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212
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
62e2055c06bdab8ebe9363f8cb6ba7382d3af888
|
4577d8169613b1620d70e3c2f50b6f36e6c46993
|
/students/1798177/homework04/program02.py
|
2afb7299a76787aa239a4beaaac8f0e9130c4d9e
|
[] |
no_license
|
Fondamenti18/fondamenti-di-programmazione
|
cbaf31810a17b5bd2afaa430c4bf85d05b597bf0
|
031ec9761acb1a425fcc4a18b07884b45154516b
|
refs/heads/master
| 2020-03-24T03:25:58.222060
| 2018-08-01T17:52:06
| 2018-08-01T17:52:06
| 142,419,241
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 11,331
|
py
|
# Il tris è un popolarissimo gioco. Si gioca su una griglia quadrata di 3x3
# caselle. A turno, i due giocatori scelgono una cella vuota e vi disegnano il
# proprio simbolo (un giocatore ha come simbolo una 'o' e l'avversario una 'x').
# Vince il giocatore che riesce a disporre tre dei propri simboli in linea retta
# orizzontale, verticale o diagonale. Se la griglia viene riempita senza che
# nessuno dei giocatori sia riuscito a completare una linea retta di tre
# simboli, il gioco finisce in parità. Nel caso in cui il gioco finisse in
# parità, la partita è detta "patta". Per convenzione a griglia vuota la prima
# mossa spetta sempre al giocatore 'o'.
#
# Una configurazione del gioco è dunque univocamente determinata dal contenuto
# della griglia.
#
# Nel seguito assumiamo che il contenuto della griglia sia rappresentato tramite
# lista di liste. La dimensione della lista di liste M è 3x3 ed M[i][j] contiene
# '', 'x', o 'o' a seconda che la cella della griglia appartenente all'iesima
# riga e j-ma colonna sia ancora libera, contenga il simbolo 'x' o contenga il
# simbolo 'o'.
#
# Data una configurazione C del gioco, l'albero di gioco per C è l'albero che
# si ottiene ricorsivamente partendo dalla configurazione C e assegnando come
# figli le configurazioni che è possibile ottenere da C con una mossa ulteriore
# del gioco. Ovviamente risulteranno foglie dell'albero i possibili esiti della
# partita vale a dire le diverse configurazioni cui è possibile arrivare
# partendo da C e che rappresentano patte, vittorie per 'o' o vittorie per 'x'.
# Se veda ad esempio l'immagine albero_di_gioco.png che mostra l'albero di
# gioco che si ottiene a partire dalla configurazione rappresentata da
# [['x', 'o', 'o'], ['x', 'x', 'o'], ['', '', '']].
#
# Si consideri la seguente Classe di oggetti:
#
# class NodoTris:
# def __init__(self, griglia):
# self.nome = griglia
# self.lista_figli = []
#
# Bisogna progettare le seguente funzione gen_tree(griglia) che, data la
# configurazione di gioco griglia, costruisce l'albero di gioco che si ottiene a
# partire dalla configurazione griglia e ne restituisce la radice. I nodi
# dell'albero devono essere oggetti della classe NodoTris.
#
# Per testare la correttezza della vostra implementazione di gen_tree() il grade
# utilizzerà quattro metodi della classe NodoTris che dovete comunque
# implementare:
#
# 1) tipo(self)
# che, dato un nodo NodoTris, restituisce:
# - 'o' se la configurazione rappresentata dal nodo è una configurazione di
# vittoria per il giocatore 'o';
# - 'x' se la configurazione rappresentata dal nodo è una configurazione di
# vittoria per il giocatore 'x';
# - '-' se la configurazione rappresentata dal nodo è una configurazione di
# patta;
# - '?' se la configurazione rappresentata dal nodo è una configurazione di
# gioco non ancora terminato.
#
# 2) esiti(self)
# che, dato un nodo radice di un albero di gioco, restituisce una tripla con i
# possibili esiti della partita che ha come configurazione iniziale quella
# rappresentata dal nodo. Più precisamente: il primo elemento della tripla è il
# numero di patte possibili, il secondo è il numero di possibili vittorie per
# il giocatore 'o' mentre il terzo elemento è il numero di possibili vittorie
# per il giocatore 'x'.
#
# 3) vittorie_livello(self, giocatore, h)
# che, dato un nodo radice di un albero di gioco, uno dei due giocatori ed un
# intero h, restituisce il numero di nodi che rappresentano una vittoria per il
# giocatore e si trovano ad altezza h nell'albero. In altri termini restituisce
# il numero di vittorie possibili per giocatore in esattamente h mosse, nella
# partita che ha come configurazione iniziale quella rappresentata dalla radice
# dell'albero.
#
# 4) strategia_vincente(self, giocatore)
# che, dato un nodo radice di un albero di gioco ed uno dei due giocatori,
# restituisce True o False. Restituisce True se giocatore ha una strategia
# vincente nella partita che ha come configurazione iniziale quella
# rappresentata dal nodo radice, False altrimenti.
#
# Nota che un giocatore ha una strategia vincente rispetto ad una certa
# configurazione se, qualunque siano le mosse dell'avversario ha sempre la
# possibilità di rispondere in modo che la partita termini con la sua vittoria.
#
# Potete ovviamente definire ulteriori funzioni e altri metodi per la Classe
# NodiTris se li ritenete utili al fine della risoluzione del compito.
#
# Potete assumere che le configurazioni di gioco rappresentate da griglia siano
# sempre configurazioni lecite (vale a dire ottenute dopo un certo numero di
# mosse a parire dalla griglia vuota).
#
# AVVERTENZE: non usare caratteri non ASCII, come le lettere accentate; non
# importare moduli che non sono nella libreria standard.
#
# ATTENZIONE: i test vengono eseguiti con un timeout globale di 2*N secondi (se
# il grader esegue N test).
class NodoTris:
def __init__(self, grid):
self.nome = grid # La griglia con i valori.
self.lista_figli = set() # Insieme dei sviluppi del nodo.
self.status = '' # Lo stato del nodo ('o', 'x', '?', '-').
self.turn = '' # 0 -> 'o'; 1 -> 'x'.
self.score = []
def tipo(self):
return self.status # Viene calcolato durante la creazione dell'albero.
def esiti(self):
result = [0, 0, 0]
perform_endings(self, result)
return tuple(result)
def vittorie_livello(self, player, dest_lvl, current_lvl = 0):
if dest_lvl == current_lvl:
return int(self.status == player) # Torna 1 o 0.
else: # Ancora non si raggiunge 'dest_lvl'.
wins = 0
for sub_config in self.lista_figli:
wins += sub_config.vittorie_livello(player,
dest_lvl,
current_lvl + 1)
return wins
def strategia_vincente(self,giocatore):
if giocatore == 'o':
opposite = 'x'
else:
opposite = 'o'
result = strategy(self, giocatore, opposite)
if result == -1:
return False
else:
return True
# ------------------------------------------------------------------------------
def perform_endings(node, result):
exit = { '-' : 0, 'o' : 1, 'x' : 2 }
if node.status != '?':
result[exit[node.status]] += 1
return
for sub_config in node.lista_figli:
perform_endings(sub_config, result)
def score(node, player, opponent):
'''Ritorna il punteggio del giocatore sul nodo 'node'.'''
if node.status == player:
return 1
else: # Se vince il nemico o pareggia è sempre una cosa negativa.
return -1
def get_single_score(scores, value):
'''Ritorna il punteggio in base a 'value'.'''
if value in scores:
return value
else:
return -value # Opposto.
def evalutate_strategy(node, opponent, scores):
'''Valuta se è presente o meno una strategia vincente su 'node'.'''
if node.turn == opponent:
return get_single_score(scores, -1)
else:
return get_single_score(scores, 1)
def strategy(node, player, opponent):
'''Ritorna la presenza di una strategia vincente per il giocatore.'''
if not node.lista_figli:
return score(node, player, opponent)
scores = set()
# Micro-ottimizzazione: la risoluzione dei nomi in Python è molto lenta,
# sopratutto in casi di cicli come il for.
add = scores.add
for sub_config in node.lista_figli:
add(strategy(sub_config, player, opponent))
return evalutate_strategy(node, opponent, scores)
# ------------------------------------------------------------------------------
GRID_X = 1
GRID_O = 0
GRID_EMPTY = 10
def get_translated_cell(cell):
'''Converte la cella dal formato della griglia di partenza a quello con
la codifica numerica. Restituisce la cella convertita.
'''
if not cell:
return GRID_EMPTY
return int(cell != 'o') # 0 -> 'o', 1 -> 'x'.
def convert_grid(grid):
'''Converte la griglia dal formato originale ad uno con le celle codificate
in numeri.
'''
for row in range(3):
for column in range(3):
grid[row][column] = get_translated_cell(grid[row][column])
def calculate_sums(grid, sums):
'''Calcola i risultati delle somme delle righe, colonne e diagonali e salva
tutto sulla lista 'sums'.
'''
first_row = 0
second_row = 1
third_row = 2
first_column = 3
second_column = 4
third_column = 5
diag = 6
rdiag = 7
for step in range(0, 3):
sums[first_row] += grid[0][step]
sums[second_row] += grid[1][step]
sums[third_row] += grid[2][step]
sums[first_column] += grid[step][0]
sums[second_column] += grid[step][1]
sums[third_column] += grid[step][2]
sums[diag] = grid[0][0] + grid[1][1] + grid[2][2]
sums[rdiag] = grid[0][2] + grid[1][1] + grid[2][0]
def get_default_status(sums):
'''Ritorna il simbolo di patta oppure partita non terminata, in base ai
valori di 'sums'.
'''
if max(sums) >= GRID_EMPTY:
return '?'
else:
return '-'
def get_status(grid):
'''Ritorna lo stato delle griglia, che può essere '-', '?', 'x', 'o'.'''
sums = [0, 0, 0, 0, 0, 0, 0, 0]
calculate_sums(grid, sums)
if 3 in sums: # tre 'x' (ossia 1) in fila.
return 'x'
elif 0 in sums: # tre 'o' (ossa 0) in fila.
return 'o'
return get_default_status(sums)
def get_copy_of(grid):
'''Restituisce una copia della griglia.'''
new_grid = []
for row in range(3):
new_grid += [[grid[row][0], grid[row][1], grid[row][2]]]
return new_grid
def get_player(player):
if player:
return 'o'
else:
return 'x'
def next_move(tree, grid, row, column, player):
if grid[row][column] == GRID_EMPTY:
child_grid = get_copy_of(grid)
child_grid[row][column] = player
tree.lista_figli.add(get_tree(child_grid, player))
def get_tree(griglia, player):
tree = NodoTris(griglia)
tree.status = get_status(griglia)
tree.turn = get_player(player)
if tree.status != '?':
return tree
player = (player + 1) % 2
for row in range(3):
next_move(tree, griglia, row, 0, player)
next_move(tree, griglia, row, 1, player)
next_move(tree, griglia, row, 2, player)
return tree
def get_start_player(grid):
'''Ritorna il giocatore (codificato in numero) che deve effettuare la mossa
al turno successivo rispetto alla griglia 'grid' convertita in numeri.
'''
first_row = 0
second_row = 0
third_row = 0
for column in range(3):
first_row += grid[0][column]
second_row += grid[1][column]
third_row += grid[2][column]
# La somma di tutti i valori indica chi inizierà per primo.
total = first_row + second_row + third_row
if total == 90: # Griglia vuota.
return 1
else:
return total % 2
def gen_tree(griglia):
griglia = get_copy_of(griglia)
convert_grid(griglia)
start_player = get_start_player(griglia)
return get_tree(griglia, start_player)
|
[
"a.sterbini@gmail.com"
] |
a.sterbini@gmail.com
|
e5edc21a34b45ca67e7abb9b03ee9215880f212d
|
c440bcb0e566ed107d198593bfeb482c59276dd8
|
/advent_of_code/2021/day10_1.py
|
2d34868acd58b3462cc9f7332e432aea3f23b3a6
|
[] |
no_license
|
TheCDC/Musings
|
1ee917bbf2fd39f6fa97b268568053ca6ad7fbbf
|
7b07e315230248239bbccad5d85d0a5e8a54d5d8
|
refs/heads/master
| 2022-11-30T23:37:24.608955
| 2021-12-19T08:12:03
| 2021-12-19T08:12:03
| 175,046,297
| 0
| 0
| null | 2022-11-22T07:20:49
| 2019-03-11T17:01:54
|
Python
|
UTF-8
|
Python
| false
| false
| 1,751
|
py
|
from typing import List, Optional, Tuple
with open("inputs/day10.txt") as f:
lines = f.read().split()
openers = "([{<"
closers = ")]}>"
points_corruption = {")": 3, "]": 57, "}": 1197, ">": 25137}
def complete(opens: List[str]):
to_complete = opens[:]
completion: List[str] = []
while to_complete:
c = to_complete.pop()
completion.append(closers[openers.find(c)])
return completion
def score_corruption(s: str):
return points_corruption[s]
def is_matched_pair(a: str, b: str):
assert len(a) == 1 and len(b) == 1
assert a in openers
assert b in closers
matching = openers.find(a) == closers.find(b)
return matching
def doline(line: str):
chars = list(reversed(list(enumerate(line))))
left: List[str] = []
right: List[str] = []
corrupted: Optional[Tuple[int, str]] = None
while chars:
i, c = chars.pop()
if c in openers:
left.append(c)
else:
right.append(c)
if not is_matched_pair(left[-1], c):
corrupted = (i, c) if corrupted is None else corrupted
while len(left) and len(right) and is_matched_pair(left[-1], right[-1]):
left.pop()
right.pop()
completion = complete(left)
return (left, right, completion, corrupted)
def solve(lines):
score_total = 0
results = [doline(line) for line in lines]
score_total = sum(score_corruption(cor[1]) for l, r, comp, cor in results if cor)
return (score_total, results)
def main():
solved = solve(lines)
print(
solved[0],
*[tuple("".join(x) for x in (t[0], t[1], t[2])) + (t[3],) for t in solved[1]],
sep="\n"
)
if __name__ == "__main__":
main()
|
[
"christopher.chen1995@gmail.com"
] |
christopher.chen1995@gmail.com
|
2dd0f2b4ecbe9a9e488c7bbbb3b682fe8d7cc773
|
108f35831d5f81dd1e8eaace0b05e7202d2258dd
|
/searchr_app/file_analyzer/FileAnalyzer.py
|
2a8d62151de3763affead234dd4f587e91b2b260
|
[] |
no_license
|
cebul55/Searchr_Project
|
d54c997bb5cc5eda8a066d74adf8bb06dead9ebe
|
f629439912cdd4e822f549c48740611cab2e9d6f
|
refs/heads/master
| 2020-08-05T20:15:02.560090
| 2020-01-26T23:28:22
| 2020-01-26T23:28:22
| 212,691,605
| 0
| 0
| null | 2020-01-26T18:55:51
| 2019-10-03T22:21:09
|
Python
|
UTF-8
|
Python
| false
| false
| 3,940
|
py
|
import itertools
import json
from bs4 import BeautifulSoup
from searchr_app.file_analyzer.HTMLFileAnalyzer import HTMLFileAnalyzer
from searchr_app.file_analyzer.TextFileAnalyzer import TextFileAnalyzer
class FileAnalyzer(object):
search_result = None
search_phrases_combination = None
# phrases defined by user
search_phrases = None
# query defined by user
search_query = None
html_doc = None
text_doc = None
accuracy = 0.0
def __init__(self, search_result):
self.search_result = search_result
phrases_list = str(search_result.search.phrases_list)
self.search_phrases_combination = self.generate_phrase_combinations_as_text(phrases_list)
self.search_query = self.get_search_query(search_result.search)
if 'pdf' in search_result.content_type or 'word' in search_result.content_type:
self.text_doc = search_result.html_file
elif search_result.html_file is not None:
self.html_doc = search_result.html_file
self.start_analyzing()
# def __init__(self, search_result, phrases_combitation=None, html_doc=None, text_doc=None):
# if phrases_combitation is None:
# self.search_result = search_result
# phrases_list = str(search_result.search.phrases_list)
# self.search_phrases_combination = self.generate_phrase_combinations_as_text(phrases_list)
# if 'pdf' in search_result.content_type or 'word' in search_result.content_type:
# self.text_doc = search_result.html_file
# elif search_result.html_file is not None:
# self.html_doc = search_result.html_file
# self.start_analyzing()
# else:
# self.search_result = search_result
# self.search_phrases_combination = phrases_combitation
# self.html_doc = html_doc
# self.text_doc = text_doc
def start_analyzing(self):
if self.text_doc is not None:
print('startin doc analisys')
self.analyze_text()
elif self.html_doc is not None:
self.analyze_html()
# set status to 'analyzed' after end of analisys
# self.search_result.set_status_to_analyzed()
def generate_phrase_combinations_as_text(self, phrases_list):
phrases_list = self.convert_literal_list_to_list(phrases_list)
self.search_phrases = phrases_list
combinations = []
for L in range(1, len(phrases_list) + 1):
for subset in itertools.combinations(phrases_list, L):
combinations.append(subset)
return combinations
@staticmethod
def convert_literal_list_to_list(literal_list):
import ast
x = u'' + literal_list
x = ast.literal_eval(x)
return_list = x
return_list = [n.strip() for n in return_list]
return return_list
def analyze_text(self):
print('startin doc analisys')
text_analyzer = TextFileAnalyzer(self.search_result, self.search_phrases_combination, self.search_phrases, self.search_query, self.text_doc)
text_analyzer.analyze_text_file()
self.accuracy = text_analyzer.count_result_accuracy()
pass
def analyze_html(self):
html_analyzer = HTMLFileAnalyzer(self.search_result, self.search_phrases_combination, self.search_phrases, self.search_query, self.html_doc)
html_analyzer.analyze_html_file()
self.accuracy = html_analyzer.count_result_accuracy()
def get_accuracy(self):
return self.accuracy
def get_search_query(self, search):
saved_attribs = search.attributes.replace('\"', '#DOUBLEQUOTE#')
saved_attribs = saved_attribs.replace('\'', '\"')
attr_dict = json.loads(saved_attribs)
search_query = attr_dict['query']
search_query = search_query.replace('#DOUBLEQUOTE#', '\"')
return search_query
|
[
"B.Cybulski@stud.elka.pw.edu.pl"
] |
B.Cybulski@stud.elka.pw.edu.pl
|
13499e083165756c2b409cacdc7e80ddbc1bb0ee
|
d7ff534ef84ca89c5e0505f249374a77b74559f9
|
/services/users/project/api/utils.py
|
7e051efb9fce1a609dbfaf5feb7d5dd7296d3316
|
[] |
no_license
|
Muhinyuzi/testdriven-app
|
10095c25cb0473f73734a3356bfd8d1ce7917c14
|
625982c8e2bdea3200f6962751210d158365a6a3
|
refs/heads/master
| 2022-06-21T06:39:03.323832
| 2019-12-03T11:28:24
| 2019-12-03T11:28:24
| 154,828,668
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,016
|
py
|
# services/users/project/api/utils.py
from functools import wraps
from flask import request, jsonify
from project.api.models import User
def authenticate(f):
@wraps(f)
def decorated_function(*args, **kwargs):
response_object = {
'status': 'fail',
'message': 'Provide a valid auth token.'
}
auth_header = request.headers.get('Authorization')
if not auth_header:
return jsonify(response_object), 403
auth_token = auth_header.split(" ")[1]
resp = User.decode_auth_token(auth_token)
if isinstance(resp, str):
response_object['message'] = resp
return jsonify(response_object), 401
user = User.query.filter_by(id=resp).first()
if not user or not user.active:
return jsonify(response_object), 401
return f(resp, *args, **kwargs)
return decorated_function
def is_admin(user_id):
user = User.query.filter_by(id=user_id).first()
return user.admin
|
[
"mujecla007@gmail.com"
] |
mujecla007@gmail.com
|
dc47da1229fb1fe7b1bab77e741c3d578297a343
|
d205793571b39fe254f513f4437f37c47f4db011
|
/src_py/ztf_data.py
|
8c01c96019f18722082a48d7ffa032ab7d4514e1
|
[] |
no_license
|
memanuel/kepler-sieve
|
ba996c4008edec3458ac0afbfa3bee95367467cd
|
9f11f377d82cb941d77159fd7b97ae2300b6ca6a
|
refs/heads/main
| 2021-11-22T19:07:13.725263
| 2021-08-08T20:44:47
| 2021-08-08T20:44:47
| 236,581,914
| 1
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 3,965
|
py
|
"""
ZTF Data
Calculations to enrich ZTF data after it is loaded.
Michael S. Emanuel
24-Mar-2021
"""
# Standard libraries
import numpy as np
import pandas as pd
# Astronomy related
from astropy.units import deg
# UI
from tqdm.auto import tqdm as tqdm_auto
# MSE imports
from db_utils import df2db, sp2df, sp_run
from astro_utils import mjd_to_datetime
from ra_dec import radec2dir, calc_topos
from rebound_sim import make_sim_planets
from rebound_integrate import integrate_mjds
# *********************************************************************************************************************
def calc_ztf_detection_times():
"""Update entries in table KS.DetectionTime associated with ZTF observations"""
# Distinct ZTF detection times
df = sp2df('ZTF.GetDetectionTimes')
# Array of observations times as MJDs
mjds = df['mjd'].values
# Populate the CalendarDateTime field
df['CalendarDateTime'] = np.array([mjd_to_datetime(mjd) for mjd in mjds])
# All available data sources as a DataFrame
ds = sp2df('KS.GetDataSources')
# Populate DataSourceID and ObservatoryID fields
df['DataSourceID'] = ds.DataSourceID[ds.DataSourceCD=='ZTF'].values[0]
df['ObservatoryID'] = ds.ObservatoryID[ds.DataSourceCD=='ZTF'].values[0]
# Integrate the planets saving outputs at these observation times
print(f'Integrating planets on {df.shape[0]} distinct observation times...')
sim_epoch = make_sim_planets(epoch=mjds[0])
body_ids, body_names, q, v, elts = integrate_mjds(sim_epoch=sim_epoch, mjds=mjds, save_elements=False, progbar=True)
# Earth position at these observation times
earth_idx = np.argmax(body_names=='Earth')
q_earth = q[:,earth_idx,:]
v_earth = v[:,earth_idx,:]
# Calculate topos adjustment
dq_topos, dv_topos = calc_topos(obstime_mjd=mjds, site_name='Palomar')
# The position and velocity of the observatory
q_obs = q_earth + dq_topos.value # in AU
v_obs = v_earth + dv_topos.value # in AU / day
# Position of the Sun
sun_idx = np.argmax(body_names=='Sun')
q_sun = q[:,sun_idx,:]
# Save positions of observatory and sun to DataFrame
df[['qObs_x', 'qObs_y', 'qObs_z']] = q_obs
df[['vObs_x', 'vObs_y', 'vObs_z']] = v_obs
df[['qSun_x', 'qSun_y', 'qSun_z']] = q_sun
# Save these observation times and positions to DB
df2db(df=df, schema='KS', table='DetectionTime', verbose=False, progbar=False)
# *********************************************************************************************************************
def ztf_raw_detections(sz: int = 1000000):
"""Populate missing rows of table KS.RawDetections from ZTF.Detections"""
# Get the number of missing rows
rMax = sp2df('KS.MakeTable_RawDetection_ZTF_RowCount').RowCount[0]
# Set up a tqdm index counter to process the rows in chunks of sz at a time
iMax: int = rMax // sz + 1
idx = tqdm_auto(np.arange(iMax))
# Process the rows in chunks of sz
params={'sz':sz}
print(f'KS.RawDetections missing {rMax} rows from ZTF.Detections. Processing now...')
for i in idx:
sp_run('KS.MakeTable_RawDetection_ZTF', params=params)
# *********************************************************************************************************************
def main():
"""
Main routine for console program
"""
# Call SQL procedure to add new rows to ZTF.DetectionTime from ZTF.Detection
sp_run('ZTF.MakeTable_DetectionTime')
# Call SQL procedure to insert new records to DetectionTimeSlice
sp_run('KS.MakeTable_DetectionTimeSlice', {'sz': 30});
# Rebuild the KS.DetectionTime entries coming from ZTF
calc_ztf_detection_times()
# Update KS.DetectionTimePair
sp_run('KS.MakeTable_DetectionTimePair', {'sz':60})
# *********************************************************************************************************************
if __name__ == '__main__':
main()
|
[
"michael.s.emanuel@gmail.com"
] |
michael.s.emanuel@gmail.com
|
2466a150d32b4e609db3ff2436a9814b440d4592
|
764cd26665124fdba7ad36e1dbb5c45cfc3c8ac2
|
/0x0B-python-input_output/0-read_file.py
|
c4c3d7507a44edf365cf57fc51ce838ff5cf6eb2
|
[] |
no_license
|
Athesto/holbertonschool-higher_level_programming
|
633dbc006ba895e52a7db10b0a6e2f7b65a3f283
|
5de9c7fa35247ae27c488f1a4ed1db8f7aa6bd5e
|
refs/heads/master
| 2023-05-02T21:03:11.872649
| 2021-05-25T21:09:54
| 2021-05-25T21:10:59
| 291,834,251
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 196
|
py
|
#!/usr/bin/python3
'''task 0'''
def read_file(filename=""):
'''read file'''
with open(filename, 'r', encoding="utf-8") as file:
for line in file:
print(line, end='')
|
[
"gamez.live@gmail.com"
] |
gamez.live@gmail.com
|
3b8e576a97ba4ac059b20ebad2031a6905f954f0
|
e75d8c82d32db8544f90cd298fcbc9bf99570322
|
/beginnerModel.py
|
b48fe82c944aa8947bbe9777a2b6be54119edf60
|
[] |
no_license
|
szr712/SkipRope2
|
d5069d661bbc84d3f18234e035185e90a4db368d
|
60941a6362ab200afa3642a4b4708feef44dd7f6
|
refs/heads/master
| 2023-04-21T07:04:26.898651
| 2021-05-12T06:39:19
| 2021-05-12T06:39:19
| 360,069,902
| 1
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 7,082
|
py
|
import os
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.layers import LSTM
import tensorflow as tf
from tensorflow.python.keras import Input
from tensorflow.python.keras.layers import concatenate, add
from tensorflow.python.keras.models import Model, load_model
from tensorflow.python.keras.optimizer_v2.adam import Adam
from tensorflow.python.keras.optimizer_v2.rmsprop import RMSprop
from tensorflow.python.keras.utils.version_utils import callbacks
from tensorflow.python.keras.utils.vis_utils import plot_model
from dataReader import padding, load_dataset_beginner, load_dataset_beginner_reg
from datetime import datetime
modelName = "初学者动作标准度_Dense_有扩容_无finetuning_"
# os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
epochs, batch_size = 200, 256
dataSet = "./data"
className = "RopeSwinging"
logDir = "./logs"
curTime = datetime.now().strftime("_%Y%m%d_%H_%M_%S")
modelPath = "./model"
zuoyou = "zuoyou/左右得分_class_weight_0.997__20210419_19_16_24.h5"
shoubi = "shoubi/手臂得分_class_weight_0.974__20210419_20_14_55.h5"
shouwan = "shouwan/手腕得分_class_weight_0.969__20210419_20_13_17.h5"
def to_circleList(data):
hallsensor = -1
circleList = []
pre = 0
for i in range(0, data.shape[0]):
if data[i][9] == hallsensor:
hallsensor = -hallsensor
# 切割进入list
circleList.append(data[pre:i, 0:9].copy())
pre = i
circleList.append(data[pre:, 0:9].copy())
for i in range(0, len(circleList)):
circleList[i] = padding(circleList[i])
return circleList
def zuoyou_model():
model = load_model(os.path.join(modelPath, zuoyou))
model = Model(inputs=model.input, outputs=model.layers[1].output, name="zuoyou_model")
# model = Sequential(name="zuoyou_model")
# model.add(LSTM(64, input_shape=(30, 9), return_sequences=True, kernel_regularizer=tf.keras.regularizers.l2(0.0001)))
# model.add(LSTM(64, kernel_regularizer=tf.keras.regularizers.l2(0.0001)))
return model
def shoubi_model():
model = load_model(os.path.join(modelPath, shoubi))
model = Model(inputs=model.input, outputs=model.layers[1].output, name="shoubi_model")
model = Sequential(name="shoubi_model")
model.add(LSTM(64, input_shape=(30, 9), return_sequences=True, kernel_regularizer=tf.keras.regularizers.l2(0.0001)))
model.add(LSTM(64, kernel_regularizer=tf.keras.regularizers.l2(0.0001)))
# model.trainable = False
return model
def shouwan_model():
# model = load_model(os.path.join(modelPath, shouwan))
# model = Model(inputs=model.input, outputs=model.layers[1].output, name="shouwan_model")
model = Sequential(name="shouwan_model")
model.add(LSTM(64, input_shape=(30, 9), return_sequences=True, kernel_regularizer=tf.keras.regularizers.l2(0.0001)))
model.add(LSTM(64, kernel_regularizer=tf.keras.regularizers.l2(0.0001)))
# model.trainable = False
return model
def get_callbacks():
return [
callbacks.EarlyStopping(monitor='val_acc', patience=20, restore_best_weights=True), # 就是需要对验证集的loss监听
# callbacks.EarlyStopping(monitor='val_loss', patience=20),
callbacks.TensorBoard(log_dir=os.path.join(logDir, className, modelName + curTime)),
]
def my_loss_fn(y_true, y_pred):
zeros = tf.zeros_like(y_pred, dtype=y_pred.dtype)
ones = tf.ones_like(y_pred, dtype=y_pred.dtype)
filter = tf.where(tf.abs(y_true - y_pred) > 1, ones, zeros)
return tf.reduce_mean(filter * tf.square(y_pred - y_true), axis=-1)
def postion_model():
inputs = []
for i in range(0, 70):
inputs.append(Input(shape=(30, 9)))
print("inputs complicated")
feature = zuoyou_model()
# feature.trainable = False
outs = []
for input in inputs:
outs.append(feature(input))
print("outs complicated")
x = concatenate(outs)
x = Dropout(0.2)(x)
# x = Dense(64, activation="relu", kernel_regularizer=tf.keras.regularizers.l2(0.0001))(x)
# x = tf.expand_dims(x, axis=-1)
# x = LSTM(64, kernel_regularizer=tf.keras.regularizers.l2(0.0001))(x)
# x = LSTM(96, kernel_regularizer=tf.keras.regularizers.l2(0.0001))(x)
out = Dense(3, activation='softmax')(x)
# out = Dense(1)(x)
model = Model(inputs, out)
return model
def rope_model():
inputs = []
for i in range(0, 70):
inputs.append(Input(shape=(30, 9)))
print("inputs complicated")
feature1 = shoubi_model()
feature2 = shouwan_model()
# feature.trainable = False
outs = []
for input in inputs:
x1 = feature1(input)
x2 = feature2(input)
out = concatenate([x1, x2])
outs.append(out)
# outs.append(x1)
# for input in inputs:
# # x1 = feature1(input)
# x2 = feature2(input)
# # out = concatenate(x1)
# outs.append(x2)
# # outs.append(x2)
print("outs complicated")
x = concatenate(outs, axis=1)
x = Dropout(0.2)(x)
# x = Dense(64, activation="relu", kernel_regularizer=tf.keras.regularizers.l2(0.0001))(x)
# x = tf.expand_dims(x, axis=-1)
# x = LSTM(64, kernel_regularizer=tf.keras.regularizers.l2(0.0001))(x)
# x = LSTM(96, kernel_regularizer=tf.keras.regularizers.l2(0.0001))(x)
out = Dense(3, activation='softmax')(x)
# out = Dense(1)(x)
model = Model(inputs, out)
return model
def compile_model(model):
learning_rate = tf.keras.optimizers.schedules.ExponentialDecay(
0.0003,
decay_steps=3000,
decay_rate=0.8)
model.compile(loss='categorical_crossentropy', optimizer=Adam(learning_rate), metrics=['acc'])
# model.compile(loss='mse', optimizer=RMSprop(learning_rate), metrics=['mse', 'mae'])
# model.compile(loss=my_loss_fn, optimizer=RMSprop(learning_rate), metrics=my_loss_fn)
model.summary()
return model
def train_model(model, trainX, trainy, testX, testy, class_weights):
history = model.fit(trainX, trainy, epochs=epochs, batch_size=batch_size, validation_data=(testX, testy),
class_weight=class_weights, callbacks=get_callbacks(), shuffle=True)
# history = model.fit(trainX, trainy, epochs=epochs, batch_size=batch_size, validation_data=(testX, testy),
# callbacks=get_callbacks(), shuffle=True)
result = model.evaluate(testX, testy, batch_size=batch_size)
return history, result
if __name__ == "__main__":
print(modelName)
X_train, X_test, y_train, y_test, class_weights, _ = load_dataset_beginner(dataSet, className,augment=True,times=150)
model = rope_model()
model.summary()
# model = postion_model()
compile_model(model)
# plot_model(model, to_file='./rope_model.png')
history, result = train_model(model, X_train, y_train, X_test, y_test, class_weights)
saveName = modelName + str(round(result[1], 3)) + "_" + curTime + ".h5"
model.save(os.path.join(modelPath, className, saveName))
# model.save(saveName)
|
[
"zirui990712@163.com"
] |
zirui990712@163.com
|
3537aa0fb0be8afecb49e6666914e44afa4ab13f
|
3e01b405b99c95c48cf97f6d2f0609132e7aa80f
|
/Project Code/ICG with Quadruples/a.py
|
48e641adc9f5c9aa8884652dfc5fd106b872f2ad
|
[] |
no_license
|
rishabh15b/Compiler-Design-CPP
|
a1ee6944b98295561842795d6f229f37a9d89f53
|
fe1b43c8dee0c9afa48393aabeec66eaf8c6922e
|
refs/heads/master
| 2022-09-22T18:11:48.829062
| 2020-06-05T10:51:14
| 2020-06-05T10:51:14
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 3,295
|
py
|
fp=open("out.txt","r")
lines=fp.readlines()
#print(lines)
#lines.pop()
c=0
d_value={}
d_reg={}
used=[0]*16
#print(lines)
i=0
while(i<len(lines)):
stmt=lines[i]
stmt=stmt.strip("\n")
#print(stmt)
if("=" in stmt):
index=stmt.index("=")
lhs=stmt[0:index]
exp=stmt[index+1::]
lhs=lhs.strip()
exp=exp.strip()
#print(lhs,exp)
if(exp.isdigit()):
i=i+1
if(lhs not in d_reg):
used[c]=1
s="R"+(str(c))
print("MOV ",s,",#",exp,sep="")
c=c+1
d_value[lhs]=int(exp)
d_reg[lhs]=s
else:
d_value[lhs]=int(exp)
print("MOV ",d_reg[lhs],",#",exp,sep="")
elif(("*" in exp) or ("/" in exp) or ("%" in exp) or("+" in exp) or ("-" in exp)):
operand1,op,operand2=exp.split()
operand1=operand1.strip()
operand2=operand2.strip()
op=op.strip()
#print(operand1,op,operand2)
if(not operand1.isdigit() or not operand2.isdigit()):
if(d_reg.get(operand1)==None and not operand1.isdigit()):
s="R"+str(c)
c=c+1
d_reg[operand1]=s
print("LDR ",s,",",operand1,sep="")
if(d_reg.get(operand2)==None and not operand2.isdigit()):
s="R"+str(c)
c=c+1
d_reg[operand2]=s
print("LDR ",s,",",operand2,sep="")
if(d_reg.get(operand1)!=None and d_reg.get(operand2)!=None):
str1=d_reg[operand1]
str2=d_reg[operand2]
elif(operand1.isdigit()):
str1="#"+operand1
str2=d_reg[operand2]
elif(operand2.isdigit()):
str1=d_reg[operand1]
str2="#"+operand2
next_stmt=lines[i+1]
next_stmt.strip()
index=next_stmt.index("=")
lhs=next_stmt[0:index]
lhs=lhs.strip()
if(d_reg.get(lhs)==None):
s="R"+str(c)
c=c+1
d_reg[lhs]=s
print("LDR ",s,",",lhs,sep="")
if(op=="*"):
print("MUL ",d_reg[lhs],",",str1,",",str2,sep="")
elif(op=="+"):
print("ADD ",d_reg[lhs],",",str1,",",str2,sep="")
elif(op=="-"):
print("SUB ",d_reg[lhs],",",str1,",",str2,sep="")
elif(op=="/"):
print("DIV ",d_reg[lhs],",",str1,",",str2,sep="")
elif(op=="%"):
print("MOD ",d_reg[lhs],",",str1,",",str2,sep="")
i=i+2
elif((">" in exp) or ("<" in exp)):
#print(exp)
operand1,op,operand2=exp.split()
operand1=operand1.strip()
operand2=operand2.strip()
op=op.strip()
if(d_reg.get(operand1)==None and not operand1.isdigit()):
s="R"+str(c)
c=c+1
d_reg[operand1]=s
print("LDR ",s,",",operand1,sep="")
if(d_reg.get(operand2)==None and not operand2.isdigit()):
s="R"+str(c)
c=c+1
d_reg[operand2]=s
print("LDR ",s,",",operand2,sep="")
if(d_reg.get(operand1)!=None and d_reg.get(operand2)!=None):
str1=d_reg[operand1]
str2=d_reg[operand2]
elif(operand1.isdigit()):
str1="#"+operand1
str2=d_reg[operand2]
elif(operand2.isdigit()):
str2="#"+operand2
str1=d_reg[operand1]
s="R"+str(c)
c=c
print("SUB ",s,",",str1,",",str2,sep="")
if(op=="<"):
print("BGZ ",s,",",sep="",end="")
elif(op==">"):
print("BLZ ",s,",",sep="",end="")
elif(op==">="):
print("BLEZ ",s,",",sep="",end="")
elif(op=="<="):
print("BGEZ ",s,",",sep="",end="")
next_stmt=lines[i+2]
print(next_stmt.split()[3])
i=i+3
elif("L" in stmt.split()[0]):
print(stmt)
i=i+1
elif(stmt.split()[0]=="goto"):
print("BR ",stmt.split()[1])
i=i+1
# handle "<="
|
[
"kashishoberoi00@gmail.com"
] |
kashishoberoi00@gmail.com
|
bededd6fe791e5c9a093dd367ca0f71eb18b58a2
|
10be352a4d80672c108fc66e86afec977ec830de
|
/cs1_final/DissemblerSolver.py
|
4f3206e839c973618fadcc7e275b56dfa5e9afda
|
[] |
no_license
|
MeganTj/CS1-Python
|
cef5a7cd812f294812beff58c69514d52e2c1541
|
09b4a7cfb9bfb6bc64b631e6fd8c35ff35a5ca56
|
refs/heads/master
| 2022-02-16T17:49:34.218252
| 2022-01-27T06:22:29
| 2022-01-27T06:22:29
| 178,276,099
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 16,984
|
py
|
# Name: Megan Tjandrasuwita
# CMS cluster login name: mtjandra
'''
Solves a Dissembler puzzle given a puzzle in the form of a one-line string.
'''
import string
from utils import *
# ----------------------------------------------------------------------
# Global data.
# ----------------------------------------------------------------------
# A list of dissembler puzzles.
# Each puzzle is represented as a single string.
# Blank squares are indicated by '.' and colored squares
# are indicated by a single lowercase letter.
# The letters have no meaning (they aren't short for a specific color).
# The blanks in the string are used to separate different rows of the
# puzzle.
puzzles = [
'aababb',
'aa. b.. abb',
'a.. aba bab ..b',
'abba a..b b..a abba',
'.aa. ..b. baab .b.. .aa.',
'a...a babab a...a',
'....a ababa b.... a.aba b.a.b a...a babab',
'aabb .ba. .ac. .cd. ddcd',
'ababc d...b ad.ec .f.c. fd.eg f...e hhghg',
'aabaa bbcbb ccdcc ddedd eeaee',
'.aabb. .c..c. ca..bc d....d cdccdc .cddc.',
'..aab .ccda .b.cb db.db da.d. cbaa. dcc..',
'abbcbc adaddc dccbcb dadaab',
'ababb b.b.a a.a.a a.b.b bbaba',
'.ab. .ba. .ab. abab a..b',
# Harder puzzles:
'...a... ..bac.. .bdbad. cca.dee .afbeb. ..afb.. ...f...',
'aaaaab cbdcdb cbeadb cabfeb cafefb cddddd',
'abcdded adb.ecd abcccad afggged agf.bad afbbgad',
'abcacc daedfe dbgfef ccbhhi gjcijh gfjffi',
'aabcbcb c.a.d.b cbcdcaa d.a.a.b adcabda d.b.d.b acadcdd'
]
# ----------------------------------------------------------------------
# Helper functions.
# ----------------------------------------------------------------------
def is_adjacent(loc1, loc2):
'''
Arguments:
loc1, loc2 -- (row, column) locations
Return value:
True if two locations are orthogonally adjacent, otherwise False.
'''
assert is_loc(loc1)
assert is_loc(loc2)
x_adj = abs(loc1[0] - loc2[0]) == 1
y_adj = abs(loc1[1] - loc2[1]) == 1
if (abs(loc1[0] - loc2[0]) == 1 and loc1[1] == loc2[1]) or \
(abs(loc1[1] - loc2[1]) == 1 and loc1[0] == loc2[0]):
return True
return False
def adjacent_to_any(loc, locset):
'''
Arguments:
loc -- a (row, column) location
locset -- a set of locations
Return value:
True if `loc` is not in `locset` and at least one location
in `locset` is adjacent to `loc`, otherwise False.
The set `locset` is not altered.
'''
assert is_loc(loc)
assert is_locset(locset)
if loc not in locset:
for loc2 in locset:
if is_adjacent(loc, loc2):
return True
return False
def collect_adjacent(locset, target_set):
'''
Arguments:
locset -- a set of (row, column) locations
target_set -- another set of (row, column) locations
Return value:
A set of all the locations in `locset` that are adjacent
to any location in `target_set`.
The sets `locset` and `target_set` are not altered.
'''
assert is_locset(locset)
assert is_locset(target_set)
adj = set()
for loc in locset:
if adjacent_to_any(loc, target_set):
adj.add(loc)
return adj
def collect_connected(loc, locset):
'''
Arguments:
loc -- a (row, column) location
locset -- a set of locations
Return value:
A set of all the locations in `locset` which are connected to `loc`
via a chain of adjacent locations. Include `loc` in the resulting set.
The set `locset` is not altered.
'''
assert is_loc(loc)
assert is_locset(locset)
connected = set([loc])
orig = locset.copy()
adj = collect_adjacent(orig, connected)
adj.add(loc)
while len(adj.difference(connected)) > 0:
connected = connected.union(adj)
for i in adj:
if i in orig:
orig.remove(i)
adj = adj.union(collect_adjacent(orig, connected))
return connected
def partition_connected(locset):
'''
Partition a set of locations based on being connected via a chain of
adjacent locations. The original locset is not altered.
Return a list of subsets. The subsets must all be disjoint i.e.
the intersection of any two subsets must be the empty set.
Arguments:
locset -- a set of (row, column) locations
Return value:
The list of partitioned subsets.
The set `locset` is not altered.
'''
assert is_locset(locset)
orig = locset.copy()
lst = []
lst2 = []
while len(orig) > 0:
lst.append(orig.pop())
while len(lst) > 0:
adj = collect_connected(lst[0], locset)
lst2.append(adj)
for x in adj:
lst.remove(x)
return lst2
def filter_locset(locset):
'''
Given a locset, partition it into subsets which are connected via a
chain of adjacent locations. Compute two sets:
-- the union of all partitions whose length is < 3
-- the union of all partitions whose length is >= 3
and return them as a tuple of two sets (in that order).
Arguments:
locset -- a set of (row, column) locations
Return value:
The two sets as described above.
The set `locset` is not altered.
'''
assert is_locset(locset)
lst = partition_connected(locset)
all_less = set()
all_three = set()
for i in lst:
if len(i) >= 3:
all_three.update(i)
else:
all_less.update(i)
return (all_less, all_three)
def invert_rep(rep):
'''
Invert the board representation which maps locations to colors.
The inverted representation will map colors to sets of locations.
Arguments:
rep -- a dictionary mapping locations to one-character strings
representing colors
Return value:
a dictionary mapping one-character strings (representing colors)
to sets of locations
The input dictionary 'rep' is not altered.
'''
assert is_rep(rep)
colors = list(rep.values())
locations = list(rep.keys())
loc = []
unique = []
for i in range(len(colors)):
if colors[i] in unique:
index = unique.index(colors[i])
loc[index].add(locations[i])
else:
unique.append(colors[i])
loc.append({locations[i]})
inv = {}
for i in range(len(unique)):
inv[unique[i]] = loc[i]
return inv
def revert_rep(inverted):
'''
Invert the board representation which maps colors to sets of
locations. The new representation will map locations to colors.
Arguments:
inverted -- a dictionary mapping one-character strings
(representing colors) to sets of locations
Return value:
a dictionary mapping locations to one-character strings
representing colors
The input dictionary 'inverted' is not altered.
'''
assert is_inverted_rep(inverted)
locations = list(inverted.values())
colors = list(inverted.keys())
rev = {}
for i in range(len(locations)):
loc_set = locations[i].copy()
while len(loc_set) > 0:
rev[loc_set.pop()] = colors[i]
return rev
def swap_locations(rep, loc1, loc2):
'''
Exchange the contents of two locations.
Arguments:
rep -- a dictionary mapping locations to one-character strings
representing colors
loc1, loc2 -- adjacent locations which are in the board rep
Return value:
a new dictionary with the same structure of 'rep' with the
specified locations having each others' contents
The input dictionary 'rep' is not altered.
'''
assert is_rep(rep)
assert is_loc(loc1)
assert is_loc(loc2)
#assert ls.is_adjacent(loc1, loc2)
assert loc1 in rep
assert loc2 in rep
new_rep = rep.copy()
color1 = new_rep[loc1]
new_rep[loc1] = new_rep[loc2]
new_rep[loc2] = color1
return new_rep
def remove_connected_groups(rep):
'''
Remove all connected color groups covering at least three squares
from a board representation.
Arguments:
rep -- a dictionary mapping locations to one-character strings
representing colors
Return value:
a tuple of two dictionaries of the same kind as the input
(i.e. a mapping between locations and color strings);
the first contains the remaining locations only,
and the second contains the removed locations only
The input dictionary 'rep' is not altered.
'''
assert is_rep(rep)
inv = invert_rep(rep)
colors = list(inv.keys())
keep = {}
discard = {}
for i in colors:
locset = inv[i]
short, connected = filter_locset(locset)
discard[i] = connected
keep[i] = short
return (revert_rep(keep), revert_rep(discard))
class DissemblerSolver:
def __init__(self, puzzle):
self.nrows = 0
self.ncols = 0
self.puzzle = puzzle
self.rep = {}
self.load(self.puzzle)
self.possible = self.possible_moves()
self.visited = [[]]
self.moves = []
self.history = []
self.level = 0
#self.visited_level = 0
self.count = []
def solve_puzzle(self, prev):
if len(self.rep) == 0:
return True
if len(self.possible) == 0:
self.undo()
return
lst = []
for move in self.possible:
lst.append(move)
self.solve_puzzle(lst[0])
self.solve_puzzle(lst[1])
def solve_puzzle2(self):
'''Depth-first traversal. Check subtrees from left to right '''
#for i in self.possible:
#print(i, end = ' ')
#print()
while self.level >= 0:
#for i in self.possible:
#print(i, end = ' ')
#print()
if len(self.rep) == 0:
return True
if len(self.possible) == 0:
#print('UNDO')
self.visited.pop()
self.undo()
else:
#if self.level == 1 and len(self.visited) > 1:
#self.visited = [self.visited[0]]
made = False
for move in self.possible:
if move not in self.visited[self.level]:
#print(move)
#self.prev = self.possible
self.make_move(move)
#self.visited.add(move)
made = True
break
if made == False:
#print('UNDO')
self.visited.pop()
self.undo()
return False
#if len(self.rep) == 0:
#return True
#if len(self.possible) == 0:
#if self.undo():
#self.solve_puzzle()
#return False
#for move in self.possible:
##print('({0}, {1})'.format(move[0], move[1]), end = ' ')
#if move not in self.visited:
##print(move)
#self.make_move(move)
#self.visited.add(move)
#self.solve_puzzle()
#return True
def give_solution(self):
if self.solve_puzzle2():
for i in self.moves:
print('({0}, {1})'.format(i[0], i[1]), end = ' ')
else:
print('No solution :(')
def load(self, puzzle):
'''
Load a puzzle from a string representation of the puzzle.
Convert the string representation into a dictionary representation
mapping (row, column) coordinates to colors.
Arguments:
puzzle -- a string representing the puzzle
Return value: none
'''
rep = {}
lines = puzzle.split()
self.nrows = len(lines)
self.ncols = len(lines[0])
for row in lines:
assert len(row) == self.ncols
for row in range(self.nrows):
for col in range(self.ncols):
color = lines[row][col]
if color == '.':
continue
rep[(row, col)] = color
self.rep = rep
def possible_moves(self):
'''
Compute and return a set of all the possible moves. A "possible move"
is a move where:
-- both locations of the move are adjacent
-- both locations on the board rep are occupied by colors
-- making the move will cause some locations to be vacated
Arguments:
rep -- a dictionary mapping locations to one-character strings
representing colors
nrows -- the number of rows on the board
ncols -- the number of columns on the board
Return value:
the set of possible moves
The input dictionary 'rep' is not altered.
'''
rep = self.rep
nrows = self.nrows
ncols = self.ncols
assert type(nrows) is int and type(ncols) is int
assert nrows > 0 and ncols > 0
adj = self.adjacent_moves(nrows, ncols)
possible = set()
for i in adj:
if i[0] in rep and i[1] in rep:
new_rep = swap_locations(rep, i[0], i[1])
kept, discarded = remove_connected_groups(new_rep)
if len(discarded) >= 3:
possible.add(i)
return possible
def adjacent_moves(self, nrows, ncols):
'''
Create and return a set of all moves on a board with 'nrows' rows and
'ncols' columns. The moves consist of two adjacent (row, column)
locations.
