scmd += cmd
print(f"{socket.gethostname()}: In MULTI NODE run_per_ip(): scmd = {scmd}")
run_cmd_as_subprocess(scmd, use_devnull)
# Generate the MPI hostfile
def generate_mpi_hostfile(file_path, devices_per_hls=8):
mpi_hostfile_path = ''
if is_valid_multi_node_config():
multi_hls_nodes = get_multi_node_config_nodes()
print("Generating MPI hostfile...")
file_name = "hostfile"
os.makedirs(get_canonical_path(file_path), mode=0o777, exist_ok=True)
mpi_hostfile_path = get_canonical_path(file_path).joinpath(file_name)
if os.path.exists(mpi_hostfile_path):
cmd = f"rm -f {str(mpi_hostfile_path)}"
run_cmd_as_subprocess(cmd)
print(f"Path: {mpi_hostfile_path}")
out_fid = open(mpi_hostfile_path, 'a')
config_str = ''
for node in multi_hls_nodes:
config_str += f"{node} slots={devices_per_hls}\n"
print(f"MPI hostfile: \n{config_str}")
out_fid.write(config_str)
out_fid.close()
return mpi_hostfile_path
def print_file_contents(file_name):
with open(file_name, 'r') as fl:
for line in fl:
print(line, end='')
def _is_relevant_env_var(env_var: str):
""" Given an environment variable name, determines whether is it "relevant" for the child processes spawned by OpenMPI.
OpenMPI passes the local environment only to the local child processes.
"""
RELEVANT_ENV_VAR_INFIXES = [
"PATH", "LD_", # System-specific: PATH, PYTHONPATH, LD_LIBRARY_PATH, LD_PRELOAD
"TF_", # TensorFlow-specific, e.g.: TF_BF16_CONVERSION
"TPC_", # TPC-specific, e.g.: RUN_TPC_FUSER
"GC_", # GC-specific, e.g.: GC_KERNEL_PATH
"HABANA", "HBN", # Other Habana-specific, e.g.: HABANA_INITIAL_WORKSPACE_SIZE_MB
"HOROVOD", # Horovod-specific, e.g.: HOROVOD_LOG_LEVEL
"SYN", # Synapse-specific
"HCL", # HCL-specific, e.g.: HCL_CONFIG_PATH
"HCCL", "NCCL", # HCCL-specific: HCCL_SOCKET_IFNAME, HABANA_NCCL_COMM_API
"LOG_LEVEL", # Logger-specific, e.g.: LOG_LEVEL_HCL, LOG_LEVEL_SYN_API
]
OTHER_RELEVANT_ENV_VARS = [
"VIRTUAL_ENV",
"ENABLE_CONSOLE",
"MULTI_HLS_IPS"
]
ENV_VARS_DEPRECATIONS = {
"TF_ENABLE_BF16_CONVERSION": "Superceeded by TF_BF16_CONVERSION.",
"HABANA_USE_PREALLOC_BUFFER_FOR_ALLREDUCE": "'same address' optimization for collective operations is no longer supported.",
"HABANA_USE_STREAMS_FOR_HCL": "HCL streams are always used. Setting to 0 enforces blocking synchronization after collective operations (debug feature).",
}
if env_var in ENV_VARS_DEPRECATIONS:
print(
f"warninig: Environment variable '{env_var}' is deprecated: {ENV_VARS_DEPRECATIONS[env_var]}")
if env_var in OTHER_RELEVANT_ENV_VARS:
return True
for infix in RELEVANT_ENV_VAR_INFIXES:
if infix in env_var:
return True
return False
@lru_cache()
def get_relevant_env_vars():
""" Retrieves the list of those environment variables, which should be passed to the child processes spawned by OpenMPI.
"""
return [env_var for env_var in os.environ if _is_relevant_env_var(env_var)]
| [
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] | |
f871cb804836e1732c5ccb6ac5804e5a11670af4 | 64a1a418c6e0794dcf7be5605f1e254f2beaa65d | /tutorial/ch1/qt5_2.py | fc15b92cc431579a59972a827e95fbdeb97dd546 | [] | no_license | notmikeb/python_desk | 792c93f68b69dcd8c24384883421166771d40c15 | 1685d6e9e0c8b4d8201625ce58fa2420bc1b930e | refs/heads/master | 2021-01-20T09:36:46.832716 | 2016-02-03T13:51:20 | 2016-02-03T13:51:20 | 40,387,601 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 496 | py | """
ZetCode pyQT5 tutorial
"""
import sys
from PyQt5.QtWidgets import QApplication, QWidget
from PyQt5.QtGui import QIcon
class Example(QWidget):
def __init__(self):
super().__init__()
self.initUI()
def initUI(self):
self.setGeometry(300,300, 300, 220)
self.setWindowTitle('Icon')
self.setWindowIcon(QIcon('web.jpg'))
self.show()
if __name__ == '__main__':
app = QApplication(sys.argv)
print ("done app")
print ("show done")
ex = Example()
app.exec_()
| [
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] | |
e99a6cd94845610e54407e4cd435907ff9efacc5 | cb25407fc1480f771391bb09e36dad123ec9fca2 | /zeta/ccore.py | ae1284a066b89b51957c83e4cab38fb7716f9b45 | [] | no_license | prataprc/zeta | f68925c9dfbf70331eae59ff5cf173956d249696 | 9c3bc88c56c67d0fff5c0790d768ad6cac79642f | refs/heads/master | 2021-01-20T01:51:20.194893 | 2017-04-25T08:07:41 | 2017-04-25T08:07:41 | 89,334,343 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 8,435 | py | # -*- coding: utf-8 -*-
"""Everything can be a component.
Component :
* The ComponentMeta acts as the metaclass for all the Component derived
classes.
* Components with 'abstract' attribute set to True will not be registered
for interface.
* Components can only have no-argument initializers, like,
def __init__( self )
* implements. When a component implements an interface, it can declare so
by calling the implements() api.
* ExtensionPoint property can be instantiated to extend the functionality
of a component through interfaces.
Component Manager :
* Normally ComponentManager class is instantiated as an environment object
and binded to the Component objects as and when the Component objects are
instantiated.
Interfaces and Extension points :
* Interfaces are used to extend the functionality of Zeta framework and
currently it is used to implement view templates, plugins and more.
"""
from zeta.lib.error import ZetaComponentError
class Interface( object ) :
"""Marker base class for extension point interfaces."""
class ExtensionPoint( property ) :
"""Marker class for extension points in components."""
def __init__( self, interface ) :
"""Create the extension point.
@param interface: the `Interface` subclass that defines the protocol
for the extension point
"""
property.__init__( self, self.extensions )
self.interface = interface
self.__doc__ = 'List of components that implement `%s`' % \
self.interface.__name__
def extensions(self, component):
"""Return a list of components that declare to implement the extension
point interface.
"""
extensions = ComponentMeta._registry.get( self.interface, [] )
return filter( None, [ component.compmgr[cls] for cls in extensions ])
def __repr__( self ):
"""Return a textual representation of the extension point."""
return '<ExtensionPoint %s>' % self.interface.__name__
class ComponentMeta( type ) :
"""Meta class for components.
Takes care of component and extension point registration.
"""
_components = []
_registry = {}
_formcomps = {}
def __new__( cls, name, bases, d ) :
"""Create the component class."""
new_class = type.__new__( cls, name, bases, d )
if name == 'Component':
# Don't put the Component base class in the registry
return new_class
# Only override __init__ for Components not inheriting ComponentManager
if True not in [ issubclass(x, ComponentManager) for x in bases ] :
# Allow components to have a no-argument initializer so that
# they don't need to worry about accepting the component manager
# as argument and invoking the super-class initializer
init = d.get( '__init__' )
if not init:
# Because we're replacing the initializer, we need to make sure
# that any inherited initializers are also called.
for init in [ b.__init__._original for b in new_class.mro()
if issubclass(b, Component)
and '__init__' in b.__dict__ ] :
break
def maybe_init( self, compmgr, init=init, cls=new_class, **kwargs ) :
"""Component Init function hooked in by ComponentMeta."""
if cls not in compmgr.components:
compmgr.components[cls] = self
if init:
try:
init( self, **kwargs )
except:
del compmgr.components[cls]
raise
maybe_init._original = init
new_class.__init__ = maybe_init
if d.get( 'abstract' ):
# Don't put abstract component classes in the registry
return new_class
ComponentMeta._components.append( new_class )
registry = ComponentMeta._registry
for interface in d.get( '_implements', [] ) :
registry.setdefault( interface, [] ).append( new_class )
for base in [ base for base in bases if hasattr(base, '_implements') ] :
for interface in base._implements :
registry.setdefault( interface, [] ).append( new_class )
if 'formname' in d :
if isinstance( d.get('formname'), str ) :
ComponentMeta._formcomps.setdefault( d.get( 'formname' ),
new_class )
elif isinstance( d.get('formname'), list ) :
[ ComponentMeta._formcomps.setdefault( f, new_class )
for f in d.get('formname') ]
return new_class
class Component( object ) :
"""Base class for components.
Every component can declare what extension points it provides, as well as
what extension points of other components it extends.
"""
__metaclass__ = ComponentMeta
def __new__( cls, *args, **kwargs ) :
"""Return an existing instance of the component if it has already been
activated, otherwise create a new instance.
"""
# If this component is also the component manager, just invoke that
if issubclass( cls, ComponentManager ) :
self = super( Component, cls ).__new__( cls )
self.compmgr = self
return self
# The normal case where the component is not also the component manager
compmgr = args[0]
self = compmgr.components.get( cls )
if self is None:
self = super( Component, cls ).__new__( cls )
self.compmgr = compmgr
compmgr.component_activated( self )
return self
@staticmethod
def implements( *interfaces ):
"""Can be used in the class definiton of `Component` subclasses to
declare the extension points that are extended.
"""
import sys
frame = sys._getframe(1)
locals_ = frame.f_locals
# Some sanity checks
assert locals_ is not frame.f_globals and '__module__' in locals_, \
'implements() can only be used in a class definition'
locals_.setdefault( '_implements', [] ).extend( interfaces )
implements = Component.implements
class ComponentManager( object ) :
"""The component manager keeps a pool of active components."""
def __init__( self ) :
"""Initialize the component manager."""
self.components = {}
self.enabled = {}
if isinstance( self, Component ) :
self.components[self.__class__] = self
def __contains__( self, cls ) :
"""Return wether the given class is in the list of active components."""
return cls in self.components
def __getitem__( self, cls ) :
"""Activate the component instance for the given class, or return the
existing the instance if the component has already been activated.
"""
if cls not in self.enabled :
self.enabled[cls] = self.is_component_enabled( cls )
if not self.enabled[cls]:
return None
component = self.components.get( cls )
if not component:
if cls not in ComponentMeta._components :
raise ZetaComponentError('Component "%s" not registered' % cls.__name__)
try:
component = cls( self )
except TypeError, e :
raise ZetaComponentError('Unable to instantiate component %r (%s)' % (cls, e))
return component
def component_activated( self, component ) :
"""Can be overridden by sub-classes so that special initialization for
components can be provided.
"""
def is_component_enabled( self, cls ) :
"""Can be overridden by sub-classes to veto the activation of a
component.
If this method returns False, the component with the given class will
not be available.
"""
return True
def formcomponent( formname ) :
"""Get the component class implementing `formname` component"""
return ComponentMeta._formcomps.get( formname, None )
| [
"[email protected]"
] | |
42827266ae0abe6cf4bb9c8f2a439f58df2b3e9a | 8781d392b65ccf7ebad7332de07eddea3349f55c | /examples/vtk-structured-2d-curved.py | e322305c0b6b03fe6e91706abff7bb1edf09c799 | [
"MIT"
] | permissive | mattwala/pyvisfile | 95006fb7771cb7b51264543344fcbb1da0504432 | 41c908e1530d73a2af83fa9436751cb46c0f922a | refs/heads/master | 2022-11-11T11:19:15.474390 | 2020-06-23T05:14:31 | 2020-06-23T05:14:31 | 276,822,466 | 0 | 0 | MIT | 2020-07-03T06:14:45 | 2020-07-03T06:14:44 | null | UTF-8 | Python | false | false | 482 | py | from __future__ import absolute_import
from pyvisfile.vtk import write_structured_grid
import numpy as np
angle_mesh = np.mgrid[1:2:10j, 0:2*np.pi:20j]
r = angle_mesh[0, np.newaxis]
phi = angle_mesh[1, np.newaxis]
mesh = np.vstack((
r*np.cos(phi),
r*np.sin(phi),
))
from pytools.obj_array import make_obj_array
vec = make_obj_array([
np.cos(phi),
np.sin(phi),
])
write_structured_grid("yo-2d.vts", mesh,
point_data=[("phi", phi), ("vec", vec)])
| [
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] | |
12d2b319e45161b64b8c5e2a24a13222bf797dc1 | 43e900f11e2b230cdc0b2e48007d40294fefd87a | /Facebook/1763.longest-nice-substring.py | 6c10cfdaef09b7133bf7adcc369d6f99c980d0c9 | [] | no_license | DarkAlexWang/leetcode | 02f2ed993688c34d3ce8f95d81b3e36a53ca002f | 89142297559af20cf990a8e40975811b4be36955 | refs/heads/master | 2023-01-07T13:01:19.598427 | 2022-12-28T19:00:19 | 2022-12-28T19:00:19 | 232,729,581 | 3 | 1 | null | null | null | null | UTF-8 | Python | false | false | 539 | py | #
# @lc app=leetcode id=1763 lang=python3
#
# [1763] Longest Nice Substring
#
# @lc code=start
class Solution:
def longestNiceSubstring(self, s: str) -> str:
if len(s) < 2:
return ""
chars = set(list(s))
for i in range(len(s)):
if not (s[i].lower() in chars and s[i].upper() in chars):
s1 = self.longestNiceSubstring(s[:i])
s2 = self.longestNiceSubstring(s[i + 1:])
return s2 if len(s2) > len(s1) else s1
return s
# @lc code=end
| [
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] | |
d045c1f2d9bbf9c9ae7d90b60a3d2acae0704637 | acd41dc7e684eb2e58b6bef2b3e86950b8064945 | /res/packages/scripts/scripts/client/gui/miniclient/tech_tree/pointcuts.py | fb5d3bd3f860d356b28b23a159f21b25c9372f1e | [] | no_license | webiumsk/WoT-0.9.18.0 | e07acd08b33bfe7c73c910f5cb2a054a58a9beea | 89979c1ad547f1a1bbb2189f5ee3b10685e9a216 | refs/heads/master | 2021-01-20T09:37:10.323406 | 2017-05-04T13:51:43 | 2017-05-04T13:51:43 | 90,268,530 | 0 | 0 | null | null | null | null | WINDOWS-1250 | Python | false | false | 1,103 | py | # 2017.05.04 15:21:52 Střední Evropa (letní čas)
# Embedded file name: scripts/client/gui/miniclient/tech_tree/pointcuts.py
import aspects
from helpers import aop
class OnTechTreePopulate(aop.Pointcut):
def __init__(self):
aop.Pointcut.__init__(self, 'gui.Scaleform.daapi.view.lobby.techtree.TechTree', 'TechTree', '_populate', aspects=(aspects.OnTechTreePopulate,))
class OnBuyVehicle(aop.Pointcut):
def __init__(self, config):
aop.Pointcut.__init__(self, 'gui.Scaleform.daapi.view.lobby.vehicle_obtain_windows', 'VehicleBuyWindow', 'submit', aspects=(aspects.OnBuyVehicle(config),))
class OnRestoreVehicle(aop.Pointcut):
def __init__(self, config):
aop.Pointcut.__init__(self, 'gui.Scaleform.daapi.view.lobby.vehicle_obtain_windows', 'VehicleRestoreWindow', 'submit', aspects=(aspects.OnRestoreVehicle(config),))
# okay decompyling C:\Users\PC\wotmods\files\originals\res\packages\scripts\scripts\client\gui\miniclient\tech_tree\pointcuts.pyc
# decompiled 1 files: 1 okay, 0 failed, 0 verify failed
# 2017.05.04 15:21:52 Střední Evropa (letní čas)
| [
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] | |
5573c985cf87f71b6dc605b266b0413315c3457f | 15e818aada2b18047fa895690bc1c2afda6d7273 | /analysis/control/optimize.py | 84bb77fa593651b7c63536c6d78e82f883a5d0b6 | [
"Apache-2.0"
] | permissive | ghomsy/makani | 4ee34c4248fb0ac355f65aaed35718b1f5eabecf | 818ae8b7119b200a28af6b3669a3045f30e0dc64 | refs/heads/master | 2023-01-11T18:46:21.939471 | 2020-11-10T00:23:31 | 2020-11-10T00:23:31 | 301,863,147 | 0 | 0 | Apache-2.0 | 2020-11-10T00:23:32 | 2020-10-06T21:51:21 | null | UTF-8 | Python | false | false | 3,865 | py | # Copyright 2020 Makani Technologies LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# 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.
"""Wrappers around third-party optimization libraries."""
import cvxopt
import cvxopt.solvers
from makani.analysis.control import type_util
import numpy as np
from numpy import linalg
# pylint doesn't like capital letters in variable names, in contrast
# to numerical optimization conventions.
# pylint: disable=invalid-name
class ArgumentException(Exception):
pass
class BadSolutionException(Exception):
pass
class NoSolutionException(Exception):
pass
def SolveQp(P, q, G, h, A, b, ineq_tolerance=1e-6, eq_tolerance=1e-6):
"""Solve a quadratic program (QP).
Solves a QP of the form:
min. 0.5 * x^T * P x + q^T * x
subj. to (G * x)[i] <= h[i] for i in len(x)
A * x = b
Arguments:
P: Quadratic term of the cost (n-by-n positive semidefinite np.matrix).
q: Linear term of the cost (n-by-1 np.matrix).
G: Linear term of inequality constraints (k-by-n np.matrix).
h: constant term of the inequality constraints (k-by-1 np.matrix).
A: Linear term of the equations (l-by-n np.matrix).
b: Constant term of the equations (l-by-1 np.matrix).
ineq_tolerance: Required tolerance for inequality constraints.
eq_tolerance: Required tolerance for equality constraints.
Raises:
ArgumentException: If the arguments are not of the right type or shape.
NoSolutionException: If the solver reports any errors.
BadSolutionException: If the solver returns a solution that does not respect
constraints conditions.
Returns:
A n-by-1 np.matrix containing the (not necessarily unique) optimal solution.
"""
if (not isinstance(q, np.matrix) or not isinstance(h, np.matrix)
or not isinstance(b, np.matrix)):
raise ArgumentException()
num_var = q.shape[0]
num_ineq = h.shape[0]
num_eq = b.shape[0]
if (not type_util.CheckIsMatrix(P, shape=(num_var, num_var))
or not np.all(P.A1 == P.T.A1)
or not type_util.CheckIsMatrix(q, shape=(num_var, 1))
or not type_util.CheckIsMatrix(G, shape=(num_ineq, num_var))
or not type_util.CheckIsMatrix(h, shape=(num_ineq, 1))
or not type_util.CheckIsMatrix(A, shape=(num_eq, num_var))
or not type_util.CheckIsMatrix(b, shape=(num_eq, 1))):
raise ArgumentException(num_var, num_ineq, num_eq)
eig, _ = linalg.eigh(P)
if not np.all(eig >= -1e-6):
raise ArgumentException('P is not positive definite')
old_options = cvxopt.solvers.options
cvxopt.solvers.options['show_progress'] = False
try:
result = cvxopt.solvers.qp(cvxopt.matrix(P),
cvxopt.matrix(q),
cvxopt.matrix(G),
cvxopt.matrix(h),
cvxopt.matrix(A),
cvxopt.matrix(b))
except Exception as e:
raise NoSolutionException(e)
if result['status'] != 'optimal':
raise NoSolutionException(result)
x = np.matrix(result['x'])
# Check primal feasibility.
if np.any(G * x - h >= ineq_tolerance):
raise BadSolutionException('Inequalities mismatch.')
if np.any(np.abs(b - A * x) >= eq_tolerance):
raise BadSolutionException('Equalities mismatch.')
# TODO: Test optimality.
cvxopt.solvers.options = old_options
return np.matrix(result['x'])
| [
"[email protected]"
] | |
87d64712d998d3b868640d48f892d7e959c074cc | 87dae6d55c66df1d40d6881272009319a1600cb3 | /PROGRAMACION I SEGUNDO CUATRIMESTE TP1 EJ 1.py | fdb33f593f6082dcfbe9ff1eb7b7f96ae7012bcd | [] | no_license | abaldeg/EjerciciosPython | 92a30a82c05ec75aa7f313c8a6fa0dd052a8db11 | c8a3238587ebf6b10dbff32516c81bf00bb01630 | refs/heads/master | 2021-07-09T07:46:11.584855 | 2020-11-09T11:51:50 | 2020-11-09T11:51:50 | 210,438,930 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 679 | py | """
Desarrollar una función que reciba tres números positivos y devuelva el mayor de
los tres, sólo si éste es único (mayor estricto). En caso de no existir el mayor estricto
devolver -1. No utilizar operadores lógicos (and, or, not). Desarrollar también
un programa para ingresar los tres valores, invocar a la función y mostrar el
máximo hallado, o un mensaje informativo si éste no existe.
"""
def obtenerMayor(n1,n2,n3):
if n1>n2:
if n1>n3:
mayor=n1
elif n2>n1:
if n2>n3:
mayor=n2
elif n3>n1:
if n3>n2:
mayor=n3
else:
mayor=-1
return(mayor)
print(obtenerMayor(1,1,1))
| [
"[email protected]"
] | |
9de584dc3b0a4e354a76f9b19a508aacad68619d | d79ec3cb3e59778d2b4a17846434ef2d027d5f98 | /polls/migrations/0001_initial.py | d025fcfa894499c76821c28ea2673060914a4cd2 | [] | no_license | Marlysson/PollsApp | df08a9eae257f947f93ffa4130aec26186a087a8 | 57b1477ecc3732444123dbfa996a7287dc5d7052 | refs/heads/master | 2020-12-02T10:13:55.554610 | 2017-11-11T14:50:21 | 2017-11-11T14:50:21 | 96,703,281 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 700 | py | # -*- coding: utf-8 -*-
# Generated by Django 1.11.2 on 2017-07-06 19:29
from __future__ import unicode_literals
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='Question',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('question_text', models.CharField(max_length=50)),
('closed', models.BooleanField(default=False)),
('pub_date', models.DateField()),
],
),
]
| [
"[email protected]"
] | |
8e10a71488576fd11c908c3ed8dcc5e6ab0dfff5 | 8c9d5bc245b343d9a36882d6d1c850ce6a86442d | /dbdaora/geospatial/_tests/datastore/conftest.py | 3fa3329a173ddadfe0ab7827399e84af9c563b7e | [
"MIT"
] | permissive | dutradda/dbdaora | 1089fa9c80a93c38357818680327b24d48ac8b64 | 5c87a3818e9d736bbf5e1438edc5929a2f5acd3f | refs/heads/master | 2021-07-04T10:06:59.596720 | 2020-12-05T12:24:45 | 2020-12-05T12:24:45 | 205,046,848 | 23 | 1 | null | null | null | null | UTF-8 | Python | false | false | 446 | py | import pytest
from dbdaora import DatastoreGeoSpatialRepository, KindKeyDatastoreDataSource
@pytest.fixture
def fallback_data_source():
return KindKeyDatastoreDataSource()
@pytest.fixture
def fake_repository_cls(fake_entity_cls):
class FakeGeoSpatialRepository(DatastoreGeoSpatialRepository):
name = 'fake'
key_attrs = ('fake2_id', 'fake_id')
entity_cls = fake_entity_cls
return FakeGeoSpatialRepository
| [
"[email protected]"
] | |
f5c61dfd6024796264c0b50d239d70e492c8e49e | e83e8a3b7ef31b36b2c590b37bf2d1df1487fe5a | /tests/test_docs/test_query.py | a7d055d10b61f98b14ac194e3ebf9d696b3d26ff | [
"MIT"
] | permissive | duilio/django-ninja | 19d66eae1b3b01f9910f3ea0f569ed6d3a561707 | 8dac3c981bcf431322d32acd34c8179564a3698d | refs/heads/master | 2023-01-21T07:17:02.544071 | 2020-11-25T10:48:30 | 2020-11-25T10:48:30 | 316,243,580 | 0 | 0 | MIT | 2020-11-26T13:56:19 | 2020-11-26T13:45:12 | null | UTF-8 | Python | false | false | 3,259 | py | from unittest.mock import patch
from ninja import NinjaAPI
from client import NinjaClient
def test_examples():
api = NinjaAPI()
with patch("builtins.api", api, create=True):
import docs.src.tutorial.query.code01
import docs.src.tutorial.query.code02
import docs.src.tutorial.query.code03
import docs.src.tutorial.query.code010
client = NinjaClient(api)
# Defaults
assert client.get("/weapons").json() == [
"Ninjato",
"Shuriken",
"Katana",
"Kama",
"Kunai",
"Naginata",
"Yari",
]
assert client.get("/weapons?offset=0&limit=3").json() == [
"Ninjato",
"Shuriken",
"Katana",
]
assert client.get("/weapons?offset=2&limit=2").json() == [
"Katana",
"Kama",
]
# Required/Optional
assert client.get("/weapons/search?offset=1&q=k").json() == [
"Katana",
"Kama",
"Kunai",
]
# Coversion
# fmt: off
assert client.get("/example?b=1").json() == [None, True, None, None]
assert client.get("/example?b=True").json() == [None, True, None, None]
assert client.get("/example?b=true").json() == [None, True, None, None]
assert client.get("/example?b=on").json() == [None, True, None, None]
assert client.get("/example?b=yes").json() == [None, True, None, None]
assert client.get("/example?b=0").json() == [None, False, None, None]
assert client.get("/example?b=no").json() == [None, False, None, None]
assert client.get("/example?b=false").json() == [None, False, None, None]
assert client.get("/example?d=1577836800").json() == [None, None, "2020-01-01", None]
assert client.get("/example?d=2020-01-01").json() == [None, None, "2020-01-01", None]
# fmt: on
# Schema
assert client.get("/filter").json() == {
"filters": {"limit": 100, "offset": None, "query": None}
}
assert client.get("/filter?limit=10").json() == {
"filters": {"limit": 10, "offset": None, "query": None}
}
assert client.get("/filter?offset=10").json() == {
"filters": {"limit": 100, "offset": 10, "query": None}
}
assert client.get("/filter?query=10").json() == {
"filters": {"limit": 100, "offset": None, "query": "10"}
}
schema = api.get_openapi_schema("")
params = schema["paths"]["/filter"]["get"]["parameters"]
assert params == [
{
"in": "query",
"name": "limit",
"required": False,
"schema": {"title": "Limit", "default": 100, "type": "integer"},
},
{
"in": "query",
"name": "offset",
"required": False,
"schema": {"title": "Offset", "type": "integer"},
},
{
"in": "query",
"name": "query",
"required": False,
"schema": {"title": "Query", "type": "string"},
},
]
| [
"[email protected]"
] | |
edcc63746e71ca4f277f371dacf3b89403bc4ebd | 3ee1bb0d0acfa5c412b37365a4564f0df1c093fb | /lotte/31_eff_cnn.py | 8635f32148d2aa946e79fef91e3e007516f0af09 | [] | no_license | moileehyeji/Study | 3a20bf0d74e1faec7a2a5981c1c7e7861c08c073 | 188843c6415a4c546fdf6648400d072359d1a22b | refs/heads/main | 2023-04-18T02:30:15.810749 | 2021-05-04T08:43:53 | 2021-05-04T08:43:53 | 324,901,835 | 3 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,598 | py | import numpy as np
import pandas as pd
import tensorflow as tf
from keras.preprocessing.image import ImageDataGenerator
from numpy import expand_dims
from keras import Sequential
from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
from keras.optimizers import Adam,SGD
from sklearn.model_selection import train_test_split
from tensorflow.keras.applications import EfficientNetB4,EfficientNetB2,EfficientNetB1
from tensorflow.keras.applications.efficientnet import preprocess_input
from tqdm import tqdm
from sklearn.model_selection import train_test_split
from tensorflow.keras.models import Model
from tensorflow.keras.layers import GlobalAveragePooling2D, Flatten, BatchNormalization, Dense, Activation, Conv2D, Dropout
from tensorflow.keras import regularizers
#data load
x = np.load("C:/data/lotte/npy/128_project_x.npy",allow_pickle=True)
y = np.load("C:/data/lotte/npy/128_project_y.npy",allow_pickle=True)
x_pred = np.load('C:/data/lotte/npy/128_test.npy',allow_pickle=True)
x = preprocess_input(x)
x_pred = preprocess_input(x_pred)
idg = ImageDataGenerator(
width_shift_range=(-1,1),
height_shift_range=(-1,1),
rotation_range=45,
zoom_range=0.2,
horizontal_flip=True,
fill_mode='nearest')
idg2 = ImageDataGenerator()
x_train, x_valid, y_train, y_valid = train_test_split(x,y, train_size = 0.9, shuffle = True, random_state=66)
train_generator = idg.flow(x_train,y_train,batch_size=32, seed = 42)
valid_generator = idg2.flow(x_valid,y_valid)
efficientnet = EfficientNetB2(include_top=False,weights='imagenet',input_shape=x_train.shape[1:])
a = efficientnet.output
a = Conv2D(filters = 32,kernel_size=(12,12), strides=(1,1),padding='same',kernel_regularizer=regularizers.l2(1e-5)) (a)
a = BatchNormalization() (a)
a = Activation('swish') (a)
a = GlobalAveragePooling2D() (a)
a = Dense(512, activation= 'swish') (a)
a = Dropout(0.5) (a)
a = Dense(1000, activation= 'softmax') (a)
model = Model(inputs = efficientnet.input, outputs = a)
# efficientnet.summary()
mc = ModelCheckpoint('C:/data/lotte/h5/[0.01902]31_eff_cnn.h5',save_best_only=True, verbose=1)
early_stopping = EarlyStopping(patience= 10)
lr = ReduceLROnPlateau(patience= 5, factor=0.4)
model.compile(loss='categorical_crossentropy',
optimizer=tf.keras.optimizers.SGD(learning_rate=0.02, momentum=0.9),
metrics=['acc'])
# learning_history = model.fit_generator (train_generator,epochs=100, steps_per_epoch= len(x_train) / 32,
# validation_data=valid_generator, callbacks=[early_stopping,lr,mc])
# predict
model.load_weights('C:/data/lotte/h5/[0.01902]31_eff_cnn.h5')
result = model.predict(x_pred,verbose=True)
sub = pd.read_csv('C:/data/lotte/csv/sample.csv')
sub['prediction'] = np.argmax(result,axis = 1)
sub.to_csv('C:/data/lotte/csv/31_1.csv',index=False)
""" tta_steps = 30
predictions = []
for i in tqdm(range(tta_steps)):
# generator 초기화
test_generator.reset()
preds = model.predict_generator(generator = test_generator, verbose = 1)
predictions.append(preds)
sub = pd.read_csv('C:/data/lotte/sample.csv')
sub['prediction'] = np.argmax(result,axis = 1)
sub.to_csv('C:/data/lotte/csv/31_tta_i.csv',index=False)
pred = np.mean(predictions, axis=0)
sub = pd.read_csv('C:/data/lotte/sample.csv')
sub['prediction'] = np.argmax(result,axis = 1)
sub.to_csv('C:/data/lotte/31_tta.csv',index=False) """
# 최종 31 : 77.919
# [0.01902]31_eff_cnn 31_1: 73.894
# [0.00776]31_eff_cnn 31_2: 77.403 | [
"[email protected]"
] | |
e3960fbac34d0ef3d8fa9bce7b4dbdacb6e98791 | 1b68ecd64590474d212547bf4356544ae1f183c9 | /logic_from_site.py | f1c1a5784830e4603f8b535aa654224bf268066d | [] | no_license | wnd2da/rss2 | 833f7b08c5bdb7a8378d655367ca51d4d6c06cf0 | 2bf4f579e5c53eb200c0eeab886376b772587668 | refs/heads/master | 2022-11-16T03:12:24.609871 | 2020-07-15T08:33:16 | 2020-07-15T08:33:16 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 24,376 | py | # -*- coding: utf-8 -*-
#########################################################
# python
import traceback
import re
import logging
from time import sleep
import urllib
import urllib2
import os
# third-party
import requests
from lxml import html
from xml.sax.saxutils import escape, unescape
# sjva 공용
from framework import app, db, scheduler, path_data#, celery
from framework.job import Job
from framework.util import Util
from system.logic import SystemLogic
# 패키지
from .plugin import logger, package_name
from .model import ModelSetting
#########################################################
#토렌트퐁 Accept 2개 필요함
headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/71.0.3578.98 Safari/537.36',
'Accept' : 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8',
'Accept-Language' : 'ko-KR,ko;q=0.9,en-US;q=0.8,en;q=0.7',
'Referer' : '',
# 'Cookie' :'over18=1'
}
class LogicFromSite(object):
proxyes = None
session = requests.Session()
# 스케쥴러에서도 호출할 수 있고, 직접 RSS에서도 호출할수 있다
@staticmethod
def get_list(site_instance, board, query=None, page=1, max_id=0, max_count=999, scheduler_instance=None):
try:
LogicFromSite.set_proxy(scheduler_instance)
max_page = int(page) if page is not None else 1
xpath_list_tag_name = 'XPATH_LIST_TAG'
if 'BOARD_LIST' in site_instance.info:
tmp = board.split('&')[0]
if board.split('&')[0] in site_instance.info['BOARD_LIST']:
xpath_list_tag_name = site_instance.info['BOARD_LIST'][tmp]
xpath_dict = site_instance.info[xpath_list_tag_name]
bbs_list = LogicFromSite.__get_bbs_list(site_instance, board, max_page, max_id, xpath_dict, is_test=(max_count!=999))
count = 0
for item in bbs_list:
try:
cookie = site_instance.info['COOKIE'] if 'COOKIE' in site_instance.info else None
selenium_tag = None
if 'USE_SELENIUM' in site_instance.info['EXTRA']:
selenium_tag = site_instance.info['SELENIUM_WAIT_TAG']
data = LogicFromSite.get_html(item['url'], cookie=cookie, selenium_tag=selenium_tag)
tree = html.fromstring(data)
# Step 2. 마그넷 목록 생성
item['magnet'] = LogicFromSite.__get_magnet_list(data, tree, site_instance)
# Step 3. 다운로드 목록 생성
item['download'] = LogicFromSite.__get_download_list(data, tree, site_instance, item)
# Stem 4. Torrent_info
item['torrent_info'] = LogicFromSite.__get_torrent_info(item['magnet'], scheduler_instance)
#if item['torrent_info']:
# item['title2'] = item['torrent_info'][0]['name']
if 'SLEEP_5' in site_instance.info['EXTRA']:
sleep(5)
if 'DELAY' in site_instance.info:
sleep(site_instance.info['DELAY'])
except Exception as e:
logger.error('Exception:%s', e)
logger.error(traceback.format_exc())
item['magnet'] = None
count += 1
if count >= max_count:
break
return bbs_list
except Exception as e:
logger.error('Exception:%s', e)
logger.error(traceback.format_exc())
return None
@staticmethod
def __get_bbs_list(site_instance, board, max_page, max_id, xpath_dict, is_test=False):
bbs_list = []
index_step = xpath_dict['INDEX_STEP'] if 'INDEX_STEP' in xpath_dict else 1
index_start = xpath_dict['INDEX_START'] if 'INDEX_START' in xpath_dict else 1
stop_by_maxid = False
if 'FORCE_FIRST_PAGE' in site_instance.info['EXTRA']:
max_page = 1
cookie = None
if 'COOKIE' in site_instance.info:
cookie = site_instance.info['COOKIE']
for p in range(max_page):
url = LogicFromSite.get_board_url(site_instance, board, str(p+1))
list_tag = xpath_dict['XPATH'][:xpath_dict['XPATH'].find('[%s]')]
#list_tag = '/html/body/main/div/div/div[3]/div/table/tbody'
logger.debug('list_tag : %s', list_tag)
logger.debug('Url : %s', url)
if 'USE_SELENIUM' in site_instance.info['EXTRA']:
from system import SystemLogicSelenium
tmp = SystemLogicSelenium.get_pagesoruce_by_selenium(url, list_tag)
else:
tmp = LogicFromSite.get_html(url, cookie=cookie)
#logger.debug(tmp)
tree = html.fromstring(tmp)
#tree = html.fromstring(LogicFromSite.get_html(url)))
lists = tree.xpath(list_tag)
logger.debug('Count : %s', len(lists))
for i in range(index_start, len(lists)+1, index_step):
try:
a_tag = tree.xpath(xpath_dict['XPATH'] % i)
a_tag_index = len(a_tag)-1
if a_tag_index == -1:
logger.debug('a_tag_index : %s', a_tag_index)
continue
item = {}
#
if 'TITLE_XPATH' in xpath_dict:
#logger.debug(a_tag[a_tag_index].xpath(xpath_dict['TITLE_XPATH']))
if xpath_dict['TITLE_XPATH'].endswith('text()'):
logger.debug(a_tag[a_tag_index].xpath(xpath_dict['TITLE_XPATH']))
item['title'] = urllib.unquote(a_tag[a_tag_index].xpath(xpath_dict['TITLE_XPATH'])[-1]).strip()
else:
item['title'] = urllib.unquote(a_tag[a_tag_index].xpath(xpath_dict['TITLE_XPATH'])[0].text_content()).strip()
else:
item['title'] = urllib.unquote(a_tag[a_tag_index].text_content()).strip()
if 'TITLE_SUB' in xpath_dict:
item['title'] = re.sub(xpath_dict['TITLE_SUB'][0], xpath_dict['TITLE_SUB'][1], item['title']).strip()
# 일반적이 제목 처리 후 정규식이 있으면 추출
if 'TITLE_REGEX' in xpath_dict:
match = re.compile(xpath_dict['TITLE_REGEX']).search(item['title'])
if match:
item['title'] = match.group('title')
item['url'] = a_tag[a_tag_index].attrib['href']
if 'DETAIL_URL_SUB' in site_instance.info:
#item['url'] = item['url'].replace(site_instance.info['DETAIL_URL_RULE'][0], site_instance.info['DETAIL_URL_RULE'][1].format(URL=site_instance.info['TORRENT_SITE_URL']))
item['url'] = re.sub(site_instance.info['DETAIL_URL_SUB'][0], site_instance.info['DETAIL_URL_SUB'][1].format(URL=site_instance.info['TORRENT_SITE_URL']), item['url'])
if not item['url'].startswith('http'):
form = '%s%s' if item['url'].startswith('/') else '%s/%s'
item['url'] = form % (site_instance.info['TORRENT_SITE_URL'], item['url'])
item['id'] = ''
if 'ID_REGEX' in site_instance.info:
id_regexs = [site_instance.info['ID_REGEX']]
#id_regexs.insert(0, site_instance.info['ID_REGEX'])
else:
id_regexs = [r'wr_id\=(?P<id>\d+)', r'\/(?P<id>\d+)\.html', r'\/(?P<id>\d+)$']
for regex in id_regexs:
match = re.compile(regex).search(item['url'])
if match:
item['id'] = match.group('id')
break
if item['id'] == '':
for regex in id_regexs:
match = re.compile(regex).search(item['url'].split('?')[0])
if match:
item['id'] = match.group('id')
break
logger.debug('ID : %s, TITLE : %s', item['id'], item['title'])
if item['id'].strip() == '':
continue
if is_test:
bbs_list.append(item)
else:
if 'USING_BOARD_CHAR_ID' in site_instance.info['EXTRA']:
# javdb
from .model import ModelBbs2
entity = ModelBbs2.get(site=site_instance.info['NAME'], board=board, board_char_id=item['id'])
if entity is None:
bbs_list.append(item)
logger.debug('> Append..')
