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20fd33920d2f04fa9ff9291c2a1a4cec9afa66e0 | b7add0d1b1effc50b27d3316fa5889a5227e5b19 | /Robots/roboquasar0.1/tests/video_test.py | d5c6f649c8e6c0ec3106fabdaff3cdec653dfe20 | [] | no_license | Woz4tetra/Atlas | efb83a7c7b2698bf8b36b023f7aa573cc38284f6 | c7380868a9efef9d1594ed7aa87187f03a7e4612 | refs/heads/master | 2020-04-04T06:25:50.657631 | 2017-04-05T01:53:15 | 2017-04-05T01:53:15 | 50,269,756 | 3 | 1 | null | null | null | null | UTF-8 | Python | false | false | 4,056 | py | from atlasbuggy.interface import RobotSimulator
from atlasbuggy.interface import RobotRunner
from atlasbuggy.robot import Robot
from atlasbuggy.vision.camera import Camera
import time
import cv2
from multiprocessing import Process, Queue, Lock, Event
class Pipeline(Process):
def __init__(self):
self.frames_queue = Queue()
self.results_queue = Queue()
self.pipeline_lock = Lock()
self.exit_event = Event()
self.exit_lock = Lock()
self.capture = cv2.VideoCapture(1)
super(Pipeline, self).__init__(target=self.update)
def update(self):
while True:
success, frame = self.capture.read()
with self.exit_lock:
if self.exit_event.is_set():
break
with self.pipeline_lock:
self.results_queue.put(self.pipeline(frame))
print("1", self.results_queue.empty())
def pipeline(self, frame):
return cv2.medianBlur(frame, 111)
def put(self, frame):
with self.pipeline_lock:
self.frames_queue.put(frame)
def close(self):
with self.exit_lock:
self.exit_event.set()
class CaptureTester(Robot):
def __init__(self, enable_recording):
super(CaptureTester, self).__init__()
self.logitech = Camera("logitech")
self.ps3eye = Camera("ps3eye", enabled=False)
self.start_time = time.time()
super(CaptureTester, self).__init__()
def start(self):
logitech_name = "%s%s.avi" % (self.get_path_info("file name no extension"), self.logitech.name)
ps3eye_name = "%s%s.avi" % (self.get_path_info("file name no extension"), self.ps3eye.name)
directory = self.get_path_info("input dir")
if self.is_live:
status = self.logitech.launch_camera(
logitech_name, directory, self.logger.is_open(), capture_number=1
)
if status is not None:
return status
status = self.ps3eye.launch_camera(
ps3eye_name, directory, self.logger.is_open(), capture_number=2
)
if status is not None:
return status
else:
self.logitech.launch_video(logitech_name, directory)
self.ps3eye.launch_video(ps3eye_name, directory)
self.start_time = time.time()
def loop(self):
if self.logitech.get_frame(self.dt()) is None:
return "exit"
if self.ps3eye.get_frame(self.dt()) is None:
return "exit"
self.logitech.show_frame()
self.ps3eye.show_frame()
key = self.logitech.key_pressed()
if key == 'q' or key == -2:
return "done"
def close(self, reason):
self.logitech.close()
self.ps3eye.close()
class PipelineTest:
def __init__(self, use_pipeline):
self.use_pipeline = use_pipeline
self.pipeline = Pipeline()
self.mac_cam = Camera("mac")
if self.use_pipeline:
self.pipeline.start()
else:
self.mac_cam.launch_camera(None, None, False, 0)
def show(self):
if self.use_pipeline:
# self.pipeline.put(self.mac_cam.get_frame())
with self.pipeline.pipeline_lock:
if not self.pipeline.results_queue.empty():
print("2", self.pipeline.results_queue.empty())
cv2.imshow("pipeline", self.pipeline.results_queue.get())
# self.mac_cam.show_frame(frame)
else:
frame = self.mac_cam.get_frame()
self.mac_cam.show_frame(cv2.medianBlur(frame, 111))
def run(live):
video_tester = CaptureTester(False)
if live:
runner = RobotRunner(video_tester, log_data=False)
runner.run()
else:
simulator = RobotSimulator("18;54", "2017_Mar_12", video_tester)
simulator.run()
def pipeline():
test = PipelineTest(True)
while True:
test.show()
run(True)
# pipeline()
| [
"[email protected]"
] | |
097bda6380cf4c3c5d7e9e5bef07aeab97a3127f | a90215c7f54fa7ea5792c673aac5eebc0bccdc10 | /CS3243_P2_Sudoku_XX_ac3.py | 4239cbf11710ffa65360daf94882e63dbde1ce2d | [] | no_license | FEIHONGYI857/CS3243_project2_group8 | c137af5c198704eba0e4b0234fcc8b233f3d667f | 28f0a64111a80be6fcd617e7587e7b9f669e0977 | refs/heads/master | 2021-05-17T21:21:38.463227 | 2020-03-30T10:46:38 | 2020-03-30T10:46:38 | 250,958,329 | 0 | 0 | null | 2020-03-29T05:08:50 | 2020-03-29T05:08:49 | null | UTF-8 | Python | false | false | 7,139 | py | import sys
import copy
# Running script: given code can be run with the command:
# python file.py, ./path/to/init_state.txt ./output/output.txt
class Sudoku(object):
def __init__(self, puzzle):
# you may add more attributes if you need
self.puzzle = puzzle # self.puzzle is a list of lists
self.ans = copy.deepcopy(puzzle) # self.ans is a list of lists
self.domains = list()
self.unassignedVars = set()
self.prunes = list() #used to recall
self.prune = list()
self.constraints = list()#store (a1,a2) for ac-3
self.build_constraints()
self.neighbors = dict()
self.build_neighbors()#store (a1,a2) for ac-3 and forward checking
def build_constraints(self):
for a in range(9):
for b in range(9):#get one node
for c in range(9):
for r in range(9):#get another node
if a == c or b == r:
if not(a == c and b == r):
if [a,b,c,r] not in self.constraints:
self.constraints.append([a,b,c,r])
layers = [[0,1,2], [3,4,5], [6,7,8]]
for layer in layers:
if a in layer:
neighbourr = layer
if b in layer:
neighbourc = layer
for d in neighbourr:
for e in neighbourc:
if [a,b,d,e] not in self.constraints:
if a != d and b != e:
self.constraints.append([a,b,d,e])
#print(self.constraints)
def build_neighbors(self):
for a in range(9):
for b in range(9):
self.neighbors[a*9 + b] = list()
for c in self.constraints:
if a == c[0] and b == c[1]:
self.neighbors[a*9 + b].append([c[2],c[3]])
#print(self.neighbors[42])
def ac3(self):
queue = list(self.constraints)
while queue:
xa,xb,xc,xd = queue.pop(0)
if self.revise(xa,xb,xc,xd):
if len(self.domains[xa*9+xb]) == 0:
if xa * 9 + xb == 42:
print(self.domains[42])
return False
for xk in self.neighbors[xa*9+xb]:
if xk == xa*9+xb:
queue.append([xa,xb,xk[0],xk[1]])
return True
def revise(self,xa,xb,xc,xd):
revised = False
for x in self.domains[xa * 9 + xb]:
if not any([self.constraint(x, y) for y in self.domains[xc * 9 + xd]]):
self.domains[xa * 9 + xb].remove(x)
revised = True
return revised
def constraint(self, xi, xj):
return xi != xj
def solve(self):
self.initDomains()
#print(self.domains)
self.ac3()
# print(self.domains)
self.backtrack()
#print(puzzle)
return self.ans
def backtrack(self):
if len(self.unassignedVars) == 0:
return True
#variable selection
currVar = self.mostConstrVar(self.unassignedVars)
#value selection
domain = self.varDomain(self.domains,currVar)
#print(len(domain))
for index in range(len(domain)):
self.assignVal(currVar,domain[index])
if self.validCheck(currVar,domain[index]) == False:
self.unassignVal(currVar,domain[index])
continue
if self.backtrack() == False:
self.unassignVal(currVar,domain[index])
continue
else:
return True
#add back to unassigned
self.unassignedVars.add(currVar)
return False
def mostConstrVar(self,unassignedVars):
MCV = unassignedVars.pop()
return MCV
def varDomain(self,domainList,currVar):
varIndex = currVar[0]*9 + currVar[1]
currDomain = self.domains[varIndex]
return currDomain
def assignVal(self,currVar,value):
self.puzzle[currVar[0]][currVar[1]] = value
return 1
def unassignVal(self,currVar,value):
self.puzzle[currVar[0]][currVar[1]] = 0
return 1
def initDomains(self):
domainSet = [1,2,3,4,5,6,7,8,9]
for row in range(9):
for col in range(9):
if self.puzzle[row][col] == 0:
self.domains.append(copy.copy(domainSet))
self.unassignedVars.add((row,col))
else:
list_addvalue = []
self.domains.append([puzzle[row][col]])
return 1
def validCheck(self,currVar,value):
row = currVar[0]
col = currVar[1]
for c in range(9):
if c != col:
if self.puzzle[row][c] == value:
return False
for r in range(9):
if r != row:
if self.puzzle[r][col] == value:
return False
layers = [[0,1,2],[3,4,5],[6,7,8]]
for layer in layers:
if row in layer:
neighbourRow = layer
if col in layer:
neighbourCol = layer
#print(neighbourCol)
# print(neighbourRow)
for sqRow in neighbourRow:
for sqCol in neighbourCol:
if sqRow != row or sqCol != col:
if self.puzzle[sqRow][sqCol] == value:
return False
return True
# you may add more classes/functions if you think is useful
# However, ensure all the classes/functions are in this file ONLY
# Note that our evaluation scripts only call the solve method.
# Any other methods that you write should be used within the solve() method.
if __name__ == "__main__":
# STRICTLY do NOT modify the code in the main function here
if len(sys.argv) != 3:
print ("\nUsage: python CS3243_P2_Sudoku_XX.py input.txt output.txt\n")
raise ValueError("Wrong number of arguments!")
try:
f = open(sys.argv[1], 'r')
except IOError:
print ("\nUsage: python CS3243_P2_Sudoku_XX.py input.txt output.txt\n")
raise IOError("Input file not found!")
puzzle = [[0 for i in range(9)] for j in range(9)]
lines = f.readlines()
i, j = 0, 0
for line in lines:
for number in line:
if '0' <= number <= '9':
puzzle[i][j] = int(number)
j += 1
if j == 9:
i += 1
j = 0
sudoku = Sudoku(puzzle)
ans = sudoku.solve()
with open(sys.argv[2], 'a') as f:
for i in range(9):
for j in range(9):
f.write(str(ans[i][j]) + " ")
f.write("\n") | [
"[email protected]"
] | |
bd90bac4da03e24fabe6cb8322a8367a1012cd2b | 243d3241959f1aa07f53b4d2972ba7b72df5f8c9 | /webapp/static/fusioncharts/samples/xy_chart.py | d4c4d70d74768fcf77b10f29ce0201cc60c10b89 | [] | no_license | akr888/Creating-and-Querying-a-NHTS-Database | 51015bbe384c634a44f6c270714be59da3711190 | 87e0022f4ec93d8b0a3749286235b431de17a471 | refs/heads/master | 2020-03-19T08:46:48.278423 | 2018-06-06T18:26:21 | 2018-06-06T18:26:21 | 136,234,221 | 1 | 1 | null | null | null | null | UTF-8 | Python | false | false | 8,477 | py | from django.shortcuts import render
from django.http import HttpResponse
# Include the `fusioncharts.py` file that contains functions to embed the charts.
from ..fusioncharts import FusionCharts
# Loading Data from a Static JSON String
# Example to create a Scatter chart with the chart data passed as JSON string format.
# The `chart` method is defined to load chart data from a JSON string.
def chart(request):
# Create an object for the Scatter chart using the FusionCharts class constructor
scatterChart = FusionCharts("scatter", "ex1" , "600", "350", "chart-1", "json",
# The chart data is passed as a string to the `dataSource` parameter.
"""{
"chart": {
"theme": "fint",
"caption": "Sales of Beer & Ice-cream vs Temperature",
"subCaption": "Los Angeles Topanga",
"xAxisName": "Average Day Temperature",
"xAxisMinValue": "23",
"xAxisMaxValue": "95",
"yNumberPrefix": "$",
"xNumberSuffix": "° F",
"theme": "fint",
"xAxisNamePadding": "-5",
"legendpadding": "0",
"plotToolText": "<div><b>$seriesname</b><br/>Temperature : <b>$xValue° F</b><br/>Sales : <b>$$yValue</b></div>"
},
"categories": [{
"category": [{
"x": "23",
"label": "23 F",
"showverticalline": "0"
}, {
"x": "32",
"label": "32 F",
"showverticalline": "1"
}, {
"x": "50",
"label": "50 F",
"showverticalline": "1"
}, {
"x": "68",
"label": "68 F",
"showverticalline": "1"
}, {
"x": "80",
"label": "80 F",
"showverticalline": "1"
}, {
"x": "95",
"label": "95 F",
"showverticalline": "1"
}]
}],
"dataset": [{
"seriesname": "Ice Cream",
"showregressionline": "1",
"data": [{
"x": "23",
"y": "1560"
}, {
"x": "24",
"y": "1500"
}, {
"x": "24",
"y": "1680"
}, {
"x": "25",
"y": "1780"
}, {
"x": "25",
"y": "1620"
}, {
"x": "26",
"y": "1810"
}, {
"x": "27",
"y": "2310"
}, {
"x": "29",
"y": "2620"
}, {
"x": "31",
"y": "2500"
}, {
"x": "32",
"y": "2410"
}, {
"x": "35",
"y": "2880"
}, {
"x": "36",
"y": "3910"
}, {
"x": "34",
"y": "3960"
}, {
"x": "38",
"y": "4080"
}, {
"x": "40",
"y": "4190"
}, {
"x": "41",
"y": "4170"
}, {
"x": "42",
"y": "4280"
}, {
"x": "54",
"y": "5180"
}, {
"x": "53",
"y": "5770"
}, {
"x": "55",
"y": "5900"
}, {
"x": "56",
"y": "5940"
}, {
"x": "58",
"y": "6090"
}, {
"x": "61",
"y": "6086"
}, {
"x": "67",
"y": "6100"
}, {
"x": "68",
"y": "6200"
}, {
"x": "70",
"y": "6360"
}, {
"x": "75",
"y": "6450"
}, {
"x": "79",
"y": "6650"
}, {
"x": "80",
"y": "6710"
}, {
"x": "79",
"y": "6975"
}, {
"x": "82",
"y": "7000"
}, {
"x": "85",
"y": "7150"
}, {
"x": "86",
"y": "7160"
}, {
"x": "86",
"y": "7200"
}, {
"x": "88",
"y": "7230"
}, {
"x": "87",
"y": "7210"
}, {
"x": "86",
"y": "7480"
}, {
"x": "89",
"y": "7540"
}, {
"x": "89",
"y": "7400"
}, {
"x": "90",
"y": "7500"
}, {
"x": "92",
"y": "7640"
}]
}, {
"seriesname": "Beer",
"showregressionline": "1",
"data": [{
"x": "23",
"y": "3160"
}, {
"x": "24",
"y": "3000"
}, {
"x": "24",
"y": "3080"
}, {
"x": "25",
"y": "3680"
}, {
"x": "25",
"y": "3320"
}, {
"x": "26",
"y": "3810"
}, {
"x": "27",
"y": "5310"
}, {
"x": "29",
"y": "3620"
}, {
"x": "31",
"y": "4100"
}, {
"x": "32",
"y": "3910"
}, {
"x": "35",
"y": "4280"
}, {
"x": "36",
"y": "4210"
}, {
"x": "34",
"y": "4160"
}, {
"x": "38",
"y": "4480"
}, {
"x": "40",
"y": "4890"
}, {
"x": "41",
"y": "4770"
}, {
"x": "42",
"y": "4880"
}, {
"x": "54",
"y": "4980"
}, {
"x": "53",
"y": "4770"
}, {
"x": "55",
"y": "4900"
}, {
"x": "56",
"y": "4940"
}, {
"x": "58",
"y": "4990"
}, {
"x": "61",
"y": "5086"
}, {
"x": "67",
"y": "5100"
}, {
"x": "68",
"y": "4700"
}, {
"x": "70",
"y": "5360"
}, {
"x": "75",
"y": "5150"
}, {
"x": "79",
"y": "5450"
}, {
"x": "80",
"y": "5010"
}, {
"x": "79",
"y": "4975"
}, {
"x": "82",
"y": "5400"
}, {
"x": "85",
"y": "5150"
}, {
"x": "86",
"y": "5460"
}, {
"x": "86",
"y": "5000"
}, {
"x": "88",
"y": "5200"
}, {
"x": "87",
"y": "5410"
}, {
"x": "86",
"y": "5480"
}, {
"x": "89",
"y": "5440"
}, {
"x": "89",
"y": "5300"
}, {
"x": "90",
"y": "5500"
}, {
"x": "92",
"y": "5240"
}]
}],
"vtrendlines": [{
"line": [{
"startvalue": "23",
"endvalue": "32",
"istrendzone": "1",
"displayvalue": " ",
"color": "#adebff",
"alpha": "25"
}, {
"startvalue": "23",
"endvalue": "32",
"istrendzone": "1",
"alpha": "0",
"displayvalue": "Very cold"
}, {
"startvalue": "32",
"endvalue": "50",
"istrendzone": "1",
"displayvalue": " ",
"color": "#adebff",
"alpha": "15"
}, {
"startvalue": "32",
"endvalue": "50",
"istrendzone": "1",
"alpha": "0",
"displayvalue": "Cold"
}, {
"startvalue": "50",
"endvalue": "68",
"istrendzone": "1",
"alpha": "0",
"displayvalue": "Moderate"
}, {
"startvalue": "68",
"endvalue": "80",
"istrendzone": "1",
"alpha": "0",
"displayvalue": "Hot"
}, {
"startvalue": "68",
"endvalue": "80",
"istrendzone": "1",
"displayvalue": " ",
"color": "#f2a485",
"alpha": "15"
}, {
"startvalue": "80",
"endvalue": "95",
"istrendzone": "1",
"alpha": "0",
"displayvalue": "Very hot"
}, {
"startvalue": "80",
"endvalue": "95",
"istrendzone": "1",
"displayvalue": " ",
"color": "#f2a485",
"alpha": "25"
}]
}]
}""")
# Alternatively, you can assign this string to a string variable in a separate JSON file and
# pass the URL of that file to the `dataSource` parameter.
return render(request, 'index.html', {'output' : scatterChart.render()}) | [
"[email protected]"
] | |
125eb52072b17b6481375543446e5ef10ba04e01 | 7b5e64b2960260b5dd240cf1e2bea2c07697eaed | /interview_prep/strings/common_child.py | ed85815729ce2d7cb37a492efef2b176041664ce | [] | no_license | sotsoguk/hackerrank | 04f2d25ce94c878af7ed60f24817081e20ee551d | bbc87d8bd78df2e9b17c1a983f0424cbc6f8ca80 | refs/heads/master | 2021-09-08T05:51:23.667282 | 2021-08-30T23:38:10 | 2021-08-30T23:38:10 | 181,692,027 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 12,021 | py | from collections import defaultdict
from typing import DefaultDict
def cc(s1,s2):
table = defaultdict(int)
for r,c1 in enumerate(s1):
for c,c2 in enumerate(s2):
if c1 == c2:
table[(r,c)] = 1+ table[(r-1,c-1)]
else:
table[(r,c)] = max(table[(r-1,c)],table[(r,c-1)])
# print(table)
return table[(len(s1)-1,len(s2)-1)]
def cc3(s1,s2):
prev = [0] * (len(s2) +1)
curr = [0] * (len(s2) +1)
for r in s1:
for j,c in enumerate(s2,1):
curr[j] = prev[j-1] +1 if r==c else max(prev[j],curr[j-1])
prev, curr = curr,prev
return prev[-1]
def cc2(s1,s2):
l1, l2 = len(s1),len(s2)
t = [0]* (l1*l2)
#t = list(range(12))
def printt():
for i in range(l1):
for j in range(l2):
print(getc(i,j),sep=",",end="")
print("")
print("")
def getc(x,y):
if x<0 or x>=l1 or y<0 or y>= l2:
return 0
return t[l1*y + x]
def setc(x,y,v):
if x<0 or x>=l1 or y<0 or y>= l2:
return
else:
t[l1*y+x] = v
# printt()
# print(t)
# print("Starting")
for x,c1 in enumerate(s1):
for y,c2 in enumerate(s2):
if c1 == c2:
setc(x,y,1+getc(x-1,y-1))
else:
setc(x,y,max(getc(x-1,y),getc(x,y-1)))
# printt()
return t[-1]
def commonChild(s1, s2):
m = [[0]*(len(s2)+1) for _ in range(len(s1)+1)]
for i,c in enumerate(s1,1):
for j,d in enumerate(s2,1):
if c == d:
m[i][j] = m[i-1][j-1]+1
else:
m[i][j] = max(m[i][j-1],m[i-1][j])
return m[-1][-1]
#print(commonChild(input(), input()))
def main():
# print(cc2("ABCD","ABDC"))
s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
ss1 = "VGXGPUAMKXKSZHKBPPHYKINKEZ"
ss2 = "EVYRFZJIZJBKRFPPCJBHDPBYIC"
s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
print(cc3(s1,s2))
return 0
if __name__ == "__main__":
main() | [
"[email protected]"
] | |
ce0f094449f7839a9961db18847e127b63203a3b | 3edaad67f993f7e798cf47ffca774cb0d31d4afd | /store_server/wsgi.py | 37f3a4193096476d8a38c7c363ed64a3db5bb997 | [] | no_license | AnyiYim/book_store | 2150bb5f9edad05102a6ea8f3efccf3bbcd5e1d2 | 43f3da8770dcbe65b0d6c249d328daeb6f6b5239 | refs/heads/master | 2020-04-10T22:20:15.689190 | 2018-12-12T08:25:08 | 2018-12-12T08:25:08 | 161,321,275 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 401 | py | """
WSGI config for store_server project.
It exposes the WSGI callable as a module-level variable named ``application``.
For more information on this file, see
https://docs.djangoproject.com/en/2.1/howto/deployment/wsgi/
"""
import os
from django.core.wsgi import get_wsgi_application
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'store_server.settings')
application = get_wsgi_application()
| [
"[email protected]"
] | |
97710b3e759ea8389e52f4ccf09e2ee30031d846 | f445450ac693b466ca20b42f1ac82071d32dd991 | /generated_tempdir_2019_09_15_163300/generated_part001836.py | 441c60f8074c8598531da9bea71fd2f496347da7 | [] | no_license | Upabjojr/rubi_generated | 76e43cbafe70b4e1516fb761cabd9e5257691374 | cd35e9e51722b04fb159ada3d5811d62a423e429 | refs/heads/master | 2020-07-25T17:26:19.227918 | 2019-09-15T15:41:48 | 2019-09-15T15:41:48 | 208,357,412 | 4 | 1 | null | null | null | null | UTF-8 | Python | false | false | 1,292 | py | from sympy.abc import *
from matchpy.matching.many_to_one import CommutativeMatcher
from matchpy import *
from matchpy.utils import VariableWithCount
from collections import deque
from multiset import Multiset
from sympy.integrals.rubi.constraints import *
from sympy.integrals.rubi.utility_function import *
from sympy.integrals.rubi.rules.miscellaneous_integration import *
from sympy import *
class CommutativeMatcher81047(CommutativeMatcher):
_instance = None
patterns = {
0: (0, Multiset({}), [
(VariableWithCount('i2.4.1.0', 1, 1, None), Mul),
(VariableWithCount('i2.4.1.0_1', 1, 1, S(1)), Mul)
])
}
subjects = {}
subjects_by_id = {}
bipartite = BipartiteGraph()
associative = Mul
max_optional_count = 1
anonymous_patterns = set()
def __init__(self):
self.add_subject(None)
@staticmethod
def get():
if CommutativeMatcher81047._instance is None:
CommutativeMatcher81047._instance = CommutativeMatcher81047()
return CommutativeMatcher81047._instance
@staticmethod
def get_match_iter(subject):
subjects = deque([subject]) if subject is not None else deque()
subst0 = Substitution()
# State 81046
return
yield
from collections import deque | [
"[email protected]"
] | |
7b1dc366aad385a87238c269d4d4385def923a70 | 688b20033dba33755e0fb5e5e2b3fdd448885126 | /scene/scene.py | ec8608ef154bf9a5f505f567bb938f4088b6d1ed | [] | no_license | KaappoRaivio/mycurse | 9db0d149ec7a875d191effcedcd11ecc9211ec0d | 109b77640a64ff6782839565673c6a1847490351 | refs/heads/master | 2020-06-08T12:44:41.195259 | 2019-10-24T06:06:33 | 2019-10-24T06:06:33 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,255 | py | from __future__ import annotations
import enum
import inspect
import itertools
import os
from typing import List, Callable, Text
from _curses import curseswrapper
def converterFactory(pallette: str, lower_bound: int, upper_bound:int):
step = (upper_bound - lower_bound) / len(pallette)
def wrapper(value: int):
for index, char in enumerate(pallette):
if step * (index - 1) < value <= step * index:
return char
else:
return pallette[-1]
return wrapper
class Scene:
def __init__(self, dim_x: int=-1, dim_y: int=-1):
self.layers = []
self.wrapper = curseswrapper.CursesWrapper(transparent_character=" ", width=dim_x, height=dim_y)
def addLayer(self, layer: Layer) -> None:
self.layers.append(layer)
def commit(self):
for layer in self.layers:
self.wrapper.updateByList(layer.render(), offset_x=layer.pos_x, offset_y=layer.pos_y,
transparent_char=layer.background_char)
self.wrapper.flush()
def __enter__(self):
self.wrapper.__enter__()
def __exit__(self, exc_type, exc_val, exc_tb):
self.wrapper.__exit__(exc_type, exc_val, exc_tb)
@property
def dimX(self) -> int:
return self.wrapper.width
@property
def dimY(self) -> int:
return self.wrapper.height
class Sprite:
def __init__(self, data, converter: Callable=lambda x: str(x)):
self._data = data
self.dim_y = len(self._data)
self.dim_x = len(self._data[0])
for y in range(self.dim_y):
for x in range(self.dim_x):
self._data[y][x] = converter(self._data[y][x])
@classmethod
def fromFile(cls, path) -> Sprite:
path = os.path.join(os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))), path)
with open(path, "r") as file:
data = list(map(lambda x: list(str.strip(x)), file.readlines()))
# print(data)
return cls(data)
def render(self) -> List[List]:
return self._data
def __str__(self):
return "\n".join(map("".join, self.render()))
class LayerMode(enum.Enum):
CORNER = 0
CENTER_HORISONTAL = 1
CENTER_VERTICAL = 2
CENTERED = 4
class Layer:
def __init__(self, sprite: Sprite, background_char: str="@", mode: LayerMode=LayerMode.CORNER, pos_x: int=0, pos_y: int=0):
self.mode: LayerMode = mode
self.sprite = sprite
self.background_char = background_char
self.pos_y = pos_y
self.pos_x = pos_x
def isTransparent(self, char) -> bool:
return char == self.background_char
def render(self) -> List[List]:
return self.sprite.render()
def __str__(self):
return "\n".join(map("".join, self.render()))
if __name__ == '__main__':
scene = Scene(dim_x=80, dim_y=10)
dino = Sprite.fromFile("assets/dino.txt")
# print(dino)
layer = Layer(dino, background_char="§")
layer2 = Layer(dino, background_char="§", pos_x=2)
# layer.typeset(30, 30)
# print(layer)
scene.addLayer(layer)
scene.addLayer(layer2)
# print(scene.layers)
with scene:
scene.commit()
| [
"[email protected]"
] | |
ec230dede7ff241b77416a35d0d60224bf633c9b | 66198805c5e46d7f10f28a25a566a3e9764ff4a2 | /homeassistant/util/percentage.py | 10a72a85dff438a95a73a304fd10807f8787b011 | [
"Apache-2.0"
] | permissive | pgenera/home-assistant | 4540bd7e180bc48c4ad1ecd1ce74c9e8aa2cb1b7 | 372ed2db910cd6c73c6681fcc34a69645819cf0c | refs/heads/dev | 2022-05-20T12:27:13.176262 | 2021-02-25T12:40:01 | 2021-02-25T12:40:01 | 76,890,157 | 0 | 0 | Apache-2.0 | 2022-04-12T10:57:32 | 2016-12-19T19:17:22 | Python | UTF-8 | Python | false | false | 2,836 | py | """Percentage util functions."""
from typing import List, Tuple
def ordered_list_item_to_percentage(ordered_list: List[str], item: str) -> int:
"""Determine the percentage of an item in an ordered list.
When using this utility for fan speeds, do not include "off"
Given the list: ["low", "medium", "high", "very_high"], this
function will return the following when when the item is passed
in:
low: 25
medium: 50
high: 75
very_high: 100
"""
if item not in ordered_list:
raise ValueError
list_len = len(ordered_list)
list_position = ordered_list.index(item) + 1
return (list_position * 100) // list_len
def percentage_to_ordered_list_item(ordered_list: List[str], percentage: int) -> str:
"""Find the item that most closely matches the percentage in an ordered list.
When using this utility for fan speeds, do not include "off"
Given the list: ["low", "medium", "high", "very_high"], this
function will return the following when when the item is passed
in:
1-25: low
26-50: medium
51-75: high
76-100: very_high
"""
list_len = len(ordered_list)
if not list_len:
raise ValueError
for offset, speed in enumerate(ordered_list):
list_position = offset + 1
upper_bound = (list_position * 100) // list_len
if percentage <= upper_bound:
return speed
return ordered_list[-1]
def ranged_value_to_percentage(
low_high_range: Tuple[float, float], value: float
) -> int:
"""Given a range of low and high values convert a single value to a percentage.
When using this utility for fan speeds, do not include 0 if it is off
Given a low value of 1 and a high value of 255 this function
will return:
(1,255), 255: 100
(1,255), 127: 50
(1,255), 10: 4
"""
return int((value * 100) // states_in_range(low_high_range))
def percentage_to_ranged_value(
low_high_range: Tuple[float, float], percentage: int
) -> float:
"""Given a range of low and high values convert a percentage to a single value.
When using this utility for fan speeds, do not include 0 if it is off
Given a low value of 1 and a high value of 255 this function
will return:
(1,255), 100: 255
(1,255), 50: 127.5
(1,255), 4: 10.2
"""
return states_in_range(low_high_range) * percentage / 100
def states_in_range(low_high_range: Tuple[float, float]) -> float:
"""Given a range of low and high values return how many states exist."""
return low_high_range[1] - low_high_range[0] + 1
def int_states_in_range(low_high_range: Tuple[float, float]) -> int:
"""Given a range of low and high values return how many integer states exist."""
return int(states_in_range(low_high_range))
| [
"[email protected]"
] | |
d14f03817c49777e94f1617af2ba6743424c2478 | ae326c4e6a2b2d5b67fa8d175249ef90f6a3021a | /leo/plugins/importers/otl.py | 9921bc4aeaab3c51e8ae28a0d40df3d03fbfb639 | [
"MIT"
] | permissive | frakel/leo-editor | f95e6c77d60485d80fddfbeaf35db961cf691177 | b574118ee3b7ffe8344fa0d00dac603096117ac7 | refs/heads/master | 2020-03-28T10:40:24.621077 | 2018-10-23T14:39:31 | 2018-10-23T14:39:31 | 148,132,817 | 0 | 0 | MIT | 2018-09-10T09:40:18 | 2018-09-10T09:40:18 | null | UTF-8 | Python | false | false | 3,081 | py | #@+leo-ver=5-thin
#@+node:ekr.20140723122936.18150: * @file importers/otl.py
'''The @auto importer for vim-outline files.'''
import re
import leo.core.leoGlobals as g
import leo.plugins.importers.linescanner as linescanner
Importer = linescanner.Importer
#@+others
#@+node:ekr.20161124034614.2: ** class Otl_Importer
class Otl_Importer(Importer):
'''The importer for the otl lanuage.'''
def __init__(self, importCommands, **kwargs):
'''Otl_Importer.__init__'''
# Init the base class.
Importer.__init__(self,
importCommands,
language = 'plain',
state_class = None,
strict = False,
)
#@+others
#@+node:ekr.20161124035243.1: *3* otl_i.gen_lines & helper
# Must match body pattern first.
otl_body_pattern = re.compile(r'^: (.*)$')
otl_pattern = re.compile(r'^[ ]*(\t*)(.*)$')
def gen_lines(self, s, parent):
'''Node generator for otl (vim-outline) mode.'''
self.inject_lines_ivar(parent)
self.parents = [parent]
for line in g.splitLines(s):
m = self.otl_body_pattern.match(line)
if m:
p = self.parents[-1]
self.add_line(p, m.group(1))
else:
m = self.otl_pattern.match(line)
if m:
# Cut back the stack, then allocate a new node.
level = 1 + len(m.group(1))
self.parents = self.parents[:level]
self.find_parent(
level = level,
h = m.group(2).strip())
else:
self.error('Bad otl line: %r' % line)
#@+node:ekr.20161124035243.2: *4* otl_i.find_parent
def find_parent(self, level, h):
'''
Return the parent at the indicated level, allocating
place-holder nodes as necessary.
'''
assert level >= 0
while level >= len(self.parents):
child = self.create_child_node(
parent = self.parents[-1],
body = None,
headline = h,
)
self.parents.append(child)
return self.parents[level]
#@+node:ekr.20161125221742.1: *3* otl_i.delete_all_empty_nodes
def delete_all_empty_nodes(self, parent):
'''Override the base class so we *dont* delete empty nodes!'''
#@+node:ekr.20161126074028.1: *3* otl_i.post_pass
def post_pass(self, parent):
'''
Optional Stage 2 of the importer pipeline, consisting of zero or more
substages. Each substage alters nodes in various ways.
Subclasses may freely override this method, **provided** that all
substages use the API for setting body text. Changing p.b directly will
cause asserts to fail later in i.finish().
'''
# Do nothing!
#@-others
#@-others
importer_dict = {
'@auto': ['@auto-otl', '@auto-vim-outline',],
'class': Otl_Importer,
'extensions': ['.otl',],
}
#@@language python
#@@tabwidth -4
#@-leo
| [
"[email protected]"
] | |
877e6765c899ba23a828554f16417234025d6583 | 79e40a5f39ccc173a9c1341a3b54ede6bf2c0325 | /django_project/users/migrations/0001_initial.py | 9f3ff1b8dde9f18cf5ffd2d74593bd9528b2ea90 | [] | no_license | Seifeldin7/Django-Blog | 5677a10a82068ac06715d38464ddc228feae0d50 | 3d1fa5cb6a089cda18465915d8f8e5643674b7c1 | refs/heads/master | 2020-04-10T10:57:25.234154 | 2018-12-09T22:39:31 | 2018-12-09T22:39:31 | 160,980,611 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 800 | py | # Generated by Django 2.1 on 2018-12-03 19:00
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
]
operations = [
migrations.CreateModel(
name='Profile',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('image', models.ImageField(default='default.jpg', upload_to='profile_pics')),
('user', models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)),
],
),
]
| [
"[email protected]"
] | |
29a390a573c00034b79060277efec0083dac3d4b | 4a6c81f79121a46071ac2861f7d34a09fe25d5c3 | /baekjoon/1018_bruteForce.py | 8ec2533ada9afe7a00a68e671a29f415712f99f2 | [] | no_license | ghleokim/codeTestProblems | 3faa6913b7ea1822f6fd08edba559e562499d188 | 8d329c401979cfd8d9be0480ccc5b0a22aaf7fcf | refs/heads/master | 2020-06-17T17:27:32.441855 | 2019-09-07T16:22:17 | 2019-09-07T16:22:17 | 195,992,392 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,035 | py | # https://www.acmicpc.net/problem/1018
"""
8*8 타일이 정해진 이후
B first or W first?
B first일 때 타일
W first일 때 타일
둘 중 최소값 저장
"""
# a = 'abc'
# b = 'abb'
# c = sum(map(lambda x: int(*map(lambda y, z: 1 if y == z else 0, *x)), zip(a,b)))
refs = ('BWBWBWBW', 'WBWBWBWB')
def compare(tar, row):
compA = sum(map(lambda x: int(*map(lambda y, z: 1 if y == z else 0, *x)), zip(tar, refs[0])))
compB = sum(map(lambda x: int(*map(lambda y, z: 1 if y == z else 0, *x)), zip(tar, refs[1])))
if row % 2:
return (compA, compB)
else:
return (compB, compA)
N, M = map(int, input().split())
board = []
for _ in range(N):
board.append(input())
result = 64
for rowOffset in range(N-7):
for colOffset in range(M-7):
r = 0
res = []
for i in range(8):
a = board[i+rowOffset][colOffset:colOffset+8]
res.append(compare(a, i))
r = min(map(sum, zip(*res)))
if r < result:
result = r
print(result) | [
"[email protected]"
] | |
fe99c02caa8d8a8af0c8ca665b52512ea6c9506f | d68fb4c7bd82f4adcde5ffedca95fc38d2be6221 | /timetable/migrations/0002_department_dept_id.py | 195c6aafa14f6ad96cd2cd92565a24bb465054b3 | [] | no_license | proflamyt/timetable-generator- | 24e6c8b99892385ad606161204ba3e61d5bea7a9 | 13f5c4a3aeb0b9f2409bd89d0107e6a05737c437 | refs/heads/main | 2023-08-15T00:18:21.052553 | 2021-09-16T08:01:52 | 2021-09-16T08:01:52 | 407,078,298 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 389 | py | # Generated by Django 3.2 on 2021-09-11 11:38
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('timetable', '0001_initial'),
]
operations = [
migrations.AddField(
model_name='department',
name='dept_id',
field=models.IntegerField(blank=True, null=True),
),
]
| [
"[email protected]"
] | |
7a3a01ea717ebdfb94b9a709d0e8e04b402209bd | 65329299fca8dcf2e204132624d9b0f8f8f39af7 | /napalm_yang/models/openconfig/network_instances/network_instance/protocols/protocol/isis/levels/level/link_state_database/lsp/tlvs/tlv/is_reachability/neighbors/neighbors_/default_metric/__init__.py | 318c92982bc5ea8bbcc2a34b3d19ea00bf9daa1a | [
"Apache-2.0"
] | permissive | darylturner/napalm-yang | bf30420e22d8926efdc0705165ed0441545cdacf | b14946b884ad2019b896ee151285900c89653f44 | refs/heads/master | 2021-05-14T12:17:37.424659 | 2017-11-17T07:32:49 | 2017-11-17T07:32:49 | 116,404,171 | 0 | 0 | null | 2018-01-05T16:21:37 | 2018-01-05T16:21:36 | null | UTF-8 | Python | false | false | 9,620 | py |
from operator import attrgetter
from pyangbind.lib.yangtypes import RestrictedPrecisionDecimalType, RestrictedClassType, TypedListType
from pyangbind.lib.yangtypes import YANGBool, YANGListType, YANGDynClass, ReferenceType
from pyangbind.lib.base import PybindBase
from decimal import Decimal
from bitarray import bitarray
import __builtin__
import state
class default_metric(PybindBase):
"""
This class was auto-generated by the PythonClass plugin for PYANG
from YANG module openconfig-network-instance - based on the path /network-instances/network-instance/protocols/protocol/isis/levels/level/link-state-database/lsp/tlvs/tlv/is-reachability/neighbors/neighbors/default-metric. Each member element of
the container is represented as a class variable - with a specific
YANG type.
YANG Description: This container defines ISIS Default Metric.
"""
__slots__ = ('_pybind_generated_by', '_path_helper', '_yang_name', '_extmethods', '__state',)
_yang_name = 'default-metric'
_pybind_generated_by = 'container'
def __init__(self, *args, **kwargs):
self._path_helper = False
self._extmethods = False
self.__state = YANGDynClass(base=state.state, is_container='container', yang_name="state", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='container', is_config=False)
load = kwargs.pop("load", None)
if args:
if len(args) > 1:
raise TypeError("cannot create a YANG container with >1 argument")
all_attr = True
for e in self._pyangbind_elements:
if not hasattr(args[0], e):
all_attr = False
break
if not all_attr:
raise ValueError("Supplied object did not have the correct attributes")
for e in self._pyangbind_elements:
nobj = getattr(args[0], e)
if nobj._changed() is False:
continue
setmethod = getattr(self, "_set_%s" % e)
if load is None:
setmethod(getattr(args[0], e))
else:
setmethod(getattr(args[0], e), load=load)
def _path(self):
if hasattr(self, "_parent"):
return self._parent._path()+[self._yang_name]
else:
return [u'network-instances', u'network-instance', u'protocols', u'protocol', u'isis', u'levels', u'level', u'link-state-database', u'lsp', u'tlvs', u'tlv', u'is-reachability', u'neighbors', u'neighbors', u'default-metric']
def _get_state(self):
"""
Getter method for state, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/levels/level/link_state_database/lsp/tlvs/tlv/is_reachability/neighbors/neighbors/default_metric/state (container)
YANG Description: State parameters for default-metric.
"""
return self.__state
def _set_state(self, v, load=False):
"""
Setter method for state, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/levels/level/link_state_database/lsp/tlvs/tlv/is_reachability/neighbors/neighbors/default_metric/state (container)
If this variable is read-only (config: false) in the
source YANG file, then _set_state is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_state() directly.
YANG Description: State parameters for default-metric.
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=state.state, is_container='container', yang_name="state", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='container', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """state must be of a type compatible with container""",
'defined-type': "container",
'generated-type': """YANGDynClass(base=state.state, is_container='container', yang_name="state", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='container', is_config=False)""",
})
self.__state = t
if hasattr(self, '_set'):
self._set()
def _unset_state(self):
self.__state = YANGDynClass(base=state.state, is_container='container', yang_name="state", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='container', is_config=False)
state = __builtin__.property(_get_state)
_pyangbind_elements = {'state': state, }
import state
class default_metric(PybindBase):
"""
This class was auto-generated by the PythonClass plugin for PYANG
from YANG module openconfig-network-instance-l2 - based on the path /network-instances/network-instance/protocols/protocol/isis/levels/level/link-state-database/lsp/tlvs/tlv/is-reachability/neighbors/neighbors/default-metric. Each member element of
the container is represented as a class variable - with a specific
YANG type.
YANG Description: This container defines ISIS Default Metric.
"""
__slots__ = ('_pybind_generated_by', '_path_helper', '_yang_name', '_extmethods', '__state',)
_yang_name = 'default-metric'
_pybind_generated_by = 'container'
def __init__(self, *args, **kwargs):
self._path_helper = False
self._extmethods = False
self.__state = YANGDynClass(base=state.state, is_container='container', yang_name="state", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='container', is_config=False)
load = kwargs.pop("load", None)
if args:
if len(args) > 1:
raise TypeError("cannot create a YANG container with >1 argument")
all_attr = True
for e in self._pyangbind_elements:
if not hasattr(args[0], e):
all_attr = False
break
if not all_attr:
raise ValueError("Supplied object did not have the correct attributes")
for e in self._pyangbind_elements:
nobj = getattr(args[0], e)
if nobj._changed() is False:
continue
setmethod = getattr(self, "_set_%s" % e)
if load is None:
setmethod(getattr(args[0], e))
else:
setmethod(getattr(args[0], e), load=load)
def _path(self):
if hasattr(self, "_parent"):
return self._parent._path()+[self._yang_name]
else:
return [u'network-instances', u'network-instance', u'protocols', u'protocol', u'isis', u'levels', u'level', u'link-state-database', u'lsp', u'tlvs', u'tlv', u'is-reachability', u'neighbors', u'neighbors', u'default-metric']
def _get_state(self):
"""
Getter method for state, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/levels/level/link_state_database/lsp/tlvs/tlv/is_reachability/neighbors/neighbors/default_metric/state (container)
YANG Description: State parameters for default-metric.
"""
return self.__state
def _set_state(self, v, load=False):
"""
Setter method for state, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/levels/level/link_state_database/lsp/tlvs/tlv/is_reachability/neighbors/neighbors/default_metric/state (container)
If this variable is read-only (config: false) in the
source YANG file, then _set_state is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_state() directly.
YANG Description: State parameters for default-metric.
