content
stringlengths 1
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sequencelengths 1
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int64 1
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from sklearn.metrics import f1_score
import torch
from cogdl.utils import build_args_from_dict
from cogdl.utils import accuracy, multiclass_f1, multilabel_f1, bce_with_logits_loss, cross_entropy_loss
if __name__ == "__main__":
test_build_args_from_dict()
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# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from analogistics.statistics import time_series as ts
from analogistics.supply_chain.information_framework import movementfunctionfromInventory
from analogistics.explore import paretoDataframe
def calculatePopularity(movements: pd.Series):
"""
Define the popularity for a SKU
Args:
movements (pd.Series): series of the movement with one item per day.
Returns:
pop_in (float): relative popularity IN per day.
pop_out (float): realtiva popularity OUT per day.
pop_absolute_in (float): popularity IN per day.
pop_absolute_out (float): popularity OUT per day.
"""
pop_in = len(movements[movements > 0]) / len(movements)
pop_out = len(movements[movements < 0]) / len(movements)
pop_absolute_in = len(movements[movements > 0])
pop_absolute_out = len(movements[movements < 0])
return pop_in, pop_out, pop_absolute_in, pop_absolute_out
def calculateCOI(inventory: pd.Series):
"""
Calculate the COI index of an SKU, given the inventory function
Args:
inventory (pd.Series): series of the inventory of an SKU.
Returns:
COI_in (float): COI index IN.
COI_out (float): COI index OUT.
"""
# define inventory from movements
movements = movementfunctionfromInventory(inventory)
movements = movements.dropna()
pop_in, pop_out, _, _ = calculatePopularity(movements['QUANTITY'])
# calculate daily COI
I_t_avg = np.nanmean(inventory)
if I_t_avg > 0:
COI_in = pop_in / I_t_avg
COI_out = pop_out / I_t_avg
else:
COI_in = COI_out = np.nan
return COI_in, COI_out
def calculateTurn(inventory: pd.Series):
"""
Calculate the TURN index of an SKU, given the inventory function
Args:
inventory (pd.series): series of the inventory of an SKU.
Returns:
turn (float): Output turn index.
"""
# define inventory from movements
movements = movementfunctionfromInventory(inventory)
movements = movements.dropna()
# calculate the average outbound quantity per day
out_qty_day = -np.sum(movements[movements['QUANTITY'] < 0]['QUANTITY']) / len(movements)
# calculate average inventory quantity
I_t_avg = np.nanmean(inventory)
if I_t_avg > 0:
turn = out_qty_day / I_t_avg
else:
turn = np.nan
return turn
def calculateOrderCompletion(D_mov: pd.DataFrame, itemcode: str,
itemfield: str = 'ITEMCODE', ordercodefield: str = 'ORDERCODE'):
"""
calculate the Order Completion (OC) index
Args:
D_mov (pd.DataFrame): dataframe with movements reporting ordercode and itemcode columns.
itemcode (str): itemcode to calculate the order competion (OC) index.
itemfield (str, optional): string name of D_mov clumn with itemcode. Defaults to 'ITEMCODE'.
ordercodefield (str, optional): string name of D_mov clumn with ordercode. Defaults to 'ORDERCODE'.
Returns:
OC (float): Output OC index.
"""
# clean data
D_mov = D_mov[[itemfield, ordercodefield]]
D_mov = D_mov[D_mov[ordercodefield] != 'nan']
D_mov = D_mov.dropna()
D_mov = D_mov.reset_index()
orders = list(set(D_mov[D_mov[itemfield] == itemcode][ordercodefield]))
idx = [j in orders for j in D_mov[ordercodefield]]
D_orders = D_mov.loc[idx]
OC = 0
for ordercode in orders:
D_orders_filtered = D_orders[D_orders[ordercodefield] == ordercode]
OC = OC + 1 / len(D_orders_filtered)
return OC
def fourierAnalysisInventory(inventory: pd.Series):
"""
fourier analysis of the inventory curve
Args:
inventory (pd.series): list of inventory values.
Returns:
first_carrier (TYPE): frequency (in 1/days) with the highest amplitude value.
period (TYPE): period (in days) associated with the frequency with the highest amplitude value.
"""
D = ts.fourierAnalysis(np.array(inventory))
D = D.sort_values(by='Amplitude', ascending=False)
first_carrier = D.iloc[0]['Frequency_domain_value'] # 1 / days
period = 1 / first_carrier
return first_carrier, period
def updatePopularity(D_SKUs: pd.DataFrame):
"""
Update the popularity index
Args:
D_SKUs (pd.dataFrame): Input dataframe with SKUs.
Returns:
D_SKUs (pd.DataFrame): Output DataFrame with updated popularity.
"""
# create results columns
D_SKUs['POP_IN'] = np.nan
D_SKUs['POP_OUT'] = np.nan
D_SKUs['POP_IN_TOT'] = np.nan
D_SKUs['POP_OUT_TOT'] = np.nan
for index, row in D_SKUs.iterrows():
# select inventory curve
I_t = D_SKUs.loc[index]['INVENTORY_QTY']
# calculate the popularity
movements = movementfunctionfromInventory(I_t)
movements = movements.dropna()
if len(movements) > 0:
POP_IN, POP_OUT, POP_IN_TOT, POP_OUT_TOT = calculatePopularity(movements['QUANTITY'])
# update the dataframe
D_SKUs.at[index, 'POP_IN'] = POP_IN
D_SKUs.at[index, 'POP_OUT'] = POP_OUT
D_SKUs.at[index, 'POP_IN_TOT'] = POP_IN_TOT
D_SKUs.at[index, 'POP_OUT_TOT'] = POP_OUT_TOT
return D_SKUs
def updateCOI(D_SKUs: pd.DataFrame):
"""
Update the COI index
Args:
D_SKUs (pd.DataFrame): Input dataframe with SKUs.
Returns:
D_SKUs (pd.DataFrame): Output DataFrame with updated COI.
"""
# create result columns
D_SKUs['COI_IN'] = np.nan
D_SKUs['COI_OUT'] = np.nan
for index, row in D_SKUs.iterrows():
# select inventory curve
I_t = D_SKUs.loc[index]['INVENTORY_QTY']
# calculate the popularity
movements = movementfunctionfromInventory(I_t)
movements = movements.dropna()
if len(movements) > 0:
COI_IN, COI_OUT = calculateCOI(I_t)
# update the dataframe
D_SKUs.at[index, 'COI_IN'] = COI_IN
D_SKUs.at[index, 'COI_OUT'] = COI_OUT
return D_SKUs
def updateTURN(D_SKUs: pd.DataFrame):
"""
Update TURN index
Args:
D_SKUs (pd.DataFrame): Input dataframe with SKUs.
Returns:
D_SKUs (TYPE): Output DataFrame with updated TURN.
"""
# create result columns
D_SKUs['TURN'] = np.nan
for index, row in D_SKUs.iterrows():
# select inventory curve
I_t = D_SKUs.loc[index]['INVENTORY_QTY']
# calculate the popularity
movements = movementfunctionfromInventory(I_t)
movements = movements.dropna()
if len(movements) > 0:
TURN = calculateTurn(I_t)
# update the dataframe
D_SKUs.at[index, 'TURN'] = TURN
return D_SKUs
def updateOrderCompletion(D_SKUs: pd.DataFrame, D_mov: pd.DataFrame):
"""
Update OC index
Args:
D_SKUs (pd.DataFrame): Input dataframe with SKUs.
D_mov (pd.DataFrame): Input dataframe with movements.
Returns:
D_SKUs (pd.dataFrame): Output DataFrame with updated OC.
"""
# create result columns
D_SKUs['OC'] = np.nan
for index, row in D_SKUs.iterrows():
part = row['ITEMCODE']
# calculate the popularity
OC = calculateOrderCompletion(D_mov, part, itemfield='ITEMCODE', ordercodefield='ORDERCODE')
# update the dataframe
D_SKUs.at[index, 'OC'] = OC
return D_SKUs
def updateFourieranalysis(D_SKUs: pd.DataFrame):
"""
Update the Fourier Analysis
Args:
D_SKUs (pd.DataFrame): Input dataframe with SKUs.
Returns:
D_SKUs (pd.DataFrame): Output DataFrame with updated fourier analysis.
"""
# create result columns
D_SKUs['FOURIER_CARRIER'] = np.nan
D_SKUs['FOURIER_PERIOD'] = np.nan
for index, row in D_SKUs.iterrows():
# select inventory curve
I_t = D_SKUs.loc[index]['INVENTORY_QTY']
# calculate the popularity
movements = movementfunctionfromInventory(I_t)
movements = movements.dropna()
if len(movements) > 0:
carrier, period = fourierAnalysisInventory(I_t)
# update the dataframe
D_SKUs.at[index, 'FOURIER_CARRIER'] = carrier
D_SKUs.at[index, 'FOURIER_PERIOD'] = period
return D_SKUs
# %% PARETO AND HISTOGRAM PLOT
def whIndexParetoPlot(D_SKUs: pd.DataFrame, columnIndex: str):
"""
Define the Pareto and histogram plot for a WH index
Args:
D_SKUs (pd.DataFrame): Input dataframe with SKUs.
columnIndex (str): Name of the index to plot.
Returns:
output_figures (dict): Output dictionary with figures.