else:
logger.debug('> exist..')
else:
# 2019-04-04 토렌트퐁
try:
if 'NO_BREAK_BY_MAX_ID' in site_instance.info['EXTRA']:
if int(item['id']) <= max_id:
continue
else:
bbs_list.append(item)
else:
if int(item['id']) <= max_id:
logger.debug('STOP by MAX_ID(%s)', max_id)
stop_by_maxid = True
break
bbs_list.append(item)
#logger.debug(item)
except Exception as e:
logger.error('Exception:%s', e)
logger.error(traceback.format_exc())
except Exception as e:
logger.error('Exception:%s', e)
logger.error(traceback.format_exc())
logger.error(site_instance.info)
if stop_by_maxid:
break
logger.debug('Last count :%s', len(bbs_list))
return bbs_list
# Step2. 마그넷 목록을 생성한다.
@staticmethod
def __get_magnet_list(html, tree, site_instance):
magnet_list = []
try:
if 'MAGNET_REGAX' not in site_instance.info:
try:
link_element = tree.xpath("//a[starts-with(@href,'magnet')]")
if link_element:
for magnet in link_element:
# text가 None인걸로 판단하면 안된다.
# 텍스트가 child tag 안에 있을 수 있음.
#if magnet.text is None or not magnet.text.startswith('magnet'):
# break
if 'MAGNET_EXIST_ON_LIST' in site_instance.info['EXTRA']:
if magnet.text is None or not magnet.text.startswith('magnet'):
break
tmp = (magnet.attrib['href']).lower()[:60]
if tmp not in magnet_list:
magnet_list.append(tmp)
#logger.debug('MARNET : %s', item['magnet'])
except Exception as e:
logger.debug('Exception:%s', e)
logger.debug(traceback.format_exc())
#마그넷 regex
#elif site['HOW'] == 'USING_MAGNET_REGAX':
else:
try:
match = re.compile(site_instance.info['MAGNET_REGAX'][0]).findall(html)
for m in match:
tmp = (site_instance.info['MAGNET_REGAX'][1] % m).lower()
if tmp not in magnet_list:
magnet_list.append(tmp)
#logger.debug('MARNET : %s', magnet_list)
except Exception as e:
logger.debug('Exception:%s', e)
logger.debug(traceback.format_exc())
except Exception as e:
logger.debug('Exception:%s', e)
logger.debug(traceback.format_exc())
return magnet_list
# Step 3. 다운로드 목록 생성
@staticmethod
def __get_download_list(html, tree, site_instance, item):
download_list = []
try:
if 'DOWNLOAD_REGEX' not in site_instance.info:
return download_list
#logger.debug(html)
#tmp = html.find('a href="https://www.rgtorrent.me/bbs/download.php')
#if tmp != -1:
# logger.debug(html[tmp-300:tmp+300])
#logger.debug(site_instance.info['DOWNLOAD_REGEX'])
tmp = re.compile(site_instance.info['DOWNLOAD_REGEX'], re.MULTILINE).finditer(html)
for t in tmp:
#logger.debug(t.group('url'))
#logger.debug(t.group('filename'))
if t.group('filename').strip() == '':
continue
entity = {}
entity['link'] = urllib.unquote(t.group('url').strip()).strip()
entity['link'] = unescape(entity['link'])
logger.debug(entity['link'])
entity['filename'] = urllib.unquote(t.group('filename').strip())
entity['filename'] = unescape(entity['filename'])
if 'DOWNLOAD_URL_SUB' in site_instance.info:
logger.debug(entity['link'])
entity['link'] = re.sub(site_instance.info['DOWNLOAD_URL_SUB'][0], site_instance.info['DOWNLOAD_URL_SUB'][1].format(URL=site_instance.info['TORRENT_SITE_URL']), entity['link']).strip()
if not entity['link'].startswith('http'):
form = '%s%s' if entity['link'].startswith('/') else '%s/%s'
entity['link'] = form % (site_instance.info['TORRENT_SITE_URL'], entity['link'])
if 'FILENAME_SUB' in site_instance.info:
entity['filename'] = re.sub(site_instance.info['FILENAME_SUB'][0], site_instance.info['FILENAME_SUB'][1], entity['filename']).strip()
exist = False
for tt in download_list:
if tt['link'] == entity['link']:
exist = True
break
if not exist:
if app.config['config']['is_sjva_server'] and len(item['magnet'])>0:# or True:
try:
ext = os.path.splitext(entity['filename'])[1].lower()
#item['magnet']
if ext in ['.smi', '.srt', '.ass']:
#if True:
import io
if 'USE_SELENIUM' in site_instance.info['EXTRA']:
from system import SystemLogicSelenium
driver = SystemLogicSelenium.get_driver()
driver.get(entity['link'])
import time
time.sleep(10)
files = SystemLogicSelenium.get_downloaded_files()
logger.debug(files)
# 파일확인
filename_no_ext = os.path.splitext(entity['filename'].split('/')[-1])
file_index = 0
for idx, value in enumerate(files):
if value.find(filename_no_ext[0]) != -1:
file_index = idx
break
logger.debug('fileindex : %s', file_index)
content = SystemLogicSelenium.get_file_content(files[file_index])
byteio = io.BytesIO()
byteio.write(content)
else:
data = LogicFromSite.get_html(entity['link'], referer=item['url'], stream=True)
byteio = io.BytesIO()
for chunk in data.iter_content(1024):
byteio.write(chunk)
from discord_webhook import DiscordWebhook, DiscordEmbed
webhook_url = app.config['config']['rss_subtitle_webhook']
text = '%s\n<%s>' % (item['title'], item['url'])
webhook = DiscordWebhook(url=webhook_url, content=text)
webhook.add_file(file=byteio.getvalue(), filename=entity['filename'])
response = webhook.execute()
discord = response.json()
logger.debug(discord)
if 'attachments' in discord:
entity['direct_url'] = discord['attachments'][0]['url']
except Exception as e:
logger.debug('Exception:%s', e)
logger.debug(traceback.format_exc())
download_list.append(entity)
return download_list
except Exception as e:
logger.debug('Exception:%s', e)
logger.debug(traceback.format_exc())
return download_list
# Step 4. Torrent Info
@staticmethod
def __get_torrent_info(magnet_list, scheduler_instance):
ret = None
try:
#if not ModelSetting.get_bool('use_torrent_info'):
# return ret
# 스케쥴링
if scheduler_instance is not None:
if not scheduler_instance.use_torrent_info:
return ret
else:
#테스트
if not ModelSetting.get_bool('use_torrent_info'):
return ret
ret = []
from torrent_info import Logic as TorrentInfoLogic
for m in magnet_list:
logger.debug('Get_torrent_info:%s', m)
for i in range(3):
tmp = None
try:
tmp = TorrentInfoLogic.parse_magnet_uri(m, no_cache=True)
except:
logger.debug('Timeout..')
if tmp is not None:
break
if tmp is not None:
ret.append(tmp)
#ret[m] = tmp
except Exception as e:
logger.debug('Exception:%s', e)
logger.debug(traceback.format_exc())
return ret
@staticmethod
def get_data(url):
import ssl
data = None
for i in range(3):
try:
request = urllib2.Request(url, headers=headers)
logger.debug(url)
data = urllib2.urlopen(request).read()
except Exception as e:
logger.debug('Exception:%s', e)
logger.debug(traceback.format_exc())
try:
data = urllib2.urlopen(request, headers=headers, context=ssl.SSLContext(ssl.PROTOCOL_TLSv1)).read()
except:
try:
data = urllib2.urlopen(request, headers=headers, context=ssl.SSLContext(ssl.PROTOCOL_TLSv1_2)).read()
except:
try:
data = urllib2.urlopen(request, headers=headers, context=ssl.SSLContext(ssl.PROTOCOL_TLSv1_3)).read()
except:
pass
if data is not None:
return data
sleep(5)
@staticmethod
def set_proxy(scheduler_instance):
try:
LogicFromSite.session.keep_alive = False
flag = False
proxy_url = ModelSetting.get('proxy_url')
if scheduler_instance:
logger.debug('USE_PROXY : %s', scheduler_instance.use_proxy)
if scheduler_instance.use_proxy:
flag = True
else:
flag = ModelSetting.get_bool('use_proxy')
if flag:
LogicFromSite.proxyes = {
"http" : proxy_url,
"https" : proxy_url,
}
else:
LogicFromSite.proxyes = None
except Exception as e:
logger.error('Exception:%s', e)
logger.error(traceback.format_exc())
@staticmethod
def get_html(url, referer=None, stream=False, cookie=None, selenium_tag=None):
try:
logger.debug('get_html :%s', url)
if selenium_tag:
#if 'USE_SELENIUM' in site_instance.info['EXTRA']:
from system import SystemLogicSelenium
data = SystemLogicSelenium.get_pagesoruce_by_selenium(url, selenium_tag)
else:
headers['Referer'] = '' if referer is None else referer
if cookie is not None:
headers['Cookie'] = cookie
if LogicFromSite.proxyes:
page_content = LogicFromSite.session.get(url, headers=headers, proxies=LogicFromSite.proxyes, stream=stream, verify=False)
else:
page_content = LogicFromSite.session.get(url, headers=headers, stream=stream, verify=False)
if cookie is not None:
del headers['Cookie']
if stream:
return page_content
data = page_content.content
#logger.debug(data)
except Exception as e:
logger.error('Exception:%s', e)
logger.error(traceback.format_exc())
logger.error('Known..')
data = LogicFromSite.get_data(url)
return data
@staticmethod
def get_board_url(site_instance, board, page):
try:
if board == "NONE":
board = ""
if 'BOARD_URL_RULE' in site_instance.info:
if site_instance.info['BOARD_URL_RULE'].find('{BOARD_NAME_1}') != -1:
tmp = board.split(',')
url = site_instance.info['BOARD_URL_RULE'].format(URL=site_instance.info['TORRENT_SITE_URL'], BOARD_NAME_1=tmp[0], BOARD_NAME_2=tmp[1], PAGE=page)
else:
url = site_instance.info['BOARD_URL_RULE'].format(URL=site_instance.info['TORRENT_SITE_URL'], BOARD_NAME=board, PAGE=page)
else:
url = '%s/bbs/board.php?bo_table=%s&page=%s' % (site_instance.info['TORRENT_SITE_URL'], board, page)
return url
except Exception as e:
logger.error('Exception:%s', e)
logger.error(traceback.format_exc())
| [
"[email protected]"
] | |
75f8a59901744e93a6796e12fb712f2638bec6b5 | 90f52d0348aa0f82dc1f9013faeb7041c8f04cf8 | /wxPython3.0 Docs and Demos/samples/ide/activegrid/tool/IDE.py | caf2b4ca67dbd70a3cd483490938890dd0c1e187 | [] | no_license | resource-jason-org/python-wxPythonTool | 93a25ad93c768ca8b69ba783543cddf7deaf396b | fab6ec3155e6c1ae08ea30a23310006a32d08c36 | refs/heads/master | 2021-06-15T10:58:35.924543 | 2017-04-14T03:39:27 | 2017-04-14T03:39:27 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 368,697 | py | #----------------------------------------------------------------------------
# Name: IDE.py
# Purpose: IDE using Python extensions to the wxWindows docview framework
#
# Author: Peter Yared
#
# Created: 5/15/03
# Copyright: (c) 2003-2005 ActiveGrid, Inc.
# CVS-ID: $Id$
# License: wxWindows License
#----------------------------------------------------------------------------
import wx
import wx.lib.docview
import wx.lib.pydocview
import sys
import wx.grid
import os.path
import activegrid.util.sysutils as sysutilslib
import activegrid.util.appdirs as appdirs
import shutil
# Required for Unicode support with python
# site.py sets this, but Windows builds don't have site.py because of py2exe problems
# If site.py has already run, then the setdefaultencoding function will have been deleted.
if hasattr(sys,"setdefaultencoding"):
sys.setdefaultencoding("utf-8")
_ = wx.GetTranslation
ACTIVEGRID_BASE_IDE = False
USE_OLD_PROJECTS = False
#----------------------------------------------------------------------------
# Helper functions for command line args
#----------------------------------------------------------------------------
# Since Windows accept command line options with '/', but this character
# is used to denote absolute path names on other platforms, we need to
# conditionally handle '/' style arguments on Windows only.
def printArg(argname):
output = "'-" + argname + "'"
if wx.Platform == "__WXMSW__":
output = output + " or '/" + argname + "'"
return output
def isInArgs(argname, argv):
result = False
if ("-" + argname) in argv:
result = True
if wx.Platform == "__WXMSW__" and ("/" + argname) in argv:
result = True
return result
# The default log action in wx is to prompt with a big message box
# which is often inappropriate (for example, if the clipboard data
# is not readable on Mac, we'll get one of these messages repeatedly)
# so just log the errors instead.
# NOTE: This does NOT supress fatal system errors. Only non-fatal ones.
class AppLog(wx.PyLog):
def __init__(self):
wx.PyLog.__init__(self)
self.items = []
def DoLogString(self, message, timeStamp):
self.items.append(str(timeStamp) + u" " + message.decode())
#----------------------------------------------------------------------------
# Classes
#----------------------------------------------------------------------------
class IDEApplication(wx.lib.pydocview.DocApp):
def __init__(self, redirect=False):
wx.lib.pydocview.DocApp.__init__(self, redirect=redirect)
def OnInit(self):
global ACTIVEGRID_BASE_IDE
global USE_OLD_PROJECTS
args = sys.argv
# Suppress non-fatal errors that might prompt the user even in cases
# when the error does not impact them.
wx.Log_SetActiveTarget(AppLog())
if "-h" in args or "-help" in args or "--help" in args\
or (wx.Platform == "__WXMSW__" and "/help" in args):
print "Usage: ActiveGridAppBuilder.py [options] [filenames]\n"
# Mac doesn't really support multiple instances for GUI apps
# and since we haven't got time to test this thoroughly I'm
# disabling it for now.
if wx.Platform != "__WXMAC__":
print " option " + printArg("multiple") + " to allow multiple instances of application."
print " option " + printArg("debug") + " for debug mode."
print " option '-h' or " + printArg("help") + " to show usage information for command."
print " option " + printArg("baseide") + " for base IDE mode."
print " [filenames] is an optional list of files you want to open when application starts."
return False
elif isInArgs("dev", args):
self.SetAppName(_("ActiveGrid Application Builder Dev"))
self.SetDebug(False)
elif isInArgs("debug", args):
self.SetAppName(_("ActiveGrid Application Builder Debug"))
self.SetDebug(True)
self.SetSingleInstance(False)
elif isInArgs("baseide", args):
self.SetAppName(_("ActiveGrid IDE"))
ACTIVEGRID_BASE_IDE = True
elif isInArgs("tools", args):
USE_OLD_PROJECTS = True
else:
self.SetAppName(_("ActiveGrid Application Builder"))
self.SetDebug(False)
if isInArgs("multiple", args) and wx.Platform != "__WXMAC__":
self.SetSingleInstance(False)
if not ACTIVEGRID_BASE_IDE:
import CmdlineOptions
if isInArgs(CmdlineOptions.DEPLOY_TO_SERVE_PATH_ARG, args):
CmdlineOptions.enableDeployToServePath()
if not wx.lib.pydocview.DocApp.OnInit(self):
return False
if not ACTIVEGRID_BASE_IDE:
self.ShowSplash(getSplashBitmap())
else:
self.ShowSplash(getIDESplashBitmap())
import STCTextEditor
import FindInDirService
import MarkerService
import project as projectlib
import ProjectEditor
import PythonEditor
import OutlineService
import XmlEditor
import HtmlEditor
import TabbedView
import MessageService
import Service
import ImageEditor
import PerlEditor
import PHPEditor
import wx.lib.ogl as ogl
import DebuggerService
import AboutDialog
import SVNService
import ExtensionService
## import UpdateLogIniService
if not ACTIVEGRID_BASE_IDE:
import activegrid.model.basedocmgr as basedocmgr
import UpdateService
import DataModelEditor
import ProcessModelEditor
import DeploymentService
import WebServerService
import WelcomeService
import XFormEditor
import PropertyService
import WSDLEditor
import WsdlAgEditor
import XPathEditor
import XPathExprEditor
import ImportServiceWizard
import RoleEditor
import HelpService
import WebBrowserService
import SQLEditor
_EDIT_LAYOUTS = True
if not ACTIVEGRID_BASE_IDE:
import BPELEditor
if _EDIT_LAYOUTS:
import LayoutEditor
import SkinEditor
# This creates some pens and brushes that the OGL library uses.
# It should be called after the app object has been created, but
# before OGL is used.
ogl.OGLInitialize()
config = wx.Config(self.GetAppName(), style = wx.CONFIG_USE_LOCAL_FILE)
if not config.Exists("MDIFrameMaximized"): # Make the initial MDI frame maximize as default
config.WriteInt("MDIFrameMaximized", True)
if not config.Exists("MDIEmbedRightVisible"): # Make the properties embedded window hidden as default
config.WriteInt("MDIEmbedRightVisible", False)
docManager = IDEDocManager(flags = self.GetDefaultDocManagerFlags())
self.SetDocumentManager(docManager)
# Note: These templates must be initialized in display order for the "Files of type" dropdown for the "File | Open..." dialog
defaultTemplate = wx.lib.docview.DocTemplate(docManager,
_("Any"),
"*.*",
_("Any"),
_(".txt"),
_("Text Document"),
_("Text View"),
STCTextEditor.TextDocument,
STCTextEditor.TextView,
wx.lib.docview.TEMPLATE_INVISIBLE,
icon = STCTextEditor.getTextIcon())
docManager.AssociateTemplate(defaultTemplate)
if not ACTIVEGRID_BASE_IDE:
dplTemplate = DeploymentService.DeploymentTemplate(docManager,
_("Deployment"),
"*.dpl",
_("Deployment"),
_(".dpl"),
_("Deployment Document"),
_("Deployment View"),
XmlEditor.XmlDocument,
XmlEditor.XmlView,
wx.lib.docview.TEMPLATE_INVISIBLE,
icon = DeploymentService.getDPLIcon())
docManager.AssociateTemplate(dplTemplate)
htmlTemplate = wx.lib.docview.DocTemplate(docManager,
_("HTML"),
"*.html;*.htm",
_("HTML"),
_(".html"),
_("HTML Document"),
_("HTML View"),
HtmlEditor.HtmlDocument,
HtmlEditor.HtmlView,
icon = HtmlEditor.getHTMLIcon())
docManager.AssociateTemplate(htmlTemplate)
if not ACTIVEGRID_BASE_IDE:
identityTemplate = wx.lib.docview.DocTemplate(docManager,
_("Identity"),
"*.xacml",
_("Identity"),
_(".xacml"),
_("Identity Configuration"),
_("Identity View"),
RoleEditor.RoleEditorDocument,
RoleEditor.RoleEditorView,
wx.lib.docview.TEMPLATE_NO_CREATE,
icon = XmlEditor.getXMLIcon())
docManager.AssociateTemplate(identityTemplate)
imageTemplate = wx.lib.docview.DocTemplate(docManager,
_("Image"),
"*.bmp;*.ico;*.gif;*.jpg;*.jpeg;*.png",
_("Image"),
_(".png"),
_("Image Document"),
_("Image View"),
ImageEditor.ImageDocument,
ImageEditor.ImageView,
wx.lib.docview.TEMPLATE_NO_CREATE,
icon = ImageEditor.getImageIcon())
docManager.AssociateTemplate(imageTemplate)
if not ACTIVEGRID_BASE_IDE and _EDIT_LAYOUTS:
layoutTemplate = wx.lib.docview.DocTemplate(docManager,
_("Layout"),
"*.lyt",
_("Layout"),
_(".lyt"),
_("Renderer Layouts Document"),
_("Layout View"),
# Fix the fonts for CDATA XmlEditor.XmlDocument,
# XmlEditor.XmlView,
LayoutEditor.LayoutEditorDocument,
LayoutEditor.LayoutEditorView,
icon = LayoutEditor.getLytIcon())
docManager.AssociateTemplate(layoutTemplate)
perlTemplate = wx.lib.docview.DocTemplate(docManager,
_("Perl"),
"*.pl",
_("Perl"),
_(".pl"),
_("Perl Document"),
_("Perl View"),
PerlEditor.PerlDocument,
PerlEditor.PerlView,
icon = PerlEditor.getPerlIcon())
docManager.AssociateTemplate(perlTemplate)
phpTemplate = wx.lib.docview.DocTemplate(docManager,
_("PHP"),
"*.php",
_("PHP"),
_(".php"),
_("PHP Document"),
_("PHP View"),
PHPEditor.PHPDocument,
PHPEditor.PHPView,
icon = PHPEditor.getPHPIcon())
docManager.AssociateTemplate(phpTemplate)
if not ACTIVEGRID_BASE_IDE:
processModelTemplate = ProcessModelEditor.ProcessModelTemplate(docManager,
_("Process"),
"*.bpel",
_("Process"),
_(".bpel"),
_("Process Document"),
_("Process View"),
ProcessModelEditor.ProcessModelDocument,
ProcessModelEditor.ProcessModelView,
wx.lib.docview.TEMPLATE_NO_CREATE,
icon = ProcessModelEditor.getProcessModelIcon())
docManager.AssociateTemplate(processModelTemplate)
projectTemplate = ProjectEditor.ProjectTemplate(docManager,
_("Project"),
"*.agp",
_("Project"),
_(".agp"),
_("Project Document"),
_("Project View"),
ProjectEditor.ProjectDocument,
ProjectEditor.ProjectView,
wx.lib.docview.TEMPLATE_NO_CREATE,
icon = ProjectEditor.getProjectIcon())
docManager.AssociateTemplate(projectTemplate)
pythonTemplate = wx.lib.docview.DocTemplate(docManager,
_("Python"),
"*.py",
_("Python"),
_(".py"),
_("Python Document"),
_("Python View"),
PythonEditor.PythonDocument,
PythonEditor.PythonView,
icon = PythonEditor.getPythonIcon())
docManager.AssociateTemplate(pythonTemplate)
if not ACTIVEGRID_BASE_IDE:
dataModelTemplate = DataModelEditor.DataModelTemplate(docManager,
_("Schema"),
"*.xsd",
_("Schema"),
_(".xsd"),
_("Schema Document"),
_("Schema View"),
DataModelEditor.DataModelDocument,
DataModelEditor.DataModelView,
icon = DataModelEditor.getDataModelIcon())
docManager.AssociateTemplate(dataModelTemplate)
if not ACTIVEGRID_BASE_IDE:
wsdlagTemplate = wx.lib.docview.DocTemplate(docManager,
_("Service Reference"),
"*.wsdlag",
_("Project"),
_(".wsdlag"),
_("Service Reference Document"),
_("Service Reference View"),
WsdlAgEditor.WsdlAgDocument,
WsdlAgEditor.WsdlAgView,
wx.lib.docview.TEMPLATE_NO_CREATE,
icon = WSDLEditor.getWSDLIcon())
docManager.AssociateTemplate(wsdlagTemplate)
if not ACTIVEGRID_BASE_IDE and _EDIT_LAYOUTS:
layoutTemplate = wx.lib.docview.DocTemplate(docManager,
_("Skin"),
"*.skn",
_("Skin"),
_(".skn"),
_("Application Skin"),
_("Skin View"),
SkinEditor.SkinDocument,
SkinEditor.SkinView,
wx.lib.docview.TEMPLATE_NO_CREATE,
icon = getSkinIcon())
docManager.AssociateTemplate(layoutTemplate)
if not ACTIVEGRID_BASE_IDE:
sqlTemplate = wx.lib.docview.DocTemplate(docManager,
_("SQL"),
"*.sql",
_("SQL"),
_(".sql"),
_("SQL Document"),
_("SQL View"),
SQLEditor.SQLDocument,
SQLEditor.SQLView,
wx.lib.docview.TEMPLATE_NO_CREATE,
icon = SQLEditor.getSQLIcon())
docManager.AssociateTemplate(sqlTemplate)
textTemplate = wx.lib.docview.DocTemplate(docManager,
_("Text"),
"*.text;*.txt",
_("Text"),
_(".txt"),
_("Text Document"),
_("Text View"),
STCTextEditor.TextDocument,
STCTextEditor.TextView,
icon = STCTextEditor.getTextIcon())
docManager.AssociateTemplate(textTemplate)
if not ACTIVEGRID_BASE_IDE:
wsdlTemplate = WSDLEditor.WSDLTemplate(docManager,
_("WSDL"),
"*.wsdl",
_("WSDL"),
_(".wsdl"),
_("WSDL Document"),
_("WSDL View"),
WSDLEditor.WSDLDocument,
WSDLEditor.WSDLView,
wx.lib.docview.TEMPLATE_NO_CREATE,
icon = WSDLEditor.getWSDLIcon())
docManager.AssociateTemplate(wsdlTemplate)
if not ACTIVEGRID_BASE_IDE:
xformTemplate = wx.lib.docview.DocTemplate(docManager,
_("XForm"),
"*.xform",
_("XForm"),
_(".xform"),
_("XForm Document"),
_("XForm View"),
XFormEditor.XFormDocument,
XFormEditor.XFormView,
wx.lib.docview.TEMPLATE_NO_CREATE,
icon = XFormEditor.getXFormIcon())
docManager.AssociateTemplate(xformTemplate)
xmlTemplate = wx.lib.docview.DocTemplate(docManager,
_("XML"),
"*.xml",
_("XML"),
_(".xml"),
_("XML Document"),
_("XML View"),
XmlEditor.XmlDocument,
XmlEditor.XmlView,
icon = XmlEditor.getXMLIcon())
docManager.AssociateTemplate(xmlTemplate)
# Note: Child document types aren't displayed in "Files of type" dropdown
if not ACTIVEGRID_BASE_IDE:
viewTemplate = wx.lib.pydocview.ChildDocTemplate(docManager,
_("XForm"),
"*.none",
_("XForm"),
_(".bpel"),
_("XFormEditor Document"),
_("XFormEditor View"),
XFormEditor.XFormDocument,
XFormEditor.XFormView,
icon = XFormEditor.getXFormIcon())
docManager.AssociateTemplate(viewTemplate)
if not ACTIVEGRID_BASE_IDE:
bpelTemplate = wx.lib.pydocview.ChildDocTemplate(docManager,
_("BPEL"),
"*.none",
_("BPEL"),
_(".bpel"),
_("BPELEditor Document"),
_("BPELEditor View"),
BPELEditor.BPELDocument,
BPELEditor.BPELView,
icon = ProcessModelEditor.getProcessModelIcon())
docManager.AssociateTemplate(bpelTemplate)
if not ACTIVEGRID_BASE_IDE:
dataModelChildTemplate = wx.lib.pydocview.ChildDocTemplate(docManager,
_("Schema"),
"*.none",
_("Schema"),
_(".xsd"),
_("Schema Document"),
_("Schema View"),
DataModelEditor.DataModelChildDocument,
DataModelEditor.DataModelView,
icon = DataModelEditor.getDataModelIcon())
docManager.AssociateTemplate(dataModelChildTemplate)
textService = self.InstallService(STCTextEditor.TextService())
pythonService = self.InstallService(PythonEditor.PythonService())
perlService = self.InstallService(PerlEditor.PerlService())
phpService = self.InstallService(PHPEditor.PHPService())
if not ACTIVEGRID_BASE_IDE:
propertyService = self.InstallService(PropertyService.PropertyService("Properties", embeddedWindowLocation = wx.lib.pydocview.EMBEDDED_WINDOW_RIGHT))
projectService = self.InstallService(ProjectEditor.ProjectService("Projects", embeddedWindowLocation = wx.lib.pydocview.EMBEDDED_WINDOW_TOPLEFT))
findService = self.InstallService(FindInDirService.FindInDirService())
if not ACTIVEGRID_BASE_IDE:
webBrowserService = self.InstallService(WebBrowserService.WebBrowserService()) # this must be before webServerService since it sets the proxy environment variable that is needed by the webServerService.