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=state.state, is_container='container', yang_name="state", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='container', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """state must be of a type compatible with container""",
'defined-type': "container",
'generated-type': """YANGDynClass(base=state.state, is_container='container', yang_name="state", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='container', is_config=False)""",
})
self.__state = t
if hasattr(self, '_set'):
self._set()
def _unset_state(self):
self.__state = YANGDynClass(base=state.state, is_container='container', yang_name="state", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='container', is_config=False)
state = __builtin__.property(_get_state)
_pyangbind_elements = {'state': state, }
| [
"[email protected]"
] | |
ce4d80e03914f4245644cca58c8382e45737940a | 163bbb4e0920dedd5941e3edfb2d8706ba75627d | /Code/CodeRecords/2506/60668/280309.py | 8f06c33b8bb93614f251459d21e5f94a059a400f | [] | no_license | AdamZhouSE/pythonHomework | a25c120b03a158d60aaa9fdc5fb203b1bb377a19 | ffc5606817a666aa6241cfab27364326f5c066ff | refs/heads/master | 2022-11-24T08:05:22.122011 | 2020-07-28T16:21:24 | 2020-07-28T16:21:24 | 259,576,640 | 2 | 1 | null | null | null | null | UTF-8 | Python | false | false | 396 | py | def find_12_longest(list = []):
re = []
for i in range(len(list)):
j = i
co = list[i]
con = 1
while(j <= len(list)):
if(list[j]>co):
co = list[j]
con+=1
j += 1
re.append(con)
print(max(re))
if __name__=='__main__':
list = [int(i) for i in input().split(',')]
find_12_longest(list) | [
"[email protected]"
] | |
79e26aa8935cce4fae4215c88fbb720f1016ecc0 | 8b46c8c7c8d30bf3c3e6bc1e075c1a5cc31bbeeb | /posts/migrations/0007_follow.py | 68ed9582949d5fd67d9e7d1e8d6201560bdad621 | [
"MIT"
] | permissive | Lokteved/hw05_final | e5f07f6636d99b98986b972ae45d7decbef5e135 | faf2f21ad2f9a263d3e024038a20e2dfa08ba9e2 | refs/heads/master | 2022-11-15T17:34:05.588565 | 2020-07-03T12:23:52 | 2020-07-03T12:23:52 | 275,792,668 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 865 | py | # Generated by Django 2.2.9 on 2020-06-30 11:11
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
('posts', '0006_comment'),
]
operations = [
migrations.CreateModel(
name='Follow',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('author', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='following', to=settings.AUTH_USER_MODEL)),
('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='follower', to=settings.AUTH_USER_MODEL)),
],
),
]
| [
"[email protected]"
] | |
baee9cb9186853fe6ff19a43e4d8a480946760b3 | d6ec20fe9f0ae0a12ce55e97b9965e14fc390416 | /School/urls.py | 9aec2b13b3a0b359d2800c26f88eb86eecaca447 | [] | no_license | declo32/School | b44fc4430845d016d373b89c8178a169a19f8694 | 6531e2bb4f14e2e9e12d2aa8d4e1fb787d0ce104 | refs/heads/master | 2020-06-19T07:10:09.497085 | 2017-01-26T18:03:34 | 2017-01-26T18:03:34 | 74,913,907 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 509 | py | from django.conf.urls import url, include
from django.contrib import admin
from django.conf import settings
from django.conf.urls.static import static
import belle.urls
urlpatterns = [
url(r"^belle/", include(belle.urls)),
url(r'^admin/', admin.site.urls),
]
# shouldn't be in the same place as the rest upon deploy
if settings.DEBUG:
urlpatterns += static(settings.STATIC_URL, document_root=settings.STATIC_ROOT)
urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)
| [
"[email protected]"
] | |
83d2857eeb81a257f21753dcc2fa3bda43210f56 | f583ca041b31a6fbe8160df4701282cf43e835c5 | /0099-init.py | 2f84766d81a3e8a14fd353f8ef7799dc8dd5ce40 | [] | no_license | texcoffier/StoX | 6a5fc47f73ec3a88b9e09b94efa1dbdac9b0d7cd | 0bfc40dfb333452ee50c75c9ba4953d244bd0fac | refs/heads/master | 2020-03-19T06:14:31.219490 | 2018-06-26T06:06:23 | 2018-06-26T06:09:35 | 136,003,052 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 70 | py | """Core: initialize by calling «init» hook"""
blocks.call('init')
| [
"[email protected]"
] | |
4230ee9753690ab37214d05eb6fdf49fe8d617e8 | 424014210e0e029fd6525105508e9a802ac83573 | /misc/py/common.py | 6e614d29d72ca46c4ddfa7eb0ba5b9765f3fdee0 | [
"Apache-2.0"
] | permissive | sampotter/olim | c6892d4212f823024ee1098121c1391dbf28534a | e86e19ebc498a802adfe034f4c84b9919bace857 | refs/heads/master | 2021-03-30T18:17:54.876894 | 2020-10-05T20:45:56 | 2020-10-05T20:45:56 | 93,203,491 | 1 | 1 | null | null | null | null | UTF-8 | Python | false | false | 2,589 | py | BUILD_TYPE='Release'
import os
import sys
build_path = os.path.abspath('../../build/%s' % BUILD_TYPE)
print(build_path)
if build_path not in sys.path:
sys.path.insert(0, build_path)
import pyolim as olim
import numpy as np
import time
marchers = [olim.BasicMarcher, olim.Olim4Mid0, olim.Olim4Mid1, olim.Olim4Rect,
olim.Olim8Mid0, olim.Olim8Mid1, olim.Olim8Rect]
olim4_marchers = [olim.Olim4Mid0, olim.Olim4Mid1, olim.Olim4Rect]
olim8_marchers = [olim.Olim8Mid0, olim.Olim8Mid1, olim.Olim8Rect]
mid0_marchers = [olim.Olim4Mid0, olim.Olim8Mid0]
mid1_marchers = [olim.Olim4Mid1, olim.Olim8Mid1]
rect_marchers = [olim.Olim4Rect, olim.Olim8Rect]
_marcher_names = {
olim.BasicMarcher: 'basic',
olim.Olim4Mid0: 'olim4 mp0',
olim.Olim4Mid1: 'olim4 mp1',
olim.Olim4Rect: 'olim4 rhr',
olim.Olim8Mid0: 'olim8 mp0',
olim.Olim8Mid1: 'olim8 mp1',
olim.Olim8Rect: 'olim8 rhr'}
def get_marcher_name(marcher):
return _marcher_names[marcher]
_marcher_plot_names = {
olim.BasicMarcher: '\\texttt{basic}',
olim.Olim4Mid0: '\\texttt{olim4\_mp0}',
olim.Olim4Mid1: '\\texttt{olim4\_mp1}',
olim.Olim4Rect: '\\texttt{olim4\_rhr}',
olim.Olim8Mid0: '\\texttt{olim8\_mp0}',
olim.Olim8Mid1: '\\texttt{olim8\_mp1}',
olim.Olim8Rect: '\\texttt{olim8\_rhr}'}
def get_marcher_plot_name(marcher):
return _marcher_plot_names[marcher]
_marchers_by_name = {v: k for k, v in _marcher_names.items()}
def get_marcher_by_name(name):
return _marchers_by_name[name]
def relerr(x, y, ord_):
norm = lambda x: np.linalg.norm(x.flat, ord_)
distxy = norm(x - y)
return max(distxy/norm(x), distxy/norm(y))
def get_exact_soln(f, M):
l = np.linspace(-1, 1, M)
return f(*np.meshgrid(l, l))
def compute_soln(marcher, s, M):
l = np.linspace(-1, 1, M)
m = marcher(s(*np.meshgrid(l, l)), 2/(M - 1))
m.addBoundaryNode(int(M/2), int(M/2))
m.run()
U = np.array([[m.getValue(i, j) for j in range(M)] for i in range(M)])
return U
def tic():
tic.t0 = time.time()
tic.t0 = None
def toc():
if tic.t0:
return time.time() - tic.t0
else:
raise RuntimeError("tic() hasn't been called")
def time_marcher(Marcher, s, n, ntrials=10):
print(' - n = %d' % n)
h = 2/(n - 1)
l = np.linspace(-1, 1, n)
i = int(n/2)
x, y = np.meshgrid(l, l)
s_cache = s(x, y)
def do_trial(trial):
tic()
m = Marcher(s_cache, h)
m.addBoundaryNode(i, i)
m.run()
return toc()
times = min(do_trial(t) for t in range(ntrials))
return times
| [
"[email protected]"
] | |
11b8d41fec616cc67b91713940f64d5a879b29f7 | 847273de4b1d814fab8b19dc651c651c2d342ede | /.history/Sudoku_II_006_20180621095510.py | 05cab38ebdd0db44f1cf1174db8ec3a7208062db | [] | no_license | Los4U/sudoku_in_python | 0ba55850afcffeac4170321651620f3c89448b45 | 7d470604962a43da3fc3e5edce6f718076197d32 | refs/heads/master | 2020-03-22T08:10:13.939424 | 2018-07-04T17:21:13 | 2018-07-04T17:21:13 | 139,749,483 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 4,836 | py | from random import randint
sudoku1 = [
[5, 9, 8, 6, 1, 2, 3, 4, 7],
[2, 1, 7, 9, 3, 4, 8, 6, 5],
[6, 4, 3, 5, 8, 7, 1, 2, 9],
[1, 6, 5, 4, 9, 8, 2, 7, 3],
[3, 2, 9, 7, 6, 5, 4, 1, 8],
[7, 8, 4, 3, 2, 1, 5, 9, 6],
[8, 3, 1, 2, 7, 6, 9, 5, 4],
[4, 7, 2, 8, 5, 9, 6, 3, 1],
[9, 5, 6, 1, 4, 3, 7, 8, " "]
]
sudoku2 = [
[9, 8, 7, 4, 3, 2, 5, 6, 1],
[2, 4, 3, 5, 1, 6, 8, 7, 9],
[5, 6, 1, 7, 9, 8, 4, 3, 2],
[3, 9, 5, 6, 4, 7, 2, 1, 8],
[8, 2, 4, 3, 5, 1, 6, 9, 7],
[1, 7, 6, 2, 8, 9, 3, 4, 5],
[7, 1, 2, 8, 6, 3, 9, 5, 4],
[4, 3, 8, 9, 7, 5, 1, 2, 6],
[' ', 5, ' ', ' ', 2, ' ', 7, ' ', ' ']
]
sudoku3 = [
[9, 8, 7, 4, 3, 2, 5, 6, 1],
[2, 4, 3, 5, 1, 6, 8, 7, 9],
[5, 6, 1, 7, 9, 8, 4, 3, 2],
[3, 9, 5, 6, 4, 7, 2, 1, 8],
[8, 2, 4, 3, 5, 1, 6, 9, 7],
[1, 7, 6, 2, 8, 9, 3, 4, 5],
[7, 1, 2, 8, 6, 3, 9, 5, 4],
[4, 3, 8, 9, 7, 5, 1, 2, 6],
[' ', 5, ' ', ' ', 2, ' ', 7, ' ', ' ']
]
def printSudoku():
i = 0
while i < 10:
if i == 0:
print(" 1 2 3 4 5 6 7 8 9")
print(" -------------------------")
elif i == 3 or i == 6 or i == 9:
print(" -------------------------")
line = "|"
if i < 9:
print('{2} {1} {0[0]} {0[1]} {0[2]} {1} {0[3]} {0[4]} {0[5]} {1} {0[6]} {0[7]} {0[8]} {1}'.format(sudoku[i], line, i+1))
i = i + 1
print(" ")
print(" %@@@@@@@ @@@ @@@ (@@@@@@@@@ ,@@@@2@@@@@ @@@, /@@@/ @@@, @@@ ")
print(" @@@* @@@ @@@ (@@( /@@@# .@@@% (@@@ @@@, @@@% @@@, @@@. ")
print(" @@@& @@@ @@@ (@@( @@@* @@@% #@@% @@@,.@@@. @@@, @@@. ")
print(" ,@@@@@@* @@@ @@@ (@@( (@@% .@@@* ,@@@ @@@%@@% @@@, @@@. ")
print(" /@@@@@# @@@ @@@ (@@( (@@% .@@@* ,@@@ @@@,@@@( @@@, @@@. ")
print(" *@@@. @@@ .@@& (@@( @@@. @@@% &@@( @@@, &@@@. @@@* .@@@. ")
print(" &, &@@@ #@@@. ,@@@, (@@( ,&@@@* ,@@@& .@@@@ @@@, (@@@/ #@@@* @@@# ")
print(",@@@@@@@@( (@@@@@@@@% (@@@@@@@@@( #@@@@@@@@@, @@@, ,@@@% ,@@@@@@@@@. \n ")
print("To start game input:")
print(" r - to load random puzzle:")
print(" 1 - to load chart nr 1:")
print(" 2 - to load chart nr 2:")
print(" 3 - to load chart nr 3:")
choice = input("Input here: ")
s = 0
if choice == "R" or choice == "r":
listaSudoku = [sudoku1, sudoku2, sudoku3]
sudoku_number = randint(0, 2)
print("dupa", sudoku_number)
sudoku = listaSudoku[sudoku_number][:]
elif int(choice) == 1:
s = 1
sudoku = list(sudoku1)
elif int(choice) == 2:
s = 2
sudoku = list(sudoku2)
elif int(choice) == 3:
s = 3
sudoku = list(sudoku3)
while True: # prints Sudoku until is solved
print("Your sudoku to solve:")
printSudoku()
print("Input 3 numbers in format a b c, np. 4 5 8")
print(" a - row number")
print(" b - column number ")
print(" c - value")
# vprint(" r - reset chart to start\n ")
x = input("Input a b c: ")
print("")
numbers = " 0123456789" # conditions of entering the numbers !
if (len(x) != 5) or (str(x[0]) not in numbers) or (str(x[2]) not in numbers) or (
str(x[4]) not in numbers) or (str(x[1]) != " ") or (str(x[3]) != " "):
if x == "r": # reset
if s == 1:
print("RESET TO = ", s)
sudoku = sudoku1[:]
elif s == 2:
sudoku = sudoku2[:]
print("RESET TO = ", s)
elif s == 3:
sudoku = sudoku3[:]
print("RESET TO = ", s)
# printSudoku()
print(" Function reset() will be ready in Next Week")
else:
print("Error - wrong number format \n ")
continue
else:
sudoku[int(x[0])-1][int(x[2])-1] = int(x[4])
column1 = 0
column2 = 0
try: # check if sudoku is solved
i = 0
list = []
while i < 9: # check is
column = 0
for item in sudoku:
column = column + item[i]
list.append(column)
i += 1
is45 = 0
for listElement in list:
if listElement == 45:
is45 = is45 + 1
#
i = 0
for item in sudoku:
if sum(item) == 45 and is45 == 9:
i = i + 1
if i == 9:
printSudoku()
print("@@@@@@@@@@ YOU WIN @@@@@@@@@@")
break
except TypeError:
print()
| [
"[email protected]"
] | |
3545ea068936863c0e7c64f5432d8f79f5bb77f3 | f8d14d10d8d9a9a43ec454db3f3d95ace3ee520c | /sensors/Adafruit/Adafruit_GPIO_Platform.py | 49c7ac2189b24ecbc6e1c1fdf389db2194942efd | [] | no_license | erhnam/Raspirover | 288f306a5b6cb48b6a677c9f26ff78c9a917ff56 | cb46e7b2fa703362f83f1caea94d7f689509b84d | refs/heads/master | 2021-06-25T19:54:03.676658 | 2021-02-14T13:00:37 | 2021-02-14T13:00:37 | 73,689,132 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,190 | py | # Copyright (c) 2014 Adafruit Industries
# Author: Tony DiCola
# 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.
import platform
import re
# Platform identification constants.
UNKNOWN = 0
RASPBERRY_PI = 1
NANOPI = 2
MINNOWBOARD = 3
def platform_detect():
"""Detect if running on the Raspberry Pi or Beaglebone Black and return the
platform type. Will return RASPBERRY_PI, NANOPI, or UNKNOWN."""
# Handle Raspberry Pi
pi = pi_version()
if pi is not None:
return RASPBERRY_PI
# Handle Beaglebone Black
# TODO: Check the Beaglebone Black /proc/cpuinfo value instead of reading
# the platform.
plat = platform.platform()
if plat.lower().find('armv7l-with-debian') > -1:
return NANOPI
elif plat.lower().find('armv7l-with-ubuntu') > -1:
return NANOPI
elif plat.lower().find('armv7l-with-glibc2.4') > -1:
return NANOPI
# Handle Minnowboard
# Assumption is that mraa is installed
try:
import mraa
if mraa.getPlatformName()=='MinnowBoard MAX':
return MINNOWBOARD
except ImportError:
pass
# Couldn't figure out the platform, just return unknown.
return UNKNOWN
def pi_revision():
"""Detect the revision number of a Raspberry Pi, useful for changing
functionality like default I2C bus based on revision."""
# Revision list available at: http://elinux.org/RPi_HardwareHistory#Board_Revision_History
with open('/proc/cpuinfo', 'r') as infile:
for line in infile:
# Match a line of the form "Revision : 0002" while ignoring extra
# info in front of the revsion (like 1000 when the Pi was over-volted).
match = re.match('Revision\s+:\s+.*(\w{4})$', line, flags=re.IGNORECASE)
if match and match.group(1) in ['0000', '0002', '0003']:
# Return revision 1 if revision ends with 0000, 0002 or 0003.
return 1
elif match:
# Assume revision 2 if revision ends with any other 4 chars.
return 2
# Couldn't find the revision, throw an exception.
raise RuntimeError('Could not determine Raspberry Pi revision.')
def pi_version():
"""Detect the version of the Raspberry Pi. Returns either 1, 2 or
None depending on if it's a Raspberry Pi 1 (model A, B, A+, B+),
Raspberry Pi 2 (model B+), or not a Raspberry Pi.
"""
# Check /proc/cpuinfo for the Hardware field value.
# 2708 is pi 1
# 2709 is pi 2
# Anything else is not a pi.
with open('/proc/cpuinfo', 'r') as infile:
cpuinfo = infile.read()
# Match a line like 'Hardware : BCM2709'
match = re.search('^Hardware\s+:\s+(\w+)$', cpuinfo,
flags=re.MULTILINE | re.IGNORECASE)
if not match:
# Couldn't find the hardware, assume it isn't a pi.
return None
if match.group(1) == 'BCM2708':
# Pi 1
return 1
elif match.group(1) == 'BCM2709':
# Pi 2
return 2
elif match.group(1) == 'BCM2835':
return 2
else:
# Something else, not a pi.
return None
# eof # | [
"[email protected]"
] | |
8cd5555a8cd5e94ba5e900118102a1b6074b01c3 | 167face5e34f69ba36b8a8d93306387dcaa50d24 | /23funcoes_cast.py | e9044e64e2e7b6fe1532c4d2a85bc7a0008330af | [] | no_license | william-cirico/python-study | 4fbe20936c46af6115f0d88ad861c71e6273db71 | 5923268fea4c78707fe82f1f609535a69859d0df | refs/heads/main | 2023-04-19T03:49:23.237829 | 2021-05-03T01:24:56 | 2021-05-03T01:24:56 | 309,492,617 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 382 | py | import re
def is_float(val):
if isinstance(val, float):
return True
if re.search(r'^\-{,1}[0-9]+\.{1}[0-9]+$', val):
return True
return False
def is_int(val):
if isinstance(val, int):
return True
if re.search(r'^\-{,1}[0-9]+$', val):
return True
return False
def is_number(val):
return is_int(val) or is_float(val) | [
"[email protected]"
] | |
59274581f706141dcf76b6c0ad16c88a286f6c4a | 64832dd1e64cfc6de83c5abf099461eda50f648c | /school/models.py | 9c0012ca777243bedcb7b07afbec1e52a7e3a42a | [] | no_license | showzvan/mysite | d7e79e16eb8c2d3598d00d1a96fa0b1940765913 | 32826c83915a2f95440c04ed20a7800d2c343ac1 | refs/heads/master | 2020-04-27T17:05:19.143296 | 2019-03-08T09:09:45 | 2019-03-08T09:09:45 | 174,504,656 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,989 | py | from django.db import models
from user.models import Areas, Provinces, Citys
# 院校类型表
class SchoolType(models.Model):
type_name = models.CharField("类型名称", max_length=255)
notes = models.TextField("分类说明", null=True)
is_status = models.BooleanField("状态", default=True) # true 可用 false 不可用
class Meta:
verbose_name = "院校类型表"
verbose_name_plural = "院校类型"
db_table = "zhouju_school_type"
# 院校特征表
class SchoolFeatures(models.Model):
feature_name = models.CharField("院校特征名称", max_length=255)
notes = models.TextField("院校特征说明", null=True)
is_status = models.BooleanField("状态", default=True) # true 可用 false 不可用
class Meta:
verbose_name = "院校特征表"
verbose_name_plural = "院校特征"
db_table = "zhouju_school_features"
# 院校表
class Schools(models.Model):
name = models.CharField("院校名称", max_length=255)
banner = models.FileField("院校banner图", upload_to='media', null=True) # 图片地址
description = models.TextField("院校描述")
motto = models.TextField("校训", null=True)
emblem = models.FileField("校徽", upload_to='media', null=True) # 校徽图片地址
enrol_notes = models.TextField("报名须知", null=True)
diploma_images = models.FileField("毕业证图片地址", upload_to='media', null=True)
degree_images = models.FileField("学位证图片地址", upload_to='media', null=True)
is_985 = models.BooleanField("是否是985", default=False) # True 是985 False 不是985
is_211 = models.BooleanField("是否是211", default=False) # True 是211，False 不是211
is_double = models.BooleanField("是否是双一流", default=False) # True 是双一流， False 不是
brief = models.TextField("招生简章", null=True)
exam = models.TextField("考试与毕业", null=True)
count = models.BigIntegerField("累计报读人数", null=True)
is_status = models.BooleanField("状态", default=True) # true 可用 false 不可用
sch_pro = models.ForeignKey("user.Provinces", verbose_name="院校所在省份", on_delete=models.DO_NOTHING,
related_name='sch_pro', null=True)
sch_city = models.ForeignKey("user.Citys", verbose_name="院校所在城市", on_delete=models.DO_NOTHING,
related_name='sch_city', null=True)
school_type = models.ManyToManyField("SchoolType", verbose_name="学校类型")
scholl_feature = models.ManyToManyField("SchoolFeatures", verbose_name="学校特性")
class Meta:
verbose_name = "院校表"
verbose_name_plural = "院校列表"
db_table = "zhouju_schools"
# 院校招生简章表
class SchoolIntro(models.Model):
school_id = models.ForeignKey("Schools", verbose_name="院校id", on_delete=models.DO_NOTHING)
tesezhuanye = models.TextField("特色专业")
yuanxiaojieshao = models.TextField("院校介绍")
zhaoshengduixiang = models.TextField("招生对象")
zhaoshengzhuanye = models.TextField("招生专业")
baomingbanfa = models.TextField("报名办法")
ruxuefangshi = models.TextField("入学方式")
ruxueceshi = models.TextField("入学测试")
ruxuezigeshencha = models.TextField("入学资格审查")
luqujiaofei = models.TextField("录取缴费")
jiaoxuejixuexi = models.TextField("教学及学习")
zhongdianzhuanyekechengshezhi = models.TextField("重点专业课程设置")
tongkao = models.TextField("统考")
biyezhengshu = models.TextField("毕业证书学位证书")
biyeimage = models.FileField("毕业证图片地址", upload_to='media')
xueweiimage = models.FileField("学位证图片地址", upload_to='media')
class Meta:
verbose_name = "院校招生简章"
verbose_name_plural = verbose_name
db_table = "zhouju_school_intro"
| [
"[email protected]"
] | |
093becada6d26d3731529fc50481a3f9baed37ab | afd5dd9948d4a12f0413b46892648601a3df78bf | /venv/bin/easy_install | c732d8baa0c1cfbf1adcc4723d0e79c228cc3a77 | [
"MIT"
] | permissive | radekwarowny/goldlist_db | d0d74e60a66fee27cb890d581bb49035e16b92d6 | 5b5581a1bf9a46747300e26f04660ddb1b98b00b | refs/heads/master | 2023-01-20T17:45:56.673296 | 2020-11-30T13:19:32 | 2020-11-30T13:19:32 | 317,227,688 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 263 | #!/home/radek/PycharmProjects/gl_db/venv/bin/python
# -*- 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())
| [
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] | ||
b4d8fc8ab2d6d7827b14fa303d91b84881b51a4f | a56d492e99fc0e77da8d4be795e4b31c0e481a5d | /oldz/themain.py | 28c6e16ba28ebe0d6f5d40183e5abcac3417f58d | [] | no_license | samuel637/Misadventure | f74a00ae54465fd7c77884d8af718066add59562 | 649dbb4bbd4aaa729c4f4fa141b64e1d086adf8a | refs/heads/master | 2020-07-20T21:34:11.713916 | 2019-09-06T04:58:55 | 2019-09-06T04:58:55 | 206,712,678 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,940 | py | from time import sleep
import sys
from os import system
import textstorage as ts
#initial clear screen to pretty up terminal
system('cls')
#loop so user can access multiple pieces of data in one session. type exit to exit (so exit can't be a key in the dictionary)
while True:
i = input("Enter Key\n")
if i == "exit":
system('cls')
break
#call a function from another file!
ts.textFunc(i)
'''
filename = "texts/test.txt"
def slowPrint(filename):
text = open(filename, "r")
# print(text.read())
while True:
c = text.read(1)
if not c:
print("End of file")
break
print(c, end="")
sys.stdout.flush()
sleep(.05)
slowPrint(filename)
from os import system
from time import sleep
import random
class snoflake:
def __init__(self, x, y):
self.x = x
self.y = y
def loadIt():
system('cls')
loading = ["|", "\\", "--", "/"]
i = 0
while i < 30:
system('cls')
print("Loading " + loading[i % 4])
sleep(.15)
i += 1
system('cls')
print("Loading Complete\n")
def youdunfuckedupnow():
# get size of terminal screen make it resizeable
termheight = 30
termwidth = 30
numflakes = 30
# make the 2d array of sno flakes
i = 0
snowArray = []
while i < termheight:
j = 0
temparr = []
while j < termwidth:
temparr.append(" ")
j += 1
snowArray.append(temparr)
i += 1
# add some muffugga snoflasks
i = 0
flakeArray = []
while i < numflakes:
flakeArray.append(snoflake(random.randint(
0, termheight), random.randint(0, termwidth)))
i += 1
# while loop to draw frames to screen
# while True:
for arr in snowArray:
for cha in arr:
print(cha, end="")
print("")
youdunfuckedupnow()
'''
| [
"[email protected]"
] | |
8ec7a7b1c085ad2e6b7846578fd1ce5fc96e5685 | ea2535aff6cc6322dfc65002d39520defdc30d18 | /status/rc.spec | b41142d07fa9b0c25c0bbfe9c562f86df94651e6 | [
"Apache-2.0"
] | permissive | tgraf/apisim | 7dbb441fc75db412c4c20cfe7b9863e376378ef9 | 71751e71d34111a210a81e7f69d4971d2ebf8c75 | refs/heads/master | 2021-04-29T04:47:23.624255 | 2017-01-14T19:53:16 | 2017-01-14T19:53:16 | 78,020,383 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 896 | spec | {
"kind":"ReplicationController",
"apiVersion":"v1",
"metadata":{
"name":"status",
"labels":{
"k8s-app.apisim":"status"
}
},
"spec":{
"replicas":1,
"selector":{
"k8s-app.apisim":"status"
},
"template":{
"metadata":{
"labels":{
"k8s-app.apisim":"status"
}
},
"spec":{
"nodeSelector":{
"kubernetes.io/hostname": "worker2"
},
"containers":[{
"name":"status",
"image":"tgraf/apisim:latest",
"command": ["/go/bin/app", "status-server"],
"ports":[
{"containerPort": 8888, "name": "apisim-status"}
]
}]
}
}
}
}
| [
"[email protected]"
] | |
377652f054eed8f11a04e077f338ae614799d64b | afde732d20cfc0500f0681a47a5984088f6e6ec9 | /www/cgi-bin/mysite_latest/testing 18-6/scan_cluster.py | cbb24749c99f43864e98cfb4caeb804c38dca3a1 | [] | no_license | lordzuko/IVORY-B.R.I.G.H.T | e4de2631ddca5c2daeafa7054a30b9f137518d72 | cc2e4aeb1d4b172af95b0368a995269573936bcb | refs/heads/master | 2020-04-05T22:57:48.864476 | 2016-10-01T17:01:21 | 2016-10-01T17:01:21 | 41,604,963 | 1 | 1 | null | null | null | null | UTF-8 | Python | false | false | 262 | py | #!/usr/bin/python2
import os
import sys
import commands as cmd
def main():
#os.popen("mkdir -p /root/Desktop/testing/hadoop_setup")
os.popen("nmap -sP -n 192.168.0.0/24 | grep 192. | awk -F' ' '{print $5}' > nodes")
if __name__ == '__main__':
main()
| [
"[email protected]"
] | |
b2e7e2c98c8e10eb0cbb013f3212657a91015ba7 | d80aad3af9349861c164f5102f4a4fd62bbb7b35 | /train/util.py | 3fef6ba032afe47d031105074acaabf5dfff8a72 | [
"Apache-2.0"
] | permissive | yucongo/AI-writer_Data2Doc | 303edf3568afe0c707794a3b640eb80eec9df027 | 5d6044640a0e0ea68cc729212de7572ea24ef9ba | refs/heads/master | 2020-03-15T19:41:36.125974 | 2018-05-06T07:11:36 | 2018-05-06T07:11:36 | 132,315,065 | 0 | 0 | Apache-2.0 | 2018-05-06T07:07:21 | 2018-05-06T07:07:21 | null | UTF-8 | Python | false | false | 1,973 | py | """Some useful utilizations. Borrowed from Pytorch Tutorial."""
import time
import math
import heapq
import torch
def asMinutes(s):
m = math.floor(s / 60)
s -= m * 60
return '%dm %ds' % (m, s)
def gettime(start):
now = time.time()
return asMinutes(now - start)
def timeSince(since, percent):
now = time.time()
s = now - since
es = s / (percent)
rs = es - s
return '%s (- %s)' % (asMinutes(s), asMinutes(rs))
def load_model(model, model_src, mode='eval'):
state_dict = torch.load(model_src, map_location=lambda storage, loc: storage)
model.load_state_dict(state_dict)
if mode == 'eval':
model.eval()
else:
model.train()
return model
def show_attention(inputs, outputs, attentions):
"""The function to show attention scores.
Args:
inputs: A list of tuples, indicating the input triplets.
outputs: A list of strings, indicating the output texts.
attentions: A matrix of attention scores.
"""
score = attentions.numpy()
for i, text in enumerate(outputs):
max_score = ['N\A', 0]
for j, triplet in enumerate(inputs):
if score[i, j] > max_score[1]:
max_score = [triplet, score[i, j]]
print('{} <-> {} = {}'.format(text, max_score[0], max_score[1]))
if text == '.':
print('')
def show_triplets(triplets):
"""The function to show input triplets.
Args:
triplets: A list of tuples, indicating the input triplets.
"""
for triplet in triplets:
print(triplet, end=',')
if triplet[2] == '<EOB>':
print('\n==============')
return
class PriorityQueue:
def __init__(self):
self._queue = []
self._index = 0
def push(self, item, priority):
heapq.heappush(self._queue, (-priority, self._index, item))
self._index += 1
def pop(self):
return heapq.heappop(self._queue)[-1]
| [
"[email protected]"
] | |
f78d1b8c57a2e9547fea4a3d195c89890d1bb9fa | f5d39762fabef6c50d98584d051070e04ef7cb46 | /Hyperskill/Python/Easy/Hangman/Problems/Chaos/main.py | f5c197983524f0829d74d4b8979421f3ce4ab903 | [] | no_license | DanielJBurbridge/Jetbrains-Academy | e2c29cb9e3cca5361d24851493b5989a9207d8dd | da67caee1223b62acc87522f183e382fec043909 | refs/heads/main | 2023-03-23T05:56:50.226606 | 2021-03-12T10:45:17 | 2021-03-12T10:45:17 | 280,210,460 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 48 | py | print(45/9 + 16 * (5 + 8))
print("mathematics")
| [
"[email protected]"
] | |
6f15f68ff9faa5be730cce549e7624788d52927f | e8f99a162207cba82d4e0f969d7bcdb2b9d8b522 | /nowcoder/2019test/internal_network_ip.py | 65c87cc3a5715793a6b8bbb7fb1227344454ba7f | [] | no_license | TesterCC/Python3Scripts | edb5446278ebf13edb64336001081941ca27d67d | 58be67e1ffc74ef50289a885aa4ad05f58e2c383 | refs/heads/master | 2023-08-30T21:16:38.328045 | 2023-08-17T11:23:08 | 2023-08-17T11:23:08 | 93,401,996 | 6 | 3 | null | null | null | null | UTF-8 | Python | false | false | 1,692 | py | #!/usr/bin/env python
# -*- coding:utf-8 -*-
__author__ = 'MFC'
__time__ = '2019-09-30 01:57'
"""
2019 bilibili
https://www.nowcoder.com/practice/80ce674313ff43af9d7ac7a41ae21527?tpId=98&tqId=33025&tPage=11&rp=11&ru=/ta/2019test&qru=/ta/2019test/question-ranking
时间限制：1秒 空间限制：32768K
题目描述
从业 666 年的 BILIBILI 网络安全工程师 KindMo 最近很困惑，公司有一个业务总是受到 SSRF 攻击。请帮他写一个程序，判断输入的字符串是否属于内网IP，用于防御该漏洞。
我们知道常见的内网IP有，127.0.0.1，192.168.0.1 等。
输入描述:
每次输入仅包含一个IP字符串，即一个测试样例
输出描述:
对于每个测试实例输出整数1或0，
1代表True，即输入属于内网IP，
0代表False，即输入不属于内网IP或不是IP字符串。
示例1
输入
42.96.146.169
输出
0
"""
# 判断段首，不然累遍历肯定超内存限制啊
# 运行时间：30ms, 占用内存：3560k
def check_internal_ip(check_ip):
check_ip_list = check_ip.split(".")
if len(check_ip_list) == 4:
a, b, c, d = check_ip_list
a = int(a)
b = int(b)
c = int(c)
d = int(d)
if a > 255 or b > 255 or c > 255 or d > 255:
return 0
elif a == 10 and b >= 0 and c >= 0 and d >= 0:
return 1
elif a == 172 and (16 <= b <= 31) and c >= 0 and d >= 0:
return 1
elif a == 192 and b == 168 and c >= 0 and d >= 0:
return 1
else:
return 0
else:
return 0
if __name__ == '__main__':
check_ip = input()
print(check_internal_ip(check_ip))
| [
"[email protected]"
] | |
54107b6985d401fa0d316f5ea9cbd1ea3c652877 | df1254b56f35b24644e00493c50d4b6eb3c15b7b | /colour/examples/models/examples_rgb.py | 9cb77791edfb081c02f842df37b99e12120c0531 | [
"BSD-3-Clause"
] | permissive | colour-science/colour | 908400b227cf81668675e41099256ce50b23ae4b | 1fdf3b3042922e8d4f86b989b00a06e7e5d81102 | refs/heads/develop | 2023-09-01T23:17:07.186869 | 2023-08-26T09:40:45 | 2023-08-26T09:40:45 | 17,114,363 | 1,756 | 301 | BSD-3-Clause | 2023-09-14T10:24:37 | 2014-02-23T18:55:40 | Python | UTF-8 | Python | false | false | 2,371 | py | """Showcases *RGB* *colourspaces* computations."""
import numpy as np
from pprint import pprint
import colour
from colour.utilities import message_box
message_box('"RGB" Colourspaces Computations')
message_box('"RGB" colourspaces dataset.')
pprint(sorted(colour.RGB_COLOURSPACES.keys()))
print("\n")
message_box('"ACES2065-1" colourspaces data.')
colourspace = colour.RGB_COLOURSPACES["ACES2065-1"]
print(f'Name:\n"{colourspace.name}"')
print(f"\nPrimaries:\n{colourspace.primaries}")
print(
f'\nNormalised primary matrix to "CIE XYZ" tristimulus values:\n'
f"{colourspace.matrix_RGB_to_XYZ}"
)
print(
f'\nNormalised primary matrix to "ACES2065-1":\n'
f"{colourspace.matrix_XYZ_to_RGB}"
)
print(
f"\nOpto-electronic transfer function from linear to colourspace:\n"
f"{colourspace.cctf_encoding}"
)
print(
f"\nElectro-optical transfer function from colourspace to linear:\n"
f"{colourspace.cctf_decoding}"
)
print("\n")
message_box(
'Computing the "ACES2065-1" colourspace to "ITU-R BT.709" colourspace '
"matrix."
)
cat = colour.adaptation.matrix_chromatic_adaptation_VonKries(
colour.xy_to_XYZ(colourspace.whitepoint),
colour.xy_to_XYZ(colour.RGB_COLOURSPACES["ITU-R BT.709"].whitepoint),
)
print(
np.dot(
colour.RGB_COLOURSPACES["ITU-R BT.709"].matrix_XYZ_to_RGB,
np.dot(cat, colourspace.matrix_RGB_to_XYZ),
)
)
print("\n")
RGB = np.array([0.45620519, 0.03081071, 0.04091952])
message_box(
f'Converting from the "ITU-R BT.709" colourspace to the "ACEScg" '
f'colourspace given "RGB" values:\n\n\t{RGB}'
)
print(
colour.RGB_to_RGB(
RGB,
colour.RGB_COLOURSPACES["ITU-R BT.709"],
colour.RGB_COLOURSPACES["ACEScg"],
)
)
print("\n")
message_box(
'"Recommendation ITU-T H.273" '
"Code points for Video Signal Type Identification"
)
message_box(
f"Colour Primaries: {list(colour.COLOUR_PRIMARIES_ITUTH273.keys())}"
)
colour.models.describe_video_signal_colour_primaries(1)
print("\n")
message_box(
f"Transfer Characteristics: "
f"{list(colour.TRANSFER_CHARACTERISTICS_ITUTH273.keys())}"
)
colour.models.describe_video_signal_transfer_characteristics(1)
print("\n")
message_box(
f"Matrix Coefficients: "
f"{list(colour.MATRIX_COEFFICIENTS_ITUTH273.keys())}"
)
colour.models.describe_video_signal_matrix_coefficients(1)
| [
"[email protected]"
] | |
a3f5eabaab314062ef6ff511debc3e23ba3d7cc4 | e40b164391060a7c66fa480996f751c4797d6f29 | /QuoteEngine/PDFIngestor.py | bb4cb9e0681c9c203996a5fe5e9d94e49035c363 | [] | no_license | Mehaktoor/Motivational-Meme-Generator | f4fdd745fb37935c07975cf02645108e463ff38d | ec8f191f1c049f41801989e142b6625e64e004f8 | refs/heads/main | 2023-03-17T12:51:27.922306 | 2021-03-07T00:14:06 | 2021-03-07T00:14:06 | 338,726,769 | 0 | 1 | null | 2021-03-02T21:56:35 | 2021-02-14T04:13:34 | Python | UTF-8 | Python | false | false | 1,026 | py | from typing import List
from time import time
import os
import subprocess
from .QuoteModel import QuoteModel
from .IngestorInterface import IngestorInterface
# ingest PDF file format. Create list of QuoteModel objects.
class PDFIngestor(IngestorInterface):
allowed_extensions = ['pdf']
@classmethod
def parse(cls, path: str) -> List[QuoteModel]:
if not cls.can_ingest(path):
raise Exception('cannot ingest exception')
# CLI tool - pdftotext used to convert pdf format to txt format.
tmp = f'./tmp/{int(time())}.txt'
call = subprocess.call(['pdftotext', path, tmp])
with open(tmp, 'r') as f:
quotes = []
for line in f.readlines():
line = line.strip('\n\r').strip()
if len(line) == 0:
continue
parse = line.split('-')
new_quote = QuoteModel(parse[0], parse[1])
quotes.append(new_quote)
os.remove(tmp)
return quotes
| [
"[email protected]"
] | |
654fa1db75fd03efbbb973cc2d28997f58c507b5 | bf0901b2cf3bbaf7c6e44030e0139376e0f90b4b | /cee6110hydroinfo/hw/hw1/hw1.py | 28c6b3515b34ffe8ff21644a8f44c3ddec5bfcad | [] | no_license | karunmj/usu-coursework | a978fb859932e0894f8ada9e769fde51bf6f021a | 40dfb9447b34d5476e390a7e53e748706a37af3e | refs/heads/master | 2020-09-24T21:25:43.059344 | 2016-11-22T08:22:44 | 2016-11-22T08:22:44 | 67,301,294 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 606 | py | ##Plotting pH and dissolved oxygen levels
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
#csv file contains logan river data for 2015 from water lab site
loganriverdata = pd.read_csv('iUTAH_GAMUT_LR_WaterLab_AA_RawData_2015.csv', skiprows=68)
#plotting ph
plt.plot(loganriverdata['pH'])
plt.ylabel('ph')
plt.xlabel('Time units [every 15 min]')
plt.title('pH levels, 2015')
plt.show()
#plotting dissolved oxygen
plt.plot(loganriverdata['ODO'])
plt.ylabel('Dissolved oxgen [mg/L]')
plt.xlabel('Time units [every 15 min]')
plt.title('Dissolved oxygen levels, 2015')
plt.show()
| [
"[email protected]"
] | |
26e1176c7c2ac4ffa6357771cf0eaf657df56097 | aa8ad65d801cf9c292e318e41f106c1974fe96b9 | /inflight-entertainment.py | 25f0f5786faab7df4fb96eaf6fafbbf29aaf0ed4 | [] | no_license | gautamgitspace/interview-cake | fbc3f95d2af82a46d735f07e7d5746ec3d7412ce | 0f3c533df091571661b7b95db514870b3b790aef | refs/heads/master | 2021-01-20T07:53:47.161093 | 2017-06-09T23:53:38 | 2017-06-09T23:53:38 | 90,061,214 | 5 | 1 | null | null | null | null | UTF-8 | Python | false | false | 846 | py | #nested loop approach: outer - first_movie_length, inner - check for condition
#flight_length-first_movie_length = second_movie_length for every item in outer loop.
#this gives O(n^2)
"""
for item in m:
fml = item
for item2 in m:
if (fl-fml==item2):
return True
"""
#A better approach is to use set() which will give us constant-time lookups:
#this gives: O(n) time, and O(n) space
def can_two_movies_fill_flight(movie_lengths, flight_length):
movie_lengths_seen = set()
for i in movie_lengths:
second_movie_length = flight_length-i
if second_movie_length in movie_lengths_seen:
return True
movie_lengths_seen.add(i)
movie_lengths = [2.5, 2.45, 2.35, 2.4, 2.67, 3.1, 2.33]
flight_length = 5
result = can_two_movies_fill_flight(movie_lengths,flight_length)
print result
| [
"[email protected]"
] | |
1c9920c6563c7225e4224537335ab439b2822a2b | 4b31372a907f446a36e2461d3160e809603cf7ef | /Python 3/1002.py | cbecad92dd877a8a0d1fefa56a97547e223b0a1a | [] | no_license | lcar99/URI | 60c65438f557fcc2b846444d8daf79ddd782d3eb | bded0ac6564036976a9acab451981752a4dac36d | refs/heads/main | 2023-02-24T03:27:23.963643 | 2021-02-03T07:06:01 | 2021-02-03T07:06:01 | 335,168,807 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 65 | py | r = float(input())
pi = 3.14159
a = pi*r*r
print ("A="+'%.4f'%a)
| [
"[email protected]"
] | |
667ad84dd8023e2e0b9f6efb93424a1527f4165d | 9ae6ce54bf9a2a86201961fdbd5e7b0ec913ff56 | /google/ads/googleads/v11/enums/types/feed_origin.py | 7b7ab37117859b624d16463ece35dabae7420511 | [
"Apache-2.0"
] | permissive | GerhardusM/google-ads-python | 73b275a06e5401e6b951a6cd99af98c247e34aa3 | 676ac5fcb5bec0d9b5897f4c950049dac5647555 | refs/heads/master | 2022-07-06T19:05:50.932553 | 2022-06-17T20:41:17 | 2022-06-17T20:41:17 | 207,535,443 | 0 | 0 | Apache-2.0 | 2019-09-10T10:58:55 | 2019-09-10T10:58:55 | null | UTF-8 | Python | false | false | 1,121 | py | # -*- coding: utf-8 -*-
# Copyright 2022 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# 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 proto # type: ignore
__protobuf__ = proto.module(
package="google.ads.googleads.v11.enums",
marshal="google.ads.googleads.v11",
manifest={"FeedOriginEnum",},
)
class FeedOriginEnum(proto.Message):
r"""Container for enum describing possible values for a feed
origin.
"""
class FeedOrigin(proto.Enum):
r"""Possible values for a feed origin."""
UNSPECIFIED = 0
UNKNOWN = 1
USER = 2
GOOGLE = 3
__all__ = tuple(sorted(__protobuf__.manifest))
| [
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] | |
e7b1c65afc5f4fe30f3a62488bda704c7df580ec | b49a54a159caf47fba66316256169fb8062eff11 | /currency/admin.py | b33c7a9b10e67dec23bbfdb5cca6282a2491b599 | [] | no_license | Minyeob/Currency-Calculator | d4b3f54ad268fbdf4fa5a73a0a7c2200593c89e2 | 4ae9abf265d968550ffe6817f1e3d928db93b0cd | refs/heads/master | 2021-01-12T17:07:33.763190 | 2016-10-04T16:00:51 | 2016-10-04T16:00:51 | 69,980,399 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 326 | py | from django.contrib import admin
from currency.models import CurrencyEUR, CurrencyCNY, CurrencyGBP, CurrencyJPY, CurrencyKRW, Choice
admin.site.register(CurrencyKRW)
admin.site.register(CurrencyEUR)
admin.site.register(CurrencyCNY)
admin.site.register(CurrencyGBP)
admin.site.register(CurrencyJPY)
admin.site.register(Choice) | [
"[email protected]"
] | |
8300bac3852392251d6b3d01ec530edeffea27fc | 80d1e7d12e21383c795eab1b7a59fabece827f45 | /10 Days of Statistics/Quartiles.py | a3c4cce799b52ae54de591077d2fbad6d610b014 | [] | no_license | nikhiljsk/HackerRank | df7716d3bae91defee97df1efd34273c5fa48cc3 | 33a0925c8a03f3119348f3abf440b2b3b9082474 | refs/heads/master | 2022-11-08T11:16:27.535386 | 2020-06-19T17:59:46 | 2020-06-19T17:59:46 | 272,817,936 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 519 | py | def cal_median(numbers):
# print(numbers)
n = len(numbers)
if n%2 == 0:
return (numbers[n//2 - 1] + numbers[n//2])//2
return numbers[n//2]
n = int(input())
numbers = list(sorted(map(int, input().rstrip().split())))
# print(numbers)
middle = n//2
if n%2 != 0:
print(cal_median(numbers[:middle]))
print(cal_median(numbers))
print(cal_median(numbers[middle+1:]))
else:
print(cal_median(numbers[:middle]))
print(cal_median(numbers))
print(cal_median(numbers[middle:]))
| [
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] | |
564f13656023dfb3964fd387bb6d1581976fb9df | 893ccb3eafc2bf91667c755bda0534850c85986a | /proxy_api/server_run.py | 145136c05ca91f6d7a18865ddea1ae21d6750cba | [] | no_license | BoyYongXin/proxy_pool | 978443e5d5aebddbecf1b85997691848003324d2 | 1fc05097bfdfd1906fa81a5fa48a42f85144e642 | refs/heads/master | 2020-08-13T15:01:31.227107 | 2019-10-14T08:28:02 | 2019-10-14T08:28:02 | 214,988,763 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,008 | py | # -*- coding: utf-8 -*-
'''
Created on 2019-10-10 01:55
---------
@summary: 启动代理接口程序
---------
@author: Yongxin_Yang
'''
import tornado.web
import tornado.httpserver
import tornado.ioloop
from tornado import gen
import os
import json
from utils import tools
import random
from db.redisdb import RedisDB
redis = RedisDB()
config = os.path.join(os.path.dirname(__file__) + '/../config.conf')
redis_key = tools.get_conf_value(config, 'redis', 'redis_key2')
class IndexHandler(tornado.web.RequestHandler):
async def get(self):
num = int(self.get_argument('num'))#获取url参数
total_count = redis.sget_count(table=redis_key)
ip_pools = redis.sget(table=redis_key, count=total_count)
ip_Random = [] # 定义随机数列表
random.shuffle(ip_pools) # 打乱列表顺序
ip_Random = ip_pools[0:num] # 截取打乱后的前num个值，赋值给新列表iRandom
if ip_Random:
result = {'result':'获取ip成功','get_count':len(ip_Random),'proxy':ip_Random}
else:
result = {'result': '未知原因，请联系开发人员','get_count':len(ip_Random), 'proxy': []}
self.write(result)
class Application(tornado.web.Application): #创建 Application 对象， 定义 setting 和 URL 映射规则
def __init__(self):
handlers = [
(r"/GetProxy", IndexHandler),
]
settings = dict(
debug=True,
)
tornado.web.Application.__init__(self, handlers, **settings) # 将参数设置传递到父类 Application中
if __name__ == "__main__":
http_server = tornado.httpserver.HTTPServer(Application()) # 传递 Application 对象，封装成 HTTPServer 对象
http_server.listen(8888,address="192.168.80.60") # 启动 HTTPServer 监听，实际上 HTTPServer 继承自 TCPServer，是在TCPServer 中启动 listen Socket 端口
tornado.ioloop.IOLoop.instance().start()#获取全局IOLoop单例，启动IOLoop大循环
| [
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] | |
03e9120f357635fb7ffca4d6a3fe9f71d28a8685 | 3dbf79d16490d03001abcfc29b7c674fdcb8af38 | /src/Torcs_5/Torcs_5/splash/component/source_linear_x.py | f39cbbeebe8c5506bdb6e1111e9b493adba46e2d | [] | no_license | cheonghwa-lee/dev_ws | 113c58d87f6408c6a18778c8d74d12a3a656f805 | e185ab1961dcb97f0ef3de50f192a206cf7226b3 | refs/heads/main | 2023-06-25T03:29:54.334763 | 2021-07-22T00:33:45 | 2021-07-22T00:33:45 | 315,225,399 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 881 | py | '''
Generated automatically by Splash Code Generator for source_linear_x
'''
from std_msgs.msg import Float32
from geometry_msgs.msg import TwistStamped
from scl.components import *
from scl.impl.singleton import Singleton
from ..factory.factory_0 import Factory0
class SourceLinearX(Component, metaclass=Singleton):
def __init__(self):
super().__init__(name="source_linear_x", factory=Factory0(), mode="Mode A")
def setup(self):
self.create_subscription(TwistStamped, "/torcs_speed", self.torcs_speed_callback, 1)
self.attach_stream_output_port(msg_type=Float32, channel="source_x")
def run(self):
pass
def torcs_speed_callback(self, msg):
linear_x = msg.twist.linear.x
# print(linear_x)
new_msg = Float32()
new_msg.data = linear_x
self.write("source_x", new_msg)
| [
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] | |
9ba27ec47692291528c3632b1221583c88a010aa | 1d640906754a591ecca623a7531196976985d9f2 | /tictac/transform.py | 1a586093c625cd906dae645efe1ee55c580a0272 | [] | no_license | nestedsoftware/tictac | 971f971c2eb4c77aed0c3231a9000cf720baae23 | 220bbdc6103ff012ec60b5b424e1566205349588 | refs/heads/master | 2021-09-26T04:35:01.768442 | 2020-01-15T04:32:17 | 2020-01-15T04:32:17 | 192,117,298 | 24 | 26 | null | 2021-09-22T19:11:29 | 2019-06-15T19:37:37 | Python | UTF-8 | Python | false | false | 1,175 | py | import numpy as np
class Transform:
def __init__(self, *operations):
self.operations = operations
def transform(self, target):
for op in self.operations:
target = op.transform(target)
return target
def reverse(self, target):
for op in reverse(self.operations):
target = op.reverse(target)
return target
class Identity:
@staticmethod
def transform(matrix2d):
return matrix2d
@staticmethod
def reverse(matrix2d):
return matrix2d
class Rotate90:
def __init__(self, number_of_rotations):
self.number_of_rotations = number_of_rotations
self.op = np.rot90
def transform(self, matrix2d):
return self.op(matrix2d, self.number_of_rotations)
def reverse(self, transformed_matrix2d):
return self.op(transformed_matrix2d, -self.number_of_rotations)
class Flip:
def __init__(self, op):
self.op = op
def transform(self, matrix2d):
return self.op(matrix2d)
def reverse(self, transformed_matrix2d):
return self.transform(transformed_matrix2d)
def reverse(items):
return items[::-1]
| [
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] | |
f9cd8f7fa1c0852d57d2bb121a13e89181237fff | 08b22b6619ad84613484018fd8445212d18445ba | /setup.py | 32103883e3399c3b89d4282870acc8b14687cbc2 | [
"MIT"
] | permissive | sravel/pibooth-pimoroni11x7 | 6cc21c20526c205ca203b85e15460363fc44dbb8 | 53ce5bd79e91c68a5947ece242b1ddbe79191219 | refs/heads/master | 2023-06-27T12:58:17.489182 | 2021-07-22T08:57:13 | 2021-07-22T08:57:13 | 268,804,770 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,970 | py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import sys
from io import open
import os.path as osp
from setuptools import setup
HERE = osp.abspath(osp.dirname(__file__))
sys.path.insert(0, HERE)
import pibooth_pimoroni11x7 as plugin
def main():
setup(
name=plugin.__name__,
version=plugin.__version__,
description=plugin.__doc__,
long_description=open(osp.join(HERE, 'README.rst'), encoding='utf-8').read(),
long_description_content_type='text/x-rst',
classifiers=[
'Development Status :: 5 - Production/Stable',
'Environment :: Other Environment',
'Intended Audience :: Developers',
'Intended Audience :: End Users/Desktop',
'License :: OSI Approved :: MIT License',
'Operating System :: POSIX :: Linux',
'Programming Language :: Python :: 2.7',
'Programming Language :: Python :: 3.4',
'Programming Language :: Python :: 3.6',
'Natural Language :: English',
'Topic :: Multimedia :: Graphics :: Capture :: Digital Camera',
],
author="Sébastien Ravel",
url="https://github.com/pibooth/pibooth-pimoroni11x7",
download_url="https://github.com/pibooth/pibooth-pimoroni11x7/archive/{}.tar.gz".format(plugin.__version__),
license='MIT license',
platforms=['unix', 'linux'],
keywords=[
'Raspberry Pi',
'camera',
'photobooth',
'qr code'
],
py_modules=['pibooth_pimoroni11x7'],
install_requires=[
'pibooth>=2.0.0',
'matrix11x7>=0.0.1'
],
options={
'bdist_wheel':
{'universal': True}
},
zip_safe=False, # Don't install the lib as an .egg zipfile
entry_points={'pibooth': ["pibooth_pimoroni11x7 = pibooth_pimoroni11x7"]},
)
if __name__ == '__main__':
main()
| [
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] | |
03af47df44cbfb9bfd1864ba2a4f4483c9fbca4f | 7d748aa915be71588ce96605dc7050a86b10f5ae | /src/oryxbot_arm_controller/script/oryxbot_pick_arpy.py | dbf9dcd2fff3f946acb0e682aa2f5189e2412771 | [] | no_license | DylanLN/oryxbot-sim | 8da3924663eb8e59e6f51325df62af0eefc1bdb6 | 49a1411b17d7be5d327eef7ce4395c963887a94a | refs/heads/master | 2022-04-23T13:24:41.791537 | 2020-04-26T09:36:08 | 2020-04-26T09:36:08 | 258,961,974 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,192 | py | #!/usr/bin/env python
# -*- coding: UTF-8 -*-
'''robot_pick ROS Node'''
# license removed for brevity
import sys
import os
import math
import threading
import rospy
import time
from oryxbot_msgs.srv import *
from oryxbot_msgs.msg import *
import tf
class ORYXBOT_PICK(object):
CAMREA_X_MAX=20.0
CAMREA_Y_MAX=205.0
CAMREA_Z_MAX=170.0
position=[0.0,0.0,0.0,0.0]
pick_server=''
place_server=''
client_pick=''
client_place=''
client_goto=''
listener=''
def thread_job(self):
rospy.spin()
def callback(self,msg):
self.position[0] = msg.x/1000.0
self.position[1] = msg.y/1000.0
self.position[2] = msg.z/1000.0
self.position[3] = msg.r
br = tf.TransformBroadcaster()
br.sendTransform((self.position[0], self.position[1], self.position[2]),
tf.transformations.quaternion_from_euler(0, 0, math.atan2(self.position[1],self.position[0])),
rospy.Time.now(),
"end",
"robot")
def pick_callback(self,req):
xx=self.position[0]
yy=self.position[1]
zz=self.position[2]
a=req.number
marker="/ar_marker_"+str(req.number)
print marker
if req.mode==0:
goresp = self.client_goto(self.CAMREA_X_MAX,self.CAMREA_Y_MAX,self.CAMREA_Z_MAX,0.0)
if goresp.ret==False:
return pick_markerResponse([self.CAMREA_X_MAX,self.CAMREA_Y_MAX,self.CAMREA_Z_MAX],False," goto error !")