Arguments:
nrows -- the number of rows on the board
ncols -- the number of columns on the board
Return value:
the set of moves, where each move is a pair of adjacent locations
and each location is a (row, column) pair; also the two locations
are ordered in the tuple (the "smallest" comes first)
Note that the moves are independent of the contents of any board
representation; we aren't considering whether the moves would actually
change anything on a board or whether the locations of each move are
occupied by color squares.
'''
assert type(nrows) is int and type(ncols) is int
assert nrows > 0 and ncols > 0
moves = set()
for i in range(nrows):
for j in range(ncols):
current = (i, j)
top = (i - 1, j)
bottom = (i + 1, j)
left = (i, j - 1)
right = (i, j + 1)
if i - 1 >= 0:
if (top, current) not in moves:
moves.add((current, top))
if i + 1 < nrows:
if (bottom, current) not in moves:
moves.add((current, bottom))
if j - 1 >= 0:
if (left, current) not in moves:
moves.add((current, left))
if j + 1 < ncols:
if (right, current) not in moves:
moves.add((current, right))
return moves
def make_move(self, move):
self.history.append(self.rep.copy())
self.rep = swap_locations(self.rep, move[0], move[1])
(self.rep, removed) = remove_connected_groups(self.rep)
#self.history.append(self.rep.copy())
self.moves.append(move)
self.possible = self.possible_moves()
self.level += 1
self.visited.append([])
def undo(self):
if self.history != []:
self.rep = self.history.pop()
move = self.moves.pop()
#if self.visited_level == 0:
#self.visited_level = self.level + 1
#elif (self.visited_level - self.level > 1):
#self.visited_level -= 1
#self.visited = []
#else:
#self.visited_level = self.level + 1
self.visited[self.level - 1].append(move)
self.possible = self.possible_moves()
#all_in = True
#for i in self.possible:
#if i not in self.visited[self.level]:
#all_in = False
#break
#if all_in == True:
#self.visited.pop()
#self.undo()
#self.visited[self.level - 1].append(moves[len(moves) - 1])
#self.possible = set()
self.level -= 1
if __name__ == '__main__':
puzzle = input("Enter a Dissembler puzzle to be solved: ")
solver = DissemblerSolver(puzzle)
solver.give_solution()
|
[
"26992324+MeganTj@users.noreply.github.com"
] |
26992324+MeganTj@users.noreply.github.com
|
5cc782ae961498b0b58560713f086c5bc4bfa0ed
|
4f48e707f6a50adb40c13aaee1d10d9d76c81780
|
/logsapp/migrations/0001_initial.py
|
86dd00cf84cba4766c5eb9bfc533c575d077c607
|
[] |
no_license
|
rishanexe/logistics
|
a7c3421e4a8493bc71559b7e76ed2824a880bdc3
|
e383929d8247842bfbc2e53e6822b07a4bf85402
|
refs/heads/master
| 2020-04-26T08:36:46.057822
| 2019-04-04T17:29:22
| 2019-04-04T17:29:22
| 173,428,159
| 1
| 1
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,212
|
py
|
# Generated by Django 2.1.7 on 2019-03-03 09:28
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='Details',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('customer_name', models.CharField(max_length=200)),
('refid', models.CharField(max_length=8)),
('good_type', models.CharField(max_length=10)),
('packs', models.IntegerField(default=0)),
('weight', models.IntegerField(default=200)),
('location', models.CharField(max_length=10)),
],
),
migrations.CreateModel(
name='Login',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('username', models.CharField(max_length=10)),
('password', models.CharField(max_length=16)),
('usertype', models.CharField(max_length=10)),
],
),
]
|
[
"ri5han@github.com"
] |
ri5han@github.com
|
53a60bedfce82eb6c8617cb9f78d86af5ec5433b
|
0c8174248dedf4b1dc152d4f944165c0313048ea
|
/220. Contains Duplicate III.py
|
dcb506cf2bedab2a3a92862caca1119f7b483c30
|
[] |
no_license
|
linruili/leetcode-python3.5
|
6c31b4ff8985a4c3f94930698df2de13bf2a1420
|
01680c813150e5fb9ddd52b0746add5d416104f2
|
refs/heads/master
| 2021-01-20T18:45:05.059677
| 2016-07-01T15:03:08
| 2016-07-01T15:03:08
| 61,994,520
| 0
| 1
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 108,153
|
py
|
def containsNearbyAlmostDuplicate(nums, k, t):
"""
:type nums: List[int]
:type k: int
:type t: int
:rtype: bool
"""
for i in range(len(nums)-1):
for j in range(i+1 , min(len(nums) , i+k+1)):
if abs(nums[i]-nums[j]) <= t:
return True
return False
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print(containsNearbyAlmostDuplicate(L , 10000 ,0))
|
[
"linruili1113@gmail.com"
] |
linruili1113@gmail.com
|
d6cbfefec0772206808b2dbfbe06b5750a86f444
|
c3abc9e55a0a57027ef6928e83443e2ba7fcfbdf
|
/lhzutil.py
|
41e0dbf406614ce7b1b0dc40b4c0b0e077e0b428
|
[
"Apache-2.0"
] |
permissive
|
Q5EbA7Vdyw84efER/wxBot
|
590de2355f1f8be7e3aff2846ef1900459eaf958
|
98c7acee5a076332c8de2bac091e04d7f661edb6
|
refs/heads/master
| 2020-03-24T14:21:22.283861
| 2018-12-25T06:13:26
| 2018-12-25T06:13:26
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 520
|
py
|
# -*- coding: utf-8 -*-
# 获取字符串中匹配子串的最后一个位置
def find_last(string, str):
last_position = -1
while True:
position = string.find(str, last_position+1)
if position == -1:
return last_position
last_position = position
# 将文件名改写成小文件名
def thumbFilePath(filepath):
lastindexofdot = find_last(filepath, '.')
filepath = filepath[:lastindexofdot]+'_thumb'+filepath[lastindexofdot:]
return filepath
|
[
"lhz@lhzs-Macbook-Pro.local"
] |
lhz@lhzs-Macbook-Pro.local
|
20675f33d29bf73a3bcfb2d44baa70a43ba27ed0
|
e002510c48f271ffcc36449f2c866d29e0bb2a4b
|
/AoC/2018/05/05.py
|
330ff11fe3cf88c9c56de54ee8ab57c0ea92d5e8
|
[] |
no_license
|
petreleon/CodingChallanges
|
6da5b45abcb6d9e10acbf78d6eb695eaa413fff7
|
3b213938a90c0e477ec592b0cb5cb2826971b8ea
|
refs/heads/master
| 2023-02-07T23:32:05.390242
| 2020-12-25T23:17:32
| 2020-12-25T23:17:32
| 317,426,902
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 758
|
py
|
from parse import parse
from operator import methodcaller
file_opened = open("input.txt", 'r')
lines = file_opened.read().splitlines()
def reactorMatch(first,second):
if first.upper() == second.upper() and first != second:
return True
return False
sum_ = 0
for line in lines:
beforeReact = ''
afterReact = line
while afterReact != beforeReact:
beforeReact = afterReact
for reactorIndex, reactor in enumerate(beforeReact):
if reactorIndex < len(beforeReact) - 1:
if reactorMatch(reactor, beforeReact[reactorIndex+1]):
afterReact = beforeReact[:reactorIndex]+beforeReact[reactorIndex+2:]
break
sum_ += len(afterReact)
print(sum_)
|
[
"petreleonardos@gmail.com"
] |
petreleonardos@gmail.com
|
a63796007e54fbdde56559bc9935e176c6ce52c4
|
a392216464aea36f81c7dc29de5595c0f5331565
|
/test.py
|
2d66f54a7d4b983057934fc3f61f705574d65af9
|
[] |
no_license
|
renecotyfanboy/bh
|
98a3722411383e08c9f7aa9aa3403ed8d13c9a5f
|
8b601d96db5c22360d0d41e52a4ef1d69c23d511
|
refs/heads/master
| 2022-09-01T22:53:36.554676
| 2020-05-25T10:42:25
| 2020-05-25T10:42:25
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 569
|
py
|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun May 24 01:54:00 2020
@author: simon
"""
#%% Example code
import numpy as np
import matplotlib.pyplot as plt
import tempfile
from os.path import join
from tqdm import tqdm
from matplotlib import cm
from matplotlib import colors,image
from tools.imager import BH_imager
import animatplot as amp
#with tempfile.TemporaryDirectory() as tmpdirname:
bh = BH_imager(angle=10,pixel_size=0.0125)
img = bh.compute_img()
np.savetxt('img.txt',img)
# plt.imshow(img,cmap=cm.hot,origin='lower')
# plt.axis('off')
|
[
"49200287+renecotyfanboy@users.noreply.github.com"
] |
49200287+renecotyfanboy@users.noreply.github.com
|
5a2d06f06eacfc9d696d721d68dc22817b5a540d
|
f3bb68a5fd99b5fc4e8365375fa85b88b4b84ea1
|
/main.py
|
49e025aadc1081545e37d2df2eb2ce53d7dc2d8a
|
[] |
no_license
|
AndreYanny/calculator
|
bc972f0ed4eb1354d3363be681c463d6d66ae91f
|
7b9bcbf8b2daf2ce1dcdd20647dbf041a8fe7c63
|
refs/heads/master
| 2023-04-11T19:18:51.300737
| 2021-03-31T16:30:31
| 2021-03-31T16:30:31
| 353,417,368
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,159
|
py
|
# Program make a simple calculator
# This function adds two numbers
def add(x, y):
print(x, "+", y, "=", x + y)
# This function subtracts two numbers
def subtract(x, y):
print(x, "-", y, "=", x - y)
# This function multiplies two numbers
def multiply(x, y):
print(x, "*", y, "=", x * y)
# This function divides two numbers
def divide(x, y):
try:
print(x, "/", y, "=", x / y)
except ZeroDivisionError:
print("Division by zero error!")
print("Select operation.")
print("1.Add")
print("2.Subtract")
print("3.Multiply")
print("4.Divide")
while True:
# Take input from the user
choice = input("\nEnter choice(1/2/3/4): ")
# Check if choice is one of the four options
if choice in ('1', '2', '3', '4'):
num1 = float(input("Enter first number: "))
num2 = float(input("Enter second number: "))
if choice == '1':
add(num1, num2)
elif choice == '2':
subtract(num1, num2)
elif choice == '3':
multiply(num1, num2)
elif choice == '4':
divide(num1, num2)
break
else:
print("Invalid Input")
|
[
"andre_osama@yahoo.com"
] |
andre_osama@yahoo.com
|
5547b3e9164be8dae5f3be87c30fc50b9e4ece64
|
91d70927a526f11e9c093ef62f5d83a3e613d9ee
|
/exercise13/Tehtävä_L13T02.py
|
99d355988a478c050902c785fba7d19d0646a52e
|
[] |
no_license
|
RiikkaKokko/JAMK_ohjelmoinnin_perusteet
|
1812b92c9b6d8b1127ca2b54e93d17fa687920b6
|
2be91f6346a301232f06023425408d83196cab63
|
refs/heads/master
| 2023-07-23T02:58:06.639330
| 2021-08-23T13:20:06
| 2021-08-23T13:20:06
| 399,109,539
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 619
|
py
|
Arvosanat = []
arvosana = 0
number_of_empty_responses = 0
while True:
data = (input("Kirjoita arvosana: "))
arvosana += 1
Arvosanat.append(data)
if data == "":
number_of_empty_responses += 1
if number_of_empty_responses == 1:
Arvosanat.remove("")
#print(Arvosanat)
ints = [int(item) for item in Arvosanat]
break
uusi_lista = [i for i in ints if i > -1 and i < 6]
#print(uusi_lista)
keskiarvo = sum(uusi_lista) / len(uusi_lista)
print("Arvosanoja on yhteensä: " + str(arvosana - 1))
print("numeroiden keskiarvo on: " + str(keskiarvo))
|
[
"40693952+RiikkaKokko@users.noreply.github.com"
] |
40693952+RiikkaKokko@users.noreply.github.com
|
4699041df8bc845885513fbf247fa04518328cbd
|
14afcc5e2b8bdb3d91b500f6e7985d8a3378e929
|
/src/68.文本左右对齐.py
|
b3689a9c97bc0475d281eab692c085002b906bbc
|
[] |
no_license
|
hysapphire/leetcode-python
|
8569a0e76f8917165e6b9fb25bfef1afc1186e3c
|
8e338ee7a5c9f124e897491d6a1f4bcd1d1a6270
|
refs/heads/master
| 2022-12-03T15:17:52.557115
| 2020-08-17T14:19:59
| 2020-08-17T14:19:59
| 278,781,919
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,782
|
py
|
#
# @lc app=leetcode.cn id=68 lang=python3
#
# [68] 文本左右对齐
#
# @lc code=start
class Solution:
def fullJustify(self, words: List[str], maxWidth: int) -> List[str]:
splited_words = []
s = []
cnt = 0
for word in words:
t = cnt + len(word)
if t > maxWidth:
splited_words.append(s)
s = [word]
cnt = len(word) + 1
else:
s.append(word)
cnt = t + 1
splited_words.append(s)
res = []
for splited_word in splited_words[:-1]:
s = ""
if len(splited_word) == 1:
num_space = 0
else:
num_space = (maxWidth - sum([len(word) for word in splited_word])) // (len(splited_word) - 1)
delta_num_space = (maxWidth - sum([len(word) for word in splited_word])) - (len(splited_word) - 1) * num_space
if len(splited_word) == 1:
s = ""
s += splited_word[0]
for _ in range(delta_num_space):
s += " "
else:
for word in splited_word[:-1]:
s += word
for _ in range(num_space):
s += " "
if delta_num_space > 0:
s += " "
delta_num_space -= 1
s += splited_word[-1]
res.append(s)
s = ""
for word in splited_words[-1][:-1]:
s += word
s += " "
s += splited_words[-1][-1]
for _ in range(maxWidth - len(s)):
s += " "
res.append(s)
return res
# @lc code=end
|
[
"huoyang93@qq.com"
] |
huoyang93@qq.com
|
fac016a72c1b7602d5ce283cbfdbcf983e781f19
|
d83ad2d6dd921cff284db58bc8a212d7e97d132c
|
/python/SparseMatrix.py
|
9140c87fa65e4e385aea83d0b591e281e45189a3
|
[] |
no_license
|
BradleyMorgan/COMP7970-004-FinalProject
|
493f8e62b96f34d647a3b15c635f611367cd8ab5
|
dba2bb72142bfcf94c25448948466af267ed15fa
|
refs/heads/master
| 2021-08-22T23:09:11.143733
| 2017-12-01T15:34:30
| 2017-12-01T15:34:30
| 109,397,317
| 0
| 0
| null | 2017-11-03T13:14:07
| 2017-11-03T13:14:06
| null |
UTF-8
|
Python
| false
| false
| 3,216
|
py
|
import random
import scipy.io as sio
from math import sqrt
class SparseMatrix():
def __init__(self, fileName = 'blogcatalog.mat'):
self._data = None
self.edge_size = 0
self.node_size = 0
self.InstanceToFeature = list()
self.FeatureToInstance = list()
self.load_from_mat(fileName)
def load_from_mat(self,data):
# construct a mapping from features to instances
mat_contents = sio.loadmat(data)
network = mat_contents['network']
self.feature_size = network.shape[0]
self.instance_size = network.nnz // 2
rows,columns = network.nonzero()
network_size = network.nnz
count_element = 0
count_row = 0
for i in range(self.instance_size):
self.InstanceToFeature.append(list())
for featureIndex in range(self.feature_size):
self.FeatureToInstance.append(list())
for i in range(network_size):
node_1 = rows[i]
node_2 = columns[i]
if node_2 > node_1:
# instnace to feature
self.InstanceToFeature[count_row].append(node_1)
self.InstanceToFeature[count_row].append(node_2)
# feature to instance
self.FeatureToInstance[node_1].append(count_row)
self.FeatureToInstance[node_2].append(count_row)
count_element = count_element + 2
count_row = count_row +1
def getRelevantInstanceSetByFeatureIndexAndCentroid(self,centroid):
relevantInstanceSet = set()
# centroid contains featureIndex and value
for featureIndex in centroid:
for instanceIndex in self.FeatureToInstance[featureIndex]:
relevantInstanceSet.add(instanceIndex)
return relevantInstanceSet
def pickRandomInstance(self):
'''
will return a feature instance as a collection of two
values mapped to a random index between 0 and the
size of input
:return: instance
'''
instanceIndex = random.randint(0, self.getInstanceSize() - 1)
instance = self.dataToInstance(instanceIndex)
return instance
def pickInstanceFromIsolated(self, isolated):
instanceIndex = 0
if isolated:
instanceIndex = isolated.pop()
return self.dataToInstance(instanceIndex);
else:
return self.pickRandomInstance()
def dataToInstance(self,instanceIndex):
instance = {}
value = 1 / sqrt(2)
for featureIndex in self.InstanceToFeature[instanceIndex]:
# normalization
instance[featureIndex] = value
return instance
def calculateSimilarity(self, instanceIndex, centroid):
similarity = 0
for featureIndex in self.InstanceToFeature[instanceIndex]:
if featureIndex in centroid:
value = 1 / sqrt(2)
similarity += centroid[featureIndex] * value
return similarity
def getFeatureSize(self):
return self.feature_size
def getInstanceSize(self):
return self.instance_size
def getMatrix(self):
return self._data
|
[
"robinsa87@gmail.com"
] |
robinsa87@gmail.com
|
92a2c9a2ec7f194f864497a4a7795eeba899fab1
|
0f727fdca3f37e08ed057abcfeb068b3f4acb84b
|
/gps_helper/test/test_kalman.py
|
1d732c3f244605a4ce37104b91e1cf6df728c182
|
[
"MIT"
] |
permissive
|
Miguel-O-Matic/gps-helper
|
e7939902201f2e5b1260e2e801439660fe3f4d32
|
dae3f143281ef930de84a1b783d33aa6e4446040
|
refs/heads/master
| 2020-06-24T20:51:34.604777
| 2019-08-15T12:42:32
| 2019-08-15T12:42:32
| 199,085,652
| 0
| 0
|
MIT
| 2019-07-26T22:08:41
| 2019-07-26T22:08:41
| null |
UTF-8
|
Python
| false
| false
| 15,770
|
py
|
from .test_helper import GPSTest
from .. import kalman as kf
from .. import simulator as sim
import numpy as np
from numpy.linalg import norm
from numpy import testing as npt
class TestKalman(GPSTest):
"""
Test class for the kalman functions.
"""
_multiprocess_can_split_ = True
def test_simple_kalman_x(self):
"""
If only ten measurements are used, the tests do not cover all the way to convergence. Therefore, every tenth
sample is used for the test.
:return:
"""
dt = 0.1
t = np.arange(0, 10 + dt, dt)
x_saved = np.zeros((len(t), 2))
x_test = np.array([[13.50229134, 13.50229134],
[13.67920055, 13.67920055],
[13.35742003, 13.35742003],
[13.74166822, 13.74166822],
[13.69347514, 13.69347514],
[13.81459412, 13.81459412],
[13.83609622, 13.83609622],
[13.8665808 , 13.8665808 ],
[13.77658251, 13.77658251],
[13.85373983, 13.85373983],
[13.7394069 , 13.7394069 ]])
# Create objects for the simulation
gv = sim.GetVoltage(14.0, dt, sigma_w=2)
sk = kf.SimpleKalman(initial_state=14)
for k in range(len(t)):
z = gv.measurement()
x_saved[k, :] = sk.next_sample(z)
npt.assert_almost_equal(x_test, x_saved[::10])
def test_simple_kalman_k(self):
"""
If only ten measurements are used, the tests do not cover all the way to convergence. Therefore, every tenth
sample is used for the test.
:return:
"""
dt = 0.1
t = np.arange(0, 10 + dt, dt)
k_saved = np.zeros(len(t))
k_test = np.array([0.6, 0.08571429, 0.04615385, 0.03157895, 0.024,
0.01935484, 0.01621622, 0.01395349, 0.0122449 , 0.01090909,
0.00983607])
# Create objects for the simulation
gv = sim.GetVoltage(14.0, dt, sigma_w=2)
sk = kf.SimpleKalman(initial_state=14)
for k in range(len(t)):
z = gv.measurement()
sk.next_sample(z)
k_saved[k] = sk.K
npt.assert_almost_equal(k_test, k_saved[::10])
def test_simple_kalman_p(self):
"""
If only ten measurements are used, the tests do not cover all the way to convergence. Therefore, every tenth
sample is used for the test.
:return:
"""
dt = 0.1
t = np.arange(0, 10 + dt, dt)
p_saved = np.zeros(len(t))
p_test = np.array([2.4, 0.34285714, 0.18461538, 0.12631579, 0.096,
0.07741935, 0.06486486, 0.05581395, 0.04897959, 0.04363636,
0.03934426])
# Create objects for the simulation
gv = sim.GetVoltage(14.0, dt, sigma_w=2)
sk = kf.SimpleKalman(initial_state=14)
for k in range(len(t)):
z = gv.measurement()
sk.next_sample(z)
p_saved[k] = sk.P
npt.assert_almost_equal(p_test, p_saved[::10])
def test_pos_kalman_x_pos(self):
"""
If only ten measurements are used, the tests do not cover all the way to convergence. Therefore, every tenth
sample is used for the test.
:return:
"""
dt = 0.1
t = np.arange(0, 10 + dt, dt)
x_saved = np.zeros((2, len(t)))
x_test = np.array([2.40104478, 85.65186517, 167.37150554, 248.87502207,
328.96915024, 406.71764263, 486.88690622, 566.02913866,
645.68324187, 727.69351353, 810.20295338])
# Create objects for the simulation
Q = np.array([[1, 0], [0, 3]])
R = np.array([[10, 0], [0, 2]])
gpv = sim.GetPosVel(Q=Q, R=R, dt=dt)
pk = kf.PosKalman(Q, R, initial_state=[0, 80])
for k in range(len(t)):
# take a measurement
z = gpv.measurement()
# Update the Kalman filter
x_saved[:, k, None] = pk.next_sample(z)
npt.assert_almost_equal(x_test, x_saved[0, ::10])
def test_pos_kalman_x_vel(self):
"""
If only ten measurements are used, the tests do not cover all the way to convergence. Therefore, every tenth
sample is used for the test.
:return:
"""
dt = 0.1
t = np.arange(0, 10 + dt, dt)
x_saved = np.zeros((2, len(t)))
x_test = np.array([63.71165764, 81.26369543, 79.46747731, 79.05128724, 77.47045411,
78.00121222, 80.21543726, 80.60963484, 81.56581114, 81.90123253,
80.31546328])
# Create objects for the simulation
Q = np.array([[1, 0], [0, 3]])
R = np.array([[10, 0], [0, 2]])
gpv = sim.GetPosVel(Q=Q, R=R, dt=dt)
pk = kf.PosKalman(Q, R, initial_state=[0, 80])
for k in range(len(t)):
# take a measurement
z = gpv.measurement()
# Update the Kalman filter
x_saved[:, k, None] = pk.next_sample(z)
npt.assert_almost_equal(x_test, x_saved[1, ::10])
def test_pos_kalman_p_pos(self):
"""
If only ten measurements are used, the tests do not cover all the way to convergence. Therefore, every tenth
sample is used for the test.
:return:
"""
dt = 0.1
t = np.arange(0, 10 + dt, dt)
x_saved = np.zeros((2, len(t)))
p_diag = np.zeros((len(t),2))
p_test = np.array([3.76669267, 2.76774029, 2.76843135, 2.7685862, 2.76859725,
2.76859802, 2.76859808, 2.76859808, 2.76859808, 2.76859808,
2.76859808])
# Create objects for the simulation
Q = np.array([[1, 0], [0, 3]])
R = np.array([[10, 0], [0, 2]])
gpv = sim.GetPosVel(Q=Q, R=R, dt=dt)
pk = kf.PosKalman(Q, R, initial_state=[0, 80])
for k in range(len(t)):
# take a measurement
z = gpv.measurement()
# Update the Kalman filter
x_saved[:, k, None] = pk.next_sample(z)
p_diag[k, :] = pk.P.diagonal()
npt.assert_almost_equal(p_test, p_diag[::10, 0])
def test_pos_kalman_p_vel(self):
"""
If only ten measurements are used, the tests do not cover all the way to convergence. Therefore, every tenth
sample is used for the test.
:return:
"""
dt = 0.1
t = np.arange(0, 10 + dt, dt)
x_saved = np.zeros((2, len(t)))
p_diag = np.zeros((len(t),2))
p_test = np.array([1.59079563, 1.36849424, 1.36839693, 1.36838998, 1.3683895 ,
1.36838946, 1.36838946, 1.36838946, 1.36838946, 1.36838946,
1.36838946])
# Create objects for the simulation
Q = np.array([[1, 0], [0, 3]])
R = np.array([[10, 0], [0, 2]])
gpv = sim.GetPosVel(Q=Q, R=R, dt=dt)
pk = kf.PosKalman(Q, R, initial_state=[0, 80])
for k in range(len(t)):
# take a measurement
z = gpv.measurement()
# Update the Kalman filter
x_saved[:, k, None] = pk.next_sample(z)
p_diag[k, :] = pk.P.diagonal()
npt.assert_almost_equal(p_test, p_diag[::10, 1])
def test_dv_kalman_x_pos(self):
dt = 0.1
t = np.arange(0, 10 + dt, dt)
x_saved = np.zeros((len(t), 2))
x_test = np.array([3.00505891, 81.24273033, 159.15363574, 239.96205645,
306.83657799, 390.95995075, 482.18752904, 560.48756223,
638.8627147, 716.34708371, 796.09003321])
# Create objects for the simulation
gp = sim.GetPos()
dk = kf.DvKalman()
for k in range(len(t)):
z = gp.measurement()
x_saved[k, :] = dk.next_sample(z)
npt.assert_almost_equal(x_test, x_saved[::10, 0])
def test_dv_kalman_x_vel(self):
dt = 0.1
t = np.arange(0, 10 + dt, dt)
x_saved = np.zeros((len(t), 2))
x_test = np.array([20.08306272, 59.87779911, 72.3189212 , 73.53420293, 64.12422919,
66.87463733, 84.32133029, 79.23214373, 82.47667741, 86.30916162,
80.29449928])
# Create objects for the simulation
gp = sim.GetPos()
dk = kf.DvKalman()
for k in range(len(t)):
z = gp.measurement()
x_saved[k, :] = dk.next_sample(z)
npt.assert_almost_equal(x_test, x_saved[::10, 1])
def test_int_kalman_x_vel(self):
dt = 0.1
t = np.arange(0, 10 + dt, dt)
x_saved = np.zeros((len(t), 2))
x_test = np.array([44.82330127, 80.66266235, 72.73385292, 82.05528032, 74.8722922 ,
82.66549625, 86.53350242, 80.31837348, 80.74624911, 83.77797869,
80.57758014])
# Create objects for the simulation
gv = sim.GetVel()
ik = kf.IntKalman()
for k in range(len(t)):
z = gv.measurement()
x_saved[k, :] = ik.next_sample(z)
npt.assert_almost_equal(x_test, x_saved[::10, 1])
def test_int_kalman_x_pos(self):
dt = 0.1
t = np.arange(0, 10 + dt, dt)
x_saved = np.zeros((len(t), 2))
x_test = np.array([3.55145633, 77.7381104 , 153.44154205, 235.38042071,
313.80303816, 394.62000052, 473.96915187, 554.86351,
630.47869611, 712.59481293, 786.37162823])
# Create objects for the simulation
gv = sim.GetVel()
ik = kf.IntKalman()
for k in range(len(t)):
z = gv.measurement()
x_saved[k, :] = ik.next_sample(z)
npt.assert_almost_equal(x_test, x_saved[::10, 0])
def test_ekf_x(self):
dt = 0.05
n_samples = 500
t = np.arange(n_samples) * dt
n_samples = len(t)
x_saved = np.zeros((n_samples, 3))
x_test = np.array([[ 4.5 , 90. , 1048.35700227],
[ 340.92701179, 129.19059199, 1007.64740711],
[ 793.01850282, 153.83039049, 1003.76887912],
[1187.81309238, 156.6880102 , 1003.96499988],
[1635.12480478, 169.35387149, 1006.01391596],
[1965.96084856, 148.35186341, 1000.27827996],
[2411.82207964, 162.38496029, 1004.06328925],
[2811.82330229, 161.8190544 , 1003.97565162],
[3254.16219351, 168.74746912, 1004.5385335 ],
[3630.89648321, 154.58882883, 1004.22494179]])
gr = sim.GetRadar()
ekf = kf.RadarEKF(dt, initial_state=[0, 90, 1100])
for k in range(n_samples):
xm = gr.measurement()
x_saved[k, :] = ekf.next_sample(xm)
npt.assert_almost_equal(x_test, x_saved[::50, :])
def test_ekf_z(self):
dt = 0.05
n_samples = 500
t = np.arange(n_samples) * dt
n_samples = len(t)
x_saved = np.zeros((n_samples, 3))
z_saved = np.zeros(n_samples)
z_test = np.array([1052.22272082, 1071.57581789, 1288.44638908, 1563.13684492,
1927.27212923, 2210.78424339, 2617.51747043, 2990.06735689,
3409.85996124, 3770.3812422 ])
gr = sim.GetRadar()
ekf = kf.RadarEKF(dt, initial_state=[0, 90, 1100])
for k in range(n_samples):
xm = gr.measurement()
x_saved[k, :] = ekf.next_sample(xm)
z_saved[k] = norm(x_saved[k])
npt.assert_almost_equal(z_test, z_saved[::50])
def test_sigma_points(self):
xm = np.array([[5], [5]])
px = 9 * np.eye(2)
kappa = 2
xi, w = kf.sigma_points(xm, px, kappa) # sigma points and weights
xi_test = [[ 5., 11., 5., -1., 5.],
[ 5., 5., 11., 5., -1.]]
w_test = [0.5, 0.125, 0.125, 0.125, 0.125]
npt.assert_almost_equal(xi_test, xi)
npt.assert_almost_equal(w_test, w)
def test_ut(self):
xm = np.array([[5], [5]])
px = 9 * np.eye(2)
kappa = 2
x_avg_test = [[5.], [5.]]
x_cov_test = [[9., 0.], [0., 9.]]
xi, w = kf.sigma_points(xm, px, kappa) # sigma points and weights
xAvg, xCov = kf.ut(xi, w) # estimate mean vector and covariance matrix using sigma points
npt.assert_almost_equal(x_avg_test, xAvg)
npt.assert_almost_equal(x_cov_test, xCov)
def test_radar_ukf_x(self):
x_test = np.array([[ 4.11483289, 89.9807873 , 1006.07122023],
[ 352.3105395 , 111.00439655, 1007.21596109],
[ 760.28088421, 129.09475887, 1013.98092468],
[1146.57543567, 135.45782881, 1023.0940802 ],
[1570.56204941, 142.75711716, 1041.58425653],
[1920.57768989, 141.88223322, 1039.88681358],
[2352.56816971, 149.54007223, 1056.58613916],
[2759.93367043, 153.00065999, 1063.05210355],
[3197.83961482, 158.44281893, 1072.24895062],
[3601.64732546, 158.77005313, 1073.01176455]])
dt = 0.05
n_samples = 500
t = np.arange(n_samples) * dt
n_samples = len(t)
x_saved = np.zeros((n_samples, 3))
gr = sim.GetRadar()
r_ukf = kf.RadarUKF(dt, initial_state=[0, 90, 1100])
for k in range(n_samples):
xm = gr.measurement()
x_saved[k, :] = r_ukf.next_sample(xm)
npt.assert_almost_equal(x_test, x_saved[::50, :])
def test_radar_ukf_z(self):
z_test = np.array([1010.09542821, 1072.81344351, 1273.91121954, 1542.62942731,
1889.95833525, 2188.63286461, 2583.27572973, 2961.54061968,
3376.5367081 , 3761.43930347])
dt = 0.05
n_samples = 500
t = np.arange(n_samples) * dt
n_samples = len(t)
x_saved = np.zeros((n_samples, 3))
z_saved = np.zeros(n_samples)
gr = sim.GetRadar()
r_ukf = kf.RadarUKF(dt, initial_state=[0, 90, 1100])
for k in range(n_samples):
xm = gr.measurement()
x_saved[k, :] = r_ukf.next_sample(xm)
z_saved[k] = norm(x_saved[k])
npt.assert_almost_equal(z_test, z_saved[::50])
def test_radar_ukf_k(self):
k_test = np.array([[3.72767389e-03, 1.85941828e-04, 9.09049215e-01],
[3.61211252e-01, 7.79001494e-02, 1.78803480e-02],
[8.96156438e-02, 1.19831691e-02, 1.10461769e-02],
[4.37106654e-02, 4.44888833e-03, 8.91105184e-03],
[2.83271141e-02, 2.62417475e-03, 7.81276553e-03],
[2.25960281e-02, 2.85581248e-03, 6.27331967e-03],
[2.09285314e-02, 2.47122914e-03, 4.92643172e-03],
[1.99822813e-02, 2.31160049e-03, 4.09337298e-03],
[1.94611058e-02, 2.22562871e-03, 3.51757877e-03],
[1.91697107e-02, 2.17315151e-03, 3.11253739e-03]])
dt = 0.05
n_samples = 500
t = np.arange(n_samples) * dt
n_samples = len(t)
k_saved = np.zeros((n_samples, 3))
gr = sim.GetRadar()
r_ukf = kf.RadarUKF(dt, initial_state=[0, 90, 1100])
for k in range(n_samples):
xm = gr.measurement()
r_ukf.next_sample(xm)
k_saved[k, :] = r_ukf.K.T
npt.assert_almost_equal(k_test, k_saved[::50, :])
|
[
"chiranthsiddappa@gmail.com"
] |
chiranthsiddappa@gmail.com
|
6a2f2c5f0d35f4f8e7e2af202d2a7b638e94bd51
|
255daf96ae2641a06fd5020a65622e41b1b22ca8
|
/insert_sort1.py
|
1b11a46d3a082f3b965df4a223babe8df9a71b5e
|
[] |
no_license
|
duanyiting2018/learning_python
|
11f7aab080e16da5dcf9bb14a4d81590eabd4c85
|
3041d6a6369641a0bb8e7c3161cbb988d3c37abd
|
refs/heads/master
| 2021-08-18T05:51:25.827615
| 2021-06-11T13:39:52
| 2021-06-11T13:39:52
| 151,018,624
| 4
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 667
|
py
|
# -*- coding: utf-8 -*-
"""
Created on Mon Feb 24 14:05:50 2020
@author: duanyiting
"""
size=8
def showdata(data2):
for i in range(size):
print('%1d'%data2[i],end=' ')
print()
def insert_sort(data3):
for i in range(1,size):
tmp=data3[i]#tmp用来暂存数据
no=i-1
while no>=0 and tmp<data3[no]:
data3[no+1]=data3[no]#将所有元素后移1位
no=no-1
data3[no+1]=tmp#最小的元素放在data3[0]
data1=[]
print("插入排序,请输入8个数：")
for i in range(0,8):
data1.append(int(input()))
#showdata(data1)
insert_sort(data1)
print("排序后的数组是：")
showdata(data1)
|
[
"1036179833@qq.com"
] |
1036179833@qq.com
|
72222da4ae1741a0fe83d540d008fd9bae0c1a83
|
51b6d2fc53d5c632fcf01319842baebf13901e84
|
/atcoder.jp/abc131/abc131_a/Main.py
|
68ba8c087a27f58b969015b21503fb2ab8a823b3
|
[] |
no_license
|
mono-0812/procon
|
35db3b2c21eff74fbd7b52db07f249380f6834ef
|
68a4b53880a228a0164052b23d1326363efcbc20
|
refs/heads/master
| 2023-05-30T17:02:58.935074
| 2021-06-27T12:15:10
| 2021-06-27T12:15:10
| 345,896,553
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 103
|
py
|
s=input()
las=""
for i in s:
if las==i:
print("Bad")
exit()
las=i
print("Good")
|
[
"frisk02.jar@gmail.com"
] |
frisk02.jar@gmail.com
|
1dd017ce55ba23554439bf43ec6c811e5a816567
|
4a06d9c889b5db2b7f9cbce0c39dedfce27876c4
|
/Application3_Q7.py
|
521100e0e901931296709c938ffeba32c9daa8cc
|
[] |
no_license
|
Oleksandr-Olefirenko/AlgorithmicThinking
|
f75fe8a98877deb4ed724cdb680f893fe5830f4b
|
3c5986d6003ee1a00e05e736f81540001480469e
|
refs/heads/master
| 2021-05-29T22:03:01.457838
| 2015-08-04T19:08:25
| 2015-08-04T19:08:25
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 9,156
|
py
|
"""
Student template code for Project 3
Student will implement five functions:
slow_closest_pair(cluster_list)
fast_closest_pair(cluster_list)
closest_pair_strip(cluster_list, horiz_center, half_width)
hierarchical_clustering(cluster_list, num_clusters)
kmeans_clustering(cluster_list, num_clusters, num_iterations)
where cluster_list is a 2D list of clusters in the plane
"""
import math
import alg_cluster
######################################################
# Code for closest pairs of clusters
def pair_distance(cluster_list, idx1, idx2):
"""
Helper function that computes Euclidean distance between two clusters in a list
Input: cluster_list is list of clusters, idx1 and idx2 are integer indices for two clusters
Output: tuple (dist, idx1, idx2) where dist is distance between
cluster_list[idx1] and cluster_list[idx2]
"""
return (cluster_list[idx1].distance(cluster_list[idx2]), min(idx1, idx2), max(idx1, idx2))
def slow_closest_pair(cluster_list, l_b, r_b):
"""
Compute the distance between the closest pair of clusters in a list (slow)
Input: cluster_list is the list of clusters
Output: tuple of the form (dist, idx1, idx2) where the centers of the clusters
cluster_list[idx1] and cluster_list[idx2] have minimum distance dist.
"""
result = (float("inf"), -1, -1)
if l_b == r_b:
return result
for idx1 in xrange(l_b, r_b):
for idx2 in xrange(idx1 + 1, r_b + 1):
current_d = pair_distance(cluster_list, idx1, idx2)
if current_d < result:
result = current_d
return result
def fast_closest_pair(cluster_list, l_b, r_b, v_i):
"""
Compute the distance between the closest pair of clusters in a list (fast)
Input: cluster_list is list of clusters SORTED such that horizontal positions of their
centers are in ascending order
Output: tuple of the form (dist, idx1, idx2) where the centers of the clusters
cluster_list[idx1] and cluster_list[idx2] have minimum distance dist.
"""
num = r_b - l_b + 1
if num <= 3:
return slow_closest_pair(cluster_list, l_b, r_b)
else:
mid = int(math.floor(0.5 * (r_b + l_b)))
v_i_l = [v_i[idx] for idx in xrange(len(v_i))
if v_i[idx] < mid]
v_i_r = [v_i[idx] for idx in xrange(len(v_i))
if v_i[idx] >= mid]
result = fast_closest_pair(cluster_list, l_b, mid - 1, v_i_l)
new_res = fast_closest_pair(cluster_list, mid, r_b, v_i_r)
#new_res = (new_res[0], new_res[1] + mid, new_res[2] + mid)
if new_res < result:
result = new_res
mid = 0.5 * (cluster_list[mid - 1].horiz_center()
+ cluster_list[mid].horiz_center())
new_res = closest_pair_strip(cluster_list,
mid, result[0], v_i)
if new_res < result:
result = new_res
return result
def closest_pair_strip(cluster_list, horiz_center, half_width, v_i):
"""
Helper function to compute the closest pair of clusters in a vertical strip
Input: cluster_list is a list of clusters produced by fast_closest_pair
horiz_center is the horizontal position of the strip's vertical center line
half_width is the half the width of the strip (i.e; the maximum horizontal distance
that a cluster can lie from the center line)
Output: tuple of the form (dist, idx1, idx2) where the centers of the clusters
cluster_list[idx1] and cluster_list[idx2] lie in the strip and have minimum distance dist.
"""
mid = [v_i[idx] for idx in xrange(len(v_i))
if abs(cluster_list[v_i[idx]].horiz_center()
- horiz_center) < half_width]
#mid.sort(key = lambda idx: cluster_list[idx].vert_center())
num = len(mid)
result = (float("inf"), -1, -1)
for idx1 in xrange(num - 1):
for idx2 in xrange(idx1 + 1, min(idx1 + 4, num)):
current_d = pair_distance(cluster_list, mid[idx1], mid[idx2])
if current_d < result:
result = current_d
if result[1] > result[2]:
result = (result[0], result[2], result[1])
return result
######################################################################
# Code for hierarchical clustering
def hierarchical_clustering(cluster_list, num_clusters):
"""
Compute a hierarchical clustering of a set of clusters
Note: the function may mutate cluster_list
Input: List of clusters, integer number of clusters
Output: List of clusters whose length is num_clusters
"""
num = len(cluster_list)
cluster_list.sort(key = lambda clu: clu.horiz_center())
v_i = [idx for idx in xrange(num)]
v_i.sort(key = lambda idx: cluster_list[idx].vert_center())
while num > num_clusters:
if num % 50 == 0:
print num_clusters, num
#cluster_list.sort(key = lambda clu: clu.horiz_center())
idx = fast_closest_pair(cluster_list, 0, num - 1, v_i)
#cluster_list[idx[1]].merge_clusters(cluster_list[idx[2]])
#cluster_list.pop(idx[2])
arrange_h(cluster_list, idx[1], idx[2])
arrange(v_i, cluster_list, idx[1], idx[2])
num -= 1
return cluster_list
def arrange(v_i, cluster_list, idx1, idx2):
pos = min(v_i.index(idx1), v_i.index(idx2))
vert = cluster_list[idx1].vert_center()
v_i.remove(idx1)
v_i.remove(idx2)
for idx in xrange(len(v_i)):
if v_i[idx] > idx2:
v_i[idx] -= 1
while pos < len (v_i):
if vert < cluster_list[pos].vert_center():
break
else:
pos += 1
v_i.insert(pos, idx1)
def arrange_h(cluster_list, idx1, idx2):
pos = idx1
cluster = cluster_list[idx1].copy()
cluster = cluster.merge_clusters(cluster_list[idx2])
horiz = cluster_list[idx1].horiz_center()
cluster_list.pop(idx2)
cluster_list.pop(idx1)
while pos < len (cluster_list):
if horiz < cluster_list[pos].horiz_center():
break
else:
pos += 1
cluster_list.insert(pos, cluster)
######################################################################
# Code for k-means clustering
def kmeans_clustering(cluster_list, num_clusters, num_iterations):
"""
Compute the k-means clustering of a set of clusters
Note: the function may not mutate cluster_list
Input: List of clusters, integers number of clusters and number of iterations
Output: List of clusters whose length is num_clusters
"""
# position initial clusters at the location of clusters with largest populations
num = len(cluster_list)
points = [idx for idx in xrange(num)]
points.sort(reverse = True, key = lambda idx:
cluster_list[idx].total_population())
points = [[cluster_list[points[idx]].horiz_center(),
cluster_list[points[idx]].vert_center()]
for idx in xrange(num_clusters)]
clusters = [-1 for _ in xrange(num)]
population = [0 for _ in xrange(num_clusters)]
for _ in xrange(num_iterations):
for cidx in xrange(num):
mind = (float("inf"), -1, -1)
for idx in xrange(num_clusters):
dist = cluster_point_distance(cluster_list,
points,
cidx, idx)
if mind > dist:
mind = dist
clusters[cidx] = mind[2]
for idx in xrange(num_clusters):
points[idx][0] = 0.0
points[idx][1] = 0.0
population[idx] = 0
for cidx in xrange(num):
idx = clusters[cidx]
cpopul = cluster_list[cidx].total_population()
population[idx] += cpopul
points[idx][0] += cluster_list[cidx].horiz_center() * cpopul
points[idx][1] += cluster_list[cidx].vert_center() * cpopul
for idx in xrange(num_clusters):
points[idx][0] /= population[idx]
points[idx][1] /= population[idx]
result = [0 for _ in xrange(num_clusters)]
for cidx in xrange(num):
idx = clusters[cidx]
if result[idx] == 0:
result[idx] = cluster_list[cidx].copy()
else:
result[idx].merge_clusters(cluster_list[cidx])
return result
def cluster_point_distance(cluster_list, points, cidx, idx):
"""
Helper function that computes Euclidean distance between cluster and point
Input: cluster_list is list of clusters, points is list of points,
cidx1 and idx are integer indices for cluster and point
Output: tuple (dist, cidx, idx) where dist is distance between
cluster_list[cidx] and points[idx]
"""
d_x = cluster_list[cidx].horiz_center() - points[idx][0]
d_y = cluster_list[cidx].vert_center() - points[idx][1]
return (math.sqrt(d_x ** 2 + d_y ** 2), cidx, idx)
def compute_distortion(cluster_list, data_table):
return sum([cluster.cluster_error(data_table) for cluster in cluster_list])
|
[
"TogusaRusso@gmail.com"
] |
TogusaRusso@gmail.com
|
3ffd4d86eac5f5c2dd6d48d15c7872a2d6e8ef95
|
abbc500b22e8d136550b3994095d79f3a2dc2f38
|
/answers/98.py
|
1ce7880153465dda684a3db96cd59d7912450d43
|
[] |
no_license
|
badandworse/leetcodeAns
|
956507f90eddcff3acb909275f3c8d0ae62e83dc
|
b1d141272d7ae02552a5e0bc88ae2dd5a9fd3a08
|
refs/heads/master
| 2021-05-15T00:04:33.414555
| 2018-04-13T15:07:10
| 2018-04-13T15:07:10
| 103,942,544
| 1
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 715
|
py
|
# Definition for a binary tree node.
# class TreeNode(object):
# def __init__(self, x):
# self.val = x
# self.left = None
# self.right = None
class Solution(object):
def isValidBST(self, root):
"""
:type root: TreeNode
:rtype: bool
"""
inOrderLists=[]
stacks=[]
while root or len(stacks)>0:
while root:
stacks.append(root)
root=root.left
root=stacks.pop()
inOrderLists.append(root.val)
root=root.right
for i in range(len(inOrderLists)-1):
if inOrderLists[i]>=inOrderLists[i+1]:
return False
return True
|
[
"zilaixv@gmail.com"
] |
zilaixv@gmail.com
|
2850dbedb93f513dc0ee15666df35c5ff685c000
|
1302c48beae789b1b7837f34325a8f2b203d69df
|
/src/byro/bookkeeping/models/account.py
|
866ae96ca5bbdf954ac3dddf73f44b8cdd0bb526
|
[] |
no_license
|
grince/byro
|
b9a8ad0d54b78ee220af6dedee119ab9ec0036df
|
abe8743c04ba828fdd5ff50c55c43a3b32bc26bd
|
refs/heads/master
| 2021-01-25T12:31:12.461853
| 2018-02-26T17:42:12
| 2018-02-26T17:42:12
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 2,496
|
py
|
from django.db import models
from django.db.models import Q
from django.utils.decorators import classproperty
from django.utils.timezone import now
from django.utils.translation import ugettext_lazy as _
from byro.common.models.auditable import Auditable
from byro.common.models.choices import Choices
class AccountCategory(Choices):
# Regular Categories
MEMBER_DONATION = 'member_donation'
MEMBER_FEES = 'member_fees'
# Categories for double-entry bookkeeping
ASSET = 'asset'
LIABILITY = 'liability'
INCOME = 'income'
EXPENSE = 'expense'
@classproperty
def choices(cls):
return (
(cls.MEMBER_DONATION, _('Donation account')),
(cls.MEMBER_FEES, _('Membership fee account')),
(cls.ASSET, _('Asset account')),
(cls.LIABILITY, _('Liability account')),
(cls.INCOME, _('Income account')),
(cls.EXPENSE, _('Expense account')),
)
class Account(Auditable, models.Model):
account_category = models.CharField(
choices=AccountCategory.choices,
max_length=AccountCategory.max_length,
)
name = models.CharField(max_length=300, null=True) # e.g. 'Laser donations'
class Meta:
unique_together = (
('account_category', 'name'),
)
def __str__(self):
if self.name:
return self.name
return f'{self.account_category} account #{self.id}'
@property
def transactions(self):
from byro.bookkeeping.models import VirtualTransaction
return VirtualTransaction.objects.filter(
Q(source_account=self) | Q(destination_account=self)
)
def total_in(self, start=None, end=now()):
qs = self.incoming_transactions
if start:
qs = qs.filter(value_datetime__gte=start)
if end:
qs = qs.filter(value_datetime__lte=end)
return qs.aggregate(incoming=models.Sum('amount'))['incoming'] or 0
def total_out(self, start=None, end=now()):
qs = self.outgoing_transactions
if start:
qs = qs.filter(value_datetime__gte=start)
if end:
qs = qs.filter(value_datetime__lte=end)
return qs.aggregate(outgoing=models.Sum('amount'))['outgoing'] or 0
def balance(self, start=None, end=now()):
incoming_sum = self.total_in(start=start, end=end)
outgoing_sum = self.total_out(start=start, end=end)
return incoming_sum - outgoing_sum
|
[
"rixx@cutebit.de"
] |
rixx@cutebit.de
|
fd00b70e65bcfa00a449b04cf78677a2199d7d70
|
0aad1bfab7ff39bdadd66d048de4c8d0670ca325
|
/gui/setup.py
|
6a1aa6738ccebd0501e6827d2343cc3804c497c1
|
[] |
no_license
|
RidaShamasneh/SQLAlchamy
|
ffe50c1c9eec54cb46255071a1c9d7ff693a8800
|
cb56c27e6a8dabfc53b7890b33ecdfeb68343b7d
|
refs/heads/master
| 2021-02-18T08:40:08.294409
| 2020-07-09T14:45:13
| 2020-07-09T14:45:13
| 245,178,704
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 3,476
|
py
|
#################################################################
# Do not remove the py2exe package, it is needed somewhere else #
#################################################################
import sys
sys.path.append(r'..')