webServerService = self.InstallService(WebServerService.WebServerService()) # this must be after webBrowserService since that service sets the proxy environment variables.
outlineService = self.InstallService(OutlineService.OutlineService("Outline", embeddedWindowLocation = wx.lib.pydocview.EMBEDDED_WINDOW_BOTTOMLEFT))
filePropertiesService = self.InstallService(wx.lib.pydocview.FilePropertiesService())
markerService = self.InstallService(MarkerService.MarkerService())
messageService = self.InstallService(MessageService.MessageService("Messages", embeddedWindowLocation = wx.lib.pydocview.EMBEDDED_WINDOW_BOTTOM))
debuggerService = self.InstallService(DebuggerService.DebuggerService("Debugger", embeddedWindowLocation = wx.lib.pydocview.EMBEDDED_WINDOW_BOTTOM))
if not ACTIVEGRID_BASE_IDE:
processModelService = self.InstallService(ProcessModelEditor.ProcessModelService())
viewEditorService = self.InstallService(XFormEditor.XFormService())
deploymentService = self.InstallService(DeploymentService.DeploymentService())
dataModelService = self.InstallService(DataModelEditor.DataModelService())
dataSourceService = self.InstallService(DataModelEditor.DataSourceService())
wsdlService = self.InstallService(WSDLEditor.WSDLService())
welcomeService = self.InstallService(WelcomeService.WelcomeService())
if not ACTIVEGRID_BASE_IDE and _EDIT_LAYOUTS:
layoutService = self.InstallService(LayoutEditor.LayoutEditorService())
extensionService = self.InstallService(ExtensionService.ExtensionService())
optionsService = self.InstallService(wx.lib.pydocview.DocOptionsService(supportedModes=wx.lib.docview.DOC_MDI))
aboutService = self.InstallService(wx.lib.pydocview.AboutService(AboutDialog.AboutDialog))
svnService = self.InstallService(SVNService.SVNService())
if not ACTIVEGRID_BASE_IDE:
helpPath = os.path.join(sysutilslib.mainModuleDir, "activegrid", "tool", "data", "AGDeveloperGuideWebHelp", "AGDeveloperGuideWebHelp.hhp")
helpService = self.InstallService(HelpService.HelpService(helpPath))
if self.GetUseTabbedMDI():
windowService = self.InstallService(wx.lib.pydocview.WindowMenuService())
if not ACTIVEGRID_BASE_IDE:
projectService.AddRunHandler(processModelService)
# order of these added determines display order of Options Panels
optionsService.AddOptionsPanel(ProjectEditor.ProjectOptionsPanel)
optionsService.AddOptionsPanel(DebuggerService.DebuggerOptionsPanel)
if not ACTIVEGRID_BASE_IDE:
optionsService.AddOptionsPanel(WebServerService.WebServerOptionsPanel)
optionsService.AddOptionsPanel(DataModelEditor.DataSourceOptionsPanel)
optionsService.AddOptionsPanel(DataModelEditor.SchemaOptionsPanel)
optionsService.AddOptionsPanel(WebBrowserService.WebBrowserOptionsPanel)
optionsService.AddOptionsPanel(ImportServiceWizard.ServiceOptionsPanel)
optionsService.AddOptionsPanel(PythonEditor.PythonOptionsPanel)
optionsService.AddOptionsPanel(PHPEditor.PHPOptionsPanel)
optionsService.AddOptionsPanel(PerlEditor.PerlOptionsPanel)
optionsService.AddOptionsPanel(XmlEditor.XmlOptionsPanel)
optionsService.AddOptionsPanel(HtmlEditor.HtmlOptionsPanel)
optionsService.AddOptionsPanel(STCTextEditor.TextOptionsPanel)
optionsService.AddOptionsPanel(SVNService.SVNOptionsPanel)
optionsService.AddOptionsPanel(ExtensionService.ExtensionOptionsPanel)
filePropertiesService.AddCustomEventHandler(projectService)
outlineService.AddViewTypeForBackgroundHandler(PythonEditor.PythonView)
outlineService.AddViewTypeForBackgroundHandler(PHPEditor.PHPView)
outlineService.AddViewTypeForBackgroundHandler(ProjectEditor.ProjectView) # special case, don't clear outline if in project
outlineService.AddViewTypeForBackgroundHandler(MessageService.MessageView) # special case, don't clear outline if in message window
if not ACTIVEGRID_BASE_IDE:
outlineService.AddViewTypeForBackgroundHandler(DataModelEditor.DataModelView)
outlineService.AddViewTypeForBackgroundHandler(ProcessModelEditor.ProcessModelView)
outlineService.AddViewTypeForBackgroundHandler(PropertyService.PropertyView) # special case, don't clear outline if in property window
outlineService.StartBackgroundTimer()
if not ACTIVEGRID_BASE_IDE:
propertyService.AddViewTypeForBackgroundHandler(DataModelEditor.DataModelView)
propertyService.AddViewTypeForBackgroundHandler(ProcessModelEditor.ProcessModelView)
propertyService.AddViewTypeForBackgroundHandler(XFormEditor.XFormView)
propertyService.AddViewTypeForBackgroundHandler(BPELEditor.BPELView)
propertyService.AddViewTypeForBackgroundHandler(WSDLEditor.WSDLView)
propertyService.StartBackgroundTimer()
propertyService.AddCustomCellRenderers(DataModelEditor.GetCustomGridCellRendererDict())
propertyService.AddCustomCellRenderers(BPELEditor.GetCustomGridCellRendererDict())
propertyService.AddCustomCellRenderers(XFormEditor.GetCustomGridCellRendererDict())
propertyService.AddCustomCellRenderers(XPathEditor.GetCustomGridCellRendererDict())
propertyService.AddCustomCellRenderers(XPathExprEditor.GetCustomGridCellRendererDict())
propertyService.AddCustomCellRenderers(WSDLEditor.GetCustomGridCellRendererDict())
propertyService.AddCustomCellRenderers(WsdlAgEditor.GetCustomGridCellRendererDict())
propertyService.AddCustomCellEditors(DataModelEditor.GetCustomGridCellEditorDict())
propertyService.AddCustomCellEditors(BPELEditor.GetCustomGridCellEditorDict())
propertyService.AddCustomCellEditors(XFormEditor.GetCustomGridCellEditorDict())
propertyService.AddCustomCellEditors(XPathEditor.GetCustomGridCellEditorDict())
propertyService.AddCustomCellEditors(XPathExprEditor.GetCustomGridCellEditorDict())
propertyService.AddCustomCellEditors(WSDLEditor.GetCustomGridCellEditorDict())
propertyService.AddCustomCellEditors(WsdlAgEditor.GetCustomGridCellEditorDict())
if not ACTIVEGRID_BASE_IDE:
projectService.AddNameDefault(".bpel", projectService.GetDefaultNameCallback)
projectService.AddNameDefault(".xsd", dataModelService.GetDefaultNameCallback)
projectService.AddNameDefault(".xform", projectService.GetDefaultNameCallback)
projectService.AddNameDefault(".wsdl", projectService.GetDefaultNameCallback)
projectService.AddNameDefault(".wsdlag", projectService.GetDefaultNameCallback)
projectService.AddNameDefault(".skn", projectService.GetDefaultNameCallback)
projectService.AddNameDefault(".xacml", projectService.GetDefaultNameCallback)
projectService.AddFileTypeDefault(".lyt", basedocmgr.FILE_TYPE_LAYOUT)
projectService.AddFileTypeDefault(".bpel", basedocmgr.FILE_TYPE_PROCESS)
projectService.AddFileTypeDefault(".xsd", basedocmgr.FILE_TYPE_SCHEMA)
projectService.AddFileTypeDefault(".wsdlag", basedocmgr.FILE_TYPE_SERVICE)
projectService.AddFileTypeDefault(".skn", basedocmgr.FILE_TYPE_SKIN)
projectService.AddFileTypeDefault(".xacml", basedocmgr.FILE_TYPE_IDENTITY)
projectService.AddFileTypeDefault(".css", basedocmgr.FILE_TYPE_STATIC)
projectService.AddFileTypeDefault(".js", basedocmgr.FILE_TYPE_STATIC)
projectService.AddFileTypeDefault(".gif", basedocmgr.FILE_TYPE_STATIC)
projectService.AddFileTypeDefault(".jpg", basedocmgr.FILE_TYPE_STATIC)
projectService.AddFileTypeDefault(".jpeg", basedocmgr.FILE_TYPE_STATIC)
projectService.AddFileTypeDefault(".xform", basedocmgr.FILE_TYPE_XFORM)
projectService.AddLogicalViewFolderDefault(".agp", _("Projects"))
projectService.AddLogicalViewFolderDefault(".wsdlag", _("Services"))
projectService.AddLogicalViewFolderDefault(".wsdl", _("Services"))
projectService.AddLogicalViewFolderDefault(".xsd", _("Data Models"))
projectService.AddLogicalViewFolderDefault(".bpel", _("Page Flows"))
projectService.AddLogicalViewFolderDefault(".xform", _("Pages"))
projectService.AddLogicalViewFolderDefault(".xacml", _("Security"))
projectService.AddLogicalViewFolderDefault(".lyt", _("Presentation/Layouts"))
projectService.AddLogicalViewFolderDefault(".skn", _("Presentation/Skins"))
projectService.AddLogicalViewFolderDefault(".css", _("Presentation/Stylesheets"))
projectService.AddLogicalViewFolderDefault(".js", _("Presentation/Javascript"))
projectService.AddLogicalViewFolderDefault(".html", _("Presentation/Static"))
projectService.AddLogicalViewFolderDefault(".htm", _("Presentation/Static"))
projectService.AddLogicalViewFolderDefault(".gif", _("Presentation/Images"))
projectService.AddLogicalViewFolderDefault(".jpeg", _("Presentation/Images"))
projectService.AddLogicalViewFolderDefault(".jpg", _("Presentation/Images"))
projectService.AddLogicalViewFolderDefault(".png", _("Presentation/Images"))
projectService.AddLogicalViewFolderDefault(".ico", _("Presentation/Images"))
projectService.AddLogicalViewFolderDefault(".bmp", _("Presentation/Images"))
projectService.AddLogicalViewFolderDefault(".py", _("Code"))
projectService.AddLogicalViewFolderDefault(".php", _("Code"))
projectService.AddLogicalViewFolderDefault(".pl", _("Code"))
projectService.AddLogicalViewFolderDefault(".sql", _("Code"))
projectService.AddLogicalViewFolderDefault(".xml", _("Code"))
projectService.AddLogicalViewFolderDefault(".dpl", _("Code"))
projectService.AddLogicalViewFolderCollapsedDefault(_("Page Flows"), False)
projectService.AddLogicalViewFolderCollapsedDefault(_("Pages"), False)
self.SetDefaultIcon(getActiveGridIcon())
if not ACTIVEGRID_BASE_IDE:
embeddedWindows = wx.lib.pydocview.EMBEDDED_WINDOW_TOPLEFT | wx.lib.pydocview.EMBEDDED_WINDOW_BOTTOMLEFT |wx.lib.pydocview.EMBEDDED_WINDOW_BOTTOM | wx.lib.pydocview.EMBEDDED_WINDOW_RIGHT
else:
embeddedWindows = wx.lib.pydocview.EMBEDDED_WINDOW_TOPLEFT | wx.lib.pydocview.EMBEDDED_WINDOW_BOTTOMLEFT |wx.lib.pydocview.EMBEDDED_WINDOW_BOTTOM
if self.GetUseTabbedMDI():
self.frame = IDEDocTabbedParentFrame(docManager, None, -1, wx.GetApp().GetAppName(), embeddedWindows=embeddedWindows, minSize=150)
else:
self.frame = IDEMDIParentFrame(docManager, None, -1, wx.GetApp().GetAppName(), embeddedWindows=embeddedWindows, minSize=150)
self.frame.Show(True)
wx.lib.pydocview.DocApp.CloseSplash(self)
self.OpenCommandLineArgs()
if not projectService.OpenSavedProjects() and not docManager.GetDocuments() and self.IsSDI(): # Have to open something if it's SDI and there are no projects...
projectTemplate.CreateDocument('', wx.lib.docview.DOC_NEW).OnNewDocument()
tips_path = os.path.join(sysutilslib.mainModuleDir, "activegrid", "tool", "data", "tips.txt")
# wxBug: On Mac, having the updates fire while the tip dialog is at front
# for some reason messes up menu updates. This seems a low-level wxWidgets bug,
# so until I track this down, turn off UI updates while the tip dialog is showing.
if not ACTIVEGRID_BASE_IDE:
wx.UpdateUIEvent.SetUpdateInterval(-1)
UpdateService.UpdateVersionNag()
appUpdater = UpdateService.AppUpdateService(self)
appUpdater.RunUpdateIfNewer()
if not welcomeService.RunWelcomeIfFirstTime():
if os.path.isfile(tips_path):
self.ShowTip(docManager.FindSuitableParent(), wx.CreateFileTipProvider(tips_path, 0))
else:
if os.path.isfile(tips_path):
self.ShowTip(docManager.FindSuitableParent(), wx.CreateFileTipProvider(tips_path, 0))
iconPath = os.path.join(sysutilslib.mainModuleDir, "activegrid", "tool", "bmp_source", "activegrid.ico")
if os.path.isfile(iconPath):
ib = wx.IconBundle()
ib.AddIconFromFile(iconPath, wx.BITMAP_TYPE_ANY)
wx.GetApp().GetTopWindow().SetIcons(ib)
wx.UpdateUIEvent.SetUpdateInterval(1000) # Overhead of updating menus was too much. Change to update every n milliseconds.
return True
class IDEDocManager(wx.lib.docview.DocManager):
# Overriding default document creation.
def OnFileNew(self, event):
import NewDialog
newDialog = NewDialog.NewDialog(wx.GetApp().GetTopWindow())
if newDialog.ShowModal() == wx.ID_OK:
isTemplate, object = newDialog.GetSelection()
if isTemplate:
object.CreateDocument('', wx.lib.docview.DOC_NEW)
else:
import ProcessModelEditor
if object == NewDialog.FROM_DATA_SOURCE:
wiz = ProcessModelEditor.CreateAppWizard(wx.GetApp().GetTopWindow(), title=_("New Database Application"), minimalCreate=False, startingType=object)
wiz.RunWizard()
elif object == NewDialog.FROM_DATABASE_SCHEMA:
wiz = ProcessModelEditor.CreateAppWizard(wx.GetApp().GetTopWindow(), title=_("New Database Application"), minimalCreate=False, startingType=object)
wiz.RunWizard()
elif object == NewDialog.FROM_SERVICE:
wiz = ProcessModelEditor.CreateAppWizard(wx.GetApp().GetTopWindow(), title=_("New Service Application"), minimalCreate=False, startingType=object)
wiz.RunWizard()
elif object == NewDialog.CREATE_SKELETON_APP:
wiz = ProcessModelEditor.CreateAppWizard(wx.GetApp().GetTopWindow(), title=_("New Skeleton Application"), minimalCreate=False, startingType=object)
wiz.RunWizard()
elif object == NewDialog.CREATE_PROJECT:
import ProjectEditor
for temp in self.GetTemplates():
if isinstance(temp,ProjectEditor.ProjectTemplate):
temp.CreateDocument('', wx.lib.docview.DOC_NEW)
break
else:
assert False, "Unknown type returned from NewDialog"
class IDEDocTabbedParentFrame(wx.lib.pydocview.DocTabbedParentFrame):
# wxBug: Need this for linux. The status bar created in pydocview is
# replaced in IDE.py with the status bar for the code editor. On windows
# this works just fine, but on linux the pydocview status bar shows up near
# the top of the screen instead of disappearing.
def CreateDefaultStatusBar(self):
pass
class IDEMDIParentFrame(wx.lib.pydocview.DocMDIParentFrame):
# wxBug: Need this for linux. The status bar created in pydocview is
# replaced in IDE.py with the status bar for the code editor. On windows
# this works just fine, but on linux the pydocview status bar shows up near
# the top of the screen instead of disappearing.
def CreateDefaultStatusBar(self):
pass
#----------------------------------------------------------------------------
# Icon Bitmaps - generated by encode_bitmaps.py
#----------------------------------------------------------------------------
from wx import ImageFromStream, BitmapFromImage
import cStringIO
def getSplashData():
return \
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def getSplashBitmap():
return BitmapFromImage(getSplashImage())
def getSplashImage():
stream = cStringIO.StringIO(getSplashData())
return ImageFromStream(stream)
#----------------------------------------------------------------------
def getIDESplashData():
return \
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#\xc7\xab\x05\xa1\x8d\x00\xed\x08#\x86\x84\x10D4)99m<\xd9\xaa\xfbfG"\xe5\xbd\
\x12\x1f\x080\xe5\x80\xe7\x11@\x00\xb3\x0c\x95\xdf;\xd8.\xec3:\xce]\xa3\xd1L\
\x00I\x12\xc7LJ\xb6\xe9\xdb\xbc\\0-\xcb4\rbP\xf0lZ.\x18I\xc2\xa4\x88\x9a!f\
\x95\xdd \xe0\xda\x04$\x00\x02\xb0\x06t\xee\x1cl\x17\xf6\x19\x1d-\xa3\xd1h&\
\x00\xc1Y\xab\x03&e\x08\x1b\xad\xa8R.\x94<\x0b%J\xd3 \x82\x85\xcd\xf6S)\x05"\
\xd9\xae\xa6\x8d\xe0:@\xb2\xeb\x9f\xda\xab`\xcd\x80\xf5\xaa\xb8\xd1\xb4[F\
\xa3\xd1\x1cRP2d\x1d\xe4M\xca\x1b\x8f\xd6\x9fD\x894)\xf8\x0e$\x1c\x01LBM \
\x04%\x97\x82\x03 "\x91\x08\xbeC\xa0g\xb6W\xa7\xfa\x8ap\x18>T\xdf\x82\xa3\
\x98\xbd\xbd\x1fm\xb9k4\x9aC\x04J\x86\xbc#\xe3\x86L\x82n\xcc"\x01D\x10\x02\
\x95P\x87\t\x98\x94\x00p\x90\x1c\x00\x08\x80i\x10\x00\x92\xf0\xed\xdc\xbd\
\x88\xe0\xe5\xccv\x85\xe8@\xfb.L\x9d\xdf\xd7.\x1d\x10Z\xdc5\x1a\xcd\x81\x82\
\x02%G\xde\x91IC&Md-\x94\xac\xab\xca=i\x96r;\xa2Q\x15J5\x08I\xa3\xddew\x03\
\xa4\x94\x10\x02R\x82I\xc1\xb5\xc9\xe0uO\xe1\x03\xf0N\x80\xe1\xefC\xcf\x0e\
\x16-\xee\x1a\x8df\xdf\x91\t\xf06\xb2\x80G\r\xc9C\x90,\x9b\xa91\x07\x01\x90\
\x08\x123\xff\xce\x80\x00\x80\xa0\x12\t\xd0\xde\n\xa6\x82;\xc8lO\xf7h\xfe\
\x16\xca\xbf\x0bG4\x8d{\xca\xff\x0fY\xef?\x965\xa5@\x17\x00\x00\x00\x00IEND\
\xaeB`\x82'
def getIDESplashBitmap():
return BitmapFromImage(getIDESplashImage())
def getIDESplashImage():
stream = cStringIO.StringIO(getIDESplashData())
return ImageFromStream(stream)
#----------------------------------------------------------------------
def getActiveGridData():
return \
'\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x10\x00\x00\x00\x10\x08\x02\
\x00\x00\x00\x90\x91h6\x00\x00\x00\x03sBIT\x08\x08\x08\xdb\xe1O\xe0\x00\x00\
\x02\x10IDAT(\x91\x95\x92Kk\x13a\x14\x86\xcf7\xb7d\x92I\xa7I\'7\xa9)\x1a\xab\
\xb5\xc1R\xabh\x15\x04\x91\xd2\x8d\x08\xaet\'\xf4?\xf87\xfc\r\xee\xdc\xb8\
\xd0E\xc1\x8d(bP\xf0\x86Z/i\x02m\x9a\x98\xa9\xc9d&\x9d\xc9\xdc\xe7\x9b\xf9\
\xdc\x84Rb@|\x97\xe7\x9c\x07^x\x0e"\x84\xc0\xff\x84\x998m\x7f}j\xd4\x9f\xc8n\
\xd1d\xe7o\xdf\xd9@\x88:\\Q\x7f_\xebJ\xb3\xbd\xb59-\xb2y\xde\xc8\xe3\xf7\xb5\
7\x8f\x8e\xb6\x98\x00\xb4\xb66\tv\xf6~\xfb\x10\x1a\t\xc6\xea\xec~&Q8\xb9R\
\x14a\xa3\xbf\xa7\xb6\xbf$hp\xfc\xa0\xa6\x10u\x18\x9d\xb9P\xa1hf\x1c\xc0\xbe\
\xd3\xf9\xf1Lm\xbeS\x15\x99\xa1B+ \x1e\x06\x96\x02\x9a\xa6OWV}[e\xe3"\xa2\
\x98\x11\xe0Y\x83\xed\x97\x0f8\xbf)q H\xa4\xa3\x11\xdb\x8b,\x8f\xeckAnv\xc5\
\xb4\xd9~\xf5q\x02\xf6sgoN\x1f\xbf\xc4\x00@\xe3\xedC\xceo\n1\x12ci!\x81x6\
\xdc\xe9\xa1\xbe\x11F\x84.\xcc\x9d\xcag\x93;\xdb\xbf\x1c\xaf^\xab\x0eS\xd2+\
\n\x00\xec\xeeG\x8e&b:#-,%\xc5l\x8c\xa3\xae,\x1d\xbbq1wn\x8e\xf9\xf6\xe1E*\
\x9d\xe1\xd3E3\x10\xf2\x8bk\xf9\xf2U\x06\x00\x10\x10\x00\xc4\xcf\xe4P\xa1\
\x14*\xdd\x08h\x96\x17y\xd7\x88s(I\xe9\x8d\xfa\xcf\xd2\xca]~\xba\x14\xf4?iz\
\x86\x01\x00N<\xe9\xb9MM\x96\x13\xba\xae\xabj\x80#\xa5\xd7\x1b\x98\x9e\x87!\
\x19G\xc3AO\xa8,\x0b\xe7oEx]\xdb}M\x01\xc0\x89\xcb\x1b.\x11z\x8a\xd1i\xc9\
\x86\xe5\x99\x0e\x96\xbb\x9a6\xb0\\\x0f|\x8cf2\xe2H\x19\x13\x93\xe6\xd7(\x00\
\x98\xca\x96\xcb\xd7\xef\xe3\xd8\xec\x81\x03\xa6\x0b\xa6K\x0c;\xd4\xed\xe8\
\xc0\x8e0\x95,\x96\x16\x8e\xbaB\x87\xda\xb1o\xb7\xbe?\x97\x1bUC\x95]\x0f\x0f\
\x1d\x12\xd2S\xab\xeb\xf7\x16\x97\xafM\x06F\xb2\xc3@W\xe5\xa1\xaeF@K\x85\x92\
\x90J\x8f=\xce8\xf0\xcf\xfc\x01\xc1h\x0bqbR\xd1\'\x00\x00\x00\x00IEND\xaeB`\
\x82'
def getActiveGridBitmap():
return BitmapFromImage(getActiveGridImage())
def getActiveGridImage():
stream = cStringIO.StringIO(getActiveGridData())
return ImageFromStream(stream)
def getActiveGridIcon():
return wx.IconFromBitmap(getActiveGridBitmap())
#----------------------------------------------------------------------
def getSkinData():
return \
"\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x10\x00\x00\x00\x10\x08\x06\
\x00\x00\x00\x1f\xf3\xffa\x00\x00\x00\x04sBIT\x08\x08\x08\x08|\x08d\x88\x00\
\x00\x01CIDAT8\x8d\x85\x931K\xc3@\x14\x80\xbf\xd4\xba\xb5\xfe\x04\x03J\xe9Vp\
5\x83Z\x97\xacB;dppw\x10\x04\x05\xa9\xa0\x90:\xa8\xa3\x14qs(8\xd4\xa1\x9bd\
\xa9\x8b\x08\x15\xe2$\x82\xb4)U\x8b\x93\x8b\x11\x1d\x14\xe2\x10\x936\xb9$}\
\x10\xc8\xe3\xe5\xfb\xde\xbb\xcb\x9dt\xdb6\x1dB\xd1\xee}\xf9\xef\x1b\xda\x82\
\x14\xae\x8fF\n \x93\x9f\x0b<EU\x01@V\x14\xd6\xab\x17B\x03A\x10\x17\xb3YX^-%\
J\xd2I\x82\xb3Z#\xa9\x9c,\x90\x15\x05\xd9]\t\xa7\xbbGB\xfd\xa7\xba\xe2\x00\
\xa4F7\xcc\x8b\xae\x9d\xdc\xd5\x83#'\x08\xc3\xe1\xdc\x83_\xee\xbf9\xb7\x1e\
\x87\x82\xa8\xae\xe3\xe0\xfa\xd3\xaf+\x18\xd75\x0e\xee|\x0e\xa4t\xc7z\xf37+v\
\x92\xcb\x13\xeaK:\x00\xbd\xc1\x9e\x0fC\xe8\x1c\x84\xe1BS\xa3\xd0\xd4\xa8\
\xed\xcc\xa3\xb7*\x00\x01\xd8\x17t\xedh\x18 S\xdc\x02`[\x9dDoU\x020\x80\xa4\
\xae\x1d\n\xa7l3o\x06\xe0\x87\xe32\xaf\x96\xfb\xd9\xbe\xd9\x0f\n\xa4\xd4\x84\
\x9f\x18\x07eA\xd67\xef\xc8\x19\xef\x00,~\xd8\xc2\xc5\xf2\x7f\xe3\xf5T\xd6\
\x996\x87\xebx6n\x00\xc8\xfd\xe7Q\xb00\x81UR\x85\tf\x1aW\x89\xd7\xf9\x0f\x9f\
\xff\x90p\xb7%\x8e\xf9\x00\x00\x00\x00IEND\xaeB`\x82"
def getSkinBitmap():
return BitmapFromImage(getSkinImage())
def getSkinImage():
stream = cStringIO.StringIO(getSkinData())
return ImageFromStream(stream)
def getSkinIcon():
return wx.IconFromBitmap(getSkinBitmap())
| [
"[email protected]"
] | |
da2e3218485d698bc9cf720d1e8e480446b29cbd | 66160ae77aed90e1e8eb32708ec251d098f94255 | /Python/Python Basics/.history/ClassBasics_20200618233336.py | 6b9928a122b44674bd19a1876ed2a74030d5567c | [] | no_license | sarthikg/cheat-sheets | 8cf60825bb510d38b57c48da06d41bfef9698c46 | 027ec9d607e5643eed2aa46e06eab3602fc3072c | refs/heads/master | 2022-12-23T18:57:31.574682 | 2020-09-21T00:28:24 | 2020-09-21T00:28:24 | 297,188,205 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 25 | py | # Defining a Class
class | [
"[email protected]"
] | |
3150e892a3d4da245621a1bb195d4cbfa8821d0b | ba09a6cc089e408e2a865810b0e365d004712ce8 | /Calculator/calculator.py | b858bb30a91766785f55b12d25b67dfc1136b2cd | [] | no_license | ephreal/Silliness | 5459977e1bca44cde4639f8bf83bd8a976b2ee63 | bce290f86f818ad926613e84a07e5d354a112c4b | refs/heads/master | 2021-06-05T02:41:42.467732 | 2020-02-20T16:31:35 | 2020-02-20T16:31:35 | 133,086,053 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 6,289 | py | import tkinter as tk
from decimal import Decimal
from tkinter import ttk
class Calculator(ttk.Frame):
def __init__(self, master=tk.Tk()):
super().__init__(master)
self.master = master
self.master.title("tKalculator")
self.operator = None
self.prev_num = 0
self.completed_calculation = False
self.grid()
self.create_widgets()
self.bind_keys()
self.arrange_widgets()
self.mainloop()
def create_widgets(self):
"""
Create all calculator buttons
and widgets
"""
self.number_buttons = []
# Number display
self.top_display_space = ttk.Label(self, text=" ")
self.display = tk.Text(self, height=1, width=30, pady=4)
self.display.insert(1.0, "0")
self.display["state"] = "disabled"
self.bottom_display_space = ttk.Label(self, text=" ")
# Number buttons
# For some reason, the for loop makes everything send
# a "9" to the update function
for num in range(0, 10):
num = str(num)
self.number_buttons.append(
# note to self: The num=num is necessary here
ttk.Button(self, text=num, command=lambda num=num: self.update(num))
)
self.decimal_button = ttk.Button(self, text=".",
command=lambda: self.update("."))
# Special Buttons
self.clearall_button = ttk.Button(self, text="A/C", command=self.all_clear)
self.clear_button = ttk.Button(self, text="C", command=self.clear)
# Math operators
self.add_button = ttk.Button(self, text="+", command=lambda: self.math("+"))
self.sub_button = ttk.Button(self, text="-", command=lambda: self.math("-"))
self.mult_button = ttk.Button(self, text="X", command=lambda: self.math("x"))
self.div_button = ttk.Button(self, text="/", command=lambda: self.math("/"))
self.eql_button = ttk.Button(self, text="=", command=lambda: self.math("="))
def arrange_widgets(self):
"""
Arrange all calculator widgets.