time.sleep(1)
elif req.mode==1:
pass
else:
return pick_markerResponse([0,0,0],False," Pattern error !")
try:
(trans,rot) = self.listener.lookupTransform('/robot', marker, rospy.Time(0))
except (tf.LookupException, tf.ConnectivityException, tf.ExtrapolationException):
return pick_markerResponse([0,0,0],False," tf erro !")
x_=trans[0]*1000.0
y_=trans[1]*1000.0
z_=trans[2]*1000.0
print trans
print x_,y_,z_
goresp = self.client_goto(x_,y_,z_+50,0.0)
if goresp.ret==False:
return pick_markerResponse([x_,y_,z_+50],False," goto error !")
piresp = self.client_pick(x_,y_,z_,0.0)
if piresp.ret==False:
return pick_markerResponse([x_,y_,z_],False," Manipulator unattainable !")
goresp = self.client_goto(x_,y_,z_+50,0.0)
if goresp.ret==False:
return pick_markerResponse([x_,y_,z_+50],False," goto error !")
time.sleep(1)
if abs(req.position[0])<0.01 and abs(req.position[1])<0.01 and abs(req.position[2])<0.01 :
if req.mode == 0:
goresp = self.client_goto(self.CAMREA_X_MAX,self.CAMREA_Y_MAX,self.CAMREA_Z_MAX,0.0)
if goresp.ret==False:
return pick_markerResponse([self.CAMREA_X_MAX,self.CAMREA_Y_MAX,self.CAMREA_Z_MAX],False," goto error !")
elif req.mode == 1:
goresp = self.client_goto(xx,yy,zz,0.0)
if goresp.ret==False:
return pick_markerResponse([xx,yy,zz],False," goto error !")
else:
goresp = self.client_goto(req.position[0],req.position[1],req.position[2]+50,0.0)
if goresp.ret==False:
return pick_markerResponse([req.position[0],req.position[1],req.position[2]+50],False," goto error !")
plresp = self.client_place(req.position[0],req.position[1],req.position[2],0.0)
if plresp.ret==False:
return pick_markerResponse([req.position[0],req.position[1],req.position[2]],False," place error !")
return pick_markerResponse([0,0,0],True," ok !")
def place_callback(self,req):
print req
goresp = self.client_goto(req.x,req.y,req.z+40,0.0)
if goresp.ret==False:
return pick_placeResponse(False)
plresp = self.client_place(req.x,req.y,req.z,0.0)
if plresp.ret==False:
return pick_placeResponse(False)
return pick_placeResponse(True)
def __init__(self):
'''robot_pick'''
rospy.init_node('robot_pick', anonymous=True)
self.listener = tf.TransformListener()
self.pick_server = rospy.Service('pick_ar', pick_marker, self.pick_callback)
self.place_server = rospy.Service('place_ar', pick_place, self.place_callback)
rospy.Subscriber('/oryxbot_arm_controller/position_info', dobot_control, self.callback)
rospy.wait_for_service('pick')
rospy.wait_for_service('place')
rospy.wait_for_service('goto_position')
self.client_pick=rospy.ServiceProxy("pick",pick_place)
self.client_place=rospy.ServiceProxy("place",pick_place)
self.client_goto=rospy.ServiceProxy("goto_position",pick_place)
add_thread = threading.Thread(target = self.thread_job)
add_thread.start()
rospy.spin()
if __name__ == '__main__':
try:
ORYXBOT_PICK()
except rospy.ROSInterruptException:
rospy.loginfo("Navigation test finished.")
| [
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] | |
a5e68b11c8a13478b4dfbb1872a987caed729e92 | 5e26a16d75c408399c4e434ca187a96e4e79b6cc | /6Benches/performetrics/comma seperated binary.py | ffaf925e8711251f3182c8c6c3f58776ab87b71b | [] | no_license | Gaayathre7/mylib | 6005df5f241627865ca21cd3c79ebae9cdbb00aa | c0a9e5d262bedf8fa742fd766e6475a500a33436 | refs/heads/master | 2022-04-08T22:08:58.606045 | 2020-03-10T06:40:35 | 2020-03-10T06:40:35 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 397 | py | # -*- coding: utf-8 -*-
"""
Created on Tue Mar 3 16:18:24 2020
@author: rajas
"""
#write an prgm which accepts a sequence of comma seperated 4 digit binary numbers as its input and then check whether they r divisible by 5 or not
items=[]
num=[x for x in input().split(',')]
for p in num:
x=int(p,2)
if x%5==0:
items.append(p)
print(",".join(items))
| [
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] | |
b671d1899ce6df4b4f5966b87c864960f9a7c4bb | 8bc537deb77d32ff42b226200af494b1351de01e | /dexenv/bin/pyrsa-decrypt | 1844b4d391de0bf2a36811bc1d3ddb4c289231c7 | [] | no_license | bopopescu/ELearning | 00478324f898dc8767d23b61d10b282945bb9058 | 2484b8921ee22871a8dc478696e1e3e0c27690b9 | refs/heads/master | 2022-11-26T08:38:33.739521 | 2020-05-08T22:27:03 | 2020-05-08T22:27:03 | 280,945,464 | 0 | 0 | null | 2020-07-19T20:31:36 | 2020-07-19T20:31:35 | null | UTF-8 | Python | false | false | 228 | #!/home/dex/Dex/dexenv/bin/python3
# -*- coding: utf-8 -*-
import re
import sys
from rsa.cli import decrypt
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
sys.exit(decrypt())
| [
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] | ||
32326006e5bb1689d8ba85188848f20fbc1eef7b | fb7e2563f78008bb2fd1e082c46309bf86d2644f | /tests/python/unittest/test_operator.py | 5ef79cfb63145d2a53943df5c5fe3a513f495983 | [
"Apache-2.0"
] | permissive | achao2013/mxnet-quantify | ddbf03d8f5a4dc4694b4d729e364c6465f4fb887 | ae77c896da6db35530390e3cf8e524d553bba112 | refs/heads/master | 2021-01-18T22:41:24.798766 | 2017-04-05T02:05:51 | 2017-04-05T02:05:51 | 87,065,611 | 4 | 0 | null | null | null | null | UTF-8 | Python | false | false | 44,528 | py | # pylint: skip-file
import numpy as np
import mxnet as mx
import random
from numpy.testing import assert_allclose
from check_utils import (check_numeric_gradient, check_symbolic_backward,
check_symbolic_forward, reldiff, _np_reduce)
def same(a, b):
return np.sum(a != b) == 0
def np_softmax(x):
x = x - np.max(x, axis=1).reshape(x.shape[0], 1)
x = np.exp(x)
x /= np.sum(x, axis=1).reshape(x.shape[0], 1)
return x
def check_elementwise_sum_with_shape(shape, n):
# forward
inputs = [mx.symbol.Variable('arg%d' % i) for i in range(n)]
out = mx.symbol.ElementWiseSum(*inputs, name='esum')
arr = [mx.nd.empty(shape) for i in range(n)]
arr_grad = [mx.nd.empty(shape) for i in range(n)]
for i in range(n):
arr[i][:] = np.random.uniform(-10, 10, shape)
exec1 = out.bind(mx.Context('cpu'),
args=arr,
args_grad=arr_grad)
out1 = exec1.outputs[0].asnumpy()
exec1.forward()
out1 = exec1.outputs[0].asnumpy()
out = sum(a.asnumpy() for a in arr)
assert reldiff(out, out1) < 1e-6
out_grad = mx.nd.empty(shape)
out_grad[:] = np.random.uniform(-10, 10, shape)
# backward
exec1.backward([out_grad])
for a in arr_grad:
assert same(a.asnumpy(), out_grad.asnumpy())
def test_elementwise_sum():
np.random.seed(0)
nrepeat = 2
maxdim = 4
for repeat in range(nrepeat):
for dim in range(1, maxdim):
shape = tuple(np.random.randint(1, int(1000**(1.0/dim)), size=dim))
check_elementwise_sum_with_shape(shape, np.random.randint(1, 8))
def check_slice_channel(dim, num):
ins = []
if dim == 2:
shape = (2, 2)
else:
shape = (2, 2, 2 ,3)
ins = [np.ones(shape) * i for i in range(num)]
e = np.hstack(ins)
e_nd = mx.nd.empty(e.shape)
e_nd[:] = e
data = mx.sym.Variable('data')
op = mx.sym.SliceChannel(data=data, num_outputs=num)
arg_shape, output_shape, aux_shape = op.infer_shape(data=e_nd.shape)
grad_nd = [mx.nd.empty(shape) for shape in arg_shape]
exe = op.bind(mx.cpu(), args=[e_nd], args_grad=grad_nd)
assert len(exe.outputs) == num
o_nd = [exe.outputs[i] for i in range(num)]
# test forward
exe.forward()
for i in range(num):
assert reldiff(o_nd[i].asnumpy(), ins[i]) < 1e-5
# test backward
for i in range(num):
o_nd[i] += i
exe.backward(o_nd)
assert reldiff(grad_nd[0].asnumpy(), np.hstack([ins[i] + i for i in range(num)])) < 1e-5
# test slice channel with squeeze_axis
op = mx.sym.SliceChannel(data=data, num_outputs=shape[1], squeeze_axis=1)
arg_shape, output_shape, aux_shape = op.infer_shape(data=shape)
assert len(output_shape) == shape[1]
for o_shape in output_shape:
assert len(o_shape) == len(shape) - 1
assert o_shape == tuple([shape[0]] + list(shape[2:]))
def check_concat_with_shape(shapes, dimension, skip_second):
# if skip_second is True, second argument will not have gradient.
# it is to test #1130
n = len(shapes)
# forward
target_dim = 0
for shape in shapes:
target_dim += shape[dimension]
inputs = [mx.symbol.Variable('arg%d' % i) for i in range(n)]
out = mx.symbol.Concat(*inputs, name='conc',dim=dimension)
arr = [mx.nd.empty(shape) for shape in shapes]
for i in range(n):
arr[i][:] = shapes[i][dimension]
arr_np = [np.copy(narray.asnumpy()) for narray in arr]
arr_grad = [mx.nd.empty(shape) for shape in shapes]
dict_grad = {}
arg_names = out.list_arguments()
for name, g in zip(arg_names, arr_grad):
if not skip_second or name != 'arg1':
dict_grad[name] = g
args = out.list_arguments()
arg_shapes, out_shapes, aux_shapes = out.infer_shape(**dict(zip(args, shapes)))
out_grad = mx.nd.empty(out_shapes[0])
exec1 = out.bind(mx.Context('cpu'),
args=arr,
args_grad=dict_grad)
exec1.forward()
out1 = exec1.outputs[0]
ret = np.concatenate([narray.asnumpy() for narray in arr], axis=dimension)
assert same(out1.asnumpy(), ret)
# backward
out1.copyto(out_grad)
out_grad[:] += 1
exec1.backward([out_grad])
for i, name in enumerate(arg_names):
if not skip_second or name != 'arg1':
grad = dict_grad[name]
np_grad = arr_np[i]
assert same(grad.asnumpy(), np_grad + 1)
def test_concat():
for dimension in range(4):
n = 2
merge = [2, 3, 4, 5, 6]
a = 2
b = 3
c = 4
# test 2D
if dimension<2:
for dim in range(2, 6):
shapes = []
for i in range(dim):
if dimension == 0:
shapes.append((merge[i], a))
elif dimension == 1:
shapes.append((a, merge[i]))
check_concat_with_shape(shapes,dimension,True)
check_concat_with_shape(shapes,dimension,False)
#test 3D
if dimension<3:
for dim in range(2, 6):
shapes = []
for i in range(dim):
if dimension == 0:
shapes.append((merge[i], a,b))
elif dimension ==1:
shapes.append((a,merge[i],b))
elif dimension ==2:
shapes.append((a,b,merge[i]))
check_concat_with_shape(shapes,dimension,True)
check_concat_with_shape(shapes,dimension,False)
# test 4D
for dim in range(2, 6):
shapes = []
for i in range(dim):
if dimension == 0:
shapes.append((merge[i],a,b,c))
elif dimension == 1:
shapes.append((a,merge[i],b,c))
elif dimension ==2:
shapes.append((a,b,merge[i],c))
elif dimension ==3:
shapes.append((a,b,c,merge[i]))
check_concat_with_shape(shapes,dimension,True)
check_concat_with_shape(shapes,dimension,False)
def test_slice_channel():
check_slice_channel(2, 4)
check_slice_channel(4, 4)
check_slice_channel(2, 16)
def check_regression(symbol, forward, backward):
data = mx.symbol.Variable('data')
label = mx.symbol.Variable('label')
out = symbol(data, label)
shape = (3, 1)
arr_data = mx.random.uniform(-1, 1, shape)
arr_label = mx.random.uniform(0, 1, shape[0])
arr_grad = mx.nd.empty(shape)
exec1 = out.bind(mx.cpu(),
args=[arr_data, arr_label],
args_grad={"data" : arr_grad})
exec1.forward()
out1 = exec1.outputs[0].asnumpy()
npout = forward(arr_data.asnumpy())
assert reldiff(npout, out1) < 1e-6
exec1.backward()
npout = backward(npout, arr_label.asnumpy().reshape(npout.shape))
assert reldiff(npout, arr_grad.asnumpy()) < 1e-6
def test_regression():
check_regression(mx.symbol.LogisticRegressionOutput,
lambda x: 1.0 / (1.0 + np.exp(-x)),
lambda x, y : x - y)
check_regression(mx.symbol.LinearRegressionOutput,
lambda x: x,
lambda x, y : x - y)
def check_softmax_with_ignore_label(xpu):
X = mx.symbol.Variable('X')
L = mx.symbol.Variable('L')
Y = mx.symbol.SoftmaxOutput(data=X, label=L, ignore_label=0, use_ignore=True)
shape = (20, 10)
x = mx.nd.empty(shape, ctx = xpu)
l = mx.nd.empty((shape[0],), ctx = xpu)
x_np = np.random.rand(*shape)
l_np = np.random.randint(0, shape[1]-1, (shape[0],))
x[:] = x_np
l[:] = l_np
grad = mx.nd.empty(shape, ctx = xpu)
exec1 = Y.bind(xpu, args = [x, l], args_grad = {'X': grad})
exec1.forward()
exec1.backward()
grad0 = grad.asnumpy()
for i in range(int(shape[0]/2)):
l_np[i] = 0
l[:] = l_np
exec1.forward()
exec1.backward()
grad1 = grad.asnumpy()
assert(abs(np.sum(grad1[:int(shape[0]/2)])) < 1e-5)
assert(reldiff(grad0[int(shape[0]/2):], grad1[int(shape[0]/2):]) < 1e-5)
def check_softmax_with_shape(shape, xpu):
# bind with label
X = mx.symbol.Variable('X')
L = mx.symbol.Variable('L')
Y = mx.symbol.SoftmaxOutput(data=X, label=L)
x = mx.random.uniform(-1, 1, shape, ctx = xpu)
l = mx.random.uniform(-1, 1, shape, ctx = xpu)
l[:] = np_softmax(l.asnumpy())
grad = mx.nd.empty(shape, ctx = xpu)
exec1 = Y.bind(xpu, args = [x, l], args_grad = {'X': grad})
exec1.forward()
out = exec1.outputs[0].asnumpy()
assert_allclose(out, np_softmax(x.asnumpy()))
exec1.backward()
assert_allclose(grad.asnumpy(), np_softmax(x.asnumpy()) - l.asnumpy())
def test_softmax():
check_softmax_with_shape((3, 4), mx.cpu())
def check_multi_softmax_with_shape(shape, xpu):
X = mx.symbol.Variable('X')
L = mx.symbol.Variable('L')
Y = mx.symbol.SoftmaxOutput(data=X, label=L, multi_output=True)
x = mx.random.uniform(-1, 1, shape, ctx = xpu)
l = mx.nd.empty((shape[0], shape[2]), ctx = xpu)
l[:] = np.random.randint(0, shape[1]-1, (shape[0], shape[2]))
grad = mx.nd.empty(shape, ctx = xpu)
exec1 = Y.bind(xpu, args = [x, l], args_grad = {'X': grad})
exec1.forward()
print(exec1.outputs[0].asnumpy())
exec1.backward()
print(grad.asnumpy())
def test_python_op():
X = mx.symbol.Variable('X')
op = mx.operator.NumpyOp()
s = op.get_symbol(X, name='numpy_op')
x = mx.ndarray.ones((10))*10
dx = mx.ndarray.zeros((10))
dy = mx.ndarray.ones((10))
exec1 = s.bind(mx.cpu(), args=[x], args_grad = {'X': dx})
exec1.forward()
assert reldiff(x.asnumpy(), exec1.outputs[0].asnumpy()) < 1e-5
exec1.backward(dy)
assert reldiff(dy.asnumpy(), dx.asnumpy()) < 1e-5
def test_swapaxes():
data = mx.symbol.Variable('data')
shape = (2, 3, 4)
data_tmp = np.ones(shape)
data_tmp[0] = 1
data_tmp[1] = 2
arr_data = mx.nd.array(data_tmp)
swap0 = mx.symbol.SwapAxis(data=data, dim1=0, dim2=2)
swap = mx.symbol.SwapAxis(data=swap0, dim1=1, dim2=2)
exe_c = swap.bind(mx.cpu(), args=[arr_data])
exe_c.forward()
out = exe_c.outputs[0].asnumpy()
swap0_ = np.swapaxes(data_tmp, 0, 2)
swap_ = np.swapaxes(swap0_, 1, 2)
assert reldiff(out, swap_) < 1e-6
def test_scalarop():
data = mx.symbol.Variable('data')
shape = (3, 4)
data_tmp = np.ones(shape)*5
arr_data = mx.nd.array(data_tmp)
arr_grad = mx.nd.empty(shape)
arr_grad[:]=3
test = 2 / (4-((1+data+1)*2/5)-0.2)
npout_1 = (4-((1+data_tmp+1)*2/5)-0.2)
npout = 2/npout_1
check_symbolic_forward(test, [data_tmp], [npout])
npout_grad = 2.*2/5
npout_grad = 2*npout_grad /(npout_1 *npout_1 )
check_symbolic_backward(test, [data_tmp], [np.ones(shape)*2], [npout_grad])
def test_scalar_pow():
data = mx.symbol.Variable('data')
shape = (1, 1)
data_tmp = np.ones(shape)
test = data ** 2
check_numeric_gradient(test, [data_tmp])
check_symbolic_forward(test, [data_tmp], [data_tmp ** 2])
check_symbolic_backward(test, [data_tmp], [np.ones(shape)], [2 * data_tmp])
def test_symbol_pow():
shape = (1, 1)
data = mx.symbol.Variable('data')
data_tmp = np.ones(shape)*2
exp = mx.symbol.Variable('exp')
exp_tmp = np.ones(shape)*3
test = data**exp
check_numeric_gradient(test, [data_tmp, exp_tmp])
check_symbolic_forward(test, [data_tmp, exp_tmp], [data_tmp**exp_tmp])
data_dir = data_tmp**(exp_tmp - 1) * exp_tmp
exp_dir = data_tmp**(exp_tmp) * np.log(data_tmp)
check_symbolic_backward(test, [data_tmp, exp_tmp], [np.ones(shape)], [data_dir, exp_dir])
def test_pow_fn():
shape = (3, 4)
exp = mx.symbol.Variable("exp")
y = mx.sym.pow(2, exp)
x = np.ones(shape)*3
check_numeric_gradient(y, [x])
check_symbolic_forward(y, [x], [2**x])
check_symbolic_backward(y, [x], [np.ones(shape)], [np.log(2) * 2**x])
def test_embedding():
in_dim = 10
out_dim = 4
batch = 24
data = mx.sym.Variable("data")
embed = mx.sym.Embedding(data=data, input_dim=in_dim, output_dim=out_dim, name="embed")
exe_test = embed.simple_bind(mx.cpu(), grad_req={'data': 'null', 'embed_weight': 'write'}, data=(batch,))
arg_map = dict(zip(embed.list_arguments(), exe_test.arg_arrays))
grad_map = dict(zip(embed.list_arguments(), exe_test.grad_arrays))
np_data = np.random.randint(low=0, high=in_dim, size=batch)
np_weight = np.random.uniform(-0.01, 0.01, arg_map["embed_weight"].shape)
np_onehot = np.zeros((batch, in_dim))
np_onehot[np.arange(batch), np_data] = 1.0
# forward
arg_map["data"][:] = np_data
arg_map["embed_weight"][:] = np_weight
exe_test.forward()
assert reldiff(exe_test.outputs[0].asnumpy(), np.dot(np_onehot, np_weight)) < 1e-6
# backward
np_grad = np.random.uniform(-1, 1, exe_test.outputs[0].shape)
grad = mx.nd.zeros(np_grad.shape)
grad[:] = np_grad
exe_test.backward([grad])
assert reldiff(grad_map["embed_weight"].asnumpy(), np.dot(np_onehot.T, np_grad)) < 1e-6
# check ops handle duplicate input correctly.
def test_binary_op_duplicate_input():
data = mx.symbol.Variable('data')
shape = (3, 4)
data_tmp = np.ones(shape)
data_tmp[:] = 5
arr_data = mx.nd.array(data_tmp)
arr_grad = mx.nd.empty(shape)
arr_grad[:] = 3
out_grad = mx.nd.empty(shape)
out_grad[:] = 1
square = data * data
exe_square = square.bind(mx.cpu(), args=[arr_data], args_grad=[arr_grad])
exe_square.forward()
assert reldiff(exe_square.outputs[0].asnumpy(), data_tmp * data_tmp) < 1e-6
exe_square.backward(out_grad)
assert reldiff(arr_grad.asnumpy(), 2.0 * data_tmp) < 1e-6
def test_sign():
data = mx.symbol.Variable('data')
shape = (3, 4)
data_tmp = np.ones(shape)
data_tmp[:]=5
arr_data = mx.nd.array(data_tmp)
arr_grad = mx.nd.empty(shape)
arr_grad[:]=3
test = mx.sym.sign(data)
exe_test = test.bind(mx.cpu(), args=[arr_data], args_grad=[arr_grad])
exe_test.forward()
out = exe_test.outputs[0].asnumpy()
npout = np.sign(data_tmp)
assert reldiff(out, npout) < 1e-6
out_grad = mx.nd.empty(shape)
out_grad[:] = 2;
npout_grad = out_grad.asnumpy()
npout_grad = 0;
exe_test.backward(out_grad)
assert reldiff(arr_grad.asnumpy(), npout_grad) < 1e-6
def test_round_ceil_floor():
data = mx.symbol.Variable('data')
shape = (3, 4)
data_tmp = np.ones(shape)
data_tmp[:]=5.543
arr_data = mx.nd.array(data_tmp)
arr_grad = mx.nd.empty(shape)
arr_grad[:]= 2
test = mx.sym.round(data) + mx.sym.ceil(data) + mx.sym.floor(data)
exe_test = test.bind(mx.cpu(), args=[arr_data])
exe_test.forward()
out = exe_test.outputs[0].asnumpy()
npout = np.round(data_tmp) + np.ceil(data_tmp) + np.floor(data_tmp)
assert reldiff(out, npout) < 1e-6
def test_rsqrt_cos_sin():
data = mx.symbol.Variable('data')
shape = (3, 4)
data_tmp = np.ones(shape)
data_tmp[:]=5
arr_data = mx.nd.array(data_tmp)
arr_grad = mx.nd.empty(shape)
arr_grad[:]=3
test = mx.sym.rsqrt(data) + mx.sym.cos(data) + mx.sym.sin(data)
exe_test = test.bind(mx.cpu(), args=[arr_data], args_grad=[arr_grad])
exe_test.forward()
out = exe_test.outputs[0].asnumpy()
npout = 1/ np.sqrt(data_tmp) + np.cos(data_tmp) + np.sin(data_tmp)
assert reldiff(out, npout) < 1e-6
out_grad = mx.nd.empty(shape)
out_grad[:] = 2;
npout_grad = out_grad.asnumpy()
npout_grad = npout_grad * -(1.0 / (2.0 * data_tmp * np.sqrt(data_tmp))) + npout_grad * -1 * np.sin(data_tmp) + npout_grad * np.cos(data_tmp)
exe_test.backward(out_grad)
assert reldiff(arr_grad.asnumpy(), npout_grad) < 1e-6
def test_maximum_minimum():
data1 = mx.symbol.Variable('data')
data2 = mx.symbol.Variable('data')
shape = (3, 4)
data_tmp1 = np.random.rand(3,4)
data_tmp2 = np.random.rand(3,4)
data_tmp1[:] = 2
data_tmp2[:] = 3
arr_data1 = mx.nd.array(data_tmp1)
arr_data2 = mx.nd.array(data_tmp2)
arr_grad1 = mx.nd.empty(shape)
arr_grad2 = mx.nd.empty(shape)
test = mx.sym.maximum(data1,data2) + mx.sym.minimum(data1,data2);
exe_test = test.bind(mx.cpu(), args=[arr_data1,arr_data2], args_grad=[arr_grad1,arr_grad2])
exe_test.forward()
out = exe_test.outputs[0].asnumpy()
npout = np.maximum(data_tmp1,data_tmp2) + np.minimum(data_tmp1,data_tmp2)
assert reldiff(out, npout) < 1e-6
out_grad = mx.nd.empty(shape)
out_grad[:] = 2
exe_test.backward(out_grad)
npout_grad = np.ones(shape)
npout_grad[:] = 2
mask1 = (data_tmp1 > data_tmp2).astype('float')
mask2 = (data_tmp1 < data_tmp2).astype('float')
npout_grad1 = npout_grad * mask1 + npout_grad * mask2
npout_grad2 = (npout_grad - npout_grad * mask1) + (npout_grad - npout_grad * mask2)
assert reldiff(arr_grad1.asnumpy(), npout_grad1) < 1e-6
assert reldiff(arr_grad2.asnumpy(), npout_grad2) < 1e-6
def test_maximum_minimum_scalar():
data1 = mx.symbol.Variable('data')
shape = (3, 4)
data_tmp1 = np.random.rand(3,4)
data_tmp1[:] = 2
arr_data1 = mx.nd.array(data_tmp1)
arr_grad1 = mx.nd.empty(shape)
test = mx.sym.maximum(data1,3) + mx.sym.maximum(9,data1) + mx.sym.minimum(5,data1) + mx.sym.minimum(data1,4)
exe_test = test.bind(mx.cpu(), args=[arr_data1], args_grad=[arr_grad1])
exe_test.forward()
out = exe_test.outputs[0].asnumpy()
npout = np.maximum(data_tmp1,3) + np.maximum(9,data_tmp1) + np.minimum(5,data_tmp1) + np.minimum(data_tmp1,4)
assert reldiff(out, npout) < 1e-6
out_grad = mx.nd.empty(shape)
out_grad[:] = 2
exe_test.backward(out_grad)
npout_grad = np.ones(shape)
npout_grad[:] = 2
mask1 = (data_tmp1 > 3).astype('float')
mask2 = (9 > data_tmp1).astype('float')
mask3 = (5 < data_tmp1).astype('float')
mask4 = (data_tmp1 < 4).astype('float')
npout_grad1 = npout_grad * mask1 + (npout_grad - npout_grad * mask2) + (npout_grad - npout_grad * mask3) + npout_grad * mask4
assert reldiff(arr_grad1.asnumpy(), npout_grad1) < 1e-6
def test_abs():
data = mx.symbol.Variable('data')
shape = (3, 4)
data_tmp = np.ones(shape)
data_tmp[:]=5
arr_data = mx.nd.array(data_tmp)
arr_grad = mx.nd.empty(shape)
arr_grad[:]=3
test = mx.sym.abs(data)
exe_test = test.bind(mx.cpu(), args=[arr_data], args_grad=[arr_grad])
exe_test.forward()
out = exe_test.outputs[0].asnumpy()
npout = abs(data_tmp)
assert reldiff(out, npout) < 1e-6
out_grad = mx.nd.empty(shape)
out_grad[:] = 2;
npout_grad = out_grad.asnumpy()
npout_grad = npout_grad * np.sign(data_tmp)
exe_test.backward(out_grad)
assert reldiff(arr_grad.asnumpy(), npout_grad) < 1e-6
def check_deconvolution_forward_backward(input_shape, num_filter, kernel, stride, pad):
"""configure A: input --> conv --> deconv --> output.
the convolution and deconvoluiton has similar parameter which ensure
the input shape is the same as output, and the same weights between conv
and deconv;
If the input value of forward() and backwrad() is the same, then
the output value of them should also the same;
"""
assert input_shape[1] == num_filter
data = mx.sym.Variable(name="data")
conv = mx.sym.Convolution(
data=data, kernel=kernel, stride=stride, pad=pad,
num_filter=num_filter, no_bias = "true", name = "conv")
deconv = mx.sym.Deconvolution(
data=conv, kernel=kernel, stride=stride, pad=pad,
num_filter=num_filter, no_bias = "true", name = "deconv")
arg_names = deconv.list_arguments()
arg_shapes, out_shapes, _ = deconv.infer_shape(data=input_shape)
input_data = mx.random.uniform(-5, 5, input_shape)
out_grad = input_data
args = {}
args["data"] = input_data
args['conv_weight'] = args['deconv_weight'] = mx.random.normal(0, 1,
(num_filter, input_shape[1]) + kernel)
args_grad = [mx.nd.empty(s) for s in arg_shapes]
exe = deconv.bind(mx.cpu(), args=args, args_grad=args_grad)
exe.forward()
out = exe.outputs[0].asnumpy()
exe.backward(out_grad)
assert reldiff(out, args_grad[0].asnumpy()) < 1e-6
def check_deconvolution_gradient(input_shape, num_filter, pad):
"""configure A: input --> conv --> output.