"""
output_figures = {}
# define the pareto values
D_SKUs_pop = paretoDataframe(D_SKUs, columnIndex)
# build the pareto figures
fig1 = plt.figure()
plt.plot(np.arange(0, len(D_SKUs_pop)), D_SKUs_pop[f"{columnIndex}_CUM"], color='orange')
plt.title(f"{columnIndex} Pareto curve")
plt.xlabel("N. of SKUs")
plt.ylabel("Popularity percentage")
# save the Pareto figure
output_figures[f"{columnIndex}_pareto"] = fig1
fig2 = plt.figure()
plt.hist(D_SKUs_pop[columnIndex], color='orange')
plt.title(f"{columnIndex} histogram")
plt.xlabel(f"{columnIndex}")
plt.ylabel("Frequency")
# save the Pareto figure
output_figures[f"{columnIndex}_hist"] = fig2
return output_figures
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] | 2.352081 | 4,036 |
from gazette.spiders.base.fecam import FecamGazetteSpider
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] | 2.681818 | 22 |
import math
import pandas as pd
from causallearn.utils.ScoreUtils import *
def local_score_BIC(Data, i, PAi, parameters=None):
'''
Calculate the *negative* local score with BIC for the linear Gaussian continue data case
Parameters
----------
Data: ndarray, (sample, features)
i: current index
PAi: parent indexes
parameters: lambda_value, the penalty discount of bic
Returns
-------
score: local BIC score
'''
if parameters is None:
lambda_value = 1
else:
lambda_value = parameters['lambda_value']
Data = np.mat(Data)
T = Data.shape[0]
X = Data[:, i]
if len(PAi) != 0:
PA = Data[:, PAi]
D = PA.shape[1]
# derive the parameters by maximum likelihood
H = PA * pdinv(PA.T * PA) * PA.T
E = X - H * X
sigma2 = np.sum(np.power(E, 2)) / T
# BIC
score = T * np.log(sigma2) + lambda_value * D * np.log(T)
else:
sigma2 = np.sum(np.power(X, 2)) / T
# BIC
score = T * np.log(sigma2)
return score
def local_score_BDeu(Data, i, PAi, parameters=None):
'''
Calculate the *negative* local score with BDeu for the discrete case
Parameters
----------
Data: (sample, features)
i: current index
PAi: parent indexes
parameters:
sample_prior: sample prior
structure_prior: structure prior
r_i_map: number of states of the finite random variable X_{i}
Returns
-------
score: local BDeu score
'''
if parameters is None:
sample_prior = 1 # default sample_prior = 1
structure_prior = 1 # default structure_prior = 1
r_i_map = {i: len(np.unique(np.asarray(Data[:, i]))) for i in range(Data.shape[1])}
else:
sample_prior = parameters['sample_prior']
structure_prior = parameters['structure_prior']
r_i_map = parameters['r_i_map']
# calculate q_{i}
q_i = 1
for pa in PAi:
q_i *= r_i_map[pa]
if len(PAi) != 0:
# calculate N_{ij}
names = ['x{}'.format(i) for i in range(Data.shape[1])]
Data_pd = pd.DataFrame(Data, columns=names)
parant_names = ['x{}'.format(i) for i in PAi]
Data_pd_group_Nij = Data_pd.groupby(parant_names)
Nij_map = {key: len(Data_pd_group_Nij.indices.get(key)) for key in Data_pd_group_Nij.indices.keys()}
Nij_map_keys_list = list(Nij_map.keys())
# calculate N_{ijk}
Nijk_map = {ij: Data_pd_group_Nij.get_group(ij).groupby('x{}'.format(i)).apply(len).reset_index() for ij in
Nij_map.keys()}
for v in Nijk_map.values():
v.columns = ['x{}'.format(i), 'times']
else:
# calculate N_{ij}
names = ['x{}'.format(i) for i in range(Data.shape[1])]
Nij_map = {'': len(Data[:, i])}
Nij_map_keys_list = ['']
Data_pd = pd.DataFrame(Data, columns=names)
# calculate N_{ijk}
Nijk_map = {ij: Data_pd.groupby('x{}'.format(i)).apply(len).reset_index() for ij in Nij_map_keys_list}
for v in Nijk_map.values():
v.columns = ['x{}'.format(i), 'times']
BDeu_score = 0
# first term
vm = Data.shape[0] - 1
BDeu_score += len(PAi) * np.log(structure_prior / vm) + (vm - len(PAi)) * np.log(1 - (structure_prior / vm))
# second term
for pa in range(len(Nij_map_keys_list)):
Nij = Nij_map.get(Nij_map_keys_list[pa])
first_term = math.lgamma(sample_prior / q_i) - math.lgamma(Nij + sample_prior / q_i)
second_term = 0
Nijk_list = Nijk_map.get(Nij_map_keys_list[pa])['times'].to_numpy()
for Nijk in Nijk_list:
second_term += math.lgamma(Nijk + sample_prior / (r_i_map[i] * q_i)) - math.lgamma(
sample_prior / (r_i_map[i] * q_i))
BDeu_score += first_term + second_term
return -BDeu_score
def local_score_cv_general(Data, Xi, PAi, parameters):
'''
Calculate the local score
using negative k-fold cross-validated log likelihood as the score
based on a regression model in RKHS
Parameters
----------
Data: (sample, features)
i: current index
PAi: parent indexes
parameters:
kfold: k-fold cross validation
lambda: regularization parameter
Returns
-------
score: local score
'''
Data = np.mat(Data)
PAi = list(PAi)
T = Data.shape[0]
X = Data[:, Xi]
var_lambda = parameters['lambda'] # regularization parameter
k = parameters['kfold'] # k-fold cross validation
n0 = math.floor(T / k)
gamma = 0.01
Thresh = 1E-5
if (len(PAi)):
PA = Data[:, PAi]
# set the kernel for X
GX = np.sum(np.multiply(X, X), axis=1)
Q = np.tile(GX, (1, T))
R = np.tile(GX.T, (T, 1))
dists = Q + R - 2 * X * X.T
dists = dists - np.tril(dists)
dists = np.reshape(dists, (T ** 2, 1))
width = np.sqrt(0.5 * np.median(dists[np.where(dists > 0)], axis=1)[0, 0]) # median value
width = width * 2
theta = 1 / (width ** 2)
Kx, _ = kernel(X, X, (theta, 1)) # Gaussian kernel
H0 = np.mat(np.eye(T)) - np.mat(np.ones((T, T))) / (T) # for centering of the data in feature space
Kx = H0 * Kx * H0 # kernel matrix for X
# eig_Kx, eix = eigdec((Kx + Kx.T)/2, np.min([400, math.floor(T/2)]), evals_only=False) # /2
# IIx = np.where(eig_Kx > np.max(eig_Kx) * Thresh)[0]
# eig_Kx = eig_Kx[IIx]
# eix = eix[:, IIx]
# mx = len(IIx)
# set the kernel for PA
Kpa = np.mat(np.ones((T, T)))
for m in range(PA.shape[1]):
G = np.sum((np.multiply(PA[:, m], PA[:, m])), axis=1)
Q = np.tile(G, (1, T))
R = np.tile(G.T, (T, 1))
dists = Q + R - 2 * PA[:, m] * PA[:, m].T
dists = dists - np.tril(dists)
dists = np.reshape(dists, (T ** 2, 1))
width = np.sqrt(0.5 * np.median(dists[np.where(dists > 0)], axis=1)[0, 0]) # median value
width = width * 2
theta = 1 / (width ** 2)
Kpa = np.multiply(Kpa, kernel(PA[:, m], PA[:, m], (theta, 1))[0])
H0 = np.mat(np.eye(T)) - np.mat(np.ones((T, T))) / (T) # for centering of the data in feature space
Kpa = H0 * Kpa * H0 # kernel matrix for PA
CV = 0
for kk in range(k):