sys.path.append(r'..\libs')
sys.path.append(r'C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\redist\x64\Microsoft.VC140.CRT')
sys.path.append(r'C:\Windows\WinSxS\amd64_microsoft.vc90.crt_1fc8b3b9a1e18e3b_9.0.21022.8_none_750b37ff97f4f68b')
sys.path.append(r'C:\Windows\SysWOW64\downlevel')
import py2exe
import resources.gui_resources
from distutils.core import setup
from glob import glob
import site
import os.path
data_files = [
(".", glob(r'C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\redist\x64\Microsoft.VC140.CRT\*.*')),
(".", glob(r'C:\Windows\SysWOW64\downlevel\api-ms-win-crt-heap-l1-1-0.dll')),
(".", glob(r'C:\Windows\SysWOW64\downlevel\api-ms-win-crt-runtime-l1-1-0.dll')),
(".", glob(r'C:\Windows\SysWOW64\downlevel\api-ms-win-crt-stdio-l1-1-0.dll')),
(".", glob(r'C:\Windows\SysWOW64\downlevel\api-ms-win-crt-string-l1-1-0.dll')),
("imageformats", [site.getsitepackages()[1] + "\\PyQt4" + "\\plugins\\imageformats\\qjpeg4.dll"]),
("imageformats", [site.getsitepackages()[1] + "\\PyQt4" + "\\plugins\\imageformats\\qico4.dll"]),
("imageformats", [site.getsitepackages()[1] + "\\PyQt4" + "\\plugins\\imageformats\\qgif4.dll"])]
excludes = ['Carbon', 'Carbon.Files', 'IronPythonConsole', 'System', 'System.Windows.Forms.Clipboard', '_imp',
'_scproxy', '_sysconfigdata', '_thread', 'clr', 'com.sun', 'com.sun.jna', 'com.sun.jna.platform',
'console', 'dummy.Process',
'importlib.machinery', 'modes.editingmodes', 'ordereddict',
'pkg_resources.extern.appdirs', 'pkg_resources.extern.packaging', 'pkg_resources.extern.six',
'pkg_resources.extern.six.moves', 'pyreadline.keysyms.make_KeyPress',
'pyreadline.keysyms.make_KeyPress_from_keydescr', 'pyreadline.keysyms.make_keyinfo',
'pyreadline.keysyms.make_keysym', 'six.moves.urllib', 'startup', 'win32com.gen_py',
'win32com.shell', 'winreg', 'cffi']
includes = ['sip', 'pkg_resources', 'PyQt4', 'sqlalchemy.sql.default_comparator', 'sqlalchemy.ext.baked']
packages = ['appdirs', 'packaging', 'encodings', 'sqlalchemy_utils']
dll_excludes = ['IPHLPAPI.DLL',
'api-ms-win-crt-convert-l1-1-0.dll',
'api-ms-win-crt-math-l1-1-0.dll',
'api-ms-win-crt-math-l1-1-0.dll',
'api-ms-win-crt-utility-l1-1-0.dll']
# important note: There is a bug in py2exe which requires setup.py to be executed twice to set an app icon
for _ in range(1, 3):
setup(data_files=data_files,
name='SQL Alchemy Viewer',
author='Rida-Shamasneh',
version="v0.0.1",
description='SQL Alchemy Viewer, CSV viewer (POC)',
packages=[os.path.abspath(os.path.join(os.getcwd(), os.pardir)) + '\\libs'],
options={'py2exe': {'excludes': excludes,
'includes': includes,
'packages': packages,
'dll_excludes': dll_excludes}},
zipfile=None,
windows=[{'script': 'main.py',
'dest_base': 'sql_alchemy',
'icon_resources': [(1, os.getcwd() + '\\resources\\images\\app_icon.ico')]
}]
)
|
[
"rshamasneh@asaltech.com"
] |
rshamasneh@asaltech.com
|
127f915c4b516382f9f8475573aee8803aa3fb0f
|
d83cedb26a1f40b4e129416165fe7ae0947a725d
|
/斐波那契 列表 网上代码.py
|
1b473c28e6529067b98df0df1c5f18a7294c5138
|
[] |
no_license
|
VestigesH/python
|
84ce1c188495748632edb202177db67cd75f1ebe
|
5b8561d171cab5cb93c5577b24d84fd01524b168
|
refs/heads/master
| 2020-06-16T14:00:35.510453
| 2019-07-07T02:33:08
| 2019-07-07T02:33:08
| 195,601,138
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 60
|
py
|
L=[1,1]
while len(L)<10:
L.append(L[-1]+L[-2])
print(L)
|
[
"1541945594@qq.com"
] |
1541945594@qq.com
|
d411f2560b7fea2dbce270905b09789a7a00e732
|
8e524f23d18c02d5b4e4d8cccff5330229a8d7eb
|
/EstruturaDeRepeticao/exerc8.py
|
c6ca2af03682e1f7ab2272b3135e42916d5cffb2
|
[] |
no_license
|
Louissilver/exercicios_python
|
dfe8e1e37fb9300f357c0e5ceffcc559666161fe
|
39d7d53b83177b1dcd64d105eb13f75eed557b17
|
refs/heads/master
| 2022-12-27T03:41:31.238035
| 2020-10-05T23:15:32
| 2020-10-05T23:15:32
| 295,038,319
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 275
|
py
|
#8.Faça um programa que leia 5 números e informe a soma e a média dos números.
num = 0
soma = 0
media = 0
for i in range(1, 6):
num = int(input("Digite um número: "))
soma += num
media = soma/i
print(f"A soma dos números é {soma} e a média deles é {media:.2f}")
|
[
"luisfernandosilveira23@gmail.com"
] |
luisfernandosilveira23@gmail.com
|
c2b6c76ca7aba1eb22ec95bfa7914deef8ef88e7
|
93ab9665078d49028e5094e23d673574031d47e5
|
/Data_structures/list/meeting_point.py
|
b1d9fd087e06d641949969cbfa1187c8b775cc1e
|
[] |
no_license
|
raghulrage/Python-programs
|
7072429108c2932323b5f636d06f97a07c4cb6a4
|
8d134fade8626c99237c48068a2d1f5c6b04a0cc
|
refs/heads/master
| 2023-05-05T23:11:36.407458
| 2020-10-17T05:45:58
| 2020-10-17T05:45:58
| 198,346,485
| 1
| 9
| null | 2021-05-22T12:50:28
| 2019-07-23T03:41:20
|
Python
|
UTF-8
|
Python
| false
| false
| 1,612
|
py
|
class Node:
def __init__(self,data):
self.data = data
self.next = None
class LinkedList():
def __init__(self):
self.head = None
def push(self,data):
new_node = Node(data)
if(self.head == None):
self.head = new_node
return
temp = self.head
while temp.next!=None:
temp = temp.next
temp.next = new_node
def input_push(self,l):
for i in l:
self.push(i)
def display(self):
temp = self.head
print('-----------')
while temp:
print(temp.data)
temp = temp.next
def meeting_point(l1,l2):
temp1 = l1.head
while temp1:
temp2 = l2.head
while temp2:
if temp1.data == temp2.data:
t1 = temp1
t2 = temp2
f=0
while t2!=None and t1!=None:
if t1.data == t2.data:
f = 1
else:
f = 0
break
t1 = t1.next
t2 = t2.next
if f == 1 :
print('Meeting point: ',temp1.data)
return
temp2 = temp2.next
temp1 = temp1.next
print('No meeting point')
lst1 = LinkedList()
lst2 = LinkedList()
lst1.input_push(list(map(int,input().split())))
lst2.input_push(list(map(int,input().split())))
lst1.display()
lst2.display()
meeting_point(lst1,lst2)
|
[
"noreply@github.com"
] |
raghulrage.noreply@github.com
|
d175ae5e78fe281e79ff378a83c197c643eda9d6
|
7ab5402a6de3eb6bdda78d1bc42a2fddc920db44
|
/Algorithms/Implementation/Apple and Orange/code.py
|
fc29e634c07686cc80db997da8fb7fd5205a3ab9
|
[] |
no_license
|
elmoallistair/hackerrank
|
14f29c8960fca81f578f19bcb4df8978da7730a0
|
0d119fbe39ded3f9dc17d35661575d7822d82562
|
refs/heads/master
| 2023-06-09T18:54:24.661721
| 2022-10-20T12:29:34
| 2022-10-20T12:29:34
| 237,128,803
| 7
| 3
| null | 2023-05-31T10:32:59
| 2020-01-30T03:04:33
|
Python
|
UTF-8
|
Python
| false
| false
| 1,170
|
py
|
# Written: 02-Jan-2020
# https://www.hackerrank.com/challenges/apple-and-orange/problem
#!/bin/python3
import math
import os
import random
import re
import sys
# Complete the countApplesAndOranges function below.
def countApplesAndOranges(s, t, a, b, apples, oranges):
ap_count = or_count = 0
for i in apples:
fall_loc = a + i
if s <= fall_loc <= t:
ap_count += 1
for i in oranges:
fall_loc = b + i
if s <= fall_loc <= t:
or_count += 1
# Shorter Code:
# ap_count = len([i for i in apples if s <= a+i <=t])
# or_count = len([i for i in oranges if s <= b+i <=t])
print(ap_count)
print(or_count)
if __name__ == '__main__':
st = input().split()
s = int(st[0]) # Starting point
t = int(st[1]) # Ending Point
ab = input().split()
a = int(ab[0]) # Location of the Apple tree
b = int(ab[1]) # Location of the Orange tree
mn = input().split()
m = int(mn[0])
n = int(mn[1])
apples = list(map(int, input().rstrip().split()))
oranges = list(map(int, input().rstrip().split()))
countApplesAndOranges(s, t, a, b, apples, oranges)
|
[
"noreply@github.com"
] |
elmoallistair.noreply@github.com
|
88be9db0202831cb66b600f8534377dc0698b3d3
|
c4b86ca323a9725e80e17c5a56a2b60a6194fcda
|
/adaboost.py
|
7047d283957f420c610c60ea7b902fb6b09fc04b
|
[] |
no_license
|
shivgupt/Whats-the-Correct-Orientation
|
3d8c2991bb1c65b5de1d1a7d5ed6a230884bfa0c
|
6f32609ba1aad11b10f993456ad7e1680bba64df
|
refs/heads/master
| 2021-06-10T21:46:18.892800
| 2016-12-31T22:34:40
| 2016-12-31T22:34:40
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 336
|
py
|
#!/usr/bin/python
"""
Adaboost
"""
import time
def train(traind, N):
starttime = time.time()
print " Training...",
print "Done in", round(time.time() - starttime, 5), "seconds!"
def test(testd, N):
starttime = time.time()
print " Testing...",
print "Done in", round(time.time() - starttime, 5), "seconds!"
|
[
"bohenderson93@gmail.com"
] |
bohenderson93@gmail.com
|
c8a176d73ce4de43a0c744f3ba4ba152b13f907d
|
9c968f7cdf390f8417912519b53f1b7f6ea8b7e8
|
/HJ_AL/brute_force/b1065_brute.py
|
9f0cd478cfd5e6253c843f1b729ba4e7aabdc19b
|
[] |
no_license
|
hhongjoon/TIL
|
aa33ce2973552a0baa0e0da5bd7d20824fd2e322
|
a33b20af15d3f671ea7c7b2855291e50a9036c1c
|
refs/heads/master
| 2021-08-07T17:33:39.722880
| 2020-04-25T08:11:02
| 2020-04-25T08:11:02
| 162,099,245
| 4
| 0
| null | 2019-10-30T09:06:21
| 2018-12-17T08:34:07
|
Jupyter Notebook
|
UTF-8
|
Python
| false
| false
| 427
|
py
|
num = int(input())
count=0
for i in range(1,num+1):
if len(str(i)) == 1 or len(str(i))==2:
count += 1
continue
str_num=str(i)
judge = True
for j in range(0,len(str_num)-2):
if int(str_num[j]) - int(str_num[j+1]) == int(str_num[j+1]) - int(str_num[j+2]):
continue
else:
judge = False
break
if judge == True:
count+=1
print(count)
|
[
"sungsung129@gmail.com"
] |
sungsung129@gmail.com
|
30e02ab643584aa79a5e1571a8f813fc19a66bf0
|
a650a94b2ee02c5ac4160377105059eda51b3c32
|
/scraper/ont_muni_list.py
|
22f4dfc701a2e47a0deb9342ae71f07a821c5d04
|
[
"MIT"
] |
permissive
|
asterix135/votefor_data
|
566dc082eb9137f2245f13a637266d33e386985d
|
db54a424e1455868d9d47f0726e5fbce4679aacf
|
refs/heads/master
| 2021-01-23T15:50:56.415394
| 2017-09-18T02:39:50
| 2017-09-18T02:39:50
| 93,273,254
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 497
|
py
|
import csv
import requests
from lxml import html
page = requests.get('http://www.mah.gov.on.ca/page1591.aspx')
tree = html.fromstring(page.content)
muni_names = tree.xpath(
'//*[@id="content"]/div/table/tbody[2]/tr/td[1]/p/a[contains(@href, "h")]/text()'
)
muni_urls = tree.xpath(
'//*[@id="content"]/div/table/tbody[2]/tr/td[1]/p/a/@href'
)
muni_geo = None
muni_phone = None
print(len(muni_names))
print(len(muni_urls))
muni_data = list(zip(muni_names, muni_urls))
print(muni_data[-5:])
|
[
"chgraham@gmail.com"
] |
chgraham@gmail.com
|
a4e9e0947887ea83249ad5ea387bebbd6ff85e0a
|
8793b5a3c6819ff3a349faaa80868035dfdf424c
|
/6/at_lesson/star.py
|
895b6681026d8de02db9a5c372d432b5b308a614
|
[] |
no_license
|
MarinaFirefly/Python_homeworks
|
0a331d5c2235e6f9b791c136d0f08020bf688b4a
|
c334c828fac9d163b45fe3710db45ec393830bff
|
refs/heads/master
| 2020-09-06T15:33:41.456129
| 2020-02-08T09:46:30
| 2020-02-08T09:46:30
| 220,466,582
| 2
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 326
|
py
|
#briliant. Only odd numbers must be entered!
def star(number):
for i in range(number):
quantity_slash = abs(number // 2 - i)
print(f"{' ' * quantity_slash}{'*' * (number - quantity_slash * 2)}")
return
num = int(input("Enter odd number! "))
if num%2 == 1:
star(num)
else: print ("Number isn't odd!")
|
[
"laktionova.marina@gmail.com"
] |
laktionova.marina@gmail.com
|
b67369e584f1033bb94ffe25f1989624e70acf7b
|
a219071bf28605b7504d0aea2c0e35cdbaa10460
|
/helpdesk_luismiguel/__manifest__.py
|
8ff31aacd8653184c58cd827b188b554ddf6118b
|
[] |
no_license
|
e-lrl/curso2020-2
|
3c3106da39ad5a6e53dc6c1050e0ea08019c808a
|
8fa5b0d702892b4ffceaf45ba7e75e781f3f2c76
|
refs/heads/13.0
| 2023-02-03T04:19:07.290378
| 2020-12-20T21:11:55
| 2020-12-20T21:11:55
| 316,099,365
| 0
| 0
| null | 2020-11-26T02:06:22
| 2020-11-26T02:06:22
| null |
UTF-8
|
Python
| false
| false
| 789
|
py
|
# Copyright 2020 Hergar
# License AGPL-3.0 or later (https://www.gnu.org/licenses/agpl).
{
"name": "helpdesk.ticket",
"summary": "Helpdesk Ticket",
"version": "13.0.1.0.0",
"category": "Helpdesk",
"website": "https://github.com/OCA/helpdesk",
"author": "Hergar, Odoo Community Association (OCA)",
# see https://odoo-community.org/page/maintainer-role for a description of the maintainer role and responsibilities
"maintainers": ["luismiguelarpon"],
"license": "AGPL-3",
"application": True,
"installable": True,
"depends": [
"base",
],
"data": [
"security/helpdesk_security.xml",
"security/ir.model.access.csv",
"views/helpdesk_ticket_views.xml",
],
"demo": [
]
}
|
[
"l_arpon@hergar.com"
] |
l_arpon@hergar.com
|
dfa17b78951d1872ed8fc4f817a8579389a5a042
|
f9d564f1aa83eca45872dab7fbaa26dd48210d08
|
/huaweicloud-sdk-dws/huaweicloudsdkdws/v2/model/cancel_readonly_cluster_response.py
|
d682b2b92dc9ed1fe6c03084b6e00be2e4fc2041
|
[
"Apache-2.0"
] |
permissive
|
huaweicloud/huaweicloud-sdk-python-v3
|
cde6d849ce5b1de05ac5ebfd6153f27803837d84
|
f69344c1dadb79067746ddf9bfde4bddc18d5ecf
|
refs/heads/master
| 2023-09-01T19:29:43.013318
| 2023-08-31T08:28:59
| 2023-08-31T08:28:59
| 262,207,814
| 103
| 44
|
NOASSERTION
| 2023-06-22T14:50:48
| 2020-05-08T02:28:43
|
Python
|
UTF-8
|
Python
| false
| false
| 2,467
|
py
|
# coding: utf-8
import six
from huaweicloudsdkcore.sdk_response import SdkResponse
from huaweicloudsdkcore.utils.http_utils import sanitize_for_serialization
class CancelReadonlyClusterResponse(SdkResponse):
"""
Attributes:
openapi_types (dict): The key is attribute name
and the value is attribute type.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
"""
sensitive_list = []
openapi_types = {
}
attribute_map = {
}
def __init__(self):
"""CancelReadonlyClusterResponse
The model defined in huaweicloud sdk
"""
super(CancelReadonlyClusterResponse, self).__init__()
self.discriminator = None
def to_dict(self):
"""Returns the model properties as a dict"""
result = {}
for attr, _ in six.iteritems(self.openapi_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
))
elif hasattr(value, "to_dict"):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map(
lambda item: (item[0], item[1].to_dict())
if hasattr(item[1], "to_dict") else item,
value.items()
))
else:
if attr in self.sensitive_list:
result[attr] = "****"
else:
result[attr] = value
return result
def to_str(self):
"""Returns the string representation of the model"""
import simplejson as json
if six.PY2:
import sys
reload(sys)
sys.setdefaultencoding("utf-8")
return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)
def __repr__(self):
"""For `print`"""
return self.to_str()
def __eq__(self, other):
"""Returns true if both objects are equal"""
if not isinstance(other, CancelReadonlyClusterResponse):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""Returns true if both objects are not equal"""
return not self == other
|
[
"hwcloudsdk@huawei.com"
] |
hwcloudsdk@huawei.com
|
b4894f135a3440bcec49be233de785a09e58a382
|
7f32834adae48f49d088aef58718b9d7a473af26
|
/venv/Scripts/pip3-script.py
|
9986486fc369508d184eb01eefd50e5983b032fa
|
[] |
no_license
|
PeiYunluo/spider-scrapy
|
9f6e4f38aa15f7ea4d997c1139254cd5be259981
|
6d91e59b509f141930de45b83a3de47db4b72346
|
refs/heads/master
| 2020-12-14T10:19:06.055652
| 2020-01-18T08:49:17
| 2020-01-18T08:49:17
| 234,706,039
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 407
|
py
|
#!D:\PyCharmProjects\spider-scrapy\venv\Scripts\python.exe
# EASY-INSTALL-ENTRY-SCRIPT: 'pip==19.0.3','console_scripts','pip3'
__requires__ = 'pip==19.0.3'
import re
import sys
from pkg_resources import load_entry_point
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0])
sys.exit(
load_entry_point('pip==19.0.3', 'console_scripts', 'pip3')()
)
|
[
"peiyunluo@icloud.com"
] |
peiyunluo@icloud.com
|
e1dde18ada9d2bd785b48f8d297f7672a2c033fc
|
83d30fd71aed81e391efa95b77cad4e3ab0f9522
|
/Копия task1_18 Vector Class.py
|
7da64689f87f12fc192b592fe189bb35b36dda15
|
[] |
no_license
|
jooker33/learning_tasks
|
b59a8daceb3592871c86873ed2d0522ad9d7927b
|
072212540e9b3ab98978e2e2801796e2c1174a89
|
refs/heads/master
| 2023-08-28T06:09:05.908910
| 2021-11-01T15:57:12
| 2021-11-01T15:57:12
| 390,347,899
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 441
|
py
|
class vector():
def __init__(self,x_coord,y_coord):
self.x_coord=x_coord
self.y_coord=y_coord
def __add__(self,other):
return vector(self.x_coord + other.x_coord,self.y_coord+other.y_coord)
def __repr__(self):
return 'Vector({}, {})'.format(self.x_coord,self.y_coord)
vector_1=vector(1,3)
#Вывели значения координат Х и Y
print(vector_1.x_coord, vector_1.y_coord)
print(vector_1+vector_1)
print(1+2)
|
[
"noreply@github.com"
] |
jooker33.noreply@github.com
|
3597afdc6cc44123ef9e2f78547e45c252287aac
|
6f9e4bec6bbe9a37dc69c991a9ba0090b9720bfa
|
/project_rango/settings/base.py
|
4504d734ffd99dae77f5dbb368bee1ad1005894f
|
[] |
no_license
|
hkfs15/rango
|
13e10010e259ef88777a898f4f54621bf4677bef
|
0ec0902bfc070667bc3e21c8403103dafac0f2e9
|
refs/heads/master
| 2021-01-10T10:01:42.345526
| 2015-10-14T04:18:33
| 2015-10-14T04:18:33
| 44,223,643
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 3,096
|
py
|
"""
Django settings for project_rango project.
Generated by 'django-admin startproject' using Django 1.8.5.
For more information on this file, see
https://docs.djangoproject.com/en/1.8/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/1.8/ref/settings/
"""
# Build paths inside the project like this: os.path.join(BASE_DIR, ...)
import os
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/1.8/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = 'r@ork-5o5hnkmkb_l^to&m!skn!a=(xt+1la274t&g@&vqoehl'
# SECURITY WARNING: don't run with debug turned on in production!
DEBUG = True
ALLOWED_HOSTS = []
# Application definition
INSTALLED_APPS = (
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
'project_rango.apps.rango',
'registration',
'bootstrap_toolkit',
)
MIDDLEWARE_CLASSES = (
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.auth.middleware.SessionAuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
'django.middleware.security.SecurityMiddleware',
)
ROOT_URLCONF = 'project_rango.urls'
TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'DIRS': [os.path.join(BASE_DIR,'templates')],
'APP_DIRS': True,
'OPTIONS': {
'context_processors': [
'django.template.context_processors.debug',
'django.template.context_processors.request',
'django.contrib.auth.context_processors.auth',
'django.contrib.messages.context_processors.messages',
],
},
},
]
WSGI_APPLICATION = 'project_rango.wsgi.application'
# Database
# https://docs.djangoproject.com/en/1.8/ref/settings/#databases
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': os.path.join(BASE_DIR, 'db.sqlite3'),
}
}
# Internationalization
# https://docs.djangoproject.com/en/1.8/topics/i18n/
LANGUAGE_CODE = 'en-us'
TIME_ZONE = 'UTC'
USE_I18N = True
USE_L10N = True
USE_TZ = True
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/1.8/howto/static-files/
STATIC_URL = '/static/'
STATICFILES_DIRS = (
os.path.join(BASE_DIR,"static"),
)
LOCALE_PATHS = (
os.path.join(BASE_DIR,"locale"),
)
MEDIA_ROOT = '/upload/'
LOGIN_URL = '/rango/login/'
REGISTRATION_OPEN = True
ACCOUNT_ACTIVATION_DAYS = 7
REGISTRATION_AUTO_LOGIN = True
LOGIN_REDIRECT_URL = '/rango/'
LOGIN_URL = '/accounts/login/'
|
[
"hkfs15@163.com"
] |
hkfs15@163.com
|
028660a24e92f54b0bc846a5d68b6e90ac21cddf
|
41710e9133d660739f8f9f17040a2a8a6082e9fb
|
/python/aa_modules/fitsio_has_errors/eg2.py
|
d4a0e6e5e75796a2ec451845dfda65e7d12df200
|
[] |
no_license
|
hanjiangxue007/Programming
|
591678150e2e300051fdeaf09124d3893076d3a9
|
7a545ef2300b004497f30d27d1f2aaa032e26af5
|
refs/heads/master
| 2020-06-29T18:50:27.776557
| 2016-10-27T18:31:39
| 2016-10-27T18:31:39
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,094
|
py
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Author : Bhishan Poudel; Physics PhD Student, Ohio University
# Date : Oct-15-2016 Sat
# Last update :
#
#
# Imports
import fitsio
from fitsio import FITS,FITSHDR
# Often you just want to quickly read or write data without bothering to
# create a FITS object. In that case, you can use the read and write
# convienience functions.
# read all data from the first hdu with data
filename='test.fits'
data = fitsio.read(filename)
# read a subset of rows and columns from a table
data = fitsio.read(filename, rows=[35,1001], columns=['x','y'], ext=2)
# read the header, or both at once
h = fitsio.read_header(filename, extension)
data,h = fitsio.read(filename, ext=ext, header=True)
# open the file, write a new binary table extension, and then write the
# data from "recarray" into the table. By default a new extension is
# added to the file. use clobber=True to overwrite an existing file
# instead. To append rows to an existing table, see below.
fitsio.write(filename, recarray)
# write an image
fitsio.write(filename, image)
|
[
"bhishantryphysics@gmail.com"
] |
bhishantryphysics@gmail.com
|
bddbb9c1ef1aa13ff2a9a7ce5aad4cdea29336db
|
75915b819739c338b61e664ede0849a1cc0a17d1
|
/webSauna/my.app/my/new_app/views.py
|
9c92bedbda25df6a1573b61f20db7c932551e1d7
|
[] |
no_license
|
gaurav7goyal/pyramid_framework
|
d3f604aa4703a878829ccbc1e50fe0ea74712985
|
c472c6de7e2dcd7deb4ffa187fb46165905505fe
|
refs/heads/master
| 2020-09-23T02:33:37.065084
| 2019-12-27T05:35:13
| 2019-12-27T05:35:13
| 225,379,442
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 348
|
py
|
"""My Application views."""
from websauna.system.http import Request
from websauna.system.core.route import simple_route
# Configure a sample view provided by this addon
@simple_route("/", route_name="home", renderer='my.new_app/home.html')
def home(request: Request):
"""Render site homepage."""
return {"project": "My new Application"}
|
[
"gaurav.goyal@d10x.io"
] |
gaurav.goyal@d10x.io
|
3851500e0770a527347f3612bf3cb70c49f66473
|
ae9fb8a01419c9f405142ec8c878608f0c0568f4
|
/mapit_bulk_processing/migrations/0013_auto_20160408_1040.py
|
b9a4155a1d098424f5490475c7e2cc73a23bd699
|
[] |
no_license
|
mysociety/mapit-bulk-processing
|
dfbd41c6a06b9d0625a3dde797cdf9dcc07e6f7e
|
d3a407877145fee657bde2ca6b9d5c5ae1fe96e3
|
refs/heads/master
| 2021-01-19T03:59:16.393571
| 2016-08-02T12:16:30
| 2016-08-02T12:16:30
| 55,393,025
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 949
|
py
|
# -*- coding: utf-8 -*-
# Generated by Django 1.9.4 on 2016-04-08 09:40
from __future__ import unicode_literals
from django.db import migrations, models
import mapit_bulk_processing.models
class Migration(migrations.Migration):
dependencies = [
('mapit_bulk_processing', '0012_auto_20160401_1740'),
]
operations = [
migrations.AddField(
model_name='bulklookup',
name='bad_rows',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='bulklookup',
name='original_file',
field=models.FileField(upload_to=mapit_bulk_processing.models.original_file_upload_to),
),
migrations.AlterField(
model_name='bulklookup',
name='output_file',
field=models.FileField(blank=True, upload_to=mapit_bulk_processing.models.output_file_upload_to),
),
]
|
[
"steve@mysociety.org"
] |
steve@mysociety.org
|
bc5f1733b0803e237f45c8a20bd76e628c440728
|
5f0f3823e4f637dec72ec155dfda9ad485fca190
|
/Chapter-1/01.py
|
2b9ef64a6a4d1ed83eb9ea749298ae772ee2852e
|
[] |
no_license
|
Loliver1224/NLP100
|
41eacdf45886b2c16589129812d7cc695b6fde42
|
458f7a8383de83840d23fb64e0047df372f3db55
|
refs/heads/master
| 2020-05-01T09:18:12.122776
| 2019-09-05T16:22:28
| 2019-09-05T16:22:28
| 177,397,026
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 245
|
py
|
# 01. 「パタトクカシーー」
# 「パタトクカシーー」という文字列の1,3,5,7文字目を取り出して連結した文字列を得よ
s = "パタトクカシーー"
print(s[::2])
# 前回と同様にスライスを用いる
|
[
"swim.01060808@gmail.com"
] |
swim.01060808@gmail.com
|
7c6ad8291a41bca71e27bfbe2cc3b423b82fcc5d
|
121a430d428cda6b854a40ebd371cf6210b2d05a
|
/platformer.py
|
d262ac828d8569c32d457774e22c373d4dbff307
|
[
"MIT"
] |
permissive
|
oliviaosimon/Platformer
|
0be14eb4d163fee51e4347695cd1badd73dae12b
|
d70db0f969f73fe5a7bbe6babb4f77c2e35061ec
|
refs/heads/master
| 2020-04-02T23:08:31.090310
| 2018-12-11T16:55:24
| 2018-12-11T16:55:24
| 154,857,055
| 0
| 0
|
MIT
| 2018-10-26T15:39:41
| 2018-10-26T15:39:41
| null |
UTF-8
|
Python
| false
| false
| 9,228
|
py
|
"""
platformer.py
Author: Olivia Simon
Credit:
Assignment:
Write and submit a program that implements the sandbox platformer game:
https://github.com/HHS-IntroProgramming/Platformer
"""
from ggame import App, Color, LineStyle, Sprite, RectangleAsset, CircleAsset, EllipseAsset, PolygonAsset, ImageAsset, Frame
myapp = App()
SCREEN_WIDTH = 1000
SCREEN_HEIGHT = 800
lightBlue = Color(0x2EFEC8, 1.0)
black = Color(0x000000, 1.0)
pink = Color(0xFF00FF, 1.0)
red = Color(0xFF5733, 1.0)
white = Color(0xFFFFFF, 1.0)
red = Color(0xff0000, 1.0)
green = Color(0x00ff00, 1.0)
darkBlue = Color(0x0000ff, 1.0)
black = Color(0x000000, 1.0)
white = Color(0xffffff, 1.0)
grey = Color(0xC0C0C0, 1.0)
thinline = LineStyle(2, black)
blkline = LineStyle(1, black)
noline = LineStyle(0, white)
coolline = LineStyle(1, grey)
blueline = LineStyle(2, darkBlue)
redline = LineStyle(1, red)
greenline = LineStyle(1, green)
gridline = LineStyle(1, grey)
grid=RectangleAsset(30,30,gridline,white)
class Blocks(Sprite):
def __init__(self, x, y, w, h, color):
grid = lambda X : X - X % w
super().__init__(
RectangleAsset(w-1, h-1, LineStyle(0,Color(0, 1.0)), color),(grid(x), grid(y)))
#collisions
collisioncontra =self.collidingWithSprites(type(self))
if len(collisioncontra):
collisioncontra[0].destroy()
class Wall(Blocks):
def __init__(self,x,y):
super().__init__(x,y,60,60,grey) #(self, x, y, w, h, color)
class MarioWall(Blocks):
def __init__(self, x, y):
super().__init__(x, y, 50, 15, black)
class Newton(Sprite):
def __init__(self, x, y, width, height, color, app):
self.vx = self.vy = 0
self.stuck = False
self.app = app
self.resting = False
super().__init__(
RectangleAsset(
width, height,
LineStyle(0, white),
color),
(x, y))
def step(self):
# process movement in horizontal direction first
self.x += self.vx
collides = self.collidingWithSprites(Wall)
collides.extend(self.collidingWithSprites(MarioWall))
for collider in collides:
if self.vx > 0 or self.vx < 0:
if self.vx > 0:
self.x = collider.x - self.width - 1
else:
self.x = collider.x + collider.width + 1
self.vx = 0
# process movement in vertical direction second
self.y += self.vy
collides = self.collidingWithSprites(Wall)
collides.extend(self.collidingWithSprites(MarioWall))
for collider in collides:
if self.vy > 0 or self.vy < 0:
if self.vy > 0:
self.y = collider.y - self.height - 1
if not self.resting:
self.vx = 0
self.resting = True
self.vy = 0
# upward collisions for true Wall only
elif isinstance(collider, Wall):
pass
#self.y = collider.y + collider.height
#self.vy = 0
# adjust vertical velocity for acceleration due to gravity
self.vy += 1
# check for out of bounds
if self.y > self.app.height:
self.app.killMe(self)
class Playah(Newton):
def __init__(self, x, y, app):
w = 10
h = 20
super().__init__(x-w//2, y-h//2, w, h, lightBlue, app)
def step(self):
Jumpers = self.collidingWithSprites(Jumper) #interference with Jumpers
if len(Jumpers):
self.vy = -16 #y -- y positioning jump boost
self.resting = False
super().step()
def move(self, key):
if key == "left arrow":
if self.vx > 0:
self.vx = 0
else:
self.vx = -5
elif key == "right arrow":
if self.vx < 0:
self.vx = 0
else:
self.vx = 5
elif key == "space" and self.resting:
self.vy = -12
self.resting = False
def stopMove(self, key):
if key == "left arrow" or key == "right arrow":
if self.resting:
self.vx = 0
class Pellets(Sprite):
def __init__(self, direction, x, y, app):
w = 15
h = 5
self.direction = direction
self.app = app
super().__init__(RectangleAsset(w, h,
LineStyle(0, Color(0, 1.0)),
Color(0x00ffff, 1.0)),
(x-w//2, y-h//2))
def step(self):
self.x += self.direction
# check for out of bounds
if self.x > self.app.width or self.x < 0:
self.app.killMe(self)
# check for any collisions
hits = self.collidingWithSprites()
selfdestruct = False
for target in hits:
# destroy players and other Pellets
if isinstance(target, Playah) or isinstance(target, Pellets):
self.app.killMe(target)
# self destruct on anything but a Machina
if not isinstance(target, Machina):
selfdestruct = True
if selfdestruct:
self.app.killMe(self)
class Machina(Newton):
def __init__(self, x, y, app):
w = 20
h = 35
r = 10
self.time = 0
self.direction = 1
super().__init__(x-w//2, y-h//2, w, h, Color(0xff8800, 1.0), app)
def step(self):
super().step()
self.time += 1
if self.time % 100 == 0:
Pellets(self.direction,
self.x+self.width//2,
self.y+10,
self.app)
self.direction *= -1
class Jumper(Newton):
def __init__(self, x, y, app):
w = 20
h = 3
super().__init__(x-w//2, y-h//2, w, h,green, app)
def step(self):
if self.resting:
self.app.FallingJumpers.remove(self)
super().step()
#Index/Glossarix
class Game(App):
def __init__(self):
super().__init__()
self.p = None
self.pos = (0,0)
self.listenKeyEvent("keydown", "w", self.newWall)
self.listenKeyEvent("keydown", "p", self.newPlayah)
self.listenKeyEvent("keydown", "j", self.newJumper)
self.listenKeyEvent("keydown", "m", self.newStepThrough)
self.listenKeyEvent("keydown", "left arrow", self.moveKey)
self.listenKeyEvent("keydown", "right arrow", self.moveKey)
self.listenKeyEvent("keydown", "space", self.moveKey)
self.listenKeyEvent("keyup", "left arrow", self.stopMoveKey)
self.listenKeyEvent("keyup", "right arrow", self.stopMoveKey)
self.listenKeyEvent("keyup", "space", self.stopMoveKey)
self.listenMouseEvent("mousemove", self.moveMouse)
self.FallingJumpers = [] #jummpers appended
self.KillList = []
print("press w to make a wall appear.")
print("press p for your Playah to appear.")
print("press m for a ghost wall to appear.")
print("press j for a jumper to appear.")
print("use the arrowkeys to move left and right.")
print("use the spacebar to jump.")
print("Have fun!")
def moveMouse(self, event):
self.pos = (event.x, event.y)
def newWall(self, event):
Wall(self.pos[0], self.pos[1])
def newPlayah(self, event):
for p in Game.getSpritesbyClass(Playah):
p.destroy()
self.p = None
self.p = Playah(self.pos[0], self.pos[1], self)
def newJumper(self, event):
self.FallingJumpers.append(Jumper(self.pos[0], self.pos[1], self))
def newStepThrough(self, event):
MarioWall(self.pos[0], self.pos[1],) #how to place it directly on mouse and not higher???
def moveKey(self, event):
if self.p:
self.p.move(event.key)
def stopMoveKey(self, event):
if self.p:
self.p.stopMove(event.key)
#########
def step(self):
if self.p:
self.p.step()
for s in self.FallingJumpers: # problem fixed, empty list in Game added for fallingjumpers
s.step()
for t in Game.getSpritesbyClass(Machina):
t.step()
for b in Game.getSpritesbyClass(Pellets):
b.step()
for k in self.KillList:
k.destroy()
self.KillList = []
def killMe(self, obj):
if obj in self.FallingJumpers:
self.FallingJumpers.remove(obj)
elif obj == self.p:
self.p = None
if not obj in self.KillList:
self.KillList.append(obj)
app = Game()
app.run()
|
[
"42871888+oliviaosimon@users.noreply.github.com"
] |
42871888+oliviaosimon@users.noreply.github.com
|
6f6408c6315620f3287858f8f6276f435d001ecd
|
9d939421402f98030e41d8f83c5cb3c5f8eadb45
|
/app/decorated.py
|
ace26787c86114b65840ed451ff4848f3559046c
|
[] |
no_license
|
miniYYan/xiaoniu_cron
|
3819a47c474d4a98c3959acf54ad1eaf9c3bc14a
|
f84902b45f58eba5b811e049154c4bf6cae2ae91
|
refs/heads/master
| 2022-12-20T04:28:05.052900
| 2020-09-28T01:02:57
| 2020-09-28T01:02:57
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,094
|
py
|
#!/usr/bin/python3
# -*- coding:utf-8 -*-
from functools import wraps
from datas.utils.json import api_return
def api_err_return(code=1,msg='',data=''):
return code,msg,data
'''
接口 api返回
'''
def api_deal_return(func):
@wraps(func)
def gen_status(*args, **kwargs):
try:
result = func(*args, **kwargs)
if type(result)==str:
return api_return(errcode=0,errmsg=result)
if type(result)==list or type(result)==dict:
return api_return(errcode=0,errmsg='success',data=result)
if type(result)==tuple:
if len(result)==2:
errmsg=result[0]
if errmsg is None or errmsg=="":
errmsg='success'
return api_return(errcode=0, errmsg=errmsg, data=result[1])
else:
return api_return(errcode=result[0],errmsg=result[1],data=result[2])
except Exception as e:
error = str(e)
return api_return(errcode=1,errmsg=error)
return gen_status
|
[
"aniulee@qq.com"
] |
aniulee@qq.com
|
99993322ae5d3b12cf215d06c988de7930420701
|
6ea03449de7e8b3efcd980995eef9d2a6226fa90
|
/cnn_minist.py
|
4011f88c9c22d738db7bd4b19deccf6e3f5443c8
|
[] |
no_license
|
wuzhouqiang/tensorflow2
|
cab6c05fa89aab7e725a54c8112bae34ba8645f7
|
a5bc48972cb6a7e8355dc44dc0f36ad571f7a9b6
|
refs/heads/master
| 2020-06-27T14:03:26.444538
| 2019-08-06T04:12:06
| 2019-08-06T04:12:06
| 199,972,058
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,409
|
py
|
import tensorflow as tf
from tensorflow import keras
lr = 1e-3
batchsz = 256
epochs = 50
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
# print(x_train.shape, y_train.shape) # (60000, 28, 28) (60000,)
x_train = x_train.reshape(60000, 28, 28, 1)
x_test = x_test.reshape(10000, 28, 28, 1)
x_train = x_train / 255
x_test = x_test / 255
train_ds = tf.data.Dataset.from_tensor_slices((x_train, y_train)).shuffle(10000).batch(batchsz)
test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(batchsz)
model = keras.models.Sequential()
model.add(keras.layers.Conv2D(filters=32, kernel_size=(3, 3), input_shape=(28, 28, 1), activation='relu', ))
model.add(keras.layers.MaxPooling2D((2, 2)))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.Conv2D(64, (3, 3), activation='relu'))
model.add(keras.layers.MaxPooling2D((2, 2)))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.Conv2D(64, (3, 3), activation='relu'))
model.add(keras.layers.Flatten())
model.add(keras.layers.Dense(64, activation='relu'))
model.add(keras.layers.Dropout(0.5))
model.add(keras.layers.Dense(10, activation='softmax'))
print(model.summary())
model.compile(optimizer=tf.keras.optimizers.Adam(lr=lr),
loss=tf.losses.SparseCategoricalCrossentropy(),
metrics=['accuracy'])
model.fit(train_ds, epochs=epochs)
model.evaluate(test_ds)
|
[
"w943554532@126.com"
] |
w943554532@126.com
|
ac86c90afb0aadf8e7f618ee3cf32b49cf3f6cca
|
6c28f51864cf2d2582c582f168bbd7aaf9207d71
|
/14_week/이레/5676_음주 코딩.py
|
212f133e297e07a2901ca2e9de5d4b3fd87a7de0
|
[] |
no_license
|
brrgi/Algorithms
|
a4ae5319703b9f5daa359bb1afdb82a1b17797cc
|
2b7003ef0463a56f4595656d46fdae11f88d246a
|
refs/heads/main
| 2023-08-29T00:17:39.255573
| 2021-10-31T14:21:36
| 2021-10-31T14:21:36
| 386,301,933
| 2
| 1
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 651
|
py
|
while 1:
try:
n,k=map(int, input().split())
tree=(n*4)*[0]
nodes=list(map(int, input().split()))
answer=''
init(0,n-1,1)
for _ in range(k):
lst=input().split()
if lst[0]=='C':
i,V=map(int, (lst[1],lst[2]))
nodes[i-1]=pmz(V)
update(0,n-1,1,i-1,pmz(V))
else:
i,j=map(int, (lst[1],lst[2]))
res=query(0,n-1,1,i-1,j-1)
if(res==0):answer+='0'
elif(res>0):answer+='+'
else:answer+='-'
print(answer)
except Exception:
break
|
[
"48500985+brrgi@users.noreply.github.com"
] |
48500985+brrgi@users.noreply.github.com
|
f5d619cb1c3318a7c4daf4cc4c3df14dbff8b35c
|
37279a6b70fd432d96087f2154ded529ffbc0c9e
|
/binary/190-Reverse-Bits.py
|
cd559594ce7b9ec23bb1a474deca59b1404b5570
|
[] |
no_license
|
abhishek-jana/Leetcode-Solutions
|
cfe1bad64fda2421ba85f23121ca50ffc59357da
|
9cd5d12b7438c646226a5e174571e3dbf339a179
|
refs/heads/master
| 2020-12-02T02:04:41.784082
| 2020-01-22T13:07:13
| 2020-01-22T13:07:13
| 230,852,748
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,496
|
py
|
'''
Reverse bits of a given 32 bits unsigned integer.
Example 1:
Input: 00000010100101000001111010011100
Output: 00111001011110000010100101000000
Explanation: The input binary string 00000010100101000001111010011100 represents the unsigned integer 43261596, so return 964176192 which its binary representation is 00111001011110000010100101000000.
Example 2:
Input: 11111111111111111111111111111101
Output: 10111111111111111111111111111111
Explanation: The input binary string 11111111111111111111111111111101 represents the unsigned integer 4294967293, so return 3221225471 which its binary representation is 10111111111111111111111111111111.
'''
class Solution:
# @param n, an integer
# @return an integer
def reverseBits(self, n):
res = 0
for i in range(32):
res += n & 1
n = n >> 1
if i != 31:
res = res << 1
return res
print (Solution().reverseBits(12))
# Time : O(logn) = O(32)
# Space: O(1)
class Solution(object):
# @param n, an integer
# @return an integer
def reverseBits(self, n):
result = 0
for i in range(32):
result <<= 1
result |= n & 1
n >>= 1
return result
def reverseBits2(self, n):
string = bin(n)
if '-' in string:
string = string[:3] + string[3:].zfill(32)[::-1]
else:
string = string[:2] + string[2:].zfill(32)[::-1]
return int(string, 2)
|
[
"abhishekjana6@gmail.com"
] |
abhishekjana6@gmail.com
|
243f9502d6d493755fd42afd6fe5df30df8792a6
|
8d1b2c704368e4e40dbe564f54a16658be334687
|
/Netflix-based/py/fdt_process.py
|
972cb26a617eb34c6099a20729d5ed8249673d5e
|
[] |
no_license
|
zzhyzzh/Factorized-Decision-Tree
|
ca9b4b7bd7cdcaad4e52d98426e856efd290f167
|
0057c1d464d10efc0ee8f3ec30402af58a046774
|
refs/heads/master
| 2021-09-07T19:05:54.612325
| 2018-02-27T16:29:39
| 2018-02-27T16:29:39
| 110,657,366
| 0
| 1
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,799
|
py
|
############# Input Package ################
from scipy.sparse import load_npz
import dt_model as dt
import tool_function as tf
import klepto
############################################
############### Load Data ##################
rating_matrix_csc = load_npz('./netflix/sparse_matrix_100%.npz').tocsc()
rating_matrix_val_csc = load_npz('./netflix/sparse_matrix_validation_75%.npz').tocsc()
print("file load DONE")
############################################
############### Build Tree #################
start = 0
end = int(rating_matrix_csc.shape[1] * 0.75)
dtmodel_realdata = dt.DecisionTreeModel(rating_matrix_csc[:, start:end], depth_threshold = 10)
dtmodel_realdata.build_model()
Tree = klepto.archives.dir_archive('treeFile', cached=True, serialized=True)
Tree['lr_bound'] = dtmodel_realdata.lr_bound
Tree['tree'] = dtmodel_realdata.tree
Tree['split_item'] = dtmodel_realdata.split_item
Tree['rI'] = dtmodel_realdata.rI
Tree.dump()
Tree.clear()
############################################
######################## Build Predict Model #########################
Tree = klepto.archives.dir_archive('treeFile', cached=True, serialized=True)
Tree.load('treeFile')
plambda_candidates = {}
for level in Tree['lr_bound']:
plambda_candidates[level] = list(np.arange(0.001, 0.05, 0.0005))
prediction_model = tf.generate_prediction_model(Tree['lr_bound'], Tree['tree'], Tree['rI'], rating_matrix_csc[:, start:end].tocsr(), plambda_candidates, rating_matrix_val_csc)
######################################################################
######################### Test for New-user ##########################
rmse_result = tf.pred_RMSE_for_new_user(Tree['split_item'], rI, prediction_model, rating_matrix_csc[:, end:])
######################################################################
|
[
"zzhno50@hotmail.com"
] |
zzhno50@hotmail.com
|
170ca8d188aacad28ab3a8be69a38b02bb931402
|
9e4ab50f5822941ab70fefb8ac8f2d91d702d9df
|
/suorganizer/views.py
|
a8c5954ad810ac9b24b4425723169efa4e7b3098
|
[] |
no_license
|
andyk1278/startuptracker
|
cf3b51a82aa6018b990c605cff47398636b4643c
|
b2b07db3a6213249588214200b52a705ed50b339
|
refs/heads/master
| 2021-01-02T23:00:29.839108
| 2017-08-13T09:18:51
| 2017-08-13T09:18:51
| 99,437,365
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 115
|
py
|
from django.http import HttpResponseRedirect
def redirect_root(request):
return HttpResponseRedirect('/blog/')
|
[
"andyk1278@gmail.com"
] |
andyk1278@gmail.com
|
c197ade7c48dc6d9abbc95297012c1ba1bc2f6b2
|
f3591f53c7a4ab6f1babdeb1ff4e21843054d2f2
|
/train_blog.py
|
decd987287d9c24bcf358e43d9a11363a7c72cf6
|
[
"MIT"
] |
permissive
|
forks-learning/self_supervised
|
3bf0ea8d7402bb780ba68e97806798340cc921c3
|
a3addb9c4bc5277224eee040c6ba30230f7586fc
|
refs/heads/master
| 2022-12-08T00:24:46.495732
| 2020-08-26T18:01:21
| 2020-08-26T18:01:21
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,338
|
py
|
import pytorch_lightning as pl
from attr import evolve
from pytorch_lightning.loggers import TensorBoardLogger
from moco import MoCoMethod
from moco import MoCoMethodParams
def main():
base_config = MoCoMethodParams(
lr=0.8,
batch_size=256,
multi_gpu_training=False,
loss_type="ip",
use_negative_examples=False,
use_both_augmentations_as_queries=True,
mlp_normalization="bn",
prediction_mlp_layers=2,
projection_mlp_layers=2,
)
configs = {
"base": base_config,
"pred_only": evolve(base_config, mlp_normalization=None, prediction_mlp_normalization="bn"),
"proj_only": evolve(base_config, mlp_normalization="bn", prediction_mlp_normalization=None),
"no_norm": evolve(base_config, mlp_normalization=None),
"layer_norm": evolve(base_config, mlp_normalization="ln"),
"xent": evolve(base_config, use_negative_examples=True, loss_type="ce", mlp_normalization=None, lr=0.02),
}
for seed in range(3):
for name, config in configs.items():
method = MoCoMethod(config)
logger = TensorBoardLogger("tb_logs", name=f"{name}_{seed}")
trainer = pl.Trainer(gpus=1, max_epochs=10, logger=logger)
trainer.fit(method)
if __name__ == "__main__":
main()
|
[
"abe@sourceress.co"
] |
abe@sourceress.co
|
a3fcd188a0e4b21306c2d67dfab0717e25089ac6
|
7650c4b0404ff25c26ed5c29e544ed00e259ca50
|
/examples/EXAMPLE_config_DST_LC.py
|
c230007afcb3242d4823f688fe1c22e8b2fc12ef
|
[
"Apache-2.0"
] |
permissive
|
ajeldorado/falco-python
|
b32e1ab24d521a6790b4c18a96a4fa9d239c7882
|
406ccf60392542630a7f1f629fc020b8c8e613d2
|
refs/heads/master
| 2022-11-06T03:27:12.604912
| 2022-10-07T15:22:19
| 2022-10-07T15:22:19
| 137,521,218
| 5
| 2
|
Apache-2.0
| 2022-10-07T15:22:58
| 2018-06-15T18:43:56
|
Python
|
UTF-8
|
Python
| false
| false
| 13,962
|
py
|
# import sys
# sys.path.append('../')
import numpy as np
import falco
mp = falco.config.ModelParameters()
mp.SeriesNum = 1
mp.TrialNum = 34
# Special Computational Settings
mp.flagParallel = True;
mp.useGPU = False;
mp.flagPlot = False;
# General
mp.centering = 'pixel';
# Method of computing core throughput:
# - 'HMI' for energy within half-max isophote divided by energy at telescope pupil
# - 'EE' for encircled energy within a radius (mp.thput_radius) divided by energy at telescope pupil
mp.thput_metric = 'EE'
mp.thput_radius = 0.7; # photometric aperture radius [lambda_c/D]. Used ONLY for 'EE' method.
mp.thput_eval_x = 6; # x location [lambda_c/D] in dark hole at which to evaluate throughput
mp.thput_eval_y = 0; # y location [lambda_c/D] in dark hole at which to evaluate throughput
# Where to shift the source to compute the intensity normalization value.
mp.source_x_offset_norm = 6; # x location [lambda_c/D] in dark hole at which to compute intensity normalization
mp.source_y_offset_norm = 0; # y location [lambda_c/D] in dark hole at which to compute intensity normalization
# Bandwidth and Wavelength Specs
mp.lambda0 = 550e-9; # Central wavelength of the whole spectral bandpass [meters]
mp.fracBW = 0.10; # fractional bandwidth of the whole bandpass (Delta lambda / lambda0)
mp.Nsbp = 5; # Number of sub-bandpasses to divide the whole bandpass into for estimation and control
mp.Nwpsbp = 1; # Number of wavelengths to used to approximate an image in each sub-bandpass
# Wavefront Estimation
# Estimator Options:
# - 'perfect' for exact numerical answer from full model
# - 'pairwise' for pairwise probing with batch process estimation
mp.estimator = 'pairwise'
# Pairwise probing:
mp.est = falco.config.Object()
mp.est.probe = falco.config.Probe()
mp.est.probe.Npairs = 3 # Number of pair-wise probe PAIRS to use.
mp.est.probe.whichDM = 1 # Which DM # to use for probing. 1 or 2. Default is 1
mp.est.probe.radius = 12 # Max x/y extent of probed region [lambda/D].
mp.est.probe.xOffset = 0 # offset of probe center in x [actuators]. Use to avoid central obscurations.
mp.est.probe.yOffset = 10 # offset of probe center in y [actuators]. Use to avoid central obscurations.
mp.est.probe.axis = 'alternate' # which axis to have the phase discontinuity along [x or y or xy/alt/alternate]
mp.est.probe.gainFudge = 1 # empirical fudge factor to make average probe amplitude match desired value.