configure B: input --> deconv --> output
the convolution and deconvoluiton has similar parameter which ensure
the input shape is the same as output;
During backward(), if the input of A equals output of B, and the output
of A equals input of B, then the grad of weight should be the same;
"""
stride = (1, 1)
kernel = (2*pad[0]+1, 2*pad[1]+1)
data_conv = mx.sym.Variable(name="data_conv")
conv = mx.sym.Convolution(
data=data_conv, kernel=kernel, stride=stride, pad=pad,
num_filter=num_filter, no_bias = "true", name = "conv")
data_deconv = mx.sym.Variable(name="data_deconv")
deconv = mx.sym.Deconvolution(
data=data_deconv, kernel=kernel, stride=stride, pad=pad,
num_filter=num_filter, no_bias = "true", name = "deconv")
conv_data = mx.random.uniform(-5, 5, input_shape)
conv_args = {}
conv_args["data_conv"] = conv_data
conv_args['conv_weight'] = \
mx.random.normal(0, 1,(num_filter, input_shape[1]) + kernel)
conv_args_grad = [mx.nd.zeros(conv_data.shape),
mx.nd.zeros((num_filter, input_shape[1]) + kernel)]
exe_conv = conv.bind(mx.cpu(), args=conv_args, args_grad=conv_args_grad)
conv_out_grad = mx.random.normal(0, 2, exe_conv.outputs[0].shape)
exe_conv.backward(conv_out_grad)
deconv_data = conv_out_grad
deconv_args = {}
deconv_args['data_deconv'] = deconv_data
deconv_args['deconv_weight'] = conv_args['conv_weight']
deconv_args_grad = [mx.nd.zeros(deconv_data.shape),
mx.nd.zeros((num_filter, input_shape[1]) + kernel)]
exe_deconv = deconv.bind(mx.cpu(), args=deconv_args, args_grad=deconv_args_grad)
deconv_out_grad = conv_data[:]
exe_deconv.backward(deconv_out_grad)
assert reldiff(conv_args_grad[1].asnumpy(), deconv_args_grad[1].asnumpy()) < 1e-6
def check_deconvolution_target_shape(input_shape, kernel, stride, pad, adj, target_shape=None):
data = mx.sym.Variable(name="data")
deconv = mx.sym.Deconvolution(
data=data, kernel=kernel, stride=stride, pad=pad, adj=adj, num_filter=5,
target_shape = target_shape if target_shape is not None else (0, 0))
arg_names = deconv.list_arguments()
arg_shapes, out_shapes, _ = deconv.infer_shape(data=input_shape)
assert out_shapes[0] == (input_shape[0], 5, 8, 8)
def test_deconvolution():
check_deconvolution_target_shape(
input_shape = (2,3,4,4),
kernel = (3,3),
stride = (2,2),
target_shape = (8,8),
pad = (99,99), # will be ignored
adj = (101,101), # will be ignored
)
check_deconvolution_target_shape(
input_shape = (2,3,4,4),
kernel = (3,3),
stride = (2,2),
pad = (1,1),
adj = (1,1),
)
check_deconvolution_forward_backward(
input_shape = (1,1,5,5),
num_filter = 1,
kernel = (3,3),
stride = (1,1),
pad = (1,1)
)
check_deconvolution_forward_backward(
input_shape = (32,3,28,28),
num_filter = 3,
kernel = (3,3),
stride = (1,1),
pad = (1,1)
)
check_deconvolution_forward_backward(
input_shape = (10, 3, 403, 403),
num_filter = 3,
kernel = (7,7),
stride = (5,5),
pad = (2,2)
)
check_deconvolution_gradient(
input_shape = (1,3,5,5),
num_filter = 3,
pad = (1,1)
)
check_deconvolution_gradient(
input_shape = (5,3,100,100),
num_filter = 3,
pad = (3,3)
)
def check_nearest_upsampling_with_shape(shapes, scale, root_scale):
arr = {'arg_%d'%i: mx.random.uniform(-10.0, 10.0, shape) for i, shape in zip(range(len(shapes)), shapes)}
arr_grad = {'arg_%d'%i: mx.nd.zeros(shape) for i, shape in zip(range(len(shapes)), shapes)}
up = mx.sym.UpSampling(*[mx.sym.Variable('arg_%d'%i) for i in range(len(shapes))], sample_type='nearest', scale=root_scale)
exe = up.bind(mx.cpu(), args=arr, args_grad=arr_grad)
exe.forward(is_train=True)
exe.backward(exe.outputs)
for k in range(len(shapes)):
name = 'arg_%d'%k
assert_allclose(arr[name].asnumpy()*root_scale**2*scale**(2*k), arr_grad[name].asnumpy(), rtol=1e-4)
def test_nearest_upsampling():
for root_scale in [1,2,3]:
for scale in [1,2,3]:
for num_shape in [1,2,3]:
for base in [1,2,3]:
shapes = [(1,3,base*root_scale*scale**(num_shape-1-i),base*root_scale*scale**(num_shape-1-i)) for i in range(num_shape)]
check_nearest_upsampling_with_shape(shapes, scale, root_scale)
def test_batchnorm_training():
for shape in [(2, 3), (2, 3, 2, 2)]:
data_tmp = np.random.normal(size=shape)
s = shape[1],
gamma = np.ones(s)
beta = np.ones(s)
gamma[1] = 3
beta[0] = 3
rolling_mean = np.random.uniform(size=s)
rolling_std = np.random.uniform(size=s)
data = mx.symbol.Variable('data')
test = mx.symbol.BatchNorm(data, fix_gamma=False)
check_numeric_gradient(test, [data_tmp, gamma, beta], [rolling_mean, rolling_std], numeric_eps=1e-3, check_eps=5e-2)
# Gamma needs to be fixed at one when fix_gamma is true,
gamma = np.ones(s)
test = mx.symbol.BatchNorm(data, fix_gamma=True)
check_numeric_gradient(test, [data_tmp, gamma, beta], [rolling_mean, rolling_std], numeric_eps=1e-3, check_eps=5e-2)
def test_convolution_grouping():
num_filter = 4
num_group = 2
kernel = (3, 3)
shape = (1, 4, 9, 9)
x = mx.sym.Variable('x')
w = mx.sym.Variable('w')
b = mx.sym.Variable('b')
y1 = mx.sym.Convolution(data=x, weight=w, bias=b, num_filter=num_filter, num_group=num_group, kernel=kernel)
xslice = mx.sym.SliceChannel(data=x, num_outputs=num_group, axis=1)
wslice = mx.sym.SliceChannel(data=w, num_outputs=num_group, axis=0)
bslice = mx.sym.SliceChannel(data=b, num_outputs=num_group, axis=0)
y2 = mx.sym.Concat(*[mx.sym.Convolution(data=xslice[i], weight=wslice[i], bias=bslice[i],
num_filter=num_filter//num_group, kernel=kernel)
for i in range(num_group)])
exe1 = y1.simple_bind(mx.cpu(), x=shape)
exe2 = y2.simple_bind(mx.cpu(), x=shape, w=(num_filter, shape[1]//num_group, kernel[0], kernel[1]), b=(num_filter,))
for arr1, arr2 in zip(exe1.arg_arrays, exe2.arg_arrays):
arr1[:] = np.random.normal(size=arr1.shape)
arr2[:] = arr1
exe1.forward(is_train=True)
exe1.backward(exe1.outputs[0])
exe2.forward(is_train=True)
exe2.backward(exe2.outputs[0])
for arr1, arr2 in zip(exe1.outputs + exe1.grad_arrays, exe2.outputs + exe2.grad_arrays):
np.testing.assert_allclose(arr1.asnumpy(), arr2.asnumpy(), rtol=1e-3)
def _gen_broadcast_data():
# Generate random data that has ndim between 1-7 and all the shape dims between 1-10
ndim = np.random.randint(1, 8)
shape = np.random.randint(1, 11, size=(ndim,))
l_same_dim = np.random.randint(0, 5)
r_same_dim = np.random.randint(0, 5)
l_axis_flags = np.random.randint(0, 2, size=ndim)
r_axis_flags = np.random.randint(0, 2, size=ndim)
if l_same_dim == 4:
l_axis_flags = np.ones(ndim)
if r_same_dim == 4:
r_axis_flags = np.ones(ndim)
l_shape = shape.copy()
r_shape = shape.copy()
l_shape[np.where(l_axis_flags == 0)] = 1
r_shape[np.where(r_axis_flags == 0)] = 1
return [np.random.random(l_shape), np.random.random(r_shape)]
def _check_broadcast_op_forward(symbol, baseline):
sample_num = 200
for i in range(sample_num):
d = _gen_broadcast_data()
x = baseline(d[0], d[1])
y = symbol.bind(mx.cpu(), args={'a': mx.nd.array(d[0]), 'b' : mx.nd.array(d[1])})
y.forward()
err = np.sum(np.abs(x - y.outputs[0].asnumpy())) / np.sum(np.abs(x))
assert err < 1e-4, 'error %f, shapes are %s, %s' % (
err, d[0].shape, d[1].shape)
def _check_broadcast_op_backward(symbol, baseline):
sample_num = 200
for i in range(sample_num):
d = _gen_broadcast_data()
out = np.random.random((d[0] + d[1]).shape)
def reduce_op(shape, x):
if shape == x.shape:
return x
keepdims_shape = list(x.shape)
for i in range(len(shape)):
if x.shape[i] != shape[i]:
keepdims_shape[i] = 1
x = np.sum(x, axis=i).reshape(keepdims_shape)
return x
baseline_grad1, baseline_grad2 = baseline(out, d[0], d[1])
x_1 = reduce_op(d[0].shape, baseline_grad1)
x_2 = reduce_op(d[1].shape, baseline_grad2)
y_1 = mx.nd.empty(d[0].shape)
y_2 = mx.nd.empty(d[1].shape)
y = symbol.bind(mx.cpu(), args={'a': mx.nd.array(d[0]), 'b' : mx.nd.array(d[1])},
args_grad=[y_1, y_2])
y.forward()
y.backward([mx.nd.array(out)])
err = lambda x, y: np.sum(np.abs(x-y)) / np.sum(np.abs(x))
err_1 = err(x_1, y_1.asnumpy())
err_2 = err(x_2, y_2.asnumpy())
assert err_1 < 1e-5 and err_2 < 1e-5, 'lhs error %f, rhs error %f, shapes are %s %s' % (
err_1, err_2, d[0].shape, d[1].shape)
def test_broadcast_binary_op():
a = mx.sym.Variable('a')
b = mx.sym.Variable('b')
def test_bplus(a, b):
c = mx.sym.broadcast_plus(a, b)
_check_broadcast_op_forward(c, lambda a, b: a + b)
_check_broadcast_op_backward(c, lambda g_out, a, b: (g_out, g_out))
def test_bminus(a, b):
c = mx.sym.broadcast_minus(a, b)
_check_broadcast_op_forward(c, lambda a, b: a - b)
_check_broadcast_op_backward(c, lambda g_out, a, b: (g_out, - g_out))
def test_bmul(a, b):
c = mx.sym.broadcast_mul(a, b)
_check_broadcast_op_forward(c, lambda a, b: a * b)
_check_broadcast_op_backward(c, lambda g_out, a, b: (g_out * b, g_out * a))
def test_bdiv(a, b):
c = mx.sym.broadcast_div(a, b)
_check_broadcast_op_forward(c, lambda a, b: a / b)
_check_broadcast_op_backward(c, lambda g_out, a, b: (g_out / b, - g_out * a / (b * b)))
def test_bpow(a, b):
c = mx.sym.broadcast_power(a, b)
_check_broadcast_op_forward(c, lambda a, b: a ** b)
_check_broadcast_op_backward(c, lambda g_out, a, b: (g_out * a **(b - 1) * b,
g_out * a ** b * np.log(a)))
test_bplus(a, b)
test_bminus(a, b)
test_bmul(a, b)
test_bdiv(a, b)
test_bpow(a, b)
def test_run_convolution_dilated_impulse_response(dil=(1,1), kernel_shape=(3,3), verbose=False):
# Input for spike response
spike_imgs = np.zeros(shape=(1,1,33,33), dtype=np.float32)
spike_imgs[0,0,16,16] = 1.0
spike_img = mx.nd.array(spike_imgs)
spike_img2 = mx.nd.array(spike_imgs)
kernel_weights = mx.nd.ones(shape=tuple([1,1]+list(kernel_shape)), dtype=np.float32)
kernel_weights2 = mx.nd.ones(shape=tuple([1,1]+list(kernel_shape)), dtype=np.float32)
kernel = mx.symbol.Variable('kernel')
in_img = mx.symbol.Variable('input')
net = mx.symbol.Convolution(in_img, num_filter=1,kernel=kernel_shape, dilate=dil, no_bias="true", name='test_convolution')
net.list_arguments()
be = net.bind(mx.cpu(), args={ 'input' : spike_img, 'test_convolution_weight' : kernel_weights},
args_grad={'input' : spike_img2, 'test_convolution_weight' : kernel_weights2 } )
be.forward(True)
out_o = be.outputs[0].asnumpy()
ndo = be.outputs[0]
out_grads = np.zeros(shape=be.outputs[0].shape, dtype=np.float32)
out_grads[0,0, 16,16] = 1.0
out_grad = mx.nd.array(out_grads)
be.backward([out_grad])
vgrad = be.grad_arrays[0].asnumpy()
out = out_o.reshape((out_o.shape[2],out_o.shape[3]))
nzx,nzy = np.nonzero(out)
assert(np.sum(out)==np.prod(kernel_shape))
assert(np.sum(vgrad)==np.prod(kernel_shape))
# Now check whether the input gradient was computed correctly
input_grad = mx.nd.array(vgrad)
be = net.bind(mx.cpu(), args={ 'input' : input_grad, 'test_convolution_weight' : kernel_weights})
be.forward(True)
out_o = be.outputs[0].asnumpy()
assert(out_o[0,0,16,16]==np.prod(kernel_shape))
rnd_kernel_s = np.random.uniform(low=0.0, high=1.0, size=tuple([1,1]+list(kernel_shape))).astype(np.float32)
impulse_error = mx.nd.array(out_o/np.sum(out_o)) # This should be 1.0 at [0,0,16,16]
rnd_kernel = mx.nd.array(rnd_kernel_s)
rnd_kernel2 = mx.nd.array(rnd_kernel_s)
white_in = mx.nd.ones(shape=(1,1,33,33))
white_in2 = mx.nd.ones(shape=(1,1,33,33))
be = net.bind(mx.cpu(), args={ 'input' : white_in, 'test_convolution_weight' : rnd_kernel},
args_grad={'input' : white_in2, 'test_convolution_weight' : rnd_kernel2 } )
be.forward(True)
be.backward([impulse_error])
out_orig = be.outputs[0].asnumpy()
kernel_gradient = be.grad_arrays[1].asnumpy()
dkernel = mx.nd.array(rnd_kernel_s + kernel_gradient)
be = net.bind(mx.cpu(), args={ 'input' : white_in, 'test_convolution_weight' : dkernel})
be.forward(True)
out = be.outputs[0].asnumpy()
# Now do a simple check of the kernel gradient
assert(out[0,0,16,16] - np.sum(kernel_gradient) - out_orig[0,0,16,16] < 0.001)
def test_convolution_dilated_impulse_response():
for dil in [ (1,1), (2,2), (3,3) ]:
for ks in [ (3,3), (4,4), (2,3), (3,2), (1,1) ]:
test_run_convolution_dilated_impulse_response(dil=dil, kernel_shape=ks)
def test_reshape():
def test_reshape_new(src_shape, shape_args, dst_shape):
net = mx.sym.Variable("data")
net = mx.sym.Reshape(net, shape=shape_args)
js = net.tojson()
net = mx.sym.load_json(js)
_, output_shape, __ = net.infer_shape(data=src_shape)
assert output_shape[0] == dst_shape, \
'Src Shape = %s, Shape Arguments = %s, Dst Shape = %s, Output Shape = %s' \
%(str(src_shape), str(shape_args), str(dst_shape), str(output_shape[0]))
dat_npy = np.random.rand(*src_shape)
grad_npy = np.random.rand(*dst_shape)
exe = net.simple_bind(mx.cpu(), data=src_shape)
exe.arg_dict['data'][:] = dat_npy
exe.forward(is_train=True)
assert np.square(exe.outputs[0].asnumpy() - dat_npy.reshape(dst_shape)).mean() < 1E-7, \
'Src Shape = %s, Shape Arguments = %s, Dst Shape = %s' %(str(src_shape),
str(shape_args), str(dst_shape))
exe.backward(out_grads=mx.nd.array(grad_npy))
assert np.square(exe.grad_dict['data'].asnumpy() - grad_npy.reshape(src_shape)).mean() < 1E-7, \
'Src Shape = %s, Shape Arguments = %s, Dst Shape = %s' %(str(src_shape),
str(shape_args), str(dst_shape))
# Test new api (Using shape)
test_cases = [[(2, 3, 5, 5), (0, -1), (2, 75)],
[(2, 3, 5, 5), (0, 0, -1), (2, 3, 25)],
[(5, 3, 4, 5), (0, -1, 0), (5, 15, 4)],
[(2, 3, 5, 4), (-1, 0, 0), (8, 3, 5),
[(2, 3, 4, 5), (3, -1, 0), (3, 10, 4)],
[(2, 3, 5, 5), (5, 3, 0, -1), (5, 3, 5, 2)],
[(2, 3, 5, 5), (0, 0, 0, 0), (2, 3, 5, 5)],
[(2, 4, 5, 3), (-1, 2, 2, 1), (30, 2, 2, 1)]]]
for test_case in test_cases:
test_reshape_new(test_case[0], test_case[1], test_case[2])
# Test old api
net = mx.sym.Variable("data")
net = mx.sym.Reshape(net, target_shape=(2, 0))
js = net.tojson()
net = mx.sym.load_json(js)
_, output_shape, __ = net.infer_shape(data=(2, 3, 5, 5))
assert(output_shape[0] == (2, 75))
def test_reduce():
sample_num = 200
def test_reduce_inner(numpy_reduce_func, numpy_reduce_grad_func, mx_reduce_sym):
for i in range(sample_num):
# Generate random data that has ndim between 1-7 and all the shape dims between 1-10
ndim = np.random.randint(1, 8)
shape = np.random.randint(1, 11, size=(ndim,))
axis_num = np.random.randint(0, ndim, size=1)
axis_flags = np.random.randint(0, 2, size=ndim)
axes = []
for (axis, flag) in enumerate(axis_flags):
if flag:
axes.append(axis)
if 0 == len(axes):
axes = None
elif 1 == len(axes):
axes = axes[0]
else:
axes = tuple(axes)
keepdims = np.random.randint(0, 2)
a = mx.symbol.Variable('a')
if axes is None:
b = mx_reduce_sym(a, keepdims=keepdims)
else:
b = mx_reduce_sym(a, axis=axes, keepdims=keepdims)
dat_npy = np.random.rand(*shape)
sum_groundtruth = np.array(numpy_reduce_func(dat_npy, axis=axes, keepdims=keepdims))
if sum_groundtruth.shape == ():
sum_groundtruth = np.array([sum_groundtruth])
grad_nd = mx.nd.empty(shape)
outgrad_npy = np.array(np.random.rand(*sum_groundtruth.shape))
grad_groundtruth = numpy_reduce_grad_func(outgrad=outgrad_npy, data=dat_npy,
axis=axes, keepdims=keepdims)
net = b.bind(mx.cpu(), args={'a': mx.nd.array(dat_npy)},
args_grad={'a': grad_nd})
net.forward(is_train=True)
err_forward = reldiff(net.outputs[0].asnumpy(), sum_groundtruth)
assert err_forward < 1E-4
net.backward(out_grads=mx.nd.array(outgrad_npy))
err_backward = reldiff(grad_nd.asnumpy(), grad_groundtruth)
assert err_backward < 1E-4
test_reduce_inner(lambda data, axis, keepdims:_np_reduce(data, axis, keepdims, np.sum),
lambda outgrad, data, axis, keepdims:
outgrad.reshape(_np_reduce(data, axis, 1, np.sum).shape),
mx.symbol.sum)
def test_broadcast():
sample_num = 200
for i in range(sample_num):
# Generate random data that has ndim between 1-7 and all the shape dims between 1-10
ndim = np.random.randint(1, 8)
target_shape = np.random.randint(1, 11, size=(ndim,))
axis = tuple(set(np.random.randint(0, ndim, np.random.randint(1, ndim + 1))))
shape = target_shape.copy()
size = tuple([shape[ele] for ele in axis])
for ele in axis:
shape[ele] = 1
a = mx.symbol.Variable('a')
sym_bcast_axis = mx.symbol.broadcast_axis(a, axis=axis, size=size)
sym_bcast_to = mx.symbol.broadcast_to(a, shape=tuple(target_shape))
def test_broadcasting_ele(sym_bcast):
dat_npy = np.random.rand(*shape)
groundtruth = dat_npy
grad_nd = mx.nd.empty(shape)
outgrad_npy = np.random.rand(*target_shape)
grad_groundtruth = _np_reduce(outgrad_npy, axis=axis, keepdims=True,
numpy_reduce_func=np.sum)
net = sym_bcast.bind(mx.cpu(), args={'a': mx.nd.array(dat_npy)},
args_grad={'a': grad_nd})
net.forward(is_train=True)
assert (net.outputs[0].shape == target_shape).all()
err_forward = reldiff(net.outputs[0].asnumpy(), groundtruth)
assert err_forward < 1E-4
net.backward(out_grads=mx.nd.array(outgrad_npy))
err_backward = reldiff(grad_nd.asnumpy(), grad_groundtruth)
assert err_backward < 1E-4
test_broadcasting_ele(sym_bcast_axis)
test_broadcasting_ele(sym_bcast_to)
def test_transpose():
for ndim in range(1, 6):
for t in range(5):
dims = list(np.random.randint(1, 10, size=ndim))
axes = list(range(ndim))
random.shuffle(axes)
axes = tuple(axes)
x = mx.nd.array(np.random.normal(size=dims))
y = mx.nd.transpose(x, axes=axes)
assert_allclose(np.transpose(x.asnumpy(), axes=axes), y.asnumpy())
y = mx.nd.transpose(x)
assert_allclose(np.transpose(x.asnumpy()), y.asnumpy())
def test_expand_dims():
for ndim in range(1, 6):
for t in range(5):
dims = list(np.random.randint(1, 10, size=ndim))
axis = np.random.randint(1, ndim+1)
x = mx.nd.array(np.random.normal(size=dims))
y = mx.nd.expand_dims(x, axis=axis)
assert_allclose(np.expand_dims(x.asnumpy(), axis=axis), y.asnumpy())
def test_crop():
for ndim in range(1, 6):
for t in range(5):
dims = []
begin = []
end = []
idx = []
for i in range(ndim):
d = random.randint(1, 10)
b = random.randint(0, d-1)
e = random.randint(b+1, d)
dims.append(d)
begin.append(b)
end.append(e)
idx.append(slice(b, e))
x = mx.nd.array(np.random.normal(size=dims))
y = mx.nd.crop(x, begin=tuple(begin), end=tuple(end))
assert_allclose(x.asnumpy()[idx], y.asnumpy())
def test_slice_axis():
for ndim in range(1, 6):
shape = np.random.randint(1, 11, size=(ndim,))
for t in range(ndim):
d = shape[t]
b = random.randint(0, d-1)
e = random.randint(b+1, d)
idx = []
for i in range(ndim):
idx.append(slice(0, shape[i]))
idx[t] = slice(b, e)
X = mx.symbol.Variable('X')
x = mx.nd.array(np.random.normal(size=shape))
Y = mx.symbol.slice_axis(data=X, axis=t, begin=b, end=e)
xgrad = mx.nd.empty(x.shape)
exec1 = Y.bind(mx.cpu(), args = [x], args_grad = {'X': xgrad})
exec1.forward()
y = exec1.outputs[0]
assert_allclose(x.asnumpy()[idx], y.asnumpy())
exec1.backward([y])
xx = x.asnumpy()
xx[:] = 0.0
xx[idx] = x.asnumpy()[idx]
assert_allclose(xx, xgrad.asnumpy())
def test_flip():
for ndim in range(1, 6):
for t in range(5):
dims = [random.randint(1,10) for i in range(ndim)]
axis = random.randint(0, ndim-1)
idx = [slice(None, None, -1) if i == axis else slice(None, None) for i in range(ndim)]
x = mx.nd.array(np.random.normal(size=dims))
y = mx.nd.flip(x, axis=axis)
assert_allclose(x.asnumpy()[idx], y.asnumpy())
if __name__ == '__main__':
test_expand_dims()
test_slice_axis()
test_softmax()
test_broadcast_binary_op()
test_flip()
test_crop()
test_transpose()
test_convolution_grouping()
test_nearest_upsampling()
test_binary_op_duplicate_input()
test_elementwise_sum()
test_concat()
test_slice_channel()
test_regression()
test_python_op()
test_swapaxes()
test_scalarop()
test_scalar_pow()
test_symbol_pow()
test_pow_fn()
test_embedding()
test_rsqrt_cos_sin()
test_maximum_minimum()
test_maximum_minimum_scalar()
test_abs()
test_round_ceil_floor()
test_deconvolution()
test_batchnorm_training()
check_softmax_with_ignore_label(mx.cpu())
test_convolution_dilated_impulse_response()
test_reshape()
test_reduce()
test_broadcast()
| [
"[email protected]"
] | |
b695c25a6c407051dcf0672150ad0a2044ae0099 | 5fae2c70329586526b83f1343397b23b065448b2 | /indoor_load.py | 635ee49de29f65bc34030b66f33d5cf32a674582 | [] | no_license | salbali/simulation-of-vav-system- | 735b9e569efae7979250bdfb226b0f42e62f12ca | e5192553d25bd52af0a8457eb9b060e75cc4aec5 | refs/heads/master | 2021-09-22T09:33:54.448674 | 2018-03-08T13:59:42 | 2018-03-08T13:59:42 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,894 | py | # !/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
import difference_methods
import readcsv
import air
import solar_radiation
import load_window
import matplotlib.pyplot as plt
## 热负荷计算programme
# 1. 数据输入和计算准备
# 1.1 air.py的import和系数
# 表面热传达率
alpha_o = 23
alpha_i = 9.3
# 室内侧热传达对流辐射比
kc = 0.45
kr = 0.55
# 时间间隔
dt = 60
# 1.2 建筑尺寸和外壁材料输入
w_alpha = 45 # 朝向
rho_g = 0.2 # 地面反射率
a_s = 0.7 # 外表面日射吸收率
epsilon = 0.9 # 长波辐射率
# 窗
A1 = 28 # 开口面积
K1 = 6.4 # 热贯流率
AG = 24 # 玻璃面积
tau_TN = 0.79 # 综合透过率
B_N = 0.04 # 吸收日射取得率
# 外壁
A2 = 22.4
A2_material = ["concrete", "rock_wool", "air", "arumi"]
A2_d = [0.150, 0.050, 0, 0.2]
A2_m = [7, 2, 1, 1]
# 内壁
A3 = 100.8
A3_material = ["concrete"]
A3_d = [0.120]
A3_m = [6]
# 床
A4 = 98
A4_material = ["carpet", "concrete", "air", "stonebodo"]
A4_d = [0.015, 0.150, 0, 0.012]
A4_m = [1, 7, 1, 1]
# 天井
A5 = 98
A5_material = ["stonebodo", "air", "concrete", "carpet"]
A5_d = [0.012, 0, 0.150, 0.015]
A5_m = [1, 1, 7, 1]
# 室容积
VOL = 353
CPF = 3500
# 1.3 外表面的方向余弦(参考表6.5)
# 1.4 后退差分的ul, ur, ux计算 调用difference_method
A2_ul, A2_ur, A2_ux = difference_methods.diff_ux(A2_material, A2_d, A2_m, dt, alpha_i, alpha_o)
A3_ul, A3_ur, A3_ux = difference_methods.diff_ux(A3_material, A3_d, A3_m, dt, alpha_i, alpha_i)
A4_ul, A4_ur, A4_ux = difference_methods.diff_ux(A4_material, A4_d, A4_m, dt, alpha_i, alpha_i)
A5_ul, A5_ur, A5_ux = difference_methods.diff_ux(A5_material, A5_d, A5_m, dt, alpha_i, alpha_i)
# 1.5 FIn FOn
# 窗 定常
FI = [1 - K1/alpha_i]
FO = [K1/alpha_i]
# 外壁 # 内壁 # 床 # 天井 (特殊处理：天井看成是床的另一面？)
FI.extend([eval("A"+str(i+2)+"_ux[0][0] * A"+str(i+2)+"_ul[0]") for i in range(4)])
FO.extend([eval("A"+str(i+2)+"_ux[0][-1] * A"+str(i+2)+"_ur[-1]") for i in range(4)])
# 1.6 全室内表面积的计算 室内表面辐射吸收率的设定
A_list = [eval("A" + str(i+1)) for i in range(5)]
Arm = sum(A_list)
# S_Gn = S_Hn = S_Ln = S_An = S_n
S_list = [0.7 * A_list[i] / Arm for i in range(5)]
S_list[3] = 0.3 + 0.7 * A4 / Arm
# 1.7 求ANF SDT AR RMDT RMDTX
ANF = np.sum(np.multiply(A_list, FI))
SDT = Arm - kr * ANF
AR = Arm * alpha_i * kc * (1 - kc * ANF / SDT)
RMDT = (1005 * 1.2 * VOL + CPF) / dt
RMDTX = 1.2 * VOL / dt
# 2. 运转条件 气象条件的输入
# 2.1 输入 气象读入
# 换气回数
n_air = 0.2
# 室内发热
# 人体
N_H = 16 # 在室人数
H_T = 119 # 全放热量
H_S24 = 62 # 24度的显热量
H_d = 4 # 勾配
# 照明
W_LI = 2900
# 机器
W_AS = 500
W_AL = 0
# 空调设定
T_R_set = 26
phi_R_set = 60
# 气象数据
weather_data = readcsv.weather()
RN = weather_data["RN"]
outdoor_temp = weather_data["outdoor_temp"]
# 2.2 绝对湿度
x_R_set = air.pw2x(air.t_phi2pw(T_R_set+273.15, phi_R_set))
# 2.3 间隙风 [kg/s]
Go = 1.2 * n_air * VOL / 3600
# 2.4 室内发热
# 人体
H_s = H_S24 - H_d * (T_R_set - 24)
H_l = H_T - H_s
Q_HS = N_H * H_s
Q_HL = N_H * H_l
# 室内发热对流成分
HG_c = 0.5 * Q_HS + 0.6 * W_LI + 0.6 * W_AS
# 室内发热辐射成分
HG_r = 0.5 * Q_HS + 0.4 * W_LI + 0.4 * W_AS
# 潜热
HLG = Q_HL + W_AL
# 3. 初期値设定
# 差分法的初期値
T_R = T_R_set
# 4. 边界条件设定 (全年！)
# 4.3 日射量的计算
# 窗
[I_d, I_s, I_r, cos_theta, Fs] = solar_radiation.solar_radiation(w_alpha)
[Q_GT, Q_GA, Q_GO] = load_window.load_window(26, w_alpha, AG, k=0.85, tau=tau_TN, Bn=B_N, K=K1)
# Q_GO是没有用的，所以26度也无所谓
I_w = I_d + I_s + I_r
# 4.4 Ten的计算
T_e_1 = Q_GA / A1 / K1 - (epsilon * Fs * RN) / alpha_o + outdoor_temp
T_e_2 = (a_s * I_w - epsilon * Fs * RN) / alpha_o + outdoor_temp
T_e_3 = 0.7 * T_R + 0.3 * outdoor_temp
| [
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f1eb1e35f47c959e4faab077721558c55ff4e254 | 0be76c7ff5fcaf526fa2d12b0c647ae7dac4ee01 | /MODUL_6/No8.py | 958a10d772865fa9dd529f0b1ee1f5e89cffe9e3 | [] | no_license | L200180148/algostruk_f | 3d9723987942d5b64144501883bed5ec69c51151 | 85173cac3a3c4b274ef9d64edb57ff493e5e2d3c | refs/heads/master | 2021-02-13T05:51:17.191202 | 2020-04-26T06:12:18 | 2020-04-26T06:12:18 | 244,667,933 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,596 | py | #Nomor 8
class Node:
def __init__(self, data):
self.data = data
self.next = None
class LinkedList:
def __init__(self):
self.head = None
def appendList(self, data):
node = Node(data)
if self.head == None:
self.head = node
else:
curr = self.head
while curr.next != None:
curr = curr.next
curr.next = node
def appendSorted(self, data):
node = Node(data)
curr = self.head
prev = None
while curr is not None and curr.data < data:
prev = curr
curr = curr.next
if prev == None:
self.head = node
else:
prev.next = node
node.next = curr
def printList(self):
curr = self.head
while curr != None:
print ("%d"%curr.data),
curr = curr.next
def mergeSorted(self, list1, list2):
if list1 is None:
return list2
if list2 is None:
return list1
if list1.data < list2.data:
temp = list1
temp.next = self.mergeSorted(list1.next, list2)
else:
temp = list2
temp.next = self.mergeSorted(list1, list2.next)
return temp
list1 = LinkedList()
list1.appendSorted(13)
list1.appendSorted(12)
list1.appendSorted(3)
list1.appendSorted(16)
list1.appendSorted(7)
print("List 1 :"),
list1.printList()
list2 = LinkedList()
list2.appendSorted(9)
list2.appendSorted(10)
list2.appendSorted(1)
print("List 2 :"),
list2.printList()
list3 = LinkedList()
list3.head = list3.mergeSorted(list1.head, list2.head)
print("Merged List :"),
list3.printList()
| [
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d22003a046bb6dd576dff86e3577f296aea4223d | d9e68125b8f02a618f6cf83c18face5bead8e43d | /recommendation/autoencoder/user_per_cost.py | ccbe795bbf7bbbdd2a6b7c1eedf9051b8b970e9c | [] | no_license | CHENLONGREN/Autoencoder-research | ec201d5375f76c2a76cf864395b6adccaa440606 | f2e5264f87a793ee40457324dca6a93a355d253c | refs/heads/master | 2023-03-07T00:12:04.277901 | 2021-02-23T08:16:23 | 2021-02-23T08:16:23 | 204,639,844 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 321 | py | import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv("C:/Users/chenl/PycharmProjects/research/traning/per_auto.csv")
plt.plot(data['epoch'], data['cost'])
plt.xticks(rotation=2)
plt.xlabel('epoch')
plt.ylabel('avg_cost')
plt.title('traning cost')
plt.savefig('./user_per_cost.png')
plt.show
| [
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8797c07640c0f21cdbed37a5c6c1746df8d0c1cd | f22642f05adc6a35d226b32623990f71d41db8e4 | /ele/wsgi.py | 32bf8f35a7e79c119cfea7baabf180a4f1d45bbe | [] | no_license | wormchaos/ele | 6ee1faf3dafe8478e97fb0b8e36867f01380776f | db99a3f6fa331d226616f7dbe499bb7b545bcda1 | refs/heads/master | 2021-01-22T04:57:21.713747 | 2016-09-18T11:30:32 | 2016-09-18T11:30:32 | 68,516,993 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 384 | py | """
WSGI config for ele project.
It exposes the WSGI callable as a module-level variable named ``application``.
For more information on this file, see
https://docs.djangoproject.com/en/1.10/howto/deployment/wsgi/
"""
import os
from django.core.wsgi import get_wsgi_application
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "ele.settings")
application = get_wsgi_application()
| [
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] | |
838ad524a7ec16b8d6a02bc3bd7163119b6ec67d | d52413173437ba73ecdf822ca895e659f00a8ce7 | /kiwibackend/application/submodule/fanyoy/reset_template.py | 0c8e2e20620eb8a9ce28057d1fa7136f012b03b0 | [] | no_license | whiteprism/mywork | 2329b3459c967c079d6185c5acabd6df80cab8ea | a8e568e89744ca7acbc59e4744aff2a0756d7252 | refs/heads/master | 2021-01-21T11:15:49.090408 | 2017-03-31T03:28:13 | 2017-03-31T03:28:13 | 83,540,646 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 379 | py | # /usr/bin/env python
# -*- coding: utf-8 -*
from django.core.cache import get_cache
def decorate_active_player(view_func):
@wraps(view_func)
def decorate(request, *args, **kwds):
player = request.player
if player and player.is_end_tutorial():
_add_active_player(player)
return view_func(request, *args, **kwds)
return decorate
| [
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] | |
c2cb537f158dd780c9a2af8848369339e53f997f | c8a0a370e3bdba7a159911ef9bee0c2bf9401dc7 | /Problem Solving with Algorithms and Data Structures using python/05 Sorting and Searching/Sort/01 bubble sort/bubble_sort.py | 80ee3147218deed1736d2b5f7fb8255f54b89013 | [] | no_license | ht-dep/Algorithms-by-Python | c47d08c27b3e8cb8b9f3f5ebd9c50f6b099281b5 | 5c5ed701944f6b5ebed1f933d65cc8c31ec42245 | refs/heads/master | 2020-03-06T23:43:10.988589 | 2018-01-21T14:54:20 | 2018-01-21T14:54:20 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 484 | py | # coding:utf-8
# bubble sort:循环比较两个相邻位置之间的大小关系
def bubbleSort(num_list):
L = len(num_list)
while L > 1:
for i in range(L-1):
if num_list[i]>num_list[i+1]:
temp = num_list[i]
num_list[i] = num_list[i+1]
num_list[i+1] = temp
L -= 1
return num_list
if __name__ == "__main__":
num_list = [54, 26, 93, 17, 77, 31, 44, 55, 20]
print bubbleSort(num_list)
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] | |
bea9db81ee8001e8b8990c331daaa096dabb2f2a | 37b5947f77a2e84f17831642f8f610fd92738774 | /docs/ch7/anonymous/238.py | cfea98b0656605a0d8fa39635b90b21f8523eff0 | [] | no_license | reversalSpring/algoStudy | a75cad011fff19739ef28dc7bcb6d66e8fc66c5b | 64408f04ca00ff377f972b3aaa4e067efeaac3df | refs/heads/master | 2023-06-26T06:13:30.499499 | 2021-07-24T09:15:42 | 2021-07-24T09:15:42 | 389,052,360 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 845 | py | from typing import List
# https://leetcode.com/problems/product-of-array-except-self/
"""
Given an array nums of n integers where n > 1,
return an array output such that output[i] is equal to the product of all the elements of nums except nums[i].
Constraint:
It's guaranteed that the product of the elements of any prefix or suffix of the array (including the whole array)
fits in a 32 bit integer.
"""
class Solution:
def productExceptSelf(self, nums: List[int]) -> List[int]:
left = []
p = 1
for i in range(0, len(nums)):
left.append(p)
p = p * nums[i]
p = 1
for i in range(len(nums) - 1, -1, -1):
left[i] = left[i] * p
p = p * nums[i]
return left
s = Solution()
case = [1,2,3,4]
print(s.productExceptSelf(case)) | [
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] | |
f839f33d95a315e4f7e94c2daf3afdac8b04f301 | 4f3b92efcd5202d1242b7a833816674f30bb3af2 | /tools.py | b3a75b08393c8f5bbcb899ac5f4b4f4eb25e92c5 | [] | no_license | niyasl2/auto_gui | 9ea9d9fa1f7c78c03fea83463d04231c15956bfa | 59dee5492b819b32713e60aa04073e60fc56b097 | refs/heads/master | 2016-09-06T17:32:56.540743 | 2012-10-26T14:38:40 | 2012-10-26T14:38:40 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 9,251 | py | #-------------------------------------------------------------------------------
# Name: module1
# Purpose:
#
# Author: Administrator
#
# Created: 29/06/2012
# Copyright: (c) Administrator 2012
# Licence: <your licence>
#-------------------------------------------------------------------------------
#!/usr/bin/env python
import os,sys,shutil,subprocess,time,ssh,re
from global_var import *
from FindRegression import *
import chart
import common
class Tools:
def checkBuild (self,file):
print "Checking build..."
FILE_STATUS = open(file,'r')
# search_for_build_ok = "Wrapped Integrity check successful"
search_for_build_ok = "build archive with header using"
for line in reversed(FILE_STATUS.readlines()):
if re.search(search_for_build_ok, line):
return BUILD_OK
return BUILD_FAILED
def find_index(self,lst,item):
if not isinstance(item,basestring):
item = item[0]
for i in range(0,len(lst)):
if(lst[i]==item):
if lst == BRANCH_ALLOWED and common.CARDHU:
return ((len(BRANCH_ALLOWED)*(len(PLATFORM_ALLOWED)-1))+i)
return i
return 0
def distclean(self,branch,variant="tango-internal"):
i = self.find_index(BRANCH_ALLOWED,branch)
cmd = "cd %s ;qjob -noxterm make distclean VARIANT=%s "%(build_dir[i],variant)
result = self.ssh_client(cmd)
def build(self,branch,cl,reg=False,variant="tango-internal"):
print "Building ",branch,cl,variant
i = self.find_index(BRANCH_ALLOWED,branch)
if os.path.exists(BINARY_LIB+str(cl)+branch+".zlib.wrapped"):
return BUILD_OK
if os.path.exists(MODEM_BINARY_LOC[i]+"modem-rsa-key0.zlib.wrapped"):
os.remove(MODEM_BINARY_LOC[i]+"modem-rsa-key0.zlib.wrapped")
#cmd = "cd %s ;p4 sync %s...@%s; make -l ncpus=2 -j6 bin VARIANT=%s "%(build_dir[i],P4BRANCH[i],cl,variant)
cmd = "cd %s ;p4 sync %s...@%s; qjob -noxterm make -l ncpus=2 -j6 bin VARIANT=%s "%(build_dir[i],P4BRANCH[i],cl,variant)
cmd = "cd %s ;p4 sync %s...@%s; qjob -noxterm make -l ncpus=2 -j6 bin VARIANT=%s "%(build_dir[i],P4BRANCH[i],cl,variant)
result = self.ssh_client(cmd)
## if self.checkBuild(BUILD_STATUS_LOC[i]+"%s_build_status.txt"%variant) == BUILD_OK :
## print "Finished"
## return BUILD_OK
if os.path.exists(MODEM_BINARY_LOC[i]+"modem-rsa-key0.zlib.wrapped"):
print " Build Successful"
shutil.copy2(MODEM_BINARY_LOC[i]+"modem-rsa-key0.zlib.wrapped",(BINARY_LIB+str(cl)+branch+".zlib.wrapped"))
return BUILD_OK
else:
if reg:
self.distclean(branch)
result = self.ssh_client(cmd) # build again
if os.path.exists(MODEM_BINARY_LOC[i]+"modem-rsa-key0.zlib.wrapped"):
print " Build Successful After cleaning build dir"
shutil.copy2(MODEM_BINARY_LOC[i]+"modem-rsa-key0.zlib.wrapped",(BINARY_LIB+str(cl)+branch+".zlib.wrapped"))
return BUILD_OK
msg = r"BUILD FAILURE FOR CL %s BRANCH %s VARIANT %s\n"%(cl,branch,variant)
self.sendMail(msg)
file_name = "BUILD_FAILURE_BRANCH_%s_CL_%s.txt"%(branch,str(cl))
try:
FILE = open('regression\\'+file_name,'a')
for line in result :
msg += r'%s'%line
FILE.write(line)
#print msg
FILE.close()
#p = subprocess.Popen("echo %s > build_failure_mail"%msg, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True)
self.sendMail(msg)
except:
pass
return BUILD_FAILED
def ssh_client(self,cmd):
server = ssh.Connection(host='frsys1', username='gcflab', password='LG!)67wn')
result = server.execute(cmd)
return result
def get_CL_list(self,br,p4br,startCl,endCl):
cl_list = []
cmd = "cd %s;p4 changes %s...@%s,@%s" % (br,p4br, startCl, endCl)
#print "[get_CL_list]",cmd
result = self.ssh_client(cmd)
#print result
for line in result:
#print line
try:
cl = re.search(re.compile(r'Change (\S+) on'),line).group(1)
#if self.failed_build(cl) == False :
cl_list.append(cl)
except:
pass
#print cl_list
return cl_list
def build_regression(self,branch,ko_cl):
print "[BUILD_REGRESSION]BUILD FAILURE BRANCH %s CL %s"%(branch,str(ko_cl))
#self.sendMail(r"[BUILD_REGRESSION]BUILD FAILURE BRANCH %s CL %s"%(branch,ko_cl))
i = self.find_index(BRANCH_ALLOWED,branch)
ok_cl = chart.Chart().last_build_success(branch,ko_cl)
print "OK_CL",ok_cl
while True :
cl_list = self.get_CL_list(build_dir[i],P4BRANCH[i],ok_cl,ko_cl)
print cl_list
if(len(cl_list)) == 2 :
print "Final OK ",cl_list[1]
print "Final KO",cl_list[0]
try:
self.sendMail(r"BUILD FAILURE BRANCH %s KO_CL %s OK_CL%s"%(branch,cl_list[0],cl_list[1]))
file_name = "BUILD_FAILURE_BRANCH_%s_KO_CL_%s_OK_CL%s.txt"%(branch,cl_list[0],cl_list[1])
FILE = open('regression\\'+file_name,'a')
FILE.write(file_name)
FILE.close()
except:
pass
return cl_list[0],cl_list[1]
if(len(cl_list)) == 0 :
print "End"
return
nxt_cl = cl_list[len(cl_list)/2]
print nxt_cl
build_status = self.build(branch,nxt_cl)
if build_status == BUILD_OK :
ok_cl = nxt_cl
else:
ko_cl = nxt_cl
def _build(self,branch,cl=0,variant="tango-internal"):
#print "Building Branch : ",branch
i = self.find_index(BRANCH_ALLOWED,branch)
if cl == 0:
cl = self.latest_cl(branch)
#print "CL",cl
if cl != 0 :
status = self.build(branch,cl,variant)
if status == BUILD_OK :
return BUILD_OK,int(cl)
else:
self.build_regression(branch,cl)
print "Build Regression End"
return BUILD_FAILED,0
return BUILD_FAILED,0
def sendMail(self,msg):
cmd = "echo \"%s\" > txt_msg ; cat txt_msg | ssh frlts mail -s \"Test_Issues\" %s " %(msg,SUPERVISOR)
self.ssh_client(cmd)
# def sendMail(self,msg):
# print "Msg to Send",msg
# cmd = "echo \"%s\" > txt_msg ; cat txt_msg | ssh frlts mail -s \"Test_Issues\" %s " %(msg,SUPERVISOR)
# self.ssh_client(cmd)
# cmd = "echo \"%s\" > txt_msg ; cat txt_msg | mail -s \"Test_Issues2\" [email protected] " %(msg)
# self.ssh_client(cmd)
# cmd = "echo \"%s\" > txt_msg ; mail -s \"Test_Issues3\" [email protected] < txt_msg" %(msg)
# self.ssh_client(cmd)
# self.sendMailGCF(msg)
def latest_cl(self,branch):
i = self.find_index(BRANCH_ALLOWED,branch)
#print i
#print "cd %s ; p4 changes -m1 %s..."%(build_dir[i],P4BRANCH[i])
result = self.ssh_client("cd %s ; p4 changes -m1 %s..."%(build_dir[i],P4BRANCH[i]))
print result
for line in result:
print line
try:
cl = re.search(re.compile(r'Change (\S+) on'),line).group(1)
except:
cl = 0
return cl
def string_array(self,element):
#element = str(element)
if isinstance(element, basestring) :#or isinstance(element,int):
scarray = []
scarray.append(element)
element = scarray
elif isinstance(element,int):
scarray=[]
scarray.append(element)
element = scarray
return element
def generateBuildReady (self,branch,cl):
cmd = "echo \"CL%s\" > %s" % (str(cl),branch)
p=subprocess.Popen(cmd,stderr=subprocess.PIPE,shell=True) #NSAIT
#print "Generate Build Ready with type=%s, owner=%s, branch=%s, variant=%s, CL%s, %s, %s" % (type, owner, branch, variant, cl, test, band)
def find_average(self,list,threshold):
tList = []
for i in range(0,len(list)):
if(list[i] > threshold):
tList.append(list[i])
return int(sum(tList)/len(tList))
def main():
Tools()._build('main')
#Tools().sendMail("Hello 2")
pass
if __name__ == '__main__':
#main()
#print Tools().get_CL_list(build_dir[0],P4BRANCH[0],472914,472920)#_build('cr3')
for platform in PLATFORM_ALLOWED:
if platform == 'cardhu':
common.CARDHU = True
for branch in BRANCH_ALLOWED:
i = Tools().find_index(BRANCH_ALLOWED,branch)
print EXCEL_FLIST[i],i | [
"[email protected]"
] | |
c6d78cf5cce4c612fcd874fb058bfddc9d24d3d7 | 0be66219fa89953dfbd3fdce0d4f5cc1127722d2 | /trans_pb/pb.py | b34e951dcdc5fa0e14df148cb508268b40d54f80 | [] | no_license | qaz734913414/transfer-.meta-to-.pb | ce60f082073d16bb9191e8e4c7313a225f36fd16 | 5648e5dd3193d7526b49dc6f96c35359477841e4 | refs/heads/master | 2020-08-05T04:29:51.602122 | 2018-07-12T05:45:35 | 2018-07-12T05:45:35 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,865 | py | #coding:utf-8
import tensorflow as tf
from mtcnn_model import P_Net,R_Net, O_Net
#1.input placeholder
#2.定义网络结构，输入是placeholder;
# 定义网络结构时把“if training”部分去掉了，只留下inference部分
#3.restore 旧的ckpt文件（名称类似于xxx.ckpt.data-xxx）的参数值（如，weight）；
# 如果直接freeze旧的ckpt文件和meta文件为pb文件，会发现print出的节点名字会对不上，直接拿来用也会报错，是不是可以认为训练图和inference的图中节点对不上
#4.save->新的meta文件和ckpt文件
#5.freeze.py freeze graph 生成pb文件
#6.测试是不是可以用。
with tf.Graph().as_default():
with tf.Session() as sess:
#inputs_placeholder = tf.placeholder(tf.float32, shape=[None,None,None,3], name='input') #1 ；pnet是全卷积网络，输入大小任意
#inputs_placeholder = tf.placeholder(tf.float32, shape=[None,24,24,3], name='input') #1 ；rnet有全连接层，所以输入是固定大小，onet同理
inputs_placeholder = tf.placeholder(tf.float32, shape=[None,48,48,3], name='input') #1
#cls_pro,bbox_pred,landmark_pred = P_Net(inputs_placeholder) #2
#cls_pro,bbox_pred,landmark_pred = R_Net(inputs_placeholder) #2
cls_pro,bbox_pred,landmark_pred = O_Net(inputs_placeholder) #2
saver = tf.train.Saver() #2
#saver.restore(sess, './old_ckpt/PNet_landmark/PNets/PNet-30') #3
#saver.restore(sess, './old_ckpt/RNet_landmark/RNets/RNet-22') #3
saver.restore(sess, './old_ckpt/ONet_landmark/ONets/ONet-22') #3
for op in tf.get_default_graph().get_operations(): #打印网络中的节点
for t in op.values():
print(t.name)
#saver.save(sess,'./new_ckpt_pb/pnet/pnet.ckpt') #4
#saver.save(sess,'./new_ckpt_pb/rnet/rnet.ckpt') #4
saver.save(sess,'./new_ckpt_pb/onet/onet.ckpt') #4 | [
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] | |
e8bc12338eb61113054e424095b8b5968be09657 | 30a6024818586766020ce957be386f9240986acb | /mysite/mysite/settings.py | a71bf8e575a7eca99b71d9df16e5d14870a560cb | [] | no_license | Sobakenshi/my-first-blog | 474b881623ff66ca98c05f207deb586b391e94f8 | a6540c959c068c57ea8649d31cdc4ddac688617d | refs/heads/master | 2020-06-02T02:09:22.856261 | 2019-06-09T20:56:55 | 2019-06-09T20:56:55 | 191,002,278 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,221 | py | """
Django settings for mysite project.
Generated by 'django-admin startproject' using Django 2.2.2.
For more information on this file, see
https://docs.djangoproject.com/en/2.2/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/2.2/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/2.2/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = '_x3h)-(t%p$&!d=i#8vl_+2059jl04us4-n6s&0&79v*ozvfm-'
# SECURITY WARNING: don't run with debug turned on in production!
DEBUG = True
# ALLOWED_HOSTS = []
ALLOWED_HOSTS = ['127.0.0.1', 'Sobakenshi.pythonanywhere.com']
# Application definition
INSTALLED_APPS = [
'blog',
'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 = 'mysite.urls'
TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'DIRS': [],
'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 = 'mysite.wsgi.application'
# Database
# https://docs.djangoproject.com/en/2.2/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/2.2/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/2.2/topics/i18n/
LANGUAGE_CODE = 'ru-ru'
TIME_ZONE = 'Europe/Moscow'
USE_I18N = True
USE_L10N = True
USE_TZ = True
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/2.2/howto/static-files/
STATIC_URL = '/static/'
STATIC_ROOT = os.path.join(BASE_DIR, 'static')
| [
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ffabe999a7beb5c6360d7919db313c9839e0d31f | c279b5c30e2b782efc1b58cd1f8e0b8b325ede6d | /quizy/admin.py | d124df10841f90438ce350f0c12f57896b9036af | [] | no_license | kapisolec/examsite | 00f1fdc986903b8b2f670f34a245b490bca6554d | 48bb854c759465fbca02baa1ef40723146a5fb3e | refs/heads/master | 2022-12-24T03:13:23.421588 | 2020-10-05T14:09:40 | 2020-10-05T14:09:40 | 267,034,896 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,160 | py | from .models import *
# Register your models here.
from django.contrib import admin
from django.utils.translation import gettext_lazy as _
# class OdpowiedziAdminListFilter(admin.SimpleListFilter):
# title = _('pytanie')
# parameter_name = 'pytanie'
# def lookups(self, request, model_admin):
# pytania = Odpowiedzi.pytanie.get_queryset()
# return [(pytania.pk, pytania.tresc) for pytania in pytania]
# def queryset(self, request, queryset):
# value = self.value()
# if value is not None:
# return queryset.filter(pytania_id = self.value())
# return queryset
class OdpowiedziAdminModel(admin.ModelAdmin):
pytanie = list(Odpowiedzi.pytanie.get_queryset())
list_filter = ('pytanie',)
class WynikiAdminModel(admin.ModelAdmin):
list_display = ('uzytkownik', 'wynik')
list_filter = ('uzytkownik','wynik')
admin.site.site_header = 'Panel administracyjny projektu Django'
admin.site.register(WynikiModel, WynikiAdminModel)
admin.site.register(Pytania)
admin.site.register(Odpowiedzi,OdpowiedziAdminModel)
admin.site.register(WyborOdpowiedzi)
admin.site.register(Uzytkownicy)
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9cd23e4e159c5e653cde08597a57d5b95149950f | cb1e63db3a045680997315076c950d8f8bdb9ada | /docs/conf.py | 6303a36e20ead580b4eb3593976902c779864cfc | [] | no_license | cloverfeed/ranko | c222979ffad45af71edf88f3f92ee0190f44eaf7 | 92075b5337097e9e6baf43442ef7f4f5b7177b69 | refs/heads/master | 2016-09-05T12:27:17.685142 | 2016-01-16T17:08:19 | 2016-01-16T17:08:19 | 19,494,776 | 1 | 0 | null | 2016-01-20T20:21:18 | 2014-05-06T13:15:31 | Python | UTF-8 | Python | false | false | 8,266 | py | # -*- coding: utf-8 -*-
#
# ranko documentation build configuration file, created by
# sphinx-quickstart on Wed Oct 22 09:29:08 2014.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All configuration values have a default; values that are commented out
# serve to show the default.
import sys
import os
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
sys.path.insert(0, os.path.abspath('..'))
# -- General configuration ------------------------------------------------
# If your documentation needs a minimal Sphinx version, state it here.
#needs_sphinx = '1.0'
# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
# ones.
extensions = [
'sphinx.ext.autodoc',
'sphinx.ext.autosummary',
'sphinx.ext.todo',
'sphinx.ext.coverage',
'sphinxcontrib.autohttp.flask',
]
# Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates']
# The suffix of source filenames.
source_suffix = '.rst'
# The encoding of source files.
#source_encoding = 'utf-8-sig'
# The master toctree document.
master_doc = 'index'
# General information about the project.
project = u'ranko'
copyright = u'2014, Etienne Millon'
# The version info for the project you're documenting, acts as replacement for
# |version| and |release|, also used in various other places throughout the
# built documents.
#
# The short X.Y version.
version = '0'
# The full version, including alpha/beta/rc tags.
release = '0'
# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
#language = None
# There are two options for replacing |today|: either, you set today to some
# non-false value, then it is used:
#today = ''
# Else, today_fmt is used as the format for a strftime call.
#today_fmt = '%B %d, %Y'
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
exclude_patterns = ['_build']
# The reST default role (used for this markup: `text`) to use for all
# documents.
#default_role = None
# If true, '()' will be appended to :func: etc. cross-reference text.
#add_function_parentheses = True
# If true, the current module name will be prepended to all description
# unit titles (such as .. function::).
#add_module_names = True
# If true, sectionauthor and moduleauthor directives will be shown in the
# output. They are ignored by default.
#show_authors = False
# The name of the Pygments (syntax highlighting) style to use.
pygments_style = 'sphinx'
# A list of ignored prefixes for module index sorting.
#modindex_common_prefix = []
# If true, keep warnings as "system message" paragraphs in the built documents.
#keep_warnings = False
# -- Options for HTML output ----------------------------------------------
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
html_theme = 'default'
# Theme options are theme-specific and customize the look and feel of a theme
# further. For a list of options available for each theme, see the
# documentation.
#html_theme_options = {}
# Add any paths that contain custom themes here, relative to this directory.
#html_theme_path = []
# The name for this set of Sphinx documents. If None, it defaults to
# "<project> v<release> documentation".
#html_title = None
# A shorter title for the navigation bar. Default is the same as html_title.
#html_short_title = None
# The name of an image file (relative to this directory) to place at the top
# of the sidebar.
#html_logo = None
# The name of an image file (within the static path) to use as favicon of the
# docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32
# pixels large.
#html_favicon = None
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ['_static']
# Add any extra paths that contain custom files (such as robots.txt or
# .htaccess) here, relative to this directory. These files are copied
# directly to the root of the documentation.
#html_extra_path = []
# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
# using the given strftime format.
#html_last_updated_fmt = '%b %d, %Y'
# If true, SmartyPants will be used to convert quotes and dashes to
# typographically correct entities.
#html_use_smartypants = True
# Custom sidebar templates, maps document names to template names.
#html_sidebars = {}
# Additional templates that should be rendered to pages, maps page names to
# template names.
#html_additional_pages = {}
# If false, no module index is generated.
#html_domain_indices = True
# If false, no index is generated.
#html_use_index = True
# If true, the index is split into individual pages for each letter.
#html_split_index = False
# If true, links to the reST sources are added to the pages.
#html_show_sourcelink = True
# If true, "Created using Sphinx" is shown in the HTML footer. Default is True.
#html_show_sphinx = True
# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True.