if (kk == 0):
Kx_te = Kx[0:n0, 0:n0]
Kx_tr = Kx[n0: T, n0: T]
Kx_tr_te = Kx[n0: T, 0: n0]
Kpa_te = Kpa[0:n0, 0: n0]
Kpa_tr = Kpa[n0: T, n0: T]
Kpa_tr_te = Kpa[n0: T, 0: n0]
nv = n0 # sample size of validated data
if (kk == k - 1):
Kx_te = Kx[kk * n0:T, kk * n0: T]
Kx_tr = Kx[0:kk * n0, 0: kk * n0]
Kx_tr_te = Kx[0:kk * n0, kk * n0: T]
Kpa_te = Kpa[kk * n0:T, kk * n0: T]
Kpa_tr = Kpa[0: kk * n0, 0: kk * n0]
Kpa_tr_te = Kpa[0:kk * n0, kk * n0: T]
nv = T - kk * n0
if (kk < k - 1 and kk > 0):
Kx_te = Kx[kk * n0: (kk + 1) * n0, kk * n0: (kk + 1) * n0]
Kx_tr = Kx[np.ix_(np.concatenate([np.arange(0, kk * n0), np.arange((kk + 1) * n0, T)]),
np.concatenate([np.arange(0, kk * n0), np.arange((kk + 1) * n0, T)]))]
Kx_tr_te = Kx[np.ix_(np.concatenate([np.arange(0, kk * n0), np.arange((kk + 1) * n0, T)]),
np.arange(kk * n0, (kk + 1) * n0))]
Kpa_te = Kpa[kk * n0: (kk + 1) * n0, kk * n0: (kk + 1) * n0]
Kpa_tr = Kpa[np.ix_(np.concatenate([np.arange(0, kk * n0), np.arange((kk + 1) * n0, T)]),
np.concatenate([np.arange(0, kk * n0), np.arange((kk + 1) * n0, T)]))]
Kpa_tr_te = Kpa[np.ix_(np.concatenate([np.arange(0, kk * n0), np.arange((kk + 1) * n0, T)]),
np.arange(kk * n0, (kk + 1) * n0))]
nv = n0
n1 = T - nv
tmp1 = pdinv(Kpa_tr + n1 * var_lambda * np.mat(np.eye(n1)))
tmp2 = tmp1 * Kx_tr * tmp1
tmp3 = tmp1 * pdinv(np.mat(np.eye(n1)) + n1 * var_lambda ** 2 / gamma * tmp2) * tmp1
A = (Kx_te + Kpa_tr_te.T * tmp2 * Kpa_tr_te - 2 * Kx_tr_te.T * tmp1 * Kpa_tr_te
- n1 * var_lambda ** 2 / gamma * Kx_tr_te.T * tmp3 * Kx_tr_te
- n1 * var_lambda ** 2 / gamma * Kpa_tr_te.T * tmp1 * Kx_tr * tmp3 * Kx_tr * tmp1 * Kpa_tr_te
+ 2 * n1 * var_lambda ** 2 / gamma * Kx_tr_te.T * tmp3 * Kx_tr * tmp1 * Kpa_tr_te) / gamma
B = n1 * var_lambda ** 2 / gamma * tmp2 + np.mat(np.eye(n1))
L = np.linalg.cholesky(B)
C = np.sum(np.log(np.diag(L)))
# CV = CV + (nv*nv*log(2*pi) + nv*C + nv*mx*log(gamma) + trace(A))/2;
CV = CV + (nv * nv * np.log(2 * np.pi) + nv * C + np.trace(A)) / 2
CV = CV / k
else:
# set the kernel for X
GX = np.sum(np.multiply(X, X), axis=1)
Q = np.tile(GX, (1, T))
R = np.tile(GX.T, (T, 1))
dists = Q + R - 2 * X * X.T
dists = dists - np.tril(dists)
dists = np.reshape(dists, (T ** 2, 1))
width = np.sqrt(0.5 * np.median(dists[np.where(dists > 0)], axis=1)[0, 0]) # median value
width = width * 2
theta = 1 / (width ** 2)
Kx, _ = kernel(X, X, (theta, 1)) # Gaussian kernel
H0 = np.mat(np.eye(T)) - np.mat(np.ones((T, T))) / (T) # for centering of the data in feature space
Kx = H0 * Kx * H0 # kernel matrix for X
# eig_Kx, eix = eigdec((Kx + Kx.T) / 2, np.min([400, math.floor(T / 2)]), evals_only=False) # /2
# IIx = np.where(eig_Kx > np.max(eig_Kx) * Thresh)[0]
# mx = len(IIx)
CV = 0
for kk in range(k):
if (kk == 0):
Kx_te = Kx[kk * n0: (kk + 1) * n0, kk * n0: (kk + 1) * n0]
Kx_tr = Kx[(kk + 1) * n0:T, (kk + 1) * n0: T]
Kx_tr_te = Kx[(kk + 1) * n0:T, kk * n0: (kk + 1) * n0]
nv = n0
if (kk == k - 1):
Kx_te = Kx[kk * n0: T, kk * n0: T]
Kx_tr = Kx[0: kk * n0, 0: kk * n0]
Kx_tr_te = Kx[0:kk * n0, kk * n0: T]
nv = T - kk * n0
if (kk < k - 1 and kk > 0):
Kx_te = Kx[kk * n0: (kk + 1) * n0, kk * n0: (kk + 1) * n0]
Kx_tr = Kx[np.ix_(np.concatenate([np.arange(0, kk * n0), np.arange((kk + 1) * n0, T)]),
np.concatenate([np.arange(0, kk * n0), np.arange((kk + 1) * n0, T)]))]
Kx_tr_te = Kx[np.ix_(np.concatenate([np.arange(0, kk * n0), np.arange((kk + 1) * n0, T)]),
np.arange(kk * n0, (kk + 1) * n0))]
nv = n0
n1 = T - nv
A = (Kx_te - 1 / (gamma * n1) * Kx_tr_te.T * pdinv(
np.mat(np.eye(n1)) + 1 / (gamma * n1) * Kx_tr) * Kx_tr_te) / gamma
B = 1 / (gamma * n1) * Kx_tr + np.mat(np.eye(n1))
L = np.linalg.cholesky(B)
C = np.sum(np.log(np.diag(L)))
# CV = CV + (nv*nv*log(2*pi) + nv*C + nv*mx*log(gamma) + trace(A))/2;
CV = CV + (nv * nv * np.log(2 * np.pi) + nv * C + np.trace(A)) / 2
CV = CV / k
score = CV # negative cross-validated likelihood
return score
def local_score_cv_multi(Data, Xi, PAi, parameters):
'''
Calculate the local score
using negative k-fold cross-validated log likelihood as the score
based on a regression model in RKHS
for variables with multi-variate dimensions
Parameters
----------
Data: (sample, features)
i: current index
PAi: parent indexes
parameters:
kfold: k-fold cross validation
lambda: regularization parameter
dlabel: for variables with multi-dimensions,
indicate which dimensions belong to the i-th variable.
Returns
-------
score: local score
'''
T = Data.shape[0]
X = Data[:, parameters['dlabel'][Xi]]
var_lambda = parameters['lambda'] # regularization parameter
k = parameters['kfold'] # k-fold cross validation
n0 = math.floor(T / k)
gamma = 0.01
Thresh = 1E-5
if (len(PAi)):
# set the kernel for X
GX = np.sum(np.multiply(X, X), axis=1)
Q = np.tile(GX, (1, T))
R = np.tile(GX.T, (T, 1))
dists = Q + R - 2 * X * X.T
dists = dists - np.tril(dists)
dists = np.reshape(dists, (T ** 2, 1))
width = np.sqrt(0.5 * np.median(dists[np.where(dists > 0)], axis=1)[0, 0]) # median value
width = width * 3 ###
theta = 1 / (width ** 2 * X.shape[1]) #
Kx, _ = kernel(X, X, (theta, 1)) # Gaussian kernel
H0 = np.mat(np.eye(T)) - np.mat(np.ones((T, T))) / (T) # for centering of the data in feature space
Kx = H0 * Kx * H0 # kernel matrix for X
# set the kernel for PA
Kpa = np.mat(np.ones((T, T)))
for m in range(len(PAi)):
PA = Data[:, parameters['dlabel'][PAi[m]]]
G = np.sum((np.multiply(PA, PA)), axis=1)
Q = np.tile(G, (1, T))
R = np.tile(G.T, (T, 1))
dists = Q + R - 2 * PA * PA.T
dists = dists - np.tril(dists)
dists = np.reshape(dists, (T ** 2, 1))
width = np.sqrt(0.5 * np.median(dists[np.where(dists > 0)], axis=1)[0, 0]) # median value
width = width * 3 ###
theta = 1 / (width ** 2 * PA.shape[1])
Kpa = np.multiply(Kpa, kernel(PA, PA, (theta, 1))[0])
H0 = np.mat(np.eye(T)) - np.mat(np.ones((T, T))) / (T) # for centering of the data in feature space
Kpa = H0 * Kpa * H0 # kernel matrix for PA
CV = 0
for kk in range(k):
if (kk == 0):
Kx_te = Kx[0:n0, 0:n0]
Kx_tr = Kx[n0: T, n0: T]
Kx_tr_te = Kx[n0: T, 0: n0]
Kpa_te = Kpa[0:n0, 0: n0]
Kpa_tr = Kpa[n0: T, n0: T]
Kpa_tr_te = Kpa[n0: T, 0: n0]
nv = n0 # sample size of validated data
if (kk == k - 1):
Kx_te = Kx[kk * n0:T, kk * n0: T]
Kx_tr = Kx[0:kk * n0, 0: kk * n0]