## Wavefront Control: General
# Threshold for culling weak actuators from the Jacobian:
mp.logGmin = -6; # 10^(mp.logGmin) used on the intensity of DM1 and DM2 Jacobians to weed out the weakest actuators
# Zernikes to suppress with controller
mp.jac = falco.config.Object()
mp.jac.zerns = np.array([1]) # Which Zernike modes to include in Jacobian. Given as the max Noll index. Always include the value "1" for the on-axis piston mode.
mp.jac.Zcoef = 1e-9*np.ones_like(mp.jac.zerns) # meters RMS of Zernike aberrations. (piston value is reset to 1 later)
# Zernikes to compute sensitivities for
mp.eval = falco.config.Object()
mp.eval.indsZnoll = np.array([2, 3]) # Noll indices of Zernikes to compute values for [1-D ndarray]
# Annuli to compute 1nm RMS Zernike sensitivities over. Columns are [inner radius, outer radius]. One row per annulus.
mp.eval.Rsens = np.array([[3., 4.], [4., 8.]]); # [2-D ndarray]
# Grid- or Line-Search Settings
mp.ctrl = falco.config.Object()
mp.ctrl.log10regVec = np.arange(-6, -1.5, 1) # log10 of the regularization exponents (often called Beta values)
mp.ctrl.dmfacVec = np.array([1., ]) # Proportional gain term applied to the total DM delta command. Usually in range [0.5,1]. [1-D ndarray]
# Spatial pixel weighting
mp.WspatialDef = [];# [3, 4.5, 3]; # spatial control Jacobian weighting by annulus: [Inner radius, outer radius, intensity weight; (as many rows as desired)] [ndarray]
# DM weighting
mp.dm1.weight = 1.
mp.dm2.weight = 1.
## Wavefront Control: Controller Specific (case insensitive)
# Controller options:
# - 'gridsearchEFC' for EFC as an empirical grid search over tuning parameters
# - 'plannedEFC' for EFC with an automated regularization schedule
# # # GRID SEARCH EFC DEFAULTS
# WFSC Iterations and Control Matrix Relinearization
# mp.controller = 'gridsearchEFC';
# mp.Nitr = 4 # Number of estimation+control iterations to perform
# mp.relinItrVec = np.arange(mp.Nitr+1) #1:mp.Nitr; # Which correction iterations at which to re-compute the control Jacobian [1-D ndarray]
# mp.dm_ind = np.array([1, 2]) # Which DMs to use [1-D ndarray]
# PLANNED SEARCH EFC DEFAULTS
mp.controller = 'plannedefc'
mp.dm_ind = np.array([1, 2]) # vector of DMs used in controller at ANY time (not necessarily all at once or all the time).
mp.ctrl.dmfacVec = [1]
# CONTROL SCHEDULE. Columns of mp.ctrl.sched_mat are:
# Column 1: # of iterations,
# Column 2: log10(regularization),
# Column 3: which DMs to use (12, 128, 129, or 1289) for control
# Column 4: flag (0 = False, 1 = True), whether to re-linearize
# at that iteration.
# Column 5: flag (0 = False, 1 = True), whether to perform an
# EFC parameter grid search to find the set giving the best
# contrast .
# The imaginary part of the log10(regularization) in column 2 is
# replaced for that iteration with the optimal log10(regularization)
# A row starting with [0, 0, 0, 1...] is for relinearizing only at that time
partA = np.tile(np.array([1, 1j, 12, 1, 1]), (4, 1))
partB = np.tile(np.array([1, 1j-1, 12, 1, 1]), (25, 1))
partC = np.tile(np.array([1, 1j, 12, 1, 1]), (1, 1))
sched_mat = np.concatenate((partA, partB, partC), axis=0)
mp.Nitr, mp.relinItrVec, mp.gridSearchItrVec, mp.ctrl.log10regSchedIn, \
mp.dm_ind_sched = falco.ctrl.efc_schedule_generator(sched_mat)
# Deformable Mirrors: Influence Functions
## Influence Function Options:
## - falco.INFLUENCE_XINETICS uses the file 'influence_dm5v2.fits' for one type of Xinetics DM
## - INFLUENCE_BMC_2K uses the file 'influence_BMC_2kDM_400micron_res10.fits' for BMC 2k DM
## - INFLUENCE_BMC_KILO uses the file 'influence_BMC_kiloDM_300micron_res10_spline.fits' for BMC kiloDM
mp.dm1.inf_fn = falco.INFLUENCE_XINETICS
mp.dm2.inf_fn = falco.INFLUENCE_XINETICS
mp.dm1.dm_spacing = 0.9906e-3;#1e-3; # User defined actuator pitch
mp.dm2.dm_spacing = 0.9906e-3;#1e-3; # User defined actuator pitch
mp.dm1.inf_sign = '+';
mp.dm2.inf_sign = '+';
# Deformable Mirrors: Optical Layout Parameters
## DM1 parameters
mp.dm1.Nact = 48; # # of actuators across DM array
mp.dm1.VtoH = 1e-9*np.ones((48,48)) # gains of all actuators [nm/V of free stroke]
mp.dm1.xtilt = 0; # for foreshortening. angle of rotation about x-axis [degrees]
mp.dm1.ytilt = 0 # for foreshortening. angle of rotation about y-axis [degrees]
mp.dm1.zrot = 0; # clocking of DM surface [degrees]
mp.dm1.xc = (48/2 - 1/2); # x-center location of DM surface [actuator widths]
mp.dm1.yc = (48/2 - 1/2); # y-center location of DM surface [actuator widths]
mp.dm1.edgeBuffer = 1; # max radius (in actuator spacings) outside of beam on DM surface to compute influence functions for. [actuator widths]
## DM2 parameters
mp.dm2.Nact = 48; # # of actuators across DM array
mp.dm2.VtoH = 1e-9*np.ones((48,48)) # gains of all actuators [nm/V of free stroke]
mp.dm2.xtilt = 0; # for foreshortening. angle of rotation about x-axis [degrees]
mp.dm2.ytilt = 0 # for foreshortening. angle of rotation about y-axis [degrees]
mp.dm2.zrot = 0; # clocking of DM surface [degrees]
mp.dm2.xc = (48/2 - 1/2); # x-center location of DM surface [actuator widths]
mp.dm2.yc = (48/2 - 1/2); # y-center location of DM surface [actuator widths]
mp.dm2.edgeBuffer = 1; # max radius (in actuator spacings) outside of beam on DM surface to compute influence functions for. [actuator widths]
## Aperture stops at DMs
mp.flagDM1stop = False; # Whether to apply an iris or not
mp.dm1.Dstop = 100e-3; # Diameter of iris [meters]
mp.flagDM2stop = False; # Whether to apply an iris or not
mp.dm2.Dstop = 50e-3; # Diameter of iris [meters]
## DM separations
mp.d_P2_dm1 = 0; # distance (along +z axis) from P2 pupil to DM1 [meters]
mp.d_dm1_dm2 = 1.000; # distance between DM1 and DM2 [meters]
# Optical Layout: All models
## Key Optical Layout Choices
mp.flagSim = True; # Simulation or not
mp.layout = 'Fourier'; # Which optical layout to use
mp.coro = 'LC'
mp.flagApod = False # Whether to use an apodizer or not
### NEED TO DETERMINE
mp.Fend = falco.config.Object()
## Final Focal Plane Properties
mp.Fend.res = 3.0; # Sampling [ pixels per lambda0/D]
mp.Fend.FOV = 11.; # half-width of the field of view in both dimensions [lambda0/D]
### NEED TO DETERMINE
## Correction and scoring region definition
mp.Fend.corr = falco.config.Object()
mp.Fend.corr.Rin = 2.8; # inner radius of dark hole correction region [lambda0/D]
mp.Fend.corr.Rout = 10; # outer radius of dark hole correction region [lambda0/D]
mp.Fend.corr.ang = 180; # angular opening of dark hole correction region [degrees]
#
mp.Fend.score = falco.config.Object()
mp.Fend.score.Rin = 2.8; # inner radius of dark hole scoring region [lambda0/D]
mp.Fend.score.Rout = 10; # outer radius of dark hole scoring region [lambda0/D]
mp.Fend.score.ang = 180; # angular opening of dark hole scoring region [degrees]
#
mp.Fend.sides = 'leftright' # Which side(s) for correction: 'left', 'right', 'top', 'up', 'bottom', 'down', 'lr', 'rl', 'leftright', 'rightleft', 'tb', 'bt', 'ud', 'du', 'topbottom', 'bottomtop', 'updown', 'downup'
# Optical Layout: Compact Model (and Jacobian Model)
## NOTE for HLC and LC: Lyot plane resolution must be the same as input pupil's in order to use Babinet's principle
## Focal Lengths
mp.fl = 1.; # [meters] Focal length value used for all FTs in the compact model. Don't need different values since this is a Fourier model.
## Pupil Plane Diameters
mp.P2.D = 46.3e-3;
mp.P3.D = 46.3e-3;
mp.P4.D = 46.3e-3;
### NEED TO DETERMINE
## Pupil Plane Resolutions
mp.P1.compact.Nbeam = 300
# mp.P2.compact.Nbeam = 300
# mp.P3.compact.Nbeam = 300
mp.P4.compact.Nbeam = 300
## Number of re-imaging relays between pupil planesin compact model. Needed
## to keep track of 180-degree rotations compared to the full model, which
## in general can have probably has extra collimated beams compared to the
## compact model.
mp.Nrelay1to2 = 1
mp.Nrelay2to3 = 1
mp.Nrelay3to4 = 1
mp.NrelayFend = 0 # How many times to rotate the final image by 180 degrees
# Optical Layout: Full Model
## Focal Lengths
## mp.fl = 1;
#
## Pupil Plane Resolutions
mp.P1.full.Nbeam = 300
# mp.P2.full.Nbeam = 300
# mp.P3.full.Nbeam = 300
mp.P4.full.Nbeam = 300
# %% Entrance Pupil (P1) Definition and Generation
##Pupil definition
mp.whichPupil = 'Simple'
mp.P1.IDnorm = 0.00 # ID of the central obscuration [diameter]. Used only for computing the RMS DM surface from the ID to the OD of the pupil. OD is assumed to be 1.
mp.P1.ODnorm = 1.00
# mp.P1.IDnorm = 0.303; # ID of the central obscuration [diameter]. Used only for computing the RMS DM surface from the ID to the OD of the pupil. OD is assumed to be 1.
mp.P1.D = 4.0; # telescope diameter [meters]. Used only for converting milliarcseconds to lambda0/D or vice-versa.
# mp.P1.Dfac = 1; # Factor scaling inscribed OD to circumscribed OD for the telescope pupil.
# Inputs common to both the compact and full models
inputs = {"OD": 1.00}
# Full model only
inputs["Nbeam"] = mp.P1.full.Nbeam
inputs["Npad"] = falco.util.ceil_even(mp.P1.full.Nbeam+2) # 2**(falco.util.nextpow2(mp.P1.full.Nbeam))
mp.P1.full.mask = falco.mask.falco_gen_pupil_Simple(inputs)
# Compact model only
inputs["Nbeam"] = mp.P1.compact.Nbeam
inputs["Npad"] = falco.util.ceil_even(mp.P1.compact.Nbeam+2) #2**(falco.util.nextpow2(mp.P1.compact.Nbeam))
mp.P1.compact.mask = falco.mask.falco_gen_pupil_Simple(inputs)
# %% "Apodizer" (P3) Definition and Generation
mp.flagApod = False # Whether to use an apodizer or not
# %% Lyot stop (P4) Definition and Generation
# Lyot stop geometry
mp.P4.wStrut = 0.005 # nominal pupil's value is 76mm = 3.216#
mp.P4.IDnorm = 47.36/227.86 # Lyot stop ID [Dtelescope]
mp.P4.ODnorm = 156.21/227.86 # Lyot stop OD [Dtelescope]
mp.P4.angStrut = [90, 210, 330] # degrees
# Inputs common to both the compact and full models
inputs = {
'ID': mp.P4.IDnorm,
'OD': mp.P4.ODnorm,
'angStrut': mp.P4.angStrut,
'wStrut': mp.P4.wStrut,
}
# Full model
inputs["Nbeam"] = mp.P4.full.Nbeam
inputs["Npad"] = 2**(falco.util.nextpow2(mp.P4.full.Nbeam))
mp.P4.full.mask = falco.mask.falco_gen_pupil_Simple(inputs)
# Compact model
inputs["Nbeam"] = mp.P4.compact.Nbeam
inputs["Npad"] = 2**(falco.util.nextpow2(mp.P4.compact.Nbeam))
mp.P4.compact.mask = falco.mask.falco_gen_pupil_Simple(inputs)
# %% FPM (F3) Definition and Generation
# FPM size
mp.F3.Rin = 2.8 # maximum radius of inner part of the focal plane mask [lambda0/D]
mp.F3.Rout = np.Inf # radius of outer opaque edge of FPM [lambda0/D]
mp.F3.ang = 180 # on each side, opening angle [degrees]
mp.F3.FPMampFac = 10**(-3.7/2.0) # amplitude transmission of the FPM
mp.F3.compact.res = 6 # sampling of FPM for full model [pixels per lambda0/D]
mp.F3.full.res = 6 # sampling of FPM for full model [pixels per lambda0/D]
# Both models
FPM = {}
FPM["rhoInner"] = mp.F3.Rin # radius of inner FPM amplitude spot (in lambda_c/D)
FPM["rhoOuter"] = mp.F3.Rout # radius of outer opaque FPM ring (in lambda_c/D)
FPM["centering"] = mp.centering
FPM["FPMampFac"] = mp.F3.FPMampFac # amplitude transmission of inner FPM spot
# Full model
FPM["pixresFPM"] = mp.F3.full.res
mp.F3.full.mask = falco.mask.gen_annular_fpm(FPM)
# Compact model
FPM["pixresFPM"] = mp.F3.compact.res;
mp.F3.compact.mask = falco.mask.gen_annular_fpm(FPM)
|
[
"aj.riggs@jpl.nasa.gov"
] |
aj.riggs@jpl.nasa.gov
|
b65f91b5d0820bef879b4902b41d7a79e7fe245a
|
33f304bbd8536045a63dea909031576ea3f7b488
|
/census_area/core.py
|
c3fe06979410922dd4552eca320be2f8349c5c06
|
[
"MIT"
] |
permissive
|
LindaLv11/census_area
|
859c92cd5ca6a8537ff45014b42771804dc29913
|
48d8bc7e73c12b58e796307e36c93029b1ec0044
|
refs/heads/master
| 2020-04-20T08:25:32.838867
| 2019-01-04T03:00:47
| 2019-01-04T03:00:47
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 4,436
|
py
|
import shapely.geometry
import shapely.geos
import esridump
GEO_URLS = {
'tracts' : {
1990 : 'https://gis.uspatial.umn.edu/arcgis/rest/services/nhgis/Census_Tracts_1910_2014/MapServer/8',
2000 : 'https://tigerweb.geo.census.gov/arcgis/rest/services/Census2010/tigerWMS_Census2000/MapServer/6',
2010 : 'https://tigerweb.geo.census.gov/arcgis/rest/services/TIGERweb/tigerWMS_Census2010/MapServer/14',
2011 : 'https://tigerweb.geo.census.gov/arcgis/rest/services/TIGERweb/tigerWMS_Census2010/MapServer/14',
2012 : 'https://tigerweb.geo.census.gov/arcgis/rest/services/TIGERweb/tigerWMS_Census2010/MapServer/14',
2013 : 'https://tigerweb.geo.census.gov/arcgis/rest/services/TIGERweb/tigerWMS_ACS2013/MapServer/8',
2014 : 'https://tigerweb.geo.census.gov/arcgis/rest/services/TIGERweb/tigerWMS_ACS2014/MapServer/8',
2015 : 'https://tigerweb.geo.census.gov/arcgis/rest/services/TIGERweb/tigerWMS_ACS2015/MapServer/8',
2016 : 'https://tigerweb.geo.census.gov/arcgis/rest/services/TIGERweb/tigerWMS_ACS2015/MapServer/8'},
'block groups' : {
2000 : 'https://tigerweb.geo.census.gov/arcgis/rest/services/Census2010/tigerWMS_Census2000/MapServer/8',
2010 : 'https://tigerweb.geo.census.gov/arcgis/rest/services/TIGERweb/tigerWMS_Census2010/MapServer/16',
2011 : 'https://tigerweb.geo.census.gov/arcgis/rest/services/TIGERweb/tigerWMS_Census2010/MapServer/16',
2012 : 'https://tigerweb.geo.census.gov/arcgis/rest/services/TIGERweb/tigerWMS_Census2010/MapServer/16',
2013 : 'https://tigerweb.geo.census.gov/arcgis/rest/services/TIGERweb/tigerWMS_ACS2013/MapServer/10',
2014 : 'https://tigerweb.geo.census.gov/arcgis/rest/services/TIGERweb/tigerWMS_ACS2014/MapServer/10',
2015 : 'https://tigerweb.geo.census.gov/arcgis/rest/services/TIGERweb/tigerWMS_ACS2015/MapServer/10',
2016 : 'https://tigerweb.geo.census.gov/arcgis/rest/services/TIGERweb/tigerWMS_ACS2015/MapServer/10'},
'blocks' : {
2000 : 'https://tigerweb.geo.census.gov/arcgis/rest/services/Census2010/tigerWMS_Census2000/MapServer/10',
2010 : 'https://tigerweb.geo.census.gov/arcgis/rest/services/TIGERweb/tigerWMS_Current/MapServer/12'},
'incorporated places' : {
1990 : 'https://gis.uspatial.umn.edu/arcgis/rest/services/nhgis/Places_1980_2014/MapServer/1',
2000 : 'https://tigerweb.geo.census.gov/arcgis/rest/services/Census2010/tigerWMS_Census2000/MapServer/24',
2010 : 'https://tigerweb.geo.census.gov/arcgis/rest/services/TIGERweb/tigerWMS_Census2010/MapServer/34',
2011 : 'https://tigerweb.geo.census.gov/arcgis/rest/services/TIGERweb/tigerWMS_Census2010/MapServer/34',
2012 : 'https://tigerweb.geo.census.gov/arcgis/rest/services/TIGERweb/tigerWMS_Census2010/MapServer/34',
2013 : 'https://tigerweb.geo.census.gov/arcgis/rest/services/TIGERweb/tigerWMS_ACS2013/MapServer/26',
2014 : 'https://tigerweb.geo.census.gov/arcgis/rest/services/TIGERweb/tigerWMS_ACS2014/MapServer/26',
2015 : 'https://tigerweb.geo.census.gov/arcgis/rest/services/TIGERweb/tigerWMS_ACS2015/MapServer/26',
2016 : 'https://tigerweb.geo.census.gov/arcgis/rest/services/TIGERweb/tigerWMS_ACS2016/MapServer/26'}
}
class AreaFilter(object):
def __init__(self, geojson_geometry, sub_geography_url):
self.geo = shapely.geometry.shape(geojson_geometry)
geo_query_args = {'geometry': ','.join(str(x) for x in self.geo.bounds),
'geometryType': 'esriGeometryEnvelope',
'spatialRel': 'esriSpatialRelEnvelopeIntersects',
'inSR' : '4326',
'geometryPrecision' : 9,
'orderByFields': 'OID'}
self.area_dumper = esridump.EsriDumper(sub_geography_url,
extra_query_args = geo_query_args)
def __iter__(self):
for area in self.area_dumper:
area_geo = shapely.geometry.shape(area['geometry'])
if self.geo.intersects(area_geo):
try:
intersection = self.geo.intersection(area_geo)
except shapely.geos.TopologicalError:
intersection = self.geo.buffer(0).intersection(area_geo.buffer(0))
if intersection.area/area_geo.area > 0.1:
yield area
|
[
"fgregg@uchicago.edu"
] |
fgregg@uchicago.edu
|
ae5725c9cb4a24fb714b7b102620f0ddc2126997
|
e0ba27b8b9894ccffa0bff1b3e0e35d135e5966f
|
/src/api/__init__.py
|
c9932624f8ac5a7077a105c64116fa4199bd5482
|
[] |
no_license
|
tiagodread/lumen_api
|
0188969d178a9bca2897cb8fff0181d7ab6ee9d1
|
41898d2691550c530703b5771ad1176b1b4c2b9f
|
refs/heads/master
| 2020-04-04T01:08:49.312051
| 2018-11-06T15:49:05
| 2018-11-06T15:49:05
| 155,669,556
| 1
| 1
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 22
|
py
|
from .api import Api
|
[
"tiago.goes2009@hotmail.com"
] |
tiago.goes2009@hotmail.com
|
b5a06168a7891d65d6d1f2dc37cc42b31c3f9075
|
14b8cf0b67104b53534678b8c0e9525ace4714ff
|
/codeeval/spiral.py
|
8ce3b47e920f2d0e9c03bbd1d9e3a51d4092b051
|
[] |
no_license
|
bhfwg/py_learn
|
bb11898fd81f653643fc61949f43df751d317fcb
|
eca9da748bada67357961d1581d8ec890a3385f8
|
refs/heads/master
| 2020-03-27T15:01:25.881792
| 2018-06-05T01:36:26
| 2018-06-05T01:36:26
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,433
|
py
|
from sys import argv
def spiral_printing(n, m, one):
if n * m == 1:
yield one[0]
return
def point_2_index(x, y):
return x * m + y
ax = 0
ay = 0
bx = 0
by = m - 1
cx = n - 1
cy = m - 1
dx = n - 1
dy = 0
while 1:
for i in xrange(ay, by):
index = point_2_index(ax, i)
yield one[index]
for i in xrange(bx, cx):
index = point_2_index(i, cy)
yield one[index]
for i in xrange(cy, dy, -1):
index = point_2_index(dx, i)
yield one[index]
for i in xrange(dx, ax, -1):
index = point_2_index(i, ax)
yield one[index]
ax += 1
ay += 1
bx += 1
by -= 1
cx -= 1
cy -= 1
dx -= 1
dy += 1
if ay > by or ax > dx:
break
if ay == by:
for i in xrange(bx, cx + 1):
index = point_2_index(i, cy)
yield one[index]
break
elif ax == dx:
for i in xrange(ay, by + 1):
index = point_2_index(ax, i)
yield one[index]
break
f = open(argv[1], 'r')
for one in f:
one = one.strip()
if one:
n, m, one = one.split(';')
n = int(n)
m = int(m)
one = one.split(' ')
print ' '.join(spiral_printing(n, m, one))
f.close()
|
[
"metathinkerk@gmail.com"
] |
metathinkerk@gmail.com
|
0f2da2a3a3f2c17a50f1b0f5234713f5baf9597f
|
3aa1e554a4895252f36bf2c423d7813af67b11c9
|
/algoprac3/backjun_2609.py
|
1f8163b7259309b8a0d24d45e73117cebda959d5
|
[] |
no_license
|
rlagudals95/Algorythm
|
10e18b4d83b287144545f7129f66e0f571b814b0
|
055d1eae986071b0cfd3f00f1669043f364c090c
|
refs/heads/main
| 2023-04-28T23:49:59.926153
| 2021-05-20T01:32:16
| 2021-05-20T01:32:16
| 369,041,015
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 521
|
py
|
# C = list(map(int, input().split()))
# a = C[0]
# b = C[1]
# low = 6
# high = a*b//low
# low = a*b//high
# print(low,high)
import sys
A, B = map(int, sys.stdin.readline().split())
# a=24 b=18
a, b = A, B
while b != 0:
a = a % b # a = 24를 18로 나눈 나머지 6 // 6이 최대공약수
a, b = b, a # a는 6이 되어 b가 되고 6,18 = 18,6 이제 18이 또들어가 b로 나뉘면 0
print(a)
print(A*B//a) # 최소 공배수는 두수 곱한것 나누기 최대공약수
|
[
"76252074+rlagudals95@users.noreply.github.com"
] |
76252074+rlagudals95@users.noreply.github.com
|
30faee71e2caf0f4591612bbe4528e2edd9a5b1e
|
684f8a81a212b58d038a353ae247fec89cde5bde
|
/newclient.py
|
89ea5422724c78eb2667f0e4e96e7a9748eecb8f
|
[
"MIT"
] |
permissive
|
saini1998/Computer-Networks
|
418731ebef4bc10b31663f86d4bd76d467ca4560
|
dd47093231edc6a11774d75470a6064818c48a0d
|
refs/heads/master
| 2021-02-13T04:55:04.197853
| 2020-03-05T17:51:01
| 2020-03-05T17:51:01
| 244,663,347
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 625
|
py
|
import socket
import os
import subprocess
s = socket.socket()
host = '172.20.10.3'
port = 9999
s.connect((host, port))
while True:
data = s.recv(1024)
if data[:2].decode("utf-8") == 'cd':
os.chdir(data[3:].decode("utf-8"))
if len(data) > 0:
cmd = subprocess.Popen(data[:].decode("utf-8"),shell=True, stdout=subprocess.PIPE, stdin=subprocess.PIPE, stderr=subprocess.PIPE)
output_byte = cmd.stdout.read() + cmd.stderr.read()
output_str = str(output_byte,"utf-8")
currentWD = os.getcwd() + "> "
s.send(str.encode(output_str + currentWD))
print(output_str)
|
[
"sainiaaryaman1998@hmail.com"
] |
sainiaaryaman1998@hmail.com
|
ed7b2c19a04dae0bca45ca6da925bc7be5c2972b
|
2677510320f5d4e111d9262e7c76bd2eec1444e3
|
/src/control/sac_script/discrete_actor.py
|
a56d8e714ac784b441bc0ad032465c394d5ea915
|
[] |
no_license
|
amiani/spacerl
|
1037da385ab7f27aaffbd7df58cbdb01164329e0
|
ee11a5e2b2604eb06279e76964e10aad6017ebe6
|
refs/heads/master
| 2022-06-28T20:39:45.004294
| 2020-05-04T00:13:35
| 2020-05-04T00:13:35
| 249,797,554
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 747
|
py
|
import torch
from torch.distributions import Categorical
obdim, h1, h2, acdim = 6+3, 256, 256, 3
class DiscreteActor(torch.nn.Module):
def __init__(self):
super().__init__()
self.net = torch.nn.Sequential(
torch.nn.Linear(obdim, h1),
torch.nn.ReLU(),
torch.nn.Linear(h1, h2),
torch.nn.ReLU(),
torch.nn.Linear(h2, acdim),
torch.nn.LogSoftmax(dim=1),
)
def forward(self, input):
logits = self.net(input)
action = Categorical(logits=logits).sample()
return action, logits.exp(), logits
model = DiscreteActor()
traced = torch.jit.trace(model, torch.randn(1, obdim), check_trace=False)
traced.save('discrete_actor.pt')
|
[
"amaianijohns@gmail.com"
] |
amaianijohns@gmail.com
|
083fe36854fbd371229788e78188c0979a5c2992
|
9136059cc782460a1cd4d74aa7e4570037996202
|
/testingoutputfromreducerc1.py
|
8e3665636e8afbbd913287e86d3a3f4cf89ed0e4
|
[] |
no_license
|
sheikhusmanshakeel/ExtremeComputing2
|
d26dfb02d32e64dcee4f5252ee138b2925f58a26
|
e634100fa4404e540fefe7ad864791a93aaa483f
|
refs/heads/master
| 2021-08-15T05:41:47.942314
| 2017-11-17T11:58:37
| 2017-11-17T11:58:37
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 781
|
py
|
#!/usr/bin/env python
import sys
ownerRowIdDict = dict()
maxCount = 0
currentCount = 0
previousOwnerId = ''
currentOwenrId = ''
answerIds = ''
fileHandle = open('/afs/inf.ed.ac.uk/user/s15/s1579769/excassignment2/outputfromreducerc1', 'r')
# fileHandle = open('/home/raven/PycharmProjects/excassignment2/stacksorted.txt', 'r')
for line in fileHandle.readlines():
currentOwenrId, payload = line.strip('\n').split('\t')
if currentOwenrId not in ownerRowIdDict:
ownerRowIdDict[currentOwenrId] = payload + ' , '
else:
answers = ownerRowIdDict[currentOwenrId]
ownerRowIdDict[currentOwenrId] = answers + ' , ' + payload
for key in ownerRowIdDict.keys():
count = len(ownerRowIdDict[key].split(',')) -1
print("{0}\t{1}".format(key,count))
|
[
"sheikh_usman3@yahoo.com"
] |
sheikh_usman3@yahoo.com
|
6c45e72f32ca223fecfcc490073f0cd0d14b4b65
|
0130c8b14927097663157846adc4b146d67d2fda
|
/tests/common/test_run/div_no_nan_run.py
|
1a2c66c665dc13f6f5900b55ab27ee71b9d67109
|
[
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference",
"Unlicense",
"BSD-3-Clause",
"NCSA",
"LLVM-exception",
"Zlib",
"BSD-2-Clause",
"MIT"
] |
permissive
|
Shigangli/akg
|
e8be3e0ee1eafe3e42b4cc4d424c28f08ef4c0bc
|
3766c54e0b109541932d147a6b5643a334b82403
|
refs/heads/master
| 2023-09-06T05:13:40.571583
| 2021-11-23T03:44:54
| 2021-11-23T03:44:54
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 2,497
|
py
|
# Copyright 2020 Huawei Technologies Co., Ltd
#
# 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 numpy as np
from akg.utils import kernel_exec as utils
from tests.common.test_op import div_no_nan
from tests.common.tensorio import compare_tensor
from tests.common.base import get_rtol_atol
from tests.common.gen_random import random_gaussian
def div_no_nan_execute(shapes, dtype, attrs):
exp_output, inputs, args = gen_data(dtype, shapes)
mod = div_no_nan_compile(shapes, dtype, attrs)
# result_tvm
acu_output = utils.mod_launch(mod, args, expect=exp_output)
# compare result
rtol, atol = get_rtol_atol("div_no_nan", dtype)
TestCase_Result = compare_tensor(acu_output, exp_output, rtol=rtol, atol=atol, equal_nan=True)
return inputs, acu_output, exp_output, TestCase_Result
def gen_data(dtype, shapes):
# Result_Numpy
data_x = random_gaussian(shapes[0], miu=1, sigma=0.1).astype(dtype)
data_y = random_gaussian(shapes[1], miu=0, sigma=2**-64).astype(dtype)
if dtype in ["uint8", "int8", "int32"]:
is_zero = np.equal(0, data_y)
if dtype in ["float16"]:
is_zero = np.less(np.abs(data_y), 2**-12)
if dtype in ["float32"]:
is_zero = np.less(np.abs(data_y), 2**-64)
if dtype in ["uint8", "int8", "int32"]:
exp_output = np.floor_divide(np.multiply(data_x, (1 - is_zero)), data_y + is_zero)
if dtype in ["float16", "float32"]:
exp_output = np.true_divide(np.multiply(data_x, (1 - is_zero)), data_y + is_zero)
# inputs and output to hold the data
output = np.full(exp_output.shape, np.nan, dtype)
inputs = [data_x, data_y]
args = [data_x, data_y, output]
return exp_output, inputs, args
def div_no_nan_compile(shapes, dtype, attrs, kernel_name='div_no_nan', runing=False):
return utils.op_build_test(div_no_nan.div_no_nan, [shapes[0], shapes[1]], [dtype, dtype], kernel_name=kernel_name, attrs=attrs, tuning=runing)
|
[
"1027252281@qq.com"
] |
1027252281@qq.com
|
17a5a316fa55654604037c588471a6476c68239c
|
04ce9939a13dab2dd306d06f2c420e074de87a03
|
/SiamDW_D/libs/FPNlib/mmdet/ops/__init__.py
|
e24d41db63b2b6739c87dc709f7f552a2b50795e
|
[
"MIT"
] |
permissive
|
cy-sohn/VOT2019
|
d0ae7a083dc96eb2a1bd6f9340dbf36b1583cfc7
|
eaf84c2b58a8ed3ff6ca464dcfdd52519507ae36
|
refs/heads/master
| 2022-03-03T07:43:10.778201
| 2019-10-23T06:32:24
| 2019-10-23T06:32:24
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 68
|
py
|
from .nms import nms, soft_nms
__all__ = [
'nms', 'soft_nms'
]
|
[
"henry.hw.peng@gmail.com"
] |
henry.hw.peng@gmail.com
|
9fc39c434aeb8db7e69c85650d79dea51a686666
|
5d2404f62e58d5fd1f6112744ff32c3166183ac7
|
/Geek University/Seção 4/Exercicios/EX49.py
|
de8275af5902ac3f09895155461a32956779a2ef
|
[] |
no_license
|
Leownhart/My_Course_of_python
|
236cfc84d841c5883e5aa1cc0c0730e7a9a83c40
|
5abb21f8cdad91ab54247a007d40bf9ecd2cff8c
|
refs/heads/master
| 2020-08-28T15:04:33.628086
| 2020-08-24T19:25:39
| 2020-08-24T19:25:39
| 217,733,877
| 1
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 654
|
py
|
'''
49 - Faça um programa que leia um horário (hora, minuto, segundo) de inicio e a duração, em
segundos, de uma experiência biológica. O programa de resultar com o novo horário
(hora, minuto, segundo) do termino da mesma.
from datetime import datetime
now = datetime.now()
print now.year
print now.month
print now.day
print now.hour
print now.minute
print now.second
'''
# RESPOSTAS
from datetime import datetime
Hora = int(input('Informe a Hora: '))
Minuto = int(input('Informe os Minutos: '))
Segundos = int(input('Informe os Segundos: '))
print(f'Passaram-se {Hora * 3600 + Minuto * 60 + Segundos} Segundos')
print(f'{datetime.now()}')
|
[
"francisco.amartins.al@gmail.com"
] |
francisco.amartins.al@gmail.com
|
6dc940a6b8466d401e91dddd33c3ef52e54a5242
|
cb97215bde0c6d15e454b03ab9f264801772bf83
|
/Notes/Plotting/folium_extended.py
|
6c5d81c32ea59b71d4110067cc63feacac6af12f
|
[] |
no_license
|
FWP-Computer-Science/programming2-sp2021
|
331f1eb2eeea70cc10bbdeaf42fc19d031fd9b00
|
ce407b6fa8481ece0ee1ab45128c9976e8a69f6b
|
refs/heads/main
| 2023-04-11T18:12:36.024182
| 2021-05-19T14:21:37
| 2021-05-19T14:21:37
| 331,132,320
| 0
| 3
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,418
|
py
|
import folium
import csv
# https://python-visualization.github.io/folium/quickstart.html#
art_map = folium.Map(location=[41.8781, -87.6298], zoom_start=11)
# Parker 41.923000, -87.638461
# placing a marker on the map
folium.Marker(location=[41.923000, -87.638461],
popup="Our School",
icon=folium.Icon(color='red', icon='graduation-cap', prefix='fa'),
).add_to(art_map)
art_map.save('my_artmap.html')
"""
marker options:
with prefix='fa': https://fontawesome.com/v4.7.0/icons/
(https://maxcdn.bootstrapcdn.com/font-awesome/4.1.0/css/font-awesome.min.css)
without: https://getbootstrap.com/docs/3.3/components/
"""
# open the file and read through it
import csv
import random
art_file = open("/Users/bifft/PycharmProjects/programming2-sp2021/Resources/parks_public_art.csv")
art_data = list(csv.DictReader(art_file))
color_list = ['darkgreen', 'gray', 'lightgreen', 'darkpurple', 'cadetblue', 'lightblue', 'lightgray', 'orange', 'darkred', 'purple', 'pink', 'black', 'darkblue', 'beige', 'lightred', 'green', 'blue']
# make separate lists for art, location
for i in art_data:
folium.Marker(location=[i["LATITUDE"], i["LONGITUDE"]],
popup="<b>{0}</b>".format(i["ART"]),
icon=folium.Icon(color=random.choice(color_list), icon="cubes", prefix="fa")).add_to((art_map))
# plot the data from there
art_map.save('my_artmap.html')
|
[
"bifft95@gmail.com"
] |
bifft95@gmail.com
|
e4a2ebe390211d6803336853fae8ab415dc3c629
|
4baf2e3f52bcbf447b368a5832ae18fd0339431c
|
/itchanged/diff_match_patch.py
|
4dae2a004574271885d834205ab24de64bf56e12
|
[] |
no_license
|
markng/itchanged-server
|
a18798a6670ba2dc210e783d1dd140331aa36b4f
|
16765f3a6c51ff6a581bdfff3ee259fcf8520e1c
|
refs/heads/master
| 2016-09-06T04:06:41.132101
| 2010-07-12T14:27:21
| 2010-07-12T14:27:21
| 530,826
| 1
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 64,638
|
py
|
#!/usr/bin/python2.4
"""Diff Match and Patch
Copyright 2006 Google Inc.
http://code.google.com/p/google-diff-match-patch/
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.
"""
"""Functions for diff, match and patch.
Computes the difference between two texts to create a patch.
Applies the patch onto another text, allowing for errors.
"""
__author__ = 'fraser@google.com (Neil Fraser)'
import math
import time
import urllib
import re
class diff_match_patch:
"""Class containing the diff, match and patch methods.
Also contains the behaviour settings.
"""
def __init__(self):
"""Inits a diff_match_patch object with default settings.
Redefine these in your program to override the defaults.
"""
# Number of seconds to map a diff before giving up (0 for infinity).
self.Diff_Timeout = 1.0
# Cost of an empty edit operation in terms of edit characters.
self.Diff_EditCost = 4
# The size beyond which the double-ended diff activates.
# Double-ending is twice as fast, but less accurate.
self.Diff_DualThreshold = 32
# At what point is no match declared (0.0 = perfection, 1.0 = very loose).
self.Match_Threshold = 0.5
# How far to search for a match (0 = exact location, 1000+ = broad match).
# A match this many characters away from the expected location will add
# 1.0 to the score (0.0 is a perfect match).
self.Match_Distance = 1000
# When deleting a large block of text (over ~64 characters), how close does
# the contents have to match the expected contents. (0.0 = perfection,
# 1.0 = very loose). Note that Match_Threshold controls how closely the
# end points of a delete need to match.
self.Patch_DeleteThreshold = 0.5
# Chunk size for context length.
self.Patch_Margin = 4
# How many bits in a number?
# Python has no maximum, thus to disable patch splitting set to 0.
# However to avoid long patches in certain pathological cases, use 32.
# Multiple short patches (using native ints) are much faster than long ones.
self.Match_MaxBits = 32
# DIFF FUNCTIONS
# The data structure representing a diff is an array of tuples:
# [(DIFF_DELETE, "Hello"), (DIFF_INSERT, "Goodbye"), (DIFF_EQUAL, " world.")]
# which means: delete "Hello", add "Goodbye" and keep " world."
DIFF_DELETE = -1
DIFF_INSERT = 1
DIFF_EQUAL = 0
def diff_main(self, text1, text2, checklines=True):
"""Find the differences between two texts. Simplifies the problem by
stripping any common prefix or suffix off the texts before diffing.
Args:
text1: Old string to be diffed.
text2: New string to be diffed.
checklines: Optional speedup flag. If present and false, then don't run
a line-level diff first to identify the changed areas.
Defaults to true, which does a faster, slightly less optimal diff.
Returns:
Array of changes.
"""
# Check for equality (speedup)
if text1 == text2:
return [(self.DIFF_EQUAL, text1)]
# Trim off common prefix (speedup)
commonlength = self.diff_commonPrefix(text1, text2)
commonprefix = text1[:commonlength]
text1 = text1[commonlength:]
text2 = text2[commonlength:]
# Trim off common suffix (speedup)
commonlength = self.diff_commonSuffix(text1, text2)
if commonlength == 0:
commonsuffix = ''
else:
commonsuffix = text1[-commonlength:]
text1 = text1[:-commonlength]
text2 = text2[:-commonlength]
# Compute the diff on the middle block
diffs = self.diff_compute(text1, text2, checklines)
# Restore the prefix and suffix
if commonprefix:
diffs[:0] = [(self.DIFF_EQUAL, commonprefix)]
if commonsuffix:
diffs.append((self.DIFF_EQUAL, commonsuffix))
#self.diff_cleanupMerge(diffs)
self.diff_cleanupSemantic(diffs)
return diffs
def diff_compute(self, text1, text2, checklines):
"""Find the differences between two texts. Assumes that the texts do not
have any common prefix or suffix.
Args:
text1: Old string to be diffed.
text2: New string to be diffed.
checklines: Speedup flag. If false, then don't run a line-level diff
first to identify the changed areas.
If true, then run a faster, slightly less optimal diff.
Returns:
Array of changes.
"""
if not text1:
# Just add some text (speedup)
return [(self.DIFF_INSERT, text2)]
if not text2:
# Just delete some text (speedup)
return [(self.DIFF_DELETE, text1)]
if len(text1) > len(text2):
(longtext, shorttext) = (text1, text2)
else:
(shorttext, longtext) = (text1, text2)
i = longtext.find(shorttext)
if i != -1:
# Shorter text is inside the longer text (speedup)
diffs = [(self.DIFF_INSERT, longtext[:i]), (self.DIFF_EQUAL, shorttext),
(self.DIFF_INSERT, longtext[i + len(shorttext):])]
# Swap insertions for deletions if diff is reversed.
if len(text1) > len(text2):
diffs[0] = (self.DIFF_DELETE, diffs[0][1])
diffs[2] = (self.DIFF_DELETE, diffs[2][1])
return diffs
longtext = shorttext = None # Garbage collect.
# Check to see if the problem can be split in two.
hm = self.diff_halfMatch(text1, text2)
if hm:
# A half-match was found, sort out the return data.
(text1_a, text1_b, text2_a, text2_b, mid_common) = hm
# Send both pairs off for separate processing.
diffs_a = self.diff_main(text1_a, text2_a, checklines)
diffs_b = self.diff_main(text1_b, text2_b, checklines)
# Merge the results.
return diffs_a + [(self.DIFF_EQUAL, mid_common)] + diffs_b
# Perform a real diff.
if checklines and (len(text1) < 100 or len(text2) < 100):
checklines = False # Too trivial for the overhead.
if checklines:
# Scan the text on a line-by-line basis first.
(text1, text2, linearray) = self.diff_linesToChars(text1, text2)
diffs = self.diff_map(text1, text2)
if not diffs: # No acceptable result.
diffs = [(self.DIFF_DELETE, text1), (self.DIFF_INSERT, text2)]
if checklines:
# Convert the diff back to original text.
self.diff_charsToLines(diffs, linearray)
# Eliminate freak matches (e.g. blank lines)
self.diff_cleanupSemantic(diffs)
# Rediff any replacement blocks, this time character-by-character.
# Add a dummy entry at the end.
diffs.append((self.DIFF_EQUAL, ''))
pointer = 0
count_delete = 0
count_insert = 0
text_delete = ''
text_insert = ''
while pointer < len(diffs):
if diffs[pointer][0] == self.DIFF_INSERT:
count_insert += 1
text_insert += diffs[pointer][1]
elif diffs[pointer][0] == self.DIFF_DELETE:
count_delete += 1
text_delete += diffs[pointer][1]
elif diffs[pointer][0] == self.DIFF_EQUAL:
# Upon reaching an equality, check for prior redundancies.
if count_delete >= 1 and count_insert >= 1:
# Delete the offending records and add the merged ones.
a = self.diff_main(text_delete, text_insert, False)
diffs[pointer - count_delete - count_insert : pointer] = a
pointer = pointer - count_delete - count_insert + len(a)
count_insert = 0
count_delete = 0
text_delete = ''
text_insert = ''
pointer += 1
diffs.pop() # Remove the dummy entry at the end.
return diffs
def diff_linesToChars(self, text1, text2):
"""Split two texts into an array of strings. Reduce the texts to a string
of hashes where each Unicode character represents one line.
Args:
text1: First string.
text2: Second string.
Returns:
Three element tuple, containing the encoded text1, the encoded text2 and
the array of unique strings. The zeroth element of the array of unique
strings is intentionally blank.
"""
lineArray = [] # e.g. lineArray[4] == "Hello\n"
lineHash = {} # e.g. lineHash["Hello\n"] == 4
# "\x00" is a valid character, but various debuggers don't like it.
# So we'll insert a junk entry to avoid generating a null character.
lineArray.append('')
def diff_linesToCharsMunge(text):
"""Split a text into an array of strings. Reduce the texts to a string
of hashes where each Unicode character represents one line.
Modifies linearray and linehash through being a closure.
Args:
text: String to encode.
Returns:
Encoded string.
"""
chars = []
# Walk the text, pulling out a substring for each line.
# text.split('\n') would would temporarily double our memory footprint.
# Modifying text would create many large strings to garbage collect.
lineStart = 0
lineEnd = -1
while lineEnd < len(text) - 1:
lineEnd = text.find('\n', lineStart)
if lineEnd == -1:
lineEnd = len(text) - 1
line = text[lineStart:lineEnd + 1]
lineStart = lineEnd + 1
if line in lineHash:
chars.append(unichr(lineHash[line]))
else:
lineArray.append(line)
lineHash[line] = len(lineArray) - 1
chars.append(unichr(len(lineArray) - 1))
return "".join(chars)
chars1 = diff_linesToCharsMunge(text1)
chars2 = diff_linesToCharsMunge(text2)
return (chars1, chars2, lineArray)
def diff_charsToLines(self, diffs, lineArray):
"""Rehydrate the text in a diff from a string of line hashes to real lines
of text.
Args:
diffs: Array of diff tuples.
lineArray: Array of unique strings.
"""
for x in xrange(len(diffs)):
text = []
for char in diffs[x][1]:
text.append(lineArray[ord(char)])
diffs[x] = (diffs[x][0], "".join(text))
def diff_map(self, text1, text2):
"""Explore the intersection points between the two texts.
Args:
text1: Old string to be diffed.
text2: New string to be diffed.
Returns:
Array of diff tuples or None if no diff available.
"""
# Unlike in most languages, Python counts time in seconds.
s_end = time.time() + self.Diff_Timeout # Don't run for too long.
# Cache the text lengths to prevent multiple calls.
text1_length = len(text1)
text2_length = len(text2)
max_d = text1_length + text2_length - 1
doubleEnd = self.Diff_DualThreshold * 2 < max_d
v_map1 = []
v_map2 = []
v1 = {}
v2 = {}
v1[1] = 0
v2[1] = 0
footsteps = {}
done = False
# If the total number of characters is odd, then the front path will
# collide with the reverse path.
front = (text1_length + text2_length) % 2
for d in xrange(max_d):
# Bail out if timeout reached.
if self.Diff_Timeout > 0 and time.time() > s_end:
return None
# Walk the front path one step.
v_map1.append({})
for k in xrange(-d, d + 1, 2):
if k == -d or k != d and v1[k - 1] < v1[k + 1]:
x = v1[k + 1]
else:
x = v1[k - 1] + 1
y = x - k
if doubleEnd:
footstep = (x, y)
if front and footstep in footsteps:
done = True
if not front:
footsteps[footstep] = d
while (not done and x < text1_length and y < text2_length and
text1[x] == text2[y]):
x += 1
y += 1
if doubleEnd:
footstep = (x, y)
if front and footstep in footsteps:
done = True
if not front:
footsteps[footstep] = d
v1[k] = x
v_map1[d][(x, y)] = True
if x == text1_length and y == text2_length:
# Reached the end in single-path mode.
return self.diff_path1(v_map1, text1, text2)
elif done:
# Front path ran over reverse path.
v_map2 = v_map2[:footsteps[footstep] + 1]
a = self.diff_path1(v_map1, text1[:x], text2[:y])
b = self.diff_path2(v_map2, text1[x:], text2[y:])
return a + b
if doubleEnd:
# Walk the reverse path one step.
v_map2.append({})
for k in xrange(-d, d + 1, 2):
if k == -d or k != d and v2[k - 1] < v2[k + 1]:
x = v2[k + 1]
else:
x = v2[k - 1] + 1
y = x - k
footstep = (text1_length - x, text2_length - y)
if not front and footstep in footsteps:
done = True
if front:
footsteps[footstep] = d
while (not done and x < text1_length and y < text2_length and
text1[-x - 1] == text2[-y - 1]):
x += 1
y += 1
footstep = (text1_length - x, text2_length - y)
if not front and footstep in footsteps:
done = True
if front:
footsteps[footstep] = d
v2[k] = x
v_map2[d][(x, y)] = True
if done:
# Reverse path ran over front path.
v_map1 = v_map1[:footsteps[footstep] + 1]
a = self.diff_path1(v_map1, text1[:text1_length - x],
text2[:text2_length - y])
b = self.diff_path2(v_map2, text1[text1_length - x:],
text2[text2_length - y:])
return a + b
# Number of diffs equals number of characters, no commonality at all.
return None
def diff_path1(self, v_map, text1, text2):
"""Work from the middle back to the start to determine the path.