#html_show_copyright = True
# If true, an OpenSearch description file will be output, and all pages will
# contain a <link> tag referring to it. The value of this option must be the
# base URL from which the finished HTML is served.
#html_use_opensearch = ''
# This is the file name suffix for HTML files (e.g. ".xhtml").
#html_file_suffix = None
# Output file base name for HTML help builder.
htmlhelp_basename = 'rankodoc'
# -- Options for LaTeX output ---------------------------------------------
latex_elements = {
# The paper size ('letterpaper' or 'a4paper').
#'papersize': 'letterpaper',
# The font size ('10pt', '11pt' or '12pt').
#'pointsize': '10pt',
# Additional stuff for the LaTeX preamble.
#'preamble': '',
}
# Grouping the document tree into LaTeX files. List of tuples
# (source start file, target name, title,
# author, documentclass [howto, manual, or own class]).
latex_documents = [
('index', 'ranko.tex', u'ranko Documentation',
u'Etienne Millon', 'manual'),
]
# The name of an image file (relative to this directory) to place at the top of
# the title page.
#latex_logo = None
# For "manual" documents, if this is true, then toplevel headings are parts,
# not chapters.
#latex_use_parts = False
# If true, show page references after internal links.
#latex_show_pagerefs = False
# If true, show URL addresses after external links.
#latex_show_urls = False
# Documents to append as an appendix to all manuals.
#latex_appendices = []
# If false, no module index is generated.
#latex_domain_indices = True
# -- Options for manual page output ---------------------------------------
# One entry per manual page. List of tuples
# (source start file, name, description, authors, manual section).
man_pages = [
('index', 'ranko', u'ranko Documentation',
[u'Etienne Millon'], 1)
]
# If true, show URL addresses after external links.
#man_show_urls = False
# -- Options for Texinfo output -------------------------------------------
# Grouping the document tree into Texinfo files. List of tuples
# (source start file, target name, title, author,
# dir menu entry, description, category)
texinfo_documents = [
('index', 'ranko', u'ranko Documentation',
u'Etienne Millon', 'ranko', 'One line description of project.',
'Miscellaneous'),
]
# Documents to append as an appendix to all manuals.
#texinfo_appendices = []
# If false, no module index is generated.
#texinfo_domain_indices = True
# How to display URL addresses: 'footnote', 'no', or 'inline'.
#texinfo_show_urls = 'footnote'
# If true, do not generate a @detailmenu in the "Top" node's menu.
#texinfo_no_detailmenu = False
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a1fc2862dd9837a78a4d8d46bbf085b30291de55 | b835a371d6e96e3bf01e157825367fa8cb977101 | /website/userprofile/apps.py | 70d5a409abeb2888060e2f4758c296436885962c | [
"MIT"
] | permissive | SebastiaanZ/minigigscyclingteam | c832858c5164c66782a082301bf46df15ded7fc4 | 6c8c4f7ae41a5b01a551c592dc81fd37fd4f686e | refs/heads/master | 2022-11-29T18:22:58.005902 | 2020-01-28T22:15:42 | 2020-01-28T22:15:42 | 196,997,653 | 0 | 0 | MIT | 2022-11-22T05:16:55 | 2019-07-15T12:49:03 | HTML | UTF-8 | Python | false | false | 616 | py | import logging
from django.apps import AppConfig
from django.contrib.auth import get_user_model
from django.db.models.signals import post_save
log = logging.getLogger(__name__)
class UserprofileConfig(AppConfig):
"""App configuration for the extended user profile."""
name = 'website.userprofile'
def ready(self):
"""Add signal handlers for User post_save."""
from website.userprofile.signals import create_user_profile, save_user_profile
post_save.connect(create_user_profile, sender=get_user_model())
post_save.connect(save_user_profile, sender=get_user_model())
| [
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86f0947a9ea548fee059c7e04aef7f9aa0889a43 | b46f5825b809c0166622149fc5561c23750b379c | /AppImageBuilder/app_dir/runtimes/classic/helpers/factory.py | 9d6ba258dac58c56ed53f44df74516b7789429da | [
"MIT"
] | permissive | gouchi/appimage-builder | 22b85cb682f1b126515a6debd34874bd152a4211 | 40e9851c573179e066af116fb906e9cad8099b59 | refs/heads/master | 2022-09-28T09:46:11.783837 | 2020-06-07T19:44:48 | 2020-06-07T19:44:48 | 267,360,199 | 0 | 0 | MIT | 2020-05-27T15:42:25 | 2020-05-27T15:42:24 | null | UTF-8 | Python | false | false | 1,768 | py | # Copyright 2020 Alexis Lopez Zubieta
#
# 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.
from .dynamic_loader import DynamicLoader
from .base_helper import BaseHelper
from .fontconfig import FontConfig
from .gstreamer import GStreamer
from .java import Java
from .libgl import LibGL
from .openssl import OpenSSL
from .qt import Qt
from .gdk_pixbuf import GdkPixbuf
from .glib_schemas import GLibSchemas
class HelperFactoryError(RuntimeError):
pass
class HelperFactory:
def __init__(self, app_dir, app_dir_files):
self.app_dir = app_dir
self.app_dir_files = app_dir_files
self.helpers = {
'loader': DynamicLoader,
'fontconfig': FontConfig,
'openssl': OpenSSL,
'qt': Qt,
'libgl': LibGL,
'gstreamer': GStreamer,
'gdk_pixbuf': GdkPixbuf,
'glib_schemas': GLibSchemas,
'java': Java
}
def get(self, id) -> BaseHelper:
if id in self.helpers:
obj = self.helpers[id](self.app_dir, self.app_dir_files)
return obj
else:
raise HelperFactoryError('%s: unknown helper id' % id)
def list(self):
return self.helpers.keys()
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bcca1deb423debb367d356f3667ac15b70b767b0 | fc5f1e70b48651e3bcd983a8ea2f2e8c64cd25eb | /method_plain_cvxopt.py | 95a3d82d84f07479421451e1605f9328e022fb3e | [] | no_license | vishalbelsare/multitask | 62d69df764436347a42bdeb6c1681a9b9bb2c819 | b8ce676ecb778b17dc39d06878523299fd693bc3 | refs/heads/master | 2020-04-07T10:30:27.518466 | 2014-02-01T12:59:33 | 2014-02-01T12:59:33 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,567 | py | #!/usr/bin/env python2.5
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# (at your option) any later version.
#
# Written (W) 2009 Christian Widmer
# Copyright (C) 2009 Max-Planck-Society
"""
Created on 02.06.2009
@author: Christian Widmer
@summary: Implementation of the plain svm method
using openopt as solver backend
"""
import unittest
import numpy
import helper
from shogun.Shogun import LibSVM, SVMLight, StringCharFeatures, Labels
from shogun.Shogun import DNA, WeightedDegreeStringKernel
from base_method import MultiMethod
from expenv_runner import fetch_gammas
from openopt import QP
debug = False
class Method(MultiMethod):
def _train(self, train_data, param):
"""
training procedure using training examples and labels
@param train_data: Data relevant to SVM training
@type train_data: dict<str, list<instances> >
@param param: Parameters for the training procedure
@type param: ParameterSvm
"""
# fix parameters
M = len(train_data)
predictors = {}
# extract training data
for (task_id, instance_set) in train_data.items():
print "train task id:", task_id
assert(instance_set[0].dataset.organism==task_id)
examples = [inst.example for inst in instance_set]
labels = [inst.label for inst in instance_set]
# shogun data
feat = StringCharFeatures(DNA)
feat.set_string_features(examples)
lab = Labels(numpy.double(labels))
# create kernel
k = WeightedDegreeStringKernel(feat, feat, param.wdk_degree, 0)
y = numpy.array(labels)
km = k.get_kernel_matrix()
km = numpy.transpose(y.flatten() * (km*y.flatten()).transpose())
N = len(labels)
f = -numpy.ones(N)
# set up QP
p = QP(km, f, Aeq=y, beq=0, lb=numpy.zeros(N), ub=param.cost*numpy.ones(N))
# run solver
r = p.solve('cvxopt_qp', iprint = 0)
alphas = r.xf
objective = r.ff
print "objective:", objective
predictors[task_id] = (alphas, param.wdk_degree, examples, labels)
return predictors
def _predict(self, predictor, examples, task_id):
"""
make prediction on examples using trained predictor
@param predictor: trained predictor
@type predictor: array
@param examples: list of examples
@type examples: list
@param task_id: task identifier
@type task_id: str
"""
(alphas, wdk_degree, train_examples, train_labels) = predictor
print "length alphas:", len(alphas), ", length train_examples:", len(train_examples), ", length train_labels:", len(train_labels)
# shogun data
feat_train = StringCharFeatures(DNA)
feat_train.set_string_features(list(train_examples))
feat_test = StringCharFeatures(DNA)
feat_test.set_string_features(list(examples))
k = WeightedDegreeStringKernel(feat_train, feat_test, wdk_degree, 0)
km = k.get_kernel_matrix()
alphas = numpy.array(alphas)
print "warning: labels missing" #TODO FIX
out = numpy.dot(alphas, km)
####################
return out
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3c4f9c3b72ec831f2ab07a24009b2d30da34cdb4 | 60f1ecf60ffd240ab30a7282d00063fdf9610c03 | /scripts/lightsensors.py | 77df8c6cd7fb3cc74a923cbc6717184ca77d4ad8 | [
"BSD-3-Clause"
] | permissive | nzhoo/pimouse_ros | e5d4b9220b0069349c2103e077fe65c20da083b3 | 47f3c3aed88bca45722849b9cf7383cfd1e25f0d | refs/heads/master | 2021-05-14T00:02:07.282355 | 2018-01-25T15:18:21 | 2018-01-25T15:18:21 | 116,532,134 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,260 | py | #!/usr/bin/env python
#encoding: utf8
import sys, rospy
from pimouse_ros.msg import LightSensorValues
def get_freq():
f = rospy.get_param('lightsensors_freq',10)
try:
if f <= 0.0:
raise Exception()
except:
rospy.logerr("value error: lightsensors_freq")
sys.exit(1)
return f
if __name__ == '__main__':
devfile = '/dev/rtlightsensor0'
rospy.init_node('lightsensors')
pub = rospy.Publisher('lightsensors', LightSensorValues, queue_size=1)
freq = get_freq()
rate = rospy.Rate(freq)
while not rospy.is_shutdown():
try:
with open(devfile,'r') as f:
data = f.readline().split()
data = [ int(e) for e in data ]
d = LightSensorValues()
d.right_forward = data[0]
d.right_side = data[1]
d.left_side = data[2]
d.left_forward = data[3]
d.sum_all = sum(data)
d.sum_forward = data[0] + data[3]
pub.publish(d)
except IOError:
rospy.logerr("cannot write to " + devfile)
f = get_freq()
if f != freq:
freq = f
rate = rospy.Rate(freq)
rate.sleep()
| [
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] | |
8ad9d860ae0c5298d003124bad94cdfcca6b2a0f | 8fb49480682a5085e7bc3b31a37e209598a1aa72 | /TimeEval/NetworkPolicy_0/t1.py | 74b1c6d001dc65d190ca98d1e42fd2156766e809 | [] | no_license | vasu018/IoTSecurity | 3063edad1d67f9bd1b93d0a2c322d52b2bb78c18 | 62de6b64dd27109192258f0bfa78db22d2398c28 | refs/heads/master | 2021-09-17T23:48:28.684601 | 2018-04-03T17:01:13 | 2018-04-03T17:01:13 | 113,246,729 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,536 | py | from c4 import *
import sys
from timeit import default_timer as timer
def get_nnodes(n,parent,poltype,translations):
pass
def get_network(node):
if node in nn:
net = nn[node]
else:
i = re.search('(.*){(.*)}',node)
net = "->".join([i.group(2),i.group(1)]) if i.group(2) else i.group(1)
if net in ipmap:
if node in poltypes and (poltypes[node] in ipmap[net]):
return ipmap[net][poltypes[node]]
elif node in poltypes:
return ipmap[net]['networks']
else:
return ""
else:
print("No mapping found for "+net)
return ""
if __name__ == "__main__":
starttime = timer()
Graphs = create_graphs()
print(timer()-starttime)
sys.exit(0)
translations = json.load(open("translate.json", "r"))
comp = nx.MultiDiGraph()
for g in Graphs:
user1_graph = Graphs[g]['main']
mappings= {}
#parents = c4.nx.get_node_attributes(user1_graph,"parent")
poltypes = nx.get_node_attributes(user1_graph,"polabstract")
netnodes = []
for n in user1_graph:
if poltypes[n]=='networks':
netnodes.append(n)
continue
elif poltypes[n]=='Hosts':
continue
mappings[n] = translations[n] if n in translations else 'default'
nx.set_node_attributes(user1_graph,"network",mappings)
nn = nx.get_node_attributes(user1_graph,"network")
for nodes in nn:
for networknodes in netnodes:
i = re.search('(.*){(.*)}',networknodes)
loc1 = "->".join([i.group(2),i.group(1)]) if i.group(2) else i.group(1)
if nn[nodes].startswith(loc1):
for dest in user1_graph[networknodes]:
for e in user1_graph[networknodes][dest]:
add_edge([nodes], [dest], user1_graph[networknodes][dest][e], user1_graph,True)
comp = nx.compose(comp, user1_graph)
# print("\nFollowing is the composed graph:")
# for n in comp.adjacency_iter():
# if n[1]:
# print(n)
composenetnodes = []
poltypes = nx.get_node_attributes(comp,"polabstract")
for n in comp:
if n in poltypes and poltypes[n]=='networks':
composenetnodes.append(n)
nn = nx.get_node_attributes(comp,"network")
# print("\nPrinting network of other abstraction:")
# print(nn)
for nodes in nn:
for networknodes in composenetnodes:
i = re.search('(.*){(.*)}',networknodes)
loc1 = "->".join([i.group(2),i.group(1)]) if i.group(2) else i.group(1)
if nn[nodes].startswith(loc1):
for dest in comp[networknodes]:
for e in comp[networknodes][dest]:
add_edge([nodes], [dest], comp[networknodes][dest][e], comp,True)
finalgraph = nx.MultiDiGraph()
ipmap = json.load(open("ipmapping.json", "r"))
for n in comp:
for dest in comp[n]:
s = get_network(n)
t = get_network(dest)
for e in comp[n][dest]:
add_edge([s],[t],comp[n][dest][e],finalgraph)
# print("\nFinal Graph after mapping:")
# for n in finalgraph.adjacency_iter():
# if n[1]:
# print(n)
#print(timer()-starttime)
| [
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] | |
6d0a7d8c0ab32af15ba6dc222b31a043a5970055 | ce6ace34704e74c2a53e9b38b2630876d9cd52e2 | /mdias_addons/metro_park_maintenance/models/export_produce_plan_wizard.py | 73bbf97f7f5bd04a847592c2975e317e4c8985b6 | [] | no_license | rezaghanimi/main_mdias | e3cfd8033204d8e7e484041f506892621a3e3479 | 13b428a5c4ade6278e3e5e996ef10d9fb0fea4b9 | refs/heads/master | 2022-09-17T20:15:42.305452 | 2020-05-29T05:38:35 | 2020-05-29T05:38:35 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,164 | py |
# -*- coding: utf-8 -*-
from odoo import models, fields, api
import pendulum
class ExportProducePlanWizard(models.TransientModel):
'''
导出生产计划
'''
_name = 'metro_park_maintenance.export_produce_plan_wizard'
end_month = fields.Many2one(string='结束月份',
comodel_name='metro_park_maintenance.month', required=True)
start_year = fields.Many2one(string='开始年份',
comodel_name='metro_park_maintenance.year', required=True)
start_month = fields.Many2one(string='开始月份',
comodel_name='metro_park_maintenance.month', required=True)
end_year = fields.Many2one(string='结束年份',
comodel_name='metro_park_maintenance.year', required=True)
@api.multi
def on_ok(self):
'''
确定按扭点击, 导出对应年月的计划
:return:
'''
return {
'name': '月计划下载',
'type': 'ir.actions.act_url',
'url': '/export_produce_plan/{month_plan_id}'.format(month_plan_id=self.id)
}
| [
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] | |
ded5d4b6cbbedfbc0b3d0a7450050a050dd73c7a | 079d34639c394e511f772af7cd7f01a7ccdc5111 | /flask-structure/web/resource/sample.py | 3c9d2ff89a9e24739310ec839981fb6e0cd58bb2 | [] | no_license | hinczhang/demo-repository | ffd03013137ba68fbb8f96f04931b89f5c572a4b | f980a00cf8a5bb9e14f7a75be149678657bf5710 | refs/heads/main | 2023-01-03T02:54:14.038388 | 2020-11-03T12:28:35 | 2020-11-03T12:28:35 | 308,655,500 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 506 | py | from flask_restful import Resource
from flask import Response,request
import json
#return useful information
class Sample(Resource):
def get(self):#block get method
pass
def post(self):#open post method
print(json.loads(request.data)['mode'])#the data delivered from frontend to backend are stored in the request.data, which should be jsonfied.
data = json.dumps({'sample':'success!'})
res = Response(response=data, status=200, mimetype="application/json")#send message to frontend
return res
| [
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] | |
119f5a12bb5beddeb2cede2d0f3562ae1b6a9630 | 6d8bb00e42aa128cd47dab310c75be2e5b9d1c97 | /crawl2/douban/douban/middlewares.py | 1d79896b30e8c5fe16195f046f178dd5c215c758 | [] | no_license | LayneIns/CrawlerProject | dcff0dbc7cdfd0a5496f7d03a5d03b31777cbfb6 | 48cdae511dfb671f3e6d52040f4d98069f2bac33 | refs/heads/master | 2021-04-15T14:43:58.669794 | 2018-03-25T16:33:12 | 2018-03-25T16:33:12 | 126,717,553 | 1 | 1 | null | null | null | null | UTF-8 | Python | false | false | 4,459 | py | # -*- coding: utf-8 -*-
# Define here the models for your spider middleware
#
# See documentation in:
# https://doc.scrapy.org/en/latest/topics/spider-middleware.html
from scrapy import signals
import random,base64
from fetch_free_proxyes import fetch_all
class DoubanSpiderMiddleware(object):
# Not all methods need to be defined. If a method is not defined,
# scrapy acts as if the spider middleware does not modify the
# passed objects.
@classmethod
def from_crawler(cls, crawler):
# This method is used by Scrapy to create your spiders.
s = cls()
crawler.signals.connect(s.spider_opened, signal=signals.spider_opened)
return s
def process_spider_input(self, response, spider):
# Called for each response that goes through the spider
# middleware and into the spider.
# Should return None or raise an exception.
return None
def process_spider_output(self, response, result, spider):
# Called with the results returned from the Spider, after
# it has processed the response.
# Must return an iterable of Request, dict or Item objects.
for i in result:
yield i
def process_spider_exception(self, response, exception, spider):
# Called when a spider or process_spider_input() method
# (from other spider middleware) raises an exception.
# Should return either None or an iterable of Response, dict
# or Item objects.
pass
def process_start_requests(self, start_requests, spider):
# Called with the start requests of the spider, and works
# similarly to the process_spider_output() method, except
# that it doesn’t have a response associated.
# Must return only requests (not items).
for r in start_requests:
yield r
def spider_opened(self, spider):
spider.logger.info('Spider opened: %s' % spider.name)
class DoubanDownloaderMiddleware(object):
# Not all methods need to be defined. If a method is not defined,
# scrapy acts as if the downloader middleware does not modify the
# passed objects.
@classmethod
def from_crawler(cls, crawler):
# This method is used by Scrapy to create your spiders.
s = cls()
crawler.signals.connect(s.spider_opened, signal=signals.spider_opened)
return s
def process_request(self, request, spider):
# Called for each request that goes through the downloader
# middleware.
# Must either:
# - return None: continue processing this request
# - or return a Response object
# - or return a Request object
# - or raise IgnoreRequest: process_exception() methods of
# installed downloader middleware will be called
return None
def process_response(self, request, response, spider):
# Called with the response returned from the downloader.
# Must either;
# - return a Response object
# - return a Request object
# - or raise IgnoreRequest
return response
def process_exception(self, request, exception, spider):
# Called when a download handler or a process_request()
# (from other downloader middleware) raises an exception.
# Must either:
# - return None: continue processing this exception
# - return a Response object: stops process_exception() chain
# - return a Request object: stops process_exception() chain
pass
def spider_opened(self, spider):
spider.logger.info('Spider opened: %s' % spider.name)
class RandomUserAgent(object):
"""Randomly rotate user agents based on a list of predefined ones"""
def __init__(self, agents):
self.agents = agents
@classmethod
def from_crawler(cls, crawler):
return cls(crawler.settings.getlist('USER_AGENTS'))
def process_request(self, request, spider):
request.headers.setdefault('User-Agent', random.choice(self.agents))
class ProxyMiddleware(object):
def __init__(self):
self.count = 0
self.PROXIES = []
def process_request(self, request, spider):
if self.count % 200 == 0:
print 'fetcing proxys ..........'
self.PROXIES = fetch_all()
proxy = random.choice(self.PROXIES)
request.meta['proxy'] = "http://%s" % proxy
self.count += 1 | [
"[email protected]"
] | |
f51656b89eb01e0650b076ccb826d8a2aecd11c4 | 0605abd822f659156b290c75d93006ec6a91e13f | /accounts/models.py | abf8f62baf08471b6310dd7a09ee366bac7cc748 | [] | no_license | nickcarva/django-agenda | 0bf06b7e0aae22f79914d961bbd50c1c2c6b5f3b | 5ddaf7255122ccfdcc58b6e408c352eadbc8b9d6 | refs/heads/master | 2022-12-08T04:31:04.280272 | 2020-08-25T15:07:09 | 2020-08-25T15:07:09 | 289,528,569 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 160 | py | from contatos.models import Contato
from django import forms
class FormContato(forms.ModelForm):
class Meta:
model = Contato
exclude = ()
| [
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] | |
edec94f06021805f99c337437bd84728ff5f22fe | 8853462a79608b7e5b7af94dbfa6c0a63c1f6b6a | /CustomEnvs/Gym envs/2. Crawler/Quadruped_54/Quadruped_54/envs/Quadruped_54.py | 3e119be601c9806a60345098a9fbe20c4f692a7b | [] | no_license | Ashish017/CASNET | eaae2552f8d56413f756c7d3839cd6f548a6e1ef | 73ec542c4c3fa1f97686796f0c385c71cad3e8d5 | refs/heads/master | 2023-02-06T06:53:27.362356 | 2020-12-27T04:43:34 | 2020-12-27T04:43:34 | 270,657,078 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,433 | py | import numpy as np
from gym import utils
from gym.envs.mujoco import mujoco_env
import time
class model_data:
def __init__(self):
self.num_legs = 4
self.leg_lengths = [[0.2828, 0.1697, 0.5656],[0.2828, 0.2828, 0.5656],[0.2828, 0.2828, 0.5656],[0.2828,0.2828, 0.6221]]
self.leg_starts = [[0.2, 0.2],[0.2, -0.2],[-0.23, -0.23],[-0.2, 0.2]]
self.joint_ranges = [[[-0.5234, 0.5234],[-0.5234, 0.5234],[0.1745, 1.57]],[[-0.5234, 0.5234],[-0.5234, 0.5234],[0.3490, 1.1344]],[[-0.5234, 0.5234],[-0.5234, 0.5234],[0.1745, 1.57]],[[-0.5234, 0.5234],[-0.5234, 0.5234],[0.1745, 1.57]]]
self.joint_axes = [[[0, 0, 1],[-1, 1, 0],[-1, 1, 0]],[[0, 0, 1],[1, 1, 0],[1, 1, 0]],[[0, 0, -1],[1, -1, 0],[1, -1, 0]],[[0, 0, -1],[-1, -1, 0],[-1, -1, 0]]]
self.joint_names = ['hip', 'ankle']
modelData = model_data()
def mod(vector):
value = ((vector[0]**2 + vector[1]**2)**0.5)
return value
class Quadruped_v54(mujoco_env.MujocoEnv, utils.EzPickle):
def __init__(self, add_dummy=False, max_segments=3, max_legs=6):
self.add_dummy = add_dummy
self.max_legs = max_legs
self.max_segments = max_segments
if add_dummy:
self.dummy_segment = [[0.0,0.0,0.0],[0.0,0.0],[0.0],[0.0]]
self.dummy_leg = [[0.0,0.0]]
for _ in range(max_segments):
self.dummy_leg.extend(self.dummy_segment)
mujoco_env.MujocoEnv.__init__(self, 'Quadruped_54.xml', 5)
utils.EzPickle.__init__(self)
def step(self, a, goal=[0, 1]):
goal = np.array(goal)
#Pervious xy pos and simulation step
x_before = self.get_body_com("torso")[0]
y_before = self.get_body_com("torso")[1]
self.do_simulation(a, self.frame_skip)
#Calculating reward
x_after = self.get_body_com("torso")[0]
y_after = self.get_body_com("torso")[1]
x_movement = x_after - x_before
y_movement = y_after - y_before
distance_covered = ((x_movement**2) + (y_movement**2))**0.5
speed = distance_covered / self.dt
direction = [x_movement/distance_covered, y_movement/distance_covered]
dot_product = sum(goal * np.array(direction))
cos_theeta = dot_product / (mod(goal) * mod(direction))
#Whether done
state = self.state_vector()
not_done = np.isfinite(state).all() and state[2] >= 0.2
done = not not_done
ob = self._get_obs()
movement_reward = speed * cos_theeta
weighted_movement_reward = 2*movement_reward
ctrl_cost = (.5 * np.square(a).sum())/(modelData.num_legs+2.0)
survive_reward = 1.0
if done:
survive_reward = 0
total_reward = weighted_movement_reward - ctrl_cost + survive_reward
return ob, total_reward, done, dict(
forward_reward=weighted_movement_reward,
ctrl_reward = -ctrl_cost,
survive_reward = survive_reward
)
def _get_obs(self):
#Extracting joint_pos
joint_pos = []
for leg_number in range(modelData.num_legs):
leg_joint_pos = []
added_extra = False
for joint in range(len(modelData.joint_names)):
joint_qpos_addr = self.model.get_joint_qpos_addr(modelData.joint_names[joint]+ "_" + str(leg_number+1))
joint_qpos = self.data.qpos[joint_qpos_addr]
leg_joint_pos.append(float(joint_qpos))
if leg_number == 0 and not added_extra:
joint_qpos_addr = self.model.get_joint_qpos_addr('extra_joint_1')
joint_qpos = self.data.qpos[joint_qpos_addr]
leg_joint_pos.append(float(joint_qpos))
added_extra = True
if leg_number == 1 and not added_extra:
joint_qpos_addr = self.model.get_joint_qpos_addr('extra_joint_2')
joint_qpos = self.data.qpos[joint_qpos_addr]
leg_joint_pos.append(float(joint_qpos))
added_extra = True
if leg_number == 2 and not added_extra:
joint_qpos_addr = self.model.get_joint_qpos_addr('extra_joint_3')
joint_qpos = self.data.qpos[joint_qpos_addr]
leg_joint_pos.append(float(joint_qpos))
added_extra = True
if leg_number == 3 and not added_extra:
joint_qpos_addr = self.model.get_joint_qpos_addr('extra_joint_4')
joint_qpos = self.data.qpos[joint_qpos_addr]
leg_joint_pos.append(float(joint_qpos))
added_extra = True
joint_pos.append(leg_joint_pos)
self.joint_pos = joint_pos
#Preparing observations
obs = []
for leg_num in range(modelData.num_legs):
obs.append(modelData.leg_starts[leg_num]) #leg_start
obs.append(modelData.joint_axes[leg_num][0]) #j1_range
obs.append(modelData.joint_ranges[leg_num][0]) #j1_range
obs.append([self.joint_pos[leg_num][0]]) #j1_pos
obs.append([modelData.leg_lengths[leg_num][0]]) #length_1
obs.append(modelData.joint_axes[leg_num][1]) #j1_range
obs.append(modelData.joint_ranges[leg_num][1]) #j2_range
obs.append([self.joint_pos[leg_num][1]]) #j2_pos
obs.append([modelData.leg_lengths[leg_num][1]]) #length_2
obs.append(modelData.joint_axes[leg_num][2]) #j1_range
obs.append(modelData.joint_ranges[leg_num][2]) #j2_range
obs.append([self.joint_pos[leg_num][2]]) #j2_pos
obs.append([modelData.leg_lengths[leg_num][2]])
if self.add_dummy:
for _ in range(self.max_segments - len(modelData.leg_lengths[leg_num])):
obs.extend(self.dummy_segment)
if self.add_dummy:
for _ in range(self.max_legs - modelData.num_legs):
obs.extend(self.dummy_leg)
obs = [j for i in obs for j in i]
obs = np.array(obs)
return obs
def reset_model(self):
qpos = self.init_qpos
qvel = self.init_qvel
self.set_state(qpos, qvel)
return self._get_obs()
def viewer_setup(self):
self.viewer.cam.distance = self.model.stat.extent * 0.5 | [
"[email protected]"
] | |
e8aa0117f38aa251e8d8d3669a7a3773d6d5901d | fc978655927aa593f13690b9622217fd3b6b2930 | /other/plot_missing_rate.py | d65568a6c28e40e7d799bb49f23a71cc5718c041 | [] | no_license | QixinWangCpsLab/Hypoxemia-MLPred | c159dcf1da9fb4a460dafcc1f3afb7bc78c67e29 | f18a60dac0a42f504026b9791f1a1e01f5ed6735 | refs/heads/master | 2023-05-03T11:07:18.343397 | 2021-05-27T19:26:45 | 2021-05-27T19:26:45 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,973 | py | from utils.utility_analysis import *
from utils.utility_preprocess import PatientFilter, LabelAssignment, DataImputation
import numpy as np
import matplotlib.pyplot as plt
df_dynamic = pd.read_csv('../data/data_frame/dynamic_dataframe.csv')
filling_ratio = feature_value_filling_ratio(df_dynamic)
feat_name = ['invDiastolic', 'invMeanBP', 'invSystolic', 'HR', 'Diastolic', 'MeanBP', 'Systolic', 'SpO2',
'RespRate', 'PEEP', 'PIP', 'FiO2', 'TidalVolume', 'Pulse', 'ETCO2', 'O2Flow', 'AirFlow',
'N2OFlow', 'Temp', 'coreTemp']
# interpolate if gap is less than 10 timestep
mask = df_dynamic[feat_name].copy()
df_dynamic_imp = df_dynamic.copy()
for column in feat_name:
df = pd.DataFrame(df_dynamic_imp[column])
df['new'] = ((df.notnull() != df.shift().notnull()).cumsum())
df['ones'] = 1
mask[column] = (df.groupby('new')['ones'].transform('count')
< 20) | df_dynamic_imp[column].notnull()
df_dynamic_imp[feat_name] = df_dynamic_imp[feat_name].interpolate().bfill()[mask]
df_dynamic_imp[['FiO2', 'N2OFlow']] = df_dynamic_imp[['pid', 'FiO2', 'N2OFlow']].groupby(['pid']).ffill()
filling_ratio_imp = feature_value_filling_ratio(df_dynamic_imp)
fig = plt.figure(figsize=(9, 4))
ax = fig.add_subplot()
# set width of bar
barWidth = 0.3
# set height of bar
bars1 = list(filling_ratio.values())[3:23]
bars2 = list(filling_ratio_imp.values())[3:23]
# Set position of bar on X axis
r1 = np.arange(len(bars1))
r2 = [x + barWidth for x in r1]
# Make the plot
plt.bar(r1, bars1, width=barWidth, edgecolor='white', label='Before')
plt.bar(r2, bars2, width=barWidth, edgecolor='white', label='After')
# Add xticks on the middle of the group bars
plt.xticks([r + barWidth for r in range(len(bars1))], list(filling_ratio.keys())[3:])
fig.autofmt_xdate(rotation=45)
plt.xlabel('Input Variable', fontweight='bold')
plt.ylabel('1 - Missing Rate', fontweight='bold')
# Create legend & Show graphic
plt.legend()
plt.show()
| [
"[email protected]"
] | |
c2fdcfabae899f009f29bec8b1ab76bc09a461dc | 5e6549f2dbce8abaf6c107b320e6eb56080f88fe | /website/articles/permissions.py | de270272bd1d508cbba52c45b476ddf395132bd5 | [] | no_license | nicholasjuncos/genesis_challenge | fdde1d19ed37c4da3a53b56cda115f492bc19b73 | 1410a947e91e5996792d5fcfe2641ae8dfde656b | refs/heads/main | 2023-03-06T10:58:10.447452 | 2021-02-20T18:57:25 | 2021-02-20T18:57:25 | 340,441,697 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 938 | py | from rest_framework import permissions
class IsAuthorOrReadOnly(permissions.BasePermission):
"""
Object-level permission to only allow authors of an object to edit it.
Assumes the model instance has an `author` attribute.
"""
def has_permission(self, request, view):
return bool(
request.method in permissions.SAFE_METHODS or
request.user and
request.user.is_authenticated
)
def has_object_permission(self, request, view, obj):
# Read permissions are allowed to any request,
# so we'll always allow GET, HEAD or OPTIONS requests.
if request.method in permissions.SAFE_METHODS:
return True
if not request.user.is_authenticated:
return False
# Instance must have an attribute named `author`.
if request.user.is_superuser:
return True
return obj.author == request.user
| [
"[email protected]"
] | |
346f11569af34e57c2985048448a6df2c9cf2053 | 556db265723b0cc30ad2917442ed6dad92fd9044 | /tensorflow/python/kernel_tests/matrix_exponential_op_test.py | 19091a7b070247232290715794580bd2fc461ee0 | [
"MIT",
"Apache-2.0",
"BSD-2-Clause"
] | permissive | graphcore/tensorflow | c1669b489be0e045b3ec856b311b3139858de196 | 085b20a4b6287eff8c0b792425d52422ab8cbab3 | refs/heads/r2.6/sdk-release-3.2 | 2023-07-06T06:23:53.857743 | 2023-03-14T13:04:04 | 2023-03-14T13:48:43 | 162,717,602 | 84 | 17 | Apache-2.0 | 2023-03-25T01:13:37 | 2018-12-21T13:30:38 | C++ | UTF-8 | Python | false | false | 9,441 | py | # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for tensorflow.ops.linalg.linalg_impl.matrix_exponential."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import itertools
import numpy as np
from tensorflow.python.client import session
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import ops
from tensorflow.python.framework import test_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.ops import variables
from tensorflow.python.ops.linalg import linalg_impl
from tensorflow.python.platform import benchmark
from tensorflow.python.platform import test
def np_expm(x): # pylint: disable=invalid-name
"""Slow but accurate Taylor series matrix exponential."""
y = np.zeros(x.shape, dtype=x.dtype)
xn = np.eye(x.shape[0], dtype=x.dtype)
for n in range(40):
if n > 0:
xn /= float(n)
y += xn
xn = np.dot(xn, x)
return y
class ExponentialOpTest(test.TestCase):
def _verifyExponential(self, x, np_type):
inp = x.astype(np_type)
with test_util.use_gpu():
tf_ans = linalg_impl.matrix_exponential(inp)
if x.size == 0:
np_ans = np.empty(x.shape, dtype=np_type)
else:
if x.ndim > 2:
np_ans = np.zeros(inp.shape, dtype=np_type)
for i in itertools.product(*[range(x) for x in inp.shape[:-2]]):
np_ans[i] = np_expm(inp[i])
else:
np_ans = np_expm(inp)
out = self.evaluate(tf_ans)
self.assertAllClose(np_ans, out, rtol=1e-3, atol=1e-3)
def _verifyExponentialReal(self, x):
for np_type in [np.float32, np.float64]:
self._verifyExponential(x, np_type)
def _verifyExponentialComplex(self, x):
for np_type in [np.complex64, np.complex128]:
self._verifyExponential(x, np_type)
def _makeBatch(self, matrix1, matrix2):
matrix_batch = np.concatenate(
[np.expand_dims(matrix1, 0),
np.expand_dims(matrix2, 0)])
matrix_batch = np.tile(matrix_batch, [2, 3, 1, 1])
return matrix_batch
def testNonsymmetricReal(self):
# 2x2 matrices
matrix1 = np.array([[1., 2.], [3., 4.]])
matrix2 = np.array([[1., 3.], [3., 5.]])
self._verifyExponentialReal(matrix1)
self._verifyExponentialReal(matrix2)
# A multidimensional batch of 2x2 matrices
self._verifyExponentialReal(self._makeBatch(matrix1, matrix2))
@test_util.run_deprecated_v1
def testNonsymmetricComplex(self):
matrix1 = np.array([[1., 2.], [3., 4.]])
matrix2 = np.array([[1., 3.], [3., 5.]])
matrix1 = matrix1.astype(np.complex64)
matrix1 += 1j * matrix1
matrix2 = matrix2.astype(np.complex64)
matrix2 += 1j * matrix2
self._verifyExponentialComplex(matrix1)
self._verifyExponentialComplex(matrix2)
# Complex batch
self._verifyExponentialComplex(self._makeBatch(matrix1, matrix2))
def testSymmetricPositiveDefiniteReal(self):
# 2x2 matrices
matrix1 = np.array([[2., 1.], [1., 2.]])
matrix2 = np.array([[3., -1.], [-1., 3.]])
self._verifyExponentialReal(matrix1)
self._verifyExponentialReal(matrix2)
# A multidimensional batch of 2x2 matrices
self._verifyExponentialReal(self._makeBatch(matrix1, matrix2))
def testSymmetricPositiveDefiniteComplex(self):
matrix1 = np.array([[2., 1.], [1., 2.]])
matrix2 = np.array([[3., -1.], [-1., 3.]])
matrix1 = matrix1.astype(np.complex64)
matrix1 += 1j * matrix1
matrix2 = matrix2.astype(np.complex64)
matrix2 += 1j * matrix2
self._verifyExponentialComplex(matrix1)
self._verifyExponentialComplex(matrix2)
# Complex batch
self._verifyExponentialComplex(self._makeBatch(matrix1, matrix2))
@test_util.run_deprecated_v1
def testNonSquareMatrix(self):
# When the exponential of a non-square matrix is attempted we should return
# an error
with self.assertRaises(ValueError):
linalg_impl.matrix_exponential(np.array([[1., 2., 3.], [3., 4., 5.]]))
@test_util.run_deprecated_v1
def testWrongDimensions(self):
# The input to the exponential should be at least a 2-dimensional tensor.
tensor3 = constant_op.constant([1., 2.])
with self.assertRaises(ValueError):
linalg_impl.matrix_exponential(tensor3)
def testInfinite(self):
# Check that the op does not loop forever on infinite inputs. (b/158433036)
in_tensor = [[np.inf, 1.], [1., 1.]]
result = self.evaluate(linalg_impl.matrix_exponential(in_tensor))
self.assertTrue(np.all(np.isnan(result)))
def testEmpty(self):
self._verifyExponentialReal(np.empty([0, 2, 2]))
self._verifyExponentialReal(np.empty([2, 0, 0]))
@test_util.run_deprecated_v1
def testDynamic(self):
with self.session() as sess:
inp = array_ops.placeholder(ops.dtypes.float32)
expm = linalg_impl.matrix_exponential(inp)
matrix = np.array([[1., 2.], [3., 4.]])
sess.run(expm, feed_dict={inp: matrix})
@test_util.run_deprecated_v1
def testConcurrentExecutesWithoutError(self):
with self.session():
matrix1 = random_ops.random_normal([5, 5], seed=42)
matrix2 = random_ops.random_normal([5, 5], seed=42)
expm1 = linalg_impl.matrix_exponential(matrix1)
expm2 = linalg_impl.matrix_exponential(matrix2)
expm = self.evaluate([expm1, expm2])
self.assertAllEqual(expm[0], expm[1])
class MatrixExponentialBenchmark(test.Benchmark):
shapes = [
(4, 4),
(10, 10),
(16, 16),
(101, 101),
(256, 256),
(1000, 1000),
(1024, 1024),
(2048, 2048),
(513, 4, 4),
(513, 16, 16),
(513, 256, 256),
]
def _GenerateMatrix(self, shape):
batch_shape = shape[:-2]
shape = shape[-2:]
assert shape[0] == shape[1]
n = shape[0]
matrix = np.ones(shape).astype(np.float32) / (2.0 * n) + np.diag(
np.ones(n).astype(np.float32))
return variables.Variable(np.tile(matrix, batch_shape + (1, 1)))
def benchmarkMatrixExponentialOp(self):
for shape in self.shapes:
with ops.Graph().as_default(), \
session.Session(config=benchmark.benchmark_config()) as sess, \
ops.device("/cpu:0"):
matrix = self._GenerateMatrix(shape)
expm = linalg_impl.matrix_exponential(matrix)
self.evaluate(variables.global_variables_initializer())
self.run_op_benchmark(
sess,
control_flow_ops.group(expm),
min_iters=25,
name="matrix_exponential_cpu_{shape}".format(shape=shape))
if test.is_gpu_available(True):
with ops.Graph().as_default(), \
session.Session(config=benchmark.benchmark_config()) as sess, \
ops.device("/gpu:0"):
matrix = self._GenerateMatrix(shape)
expm = linalg_impl.matrix_exponential(matrix)
self.evaluate(variables.global_variables_initializer())
self.run_op_benchmark(
sess,
control_flow_ops.group(expm),
min_iters=25,
name="matrix_exponential_gpu_{shape}".format(shape=shape))
def _TestRandomSmall(dtype, batch_dims, size):
def Test(self):
np.random.seed(42)
shape = batch_dims + (size, size)
matrix = np.random.uniform(low=-1.0, high=1.0, size=shape).astype(dtype)
self._verifyExponentialReal(matrix)
return Test
def _TestL1Norms(dtype, shape, scale):
def Test(self):
np.random.seed(42)
matrix = np.random.uniform(
low=-1.0, high=1.0, size=np.prod(shape)).reshape(shape).astype(dtype)
print(dtype, shape, scale, matrix)
l1_norm = np.max(np.sum(np.abs(matrix), axis=matrix.ndim - 2))
matrix /= l1_norm
self._verifyExponentialReal(scale * matrix)
return Test
if __name__ == "__main__":
for dtype_ in [np.float32, np.float64, np.complex64, np.complex128]:
for batch_ in [(), (2,), (2, 2)]:
for size_ in [4, 7]:
name = "%s_%d_%d" % (dtype_.__name__, len(batch_), size_)
setattr(ExponentialOpTest, "testL1Norms_" + name,
_TestRandomSmall(dtype_, batch_, size_))
for shape_ in [(3, 3), (2, 3, 3)]:
for dtype_ in [np.float32, np.complex64]:
for scale_ in [0.1, 1.5, 5.0, 20.0]:
name = "%s_%d_%d" % (dtype_.__name__, len(shape_), int(scale_ * 10))
setattr(ExponentialOpTest, "testL1Norms_" + name,
_TestL1Norms(dtype_, shape_, scale_))
for dtype_ in [np.float64, np.complex128]:
for scale_ in [0.01, 0.2, 0.5, 1.5, 6.0, 25.0]:
name = "%s_%d_%d" % (dtype_.__name__, len(shape_), int(scale_ * 100))
setattr(ExponentialOpTest, "testL1Norms_" + name,
_TestL1Norms(dtype_, shape_, scale_))
test.main()
| [
"[email protected]"
] | |
308b0d02dca29100fbdf8b0d39b67404e91d0d0d | 4da25f06c5b24c7820fbfb84b21c57ffc3fcf884 | /button_serial_port.py | 9cdb186c8bb49016859e23c89ac107784832a722 | [] | no_license | aplinxy9plin/coffee-bot | 4ca9026dea5e14639e06947b40722d81efbe2922 | 13aad2e754867e6ea43682da14af7b237ee18753 | refs/heads/master | 2020-04-02T01:41:03.594832 | 2019-01-18T02:53:24 | 2019-01-18T02:53:24 | 153,869,451 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 392 | py | import RPi.GPIO as GPIO
import requests
url = "http://localhost:1337/"
payload = "YES"
# led = 4
key = 3
GPIO.cleanup()
# GPIO.setmode(GPIO.BCM)
# GPIO.setup(LED, GPIO.OUT)
# GPIO.output(LED, GPIO.LOW)
GPIO.setup(KEY, GPIO.IN)
print("IT WORDS!")
while True:
if GPIO.input(KEY) == False:
response = requests.request("GET", url, data=payload)
# ts_alvlvl
# print(response.text) | [
"[email protected]"
] | |
9bba0eea777a34656bae8ba6fecf68e5ac398b7d | 65a1c4b7a9b1fcc8717b7d6c8ff7208f629d4c18 | /examples/general_image-fetch_py/general_image-fetch.py | 248bddf8afbc58b295cc1577cec1b467beae1846 | [
"MIT"
] | permissive | ScranchNew/AI_Soccer | 7da2e16027f7a59590bbc9be0943711332029d7a | 1bf5e29a369a2dcd577aaf8830510150a1f0302d | refs/heads/master | 2020-09-29T15:23:34.037599 | 2019-12-17T12:27:36 | 2019-12-17T12:27:36 | 227,062,336 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 9,841 | py | #!/usr/bin/python3
# Author(s): Luiz Felipe Vecchietti, Chansol Hong, Inbae Jeong
# Maintainer: Chansol Hong ([email protected])
from __future__ import print_function
from twisted.internet import reactor
from twisted.internet.defer import inlineCallbacks
from autobahn.wamp.serializer import MsgPackSerializer
from autobahn.wamp.types import ComponentConfig
from autobahn.twisted.wamp import ApplicationSession, ApplicationRunner
import argparse
import random
import sys
import base64
import numpy as np
import cv2
#reset_reason
NONE = 0
GAME_START = 1
SCORE_MYTEAM = 2
SCORE_OPPONENT = 3
GAME_END = 4
DEADLOCK = 5
GOALKICK = 6
CORNERKICK = 7
PENALTYKICK = 8
HALFTIME = 9
EPISODE_END = 10
#game_state
STATE_DEFAULT = 0
STATE_KICKOFF = 1
STATE_GOALKICK = 2
STATE_CORNERKICK = 3
STATE_PENALTYKICK = 4
#coordinates
MY_TEAM = 0
OP_TEAM = 1
BALL = 2
X = 0
Y = 1
TH = 2
ACTIVE = 3
TOUCH = 4
class Received_Image(object):
def __init__(self, resolution, colorChannels):
self.resolution = resolution
self.colorChannels = colorChannels
# need to initialize the matrix at timestep 0
self.ImageBuffer = np.zeros((resolution[1], resolution[0], colorChannels)) # rows, columns, colorchannels
def update_image(self, received_parts):
self.received_parts = received_parts
for i in range(0,len(received_parts)):
dec_msg = base64.b64decode(self.received_parts[i].b64, '-_') # decode the base64 message
np_msg = np.fromstring(dec_msg, dtype=np.uint8) # convert byte array to numpy array
reshaped_msg = np_msg.reshape((self.received_parts[i].height, self.received_parts[i].width, 3))
for j in range(0, self.received_parts[i].height): # y axis
for k in range(0, self.received_parts[i].width): # x axis
self.ImageBuffer[j+self.received_parts[i].y, k+self.received_parts[i].x, 0] = reshaped_msg[j, k, 0] # blue channel
self.ImageBuffer[j+self.received_parts[i].y, k+self.received_parts[i].x, 1] = reshaped_msg[j, k, 1] # green channel
self.ImageBuffer[j+self.received_parts[i].y, k+self.received_parts[i].x, 2] = reshaped_msg[j, k, 2] # red channel
class SubImage(object):
def __init__(self, x, y, width, height, b64):
self.x = x
self.y = y
self.width = width
self.height = height
self.b64 = b64
class Frame(object):
def __init__(self):
self.time = None
self.score = None
self.reset_reason = None
self.subimages = None
self.coordinates = None
self.half_passed = None
class Component(ApplicationSession):
"""
AI Base + Random Walk
"""
def __init__(self, config):
ApplicationSession.__init__(self, config)
def printConsole(self, message):
print(message)
sys.__stdout__.flush()
def onConnect(self):
self.join(self.config.realm)
@inlineCallbacks
def onJoin(self, details):
##############################################################################
def init_variables(self, info):
# Here you have the information of the game (virtual init() in random_walk.cpp)
# List: game_time, number_of_robots
# field, goal, penalty_area, goal_area, resolution Dimension: [x, y]
# ball_radius, ball_mass,
# robot_size, robot_height, axle_length, robot_body_mass, ID: [0, 1, 2, 3, 4]
# wheel_radius, wheel_mass, ID: [0, 1, 2, 3, 4]
# max_linear_velocity, max_torque, codewords, ID: [0, 1, 2, 3, 4]
# self.game_time = info['game_time']
self.number_of_robots = info['number_of_robots']
# self.field = info['field']
# self.goal = info['goal']
# self.penalty_area = info['penalty_area']
# self.goal_area = info['goal_area']
self.resolution = info['resolution']
# self.ball_radius = info['ball_radius']
# self.ball_mass = info['ball_mass']
# self.robot_size = info['robot_size']
# self.robot_height = info['robot_height']
# self.axle_length = info['axle_length']
# self.robot_body_mass = info['robot_body_mass']
# self.wheel_radius = info['wheel_radius']
# self.wheel_mass = info['wheel_mass']
self.max_linear_velocity = info['max_linear_velocity']
# self.max_torque = info['max_torque']
# self.codewords = info['codewords']
self.colorChannels = 3
self.end_of_frame = False
self.received_frame = Frame()
self.image = Received_Image(self.resolution, self.colorChannels)
return
##############################################################################
try:
info = yield self.call(u'aiwc.get_info', args.key)
except Exception as e:
self.printConsole("Error: {}".format(e))
else:
try:
self.sub = yield self.subscribe(self.on_event, args.key)
except Exception as e2:
self.printConsole("Error: {}".format(e2))
init_variables(self, info)
try:
yield self.call(u'aiwc.ready', args.key)
except Exception as e:
self.printConsole("Error: {}".format(e))
else:
self.printConsole("I am ready for the game!")