Kx_tr_te = Kx[0:kk * n0, kk * n0: T]
Kpa_te = Kpa[kk * n0:T, kk * n0: T]
Kpa_tr = Kpa[0: kk * n0, 0: kk * n0]
Kpa_tr_te = Kpa[0:kk * n0, kk * n0: T]
nv = T - kk * n0
if (kk < k - 1 and kk > 0):
Kx_te = Kx[kk * n0: (kk + 1) * n0, kk * n0: (kk + 1) * n0]
Kx_tr = Kx[np.ix_(np.concatenate([np.arange(0, kk * n0), np.arange((kk + 1) * n0, T)]),
np.concatenate([np.arange(0, kk * n0), np.arange((kk + 1) * n0, T)]))]
Kx_tr_te = Kx[np.ix_(np.concatenate([np.arange(0, kk * n0), np.arange((kk + 1) * n0, T)]),
np.arange(kk * n0, (kk + 1) * n0))]
Kpa_te = Kpa[kk * n0: (kk + 1) * n0, kk * n0: (kk + 1) * n0]
Kpa_tr = Kpa[np.ix_(np.concatenate([np.arange(0, kk * n0), np.arange((kk + 1) * n0, T)]),
np.concatenate([np.arange(0, kk * n0), np.arange((kk + 1) * n0, T)]))]
Kpa_tr_te = Kpa[np.ix_(np.concatenate([np.arange(0, kk * n0), np.arange((kk + 1) * n0, T)]),
np.arange(kk * n0, (kk + 1) * n0))]
nv = n0
n1 = T - nv
tmp1 = pdinv(Kpa_tr + n1 * var_lambda * np.mat(np.eye(n1)))
tmp2 = tmp1 * Kx_tr * tmp1
tmp3 = tmp1 * pdinv(np.mat(np.eye(n1)) + n1 * var_lambda ** 2 / gamma * tmp2) * tmp1
A = (Kx_te + Kpa_tr_te.T * tmp2 * Kpa_tr_te - 2 * Kx_tr_te.T * tmp1 * Kpa_tr_te
- n1 * var_lambda ** 2 / gamma * Kx_tr_te.T * tmp3 * Kx_tr_te
- n1 * var_lambda ** 2 / gamma * Kpa_tr_te.T * tmp1 * Kx_tr * tmp3 * Kx_tr * tmp1 * Kpa_tr_te
+ 2 * n1 * var_lambda ** 2 / gamma * Kx_tr_te.T * tmp3 * Kx_tr * tmp1 * Kpa_tr_te) / gamma
B = n1 * var_lambda ** 2 / gamma * tmp2 + np.mat(np.eye(n1))
L = np.linalg.cholesky(B)
C = np.sum(np.log(np.diag(L)))
# CV = CV + (nv*nv*log(2*pi) + nv*C + nv*mx*log(gamma) + trace(A))/2;
CV = CV + (nv * nv * np.log(2 * np.pi) + nv * C + np.trace(A)) / 2
CV = CV / k
else:
# set the kernel for X
GX = np.sum(np.multiply(X, X), axis=1)
Q = np.tile(GX, (1, T))
R = np.tile(GX.T, (T, 1))
dists = Q + R - 2 * X * X.T
dists = dists - np.tril(dists)
dists = np.reshape(dists, (T ** 2, 1))
width = np.sqrt(0.5 * np.median(dists[np.where(dists > 0)], axis=1)[0, 0]) # median value
width = width * 3 ###
theta = 1 / (width ** 2 * X.shape[1]) #
Kx, _ = kernel(X, X, (theta, 1)) # Gaussian kernel
H0 = np.mat(np.eye(T)) - np.mat(np.ones((T, T))) / (T) # for centering of the data in feature space
Kx = H0 * Kx * H0 # kernel matrix for X
CV = 0
for kk in range(k):
if (kk == 0):
Kx_te = Kx[kk * n0: (kk + 1) * n0, kk * n0: (kk + 1) * n0]
Kx_tr = Kx[(kk + 1) * n0:T, (kk + 1) * n0: T]
Kx_tr_te = Kx[(kk + 1) * n0:T, kk * n0: (kk + 1) * n0]
nv = n0
if (kk == k - 1):
Kx_te = Kx[kk * n0: T, kk * n0: T]
Kx_tr = Kx[0: kk * n0, 0: kk * n0]
Kx_tr_te = Kx[0:kk * n0, kk * n0: T]
nv = T - kk * n0
if (kk < k - 1 and kk > 0):
Kx_te = Kx[kk * n0: (kk + 1) * n0, kk * n0: (kk + 1) * n0]
Kx_tr = Kx[np.ix_(np.concatenate([np.arange(0, kk * n0), np.arange((kk + 1) * n0, T)]),
np.concatenate([np.arange(0, kk * n0), np.arange((kk + 1) * n0, T)]))]
Kx_tr_te = Kx[np.ix_(np.concatenate([np.arange(0, kk * n0), np.arange((kk + 1) * n0, T)]),
np.arange(kk * n0, (kk + 1) * n0))]
nv = n0
n1 = T - nv
A = (Kx_te - 1 / (gamma * n1) * Kx_tr_te.T * pdinv(
np.mat(np.eye(n1)) + 1 / (gamma * n1) * Kx_tr) * Kx_tr_te) / gamma
B = 1 / (gamma * n1) * Kx_tr + np.mat(np.eye(n1))
L = np.linalg.cholesky(B)
C = np.sum(np.log(np.diag(L)))
# CV = CV + (nv*nv*log(2*pi) + nv*C + nv*mx*log(gamma) + trace(A))/2;
CV = CV + (nv * nv * np.log(2 * np.pi) + nv * C + np.trace(A)) / 2
CV = CV / k
score = CV # negative cross-validated likelihood
return score
def local_score_marginal_general(Data, Xi, PAi, parameters):
'''
Calculate the local score by negative marginal likelihood
based on a regression model in RKHS
Parameters
----------
Data: (sample, features)
i: current index
PAi: parent indexes
parameters: None
Returns
-------
score: local score
'''
T = Data.shape[0]
X = Data[:, Xi]
dX = X.shape[1]
# set the kernel for X
GX = np.sum(np.multiply(X, X), axis=1)
Q = np.tile(GX, (1, T))
R = np.tile(GX.T, (T, 1))
dists = Q + R - 2 * X * X.T
dists = dists - np.tril(dists)
dists = np.reshape(dists, (T ** 2, 1))
width = np.sqrt(0.5 * np.median(dists[np.where(dists > 0)], axis=1)[0, 0])
width = width * 2.5 # kernel width
theta = 1 / (width ** 2)
H = np.mat(np.eye(T)) - np.mat(np.ones((T, T))) / T
Kx, _ = kernel(X, X, (theta, 1))
Kx = H * Kx * H
Thresh = 1E-5
eig_Kx, eix = eigdec((Kx + Kx.T) / 2, np.min([400, math.floor(T / 4)]), evals_only=False) # /2
IIx = np.where(eig_Kx > np.max(eig_Kx) * Thresh)[0]
eig_Kx = eig_Kx[IIx]
eix = eix[:, IIx]
if (len(PAi)):
PA = Data[:, PAi]
widthPA = np.mat(np.empty((PA.shape[1], 1)))
# set the kernel for PA
for m in range(PA.shape[1]):
G = np.sum((np.multiply(PA[:, m], PA[:, m])), axis=1)
Q = np.tile(G, (1, T))
R = np.tile(G.T, (T, 1))
dists = Q + R - 2 * PA[:, m] * PA[:, m].T
dists = dists - np.tril(dists)
dists = np.reshape(dists, (T ** 2, 1))
widthPA[m] = np.sqrt(0.5 * np.median(dists[np.where(dists > 0)], axis=1)[0, 0])
widthPA = widthPA * 2.5 # kernel width
covfunc = np.asarray(['covSum', ['covSEard', 'covNoise']])
logtheta0 = np.vstack([np.log(widthPA), 0, np.log(np.sqrt(0.1))])
logtheta, fvals, iter = minimize(logtheta0, 'gpr_multi_new', -300, covfunc, PA,
2 * np.sqrt(T) * eix * np.diag(np.sqrt(eig_Kx)) / np.sqrt(eig_Kx[0]))
nlml, dnlml = gpr_multi_new(logtheta, covfunc, PA,
2 * np.sqrt(T) * eix * np.diag(np.sqrt(eig_Kx)) / np.sqrt(eig_Kx[0]),
nargout=2)
else:
covfunc = np.asarray(['covSum', ['covSEard', 'covNoise']])
PA = np.mat(np.zeros((T, 1)))
logtheta0 = np.mat([100, 0, np.log(np.sqrt(0.1))]).T
logtheta, fvals, iter = minimize(logtheta0, 'gpr_multi_new', -300, covfunc, PA,
2 * np.sqrt(T) * eix * np.diag(np.sqrt(eig_Kx)) / np.sqrt(eig_Kx[0]))
nlml, dnlml = gpr_multi_new(logtheta, covfunc, PA,
2 * np.sqrt(T) * eix * np.diag(np.sqrt(eig_Kx)) / np.sqrt(eig_Kx[0]),
nargout=2)
score = nlml # negative log-likelihood
return score
def local_score_marginal_multi(Data, Xi, PAi, parameters):
'''
Calculate the local score by negative marginal likelihood
based on a regression model in RKHS
for variables with multi-variate dimensions
Parameters
----------
Data: (sample, features)
i: current index
PAi: parent indexes
parameters:
dlabel: for variables with multi-dimensions,
indicate which dimensions belong to the i-th variable.