Args:
v_map: Array of paths.
text1: Old string fragment to be diffed.
text2: New string fragment to be diffed.
Returns:
Array of diff tuples.
"""
path = []
x = len(text1)
y = len(text2)
last_op = None
for d in xrange(len(v_map) - 2, -1, -1):
while True:
if (x - 1, y) in v_map[d]:
x -= 1
if last_op == self.DIFF_DELETE:
path[0] = (self.DIFF_DELETE, text1[x] + path[0][1])
else:
path[:0] = [(self.DIFF_DELETE, text1[x])]
last_op = self.DIFF_DELETE
break
elif (x, y - 1) in v_map[d]:
y -= 1
if last_op == self.DIFF_INSERT:
path[0] = (self.DIFF_INSERT, text2[y] + path[0][1])
else:
path[:0] = [(self.DIFF_INSERT, text2[y])]
last_op = self.DIFF_INSERT
break
else:
x -= 1
y -= 1
assert text1[x] == text2[y], ("No diagonal. " +
"Can't happen. (diff_path1)")
if last_op == self.DIFF_EQUAL:
path[0] = (self.DIFF_EQUAL, text1[x] + path[0][1])
else:
path[:0] = [(self.DIFF_EQUAL, text1[x])]
last_op = self.DIFF_EQUAL
return path
def diff_path2(self, v_map, text1, text2):
"""Work from the middle back to the end to determine the path.
Args:
v_map: Array of paths.
text1: Old string fragment to be diffed.
text2: New string fragment to be diffed.
Returns:
Array of diff tuples.
"""
path = []
x = len(text1)
y = len(text2)
last_op = None
for d in xrange(len(v_map) - 2, -1, -1):
while True:
if (x - 1, y) in v_map[d]:
x -= 1
if last_op == self.DIFF_DELETE:
path[-1] = (self.DIFF_DELETE, path[-1][1] + text1[-x - 1])
else:
path.append((self.DIFF_DELETE, text1[-x - 1]))
last_op = self.DIFF_DELETE
break
elif (x, y - 1) in v_map[d]:
y -= 1
if last_op == self.DIFF_INSERT:
path[-1] = (self.DIFF_INSERT, path[-1][1] + text2[-y - 1])
else:
path.append((self.DIFF_INSERT, text2[-y - 1]))
last_op = self.DIFF_INSERT
break
else:
x -= 1
y -= 1
assert text1[-x - 1] == text2[-y - 1], ("No diagonal. " +
"Can't happen. (diff_path2)")
if last_op == self.DIFF_EQUAL:
path[-1] = (self.DIFF_EQUAL, path[-1][1] + text1[-x - 1])
else:
path.append((self.DIFF_EQUAL, text1[-x - 1]))
last_op = self.DIFF_EQUAL
return path
def diff_commonPrefix(self, text1, text2):
"""Determine the common prefix of two strings.
Args:
text1: First string.
text2: Second string.
Returns:
The number of characters common to the start of each string.
"""
# Quick check for common null cases.
if not text1 or not text2 or text1[0] != text2[0]:
return 0
# Binary search.
# Performance analysis: http://neil.fraser.name/news/2007/10/09/
pointermin = 0
pointermax = min(len(text1), len(text2))
pointermid = pointermax
pointerstart = 0
while pointermin < pointermid:
if text1[pointerstart:pointermid] == text2[pointerstart:pointermid]:
pointermin = pointermid
pointerstart = pointermin
else:
pointermax = pointermid
pointermid = int((pointermax - pointermin) / 2 + pointermin)
return pointermid
def diff_commonSuffix(self, text1, text2):
"""Determine the common suffix of two strings.
Args:
text1: First string.
text2: Second string.
Returns:
The number of characters common to the end of each string.
"""
# Quick check for common null cases.
if not text1 or not text2 or text1[-1] != text2[-1]:
return 0
# Binary search.
# Performance analysis: http://neil.fraser.name/news/2007/10/09/
pointermin = 0
pointermax = min(len(text1), len(text2))
pointermid = pointermax
pointerend = 0
while pointermin < pointermid:
if (text1[-pointermid:len(text1) - pointerend] ==
text2[-pointermid:len(text2) - pointerend]):
pointermin = pointermid
pointerend = pointermin
else:
pointermax = pointermid
pointermid = int((pointermax - pointermin) / 2 + pointermin)
return pointermid
def diff_halfMatch(self, text1, text2):
"""Do the two texts share a substring which is at least half the length of
the longer text?
Args:
text1: First string.
text2: Second string.
Returns:
Five element Array, containing the prefix of text1, the suffix of text1,
the prefix of text2, the suffix of text2 and the common middle. Or None
if there was no match.
"""
if len(text1) > len(text2):
(longtext, shorttext) = (text1, text2)
else:
(shorttext, longtext) = (text1, text2)
if len(longtext) < 10 or len(shorttext) < 1:
return None # Pointless.
def diff_halfMatchI(longtext, shorttext, i):
"""Does a substring of shorttext exist within longtext such that the
substring is at least half the length of longtext?
Closure, but does not reference any external variables.
Args:
longtext: Longer string.
shorttext: Shorter string.
i: Start index of quarter length substring within longtext.
Returns:
Five element Array, containing the prefix of longtext, the suffix of
longtext, the prefix of shorttext, the suffix of shorttext and the
common middle. Or None if there was no match.
"""
seed = longtext[i:i + len(longtext) / 4]
best_common = ''
j = shorttext.find(seed)
while j != -1:
prefixLength = self.diff_commonPrefix(longtext[i:], shorttext[j:])
suffixLength = self.diff_commonSuffix(longtext[:i], shorttext[:j])
if len(best_common) < suffixLength + prefixLength:
best_common = (shorttext[j - suffixLength:j] +
shorttext[j:j + prefixLength])
best_longtext_a = longtext[:i - suffixLength]
best_longtext_b = longtext[i + prefixLength:]
best_shorttext_a = shorttext[:j - suffixLength]
best_shorttext_b = shorttext[j + prefixLength:]
j = shorttext.find(seed, j + 1)
if len(best_common) >= len(longtext) / 2:
return (best_longtext_a, best_longtext_b,
best_shorttext_a, best_shorttext_b, best_common)
else:
return None
# First check if the second quarter is the seed for a half-match.
hm1 = diff_halfMatchI(longtext, shorttext, (len(longtext) + 3) / 4)
# Check again based on the third quarter.
hm2 = diff_halfMatchI(longtext, shorttext, (len(longtext) + 1) / 2)
if not hm1 and not hm2:
return None
elif not hm2:
hm = hm1
elif not hm1:
hm = hm2
else:
# Both matched. Select the longest.
if len(hm1[4]) > len(hm2[4]):
hm = hm1
else:
hm = hm2
# A half-match was found, sort out the return data.
if len(text1) > len(text2):
(text1_a, text1_b, text2_a, text2_b, mid_common) = hm
else:
(text2_a, text2_b, text1_a, text1_b, mid_common) = hm
return (text1_a, text1_b, text2_a, text2_b, mid_common)
def diff_cleanupSemantic(self, diffs):
"""Reduce the number of edits by eliminating semantically trivial
equalities.
Args:
diffs: Array of diff tuples.
"""
changes = False
equalities = [] # Stack of indices where equalities are found.
lastequality = None # Always equal to equalities[-1][1]
pointer = 0 # Index of current position.
length_changes1 = 0 # Number of chars that changed prior to the equality.
length_changes2 = 0 # Number of chars that changed after the equality.
while pointer < len(diffs):
if diffs[pointer][0] == self.DIFF_EQUAL: # equality found
equalities.append(pointer)
length_changes1 = length_changes2
length_changes2 = 0
lastequality = diffs[pointer][1]
else: # an insertion or deletion
length_changes2 += len(diffs[pointer][1])
if (lastequality != None and (len(lastequality) <= length_changes1) and
(len(lastequality) <= length_changes2)):
# Duplicate record
diffs.insert(equalities[-1], (self.DIFF_DELETE, lastequality))
# Change second copy to insert.
diffs[equalities[-1] + 1] = (self.DIFF_INSERT,
diffs[equalities[-1] + 1][1])
# Throw away the equality we just deleted.
equalities.pop()
# Throw away the previous equality (it needs to be reevaluated).
if len(equalities) != 0:
equalities.pop()
if len(equalities):
pointer = equalities[-1]
else:
pointer = -1
length_changes1 = 0 # Reset the counters.
length_changes2 = 0
lastequality = None
changes = True
pointer += 1
if changes:
self.diff_cleanupMerge(diffs)
self.diff_cleanupSemanticLossless(diffs)
def diff_cleanupSemanticLossless(self, diffs):
"""Look for single edits surrounded on both sides by equalities
which can be shifted sideways to align the edit to a word boundary.
e.g: The c<ins>at c</ins>ame. -> The <ins>cat </ins>came.
Args:
diffs: Array of diff tuples.
"""
def diff_cleanupSemanticScore(one, two):
"""Given two strings, compute a score representing whether the
internal boundary falls on logical boundaries.
Scores range from 5 (best) to 0 (worst).
Closure, but does not reference any external variables.
Args:
one: First string.
two: Second string.
Returns:
The score.
"""
if not one or not two:
# Edges are the best.
return 5
# Each port of this function behaves slightly differently due to
# subtle differences in each language's definition of things like
# 'whitespace'. Since this function's purpose is largely cosmetic,
# the choice has been made to use each language's native features
# rather than force total conformity.
score = 0
# One point for non-alphanumeric.
if not one[-1].isalnum() or not two[0].isalnum():
score += 1
# Two points for whitespace.
if one[-1].isspace() or two[0].isspace():
score += 1
# Three points for line breaks.
if (one[-1] == "\r" or one[-1] == "\n" or
two[0] == "\r" or two[0] == "\n"):
score += 1
# Four points for blank lines.
if (re.search("\\n\\r?\\n$", one) or
re.match("^\\r?\\n\\r?\\n", two)):
score += 1
return score
pointer = 1
# Intentionally ignore the first and last element (don't need checking).
while pointer < len(diffs) - 1:
if (diffs[pointer - 1][0] == self.DIFF_EQUAL and
diffs[pointer + 1][0] == self.DIFF_EQUAL):
# This is a single edit surrounded by equalities.
equality1 = diffs[pointer - 1][1]
edit = diffs[pointer][1]
equality2 = diffs[pointer + 1][1]
# First, shift the edit as far left as possible.
commonOffset = self.diff_commonSuffix(equality1, edit)
if commonOffset:
commonString = edit[-commonOffset:]
equality1 = equality1[:-commonOffset]
edit = commonString + edit[:-commonOffset]
equality2 = commonString + equality2
# Second, step character by character right, looking for the best fit.
bestEquality1 = equality1
bestEdit = edit
bestEquality2 = equality2
bestScore = (diff_cleanupSemanticScore(equality1, edit) +
diff_cleanupSemanticScore(edit, equality2))
while edit and equality2 and edit[0] == equality2[0]:
equality1 += edit[0]
edit = edit[1:] + equality2[0]
equality2 = equality2[1:]
score = (diff_cleanupSemanticScore(equality1, edit) +
diff_cleanupSemanticScore(edit, equality2))
# The >= encourages trailing rather than leading whitespace on edits.
if score >= bestScore:
bestScore = score
bestEquality1 = equality1
bestEdit = edit
bestEquality2 = equality2
if diffs[pointer - 1][1] != bestEquality1:
# We have an improvement, save it back to the diff.
if bestEquality1:
diffs[pointer - 1] = (diffs[pointer - 1][0], bestEquality1)
else:
del diffs[pointer - 1]
pointer -= 1
diffs[pointer] = (diffs[pointer][0], bestEdit)
if bestEquality2:
diffs[pointer + 1] = (diffs[pointer + 1][0], bestEquality2)
else:
del diffs[pointer + 1]
pointer -= 1
pointer += 1
def diff_cleanupEfficiency(self, diffs):
"""Reduce the number of edits by eliminating operationally trivial
equalities.
Args:
diffs: Array of diff tuples.
"""
changes = False
equalities = [] # Stack of indices where equalities are found.
lastequality = '' # Always equal to equalities[-1][1]
pointer = 0 # Index of current position.
pre_ins = False # Is there an insertion operation before the last equality.
pre_del = False # Is there a deletion operation before the last equality.
post_ins = False # Is there an insertion operation after the last equality.
post_del = False # Is there a deletion operation after the last equality.
while pointer < len(diffs):
if diffs[pointer][0] == self.DIFF_EQUAL: # equality found
if (len(diffs[pointer][1]) < self.Diff_EditCost and
(post_ins or post_del)):
# Candidate found.
equalities.append(pointer)
pre_ins = post_ins
pre_del = post_del
lastequality = diffs[pointer][1]
else:
# Not a candidate, and can never become one.
equalities = []
lastequality = ''
post_ins = post_del = False
else: # an insertion or deletion
if diffs[pointer][0] == self.DIFF_DELETE:
post_del = True
else:
post_ins = True
# Five types to be split:
# <ins>A</ins><del>B</del>XY<ins>C</ins><del>D</del>
# <ins>A</ins>X<ins>C</ins><del>D</del>
# <ins>A</ins><del>B</del>X<ins>C</ins>
# <ins>A</del>X<ins>C</ins><del>D</del>
# <ins>A</ins><del>B</del>X<del>C</del>
if lastequality and ((pre_ins and pre_del and post_ins and post_del) or
((len(lastequality) < self.Diff_EditCost / 2) and
(pre_ins + pre_del + post_ins + post_del) == 3)):
# Duplicate record
diffs.insert(equalities[-1], (self.DIFF_DELETE, lastequality))
# Change second copy to insert.
diffs[equalities[-1] + 1] = (self.DIFF_INSERT,
diffs[equalities[-1] + 1][1])
equalities.pop() # Throw away the equality we just deleted
lastequality = ''
if pre_ins and pre_del:
# No changes made which could affect previous entry, keep going.
post_ins = post_del = True
equalities = []
else:
if len(equalities):
equalities.pop() # Throw away the previous equality
if len(equalities):
pointer = equalities[-1]
else:
pointer = -1
post_ins = post_del = False
changes = True
pointer += 1
if changes:
self.diff_cleanupMerge(diffs)
def diff_cleanupMerge(self, diffs):
"""Reorder and merge like edit sections. Merge equalities.
Any edit section can move as long as it doesn't cross an equality.
Args:
diffs: Array of diff tuples.
"""
diffs.append((self.DIFF_EQUAL, '')) # Add a dummy entry at the end.
pointer = 0
count_delete = 0
count_insert = 0
text_delete = ''
text_insert = ''
while pointer < len(diffs):
if diffs[pointer][0] == self.DIFF_INSERT:
count_insert += 1
text_insert += diffs[pointer][1]
pointer += 1
elif diffs[pointer][0] == self.DIFF_DELETE:
count_delete += 1
text_delete += diffs[pointer][1]
pointer += 1
elif diffs[pointer][0] == self.DIFF_EQUAL:
# Upon reaching an equality, check for prior redundancies.
if count_delete != 0 or count_insert != 0:
if count_delete != 0 and count_insert != 0:
# Factor out any common prefixies.
commonlength = self.diff_commonPrefix(text_insert, text_delete)
if commonlength != 0:
x = pointer - count_delete - count_insert - 1
if x >= 0 and diffs[x][0] == self.DIFF_EQUAL:
diffs[x] = (diffs[x][0], diffs[x][1] +
text_insert[:commonlength])
else:
diffs.insert(0, (self.DIFF_EQUAL, text_insert[:commonlength]))
pointer += 1
text_insert = text_insert[commonlength:]
text_delete = text_delete[commonlength:]
# Factor out any common suffixies.
commonlength = self.diff_commonSuffix(text_insert, text_delete)
if commonlength != 0:
diffs[pointer] = (diffs[pointer][0], text_insert[-commonlength:] +
diffs[pointer][1])
text_insert = text_insert[:-commonlength]
text_delete = text_delete[:-commonlength]
# Delete the offending records and add the merged ones.
if count_delete == 0:
diffs[pointer - count_insert : pointer] = [
(self.DIFF_INSERT, text_insert)]
elif count_insert == 0:
diffs[pointer - count_delete : pointer] = [
(self.DIFF_DELETE, text_delete)]
else:
diffs[pointer - count_delete - count_insert : pointer] = [
(self.DIFF_DELETE, text_delete),
(self.DIFF_INSERT, text_insert)]
pointer = pointer - count_delete - count_insert + 1
if count_delete != 0:
pointer += 1
if count_insert != 0:
pointer += 1
elif pointer != 0 and diffs[pointer - 1][0] == self.DIFF_EQUAL:
# Merge this equality with the previous one.
diffs[pointer - 1] = (diffs[pointer - 1][0],
diffs[pointer - 1][1] + diffs[pointer][1])
del diffs[pointer]
else:
pointer += 1
count_insert = 0
count_delete = 0
text_delete = ''
text_insert = ''
if diffs[-1][1] == '':
diffs.pop() # Remove the dummy entry at the end.
# Second pass: look for single edits surrounded on both sides by equalities
# which can be shifted sideways to eliminate an equality.
# e.g: A<ins>BA</ins>C -> <ins>AB</ins>AC
changes = False
pointer = 1
# Intentionally ignore the first and last element (don't need checking).
while pointer < len(diffs) - 1:
if (diffs[pointer - 1][0] == self.DIFF_EQUAL and
diffs[pointer + 1][0] == self.DIFF_EQUAL):
# This is a single edit surrounded by equalities.
if diffs[pointer][1].endswith(diffs[pointer - 1][1]):
# Shift the edit over the previous equality.
diffs[pointer] = (diffs[pointer][0],
diffs[pointer - 1][1] +
diffs[pointer][1][:-len(diffs[pointer - 1][1])])
diffs[pointer + 1] = (diffs[pointer + 1][0],
diffs[pointer - 1][1] + diffs[pointer + 1][1])
del diffs[pointer - 1]
changes = True
elif diffs[pointer][1].startswith(diffs[pointer + 1][1]):
# Shift the edit over the next equality.
diffs[pointer - 1] = (diffs[pointer - 1][0],
diffs[pointer - 1][1] + diffs[pointer + 1][1])
diffs[pointer] = (diffs[pointer][0],
diffs[pointer][1][len(diffs[pointer + 1][1]):] +
diffs[pointer + 1][1])
del diffs[pointer + 1]
changes = True
pointer += 1
# If shifts were made, the diff needs reordering and another shift sweep.
if changes:
self.diff_cleanupMerge(diffs)
def diff_xIndex(self, diffs, loc):
"""loc is a location in text1, compute and return the equivalent location
in text2. e.g. "The cat" vs "The big cat", 1->1, 5->8
Args:
diffs: Array of diff tuples.
loc: Location within text1.
Returns:
Location within text2.
"""
chars1 = 0
chars2 = 0
last_chars1 = 0
last_chars2 = 0
for x in xrange(len(diffs)):
(op, text) = diffs[x]
if op != self.DIFF_INSERT: # Equality or deletion.
chars1 += len(text)
if op != self.DIFF_DELETE: # Equality or insertion.
chars2 += len(text)
if chars1 > loc: # Overshot the location.
break
last_chars1 = chars1
last_chars2 = chars2
if len(diffs) != x and diffs[x][0] == self.DIFF_DELETE:
# The location was deleted.
return last_chars2
# Add the remaining len(character).
return last_chars2 + (loc - last_chars1)
def diff_prettyHtml(self, diffs):
"""Convert a diff array into a pretty HTML report.
Args:
diffs: Array of diff tuples.
Returns:
HTML representation.
"""
html = []
i = 0
for (op, data) in diffs:
text = (data.replace("&", "&").replace("<", "<")
.replace(">", ">").replace("\n", "<br>"))
if op == self.DIFF_INSERT:
html.append("<strong>%s</strong>"
% (text))
if len(text) > 20:
html.append("<br>")
elif op == self.DIFF_DELETE:
html.append("<del>%s</del>"
% (text))
if len(text) > 20:
html.append("<br>")
elif op == self.DIFF_EQUAL:
html.append("<span>%s</span>" % (text))
if op != self.DIFF_DELETE:
i += len(data)
return "".join(html)
def diff_text1(self, diffs):
"""Compute and return the source text (all equalities and deletions).
Args:
diffs: Array of diff tuples.
Returns:
Source text.
"""
text = []
for (op, data) in diffs:
if op != self.DIFF_INSERT:
text.append(data)
return "".join(text)
def diff_text2(self, diffs):
"""Compute and return the destination text (all equalities and insertions).
Args:
diffs: Array of diff tuples.
Returns:
Destination text.
"""
text = []
for (op, data) in diffs:
if op != self.DIFF_DELETE:
text.append(data)
return "".join(text)
def diff_levenshtein(self, diffs):
"""Compute the Levenshtein distance; the number of inserted, deleted or
substituted characters.
Args:
diffs: Array of diff tuples.
Returns:
Number of changes.
"""
levenshtein = 0
insertions = 0
deletions = 0
for (op, data) in diffs:
if op == self.DIFF_INSERT:
insertions += len(data)
elif op == self.DIFF_DELETE:
deletions += len(data)
elif op == self.DIFF_EQUAL:
# A deletion and an insertion is one substitution.
levenshtein += max(insertions, deletions)
insertions = 0
deletions = 0
levenshtein += max(insertions, deletions)
return levenshtein
def diff_toDelta(self, diffs):
"""Crush the diff into an encoded string which describes the operations
required to transform text1 into text2.
E.g. =3\t-2\t+ing -> Keep 3 chars, delete 2 chars, insert 'ing'.
Operations are tab-separated. Inserted text is escaped using %xx notation.
Args:
diffs: Array of diff tuples.
Returns:
Delta text.
"""
text = []
for (op, data) in diffs:
if op == self.DIFF_INSERT:
# High ascii will raise UnicodeDecodeError. Use Unicode instead.
data = data.encode("utf-8")
text.append("+" + urllib.quote(data, "!~*'();/?:@&=+$,# "))
elif op == self.DIFF_DELETE:
text.append("-%d" % len(data))
elif op == self.DIFF_EQUAL:
text.append("=%d" % len(data))
return "\t".join(text)
def diff_fromDelta(self, text1, delta):
"""Given the original text1, and an encoded string which describes the
operations required to transform text1 into text2, compute the full diff.
Args:
text1: Source string for the diff.
delta: Delta text.
Returns:
Array of diff tuples.
Raises:
ValueError: If invalid input.
"""
if type(delta) == unicode:
# Deltas should be composed of a subset of ascii chars, Unicode not
# required. If this encode raises UnicodeEncodeError, delta is invalid.
delta = delta.encode("ascii")
diffs = []
pointer = 0 # Cursor in text1
tokens = delta.split("\t")
for token in tokens:
if token == "":
# Blank tokens are ok (from a trailing \t).
continue
# Each token begins with a one character parameter which specifies the
# operation of this token (delete, insert, equality).
param = token[1:]
if token[0] == "+":
param = urllib.unquote(param).decode("utf-8")
diffs.append((self.DIFF_INSERT, param))
elif token[0] == "-" or token[0] == "=":
try:
n = int(param)
except ValueError:
raise ValueError, "Invalid number in diff_fromDelta: " + param
if n < 0:
raise ValueError, "Negative number in diff_fromDelta: " + param
text = text1[pointer : pointer + n]
pointer += n
if token[0] == "=":
diffs.append((self.DIFF_EQUAL, text))
else:
diffs.append((self.DIFF_DELETE, text))
else:
# Anything else is an error.
raise ValueError, ("Invalid diff operation in diff_fromDelta: " +
token[0])
if pointer != len(text1):
raise ValueError, (
"Delta length (%d) does not equal source text length (%d)." %
(pointer, len(text1)))
return diffs
# MATCH FUNCTIONS
def match_main(self, text, pattern, loc):
"""Locate the best instance of 'pattern' in 'text' near 'loc'.
Args:
text: The text to search.
pattern: The pattern to search for.
loc: The location to search around.
Returns:
Best match index or -1.
"""
loc = max(0, min(loc, len(text)))
if text == pattern:
# Shortcut (potentially not guaranteed by the algorithm)
return 0
elif not text:
# Nothing to match.
return -1
elif text[loc:loc + len(pattern)] == pattern:
# Perfect match at the perfect spot! (Includes case of null pattern)
return loc
else:
# Do a fuzzy compare.
match = self.match_bitap(text, pattern, loc)
return match
def match_bitap(self, text, pattern, loc):
"""Locate the best instance of 'pattern' in 'text' near 'loc' using the
Bitap algorithm.
Args:
text: The text to search.
pattern: The pattern to search for.
loc: The location to search around.
Returns:
Best match index or -1.
"""
# Python doesn't have a maxint limit, so ignore this check.
#if self.Match_MaxBits != 0 and len(pattern) > self.Match_MaxBits:
# raise ValueError("Pattern too long for this application.")
# Initialise the alphabet.
s = self.match_alphabet(pattern)
def match_bitapScore(e, x):
"""Compute and return the score for a match with e errors and x location.
Accesses loc and pattern through being a closure.
Args:
e: Number of errors in match.
x: Location of match.
Returns:
Overall score for match (0.0 = good, 1.0 = bad).
"""
accuracy = float(e) / len(pattern)
proximity = abs(loc - x)
if not self.Match_Distance:
# Dodge divide by zero error.
return proximity and 1.0 or accuracy
return accuracy + (proximity / float(self.Match_Distance))
# Highest score beyond which we give up.
score_threshold = self.Match_Threshold
# Is there a nearby exact match? (speedup)
best_loc = text.find(pattern, loc)
if best_loc != -1:
score_threshold = min(match_bitapScore(0, best_loc), score_threshold)
# What about in the other direction? (speedup)
best_loc = text.rfind(pattern, loc + len(pattern))
if best_loc != -1:
score_threshold = min(match_bitapScore(0, best_loc), score_threshold)
# Initialise the bit arrays.
matchmask = 1 << (len(pattern) - 1)
best_loc = -1
bin_max = len(pattern) + len(text)
# Empty initialization added to appease pychecker.
last_rd = None
for d in xrange(len(pattern)):
# Scan for the best match each iteration allows for one more error.
# Run a binary search to determine how far from 'loc' we can stray at
# this error level.
bin_min = 0
bin_mid = bin_max
while bin_min < bin_mid:
if match_bitapScore(d, loc + bin_mid) <= score_threshold:
bin_min = bin_mid
else:
bin_max = bin_mid
bin_mid = (bin_max - bin_min) / 2 + bin_min
# Use the result from this iteration as the maximum for the next.
bin_max = bin_mid
start = max(1, loc - bin_mid + 1)
finish = min(loc + bin_mid, len(text)) + len(pattern)
rd = range(finish + 1)
rd.append((1 << d) - 1)
for j in xrange(finish, start - 1, -1):
if len(text) <= j - 1:
# Out of range.
charMatch = 0
else:
charMatch = s.get(text[j - 1], 0)
if d == 0: # First pass: exact match.
rd[j] = ((rd[j + 1] << 1) | 1) & charMatch
else: # Subsequent passes: fuzzy match.
rd[j] = ((rd[j + 1] << 1) | 1) & charMatch | (
((last_rd[j + 1] | last_rd[j]) << 1) | 1) | last_rd[j + 1]
if rd[j] & matchmask:
score = match_bitapScore(d, j - 1)
# This match will almost certainly be better than any existing match.
# But check anyway.
if score <= score_threshold:
# Told you so.
score_threshold = score
best_loc = j - 1
if best_loc > loc:
# When passing loc, don't exceed our current distance from loc.
start = max(1, 2 * loc - best_loc)
else:
# Already passed loc, downhill from here on in.
break
# No hope for a (better) match at greater error levels.
if match_bitapScore(d + 1, loc) > score_threshold:
break
last_rd = rd
return best_loc
def match_alphabet(self, pattern):
"""Initialise the alphabet for the Bitap algorithm.
Args:
pattern: The text to encode.
Returns:
Hash of character locations.
"""
s = {}
for char in pattern:
s[char] = 0
for i in xrange(len(pattern)):
s[pattern[i]] |= 1 << (len(pattern) - i - 1)
return s
# PATCH FUNCTIONS
def patch_addContext(self, patch, text):
"""Increase the context until it is unique,
but don't let the pattern expand beyond Match_MaxBits.
Args:
patch: The patch to grow.
text: Source text.
"""
if len(text) == 0:
return
pattern = text[patch.start2 : patch.start2 + patch.length1]
padding = 0
# Look for the first and last matches of pattern in text. If two different
# matches are found, increase the pattern length.
while (text.find(pattern) != text.rfind(pattern) and (self.Match_MaxBits ==
0 or len(pattern) < self.Match_MaxBits - self.Patch_Margin -
self.Patch_Margin)):
padding += self.Patch_Margin
pattern = text[max(0, patch.start2 - padding) :
patch.start2 + patch.length1 + padding]
# Add one chunk for good luck.
padding += self.Patch_Margin
# Add the prefix.
prefix = text[max(0, patch.start2 - padding) : patch.start2]
if prefix:
patch.diffs[:0] = [(self.DIFF_EQUAL, prefix)]
# Add the suffix.
suffix = text[patch.start2 + patch.length1 :
patch.start2 + patch.length1 + padding]
if suffix:
patch.diffs.append((self.DIFF_EQUAL, suffix))
# Roll back the start points.
patch.start1 -= len(prefix)
patch.start2 -= len(prefix)
# Extend lengths.
patch.length1 += len(prefix) + len(suffix)
patch.length2 += len(prefix) + len(suffix)
def patch_make(self, a, b=None, c=None):
"""Compute a list of patches to turn text1 into text2.
Use diffs if provided, otherwise compute it ourselves.
There are four ways to call this function, depending on what data is
available to the caller:
Method 1:
a = text1, b = text2
Method 2:
a = diffs
Method 3 (optimal):
a = text1, b = diffs
Method 4 (deprecated, use method 3):
a = text1, b = text2, c = diffs
Args:
a: text1 (methods 1,3,4) or Array of diff tuples for text1 to
text2 (method 2).
b: text2 (methods 1,4) or Array of diff tuples for text1 to
text2 (method 3) or undefined (method 2).
c: Array of diff tuples for text1 to text2 (method 4) or
undefined (methods 1,2,3).
Returns:
Array of patch objects.
"""
text1 = None
diffs = None
# Note that texts may arrive as 'str' or 'unicode'.
if isinstance(a, basestring) and isinstance(b, basestring) and c is None:
# Method 1: text1, text2
# Compute diffs from text1 and text2.
text1 = a
diffs = self.diff_main(text1, b, True)
if len(diffs) > 2:
self.diff_cleanupSemantic(diffs)
self.diff_cleanupEfficiency(diffs)
elif isinstance(a, list) and b is None and c is None:
# Method 2: diffs
# Compute text1 from diffs.
diffs = a
text1 = self.diff_text1(diffs)
elif isinstance(a, basestring) and isinstance(b, list) and c is None:
# Method 3: text1, diffs
text1 = a
diffs = b
elif (isinstance(a, basestring) and isinstance(b, basestring) and
isinstance(c, list)):
# Method 4: text1, text2, diffs
# text2 is not used.
text1 = a
diffs = c
else:
raise ValueError("Unknown call format to patch_make.")
if not diffs:
return [] # Get rid of the None case.
patches = []
patch = patch_obj()
char_count1 = 0 # Number of characters into the text1 string.
char_count2 = 0 # Number of characters into the text2 string.
prepatch_text = text1 # Recreate the patches to determine context info.
postpatch_text = text1
for x in xrange(len(diffs)):
(diff_type, diff_text) = diffs[x]
if len(patch.diffs) == 0 and diff_type != self.DIFF_EQUAL:
# A new patch starts here.
patch.start1 = char_count1
patch.start2 = char_count2
if diff_type == self.DIFF_INSERT:
# Insertion
patch.diffs.append(diffs[x])
patch.length2 += len(diff_text)
postpatch_text = (postpatch_text[:char_count2] + diff_text +
postpatch_text[char_count2:])
elif diff_type == self.DIFF_DELETE:
# Deletion.
patch.length1 += len(diff_text)
patch.diffs.append(diffs[x])
postpatch_text = (postpatch_text[:char_count2] +
postpatch_text[char_count2 + len(diff_text):])
elif (diff_type == self.DIFF_EQUAL and
len(diff_text) <= 2 * self.Patch_Margin and
len(patch.diffs) != 0 and len(diffs) != x + 1):
# Small equality inside a patch.
patch.diffs.append(diffs[x])
patch.length1 += len(diff_text)
patch.length2 += len(diff_text)
if (diff_type == self.DIFF_EQUAL and
len(diff_text) >= 2 * self.Patch_Margin):
# Time for a new patch.
if len(patch.diffs) != 0:
self.patch_addContext(patch, prepatch_text)
patches.append(patch)
patch = patch_obj()
# Unlike Unidiff, our patch lists have a rolling context.
# http://code.google.com/p/google-diff-match-patch/wiki/Unidiff
# Update prepatch text & pos to reflect the application of the
# just completed patch.
prepatch_text = postpatch_text
char_count1 = char_count2
# Update the current character count.
if diff_type != self.DIFF_INSERT:
char_count1 += len(diff_text)
if diff_type != self.DIFF_DELETE:
char_count2 += len(diff_text)
# Pick up the leftover patch if not empty.
if len(patch.diffs) != 0:
self.patch_addContext(patch, prepatch_text)
patches.append(patch)
return patches
def patch_deepCopy(self, patches):
"""Given an array of patches, return another array that is identical.
Args:
patches: Array of patch objects.
Returns:
Array of patch objects.
"""
patchesCopy = []
for patch in patches:
patchCopy = patch_obj()
# No need to deep copy the tuples since they are immutable.
patchCopy.diffs = patch.diffs[:]
patchCopy.start1 = patch.start1
patchCopy.start2 = patch.start2
patchCopy.length1 = patch.length1
patchCopy.length2 = patch.length2
patchesCopy.append(patchCopy)
return patchesCopy
def patch_apply(self, patches, text):
"""Merge a set of patches onto the text. Return a patched text, as well
as a list of true/false values indicating which patches were applied.
Args:
patches: Array of patch objects.
text: Old text.
Returns:
Two element Array, containing the new text and an array of boolean values.
"""
if not patches:
return (text, [])
# Deep copy the patches so that no changes are made to originals.
patches = self.patch_deepCopy(patches)
nullPadding = self.patch_addPadding(patches)
text = nullPadding + text + nullPadding
self.patch_splitMax(patches)
# delta keeps track of the offset between the expected and actual location
# of the previous patch. If there are patches expected at positions 10 and
# 20, but the first patch was found at 12, delta is 2 and the second patch
# has an effective expected position of 22.
delta = 0
results = []
for patch in patches:
expected_loc = patch.start2 + delta
text1 = self.diff_text1(patch.diffs)
end_loc = -1
if len(text1) > self.Match_MaxBits:
# patch_splitMax will only provide an oversized pattern in the case of
# a monster delete.
start_loc = self.match_main(text, text1[:self.Match_MaxBits],
expected_loc)
if start_loc != -1:
end_loc = self.match_main(text, text1[-self.Match_MaxBits:],
expected_loc + len(text1) - self.Match_MaxBits)
if end_loc == -1 or start_loc >= end_loc:
# Can't find valid trailing context. Drop this patch.
start_loc = -1
else:
start_loc = self.match_main(text, text1, expected_loc)
if start_loc == -1:
# No match found. :(
results.append(False)
# Subtract the delta for this failed patch from subsequent patches.
delta -= patch.length2 - patch.length1
else:
# Found a match. :)
results.append(True)
delta = start_loc - expected_loc
if end_loc == -1:
text2 = text[start_loc : start_loc + len(text1)]
else:
text2 = text[start_loc : end_loc + self.Match_MaxBits]
if text1 == text2:
# Perfect match, just shove the replacement text in.
text = (text[:start_loc] + self.diff_text2(patch.diffs) +
text[start_loc + len(text1):])
else:
# Imperfect match.
# Run a diff to get a framework of equivalent indices.
diffs = self.diff_main(text1, text2, False)
if (len(text1) > self.Match_MaxBits and
self.diff_levenshtein(diffs) / float(len(text1)) >
self.Patch_DeleteThreshold):
# The end points match, but the content is unacceptably bad.
results[-1] = False
else:
self.diff_cleanupSemanticLossless(diffs)
index1 = 0
for (op, data) in patch.diffs:
if op != self.DIFF_EQUAL:
index2 = self.diff_xIndex(diffs, index1)
if op == self.DIFF_INSERT: # Insertion
text = text[:start_loc + index2] + data + text[start_loc +
index2:]
elif op == self.DIFF_DELETE: # Deletion
text = text[:start_loc + index2] + text[start_loc +
self.diff_xIndex(diffs, index1 + len(data)):]
if op != self.DIFF_DELETE:
index1 += len(data)
# Strip the padding off.
text = text[len(nullPadding):-len(nullPadding)]
return (text, results)
def patch_addPadding(self, patches):
"""Add some padding on text start and end so that edges can match
something. Intended to be called only from within patch_apply.
Args:
patches: Array of patch objects.
Returns:
The padding string added to each side.
"""
paddingLength = self.Patch_Margin
nullPadding = ""
for x in xrange(1, paddingLength + 1):
nullPadding += chr(x)
# Bump all the patches forward.
for patch in patches:
patch.start1 += paddingLength
patch.start2 += paddingLength
# Add some padding on start of first diff.
patch = patches[0]
diffs = patch.diffs
if not diffs or diffs[0][0] != self.DIFF_EQUAL:
# Add nullPadding equality.
diffs.insert(0, (self.DIFF_EQUAL, nullPadding))
patch.start1 -= paddingLength # Should be 0.
patch.start2 -= paddingLength # Should be 0.
patch.length1 += paddingLength
patch.length2 += paddingLength
elif paddingLength > len(diffs[0][1]):
# Grow first equality.
extraLength = paddingLength - len(diffs[0][1])
newText = nullPadding[len(diffs[0][1]):] + diffs[0][1]
diffs[0] = (diffs[0][0], newText)
patch.start1 -= extraLength
patch.start2 -= extraLength
patch.length1 += extraLength
patch.length2 += extraLength
# Add some padding on end of last diff.
patch = patches[-1]
diffs = patch.diffs
if not diffs or diffs[-1][0] != self.DIFF_EQUAL:
# Add nullPadding equality.
diffs.append((self.DIFF_EQUAL, nullPadding))
patch.length1 += paddingLength
patch.length2 += paddingLength
elif paddingLength > len(diffs[-1][1]):
# Grow last equality.
extraLength = paddingLength - len(diffs[-1][1])
newText = diffs[-1][1] + nullPadding[:extraLength]
diffs[-1] = (diffs[-1][0], newText)
patch.length1 += extraLength
patch.length2 += extraLength
return nullPadding
def patch_splitMax(self, patches):
"""Look through the patches and break up any which are longer than the
maximum limit of the match algorithm.
Args:
patches: Array of patch objects.
"""
if self.Match_MaxBits == 0:
return
for x in xrange(len(patches)):
if patches[x].length1 > self.Match_MaxBits:
bigpatch = patches[x]
# Remove the big old patch.
del patches[x]
x -= 1
patch_size = self.Match_MaxBits
start1 = bigpatch.start1
start2 = bigpatch.start2
precontext = ''
while len(bigpatch.diffs) != 0:
# Create one of several smaller patches.
patch = patch_obj()
empty = True
patch.start1 = start1 - len(precontext)
patch.start2 = start2 - len(precontext)
if precontext:
patch.length1 = patch.length2 = len(precontext)
patch.diffs.append((self.DIFF_EQUAL, precontext))
while (len(bigpatch.diffs) != 0 and
patch.length1 < patch_size - self.Patch_Margin):
(diff_type, diff_text) = bigpatch.diffs[0]
if diff_type == self.DIFF_INSERT:
# Insertions are harmless.
patch.length2 += len(diff_text)
start2 += len(diff_text)
patch.diffs.append(bigpatch.diffs.pop(0))
empty = False
elif (diff_type == self.DIFF_DELETE and len(patch.diffs) == 1 and
patch.diffs[0][0] == self.DIFF_EQUAL and
len(diff_text) > 2 * patch_size):
# This is a large deletion. Let it pass in one chunk.
patch.length1 += len(diff_text)
start1 += len(diff_text)
empty = False
patch.diffs.append((diff_type, diff_text))
del bigpatch.diffs[0]
else:
# Deletion or equality. Only take as much as we can stomach.
diff_text = diff_text[:patch_size - patch.length1 -
self.Patch_Margin]
patch.length1 += len(diff_text)
start1 += len(diff_text)
if diff_type == self.DIFF_EQUAL:
patch.length2 += len(diff_text)
start2 += len(diff_text)
else:
empty = False
patch.diffs.append((diff_type, diff_text))
if diff_text == bigpatch.diffs[0][1]:
del bigpatch.diffs[0]
else:
bigpatch.diffs[0] = (bigpatch.diffs[0][0],
bigpatch.diffs[0][1][len(diff_text):])
# Compute the head context for the next patch.
precontext = self.diff_text2(patch.diffs)
precontext = precontext[-self.Patch_Margin:]
# Append the end context for this patch.
postcontext = self.diff_text1(bigpatch.diffs)[:self.Patch_Margin]
if postcontext:
patch.length1 += len(postcontext)
patch.length2 += len(postcontext)
if len(patch.diffs) != 0 and patch.diffs[-1][0] == self.DIFF_EQUAL:
patch.diffs[-1] = (self.DIFF_EQUAL, patch.diffs[-1][1] +
postcontext)
else:
patch.diffs.append((self.DIFF_EQUAL, postcontext))
if not empty:
x += 1
patches.insert(x, patch)
def patch_toText(self, patches):
"""Take a list of patches and return a textual representation.
Args:
patches: Array of patch objects.
Returns:
Text representation of patches.
"""
text = []
for patch in patches:
text.append(str(patch))
return "".join(text)
def patch_fromText(self, textline):
"""Parse a textual representation of patches and return a list of patch
objects.
Args:
textline: Text representation of patches.
Returns:
Array of patch objects.
Raises:
ValueError: If invalid input.
"""
if type(textline) == unicode:
# Patches should be composed of a subset of ascii chars, Unicode not
# required. If this encode raises UnicodeEncodeError, patch is invalid.
textline = textline.encode("ascii")
patches = []
if not textline:
return patches
text = textline.split('\n')
while len(text) != 0:
m = re.match("^@@ -(\d+),?(\d*) \+(\d+),?(\d*) @@$", text[0])
if not m:
raise ValueError, "Invalid patch string: " + text[0]
patch = patch_obj()
patches.append(patch)
patch.start1 = int(m.group(1))
if m.group(2) == '':
patch.start1 -= 1
patch.length1 = 1
elif m.group(2) == '0':
patch.length1 = 0
else:
patch.start1 -= 1
patch.length1 = int(m.group(2))
patch.start2 = int(m.group(3))
if m.group(4) == '':
patch.start2 -= 1
patch.length2 = 1
elif m.group(4) == '0':
patch.length2 = 0
else:
patch.start2 -= 1
patch.length2 = int(m.group(4))
del text[0]
while len(text) != 0:
if text[0]:
sign = text[0][0]
else:
sign = ''
line = urllib.unquote(text[0][1:])
line = line.decode("utf-8")
if sign == '+':
# Insertion.
patch.diffs.append((self.DIFF_INSERT, line))
elif sign == '-':
# Deletion.
patch.diffs.append((self.DIFF_DELETE, line))
elif sign == ' ':
# Minor equality.
patch.diffs.append((self.DIFF_EQUAL, line))
elif sign == '@':
# Start of next patch.
break
elif sign == '':
# Blank line? Whatever.
pass
else:
# WTF?
raise ValueError, "Invalid patch mode: '%s'\n%s" % (sign, line)
del text[0]
return patches
class patch_obj:
"""Class representing one patch operation.
"""
def __init__(self):
"""Initializes with an empty list of diffs.
"""
self.diffs = []
self.start1 = None
self.start2 = None
self.length1 = 0
self.length2 = 0
def __str__(self):
"""Emmulate GNU diff's format.
Header: @@ -382,8 +481,9 @@
Indicies are printed as 1-based, not 0-based.
Returns:
The GNU diff string.
"""
if self.length1 == 0:
coords1 = str(self.start1) + ",0"
elif self.length1 == 1:
coords1 = str(self.start1 + 1)
else:
coords1 = str(self.start1 + 1) + "," + str(self.length1)
if self.length2 == 0:
coords2 = str(self.start2) + ",0"
elif self.length2 == 1:
coords2 = str(self.start2 + 1)
else:
coords2 = str(self.start2 + 1) + "," + str(self.length2)
text = ["@@ -", coords1, " +", coords2, " @@\n"]
# Escape the body of the patch with %xx notation.
for (op, data) in self.diffs:
if op == diff_match_patch.DIFF_INSERT:
text.append("+")
elif op == diff_match_patch.DIFF_DELETE:
text.append("-")
elif op == diff_match_patch.DIFF_EQUAL:
text.append(" ")
# High ascii will raise UnicodeDecodeError. Use Unicode instead.
data = data.encode("utf-8")
text.append(urllib.quote(data, "!~*'();/?:@&=+$,# ") + "\n")
return "".join(text)
|
[
"mark@markng.co.uk"
] |
mark@markng.co.uk
|
85f1f80f24220aed6e73c2015e66ee635b426869
|
15d4861bf2f4748f144fbb85a128341736060bd2
|
/archive/chemproject/copypaste.py
|
7c72af112b0c881e7824d2aa062e487cae5d3a4b
|
[] |
no_license
|
wombat-drone/fema-flavor-classifier
|
283033d4e1535e3f83fefada41eb4d2a36c138c5
|
9737d5e6322f49b89e61cfa372505d8e217dd4da
|
refs/heads/master
| 2022-11-06T22:38:32.838474
| 2018-01-29T22:58:12
| 2018-01-29T22:58:12
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 514
|
py
|
"""
Tools for sending printouts to clipboard.
Only work for Mac.
"""
import subprocess
def write_to_clipboard(output):
"""
writes output to clipboard
"""
process = subprocess.Popen(
'pbcopy', env={'LANG': 'en_US.UTF-8'}, stdin=subprocess.PIPE)
process.communicate(output.encode('utf-8'))
def read_from_clipboard():
"""
Returns a string with whaterver is in the clipboard.
"""
return subprocess.check_output('pbpaste', env={'LANG': 'en_US.UTF-8'}).decode('utf-8')
|
[
"TRN@BeyondTRN.local"
] |
TRN@BeyondTRN.local
|
68eeea5ed3b7b64fa83adeca2d9a513d9c57fd1c
|
24caa6710105a060fab2e17147e6d56609939011
|
/06-Importing_Data_in_Python_(Part_2)/01-Importing_data_from_the_Internet/01-Importing_flat_files_from_the_web_your_turn!.py
|
b845373064884f87b9853e85c1360cd5849f5a64
|
[] |
no_license
|
inverseundefined/DataCamp
|
99607022ad3f899d7681ad1f70fcedab290e269a
|
7226b6b6f41888c3610a884db9a226e013d37e56
|
refs/heads/master
| 2022-01-10T00:53:21.714908
| 2019-07-24T13:27:49
| 2019-07-24T13:27:49
| 198,280,648
| 1
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,446
|
py
|
'''
Importing flat files from the web: your turn!
You are about to import your first file from the web! The flat file you will import will be 'winequality-red.csv' from the University of California, Irvine's Machine Learning repository. The flat file contains tabular data of physiochemical properties of red wine, such as pH, alcohol content and citric acid content, along with wine quality rating.
The URL of the file is
'https://s3.amazonaws.com/assets.datacamp.com/production/course_1606/datasets/winequality-red.csv'
After you import it, you'll check your working directory to confirm that it is there and then you'll load it into a pandas DataFrame.
Instructions
100 XP
Import the function urlretrieve from the subpackage urllib.request.
Assign the URL of the file to the variable url.
Use the function urlretrieve() to save the file locally as 'winequality-red.csv'.
Execute the remaining code to load 'winequality-red.csv' in a pandas DataFrame and to print its head to the shell.