@inlineCallbacks
def on_event(self, f):
@inlineCallbacks
def set_wheel(self, robot_wheels):
yield self.call(u'aiwc.set_speed', args.key, robot_wheels)
return
# initiate empty frame
if (self.end_of_frame):
self.received_frame = Frame()
self.end_of_frame = False
received_subimages = []
if 'time' in f:
self.received_frame.time = f['time']
if 'score' in f:
self.received_frame.score = f['score']
if 'reset_reason' in f:
self.received_frame.reset_reason = f['reset_reason']
if 'half_passed' in f:
self.received_frame.half_passed = f['half_passed']
if 'subimages' in f:
self.received_frame.subimages = f['subimages']
# Comment the next lines if you don't need to use the image information
for s in self.received_frame.subimages:
received_subimages.append(SubImage(s['x'],
s['y'],
s['w'],
s['h'],
s['base64'].encode('utf8')))
# This function updates the image data stored in 'self.image.ImageBuffer'
# Do not directly modify 'self.image.ImageBuffer' image updates are done in a way that
# only the parts of the old frame that have been changed are overwritten by the new data
self.image.update_image(received_subimages)
if 'coordinates' in f:
self.received_frame.coordinates = f['coordinates']
if 'EOF' in f:
self.end_of_frame = f['EOF']
#self.printConsole(self.received_frame.time)
#self.printConsole(self.received_frame.score)
#self.printConsole(self.received_frame.reset_reason)
#self.printConsole(self.end_of_frame)
if (self.end_of_frame):
# To get the image at the end of each frame use the variable:
# self.image.ImageBuffer
##############################################################################
#(virtual update())
# OpenCV uses [0, 1] range for describing an RGB image stored in a numpy array
# that ImageBuffer's [0, 255] range should be transformed to [0, 1]
cv2.imshow('image', self.image.ImageBuffer / 255.0)
cv2.waitKey(1)
##############################################################################
if(self.received_frame.reset_reason == GAME_END):
##############################################################################
#(virtual finish())
#save your data
with open(args.datapath + '/result.txt', 'w') as output:
#output.write('yourvariables')
output.close()
#unsubscribe; reset or leave
yield self.sub.unsubscribe()
try:
yield self.leave()
except Exception as e:
self.printConsole("Error: {}".format(e))
##############################################################################
self.end_of_frame = False
def onDisconnect(self):
if reactor.running:
reactor.stop()
if __name__ == '__main__':
try:
unicode
except NameError:
# Define 'unicode' for Python 3
def unicode(s, *_):
return s
def to_unicode(s):
return unicode(s, "utf-8")
parser = argparse.ArgumentParser()
parser.add_argument("server_ip", type=to_unicode)
parser.add_argument("port", type=to_unicode)
parser.add_argument("realm", type=to_unicode)
parser.add_argument("key", type=to_unicode)
parser.add_argument("datapath", type=to_unicode)
args = parser.parse_args()
ai_sv = "rs://" + args.server_ip + ":" + args.port
ai_realm = args.realm
# create a Wamp session object
session = Component(ComponentConfig(ai_realm, {}))
# initialize the msgpack serializer
serializer = MsgPackSerializer()
# use Wamp-over-rawsocket
runner = ApplicationRunner(ai_sv, ai_realm, serializers=[serializer])
runner.run(session, auto_reconnect=False)
| [
"[email protected]"
] | |
e21455b3d626aa0bc0e3bf9b392747e1f9b49898 | c942e898ab37539cd580abe165b534ce5e1091e1 | /exercise files/Ch2/classes_start.py | 4e8f8254a592d547d9b6fa3e685b255270d0e4f5 | [] | no_license | Carmiej/Learning-Python | 4c7c16b6d8c4bfe7f0a6a55fdc65b3bdd5a52fcf | 382edfbbe860125be6254dcf3e2b306d70cc3b16 | refs/heads/main | 2023-01-08T10:27:26.678034 | 2020-10-30T03:23:27 | 2020-10-30T03:23:27 | 308,513,260 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 607 | py | #
# Example file for working with classes
#
class myClass():
def method1(self):
print("myClass method1")
def method2(self, someString):
print("myClass ,method2 " + someString)
class anotherClass(myClass):
def method1(self):
myClass.method1(self)
print("anotherClass method1")
def method2(self, someString):
print("anotherClass ,method2 " + someString)
def main():
c = myClass()
c.method1()
c.method2("This is a String")
c2= anotherClass()
c2.method1()
c2.method2("This is a String")
if __name__ == "__main__":
main()
| [
"[email protected]"
] | |
f3cdae6081e906458eedf1db091b5d53cb5464ab | 3b9194499503f9e06dbebe113c53afad0b572ab2 | /Visualization/VisualizeBrainSpanExpression_plotly.py | 23b6877c726642d0d3c2209dba5b38d0c81b22ca | [] | no_license | JulseJiang/DrugKBPrototype | 0bffa42b2913996d22e40942d24c4da0feb76f33 | 34567630fe8febb1ea6827541fd3d92f42935ecc | refs/heads/main | 2023-03-19T17:57:14.822743 | 2021-03-18T11:54:22 | 2021-03-18T11:54:22 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 24,299 | py | # -*- encoding: utf-8 -*-
# -------------------------------------------------------------------------------
# @file: VisualizeBrainSpanExpression
# @Author: GuoSijia
# @Purpose:
# @Created: 2018-09-25
# @update: 2018-09-25 11:34
# @Software: PyCharm
# -------------------------------------------------------------------------------
from pyecharts import Bar, Line, Scatter, Overlap, Page
import pandas as pd
import math
import pymongo
import pyecharts
import plotly.plotly
import plotly.graph_objs as go
class DataStorage:
def __init__(self, name):
self.name = name
self.path = self.__login()
def __login(self):
client = pymongo.MongoClient("127.0.0.1", 27017)
db = client['Denovo']
# db.authenticate("tanxian123", "123456")
collection = client['Denovo'][self.name]
return collection
def FindByID(self, ID):
x = self.path.find_one({'ENTREZ_ID': ID})
return x
class Brain_Dic:
def __init__(self):
pass
def brain_dic(self):
brain_list = {'10': ['13', 'amygdala', '20-39Y'], '22': ['12', 'cerebellum', '12-19Y'],
'100': ['8', 'cortex', '0-5M'], '179': ['2', 'striatum', '8-9PCW'], '96': ['8', 'cortex', '0-5M'],
'158': ['14', 'cortex', '40Y'], '117': ['10', 'cortex', '1-5Y'],
'137': ['12', 'cortex', '12-19Y'], '113': ['10', 'cortex', '1-5Y'],
'53': ['4', 'cortex', '13-15PCW'], '140': ['12', 'cortex', '12-19Y'],
'38': ['3', 'cortex', '10-12PCW'], '70': ['6', 'cortex', '19-23PCW'],
'183': ['6', 'striatum', '19-23PCW'], '55': ['4', 'cortex', '13-15PCW'],
'24': ['14', 'cerebellum', '40Y'], '157': ['14', 'cortex', '40Y'],
'73': ['6', 'cortex', '19-23PCW'], '185': ['8', 'striatum', '0-5M'],
'126': ['11', 'cortex', '6-11Y'], '145': ['13', 'cortex', '20-39Y'],
'175': ['13', 'hippocampus', '20-39Y'], '74': ['6', 'cortex', '19-23PCW'],
'45': ['3', 'cortex', '10-12PCW'], '88': ['7', 'cortex', '24-37PCW'],
'7': ['10', 'amygdala', '1-5Y'], '65': ['5', 'cortex', '16-18PCW'],
'78': ['6', 'cortex', '19-23PCW'], '156': ['14', 'cortex', '40Y'],
'119': ['10', 'cortex', '1-5Y'], '104': ['9', 'cortex', '6-11M'],
'76': ['6', 'cortex', '19-23PCW'], '69': ['6', 'cortex', '19-23PCW'],
'184': ['7', 'striatum', '24-37PCW'], '60': ['5', 'cortex', '16-18PCW'],
'17': ['7', 'cerebellum', '24-37PCW'], '182': ['5', 'striatum', '16-18PCW'],
'39': ['3', 'cortex', '10-12PCW'], '85': ['7', 'cortex', '24-37PCW'],
'169': ['6', 'hippocampus', '19-23PCW'], '49': ['4', 'cortex', '13-15PCW'],
'177': ['2', 'striatum', '8-9PCW'], '148': ['13', 'cortex', '20-39Y'],
'18': ['8', 'cerebellum', '0-5M'], '187': ['11', 'striatum', '6-11Y'],
'149': ['13', 'cortex', '20-39Y'], '142': ['12', 'cortex', '12-19Y'],
'13': ['3', 'cerebellum', '10-12PCW'], '97': ['8', 'cortex', '0-5M'],
'20': ['10', 'cerebellum', '1-5Y'], '42': ['3', 'cortex', '10-12PCW'],
'153': ['13', 'cortex', '20-39Y'], '12': ['2', 'cerebellum', '8-9PCW'],
'71': ['6', 'cortex', '19-23PCW'], '56': ['4', 'cortex', '13-15PCW'],
'132': ['11', 'cortex', '6-11Y'], '58': ['5', 'cortex', '16-18PCW'],
'164': ['14', 'cortex', '40Y'], '84': ['7', 'cortex', '24-37PCW'],
'26': ['2', 'cortex', '8-9PCW'], '90': ['7', 'cortex', '24-37PCW'],
'89': ['7', 'cortex', '24-37PCW'], '116': ['10', 'cortex', '1-5Y'],
'87': ['7', 'cortex', '24-37PCW'], '201': ['12', 'thalamus', '12-19Y'],
'44': ['3', 'cortex', '10-12PCW'], '166': ['3', 'hippocampus', '10-12PCW'],
'8': ['11', 'amygdala', '6-11Y'], '43': ['3', 'cortex', '10-12PCW'],
'32': ['2', 'cortex', '8-9PCW'], '172': ['10', 'hippocampus', '1-5Y'],
'25': ['2', 'cortex', '8-9PCW'], '155': ['14', 'cortex', '40Y'],
'167': ['4', 'hippocampus', '13-15PCW'], '129': ['11', 'cortex', '6-11Y'],
'122': ['11', 'cortex', '6-11Y'], '178': ['2', 'striatum', '8-9PCW'],
'46': ['4', 'cortex', '13-15PCW'], '165': ['2', 'hippocampus', '8-9PCW'],
'64': ['5', 'cortex', '16-18PCW'], '1': ['3', 'amygdala', '10-12PCW'],
'174': ['12', 'hippocampus', '12-19Y'], '202': ['13', 'thalamus', '20-39Y'],
'168': ['5', 'hippocampus', '16-18PCW'], '108': ['9', 'cortex', '6-11M'],
'109': ['9', 'cortex', '6-11M'], '139': ['12', 'cortex', '12-19Y'],
'79': ['6', 'cortex', '19-23PCW'], '198': ['9', 'thalamus', '6-11M'],
'147': ['13', 'cortex', '20-39Y'], '86': ['7', 'cortex', '24-37PCW'],
'128': ['11', 'cortex', '6-11Y'], '134': ['12', 'cortex', '12-19Y'],
'59': ['5', 'cortex', '16-18PCW'], '72': ['6', 'cortex', '19-23PCW'],
'23': ['13', 'cerebellum', '20-39Y'], '127': ['11', 'cortex', '6-11Y'],
'195': ['6', 'thalamus', '19-23PCW'], '162': ['14', 'cortex', '40Y'],
'94': ['8', 'cortex', '0-5M'], '66': ['5', 'cortex', '16-18PCW'],
'57': ['5', 'cortex', '16-18PCW'], '37': ['3', 'cortex', '10-12PCW'],
'131': ['11', 'cortex', '6-11Y'], '81': ['7', 'cortex', '24-37PCW'],
'152': ['13', 'cortex', '20-39Y'], '159': ['14', 'cortex', '40Y'],
'11': ['14', 'amygdala', '40Y'], '16': ['6', 'cerebellum', '19-23PCW'],
'154': ['13', 'cortex', '20-39Y'], '144': ['13', 'cortex', '20-39Y'],
'14': ['4', 'cerebellum', '13-15PCW'], '40': ['3', 'cortex', '10-12PCW'],
'31': ['2', 'cortex', '8-9PCW'], '82': ['7', 'cortex', '24-37PCW'],
'54': ['4', 'cortex', '13-15PCW'], '123': ['11', 'cortex', '6-11Y'],
'136': ['12', 'cortex', '12-19Y'], '121': ['10', 'cortex', '1-5Y'],
'61': ['5', 'cortex', '16-18PCW'], '110': ['9', 'cortex', '6-11M'],
'67': ['5', 'cortex', '16-18PCW'], '146': ['13', 'cortex', '20-39Y'],
'91': ['7', 'cortex', '24-37PCW'], '34': ['2', 'cortex', '8-9PCW'],
'193': ['4', 'thalamus', '13-15PCW'], '80': ['6', 'cortex', '19-23PCW'],
'192': ['3', 'thalamus', '10-12PCW'], '194': ['5', 'thalamus', '16-18PCW'],
'99': ['8', 'cortex', '0-5M'], '190': ['14', 'striatum', '40Y'], '93': ['8', 'cortex', '0-5M'],
'133': ['12', 'cortex', '12-19Y'], '19': ['9', 'cerebellum', '6-11M'],
'150': ['13', 'cortex', '20-39Y'], '186': ['10', 'striatum', '1-5Y'],
'77': ['6', 'cortex', '19-23PCW'], '63': ['5', 'cortex', '16-18PCW'],
'107': ['9', 'cortex', '6-11M'], '114': ['10', 'cortex', '1-5Y'],
'200': ['11', 'thalamus', '6-11Y'], '143': ['12', 'cortex', '12-19Y'],
'124': ['11', 'cortex', '6-11Y'], '120': ['10', 'cortex', '1-5Y'],
'112': ['10', 'cortex', '1-5Y'], '203': ['14', 'thalamus', '40Y'],
'191': ['2', 'thalamus', '8-9PCW'], '130': ['11', 'cortex', '6-11Y'],
'28': ['2', 'cortex', '8-9PCW'], '196': ['7', 'thalamus', '24-37PCW'],
'15': ['5', 'cerebellum', '16-18PCW'], '105': ['9', 'cortex', '6-11M'],
'163': ['14', 'cortex', '40Y'], '138': ['12', 'cortex', '12-19Y'],
'125': ['11', 'cortex', '6-11Y'], '118': ['10', 'cortex', '1-5Y'],
'5': ['7', 'amygdala', '24-37PCW'], '173': ['11', 'hippocampus', '6-11Y'],
'141': ['12', 'cortex', '12-19Y'], '160': ['14', 'cortex', '40Y'],
'180': ['3', 'striatum', '10-12PCW'], '36': ['3', 'cortex', '10-12PCW'],
'30': ['2', 'cortex', '8-9PCW'], '3': ['5', 'amygdala', '16-18PCW'],
'151': ['13', 'cortex', '20-39Y'], '135': ['12', 'cortex', '12-19Y'],
'4': ['6', 'amygdala', '19-23PCW'], '68': ['5', 'cortex', '16-18PCW'],
'98': ['8', 'cortex', '0-5M'], '50': ['4', 'cortex', '13-15PCW'], '115': ['10', 'cortex', '1-5Y'],
'106': ['9', 'cortex', '6-11M'], '75': ['6', 'cortex', '19-23PCW'],
'176': ['14', 'hippocampus', '40Y'], '161': ['14', 'cortex', '40Y'],
'171': ['8', 'hippocampus', '0-5M'], '41': ['3', 'cortex', '10-12PCW'],
'2': ['4', 'amygdala', '13-15PCW'], '83': ['7', 'cortex', '24-37PCW'],
'52': ['4', 'cortex', '13-15PCW'], '27': ['2', 'cortex', '8-9PCW'],
'189': ['13', 'striatum', '20-39Y'], '33': ['2', 'cortex', '8-9PCW'],
'181': ['4', 'striatum', '13-15PCW'], '199': ['10', 'thalamus', '1-5Y'],
'48': ['4', 'cortex', '13-15PCW'], '21': ['11', 'cerebellum', '6-11Y'],
'47': ['4', 'cortex', '13-15PCW'], '95': ['8', 'cortex', '0-5M'], '6': ['8', 'amygdala', '0-5M'],
'111': ['10', 'cortex', '1-5Y'], '92': ['8', 'cortex', '0-5M'], '51': ['4', 'cortex', '13-15PCW'],
'9': ['12', 'amygdala', '12-19Y'], '29': ['2', 'cortex', '8-9PCW'],
'0': ['2', 'amygdala', '8-9PCW'], '103': ['9', 'cortex', '6-11M'],
'188': ['12', 'striatum', '12-19Y'], '197': ['8', 'thalamus', '0-5M'],
'170': ['7', 'hippocampus', '24-37PCW'], '62': ['5', 'cortex', '16-18PCW'],
'102': ['8', 'cortex', '0-5M'], '101': ['8', 'cortex', '0-5M'], '35': ['3', 'cortex', '10-12PCW']}
return brain_list
class dict_brainSpan():
def dict_gene2(self, id, data, brainDic):
brainSpan_key = brainDic.brain_dic()
data_brainSpan = data['BrainspanX'][0]
data_brainSpan.pop('ENTREZ_ID')
data_brainSpan_key = [int(i) for i in list(data_brainSpan.keys())] # t
data_brainSpan_value = [float(i) for i in list(data_brainSpan.values())] # c
"""
type为系列
"""
others_type = ['amygdala', 'cerebellum', 'hippocampus', 'thalamus']
"""
获取数据
"""
max_value = 0
data = pd.DataFrame({str(id): data_brainSpan_value}, index=data_brainSpan_key).sort_index()[str(id)]
# print(data)
max_value = math.log2(max(data) + 1.0)
new_df = pd.DataFrame(brainSpan_key, index=['#Period index', 'brain region', 'period info']).T
new_df[['#Period index']] = new_df[['#Period index']].apply(pd.to_numeric)
new_df = new_df.sort_values(by=['brain region', '#Period index'])
list_others_data = [['' for j in range(13)] for i in range(4)]
for i in range(len(new_df['brain region'])):
if new_df.iloc[i, 1] in others_type:
loc_type = others_type.index(new_df.iloc[i, 1])
p = new_df.iloc[i, 0]
list_others_data[loc_type][p - 2] = math.log2(data[i] + 1.0)
list_special_cortex = [[] for j in range(13)]
list_special_striatum = [[] for j in range(13)]
for i in range(len(new_df['brain region'])):
if new_df.iloc[i, 1] in "cortex":
p = new_df.iloc[i, 0]
list_special_cortex[p - 2].append(math.log2(data[i] + 1.0))
if new_df.iloc[i, 1] in "striatum":
p = new_df.iloc[i, 0]
list_special_striatum[p - 2].append(math.log2(data[i] + 1.0))
# print('*******************')
# print(others_type)
# print(list_others_data)
# print(list_special_striatum)
# print(list_special_cortex)
# print(max_value)
# print('*******************')
return others_type, list_others_data, list_special_striatum, list_special_cortex, max_value
class data_plot(object):
'''
根据处理后的数据后画图
'''
def __init__(self):
pass
def LineAndBox_plot(self, geneName, others_type, list_others_data,
list_special_striatum, list_special_cortex, max):
traces = []
grid = Page()
colors = ['rgb(127,127,127)', 'rgb(188,189,34)', '#BC3C29FF', '#20854EFF',
'rgb(0,0,0)',
'rgb(188,188,34)', 'rgb(144,238,144)']
if others_type != 0:
"""
第一张图
"""
overlap = Overlap()
line_special = Line()
# title="\tSpatio-temporal Human Developmental Brain Expression (BrainSpan)",
# title_pos="left")
i = 0
es = Scatter(geneName)
# print(es)
# print('es')
attr = ['Early fetal\n8-9PCW', 'Early fetal\n10-12PCW', 'Early mid-fetal\n13PCW-15PCW',
'Early mid-fetal\n16PCW-18PCW',
'Late mid-fetal\n19PCW-23PCW', 'Late fetal\n24PCW-37PCW',
'Neonatal and early infancy\n0M(birth)-5M',
'Late infancy\n6M-11M', 'Early childhood\n1Y-5Y', 'Middle and late childhood\n6Y-11Y',
'Adolescence\n12Y-19Y', 'Young adulthood\n20Y-39Y', 'Middle adulthood\n40Y']
"""
画多数值类型的拟合曲线
"""
"""
1.求和 ；2.取平均值；3.画smooth
"""
"""
1-1 striatum
"""
list_tmp_striatum_ave = [0 for i in range(13)]
list_tmp_striatum_num = [0 for i in range(13)]
for i in range(13):
if list_special_striatum[i] != []:
for item in list_special_striatum[i]:
list_tmp_striatum_ave[i] += item
list_tmp_striatum_num[i] += 1
if list_special_striatum[i] == []:
list_tmp_striatum_ave[i] = ''
for i in range(13):
if list_tmp_striatum_ave[i] != '':
list_tmp_striatum_ave[i] = list_tmp_striatum_ave[i] / list_tmp_striatum_num[i]
# print(list_tmp_striatum_ave)
line_special.add("striatum", attr, list_tmp_striatum_ave, yaxis_max=math.ceil(max), is_smooth=True,
xaxis_interval=0, xaxis_type="category",
xaxis_rotate=-30, xaxis_label_textsize=9, legend_selectedmode=False
, legend_pos="top", legend_orient="vertical")
traces.append(go.Scatter(
x=attr,
y=list_tmp_striatum_ave,
name='striatum',
line=dict(
shape='spline',
color='rgb(155, 48, 255)'
),
mode="lines",
))
"""
1-2 cortex
"""
list_tmp_cortex_ave = [0 for i in range(13)]
list_tmp_cortex_num = [0 for i in range(13)]
for i in range(13):
if list_special_cortex[i] != []:
for item in list_special_cortex[i]:
list_tmp_cortex_ave[i] += item
list_tmp_cortex_num[i] += 1
if list_special_cortex[i] == []:
list_tmp_cortex_ave[i] = ''
for i in range(13):
if list_tmp_cortex_ave[i] != '':
list_tmp_cortex_ave[i] = list_tmp_cortex_ave[i] / list_tmp_cortex_num[i]
# print(list_tmp_cortex_ave)
traces.append(go.Scatter(
x=attr,
y=list_tmp_cortex_ave,
name='cortex',
line=dict(
shape='spline',
color='rgb(122,197,205)',
),
mode="lines",
))
line_special.add("cortex", attr, list_tmp_cortex_ave, yaxis_max=math.ceil(max), is_smooth=True,
xaxis_interval=0, xaxis_type="category",
xaxis_rotate=-30, xaxis_label_textsize=9,
legend_selectedmode=False, yaxis_name='LOG2(RPKM+1)', yaxis_name_pos='middle'
, legend_pos="10%", legend_orient="horizontal")
overlap.add(line_special)
"""
先补齐list
"""
max_length_striatum = 0
for item in list_special_striatum:
if len(item) >= max_length_striatum:
max_length_striatum = len(item)
for i in range(13):
if len(list_special_striatum[i]) < max_length_striatum:
for j in range(max_length_striatum - len(list_special_striatum[i])):
list_special_striatum[i].append('')
max_length_cortex = 0
for item in list_special_cortex:
if len(item) >= max_length_cortex:
max_length_cortex = len(item)
for i in range(13):
if len(list_special_cortex[i]) < max_length_cortex:
for j in range(max_length_cortex - len(list_special_cortex[i])):
list_special_cortex[i].append('')
"""
画散点
"""
"""
striatum
"""
for i in range(max_length_striatum):
list_tmp_striatum = []
for j in range(13):
list_tmp_striatum.append(list_special_striatum[j][i])
# print(list_linshi_striatum)
traces.append(go.Scatter(
x=attr,
y=list_tmp_striatum,
mode='markers',
marker=dict(
# size=2,
color='rgb(155, 48, 255)',
),
showlegend=False,
hoverinfo='all',
name='striatum'
))
es.add("striatum", attr, list_tmp_striatum, xaxis_type="category", xaxis_rotate=-30,
xaxis_interval=0,
legend_selectedmode=False, yaxis_max=math.ceil(max), symbol_size=3,
is_legend_show=False, xaxis_label_textsize=9
, legend_pos="right", legend_orient="vertical")
"""
cortex
"""
for i in range(max_length_cortex):
list_linshi_cortex = []
for j in range(13):
list_linshi_cortex.append(list_special_cortex[j][i])
# print(list_linshi_cortex)
traces.append(go.Scatter(
x=attr,
y=list_linshi_cortex,
mode='markers',
marker=dict(
# size=2,
color='rgb(122,197,205)',
),
showlegend=False,
# hoverinfo='name+x+y',
name='cortex'
))
es.add("cortex", attr, list_linshi_cortex, xaxis_type="category", xaxis_rotate=-30, xaxis_interval=0,
legend_selectedmode=False, yaxis_max=max, symbol_size=3,
is_legend_show=False, xaxis_label_textsize=9, legend_pos="right", legend_orient="vertical")
# traces.append(go.Scatter(
# x=attr,
# y=list_linshi_cortex,
# mode='markers',
# marker=dict(
# # size=2,
# color='rgb(155, 48, 255)',
# ),
# showlegend=False,
# ))
overlap.add(es)
"""
单数值的线
"""
k = 0
for index, item in enumerate(others_type):
# print(item)
line = Line()
# print(list_others_data[k])
traces.append(go.Scatter(
x=attr,
y=list_others_data[k],
name=item,
line=dict(
shape='spline',
color=colors[index]
),
mode="lines",
))
line.add(str(item), attr, list_others_data[k], is_smooth=True, is_symbol_show=False, xaxis_interval=0,
legend_selectedmode=False, xaxis_type="category", yaxis_max=max, xaxis_rotate=-30,
xaxis_label_textsize=9
, legend_pos="right", legend_orient="vertical")
overlap.add(line)
k += 1
grid.add(overlap)
grid.render()
# print(traces)
layout = go.Layout(
paper_bgcolor='rgb(249, 249, 249)',
plot_bgcolor='rgb(249, 249, 249)',
height=400,
width=1000,
# title='<b>Gene Express from MouseBrain<b>',
hovermode='closest',
yaxis=dict(
autorange=True,
showgrid=True,
zeroline=True,
# dtick=10,
title='log<sub>2</sub>( RPKM + 1 )',
titlefont=dict(
family='Arial',
),
),
xaxis=dict(
showgrid=True,
zeroline=True,
showline=True,
showticklabels=True,
tickangle=25, # x轴刻度之间距离
tickfont=dict(
# size=10,
family='Arial',
),
# tickwidth=0.5
),
margin=dict(
l=50,
r=10,
b=100,
t=30,
),
showlegend=True
)
# for item in traces:
# print(item)
fig = go.Figure(data=traces, layout=layout)
plotly.offline.plot(fig, show_link=False)
try:
return plotly.offline.plot(fig, show_link=False, output_type="div", include_plotlyjs=False)
# return grid.render_embed()
except:
return '<div><p>There is no corresponding data published yet,we will update it when such data available.</p></div>'
class Main:
def __init__(self):
pass
def run(self, id):
f = DataStorage("JR")
data = f.FindByID(str(id))
geneName = data['Symbol']
brainSpan = dict_brainSpan()
draw = data_plot()
brainDic = Brain_Dic()
# print(data.get("BrainspanX"))
if data.get("BrainspanX") == None:
return '<div><p>There is no corresponding data published yet, we will update it when such data available. </p></div>'
else:
# print(11)
others_type, list_others_data, list_special_striatum, list_special_cortex, max_value = brainSpan.dict_gene2(
id=id,
data=data,
brainDic=brainDic)
"""
画图 ；为0表示该没有找到这个基因的数据，不画图
"""
return draw.LineAndBox_plot(geneName, others_type, list_others_data,
list_special_striatum, list_special_cortex, max_value)
def main(ID):
mainer = Main()
# 996
ID = str(ID)
# print(mainer.run(ID))
return mainer.run(ID)
if __name__ == '__main__':
main("9757")
| [
"[email protected]"
] | |
1305e53dfc974a72d2087f4b8cb7312977d8f002 | 365bbcc1dbcc14d4f737ec6218dcf46114c8dc9d | /rango/env/bin/pip3.6 | 24c3cff5b6d0f3f82c7368dd5bd82ee8aac1aa93 | [] | no_license | kimm9/PythonDevelopment | f52c88bd00be07c9774a2fe2f717a67f81b952dd | 03c77b10f684caca6c650eeb6c810f1fd89e0c6e | refs/heads/master | 2022-12-09T08:23:50.206527 | 2018-03-26T17:13:11 | 2018-03-26T17:13:11 | 116,835,012 | 0 | 0 | null | 2022-11-22T02:15:17 | 2018-01-09T15:37:23 | Python | UTF-8 | Python | false | false | 254 | 6 | #!/Users/Matthew/Documents/PythonDevelopment/rango/env/bin/python3
# -*- coding: utf-8 -*-
import re
import sys
from pip import main
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0])
sys.exit(main())
| [
"[email protected]"
] | |
1e7cc99a08d573612aee1a48bd305b9b768359a5 | cc264f36850fb9bda302d437f67491f7cdef69b1 | /code/nnet_layers.py | 1cd805084384a5de9b39e7822e335b3717ace550 | [] | no_license | xiayandi/Bilingual-Sentence-Classification | 7c0982fbd6de80f42fce3b6c6f05213cbfdd0293 | 56c21208553e80b71ea94b4d4bc4b49072e41b5b | refs/heads/master | 2020-04-14T13:09:45.115020 | 2016-04-19T16:45:32 | 2016-04-19T16:45:32 | 41,878,066 | 1 | 2 | null | null | null | null | UTF-8 | Python | false | false | 17,904 | py | __author__ = 'xiy1pal'
import numpy as np
import theano
import theano.tensor as T
from theano.tensor.signal import downsample
import theano.tensor.shared_randomstreams
from theano.tensor.nnet import conv
# different non-linearities
def ReLU(x):
y = T.maximum(0.0, x)
return (y)
def Sigmoid(x):
y = T.nnet.sigmoid(x)
return (y)
def Tanh(x):
y = T.tanh(x)
return (y)
def Iden(x):
y = x
return (y)
class HiddenLayer(object):
"""
Class for HiddenLayer
"""
def __init__(self, rng, input, n_in, n_out, activation, W=None, b=None,
use_bias=False):
self.input = input
self.activation = activation
if W is None:
if activation == ReLU:
W_values = np.asarray(0.01 * rng.standard_normal(size=(n_in, n_out)), dtype=theano.config.floatX)
else:
W_values = np.asarray(rng.uniform(low=-np.sqrt(6. / (n_in + n_out)), high=np.sqrt(6. / (n_in + n_out)),
size=(n_in, n_out)), dtype=theano.config.floatX)
W = theano.shared(value=W_values, name='W')
if b is None:
b_values = np.zeros((n_out,), dtype=theano.config.floatX)
b = theano.shared(value=b_values, name='b')
self.W = W
self.b = b
if use_bias:
lin_output = T.dot(input, self.W) + self.b
else:
lin_output = T.dot(input, self.W)
self.output = (lin_output if activation is None else activation(lin_output))
# parameters of the model
if use_bias:
self.params = [self.W, self.b]
else:
self.params = [self.W]
class LogisticRegression(object):
def __init__(self, input, n_in, n_out, W=None, b=None):
# initialize with 0 the weights W as a matrix of shape (n_in, n_out)
if W is None:
self.W = theano.shared(
value=np.zeros((n_in, n_out), dtype=theano.config.floatX),
name='W')
else:
self.W = W
# initialize the baises b as a vector of n_out 0s
if b is None:
self.b = theano.shared(
value=np.zeros((n_out,), dtype=theano.config.floatX),
name='b')
else:
self.b = b
# compute vector of class-membership probabilities in symbolic form
self.p_y_given_x = T.nnet.softmax(T.dot(input, self.W) + self.b)
# compute prediction as class whose probability is maximal in
# symbolic form
self.y_pred = T.argmax(self.p_y_given_x, axis=1)
# parameters of the model
self.params = [self.W, self.b]
def negative_log_likelihood(self, y):
return -T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]), y])
def errors(self, y):
# check if y has same dimension of y_pred
if y.ndim != self.y_pred.ndim:
raise TypeError('y should have the same shape as self.y_pred',
('y', y.type, 'y_pred', self.y_pred.type))
# check if y is of the correct datatype
if y.dtype.startswith('int'):
# the T.neq operator returns a vector of 0s and 1s, where 1
# represents a mistake in prediction
return T.mean(T.neq(self.y_pred, y))
else:
raise NotImplementedError()
class MLP(object):
def __init__(self, rng, input, n_in, n_hidden, n_out):
# Since we are dealing with a one hidden layer MLP, this will translate
# into a HiddenLayer with a tanh activation function connected to the
# LogisticRegression layer; the activation function can be replaced by
# sigmoid or any other nonlinear function
self.hiddenLayer = HiddenLayer(rng=rng, input=input,
n_in=n_in, n_out=n_hidden,
activation=T.tanh)
# The logistic regression layer gets as input the hidden units
# of the hidden layer
self.logRegressionLayer = LogisticRegression(
input=self.hiddenLayer.output,
n_in=n_hidden,
n_out=n_out)
# L1 norm ; one regularization option is to enforce L1 norm to
# be small
# negative log likelihood of the MLP is given by the negative
# log likelihood of the output of the model, computed in the
# logistic regression layer
self.negative_log_likelihood = self.logRegressionLayer.negative_log_likelihood
# same holds for the function computing the number of errors
self.errors = self.logRegressionLayer.errors
# the prediction from logistic regression
self.y_preds = self.logRegressionLayer.y_pred
self.p_y_given_x = self.logRegressionLayer.p_y_given_x
# the parameters of the model are the parameters of the two layer it is
# made out of
self.params = self.hiddenLayer.params + self.logRegressionLayer.params
class _LeNetConvPoolLayer(object):
"""Pool Layer of a convolutional network """
def __init__(self, rng, input, filter_shape,
poolsize,
non_linear="tanh",
params=None,
image_shape=None):
"""
Allocate a LeNetConvPoolLayer with shared variable internal parameters.
:type rng: numpy.random.RandomState
:param rng: a random number generator used to initialize weights
:type input: theano.tensor.dtensor4
:param input: symbolic image tensor, of shape image_shape
:type filter_shape: tuple or list of length 4
:param filter_shape: (number of filters, num input feature maps,
filter height,filter width)
:type image_shape: tuple or list of length 4
:param image_shape: (batch size, num input feature maps,
image height, image width)
:type poolsize: tuple or list of length 2
:param poolsize: the downsampling (pooling) factor (#rows,#cols)
"""
self.input = input
self.filter_shape = filter_shape
self.image_shape = image_shape
self.poolsize = poolsize
self.non_linear = non_linear
if params is None:
# there are "num input feature maps * filter height * filter width"
# inputs to each hidden unit
fan_in = np.prod(filter_shape[1:])
# each unit in the lower layer receives a gradient from:
# "num output feature maps * filter height * filter width" /
# pooling size
fan_out = (filter_shape[0] * np.prod(filter_shape[2:]) / np.prod(poolsize))
# initialize weights with random weights
if self.non_linear == "none" or self.non_linear == "relu":
self.W = theano.shared(np.asarray(rng.uniform(low=-0.01, high=0.01, size=filter_shape),
dtype=theano.config.floatX), borrow=True, name="W_conv")
else:
W_bound = np.sqrt(6. / (fan_in + fan_out))
self.W = theano.shared(np.asarray(rng.uniform(low=-W_bound, high=W_bound, size=filter_shape),
dtype=theano.config.floatX), borrow=True, name="W_conv")
b_values = np.zeros((filter_shape[0],), dtype=theano.config.floatX)
self.b = theano.shared(value=b_values, borrow=True, name="b_conv")
else:
filter_index = 0
bias_index = 1
self.W = params[filter_index]
self.b = params[bias_index]
# convolve input feature maps with filters
conv_out = conv.conv2d(input=input, filters=self.W, filter_shape=self.filter_shape,
image_shape=self.image_shape)
if self.non_linear == "tanh":
conv_out_tanh = T.tanh(conv_out + self.b.dimshuffle('x', 0, 'x', 'x'))
self.conv_out = conv_out_tanh
self.output = T.max(conv_out_tanh, axis=2)
self.argmax = T.argmax(conv_out_tanh, axis=2)
elif self.non_linear == "relu":
conv_out_relu = ReLU(conv_out + self.b.dimshuffle('x', 0, 'x', 'x'))
self.conv_out = conv_out_relu
self.output = T.max(conv_out_relu, axis=2)
self.argmax = T.argmax(conv_out_relu, axis=2)
else:
print 'what on earth do you want!!!'
import sys
sys.exit()
self.params = [self.W, self.b]
def conv_layer_output(self, input, image_shape):
conv_out = conv.conv2d(input=input, filters=self.W, filter_shape=self.filter_shape,
image_shape=image_shape)
if self.non_linear == "tanh":
conv_out_tanh = T.tanh(conv_out + self.b.dimshuffle('x', 0, 'x', 'x'))
output = downsample.max_pool_2d(input=conv_out_tanh, ds=self.poolsize, ignore_border=True)
elif self.non_linear == "relu":
conv_out_relu = ReLU(conv_out + self.b.dimshuffle('x', 0, 'x', 'x'))
conv_out = conv_out_relu
output = T.max(conv_out_relu, axis=2)
argmax = T.argmax(conv_out_relu, axis=2)
conv_out = conv_out_relu
return output
class LeNetConvPoolLayer(object):
"""Pool Layer of a convolutional network """
def __init__(self, rng, input, filter_shape, image_shape, poolsize=(2, 2), non_linear="tanh"):
"""
Allocate a LeNetConvPoolLayer with shared variable internal parameters.
:type rng: numpy.random.RandomState
:param rng: a random number generator used to initialize weights
:type input: theano.tensor.dtensor4
:param input: symbolic image tensor, of shape image_shape
:type filter_shape: tuple or list of length 4
:param filter_shape: (number of filters, num input feature maps,
filter height,filter width)
:type image_shape: tuple or list of length 4
:param image_shape: (batch size, num input feature maps,
image height, image width)
:type poolsize: tuple or list of length 2
:param poolsize: the downsampling (pooling) factor (#rows,#cols)
"""
self.input = input
self.filter_shape = filter_shape
self.image_shape = image_shape
self.poolsize = poolsize
self.non_linear = non_linear
# there are "num input feature maps * filter height * filter width"
# inputs to each hidden unit
fan_in = np.prod(filter_shape[1:])
# each unit in the lower layer receives a gradient from:
# "num output feature maps * filter height * filter width" /
# pooling size
fan_out = (filter_shape[0] * np.prod(filter_shape[2:]) / np.prod(poolsize))
# initialize weights with random weights
if self.non_linear == "none" or self.non_linear == "relu":
self.W = theano.shared(np.asarray(rng.uniform(low=-0.01, high=0.01, size=filter_shape),
dtype=theano.config.floatX), borrow=True, name="W_conv")
else:
W_bound = np.sqrt(6. / (fan_in + fan_out))
self.W = theano.shared(np.asarray(rng.uniform(low=-W_bound, high=W_bound, size=filter_shape),
dtype=theano.config.floatX), borrow=True, name="W_conv")
b_values = np.zeros((filter_shape[0],), dtype=theano.config.floatX)
self.b = theano.shared(value=b_values, borrow=True, name="b_conv")
# convolve input feature maps with filters
conv_out = conv.conv2d(input=input, filters=self.W, filter_shape=self.filter_shape,
image_shape=self.image_shape)
if self.non_linear == "tanh":
conv_out_tanh = T.tanh(conv_out + self.b.dimshuffle('x', 0, 'x', 'x'))
self.output = downsample.max_pool_2d(input=conv_out_tanh, ds=self.poolsize, ignore_border=True)
elif self.non_linear == "relu":
conv_out_relu = ReLU(conv_out + self.b.dimshuffle('x', 0, 'x', 'x'))
self.output = downsample.max_pool_2d(input=conv_out_relu, ds=self.poolsize, ignore_border=True)
self.conv_out = conv_out_relu
else:
pooled_out = downsample.max_pool_2d(input=conv_out, ds=self.poolsize, ignore_border=True)
self.output = pooled_out + self.b.dimshuffle('x', 0, 'x', 'x')
self.params = [self.W, self.b]
def conv_layer_output(self, input, image_shape):
conv_out = conv.conv2d(input=input, filters=self.W, filter_shape=self.filter_shape,
image_shape=image_shape)
if self.non_linear == "tanh":
conv_out_tanh = T.tanh(conv_out + self.b.dimshuffle('x', 0, 'x', 'x'))
output = downsample.max_pool_2d(input=conv_out_tanh, ds=self.poolsize, ignore_border=True)
elif self.non_linear == "relu":
conv_out_relu = ReLU(conv_out + self.b.dimshuffle('x', 0, 'x', 'x'))
output = downsample.max_pool_2d(input=conv_out_relu, ds=self.poolsize, ignore_border=True)
conv_out = conv_out_relu
else:
print 'non-linear function wrong! LeNet'
import sys
sys.exit()
return output
def _dropout_from_layer(rng, layer, p):
"""p is the probablity of dropping a unit
"""
srng = theano.tensor.shared_randomstreams.RandomStreams(rng.randint(999999))
# p=1-p because 1's indicate keep and p is prob of dropping
mask = srng.binomial(n=1, p=1 - p, size=layer.shape)
# The cast is important because
# int * float32 = float64 which pulls things off the gpu
output = layer * T.cast(mask, theano.config.floatX)
return output
class DropoutHiddenLayer(HiddenLayer):
def __init__(self, rng, input, n_in, n_out,
activation, dropout_rate, use_bias, W=None, b=None):
super(DropoutHiddenLayer, self).__init__(
rng=rng, input=input, n_in=n_in, n_out=n_out, W=W, b=b,
activation=activation, use_bias=use_bias)
self.output = _dropout_from_layer(rng, self.output, p=dropout_rate)
class MLPDropout(object):
"""A multilayer perceptron with dropout"""
def __init__(self, rng, input, layer_sizes, dropout_rate, activation=Tanh, use_bias=True):
# rectified_linear_activation = lambda x: T.maximum(0.0, x)
# Set up all the hidden layers
self.weight_matrix_sizes = zip(layer_sizes, layer_sizes[1:])
self.layers = []
self.dropout_layers = []
self.activation = activation
next_layer_input = input
# first_layer = True
# dropout the input
next_dropout_layer_input = _dropout_from_layer(rng, input, p=dropout_rate)
layer_counter = 0
for n_in, n_out in self.weight_matrix_sizes[:-1]:
next_dropout_layer = DropoutHiddenLayer(rng=rng,
input=next_dropout_layer_input,
activation=activation,
n_in=n_in, n_out=n_out, use_bias=use_bias,
dropout_rate=dropout_rate)
self.dropout_layers.append(next_dropout_layer)
next_dropout_layer_input = next_dropout_layer.output
# Reuse the parameters from the dropout layer here, in a different
# path through the graph.
next_layer = HiddenLayer(rng=rng,
input=next_layer_input,
activation=activation,
# scale the weight matrix W with (1-p)
W=next_dropout_layer.W * (1 - dropout_rate),
b=next_dropout_layer.b,
n_in=n_in, n_out=n_out,
use_bias=use_bias)
self.layers.append(next_layer)
next_layer_input = next_layer.output
# first_layer = False
layer_counter += 1
# Set up the output layer
n_in, n_out = self.weight_matrix_sizes[-1]
dropout_output_layer = LogisticRegression(
input=next_dropout_layer_input,
n_in=n_in, n_out=n_out)
self.dropout_layers.append(dropout_output_layer)
# Again, reuse paramters in the dropout output.
output_layer = LogisticRegression(
input=next_layer_input,
# scale the weight matrix W with (1-p)
W=dropout_output_layer.W * (1 - dropout_rate),
b=dropout_output_layer.b,
n_in=n_in, n_out=n_out)
self.layers.append(output_layer)
# Use the negative log likelihood of the logistic regression layer as
# the objective.
self.dropout_negative_log_likelihood = self.dropout_layers[-1].negative_log_likelihood
self.dropout_errors = self.dropout_layers[-1].errors
self.negative_log_likelihood = self.layers[-1].negative_log_likelihood
self.errors = self.layers[-1].errors
self.y_preds = self.layers[-1].y_pred
self.p_y_given_x = self.layers[-1].p_y_given_x
# Grab all the parameters together.
self.params = [param for layer in self.dropout_layers for param in layer.params]
def predict(self, input):
next_layer_input = input
for i, layer in enumerate(self.layers[:-1]):
next_layer_input = self.activation(T.dot(next_layer_input, layer.W) + layer.b)
p_y_given_x = T.nnet.softmax(T.dot(next_layer_input, self.layers[-1].W) + self.layers[-1].b)
y_pred = T.argmax(p_y_given_x, axis=1)
return y_pred
| [
"[email protected]"
] | |
b6e3f87d7064f4e4cfaea9c68abcc286b4a1f124 | 7cf945655903bb922c669cabe9e9b28bf718540d | /TinyFlow/Loss.py | 963dd834773ca2b98984e09f89b52b3ce815c0a0 | [
"MIT"
] | permissive | JyotinderSingh/TinyFlow-Deep-Learning-Framework | fe224c5694bad6135068c7a7fc8faa87fd9fdb78 | 1a57f273d05cf1ac940da61fa4713c5265b4c46d | refs/heads/master | 2023-06-13T08:19:51.937261 | 2021-07-12T05:46:55 | 2021-07-12T05:46:55 | 258,977,602 | 0 | 0 | MIT | 2020-10-27T12:02:18 | 2020-04-26T08:21:51 | Python | UTF-8 | Python | false | false | 6,640 | py | import numpy as np
# Common loss class for regularization
class Loss:
# Regularization loss calculation
def regularization_loss(self, layer):
# 0 by default
regularization_loss = 0
# L1 regularization - weights
# Only calculate when factor greater than 0
if layer.weight_regularizer_l1 > 0:
regularization_loss += layer.weight_regularizer_l1 * \
np.sum(np.abs(layer.weights))
# L2 regularization - weights
# Only calculate when factor greater than 0
if layer.weight_regularizer_l2 > 0:
regularization_loss += layer.weight_regularizer_l2 * \
np.sum(layer.weights * layer.weights)
# L1 regularization - biases
# Only calculate when factor greater than 0
if layer.bias_regularizer_l1 > 0:
regularization_loss += layer.bias_regularizer_l1 * \
np.sum(np.abs(layer.biases))
# L2 regularization - biases
# Only calculate when factor greater than 0
if layer.bias_regularizer_l2 > 0:
regularization_loss += layer.bias_regularizer_l2 * \
np.sum(layer.biases * layer.biases)
return regularization_loss
# Regularization loss calculation
def network_regularization_loss(self):
'''network_regularization_loss (self)\n
Internal method for network wrapper for auto calculation
of regularization loss of all the trainable layers
'''
# 0 by default
regularization_loss = 0
# Calculate regularization loss - iterate over all trainable layers
for layer in self.trainable_layers:
# L1 regularization - weights
# Only calculate when factor greater than 0
if layer.weight_regularizer_l1 > 0:
regularization_loss += layer.weight_regularizer_l1 * \
np.sum(np.abs(layer.weights))
# L2 regularization - weights
# Only calculate when factor greater than 0
if layer.weight_regularizer_l2 > 0:
regularization_loss += layer.weight_regularizer_l2 * \
np.sum(layer.weights * layer.weights)
# L1 regularization - biases
# Only calculate when factor greater than 0
if layer.bias_regularizer_l1 > 0:
regularization_loss += layer.bias_regularizer_l1 * \
np.sum(np.abs(layer.biases))
# L2 regularization - biases
# Only calculate when factor greater than 0
if layer.bias_regularizer_l2 > 0:
regularization_loss += layer.bias_regularizer_l2 * \
np.sum(layer.biases * layer.biases)
return regularization_loss
# Set/remember trainable layers
def remember_trainable_layers(self, trainable_layers):
'''remember_trainable_layers (self, trainable_layers)\n
internal method for Network wrapper to keep track of trainable layers
'''
self.trainable_layers = trainable_layers
# Calculates the data and regularization losses
# given model output and ground truth values
def calculate(self, output, y, *, include_regularization=False):
'''calculate(self, output, ground_truth)\n
internal method for Network wrapper\n
Calculates the data and regularization losses
given model output and ground truth values
'''
# Calculate sample losses
sample_losses = self.forward(output, y)
# Calculate the mean loss
data_loss = np.mean(sample_losses)
# If just data loss is needed, return it
if not include_regularization:
return data_loss
# Return the data and regularization losses
return data_loss, self.network_regularization_loss()
# Cross-entropy loss
class Loss_CategoricalCrossEntropy(Loss):
# Forward Pass
def forward(self, y_pred, y_true):
'''Loss_CategoricalCrossEntropy.forward (predicted_values, ground_truth)\n
Returns the negative_log_likelihood for the correct class score.\n
The loss returned is the mean loss over the batch.