Returns
-------
score: local score
'''
T = Data.shape[0]
X = Data[:, parameters['dlabel'][Xi]]
dX = X.shape[1]
# set the kernel for X
GX = np.sum(np.multiply(X, X), axis=1)
Q = np.tile(GX, (1, T))
R = np.tile(GX.T, (T, 1))
dists = Q + R - 2 * X * X.T
dists = dists - np.tril(dists)
dists = np.reshape(dists, (T ** 2, 1))
widthX = np.sqrt(0.5 * np.median(dists[np.where(dists > 0)], axis=1)[0, 0])
widthX = widthX * 2.5 # kernel width
theta = 1 / (widthX ** 2)
H = np.mat(np.eye(T)) - np.mat(np.ones((T, T))) / T
Kx, _ = kernel(X, X, (theta, 1))
Kx = H * Kx * H
Thresh = 1E-5
eig_Kx, eix = eigdec((Kx + Kx.T) / 2, np.min([400, math.floor(T / 4)]), evals_only=False) # /2
IIx = np.where(eig_Kx > np.max(eig_Kx) * Thresh)[0]
eig_Kx = eig_Kx[IIx]
eix = eix[:, IIx]
if (len(PAi)):
widthPA_all = np.mat(np.empty((1, 0)))
# set the kernel for PA
PA_all = np.mat(np.empty((Data.shape[0], 0)))
for m in range(len(PAi)):
PA = Data[:, parameters['dlabel'][PAi[m]]]
PA_all = np.hstack([PA_all, PA])
G = np.sum((np.multiply(PA, PA)), axis=1)
Q = np.tile(G, (1, T))
R = np.tile(G.T, (T, 1))
dists = Q + R - 2 * PA * PA.T
dists = dists - np.tril(dists)
dists = np.reshape(dists, (T ** 2, 1))
widthPA = np.sqrt(0.5 * np.median(dists[np.where(dists > 0)], axis=1)[0, 0])
widthPA_all = np.hstack(
[widthPA_all, widthPA * np.mat(np.ones((1, np.size(parameters['dlabel'][PAi[m]]))))])
widthPA_all = widthPA_all * 2.5 # kernel width
covfunc = np.asarray(['covSum', ['covSEard', 'covNoise']])
logtheta0 = np.vstack([np.log(widthPA_all.T), 0, np.log(np.sqrt(0.1))])
logtheta, fvals, iter = minimize(logtheta0, 'gpr_multi_new', -300, covfunc, PA_all,
2 * np.sqrt(T) * eix * np.diag(np.sqrt(eig_Kx)) / np.sqrt(eig_Kx[0]))
nlml, dnlml = gpr_multi_new(logtheta, covfunc, PA_all,
2 * np.sqrt(T) * eix * np.diag(np.sqrt(eig_Kx)) / np.sqrt(eig_Kx[0]),
nargout=2)
else:
covfunc = np.asarray(['covSum', ['covSEard', 'covNoise']])
PA = np.mat(np.zeros((T, 1)))
logtheta0 = np.mat([100, 0, np.log(np.sqrt(0.1))]).T
logtheta, fvals, iter = minimize(logtheta0, 'gpr_multi_new', -300, covfunc, PA,
2 * np.sqrt(T) * eix * np.diag(np.sqrt(eig_Kx)) / np.sqrt(eig_Kx[0]))
nlml, dnlml = gpr_multi_new(logtheta, covfunc, PA,
2 * np.sqrt(T) * eix * np.diag(np.sqrt(eig_Kx)) / np.sqrt(eig_Kx[0]),
nargout=2)
score = nlml # negative log-likelihood
return score
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] | 1.754226 | 14,493 |
import sys
import os
# make sure this is a system variable in your bashrc
NASA_ULI_ROOT_DIR = os.environ['NASA_ULI_ROOT_DIR']
XPC3_DIR = NASA_ULI_ROOT_DIR + '/src/'
sys.path.append(XPC3_DIR)
import numpy as np
import xpc3
import xpc3_helper
import time
import os
import mss
import cv2
import settings
screenShot = mss.mss()
def record(client, outDir, startPerc = 1.5, endPerc = 10, freq = 10, numEpisodes = 1):
""" Record data from training episodes in XPlane into a CSV and PNG files
Args:
client: XPlane Client
outDir: output directory for csv file and png images
-----------------
startPerc: percentage down runway to start collecting data
endPerc: percentage down runway to finish collecting data
(NOTE: this must be less than endPerc for the sinusoidal trajectory)
freq: frequency to save data
(NOTE: this is approximate due to computational overhead)
numEpisodes: number of episodes to record for
"""
# Make data folder if it doesn't exist
if not os.path.exists(outDir):
os.makedirs(outDir)
# Initialize the CSV file
csvFile = outDir + 'labels.csv'
with open(csvFile, 'w') as fd:
fd.write('image_filename,absolute_time_GMT_seconds,relative_time_seconds,distance_to_centerline_meters,')
fd.write('distance_to_centerline_NORMALIZED,downtrack_position_meters,downtrack_position_NORMALIZED,')
fd.write('heading_error_degrees,heading_error_NORMALIZED,period_of_day,cloud_type\n')
for i in range(numEpisodes):
first = True
percDownRunway = xpc3_helper.getPercDownRunway(client)
currStep = 0
while percDownRunway > endPerc:
time.sleep(0.5)
percDownRunway = xpc3_helper.getPercDownRunway(client)
while percDownRunway < endPerc:
if ((percDownRunway > startPerc) and (percDownRunway < 45.0)) or ((percDownRunway > 51.5) and (percDownRunway < endPerc)):
if first:
startTime = client.getDREF("sim/time/zulu_time_sec")[0]
addCurrData(client, outDir, csvFile, startTime, currStep, i + 1)
first = False
time.sleep(1 / freq)
else:
addCurrData(client, outDir, csvFile,
startTime, currStep, i + 1)
time.sleep(1 / freq)
currStep += 1
else:
time.sleep(1)
percDownRunway = xpc3_helper.getPercDownRunway(client)
def addCurrData(client, outDir, csvFile, startTime, currStep, episodeNum):
""" Add current data to csv file and save off a screenshot
Args:
client: XPlane Client
outDir: output directory for csv file and png images
csvFile: name of the CSV file for non-image data (should be initialized already)
startTime: absolute time that the episode started
currStep: current step of saving data (for image labeling)
episodeNum: number of the current episode (for image labeling)
"""
# Time step information
absolute_time = client.getDREF("sim/time/zulu_time_sec")[0]
time_step = absolute_time - startTime
# Plane state information
cte, dtp, he = xpc3_helper.getHomeState(client)
# Environmental conditions
local_time = client.getDREF("sim/time/local_time_sec")[0]
if local_time < 5 * 3600 or local_time > 17 * 3600:
period_of_day = 2
time_period = 'night'
elif local_time > 12 * 3600 and local_time < 17 * 3600:
period_of_day = 1
time_period = 'afternoon'
else:
period_of_day = 0
time_period = 'morning'
cloud_cover = client.getDREF("sim/weather/cloud_type[0]")[0]
weather = settings.WEATHER_TYPES[cloud_cover]
# Image information
img = cv2.cvtColor(np.array(screenShot.grab(settings.MONITOR)),
cv2.COLOR_BGRA2BGR)[230:, :, :]
img = cv2.resize(img, (settings.WIDTH, settings.HEIGHT))
img_name = 'MWH_Runway04_' + time_period + '_' + weather + '_' + str(episodeNum) + '_' + str(currStep) + '.png'
# For now, just save the image to an output directory
cv2.imwrite('%s%s' % (outDir, img_name), img)
# Append everything to the csv file
with open(csvFile, 'a') as fd:
fd.write("%s,%f,%f,%f,%f,%f,%f,%f,%f,%d,%d\n" % (img_name, absolute_time, time_step,
cte, cte / 10.0, dtp, dtp / 2982.0, he, he / 30.0, period_of_day, cloud_cover))
if __name__ == "__main__":
main()
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#!/usr/bin/python3
# -*- coding: utf-8 -*-
from rsamsg import *
#alice and bob
#alice_pubkey, alice_privkey=gen()
#bob_pubkey, bob_privkey=gen()
alice_pubkey = loadfile("keys/alice_pubkey")
alice_prikey = loadfile("keys/alice_prikey")
bob_pubkey = loadfile("keys/bob_pubkey")
bob_prikey = loadfile("keys/bob_prikey")
#from alice
message = "你好"
#to bob
crypto,signature=alice_to_bob(message,bob_pubkey,alice_prikey)
# bob verify
verifyed,msg=bob_get_msg_from_alice(crypto,signature,bob_prikey,alice_pubkey)
if(verifyed):
print(msg)
else:
print("err:{}".format(msg)) | [
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] | 2.200758 | 264 |
"""Defines the class for a node health check task"""
from __future__ import unicode_literals
import datetime
from django.conf import settings
from job.tasks.base_task import AtomicCounter
from job.tasks.node_task import NodeTask
from node.resources.node_resources import NodeResources
from node.resources.resource import Cpus, Mem
HEALTH_TASK_ID_PREFIX = 'scale_health'
COUNTER = AtomicCounter()
class HealthTask(NodeTask):
"""Represents a task that performs a health check on a node. This class is thread-safe.
"""
BAD_DAEMON_CODE = 2
LOW_DOCKER_SPACE_CODE = 3
BAD_LOGSTASH_CODE = 4
def __init__(self, framework_id, agent_id):
"""Constructor
:param framework_id: The framework ID
:type framework_id: string
:param agent_id: The agent ID
:type agent_id: string
"""
task_id = '%s_%s_%d' % (HEALTH_TASK_ID_PREFIX, framework_id, COUNTER.get_next())
super(HealthTask, self).__init__(task_id, 'Scale Health Check', agent_id)
self._uses_docker = False
self._docker_image = None
self._docker_params = []
self._is_docker_privileged = False
self._running_timeout_threshold = datetime.timedelta(minutes=15)
health_check_commands = []
# Check if docker version works (indicates if daemon is working)
bad_daemon_check = 'docker version'
bad_daemon_check = 'timeout -s SIGKILL 10s %s' % bad_daemon_check # docker version has 10 seconds to succeed
bad_daemon_check = '%s; if [[ $? != 0 ]]; then exit %d; fi' % (bad_daemon_check, HealthTask.BAD_DAEMON_CODE)
health_check_commands.append(bad_daemon_check)
# Check if docker ps works (also indicates if daemon is working)
docker_ps_check = 'docker ps'
docker_ps_check = 'timeout -s SIGKILL 10s %s' % docker_ps_check # docker ps has 10 seconds to succeed
docker_ps_check = '%s; if [[ $? != 0 ]]; then exit %d; fi' % (docker_ps_check, HealthTask.BAD_DAEMON_CODE)
health_check_commands.append(docker_ps_check)
# Check if Docker disk space is below 1 GiB (assumes /var/lib/docker, ignores check otherwise)
get_disk_space = 'df --output=avail /var/lib/docker | tail -1'
test_disk_space = 'test `%s` -lt 1048576; if [[ $? == 0 ]]; then exit %d; fi'
test_disk_space = test_disk_space % (get_disk_space, HealthTask.LOW_DOCKER_SPACE_CODE)
low_docker_space_check = 'if [[ -d /var/lib/docker ]]; then %s; fi' % test_disk_space
health_check_commands.append(low_docker_space_check)
# Check to ensure that fluentd is reachable
if settings.LOGGING_HEALTH_ADDRESS:
logging_check = 'timeout -s SIGKILL 5s curl %s; if [[ $? != 0 ]]; then exit %d; fi'
logging_check = logging_check % (settings.LOGGING_HEALTH_ADDRESS, HealthTask.BAD_LOGSTASH_CODE)
health_check_commands.append(logging_check)
self._command = ' && '.join(health_check_commands)
# Node task properties
self.task_type = 'health-check'
self.title = 'Node Health Check'
self.description = 'Checks the health status of the node'
def get_resources(self):
"""See :meth:`job.tasks.base_task.Task.get_resources`
"""
return NodeResources([Cpus(0.1), Mem(32.0)])
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] | 2.458027 | 1,358 |
#!/usr/bin/env python
try:
from components import resistor
from components import capacitor
from components import voltage
from components import tolerance
except:
from bommerge.components import resistor
from bommerge.components import capacitor
from bommerge.components import voltage
from bommerge.components import tolerance
from gui.projectConfigurationWidget import ProjectConfigurationWidget
from parsers import csvToJson
from utils import files
import os
from decimal import *
try:
import Tkinter as tk
import ttk
except ImportError:
import tkinter as tk
from tkinter import ttk
if __name__ == "__main__":
main()
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] | 3.301435 | 209 |
# Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.
import math
import numpy as np
import torch
from . import FairseqDataset
class TokenBlockDataset(FairseqDataset):
"""Break a Dataset of tokens into blocks.