Take Hint (-30 XP)
'''
# Import package
from urllib.request import urlretrieve
# Import pandas
import pandas as pd
# Assign url of file: url
url = 'https://s3.amazonaws.com/assets.datacamp.com/production/course_1606/datasets/winequality-red.csv'
# Save file locally
urlretrieve(url, 'winequality-red.csv')
# Read file into a DataFrame and print its head
df = pd.read_csv('winequality-red.csv', sep=';')
print(df.head())
|
[
"inversedrivenundefined@gmail.com"
] |
inversedrivenundefined@gmail.com
|
f34253d3cdbb977a559f8f296b19947c19542246
|
77a73704511667d41392b15d7b87e2621453a6a4
|
/Day9/q36.py
|
379321f62dfc7f99e76e2a59f1077cb0d29e0a4a
|
[] |
no_license
|
mahtab04/Python-Programming-Practice
|
0ee790aec4ae32a474800826a7da113906289b29
|
c076c6efd786059d3a4ac33dddd41f187eb0ce10
|
refs/heads/master
| 2020-04-16T18:29:28.454966
| 2019-03-11T15:49:13
| 2019-03-11T15:49:13
| 165,822,216
| 3
| 5
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 423
|
py
|
# Program to find whether a no is power of two
# Given a positive integer, write a function to find if it is a power of two or not.
# Input : n = 4
# Output : Yes
# 22 = 4
# Input : n = 7
# Output : No
# Input : n = 32
# Output : Yes
# 25 = 32
def check_Power(number):
if(number & number-1) == 0:
print("yes")
else:
print("NO")
a = 4
b = 7
c = 32
check_Power(a)
check_Power(b)
check_Power(c)
|
[
"smmalam9@gmail.com"
] |
smmalam9@gmail.com
|
b24a89fc5023e465c9d433a04830c0efbcfae800
|
19c42d7c30a38e9ef5e5ef694b00449c92ec8f8f
|
/images/serializers.py
|
a89a0ad31cecf175961b0a0955b3cca0cda68f7d
|
[] |
no_license
|
Blitzone/backend
|
219210fe82652b872df1e0c4ccdaec21b1b1b887
|
9dfb1327db304877ff08fb88ed79df8182bffcf0
|
refs/heads/master
| 2021-01-17T07:10:15.523623
| 2016-07-18T23:12:53
| 2016-07-18T23:12:53
| 55,292,590
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 4,018
|
py
|
from rest_framework import serializers
from .models import Topic, Chapter, Blitz, UserTopic, UserChapter
from accounts.serializers import BlitzUserSerializer
from accounts.models import BlitzUser
from django.db.models import Q
class UserTopicSerializer(serializers.ModelSerializer):
class Meta:
model = UserTopic
fields = ('id', 'user', 'topic', 'likes', 'dislikes')
class DailyUserTopicSerializer(serializers.ModelSerializer):
user = serializers.SerializerMethodField('getUsername')
photoChapters = serializers.SerializerMethodField('getPhotoChapters')
is_liked = serializers.SerializerMethodField('isLiked')
is_disliked = serializers.SerializerMethodField('isDisliked')
is_blitzed = serializers.SerializerMethodField('isBlitzed')
def __init__(self, *args, **kwargs):
self.requestingUser = kwargs.pop('requestingUser', None)
super(DailyUserTopicSerializer, self).__init__(*args, **kwargs)
def getUsername(self, userTopic):
return BlitzUserSerializer(userTopic.user).data
def getPhotoChapters(self, userTopic):
photoChapters = UserChapter.objects.filter(userTopic=userTopic)
return DailyUserChapterSerializer(photoChapters, many=True).data
def isLiked(self, userTopic):
blitzUser = BlitzUser.objects.get(user__username=self.requestingUser)
t = UserTopic.objects.get(pk=userTopic.pk)
return t in blitzUser.likes.all()
def isDisliked(self, userTopic):
blitzUser = BlitzUser.objects.get(user__username=self.requestingUser)
t = UserTopic.objects.get(pk=userTopic.pk)
return t in blitzUser.dislikes.all()
def isBlitzed(self, userTopic):
blitzUser = BlitzUser.objects.get(user__username=self.requestingUser)
return Blitz.objects.filter(Q(user1=blitzUser, user2=userTopic.user) | Q(user1=userTopic.user, user2=blitzUser)).count() > 0
class Meta:
model = UserTopic
fields = (
'user',
'likes',
'dislikes',
'is_liked',
'is_disliked',
'is_blitzed',
'photoChapters',
'timestampUpdated')
class TopicSerializer(serializers.ModelSerializer):
class Meta:
model = Topic
fields = ('id', 'name', 'startDate', 'endDate')
class UserChapterSerializer(serializers.ModelSerializer):
user = serializers.SerializerMethodField('getUser')
def getUser(self, userChapter):
return BlitzUserSerializer(userChapter.userTopic.user).data
class Meta:
model = UserChapter
fields = ('id', 'image', 'userTopic', 'chapter', 'user')
class DailyUserChapterSerializer(serializers.ModelSerializer):
chapter = serializers.SerializerMethodField('getChapter')
def getChapter(self, userChapter):
return userChapter.chapter.name
class Meta:
model = UserChapter
fields = ('image', 'chapter')
class SearchUserChapterSerializer(serializers.ModelSerializer):
user = serializers.SerializerMethodField('getUser')
is_followed = serializers.SerializerMethodField('isFollowed')
def __init__(self, *args, **kwargs):
self.requestingUser = kwargs.pop('requestingUser', None)
super(SearchUserChapterSerializer, self).__init__(*args, **kwargs)
def getUser(self, userChapter):
return BlitzUserSerializer(userChapter.userTopic.user).data
def isFollowed(self, userChapter):
requestingBlitzUser = BlitzUser.objects.get(user__username=self.requestingUser)
return userChapter.userTopic.user in requestingBlitzUser.follows.all()
class Meta:
model = UserChapter
fields = ('id', 'image', 'userTopic', 'chapter', 'user', 'is_followed')
class ChapterSerializer(serializers.ModelSerializer):
class Meta:
model = Chapter
fields = ('id', 'name', 'topic')
class BlitzSerializer(serializers.ModelSerializer):
class Meta:
model = Blitz
fields = ('id', 'user1', 'user2', 'winner', 'userTopic', 'startDate', 'endDate')
|
[
"mikelv92@gmail.com"
] |
mikelv92@gmail.com
|
af110594bc60b09186afd5627301dc1dbf379ca8
|
af61044c866eb85ca2c622e082090f7657431206
|
/webcli/arthur_utils/experiment.py
|
a2e95ed3a2caacf3035abf7dcdb6607dbfd126af
|
[] |
no_license
|
leepand/gridpoc
|
f7959ef099d8a5513c59dfeb682761771ffe7594
|
4c476cd0241a95a4a7d2abf53a519d3749ecfb94
|
refs/heads/master
| 2020-04-28T02:38:49.631595
| 2019-03-11T02:01:50
| 2019-03-11T02:01:50
| 174,906,542
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,502
|
py
|
from _mlflow_object import _MLflowObject
class Experiment(_MLflowObject):
"""
Experiment object.
"""
DEFAULT_EXPERIMENT_ID = 0
ACTIVE_LIFECYCLE = 'active'
DELETED_LIFECYCLE = 'deleted'
def __init__(self, experiment_id, name, artifact_location, lifecycle_stage):
super(Experiment, self).__init__()
self._experiment_id = experiment_id
self._name = name
self._artifact_location = artifact_location
self._lifecycle_stage = lifecycle_stage
@property
def experiment_id(self):
"""Integer ID of the experiment."""
return self._experiment_id
@property
def name(self):
"""String name of the experiment."""
return self._name
def _set_name(self, new_name):
self._name = new_name
@property
def artifact_location(self):
"""String corresponding to the root artifact URI for the experiment."""
return self._artifact_location
@property
def lifecycle_stage(self):
"""Lifecycle stage of the experiment. Can either be 'active' or 'deleted'."""
return self._lifecycle_stage
@classmethod
def from_proto(cls, proto):
return cls(proto.experiment_id, proto.name, proto.artifact_location, proto.lifecycle_stage)
@classmethod
def _properties(cls):
# TODO: Hard coding this list of props for now. There has to be a clearer way...
return ["experiment_id", "name", "artifact_location", "lifecycle_stage"]
|
[
"85721094@qq.com"
] |
85721094@qq.com
|
5db755421c2575707e428ae378775b1cf3462cdc
|
672f2f55cc81388ce6322350ad1fbd96d4c639dd
|
/brain_games/scripts/brain_gsd.py
|
76a1286c3aa0574f56a3cced02233f01314a042f
|
[] |
no_license
|
Nikolaevaanneta/python-project-lvl1
|
26018cf3681fc2f44991d6c781901d4c5a081f56
|
9913b2e092e6c3a414a3cf9d72fb39be03d25254
|
refs/heads/main
| 2023-07-14T19:50:42.219635
| 2021-08-26T10:41:00
| 2021-08-26T10:41:00
| 397,596,100
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 144
|
py
|
from brain_games.body import run_game
from brain_games.games import gsd
def main():
run_game(gsd)
if __name__ == '__main__':
main()
|
[
"nikolaevaanneta@gmail.com"
] |
nikolaevaanneta@gmail.com
|
33667e8b97d6c876c073bc1b32185c8188c271fa
|
a1614311937bae5204e171b2a3481fb31e61a490
|
/media/codigos/36/36sol118.py
|
0e4ccda5dba78b1aa00e7913b2e0c1bb249e5ec9
|
[] |
no_license
|
alexandre146/avaliar
|
8d406100ed72f10292a0580edac50ad061ad92e9
|
3daf247ca68962086592a356e013b07fa1569afe
|
refs/heads/master
| 2020-03-21T03:09:29.493919
| 2018-07-23T11:41:38
| 2018-07-23T11:41:38
| 137,883,682
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 178
|
py
|
n=int(input())
m=int(input())
if(m%n==0):
print(m)
elif(m%n!=0):
x=m%n
if((m-x)==n):
print("sem multiplos menores que"+str(m))
else:
print(m-x)
|
[
"alexandre146@gmail.com"
] |
alexandre146@gmail.com
|
3a30135d3a76ea4be773fdf0dbce6c8d4ce4fcf1
|
ce4c24564022d8f3bd9d224e99a66bb43eb2d870
|
/R0004_Exams_By_Weekday_BarPlot_Report/ExamsByWeekday.py
|
2ab7e09d499ffdf5ada9914ac1b8df1eda682c40
|
[] |
no_license
|
Harshit1503/Test-Center-Data-Analysis
|
a676e7de484977ac975cc2a5792c8f461a63cbdc
|
b94a43b4a01fa9c5e3f8166cf4957f7aea6e6a9a
|
refs/heads/master
| 2022-11-28T08:15:55.802896
| 2021-03-29T16:14:32
| 2021-03-29T16:14:32
| 205,307,314
| 0
| 0
| null | 2022-11-22T07:39:01
| 2019-08-30T04:56:45
|
Python
|
UTF-8
|
Python
| false
| false
| 3,310
|
py
|
# -*- coding: utf-8 -*-
"""
Created on Thu Aug 1 01:37:07 2019
@author: Harshit
"""
import os
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image, ImageDraw, ImageFont
from datetime import date
def exams_by_weekday_bar_plot(weekDay_CNT_Tuple, yearVal):
# set width of bar
barWidth = 0.25
# yearVal = 2016
plt.rc('figure', figsize=(24, 15))
font = {'weight' : 'bold',
'size' : 22}
plt.rc('font', **font)
# set height of bar
aah = [weekDay_CNT_Tuple[0][1], weekDay_CNT_Tuple[0][2], weekDay_CNT_Tuple[0][3], weekDay_CNT_Tuple[0][4], weekDay_CNT_Tuple[0][5], weekDay_CNT_Tuple[0][6], weekDay_CNT_Tuple[0][7]]
cbb = [weekDay_CNT_Tuple[1][1], weekDay_CNT_Tuple[1][2], weekDay_CNT_Tuple[1][3], weekDay_CNT_Tuple[1][4], weekDay_CNT_Tuple[1][5], weekDay_CNT_Tuple[1][6], weekDay_CNT_Tuple[1][7]]
gar = [weekDay_CNT_Tuple[2][1], weekDay_CNT_Tuple[2][2], weekDay_CNT_Tuple[2][3], weekDay_CNT_Tuple[2][4], weekDay_CNT_Tuple[2][5], weekDay_CNT_Tuple[2][6], weekDay_CNT_Tuple[2][7]]
# Set position of bar on X axis
r1 = np.arange(len(aah))
r2 = [x + barWidth for x in r1]
r3 = [x + barWidth for x in r2]
# Make the plot
plt.bar(r1, aah, color='steelblue', width=barWidth, edgecolor='white', label='AAH')
plt.bar(r2, cbb, color='darkorange', width=barWidth, edgecolor='white', label='CBB')
plt.bar(r3, gar, color='c', width=barWidth, edgecolor='white', label='GAR')
# Add xticks on the middle of the group bars
plt.xlabel('Weekday', fontsize = 25, fontweight='bold')
plt.ylabel('Number of Students', fontsize = 25, weight = "bold")
if yearVal not in ['all', 'All', 'ALL']:
plt.title("Exams By Weekday: "+str(yearVal)+"\n", fontsize = 30, weight="bold")
else:
plt.title('Exams By Weekday'+"\n", fontsize = 30, weight = "bold")
plt.xticks([r + barWidth for r in range(len(aah))], ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun'])
# Create legend & Show graphic
plt.legend(['AAH', 'CBB', 'GAR'])
#Save the Pie Chart image to respective report directory
#Absolute path of report directory
abs_path = os.path.abspath(os.path.dirname(__file__))
rel_path = "output"
path = os.path.join(abs_path, rel_path)
if not os.path.exists(path):
print("Image Folder Created!")
os.makedirs(path)
if yearVal not in ['all', 'All', 'ALL']:
file_name = 'Exams_By_Weekday_BarPlot_' + str(yearVal) +'.png'
else:
file_name = 'Exams_By_Weekday_BarPlot_all.png'
my_dpi = 72
plt.savefig(os.path.join(path, file_name), dpi = my_dpi)
plt.show()
image_path_input = path
image_path_output = path
fnt = ImageFont.truetype("arial.ttf", 25)
image_name_input = '\\' + file_name
today = date.today()
im = Image.open(image_path_input + image_name_input)
position = (1570, 1030)
message = today.strftime("%m/%d/%Y")
# initialise the drawing context with the image object as background
draw = ImageDraw.Draw(im)
draw.text(position, message, font = fnt, fill = "black")
im.show()
image_name_output = '\\' + file_name
im.save(image_path_output + image_name_output)
|
[
"harshit.singh1503@gmail.com"
] |
harshit.singh1503@gmail.com
|
5d2d9c1ac8f26a527eaf2d08e5cdd9a656e0880c
|
6fa701cdaa0d83caa0d3cbffe39b40e54bf3d386
|
/google/cloud/kms/v1/kms-v1-py/google/cloud/kms_v1/services/key_management_service/transports/base.py
|
0da50e0196d2991964005d561c4193993d0eb0a7
|
[
"Apache-2.0"
] |
permissive
|
oltoco/googleapis-gen
|
bf40cfad61b4217aca07068bd4922a86e3bbd2d5
|
00ca50bdde80906d6f62314ef4f7630b8cdb6e15
|
refs/heads/master
| 2023-07-17T22:11:47.848185
| 2021-08-29T20:39:47
| 2021-08-29T20:39:47
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 26,784
|
py
|
# -*- coding: utf-8 -*-
# Copyright 2020 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 abc
from typing import Awaitable, Callable, Dict, Optional, Sequence, Union
import packaging.version
import pkg_resources
import google.auth # type: ignore
import google.api_core # type: ignore
from google.api_core import exceptions as core_exceptions # type: ignore
from google.api_core import gapic_v1 # type: ignore
from google.api_core import retry as retries # type: ignore
from google.auth import credentials as ga_credentials # type: ignore
from google.oauth2 import service_account # type: ignore
from google.cloud.kms_v1.types import resources
from google.cloud.kms_v1.types import service
from google.iam.v1 import iam_policy_pb2 # type: ignore
from google.iam.v1 import policy_pb2 # type: ignore
try:
DEFAULT_CLIENT_INFO = gapic_v1.client_info.ClientInfo(
gapic_version=pkg_resources.get_distribution(
'google-cloud-kms',
).version,
)
except pkg_resources.DistributionNotFound:
DEFAULT_CLIENT_INFO = gapic_v1.client_info.ClientInfo()
try:
# google.auth.__version__ was added in 1.26.0
_GOOGLE_AUTH_VERSION = google.auth.__version__
except AttributeError:
try: # try pkg_resources if it is available
_GOOGLE_AUTH_VERSION = pkg_resources.get_distribution("google-auth").version
except pkg_resources.DistributionNotFound: # pragma: NO COVER
_GOOGLE_AUTH_VERSION = None
class KeyManagementServiceTransport(abc.ABC):
"""Abstract transport class for KeyManagementService."""
AUTH_SCOPES = (
'https://www.googleapis.com/auth/cloud-platform',
'https://www.googleapis.com/auth/cloudkms',
)
DEFAULT_HOST: str = 'cloudkms.googleapis.com'
def __init__(
self, *,
host: str = DEFAULT_HOST,
credentials: ga_credentials.Credentials = None,
credentials_file: Optional[str] = None,
scopes: Optional[Sequence[str]] = None,
quota_project_id: Optional[str] = None,
client_info: gapic_v1.client_info.ClientInfo = DEFAULT_CLIENT_INFO,
always_use_jwt_access: Optional[bool] = False,
**kwargs,
) -> None:
"""Instantiate the transport.
Args:
host (Optional[str]):
The hostname to connect to.
credentials (Optional[google.auth.credentials.Credentials]): The
authorization credentials to attach to requests. These
credentials identify the application to the service; if none
are specified, the client will attempt to ascertain the
credentials from the environment.
credentials_file (Optional[str]): A file with credentials that can
be loaded with :func:`google.auth.load_credentials_from_file`.
This argument is mutually exclusive with credentials.
scopes (Optional[Sequence[str]]): A list of scopes.
quota_project_id (Optional[str]): An optional project to use for billing
and quota.
client_info (google.api_core.gapic_v1.client_info.ClientInfo):
The client info used to send a user-agent string along with
API requests. If ``None``, then default info will be used.
Generally, you only need to set this if you're developing
your own client library.
always_use_jwt_access (Optional[bool]): Whether self signed JWT should
be used for service account credentials.
"""
# Save the hostname. Default to port 443 (HTTPS) if none is specified.
if ':' not in host:
host += ':443'
self._host = host
scopes_kwargs = self._get_scopes_kwargs(self._host, scopes)
# Save the scopes.
self._scopes = scopes
# If no credentials are provided, then determine the appropriate
# defaults.
if credentials and credentials_file:
raise core_exceptions.DuplicateCredentialArgs("'credentials_file' and 'credentials' are mutually exclusive")
if credentials_file is not None:
credentials, _ = google.auth.load_credentials_from_file(
credentials_file,
**scopes_kwargs,
quota_project_id=quota_project_id
)
elif credentials is None:
credentials, _ = google.auth.default(**scopes_kwargs, quota_project_id=quota_project_id)
# If the credentials is service account credentials, then always try to use self signed JWT.
if always_use_jwt_access and isinstance(credentials, service_account.Credentials) and hasattr(service_account.Credentials, "with_always_use_jwt_access"):
credentials = credentials.with_always_use_jwt_access(True)
# Save the credentials.
self._credentials = credentials
# TODO(busunkim): This method is in the base transport
# to avoid duplicating code across the transport classes. These functions
# should be deleted once the minimum required versions of google-auth is increased.
# TODO: Remove this function once google-auth >= 1.25.0 is required
@classmethod
def _get_scopes_kwargs(cls, host: str, scopes: Optional[Sequence[str]]) -> Dict[str, Optional[Sequence[str]]]:
"""Returns scopes kwargs to pass to google-auth methods depending on the google-auth version"""
scopes_kwargs = {}
if _GOOGLE_AUTH_VERSION and (
packaging.version.parse(_GOOGLE_AUTH_VERSION)
>= packaging.version.parse("1.25.0")
):
scopes_kwargs = {"scopes": scopes, "default_scopes": cls.AUTH_SCOPES}
else:
scopes_kwargs = {"scopes": scopes or cls.AUTH_SCOPES}
return scopes_kwargs
def _prep_wrapped_messages(self, client_info):
# Precompute the wrapped methods.
self._wrapped_methods = {
self.list_key_rings: gapic_v1.method.wrap_method(
self.list_key_rings,
default_retry=retries.Retry(
initial=0.1,maximum=60.0,multiplier=1.3, predicate=retries.if_exception_type(
core_exceptions.DeadlineExceeded,
core_exceptions.ServiceUnavailable,
),
deadline=60.0,
),
default_timeout=60.0,
client_info=client_info,
),
self.list_crypto_keys: gapic_v1.method.wrap_method(
self.list_crypto_keys,
default_retry=retries.Retry(
initial=0.1,maximum=60.0,multiplier=1.3, predicate=retries.if_exception_type(
core_exceptions.DeadlineExceeded,
core_exceptions.ServiceUnavailable,
),
deadline=60.0,
),
default_timeout=60.0,
client_info=client_info,
),
self.list_crypto_key_versions: gapic_v1.method.wrap_method(
self.list_crypto_key_versions,
default_retry=retries.Retry(
initial=0.1,maximum=60.0,multiplier=1.3, predicate=retries.if_exception_type(
core_exceptions.DeadlineExceeded,
core_exceptions.ServiceUnavailable,
),
deadline=60.0,
),
default_timeout=60.0,
client_info=client_info,
),
self.list_import_jobs: gapic_v1.method.wrap_method(
self.list_import_jobs,
default_retry=retries.Retry(
initial=0.1,maximum=60.0,multiplier=1.3, predicate=retries.if_exception_type(
core_exceptions.DeadlineExceeded,
core_exceptions.ServiceUnavailable,
),
deadline=60.0,
),
default_timeout=60.0,
client_info=client_info,
),
self.get_key_ring: gapic_v1.method.wrap_method(
self.get_key_ring,
default_retry=retries.Retry(
initial=0.1,maximum=60.0,multiplier=1.3, predicate=retries.if_exception_type(
core_exceptions.DeadlineExceeded,
core_exceptions.ServiceUnavailable,
),
deadline=60.0,
),
default_timeout=60.0,
client_info=client_info,
),
self.get_crypto_key: gapic_v1.method.wrap_method(
self.get_crypto_key,
default_retry=retries.Retry(
initial=0.1,maximum=60.0,multiplier=1.3, predicate=retries.if_exception_type(
core_exceptions.DeadlineExceeded,
core_exceptions.ServiceUnavailable,
),
deadline=60.0,
),
default_timeout=60.0,
client_info=client_info,
),
self.get_crypto_key_version: gapic_v1.method.wrap_method(
self.get_crypto_key_version,
default_retry=retries.Retry(
initial=0.1,maximum=60.0,multiplier=1.3, predicate=retries.if_exception_type(
core_exceptions.DeadlineExceeded,
core_exceptions.ServiceUnavailable,
),
deadline=60.0,
),
default_timeout=60.0,
client_info=client_info,
),
self.get_public_key: gapic_v1.method.wrap_method(
self.get_public_key,
default_retry=retries.Retry(
initial=0.1,maximum=60.0,multiplier=1.3, predicate=retries.if_exception_type(
core_exceptions.DeadlineExceeded,
core_exceptions.ServiceUnavailable,
),
deadline=60.0,
),
default_timeout=60.0,
client_info=client_info,
),
self.get_import_job: gapic_v1.method.wrap_method(
self.get_import_job,
default_retry=retries.Retry(
initial=0.1,maximum=60.0,multiplier=1.3, predicate=retries.if_exception_type(
core_exceptions.DeadlineExceeded,
core_exceptions.ServiceUnavailable,
),
deadline=60.0,
),
default_timeout=60.0,
client_info=client_info,
),
self.create_key_ring: gapic_v1.method.wrap_method(
self.create_key_ring,
default_retry=retries.Retry(
initial=0.1,maximum=60.0,multiplier=1.3, predicate=retries.if_exception_type(
core_exceptions.DeadlineExceeded,
core_exceptions.ServiceUnavailable,
),
deadline=60.0,
),
default_timeout=60.0,
client_info=client_info,
),
self.create_crypto_key: gapic_v1.method.wrap_method(
self.create_crypto_key,
default_retry=retries.Retry(
initial=0.1,maximum=60.0,multiplier=1.3, predicate=retries.if_exception_type(
core_exceptions.DeadlineExceeded,
core_exceptions.ServiceUnavailable,
),
deadline=60.0,
),
default_timeout=60.0,
client_info=client_info,
),
self.create_crypto_key_version: gapic_v1.method.wrap_method(
self.create_crypto_key_version,
default_timeout=60.0,
client_info=client_info,
),
self.import_crypto_key_version: gapic_v1.method.wrap_method(
self.import_crypto_key_version,
default_timeout=60.0,
client_info=client_info,
),
self.create_import_job: gapic_v1.method.wrap_method(
self.create_import_job,
default_retry=retries.Retry(
initial=0.1,maximum=60.0,multiplier=1.3, predicate=retries.if_exception_type(
core_exceptions.DeadlineExceeded,
core_exceptions.ServiceUnavailable,
),
deadline=60.0,
),
default_timeout=60.0,
client_info=client_info,
),
self.update_crypto_key: gapic_v1.method.wrap_method(
self.update_crypto_key,
default_retry=retries.Retry(
initial=0.1,maximum=60.0,multiplier=1.3, predicate=retries.if_exception_type(
core_exceptions.DeadlineExceeded,
core_exceptions.ServiceUnavailable,
),
deadline=60.0,
),
default_timeout=60.0,
client_info=client_info,
),
self.update_crypto_key_version: gapic_v1.method.wrap_method(
self.update_crypto_key_version,
default_retry=retries.Retry(
initial=0.1,maximum=60.0,multiplier=1.3, predicate=retries.if_exception_type(
core_exceptions.DeadlineExceeded,
core_exceptions.ServiceUnavailable,
),
deadline=60.0,
),
default_timeout=60.0,
client_info=client_info,
),
self.update_crypto_key_primary_version: gapic_v1.method.wrap_method(
self.update_crypto_key_primary_version,
default_retry=retries.Retry(
initial=0.1,maximum=60.0,multiplier=1.3, predicate=retries.if_exception_type(
core_exceptions.DeadlineExceeded,
core_exceptions.ServiceUnavailable,
),
deadline=60.0,
),
default_timeout=60.0,
client_info=client_info,
),
self.destroy_crypto_key_version: gapic_v1.method.wrap_method(
self.destroy_crypto_key_version,
default_retry=retries.Retry(
initial=0.1,maximum=60.0,multiplier=1.3, predicate=retries.if_exception_type(
core_exceptions.DeadlineExceeded,
core_exceptions.ServiceUnavailable,
),
deadline=60.0,
),
default_timeout=60.0,
client_info=client_info,
),
self.restore_crypto_key_version: gapic_v1.method.wrap_method(
self.restore_crypto_key_version,
default_retry=retries.Retry(
initial=0.1,maximum=60.0,multiplier=1.3, predicate=retries.if_exception_type(
core_exceptions.DeadlineExceeded,
core_exceptions.ServiceUnavailable,
),
deadline=60.0,
),
default_timeout=60.0,
client_info=client_info,
),
self.encrypt: gapic_v1.method.wrap_method(
self.encrypt,
default_retry=retries.Retry(
initial=0.1,maximum=60.0,multiplier=1.3, predicate=retries.if_exception_type(
core_exceptions.DeadlineExceeded,
core_exceptions.ServiceUnavailable,
),
deadline=60.0,
),
default_timeout=60.0,
client_info=client_info,
),
self.decrypt: gapic_v1.method.wrap_method(
self.decrypt,
default_retry=retries.Retry(
initial=0.1,maximum=60.0,multiplier=1.3, predicate=retries.if_exception_type(
core_exceptions.DeadlineExceeded,
core_exceptions.ServiceUnavailable,
),
deadline=60.0,
),
default_timeout=60.0,
client_info=client_info,
),
self.asymmetric_sign: gapic_v1.method.wrap_method(
self.asymmetric_sign,
default_retry=retries.Retry(
initial=0.1,maximum=60.0,multiplier=1.3, predicate=retries.if_exception_type(
core_exceptions.DeadlineExceeded,
core_exceptions.ServiceUnavailable,
),
deadline=60.0,
),
default_timeout=60.0,
client_info=client_info,
),
self.asymmetric_decrypt: gapic_v1.method.wrap_method(
self.asymmetric_decrypt,
default_retry=retries.Retry(
initial=0.1,maximum=60.0,multiplier=1.3, predicate=retries.if_exception_type(
core_exceptions.DeadlineExceeded,
core_exceptions.ServiceUnavailable,
),
deadline=60.0,
),
default_timeout=60.0,
client_info=client_info,
),
self.mac_sign: gapic_v1.method.wrap_method(
self.mac_sign,
default_timeout=None,
client_info=client_info,
),
self.mac_verify: gapic_v1.method.wrap_method(
self.mac_verify,
default_timeout=None,
client_info=client_info,
),
self.generate_random_bytes: gapic_v1.method.wrap_method(
self.generate_random_bytes,
default_timeout=None,
client_info=client_info,
),
}
@property
def list_key_rings(self) -> Callable[
[service.ListKeyRingsRequest],
Union[
service.ListKeyRingsResponse,
Awaitable[service.ListKeyRingsResponse]
]]:
raise NotImplementedError()
@property
def list_crypto_keys(self) -> Callable[
[service.ListCryptoKeysRequest],
Union[
service.ListCryptoKeysResponse,
Awaitable[service.ListCryptoKeysResponse]
]]:
raise NotImplementedError()
@property
def list_crypto_key_versions(self) -> Callable[
[service.ListCryptoKeyVersionsRequest],
Union[
service.ListCryptoKeyVersionsResponse,
Awaitable[service.ListCryptoKeyVersionsResponse]
]]:
raise NotImplementedError()
@property
def list_import_jobs(self) -> Callable[
[service.ListImportJobsRequest],
Union[
service.ListImportJobsResponse,
Awaitable[service.ListImportJobsResponse]
]]:
raise NotImplementedError()
@property
def get_key_ring(self) -> Callable[
[service.GetKeyRingRequest],
Union[
resources.KeyRing,
Awaitable[resources.KeyRing]
]]:
raise NotImplementedError()
@property
def get_crypto_key(self) -> Callable[
[service.GetCryptoKeyRequest],
Union[
resources.CryptoKey,
Awaitable[resources.CryptoKey]
]]:
raise NotImplementedError()
@property
def get_crypto_key_version(self) -> Callable[
[service.GetCryptoKeyVersionRequest],
Union[
resources.CryptoKeyVersion,
Awaitable[resources.CryptoKeyVersion]
]]:
raise NotImplementedError()
@property
def get_public_key(self) -> Callable[
[service.GetPublicKeyRequest],
Union[
resources.PublicKey,
Awaitable[resources.PublicKey]
]]:
raise NotImplementedError()
@property
def get_import_job(self) -> Callable[
[service.GetImportJobRequest],
Union[
resources.ImportJob,
Awaitable[resources.ImportJob]
]]:
raise NotImplementedError()
@property
def create_key_ring(self) -> Callable[
[service.CreateKeyRingRequest],
Union[
resources.KeyRing,
Awaitable[resources.KeyRing]
]]:
raise NotImplementedError()
@property
def create_crypto_key(self) -> Callable[
[service.CreateCryptoKeyRequest],
Union[
resources.CryptoKey,
Awaitable[resources.CryptoKey]
]]:
raise NotImplementedError()
@property
def create_crypto_key_version(self) -> Callable[
[service.CreateCryptoKeyVersionRequest],
Union[
resources.CryptoKeyVersion,
Awaitable[resources.CryptoKeyVersion]
]]:
raise NotImplementedError()
@property
def import_crypto_key_version(self) -> Callable[
[service.ImportCryptoKeyVersionRequest],
Union[
resources.CryptoKeyVersion,
Awaitable[resources.CryptoKeyVersion]
]]:
raise NotImplementedError()
@property
def create_import_job(self) -> Callable[
[service.CreateImportJobRequest],
Union[
resources.ImportJob,
Awaitable[resources.ImportJob]
]]:
raise NotImplementedError()
@property
def update_crypto_key(self) -> Callable[
[service.UpdateCryptoKeyRequest],
Union[
resources.CryptoKey,
Awaitable[resources.CryptoKey]
]]:
raise NotImplementedError()
@property
def update_crypto_key_version(self) -> Callable[
[service.UpdateCryptoKeyVersionRequest],
Union[
resources.CryptoKeyVersion,
Awaitable[resources.CryptoKeyVersion]
]]:
raise NotImplementedError()
@property
def update_crypto_key_primary_version(self) -> Callable[
[service.UpdateCryptoKeyPrimaryVersionRequest],
Union[
resources.CryptoKey,
Awaitable[resources.CryptoKey]
]]:
raise NotImplementedError()
@property
def destroy_crypto_key_version(self) -> Callable[
[service.DestroyCryptoKeyVersionRequest],
Union[
resources.CryptoKeyVersion,
Awaitable[resources.CryptoKeyVersion]
]]:
raise NotImplementedError()
@property
def restore_crypto_key_version(self) -> Callable[
[service.RestoreCryptoKeyVersionRequest],
Union[
resources.CryptoKeyVersion,
Awaitable[resources.CryptoKeyVersion]
]]:
raise NotImplementedError()
@property
def encrypt(self) -> Callable[
[service.EncryptRequest],
Union[
service.EncryptResponse,
Awaitable[service.EncryptResponse]
]]:
raise NotImplementedError()
@property
def decrypt(self) -> Callable[
[service.DecryptRequest],
Union[
service.DecryptResponse,
Awaitable[service.DecryptResponse]
]]:
raise NotImplementedError()
@property
def asymmetric_sign(self) -> Callable[
[service.AsymmetricSignRequest],
Union[
service.AsymmetricSignResponse,
Awaitable[service.AsymmetricSignResponse]
]]:
raise NotImplementedError()
@property
def asymmetric_decrypt(self) -> Callable[
[service.AsymmetricDecryptRequest],
Union[
service.AsymmetricDecryptResponse,
Awaitable[service.AsymmetricDecryptResponse]
]]:
raise NotImplementedError()
@property
def mac_sign(self) -> Callable[
[service.MacSignRequest],
Union[
service.MacSignResponse,
Awaitable[service.MacSignResponse]
]]:
raise NotImplementedError()
@property
def mac_verify(self) -> Callable[
[service.MacVerifyRequest],
Union[
service.MacVerifyResponse,
Awaitable[service.MacVerifyResponse]
]]:
raise NotImplementedError()
@property
def generate_random_bytes(self) -> Callable[
[service.GenerateRandomBytesRequest],
Union[
service.GenerateRandomBytesResponse,
Awaitable[service.GenerateRandomBytesResponse]
]]:
raise NotImplementedError()
@property
def set_iam_policy(
self,
) -> Callable[
[iam_policy_pb2.SetIamPolicyRequest],
Union[policy_pb2.Policy, Awaitable[policy_pb2.Policy]],
]:
raise NotImplementedError()
@property
def get_iam_policy(
self,
) -> Callable[
[iam_policy_pb2.GetIamPolicyRequest],
Union[policy_pb2.Policy, Awaitable[policy_pb2.Policy]],
]:
raise NotImplementedError()
@property
def test_iam_permissions(
self,
) -> Callable[
[iam_policy_pb2.TestIamPermissionsRequest],
Union[
iam_policy_pb2.TestIamPermissionsResponse,
Awaitable[iam_policy_pb2.TestIamPermissionsResponse],
],
]:
raise NotImplementedError()
__all__ = (
'KeyManagementServiceTransport',
)
|
[
"bazel-bot-development[bot]@users.noreply.github.com"
] |
bazel-bot-development[bot]@users.noreply.github.com
|
ec49e6c91ca97068e5bb27f4a55e242b2c3c60c3
|
d8cbe9ce0469f72b8929af01538b6ceddff10a38
|
/tests/components/calendar/test_trigger.py
|
ac2547c81f72bf1d79b0d040948536d8a80702ac
|
[
"Apache-2.0"
] |
permissive
|
piitaya/home-assistant
|
9c1ba162dac9604e4d43e035e74bad7bba327f0b
|
48893738192431f96966998c4ff7a3723a2f8f4a
|
refs/heads/dev
| 2023-03-07T16:13:32.117970
| 2023-01-10T17:47:48
| 2023-01-10T17:47:48
| 172,578,293
| 3
| 1
|
Apache-2.0
| 2023-02-22T06:15:56
| 2019-02-25T20:19:40
|
Python
|
UTF-8
|
Python
| false
| false
| 21,468
|
py
|
"""Tests for the calendar automation.
The tests create calendar based automations, set up a fake set of calendar
events, then advance time to exercise that the automation is called. The
tests use a fixture that mocks out events returned by the calendar entity,
and create events using a relative time offset and then advance the clock
forward exercising the triggers.
"""
from __future__ import annotations
from collections.abc import Callable, Generator
import datetime
import logging
import secrets
from typing import Any
from unittest.mock import patch
import pytest
from homeassistant.components import calendar
import homeassistant.components.automation as automation
from homeassistant.components.calendar.trigger import EVENT_END, EVENT_START
from homeassistant.core import HomeAssistant
from homeassistant.setup import async_setup_component
import homeassistant.util.dt as dt_util
from tests.common import async_fire_time_changed, async_mock_service
_LOGGER = logging.getLogger(__name__)
CALENDAR_ENTITY_ID = "calendar.calendar_2"
CONFIG = {calendar.DOMAIN: {"platform": "demo"}}
TEST_AUTOMATION_ACTION = {
"service": "test.automation",
"data": {
"platform": "{{ trigger.platform }}",
"event": "{{ trigger.event }}",
"calendar_event": "{{ trigger.calendar_event }}",
},
}
# The trigger sets two alarms: One based on the next event and one
# to refresh the schedule. The test advances the time an arbitrary
# amount to trigger either type of event with a small jitter.
TEST_TIME_ADVANCE_INTERVAL = datetime.timedelta(minutes=1)
TEST_UPDATE_INTERVAL = datetime.timedelta(minutes=7)
class FakeSchedule:
"""Test fixture class for return events in a specific date range."""
def __init__(self, hass, freezer):
"""Initiailize FakeSchedule."""
self.hass = hass
self.freezer = freezer
# Map of event start time to event
self.events: list[calendar.CalendarEvent] = []
def create_event(
self,
start: datetime.timedelta,
end: datetime.timedelta,
summary: str | None = None,
description: str | None = None,
location: str | None = None,
) -> dict[str, Any]:
"""Create a new fake event, used by tests."""
event = calendar.CalendarEvent(
start=start,
end=end,
summary=summary if summary else f"Event {secrets.token_hex(16)}",
description=description,
location=location,
)
self.events.append(event)
return event.as_dict()
async def async_get_events(
self,
hass: HomeAssistant,
start_date: datetime.datetime,
end_date: datetime.datetime,
) -> list[calendar.CalendarEvent]:
"""Get all events in a specific time frame, used by the demo calendar."""
assert start_date < end_date
values = []
local_start_date = dt_util.as_local(start_date)
local_end_date = dt_util.as_local(end_date)
for event in self.events:
if (
event.start_datetime_local < local_end_date
and local_start_date < event.end_datetime_local
):
values.append(event)
return values
async def fire_time(self, trigger_time: datetime.datetime) -> None:
"""Fire an alarm and wait."""
_LOGGER.debug(f"Firing alarm @ {trigger_time}")
self.freezer.move_to(trigger_time)
async_fire_time_changed(self.hass, trigger_time)
await self.hass.async_block_till_done()
async def fire_until(self, end: datetime.timedelta) -> None:
"""Simulate the passage of time by firing alarms until the time is reached."""
current_time = dt_util.as_utc(self.freezer())
if (end - current_time) > (TEST_UPDATE_INTERVAL * 2):
# Jump ahead to right before the target alarm them to remove
# unnecessary waiting, before advancing in smaller increments below.
# This leaves time for multiple update intervals to refresh the set
# of upcoming events
await self.fire_time(end - TEST_UPDATE_INTERVAL * 2)
while dt_util.utcnow() < end:
self.freezer.tick(TEST_TIME_ADVANCE_INTERVAL)
await self.fire_time(dt_util.utcnow())
@pytest.fixture
def set_time_zone(hass):
"""Set the time zone for the tests."""
# Set our timezone to CST/Regina so we can check calculations
# This keeps UTC-6 all year round
hass.config.set_time_zone("America/Regina")
@pytest.fixture
def fake_schedule(hass, freezer):
"""Fixture that tests can use to make fake events."""
# Setup start time for all tests
freezer.move_to("2022-04-19 10:31:02+00:00")
schedule = FakeSchedule(hass, freezer)
with patch(
"homeassistant.components.demo.calendar.DemoCalendar.async_get_events",
new=schedule.async_get_events,
):
yield schedule
@pytest.fixture(autouse=True)
async def setup_calendar(hass: HomeAssistant, fake_schedule: FakeSchedule) -> None:
"""Initialize the demo calendar."""
assert await async_setup_component(hass, calendar.DOMAIN, CONFIG)
await hass.async_block_till_done()
async def create_automation(hass: HomeAssistant, event_type: str, offset=None) -> None:
"""Register an automation."""
trigger_data = {
"platform": calendar.DOMAIN,
"entity_id": CALENDAR_ENTITY_ID,
"event": event_type,
}
if offset:
trigger_data["offset"] = offset
assert await async_setup_component(
hass,
automation.DOMAIN,
{
automation.DOMAIN: {
"trigger": trigger_data,
"action": TEST_AUTOMATION_ACTION,
"mode": "queued",
}
},
)
await hass.async_block_till_done()
@pytest.fixture
def calls(hass: HomeAssistant) -> Callable[[], list]:
"""Fixture to return payload data for automation calls."""
service_calls = async_mock_service(hass, "test", "automation")
def get_trigger_data() -> list:
return [c.data for c in service_calls]
return get_trigger_data
@pytest.fixture(autouse=True)
def mock_update_interval() -> Generator[None, None, None]:
"""Fixture to override the update interval for refreshing events."""
with patch(
"homeassistant.components.calendar.trigger.UPDATE_INTERVAL",
new=TEST_UPDATE_INTERVAL,
):
yield
async def test_event_start_trigger(hass, calls, fake_schedule):
"""Test the a calendar trigger based on start time."""
event_data = fake_schedule.create_event(
start=datetime.datetime.fromisoformat("2022-04-19 11:00:00+00:00"),
end=datetime.datetime.fromisoformat("2022-04-19 11:30:00+00:00"),
)
await create_automation(hass, EVENT_START)
assert len(calls()) == 0
await fake_schedule.fire_until(
datetime.datetime.fromisoformat("2022-04-19 11:15:00+00:00"),
)
assert calls() == [
{
"platform": "calendar",
"event": EVENT_START,
"calendar_event": event_data,
}
]
@pytest.mark.parametrize(
"offset_str, offset_delta",
[
("-01:00", datetime.timedelta(hours=-1)),
("+01:00", datetime.timedelta(hours=1)),
],
)
async def test_event_start_trigger_with_offset(
hass, calls, fake_schedule, offset_str, offset_delta
):
"""Test the a calendar trigger based on start time with an offset."""
event_data = fake_schedule.create_event(
start=datetime.datetime.fromisoformat("2022-04-19 12:00:00+00:00"),
end=datetime.datetime.fromisoformat("2022-04-19 12:30:00+00:00"),
)
await create_automation(hass, EVENT_START, offset=offset_str)
# No calls yet
await fake_schedule.fire_until(
datetime.datetime.fromisoformat("2022-04-19 11:55:00+00:00") + offset_delta,
)
assert len(calls()) == 0
# Event has started w/ offset
await fake_schedule.fire_until(
datetime.datetime.fromisoformat("2022-04-19 12:05:00+00:00") + offset_delta,
)
assert calls() == [
{
"platform": "calendar",
"event": EVENT_START,
"calendar_event": event_data,
}
]
async def test_event_end_trigger(hass, calls, fake_schedule):
"""Test the a calendar trigger based on end time."""
event_data = fake_schedule.create_event(
start=datetime.datetime.fromisoformat("2022-04-19 11:00:00+00:00"),
end=datetime.datetime.fromisoformat("2022-04-19 12:00:00+00:00"),
)
await create_automation(hass, EVENT_END)
# Event started, nothing should fire yet
await fake_schedule.fire_until(
datetime.datetime.fromisoformat("2022-04-19 11:10:00+00:00")
)
assert len(calls()) == 0
# Event ends
await fake_schedule.fire_until(
datetime.datetime.fromisoformat("2022-04-19 12:10:00+00:00")
)
assert calls() == [
{
"platform": "calendar",
"event": EVENT_END,
"calendar_event": event_data,
}
]
@pytest.mark.parametrize(
"offset_str, offset_delta",
[
("-01:00", datetime.timedelta(hours=-1)),
("+01:00", datetime.timedelta(hours=1)),
],
)
async def test_event_end_trigger_with_offset(
hass, calls, fake_schedule, offset_str, offset_delta
):
"""Test the a calendar trigger based on end time with an offset."""
event_data = fake_schedule.create_event(
start=datetime.datetime.fromisoformat("2022-04-19 12:00:00+00:00"),
end=datetime.datetime.fromisoformat("2022-04-19 12:30:00+00:00"),
)
await create_automation(hass, EVENT_END, offset=offset_str)
# No calls yet
await fake_schedule.fire_until(
datetime.datetime.fromisoformat("2022-04-19 12:05:00+00:00") + offset_delta,
)
assert len(calls()) == 0
# Event has started w/ offset
await fake_schedule.fire_until(
datetime.datetime.fromisoformat("2022-04-19 12:35:00+00:00") + offset_delta,
)
assert calls() == [
{
"platform": "calendar",
"event": EVENT_END,
"calendar_event": event_data,
}
]
async def test_calendar_trigger_with_no_events(hass, calls, fake_schedule):
"""Test a calendar trigger setup with no events."""
await create_automation(hass, EVENT_START)
await create_automation(hass, EVENT_END)
# No calls, at arbitrary times
await fake_schedule.fire_until(
datetime.datetime.fromisoformat("2022-04-19 11:00:00+00:00")
)
assert len(calls()) == 0
async def test_multiple_start_events(hass, calls, fake_schedule):
"""Test that a trigger fires for multiple events."""
event_data1 = fake_schedule.create_event(
start=datetime.datetime.fromisoformat("2022-04-19 10:45:00+00:00"),
end=datetime.datetime.fromisoformat("2022-04-19 11:00:00+00:00"),
)
event_data2 = fake_schedule.create_event(
start=datetime.datetime.fromisoformat("2022-04-19 11:00:00+00:00"),
end=datetime.datetime.fromisoformat("2022-04-19 11:15:00+00:00"),
)
await create_automation(hass, EVENT_START)
await fake_schedule.fire_until(
datetime.datetime.fromisoformat("2022-04-19 11:30:00+00:00")
)
assert calls() == [
{
"platform": "calendar",
"event": EVENT_START,
"calendar_event": event_data1,
},
{
"platform": "calendar",
"event": EVENT_START,
"calendar_event": event_data2,
},
]
async def test_multiple_end_events(hass, calls, fake_schedule):
"""Test that a trigger fires for multiple events."""
event_data1 = fake_schedule.create_event(
start=datetime.datetime.fromisoformat("2022-04-19 10:45:00+00:00"),
end=datetime.datetime.fromisoformat("2022-04-19 11:00:00+00:00"),
)
event_data2 = fake_schedule.create_event(
start=datetime.datetime.fromisoformat("2022-04-19 11:00:00+00:00"),
end=datetime.datetime.fromisoformat("2022-04-19 11:15:00+00:00"),
)
await create_automation(hass, EVENT_END)
await fake_schedule.fire_until(
datetime.datetime.fromisoformat("2022-04-19 11:30:00+00:00")
)
assert calls() == [
{
"platform": "calendar",
"event": EVENT_END,
"calendar_event": event_data1,
},
{
"platform": "calendar",
"event": EVENT_END,
"calendar_event": event_data2,
},
]
async def test_multiple_events_sharing_start_time(hass, calls, fake_schedule):
"""Test that a trigger fires for every event sharing a start time."""
event_data1 = fake_schedule.create_event(
start=datetime.datetime.fromisoformat("2022-04-19 11:00:00+00:00"),
end=datetime.datetime.fromisoformat("2022-04-19 11:30:00+00:00"),
)
event_data2 = fake_schedule.create_event(
start=datetime.datetime.fromisoformat("2022-04-19 11:00:00+00:00"),
end=datetime.datetime.fromisoformat("2022-04-19 11:30:00+00:00"),
)
await create_automation(hass, EVENT_START)
await fake_schedule.fire_until(
datetime.datetime.fromisoformat("2022-04-19 11:35:00+00:00")
)
assert calls() == [
{
"platform": "calendar",
"event": EVENT_START,
"calendar_event": event_data1,
},
{
"platform": "calendar",
"event": EVENT_START,
"calendar_event": event_data2,
},
]
async def test_overlap_events(hass, calls, fake_schedule):
"""Test that a trigger fires for events that overlap."""
event_data1 = fake_schedule.create_event(
start=datetime.datetime.fromisoformat("2022-04-19 11:00:00+00:00"),
end=datetime.datetime.fromisoformat("2022-04-19 11:30:00+00:00"),
)
event_data2 = fake_schedule.create_event(
start=datetime.datetime.fromisoformat("2022-04-19 11:15:00+00:00"),
end=datetime.datetime.fromisoformat("2022-04-19 11:45:00+00:00"),
)
await create_automation(hass, EVENT_START)
await fake_schedule.fire_until(
datetime.datetime.fromisoformat("2022-04-19 11:20:00+00:00")
)
assert calls() == [
{
"platform": "calendar",
"event": EVENT_START,
"calendar_event": event_data1,
},
{
"platform": "calendar",
"event": EVENT_START,
"calendar_event": event_data2,
},
]
async def test_invalid_calendar_id(hass, caplog):
"""Test creating a trigger with an invalid calendar id."""
assert await async_setup_component(
hass,
automation.DOMAIN,
{
automation.DOMAIN: {
"action": TEST_AUTOMATION_ACTION,
"trigger": {
"platform": calendar.DOMAIN,
"entity_id": "invalid-calendar-id",
},
}
},
)
await hass.async_block_till_done()
assert "Entity ID invalid-calendar-id is an invalid entity ID" in caplog.text
async def test_legacy_entity_type(hass, caplog):
"""Test creating a trigger with an invalid calendar id."""
assert await async_setup_component(
hass,
automation.DOMAIN,
{
automation.DOMAIN: {
"action": TEST_AUTOMATION_ACTION,
"trigger": {
"platform": calendar.DOMAIN,
"entity_id": "calendar.calendar_3",
},
}
},
)
await hass.async_block_till_done()
assert "is not a calendar entity" in caplog.text
async def test_update_next_event(hass, calls, fake_schedule):
"""Test detection of a new event after initial trigger is setup."""