"""
# Display
self.top_display_space.grid(row=0, column=1)
self.display.grid(row=1, column=0, columnspan=5)
self.bottom_display_space.grid(row=2, column=2)
# Number buttons
row = 3
column = 1
for i in range(1, 10):
self.number_buttons[i].grid(row=row, column=column)
column += 1
if column > 3:
column = 1
row += 1
self.number_buttons[0].grid(row=6, column=2)
self.decimal_button.grid(row=6, column=1)
# Special Buttons
self.clearall_button.grid(row=7, column=1)
self.clear_button.grid(row=6, column=3)
# Math operator buttons
self.add_button.grid(row=7, column=2)
self.sub_button.grid(row=7, column=3)
self.mult_button.grid(row=8, column=2)
self.div_button.grid(row=8, column=3)
self.eql_button.grid(row=8, column=1)
def bind_keys(self):
"""
Binds events to keyboard button presses.
"""
# Keys to bind
keys = "1234567890.caCA+-*x/="
special_keys = [
"<KP_1>", "<KP_2>", "<KP_3>",
"<KP_4>", "<KP_5>", "<KP_6>",
"<KP_7>", "<KP_8>", "<KP_9>",
"<KP_0>", "<KP_Decimal>", "<KP_Add>",
"<KP_Subtract>", "<KP_Multiply>",
"<KP_Divide>", "<KP_Enter>",
"<Return>", "<Escape>",
"<BackSpace>"
]
# A couple for loops to bind all the keys
for key in keys:
self.master.bind(key, self.keypress_handler)
for key in special_keys:
self.master.bind(key, self.keypress_handler)
def backspace(self, event):
"""
Remove one character from the display.
"""
self.display["state"] = "normal"
current = self.display.get(1.0, tk.END)
self.display.delete(1.0, tk.END)
current = current[:-2]
# Make sure that the display is never empty
if current == "":
current = "0"
self.display.insert(1.0, current)
self.display["state"] = "disabled"
def keypress_handler(self, event):
"""
Handles any bound keyboard presses.
"""
char_keycode = '01234567890.'
char_operator = "+-x*/"
if (event.char in char_keycode):
self.update(event.char)
elif event.char in char_operator:
self.math(event.char)
elif event.char == "\r" or event.char == "=":
self.math("=")
elif event.char == "\x1b":
self.master.destroy()
elif event.char == "c" or event.char == "C":
self.clear()
elif event.char == "a" or event.char == "A":
self.all_clear()
def update(self, character):
"""
Handles all updating of the number display.
"""
# Allow editing of the display
self.display["state"] = "normal"
# Get the current number
num = self.display.get(1.0, tk.END)
# clear the display
self.display.delete(1.0, tk.END)
# Remove "\n"
num = num.strip()
# Clear num provided we're not putting a
# decimal after a zero
if num == "0" and not character == ".":
num = ""
num = f"{num}{character}"
self.display.insert(1.0, f"{num}")
self.display["state"] = "disabled"
def all_clear(self):
"""
Resets everything for starting a
new calculation.
"""
self.clear()
self.prev_num = 0
self.operator = None
def clear(self):
"""
Clears the display by removing
any current text and setting the
display to 0
"""
self.display["state"] = "normal"
self.display.delete(1.0, tk.END)
self.display.insert(1.0, "0")
self.display["state"] = "disabled"
def math(self, operator):
"""
Handle any actual math.
"""
if not self.operator:
# If an operator doesn't exist, the
# calculator is waiting for a new
# input.
self.operator = operator
self.prev_num = self.display.get(1.0, tk.END)
self.clear()
else:
# The calculator is ready to do some math.
self.prev_num = Decimal(self.prev_num)
curr_num = self.display.get(1.0, tk.END)
curr_num = Decimal(curr_num)
if self.operator == "+":
self.prev_num += curr_num
elif self.operator == "-":
self.prev_num -= curr_num
elif self.operator == "x" or self.operator == "*":
self.prev_num *= curr_num
elif self.operator == "/":
self.prev_num /= curr_num
self.operator = operator
if self.operator == "=":
# It's now time to show the current result
# of all calculations.
self.display["state"] = "normal"
self.display.delete(1.0, tk.END)
self.display.insert(1.0, str(self.prev_num))
self.display["state"] = "disabled"
self.completed_calculation = True
else:
# We're ready for another number to
# perform calculations on
self.clear()
if __name__ == "__main__":
calc = Calculator() | [
"[email protected]"
] | |
3a239988545f0c87767b909a0dfbec058597d3dd | f9033131dc4d66ede2c5c22fcaa4a0be5b682152 | /Sorting/Tasks/eolymp(5089).py | 21668b326d3a8cfd9412a0d740ab1941e4accc97 | [] | no_license | Invalid-coder/Data-Structures-and-algorithms | 9bd755ce3d4eb11e605480db53302096c9874364 | 42c6eb8656e85b76f1c0043dcddc9c526ae12ba1 | refs/heads/main | 2023-04-29T08:40:34.661184 | 2021-05-19T10:57:37 | 2021-05-19T10:57:37 | 301,458,981 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 860 | py | #https://www.e-olymp.com/uk/submissions/7293961
def greater(a, b):
i = 0
j = 0
while i < len(a) and j < len(b):
if ord(a[i]) > ord(b[j]):
return a
elif ord(a[i]) < ord(b[j]):
return b
else:
i += 1
j += 1
if i == len(a) and j < len(b):
return b
elif j == len(b) and i < len(a):
return a
else:
return a
def selectionSort(array):
n = len(array)
for i in range(n - 1, 0, -1):
maxpos = 0
for j in range(1, i + 1):
if greater(array[maxpos], array[j]) == array[j]:
maxpos = j
array[i], array[maxpos] = array[maxpos], array[i]
if __name__ == '__main__':
n = int(input())
array = [input() for _ in range(n)]
selectionSort(array)
for el in array:
print(el) | [
"[email protected]"
] | |
2e81ee7aa1523d07e3ba18b0d2a4a114cea43f3b | 2a9bd67c955044294b3d07fa5367d2fbe7734a45 | /virtual/bin/easy_install | 34f4dd578c2c0695ec69cfc54a86276d7d5d7ab2 | [
"MIT"
] | permissive | KadogoKenya/BloggerCenter | 86e455b9f470ede9cc0775353dc6a8a6a2c747fa | 90776004316a88d40f56aaa8d44004f31553c48c | refs/heads/master | 2022-12-22T03:20:02.834430 | 2020-10-01T07:58:50 | 2020-10-01T07:58:50 | 298,762,799 | 0 | 0 | null | 2020-10-01T07:58:52 | 2020-09-26T07:37:45 | Python | UTF-8 | Python | false | false | 270 | #!/home/kate/Desktop/PYTHON/blogCenter/virtual/bin/python3
# -*- coding: utf-8 -*-
import re
import sys
from setuptools.command.easy_install import main
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
sys.exit(main())
| [
"[email protected]"
] | ||
1d2ff22060d90ac5eebb0f152bd66b24eb89a21d | 4d66a2b4f40e7169b00871e7f75202fd38201d98 | /linkie/tests/run_tests.py | 5bdd879b985b69fb0642864bdeba2dbaff39bfcc | [
"MIT",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | uccser/linkie | e90d97d9d22c9a1d1eb95d87eb59f3079e4da2ce | f062762c42c812f18374a064ba2ac80d9146b7d5 | refs/heads/master | 2023-03-23T10:56:12.508180 | 2020-06-08T00:07:33 | 2020-06-08T00:07:33 | 124,695,953 | 2 | 0 | MIT | 2021-03-25T21:38:39 | 2018-03-10T20:28:47 | Python | UTF-8 | Python | false | false | 3,251 | py | import os
import unittest
from subprocess import run
from linkie import Linkie
class LinkieTestSuite(unittest.TestCase):
def __init__(self, *args, **kwargs):
unittest.TestCase.__init__(self, *args, **kwargs)
self.default_working_directory = os.getcwd()
def setUp(self):
os.chdir(self.default_working_directory)
def test_basic(self):
os.chdir('./linkie/tests/assets/basic/')
linkie = Linkie()
self.assertEqual(linkie.run(), 0)
def test_broken(self):
os.chdir('./linkie/tests/assets/broken/')
linkie = Linkie()
self.assertEqual(linkie.run(), 1)
def test_multiple(self):
os.chdir('./linkie/tests/assets/multiple/')
linkie = Linkie()
self.assertEqual(linkie.run(), 0)
def test_excluded_directories(self):
os.chdir('./linkie/tests/assets/excluded_directories/')
linkie = Linkie()
self.assertEqual(linkie.run(), 0)
def test_excluded_directories_custom(self):
os.chdir('./linkie/tests/assets/excluded_directories_custom/')
linkie = Linkie(config_file_path='linkie.yaml')
self.assertEqual(linkie.run(), 0)
def test_file_types(self):
os.chdir('./linkie/tests/assets/file_types/')
linkie = Linkie()
self.assertEqual(linkie.run(), 0)
def test_file_types_custom(self):
os.chdir('./linkie/tests/assets/file_types_custom/')
linkie = Linkie(config_file_path='linkie.yaml')
self.assertEqual(linkie.run(), 1)
def test_skip_urls(self):
os.chdir('./linkie/tests/assets/skip_urls/')
linkie = Linkie()
self.assertEqual(linkie.run(), 0)
self.assertEqual(len(linkie.urls), 2)
def test_skip_urls_custom(self):
os.chdir('./linkie/tests/assets/skip_urls_custom/')
linkie = Linkie(config_file_path='linkie.yaml')
self.assertEqual(linkie.run(), 0)
self.assertEqual(len(linkie.urls), 1)
def test_command_line_basic(self):
linkie = run('linkie', cwd='./linkie/tests/assets/basic/')
self.assertEqual(linkie.returncode, 0)
def test_command_line_broken(self):
linkie = run('linkie', cwd='./linkie/tests/assets/broken/')
self.assertEqual(linkie.returncode, 1)
def test_command_line_multiple(self):
linkie = run('linkie', cwd='./linkie/tests/assets/multiple/')
self.assertEqual(linkie.returncode, 0)
def test_command_line_excluded_directories(self):
linkie = run('linkie', cwd='./linkie/tests/assets/excluded_directories/')
self.assertEqual(linkie.returncode, 0)
def test_command_line_excluded_directories_custom(self):
linkie = run(['linkie', 'linkie.yaml'], cwd='./linkie/tests/assets/excluded_directories_custom/')
self.assertEqual(linkie.returncode, 0)
def test_command_line_file_types(self):
linkie = run('linkie', cwd='./linkie/tests/assets/file_types/')
self.assertEqual(linkie.returncode, 0)
def test_command_line_file_types_custom(self):
linkie = run(['linkie', 'linkie.yaml'], cwd='./linkie/tests/assets/file_types_custom/')
self.assertEqual(linkie.returncode, 1)
if __name__ == '__main__':
unittest.main()
| [
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] | |
91ed8fe756697243b39c0671ce9ffe931e906c4a | 7ec38beb6f041319916390ee92876678412b30f7 | /src/leecode/explore/queue_stack/01.Design Circular Queue.py | 3b4c90c483baf7a706bf6efc41b4e3e9939b2ee3 | [] | no_license | hopensic/LearnPython | 3570e212a1931d4dad65b64ecdd24414daf51c73 | f735b5d865789843f06a623a4006f8883d6d1ae0 | refs/heads/master | 2022-02-18T23:11:30.663902 | 2022-02-12T17:51:56 | 2022-02-12T17:51:56 | 218,924,551 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,471 | py | class MyCircularQueue:
def __init__(self, k: int):
self.queuesize = k
self.lst = [None] * k
self.head = -1
self.tail = -1
"""
Initialize your data structure here. Set the size of the queue to be k.
"""
def enQueue(self, value: int) -> bool:
"""
Insert an element into the circular queue. Return true if the operation is successful.
"""
if self.isFull():
return False
if self.head < 0:
self.head = (self.head + 1) % self.queuesize
self.tail = (self.tail + 1) % self.queuesize
self.lst[self.tail] = value
return True
def deQueue(self) -> bool:
"""
Delete an element from the circular queue. Return true if the operation is successful.
"""
if self.isEmpty():
return False
self.lst[self.head] = None
if self.head == self.tail:
self.head = -1
self.tail = -1
else:
self.head = (self.head + 1) % self.queuesize
return True
def Front(self) -> int:
"""
Get the front item from the queue.
"""
if self.head > -1:
return self.lst[self.head]
else:
return -1
def Rear(self) -> int:
"""
Get the last item from the queue.
"""
if self.tail > -1:
return self.lst[self.tail]
else:
return -1
def isEmpty(self) -> bool:
"""
Checks whether the circular queue is empty or not.
"""
return True if self.head < 0 and self.tail < 0 else False
def isFull(self) -> bool:
"""
Checks whether the circular queue is full or not.
"""
if self.head > -1 and self.tail > -1:
if (self.tail + 1) % self.queuesize == self.head:
return True
else:
return False
else:
return False
# Your MyCircularQueue object will be instantiated and called as such:
obj = MyCircularQueue(2)
param_1 = obj.enQueue(4)
param_1 = obj.Rear()
param_1 = obj.enQueue(9)
param_2 = obj.deQueue()
param_2 = obj.Front()
param_2 = obj.deQueue()
param_2 = obj.deQueue()
param_1 = obj.Rear()
param_2 = obj.deQueue()
param_1 = obj.enQueue(5)
param_4 = obj.Rear()
param_2 = obj.deQueue()
param_2 = obj.Front()
param_2 = obj.deQueue()
param_2 = obj.deQueue()
param_2 = obj.deQueue()
| [
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] | |
18b8e0edad42cc713cfad35c0c03af6cd4ec0305 | e2cd117b1060d445d0c6825b4761dc8d9d261a59 | /2020/algorithm/앨리스코딩/6. 0 이동시키기.py | 6476493c4bdbd3ab90e1098ac3ac28208bbf5509 | [] | no_license | winterash2/TIL | 856fc55f076a6aadff60e6949a3ce44534e24eda | 9b8e93e4eacce2644a9a564e3b118bb00c3f4132 | refs/heads/master | 2023-06-12T23:58:00.192688 | 2021-07-04T14:20:30 | 2021-07-04T14:20:30 | 288,957,710 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,189 | py | # 0 이동시키기
# 여러개의 0과 양의 정수들이 섞여 있는 배열이 주어졌다고 합시다. 이 배열에서 0들은 전부 뒤로 빼내고, 나머지 숫자들의 순서는 그대로 유지한 배열을 반환하는 함수를 만들어 봅시다.
# 예를 들어서, [0, 8, 0, 37, 4, 5, 0, 50, 0, 34, 0, 0] 가 입력으로 주어졌을 경우 [8, 37, 4, 5, 50, 34, 0, 0, 0, 0, 0, 0] 을 반환하면 됩니다.
# 이 문제는 공간 복잡도를 고려하면서 풀어 보도록 합시다. 공간 복잡도 O(1)으로 이 문제를 풀 수 있을까요?
# def moveZerosToEnd(nums): #공간복잡도 높음
# result = []
# num_zero = 0
# for num in nums:
# if num == 0:
# num_zero += 1
# else:
# result.append(num)
# for i in range(num_zero):
# result.append(0)
# return result
def moveZerosToEnd(nums):
i = j = 0
for i in range(len(nums)):
if nums[i] != 0:
nums[j] = nums[i]
if i != j:
nums[i] = 0
j += 1
print(nums)
return nums
def main():
print(moveZerosToEnd([1, 8, 0, 37, 4, 5, 0, 50, 0, 34, 0, 0]))
main()
| [
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] | |
61ecce1e44b4d35a64266562120e27f0ca389441 | 7b5c1352e1a4fb8352161cc135bfd1225a633828 | /2017-cvr-tencent-final/src/data_process/shuffle.py | 7e0c621d4a5903ba07e6d2e8d4dffff21d9be4f3 | [] | no_license | zgcgreat/2017-cvr-tencent | b7f54ae8df55fbb30f2430f695a148844982aa3a | fe79d0756bbf862d45e63e35b7c28da8396bcbda | refs/heads/master | 2021-04-03T08:32:33.651705 | 2018-07-17T08:36:53 | 2018-07-17T08:36:53 | 124,724,199 | 6 | 3 | null | null | null | null | UTF-8 | Python | false | false | 559 | py | # _*_ coding: utf-8 _*_
'''
打乱数据集顺序
'''
import random
import time
start = time.time()
print('shuffling dataset...')
input = open('../../data/traincp.csv', 'r')
output = open('../../data/train_data/train.csv', 'w')
lines = input.readlines()
outlines = []
output.write(lines.pop(0)) # pop()方法, 传递的是待删除元素的index
while lines:
line = lines.pop(random.randrange(len(lines)))
output.write(line)
input.close()
output.close()
print('dataset shuffled !')
print('Time spent: {0:.2f}s'.format(time.time() - start))
| [
"[email protected]"
] | |
4d33eb0ee9a1b6f8917d7d2ffbe3b0a8693c4976 | 07bd6d166bfe69f62559d51476ac724c380f932b | /devel/lib/python2.7/dist-packages/webots_demo/srv/_motor_set_control_pid.py | cc26f674b3a03915318d6b15b213af63468ca3be | [] | no_license | Dangko/webots_differential_car | 0efa45e1d729a14839e6e318da64c7f8398edd17 | 188fe93c2fb8d2e681b617df78b93dcdf52e09a9 | refs/heads/master | 2023-06-02T16:40:58.472884 | 2021-06-14T09:19:58 | 2021-06-14T09:19:58 | 376,771,194 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 8,158 | py | # This Python file uses the following encoding: utf-8
"""autogenerated by genpy from webots_demo/motor_set_control_pidRequest.msg. Do not edit."""
import codecs
import sys
python3 = True if sys.hexversion > 0x03000000 else False
import genpy
import struct
class motor_set_control_pidRequest(genpy.Message):
_md5sum = "1ebf8f7154a3c8eec118cec294f2c32c"
_type = "webots_demo/motor_set_control_pidRequest"
_has_header = False # flag to mark the presence of a Header object
_full_text = """float64 controlp
float64 controli
float64 controld
"""
__slots__ = ['controlp','controli','controld']
_slot_types = ['float64','float64','float64']
def __init__(self, *args, **kwds):
"""
Constructor. Any message fields that are implicitly/explicitly
set to None will be assigned a default value. The recommend
use is keyword arguments as this is more robust to future message
changes. You cannot mix in-order arguments and keyword arguments.
The available fields are:
controlp,controli,controld
:param args: complete set of field values, in .msg order
:param kwds: use keyword arguments corresponding to message field names
to set specific fields.
"""
if args or kwds:
super(motor_set_control_pidRequest, self).__init__(*args, **kwds)
# message fields cannot be None, assign default values for those that are
if self.controlp is None:
self.controlp = 0.
if self.controli is None:
self.controli = 0.
if self.controld is None:
self.controld = 0.
else:
self.controlp = 0.
self.controli = 0.
self.controld = 0.
def _get_types(self):
"""
internal API method
"""
return self._slot_types
def serialize(self, buff):
"""
serialize message into buffer
:param buff: buffer, ``StringIO``
"""
try:
_x = self
buff.write(_get_struct_3d().pack(_x.controlp, _x.controli, _x.controld))
except struct.error as se: self._check_types(struct.error("%s: '%s' when writing '%s'" % (type(se), str(se), str(locals().get('_x', self)))))
except TypeError as te: self._check_types(ValueError("%s: '%s' when writing '%s'" % (type(te), str(te), str(locals().get('_x', self)))))
def deserialize(self, str):
"""
unpack serialized message in str into this message instance
:param str: byte array of serialized message, ``str``
"""
codecs.lookup_error("rosmsg").msg_type = self._type
try:
end = 0
_x = self
start = end
end += 24
(_x.controlp, _x.controli, _x.controld,) = _get_struct_3d().unpack(str[start:end])
return self
except struct.error as e:
raise genpy.DeserializationError(e) # most likely buffer underfill
def serialize_numpy(self, buff, numpy):
"""
serialize message with numpy array types into buffer
:param buff: buffer, ``StringIO``
:param numpy: numpy python module
"""
try:
_x = self
buff.write(_get_struct_3d().pack(_x.controlp, _x.controli, _x.controld))
except struct.error as se: self._check_types(struct.error("%s: '%s' when writing '%s'" % (type(se), str(se), str(locals().get('_x', self)))))
except TypeError as te: self._check_types(ValueError("%s: '%s' when writing '%s'" % (type(te), str(te), str(locals().get('_x', self)))))
def deserialize_numpy(self, str, numpy):
"""
unpack serialized message in str into this message instance using numpy for array types
:param str: byte array of serialized message, ``str``
:param numpy: numpy python module
"""
codecs.lookup_error("rosmsg").msg_type = self._type
try:
end = 0
_x = self
start = end
end += 24
(_x.controlp, _x.controli, _x.controld,) = _get_struct_3d().unpack(str[start:end])
return self
except struct.error as e:
raise genpy.DeserializationError(e) # most likely buffer underfill
_struct_I = genpy.struct_I
def _get_struct_I():
global _struct_I
return _struct_I
_struct_3d = None
def _get_struct_3d():
global _struct_3d
if _struct_3d is None:
_struct_3d = struct.Struct("<3d")
return _struct_3d
# This Python file uses the following encoding: utf-8
"""autogenerated by genpy from webots_demo/motor_set_control_pidResponse.msg. Do not edit."""
import codecs
import sys
python3 = True if sys.hexversion > 0x03000000 else False
import genpy
import struct
class motor_set_control_pidResponse(genpy.Message):
_md5sum = "0b13460cb14006d3852674b4c614f25f"
_type = "webots_demo/motor_set_control_pidResponse"
_has_header = False # flag to mark the presence of a Header object
_full_text = """int8 success
"""
__slots__ = ['success']
_slot_types = ['int8']
def __init__(self, *args, **kwds):
"""
Constructor. Any message fields that are implicitly/explicitly
set to None will be assigned a default value. The recommend
use is keyword arguments as this is more robust to future message
changes. You cannot mix in-order arguments and keyword arguments.
The available fields are:
success
:param args: complete set of field values, in .msg order
:param kwds: use keyword arguments corresponding to message field names
to set specific fields.
"""
if args or kwds:
super(motor_set_control_pidResponse, self).__init__(*args, **kwds)
# message fields cannot be None, assign default values for those that are
if self.success is None:
self.success = 0
else:
self.success = 0
def _get_types(self):
"""
internal API method
"""
return self._slot_types
def serialize(self, buff):
"""
serialize message into buffer
:param buff: buffer, ``StringIO``
"""
try:
_x = self.success
buff.write(_get_struct_b().pack(_x))
except struct.error as se: self._check_types(struct.error("%s: '%s' when writing '%s'" % (type(se), str(se), str(locals().get('_x', self)))))
except TypeError as te: self._check_types(ValueError("%s: '%s' when writing '%s'" % (type(te), str(te), str(locals().get('_x', self)))))
def deserialize(self, str):
"""
unpack serialized message in str into this message instance
:param str: byte array of serialized message, ``str``
"""
codecs.lookup_error("rosmsg").msg_type = self._type
try:
end = 0
start = end
end += 1
(self.success,) = _get_struct_b().unpack(str[start:end])
return self
except struct.error as e:
raise genpy.DeserializationError(e) # most likely buffer underfill
def serialize_numpy(self, buff, numpy):
"""
serialize message with numpy array types into buffer
:param buff: buffer, ``StringIO``
:param numpy: numpy python module
"""
try:
_x = self.success
buff.write(_get_struct_b().pack(_x))
except struct.error as se: self._check_types(struct.error("%s: '%s' when writing '%s'" % (type(se), str(se), str(locals().get('_x', self)))))
except TypeError as te: self._check_types(ValueError("%s: '%s' when writing '%s'" % (type(te), str(te), str(locals().get('_x', self)))))
def deserialize_numpy(self, str, numpy):
"""
unpack serialized message in str into this message instance using numpy for array types
:param str: byte array of serialized message, ``str``
:param numpy: numpy python module
"""
codecs.lookup_error("rosmsg").msg_type = self._type
try:
end = 0
start = end
end += 1
(self.success,) = _get_struct_b().unpack(str[start:end])
return self
except struct.error as e:
raise genpy.DeserializationError(e) # most likely buffer underfill
_struct_I = genpy.struct_I
def _get_struct_I():
global _struct_I
return _struct_I
_struct_b = None
def _get_struct_b():
global _struct_b
if _struct_b is None:
_struct_b = struct.Struct("<b")
return _struct_b
class motor_set_control_pid(object):
_type = 'webots_demo/motor_set_control_pid'
_md5sum = '712b4e401e3c9cbb098cd0435a9a13d3'
_request_class = motor_set_control_pidRequest
_response_class = motor_set_control_pidResponse
| [
"[email protected]"
] | |
3bd486d899d983a5806e09c4acf7cbbd5d851437 | c3760e71f4024a9610bdde03de84a0c406601e63 | /baiTapVN2.py | a4123564c7dfa336d2714212fc79c931f3b2bea1 | [] | no_license | thuongtran1210/Buoi3 | 3abe6040b15f48cf04bb39da10b5588254d7514f | f62fbf75ff0a0de89acfd122a0a70be44162468a | refs/heads/master | 2022-12-28T22:26:21.288946 | 2020-10-04T16:43:23 | 2020-10-04T16:43:23 | 301,168,792 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 331 | py | # ----Bài 02: Viết hàm
# def reverse_string(str)
# trả lại chuỗi đảo ngược của chuỗi str
# --------------------------------------------------------------
def reverse_string(str):
return str[::-1]
str=input("Nhập số nghịch đảo: ")
print(f"Sau khi nghịch đảo: {reverse_string(str)}") | [
"[email protected]"
] | |
f5db65735ebada9140b63f4d29e2e61a85008ae1 | ce55c319f5a78b69fefc63595d433864a2e531b5 | /前后端分离-vue-DRF/houfen_DRF-projects/14day/zhuce_denglu/util/throttle.py | e3aab6ab4c5ccccbd9b04f6a13ac2cc5b000a29b | [] | no_license | Suijng/1809_data | a072c875e8746190e3b715e53f1afe3323f4666b | 45f8a57089f5c30ccc1a3cddb03b76dc95355417 | refs/heads/master | 2022-12-21T12:38:30.458291 | 2019-09-27T01:14:41 | 2019-09-27T01:14:41 | 211,207,071 | 0 | 0 | null | 2022-11-22T03:16:18 | 2019-09-27T00:55:21 | HTML | UTF-8 | Python | false | false | 2,077 | py | from rest_framework.throttling import SimpleRateThrottle
import time
class MyScopedRateThrottle(SimpleRateThrottle):
scope = 'unlogin'
def get_cache_key(self, request, view):
"""
Should return a unique cache-key which can be used for throttling.
Must be overridden.
May return `None` if the request should not be throttled.
"""
# IP地址用户获取访问记录
return self.get_ident(request)
# raise NotImplementedError('.get_cache_key() must be overridden')
# # 节流
# VISIT_RECORD = {}
#
#
# class VisitThrottle(object):
#
# def __init__(self):
# # 获取用户历史访问记录
# self.history = []
#
# # allow_request是否允许方法
# # True 允许访问
# # False 不允许访问
# def allow_request(self, request, view):
# # 1.获取用户IP
# user_ip = request._request.META.get("REMOTE_ADDR")
# key = user_ip
# print('1.user_ip------------', key)
#
# # 2.添加到访问记录里 创建当前时间
# createtime = time.time()
# if key not in VISIT_RECORD:
# # 当前的IP地址没有访问过服务器 没有记录 添加到字典
# VISIT_RECORD[key] = [createtime]
# return True
#
# # 获取当前用户所有的访问历史记录 返回列表
# visit_history = VISIT_RECORD[key]
# print('3.history==============', visit_history)
# self.history = visit_history
#
# # 用记录里的最有一个时间 对比 < 当前时间 -60秒
# while visit_history and visit_history[-1] < createtime - 60:
# # 删除用户记录
# visit_history.pop()
#
# # 判断小于5秒 添加到 历史列表最前面
# if len(visit_history) < 5:
# visit_history.insert(0, createtime)
# return True
#
# return False # 表示访问频率过高
#
# def wait(self):
# first_time = self.history[-1]
# return 60 - (time.time() - first_time)
| [
"[email protected]"
] | |
e2ee0e7695e88092fc028728b0f13572aeebd631 | 5b771c11e8967038025376c6ec31962ca90748dd | /insurance/insurance_reminder/urls.py | f00b885bb1c629961284a04ee7869785729b4efd | [] | no_license | AsemAntar/Django_Projects | 7135eca3b4bcb656fc88e0838483c97d7f1746e1 | 4141c2c7e91845eec307f6dd6c69199302eabb16 | refs/heads/master | 2022-12-10T06:32:35.787504 | 2020-05-26T14:43:01 | 2020-05-26T14:43:01 | 216,863,494 | 0 | 0 | null | 2022-12-05T13:31:53 | 2019-10-22T16:47:28 | Python | UTF-8 | Python | false | false | 222 | py | from django.contrib import admin
from django.urls import path, include
urlpatterns = [
path('admin/', admin.site.urls),
path('', include('guest_registeration.urls')),
path('users/', include('users.urls')),
]
| [
"[email protected]"
] | |
dfece195db40ed9971ed5c97d130ee352f56a42f | 7cca7ed981242788687db90d8f3a108bbcf4c6f9 | /pretrain_CNN_fix_parameter_DQN/pretrain_env.py | 595cbcee11364c4d917fb8941b8da2fce8d8ac1a | [] | no_license | Ryanshuai/robot_round_catch | 7204e1cf9927672257d542afc7a00bd710f5e243 | 1175fb3df0ae7ec395b85dfbfbcbb60a79878024 | refs/heads/master | 2021-09-09T23:08:59.680793 | 2018-03-20T06:32:51 | 2018-03-20T06:32:51 | 94,294,080 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 7,212 | py | """
This part of code is the environment.
Using Tensorflow to build the neural network.
"""
import numpy as np
import tensorflow as tf
import pygame
from random import uniform
FPS = 90
SCREEN_WHIDTH = 672
SCREEN_HEIGHT = 672
# init the game
pygame.init()
FPSCLOCK = pygame.time.Clock()
screen = pygame.display.set_mode([SCREEN_WHIDTH, SCREEN_HEIGHT])
pygame.display.set_caption('hunting')
# load resources
background = (255, 255, 255) # white
hunter_color = [(0, 0, 255), (255, 0, 0), (0, 255, 0), (255, 255, 0)] # B #R #G #Y
escaper_color = (0, 0, 0) # bck
class ENV:
def __init__(self):
self.hunter_radius = 8
self.escaper_radius = 8
self.max_pos = np.array([SCREEN_WHIDTH, SCREEN_HEIGHT])
self.catch_angle_max = np.pi * 3 / 4 # 135°
self.catch_dis = 50.
self.collide_min = self.hunter_radius + self.escaper_radius + 2.