Args:
dataset (~torch.utils.data.Dataset): dataset to break into blocks
sizes (List[int]): sentence lengths (required for 'complete' and 'eos')
block_size (int): maximum block size (ignored in 'eos' break mode)
break_mode (str, optional): Mode used for breaking tokens. Values can
be one of:
- 'none': break tokens into equally sized blocks (up to block_size)
- 'complete': break tokens into blocks (up to block_size) such that
blocks contains complete sentences, although block_size may be
exceeded if some sentences exceed block_size
- 'eos': each block contains one sentence (block_size is ignored)
include_targets (bool, optional): return next tokens as targets
(default: False).
"""
@property
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37227,
628,
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2488,
26745,
198
] | 2.927602 | 442 |
#!/usr/bin/python3
import os
from brownie import VRF_Pizza, VRF_Pizza_RNG, accounts, network, config, interface
STATIC_SEED = 123
| [
2,
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] | 2.75 | 48 |
"""Math utilities."""
import numpy as np
from numba import njit
rel_tol = 1e-10
abs_tol = 0.
def is_close(a, b, rel_tol=rel_tol, abs_tol=abs_tol):
"""Tell whether two values are approximately equal."""
if np.isnan(a) or np.isnan(b):
return False
if np.isinf(a) or np.isinf(b):
return False
if a == b:
return True
return abs(a - b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol)
is_close_nb = njit(cache=True)(is_close)
"""Numba-compiled version of `is_close`."""
def is_close_or_less(a, b, rel_tol=rel_tol, abs_tol=abs_tol):
"""Tell whether the first value is approximately less than or equal to the second value."""
if is_close(a, b, rel_tol=rel_tol, abs_tol=abs_tol):
return True
return a < b
@njit(cache=True)
def is_close_or_less_nb(a, b, rel_tol=rel_tol, abs_tol=abs_tol):
"""Numba-compiled version of `is_close_or_less`."""
if is_close_nb(a, b, rel_tol=rel_tol, abs_tol=abs_tol):
return True
return a < b
def is_less(a, b, rel_tol=rel_tol, abs_tol=abs_tol):
"""Tell whether the first value is approximately less than the second value."""
if is_close(a, b, rel_tol=rel_tol, abs_tol=abs_tol):
return False
return a < b
@njit(cache=True)
def is_less_nb(a, b, rel_tol=rel_tol, abs_tol=abs_tol):
"""Numba-compiled version of `is_less`."""
if is_close_nb(a, b, rel_tol=rel_tol, abs_tol=abs_tol):
return False
return a < b
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] | 2.147059 | 680 |
"""This module contains `docker image rm` class"""
from docker.errors import APIError
from .command import Command
class Rm(Command):
"""This class implements `docker image rm` command"""
name = "image rm"
require = []
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# -*- coding: utf-8 -*-
"""Project and solution file reader classes."""
import abc
import re
from vstools import resources
class FileReader(object):
"""File reader."""
def __init__(self, encoding='utf-8'):
"""Initializes a file reader.
Args:
encoding (str): encoding.
"""
super(FileReader, self).__init__()
self._encoding = encoding
self._file = None
self._line = None
def _ReadBinaryData(self, size):
"""Reads binary data.
Args:
size (int): number of bytes to read.
Returns:
bytes: binary data.
"""
return self._file.read(size)
def _ReadLine(self, look_ahead=False):
"""Reads a line.
Args:
look_ahead (Optional[bool]): indicated if the line should be considered
read (False) or not (True).
Returns:
str: line stripped of leading and trailing white space or None if no
input is available.
"""
if self._line is not None:
line = self._line
if not look_ahead:
self._line = None
else:
line = self._file.readline()
if line:
line = line.strip()
if look_ahead:
self._line = line
if isinstance(line, bytes):
line = line.decode(self._encoding)
return line
def Close(self):
"""Closes the file."""
self._file.close()
def Open(self, filename):
"""Opens the file.
Args:
filename (str): path of the file.
"""
self._file = open(filename, 'rb') # pylint: disable=consider-using-with
class VSProjectFileReader(FileReader):
"""Visual Studio project file reader."""
class VS2008ProjectFileReader(VSProjectFileReader):
"""Visual Studio 2008 project file reader."""
_CONFIGURATION_OPTIONS = {
'CharacterSet': 'character_set',
'ConfigurationType': 'output_type',
'ManagedExtensions': 'managed_extensions',
'WholeProgramOptimization': 'whole_program_optimization',
}
_TOOL_COMPILER_CONFIGURATION_OPTIONS = {
'AdditionalIncludeDirectories': 'include_directories',
'BasicRuntimeChecks': 'basic_runtime_checks',
'CompileAs': 'compile_as',
'DebugInformationFormat': 'debug_information_format',
'Detect64BitPortabilityProblems': 'detect_64bit_portability_problems',
'EnableFunctionLevelLinking': 'enable_function_level_linking',
'EnableIntrinsicFunctions': 'enable_intrinsic_functions',
'Optimization': 'optimization',
'PreprocessorDefinitions': 'preprocessor_definitions',
'RuntimeLibrary': 'runtime_library',
'SmallerTypeCheck': 'smaller_type_check',
'UsePrecompiledHeader': 'precompiled_header',
'WarnAsError': 'warning_as_error',
'WarningLevel': 'warning_level',
}
_TOOL_LIBRARIAN_CONFIGURATION_OPTIONS = {
'IgnoreAllDefaultLibraries': 'librarian_ignore_defaults',
'OutputFile': 'librarian_output_file',
}
_TOOL_LINKER_CONFIGURATION_OPTIONS = {
'AdditionalDependencies': 'additional_dependencies',
'AdditionalLibraryDirectories': 'library_directories',
'DataExecutionPrevention': 'data_execution_prevention',
'EnableCOMDATFolding': 'enable_comdat_folding',
'FixedBaseAddress': 'fixed_base_address',
'GenerateDebugInformation': 'generate_debug_information',
'ImportLibrary': 'linker_values_set',
'LinkIncremental': 'link_incremental',
'ModuleDefinitionFile': 'module_definition_file',
'OptimizeReferences': 'optimize_references',
'OutputDirectory': 'linker_output_directory',
'OutputFile': 'linker_output_file',
'RandomizedBaseAddress': 'randomized_base_address',
'SubSystem': 'sub_system',
'TargetMachine': 'target_machine',
}
def _ParseConfigurationOption(
self, project_configuration, definition, name, line):
"""Parses a configuration option.
Args:
project_configuration (VSProjectConfiguration): project configuration.
definition (str): definition of the configuration value in file.
name (str): name of the configuration value in the project information.
line (str): line that contains the configuration value.
"""
regex_pattern = '{0:s}="([^"]*)"'.format(definition)
values = re.findall(regex_pattern, line)
if len(values) == 1:
setattr(project_configuration, name, values[0])
def _ParseConfigurationOptions(
self, project_configuration, configuration_options, line):
"""Parses configuration options.
Args:
project_configuration (VSProjectConfiguration): project configuration.
configuration_options (dict[str, str]): configuration options defined
as a name per definition.
line (str): line that contains the configuration value.
"""
configuration_definition, _, _ = line.partition('=')
configuration_value = configuration_options.get(
configuration_definition, None)
if configuration_value:
self._ParseConfigurationOption(
project_configuration, configuration_definition, configuration_value,
line)
def _ReadConfiguration(self, line):
"""Reads a configuration.
Args:
line (str): line that contains the start of the configuration section.
Returns:
VSProjectConfiguration: configuration or None if no configuration was
found.