event_data1 = fake_schedule.create_event(
start=datetime.datetime.fromisoformat("2022-04-19 11:00:00+00:00"),
end=datetime.datetime.fromisoformat("2022-04-19 11:15:00+00:00"),
)
await create_automation(hass, EVENT_START)
# No calls before event start
await fake_schedule.fire_until(
datetime.datetime.fromisoformat("2022-04-19 10:45:00+00:00")
)
assert len(calls()) == 0
# Create a new event between now and when the event fires
event_data2 = fake_schedule.create_event(
start=datetime.datetime.fromisoformat("2022-04-19 10:55:00+00:00"),
end=datetime.datetime.fromisoformat("2022-04-19 11:05:00+00:00"),
)
# Advance past the end of the events
await fake_schedule.fire_until(
datetime.datetime.fromisoformat("2022-04-19 11:30:00+00:00")
)
assert calls() == [
{
"platform": "calendar",
"event": EVENT_START,
"calendar_event": event_data2,
},
{
"platform": "calendar",
"event": EVENT_START,
"calendar_event": event_data1,
},
]
async def test_update_missed(hass, calls, fake_schedule):
"""Test that new events are missed if they arrive outside the update interval."""
event_data1 = fake_schedule.create_event(
start=datetime.datetime.fromisoformat("2022-04-19 11:00:00+00:00"),
end=datetime.datetime.fromisoformat("2022-04-19 11:30:00+00:00"),
)
await create_automation(hass, EVENT_START)
# Events are refreshed at t+TEST_UPDATE_INTERVAL minutes. A new event is
# added, but the next update happens after the event is already over.
await fake_schedule.fire_until(
datetime.datetime.fromisoformat("2022-04-19 10:38:00+00:00")
)
assert len(calls()) == 0
fake_schedule.create_event(
start=datetime.datetime.fromisoformat("2022-04-19 10:40:00+00:00"),
end=datetime.datetime.fromisoformat("2022-04-19 10:55:00+00:00"),
)
# Only the first event is returned
await fake_schedule.fire_until(
datetime.datetime.fromisoformat("2022-04-19 11:05:00+00:00")
)
assert calls() == [
{
"platform": "calendar",
"event": EVENT_START,
"calendar_event": event_data1,
},
]
@pytest.mark.parametrize(
"create_data,fire_time,payload_data",
[
(
{
"start": datetime.datetime.fromisoformat("2022-04-19 11:00:00+00:00"),
"end": datetime.datetime.fromisoformat("2022-04-19 11:30:00+00:00"),
"summary": "Summary",
},
datetime.datetime.fromisoformat("2022-04-19 11:15:00+00:00"),
{
"summary": "Summary",
"start": "2022-04-19T11:00:00+00:00",
"end": "2022-04-19T11:30:00+00:00",
"all_day": False,
},
),
(
{
"start": datetime.datetime.fromisoformat("2022-04-19 11:00:00+00:00"),
"end": datetime.datetime.fromisoformat("2022-04-19 11:30:00+00:00"),
"summary": "Summary",
"description": "Description",
"location": "Location",
},
datetime.datetime.fromisoformat("2022-04-19 11:15:00+00:00"),
{
"summary": "Summary",
"start": "2022-04-19T11:00:00+00:00",
"end": "2022-04-19T11:30:00+00:00",
"all_day": False,
"description": "Description",
"location": "Location",
},
),
(
{
"summary": "Summary",
"start": datetime.date.fromisoformat("2022-04-20"),
"end": datetime.date.fromisoformat("2022-04-21"),
},
datetime.datetime.fromisoformat("2022-04-20 00:00:01-06:00"),
{
"summary": "Summary",
"start": "2022-04-20",
"end": "2022-04-21",
"all_day": True,
},
),
],
ids=["basic", "more-fields", "all-day"],
)
async def test_event_payload(
hass, calls, fake_schedule, set_time_zone, create_data, fire_time, payload_data
):
"""Test the fields in the calendar event payload are set."""
fake_schedule.create_event(**create_data)
await create_automation(hass, EVENT_START)
assert len(calls()) == 0
await fake_schedule.fire_until(fire_time)
assert calls() == [
{
"platform": "calendar",
"event": EVENT_START,
"calendar_event": payload_data,
}
]
async def test_trigger_timestamp_window_edge(hass, calls, fake_schedule, freezer):
"""Test that events in the edge of a scan are included."""
freezer.move_to("2022-04-19 11:00:00+00:00")
# Exactly at a TEST_UPDATE_INTERVAL boundary the start time,
# making this excluded from the first window.
event_data = fake_schedule.create_event(
start=datetime.datetime.fromisoformat("2022-04-19 11:14:00+00:00"),
end=datetime.datetime.fromisoformat("2022-04-19 11:30:00+00:00"),
)
await create_automation(hass, EVENT_START)
assert len(calls()) == 0
await fake_schedule.fire_until(
datetime.datetime.fromisoformat("2022-04-19 11:20:00+00:00")
)
assert calls() == [
{
"platform": "calendar",
"event": EVENT_START,
"calendar_event": event_data,
}
]
|
[
"noreply@github.com"
] |
piitaya.noreply@github.com
|
42de06cba2aa491aca4aeef891e131415fe780b7
|
0981c4973af3eda0e31c1af47e03d0133d1db43a
|
/base/sitemaps.py
|
ec929cc7331b712ef532cf6603346f17e0916179
|
[] |
no_license
|
kizashi7512/mysite_test
|
327b17f6143dca3ff322c35d26c041e28c0141ca
|
27a69e5a3039ad0aa91103bc7b2d5d9f52f8673c
|
refs/heads/master
| 2023-03-29T12:36:46.641648
| 2021-03-26T02:43:57
| 2021-03-26T02:43:57
| 351,262,050
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 467
|
py
|
from django.contrib.sitemaps import Sitemap
from django.shortcuts import resolve_url
class BaseSitemap(Sitemap):
def items(self):
items = [
'base:top',
'base:policy',
'base:terms',
]
return items
def location(self,obj):
return resolve_url(obj)
def changefreq(self, obj):
if obj == 'base:top':
return 'always'
return 'never'
def priority(self, obj):
if obj == 'base:top':
return 0.8
return 0.1
|
[
"kizashi3230@gmail.com"
] |
kizashi3230@gmail.com
|
6ec31c553f72c6a11ebe59ba3afd9595af33ffd4
|
65a60b14be349d4d8f6f493f87f6f2b4ac7f6cbb
|
/jmesh/models/transformer/external.py
|
d200fdfc08675ab4bd6a30ccfb477170acc75165
|
[
"MIT"
] |
permissive
|
Exusial/MWFormer
|
3729fee8ec085930d7a67161b968402a5db59b9e
|
008e5724c933cebc110ace9b3cdcab102ee011de
|
refs/heads/master
| 2023-07-13T10:54:01.295366
| 2023-07-10T03:10:52
| 2023-07-10T03:10:52
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 22,772
|
py
|
#coding=utf-8
import jittor as jt
from jittor import nn
from jittor.misc import _pair
from functools import partial
import math
import numpy as np
from .utils import trunc_normal,load_pretrained,_conv_filter
from .config import default_cfgs
class MLP(nn.Module):
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
super(MLP,self).__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = nn.Linear(in_features, hidden_features)
self.act = act_layer()
self.fc2 = nn.Linear(hidden_features, out_features)
self.drop = nn.Dropout(drop)
def execute(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.drop(x)
x = self.fc2(x)
x = self.drop(x)
return x
class Attention(nn.Module):
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
super().__init__()
self.num_heads = num_heads
assert dim % num_heads == 0
self.coef = 16
self.up_dim = 4
self.trans_dims = nn.Linear(dim, dim * self.up_dim)
# self.relu = nn.ReLU()
self.num_heads = self.num_heads * self.coef
self.k = 32
self.linear_0 = nn.Linear(dim * self.up_dim // self.num_heads, self.k)
self.linear_1 = nn.Linear(self.k, dim * self.up_dim // self.num_heads)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim * self.up_dim, dim)
self.relu = nn.ReLU()
self.proj_drop = nn.Dropout(proj_drop)
self.apply(self._init_weights)
def apply(self,fn):
for m in self.modules():
fn(m)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
m.weight = trunc_normal(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
def execute(self, x):
# print("init", x.shape)
B, N, C = x.shape
x = self.trans_dims(x) # B, N, C
# x = self.relu(x)
x = x.view(B, N * self.num_heads, -1)
# .reshape(B, N * self.num_heads, -1)
# .permute(0, 2, 1, 3)
# x = x.view(B, N, self.num_heads, -1).permute(0, 2, 1, 3)
attn = self.linear_0(x)
attn = attn.softmax(dim=-2)
attn = attn / ((1e-9 + attn.sum(dim=-1))).unsqueeze(-1)
# print("attn", attn.shape)
attn = self.attn_drop(attn)
# x = self.linear_1(attn).permute(0,2,1,3).reshape(B, N, -1)
x = self.linear_1(attn).reshape(B, N, -1)
# .permute(0,2,1,3).reshape(B, N, -1)
x = self.proj(x)
# print("output", x.shape)
x = self.proj_drop(x)
return x
class Attention2(nn.Module):
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
super(Attention,self).__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
# NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
self.scale = qk_scale or head_dim ** -0.5
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
def execute(self, x):
b,n,c = x.shape
qkv = self.qkv(x).reshape(b, n, 3, self.num_heads, c// self.num_heads).permute(2, 0, 3, 1, 4)
q,k,v = qkv[0],qkv[1],qkv[2]
# attn = nn.bmm(q,k.transpose(0,1,3,2))*self.scale
attn = nn.bmm_transpose(q, k)*self.scale
attn = nn.softmax(attn,dim=-1)
attn = self.attn_drop(attn)
out = nn.bmm(attn,v)
out = out.transpose(0,2,1,3).reshape(b,n,c)
out = self.proj(out)
out = self.proj_drop(out)
return out
class DropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
"""
def __init__(self, drop_prob=None):
super(DropPath, self).__init__()
self.drop_prob = drop_prob
def execute(self, x):
if self.drop_prob == 0. or not self.is_training():
return x
keep_prob = 1-self.drop_prob
shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
random_tensor = keep_prob + jt.random(shape, dtype=x.dtype)
random_tensor = jt.floor(random_tensor) # binarize
output = (x / keep_prob) * random_tensor
return output
class Block(nn.Module):
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
super(Block,self).__init__()
self.norm1 = norm_layer(dim)
self.attn = Attention(dim,
num_heads=num_heads,
qkv_bias=qkv_bias,
qk_scale=qk_scale,
attn_drop=attn_drop,
proj_drop=drop)
# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = MLP(in_features=dim,
hidden_features=mlp_hidden_dim,
act_layer=act_layer,
drop=drop)
def execute(self, x):
x = x + self.drop_path(self.attn(self.norm1(x)))
x = x + self.drop_path(self.mlp(self.norm2(x)))
return x
class PatchEmbed(nn.Module):
""" Image to Patch Embedding
"""
def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
super(PatchEmbed,self).__init__()
img_size = _pair(img_size)
patch_size = _pair(patch_size)
num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
self.img_size = img_size
self.patch_size = patch_size
self.num_patches = num_patches
self.proj = nn.Conv(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
def execute(self, x):
B, C, H, W = x.shape
# FIXME look at relaxing size constraints
assert H == self.img_size[0] and W == self.img_size[1], f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
x = self.proj(x).flatten(2).transpose(0, 2, 1)
return x
class HybridEmbed(nn.Module):
""" CNN Feature Map Embedding
Extract feature map from CNN, flatten, project to embedding dim.
"""
def __init__(self, backbone, img_size=224, feature_size=None, in_chans=3, embed_dim=768):
super(HybridEmbed,self).__init__()
assert isinstance(backbone, nn.Module)
img_size = _pair(img_size)
self.img_size = img_size
self.backbone = backbone
if feature_size is None:
with jt.no_grad():
# FIXME this is hacky, but most reliable way of determining the exact dim of the output feature
# map for all networks, the feature metadata has reliable channel and stride info, but using
# stride to calc feature dim requires info about padding of each stage that isn't captured.
training = backbone.is_training()
if training:
backbone.eval()
o = self.backbone(jt.zeros((1, in_chans, img_size[0], img_size[1])))[-1]
feature_size = o.shape[-2:]
feature_dim = o.shape[1]
backbone.train()
else:
feature_size = _pair(feature_size)
feature_dim = self.backbone.feature_info.channels()[-1]
self.num_patches = feature_size[0] * feature_size[1]
self.proj = nn.Linear(feature_dim, embed_dim)
def execute(self, x):
x = self.backbone(x)[-1]
x = x.flatten(2).transpose(0,2,1)
x = self.proj(x)
return x
class VisionTransformer(nn.Module):
""" Vision Transformer with support for patch or hybrid CNN input stage
"""
def __init__(self,
img_size=224,
patch_size=16,
in_chans=3,
num_classes=1000,
embed_dim=768,
depth=12,
num_heads=12,
mlp_ratio=4.,
qkv_bias=False,
qk_scale=None,
drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.,
hybrid_backbone=None,
norm_layer=nn.LayerNorm):
super(VisionTransformer,self).__init__()
if hybrid_backbone is not None:
self.patch_embed = HybridEmbed(hybrid_backbone, img_size=img_size, in_chans=in_chans, embed_dim=embed_dim)
else:
self.patch_embed = PatchEmbed(img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
num_patches = self.patch_embed.num_patches
self.cls_token = jt.zeros((1, 1, embed_dim))
self.pos_embed = jt.zeros((1, num_patches + 1, embed_dim))
self.pos_drop = nn.Dropout(drop_rate)
dpr = [x.item() for x in np.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
self.blocks = nn.ModuleList([
Block(
dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer)
for i in range(depth)])
self.norm = norm_layer(embed_dim)
# NOTE as per official impl, we could have a pre-logits representation dense layer + tanh here
#self.repr = nn.Linear(embed_dim, representation_size)
#self.repr_act = nn.Tanh()
# Classifier head
self.head = nn.Linear(embed_dim, num_classes)
self.pos_embed = trunc_normal(self.pos_embed, std=.02)
self.cls_token = trunc_normal(self.cls_token, std=.02)
self.apply(self._init_weights)
def apply(self,fn):
for m in self.modules():
fn(m)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
m.weight = trunc_normal(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
def execute(self, x):
B = x.shape[0]
x = self.patch_embed(x)
_,i,j = self.cls_token.shape
cls_tokens = self.cls_token.expand((B, i, j)) # stole cls_tokens impl from Phil Wang, thanks
x = jt.contrib.concat((cls_tokens, x), dim=1)
x = x + self.pos_embed
x = self.pos_drop(x)
for blk in self.blocks:
x = blk(x)
x = self.norm(x)
x = self.head(x[:, 0])
return x
class PatchTransformer(nn.Module):
""" Vision Transformer with support for patch or hybrid CNN input stage
"""
def __init__(self,
num_patches=256,
num_classes=30,
embed_dim=128,
depth=4,
num_heads=4,
mlp_ratio=4.,
qkv_bias=False,
qk_scale=None,
drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.,
hybrid_backbone=None,
norm_layer=nn.LayerNorm):
super(PatchTransformer,self).__init__()
self.cls_token = jt.zeros((1, 1, embed_dim))
# self.pos_embed = jt.zeros((1, num_patches + 1, embed_dim))
# self.pos_drop = nn.Dropout(drop_rate)
dpr = [x.item() for x in np.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
self.blocks = nn.ModuleList([
Block(
dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer)
for i in range(depth)])
self.norm = norm_layer(embed_dim)
# NOTE as per official impl, we could have a pre-logits representation dense layer + tanh here
#self.repr = nn.Linear(embed_dim, representation_size)
#self.repr_act = nn.Tanh()
# Classifier head
self.head = nn.Linear(embed_dim, num_classes)
# self.pos_embed = trunc_normal(self.pos_embed, std=.02)
self.cls_token = trunc_normal(self.cls_token, std=.02)
self.apply(self._init_weights)
def apply(self,fn):
for m in self.modules():
fn(m)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
m.weight = trunc_normal(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
def execute(self, x):
B = x.shape[0]
_,i,j = self.cls_token.shape
cls_tokens = self.cls_token.expand((B, i, j)) # stole cls_tokens impl from Phil Wang, thanks
x = jt.contrib.concat((cls_tokens, x), dim=1)
# x = x + self.pos_embed
# x = self.pos_drop(x)
for blk in self.blocks:
x = blk(x)
x = self.norm(x)
x = self.head(x[:, 0])
return x
class PatchHieTransformer(nn.Module):
""" Vision Transformer with support for patch or hybrid CNN input stage
"""
def __init__(self,
num_patches=256,
num_classes=30,
embed_dim=128,
depth=4,
num_heads=4,
mlp_ratio=4.,
qkv_bias=False,
qk_scale=None,
drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.,
hybrid_backbone=None,
norm_layer=nn.LayerNorm):
super(PatchHieTransformer,self).__init__()
# self.pos_embed = jt.zeros((1, num_patches + 1, embed_dim))
# self.pos_drop = nn.Dropout(drop_rate)
dpr = [x.item() for x in np.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
self.blocks = nn.ModuleList([
Block(
dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer)
for i in range(depth)])
self.norm = norm_layer(embed_dim)
# print(embed_dim, num_classes)
self.head = nn.Linear(embed_dim, num_classes)
self.apply(self._init_weights)
def apply(self,fn):
for m in self.modules():
fn(m)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
m.weight = trunc_normal(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
def execute(self, x):
# x = x + self.pos_embed
# x = self.pos_drop(x)
# print(x.shape)
for blk in self.blocks:
x = blk(x)
x = self.norm(x)
x = self.head(x)
return x
class PatchAvgTransformer(nn.Module):
""" Vision Transformer with support for patch or hybrid CNN input stage
"""
def __init__(self,
num_patches=256,
num_classes=30,
embed_dim=128,
depth=4,
num_heads=4,
mlp_ratio=4.,
qkv_bias=False,
qk_scale=None,
drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.,
hybrid_backbone=None,
norm_layer=nn.LayerNorm):
super(PatchAvgTransformer,self).__init__()
# self.pos_embed = jt.zeros((1, num_patches + 1, embed_dim))
# self.pos_drop = nn.Dropout(drop_rate)
dpr = [x.item() for x in np.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
self.blocks = nn.ModuleList([
Block(
dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer)
for i in range(depth)])
self.norm = norm_layer(embed_dim)
# NOTE as per official impl, we could have a pre-logits representation dense layer + tanh here
#self.repr = nn.Linear(embed_dim, representation_size)
#self.repr_act = nn.Tanh()
# Classifier head
self.head = nn.Linear(embed_dim, num_classes)
# self.pos_embed = trunc_normal(self.pos_embed, std=.02)
self.apply(self._init_weights)
def apply(self,fn):
for m in self.modules():
fn(m)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
m.weight = trunc_normal(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
def execute(self, x):
# x = x + self.pos_embed
# x = self.pos_drop(x)
for blk in self.blocks:
x = blk(x)
x = self.norm(x)
x = jt.mean(x, dim=1)
x = self.head(x)
return x
def vit_small_patch16_224(pretrained=False, **kwargs):
if pretrained:
# NOTE my scale was wrong for original weights, leaving this here until I have better ones for this model
kwargs.setdefault('qk_scale', 768 ** -0.5)
model = VisionTransformer(patch_size=16, embed_dim=768, depth=8, num_heads=8, mlp_ratio=3., **kwargs)
model.default_cfg = default_cfgs['vit_small_patch16_224']
if pretrained:
load_pretrained(
model, num_classes=kwargs.get('num_classes', 0), in_chans=kwargs.get('in_chans', 3), filter_fn=_conv_filter)
return model
def vit_base_patch16_224(pretrained=False, **kwargs):
if pretrained:
# NOTE my scale was wrong for original weights, leaving this here until I have better ones for this model
kwargs.setdefault('qk_scale', 768 ** -0.5)
model = VisionTransformer(patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, **kwargs)
model.default_cfg = default_cfgs['vit_base_patch16_224']
if pretrained:
load_pretrained(
model, num_classes=kwargs.get('num_classes', 0), in_chans=kwargs.get('in_chans', 3), filter_fn=_conv_filter)
return model
def vit_base_patch16_384(pretrained=False, **kwargs):
model = VisionTransformer(
img_size=384, patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
model.default_cfg = default_cfgs['vit_base_patch16_384']
if pretrained:
load_pretrained(
model, num_classes=kwargs.get('num_classes', 0), in_chans=kwargs.get('in_chans', 3))
return model
def vit_base_patch32_384(pretrained=False, **kwargs):
model = VisionTransformer(
img_size=384, patch_size=32, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
model.default_cfg = default_cfgs['vit_base_patch32_384']
if pretrained:
load_pretrained(
model, num_classes=kwargs.get('num_classes', 0), in_chans=kwargs.get('in_chans', 3))
return model
def vit_large_patch16_224(pretrained=False, **kwargs):
model = VisionTransformer(patch_size=16, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, **kwargs)
model.default_cfg = default_cfgs['vit_large_patch16_224']
return model
def vit_large_patch16_384(pretrained=False, **kwargs):
model = VisionTransformer(
img_size=384, patch_size=16, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
model.default_cfg = default_cfgs['vit_large_patch16_384']
if pretrained:
load_pretrained(
model, num_classes=kwargs.get('num_classes', 0), in_chans=kwargs.get('in_chans', 3))
return model
def vit_large_patch32_384(pretrained=False, **kwargs):
model = VisionTransformer(
img_size=384, patch_size=32, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
model.default_cfg = default_cfgs['vit_large_patch32_384']
if pretrained:
load_pretrained(
model, num_classes=kwargs.get('num_classes', 0), in_chans=kwargs.get('in_chans', 3))
return model
def vit_huge_patch16_224(pretrained=False, **kwargs):
model = VisionTransformer(patch_size=16, embed_dim=1280, depth=32, num_heads=16, mlp_ratio=4, **kwargs)
model.default_cfg = default_cfgs['vit_huge_patch16_224']
return model
def vit_huge_patch32_384(pretrained=False, **kwargs):
model = VisionTransformer(
img_size=384, patch_size=32, embed_dim=1280, depth=32, num_heads=16, mlp_ratio=4, **kwargs)
model.default_cfg = default_cfgs['vit_huge_patch32_384']
return model
MODELS = {
'vit_huge_patch32_384':vit_huge_patch32_384,
'vit_huge_patch16_224':vit_huge_patch16_224,
'vit_large_patch32_384':vit_large_patch32_384,
'vit_large_patch16_384':vit_large_patch16_384,
'vit_large_patch16_224':vit_large_patch16_224,
'vit_base_patch32_384':vit_base_patch32_384,
'vit_base_patch16_384':vit_base_patch16_384,
'vit_base_patch16_224':vit_base_patch16_224,
'vit_small_patch16_224':vit_small_patch16_224
}
def create_model(name,**kwargs):
assert name in MODELS.keys()
return MODELS[name](**kwargs)
|
[
"bilibili39@163.com"
] |
bilibili39@163.com
|
3a9bf2b914edde4e5c397c7319864fbf32311712
|
117f066c80f3863ebef74463292bca6444f9758a
|
/finnhub_swagger_api/finnhub_swagger_api/models/revenue_estimates_info.py
|
02eb5c15a1e32e1b17eb727157f4a1affeec2537
|
[] |
no_license
|
cottrell/notebooks
|
c6de3842cbaeb71457d270cbe6fabc8695a6ee1b
|
9eaf3d0500067fccb294d064ab78d7aaa03e8b4d
|
refs/heads/master
| 2023-08-09T22:41:01.996938
| 2023-08-04T22:41:51
| 2023-08-04T22:41:51
| 26,830,272
| 3
| 1
| null | 2023-03-04T03:58:03
| 2014-11-18T21:14:23
|
Python
|
UTF-8
|
Python
| false
| false
| 7,028
|
py
|
# coding: utf-8
"""
Finnhub API
No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen) # noqa: E501
OpenAPI spec version: 1.0.0
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
import pprint
import re # noqa: F401
import six
from finnhub_swagger_api.configuration import Configuration
class RevenueEstimatesInfo(object):
"""NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
"""
"""
Attributes:
swagger_types (dict): The key is attribute name
and the value is attribute type.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
"""
swagger_types = {
'revenue_avg': 'float',
'revenue_high': 'float',
'revenue_low': 'float',
'number_analysts': 'int',
'period': 'date'
}
attribute_map = {
'revenue_avg': 'revenueAvg',
'revenue_high': 'revenueHigh',
'revenue_low': 'revenueLow',
'number_analysts': 'numberAnalysts',
'period': 'period'
}
def __init__(self, revenue_avg=None, revenue_high=None, revenue_low=None, number_analysts=None, period=None, _configuration=None): # noqa: E501
"""RevenueEstimatesInfo - a model defined in Swagger""" # noqa: E501
if _configuration is None:
_configuration = Configuration()
self._configuration = _configuration
self._revenue_avg = None
self._revenue_high = None
self._revenue_low = None
self._number_analysts = None
self._period = None
self.discriminator = None
if revenue_avg is not None:
self.revenue_avg = revenue_avg
if revenue_high is not None:
self.revenue_high = revenue_high
if revenue_low is not None:
self.revenue_low = revenue_low
if number_analysts is not None:
self.number_analysts = number_analysts
if period is not None:
self.period = period
@property
def revenue_avg(self):
"""Gets the revenue_avg of this RevenueEstimatesInfo. # noqa: E501
Average revenue estimates including Finnhub's proprietary estimates. # noqa: E501
:return: The revenue_avg of this RevenueEstimatesInfo. # noqa: E501
:rtype: float
"""
return self._revenue_avg
@revenue_avg.setter
def revenue_avg(self, revenue_avg):
"""Sets the revenue_avg of this RevenueEstimatesInfo.
Average revenue estimates including Finnhub's proprietary estimates. # noqa: E501
:param revenue_avg: The revenue_avg of this RevenueEstimatesInfo. # noqa: E501
:type: float
"""
self._revenue_avg = revenue_avg
@property
def revenue_high(self):
"""Gets the revenue_high of this RevenueEstimatesInfo. # noqa: E501
Highest estimate. # noqa: E501
:return: The revenue_high of this RevenueEstimatesInfo. # noqa: E501
:rtype: float
"""
return self._revenue_high
@revenue_high.setter
def revenue_high(self, revenue_high):
"""Sets the revenue_high of this RevenueEstimatesInfo.
Highest estimate. # noqa: E501
:param revenue_high: The revenue_high of this RevenueEstimatesInfo. # noqa: E501
:type: float
"""
self._revenue_high = revenue_high
@property
def revenue_low(self):
"""Gets the revenue_low of this RevenueEstimatesInfo. # noqa: E501
Lowest estimate. # noqa: E501
:return: The revenue_low of this RevenueEstimatesInfo. # noqa: E501
:rtype: float
"""
return self._revenue_low
@revenue_low.setter
def revenue_low(self, revenue_low):
"""Sets the revenue_low of this RevenueEstimatesInfo.
Lowest estimate. # noqa: E501
:param revenue_low: The revenue_low of this RevenueEstimatesInfo. # noqa: E501
:type: float
"""
self._revenue_low = revenue_low
@property
def number_analysts(self):
"""Gets the number_analysts of this RevenueEstimatesInfo. # noqa: E501
Number of Analysts. # noqa: E501
:return: The number_analysts of this RevenueEstimatesInfo. # noqa: E501
:rtype: int
"""
return self._number_analysts
@number_analysts.setter
def number_analysts(self, number_analysts):
"""Sets the number_analysts of this RevenueEstimatesInfo.
Number of Analysts. # noqa: E501
:param number_analysts: The number_analysts of this RevenueEstimatesInfo. # noqa: E501
:type: int
"""
self._number_analysts = number_analysts
@property
def period(self):
"""Gets the period of this RevenueEstimatesInfo. # noqa: E501
Period. # noqa: E501
:return: The period of this RevenueEstimatesInfo. # noqa: E501
:rtype: date
"""
return self._period
@period.setter
def period(self, period):
"""Sets the period of this RevenueEstimatesInfo.
Period. # noqa: E501
:param period: The period of this RevenueEstimatesInfo. # noqa: E501
:type: date
"""
self._period = period
def to_dict(self):
"""Returns the model properties as a dict"""
result = {}
for attr, _ in six.iteritems(self.swagger_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
))
elif hasattr(value, "to_dict"):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map(
lambda item: (item[0], item[1].to_dict())
if hasattr(item[1], "to_dict") else item,
value.items()
))
else:
result[attr] = value
if issubclass(RevenueEstimatesInfo, dict):
for key, value in self.items():
result[key] = value
return result
def to_str(self):
"""Returns the string representation of the model"""
return pprint.pformat(self.to_dict())
def __repr__(self):
"""For `print` and `pprint`"""
return self.to_str()
def __eq__(self, other):
"""Returns true if both objects are equal"""
if not isinstance(other, RevenueEstimatesInfo):
return False
return self.to_dict() == other.to_dict()
def __ne__(self, other):
"""Returns true if both objects are not equal"""
if not isinstance(other, RevenueEstimatesInfo):
return True
return self.to_dict() != other.to_dict()
|
[
"cottrell@users.noreply.github.com"
] |
cottrell@users.noreply.github.com
|
d39520befb46e5c0ba5180a6c69f1a0df8f3b5c0
|
2aea5f0c91922b3686eaa9fb14d0c8675c080c98
|
/node_modules/mongoose/node_modules/mongodb/node_modules/bson/build/config.gypi
|
24638528d9d329e923b933e99e31de8b4471c3a0
|
[
"MIT",
"Apache-2.0"
] |
permissive
|
Earlvin/GEEC
|
91cdd5772c6851e7fe2f8f8564cd6505ea3edf72
|
b59d31ca3a18b486f85f68397c4de0ee92b5e852
|
refs/heads/master
| 2021-01-15T13:24:46.089488
| 2014-12-10T00:51:13
| 2014-12-10T00:51:13
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 3,247
|
gypi
|
# Do not edit. File was generated by node-gyp's "configure" step
{
"target_defaults": {
"cflags": [],
"default_configuration": "Release",
"defines": [],
"include_dirs": [],
"libraries": []
},
"variables": {
"clang": 1,
"host_arch": "x64",
"node_install_npm": "false",
"node_prefix": "/usr/local/Cellar/node/0.10.33",
"node_shared_cares": "false",
"node_shared_http_parser": "false",
"node_shared_libuv": "false",
"node_shared_openssl": "false",
"node_shared_v8": "false",
"node_shared_zlib": "false",
"node_tag": "",
"node_unsafe_optimizations": 0,
"node_use_dtrace": "true",
"node_use_etw": "false",
"node_use_openssl": "true",
"node_use_perfctr": "false",
"openssl_no_asm": 0,
"python": "/usr/local/opt/python/bin/python2.7",
"target_arch": "x64",
"v8_enable_gdbjit": 0,
"v8_no_strict_aliasing": 1,
"v8_use_snapshot": "true",
"want_separate_host_toolset": 0,
"nodedir": "/Users/EricManansala/.node-gyp/0.10.33",
"copy_dev_lib": "true",
"standalone_static_library": 1,
"save_dev": "",
"browser": "",
"viewer": "man",
"rollback": "true",
"usage": "",
"globalignorefile": "/usr/local/etc/npmignore",
"init_author_url": "",
"shell": "/bin/bash",
"parseable": "",
"shrinkwrap": "true",
"email": "",
"init_license": "ISC",
"cache_max": "Infinity",
"init_author_email": "",
"sign_git_tag": "",
"cert": "",
"git_tag_version": "true",
"local_address": "",
"long": "",
"registry": "https://registry.npmjs.org/",
"fetch_retries": "2",
"npat": "",
"key": "",
"message": "%s",
"versions": "",
"globalconfig": "/usr/local/etc/npmrc",
"always_auth": "",
"spin": "true",
"cache_lock_retries": "10",
"cafile": "",
"heading": "npm",
"fetch_retry_mintimeout": "10000",
"proprietary_attribs": "true",
"json": "",
"description": "true",
"engine_strict": "",
"https_proxy": "",
"init_module": "/Users/EricManansala/.npm-init.js",
"userconfig": "/Users/EricManansala/.npmrc",
"node_version": "0.10.33",
"user": "",
"save": "true",
"editor": "vi",
"tag": "latest",
"global": "",
"optional": "true",
"username": "",
"bin_links": "true",
"force": "",
"searchopts": "",
"depth": "Infinity",
"rebuild_bundle": "true",
"searchsort": "name",
"unicode": "true",
"fetch_retry_maxtimeout": "60000",
"ca": "",
"save_prefix": "^",
"strict_ssl": "true",
"dev": "",
"fetch_retry_factor": "10",
"group": "20",
"save_exact": "",
"cache_lock_stale": "60000",
"version": "",
"cache_min": "10",
"cache": "/Users/EricManansala/.npm",
"searchexclude": "",
"color": "true",
"save_optional": "",
"user_agent": "npm/1.4.28 node/v0.10.33 darwin x64",
"ignore_scripts": "",
"cache_lock_wait": "10000",
"production": "",
"save_bundle": "",
"umask": "18",
"git": "git",
"init_author_name": "",
"onload_script": "",
"tmp": "/var/folders/2s/vvy52tqn0mzbrcrnt8qngbgh0000gn/T",
"unsafe_perm": "true",
"link": "",
"prefix": "/usr/local"
}
}
|
[
"ejmanansala@gmail.com"
] |
ejmanansala@gmail.com
|
210e5ddca76bde20cfc78dc0b2d86fc7fb822f00
|
1170dd002a501a15f51c1282638b4d2146cab6b6
|
/roboarchsim/src/roboarchsim/sensors/probeviz.py
|
a827522e9c771446bfacd0b3344f5aa2ce868992
|
[] |
no_license
|
kaosbeat/roboarch
|
7f9ce45c9d51e6f172ee5673b83430cbc1571a4b
|
010d6b7777fd3843da8b7b325dcc8552e500a103
|
refs/heads/master
| 2020-03-17T19:31:58.999814
| 2018-05-21T07:49:03
| 2018-05-21T07:49:03
| 133,867,312
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 2,991
|
py
|
import logging; logger = logging.getLogger("morse." + __name__)
import morse.core.sensor
import math
from roboarchsim.builder.sensors import perlin
# import perlin
from morse.core.services import service, async_service
from morse.core import status
from morse.helpers.components import add_data, add_property
class Probeviz(morse.core.sensor.Sensor):
"""Write here the general documentation of your sensor.
It will appear in the generated online documentation.
"""
_name = "Probeviz"
_short_desc = "visualiseses probe vector"
# define here the data fields exported by your sensor
# format is: field name, default initial value, type, description
add_data('probevalue', 0.0, 'int', 'random testdata')
add_data('distance', 0.0, 'float', 'Distance from origin in meters')
add_data('x', 0.0, 'float', 'xpos')
add_data('y', 0.0, 'float', 'ypos')
add_data('z', 0.0, 'float', 'zpos')
add_data('color', 'none', 'str', 'A dummy colorimeter, for testing purposes. Default to \'none\'.')
def __init__(self, obj, parent=None):
logger.info("%s initialization" % obj.name)
# Call the constructor of the parent class
morse.core.sensor.Sensor.__init__(self, obj, parent)
# Do here sensor specific initializations
self._distance = 0 # dummy internal variable, for testing purposes
logger.info('probeviz component initialized')
self._step = 0
self._pnf = perlin.PerlinNoiseFactory(1,2)
self._pnf2 = perlin.PerlinNoiseFactory(2,2)
@service
def get_current_distance(self):
""" This is a sample (blocking) service (use 'async_service' decorator
for non-blocking ones).
Simply returns the value of the internal counter.
You can access it as a RPC service from clients.
"""
logger.info("%s is %sm away" % (self.name, self.local_data['distance']))
return self.local_data['distance']
def default_action(self):
""" Main loop of the sensor.
Implements the component behaviour
"""
self._step = self._step + 1
import random
# implement here the behaviour of your sensor
# self.local_data['probevalue'] = random.randint(0, 1024)
# self.local_data['probevalue'] = 512 + 512*self._pnf(self._step/1000)
self.local_data['probevalue'] = 450 + 512*self._pnf2(self.position_3d.x/3, self.position_3d.y/2)
# self.local_data['probevalue'] = self._pnf(self._step/1000)
self.local_data['distance'] = math.sqrt(pow(self.position_3d.x, 2) + pow(self.position_3d.y, 2) + pow(self.position_3d.z, 2))
self.local_data['x'] = self.position_3d.x
self.local_data['y'] = self.position_3d.y
self.local_data['z'] = self.position_3d.z
# our test sensor sees a random color
self.local_data['color'] = random.choice(["blue", "red", "green", "yellow"])
def reset_step(self):
self._step = 0;
|
[
"kasper.jordaens@gmail.com"
] |
kasper.jordaens@gmail.com
|
26ec2100442d4be7cb84f871f4af39f81f332470
|
056f10d9f99506bb9b5abf7e91633f3ad0c76061
|
/CountCSVRows.py
|
f31ac1a85a8c869736b03a67223274ff65e3ce66
|
[] |
no_license
|
taers232c/GAM-Scripts3
|
5f171b620b2ac19514ab7198e39720f59a60ba9e
|
a59c5adb7b03b6bc9a4e054b9b41eabae2779f13
|
refs/heads/master
| 2023-08-31T06:43:57.645295
| 2023-08-22T17:32:21
| 2023-08-22T17:32:21
| 108,921,186
| 176
| 46
| null | 2023-02-28T15:52:32
| 2017-10-30T23:48:44
|
Python
|
UTF-8
|
Python
| false
| false
| 573
|
py
|
#!/usr/bin/env python3
"""
# Purpose: Count rows in a CSV file
#
# Python: Use python or python3 below as appropriate to your system; verify that you have version 3
# $ python -V or python3 -V
# Python 3.x.y
# Usage:
# python3 CountCSVRows.py File.csv
#
"""
import csv
import sys
QUOTE_CHAR = '"' # Adjust as needed
if sys.argv[1] != '-':
inputFile = open(sys.argv[1], 'r', encoding='utf-8')
else:
inputFile = sys.stdin
rows = 0
for row in csv.DictReader(inputFile, quotechar=QUOTE_CHAR):
rows += 1
print(rows)
if inputFile != sys.stdin:
inputFile.close()
|
[
"ross.scroggs@gmail.com"
] |
ross.scroggs@gmail.com
|
f2b5f59d3c117070dd065713dd8377902588a840
|
a535d7f6c873b0701507f3ba2286ad9d17485811
|
/test_yuk.py
|
c874ec5b211636a508d6e3d163be589de10ae32e
|
[
"MIT"
] |
permissive
|
okken/pytest-yuk
|
5ce3747b69fb82dcdc566330e9f44d9c6aeae223
|
36ec350d7e2d753ede1f89226d5e83baa5abe075
|
refs/heads/main
| 2023-03-26T14:31:32.380813
| 2021-03-26T21:19:14
| 2021-03-26T21:19:14
| 351,911,575
| 5
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 210
|
py
|
import pytest
@pytest.mark.yuk
def test_pass():
assert 1 == 1
@pytest.mark.yuk
def test_fail():
assert 1 == 2
def test_pass_unmarked():
assert 1 == 1
def test_fail_unmarked():
assert 1 == 2
|
[
"1568356+okken@users.noreply.github.com"
] |
1568356+okken@users.noreply.github.com
|
658779025acf197840a145bf6b531d133d94f75b
|
f0d864a4a26ab5a95462df0f1016039f1ba69895
|
/tests/test_equal_air_temperature.py
|
3d9827fc4209359847c1cad74f7fc40ba93fcd1c
|
[
"MIT"
] |
permissive
|
ThomasSchuetz/ThermalBuildingModel
|
7cd6f4007fc0e67ab7007392b8c71bc5aaa32546
|
f1a9417451374ac5632980df62f859a51e91851a
|
refs/heads/master
| 2022-12-19T13:24:02.118303
| 2020-09-25T07:43:01
| 2020-09-25T07:43:01
| 297,264,852
| 0
| 1
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,881
|
py
|
# -*- coding: utf-8 -*-
import numpy as np
import pytest
from thermal_building_model.eqAirTemp import equal_air_temp
class TestEqualAirTemperature:
def test_equal_air_temperature_case_08(self):
t_outside_raw = np.loadtxt("inputs/case08_t_amb.csv", delimiter=",")
t_outside = np.array([t_outside_raw[2*i,1] for i in range(24)])
q_sol_rad_win_raw = np.loadtxt("inputs/case08_q_sol_win.csv", usecols=(1,2))
solar_radiation_windows = q_sol_rad_win_raw[0:24,:]
sunblind_in = np.zeros_like(solar_radiation_windows)
sunblind_in[solar_radiation_windows > 100] = 0.85
q_sol_rad_wall_raw = np.loadtxt("inputs/case08_q_sol_wall.csv", usecols=(1,2))
solar_radiation_walls = q_sol_rad_wall_raw[0:24,:]
t_black_sky = np.zeros_like(t_outside) + 273.15
params = {"aExt": 0.7,
"eExt": 0.9,
"wfWall": [0.05796831135677373, 0.13249899738691134],
"wfWin": [0.4047663456281575, 0.4047663456281575],
"wfGro": 0,
"T_Gro": 273.15+12,
"alpha_wall_out": 20,
"alpha_rad_wall": 5,
"withLongwave": False}
t_equal_air = equal_air_temp(solar_radiation_walls, t_black_sky, t_outside, sunblind_in, params)
expected_t_equal_air = [
291.95, 290.25, 289.65, 289.25, 289.77, 291.24, 293.88, 296.64,
298.94, 301.10, 302.68, 303.68, 305.13, 306.38, 307.16, 307.20,
306.57, 305.10, 302.75, 300.15, 297.85, 296.05, 295.05, 294.05
]
for result, expected_result in zip(t_equal_air, expected_t_equal_air):
assert result == pytest.approx(expected_result, 0.01), (
f"Expected {expected_result} but actual result is {result}")
|
[
"thomas.schuetz@eon.com"
] |
thomas.schuetz@eon.com
|
7c4856b94c048615d4958703b69db3191a928ddf
|
d7195e61bc37f6b90c8bc2d6f164e5e7da98aa77
|
/landlab/grid/linkstatus.py
|
6eb74a1aadecb3b7f83bdb0915c210dc93491ae0
|
[
"MIT"
] |
permissive
|
joeljgeo/landlab
|
ffaae36b3ad3c5e1377355427bc9cfbb21074f01
|
1d2651c76a8a36a7a132f139638192df1823f8fb
|
refs/heads/master
| 2020-04-05T01:38:11.870170
| 2018-11-09T16:44:31
| 2018-11-09T16:44:31
| 156,443,219
| 0
| 0
|
MIT
| 2018-11-09T16:44:32
| 2018-11-06T20:26:54
|
Python
|
UTF-8
|
Python
| false
| false
| 5,415
|
py
|
#! /usr/bin/env python
import numpy as np
from .nodestatus import (CLOSED_BOUNDARY, CORE_NODE, FIXED_GRADIENT_BOUNDARY,
FIXED_VALUE_BOUNDARY)
from ..utils.decorators import (cache_result_in_object,
make_return_array_immutable)
# Define the link types
#: Indicates a link is *active*, and can carry flux
ACTIVE_LINK = 0
#: Indicates a link has a fixed (gradient) value, & behaves as a boundary
FIXED_LINK = 2
#: Indicates a link is *inactive*, and cannot carry flux
INACTIVE_LINK = 4
LINK_STATUS_FLAGS_LIST = [
ACTIVE_LINK,
FIXED_LINK,
INACTIVE_LINK,
]
LINK_STATUS_FLAGS = set(LINK_STATUS_FLAGS_LIST)
def is_fixed_link(node_status_at_link):
"""Find links that are fixed.
A link is fixed if it connects a core node with a fixed value
boundary node.
Parameters
----------
node_status_at_link : ndarray of int, shape `(n_links, 2)`
Node status a link tail and head.
Returns
-------
ndarray of bool, shape `(n_links, )`
True if link is fixed.
Examples
--------
>>> from landlab.grid.diagonals import is_fixed_link
>>> from landlab import CORE_NODE, FIXED_GRADIENT_BOUNDARY
>>> is_fixed_link([CORE_NODE, FIXED_GRADIENT_BOUNDARY])
array([ True], dtype=bool)
>>> from landlab import FIXED_VALUE_BOUNDARY
>>> is_fixed_link([CORE_NODE, FIXED_VALUE_BOUNDARY])
array([False], dtype=bool)
>>> is_fixed_link([[FIXED_GRADIENT_BOUNDARY, CORE_NODE],
... [CORE_NODE, CORE_NODE]])
array([ True, False], dtype=bool)
"""
node_status_at_link = np.asarray(node_status_at_link).reshape((-1, 2))
is_core_node = node_status_at_link == CORE_NODE
is_fixed_gradient_node = node_status_at_link == FIXED_GRADIENT_BOUNDARY
return ((is_core_node[:, 0] & is_fixed_gradient_node[:, 1]) |
(is_fixed_gradient_node[:, 0] & is_core_node[:, 1]))
def is_inactive_link(node_status_at_link):
"""Find links that are inactive.
A link is inactive if it connects two boundary nodes or one of
its nodes is closed.
Parameters
----------
node_status_at_link : ndarray of int, shape `(n_links, 2)`
Node status a link tail and head.
Returns
-------
ndarray of bool, shape `(n_links, )`
True if link is isactive.
Examples
--------
>>> from landlab.grid.diagonals import is_inactive_link
>>> from landlab import CORE_NODE, FIXED_GRADIENT_BOUNDARY
>>> is_inactive_link([CORE_NODE, CLOSED_BOUNDARY])
array([ True], dtype=bool)
>>> from landlab import FIXED_VALUE_BOUNDARY
>>> is_inactive_link([FIXED_GRADIENT_BOUNDARY, FIXED_VALUE_BOUNDARY])
array([ True], dtype=bool)
>>> is_inactive_link([[FIXED_GRADIENT_BOUNDARY, CLOSED_BOUNDARY],
... [CORE_NODE, CORE_NODE]])
array([ True, False], dtype=bool)
"""
node_status_at_link = np.asarray(node_status_at_link).reshape((-1, 2))
is_core = node_status_at_link == CORE_NODE
is_fixed_value = node_status_at_link == FIXED_VALUE_BOUNDARY
is_fixed_gradient = node_status_at_link == FIXED_GRADIENT_BOUNDARY
is_closed = node_status_at_link == CLOSED_BOUNDARY
is_boundary_node = is_fixed_value | is_fixed_gradient | is_closed
return ((is_boundary_node[:, 0] & is_boundary_node[:, 1]) |
(is_closed[:, 0] & is_core[:, 1]) |
(is_core[:, 0] & is_closed[:, 1]))
def is_active_link(node_status_at_link):
"""Find links that are active.
A link is active if it connects a core node with another core
node or a fixed value boundary.
Parameters
----------
node_status_at_link : ndarray of int, shape `(n_links, 2)`
Node status a link tail and head.
Returns
-------
ndarray of bool, shape `(n_links, )`
True if link is isactive.