'''
# Number of samples in a batch
samples = y_pred.shape[0]
# Probabilities for target values -
# only if categorical labels
if len(y_true.shape) == 1:
y_pred = y_pred[range(samples), y_true]
# Losses
negative_log_likelihoods = -np.log(y_pred)
# Mask values - only for one-hot encoded labels
if len(y_true.shape) == 2:
negative_log_likelihoods *= y_true
# Overall loss
data_loss = np.sum(negative_log_likelihoods) / samples
return data_loss
# Backward pass
def backward(self, dvalues, y_true):
'''Loss_CategoricalCrossEntropy.backward (upstream_gradient, labels)\n
Calculates the backward pass for the current loss function\n
---IMPLEMENTATION TO BE UPDATED SOON---'''
samples = dvalues.shape[0]
# Make a backup so we can safely modify
self.dvalues = dvalues.copy()
self.dvalues[range(samples), y_true] -= 1
self.dvalues = self.dvalues / samples
# Binary Cross-entropy loss
class Loss_BinaryCrossEntropy(Loss):
# Forward Pass
def forward(self, y_pred, y_true):
# Clip data to prevent division by 0 (log(1) gives you 0)
# Clip both sides to prevent any shifting the mean towards any value
y_pred_clipped = np.clip(y_pred, 1e-7, 1 - 1e-7)
# Calculate sample-wise loss
sample_losses = -(y_true * np.log(y_pred_clipped) +
(1 - y_true) * np.log(1 - y_pred_clipped))
# Return losses
return sample_losses
# Backward pass
def backward(self, dvalues, y_true):
# Clip data to prevent division by 0 (log(1) gives you 0)
# Clip both sides to prevent any shifting the mean towards any value
clipped_dvalues = np.clip(dvalues, 1e-7, 1 - 1e-7)
# Gradient on clipped values
self.dvalues = -(y_true / clipped_dvalues -
(1 - y_true) / (1 - clipped_dvalues))
class Loss_MeanSquaredError(Loss):
# Forward pass
def forward(self, y_pred, y_true):
# Calculate loss
data_loss = 2 * np.mean((y_true - y_pred)**2, axis=-1)
# return losses
return data_loss
# Backward pass
def backward(self, dvalues, y_true):
# Gradient on values
self.dvalues = -(y_true - dvalues)
| [
"[email protected]"
] | |
a00b7db0fb468d6c6bdac17cf46dda3eefb44c31 | 10a380e4c43ce690a989f30b37638658fa028575 | /news/urls.py | 60357954c04df8720027a8dd72cac4263a581c06 | [] | no_license | killerbees1982/pierwsza | 6c9948b473183a8ae172665533fcea7a947a6807 | f1ca0a525c0a4ecbe535825d54a9d133ba5a7c8d | refs/heads/master | 2021-01-01T19:43:34.015530 | 2017-07-28T14:13:36 | 2017-07-28T14:13:36 | 98,656,348 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 423 | py | from django.conf.urls import url
from . import views
from django.contrib.auth.views import logout
from django.conf import settings
urlpatterns = [
url(r'^$', views.post_list, name='post_list'),
url(r'^post/(?P<pk>[0-9]+)/$', views.post_detail, name='post_detail'),
url(r'^post/new/$', views.post_new, name='post_new'),
url(r'^logout/$', logout, {'next_page': settings.LOGOUT_REDIRECT_URL}, name='logout')
] | [
"[email protected]"
] | |
ef50171deae3603d843305ba9702ab004c926836 | 625d241ef84ec8b182a9f67fd5dc0a9296a3270c | /2_Python/Final_Project/bikeshare_2/Finished Functions/time_stats.py | c3c119fb6ac0669a1ea9d95edfba2c5572cb0f3f | [] | no_license | jhl0204/Udacity-Programming-for-Data-Science-Nanodegree | fd75bd10560031fe1c242bac622297c0379061e6 | 51aeef515d78da5e117e0e85b01d044bad372a4c | refs/heads/master | 2020-08-07T12:56:52.393218 | 2019-10-07T18:50:30 | 2019-10-07T18:50:30 | 213,459,789 | 1 | 3 | null | null | null | null | UTF-8 | Python | false | false | 1,230 | py |
def time_stats(df):
"""Displays statistics on the most frequent times of travel."""
print('\nCalculating The Most Frequent Times of Travel...\n')
start_time = time.time()
# Use df.mode() to compute most often data --> outputs it as a tabular data with row 0
# and then access it with indexing (ie [0])
# https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.mode.html
# look_up dictionary
look_up = {'1': 'January', '2': 'February', '3': 'March', '4': 'April', '5': 'May',
'6': 'June', '7': 'July', '8': 'August', '9': 'September', '10': 'October', '11': 'November', '12': 'December'}
# display the most common month
popular_month = df['month'].mode()[0]
month_in_string = look_up[str(popular_month)]
print("The most common month is: ", month_in_string)
# display the most common day of week
popular_day = df['day_of_week'].mode()[0]
print("The most common day of the week is: {}".format(popular_day))
# display the most common start hour
popular_hour = df['Hour'].mode()[0]
print('The most common start hour:', popular_hour)
print("\nThis took %s seconds." % (time.time() - start_time))
print('-'*40)
| [
"[email protected]"
] | |
a1b98c507f1b74e406d7754fa7373a7ca7f4e62c | b5c230cc699dec2494f82555db3c607d27f5c37d | /app/nyt_api.py | 5ce2a4cecd5a247f66aac8b7e327317a6d28c170 | [] | no_license | ande3674/avidreader | 53f1a9cb07f0d0ea68844073791bbb7b7dc363bc | 224ce08b1cd2562e4e89240f6873c94ec8a8501e | refs/heads/master | 2020-04-08T18:02:57.351501 | 2018-12-20T18:14:49 | 2018-12-20T18:14:49 | 159,591,784 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,548 | py | from config import Config
import requests
import app.api as google_api
def get_nyt_bestsellers(): # FICTION !!!
data = requests.get(Config.NYT_URL).json()
results = data['results']
books = []
for i in range(8):
book_info = {}
current_book = results[i]
book_info['title'] = current_book['book_details'][0]['title']
book_info['author'] = current_book['book_details'][0]['author']
book_info['description'] = current_book['book_details'][0]['description']
book_info['amazon_link'] = current_book['amazon_product_url']
book_info['cover_image'] = get_google_cover_image(title=current_book['book_details'][0]['title'])
books.append(book_info)
return books
def get_nyt_bestsellers_nonfiction():
data = requests.get(Config.NYT_URL_NONFICTION).json()
results = data['results']
books = []
for i in range(8):
book_info = {}
current_book = results[i]
book_info['title'] = current_book['book_details'][0]['title']
book_info['author'] = current_book['book_details'][0]['author']
book_info['description'] = current_book['book_details'][0]['description']
book_info['amazon_link'] = current_book['amazon_product_url']
book_info['cover_image'] = get_google_cover_image(title=current_book['book_details'][0]['title'])
books.append(book_info)
return books
def get_google_cover_image(title):
return google_api.get_one_book_image_link(title)
# b = get_nyt_bestsellers_nonfiction()
# print(b) | [
"[email protected]"
] | |
611309507d45b4622968a0088a144df244863380 | f77b0f2cc709b9670e6b4dc7145a6ea5368585d2 | /project/handlers/ajax/__init__.py | 1e7699ae21b1884559d32695109ecdcf81442bf6 | [] | no_license | sgammon/StonerHub | 45ccac6bd349200bbc75c494002c3ffeb082dcb8 | a81f7fdd2c7118c6cea3c25ef9f53f272d27b0cc | refs/heads/master | 2021-01-20T10:54:47.546251 | 2011-11-07T12:02:20 | 2011-11-07T12:02:20 | 2,664,437 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,336 | py | import config
import logging
from google.appengine.api import quota
from project.handlers import WebHandler
try:
import json
except ImportError:
try:
import simplejson as json
except ImportError:
try:
from django.utils import simplejson as json
except ImportError:
logging.critical('No valid JSON adapter found.')
class SPIAjaxHandler(WebHandler):
''' Renders a given template variable set as a JSON response. '''
response_params = {}
def _build_meta_obj(self):
## Retrieve config
main_config = config.config.get('wirestone.spi')
dev_config = config.config.get('wirestone.spi.dev')
## Build meta object
meta_obj = {
'platform':{
'version':{
'version_major':main_config['version_major'],
'version_minor':main_config['version_minor'],
'version_micro':main_config['version_micro'],
'version_phase':main_config['version_phase'],
}
}
}
if dev_config['dev_mode'] == True:
## Add usage information for request to dev response
meta_obj['request'] = {
'cpu_usage': quota.get_request_cpu_usage(),
'cpu_api': quota.get_request_api_cpu_usage()
}
## Add dev config to dev response
meta_obj['platform']['dev'] = {
'debug':dev_config['debug'],
'dev_mode':dev_config['dev_mode']
}
return meta_obj
def fail(self, operation, reason):
## Create the beginnings of the response object
response_object = {
'operation':operation,
'result':'failure',
'response': {'failure_reason':reason},
'meta': self._build_meta_obj()
}
## Generate JSON response, set appropriate headers
self.response.write(json.dumps(response_object))
self.response.headers['Content-Type'] = 'text/json'
def render(self, operation, **kwargs):
## Create the beginnings of the response object
response_object = {
'operation':operation,
'result':'success',
'response': {},
'meta': self._build_meta_obj()
}
if isinstance(kwargs, dict) and len(kwargs) > 0:
for key, value in kwargs.items():
self.response_params[key] = value
## Generate Response dict
response_object['response'] = self.response_params
## Return generated JSON string
self.response.write(json.dumps(response_object))
self.response.headers['Content-Type'] = 'text/json' | [
"[email protected]"
] | |
2e9c0c678b0f5efa189a956eda7cb328d897e492 | dc66def991cf9abda24f057ad8b67432d6d3d9b6 | /nsd_prepare_rois_rdms.py | 2dadab04bac86b15fde993ddaf19ef5a079ba3dc | [] | no_license | Charestlab/nsddatapaper_rsa | 4b3860717e85e2b171e52f10e60672ed85f01e4d | 59d5838d0eb661509f651351d2a23b2eed7bf59b | refs/heads/main | 2023-03-20T06:47:34.223395 | 2021-03-11T09:44:25 | 2021-03-11T09:44:25 | 343,485,836 | 4 | 2 | null | null | null | null | UTF-8 | Python | false | false | 4,342 | py | import sys
import os
import time
import numpy as np
import nibabel as nib
from scipy.spatial.distance import pdist
from nsd_access import NSDAccess
from utils.nsd_get_data import get_conditions, get_betas
from utils.utils import average_over_conditions
"""
module to gather the region of interest rdms
"""
sub = int(sys.argv[1])
n_jobs = 38
n_sessions = 40
n_subjects = 8
# set up directories
base_dir = os.path.join('/rds', 'projects', 'c')
nsd_dir = os.path.join(base_dir, 'charesti-start', 'data', 'NSD')
proj_dir = os.path.join(base_dir, 'charesti-start', 'projects', 'NSD')
nsd_dir = os.path.join(base_dir, 'charesti-start', 'data', 'NSD')
sem_dir = os.path.join(proj_dir, 'derivatives', 'ecoset')
betas_dir = os.path.join(proj_dir, 'rsa')
models_dir = os.path.join(proj_dir, 'rsa', 'serialised_models')
# initiate nsd access
nsda = NSDAccess(nsd_dir)
# path where we save the rdms
outpath = os.path.join(betas_dir, 'roi_analyses')
if not os.path.exists(outpath):
os.makedirs(outpath)
# we use the fsaverage space.
targetspace = 'fsaverage'
lh_file = os.path.join(proj_dir, 'lh.highlevelvisual.mgz')
rh_file = os.path.join(proj_dir, 'rh.highlevelvisual.mgz')
# load the lh mask
maskdata_lh = nib.load(lh_file).get_fdata().squeeze()
maskdata_rh = nib.load(rh_file).get_fdata().squeeze()
maskdata = np.hstack((maskdata_lh, maskdata_rh))
ROIS = {1: 'pVTC', 2: 'aVTC', 3: 'v1', 4: 'v2', 5: 'v3'}
roi_names = ['pVTC', 'aVTC', 'v1', 'v2', 'v3']
# sessions
n_sessions = 40
# subjects
subs = ['subj0{}'.format(x+1) for x in range(n_subjects)]
# extract conditions
conditions = get_conditions(nsd_dir, sub, n_sessions)
# we also need to reshape conditions to be ntrials x 1
conditions = np.asarray(conditions).ravel()
# then we find the valid trials for which we do have 3 repetitions.
conditions_bool = [
True if np.sum(conditions == x) == 3 else False for x in conditions]
conditions_sampled = conditions[conditions_bool]
# find the subject's unique condition list (sample pool)
sample = np.unique(conditions[conditions_bool])
betas_file = os.path.join(
outpath, f'{sub}_betas_list_{targetspace}.npy'
)
betas_mean_file = os.path.join(
outpath, f'{sub}_betas_list_{targetspace}_averaged.npy'
)
if not os.path.exists(betas_mean_file):
# get betas
betas_mean = get_betas(
nsd_dir,
sub,
n_sessions,
targetspace=targetspace,
)
print(f'concatenating betas for {sub}')
betas_mean = np.concatenate(betas_mean, axis=1).astype(np.float32)
print(f'averaging betas for {sub}')
betas_mean = average_over_conditions(
betas_mean,
conditions,
conditions_sampled,
).astype(np.float32)
# print
print(f'saving condition averaged betas for {sub}')
np.save(betas_mean_file, betas_mean)
else:
print(f'loading betas for {sub}')
betas_mean = np.load(betas_mean_file, allow_pickle=True)
# print
print(f'saving condition list for {sub}')
np.save(
os.path.join(
outpath, f'{sub}_condition_list.npy'
),
conditions_sampled
)
# save the subject's full ROI RDMs
for roi in range(1, 6):
mask_name = ROIS[roi]
rdm_file = os.path.join(
outpath, f'{sub}_{mask_name}_fullrdm_correlation.npy'
)
if not os.path.exists(rdm_file):
# logical array of mask vertices
vs_mask = maskdata == roi
print(f'working on ROI: {mask_name}')
masked_betas = betas_mean[vs_mask, :]
good_vox = [
True if np.sum(
np.isnan(x)
) == 0 else False for x in masked_betas]
if np.sum(good_vox) != len(good_vox):
print(f'found some NaN for ROI: {mask_name} - {sub}')
masked_betas = masked_betas[good_vox, :]
# prepare for correlation distance
X = masked_betas.T
print(f'computing RDM for roi: {mask_name}')
start_time = time.time()
rdm = pdist(X, metric='correlation')
if np.any(np.isnan(rdm)):
raise ValueError
elapsed_time = time.time() - start_time
print(
'elapsedtime: ',
f'{time.strftime("%H:%M:%S", time.gmtime(elapsed_time))}'
)
print(f'saving full rdm for {mask_name} : {sub}')
np.save(
rdm_file,
rdm
)
| [
"[email protected]"
] | |
29a4c87a5d495cf30a51cc3fd20fcaba4adc14ae | feef80ed0a0182e6ff74b60dc3a743d9c19c439e | /tensorflow_datasets/scripts/create_checksum_file.py | dcef257a4a7e7d8dd179d27ce11f5e5572fcba41 | [
"Apache-2.0"
] | permissive | brettkoonce/datasets | 7bd6f73ee77b5185db398290172250d499f76bf2 | 55bb2a80ab674c2f6254ac74d90bd6e5f478e895 | refs/heads/master | 2020-04-10T07:55:01.226854 | 2018-12-08T00:09:42 | 2018-12-08T00:10:04 | 160,892,533 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,300 | py | # coding=utf-8
# Copyright 2018 The TensorFlow Datasets 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.
r"""Script to create the checksums file of a dataset.
This script is meant to be used by the creator of a DatasetBuilder.
Once the DatasetBuilder has been written and run at least once, and files have
been downloaded, run this script to create the file associating URLs and
checksums.
Example of usage:
$ scripts/create_checksum_file --dest_dir=url_checksums --dataset=mnist
See documentation at:
https://github.com/tensorflow/datasets/blob/master/docs/add_dataset.md#enabling-downloads-validation
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import hashlib
import json
import os
import re
from absl import app
from absl import flags
import tensorflow as tf
from tensorflow_datasets.core.download import checksums_file
from tensorflow_datasets.core.download import util
flags.DEFINE_string('dataset', None, 'Name of dataset.')
flags.DEFINE_string('dest_dir', 'url_checksums',
'Path to directory in which to write the checksums file .')
flags.DEFINE_string('downloads_path', '~/tensorflow-datasets/downloads',
'Path to downloads directory.')
flags.DEFINE_string('extracts_path', '~/tensorflow-datasets/extracted',
'Path to extractions directory.')
FLAGS = flags.FLAGS
def _rename(old_path, new_path):
print('mv %s %s' % (old_path, new_path))
tf.gfile.Rename(old_path, new_path, overwrite=True)
def _write_checksums(dataset_name, out_path, downloads_path):
"""Write url->checksum csv file, return mapping."""
url_to_checksum = {}
info_fnames = [fname for fname in tf.gfile.ListDirectory(downloads_path)
if fname.endswith('.INFO')]
for info_fname in info_fnames:
with tf.gfile.Open(os.path.join(downloads_path, info_fname)) as info_f:
info = json.load(info_f)
if dataset_name not in info['dataset_names']:
continue
fname = info_fname[:-len('.INFO')]
path = os.path.join(downloads_path, fname)
if not tf.gfile.Exists(path):
continue
sha256 = util.read_checksum_digest(path, hashlib.sha256)
for url in info['urls']:
if url in info and url_to_checksum[url] != sha256:
msg = ('URL %s is associated with two sha256 checksums: %s (old) and '
'%s (actual). Please check the INFO files %s.' % (
url, url_to_checksum[url], sha256, info_fnames))
raise AssertionError(msg)
url_to_checksum[url] = sha256
if not url_to_checksum:
print('No files downloaded by %s could be found in %s.' % (
dataset_name, downloads_path))
return
print('Writing url->checksum associations to %s...' % out_path)
checksums_file.dump(out_path, url_to_checksum)
return url_to_checksum
def _move_already_downloaded_extracted_files(url_to_checksum, downloads_path,
extracts_path):
"""Move already downloaded files to new filenames using checksums."""
url_to_methods = {} # url_checksum -> methods, filled by following block:
for fname in tf.gfile.ListDirectory(extracts_path):
res = re.match(r'(\d+)\.url\.([a-f0-9]{64})', fname)
if res:
extraction_method = res.group(1)
url_checksum = res.group(2)
url_to_methods.setdefault(url_checksum, []).append(extraction_method)
for url, checksum in url_to_checksum.items():
url_checksum = hashlib.sha256(url.encode('utf8')).hexdigest()
# Downloaded file + INFO file
old_path = os.path.join(downloads_path, 'url.' + url_checksum)
if tf.gfile.Exists(old_path):
new_path = os.path.join(downloads_path, checksum)
_rename(old_path, new_path)
_rename(old_path + '.INFO', new_path + '.INFO')
# Extracted files:
for method in url_to_methods.get(url_checksum, []):
old_path = os.path.join(extracts_path, '%s.url.%s' % (method,
url_checksum))
new_path = os.path.join(extracts_path, '%s.%s' % (method, checksum))
_rename(old_path, new_path)
def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
dataset = FLAGS.dataset
checksums_path = os.path.join(os.path.expanduser(FLAGS.dest_dir),
'%s.csv' % dataset)
downloads_path = os.path.expanduser(FLAGS.downloads_path)
extracts_path = os.path.expanduser(FLAGS.extracts_path)
url_to_checksum = _write_checksums(dataset, checksums_path, downloads_path)
if url_to_checksum:
_move_already_downloaded_extracted_files(
url_to_checksum, downloads_path, extracts_path)
if __name__ == '__main__':
flags.mark_flag_as_required('dataset')
app.run(main)
| [
"[email protected]"
] | |
9ae1e0fe144eb142631419a238ee090ea1448322 | defec7e7c44c4bde470cd9de102a83f176311e90 | /ProjectEuler040.py | 5a090b9875e878e4e581c2358d550ecc69bb2993 | [] | no_license | edimaudo/Project-Euler | 5ebd27ed91f5fcf4697a00d0deaa239f2b8309b9 | 06f74b46fc421510d9ea9ef86784679b914eb779 | refs/heads/master | 2020-04-10T14:57:41.692212 | 2016-11-06T20:55:04 | 2016-11-06T20:55:04 | 41,200,209 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 398 | py | # Project Euler 40
beginvalue = ""
for value in range(1,1000001):
beginvalue += str(value)
tenlist = [1,10,100,1000,10000,100000,1000000]
templist = []
for value in range(len(beginvalue)):
if int(value) in tenlist:
templist.append(beginvalue[int(value - 1)])
#print (templist)
multval = 1
for value in templist:
multval *= int(value)
print (multval)
| [
"[email protected]"
] | |
d8d99df99bca6478674badf0816e5d6693c73d13 | 94838674ffd175df6194437c1ccc3f90ab409d6c | /pillowV3/log/2018-12-30 14:56:17.161287 | fa70ed1172c1f299ee4907ddba242cb4c2972552 | [] | no_license | WojciechKoz/MyFirstNeuralNetwork | 4fdb3140d8f02257599d005638598f78055c1ac8 | 3cd032aba80ecd71edb0286724ae9ba565b75a81 | refs/heads/master | 2020-04-02T03:02:48.680433 | 2020-02-29T17:57:43 | 2020-02-29T17:57:43 | 153,943,121 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 400,307 | 161287 | #!/usr/bin/env python3
# -*- coding: utf8 -*-
from __future__ import print_function # new print() on python2
from datetime import datetime
import sys
import numpy as np
from mnist import MNIST
# Display full arrays
np.set_printoptions(threshold=np.inf)
mndata = MNIST('./data')
images_full, labels_full = mndata.load_training()
images = []
labels = []
# dynamic arguments
batch_size = int(sys.argv[1])
size_1 = int(sys.argv[2])
size_2 = int(sys.argv[3])
batch_training_size = int(sys.argv[4])
data_part = 5 # only one fifth of the whole dataset to speed up training
for i in range(len(labels_full) // batch_size // data_part):
images.append(images_full[i*batch_size : (i+1)*batch_size])
labels.append(labels_full[i*batch_size : (i+1)*batch_size])
def sigmoid_prime(x):
return np.exp(-x) / ((np.exp(-x) + 1) ** 2)
def sigmoid(x):
return 1 / (1 + np.exp(-x))
# nowe, przyda się?
def relu(x):
return np.maximum(x, x * 0.01)
def relu_prime(x):
if x >= 0:
return 1
# ej nie jest tak xd
# a jak xd?
type(x) == no.ndarray
# no x to macierz xd
# np.exp jest przeładowane ale jakakoleiwk funkcja to chyba nie
# to co foreach ? :(
# właśnie nie wiem, a co z gpu?
# to miało być szybsze a nie xd
# mamy duzo mozliwosci zmian ale nie na raz trzeba ustalic jakos
# hm TODO gpu TODO wincyj procent TODO gui gotowe
# xd
# tamto myliło hah
# to co najpierw? :p
# ssh daje wglad do basha tylko tak ?
# nie, to jest taki fajny programik, byobu
# i ten pasek na dole też jest z byobu
# on udostepnia tylko basha ?
# tak, ale basha multiplayer xd
# szkoda że 2 kursorow nie ma
# hm
return 0.01 # chyba tak xd nikt nie widzial xd
# ale x to macierz :p
# ale to jest przeciazone i jak jest funkcja od macierzy to bierze po kolei kazdy element
# w sumie
# zobacze na drugiej karcie xd
#X = np.array([[0, 0],
# [0, 1],
# [1, 0],
# [1, 1]])
#X = np.array(images)
y = []
for batch in labels:
y.append([])
for label in batch:
y[-1].append([1.0 if i == label else 0.0 for i in range(10)])
y = np.array(y)
#y = np.array([[0],
# [1],
# [1],
# [0]])
np.random.seed(1)
LEN = len(labels)
SIZES = [ 784, size_1, size_2, 10 ]
syn0 = 2 * np.random.random((SIZES[0], SIZES[1])) - 1
syn1 = 2 * np.random.random((SIZES[1], SIZES[2])) - 1
syn2 = 2 * np.random.random((SIZES[2], SIZES[3])) - 1
# biases for respective layers
b0 = 2 * np.random.random((1, SIZES[1])) - 1
b1 = 2 * np.random.random((1, SIZES[2])) - 1
b2 = 2 * np.random.random((1, SIZES[3])) - 1
for i, batch in list(enumerate(images)):
X = np.array(batch)
print("x:")
print(np.shape(X))
print("======================= BATCH {} =======================".format(i))
error = 1
j = 0
while j < batch_training_size:
l0 = X
l1 = sigmoid(np.dot(l0, syn0) + b0)
l2 = sigmoid(np.dot(l1, syn1) + b1)
l3 = sigmoid(np.dot(l2, syn2) + b2)
l3_error = (y[i] - l3)#** 2
error = np.mean(np.abs(l3_error))
j += 1
if j % 20 == 0:
print(("[%d] error: " % j) + str(error))
l3_delta = l3_error * sigmoid_prime(l3)
l2_error = l3_delta.dot(syn2.T)
l2_delta = l2_error * sigmoid_prime(l2)
l1_error = l2_delta.dot(syn1.T)
l1_delta = l1_error * sigmoid_prime(l1)
syn2 += l2.T.dot(l3_delta)
syn1 += l1.T.dot(l2_delta)
syn0 += l0.T.dot(l1_delta)
b0 += l1_delta.mean(axis=0)
b1 += l2_delta.mean(axis=0)
b2 += l3_delta.mean(axis=0)
def predict(data):
l0 = [data]
l1 = sigmoid(np.dot(l0, syn0) + b0)
l2 = sigmoid(np.dot(l1, syn1) + b1)
l3 = sigmoid(np.dot(l2, syn2) + b2)
return np.argmax(l3)
print("Output after training: ")
print(l3)
for i, el in enumerate(l3):
print(labels[0][i], "=", np.argmax(el), " predictions: ", el)
testing_images, testing_labels = mndata.load_testing()
correct = 0.0
for i, (image, label) in enumerate(zip(testing_images, testing_labels)):
prediction = predict(image)
if label == prediction:
correct += 1.0
correct_rate = correct / (i + 1.0)
print("{} = {} (correct {}%)".format(label, prediction, 100 * correct_rate))
with open('log/' + str(datetime.now()), 'a') as f:
with open(__file__, 'r') as myself:
print(myself.read(), file=f)
print("", file=f)
print("#### answers:", file=f)
print("argv =", sys.argv, file=f)
print("correct_rate =", correct_rate, file=f)
print("SIZES =", SIZES, file=f)
print("syn0 =", syn0, file=f)
print("syn1 =", syn1, file=f)
print("syn2 =", syn2, file=f)
print("b0 =", b0, file=f)
print("b1 =", b1, file=f)
print("b2 =", b2, file=f)
#### answers:
argv = ['./main.py', '63', '29', '31', '6']
correct_rate = 0.1009
SIZES = [784, 29, 31, 10]
syn0 = [[-1.65955991e-01 4.40648987e-01 -9.99771250e-01 -3.95334855e-01
-7.06488218e-01 -8.15322810e-01 -6.27479577e-01 -3.08878546e-01
-2.06465052e-01 7.76334680e-02 -1.61610971e-01 3.70439001e-01
-5.91095501e-01 7.56234873e-01 -9.45224814e-01 3.40935020e-01
-1.65390395e-01 1.17379657e-01 -7.19226123e-01 -6.03797022e-01
6.01489137e-01 9.36523151e-01 -3.73151644e-01 3.84645231e-01
7.52778305e-01 7.89213327e-01 -8.29911577e-01 -9.21890434e-01
-6.60339161e-01]
[ 7.56285007e-01 -8.03306332e-01 -1.57784750e-01 9.15779060e-01
6.63305699e-02 3.83754228e-01 -3.68968738e-01 3.73001855e-01
6.69251344e-01 -9.63423445e-01 5.00288630e-01 9.77722178e-01
4.96331309e-01 -4.39112016e-01 5.78558657e-01 -7.93547987e-01
-1.04212948e-01 8.17191006e-01 -4.12771703e-01 -4.24449323e-01
-7.39942856e-01 -9.61266084e-01 3.57671066e-01 -5.76743768e-01
-4.68906681e-01 -1.68536814e-02 -8.93274910e-01 1.48235211e-01
-7.06542850e-01]
[ 1.78611074e-01 3.99516720e-01 -7.95331142e-01 -1.71888024e-01
3.88800315e-01 -1.71641461e-01 -9.00093082e-01 7.17928118e-02
3.27589290e-01 2.97782241e-02 8.89189512e-01 1.73110081e-01
8.06803831e-01 -7.25050592e-01 -7.21447305e-01 6.14782577e-01
-2.04646326e-01 -6.69291606e-01 8.55017161e-01 -3.04468281e-01
5.01624206e-01 4.51995971e-01 7.66612182e-01 2.47344414e-01
5.01884868e-01 -3.02203316e-01 -4.60144216e-01 7.91772436e-01
-1.43817620e-01]
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8.98978517e-01 -1.00175733e-01 1.56779229e-01 -1.83726394e-01
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2.34289827e-01 -3.46710196e-01 5.41162045e-02 7.71884199e-01
-2.85460480e-01 8.17070302e-01 2.46720232e-01 -9.68357514e-01
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-7.25728501e-01 8.65190926e-01 3.93636323e-01 -8.67999655e-01
5.10926105e-01]
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1.12480468e-01 -7.27089549e-01 -8.80164621e-01 -7.57313089e-01
-9.10896243e-01]
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2.33556714e-01 8.98032641e-01 9.00352238e-01 1.13306376e-01
8.31212700e-01]
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3.58137673e-01 8.37203556e-01 -9.99195950e-01 9.53518298e-01
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1.49423009e-01 2.56152397e-01 -4.28847437e-01 1.73666681e-01
5.00043527e-01]
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4.21618085 38.93531758 18.40594523 10.56330006 22.27492877
12.02858859 27.06061734 27.56492256 23.96312596 3.21865786
37.28433619]
[ 3.74107077 6.89138772 8.09566317 5.68528239 23.43377255
26.23702207 -15.981974 25.50234645 28.54712503 19.03347005
24.17344704 12.44405422 26.65572556 41.53989554 30.8709292
16.02435945 2.29291343 47.4120376 -5.00195852 35.93602783
-0.09167764 43.85886009 20.56619427 7.05504227 20.16821651
12.78348549 28.32331559 30.27428241 23.69732359 3.75642041
44.92886334]
[ 14.13459579 14.74141552 16.21896554 12.69560974 31.17984529
36.72018154 -14.6939912 42.64244342 34.31900185 22.83102015
29.89210244 20.54142034 36.08116429 47.07615299 42.54420914
25.7847958 6.9215069 49.18255003 5.8264312 42.82845816
12.26634247 50.21580847 26.70393602 17.94685848 27.85138705
19.10212994 34.36630109 33.32021787 32.59366329 7.54465511
48.421825 ]
[ 13.88408878 14.04637772 15.69282856 12.74576899 31.67931036
37.86816514 -13.13669957 42.20957087 35.86767688 23.4939262
29.38909303 20.00982637 36.29521793 47.64371602 42.36411045
25.03874648 6.94794184 50.15333211 6.3321648 43.84716174
11.13502553 48.48217866 25.59491358 16.60268467 28.177071
18.6418123 33.57632651 33.47268908 32.69362267 8.66085708
48.70250306]
[ 18.54627671 17.24866166 21.08121073 12.39758966 31.23885525
35.86407685 -13.31921131 42.6999969 36.24200871 25.32364425
31.27626344 18.04171683 38.57512382 48.2316011 43.87230622
25.44711765 9.36693842 48.91599133 5.78272153 47.35417202
14.49959563 48.81091561 25.32346776 16.06004007 28.4551382
20.90947052 34.04314745 34.35372387 32.94950826 5.13054574
48.4726775 ]]
syn2 = [[-1.21615747e-02 -8.22606302e-01 4.81575487e-01 2.08541246e+00
5.94558061e-01 -2.26073131e-01 1.63366738e+00 -1.29557686e+00
7.50336416e-01 1.64439667e+00]
[ 9.82212299e-02 3.90663990e-01 6.40839722e-01 1.56608017e+00
3.16670813e-01 3.35471199e-01 1.15941621e+00 -7.48353667e-01
3.76749548e-01 -6.98511072e-03]
[ 1.37667818e+00 -8.87354260e-03 4.51503437e-01 6.68414078e-01
1.64727467e+00 -1.89142493e-01 3.74450170e-01 -7.03262847e-01
3.65573705e-01 5.17615698e-01]
[ 1.63427758e+00 -6.15852917e-02 1.18870108e+00 1.04338438e+00
-4.11310561e-03 5.29616424e-01 1.83659872e+00 9.04188682e-02
-3.17666857e-01 6.86029667e-01]
[ 8.81150067e-01 -5.65332897e-01 -8.49376677e-01 -6.81808559e-01
-1.13434126e-01 -4.67079454e-01 -1.56764716e+00 4.99521792e-01
-1.00290831e+00 -1.29196016e+00]
[-3.75032442e-01 -1.25366078e+00 -6.25776256e-01 -8.09302276e-01
-1.25148643e+00 -9.46728418e-01 -4.79398409e-01 -5.81639959e-01
-7.95118823e-01 -1.36781685e+00]
[-5.39563973e-01 -9.11912074e-01 -6.02615390e-01 1.07924394e+00
3.50944157e-01 -1.33257457e+00 5.61922685e-01 -6.90635958e-02
-8.12804483e-01 1.67808127e-01]
[ 3.05301402e-01 -1.98668704e+00 -4.42427289e-02 7.48658178e-01
-9.82518656e-01 -1.60387903e+00 -8.45982936e-01 -1.04342627e+00
5.03930969e-01 -1.08878049e+00]
[-1.86395862e+00 -9.70956329e-01 -1.94150896e+00 -1.25866812e+00
-6.66714143e-01 -7.98881446e-02 -2.16277967e+00 -1.36534920e+00
-1.09493281e+00 -6.15956023e-01]
[-3.88268913e-01 -3.13419811e-01 -1.34502210e+00 -5.53763363e-01
-4.58755936e-01 4.00176888e-01 -5.59756595e-01 3.08755539e-01
-1.54618335e+00 -1.09813020e-01]
[-1.51370177e+00 -8.13979831e-01 -8.97091947e-01 -6.87209356e-01
-6.36522872e-03 -7.95666098e-01 -1.40986246e+00 -4.56502826e-01
-1.00169342e+00 -1.84413720e-01]
[ 2.06098073e+00 3.39508579e-01 6.63491200e-01 -5.91789678e-01
7.42022654e-02 -4.14142877e-01 -1.40510309e-01 7.08270018e-02
5.97839887e-01 2.90466272e-01]
[-6.24965551e-01 -2.05702871e+00 -1.21368146e+00 -3.85347316e-01
-5.27857729e-01 -9.70696463e-01 -1.36770227e+00 -1.60833699e+00
-3.88544619e-01 -1.07780429e+00]
[-3.16281795e+00 -1.92694158e+00 -3.06819908e+00 -2.48995692e+00
-1.15812257e+00 -1.86000230e+00 -2.27101129e+00 -1.62357805e+00
-2.76216044e+00 -1.83852170e+00]
[-3.06063127e-01 -1.33886533e+00 -1.24188797e+00 -1.12178474e+00
-1.14927378e+00 -1.84653931e+00 -1.47448278e+00 -1.08056010e+00
-8.10165311e-01 -1.09390374e+00]
[ 3.34685172e-01 -1.13338122e+00 -3.42258586e-01 -7.89307443e-01
-4.87594231e-01 -2.09284575e-01 7.26195838e-02 -3.61786705e-01
8.81389191e-03 2.33446585e-01]
[ 9.02367607e-01 1.13013612e+00 3.89799389e-01 1.74891323e+00
9.86751634e-01 5.63801095e-01 4.86269628e-01 3.59436320e-01
9.66765026e-01 8.62585787e-01]
[-3.22078563e+00 -1.64543013e+00 -3.73051291e+00 -3.59057647e+00
-1.44849445e+00 -1.76205519e+00 -3.14225454e+00 1.80015544e-01
-4.71447271e+00 -1.73763521e+00]
[ 3.77502113e+00 3.10336350e-01 2.43044770e+00 2.28710920e+00
3.22367347e-01 -6.36979638e-01 2.32457004e+00 7.24251690e-01
1.56503395e+00 -1.28999491e-01]
[-2.69829739e+00 -2.26355396e+00 -2.36060868e+00 -1.55086091e+00
-7.97433298e-01 -6.20383904e-01 -1.31191440e+00 -7.18555486e-01
-2.65333107e+00 -1.52192435e+00]
[ 1.32527242e+00 -4.03230723e-01 1.48976933e+00 2.31361326e+00
1.44548420e+00 -3.60356736e-02 1.72912310e+00 -1.03162608e+00
2.02608494e+00 4.68511654e-01]
[-3.64460955e+00 -2.59150028e+00 -2.65163803e+00 -3.16283519e+00
-1.77421497e+00 -1.79105503e+00 -2.59508117e+00 -1.06793130e+00
-3.21763922e+00 -1.75792248e+00]
[ 1.02634169e-02 5.90035276e-01 -7.01100744e-01 -4.67774499e-01
-9.01417490e-01 -6.51471870e-01 5.16638771e-01 -3.19089481e-01
-9.72157376e-01 -1.30872984e+00]
[ 2.15259790e+00 -9.90044883e-02 6.83544639e-01 1.32179884e+00
-3.29061724e-01 -1.66989898e-01 1.28053582e+00 -1.78196430e-01
1.23867933e+00 -9.05890867e-02]
[ 3.66131784e-01 -1.17562822e+00 -8.45561115e-01 -7.41590453e-01
-2.02586992e-01 -9.87510287e-01 -2.90217703e-01 -1.19044003e+00
4.55002572e-01 -8.26618605e-01]
[-8.44618345e-01 2.08514560e-01 -7.13333019e-01 7.74542406e-01
1.30615334e+00 1.37714972e-01 -7.44488972e-02 -7.38810523e-01
-1.10239813e+00 1.78754734e-01]
[-2.00797974e+00 -1.17747007e+00 -2.39648181e+00 -1.14550203e+00
-6.60142099e-01 -1.08003696e+00 -1.54788061e+00 -2.32198439e-01
-1.13474103e+00 -1.03789694e+00]
[-2.31754840e+00 -6.29309808e-01 -1.38091819e+00 -1.59747411e+00
-7.40797209e-01 -1.55907673e+00 -6.00797128e-01 4.64242565e-01
-2.45737640e+00 -1.08361734e+00]
[ 3.11413563e-01 -5.86641863e-02 -2.33646369e-01 -1.26215084e+00
-1.35510334e+00 -7.71372939e-01 2.20790272e-01 3.89035211e-01
-1.32030726e+00 -1.19291708e+00]
[ 2.78364581e+00 9.26111756e-01 5.97826425e-01 7.20192267e-01
8.24875494e-02 -3.80401375e-02 1.40460148e+00 5.95433520e-01
9.93629422e-01 9.07416624e-01]
[-3.55679345e+00 -1.49444592e+00 -4.47267642e+00 -3.47158927e+00
-1.38226352e+00 -1.39176644e+00 -2.63986199e+00 -9.84547241e-01
-3.60608081e+00 -2.76191827e+00]]
b0 = [[-497.13018726 -475.08406169 -398.33489545 -391.86456476 -398.93841539
-493.16394632 -397.56384255 -392.96056162 -492.73678021 -396.7117114
-399.23329292 -392.33211655 -497.42891115 -487.65968364 -400.11204592
-392.42028073 -392.25680081 -395.60292008 -491.76147421 -392.92678199
-478.70456176 -397.7328966 -489.23864344 -494.78252842 -397.12629624
-410.53212425 -476.36218812 -475.68423425 -494.70983754]]
b1 = [[-1.67606851 -1.87433989 -1.90089816 -2.02329403 -2.04258 -1.82966949
-2.39900086 -1.61199395 -1.69980091 -1.89331994 -1.76666996 -2.00751273
-1.76066111 -1.91580092 -1.84196573 -1.95621233 -1.97576117 -2.04048998
-2.01193608 -1.6464314 -1.70270331 -2.07021668 -2.14092695 -1.9117394
-1.91981967 -1.88777581 -1.97180949 -2.07953058 -2.03398327 -2.15038394
-2.14016235]]
b2 = [[-0.76245482 0.50716459 -0.93196644 -0.98492957 -0.66231289 0.30589298
-0.64990493 -0.47703169 0.12829097 0.33976175]]
| [
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af57c105f9af7b30476ecc5a144a100b6270c134 | 08ca3425d5f08326398b2ece9c081091a279521f | /beast/web/flaskwebapp/capture.py | ef797c976e14b870282897dc0001cd12d8f58f4d | [] | no_license | PraveerT/RPI_MDX | b7d77e1b899d3d6306b1733a70260e4bd112c61c | c284b4389e929355d60ae0b8e7a7d2e4881cc8b9 | refs/heads/master | 2021-09-22T09:00:41.359135 | 2018-09-06T19:08:31 | 2018-09-06T19:08:31 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 938 | py |
# import the necessary packages
from picamera.array import PiRGBArray
from picamera import PiCamera
import time
import cv2
# initialize the camera and grab a reference to the raw camera capture
camera = PiCamera()
camera.resolution = (640, 480)
camera.framerate = 32
rawCapture = PiRGBArray(camera, size=(640, 480))
# allow the camera to warmup
time.sleep(0.1)
# capture frames from the camera
for frame in camera.capture_continuous(rawCapture, format="bgr", use_video_port=True):
# grab the raw NumPy array representing the image, then initialize the timestamp
# and occupied/unoccupied text
image = frame.array
# show the frame
cv2.imwrite('name.jpg',image)
cv2.imwrite('name2.jpg',image)
cv2.imwrite('name3.jpg',image)
key = cv2.waitKey(1) & 0xFF
# clear the stream in preparation for the next frame
rawCapture.truncate(0)
# if the `q` key was pressed, break from the loop
if key == ord("q"):
break
| [
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f19fc5c3330d8fcb12ebc6be3886966f8a5d6b8c | 4d76bdfbe24b76b3acffc163ceaeec5fb7196d46 | /xNes.py | 90822e1d01aeea7aad84d678f053b4f028e82eef | [] | no_license | oden41/Reinforcement_OpenAIGym | 2521c4ea6e11a1b2ac8c5ae37f721d3b277df95f | 812b2a79dd228ed7163288106200bfe42db22a47 | refs/heads/master | 2021-01-16T20:41:58.990839 | 2016-08-08T14:31:13 | 2016-08-08T14:31:13 | 64,726,040 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,800 | py | #! /usr/bin/python
# -*- coding: utf-8 -*-
import gym
import numpy
from scipy.linalg import expm
# import csv
def doOneIteration(episode, env):
Lambda = 8
M = 5
T = 200
sigma = 0.5
B = numpy.identity(4)
I = numpy.identity(4)
eta_sigma = eta_B = (3 * (3 + numpy.log(4))) / (5 * 4 * numpy.sqrt(4))
weight = []
for i in range(Lambda):
denom = 0.0
for j in range(Lambda):
denom += max(0, numpy.log(Lambda/2 + 1) - numpy.log(j + 1))
weight.append(max(0, numpy.log(Lambda/2 + 1) - numpy.log(i + 1))/denom - 1.0/Lambda)
weight = numpy.asarray(weight)
theta = 2 * numpy.random.rand(4) - 1
# theta = 2 * numpy.random.rand(4) - 10
theta_best = numpy.zeros(4)
r_best = 0
g = 0
# f = open('data{0}.csv'.format(episode), 'ab')
# csvWriter = csv.writer(f)
while g < 2000 and r_best < 200 * M:
# dataList = [g]
list = []
for j in range(Lambda):
z_j = numpy.random.randn(4)
theta_j = theta + sigma * B.dot(z_j)
list.append([z_j, theta_j, 0])
for j in range(Lambda):
for m in range(M):
observation = env.reset()
for t in range(T):
# env.render()
action = 0
if list[j][1].T.dot(observation) < 0:
action = 0
else:
action = 1
observation, reward, done, info = env.step(action)
list[j][2] += 1
if done:
break
list.sort(key=lambda x: (x[2]), reverse=True)
# for j in range(Lambda):
# dataList.append(list[j][2])
# csvWriter.writerow(dataList)
r_best = list[0][2]
theta_best = list[0][1]
sum1 = numpy.zeros(4)
for j in range(Lambda):
sum1 += weight[j] * list[j][0]
G_m = sigma * B.dot(sum1)
sum2 = numpy.zeros((4, 4))
for j in range(Lambda):
sum2 += weight[j] * (list[j][0].dot(list[j][0].T) - I)
G_M = sum2
G_sigma = numpy.trace(G_M)/4
G_B = G_M - G_sigma * I
theta += G_m
sigma *= numpy.exp(eta_sigma * G_sigma / 2)
B = B.dot(expm(eta_B * G_B / 2))
g += 1
print("g:{0}, r_best:{1}, theta:{2}".format(g, r_best, theta_best))
# f.close()
if g >= 2000:
return False
else:
return True
if __name__ == "__main__":
success = 0
fail = 0
for i in range(100):
env = gym.make('CartPole-v0')
result = doOneIteration(i, env)
if result:
success += 1
else:
fail += 1
print("success:{0},fail={1}".format(success, fail))
| [
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] | |
553f25fa2fcddfe3e7ab25743929f0fafa63ec55 | 120a32a1e7ac25a37bd74995e8f466c05b24ddb4 | /yWait/models.py | 72a29ab72151cbcaea1081e4870b360dec8506b1 | [] | no_license | nkmerrill/yWait | 26b16f1d9affa61daf6a1293a9080c857fb0f6d1 | b0b6b2f60cd359e32e14d0ef4a11a0f68773b62e | refs/heads/main | 2023-08-22T04:10:53.307410 | 2021-10-07T01:40:15 | 2021-10-07T01:40:15 | 392,822,449 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,239 | py | TEST = False #WARNING: Seeing to False will use LIVE DATA that can incur a cost! Please only set to False when live data tests are needed! For testing data, edit the values in sampleresponse.json instead!