# the center pos, x : [0, SCREEN_WHIDTH], y: [0, SCREEN_HEIGHT]
self.delta_t = 0.1 # 100ms
self.hunter_acc = 20
self.escaper_acc = 10
self.hunter_spd_max = 100 # 5 pixels once
self.escaper_spd_max = 70
self.hunter_spd = np.zeros([4, 2], dtype=np.float32)
self.escaper_spd = np.zeros([2], dtype=np.float32)
self._init_pos()
def _init_pos(self):
# the boundary
x_min = SCREEN_WHIDTH / 3
x_max = 2 * SCREEN_WHIDTH / 3
y_min = SCREEN_HEIGHT / 3
y_max = 2 * SCREEN_HEIGHT / 3
self.escaper_pos = np.array([uniform(x_min + self.collide_min, x_max - self.collide_min),
uniform(y_min + self.collide_min, y_max - self.collide_min)], dtype=np.float32)
self.hunter_pos = np.zeros([4, 2], dtype=np.float32)
self.hunter_pos[0] = [uniform(0, x_min - self.collide_min), uniform(0, y_min - self.collide_min)]
self.hunter_pos[1] = [uniform(x_max + self.collide_min, SCREEN_WHIDTH), uniform(0, y_min - self.collide_min)]
self.hunter_pos[2] = [uniform(0, x_min - self.collide_min), uniform(y_max + self.collide_min, SCREEN_HEIGHT)]
self.hunter_pos[3] = [uniform(x_max + self.collide_min, SCREEN_WHIDTH),
uniform(y_max + self.collide_min, SCREEN_HEIGHT)]
def frame_step(self, input_actions):
# update the pos and speed
self.move(input_actions)
# update the display
screen.fill(background)
for i in range(len(self.hunter_pos)):
pygame.draw.rect(screen, hunter_color[i],
((self.hunter_pos[i][0] - self.hunter_radius,
self.hunter_pos[i][1] - self.hunter_radius),
(self.hunter_radius*2, self.hunter_radius*2)))
pygame.draw.rect(screen, escaper_color,
((self.escaper_pos[0] - self.escaper_radius, self.escaper_pos[1] - self.escaper_radius),
(self.escaper_radius*2, self.escaper_radius*2)))
image_data = pygame.surfarray.array3d(pygame.display.get_surface())
pygame.display.update()
FPSCLOCK.tick(FPS)
robot_state = [self.escaper_pos[0], self.hunter_pos[0][0], self.hunter_pos[1][0], self.hunter_pos[2][0],self.hunter_pos[3][0],
self.escaper_pos[1], self.hunter_pos[0][1], self.hunter_pos[1][1], self.hunter_pos[2][1],self.hunter_pos[3][1],
self.escaper_spd[0], self.hunter_spd[0][0], self.hunter_spd[1][0], self.hunter_spd[2][0],self.hunter_spd[3][0],
self.escaper_spd[1], self.hunter_spd[0][1], self.hunter_spd[1][1], self.hunter_spd[2][1],self.hunter_spd[3][1],]
return image_data, robot_state
def move(self, input_actions):
robot_n = len(input_actions)
for i in range(robot_n - 1):#hunters
if input_actions[i] == 1: # up, update y_speed
self.hunter_spd[i][1] -= self.hunter_acc * self.delta_t
elif input_actions[i] == 2: # down
self.hunter_spd[i][1] += self.hunter_acc * self.delta_t
elif input_actions[i] == 3: # left, update x_speed
self.hunter_spd[i][0] -= self.hunter_acc * self.delta_t
elif input_actions[i] == 4: # right
self.hunter_spd[i][0] += self.hunter_acc * self.delta_t
else:
pass
if self.hunter_spd[i][0] < -self.hunter_spd_max:
self.hunter_spd[i][0] = -self.hunter_spd_max
elif self.hunter_spd[i][0] > self.hunter_spd_max:
self.hunter_spd[i][0] = self.hunter_spd_max
if self.hunter_spd[i][1] < -self.hunter_spd_max:
self.hunter_spd[i][1] = -self.hunter_spd_max
elif self.hunter_spd[i][1] > self.hunter_spd_max:
self.hunter_spd[i][1] = self.hunter_spd_max
else:
pass
self.hunter_pos[i] += self.hunter_spd[i] * self.delta_t
if self.hunter_pos[i][0] < 0:
self.hunter_pos[i][0] = 0
self.hunter_spd[i][0] = 0
elif self.hunter_pos[i][0] > SCREEN_WHIDTH:
self.hunter_pos[i][0] = SCREEN_WHIDTH
self.hunter_spd[i][0] = 0
else:
pass
if self.hunter_pos[i][1] < 0:
self.hunter_pos[i][1] = 0
self.hunter_spd[i][1] = 0
elif self.hunter_pos[i][1] > SCREEN_HEIGHT:
self.hunter_pos[i][1] = SCREEN_HEIGHT
self.hunter_spd[i][1] = 0
#escaper
if input_actions[robot_n - 1] == 1: # up, update y_speed
self.escaper_spd[1] -= self.escaper_acc * self.delta_t
elif input_actions[robot_n - 1] == 2: # down
self.escaper_spd[1] += self.escaper_acc * self.delta_t
elif input_actions[robot_n - 1] == 3: # left, update x_speed
self.escaper_spd[0] -= self.escaper_acc * self.delta_t
elif input_actions[robot_n - 1] == 4: # right
self.escaper_spd[0] += self.escaper_acc * self.delta_t
else:
pass
if self.escaper_spd[0] < -self.escaper_spd_max:
self.escaper_spd[0] = -self.escaper_spd_max
elif self.escaper_spd[0] > self.escaper_spd_max:
self.escaper_spd[0] = self.escaper_spd_max
else:
pass
if self.escaper_spd[1] < -self.escaper_spd_max:
self.escaper_spd[1] = -self.escaper_spd_max
elif self.escaper_spd[1] > self.escaper_spd_max:
self.escaper_spd[1] = self.escaper_spd_max
else:
pass
self.escaper_pos += self.escaper_spd * self.delta_t
if self.escaper_pos[0] < 0:
self.escaper_pos[0] = 0
self.escaper_spd[0] = 0
elif self.escaper_pos[0] > SCREEN_WHIDTH:
self.escaper_pos[0] = SCREEN_WHIDTH
self.escaper_spd[0] = 0
if self.escaper_pos[1] < 0:
self.escaper_pos[1] = 0
self.escaper_spd[1] = 0
elif self.escaper_pos[1] > SCREEN_HEIGHT:
self.escaper_pos[1] = SCREEN_HEIGHT
self.escaper_spd[1] = 0
| [
"[email protected]"
] | |
f96b6f6fb6ba491017ce77ff995c18a718e4bf44 | d2e8ad203a37b534a113d4f0d4dd51d9aeae382a | /django-koldar-utils/django_koldar_utils/django_toolbox/conf/DictSettingMergerAppConf.py | 82a1709767e65cb47e03e5e6f5f95f2f0dbda8dc | [
"MIT"
] | permissive | Koldar/django-koldar-common-apps | 40e24a7aae78973fa28ca411e2a32cb4b2f4dbbf | 06e6bb103d22f1f6522e97c05ff8931413c69f19 | refs/heads/main | 2023-08-17T11:44:34.631914 | 2021-10-08T12:40:40 | 2021-10-08T12:40:40 | 372,714,560 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,166 | py | from appconf import AppConf
class DictSettingMergerAppConf(AppConf):
"""
A derived class of AppConf that automatically merge the configurations from settings.py and the default ones.
In the settings you should have:
.. ::code-block:: python
APPCONF_PREFIX = {
"SETTINGS_1": 3
}
In the conf.py of the django_toolbox app, you need to code:
.. ::code-block:: python
class DjangoAppGraphQLAppConf(DictSettingMergerAppConfMixIn):
class Meta:
prefix = "APPCONF_PREFIX"
def configure(self):
return self.merge_configurations()
SETTINGS_1: int = 0
After that settings will be set to 3, rather than 0.
Note that this class merges only if in the settings.py there is a dictionary with the same name of the prefix!
"""
def merge_configurations(self):
# we have imported settings here in order to allow sphinx to buidl the documentation (otherwise it needs settings.py)
from django.conf import settings
prefix = getattr(self, "Meta").prefix
if not hasattr(settings, prefix):
return self.configured_data
# the data the user has written in the settings.py
data_in_settings = getattr(settings, prefix)
# the data in the AppConf instance specyfing default values
default_data = dict(self.configured_data)
result = dict()
# specify settings which do not have default values in the conf.py
# (thus are requried) with the values specified in the settings.py
for class_attribute_name, class_attribute_value in data_in_settings.items():
result[class_attribute_name] = data_in_settings[class_attribute_name]
# overwrite settings which have default values
# with the values specified in the settings.py
for class_attribute_name, class_attribute_value in default_data.items():
if class_attribute_name not in result:
result[class_attribute_name] = default_data[class_attribute_name]
return result
| [
"[email protected]"
] | |
85d08bd8390cacd47b28abaea8585359bc9b16a8 | d0d3d2b68c7ae9c6b374bf9cc574bb9571076d5e | /models/model_selection.py | a895a9ddcf5a3cc5fddf1da42389f67e5b9bfd99 | [] | no_license | gustafholst/hitmusicnet | f5fda86fef9c3c4684b97d432d4f2403e05b6458 | eb3c43845e5cedde0c19f907cbaf9d7d71530beb | refs/heads/master | 2023-03-20T11:03:56.730327 | 2020-01-27T16:01:22 | 2020-01-27T16:01:22 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,272 | py | def select_model(model_val=1):
model_args = {}
if model_val == 1:
model_args = {'model_name': 'model_1_A', 'model_dir': 'saved_models',
'model_subDir': 'feature_compressed',
'input_dim': 97, 'output_dim': 1, 'optimizer': 'adadelta',
'metrics': ["mean_absolute_error"], 'loss': "mse", 'earlyStop': True,
'weights': 'weights.h5', 'plot_loss': True,
'neurons': {'alpha': 1, 'beta': (1/2), 'gamma': (1/3)},
'n_layers': 4, 'weights_init': 'glorot_uniform', 'dropout': .25,
'epochs': 100, 'batch_size': 256, 'early_mon': 'val_mean_absolute_error',
'mode': 'min', 'checkout_mon': 'val_loss'}
elif model_val == 2:
model_args = {'model_name': 'model_1_B', 'model_dir': 'saved_models',
'model_subDir': 'feature_compressed',
'input_dim': 97, 'output_dim': 1, 'optimizer': 'adam',
'metrics': ["mean_absolute_error"], 'loss': "mse", 'earlyStop': True,
'weights': 'weights.h5', 'plot_loss': False,
'neurons': {'alpha': 1, 'beta': (1/2), 'gamma': (1/3)},
'n_layers': 4, 'weights_init': 'glorot_uniform', 'dropout': .25,
'epochs': 100, 'batch_size': 256, 'early_mon': 'val_mean_absolute_error',
'mode': 'min', 'checkout_mon': 'val_loss'}
elif model_val == 3:
model_args = {'model_name': 'model_1_C', 'model_dir': 'saved_models',
'model_subDir': 'feature_compressed',
'input_dim': 97, 'output_dim': 1, 'optimizer': 'adadelta',
'metrics': ["mean_absolute_error"], 'loss': "mse", 'earlyStop': True,
'weights': 'weights.h5', 'plot_loss': False,
'neurons': {'alpha': 1, 'beta': (1/2), 'gamma': (1/3)},
'n_layers': 4, 'weights_init': 'he_normal', 'dropout': .25,
'epochs': 100, 'batch_size': 256, 'early_mon': 'val_mean_absolute_error',
'mode': 'min', 'checkout_mon': 'val_loss'}
elif model_val == 4:
model_args = {'model_name': 'model_1_D', 'model_dir': 'saved_models',
'model_subDir': 'feature_compressed',
'input_dim': 97, 'output_dim': 1, 'optimizer': 'adam',
'metrics': ["mean_absolute_error"], 'loss': "mse", 'earlyStop': True,
'weights': 'weights.h5', 'plot_loss': False,
'neurons': {'alpha': 1, 'beta': (1/2), 'gamma': (1/3)},
'n_layers': 4, 'weights_init': 'he_normal', 'dropout': .25,
'epochs': 100, 'batch_size': 256, 'early_mon': 'val_mean_absolute_error',
'mode': 'min', 'checkout_mon': 'val_loss'}
# ------------------------------------------------------------------------------------------------------------------
elif model_val == 5:
model_args = {'model_name': 'model_1_A', 'model_dir': 'saved_models',
'model_subDir': 'class_feature_compressed',
'input_dim': 97, 'output_dim': 3, 'optimizer': 'adadelta',
'metrics': ["accuracy"], 'loss': "categorical_crossentropy", 'earlyStop': True,
'weights': 'weights.h5', 'plot_loss': True,
'neurons': {'alpha': 1, 'beta': (1/2), 'gamma': (1/3)},
'n_layers': 4, 'weights_init': 'glorot_uniform', 'dropout': .25,
'epochs': 100, 'batch_size': 256, 'early_mon': 'val_acc',
'mode': 'max', 'checkout_mon': 'val_acc'}
elif model_val == 6:
model_args = {'model_name': 'model_2_B', 'model_dir': 'saved_models',
'model_subDir': 'class_feature_compressed',
'input_dim': 97, 'output_dim': 3, 'optimizer': 'adam',
'metrics': ["accuracy"], 'loss': "categorical_crossentropy", 'earlyStop': True,
'weights': 'weights.h5', 'plot_loss': False,
'neurons': {'alpha': 1, 'beta': (1/2), 'gamma': (1/3)},
'n_layers': 4, 'weights_init': 'glorot_uniform', 'dropout': .25,
'epochs': 100, 'batch_size': 256, 'early_mon': 'val_acc',
'mode': 'max', 'checkout_mon': 'val_acc'}
elif model_val == 7:
model_args = {'model_name': 'model_3_C', 'model_dir': 'saved_models',
'model_subDir': 'class_feature_compressed',
'input_dim': 97, 'output_dim': 3, 'optimizer': 'adadelta',
'metrics': ["accuracy"], 'loss': "categorical_crossentropy", 'earlyStop': True,
'weights': 'weights.h5', 'plot_loss': False,
'neurons': {'alpha': 1, 'beta': (1 / 2), 'gamma': (1 / 3)},
'n_layers': 4, 'weights_init': 'he_normal', 'dropout': .25,
'epochs': 100, 'batch_size': 256, 'early_mon': 'val_acc',
'mode': 'max', 'checkout_mon': 'val_acc'}
return model_args
| [
"="
] | = |
0fba8a6c3f9302d8cd7becf379dc74a91b648a1d | c00724308e01332418f539d9a1bedca09696a253 | /setup.py | 5dd977999e08b6a1a2de1dfef1be84c049522d99 | [
"MIT"
] | permissive | felipecolen/pycep-correios | f8b53efc939e48ef71caedcf1136d941d18d4d89 | 7c6c734c4bd3205bdca2f9b1bf4463045a96c8ef | refs/heads/master | 2021-01-22T08:28:18.083461 | 2016-07-31T20:10:47 | 2016-07-31T20:10:47 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,091 | py | # -*- coding: utf-8 -*-
# #############################################################################
# The MIT License (MIT)
#
# Copyright (c) 2016 Michell Stuttgart
#
# 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 setuptools import setup, find_packages
setup(
name='pycep-correios',
version='1.0.0',
keywords='correios setuptools development cep',
packages=find_packages(),
url='https://github.com/mstuttgart/pycep-correios',
license='MIT',
author='Michell Stuttgart',
author_email='[email protected]',
description='Método para busca de dados de CEP no webservice dos '
'Correios',
install_requires=[
'requests',
],
test_suite='test',
classifiers=[
'Development Status :: 5 - Production/Stable',
'Intended Audience :: Developers',
'Topic :: Software Development :: Build Tools',
'License :: OSI Approved :: MIT License',
'Programming Language :: Python :: 3.4',
],
)
| [
"[email protected]"
] | |
edb2a540151d4958fb9579f831a30f731284d00c | bde9a6e8a2aee89572dac4872b8263e42c13a142 | /gestalt/estimator_wrappers/wrap_r_ranger.py | 1a9202cf794666f79f719d6e7aa1ad2e5c8ce4f8 | [
"MIT"
] | permissive | chrinide/gestalt | b1ee66971007139b6e39b5c3dbd43a562bf5caee | f8f24d1fa879851ba989b244f71b348e995e221c | refs/heads/master | 2021-01-20T08:07:18.877738 | 2017-05-01T06:08:26 | 2017-05-01T06:08:26 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,696 | py | # import sklearn BaseEstimator etc to use
import pandas as pd
from rpy2 import robjects as ro
from rpy2.robjects import pandas2ri
from rpy2.robjects.packages import importr
from sklearn.base import BaseEstimator
from sklearn.base import ClassifierMixin
# Activate R objects.
pandas2ri.activate()
R = ro.r
# import the R packages required.
base = importr('base')
ranger = importr('ranger')
"""
An example of how to make an R plugin to use with Gestalt, this way we can run our favourite R packages using the
same stacking framework.
"""
class RangerClassifier(BaseEstimator, ClassifierMixin):
"""
From Ranger DESCRIPTION FILE:
A fast implementation of Random Forests, particularly suited for high dimensional data.
Ensembles of classification, regression, survival and probability prediction trees are supported.
We pull in all the options that ranger allows, but as this is a classifier we hard-code
probability=True, to give probability values.
"""
def __init__(self, formula='RANGER_TARGET_DUMMY~.', num_trees=500, num_threads=1, verbose=True, seed=42):
self.formula = formula
self.num_trees = num_trees
self.probability = True
self.num_threads = num_threads
self.verbose = verbose
self.seed = seed
self.num_classes = None
self.clf = None
def fit(self, X, y):
# First convert the X and y into a dataframe object to use in R
# We have to convert the y back to a dataframe to join for using with R
# We give the a meaningless name to allow the formula to work correctly.
y = pd.DataFrame(y, index=X.index, columns = ['RANGER_TARGET_DUMMY'])
self.num_classes = y.ix[:, 0].nunique()
r_dataframe = pd.concat([X, y], axis=1)
r_dataframe['RANGER_TARGET_DUMMY'] = r_dataframe['RANGER_TARGET_DUMMY'].astype('str')
self.clf = ranger.ranger(formula=self.formula,
data=r_dataframe,
num_trees=self.num_trees,
probability=self.probability,
num_threads=self.num_threads,
verbose=self.verbose,
seed=self.seed)
return
def predict_proba(self, X):
# Ranger doesnt have a specific separate predict and predict probabilities class, it is set in the params
# REM: R is not Python :)
pr = R.predict(self.clf, dat=X)
pandas_preds = ro.pandas2ri.ri2py_dataframe(pr.rx('predictions')[0])
if self.num_classes == 2:
pandas_preds = pandas_preds.ix[:, 1]
return pandas_preds.values | [
"[email protected]"
] | |
bb2e33eeedda684e9759329e952ae418c9db3e73 | b035385c986cadf0dba9dea565800d4e47a7adaa | /tests/test_numba.py | 19e72db682b11b991e839248ce5f755d6cbbf3ae | [
"MIT",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | RahulNagraj444/fluids | 74f3abf23384b76aab95b8be60f5d1adaebc8518 | 36afa5a16c0dbf22bf0541bd06d1cce68a1f7266 | refs/heads/master | 2022-10-03T10:57:36.492863 | 2020-06-07T16:56:37 | 2020-06-07T16:56:37 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 30,579 | py | # -*- coding: utf-8 -*-
'''Chemical Engineering Design Library (ChEDL). Utilities for process modeling.
Copyright (C) 2020 Caleb Bell <[email protected]>
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 __future__ import division
from fluids import *
import fluids.vectorized
from math import *
from fluids.constants import *
from fluids.numerics import assert_close, assert_close1d
import pytest
try:
import numba
import fluids.numba
import fluids.numba_vectorized
except:
numba = None
import numpy as np
@pytest.mark.numba
@pytest.mark.skipif(numba is None, reason="Numba is missing")
def test_Clamond_numba():
assert_close(fluids.numba.Clamond(10000.0, 2.0),
fluids.Clamond(10000.0, 2.0), rtol=5e-15)
assert_close(fluids.numba.Clamond(10000.0, 2.0, True),
fluids.Clamond(10000.0, 2.0, True), rtol=5e-15)
assert_close(fluids.numba.Clamond(10000.0, 2.0, False),
fluids.Clamond(10000.0, 2.0, False), rtol=5e-15)
Res = np.array([1e5, 1e6])
eDs = np.array([1e-5, 1e-6])
fast = np.array([False]*2)
assert_close1d(fluids.numba_vectorized.Clamond(Res, eDs, fast),
fluids.vectorized.Clamond(Res, eDs, fast), rtol=1e-14)
@pytest.mark.numba
@pytest.mark.skipif(numba is None, reason="Numba is missing")
def test_string_error_message_outside_function():
fluids.numba.entrance_sharp('Miller')
fluids.numba.entrance_sharp()
fluids.numba.entrance_angled(30, 'Idelchik')
fluids.numba.entrance_angled(30, None)
fluids.numba.entrance_angled(30.0)
@pytest.mark.numba
@pytest.mark.skipif(numba is None, reason="Numba is missing")
def test_interp():
assert_close(fluids.numba.CSA_motor_efficiency(100*hp, closed=True, poles=6, high_efficiency=True), 0.95)
# Should take ~10 us
powers = np.array([70000]*100)
closed = np.array([True]*100)
poles = np.array([6]*100)
high_efficiency = np.array([True]*100)
fluids.numba_vectorized.CSA_motor_efficiency(powers, closed, poles, high_efficiency)
assert_close(fluids.numba.bend_rounded_Crane(Di=.4020, rc=.4*5, angle=30),
fluids.bend_rounded_Crane(Di=.4020, rc=.4*5, angle=30))
@pytest.mark.numba
@pytest.mark.skipif(numba is None, reason="Numba is missing")
def test_constants():
assert_close(fluids.numba.K_separator_demister_York(975000), 0.09635076944244816)
@pytest.mark.numba
@pytest.mark.skipif(numba is None, reason="Numba is missing")
def test_calling_function_in_other_module():
assert_close(fluids.numba.ft_Crane(.5), 0.011782458726227104, rtol=1e-4)
@pytest.mark.numba
@pytest.mark.skipif(numba is None, reason="Numba is missing")
def test_None_is_not_multiplied_add_check_on_is_None():
assert_close(fluids.numba.polytropic_exponent(1.4, eta_p=0.78), 1.5780346820809246, rtol=1e-5)
@pytest.mark.numba
@pytest.mark.skipif(numba is None, reason="Numba is missing")
def test_core_from_other_module():
assert_close(fluids.numba.helical_turbulent_fd_Srinivasan(1E4, 0.01, .02), 0.0570745212117107)
@pytest.mark.numba
@pytest.mark.skipif(numba is None, reason="Numba is missing")
def test_string_branches():
# Currently slower
assert_close(fluids.numba.C_Reader_Harris_Gallagher(D=0.07391, Do=0.0222, rho=1.165, mu=1.85E-5, m=0.12, taps='flange'), 0.5990326277163659)
@pytest.mark.numba
@pytest.mark.skipif(numba is None, reason="Numba is missing")
def test_interp_with_own_list():
assert_close(fluids.numba.dP_venturi_tube(D=0.07366, Do=0.05, P1=200000.0, P2=183000.0), 1788.5717754177406)
@pytest.mark.numba
@pytest.mark.skipif(numba is None, reason="Numba is missing")
def test_C_Reader_Harris_Gallagher_wet_venturi_tube_numba():
assert_close(fluids.numba.C_Reader_Harris_Gallagher_wet_venturi_tube(mg=5.31926, ml=5.31926/2, rhog=50.0, rhol=800., D=.1, Do=.06, H=1), 0.9754210845876333)
@pytest.mark.numba
@pytest.mark.skipif(numba is None, reason="Numba is missing")
def test_rename_constant():
assert_close(fluids.numba.friction_plate_Martin_1999(Re=20000, plate_enlargement_factor=1.15), 2.284018089834135)
@pytest.mark.numba
@pytest.mark.skipif(numba is None, reason="Numba is missing")
def test_list_in_list_constant_converted():
assert_close(fluids.numba.friction_plate_Kumar(Re=2000, chevron_angle=30),
friction_plate_Kumar(Re=2000, chevron_angle=30))
@pytest.mark.numba
@pytest.mark.skipif(numba is None, reason="Numba is missing")
def test_have_to_make_zero_division_a_check():
# Manually requires changes, and is unpythonic
assert_close(fluids.numba.SA_ellipsoidal_head(2, 1.5),
SA_ellipsoidal_head(2, 1.5))
@pytest.mark.numba
@pytest.mark.skipif(numba is None, reason="Numba is missing")
def test_functions_used_to_return_different_return_value_signatures_changed():
assert_close1d(fluids.numba.SA_tank(D=1., L=5, sideA='spherical', sideA_a=0.5, sideB='spherical',sideB_a=0.5),
SA_tank(D=1., L=5, sideA='spherical', sideA_a=0.5, sideB='spherical',sideB_a=0.5))
@pytest.mark.numba
@pytest.mark.skipif(numba is None, reason="Numba is missing")
def test_Colebrook_ignored():
fd = fluids.numba.Colebrook(1e5, 1e-5)
assert_close(fd, 0.018043802895063684, rtol=1e-14)
@pytest.mark.numba
@pytest.mark.skipif(numba is None, reason="Numba is missing")
def test_secant_runs():
# Really feel like the kwargs should work in object mode, but it doesn't
# Just gets slower
@numba.jit
def to_solve(x):
return sin(x*.3) - .5
fluids.numba.secant(to_solve, .3, ytol=1e-10)
@pytest.mark.numba
@pytest.mark.skipif(numba is None, reason="Numba is missing")
def test_brenth_runs():
@numba.njit
def to_solve(x, goal):
return sin(x*.3) - goal
ans = fluids.numba.brenth(to_solve, .3, 2, args=(.45,))
assert_close(ans, 1.555884463490988)
@pytest.mark.numba
@pytest.mark.skipif(numba is None, reason="Numba is missing")
def test_lambertw_runs():
assert_close(fluids.numba.numerics.lambertw(5.0), 1.3267246652422002)
assert_close(fluids.numba.Prandtl_von_Karman_Nikuradse(1e7), 0.008102669430874914)
@pytest.mark.numba
@pytest.mark.skipif(numba is None, reason="Numba is missing")
def test_ellipe_runs():
assert_close(fluids.numba.plate_enlargement_factor(amplitude=5E-4, wavelength=3.7E-3),
1.1611862034509677, rtol=1e-10)
@pytest.mark.numba
@pytest.mark.skipif(numba is None, reason="Numba is missing")
def test_control_valve_noise():
dB = fluids.numba.control_valve_noise_l_2015(m=40, P1=1E6, P2=6.5E5, Psat=2.32E3, rho=997, c=1400, Kv=77.848, d=0.1, Di=0.1071, FL=0.92, Fd=0.42, t_pipe=0.0036, rho_pipe=7800.0, c_pipe=5000.0,rho_air=1.293, c_air=343.0, An=-4.6)
assert_close(dB, 81.58200097996539)
dB = fluids.numba.control_valve_noise_g_2011(m=2.22, P1=1E6, P2=7.2E5, T1=450, rho=5.3, gamma=1.22, MW=19.8, Kv=77.85, d=0.1, Di=0.2031, FL=None, FLP=0.792, FP=0.98, Fd=0.296, t_pipe=0.008, rho_pipe=8000.0, c_pipe=5000.0, rho_air=1.293, c_air=343.0, An=-3.8, Stp=0.2)
assert_close(dB, 91.67702674629604)
@pytest.mark.numba
@pytest.mark.skipif(numba is None, reason="Numba is missing")
def test_friction_factor():
fluids.numba.friction_factor(1e5, 1e-3)
assert_close(fluids.numba.friction.friction_factor(1e4, 1e-4, Method='Churchill_1973'),
fluids.friction_factor(1e4, 1e-4, Method='Churchill_1973'))
assert_close(fluids.numba.friction.friction_factor(1e4, 1e-4),
fluids.friction_factor(1e4, 1e-4))
assert_close(fluids.numba.friction.friction_factor(1e2, 1e-4),
fluids.friction_factor(1e2, 1e-4))
@pytest.mark.numba
@pytest.mark.skipif(numba is None, reason="Numba is missing")
def test_AvailableMethods_removal():
assert_close(fluids.numba.friction_factor_curved(Re=1E5, Di=0.02, Dc=0.5),
fluids.friction_factor_curved(Re=1E5, Di=0.02, Dc=0.5))
@pytest.mark.numba
@pytest.mark.skipif(numba is None, reason="Numba is missing")
def test_bisplev_uses():
K = fluids.numba.entrance_beveled(Di=0.1, l=0.003, angle=45, method='Idelchik')
assert_close(K, 0.39949999999999997)
assert_close(fluids.numba.VFD_efficiency(100*hp, load=0.2),
fluids.VFD_efficiency(100*hp, load=0.2))
@pytest.mark.numba
@pytest.mark.skipif(numba is None, reason="Numba is missing")
def test_splev_uses():
methods = ['Rennels', 'Miller', 'Idelchik', 'Harris', 'Crane']
Ks = [fluids.numba.entrance_distance(Di=0.1, t=0.0005, method=m) for m in methods]
Ks_orig = [fluids.fittings.entrance_distance(Di=0.1, t=0.0005, method=m) for m in methods]
assert_close1d(Ks, Ks_orig)
# Same speed
assert_close(fluids.numba.entrance_rounded(Di=0.1, rc=0.0235),
fluids.fittings.entrance_rounded(Di=0.1, rc=0.0235))
# Got 10x faster! no strings.
assert_close(fluids.numba.bend_rounded_Miller(Di=.6, bend_diameters=2, angle=90, Re=2e6, roughness=2E-5, L_unimpeded=30*.6),
fluids.bend_rounded_Miller(Di=.6, bend_diameters=2, angle=90, Re=2e6, roughness=2E-5, L_unimpeded=30*.6))
@pytest.mark.numba
@pytest.mark.skipif(numba is None, reason="Numba is missing")
def test_misc_fittings():
methods = ['Rennels', 'Miller', 'Crane', 'Blevins']
assert_close1d([fluids.numba.bend_miter(Di=.6, angle=45, Re=1e6, roughness=1e-5, L_unimpeded=20, method=m) for m in methods],
[fluids.fittings.bend_miter(Di=.6, angle=45, Re=1e6, roughness=1e-5, L_unimpeded=20, method=m) for m in methods])
assert_close(fluids.numba.contraction_round_Miller(Di1=1, Di2=0.4, rc=0.04),
fluids.contraction_round_Miller(Di1=1, Di2=0.4, rc=0.04))
assert_close(fluids.numba.contraction_round(Di1=1, Di2=0.4, rc=0.04),
fluids.contraction_round(Di1=1, Di2=0.4, rc=0.04))
assert_close(fluids.numba.contraction_beveled(Di1=0.5, Di2=0.1, l=.7*.1, angle=120),
fluids.contraction_beveled(Di1=0.5, Di2=0.1, l=.7*.1, angle=120),)
assert_close(fluids.numba.diffuser_pipe_reducer(Di1=.5, Di2=.75, l=1.5, fd1=0.07),
fluids.diffuser_pipe_reducer(Di1=.5, Di2=.75, l=1.5, fd1=0.07),)
assert_close(fluids.numba.K_gate_valve_Crane(D1=.1, D2=.146, angle=13.115),
fluids.K_gate_valve_Crane(D1=.1, D2=.146, angle=13.115))
assert_close(fluids.numba.v_lift_valve_Crane(rho=998.2, D1=0.0627, D2=0.0779, style='lift check straight'),
fluids.v_lift_valve_Crane(rho=998.2, D1=0.0627, D2=0.0779, style='lift check straight'))
assert_close(fluids.numba.K_branch_converging_Crane(0.1023, 0.1023, 0.018917, 0.00633),
fluids.K_branch_converging_Crane(0.1023, 0.1023, 0.018917, 0.00633),)
assert_close(fluids.numba.bend_rounded(Di=4.020, rc=4.0*5, angle=30, Re=1E5),
fluids.bend_rounded(Di=4.020, rc=4.0*5, angle=30, Re=1E5))
assert_close(fluids.numba.contraction_conical_Crane(Di1=0.0779, Di2=0.0525, l=0),
fluids.contraction_conical_Crane(Di1=0.0779, Di2=0.0525, l=0))
assert_close(fluids.numba.contraction_conical(Di1=0.1, Di2=0.04, l=0.04, Re=1E6),
fluids.contraction_conical(Di1=0.1, Di2=0.04, l=0.04, Re=1E6))
assert_close(fluids.numba.diffuser_conical(Di1=1/3., Di2=1.0, angle=50.0, Re=1E6),
fluids.diffuser_conical(Di1=1/3., Di2=1.0, angle=50.0, Re=1E6))
assert_close(fluids.numba.diffuser_conical(Di1=1., Di2=10.,l=9, fd=0.01),
fluids.diffuser_conical(Di1=1., Di2=10.,l=9, fd=0.01))
assert_close(fluids.numba.diffuser_conical_staged(Di1=1., Di2=10., DEs=np.array([2,3,4]), ls=np.array([1.1,1.2,1.3, 1.4]), fd=0.01),
fluids.diffuser_conical_staged(Di1=1., Di2=10., DEs=np.array([2,3,4]), ls=np.array([1.1,1.2,1.3, 1.4]), fd=0.01))
assert_close(fluids.numba.K_globe_stop_check_valve_Crane(.1, .02, style=1),
fluids.K_globe_stop_check_valve_Crane(.1, .02, style=1))
assert_close(fluids.numba.K_angle_stop_check_valve_Crane(.1, .02, style=1),
fluids.K_angle_stop_check_valve_Crane(.1, .02, style=1))
assert_close(fluids.numba.K_diaphragm_valve_Crane(D=.1, style=0),
fluids.K_diaphragm_valve_Crane(D=.1, style=0))
assert_close(fluids.numba.K_foot_valve_Crane(D=0.2, style=0),
fluids.K_foot_valve_Crane(D=0.2, style=0))
assert_close(fluids.numba.K_butterfly_valve_Crane(D=.1, style=2),
fluids.K_butterfly_valve_Crane(D=.1, style=2))
assert_close(fluids.numba.K_plug_valve_Crane(D1=.01, D2=.02, angle=50),
fluids.K_plug_valve_Crane(D1=.01, D2=.02, angle=50))
@pytest.mark.numba
@pytest.mark.skipif(numba is None, reason="Numba is missing")
def test_misc_filters_numba():
assert_close(fluids.numba.round_edge_screen(0.5, 100, 45),
fluids.round_edge_screen(0.5, 100, 45))
assert_close(fluids.numba.round_edge_screen(0.5, 100),
fluids.round_edge_screen(0.5, 100))
assert_close(fluids.numba.round_edge_open_mesh(0.96, angle=33.),
fluids.round_edge_open_mesh(0.96, angle=33.))