Examples
--------
>>> from landlab.grid.diagonals import is_active_link
>>> from landlab import CORE_NODE, FIXED_GRADIENT_BOUNDARY
>>> is_active_link([CORE_NODE, FIXED_GRADIENT_BOUNDARY])
array([False], dtype=bool)
>>> from landlab import FIXED_VALUE_BOUNDARY
>>> is_active_link([CORE_NODE, FIXED_VALUE_BOUNDARY])
array([ True], dtype=bool)
>>> is_active_link([[FIXED_GRADIENT_BOUNDARY, CORE_NODE],
... [CORE_NODE, CORE_NODE]])
array([False, True], dtype=bool)
"""
node_status_at_link = np.asarray(node_status_at_link).reshape((-1, 2))
is_core_node = node_status_at_link == CORE_NODE
is_fixed_value_node = node_status_at_link == FIXED_VALUE_BOUNDARY
return (
(is_core_node[:, 0] & is_core_node[:, 1]) |
(is_core_node[:, 0] & is_fixed_value_node[:, 1]) |
(is_fixed_value_node[:, 0] & is_core_node[:, 1])
)
def set_status_at_link(node_status_at_link, out=None):
n_links = len(node_status_at_link)
if out is None:
out = np.full(n_links, 255, dtype=np.uint8)
_is_fixed_link = is_fixed_link(node_status_at_link)
_is_active_link = is_active_link(node_status_at_link)
_is_inactive_link = is_inactive_link(node_status_at_link)
assert np.all(np.sum(np.vstack((_is_active_link, _is_inactive_link,
_is_fixed_link)), axis=0) == 1)
out[_is_inactive_link] = INACTIVE_LINK
out[_is_active_link] = ACTIVE_LINK
out[_is_fixed_link] = FIXED_LINK
return out
|
[
"mcflugen@gmail.com"
] |
mcflugen@gmail.com
|
6d8323e3ea02352d65d2f5f99110a013ddd2cc3d
|
1348885ccdebfcb6010a267a3440a4ccc64373d1
|
/Examples/IPlugSideChain/scripts/update_installer_version.py
|
d4c3a9886d1d11e75b3572f01e371d4ebdeff671
|
[
"LicenseRef-scancode-other-permissive",
"LicenseRef-scancode-unknown-license-reference"
] |
permissive
|
ddf/iPlug2
|
c6565343def57dbf063fefb3b875c6337d363081
|
d05d20929544b06500369208b9ec81a62eb191fb
|
refs/heads/master
| 2022-11-02T04:39:45.019866
| 2022-10-10T17:15:04
| 2022-10-10T17:15:04
| 170,179,953
| 2
| 0
|
NOASSERTION
| 2019-02-11T18:30:30
| 2019-02-11T18:30:30
| null |
UTF-8
|
Python
| false
| false
| 3,091
|
py
|
#!/usr/bin/python3
# this script will update the versions in packages and innosetup installer files to match that in config.h
import plistlib, os, datetime, fileinput, glob, sys, string
scriptpath = os.path.dirname(os.path.realpath(__file__))
projectpath = os.path.abspath(os.path.join(scriptpath, os.pardir))
IPLUG2_ROOT = "../../.."
sys.path.insert(0, os.path.join(os.getcwd(), IPLUG2_ROOT + '/Scripts'))
from parse_config import parse_config
def replacestrs(filename, s, r):
files = glob.glob(filename)
for line in fileinput.input(files,inplace=1):
string.find(line, s)
line = line.replace(s, r)
sys.stdout.write(line)
def main():
demo = 0
if len(sys.argv) != 2:
print("Usage: update_installer_version.py demo(0 or 1)")
sys.exit(1)
else:
demo=int(sys.argv[1])
config = parse_config(projectpath)
# MAC INSTALLER
print("Updating Mac Installer version info...")
plistpath = projectpath + "/installer/" + config['BUNDLE_NAME'] + ".pkgproj"
with open(plistpath, 'rb') as fp:
installer = plistlib.load(fp)
# range = number of items in the installer (VST 2, VST 3, app, audiounit, aax)
for x in range(0,5):
installer['PACKAGES'][x]['PACKAGE_SETTINGS']['VERSION'] = config['FULL_VER_STR']
if demo:
installer['PROJECT']['PROJECT_PRESENTATION']['TITLE']['LOCALIZATIONS'][0]['VALUE'] = config['BUNDLE_NAME'] + " Demo"
installer['PROJECT']['PROJECT_PRESENTATION']['INTRODUCTION']['LOCALIZATIONS'][0]['VALUE']['PATH'] = "intro-demo.rtf"
else:
installer['PROJECT']['PROJECT_PRESENTATION']['TITLE']['LOCALIZATIONS'][0]['VALUE'] = config['BUNDLE_NAME']
installer['PROJECT']['PROJECT_PRESENTATION']['INTRODUCTION']['LOCALIZATIONS'][0]['VALUE']['PATH'] = "intro.rtf"
with open(plistpath, 'wb') as fp:
plistlib.dump(installer, fp)
# replacestrs(plistpath, "//Apple//", "//Apple Computer//")
# WIN INSTALLER
print("Updating Windows Installer version info...")
for line in fileinput.input(projectpath + "/installer/" + config['BUNDLE_NAME'] + ".iss",inplace=1):
if "AppVersion" in line:
line="AppVersion=" + config['FULL_VER_STR'] + "\n"
if "OutputBaseFilename" in line:
if demo:
line="OutputBaseFilename=IPlugSideChain Demo Installer\n"
else:
line="OutputBaseFilename=IPlugSideChain Installer\n"
if 'Source: "readme' in line:
if demo:
line='Source: "readme-win-demo.rtf"; DestDir: "{app}"; DestName: "readme.rtf"; Flags: isreadme\n'
else:
line='Source: "readme-win.rtf"; DestDir: "{app}"; DestName: "readme.rtf"; Flags: isreadme\n'
if "WelcomeLabel1" in line:
if demo:
line="WelcomeLabel1=Welcome to the IPlugSideChain Demo installer\n"
else:
line="WelcomeLabel1=Welcome to the IPlugSideChain installer\n"
if "SetupWindowTitle" in line:
if demo:
line="SetupWindowTitle=IPlugSideChain Demo installer\n"
else:
line="SetupWindowTitle=IPlugSideChain installer\n"
sys.stdout.write(line)
if __name__ == '__main__':
main()
|
[
"olilarkin@googlemail.com"
] |
olilarkin@googlemail.com
|
e6598a812d349f8f87994cd30681974bef4771b0
|
6b3dc07cc118dc68b942d593041e198c8e4b82f5
|
/conftest.py
|
c95a8cd2e7c5c28ff5f77808433b913fd1577fb7
|
[] |
no_license
|
BarysTsibets/Stepik_Selenium_Python_tasks
|
e1603010aae93a5dc7c698a22126d8660f14218e
|
b0b96cc37c2276ee68623e0bfbe866261ae9bcd5
|
refs/heads/master
| 2022-12-23T12:31:49.171178
| 2020-10-01T00:10:46
| 2020-10-01T00:10:46
| 295,846,742
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 991
|
py
|
import pytest
from selenium import webdriver
PATH1 = r'C:\Users\BorisPC\PycharmProjects\untitled\HelloWorld\Automation\driver\chromedriver.exe'
PATH2 = r'C:\Users\BorisPC\PycharmProjects\untitled\HelloWorld\Automation\driver\geckodriver.exe'
def pytest_addoption(parser):
parser.addoption('--browser_name', action='store', default='chrome',
help="Choose browser: chrome or firefox")
@pytest.fixture(scope="function")
def browser(request):
browser_name = request.config.getoption("browser_name")
browser = None
if browser_name == "chrome":
print("\nstart chrome browser for test..")
browser = webdriver.Chrome(executable_path=PATH1)
elif browser_name == "firefox":
print("\nstart firefox browser for test..")
browser = webdriver.Firefox(executable_path=PATH2)
else:
raise pytest.UsageError("--browser_name should be chrome or firefox")
yield browser
print("\nquit browser..")
browser.quit()
|
[
"molekyla_08@mail.ru"
] |
molekyla_08@mail.ru
|
5e4a0ac99813e835e0faa58dd39e06c62ee81d61
|
5208a988cfe97dcc515cb26e4f39e2da2763e6b6
|
/HouseSpider/items.py
|
e0a721e96efa04573898f743f04675998808be40
|
[] |
no_license
|
xuyun0906/HouseSpider
|
f316582de448acd91faf0fa00be9d761f5569e06
|
e3fa9f57b1716fa841a44629d4b9372d4b5bae79
|
refs/heads/master
| 2020-04-10T08:22:04.239227
| 2018-12-09T15:06:58
| 2018-12-09T15:06:58
| 160,904,248
| 0
| 1
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,842
|
py
|
# -*- coding: utf-8 -*-
# Define here the models for your scraped items
#
# See documentation in:
# https://doc.scrapy.org/en/latest/topics/items.html
import scrapy
from scrapy import Item, Field
class XiaoquItem(scrapy.Item):
id = scrapy.Field() # 小区ID
url = scrapy.Field() # 小区链接
name = scrapy.Field() # 小区名字
huxingCount = scrapy.Field() # 户型数
chengjiaoCount = scrapy.Field() # 历史成交套数
zaizuCount = scrapy.Field() # 在租套数
zaishouCount = scrapy.Field() # 在售套数
avgPrice = scrapy.Field() # 均价
region = scrapy.Field() # 区域
district = scrapy.Field() # 二级区域
city = scrapy.Field() # 城市
class XiaoquDetailItem(scrapy.Item):
id = scrapy.Field() # 小区ID
url = scrapy.Field() # 小区链接
name = scrapy.Field() # 小区名字
buildYear = scrapy.Field() # 建筑年代
bulidType = scrapy.Field() # 建筑类型
wuyeFee = scrapy.Field() # 物业费用
wuyeCompany = scrapy.Field() # 物业公司
developers = scrapy.Field() # 开发商
loudongCount = scrapy.Field() # 楼栋总数
fangwuCount = scrapy.Field() # 房屋总数
class ZSHouseItem(scrapy.Item):
url = scrapy.Field() # 房屋链接
name = scrapy.Field() # 房屋名称
city = scrapy.Field() # 城市
xiaoquId = scrapy.Field() # 小区ID
xiaoquName = scrapy.Field() # 小区名字
mianJi = scrapy.Field() # 面积
floor = scrapy.Field() # 楼层
huXing = scrapy.Field() # 户型
totalPrice = scrapy.Field() # 总价
price = scrapy.Field() # 单价
direct = scrapy.Field() # 朝向
fitment = scrapy.Field() # 装修
lift = scrapy.Field() # 电梯
buildType = scrapy.Field() # 建造类型
district = scrapy.Field() # 二级区域
|
[
"yunxu_master@126.com"
] |
yunxu_master@126.com
|
0cdc66bc726905c1a108a246b3d9afde367a20d3
|
7a16c85f09c2232a3fab5933c8ad2740edecace3
|
/python_local_courses/dz-9/nine.py
|
ab06ac9c9610927989f8c00769ea47ef6e2b184c
|
[] |
no_license
|
romanovna/baseline
|
42adf5ae1eeb27c34b4684f07ad4c57882e4033a
|
c0bbef293c9e7f434d130aca89b8741f36e888df
|
refs/heads/master
| 2021-07-14T09:20:19.924159
| 2021-03-10T13:22:36
| 2021-03-10T13:22:36
| 44,129,919
| 0
| 0
| null | 2021-02-26T14:23:30
| 2015-10-12T19:40:10
|
Python
|
UTF-8
|
Python
| false
| false
| 6,207
|
py
|
__author__ = 'roman.deles'
# coding: utf-8
""" Жизнь на ферме
- Курица
- Корова
- Собака
1. У этих классов есть следующие функциональности:
- бежать
- голос
- продукт ( яйцо, молоко) все эти классы унаследованы от базового "животное"
2. Также нужен класс ферма
Программа инициализирует ферму с заданным числом каждого животного.
3. Далее запускается метод класса ферма "прошел_месяц"
Там циклом проходим по всем животным, запуская их собственный метод "прошел месяц"
(какое животное сколько раз делает продукт, как успешно, где использовать random,
какие случайные факторы внести в жизнь фермы, решайте сами)
4. Далее запускается метод класса Ферма "Сводная инфа" , который расскажет нам об изменениях на ферме. """
import random
from abc import ABCMeta, abstractmethod
# изменен базовый класс на абстрактный, с абстрактным методом bon_appetit
class base(metaclass=ABCMeta):
def __init__(self, prod=str, K_prod=int, prod_items=str, voice_per_day=0, speed=0, moves_per_day=0, food=float):
## counters ============================
self.things_current = 0
self.voices_current = 0
self.dist_current = 0
self.food_current = 0
self.things_at_all = 0
self.voices_at_all = 0
self.dist_at_all = 0
self.food_at_all = 0
##=======================================
## some attributes
self.prod = prod
self.k_prod = K_prod
self.prod_items = prod_items
self.voices_per_day = voice_per_day
self.speed = speed
self.moved_per_day = moves_per_day
self.food = food
self.voice_k = random.randint(1, 5)
self.move_k = random.randint(1, 5)
self.things_k = random.randint(1, 5)
self.summary_in_month = 0
def move(self, days=1):
self.dist_current = (self.speed * self.moved_per_day * days * self.move_k)
self.dist_at_all += self.dist_current
def voice(self, days=1):
self.voices_current = (self.voices_per_day * days * self.voice_k)
self.voices_at_all += self.voices_current
def items(self, month):
self.things_current = (self.things_k * self.k_prod * month)
self.things_at_all += self.things_current
## counting...
def deal_with_it(self, month=1):
self.voice(month * 31)
self.items(month)
self.move(month * 31)
self.bon_appetit(month * 31)
self.summary_in_month += month
@abstractmethod
def bon_appetit(self, days):
pass
class CrazyChicken(base):
def __init__(self, prod='Eggs', K_prod=8, prod_items='items', voice_per_day=25, speed=2, moves_per_day=2, food=0.3):
super().__init__(prod, K_prod, prod_items, voice_per_day, speed, moves_per_day, food)
def bon_appetit(self, days=1):
self.food_current = (self.food * days)
self.food_at_all += self.food_current
class CrazyCow(base):
def __init__(self, prod='Vodka', K_prod=5, prod_items='liters', voice_per_day=50, speed=15, moves_per_day=4,
food=1.2):
super().__init__(prod, K_prod, prod_items, voice_per_day, speed, moves_per_day, food)
def bon_appetit(self, days=1):
self.food_current = (self.food * days)
self.food_at_all += self.food_current
class CrazyDog(base):
def __init__(self, prod='Anger', K_prod=15, prod_items='tonnes', voice_per_day=30, speed=30, moves_per_day=8,
food=0.8):
super().__init__(prod, K_prod, prod_items, voice_per_day, speed, moves_per_day, food)
def bon_appetit(self, days=1):
self.food_current = (self.food * days)
self.food_at_all += self.food_current
class the_Farm:
def __init__(self):
self.cows = []
self.dogs = []
self.chickens = []
for i in range(0, 3):
self.chickens.append(CrazyChicken())
for i in range(0, 2):
self.cows.append(CrazyCow())
for i in range(0, 1):
self.dogs.append(CrazyDog())
self.animals = {'CRAZY_DOG': self.dogs, 'CRAZY_COW': self.cows, 'CRAZY_CHICKEN': self.chickens}
def past_month(self, month=1):
for animal in self.animals.keys():
for x in self.animals[animal]:
x.deal_with_it(month)
x.bon_appetit(month)
def status(self):
with open('farm9.txt', mode='a+', encoding='utf-8') as f:
print('============================================START=================================================',
file=f)
for animal in self.animals.keys():
if self.animals[animal]:
total_count = 0
for i, x in enumerate(self.animals[animal]):
print('%s' % animal, i + 1, file=f)
print('produced', x.prod, 'in an amount of ', x.things_at_all, x.prod_items, file=f)
print('runned out', x.dist_at_all, 'KM', file=f)
print('did a sound', x.voices_at_all, 'times', file=f)
print('eaten a food', x.food_at_all, 'kg', file=f)
total_count += x.things_at_all
print(animal, 'has produced at all', total_count, x.prod_items, 'products', x.prod, 'for a',
x.summary_in_month,
'months on the farm', file=f)
print('===========================================END====================================================',
file=f)
if __name__ == '__main__':
farm = the_Farm()
farm.past_month(1)
farm.status()
|
[
"bullsey@gmail.com"
] |
bullsey@gmail.com
|
b484e91c0ba83b8e1b68afae32e927f0094f3207
|
cb025bce485f2900c588dc6853b58aaba2db8e85
|
/venv/bin/pip2.7
|
6bec2a6dd4dfc14afa4c65d9de1459e799c1d0cc
|
[] |
no_license
|
jovannovkovic/athon
|
04fc0fd0729133e504e30a03e721e8c415a4b803
|
2059160a58dfebdf891097dd5b3bc4bb50daf5bc
|
refs/heads/master
| 2020-06-06T04:21:23.494297
| 2015-04-06T10:05:42
| 2015-04-06T10:05:42
| 30,805,852
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 233
|
7
|
#!/home/popara/Desktop/backend/venv/bin/python
# -*- 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())
|
[
"popara@gmail.com"
] |
popara@gmail.com
|
e18cf06b5b2dc63197823de50c35c4e582ab59e9
|
772be6c5f04273b8e53485128e625d89fa13cb18
|
/algorithms/warmup/Diagonal-difference.py
|
1db5412cd07a5a314f7e4b36645f32ab834dfafc
|
[] |
no_license
|
anandvimal/hackerrank
|
4f714c44756b65886ff56c3c42ed8bc03477f436
|
148221da61f03859035009d307e2955ecf760fe2
|
refs/heads/master
| 2021-01-20T20:08:04.392126
| 2016-06-19T22:58:24
| 2016-06-19T22:58:24
| 59,915,520
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 371
|
py
|
import sys
n = int(raw_input().strip())
a = []
for a_i in xrange(n):
a_temp = map(int,raw_input().strip().split(' '))
a.append(a_temp)
x=0
sum1 = 0
sum2 = 0
for i in a:
d1 = i[x]
d2 = i[len(i)-1-x]
#print d1
#print d2
#print " "
sum1 = d1 + sum1
sum2 = d2 + sum2
x+=1
#print sum1
#print sum2
#print " "
print abs(sum1-sum2)
|
[
"colirsweb@gmail.com"
] |
colirsweb@gmail.com
|
532430bc032a5da32c5501e5d1e2fe6e3cfaf911
|
d8e7ed4d1e89aec85f1cd0006f8caca5ddc183c4
|
/page_object/index/search_page.py
|
1d3d0a4f5a283b1f46871918217c656078dfa3d3
|
[] |
no_license
|
reach950/hangzhoubanshi-uitest-iOS
|
3bf7585e0b42a734ec8eb1db29b5a0ea82527809
|
ce25cff3da3fb6f7e3c4a96f6e92f8b62b139c3c
|
refs/heads/master
| 2021-06-19T15:27:42.801633
| 2019-11-14T08:17:05
| 2019-11-14T08:17:05
| 187,554,944
| 0
| 0
| null | 2021-06-01T23:45:36
| 2019-05-20T02:30:54
|
Python
|
UTF-8
|
Python
| false
| false
| 2,716
|
py
|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""搜索页"""
__author__ = 'kejie'
import time
from appium.webdriver.common.mobileby import MobileBy
from page_object.base_page import BasePage
class SearchPage(BasePage):
# 搜索输入框
search_field_loc = (MobileBy.CLASS_NAME, 'XCUIElementTypeSearchField')
# 搜索按钮
search_button_loc = (MobileBy.ACCESSIBILITY_ID, 'Search')
# 客服按钮
customer_service_button_loc = (MobileBy.ACCESSIBILITY_ID, 'kefu')
# 取消搜索
cancel_search_loc = (MobileBy.ACCESSIBILITY_ID, '取消')
# 热门搜索
hot_search_loc = (MobileBy.IOS_PREDICATE, 'type == "XCUIElementTypeButton" AND (rect.y == 132 OR rect.y == 175)')
# 最后一条搜索结果
last_search_result_loc = (MobileBy.IOS_CLASS_CHAIN, '**/XCUIElementTypeTable/XCUIElementTypeCell[-1]')
# 无结果图片
no_result_image_loc = (MobileBy.ACCESSIBILITY_ID, 'nothing')
# 无结果文字
no_result_text_loc = (MobileBy.IOS_PREDICATE, 'type == "XCUIElementTypeStaticText" AND name BEGINSWITH "抱歉"')
# 无结果热门事项
no_result_hot_items_loc = (MobileBy.IOS_PREDICATE, 'type == "XCUIElementTypeStaticText" AND rect.width == 345')
# 输入关键字搜索
def search(self, text):
self.send_keys(self.search_field_loc, text)
self.tap_element(self.search_button_loc)
# 点击客服按钮
def click_customer_service_button(self):
self.tap_element(self.customer_service_button_loc)
# 取消搜索
def cancel_search(self):
self.tap_element(self.cancel_search_loc)
# 获取热门搜索的所有关键词
def get_all_hot_search_words(self):
search_words = []
eles = self.find_elements(self.hot_search_loc)
for ele in eles:
search_words.append(ele.get_attribute('name'))
return search_words
# 滑动到最后一条搜索结果
def scroll_to_last_search_result(self):
count = 0
while not self.find_element(self.last_search_result_loc).is_displayed():
if count >= 5:
break
self.swipe('up')
time.sleep(0.5)
count += 1
# 是否显示无结果页
def is_no_result_page_display(self):
if self.find_element(self.no_result_image_loc) and self.find_element(self.no_result_text_loc):
return True
else:
return False
# 获取无结果页的热门事项
def get_no_result_hot_items(self):
hot_items = []
eles = self.find_elements(self.no_result_hot_items_loc)
for ele in eles:
hot_items.append(ele.get_attribute('name'))
return hot_items
|
[
"reach950@gmail.com"
] |
reach950@gmail.com
|
e51ace0d5c0e2ac2999081b7aaabcb7594237299
|
81bdddb98eaa89c2a2256d2f40be6d5f92d3f9cd
|
/edr/edsmserver.py
|
0694116964ab4dda5bc18f9ccdf17bd5feb2cf55
|
[
"Apache-2.0"
] |
permissive
|
Majorjjamo5/edr
|
e91af77512dc415ccca8496757edecb5f9cae692
|
0b05c04954bded5952d4b28886f97f599cd30792
|
refs/heads/master
| 2020-03-29T00:06:46.277576
| 2018-09-17T00:58:07
| 2018-09-17T00:58:07
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 2,769
|
py
|
import json
import urllib
import edrconfig
import edrlog
import requests
import urllib
EDRLOG = edrlog.EDRLog()
class EDSMServer(object):
def __init__(self):
config = edrconfig.EDRConfig()
self.EDSM_API_KEY = config.edsm_api_key()
self.EDSM_SERVER = config.edsm_server()
def system(self, system_name):
params = {"systemName": system_name, "showCoordinates": 1, "showInformation":1, "showId": 1}
endpoint = "{}/api-v1/systems".format(self.EDSM_SERVER)
resp = requests.get(endpoint, params=params)
if resp.status_code != requests.codes.ok:
EDRLOG.log(u"Failed to retrieve system {} from EDSM: {}.".format(system_name, resp.status_code), "ERROR")
return None
return json.loads(resp.content)
def systems_within_radius(self, system_name, radius):
params = {"systemName": system_name, "showCoordinates": 1, "radius": radius, "showInformation": 1, "showId": 1, "showPermit": 1}
endpoint = "{}/api-v1/sphere-systems".format(self.EDSM_SERVER)
resp = requests.get(endpoint, params=params)
if resp.status_code != requests.codes.ok:
EDRLOG.log(u"Failed to retrieve system {} from EDSM: {}.".format(system_name, resp.status_code), "ERROR")
return None
results = json.loads(resp.content)
if not results:
EDRLOG.log(u"Empty systems within radius.", "INFO")
return []
sorted_results = sorted(results, key=lambda t: t["distance"])
return sorted_results
def stations_in_system(self, system_name):
params = {"systemName": system_name}
endpoint = "{}/api-system-v1/stations".format(self.EDSM_SERVER)
resp = requests.get(endpoint, params=params)
if resp.status_code != requests.codes.ok:
EDRLOG.log(u"Failed to retrieve system {} from EDSM: {}.".format(system_name, resp.status_code), "ERROR")
return None
results = json.loads(resp.content)
if not results or not results.get('stations', None):
EDRLOG.log(u"No stations in system {}.".format(system_name), "INFO")
return []
sorted_results = sorted(results['stations'], key=lambda t: t["distanceToArrival"])
return sorted_results
def factions_in_system(self, system_name):
params = {"systemName": system_name}
endpoint = "{}/api-system-v1/factions".format(self.EDSM_SERVER)
resp = requests.get(endpoint, params=params)
if resp.status_code != requests.codes.ok:
EDRLOG.log(u"Failed to retrieve state for system {} from EDSM: {}.".format(system_name, resp.status_code), "ERROR")
return None
return json.loads(resp.content)
|
[
"33626494+lekeno@users.noreply.github.com"
] |
33626494+lekeno@users.noreply.github.com
|
6bedde275f4ca48faeadfd4bab076e488b4228ea
|
cdb05cd3d2e90320bdb8469d7a0a3731457afbf6
|
/base/products.py
|
40365ad96ff37abac239c249d1e2016976a4d115
|
[] |
no_license
|
lastdefiance20/Campirit_Website_Example
|
c80a9d06e15b2c855a512c869d4c1ed30a6f835c
|
ba4d80057d77a7b6484eb5c7e88caf3553035d5c
|
refs/heads/master
| 2023-07-15T12:07:40.483139
| 2021-08-31T23:50:44
| 2021-08-31T23:50:44
| 387,822,800
| 3
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 2,575
|
py
|
products = [
{
'_id': '1',
'name': 'Airpods Wireless Bluetooth Headphones',
'image': '/images/airpods.jpg',
'description':
'Bluetooth technology lets you connect it with compatible devices wirelessly High-quality AAC audio offers immersive listening experience Built-in microphone allows you to take calls while working',
'brand': 'Apple',
'category': 'Electronics',
'price': 89.99,
'countInStock': 10,
'rating': 4.5,
'numReviews': 12,
},
{
'_id': '2',
'name': 'iPhone 11 Pro 256GB Memory',
'image': '/images/phone.jpg',
'description':
'Introducing the iPhone 11 Pro. A transformative triple-camera system that adds tons of capability without complexity. An unprecedented leap in battery life',
'brand': 'Apple',
'category': 'Electronics',
'price': 599.99,
'countInStock': 7,
'rating': 4.0,
'numReviews': 8,
},
{
'_id': '3',
'name': 'Cannon EOS 80D DSLR Camera',
'image': '/images/camera.jpg',
'description':
'Characterized by versatile imaging specs, the Canon EOS 80D further clarifies itself using a pair of robust focusing systems and an intuitive design',
'brand': 'Cannon',
'category': 'Electronics',
'price': 929.99,
'countInStock': 5,
'rating': 3,
'numReviews': 12,
},
{
'_id': '4',
'name': 'Sony Playstation 4 Pro White Version',
'image': '/images/playstation.jpg',
'description':
'The ultimate home entertainment center starts with PlayStation. Whether you are into gaming, HD movies, television, music',
'brand': 'Sony',
'category': 'Electronics',
'price': 399.99,
'countInStock': 11,
'rating': 5,
'numReviews': 12,
},
{
'_id': '5',
'name': 'Logitech G-Series Gaming Mouse',
'image': '/images/mouse.jpg',
'description':
'Get a better handle on your games with this Logitech LIGHTSYNC gaming mouse. The six programmable buttons allow customization for a smooth playing experience',
'brand': 'Logitech',
'category': 'Electronics',
'price': 49.99,
'countInStock': 7,
'rating': 3.5,
'numReviews': 10,
},
{
'_id': '6',
'name': 'Amazon Echo Dot 3rd Generation',
'image': '/images/alexa.jpg',
'description':
'Meet Echo Dot - Our most popular smart speaker with a fabric design. It is our most compact smart speaker that fits perfectly into small space',
'brand': 'Amazon',
'category': 'Electronics',
'price': 29.99,
'countInStock': 0,
'rating': 4,
'numReviews': 12,
},
]
|
[
"lastdefiance20@gmail.com"
] |
lastdefiance20@gmail.com
|
e017a965c13c03a73293617f8454c31ae8b81cac
|
8ffe631d5493bb03c50e9acaf82edd864bde5abc
|
/bin/trial
|
9f2c35a964d485cd1ccce351972aaecdb992960b
|
[] |
no_license
|
aditya172926/scrapy-scrap
|
58f7f7de77aa2ebddd31d21b213a1ab58a7b6bd4
|
f4b29b329412e326c7b698a086eb75df860ca5cc
|
refs/heads/master
| 2022-11-09T20:20:57.543829
| 2020-06-28T05:57:02
| 2020-06-28T05:57:02
| 275,516,556
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 252
|
#!/Users/harshshetye/Desktop/scrapy_scrap/bin/python
# -*- coding: utf-8 -*-
import re
import sys
from twisted.scripts.trial import run
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
sys.exit(run())
|
[
"aditya26sg@gmail.com"
] |
aditya26sg@gmail.com
|
|
366ea396e642b9019bb557961a0b903fbe4531e5
|
3ae01569e10db9e4b3036a6e877ef24a3f056179
|
/website/openeuler_release_refresher/obs/util.py
|
1b8b9459e04a65dd9c973feeb9ee6743a3f3af57
|
[
"Apache-2.0",
"MIT"
] |
permissive
|
openeuler-mirror/infrastructure
|
f3bc92bdc8af9285a549045a6180593f7d9e16fe
|
d1020ab3fe7535f75da9e8b24e6221f103422ac0
|
refs/heads/master
| 2023-09-01T18:08:00.704355
| 2023-08-26T18:00:04
| 2023-08-26T18:00:04
| 246,005,363
| 0
| 2
|
Apache-2.0
| 2023-05-23T05:51:28
| 2020-03-09T10:25:26
|
Python
|
UTF-8
|
Python
| false
| false
| 12,786
|
py
|
#!/usr/bin/python
# -*- coding:utf-8 -*-
# Copyright 2019 Huawei Technologies Co.,Ltd.
# 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 re
import base64
import hashlib
import os
from obs import const
from obs import progress
if const.IS_PYTHON2:
import urllib
else:
import urllib.parse as urllib
from obs.ilog import INFO, ERROR
def to_bool(item):
try:
return True if item is not None and str(item).lower() == 'true' else False
except Exception:
return None
def to_int(item):
try:
return int(item)
except Exception:
return None
def to_long(item):
try:
return const.LONG(item)
except Exception:
return None
def to_float(item):
try:
return float(item)
except Exception:
return None
def to_string(item):
try:
return str(item) if item is not None else ''
except Exception:
return ''
def is_valid(item):
return item is not None and item.strip() != ''
class RequestFormat(object):
@staticmethod
def get_pathformat():
return PathFormat()
@staticmethod
def get_subdomainformat():
return SubdomainFormat()
@classmethod
def convert_path_string(cls, path_args, allowdNames=None):
e = ''
if isinstance(path_args, dict):
e1 = '?'
e2 = '&'
for path_key, path_value in path_args.items():
flag = True
if allowdNames is not None and path_key not in allowdNames:
flag = False
if flag:
path_key = encode_item(path_key, '/')
if path_value is None:
e1 += path_key + '&'
continue
e2 += path_key + '=' + encode_item(path_value, '/') + '&'
e = (e1 + e2).replace('&&', '&').replace('?&', '?')[:-1]
return e
def get_endpoint(self, server, port, bucket):
return
def get_pathbase(self, bucket, key):
return
def get_url(self, bucket, key, path_args):
return
class PathFormat(RequestFormat):
def get_server(self, server, bucket):
return server
def get_pathbase(self, bucket, key):
if bucket:
return '/' + bucket + '/' + encode_object_key(key) if key else '/' + bucket
return '/' + encode_object_key(key) if key else '/'
def get_endpoint(self, server, port, bucket):
if port == 80 or port == 443:
return server
return server + ':' + str(port)
def get_url(self, bucket, key, path_args):
path_base = self.get_pathbase(bucket, key)
path_arguments = self.convert_path_string(path_args)
return path_base + path_arguments
def get_full_url(self, is_secure, server, port, bucket, key, path_args):
url = 'https://' if is_secure else 'http://'
url += self.get_endpoint(server, port, bucket)
url += self.get_url(bucket, key, path_args)
return url
class SubdomainFormat(RequestFormat):
def get_server(self, server, bucket):
return bucket + '.' + server if bucket else server
def get_pathbase(self, bucket, key):
if key is None:
return '/'
return '/' + encode_object_key(key)
def get_endpoint(self, server, port, bucket):
if port == 80 or port == 443:
return self.get_server(server, bucket)
return self.get_server(server, bucket) + ':' + str(port)
def get_url(self, bucket, key, path_args):
url = self.convert_path_string(path_args)
return self.get_pathbase(bucket, key) + url
def get_full_url(self, is_secure, server, port, bucket, key, path_args):
url = 'https://' if is_secure else 'http://'
url += self.get_endpoint(server, port, bucket)
url += self.get_url(bucket, key, path_args)
return url
class delegate(object):
def __init__(self, conn):
self.conn = conn
def send(self, data, final=False, stream_id=None):
self.conn.send(data)
def conn_delegate(conn):
return delegate(conn)
def get_readable_entity(readable, chunk_size=65536, notifier=None, auto_close=True):
if notifier is None:
notifier = progress.NONE_NOTIFIER
def entity(conn):
try:
while True:
chunk = readable.read(chunk_size)
if not chunk:
conn.send('0\r\n\r\n' if const.IS_PYTHON2 else '0\r\n\r\n'.encode('UTF-8'), final=True)
break
newReadCount = len(chunk)
if newReadCount > 0:
notifier.send(newReadCount)
hex_chunk = hex(len(chunk))[2:]
conn.send(hex_chunk if const.IS_PYTHON2 else hex_chunk.encode('UTF-8'))
conn.send('\r\n' if const.IS_PYTHON2 else '\r\n'.encode('UTF-8'))
conn.send(chunk)
conn.send('\r\n' if const.IS_PYTHON2 else '\r\n'.encode('UTF-8'))
finally:
if hasattr(readable, 'close') and callable(readable.close) and auto_close:
readable.close()
return entity
def get_readable_entity_by_totalcount(readable, totalCount, chunk_size=65536, notifier=None, auto_close=True):
if notifier is None:
notifier = progress.NONE_NOTIFIER
def entity(conn):
try:
readCount = 0
while True:
readCountOnce = chunk_size if totalCount - readCount >= chunk_size else totalCount - readCount
chunk = readable.read(readCountOnce)
newReadCount = len(chunk)
readCount += newReadCount
if newReadCount > 0:
notifier.send(newReadCount)
if readCount >= totalCount:
conn.send(chunk, final=True)
break
conn.send(chunk)
finally:
if hasattr(readable, 'close') and callable(readable.close) and auto_close:
readable.close()
return entity
def get_file_entity(file_path, chunk_size=65536, notifier=None):
if notifier is None:
notifier = progress.NONE_NOTIFIER
def entity(conn):
fileSize = os.path.getsize(file_path)
readCount = 0
with open(file_path, 'rb') as f:
while True:
chunk = f.read(chunk_size)
newReadCount = len(chunk)
if newReadCount > 0:
notifier.send(newReadCount)
readCount += newReadCount
if readCount >= fileSize:
conn.send(chunk, final=True)
break
conn.send(chunk)
return entity
def get_file_entity_by_totalcount(file_path, totalCount, chunk_size=65536, notifier=None):
if notifier is None:
notifier = progress.NONE_NOTIFIER
def entity(conn):
readCount = 0
with open(file_path, 'rb') as f:
while True:
readCountOnce = chunk_size if totalCount - readCount >= chunk_size else totalCount - readCount
chunk = f.read(readCountOnce)
newReadCount = len(chunk)
if newReadCount > 0:
notifier.send(newReadCount)
readCount += newReadCount
if readCount >= totalCount:
conn.send(chunk, final=True)
break
conn.send(chunk)
return entity
def get_file_entity_by_offset_partsize(file_path, offset, partSize, chunk_size=65536, notifier=None):
if notifier is None:
notifier = progress.NONE_NOTIFIER
def entity(conn):
readCount = 0
with open(file_path, 'rb') as f:
f.seek(offset)
while True:
readCountOnce = chunk_size if partSize - readCount >= chunk_size else partSize - readCount
chunk = f.read(readCountOnce)
newReadCount = len(chunk)
if newReadCount > 0:
notifier.send(newReadCount)
readCount += newReadCount
if readCount >= partSize:
conn.send(chunk, final=True)
break
conn.send(chunk)
return entity
def is_ipaddress(item):
return re.match(const.IPv4_REGEX, item)
def md5_encode(unencoded):
m = hashlib.md5()
unencoded = unencoded if const.IS_PYTHON2 else (unencoded.encode('UTF-8') if not isinstance(unencoded, bytes) else unencoded)
m.update(unencoded)
return m.digest()
def base64_encode(unencoded):
unencoded = unencoded if const.IS_PYTHON2 else (unencoded.encode('UTF-8') if not isinstance(unencoded, bytes) else unencoded)
encodeestr = base64.b64encode(unencoded, altchars=None)
return encodeestr if const.IS_PYTHON2 else encodeestr.decode('UTF-8')
def encode_object_key(key):
return encode_item(key, '/~')
def encode_item(item, safe='/'):
return urllib.quote(to_string(item), safe)
def decode_item(item):
return urllib.unquote(item)
def safe_trans_to_utf8(item):
if not const.IS_PYTHON2:
return item
if item is not None:
item = safe_encode(item)
try:
return item.decode('GB2312').encode('UTF-8')
except Exception:
return item
return None
def safe_trans_to_gb2312(item):
if not const.IS_PYTHON2:
return item
if item is not None:
item = safe_encode(item)
try:
return item.decode('UTF-8').encode('GB2312')
except Exception:
return item
return None
def safe_decode(item):
if not const.IS_PYTHON2:
return item
if isinstance(item, str):
try:
item = item.decode('UTF-8')
except:
try:
item = item.decode('GB2312')
except Exception:
item = None
return item
def safe_encode(item):
if not const.IS_PYTHON2:
return item
if isinstance(item, const.UNICODE):
try:
item = item.encode('UTF-8')
except UnicodeDecodeError:
try:
item = item.encode('GB2312')
except Exception:
item = None
return item
def md5_file_encode_by_size_offset(file_path=None, size=None, offset=None, chuckSize=None):
if file_path is not None and size is not None and offset is not None:
m = hashlib.md5()
with open(file_path, 'rb') as fp:
CHUNKSIZE = 65536 if chuckSize is None else chuckSize
fp.seek(offset)
read_count = 0
while read_count < size:
read_size = CHUNKSIZE if size - read_count >= CHUNKSIZE else size - read_count
data = fp.read(read_size)
read_count_once = len(data)
if read_count_once <= 0:
break
m.update(data)
read_count += read_count_once
return m.digest()
def do_close(result, conn, connHolder, log_client=None):
if not result:
close_conn(conn, log_client)
elif result.getheader('connection', '').lower() == 'close':
if log_client:
log_client.log(INFO, 'server inform to close connection')
close_conn(conn, log_client)
elif to_int(result.status) >= 500 or connHolder is None:
close_conn(conn, log_client)
elif hasattr(conn, '_clear') and conn._clear:
close_conn(conn, log_client)
else:
if connHolder is not None:
try:
connHolder['connSet'].put_nowait(conn)
except:
close_conn(conn, log_client)
def close_conn(conn, log_client=None):
try:
if conn:
conn.close()
except Exception as ex:
if log_client:
log_client.log(ERROR, ex)
SKIP_VERIFY_ATTR_TYPE = False
def verify_attr_type(value, allowedAttrType):
if SKIP_VERIFY_ATTR_TYPE:
return True
if isinstance(allowedAttrType, list):
for t in allowedAttrType:
if isinstance(value, t):
return True
return False
return isinstance(value, allowedAttrType)
def lazyCallback(*args, **kwargs):
pass
|
[
"hu.husheng@huawei.com"
] |
hu.husheng@huawei.com
|
c5517dd7c08f39a902510e7f3cd4029fc8a11a87
|
900e3a70c3264d43a8a478143ec0443b7afc9083
|
/helpers.py
|
d497e49a30802ccb27deb6bfe120b39e0b050c7d
|
[] |
no_license
|
James171/nfl_team_comparison_app
|
285fa5b48f36fda67ecf469cb0ca092e458568c3
|
1428cca60faefca64e111d2e072e6d3c2924a5cc
|
refs/heads/master
| 2021-02-19T01:49:11.382554
| 2020-05-06T19:23:59
| 2020-05-06T19:23:59
| 245,265,062
| 2
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,325
|
py
|
import os
import requests
import urllib.parse
from flask import redirect, render_template, request, session
from functools import wraps
# Hide API Key from viewers
api_key = os.environ['API_KEY']
# Lookup function to to get hierarchy api information and team ID
def lookup(symbol):
# Contact API
try:
response = requests.get(f"http://api.sportradar.us/nfl/official/trial/v5/en/league/hierarchy.json?api_key={api_key}")
# print(response.headers)
response.raise_for_status()
except requests.RequestException:
return None
# Parse response
try:
data = response.json()
return data
except (KeyError, TypeError, ValueError):
return None
def lookup_stats(team_id):
# Contact API
try:
response = requests.get(f"http://api.sportradar.us/nfl/official/trial/v5/en/seasons/2019/REG/teams/{team_id}/statistics.json?api_key={api_key}")
# print(response.headers)
print(response)
response.raise_for_status()
except requests.RequestException:
return None
# Parse response
try:
data_stats = response.json()
return data_stats
except (KeyError, TypeError, ValueError):
return None
def usd(value):
"""Format value as USD."""
return f"${value:,.2f}"
|
[
"26070680+James171@users.noreply.github.com"
] |
26070680+James171@users.noreply.github.com
|
54109eabfcfbc9695ac0860bc473d43ead1adcb8
|
0266f371319c28227a948fb105440ef74a3c583d
|
/tests/test_util_methods.py
|
8f2614102db8a1e8235c6930336ef83eab1e57bc
|
[
"MIT",
"Python-2.0"
] |
permissive
|
qbicsoftware/mtb-converter-cli
|
2ff264576c07a0c383b59f817d076ebe3d76d884
|
8a2a33f511fcc1b9791aad4a5ff6a44a16f1d72a
|
refs/heads/master
| 2021-09-15T09:08:08.005762
| 2018-05-14T19:28:58
| 2018-05-14T19:28:58
| 115,716,098
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 974
|
py
|
"""
Tests for util methods
"""
import os
import unittest
from nose.tools import raises
from mtbconverter import utils
INVALID_BARCODE = "QLAUCH123AE"
VALID_BARCODE = "QAHJH006A4"
INVALID_PATH = "my/file/QLAUCH123AE_file.fastq.gz"
VALID_PATH = "my/file/QAHJH006A4_file.fastq.gz"
PATH_WITH_MULT_BARCODES = "my/file/QAHJH006A4_QDERS021AS_file.fastq.gz"
class ArchiveTests(unittest.TestCase):
"""Test suite for util methods check"""
def test_barcode_integrity(self):
self.assertTrue(utils.is_valid_barcode(VALID_BARCODE))
self.assertFalse(utils.is_valid_barcode(INVALID_BARCODE))
@raises(ValueError)
def test_invalid_path_for_barcode(self):
utils.getbarcode(INVALID_PATH)
@raises(ValueError)
def test_path_with_multiple_barcodes(self):
utils.getbarcode(PATH_WITH_MULT_BARCODES)
def test_valid_path_for_barcode(self):
barcode = utils.getbarcode(VALID_PATH)
self.assertEqual(barcode, VALID_BARCODE)
|
[
"sven.fillinger@qbic.uni-tuebingen.de"
] |
sven.fillinger@qbic.uni-tuebingen.de
|
d021d36f984ab643b089ddca6cf72adba3e0c21e
|
e3565e1ce607f60745f2a045aae8026661a6b99b
|
/resources/Onyx-1.0.511/py/onyx/grid/griddy.py
|
b7fdb67930c51b30bfc7c426ac2a4ed49d48c2c2
|
[
"Apache-2.0"
] |
permissive
|
eternity668/speechAD
|
4c08d953b2ed06b3357b1c39d8709dd088a2471c
|
f270a1be86372b7044615e4fd82032029e123bc1
|
refs/heads/master
| 2021-01-12T22:10:33.358500
| 2014-02-03T16:03:28
| 2014-02-03T16:03:28
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,152
|
py
|
###########################################################################
#
# File: griddy.py (directory: ./py/onyx/grid)
# Date: 4-Feb-2009
# Author: Hugh Secker-Walker
# Description: A function for use in testing by gridgo.py
#
# This file is part of Onyx http://onyxtools.sourceforge.net
#
# Copyright 2009 The Johns Hopkins University
#
# 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.
#
###########################################################################
"""
>>> True
True
"""
def my_func(a, b, c):
return a, b, c
if __name__ == '__main__':
from onyx import onyx_mainstartup
onyx_mainstartup()
|
[
"nassos@n12mavra.cs.ntua.gr"
] |
nassos@n12mavra.cs.ntua.gr
|
9eb02a16cb5679b043e158e4f36ae3ea11a51e80
|
162f0a636cab320ead784b33597e583e38ac432f
|
/1744.py
|
f339b7b48defbece73a4dddc7bee0dbea7c0d161
|
[] |
no_license
|
goodsosbva/BOJ_Greedy
|
fc2450df90f64790f6cc01c168ba7f19ec83e504
|
98d21af254cacf41632a4b40ca9ef643b29bb104
|
refs/heads/main
| 2023-03-31T17:26:33.863396
| 2021-04-06T07:50:57
| 2021-04-06T07:50:57
| 347,081,712
| 1
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,641
|
py
|
n = int(input())
sequence = []
negative = []
positive = []
res = 0
for i in range(n):
i = int(input())
sequence.append(i)
for k in sequence:
if k < 0:
negative.append(k)
elif k > 0:
positive.append(k)
else:
negative.append(k)
negative.sort()
positive.sort(reverse=True)
# print(negative)
u = len(negative)
if 0 in negative:
if u % 2 == 0:
for q in range(0, u, 2):
res += negative[q] * negative[q + 1]
else:
for w in range(0, u - 1, 2):
res += negative[w] * negative[w + 1]
else:
if u % 2 == 0:
for q in range(0, u, 2):
res += negative[q] * negative[q + 1]
elif u % 2 != 0 and u != 1:
for w in range(0, u - 1, 2):
res += negative[w] * negative[w + 1]
res += negative[u - 1]
else:
res += negative[0]
# print("음수합:", res)
# print(positive)
v = len(positive)
if 1 in positive:
x = positive.count(1)
# print(x)
if v - 1 > x:
if v % 2 == 0:
for s in range(0, v - x, 2):
res += positive[s] * positive[s + 1]
res += x
else:
for t in range(0, v - x, 2):
res += positive[t] * positive[t + 1]
res += x
else:
for h in positive:
res += h
else:
if v % 2 == 0:
for r in range(0, v, 2):
res += positive[r] * positive[r + 1]
else:
for f in range(0, v - 1, 2):
res += positive[f] * positive[f + 1]
res += positive[v - 1]
print(res)
|
[
"noreply@github.com"
] |
goodsosbva.noreply@github.com
|
f8e9765b859dd527defd2ce06933a55ecb70e041
|
35fdd5b42b47a1dbe6a25f6fc1865f4e48b842a5
|
/evalml/tests/component_tests/test_catboost_classifier.py
|
1ef6fd41a8656a2914d90172ce42a92330d0a24e
|
[
"BSD-3-Clause"
] |
permissive
|
skvorekn/evalml
|
41e5426f9f7d5ad625c21b74336009894c79c7de
|
2cbfa344ec3fdc0fb0f4a0f1093811135b9b97d8
|
refs/heads/main
| 2023-03-27T01:42:07.691406
| 2021-03-19T18:53:43
| 2021-03-19T18:53:43
| 349,555,689
| 0
| 0
|
BSD-3-Clause
| 2021-03-21T14:57:01
| 2021-03-19T21:08:12
| null |
UTF-8
|
Python
| false
| false
| 837
|
py
|
import pandas as pd
from pytest import importorskip
from evalml.pipelines.components import CatBoostClassifier
from evalml.utils import SEED_BOUNDS
importorskip('catboost', reason='Skipping test because catboost not installed')
def test_catboost_classifier_random_seed_bounds_seed(X_y_binary):
"""ensure catboost's RNG doesn't fail for the min/max bounds we support on user-inputted random seeds"""
X, y = X_y_binary
col_names = ["col_{}".format(i) for i in range(len(X[0]))]
X = pd.DataFrame(X, columns=col_names)
y = pd.Series(y)
clf = CatBoostClassifier(n_estimators=1, max_depth=1, random_seed=SEED_BOUNDS.min_bound)
clf.fit(X, y)
clf = CatBoostClassifier(n_estimators=1, max_depth=1, random_seed=SEED_BOUNDS.max_bound)
fitted = clf.fit(X, y)
assert isinstance(fitted, CatBoostClassifier)
|
[
"noreply@github.com"
] |
skvorekn.noreply@github.com
|
0f776e18f96167e136351a53c789777a2a35a629
|
cbc5e26bb47ae69e80a3649c90275becf25ce404
|
/xlsxwriter/test/comparison/test_chart_layout04.py
|
f377a5806721d2af1d65752bac33bb918a5d84f3
|
[
"BSD-2-Clause-Views",
"BSD-3-Clause",
"MIT"
] |
permissive
|
mst-solar-car/kicad-bom-generator
|
c3549409c3139f787ad28391372b5cb03791694a
|
2aae905056d06f3d25343a8d784049c141d05640
|
refs/heads/master
| 2021-09-07T14:00:40.759486
| 2018-02-23T23:21:13
| 2018-02-23T23:21:13
| 107,868,801
| 3
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,764
|
py
|
###############################################################################
#
# Tests for XlsxWriter.
#
# Copyright (c), 2013-2017, John McNamara, jmcnamara@cpan.org
#
from ..excel_comparsion_test import ExcelComparisonTest
from ...workbook import Workbook
class TestCompareXLSXFiles(ExcelComparisonTest):
"""
Test file created by XlsxWriter against a file created by Excel.
"""
def setUp(self):
self.maxDiff = None
filename = 'chart_layout04.xlsx'
test_dir = 'xlsxwriter/test/comparison/'
self.got_filename = test_dir + '_test_' + filename
self.exp_filename = test_dir + 'xlsx_files/' + filename
self.ignore_files = []
self.ignore_elements = {}
def test_create_file(self):
"""Test the creation of an XlsxWriter file with user defined layout."""
workbook = Workbook(self.got_filename)
worksheet = workbook.add_worksheet()
chart = workbook.add_chart({'type': 'column'})
chart.axis_ids = [68311296, 69198208]
data = [
[1, 2, 3, 4, 5],
[2, 4, 6, 8, 10],
[3, 6, 9, 12, 15],
]
worksheet.write_column('A1', data[0])
worksheet.write_column('B1', data[1])
worksheet.write_column('C1', data[2])
chart.add_series({'values': '=Sheet1!$A$1:$A$5'})
chart.add_series({'values': '=Sheet1!$B$1:$B$5'})
chart.add_series({'values': '=Sheet1!$C$1:$C$5'})
chart.set_title({
'name': 'Title',
'layout': {
'x': 0.42631933508311465,
'y': 0.14351851851851852,
}
})
worksheet.insert_chart('E9', chart)
workbook.close()
self.assertExcelEqual()
|
[
"mwrb7d@mst.edu"
] |
mwrb7d@mst.edu
|
ee6f7db289489973bebf80ffdf3c8346f71732cc
|
6d5c5fbbefb6b6dfc398adfc054f09cb95fbdcaf
|
/rh_pathfinding/src/rh_pathfinding/utils/minheap.py
|
e8cbf2a4ee3c61c7adc7c8c6290a9deff3e0d774
|
[
"MIT"
] |
permissive
|
Wuxinxiaoshifu/rh_ros
|
46a8d75d10317aef04c57785653a6b00ddcb2b63
|
e13077060bdfcc231adee9731ebfddadcd8d6b4a
|
refs/heads/master
| 2022-04-11T15:12:38.539565
| 2020-04-09T19:51:46
| 2020-04-09T19:51:46
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,479
|
py
|
import heapq
class MinHeap:
"""
What you'd expect. There is probably a better one, but this is simple and meets our needs.
"""
def __init__(self):
self._heapList = []
self._inc = 0
def isEmpty(self):
return len(self._heapList) == 0
def __len__(self):
return len(self._heapList)
def push(self, priority, data):
# This is given as a 2nd argument, after priority, to break ties. Unique incrementing value
heapq.heappush(self._heapList, (priority, self._inc, data))
self._inc += 1
def getTop(self):
(priority, dontCare, data) = self._heapList[0]
return data
def pop(self):
(cost, dontCare, data) = heapq.heappop(self._heapList)
return data
def getTopWithPriority(self):
(priority, dontCare, data) = self._heapList[0]
return (priority, data)
def popWithPriority(self):
(priority, dontCare, data) = heapq.heappop(self._heapList)
return (priority, data)
def __iter__(self):
return _heapIter(self)
def __getitem__(self, index):
return self._heapList[index]
class _heapIter:
def __init__(self, heap):
self._heap = heap
self._index = 0
def next(self):
if self._index < len(self._heap):
(priority, dontCare, data) = self._heap[self._index]
self._index += 1
return data
else:
raise StopIteration()
|
[
"stephenpheiffer1@gmail.com"
] |
stephenpheiffer1@gmail.com
|
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