TESTRESPONSE = "yWait/sampleresponse.json"
from django.db import models
from django.contrib.auth.models import User
from datetime import datetime
from django.db.models.signals import pre_delete, pre_save
from django.dispatch import receiver
import json, os, requests
#Data object
class pureData():
def __init__(self):
self.data = {
'epoch':0,
'hour': [[],[],[],[],[],[],[]],
'closed':[True,False,False,False,False,False,True],
'address':['','','','','','',''],
'name':['','','','','','','']
}
#Actual data from API regarding location traffic.
class trafficData(models.Model):
jsonData = models.TextField(default = '')
def jsonToData(self):
output = json.loads(self.jsonData)
return output
def __str__(self):
return str(self.pk) + ' trafficData'
#A location object
class Location(models.Model):
#Name of venue passed to API (also user friendly name)
venueName = models.CharField(max_length=256, unique=True)
#Address of venue passed to API
venueAddress = models.TextField()
author = models.ForeignKey(User, on_delete=models.CASCADE)
#Traffic data for this venue
data = models.ForeignKey(trafficData, on_delete = models.SET_NULL, null=True, blank=True)
def updateData(self):
outData = pureData()
if TEST == False:
#call API and gather data
apiKey = os.environ['APIKEY']
url = "https://besttime.app/api/v1/forecasts"
params = {
'api_key_private' : apiKey,
'venue_name': self.venueName,
'venue_address' : self.venueAddress
}
responseData = requests.request("POST",url, params=params).text
unParsed = json.loads(responseData)
#To ensure API usage is consistent with expectations, address will be pulled from API
self.venueAddress = unParsed["venue_info"]["venue_address"]
else:
#TEST RESPONSE#
responseData = ""
with open(TESTRESPONSE, "r") as f:
responseData = f.read()
#parse API data for needed information
unParsed = json.loads(responseData)
outData.data['epoch'] = int(unParsed["epoch_analysis"])
for i in range(7): #API response is broken down as a list of days.
outData.data['address'][i] = self.venueAddress
outData.data['name'][i] = self.venueName
if unParsed['analysis'][i]['day_info']['venue_open'] == 'Closed':
outData.data['closed'][i] = True
else:
outData.data['hour'][i] = unParsed['analysis'][i]['quiet_hours']
outData.data['closed'][i] = False
#Delete old data, if it exists
if self.data is not None:
trafficData.objects.get(pk=self.data.pk).delete()
#create trafficData using parsed information and assign it to the object.
trafData = trafficData(jsonData = json.dumps(outData.data))
trafData.save()
self.data = trafData
def __str__(self):
return self.venueName
#Set of locations to be compared.
class ComparisonSet(models.Model):
name = models.CharField(max_length=200, unique=True)
author = models.ForeignKey(User, on_delete=models.CASCADE)
#Traffic data for this comaprison set
data = models.ForeignKey(trafficData, on_delete= models.SET_NULL, null=True, blank=True)
#Comparisons for the Location objects
locations = models.ManyToManyField(Location, related_name='compLocations')
def updateData(self):
outData = pureData()
for i in range(7):
smallestCount = 0
smallestLocData = None
for locale in self.locations.all():
locData = locale.data.jsonToData()
if len(locData['hour'][i]) >= smallestCount and not locData['closed'][i] :
smallestCount = len(locData['hour'][i])
smallestLocData = locData
if smallestLocData is None:
outData.data['hour'][i] = []
outData.data['closed'][i] = True
outData.data['address'][i] = '-'
outData.data['name'][i] = '-'
else:
outData.data['hour'][i] = smallestLocData['hour'][i]
outData.data['closed'][i] = smallestLocData['closed'][i]
outData.data['address'][i] = smallestLocData['address'][i]
outData.data['name'][i] = smallestLocData['name'][i]
outData.data['epoch'] = datetime.now().timestamp()
#Data old data, if it exists
if self.data is not None:
trafficData.objects.get(pk=self.data.pk).delete()
#Create trafficData using information found
trafData = trafficData(jsonData = json.dumps(outData.data))
trafData.save()
self.data = trafData
self.save()
def __str__(self):
return self.name
@receiver(pre_delete,sender=Location, dispatch_uid="delete data Location")
@receiver(pre_delete,sender=ComparisonSet, dispatch_uid="delete data ComparisonSet")
def deleteDataSignal(sender,instance,using,**kwargs):
data = trafficData.objects.get(pk=instance.data.pk)
data.delete()
@receiver(pre_save, sender=Location, dispatch_uid="save data Location")
def updateDataSignal(sender, instance, using, **kwargs):
instance.updateData()
| [
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] | |
7ffc7004a4767c645f7ddca86bae2f5ff4bbc9df | d668a6d561f181913c50c0c94bf725198e1680a1 | /RL_basics/value_iteration.py | 5f933ff76808dc02da8b7a7daf29fbafd6f1e354 | [] | no_license | achyut-srivastava/Reinforcement-Learning | 23944ee580f305ddad509f31bcfb7639bf0d7fd8 | ab2f9a2111ab585a330e5d15cf54ab86a579a41b | refs/heads/master | 2022-06-03T19:06:51.517985 | 2020-05-05T01:33:08 | 2020-05-05T01:33:08 | 261,335,144 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,452 | py | import numpy as np
from grid_world import standard_grid, negative_grid
from iterative_policy_evaluation import print_values, print_policy
SMALL_ENOUGH = 10e-4
GAMMA = 0.9
ALL_POSSIBLE_ACTIONS = ('U', 'D', 'L', 'R')
if __name__ == "__main__":
grid = negative_grid(-.1)
print("rewards: ")
print_values(grid.rewards, grid)
states = grid.all_state()
print("\ninitial initalization: ")
policy = {}
for s in grid.actions.keys():
policy[s] = np.random.choice(ALL_POSSIBLE_ACTIONS)
print_policy(policy, grid)
# Value Initialization
V = {}
for s in states:
V[s] = 0
while True:
delta = 0
for s in states:
old_Vs = V[s]
if s in policy:
old_a = policy[s]
new_V = float('-inf')
new_a = None
for a in ALL_POSSIBLE_ACTIONS:
grid.set_state(s)
r = grid.move(a)
v = r + GAMMA * (V[grid.current_state()])
if v > new_V:
new_V = v
new_a = a
V[s] = new_V
policy[s] = new_a
delta = max(delta, np.abs(old_Vs-V[s]))
if delta < SMALL_ENOUGH:
break
print("\n\n")
print_values(V, grid)
print("\n\n")
print_policy(policy, grid)
pass
| [
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] | |
3a35a4f0d70df9f2a9d6cc4836f51145303da749 | 65aab5e31fe8d415b55d2a50ca4e9d5c5525a7c6 | /exercise-tdd/main.py | 83f155998450f122a8fabe1416f130506f8ceb6e | [
"MIT"
] | permissive | csoehnel/DSR-Testing | a4578d4ac92b339cd19c28ec71642d49e45478fd | 3a28fb893ad6a46934c46123efaf77e3a40f2977 | refs/heads/master | 2020-04-26T04:34:28.688589 | 2017-01-31T17:08:22 | 2017-01-31T17:08:22 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 75 | py | import unittest
# Returns the nth Fibonacci number
def fib(n):
return
| [
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] | |
13eb91841cc9ea67908bd23dae98c76394f14d93 | 90a63ba41e6f462ea0c869bc415ae3d8d4f8342a | /classroom/models.py | 9085146a1e5bc0d9b78522d8a72e484a6a5168ad | [] | no_license | buypolarbear/childcare-app-1 | d8ee0f5ce5cfa94f89b2e8a04eb78b6bc4a892dc | 8acecb2611de5b9b8957a239efa5eb5c787f6a06 | refs/heads/master | 2020-05-06T15:39:15.864117 | 2013-11-11T14:35:37 | 2013-11-11T14:35:37 | 180,201,165 | 0 | 0 | null | 2019-04-08T17:39:12 | 2019-04-08T17:38:55 | Python | UTF-8 | Python | false | false | 2,010 | py | from django.contrib.auth.models import User
from django.db import models
from child.imagegenerators import GalleryThumbnail
from child.models import Child
from childcare.models import Childcare
class Classroom(models.Model):
name = models.CharField(max_length=100)
description = models.CharField(max_length=255)
childcare = models.ForeignKey(Childcare)
teachers = models.ManyToManyField(User)
created = models.DateTimeField(auto_now_add=True)
modified = models.DateTimeField(auto_now=True)
disabled = models.BooleanField(default=False)
def __unicode__(self):
return self.name
def get_absolute_url(self):
return 'childcare/%s/classroom/%s' % (self.childcare.pk, self.pk)
class Attendance(models.Model):
author = models.ForeignKey(User)
date = models.DateField(blank=True)
created = models.DateTimeField(auto_now_add=True)
modified = models.DateTimeField(auto_now=True)
classroom = models.ForeignKey(Classroom)
attendance = models.ManyToManyField(Child)
class Meta:
unique_together = ['classroom', 'date']
class Diary(models.Model):
author = models.ForeignKey(User)
date = models.DateField(blank=True)
created = models.DateTimeField(auto_now_add=True)
modified = models.DateTimeField(auto_now=True)
classroom = models.ForeignKey(Classroom)
content = models.TextField()
class Meta:
unique_together = ['classroom', 'date']
class DiaryImage(models.Model):
image = models.ImageField(upload_to='images/childcare/')
thumbnail = GalleryThumbnail(source='image')
diary = models.ForeignKey(Diary)
class Plan(models.Model):
author = models.ForeignKey(User)
classrooms = models.ManyToManyField(Classroom)
start_date = models.DateField()
end_date = models.DateField()
content = models.TextField()
file = models.FileField(upload_to='files/plan/')
created = models.DateTimeField(auto_now_add=True)
modified = models.DateTimeField(auto_now=True) | [
"[email protected]"
] | |
5ba22dd5545bb52f3c63a5bf0e88a0481fe9e92b | b522a33dd1d0b42a1817ccc0d81ba75e765d51ff | /sales/sales/doctype/floor_rise_master/test_floor_rise_master.py | 3333ec4585d9676334f4172a51bc831474f6d942 | [
"MIT"
] | permissive | dngupta78/sales | ae2d2d38b5722e526ac116acd6d226d473083198 | aee6be4ac46f6b63e85f819f68f14c242f1c9480 | refs/heads/master | 2016-09-05T15:55:07.999526 | 2015-10-23T13:22:28 | 2015-10-23T13:22:28 | 42,845,935 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 270 | py | # -*- coding: utf-8 -*-
# Copyright (c) 2015, d and Contributors
# See license.txt
from __future__ import unicode_literals
import frappe
import unittest
# test_records = frappe.get_test_records('Floor Rise Master')
class TestFloorRiseMaster(unittest.TestCase):
pass
| [
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] | |
c48be57faa453f5f6f03d5164d04197f4fda3136 | 1612752a34dbd848de952430736fcabdaf28af46 | /Assignment_6/approximateUser.py | da3c088aaf01753f57160d8cd21763d48d2b5d62 | [] | no_license | pshk04/anwala.github.io | 5a4de651a8d0f47c93bdcd31315c1cb7406310a3 | 4f33cab7cbd5712ef8eb549f59fc63dcbabecf45 | refs/heads/master | 2022-10-20T12:32:48.621496 | 2018-05-01T16:20:29 | 2018-05-01T16:20:29 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,177 | py | from operator import itemgetter
matchingUsers = []
myAge = 27
myOccupation = 'student'
myGender = 'M'
userMoviesDict = {}
userMovieRatingDict = {}
finalTopThree = {}
finalBottomThree = {}
userMovieRatingsList = []
movieRatingsList = []
matches = ''
bottomCount = 0
topCount = 0
listSize = 0
with open('users.txt', 'r') as f1:
for line in f1:
userId,age,gender,occupation,zipcode = line.split('|')
# if((int(age) < int(myAge) and int(age) > int((myAge - 3))) and (gender == myGender) and (occupation == myOccupation)):
if((int(age) == myAge) and (gender == myGender) and (occupation == myOccupation)):
matchingUsers.append(userId)
print matchingUsers
with open('data.txt', 'r') as f2:
for line in f2:
userId,movieId,rating,mseconds = line.split(' ')
if(userId in matchingUsers):
if(userId in userMoviesDict):
userMoviesDict[userId] = userMoviesDict[userId] + ":" + movieId + "|" + rating
else :
userMoviesDict[userId] = movieId + "|" + rating
print('--------')
for key, value in userMoviesDict.items():
# print(key,userMoviesDict[key])
userMovieRatingsList = userMoviesDict[key].split(":")
for movieRating in userMovieRatingsList:
movie,rating = movieRating.split("|")
userMovieRatingDict[movie] = rating
# print(movie,rating)
sortedRatings = sorted(userMovieRatingDict.items(), key=lambda value: value[1])
# print("Length :",len(sortedRatings))
bottomCount = 0
topCount = 0
listSize = 0
bottomMovieData = ""
topMovieData = ""
for data in sortedRatings:
listSize = listSize + 1
if(bottomCount < 3):
if(bottomMovieData == ""):
bottomMovieData = str(data)
else :
bottomMovieData = bottomMovieData + ":" + str(data)
bottomCount = bottomCount + 1
if(listSize > len(sortedRatings) - 3):
if(topMovieData == ""):
topMovieData = str(data)
else :
topMovieData = topMovieData + ":" + str(data)
finalBottomThree[key] = bottomMovieData
finalTopThree[key] = topMovieData
print('--------------')
print(finalTopThree)
print(finalBottomThree)
print('\n')
print "User" + " " + "Movie Title" + " " + "Rating"
print "----" + " " + "-----------" + " " + "------"
for key, value in finalTopThree.items():
movieTuple = finalTopThree[key].split(":")
for movie in movieTuple:
movieId,rating = str(movie).split(",")
movieId = movieId.replace("(","").replace("'","")
with open('item.txt', 'r') as file:
for line in file:
mid,movieTitle = line.split("|")[0:2]
if(mid == movieId):
print key," "+ movieTitle+" "+rating.replace(")","").replace("'","")
print('\n')
print "User" + " " + "Movie Title" + " " + "Rating"
print "----" + " " + "-----------" + " " + "------"
for key, value in finalBottomThree.items():
movieTuple = finalBottomThree[key].split(":")
for movie in movieTuple:
movieId,rating = str(movie).split(",")
movieId = movieId.replace("(","").replace("'","")
with open('item.txt', 'r') as file:
for line in file:
mid,movieTitle = line.split("|")[0:2]
if(mid == movieId):
print key," "+ movieTitle+" "+rating.replace(")","").replace("'","")
| [
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] | |
c8ec4b2595b08a6a47592bb7d473604b5a04ce0b | bc88f23d872d52c77d7837393a2409c6c993a12f | /python/arrays_and_strings/1.1/is_unique.py | 12c1eb526cabb975240ae19f4a778628093da5e4 | [] | no_license | Shikkic/cracking-the-code | 4076c7eb55c81211996002eb6bd4e6a52507d114 | afa1b59999af2ffc84ab352d4a2c1ce4f4df8e75 | refs/heads/master | 2021-01-10T23:31:37.457545 | 2016-10-01T03:35:01 | 2016-10-01T03:35:01 | 69,712,097 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 234 | py |
def is_unique(s):
if len(s) < 1:
return False
characterDict = {}
for char in s:
if characterDict.has_key(char):
return False
else:
characterDict[char] = 1
return True
| [
"[email protected]"
] | |
4883a6e3a0a778d3603243e35ac8d859bea0f498 | e268c3b21914dba8e2a9194975ade2a64ed7b704 | /DrOptimize/optimize/test.py | 14df39dc63bac2592693662d7b0bf9677940dee5 | [] | no_license | dwinkelman/dwinkcode | 108bb84e7cd7dba66dc3daef622d660544603a26 | 6ddd783ace5c5c89cfe5df3d41de8d9cef69d07b | refs/heads/master | 2021-01-01T20:07:37.690930 | 2017-07-30T20:52:54 | 2017-07-30T20:52:54 | 98,769,342 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 415 | py | import _dijkstra as dk
cons = [
(0, 1, 7.0),
(0, 2, 2.0),
(0, 3, 3.0),
(1, 2, 3.0),
(1, 4, 4.0),
(2, 4, 4.0),
(2, 8, 1.0),
(3, 12, 2.0),
(4, 6, 5.0),
(5, 7, 2.0),
(5, 11, 5.0),
(6, 8, 3.0),
(7, 8, 2.0),
(9, 10, 6.0),
(9, 11, 4.0),
(9, 12, 4.0),
(10, 11, 4.0),
(10, 12, 4.0)
]
path, cost = dk.Dijkstra(cons, 0, 5, True)
print cost
print path
| [
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] | |
901c175641a9bff6b41da9341a5e03bb5421d8ac | 476cc933d384a3586dd80b186b37161d62494235 | /pyramid_fullauth/events.py | 47cbe3689533dde1bd80c261c1ea3612c98f57fd | [
"MIT"
] | permissive | pronebel/pyramid_fullauth | 4a28022acc77bf06f9cc3b7458de63c71645298d | 7280660bda44879d8fe2ca340868d73e5ea2d6ce | refs/heads/master | 2021-05-13T22:39:15.048808 | 2018-01-05T12:11:21 | 2018-01-05T12:11:21 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 7,132 | py | # Copyright (c) 2013 - 2016 by pyramid_fullauth authors and contributors <see AUTHORS file>
#
# This module is part of pyramid_fullauth and is released under
# the MIT License (MIT): http://opensource.org/licenses/MIT
"""pyramid_fullauth emits these events during whole cycle."""
class _BaseRegisterEvent(object):
"""
Base fullauth event.
most of the fullauth's event will provide both request and user
object with some additional data (these will be described then).
"""
def __init__(self, request, user):
"""
Initialize event.
:param pyramid.request.Request request: request object
:param pyramid_fullauth.models.User user: user object
"""
self.request = request
self.user = user
class BeforeRegister(_BaseRegisterEvent):
"""
Execute custom code at the start of registration process.
.. note::
User object is not yet in session.
"""
def __init__(self, request, user, errors):
"""
Initialize event.
:param pyramid.request.Request request: request object
:param pyramid_fullauth.models.User user: user object
:param dict errors: a dictionary with wrong/not submitted fields
with format - fields for which error occured: error message
"""
_BaseRegisterEvent.__init__(self, request, user)
self.errors = errors
class AfterRegister(_BaseRegisterEvent):
"""
Add custom post-processing code in registration process.
Can be used to add e.g. e-mail sending with registration links.
.. note::
User object is already in a session.
.. note::
Action emitting this event, should catch all HTTPRedirection that might
be risen in event listener.
.. warning::
If HTTPRedirection is risen from event listener, then response_values
will not be used!
"""
def __init__(self, request, user, response_values):
"""
Initialize event.
:param pyramid.request.Request request: request object
:param pyramid_fullauth.models.User user: user object
:param dict response_values: a dictionary with response values
"""
_BaseRegisterEvent.__init__(self, request, user)
self.response_values = response_values
class AfterActivate(_BaseRegisterEvent):
"""
Add custom post-processing logic after user gets activated.
.. note::
Action emitting this event, should catch all HTTPRedirection that might
be risen in event listener.
"""
pass
class AfterResetRequest(_BaseRegisterEvent):
"""
Add custom post-processing after user sends request to reset password.
.. note::
Action emitting this event, should catch all HTTPRedirection that might
be risen in event listener.
"""
pass
class BeforeReset(_BaseRegisterEvent):
"""Add custom pre-processing before the actual reset-password process."""
pass
class AfterReset(_BaseRegisterEvent):
"""
Add custom post-processing after the actual reset-password process.
.. note::
Action emitting this event, should catch all HTTPRedirection that might
be risen in event listener.
"""
pass
class AlreadyLoggedIn(object):
"""
Allow execute custom logic, when logged in user tries to log in again.
.. note::
Action emitting this event, should catch all HTTPRedirection that might
be risen in event listener.
"""
def __init__(self, request):
"""
Initialize event.
:param pyramid.request.Request request: request object
"""
self.request = request
class BeforeLogIn(_BaseRegisterEvent):
"""
Add custom logic before user gets logged in.
.. note::
Action emitting this event, should catch all AttributeError that might
be risen in event listener.
User param set to None when user is not found or request method is GET.
"""
pass
class AfterLogIn(_BaseRegisterEvent):
"""Add custom logic after user logs in."""
pass
# Social events
# TODO: extract to sub module
class _BaseSocialRegister(_BaseRegisterEvent):
"""Base for all social requests."""
def __init__(self, request, user, profile):
"""
Initialize base events.
:param pyramid.request.Request request: request object
:param pyramid_fullauth.models.User user: user object
:param dict profile: a dictionary with profile data
"""
_BaseRegisterEvent.__init__(self, request, user)
self.profile = profile
class BeforeSocialRegister(_BaseSocialRegister):
"""Adds custom logic before the social login process start."""
pass
class AfterSocialRegister(_BaseSocialRegister):
"""
Add custom logic after user registers through social network.
.. note::
Action emitting this event, should catch all HTTPRedirection that might
be risen in event listener.
"""
def __init__(self, request, user, profile, response_values):
"""
Initialize event.
:param pyramid.request.Request request: request object
:param pyramid_fullauth.models.User user: user object
:param dict profile: a dictionary with profile data
:param dict response_values: a dictionary with response values
"""
_BaseSocialRegister.__init__(self, request, user, profile)
self.response_values = response_values
class AfterSocialLogIn(_BaseSocialRegister):
"""
Custom logic after user logs in through social network.
.. note::
Action emitting this event, should catch all HTTPRedirection that might
be risen in event listener.
"""
pass
class SocialAccountAlreadyConnected(_BaseSocialRegister):
"""
Event raised when social account is already connected to some other user.
Allow to add custom logic, when someone tries to connect social account to
second user in application.
.. note::
Action emitting this event, should catch all HTTPRedirection that might
be risen in event listener.
"""
def __init__(self, request, user, profile, response_values):
"""
Initialize event.
:param pyramid.request.Request request: request object
:param pyramid_fullauth.models.User user: user object
:param dict profile: a dictionary with profile data
:param dict response_values: a dictionary with response values
"""
_BaseSocialRegister.__init__(self, request, user, profile)
self.response_values = response_values
# Email change events.
class BeforeEmailChange(_BaseRegisterEvent):
"""Allow to add custom validation (like checking password) before email change process."""
pass
class AfterEmailChange(_BaseRegisterEvent):
"""Allow to add some custom post-processing, like e-mail sending, after email change process."""
pass
class AfterEmailChangeActivation(_BaseRegisterEvent):
"""Allow to add custom logic, after changed email had been activated."""
pass
| [
"[email protected]"
] | |
f37a3867f87a9c74a9e8745f4624efc6a482d43f | a7f777a2dcb6a77f738d7e79fdd6629b01564182 | /funciones.py | 44387bdd64b05782a994cc4f046c37ecaedc0d11 | [] | no_license | vantoara/Proyecto-Algoritmos-2021-1 | 1d905201a8f4a7509dc59e26784c02d96132da92 | 532e61cc0a9435066ac56bc0d37b384f32f8718b | refs/heads/main | 2023-03-30T12:26:02.439070 | 2021-04-06T03:29:40 | 2021-04-06T03:29:40 | 354,208,054 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 29,987 | py | from Player import Player
from Guessing import Guessing
from Word_Search import Word_Search
from Crypto import Crypto
from Hangman import Hangman
from Math import Math
from Python import Python
from Boolean import Boolean
from Number import Number
from Shuffle import Shuffle
from Quiz import Quiz
from Memory import Memory
from Logic import Logic
from Color import Color
from Room import Room
import requests
import random
import enquiries
import json
import requests
api = requests.get("https://api-escapamet.vercel.app")
# Función utilizada para el quiz número 1
def validate_password():
print("\nSu contraseña debe tener más de 8 caracteres, tener al menos un número, una letra mayúscula, una letra minúscula y un caracter especial")
while True:
password = input("\nIngrese su nueva contraseña: ")
if len(password) < 8:
print("\nLa contraseña elegida no es válida.")
else:
#Los any iteran a través de los varios for loops y si UN solo caracter cumple la condición (regresa True) entonces, la condición regresará True, por ende, utilicé varios any (varios for loop idénticos) para ver que las condiciones de la contraseña se cumplan.
if any(character.isspace() for character in password):
print("\nLa contraseña elegida no es válida. Ingresaste un espacio.")
else:
if (not any(character.isdigit() for character in password)) or (not any(character.isalpha() for character in password)):
print("\nLa contraseña elegida no es válida. Carece de números o letras.")
else:
if any(character.islower() for character in password) and any(character.isupper() for character in password):
if any(not character.isalnum() for character in password):
print("\nContraseña ingresada correctamente")
break
else:
print("\nLa contraseña elegida no es válida. Debe añadir un caracter especial.")
else:
print("\nLa contraseña elegida no es válida. Necesita tanto letras mayúsculas como minúsculas.")
return password
def registry(database):
# Aquí se registrarán los usuarios por primera vez.
user = input("\nIngrese su nombre de usuario: ")
if user in database:
print("\nIngreso inválido, usuario ya registrado.")
return database
# Aquí no hay ningún loop ya que si es un usuario que está registrado, puede ser que a la persona simplemente se le haya olvidado, por ende, esto lo enviaría de una vez al menú principal.
while len(user) < 3 or len(user) > 8:
print("\nNombre de usuario es muy largo o muy corto. Debe de ser entre 3 y 8 caracteres.")
user = input("\nIngrese su nombre de usuario: ")
password = validate_password() # Ya validado en la función anterior
age = input("\nIngrese su edad: ")
while not age.isdigit() or int(age) < 16:
print("\nEdad es inválida. Recuerde que debe ser mayor de 16 años para poder jugar.")
age = input("\nIngrese su edad: ")
# Al registrar el usuario, como no está comenzando una partida, no me interesa en lo absoluto lo demás.
player = Player(user.strip(), password, int(age))
# Se me guarda como un diccionario siendo el user la llave, así cuando se inicie una nueva partida y pida el user, puedo jalar información fácilmente
database[user] = player
return database
def start_new_game(database):
# Vuelvo a pedir user así corroboro si está creado
user = (input("\nIngrese su nombre de usuario para poder jugar: ")).strip()
while len(user) < 3 or len(user) > 8:
print("\nNombre de usuario es muy largo o muy corto. Debe de ser entre 3 y 8 caracteres.")
user = (input("\nIngrese su nombre de usuario para poder jugar: ")).strip()
if user in database:
# Si el usuario existe es porque hay un objeto asignado a ese usuario, lo jalo y lo guardo en la variable player para mayor simplicidad
player = database[user]
while True:
password = input("\nIngrese su contraseña: ")
if password == player.get_password():
#La librería enquiries permite un menú de selección de opciones el cual no necesita validar y es bastante práctico, solo requiere pasar la lista con las opciones deseadas como parámetro.
difficulty_options = ["Fácil", "Medio", "Difícil"]
difficulty_choice = enquiries.choose("\nEscoja una dificultad: ", difficulty_options)
if difficulty_choice == "Fácil":
print("\nBienvenido, nuevo ingreso.")
player.set_difficulty(5.0, 5)
# Se implementan las distintas especificaciones dependiendo de cada . Las vidas se pasan como float por razones obvias.
elif difficulty_choice == "Medio":
print("\nVeterano de barra.")
player.set_difficulty(3.0, 3)
else:
print("\n¿Haces doble titulación, no?")
player.set_difficulty(1.0, 2)
avatars = ["Scharifker", "Eugenio Mendoza", "Pelusa", "Gandhi", "Metropavo", "Exodia", "Lorenzo Mendoza", "Becado"]
choose_avatar = enquiries.choose("\nEscoje uno de los siguientes avatares: ", avatars)
player.set_avatar(choose_avatar)
print("\nHoy 5 de marzo de 2021, la Universidad sigue en cuarentena (esto no es novedad), lo que sí es novedad es que se robaron un Disco Duro de la Universidad del cuarto de redes que tiene toda la información de SAP de estudiantes, pagos y asignaturas. Necesitamos que nos ayudes a recuperar el disco, para eso tienes minutos, antes de que el servidor se caiga y no se pueda hacer más nada. ¿Aceptas el reto?\n")
# Si te llegas a morir entonces el while loop se rompe (consecuentemente ocurre lo mismo en las funciones nested) y se para el
while player.check_lives():
won = movement(player, api) # Esta función se encuentra al final, ya que a continuación se desarrollan son los distintos juegos.
if won:
break
# Este if agarra el caso en el que perdiste tus vidas y hace un break que te arrojará al inicio del menú
if not player.check_lives():
print("\nMuy mala jugada, te has quedado sin vidas!")
break
else:
print(f"\nFelicitaciones, este trimeste ha sido muy difícil para todos, y a pesar de las desdichas y las angustias has podido entregar el Disco Duro a su lugar correspondiente. La universidad se encuentra agradecida contigo, {player.avatar}.")
break
else:
print("\nContraseña incorrecta!")
else:
# Igualmente, para qué hacer un while loop si no se encuentra registrado. Es innecesario.
print("\nEste usuario no se encuentra registrado.")
# Para toda función de juego le paso room, obj (objeto) y el objeto player; room y obj para la clase (que se detalla en Game.py) y player para poder modificar los atributos como es debido
# Adivinanzas
def guessing_game(room, obj, player):
game = Guessing(room, obj)
# Este if lo que valida es si el jugador ya completó el minijuego, es decir, si tienes la recompensa en tu inventario es porque jugaste este juego y lo completaste.
if game.get_award() in player.get_inventory():
print("\nYa completaste este juego. Anda, que el tiempo se agota.")
else:
# Valida si tengo los requisitos para jugar, se detalla más en Game.py
if "contraseña" in player.get_inventory():
print("Ingrese la contraseña...")
print("\n"*5)
print("Contraseña correcta!") # simulación de que se hizo un input de contraseña, aesthetic.
game.show_game()
game.show_question()
while player.check_lives():
options = ["Responder", "Pedir pista"]
choice = enquiries.choose("\nEscoja que hacer: ", options)
if choice == "Responder":
# No valido la respuesta a mayor profundidad ya que como el juego es una adivinanza, no lo amerita.
player_guess = input("\nIngrese su respuesta: ")
answer = game.guess(player_guess) # Retorna True o False y se valida si se resuelve o no la adivinanza
if answer == True:
print(f"\nHas ganado el siguiente objeto: {game.get_award()}")
player.add_item(game.get_award())
break
else:
player.update_lives(-0.5)
player.check_lives()
else:
# Reviso si el jugador tiene pistas disponibles y si el juego tiene más pistas que dar. Si no se cumple, hay un mensaje de advertencia
if player.check_player_clues() and game.check_clue():
game.get_clue()
player.update_clues()
# Sopa de Letras
def word_search_game(room, obj, player):
game = Word_Search(room, obj)
if "sopa_check" in player.get_inventory(): # El premio de la sopa de letras no es un objeto sino una vida extra, "sopa_check" es un string que se appendea a la lista de inventario y se tiene como un aval de que se completó el juego
print("\n¿Memoria de corto plazo?. Ya completaste este juego.")
else:
if game.check_requirements(player.get_inventory()):
game.show_game()
# Método para mostrar la sopa. Se imprime una sola vez por tamaño y comodidad
game.show_soup()
while player.check_lives():
options = ["Responder", "Pedir pista"]
choice = enquiries.choose("\nEscoja que hacer: ", options)
if choice == "Responder":
player_guess = ((input("Ingrese su respuesta: ")).lower()).strip()
while not player_guess.isalpha():
print("\nEntrada inválida, ingrese una palabra")
player_guess = ((input("Ingrese su respuesta: ")).lower()).strip()
# Podría colocar una validación para que el jugador evite ingresar números y demás, pero creo que es justo y necesario penalizarlos por su mala conducta y querer romper el programa.
# answer guarda si se completa el juego correctamente o no
answer = game.guess_word(player_guess, player)
if answer == True:
print("\nHas ganado una vida extra!")
player.update_lives(1.0)
# El premio de la sopa de letras es una vida extra, así que se agrega al contador de vidas del jugador
player.add_item("sopa_check") # Append del aval
break
else:
if player.check_player_clues() and game.check_clue():
game.get_word_clue()
player.update_clues()
# Criptograma
def cryptic_game(room, obj, player):
game = Crypto(room, obj)
if "Mensaje" in player.get_inventory(): # Aquí busco "Mensaje" justamente ya que así es como está guardado de requisito para la API y otra habitación, así que para evitar mayores complicaciones, se verifica el dato directamente en lugar de hacer un get.award()
print("\nPor aquí no es, ya resolviste este juego.")
else:
if game.check_requirements(player.get_inventory()):
game.show_game()
game.show_alphabet()
game.show_encode()
while player.check_lives():
player_decode = (input("\nDescrife el código e ingrese su respuesta: ")).lower()
# Podría colocar una validación para que el jugador evite ingresar números y demás, pero creo que es justo y necesario penalizarlos por su mala conducta y querer romper el programa.
answer = game.answer_decode(player_decode)
if answer == True:
change_award = game.get_award()
change_award = change_award.replace(": Si estas gradudado puedes pisar el Samán","")
print(f"\nHas ganado el siguiente objeto: {change_award}")
player.add_item(change_award)
break
else:
player.update_lives(-1)
# Criptograma no tiene pistas así que no nos interesa en lo absoluto.
# Ahorcado
def hangman_game(room, obj, player):
game = Hangman(room, obj)
if game.get_award() in player.get_inventory():
print("\nQue por aquí ya pasaste, vete a otra parte!")
else:
if game.check_requirements(player.get_inventory()):
game.show_game()
game.show_prompt()
while player.check_lives():
options = ["Responder", "Pedir pista"]
choice = enquiries.choose("\nEscoja que hacer: ", options)
if choice == "Responder":
player_letter = (input("\nIngrese una letra: ")).lower()
while not player_letter.isalpha(): # Valido el input, ya que aquí si se trabaja NETAMENTE con letras
print("\nEntrada inválida. Por favor, ingrese una letra.")
player_letter = (input("\nIngrese una letra: ")).lower()
answer = game.guess_hangman(player_letter, player)
if answer == True:
print(f"\nHas ganado el siguiente objeto: {game.get_award()}")
player.add_item(game.get_award())
break
elif answer == False: # Debido a como la clase y su método correspondiente están programadas, hay que ser específicos aquí
break # Como la vida se va restando dentro de la clase, no hace falta colocar nada aquí además del break
else:
if player.check_player_clues() and game.check_clue():
game.get_hangman_clue()
player.update_clues()
# Preguntas matemáticas
def math_game(room, obj, player):
game = Math(room, obj)
if "mate_check" in player.get_inventory():
print("\nAfortunadamente sabes derivar, ya que completaste este juego.")
else:
if game.check_requirements(player.get_inventory()):
game.show_game()
game.show_math()
while player.check_lives():
options = ["Responder", "Pedir pista"]
choice = enquiries.choose("\nEscoja que hacer: ", options)
if choice == "Responder":
player_math_input = validate_player_math_input() # Esta función valida el input del usuario, ya que debe ser una fracción
answer = game.answer_math(player_math_input)
if answer == True:
print("\nHas ganado una vida extra!")
player.update_lives(1.0)
player.add_item("mate_check") # Se appendea el aval de que se completó el juego
break
else:
player.update_lives(-0.25)
break
else:
if player.check_player_clues() and game.check_clue():
game.get_math_clue()
player.update_clues()
def validate_player_math_input(): # Valida el input del usuario para el juego de matemáticas
player_math_input = (input("\nIngrese su respuesta en fracciones: ")).strip()
while True:
try:
float(player_math_input)
break
except ValueError: # Si el except lo capta entonces es porque puede tener "/" es decir, ser una fracción
try:
number, denom = player_math_input.split("/")
float(number)
float(denom) # Se evalúan ambos por separado
break
except:
print("\nIngreso inválido, por favor, ingrese un número.")
player_math_input = input("\nIngrese su respuesta en fracciones: ")
return player_math_input
# Preguntas python
def python_game(room, obj, player):
game = Python(room, obj)
if game.get_award() in player.get_inventory():
print("\nDemostraste que sabes lo básico de programación al completar este juego, no hay más nada que hacer aquí.")
else:
if game.check_requirements(player.get_inventory()):
game.show_game()
game.show_phrase()
while player.check_lives():
options = ["Responder", "Pedir pista"]
choice = enquiries.choose("\nEscoja que hacer: ", options)
if choice == "Responder":
player_python_answer = input("\nIngrese su código en una línea: ")
while "frase" not in player_python_answer:
print("\nPor favor, ingrese un código decente.") # Esto evalúa que AL MENOS ingresen la variable que guarda el string
player_python_answer = input("\nIngrese su código en una línea: ")
answer = game.answer_python(player_python_answer)
if answer == True:
print(f"\nHas ganado el siguiente objeto: {game.get_award()}")
player.add_item(game.get_award())
break
else:
player.update_lives(-0.5)
break
else:
if player.check_player_clues() and game.check_clue():
game.get_py_clue()
player.update_clues()
# Preguntas booleanas
def boolean_game(room, obj, player):
game = Boolean(room, obj)
if game.get_award() in player.get_inventory():
print("\nAquí no tienes nada que hacer, ya destruiste la puerta con el martillo.")
else:
if game.check_requirements(player.get_inventory()):
game.show_game()
game.show_boolean_question()
# Únicas posibles respuestas
options = ["True", "False"]
choice = enquiries.choose("\nEscoja que hacer: ", options)
answer = game.answer_boolean(choice)
if answer == True:
print(f"\nHas ganado el siguiente objeto: {game.get_award()}")
player.add_item(game.get_award())
else:
player.update_lives(-0.5)
# Entre número
def number_game(room, obj, player):
game = Number(room, obj)
if "Titulo Universitario" in player.get_inventory():
print("\nTienes tan buena suerte como aquel que pasa las tres físicas en el primer intento. Ya terminaste este juego.")
else:
if game.check_requirements(player.get_inventory()):
game.show_game()
game.show_number_question()
while player.check_lives():
player_number = input("\nAdivine un número entero en el rango dado: ")
while not player_number.isdigit(): # Validación que sea un entero
print("\nPor favor, ingrese un número.")
player_number = input("\nAdivine un número entero en el rango dado: ")
answer = game.answer_number(player_number)
if answer == True:
change_award = game.get_award()
change_award = change_award.replace("título Universitario","Titulo Universitario") # Temas de la API...
print(f"\nHas ganado el siguiente objeto: {change_award}")
player.add_item(change_award)
break
elif answer == False:
player.update_lives(-0.25)
# No hay break, el juego no se detiene sino hasta que gane o muere.
if player.check_player_clues():
# Este prompt siempre aparecerá después de cada ingreso fallido, por si el usuario quiere una pista
options_clue = ["Si", "No"]
choice_clue = enquiries.choose("\n¿Desea obtener una pista?", options_clue)
if choice_clue == "Si":
game.get_number_clue(player_number)
player.update_clues()
# Palabras Mezcladas
def shuffle_game(room, obj, player):
game = Shuffle(room, obj)
if game.get_award() in player.get_inventory():
print("\nYa completaste este juego. Pudiste descifrar las palabras mezcladas, ahora asegúrate que no hayas mezclado tu elección de carrera.")
else:
if game.check_requirements(player.get_inventory()):
game.show_game()
game.show_information()
game.show_shuffle()
while player.check_lives():
player_shuffle_guess = (input("\nIngrese una palabra a ordenar: ")).strip()
while not player_shuffle_guess.isalpha():
print("\nIngreso inválido, por favor ingrese una palabra.")
player_shuffle_guess = (input("\nIngrese una palabra a ordenar: ")).strip()
answer = game.guess_shuffle(player_shuffle_guess)
if answer == True:
print(f"\nHas ganado el siguiente objeto: {game.get_award()}")
player.add_item(game.get_award())
break
elif answer == False:
player.update_lives(-0.5)
# Quizizz
def quiz_game(room, obj, player):
game = Quiz(room, obj)
if game.get_award() in player.get_inventory():
print("\nTe esforzaste mucho respondiendo el Quizizz, ya completaste esta actividad. Lástima que solo vale 1%")
else:
if game.check_requirements(player.get_inventory()):
game.show_game()
game.show_specs()
while player.check_lives():
menu_options = ["Responder", "Pista"]
choice = enquiries.choose("\nEscoja una respuesta", menu_options)
if choice == "Responder":
options = game.get_options()
player_choice = enquiries.choose("\nEscoja una respuesta", options)
answer = game.answer_quiz(player_choice)
if answer == True:
print(f"\nHas ganado el siguiente objeto: {game.get_award()}")
player.add_item(game.get_award())
break
else:
player.update_lives(-0.5)
break
else:
if player.check_player_clues() and game.check_clue():
game.get_quiz_clue()
player.update_clues()
# Memoria de emojis
def memory_game(room, obj, player):
game = Memory(room, obj)
if game.get_award() in player.get_inventory():
print("\nNi instalandóte una memoria de 16gb de RAM te acuerdas que ya pasaste por aquí. Este juego ya lo completaste.")
else:
if game.check_requirements(player.get_inventory()):
game.show_grid()
game.show_game()
# Para este juego en específico, se realizaron todas las funciones como métodos dentro de la clase
answer = game.guess_card(player)
if answer == True:
print(f"\nHas ganado el siguiente objeto: {game.get_award()}")
player.add_item(game.get_award())
def logic_game(room, obj, player):
game = Logic(room, obj)
if game.get_award() in player.get_inventory():
print("\nNo estés buscando problemas, ya pisaste el Samán y sobreviviste, ahora continúa.")
else:
# REVISAR POR SEPARADO REQUIREMENTS. FOR LOOP.
if "Mensaje" in player.get_inventory() and "Titulo Universitario" in player.get_inventory():
game.show_game()
game.show_logic_question()
player_logic = input("\nIngresa un número entero como respuesta: ")
while not player_logic.isdigit():
print("\nEntrada inválida.")
player_logic = input("\nIngresa un número entero como respuesta: ")
answer = game.answer_logic(int(player_logic))
if answer == True:
print(f"\nHas ganado el siguiente objeto: {game.get_award()}")
player.add_item(game.get_award())
else:
player.update_lives(-1)
else:
game.show_msg()
player.update_lives(-1) # Con esto logramos penalizar al jugador por pisar el samán
# Sudoku
def final_game(room, obj, player):
game = Color(room, obj)
if "carnet" not in player.get_inventory() and "Disco Duro" not in player.get_inventory():
game.show_msg()
else:
game.show_game()
game.color_question()
while player.check_lives():
answer = game.choose_color()
if answer == True:
print("\nGanaste el juego! Pudiste recuperar el Disco")
won = True
return won
elif answer == False:
player.update_lives(-1)
def movement(player, api):
# Comienzas en la biblioteca
room = 1
position = Room(room)
print(f"\nBienvenido {player.get_avatar()}, gracias por tu disposición a ayudarnos a resolver este inconveniente, te encuentras actualmente ubicado en la biblioteca, revisa el menú de opciones para ver qué acciones puedes realizar. Recuerda que el tiempo corre más rápido que un trimestre en este reto.")
position.show_room()
while player.check_lives():
can_break_door = api.json()[3]["objects"][0]["game"]["award"] in player.get_inventory()
options = ["Ir a otra habitación", "Interactuar"]
choice = enquiries.choose("\n¿Qué quieres hacer?: ", options)
if choice == options[0]:
# Teniendo el cuenta el mapa del doc, tomé en cuenta las habitaciones que se encuentran a la derecha e izquierda de cada uno y así el jugador sigue el flujo correcto
directions = ["Izquierda", "Derecha"]
choice_direction = enquiries.choose("\nEscoje a que dirección quieres moverte", directions)
if room == 0:
if choice_direction == directions[0]:
room = 4
else:
room = 3
elif room == 1:
if choice_direction == directions[0]:
room = 3
else:
room = 2
elif room == 2:
if choice_direction == directions[0]:
room = 1
else:
print("\nNo te puedes mover a esa dirección, ya no hay más habitaciones a tu derecha!")
elif room == 3:
if choice_direction == directions[0]:
if can_break_door:
room = 0
else:
print("\nNo puedes pasar por ahí, está cerrado! Intenta abrir la puerta.")
else:
room = 1
else:
if choice_direction == directions[0]:
print("\nNo te puedes mover a esa dirección, ya no hay más habitaciones a tu izquierda!")
else:
room = 0
position = Room(room)
print("\n"*40)
position.show_room()
else:
objects = position.get_list_objects()
choice_object = enquiries.choose("\nEscoge con cuál objeto desea interactuar: ", objects)
object_index = objects.index(choice_object)
position.show_object(object_index)
if room == 0:
if object_index == 0:
word_search_game(room, object_index, player)
elif object_index == 1:
python_game(room, object_index, player)
else:
guessing_game(room, object_index, player)
elif room == 1:
if object_index == 0:
hangman_game(room, object_index, player)
elif object_index == 1:
math_game(room, object_index, player)
else:
cryptic_game(room, object_index, player)
elif room == 2:
if object_index == 0:
logic_game(room, object_index, player)
elif object_index == 1:
quiz_game(room, object_index, player)
else:
memory_game(room, object_index, player)
elif room == 3:
if object_index == 0:
boolean_game(room, object_index, player)
elif room == 4:
if object_index == 0:
won = final_game(room, object_index, player)
if won:
break
elif object_index == 1:
shuffle_game(room, object_index, player)
else:
number_game(room, object_index, player)
return won | [
"[email protected]"
] | |
4943a44793928a8c14faa931914ef6e92390821e | d147d5c4d15ff602243716fd1833827cad97d1cb | /IFin/IFApp/migrations/0011_auto_20190103_1408.py | 83851741af5c9e3f981200ddc6d132a2b449862b | [] | no_license | makachat/IFIN | 76d75e32f820e6031933454990373e13eecb1c5c | df30c75d80028b24ee87390d50e37902f2d0dfee | refs/heads/master | 2021-06-18T01:06:47.030866 | 2019-07-03T14:31:49 | 2019-07-03T14:31:49 | 164,739,060 | 0 | 0 | null | 2021-06-10T21:06:28 | 2019-01-08T21:49:07 | Python | UTF-8 | Python | false | false | 958 | py | # Generated by Django 2.1.4 on 2019-01-03 19:08
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('IFApp', '0010_auto_20190103_1406'),
]
operations = [
migrations.AlterField(
model_name='site',
name='address',
field=models.CharField(blank=True, max_length=255, null=True),
),
migrations.AlterField(
model_name='site',
name='category',
field=models.CharField(blank=True, choices=[('Plant', 'Plant'), ('DataCenter', 'Data Center'), ('Yard', 'Yard'), ('DistributionCenter', 'Distribution Center'), ('HeadOffice', 'Head Office')], max_length=50, null=True),
),
migrations.AlterField(
model_name='site',
name='country',
field=models.CharField(blank=True, choices=[('CA', 'CANADA'), ('US', 'USA')], max_length=10, null=True),
),
]
| [
"[email protected]"
] | |
ea1686d108c62cec07db162f4db36b7d4cf71ee8 | d7bc9f6a968de928f2bfc82aee93762c3b893c23 | /applications/home/models.py | c2744a0ea2279bc94db44b0242c9e70b61c96539 | [] | no_license | Diego-David/Prueba2 | 04e47d4a1fbfd5c8f89a0f7b46deb4f728ca4916 | 261ca23d34ea1d59a6d79e3ce2ef5d524a0ad052 | refs/heads/master | 2023-07-06T19:00:24.604487 | 2021-07-22T04:00:07 | 2021-07-22T04:00:07 | 376,198,913 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 290 | py | from django.db import models
# Create your models here.
class Prueba(models.Model):
titulo = models.CharField(max_length=100)
subtitulo = models.CharField(max_length=50)
cantidad = models.IntegerField()
def __str__(self):
return self.titulo + ' - '+self.subtitulo | [
"[email protected]"
] | |
d200ffe9828f5211da2758b5183039836393cb8c | 133b46dbe2ec0acbe9e9d1c84c1598e22cc205e9 | /Contours/contours.py.py | 6426e01d0fe6836335343458446ecaa72d324b82 | [] | no_license | kanishk307/crack-detection-beproject | dbaf601c791356ffcc7a1b84094918518de8522d | a07a9e4ba017c4223dde4f681ae9229837bda60e | refs/heads/master | 2021-07-14T10:14:23.562819 | 2020-06-19T18:25:41 | 2020-06-19T18:25:41 | 172,304,081 | 15 | 6 | null | null | null | null | UTF-8 | Python | false | false | 1,551 | py | import cv2
import numpy as np
areasum=0
src = cv2.imread("a3.jpg", 1) #image path dalo
height, width, channels = src.shape
# print(channels)
gray = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY) # grayscale, jarurat naiye but incase image hoga toh rehnediya. vaise abhi binary hi hai so no issues
blur = cv2.blur(gray, (3, 3)) # iska bhi vaise jarurat naiye par better result deray thoda. not noteworthy
ret, thresh = cv2.threshold(blur, 50, 255, cv2.THRESH_BINARY)
im2, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# hull array
hull = []
# no of points for each contor
for i in range(len(contours)):
# convex hull obj for each contour
hull.append(cv2.convexHull(contours[i], False))
# empty kala dabba
drawing = np.zeros((thresh.shape[0], thresh.shape[1], 3), np.uint8)
cv2.imshow('draw',drawing)
# draw contours and hull points
for i in range(len(contours)):
color_contours = (0, 255, 0) # green contour
color = (255, 0, 0) # blue contour
# ek ek karke draw green contour
# cv2.drawContours(drawing, contours, i, color_contours, 1, 8, hierarchy)
# draw blue convex
cv2.drawContours(drawing, hull, i, color, 1, 8)
# print(cv2.contourArea(hull)) #IDHAR KAAM KARNA HAI
cnt = hull[i]
M=cv2.moments(cnt)
# print(M)
area = cv2.contourArea(cnt)
areasum=areasum+area
# print(areasum)
print(areasum)
frameSize = width * height
intensity = areasum/frameSize
print("Intensity")
print(intensity * 100)
cv2.imshow('draw',drawing)
cv2.waitKey(0) | [
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