assert_close(fluids.numba.square_edge_grill(.45, l=.15, Dh=.002, fd=.0185),
fluids.square_edge_grill(.45, l=.15, Dh=.002, fd=.0185))
assert_close(fluids.numba.round_edge_grill(.4, l=.15, Dh=.002, fd=.0185),
fluids.round_edge_grill(.4, l=.15, Dh=.002, fd=.0185))
assert_close(fluids.numba.square_edge_screen(0.99),
fluids.square_edge_screen(0.99))
@pytest.mark.numba
@pytest.mark.skipif(numba is None, reason="Numba is missing")
def test_misc_pump_numba():
assert_close(fluids.numba.motor_efficiency_underloaded(10.1*hp, .1),
fluids.motor_efficiency_underloaded(10.1*hp, .1),)
assert_close(fluids.numba.current_ideal(V=120, P=1E4, PF=1, phase=1),
fluids.current_ideal(V=120, P=1E4, PF=1, phase=1))
@pytest.mark.numba
@pytest.mark.skipif(numba is None, reason="Numba is missing")
def test_misc_separator_numba():
assert_close(fluids.numba.K_separator_Watkins(0.88, 985.4, 1.3, horizontal=True),
fluids.K_separator_Watkins(0.88, 985.4, 1.3, horizontal=True))
@pytest.mark.numba
@pytest.mark.skipif(numba is None, reason="Numba is missing")
def test_misc_mixing_numba():
assert_close(fluids.numba.size_tee(Q1=11.7, Q2=2.74, D=0.762, D2=None, n=1, pipe_diameters=5),
fluids.size_tee(Q1=11.7, Q2=2.74, D=0.762, D2=None, n=1, pipe_diameters=5))
@pytest.mark.numba
@pytest.mark.skipif(numba is None, reason="Numba is missing")
def test_misc_compressible():
assert_close(fluids.numba.isentropic_work_compression(P1=1E5, P2=1E6, T1=300, k=1.4, eta=0.78),
fluids.isentropic_work_compression(P1=1E5, P2=1E6, T1=300, k=1.4, eta=0.78),)
assert_close(fluids.numba.isentropic_efficiency(1E5, 1E6, 1.4, eta_p=0.78),
fluids.isentropic_efficiency(1E5, 1E6, 1.4, eta_p=0.78))
assert_close(fluids.numba.polytropic_exponent(1.4, eta_p=0.78),
fluids.polytropic_exponent(1.4, eta_p=0.78))
assert_close(fluids.numba.Panhandle_A(D=0.340, P1=90E5, P2=20E5, L=160E3, SG=0.693, Tavg=277.15),
fluids.Panhandle_A(D=0.340, P1=90E5, P2=20E5, L=160E3, SG=0.693, Tavg=277.15))
assert_close(fluids.numba.Panhandle_B(D=0.340, P1=90E5, P2=20E5, L=160E3, SG=0.693, Tavg=277.15),
fluids.Panhandle_B(D=0.340, P1=90E5, P2=20E5, L=160E3, SG=0.693, Tavg=277.15))
assert_close(fluids.numba.Weymouth(D=0.340, P1=90E5, P2=20E5, L=160E3, SG=0.693, Tavg=277.15),
fluids.Weymouth(D=0.340, P1=90E5, P2=20E5, L=160E3, SG=0.693, Tavg=277.15))
assert_close(fluids.numba.Spitzglass_high(D=0.340, P1=90E5, P2=20E5, L=160E3, SG=0.693, Tavg=277.15),
fluids.Spitzglass_high(D=0.340, P1=90E5, P2=20E5, L=160E3, SG=0.693, Tavg=277.15))
assert_close(fluids.numba.Spitzglass_low(D=0.154051, P1=6720.3199, P2=0, L=54.864, SG=0.6, Tavg=288.7),
fluids.Spitzglass_low(D=0.154051, P1=6720.3199, P2=0, L=54.864, SG=0.6, Tavg=288.7))
assert_close(fluids.numba.Oliphant(D=0.340, P1=90E5, P2=20E5, L=160E3, SG=0.693, Tavg=277.15),
fluids.Oliphant(D=0.340, P1=90E5, P2=20E5, L=160E3, SG=0.693, Tavg=277.15))
@pytest.mark.numba
@pytest.mark.skipif(numba is None, reason="Numba is missing")
def test_misc_control_valve():
# Not working - size_control_valve_g, size_control_valve_l
# Can take the functions out, but the dictionary return remains problematic
# fluids.numba.control_valve_choke_P_l(69682.89291024722, 22048320.0, 0.6, P2=458887.5306077305) # Willing to change this error message if the other can pass
# fluids.numba.size_control_valve_g(T=433., MW=44.01, mu=1.4665E-4, gamma=1.30,
#Z=0.988, P1=680E3, P2=310E3, Q=38/36., D1=0.08, D2=0.1, d=0.05,
#FL=0.85, Fd=0.42, xT=0.60)
assert_close(fluids.numba.Reynolds_factor(FL=0.98, C=0.015483, d=15., Rev=1202., full_trim=False),
fluids.Reynolds_factor(FL=0.98, C=0.015483, d=15., Rev=1202., full_trim=False))
assert_close(fluids.numba.convert_flow_coefficient(10, 'Kv', 'Av'),
fluids.convert_flow_coefficient(10, 'Kv', 'Av'))
@pytest.mark.numba
@pytest.mark.skipif(numba is None, reason="Numba is missing")
def test_misc_safety_valve():
assert_close(fluids.numba.API520_round_size(1E-4),
fluids.API520_round_size(1E-4))
assert_close(fluids.numba.API520_SH(593+273.15, 1066.325E3),
fluids.API520_SH(593+273.15, 1066.325E3))
assert_close(fluids.numba.API520_W(1E6, 3E5),
fluids.API520_W(1E6, 3E5))
assert_close(fluids.numba.API520_B(1E6, 5E5),
fluids.API520_B(1E6, 5E5))
assert_close(fluids.numba.API520_A_g(m=24270/3600., T=348., Z=0.90, MW=51., k=1.11, P1=670E3, Kb=1, Kc=1),
fluids.API520_A_g(m=24270/3600., T=348., Z=0.90, MW=51., k=1.11, P1=670E3, Kb=1, Kc=1))
assert_close(fluids.numba.API520_A_steam(m=69615/3600., T=592.5, P1=12236E3, Kd=0.975, Kb=1, Kc=1),
fluids.API520_A_steam(m=69615/3600., T=592.5, P1=12236E3, Kd=0.975, Kb=1, Kc=1))
@pytest.mark.numba
@pytest.mark.skipif(numba is None, reason="Numba is missing")
def test_misc_packed_bed():
assert_close(fluids.numba.Harrison_Brunner_Hecker(dp=8E-4, voidage=0.4, vs=1E-3, rho=1E3, mu=1E-3, Dt=1E-2),
fluids.Harrison_Brunner_Hecker(dp=8E-4, voidage=0.4, vs=1E-3, rho=1E3, mu=1E-3, Dt=1E-2))
assert_close(fluids.numba.Montillet_Akkari_Comiti(dp=0.0008, voidage=0.4, L=0.5, vs=0.00132629120, rho=1000., mu=1.00E-003),
fluids.Montillet_Akkari_Comiti(dp=0.0008, voidage=0.4, L=0.5, vs=0.00132629120, rho=1000., mu=1.00E-003))
assert_close(fluids.numba.dP_packed_bed(dp=8E-4, voidage=0.4, vs=1E-3, rho=1E3, mu=1E-3, Dt=0.01),
fluids.dP_packed_bed(dp=8E-4, voidage=0.4, vs=1E-3, rho=1E3, mu=1E-3, Dt=0.01))
@pytest.mark.numba
@pytest.mark.skipif(numba is None, reason="Numba is missing")
def test_misc_packed_tower():
# 12.8 us CPython, 1.4 PyPy, 1.85 numba
assert_close(fluids.numba.Stichlmair_wet(Vg=0.4, Vl = 5E-3, rhog=5., rhol=1200., mug=5E-5, voidage=0.68, specific_area=260., C1=32., C2=7., C3=1.),
fluids.Stichlmair_wet(Vg=0.4, Vl = 5E-3, rhog=5., rhol=1200., mug=5E-5, voidage=0.68, specific_area=260., C1=32., C2=7., C3=1.),)
@pytest.mark.numba
@pytest.mark.skipif(numba is None, reason="Numba is missing")
def test_misc_flow_meter():
assert_close(fluids.numba.differential_pressure_meter_beta(D=0.2575, D2=0.184, meter_type='cone meter'),
fluids.differential_pressure_meter_beta(D=0.2575, D2=0.184, meter_type='cone meter'))
assert_close(fluids.numba.C_Miller_1996(D=0.07391, Do=0.0222, rho=1.165, mu=1.85E-5, m=0.12, taps='flange', subtype='orifice'),
fluids.C_Miller_1996(D=0.07391, Do=0.0222, rho=1.165, mu=1.85E-5, m=0.12, taps='flange', subtype='orifice'))
assert_close1d(fluids.numba.differential_pressure_meter_C_epsilon(D=0.07366, D2=0.05, P1=200000.0, P2=183000.0, rho=999.1, mu=0.0011, k=1.33, m=7.702338035732168, meter_type='ISO 5167 orifice', taps='D'),
fluids.differential_pressure_meter_C_epsilon(D=0.07366, D2=0.05, P1=200000.0, P2=183000.0, rho=999.1, mu=0.0011, k=1.33, m=7.702338035732168, meter_type='ISO 5167 orifice', taps='D'))
assert_close(fluids.numba.differential_pressure_meter_dP(D=0.07366, D2=0.05, P1=200000.0, P2=183000.0, meter_type='as cast convergent venturi tube'),
fluids.differential_pressure_meter_dP(D=0.07366, D2=0.05, P1=200000.0, P2=183000.0, meter_type='as cast convergent venturi tube'))
assert_close(fluids.numba.differential_pressure_meter_solver(D=0.07366, D2=0.05, P1=200000.0, P2=183000.0, rho=999.1, mu=0.0011, k=1.33, meter_type='ISO 5167 orifice', taps='D'),
fluids.differential_pressure_meter_solver(D=0.07366, D2=0.05, P1=200000.0, P2=183000.0, rho=999.1, mu=0.0011, k=1.33, meter_type='ISO 5167 orifice', taps='D'))
@pytest.mark.numba
@pytest.mark.skipif(numba is None, reason="Numba is missing")
def test_misc_core():
# All these had issues
assert_close(fluids.numba.Reynolds(2.5, 0.25, nu=1.636e-05),
fluids.Reynolds(2.5, 0.25, nu=1.636e-05))
assert_close(fluids.numba.Peclet_heat(1.5, 2, 1000., 4000., 0.6),
fluids.Peclet_heat(1.5, 2, 1000., 4000., 0.6))
assert_close(fluids.numba.Fourier_heat(t=1.5, L=2, rho=1000., Cp=4000., k=0.6),
fluids.Fourier_heat(t=1.5, L=2, rho=1000., Cp=4000., k=0.6))
assert_close(fluids.numba.Graetz_heat(1.5, 0.25, 5, 800., 2200., 0.6),
fluids.Graetz_heat(1.5, 0.25, 5, 800., 2200., 0.6))
assert_close(fluids.numba.Schmidt(D=2E-6, mu=4.61E-6, rho=800),
fluids.Schmidt(D=2E-6, mu=4.61E-6, rho=800))
assert_close(fluids.numba.Lewis(D=22.6E-6, alpha=19.1E-6),
fluids.Lewis(D=22.6E-6, alpha=19.1E-6))
assert_close(fluids.numba.Confinement(0.001, 1077, 76.5, 4.27E-3),
fluids.Confinement(0.001, 1077, 76.5, 4.27E-3))
assert_close(fluids.numba.Prandtl(Cp=1637., k=0.010, nu=6.4E-7, rho=7.1),
fluids.Prandtl(Cp=1637., k=0.010, nu=6.4E-7, rho=7.1))
assert_close(fluids.numba.Grashof(L=0.9144, beta=0.000933, T1=178.2, rho=1.1613, mu=1.9E-5),
fluids.Grashof(L=0.9144, beta=0.000933, T1=178.2, rho=1.1613, mu=1.9E-5))
assert_close(fluids.numba.Froude(1.83, L=2., squared=True),
fluids.Froude(1.83, L=2., squared=True))
assert_close(fluids.numba.nu_mu_converter(998., nu=1.0E-6),
fluids.nu_mu_converter(998., nu=1.0E-6))
assert_close(fluids.numba.gravity(55, 1E4),
fluids.gravity(55, 1E4))
@pytest.mark.numba
@pytest.mark.skipif(numba is None, reason="Numba is missing")
def test_misc_drag():
assert_close(fluids.numba.drag_sphere(200),
fluids.drag_sphere(200))
assert_close(fluids.numba.drag_sphere(1e6, Method='Almedeij'),
fluids.drag_sphere(1e6, Method='Almedeij'))
assert_close(fluids.numba.v_terminal(D=70E-6, rhop=2600., rho=1000., mu=1E-3),
fluids.v_terminal(D=70E-6, rhop=2600., rho=1000., mu=1E-3))
assert_close(fluids.numba.time_v_terminal_Stokes(D=1e-7, rhop=2200., rho=1.2, mu=1.78E-5, V0=1),
fluids.time_v_terminal_Stokes(D=1e-7, rhop=2200., rho=1.2, mu=1.78E-5, V0=1), rtol=1e-1 )
@pytest.mark.numba
@pytest.mark.skipif(numba is None, reason="Numba is missing")
def test_misc_two_phase_voidage():
assert_close(fluids.numba.gas_liquid_viscosity(x=0.4, mul=1E-3, mug=1E-5, rhol=850, rhog=1.2, Method='Duckler'),
fluids.gas_liquid_viscosity(x=0.4, mul=1E-3, mug=1E-5, rhol=850, rhog=1.2, Method='Duckler'))
assert_close(fluids.numba.liquid_gas_voidage(m=0.6, x=0.1, rhol=915., rhog=2.67, mul=180E-6, mug=14E-6, sigma=0.0487, D=0.05),
fluids.liquid_gas_voidage(m=0.6, x=0.1, rhol=915., rhog=2.67, mul=180E-6, mug=14E-6, sigma=0.0487, D=0.05))
@pytest.mark.numba
@pytest.mark.skipif(numba is None, reason="Numba is missing")
def test_misc_two_phase():
assert_close(fluids.numba.Beggs_Brill(m=0.6, x=0.1, rhol=915., rhog=2.67, mul=180E-6, mug=14E-6, sigma=0.0487, P=1E7, D=0.05, angle=0, roughness=0, L=1),
fluids.Beggs_Brill(m=0.6, x=0.1, rhol=915., rhog=2.67, mul=180E-6, mug=14E-6, sigma=0.0487, P=1E7, D=0.05, angle=0, roughness=0, L=1))
assert_close(fluids.numba.Kim_Mudawar(m=0.6, x=0.1, rhol=915., rhog=2.67, mul=180E-6, mug=14E-6, sigma=0.0487, D=0.05, L=1),
fluids.Kim_Mudawar(m=0.6, x=0.1, rhol=915., rhog=2.67, mul=180E-6, mug=14E-6, sigma=0.0487, D=0.05, L=1))
reg_numba = fluids.numba.Mandhane_Gregory_Aziz_regime(m=0.6, x=0.112, rhol=915.12, rhog=2.67, mul=180E-6, mug=14E-6, sigma=0.065, D=0.05)
reg_normal = fluids.Mandhane_Gregory_Aziz_regime(m=0.6, x=0.112, rhol=915.12, rhog=2.67, mul=180E-6, mug=14E-6, sigma=0.065, D=0.05)
assert reg_numba == reg_normal
reg_numba = fluids.numba.Taitel_Dukler_regime(m=0.6, x=0.112, rhol=915.12, rhog=2.67, mul=180E-6, mug=14E-6, D=0.05, roughness=0, angle=0)
reg_normal = fluids.Taitel_Dukler_regime(m=0.6, x=0.112, rhol=915.12, rhog=2.67, mul=180E-6, mug=14E-6, D=0.05, roughness=0, angle=0)
assert reg_numba == reg_normal
assert_close(fluids.numba.two_phase_dP(m=0.6, x=0.1, rhol=915., rhog=2.67, mul=180E-6, mug=14E-6, sigma=0.0487, D=0.05, L=1),
fluids.two_phase_dP(m=0.6, x=0.1, rhol=915., rhog=2.67, mul=180E-6, mug=14E-6, sigma=0.0487, D=0.05, L=1))
assert_close(fluids.numba.two_phase_dP(m=0.6, x=0.1, rhol=915., rhog=2.67, mul=180E-6, mug=14E-6, sigma=0.0487, D=0.05, L=1, P=1e6),
fluids.two_phase_dP(m=0.6, x=0.1, rhol=915., rhog=2.67, mul=180E-6, mug=14E-6, sigma=0.0487, D=0.05, L=1, P=1e6))
'''Completely working submodles:
* core
* filters
* separator
* saltation
* mixing
* safety_valve
* open_flow
* pump (except CountryPower)
* flow_meter
* packed_bed
* two_phase_voidage
* two_phase
Near misses:
* fittings - Hooper2K, Darby3K
* drag - integrate_drag_sphere (odeint)
* compressible - P_isothermal_critical_flow, isothermal_gas (need lambertw, change solvers)
* packed_tower - Stichlmair_flood (newton_system)
* geometry - double quads
Not supported:
* particle_size_distribution
* atmosphere
* friction - Only nearest_material_roughness, material_roughness, roughness_Farshad
* piping - all dictionary lookups
'''
'''
Functions not working:
# Almost workk, needs support for new branches of lambertw
fluids.numba.P_isothermal_critical_flow(P=1E6, fd=0.00185, L=1000., D=0.5)
fluids.numba.lambertw(.5)
# newton_system not working
fluids.numba.Stichlmair_flood(Vl = 5E-3, rhog=5., rhol=1200., mug=5E-5, voidage=0.68, specific_area=260., C1=32., C2=7., C3=1.)
# Using dictionaries outside is broken
# Also, nopython is broken for this case - https://github.com/numba/numba/issues/5377
fluids.numba.roughness_Farshad('Cr13, bare', 0.05)
piping.nearest_pipe -> Multiplication of None type; checking of type to handle in inputs;
dictionary lookup of schedule coefficients; function in function; doesn't like something about the data either
piping.gauge_from_t -> numba type dict; once that's inside function, dying on checking
"in" of a now-numpy array; same for t_from_gauge
fluids.numba.liquid_gas_voidage(m=0.6, x=0.1, rhol=915., rhog=2.67, mul=180E-6, mug=14E-6, sigma=0.0487, D=0.05, Method='Xu Fang voidage')
* some raaguments can be done
fluids.numba.two_phase_dP(m=0.6, x=0.1, rhol=915., rhog=2.67, mul=180E-6, mug=14E-6, sigma=0.0487, D=0.05, L=1)
Most classes which have different input types
Double quads not yet supported - almost!
'''
'''Global dictionary lookup:
Darby3K, Hooper2K,
# Feels like this should work
from numba import njit, typeof, typed, types
Darby = typed.Dict.empty(types.string, types.UniTuple(types.float64, 3))
Darby['Elbow, 90°, threaded, standard, (r/D = 1)'] = (800.0, 0.14, 4.0)
Darby['Elbow, 90°, threaded, long radius, (r/D = 1.5)'] = (800.0, 0.071, 4.2)
@numba.njit
def Darby3K(NPS, Re, name):
K1, Ki, Kd = Darby[name]
return K1/Re + Ki*(1. + Kd*NPS**-0.3)
Darby3K(NPS=12., Re=10000., name='Elbow, 90°, threaded, standard, (r/D = 1)')'''
'''
numba is not up to speeding up the various solvers!
I was able to contruct a secant version which numba would optimize, mostly.
However, it took 30x the time.
Trying to improve this, it was found reducing the number of arguments to secant
imroves things ~20%. Removing ytol or the exceptions did not improve things at all.
Eventually it was discovered, the rtol and xtol arguments should be fixed values inside the function.
This makes little sense, but it is what happened.
Slighyly better performance was found than in pure-python that way, although definitely not vs. pypy.
'''
'''Having a really hard time getting newton_system to work...
@numba.njit
def to_solve_jac(x0):
return np.array([5.0*x0[0] - 3]), np.array([5.0])
# fluids.numerics.newton_system(to_solve_jac, x0=[1.0], jac=True)
fluids.numba.newton_system(to_solve_jac, x0=[1.0], jac=True)
''' | [
"[email protected]"
] | |
4257890a7490b2def67115c1e7771a15801e77b2 | ae652aec76faffce67e85922d73a7977df95b3b6 | /util.py | e7527c2069a3f57700ad3025738fe7bf868ece28 | [] | no_license | wwq0327/note | e75964e0539a9efce0889b7774f78da0bcdff7fb | f893e851cb85b974bfbf5917a7fa5845b2f20eac | refs/heads/master | 2020-05-18T07:57:33.446712 | 2011-11-13T13:40:07 | 2011-11-13T13:40:07 | 2,765,724 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 599 | py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
util
~~~~~~~~~~~~~~~~~~~~
:date: 2011-10-17
"""
def lines(file):
'''在文本的最后一行加入一个空行'''
for line in file: yield line
yield '\n'
def blocks(file):
'''收集遇到的所有行，直接遇到一个空行，然后返回已经收集到的行。
那些返回的行就是一个代码块
'''
block = []
for line in lines(file):
if line.strip():
block.append(line)
elif block:
yield ''.join(block).strip()
block = []
| [
"[email protected]"
] | |
425a35d7f2a14880501a7ed5d0ae0c3945f95335 | 1ca1799ce0abe12e37cf2893af2dd6fcf784317c | /env/bin/easy_install-3.6 | 134cb8672b89aa373c27aab0a51561c722a4558d | [] | no_license | anubishere/projekt_eita15 | 93c542e8c10ada5bc469c8e5cdca48506405f872 | d1bc9b3e11917d6aaf942a95bb2bbdd370e91c45 | refs/heads/master | 2021-02-16T05:36:26.032675 | 2020-03-04T18:10:56 | 2020-03-04T18:10:56 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 286 | 6 | #!/home/victor/skola/kurser/digitala_system/projekt_eita15/env/bin/python3
# -*- coding: utf-8 -*-
import re
import sys
from setuptools.command.easy_install import main
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
sys.exit(main())
| [
"[email protected]"
] | |
4d8a89ad46d29396654cfe3031188c065de896cd | 781b9a4a1098f3ac339f97eb1a622924bcc5914d | /Exercices/S1_04_AlgorithmesDichotomiques/TP05c.py | f1d7d9d939d0575e3055f1da3e04e0ed8b98dfbb | [] | no_license | xpessoles/Informatique | 24d4d05e871f0ac66b112eee6c51cfa6c78aea05 | e8fb053c3da847bd0a1a565902b56d45e1e3887c | refs/heads/main | 2023-08-30T21:10:56.788526 | 2023-08-30T20:17:38 | 2023-08-30T20:17:38 | 375,464,331 | 2 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,235 | py | ##### TP 5
##### Recherche dans une liste triée.
## I.1
## Q1
# g=0, d=8
# m=4, L[m]=11 et 5 < 11, on pose g=0, d=3
# m=2, L[m]=5. On a trouvé x0
# g=0, d=8
# m=4, L[m]=8 et 8<11, on pose g=5, d=8
# m=6, L[m]=13 et 11<13, on pose g=5, d=5
# m=5, L[m]=10 et 10<11, on pose g=6, d=5. On s'arrête.
## Q2
def dichotomie(x0,L):
Test=False
n=len(L)
g,d=0,n-1
while g<=d and not Test:
m=(g+d)//2
if L[m]==x0:
Test=True
elif L[m]>x0:
d=m-1
else:
g=m+1
return(Test)
## Q3
# Si x0 n_est pas présent, on exécute la boucle tant que g<=d. On sort avec g=d+1.
# A l_entrée du 1er tout de boucle, on a d-g+1=n. A chaque tour, la valeur d-g+1 diminue environ de moitié. Donc après k tours de boucles, la longueur de l_intervalle est de l_ordre de n/2**k.
# De plus, à chaque tour de boucle, il y a 2 comparaisons.
# Au dernier tour numéro k, on a g=d soit lorsque n/2**k = 1 d_ou k=log_2(n).
# On obtient donc un nombre de comparaisons équivalent à 2*ln(n)/ln(2): complexité logarithmique.
# Dans le cas séquentiel, on obtient une complexité linéaire, donc beaucoup moins intéressant.
## I.2
# L'idée est de s'arrêter lorsque d-g=1 avec L[g]\>0>L[d]
def recherche_dicho(L):
n=len(L)
g,d=0,n-1
while d-g>1:
m=(g+d)//2
if L[m]>=0:
g=m
else:
d=m
return(g,L[g])
## I.3
## 1
# Pour une valeur à epsilon près, on s_arrete lorsque 0<d-g<2*epsilon et on renvoie (g+d)/2
## 2
def recherche_zero(f,a,b,epsilon):
g,d=a,b
while d-g>2*epsilon:
m=(g+d)/2
if f(m)*f(g)<=0:
d=m
else:
g=m
return((g+d)/2)
## 3
def f(x):
return(x**2-2)
# print(recherche_zero(f,0,2,0.001)
## 4
# Avec epsilon = 1/2**p, il faut compter combien il y a de tours de boucles. En sortie du kieme tour de boucle, d-g vaut (b-a)/2**k. Il y a donc k tours de boucles avec (b-a)/2**k<=1/2**(p-1) soit k>=p-1+log_2(b-a) soit une complexité logarithmique encore.
##### II. Exponentiation rapide.
import numpy as np
import matplotlib.pyplot as plt
import time as t
import random as r
## 1.(a)
def exponaif(x,n):
p=1
for i in range(n):
p=p*x
return(p)
# Le nombre d'opérations effectuées est exactement n (1 produit à chaque tour)
## 1.(b)
def exporapide(x,n):
y=x
k=n
p=1
while k>0:
if k % 2==1:
p=p*y
y=y*y
k=k//2
return(p)
# A chaque tour de boucle, il y a au plus 1 comparaison et 2 ou 3 opérations. En sortie du ième tour, k vaut environ n/2**k. On sort de la fonction lorsque n/2**k vaut 1 soit k=ln(n)/ln(2).
# Le nombre d_opérations est donc compris entre 2*ln(n)/ln(2) et 3*ln(n)/ln(2): complexité logarithmique en O(ln(n)).
## 2
## 2;(a)
import time as t
def Pnaif(x,n):
S=0
for i in range(n):
S=S+i*exponaif(x,i)
return(S)
# n+n(n+1)/2 ~ n**2/2 opérations. Quadratique
## 2. (b)
def Prapide(x,n):
S=0
for i in range(n):
S=S+i*exporapide(x,i)
return(S)
# O(log(i)) pour chaque i*x**i. Il reste la somme des n termes.
# D'où n+somme des log(i)=O(n.ln(n)).
## 2. (c)
def Phorner(x,L):
"""L est la liste des coefficients"""
n=len(L)-1
S=0
for i in range(n+1):
S=S*x+L[n-i]
return(S)
# 2n opérations. Linéaire mais plus intéressante que ci-dessus.
## 3
# Liste des temps d'exécution pour le calcul de x**n pour n=0..50 :
N=[i for i in range(101)]
# Tracé des temps pour les calculs de P(x) avec n=0..100 avec P(x)=somme des iX**i, i=0..n
def Temps_calcul_P(x):