"""
if not line or not line.startswith('<Configuration'):
return None
project_configuration = resources.VSProjectConfiguration()
found_tool = False
found_tool_compiler = False
found_tool_librarian = False
found_tool_linker = False
while line:
line = self._ReadLine()
if line.startswith('</Configuration>'):
break
if found_tool:
if line.startswith('/>'):
found_tool = False
found_tool_compiler = False
found_tool_librarian = False
found_tool_linker = False
elif found_tool_compiler:
self._ParseConfigurationOptions(
project_configuration, self._TOOL_COMPILER_CONFIGURATION_OPTIONS,
line)
if isinstance(
project_configuration.include_directories, str):
project_configuration.include_directories = (
project_configuration.include_directories.split(';'))
elif found_tool_librarian:
self._ParseConfigurationOptions(
project_configuration, self._TOOL_LIBRARIAN_CONFIGURATION_OPTIONS,
line)
elif found_tool_linker:
self._ParseConfigurationOptions(
project_configuration, self._TOOL_LINKER_CONFIGURATION_OPTIONS,
line)
additional_dependencies = (
project_configuration.additional_dependencies)
if isinstance(additional_dependencies, str):
# pylint: disable=no-member
additional_dependencies = additional_dependencies.split(' ')
project_configuration.additional_dependencies = []
for dependency in additional_dependencies:
dependency.replace('$(ConfigurationName)', '$(Configuration)')
project_configuration.additional_dependencies.append(dependency)
if isinstance(
project_configuration.library_directories, str):
project_configuration.library_directories = (
project_configuration.library_directories.split(';'))
elif line.startswith('Name="VCCLCompilerTool"'):
found_tool_compiler = True
elif line.startswith('Name="VCLibrarianTool"'):
found_tool_librarian = True
elif line.startswith('Name="VCLinkerTool"'):
found_tool_linker = True
elif line.startswith('<Tool'):
found_tool = True
elif line.startswith('Name='):
# For more than 1 match findall will return a list with a tuple.
values = re.findall('Name="([^|]*)[|]([^"]*)"', line)[0]
if len(values) == 2:
project_configuration.name = values[0]
project_configuration.platform = values[1]
else:
self._ParseConfigurationOptions(
project_configuration, self._CONFIGURATION_OPTIONS, line)
# TODO: PlatformToolset.
# TargetFrameworkVersion ?
# Add the target machine when not defined.
if not project_configuration.target_machine:
if project_configuration.platform == 'Win32':
project_configuration.target_machine = '1'
# TODO: assuming here that 2 is x64.
elif project_configuration.platform == 'x64':
project_configuration.target_machine = '2'
return project_configuration
def _ReadConfigurations(self, project_information):
"""Reads the configurations.
Args:
project_information (VSProjectInformation): project information.
"""
# Find the start of the configurations section.
result = False
line = self._ReadLine()
while line:
result = line.startswith('<Configurations>')
if result:
break
line = self._ReadLine()
if not result:
return
while line:
line = self._ReadLine()
if line.startswith('</Configurations>'):
break
if line.startswith('<Configuration'):
project_configuration = self._ReadConfiguration(line)
if project_configuration:
project_information.configurations.Append(project_configuration)
def _ReadFiles(self, project_information):
"""Reads the files.
Args:
project_information (VSProjectInformation): project information.
"""
# Find the start of the files section.
result = False
line = self._ReadLine()
while line:
result = line.startswith('<Files>')
if result:
break
line = self._ReadLine()
if result:
found_filter = False
found_filter_source_files = False
found_filter_header_files = False
found_filter_resource_files = False
while line:
line = self._ReadLine()
if line.startswith('</Files>'):
break
if found_filter:
if line.startswith('</Filter>'):
found_filter = False
found_filter_source_files = False
found_filter_header_files = False
found_filter_resource_files = False
elif found_filter_source_files:
if line.startswith('RelativePath='):
values = re.findall('RelativePath="([^"]*)"', line)
if len(values) == 1:
project_information.source_files.append(values[0])
elif found_filter_header_files:
if line.startswith('RelativePath='):
values = re.findall('RelativePath="([^"]*)"', line)
if len(values) == 1:
project_information.header_files.append(values[0])
elif found_filter_resource_files:
if line.startswith('RelativePath='):
values = re.findall('RelativePath="([^"]*)"', line)
if len(values) == 1:
project_information.resource_files.append(values[0])
elif line.startswith('Name="Source Files"'):
found_filter_source_files = True
elif line.startswith('Name="Header Files"'):
found_filter_header_files = True
elif line.startswith('Name="Resource Files"'):
found_filter_resource_files = True
elif line.startswith('<Filter'):
found_filter = True
def _ReadProjectInformation(self, project_information):
"""Reads project information.
Args:
project_information (VSProjectInformation): project information.
"""
line = self._ReadLine()
while line:
if line.startswith('>'):
break
if line.startswith('Name='):
values = re.findall('Name="([^"]*)"', line)
if len(values) == 1:
project_information.name = values[0]
elif line.startswith('ProjectGUID='):
values = re.findall('ProjectGUID="{([^}]*)}"', line)
if len(values) == 1:
project_information.guid = values[0]
elif line.startswith('RootNamespace='):
values = re.findall('RootNamespace="([^"]*)"', line)
if len(values) == 1:
project_information.root_name_space = values[0]
elif line.startswith('Keyword='):
values = re.findall('Keyword="([^"]*)"', line)
if len(values) == 1:
project_information.keyword = values[0]
line = self._ReadLine()
def ReadHeader(self):
"""Reads a file header.
Returns:
bool: True if successful or false otherwise.
"""
# TODO check encoding?
line = self._ReadLine()
if not line or not line.startswith('<?xml version="1.0"'):
return False
line = self._ReadLine()
if not line or not line.startswith('<VisualStudioProject'):
return False
line = self._ReadLine()
if not line or not line.startswith('ProjectType="Visual C++"'):
return False
line = self._ReadLine()
if not line or not line.startswith('Version="9,00"'):
return False
return True
def ReadProject(self):
"""Reads the project.
Returns:
VSProjectInformation: project information if successful or None otherwise.
"""
project_information = resources.VSProjectInformation()
self._ReadProjectInformation(project_information)
self._ReadConfigurations(project_information)
self._ReadFiles(project_information)
return project_information
class VS2010ProjectFileReader(VSProjectFileReader):
"""Visual Studio 2010 project file reader."""
# TODO: implement.
class VS2012ProjectFileReader(VSProjectFileReader):
"""Visual Studio 2012 project file reader."""
# TODO: implement.
class VS2013ProjectFileReader(VSProjectFileReader):
"""Visual Studio 2013 project file reader."""
# TODO: implement.
class VS2015ProjectFileReader(VSProjectFileReader):
"""Visual Studio 2015 project file reader."""
# TODO: implement.
class VS2017ProjectFileReader(VSProjectFileReader):
"""Visual Studio 2017 project file reader."""
# TODO: implement.
class VS2019ProjectFileReader(VSProjectFileReader):
"""Visual Studio 2019 project file reader."""
# TODO: implement.
class VSSolutionFileReader(FileReader):
"""Visual Studio solution file reader."""
# Note that redundant-returns-doc is broken for pylint 1.7.x for abstract
# methods
# pylint: disable=redundant-returns-doc
@abc.abstractmethod
def _CheckFormatVersion(self, line):
"""Checks the format version.
Args:
line (str): line containing the Visual Studio format version.
Returns:
bool: True if successful or false otherwise.
"""
# pylint: disable=unused-argument
def _CheckVisualStudioVersion(self, line):
"""Checks the Visual Studio version.
Args:
line (str): line containing the Visual Studio format version.
Returns:
bool: True if successful or false otherwise.
"""
return False
def ReadConfigurations(self):
"""Reads the configurations.
Returns:
VSConfigurations: configurations or None if not available.
"""
solution_configurations = resources.VSConfigurations()
line = self._ReadLine(look_ahead=True)
if not line or line != 'Global':
return None
found_section = False
line = self._ReadLine()
while line and line != 'EndGlobal':
line = self._ReadLine()
if found_section:
if line == 'EndGlobalSection':
found_section = False
else:
# For more than 1 match findall will return a list with a tuple.
values = re.findall('([^|]*)[|]([^ ]*) = ([^|]*)[|]([^ ]*)', line)
if len(values) == 1:
values = values[0]
if (len(values) == 4 and values[0] == values[2] and
values[1] == values[3]):
configuration = resources.VSSolutionConfiguration()
configuration.name = values[0]
configuration.platform = values[1]
solution_configurations.Append(configuration)
elif line == ('GlobalSection(SolutionConfigurationPlatforms) = '
'preSolution'):
found_section = True
return solution_configurations
def ReadHeader(self):
"""Reads a file header.
Returns:
bool: True if successful or false otherwise.
"""
binary_data = self._ReadBinaryData(5)
if binary_data != b'\xef\xbb\xbf\r\n':
return False
line = self._ReadLine()
if not line or not line.startswith(
'Microsoft Visual Studio Solution File, Format Version '):
return False
if not self._CheckFormatVersion(line):
return False
visual_studio_version_line = None
line = self._ReadLine(look_ahead=True)
while line:
if line.startswith('# Visual C++ '):
self._ReadLine()
elif line.startswith('VisualStudioVersion = '):
visual_studio_version_line = self._ReadLine()
else:
break
line = self._ReadLine(look_ahead=True)
if visual_studio_version_line and not self._CheckVisualStudioVersion(
visual_studio_version_line):
return False
return True
def ReadProject(self):
"""Reads a project.
Returns:
VSSolutionProject: project if successful or None otherwise.
"""
# 8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942 is a Visual C++ related GUID.
line = self._ReadLine(look_ahead=True)
if not line or not line.startswith(
'Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = '):
return None
# For more than 1 match findall will return a list with a tuple.
values = re.findall(
('Project\\("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}"\\) = "([^"]*)", '
'"([^"]*)\\.vcproj", '
'"{([0-9A-F]*-[0-9A-F]*-[0-9A-F]*-[0-9A-F]*-[0-9A-F]*)}"'),
line)
if len(values) != 1:
return None
values = values[0]
if len(values) != 3:
return None
solution_project = resources.VSSolutionProject(
values[0], values[1], values[2])
found_dependencies = False
line = self._ReadLine()
while line and line != 'EndProject':
line = self._ReadLine()
if found_dependencies:
if line == 'EndProjectSection':
found_dependencies = False
else:
# The dependencies are defined as: {%GUID%} = {%GUID%}
# For more than 1 match findall will return a list with a tuple.
guids = re.findall(
('{([0-9A-F]*-[0-9A-F]*-[0-9A-F]*-[0-9A-F]*-[0-9A-F]*)} = '
'{([0-9A-F]*-[0-9A-F]*-[0-9A-F]*-[0-9A-F]*-[0-9A-F]*)}'),
line)
if len(guids) == 1:
guids = guids[0]
if len(guids) == 2 and guids[0] == guids[1]:
solution_project.AddDependency(guids[0])
elif line == 'ProjectSection(ProjectDependencies) = postProject':
found_dependencies = True
return solution_project
def ReadProjects(self):
"""Reads the projects.