# Le polynôme est donné par une liste des coefficients.
Tn,Tr,Th=[],[],[]
for n in N:
L=[k for k in range(n+1)]
tps=t.perf_counter()
Pnaif(x,n)
Tn.append(t.perf_counter()-tps)
tps=t.perf_counter()
Sr=0
Prapide(x,n)
Tr.append(t.perf_counter()-tps)
tps=t.perf_counter()
Phorner(x,L)
Th.append(t.perf_counter()-tps)
plt.plot(N,Th,label='méthode horner')
plt.plot(N,Tr,label='méthode rapide')
plt.plot(N,Tn,label='méthode naïve')
plt.legend()
plt.show()
#####
| [
"[email protected]"
] | |
f73d54fdd4af870903d5e22391dbe348021ff86f | ffc1ab091d54635c96cd4ed6098c4bf388222f02 | /nipype/nipype-master/nipype/interfaces/afni/tests/test_auto_Qwarp.py | 2848fe97f8622750e68d5cf9d3e43276808c0a92 | [
"BSD-3-Clause",
"Apache-2.0"
] | permissive | bharathlakshman/brainintensive | fc7833078bb6ad9574e015004f2b3905c85b699e | eec8a91cbec29ba27d620984394d3ee21d0db58f | refs/heads/master | 2021-01-01T19:04:25.724906 | 2017-07-28T01:48:28 | 2017-07-28T01:48:28 | 98,499,935 | 1 | 2 | null | null | null | null | UTF-8 | Python | false | false | 3,792 | py | # AUTO-GENERATED by tools/checkspecs.py - DO NOT EDIT
from __future__ import unicode_literals
from ..preprocess import Qwarp
def test_Qwarp_inputs():
input_map = dict(Qfinal=dict(argstr='-Qfinal',
),
Qonly=dict(argstr='-Qonly',
),
allsave=dict(argstr='-allsave',
xor=['nopadWARP', 'duplo', 'plusminus'],
),
args=dict(argstr='%s',
),
ballopt=dict(argstr='-ballopt',
xor=['workhard', 'boxopt'],
),
base_file=dict(argstr='-base %s',
copyfile=False,
mandatory=True,
),
baxopt=dict(argstr='-boxopt',
xor=['workhard', 'ballopt'],
),
blur=dict(argstr='-blur %s',
),
duplo=dict(argstr='-duplo',
xor=['gridlist', 'maxlev', 'inilev', 'iniwarp', 'plusminus', 'allsave'],
),
emask=dict(argstr='-emask %s',
copyfile=False,
),
environ=dict(nohash=True,
usedefault=True,
),
expad=dict(argstr='-expad %d',
xor=['nopadWARP'],
),
gridlist=dict(argstr='-gridlist %s',
copyfile=False,
xor=['duplo', 'plusminus'],
),
hel=dict(argstr='-hel',
xor=['nmi', 'mi', 'lpc', 'lpa', 'pear'],
),
ignore_exception=dict(nohash=True,
usedefault=True,
),
in_file=dict(argstr='-source %s',
copyfile=False,
mandatory=True,
),
inilev=dict(argstr='-inlev %d',
xor=['duplo'],
),
iniwarp=dict(argstr='-iniwarp %s',
xor=['duplo'],
),
iwarp=dict(argstr='-iwarp',
xor=['plusminus'],
),
lpa=dict(argstr='-lpa',
xor=['nmi', 'mi', 'lpc', 'hel', 'pear'],
),
lpc=dict(argstr='-lpc',
position=-2,
xor=['nmi', 'mi', 'hel', 'lpa', 'pear'],
),
maxlev=dict(argstr='-maxlev %d',
position=-1,
xor=['duplo'],
),
mi=dict(argstr='-mi',
xor=['mi', 'hel', 'lpc', 'lpa', 'pear'],
),
minpatch=dict(argstr='-minpatch %d',
),
nmi=dict(argstr='-nmi',
xor=['nmi', 'hel', 'lpc', 'lpa', 'pear'],
),
noXdis=dict(argstr='-noXdis',
),
noYdis=dict(argstr='-noYdis',
),
noZdis=dict(argstr='-noZdis',
),
noneg=dict(argstr='-noneg',
),
nopad=dict(argstr='-nopad',
),
nopadWARP=dict(argstr='-nopadWARP',
xor=['allsave', 'expad'],
),
nopenalty=dict(argstr='-nopenalty',
),
nowarp=dict(argstr='-nowarp',
),
noweight=dict(argstr='-noweight',
),
out_file=dict(argstr='-prefix %s',
genfile=True,
name_source=['in_file'],
name_template='%s_QW',
),
out_weight_file=dict(argstr='-wtprefix %s',
),
outputtype=dict(),
overwrite=dict(argstr='-overwrite',
),
pblur=dict(argstr='-pblur %s',
),
pear=dict(argstr='-pear',
),
penfac=dict(argstr='-penfac %f',
),
plusminus=dict(argstr='-plusminus',
xor=['duplo', 'allsave', 'iwarp'],
),
quiet=dict(argstr='-quiet',
xor=['verb'],
),
resample=dict(argstr='-resample',
),
terminal_output=dict(nohash=True,
),
verb=dict(argstr='-verb',
xor=['quiet'],
),
wball=dict(argstr='-wball %s',
),
weight=dict(argstr='-weight %s',
),
wmask=dict(argstr='-wpass %s %f',
),
workhard=dict(argstr='-workhard',
xor=['boxopt', 'ballopt'],
),
)
inputs = Qwarp.input_spec()
for key, metadata in list(input_map.items()):
for metakey, value in list(metadata.items()):
assert getattr(inputs.traits()[key], metakey) == value
def test_Qwarp_outputs():
output_map = dict(base_warp=dict(),
source_warp=dict(),
warped_base=dict(),
warped_source=dict(),
weights=dict(),
)
outputs = Qwarp.output_spec()
for key, metadata in list(output_map.items()):
for metakey, value in list(metadata.items()):
assert getattr(outputs.traits()[key], metakey) == value
| [
"[email protected]"
] | |
6ef29292a7e42861fa5c76bb43f7c074044b2706 | 2850d9adba96bc4e73185de5d6adebf363a5c534 | /tce/tcloud/clb/InquiryLBPrice.py | 8d39cc90ea056cc0f7f42b894868a5e5193ddabb | [
"Apache-2.0"
] | permissive | FatAnker/tencentcloud-sdk-python | d8f757b12ad336e78a06b68a789ecc3c86d1d331 | d6f75a41dc7053cb51f9091f4d41b8cb7a837559 | refs/heads/master | 2020-04-30T22:34:16.740484 | 2019-04-28T11:14:11 | 2019-04-28T11:14:11 | 177,122,691 | 0 | 1 | null | 2019-03-22T10:46:01 | 2019-03-22T10:46:01 | null | UTF-8 | Python | false | false | 1,376 | py | # -*- coding: utf8 -*-
from QcloudApi.qcloudapi import QcloudApi
from tce.tcloud.utils.config import global_config
# 设置需要加载的模块
module = 'lb'
# 对应接口的接口名，请参考wiki文档上对应接口的接口名
action = 'InquiryLBPrice'
region = global_config.get('regions')
params = global_config.get(region)
secretId = params['secretId']
secretKey = params['secretKey']
domain =params['domain']
# 云API的公共参数
config = {
'Region': region,
'secretId': secretId,
'secretKey': secretKey,
'method': 'GET',
'SignatureMethod': 'HmacSHA1'
}
# 接口参数，根据实际情况填写，支持json
# 例如数组可以 "ArrayExample": ["1","2","3"]
# 例如字典可以 "DictExample": {"key1": "value1", "key2": "values2"}
action_params = {
'loadBalancerType':2
}
try:
service = QcloudApi(module, config)
# 请求前可以通过下面几个方法重新设置请求的secretId/secretKey/Region/method/SignatureMethod参数
# 重新设置请求的Region
# service.setRegion('shanghai')
# 打印生成的请求URL，不发起请求
print(service.generateUrl(action, action_params))
# 调用接口，发起请求，并打印返回结果
print(service.call(action, action_params))
except Exception as e:
import traceback
print('traceback.format_exc():\n%s' % traceback.format_exc()) | [
"[email protected]"
] | |
becdb112bb47e331e0ba9f6b5eb175b7e8f43035 | 3c6b36eb1f4f9760c52903f6d0ec4a501f948c90 | /osp/test/citations/utils/test_get_text.py | 663abb8fd5fc80e075106a2ff9727effea40b0c3 | [
"Apache-2.0"
] | permissive | davidmcclure/open-syllabus-project | 38444249af845013e3f281a7a713dca83159c56e | 078cfd4c5a257fbfb0901d43bfbc6350824eed4e | refs/heads/master | 2021-06-30T21:47:07.636558 | 2021-06-27T15:15:35 | 2021-06-27T15:15:35 | 50,152,020 | 220 | 14 | Apache-2.0 | 2021-06-27T15:11:15 | 2016-01-22T02:29:57 | Python | UTF-8 | Python | false | false | 501 | py |
import pytest
from osp.citations.utils import get_text
from bs4 import BeautifulSoup
@pytest.mark.parametrize('tag,text', [
('<tag>Article Title</tag>', 'Article Title'),
# Strip whitespace.
('<tag> Article Title </tag>', 'Article Title'),
# Empty text -> None.
('<tag></tag>', None),
('<tag> </tag>', None),
# Missing tag -> None.
('', None),
])
def test_get_text(tag, text):
tree = BeautifulSoup(tag, 'lxml')
assert get_text(tree, 'tag') == text
| [
"[email protected]"
] | |
32273e37f6ed947cad8183262e2fbe2b5511c0bf | e42c337b179ea9e85c41c992d1440dbdd10e4bdb | /solution/leetcode/120.py | 18bddd5d74b8a75dc62e353f0e5eb862930701c7 | [] | no_license | harshraj22/problem_solving | 7733a43e2dcbf507257e61732430d5c0fc1b4cb9 | 2c7d1ed486ae59126244168a446d74ae4fdadbc6 | refs/heads/master | 2023-05-26T12:58:40.098378 | 2023-05-12T17:53:11 | 2023-05-12T17:53:11 | 193,202,408 | 20 | 5 | null | 2022-08-03T16:40:45 | 2019-06-22T06:58:09 | C++ | UTF-8 | Python | false | false | 501 | py | # https://leetcode.com/problems/triangle/
class Solution:
from math import inf
def minimumTotal(self, triangle: List[List[int]]) -> int:
# create a copy
dp = [[inf for _ in row] for row in triangle]
dp[0][0] = triangle[0][0]
for i, row in enumerate(dp[:-1]):
for j, cell in enumerate(row):
try:
dp[i+1][j] = max(dp[i+1][j], dp[i][j] + triangle[i+1][j])
dp[i+1][j+1] = max(dp[i+1][j+1], dp[i][j] + triangle[i+1][j+1])
except IndexError:
pass
return min(dp[-1]) | [
"[email protected]"
] | |
495ce2f9cfe10accb714b8a7ae55e59dcd8e762c | eec105101d7a82d0551503100a3bd9a1a2af3b3c | /Assignments/a5_public/code/char_decoder.py | 180d88968e8b831c9cb4d68b2702a7a64917d69a | [] | no_license | ethanenguyen/Stanford-CS224n-NLP | 807c36cf67f8aade5b1ca7bfd5878ac354e96bbb | 181c659dbb0e0a1a1c3865a336fd74c8ebc5633e | refs/heads/master | 2022-03-28T04:16:01.236240 | 2020-01-14T13:52:21 | 2020-01-14T13:52:21 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 7,318 | py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
CS224N 2018-19: Homework 5
"""
import torch
import torch.nn as nn
class CharDecoder(nn.Module):
def __init__(self, hidden_size, char_embedding_size=50, target_vocab=None):
""" Init Character Decoder.
@param hidden_size (int): Hidden size of the decoder LSTM
@param char_embedding_size (int): dimensionality of character embeddings
@param target_vocab (VocabEntry): vocabulary for the target language. See vocab.py for documentation.
"""
### YOUR CODE HERE for part 2a
### TODO - Initialize as an nn.Module.
### - Initialize the following variables:
### self.charDecoder: LSTM. Please use nn.LSTM() to construct this.
### self.char_output_projection: Linear layer, called W_{dec} and b_{dec} in the PDF
### self.decoderCharEmb: Embedding matrix of character embeddings
### self.target_vocab: vocabulary for the target language
###
### Hint: - Use target_vocab.char2id to access the character vocabulary for the target language.
### - Set the padding_idx argument of the embedding matrix.
### - Create a new Embedding layer. Do not reuse embeddings created in Part 1 of this assignment.
super(CharDecoder, self).__init__() # Initialize as an nn.Module
self.charDecoder = nn.LSTM(char_embedding_size, hidden_size)
self.char_output_projection = nn.Linear(hidden_size, len(target_vocab.char2id), bias=True)
self.decoderCharEmb = nn.Embedding(len(target_vocab.char2id), char_embedding_size, padding_idx=target_vocab.char2id['<pad>'])
self.target_vocab = target_vocab
self.loss = nn.CrossEntropyLoss(
reduction='sum', # computed as the *sum* of cross-entropy losses of all the words in the batch
ignore_index=self.target_vocab.char2id['<pad>'] # # not take into account pad character when compute loss
)
### END YOUR CODE
# When our word-level decoder produces an <unk> token, we run our character-level decoder (a character-level conditional language model)
def forward(self, x, dec_hidden=None):
""" Forward pass of character decoder.
@param x: tensor of integers, shape (length, batch)
@param dec_hidden: internal state of the LSTM before reading the input characters. A tuple of two tensors of shape (1, batch, hidden_size)
@returns scores: called s_t in the PDF, shape (length, batch, self.vocab_size)
@returns dec_hidden: internal state of the LSTM after reading the input characters. A tuple of two tensors of shape (1, batch, hidden_size)
"""
### YOUR CODE HERE for part 2b
### TODO - Implement the forward pass of the character decoder.
x_emb = self.decoderCharEmb(x)
hidden, dec_hidden = self.charDecoder(x_emb, dec_hidden)
scores = self.char_output_projection(hidden) # i.e. s_t, logits
### END YOUR CODE
return scores, dec_hidden
# When we train the NMT system, we train the character decoder on every word in the target sentence
# (not just the words reparesented by <unk>)
def train_forward(self, char_sequence, dec_hidden=None):
""" Forward computation during training.
@param char_sequence: tensor of integers, shape (length, batch). Note that "length" here and in forward() need not be the same.
@param dec_hidden: initial internal state of the LSTM, obtained from the output of the word-level decoder. A tuple of two tensors of shape (1, batch, hidden_size)
@returns The cross-entropy loss, computed as the *sum* of cross-entropy losses of all the words in the batch.
"""
### YOUR CODE HERE for part 2c
### TODO - Implement training forward pass.
###
### Hint: - Make sure padding characters do not contribute to the cross-entropy loss.
### - char_sequence corresponds to the sequence x_1 ... x_{n+1} from the handout (e.g., <START>,m,u,s,i,c,<END>).
x = char_sequence[:-1] # exclude the <END> token
scores, dec_hidden = self.forward(x, dec_hidden)
targets = char_sequence[1:] # exclude the <START> token
targets = targets.reshape(-1) # squeeze into 1D (embed_size * batch_size)
scores = scores.reshape(-1, scores.shape[-1]) # (embed_size * batch_size, V_char)
ce_loss = self.loss(scores, targets)
### END YOUR CODE
return ce_loss
def decode_greedy(self, initialStates, device, max_length=21):
""" Greedy decoding
@param initialStates: initial internal state of the LSTM, a tuple of two tensors of size (1, batch, hidden_size)
@param device: torch.device (indicates whether the model is on CPU or GPU)
@param max_length: maximum length of words to decode
@returns decodedWords: a list (of length batch) of strings, each of which has length <= max_length.
The decoded strings should NOT contain the start-of-word and end-of-word characters.
"""
### YOUR CODE HERE for part 2d
### TODO - Implement greedy decoding.
### Hints:
### - Use target_vocab.char2id and target_vocab.id2char to convert between integers and characters
### - Use torch.tensor(..., device=device) to turn a list of character indices into a tensor.
### - We use curly brackets as start-of-word and end-of-word characters. That is, use the character '{' for <START> and '}' for <END>.
### Their indices are self.target_vocab.start_of_word and self.target_vocab.end_of_word, respectively.
# initial constant
batch_size = initialStates[0].shape[1]
start_index = self.target_vocab.start_of_word
end_index = self.target_vocab.end_of_word
# initial state
dec_hidden = initialStates
# char for each entry in a batch (1, batch_size) <- unsqueeze for the LSTM dim
current_chars = torch.tensor([start_index] * batch_size, device=device).unsqueeze(0)
decodeTuple = [['', False] for _ in range(batch_size)] # output words for each entry (output string, if this entry has already reached the end)
for t in range(max_length):
scores, dec_hidden = self.forward(current_chars, dec_hidden)
prob = torch.softmax(scores, dim=2)
current_chars = torch.argmax(scores, dim=2) # greedy pick a word with highest score
char_indices = current_chars.detach().squeeze(0) # returns a new Tensor, detached from the current graph
for i, char_index in enumerate(char_indices):
if not decodeTuple[i][1]: # this entry in a batch has not reached the end
if char_index == end_index:
# reach the end
decodeTuple[i][1] = True
else:
# concate the predict word at the bottom
decodeTuple[i][0] += self.target_vocab.id2char[char_index.item()]
decodedWords = [item[0] for item in decodeTuple]
### END YOUR CODE
return decodedWords
| [
"[email protected]"
] | |
8308f1bda2732b3dd68b53bd9a2e8a06d873fe8c | 9ba55397d0d8cad9a40260fc23860d2097a615f9 | /fitter/unimodal.py | 3150fdb0e8d4e7b808f833b263b694e9e52219c2 | [] | no_license | wombaugh/modefit | 32bf927ade786a38309d925e88bde765f3025ab6 | 855cafa799db9fbea724347d0bb9db0d101a6452 | refs/heads/master | 2020-06-20T04:38:32.010125 | 2016-11-27T10:47:45 | 2016-11-27T10:47:45 | 74,881,429 | 0 | 0 | null | 2016-11-27T10:41:51 | 2016-11-27T10:41:51 | null | UTF-8 | Python | false | false | 15,666 | py | #! /usr/bin/env python
# -*- coding: utf-8 -*-
""" Module based on virtualfitter to fit bimodal distributions. Does not have to be a step """
import numpy as np
from scipy import stats
import matplotlib.pyplot as mpl
# - local dependencies
from astrobject.utils.tools import kwargs_update
from .baseobjects import BaseModel,BaseFitter, DataHandler
__all__ = ["normal", 'truncnormal']
# ========================= #
# Main Methods #
# ========================= #
def normal(data,errors,names=None,
masknan=True,**kwargs):
""" Fit the weighted mean and intrinsic dispersion
on the data"""
if masknan:
flagnan = (data!=data) | (errors !=errors)
return UnimodalFit(data[~flagnan],errors[~flagnan],
names=names[~flagnan] if names is not None else None,**kwargs)
return UnimodalFit(data,errors,names=names,**kwargs)
def truncnormal(data,boundaries,
errors=None,names=None,
masknan=True,
**kwargs):
""" Fit a truncated normal distribution on the data
Parameters:
-----------
data: [array]
data following a normal distribution
boundaries: [float/None, float/None]
boundaries for the data. Set None for no boundaries
errors, names: [array, array] -optional-
error and names of the datapoint, respectively
masknan: [bool] -optional-
Remove the NaN values entries of the array if True.
**kwargs
Return
------
UnimodalFit
"""
# ----------
# - Input
if errors is None:
errors = np.zeros(len(data))
if masknan:
flagnan = (data!=data) | (errors !=errors)
fit = UnimodalFit(data[~flagnan],errors[~flagnan],
names=names[~flagnan] if names is not None else None,
modelname="TruncNormal",**kwargs)
else:
fit = UnimodalFit(data,errors,
names=names if names is not None else None,
modelname="TruncNormal",**kwargs)
fit.model.set_databounds(boundaries)
return fit
# ========================== #
# #
# Fitter #
# #
# ========================== #
class UnimodalFit( BaseFitter, DataHandler ):
""" """
PROPERTIES = []
SIDE_PROPERTIES = []
DERIVED_PROPERTIES = []
# =================== #
# Initialization #
# =================== #
def __init__(self,data, errors,
names=None, use_minuit=True,
modelname="Normal"):
""" low-level class to enable to fit a unimodal model on data
the given their errors.
Parameters
----------
data: [array]
The data that potentially have a bimodal distribution
(like a step). In case of Cosmological fit, this could be
the Hubble Residual for instance.
errors: [array]
Errors associated to the data.
names: [string-array/None] - optional -
Names associated with the data. This enable to follow the data
more easily.
In Development: If provided, you will soon be able to use
interactive ploting and see which points corresponds to
which object.
use_minuit: [bool] - default True -
Set the technique used to fit the model to the data.
Minuit is the iminuit library.
If not used, the scipy minimisation is used.
modelname: [string] - deftault Binormal -
The name of the class used to define the bimodal model.
This name must be an existing class of this library.
Return
-------
Defines the object
"""
self.__build__()
self.set_data(data,errors,names)
# -- for the fit
# use_minuit has a setter
self.use_minuit = use_minuit
self.set_model(eval("Model%s()"%modelname))
# =================== #
# Main #
# =================== #
# -------- #
# SETTER #
# -------- #
def set_data(self,data,errors,names=None):
""" set the information for the fit.
Parameters
----------
data: [array]
The data that potentially have a bimodal distribution
(like a step). In case of Cosmological fit, this could be
the Hubble Residual for instance.
errors: [array]
Errors associated to the data.
names: [string-array/None] - optional -
Names associated with the data. This enable to follow the data
more easily.
In Development: If provided, you will soon be able to use
interactive ploting and see which points corresponds to
which object.
Returns
-------
Void
"""
# ------------------------ #
# -- Fatal Input Errors -- #
if len(errors)!= len(data):
raise ValueErrors("data and errors must have the same size")
# -- Warning -- #
if names is not None and len(names) != len(data):
warnings.warn("names size does not match the data one. => names ignored")
names = None
self._properties["data"] = np.asarray(data)
self._properties["errors"] = np.asarray(errors)
self._side_properties["names"] = np.asarray(names) if names is not None else None
def _get_model_args_(self):
return self.data[self.used_indexes],self.errors[self.used_indexes]
def get_model(self, parameter):
""" Model distribution (from scipy) estiamted for the
given parameter. The model dispersion (scale) is the
square-root quadratic sum of the model's dispersion
and the median data error
Return
------
a scipy distribution
"""
return self.model.get_model(parameter, np.median(self.errors))
def show(self, parameter, ax=None,savefile=None,show=None,
propmodel={},**kwargs):
""" show the data and the model for the given parameters
Parameters
----------
parameter: [array]
Parameters setting the model
ax: [matplotlib.pyplot Axes] -optional-
Where the model should be display. if None this
will create a new figure and a new axes.
savefile: [string] -optional-
Save the figure at this location.
Do not give any extention, this will save a png and a pdf.
If None, no figure will be saved, but it will be displayed
(see the show arguement)
show: [bool] -optional-
If the figure is not saved, it is displayed except if show
is set to False
propmodel: [dict] -optional-
Properties passed to the matplotlib's Axes plot method for the model.
**kwargs goes to matplotlib's hist method
Return
------
dict (plot information like fig, ax, pl ; output in self._plot)
"""
from astrobject.utils.mpladdon import figout
# ----------- #
# - setting - #
# ----------- #
import matplotlib.pyplot as mpl
self._plot = {}
if ax is None:
fig = mpl.figure(figsize=[8,5])
ax = fig.add_axes([0.1,0.1,0.8,0.8])
elif "plot" not in dir(ax):
raise TypeError("The given 'ax' most likely is not a matplotlib axes. "+\
"No imshow available")
else:
fig = ax.figure
# ------------- #
# - Prop - #
# ------------- #
defprop = dict(fill=True, fc=mpl.cm.Blues(0.3,0.4), ec=mpl.cm.Blues(1.,1),
lw=2, normed=True, histtype="step")
prop = kwargs_update(defprop,**kwargs)
# ------------- #
# - Da Plots - #
# ------------- #
# model range
datalim = [self.data.min()-self.errors.max(),
self.data.max()+self.errors.max()]
datarange =datalim[1]-datalim[0]
x = np.linspace(datalim[0]-datarange*0.1,datalim[1]+datarange*0.1,
int(datarange*10))
# data
ht = ax.hist(self.data,**prop)
# model
prop = kwargs_update(dict(ls="--",color="0.5",lw=2),**propmodel)
model_ = self.get_model(parameter)
pl = ax.plot(x, model_.pdf(x),**prop)
# ------------- #
# - Output - #
# ------------- #
self._plot["figure"] = fig
self._plot["ax"] = ax
self._plot["hist"] = ht
self._plot["model"] = pl
fig.figout(savefile=savefile,show=show)
return self._plot
# =================== #
# Properties #
# =================== #
# ========================== #
# #
# Model #
# #
# ========================== #
class ModelNormal( BaseModel ):
"""
"""
FREEPARAMETERS = ["mean","sigma"]
sigma_boundaries = [0,None]
def setup(self,parameters):
""" """
self.mean,self.sigma = parameters
def get_model(self,parameter, dx):
""" Scipy Distribution associated to the
given parameters
Parameters
----------
parameter: [array]
Parameters setting the model
dx: [float]
Typical representative error on the data. This is to estimate the
effective dispersion of the gaussian
Return
------
scipy.norm
"""
mean, sigma = parameter
return stats.norm(loc=mean,scale=np.sqrt(sigma**2 + dx**2))
# ----------------------- #
# - LikeLiHood and Chi2 - #
# ----------------------- #
def get_loglikelihood(self,x,dx, pdf=False):
""" Measure the likelihood to find the data given the model's parameters.
Set pdf to True to have the array prior sum of the logs (array not in log=pdf) """
Li = stats.norm.pdf(x,loc=self.mean,scale=np.sqrt(self.sigma**2 + dx**2))
if pdf:
return Li
return np.sum(np.log(Li))
def get_case_likelihood(self,xi,dxi,pi):
""" return the log likelihood of the given case. See get_loglikelihood """
return self.get_loglikelihood([xi],[dxi])
# ----------------------- #
# - Bayesian methods - #
# ----------------------- #
def lnprior(self,parameter):
""" so far a flat prior """
for name_param,p in zip(self.FREEPARAMETERS, parameter):
if "sigma" in name_param and p<0:
return -np.inf
return 0
# ----------------------- #
# - Ploting - #
# ----------------------- #
def display(self, ax, xrange, dx, bins=1000,
ls="--", color="0.4",**kwargs):
""" Display the model on the given plot.
This median error is used.
"""
x = np.linspace(xrange[0],xrange[1],bins)
lpdf = self.get_loglikelihood(x,np.median(dx), pdf=True)
return ax.plot(x,lpdf,ls=ls, color="k",
**kwargs)
class ModelTruncNormal( ModelNormal ):
""" Normal distribution allowing for data boundaries
(e.g. amplitudes of emission lines are positive gaussian distribution
"""
PROPERTIES = ["databounds"]
def set_databounds(self,databounds):
""" boundaries for the data """
if len(databounds) != 2:
raise ValueError("databounds must have 2 entries [min,max]")
self._properties["databounds"] = databounds
def get_model(self,parameter, dx):
""" Scipy Distribution associated to the
given parameters
Parameters
----------
parameter: [array]
Parameters setting the model
dx: [float]
Typical representative error on the data. This is to estimate the
effective dispersion of the gaussian
Return
------
scipy.norm
"""
mean, sigma = parameter
tlow,tup = self.get_truncboundaries(dx, mean=mean, sigma=sigma)
return stats.truncnorm(tlow,tup,
loc=mean, scale=np.sqrt(sigma**2 + dx**2))
# ----------------------- #
# - LikeLiHood and Chi2 - #
# ----------------------- #
def get_loglikelihood(self,x,dx, pdf=False):
""" Measure the likelihood to find the data given the model's parameters """
# info about truncnorm:
# stats.truncnorm.pdf(x,f0,f1,loc=mu,scale=sigma))
# => f0 and f1 are the boundaries in sigma units !
# => e.g. stats.truncnorm.pdf(x,-2,3,loc=1,scale=2),
# the values below -2sigma and above 3 sigma are truncated
tlow,tup = self.get_truncboundaries(dx)
Li = stats.truncnorm.pdf(x,tlow,tup,
loc=self.mean, scale=np.sqrt(self.sigma**2 + dx**2))
if pdf:
return Li
return np.sum(np.log(Li))
def get_truncboundaries(self,dx, mean=None, sigma=None):
"""
"""
if mean is None:
mean = self.mean
if sigma is None:
sigma= self.sigma
min_ = -np.inf if self.databounds[0] is None else\
(self.databounds[0]-mean)/np.sqrt(sigma**2 + np.mean(dx)**2)
max_ = +np.inf if self.databounds[1] is None else\
(mean-self.databounds[1])/np.sqrt(sigma**2 + np.mean(dx)**2)
return min_,max_
# ----------------------- #
# - Ploting - #
# ----------------------- #
def display(self, ax, xrange, dx, bins=1000,
ls="--", color="0.4", show_boundaries=True,
ls_bounds="-", color_bounds="k", lw_bounds=2,
**kwargs):
""" Display the model on the given plot.
This median error is used.
The Boundaries are added
"""
if show_boundaries:
xlim = np.asarray(ax.get_xlim()).copy()
if self.databounds[0] is not None:
ax.axvline(self.databounds[0], ls=ls_bounds,
color=color_bounds, lw=lw_bounds)
if self.databounds[1] is not None:
ax.axvline(self.databounds[1], ls=ls_bounds,
color=color_bounds, lw=lw_bounds)
ax.set_xlim(*xlim)
return super(ModelTruncNormal, self).display( ax, xrange, dx, bins=1000,
ls=ls, color=color,**kwargs)
# ========================= #
# = Properties = #
# ========================= #
@property
def databounds(self):
return self._properties["databounds"]
@property
def _truncbounds_lower(self):
""" truncation boundaries for scipy's truncnorm """
if self.databounds[0] is None:
return -np.inf
return (self.databounds[0]-self.mean)/self.sigma
@property
def _truncbounds_upper(self):
""" truncation boundaries for scipy's truncnorm """
if self.databounds[1] is None:
return np.inf
return (self.mean-self.databounds[1])/self.sigma
| [
"[email protected]"
] | |
7ba7298644ea801b42016afbaa37ceaaefd61d96 | f642c054451aa3c87bb18fa63037eea0e6358bda | /my_django_app/cache/views.py | 5860e516f6bed9a965ced0893aa975acc095d93b | [] | no_license | devendraprasad1984/python | 30f3a539e92be13d893246ad28a42907457a38d5 | 0f1badabba07fbe7f5f792b7e543c0748eecd6c7 | refs/heads/master | 2023-07-21T08:22:45.193077 | 2021-08-27T15:09:28 | 2021-08-27T15:09:28 | 254,812,552 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 104 | py | # from django.shortcuts import render
# Create your views here.
def get_mod1(req):
return 'cache1'
| [
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] | |
bb5554675b64f4e7bfbfe07ba6ff2472ec4f1c0e | ac0c96ad280ac43de9c06ba21a1cbe2b1679b2f9 | /minidump/streams/HandleOperationListStream.py | 3e3e7422e7caf3d7beec41f6aea2c8b0d753690a | [
"MIT"
] | permissive | mkorman90/minidump | 5cfc9e7119f68e91e77e0039b7c364d46b769e39 | 749e6da56d0ea414450c287caa4ecfb292197b59 | refs/heads/master | 2020-05-09T23:39:29.089331 | 2018-06-17T14:51:30 | 2018-06-17T14:51:30 | 181,508,823 | 3 | 0 | MIT | 2019-04-15T14:53:20 | 2019-04-15T14:53:20 | null | UTF-8 | Python | false | false | 668 | py | #!/usr/bin/env python3
#
# Author:
# Tamas Jos (@skelsec)
#
class MINIDUMP_HANDLE_OPERATION_LIST:
def __init__(self):
self.SizeOfHeader = None
self.SizeOfEntry = None
self.NumberOfEntries = None
self.Reserved = None
def parse(dir, buff):
mhds = MINIDUMP_HANDLE_OPERATION_LIST()
mhds.SizeOfHeader = int.from_bytes(buff.read(4), byteorder = 'little', signed = False)
mhds.SizeOfEntry = int.from_bytes(buff.read(4), byteorder = 'little', signed = False)
mhds.NumberOfEntries = int.from_bytes(buff.read(4), byteorder = 'little', signed = False)
mhds.Reserved = int.from_bytes(buff.read(4), byteorder = 'little', signed = False)
return mhds
| [
"[email protected]"
] | |
c3418c0bc14439f3a396442041af6a15d8b775f9 | ca7aa979e7059467e158830b76673f5b77a0f5a3 | /Python_codes/p03096/s362138423.py | 0610f35f78b278535f070e6f3b9519ef3b0d4fe8 | [] | no_license | Aasthaengg/IBMdataset | 7abb6cbcc4fb03ef5ca68ac64ba460c4a64f8901 | f33f1c5c3b16d0ea8d1f5a7d479ad288bb3f48d8 | refs/heads/main | 2023-04-22T10:22:44.763102 | 2021-05-13T17:27:22 | 2021-05-13T17:27:22 | 367,112,348 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 754 | py | MOD = 10**9+7
N = int(input())
C = []
C.append(int(input()))
for i in range(N-1):
c = int(input())
if c == C[-1]:
continue
C.append(c)
#print(C)
N = len(C)
lis = [[0] for i in range(max(C)+1)]
for i in range(N):
lis[C[i]].append(i+1)
for i in range(len(lis)):
lis[i].append(MOD)
def binary_search(lis,i):
ok = 0
ng = len(lis)-1
while ng-ok > 1:
mid = (ok + ng)// 2
if lis[mid] < i:
ok = mid
else:
ng = mid
return lis[ok]
#print(binary_search([0,1,2,3,4],3))
#print(lis)
dp = [0] * (N+1)
dp[0] = 1
for i in range(1,N+1):
dp[i] += dp[i-1]
p = binary_search(lis[C[i-1]],i)
if p != 0:
dp[i] += dp[p]
dp[i] %= MOD
#print(p)
print(dp[-1])
| [
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] | |
88cc90c811bf40584e981843c0665460c3140aad | 3ae288daabf10b5f3dd5f09bb7bb974a6caacdaf | /processimage/cropimage/urls.py | 24083c8f8536913662bc82f1876b785f6dd67890 | [
"MIT"
] | permissive | singh1114/image-processor | 62ab01f9b87b6deda5debb010feeb4c74135c39b | a2b6e8fcb64b1449859a06775b2d7b3a0205e952 | refs/heads/master | 2022-12-13T21:44:56.316988 | 2019-02-04T06:13:05 | 2019-02-04T06:13:05 | 167,675,243 | 0 | 0 | MIT | 2022-12-08T01:35:03 | 2019-01-26T10:12:44 | Python | UTF-8 | Python | false | false | 483 | py | from django.urls import path
from cropimage.views import (
ImageUploadView,
ShowMainImagesView,
ShowCroppedImagesView
)
app_name = 'cropimage'
urlpatterns = [
path('upload_image/', ImageUploadView.as_admin_view(), name='upload'),
path('show_main_images/',
ShowMainImagesView.as_admin_view(), name='show_main_image'),
path('show_cropped_images/<uuid:main_image_uuid>',
ShowCroppedImagesView.as_admin_view(), name='show_cropped_images'),
] | [
"[email protected]"
] | |
13eb9d5df7bd1b66c602bda0c55492a22a3cfd1b | 0f946e4bbad3a44339e75893fbd185ae1fb25f67 | /lesson13/utils.py | 1444b0df757759af65c486ab2bbd1f438df459bd | [] | no_license | kirigaikabuto/distancelesson2 | e53dda7d8a07ea5c4ec2e7a39a47619b533a5df8 | 24cf61d34334fa2d6a736a81d4bc75170db2a2ad | refs/heads/master | 2022-07-03T07:16:47.588461 | 2020-05-13T13:50:54 | 2020-05-13T13:50:54 | 254,647,334 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 355 | py | def create_student(line):
parts=line.split(",")
name = parts[0]
age = int(parts[1])
marks_str = parts[2]
marks_str_arr = marks_str.split(" ")
marks_int_arr = [int(i) for i in marks_str_arr]
d={}
d['name']=name
d['age']=age
d['marks']=marks_int_arr
return d
def print_arr(arr):
for i in arr:
print(i) | [
"[email protected]"
] | |
1a46c8d568402db84e611a414452edb434b00e39 | 8982f5d9072aa9a2c7fb9466144b07760551a94e | /data/data_process.py | 21ff0c5fa4521ab1108d8895d591b5fc7458814e | [] | no_license | qw1085209368/SSLDPCA-IL-FaultDetection | 965442eac03ad996a28127c9d5513780b2738b17 | b68aa835cc127fc00ef29100908e8261ddc76998 | refs/heads/master | 2023-05-08T02:48:23.299522 | 2021-06-03T03:16:16 | 2021-06-03T03:16:35 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 11,244 | py | #!/usr/bin/env python
# @Time : 2021/3/2 15:44
# @Author : wb
# @File : data_process.py
'''
数据处理页面，将数据处理成需要的格式
'''
import h5py
import pandas as pd
import numpy as np
import os
import scipy.io as scio
import matplotlib.pyplot as plt
from PIL import Image
from matplotlib import image
from pyts.image import GramianAngularField
from tqdm import tqdm
from tslearn.piecewise import PiecewiseAggregateApproximation
from config import opt
class DataProcess(object):
'''
处理CWRU，凯斯西储大学轴承数据
CWRU原始数据分为驱动端与风扇端（DE，FE）
正常 4个
12K采样频率下的驱动端轴承故障数据 52个 没有第四种载荷的情况
48K采样频率下的驱动端轴承故障数据*（删除不用）
12K采样频率下的风扇端轴承故障数据 45个
每个采样频率下面三种故障直径，每种故障直径下面四种电机载荷，每种载荷有三种故障
内圈故障，外圈故障（三个位置），滚动体故障
总共101个数据文件
'''
def CWRU_data_1d(self, type='DE'):
'''
直接处理1d的时序数据
:type: DE或者FE，驱动端还是风扇端
:return: 保存为h5文件
'''
# 维度
dim = opt.CWRU_dim
# CWRU原始数据
CWRU_data_path = opt.CWRU_data_root
# 一维数据保存路径
save_path = opt.CWRU_data_1d_root
# 读取文件列表
frame_name = os.path.join(CWRU_data_path, 'annotations_mini.txt')
frame = pd.read_table(frame_name)
# 数据
signals = []
# 标签
labels = []
# 数据块数量
data_num = []
for idx in range(len(frame)):
mat_name = os.path.join(CWRU_data_path, frame['file_name'][idx])
raw_data = scio.loadmat(mat_name)
# raw_data.items() X097_DE_time 所以选取5:7为DE的
for key, value in raw_data.items():
if key[5:7] == type:
# 以dim的长度划分，有多少个数据块
sample_num = value.shape[0] // dim
# print('sample_num', sample_num)
# 数据取整
signal = value[0:dim * sample_num].reshape(1, -1)
# print('signals', signals.shape)
# 把数据分割成sample_num个数据块，(609,400,1)
signal_split = np.array(np.split(signal, sample_num, axis=1))
# 保存行向量
signals.append(signal_split)
# (123,)一维的label
labels.append(idx * np.ones(sample_num))
# 保存每个类别数据块的数量
data_num.append(sample_num)
# squeeze删除维度为1的维度，(1,123)->(123,)
# axis=0为纵向的拼接，axis=1为纵向的拼接
# (13477200,)
signals_np = np.concatenate(signals).squeeze()
# (33693,)
labels_np = np.concatenate(np.array(labels)).astype('uint8')
data_num_np = np.array(data_num).astype('uint16')
print(signals_np.shape, labels_np.shape, data_num_np.shape)
# 保存为h5的文件
file_name = os.path.join(save_path, 'CWRU_mini_' + type + '.h5')
f = h5py.File(file_name, 'w')
# 数据
f.create_dataset('data', data=signals_np)
# 标签
f.create_dataset('label', data=labels_np)
# 每个类别的数据块数量
f.create_dataset('data_num', data=data_num_np)
f.close()
def CWRU_data_2d_gaf(self, type='DE'):
'''
把CWRU数据集做成2d图像，使用Gramian Angular Field (GAF)，保存为png图片
因为GAF将n的时序信号转换为n*n，这样导致数据量过大，采用分段聚合近似（PAA）转换压缩时序数据的长度
97：243938
:type: DE还是FE
:return: 保存为2d图像
'''
# CWRU原始数据
CWRU_data_path = opt.CWRU_data_root
# 维度
dim = opt.CWRU_dim
# 读取文件列表
frame_name = os.path.join(CWRU_data_path, 'annotations.txt')
frame = pd.read_table(frame_name)
# 保存路径
save_path = os.path.join(opt.CWRU_data_2d_root, type)
if not os.path.exists(save_path):
os.makedirs(save_path)
# gasf文件目录
gasf_path = os.path.join(save_path, 'gasf')
if not os.path.exists(gasf_path):
os.makedirs(gasf_path)
# gadf文件目录
gadf_path = os.path.join(save_path, 'gadf')
if not os.path.exists(gadf_path):
os.makedirs(gadf_path)
for idx in tqdm(range(len(frame))):
# mat文件名
mat_name = os.path.join(CWRU_data_path, frame['file_name'][idx])
# 读取mat文件中的原始数据
raw_data = scio.loadmat(mat_name)
# raw_data.items() X097_DE_time 所以选取5:7为DE的
for key, value in raw_data.items():
if key[5:7] == type:
# dim个数据点一个划分，计算数据块的数量
sample_num = value.shape[0] // dim
# 数据取整，把列向量转换成行向量
signal = value[0:dim * sample_num].reshape(1, -1)
# PAA 分段聚合近似（PAA）转换
# paa = PiecewiseAggregateApproximation(n_segments=100)
# paa_signal = paa.fit_transform(signal)
# 按sample_num切分，每个dim大小
signals = np.split(signal, sample_num, axis=1)
for i in tqdm(range(len(signals))):
# 将每个dim的数据转换为2d图像
gasf = GramianAngularField(image_size=dim, method='summation')
signals_gasf = gasf.fit_transform(signals[i])
gadf = GramianAngularField(image_size=dim, method='difference')
signals_gadf = gadf.fit_transform(signals[i])
# 保存图像
filename_gasf = os.path.join(gasf_path, str(idx) + '.%d.png' % i)
image.imsave(filename_gasf, signals_gasf[0])
filename_gadf = os.path.join(gadf_path, str(idx) + '.%d.png' % i)
image.imsave(filename_gadf, signals_gadf[0])
# 展示图片
# images = [signals_gasf[0], signals_gadf[0]]
# titles = ['Summation', 'Difference']
#
# fig, axs = plt.subplots(1, 2, constrained_layout=True)
# for image, title, ax in zip(images, titles, axs):
# ax.imshow(image)
# ax.set_title(title)
# fig.suptitle('GramianAngularField', y=0.94, fontsize=16)
# plt.margins(0, 0)
# plt.savefig("GramianAngularField.pdf", pad_inches=0)
# plt.show()
def CWRU_data_2d_transform(self, type='DE'):
'''
使用数据拼接的方式，将一个长的时序数据拆分成小段，将小段按按行拼接
如果直接进行拼接的话样本数量比较少，采用时间窗移动切割，也就是很多数据会重复
这样可以提高图片的数量
未完成
:param type:DE or FE
:return:
'''
# CWRU原始数据
CWRU_data_path = opt.CWRU_data_root
# 维度
dim = opt.CWRU_dim
# 读取文件列表
frame_name = os.path.join(CWRU_data_path, 'annotations.txt')
frame = pd.read_table(frame_name)
# 保存路径
save_path = os.path.join(opt.CWRU_data_2d_root, type)
if not os.path.exists(save_path):
os.makedirs(save_path)
# 转换生成的图像文件目录
transform_path = os.path.join(save_path, 'transform')
if not os.path.exists(transform_path):
os.makedirs(transform_path)
for idx in tqdm(range(len(frame))):
# mat文件名
mat_name = os.path.join(CWRU_data_path, frame['file_name'][idx])
# 读取mat文件中的原始数据
raw_data = scio.loadmat(mat_name)
# raw_data.items() X097_DE_time 所以选取5:7为DE的
for key, value in raw_data.items():
if key[5:7] == type:
# dim个数据点一个划分，计算数据块的数量
sample_num = value.shape[0] // dim
# 数据取整，并转换为行向量
signal = value[0:dim * sample_num].reshape(1, -1)
# 归一化到[-1,1]，生成灰度图
signal = self.normalization(signal)
# 按sample_num切分，每一个块dim大小
signals = np.split(signal, sample_num, axis=1)
# 生成正方形的图片，正方形面积小，能生成多张图片
pic_num = sample_num // dim
pic_data = []
for i in range(pic_num-1):
pic_data.append(signals[i * dim:(i + 1) * dim])
# pic = np.concatenate(pic_data).squeeze()
# 展示图片
plt.imshow(pic_data)
plt.show()
def normalization(self, data):
'''
归一化
:param data:
:return:
'''
_range = np.max(abs(data))
return data / _range
def png2h5(self):
'''
将保存好的png图片保存到h5文件中，需要大量内存
:return: h5文件
'''
# 根目录
img_root = opt.CWRU_data_2d_DE
# 全部的图片的ID
imgs_path = [os.path.join(img_root, img) for img in os.listdir(img_root)]
# 图片数据
imgs = []
# 标签值
labels = []
for path in tqdm(imgs_path):
img = Image.open(path)
# img是Image内部的类文件，还需转换
img_PIL = np.asarray(img, dtype='uint8')
labels.append(path.split('/')[-1].split('\\')[-1].split('.')[0])
imgs.append(img_PIL)
# 关闭文件，防止多线程读取文件太多
img.close()
imgs = np.asarray(imgs).astype('uint8')
labels = np.asarray(labels).astype('uint8')
# 创建h5文件
file = h5py.File(opt.CWRU_data_2d_h5, "w")
# 在文件中创建数据集
file.create_dataset("image", np.shape(imgs), dtype='uint8', data=imgs)
# 标签
file.create_dataset("label", np.shape(labels), dtype='uint8', data=labels)
file.close()
if __name__ == '__main__':
data = DataProcess()
data.CWRU_data_1d(type='DE')
# DE(33693, 400) (33693,)
# FE(33693, 400) (33693,)
| [
"[email protected]"
] | |
5474853aa0063918f02b655556ab3268ea02e12a | edb699b1a63c3412e5a8788e32b26690c707ce2e | /rdconf.py | 1c57f37d1fefcab9072488d13dec9db4abe06cb2 | [
"MIT"
] | permissive | dkoes/rdkit-scripts | 518fe55058524ddd3a4f5ad4f0dd3d125a73f01a | 71cf2ccc4b26eb1541cc63690f40aa24b195fe8d | refs/heads/master | 2022-07-14T19:04:18.760183 | 2022-07-01T12:39:32 | 2022-07-01T12:39:32 | 72,130,300 | 48 | 32 | MIT | 2019-04-15T18:46:27 | 2016-10-27T17:09:14 | Python | UTF-8 | Python | false | false | 6,117 | py | #!/usr/bin/python3
import sys,string,argparse
from rdkit.Chem import AllChem as Chem
from optparse import OptionParser
import os, gzip
'''Given a smiles file, generate 3D conformers in output sdf.
Energy minimizes and filters conformers to meet energy window and rms constraints.
Some time ago I compared this to alternative conformer generators and
it was quite competitive (especially after RDKit's UFF implementation
added OOP terms).
'''
#convert smiles to sdf
def getRMS(mol, c1,c2):
rms = Chem.GetBestRMS(mol,mol,c1,c2)
return rms
parser = OptionParser(usage="Usage: %prog [options] <input>.smi <output>.sdf")
parser.add_option("--maxconfs", dest="maxconfs",action="store",
help="maximum number of conformers to generate per a molecule (default 20)", default="20", type="int", metavar="CNT")
parser.add_option("--sample_multiplier", dest="sample",action="store",
help="sample N*maxconfs conformers and choose the maxconformers with lowest energy (default 1)", default="1", type="float", metavar="N")
parser.add_option("--seed", dest="seed",action="store",
help="random seed (default 9162006)", default="9162006", type="int", metavar="s")
parser.add_option("--rms_threshold", dest="rms",action="store",
help="filter based on rms (default 0.7)", default="0.7", type="float", metavar="R")
parser.add_option("--energy_window", dest="energy",action="store",
help="filter based on energy difference with lowest energy conformer", default="10", type="float", metavar="E")
parser.add_option("-v","--verbose", dest="verbose",action="store_true",default=False,
help="verbose output")
parser.add_option("--mmff", dest="mmff",action="store_true",default=False,
help="use MMFF forcefield instead of UFF")
parser.add_option("--nomin", dest="nomin",action="store_true",default=False,
help="don't perform energy minimization (bad idea)")
parser.add_option("--etkdg", dest="etkdg",action="store_true",default=False,
help="use new ETKDG knowledge-based method instead of distance geometry")
(options, args) = parser.parse_args()
if(len(args) < 2):
parser.error("Need input and output")
sys.exit(-1)
input = args[0]
output = args[1]
smifile = open(input)
if options.verbose:
print("Generating a maximum of",options.maxconfs,"per a mol")
if options.etkdg and not Chem.ETKDG:
print("ETKDB does not appear to be implemented. Please upgrade RDKit.")
sys.exit(1)
split = os.path.splitext(output)
if split[1] == '.gz':
outf=gzip.open(output,'wt+')
output = split[0] #strip .gz
else:
outf = open(output,'w+')
if os.path.splitext(output)[1] == '.pdb':
sdwriter = Chem.PDBWriter(outf)
else:
sdwriter = Chem.SDWriter(outf)
if sdwriter is None:
print("Could not open ".output)
sys.exit(-1)
for line in smifile:
toks = line.split()
smi = toks[0]
name = ' '.join(toks[1:])
pieces = smi.split('.')
if len(pieces) > 1:
smi = max(pieces, key=len) #take largest component by length
print("Taking largest component: %s\t%s" % (smi,name))
mol = Chem.MolFromSmiles(smi)
if mol is not None:
if options.verbose:
print(smi)
try:
Chem.SanitizeMol(mol)
mol = Chem.AddHs(mol)
mol.SetProp("_Name",name);
if options.etkdg:
cids = Chem.EmbedMultipleConfs(mol, int(options.sample*options.maxconfs), Chem.ETKDG())
else:
cids = Chem.EmbedMultipleConfs(mol, int(options.sample*options.maxconfs),randomSeed=options.seed)
if options.verbose:
print(len(cids),"conformers found")
cenergy = []
for conf in cids:
#not passing confID only minimizes the first conformer
if options.nomin:
cenergy.append(conf)
elif options.mmff:
converged = Chem.MMFFOptimizeMolecule(mol,confId=conf)
mp = Chem.MMFFGetMoleculeProperties(mol)
cenergy.append(Chem.MMFFGetMoleculeForceField(mol,mp,confId=conf).CalcEnergy())
else:
converged = not Chem.UFFOptimizeMolecule(mol,confId=conf)
cenergy.append(Chem.UFFGetMoleculeForceField(mol,confId=conf).CalcEnergy())
if options.verbose:
print("Convergence of conformer",conf,converged,cenergy[-1])
mol = Chem.RemoveHs(mol)
sortedcids = sorted(cids,key = lambda cid: cenergy[cid])
if len(sortedcids) > 0:
mine = cenergy[sortedcids[0]]
else:
mine = 0
if(options.rms == 0):
cnt = 0;
for conf in sortedcids:
if(cnt >= options.maxconfs):
break
if(options.energy < 0) or cenergy[conf]-mine <= options.energy:
sdwriter.write(mol,conf)
cnt+=1
else:
written = {}
for conf in sortedcids:
if len(written) >= options.maxconfs:
break
#check rmsd
passed = True
for seenconf in written.keys():
rms = getRMS(mol,seenconf,conf)
if(rms < options.rms) or (options.energy > 0 and cenergy[conf]-mine > options.energy):
passed = False
break
if(passed):
written[conf] = True
sdwriter.write(mol,conf)
except (KeyboardInterrupt, SystemExit):
raise
except Exception as e:
print("Exception",e)
else:
print("ERROR:",smi)
sdwriter.close()
outf.close()
| [
"[email protected]"
] | |
e7e80f2adc30dd1979a333284f8ab5df5124745d | f533840d313af1a7be73c63f42d211c8c337aa92 | /ginga/misc/plugins/Pipeline.py | afdf9d3cf9f4577d8f65613dfaf14277eb8a0831 | [
"BSD-3-Clause"
] | permissive | godber/ginga | 2d0b5c73fa168ad33bbf7dbe0c5fd2e1b0e54900 | acb32ed422aa604681c63c5a9494ffb0ad96cf2e | refs/heads/master | 2021-01-22T18:28:04.235890 | 2014-11-20T01:09:51 | 2014-11-20T01:09:51 | 27,312,287 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 10,032 | py | #
# Pipeline.py -- Simple data reduction pipeline plugin for Ginga FITS viewer
#
# Eric Jeschke ([email protected])
#
# Copyright (c) Eric R. Jeschke. All rights reserved.
# This is open-source software licensed under a BSD license.
# Please see the file LICENSE.txt for details.
#
from __future__ import print_function
import numpy
from ginga import AstroImage
from ginga.util import dp
from ginga import GingaPlugin
from ginga.misc import Widgets
class Pipeline(GingaPlugin.LocalPlugin):
def __init__(self, fv, fitsimage):
# superclass defines some variables for us, like logger
super(Pipeline, self).__init__(fv, fitsimage)
# Load preferences
prefs = self.fv.get_preferences()
self.settings = prefs.createCategory('plugin_Pipeline')
self.settings.setDefaults(num_threads=4)
self.settings.load(onError='silent')
# For building up an image stack
self.imglist = []
# For applying flat fielding
self.flat = None
# For subtracting bias
self.bias = None
self.gui_up = False
def build_gui(self, container):
top = Widgets.VBox()
top.set_border_width(4)
vbox1, sw, orientation = Widgets.get_oriented_box(container)
vbox1.set_border_width(4)
vbox1.set_spacing(2)
self.msgFont = self.fv.getFont("sansFont", 12)
tw = Widgets.TextArea(wrap=True, editable=False)
tw.set_font(self.msgFont)
self.tw = tw
fr = Widgets.Frame("Instructions")
vbox2 = Widgets.VBox()
vbox2.add_widget(tw)
vbox2.add_widget(Widgets.Label(''), stretch=1)
fr.set_widget(vbox2)
vbox1.add_widget(fr, stretch=0)
# Main pipeline control area
captions = [
("Subtract Bias", 'button', "Bias Image:", 'label',
'bias_image', 'llabel'),
("Apply Flat Field", 'button', "Flat Image:", 'label',
'flat_image', 'llabel'),
]
w, b = Widgets.build_info(captions, orientation=orientation)
self.w.update(b)
fr = Widgets.Frame("Pipeline")
fr.set_widget(w)
vbox1.add_widget(fr, stretch=0)
b.subtract_bias.add_callback('activated', self.subtract_bias_cb)
b.subtract_bias.set_tooltip("Subtract a bias image")
bias_name = 'None'
if self.bias != None:
bias_name = self.bias.get('name', "NoName")
b.bias_image.set_text(bias_name)
b.apply_flat_field.add_callback('activated', self.apply_flat_cb)
b.apply_flat_field.set_tooltip("Apply a flat field correction")
flat_name = 'None'
if self.flat != None:
flat_name = self.flat.get('name', "NoName")
b.flat_image.set_text(flat_name)
vbox2 = Widgets.VBox()
# Pipeline status
hbox = Widgets.HBox()
hbox.set_spacing(4)
hbox.set_border_width(4)
label = Widgets.Label()
self.w.eval_status = label
hbox.add_widget(self.w.eval_status, stretch=0)
hbox.add_widget(Widgets.Label(''), stretch=1)
vbox2.add_widget(hbox, stretch=0)
# progress bar and stop button
hbox = Widgets.HBox()
hbox.set_spacing(4)
hbox.set_border_width(4)
btn = Widgets.Button("Stop")
btn.add_callback('activated', lambda w: self.eval_intr())
btn.set_enabled(False)
self.w.btn_intr_eval = btn
hbox.add_widget(btn, stretch=0)
self.w.eval_pgs = Widgets.ProgressBar()
hbox.add_widget(self.w.eval_pgs, stretch=1)
vbox2.add_widget(hbox, stretch=0)
vbox2.add_widget(Widgets.Label(''), stretch=1)
vbox1.add_widget(vbox2, stretch=0)
# Image list
captions = [
("Append", 'button', "Prepend", 'button', "Clear", 'button'),
]
w, b = Widgets.build_info(captions, orientation=orientation)
self.w.update(b)
fr = Widgets.Frame("Image Stack")
vbox = Widgets.VBox()
hbox = Widgets.HBox()
self.w.stack = Widgets.Label('')
hbox.add_widget(self.w.stack, stretch=0)
vbox.add_widget(hbox, stretch=0)
vbox.add_widget(w, stretch=0)
fr.set_widget(vbox)
vbox1.add_widget(fr, stretch=0)
self.update_stack_gui()
b.append.add_callback('activated', self.append_image_cb)
b.append.set_tooltip("Append an individual image to the stack")
b.prepend.add_callback('activated', self.prepend_image_cb)
b.prepend.set_tooltip("Prepend an individual image to the stack")
b.clear.add_callback('activated', self.clear_stack_cb)
b.clear.set_tooltip("Clear the stack of images")
# Bias
captions = [
("Make Bias", 'button', "Set Bias", 'button'),
]
w, b = Widgets.build_info(captions, orientation=orientation)
self.w.update(b)
fr = Widgets.Frame("Bias Subtraction")
fr.set_widget(w)
vbox1.add_widget(fr, stretch=0)
b.make_bias.add_callback('activated', self.make_bias_cb)
b.make_bias.set_tooltip("Makes a bias image from a stack of individual images")
b.set_bias.add_callback('activated', self.set_bias_cb)
b.set_bias.set_tooltip("Set the currently loaded image as the bias image")
# Flat fielding
captions = [
("Make Flat Field", 'button', "Set Flat Field", 'button'),
]
w, b = Widgets.build_info(captions, orientation=orientation)
self.w.update(b)
fr = Widgets.Frame("Flat Fielding")
fr.set_widget(w)
vbox1.add_widget(fr, stretch=0)
b.make_flat_field.add_callback('activated', self.make_flat_cb)
b.make_flat_field.set_tooltip("Makes a flat field from a stack of individual flats")
b.set_flat_field.add_callback('activated', self.set_flat_cb)
b.set_flat_field.set_tooltip("Set the currently loaded image as the flat field")
spacer = Widgets.Label('')
vbox1.add_widget(spacer, stretch=1)
top.add_widget(sw, stretch=1)
btns = Widgets.HBox()
btns.set_spacing(3)
btn = Widgets.Button("Close")
btn.add_callback('activated', lambda w: self.close())
btns.add_widget(btn, stretch=0)
btns.add_widget(Widgets.Label(''), stretch=1)
top.add_widget(btns, stretch=0)
container.add_widget(top, stretch=1)
self.gui_up = True
def close(self):
chname = self.fv.get_channelName(self.fitsimage)
self.fv.stop_local_plugin(chname, str(self))
self.gui_up = False
return True
def instructions(self):
self.tw.set_text("""TBD.""")
def start(self):
self.instructions()
def stop(self):
self.fv.showStatus("")
def update_status(self, text):
self.fv.gui_do(self.w.eval_status.set_text, text)
def update_stack_gui(self):
stack = [ image.get('name', "NoName") for image in self.imglist ]
self.w.stack.set_text(str(stack))
def append_image_cb(self, w):
image = self.fitsimage.get_image()
self.imglist.append(image)
self.update_stack_gui()
self.update_status("Appended image #%d to stack." % (len(self.imglist)))
def prepend_image_cb(self, w):
image = self.fitsimage.get_image()
self.imglist.insert(0, image)
self.update_stack_gui()
self.update_status("Prepended image #%d to stack." % (len(self.imglist)))
def clear_stack_cb(self, w):
self.imglist = []
self.update_stack_gui()
self.update_status("Cleared image stack.")
def show_result(self, image):
chname = self.fv.get_channelName(self.fitsimage)
name = dp.get_image_name(image)
self.imglist.insert(0, image)
self.update_stack_gui()
self.fv.add_image(name, image, chname=chname)
# BIAS
def _make_bias(self):
image = dp.make_bias(self.imglist)
self.imglist = []
self.fv.gui_do(self.show_result, image)
self.update_status("Made bias image.")
def make_bias_cb(self, w):
self.update_status("Making bias image...")
self.fv.nongui_do(self.fv.error_wrap, self._make_bias)
def subtract_bias_cb(self, w):
image = self.fitsimage.get_image()
if self.bias == None:
self.fv.show_error("Please set a bias image first")
else:
result = self.fv.error_wrap(dp.subtract, image, self.bias)
self.fv.gui_do(self.show_result, result)
def set_bias_cb(self, w):
# Current image is a bias image we should set
self.bias = self.fitsimage.get_image()
biasname = dp.get_image_name(self.bias, pfx='bias')
self.w.bias_image.set_text(biasname)
self.update_status("Set bias image.")
# FLAT FIELDING
def _make_flat_field(self):
result = dp.make_flat(self.imglist)
self.imglist = []
self.show_result(result)
self.update_status("Made flat field.")
def make_flat_cb(self, w):
self.update_status("Making flat field...")
self.fv.nongui_do(self.fv.error_wrap, self._make_flat_field)
def apply_flat_cb(self, w):
image = self.fitsimage.get_image()
if self.flat == None:
self.fv.show_error("Please set a flat field image first")
else:
result = self.fv.error_wrap(dp.divide, image, self.flat)
print(result, image)
self.fv.gui_do(self.show_result, result)
def set_flat_cb(self, w):
# Current image is a flat field we should set
self.flat = self.fitsimage.get_image()
flatname = dp.get_image_name(self.flat, pfx='flat')
self.w.flat_image.set_text(flatname)
self.update_status("Set flat field.")
def __str__(self):
return 'pipeline'
#END
| [
"[email protected]"
] | |
db508cd4bcd3fa891bbd1f0067a4bb8269d2f093 | 3378bf2ebcd3cd794b26b74f033932cb0c6a6590 | /tuframework/network_architecture/cotr/DeTrans/ops/functions/ms_deform_attn_func.py | ce8862f5fc39e40bd00747c8d9c65fcffb6118b3 | [
"Apache-2.0"
] | permissive | Magnety/tuFramework | d3d81f0663edbffbdd9b45138cbca82ffb78f03e | b31cb34d476ef306b52da955021f93c91c14ddf4 | refs/heads/master | 2023-05-01T04:36:04.873112 | 2021-05-18T01:07:54 | 2021-05-18T01:07:54 | 361,652,585 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,271 | py | import torch
import torch.nn.functional as F
from torch.autograd import Function
from torch.autograd.function import once_differentiable
def ms_deform_attn_core_pytorch_3D(value, value_spatial_shapes, sampling_locations, attention_weights):
N_, S_, M_, D_ = value.shape
_, Lq_, M_, L_, P_, _ = sampling_locations.shape
value_list = value.split([T_ * H_ * W_ for T_, H_, W_ in value_spatial_shapes], dim=1)
sampling_grids = 2 * sampling_locations - 1
# sampling_grids = 3 * sampling_locations - 1
sampling_value_list = []
for lid_, (T_, H_, W_) in enumerate(value_spatial_shapes):
value_l_ = value_list[lid_].flatten(2).transpose(1, 2).reshape(N_*M_, D_, T_, H_, W_)
sampling_grid_l_ = sampling_grids[:, :, :, lid_].transpose(1, 2).flatten(0, 1)[:,None,:,:,:]
sampling_value_l_ = F.grid_sample(value_l_, sampling_grid_l_.to(dtype=value_l_.dtype), mode='bilinear', padding_mode='zeros', align_corners=False)[:,:,0]
sampling_value_list.append(sampling_value_l_)
attention_weights = attention_weights.transpose(1, 2).reshape(N_*M_, 1, Lq_, L_*P_)
output = (torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights).sum(-1).view(N_, M_*D_, Lq_)
return output.transpose(1, 2).contiguous() | [
"[email protected]"
] | |
ef258bb106bc53bf5ed4c1a06b980d66154f5206 | 09e57dd1374713f06b70d7b37a580130d9bbab0d | /data/cirq_new/cirq_program/startCirq_Class783.py | 22d75780c4d05107a506bcec34d8229140243599 | [
"BSD-3-Clause"
] | permissive | UCLA-SEAL/QDiff | ad53650034897abb5941e74539e3aee8edb600ab | d968cbc47fe926b7f88b4adf10490f1edd6f8819 | refs/heads/main | 2023-08-05T04:52:24.961998 | 2021-09-19T02:56:16 | 2021-09-19T02:56:16 | 405,159,939 | 2 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,189 | py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 5/15/20 4:49 PM
# @File : grover.py
# qubit number=4
# total number=20
import cirq
import cirq.google as cg
from typing import Optional
import sys
from math import log2
import numpy as np
#thatsNoCode
def make_circuit(n: int, input_qubit):
c = cirq.Circuit() # circuit begin
c.append(cirq.H.on(input_qubit[0])) # number=1
c.append(cirq.H.on(input_qubit[1])) # number=2
c.append(cirq.H.on(input_qubit[1])) # number=7
c.append(cirq.H.on(input_qubit[2])) # number=3
c.append(cirq.H.on(input_qubit[3])) # number=4
c.append(cirq.H.on(input_qubit[0])) # number=17
c.append(cirq.CZ.on(input_qubit[3],input_qubit[0])) # number=18
c.append(cirq.H.on(input_qubit[0])) # number=19
c.append(cirq.H.on(input_qubit[0])) # number=14
c.append(cirq.CZ.on(input_qubit[3],input_qubit[0])) # number=15
c.append(cirq.H.on(input_qubit[0])) # number=16
c.append(cirq.Z.on(input_qubit[1])) # number=13
c.append(cirq.SWAP.on(input_qubit[1],input_qubit[0])) # number=8
c.append(cirq.SWAP.on(input_qubit[1],input_qubit[0])) # number=9
c.append(cirq.SWAP.on(input_qubit[3],input_qubit[0])) # number=10
c.append(cirq.SWAP.on(input_qubit[3],input_qubit[0])) # number=11
c.append(cirq.Z.on(input_qubit[2])) # number=12
# circuit end
return c
def bitstring(bits):
return ''.join(str(int(b)) for b in bits)
if __name__ == '__main__':
qubit_count = 4
input_qubits = [cirq.GridQubit(i, 0) for i in range(qubit_count)]
circuit = make_circuit(qubit_count,input_qubits)
circuit = cg.optimized_for_sycamore(circuit, optimizer_type='sqrt_iswap')
circuit_sample_count =2820
info = cirq.final_state_vector(circuit)
qubits = round(log2(len(info)))
frequencies = {
np.binary_repr(i, qubits): round((info[i]*(info[i].conjugate())).real,3)
for i in range(2 ** qubits)
}
writefile = open("../data/startCirq_Class783.csv","w+")
print(format(frequencies),file=writefile)
print("results end", file=writefile)
print(circuit.__len__(), file=writefile)
print(circuit,file=writefile)
writefile.close() | [
"[email protected]"
] | |
59e7efd7c9f67741580d7402f2235895e4b25a95 | eff638e128276c5d7de4fca5b0acde312e9a8a8d | /single line if-else.py | 846752636af516206408659f87bedf230522f61b | [] | no_license | Sahil4UI/Python_Jan_12-1_Regular2021 | 81e274ef2129fe43da8fbea172c055861dcda4d2 | 8e78457c2215d6bc652b14957d73423aecdd5c41 | refs/heads/main | 2023-03-04T09:22:48.582757 | 2021-02-18T07:26:47 | 2021-02-18T07:26:47 | 334,865,411 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 82 | py | x = int(input("Enter Number : "))
res = "Even" if x%2==0 else"Odd"
print(res)
| [
"[email protected]"
] |
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