Returns:
list[VSSolutionProject]: projects in preserved order.
"""
solution_projects = []
solution_project = self.ReadProject()
while solution_project:
solution_projects.append(solution_project)
solution_project = self.ReadProject()
return solution_projects
class VS2008SolutionFileReader(VSSolutionFileReader):
"""Visual Studio 2008 solution file reader."""
def _CheckFormatVersion(self, line):
"""Checks the format version.
Args:
line (str): line containing the Visual Studio format version.
Returns:
bool: True if successful or false otherwise.
"""
return line.endswith(' 10.00')
class VS2010SolutionFileReader(VSSolutionFileReader):
"""Visual Studio 2010 solution file reader."""
def _CheckFormatVersion(self, line):
"""Checks the format version.
Args:
line (str): line containing the Visual Studio format version.
Returns:
bool: True if successful or false otherwise.
"""
return line.endswith(' 11.00')
class VS2012SolutionFileReader(VSSolutionFileReader):
"""Visual Studio 2012 solution file reader."""
def _CheckFormatVersion(self, line):
"""Checks the format version.
Args:
line (str): line containing the Visual Studio format version.
Returns:
bool: True if successful or false otherwise.
"""
return line.endswith(' 12.00')
class VS2013SolutionFileReader(VS2012SolutionFileReader):
"""Visual Studio 2013 solution file reader."""
def _CheckVisualStudioVersion(self, line):
"""Checks the Visual Studio version.
Args:
line (str): line containing the Visual Studio format version.
Returns:
bool: True if successful or false otherwise.
"""
version = line.split(' = ')[1]
return version.startswith('12.')
class VS2015SolutionFileReader(VS2012SolutionFileReader):
"""Visual Studio 2015 solution file reader."""
def _CheckVisualStudioVersion(self, line):
"""Checks the Visual Studio version.
Args:
line (str): line containing the Visual Studio format version.
Returns:
bool: True if successful or false otherwise.
"""
version = line.split(' = ')[1]
return version.startswith('14.')
class VS2017SolutionFileReader(VS2012SolutionFileReader):
"""Visual Studio 2017 solution file reader."""
def _CheckVisualStudioVersion(self, line):
"""Checks the Visual Studio version.
Args:
line (str): line containing the Visual Studio format version.
Returns:
bool: True if successful or false otherwise.
"""
version = line.split(' = ')[1]
return version.startswith('15.')
class VS2019SolutionFileReader(VS2012SolutionFileReader):
"""Visual Studio 2019 solution file reader."""
def _CheckVisualStudioVersion(self, line):
"""Checks the Visual Studio version.
Args:
line (str): line containing the Visual Studio format version.
Returns:
bool: True if successful or false otherwise.
"""
version = line.split(' = ')[1]
return version.startswith('15.')
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] | 2.591374 | 8,509 |
from __future__ import unicode_literals
from subprocess import PIPE, Popen as popen
from unittest import TestCase
import responses
from packaging.utils import canonicalize_name
from pip_upgrader import __version__ as VERSION
from pip_upgrader import cli
from pip_upgrader.packages_status_detector import PackagesStatusDetector
try:
from unittest.mock import patch
except ImportError:
from mock import patch
try:
from io import StringIO
except ImportError: # pragma: nocover
from cStringIO import StringIO
@patch('pip_upgrader.packages_interactive_selector.user_input', return_value='all')
@patch('pip_upgrader.virtualenv_checker.is_virtualenv', return_value=True)
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] | 3.140909 | 220 |
#! /usr/bin/env python
import sys
speeds = dict()
# counts = dict()
# need this for extension
for line in sys.stdin:
try:
line = line.strip()
station, speed = line.split('\t')
speed = float(speed)
# Add code here to use the data
if station in speeds:
if speeds[station] < speed:
speeds[station] = speed
else:
speeds[station] = speed
except ValueError:
pass
for k, v in speeds.iteritems():
result = [k,str(v)]
print ('\t'.join(result))
# add code here to output the results | [
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] | 2.419913 | 231 |
#! /usr/bin/env python
import os
import h5py
import random
import time
import copy
import numpy as np
from tqdm import tqdm
from collections import OrderedDict, defaultdict
import torch
import torch.nn as nn
from torch.utils.data import Dataset, IterableDataset
from habitat import logger
from habitat.utils.geometry_utils import (
quaternion_from_coeff,
agent_state_target2ref,
)
from pointnav_vo.utils.geometry_utils import (
quaternion_to_array,
NormalizedDepth2TopDownViewHabitat,
NormalizedDepth2TopDownViewHabitatTorch,
)
from pointnav_vo.vo.dataset.regression_iter_dataset import BaseRegressionDataset
from pointnav_vo.vo.common.common_vars import *
FloatTensor = torch.FloatTensor
class StatePairRegressionDataset(BaseRegressionDataset):
r"""Data loader for state-pairs from Habitat simularo.
"""
def __init__(
self,
eval_flag=False,
data_file=None,
num_workers=0,
act_type=-1,
vis_size_w=256,
vis_size_h=256,
collision="-1",
discretize_depth="none",
discretized_depth_channels=0,
gen_top_down_view=False,
top_down_view_infos={},
geo_invariance_types=[],
partial_data_n_splits=1,
# data_aug=False,
):
f"""Valid combination of action and geometric consistency types are:
left OR right
|-- inverse_data_augment_only
left AND right
|-- inverse_joint_train
"""
assert (
np.sum(
[
"inverse_data_augment_only" in geo_invariance_types,
"inverse_joint_train" in geo_invariance_types,
]
)
<= 1
), f"inverse_data_augment_only and inverse_joint_train should not appear together."
if "inverse_joint_train" in geo_invariance_types:
assert (
isinstance(act_type, list)
and set(act_type) == set([TURN_LEFT, TURN_RIGHT])
) or (
isinstance(act_type, int) and act_type == -1
), f"When enabling joint-training with geometric inversion, action types must be [left, right] OR -1."
else:
assert isinstance(act_type, int)
if "inverse_data_augment_only" in geo_invariance_types:
assert (
act_type != MOVE_FORWARD
), f"Data augmentation for geometric consistency about inversion is not suitable for forward action."
self._eval = eval_flag
self._data_f = data_file
self._num_workers = num_workers
self._act_type = act_type
self._collision = collision
self._geo_invariance_types = geo_invariance_types
self._len = 0
self._act_left_right_len = 0
self._vis_size_w = vis_size_w
self._vis_size_h = vis_size_h
self._partial_data_n_splits = partial_data_n_splits
self._gen_top_down_view = gen_top_down_view
self._top_down_view_infos = top_down_view_infos
# RGB stored with uint8
self._rgb_pair_size = 2 * self._vis_size_w * self._vis_size_h * 3
# Depth stored with float16
self._depth_pair_size = 2 * self._vis_size_w * self._vis_size_h * 2
with h5py.File(data_file, "r", libver="latest") as f:
self._chunk_size = f[list(f.keys())[0]]["prev_rgbs"].shape[0]
# for rgb + depth
self._chunk_bytes = int(
np.ceil((self._rgb_pair_size + self._depth_pair_size) * self._chunk_size)
)
# for misc information
self._chunk_bytes += 20 * 2
logger.info(f"\nDataset: chunk bytes {self._chunk_bytes / (1024 * 1024)} MB\n")
self._discretize_depth = discretize_depth
self._discretized_depth_channels = discretized_depth_channels
if self._discretize_depth == "hard":
self._discretized_depth_end_vals = []
for i in np.arange(self._discretized_depth_channels):
self._discretized_depth_end_vals.append(
i * 1.0 / self._discretized_depth_channels
)
self._discretized_depth_end_vals.append(1.0)
logger.info("Get index mapping from h5py ...")
all_chunk_keys = []
with h5py.File(data_file, "r", libver="latest") as f:
for chunk_k in tqdm(sorted(f.keys())):
all_chunk_keys.append(chunk_k)
valid_idxes, transform_idxes = self._get_valid_idxes(f, chunk_k)
self._len += len(valid_idxes)
logger.info("... done.\n")
if not self._eval:
random.shuffle(all_chunk_keys)
if num_workers == 0:
# no separate worker
self._chunk_splits = all_chunk_keys
elif num_workers > 0:
self._chunk_splits = defaultdict(list)
for i, chunk_k in enumerate(all_chunk_keys):
self._chunk_splits[i % num_workers].append(chunk_k)
else:
raise ValueError
@property
@property
@property
@property
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] | 2.083876 | 2,456 |
import os
| [
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] | 3.333333 | 3 |
op = {
'plus': lambda x, y: x + y,
'minus': lambda x, y: x - y,
'multiplied': lambda x, y: x * y,
'divided': lambda x, y: x / y,
}
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import numpy as np
import torch
from torch.utils.data import Dataset
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TASK_URL = "http://code-lock.ctf.sicamp.ru"
TITLE = "Кодовый замок"
STATEMENT_TEMPLATE = f'''
"Безопасность превыше всего" - сказал ваш приятель и поставил на входную дверь кодовый замок. Но теперь вам срочно нужно попасть к нему домой, а кода вы не знаете. Что же делать?
[{TASK_URL}/{{0}}]({TASK_URL}/{{0}})
'''
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] | 1.403655 | 2,517 |
c.IPEngineApp.wait_for_url_file = 30
c.RegistrationFactory.ip = '*'
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# Copyright 2019 The Android Open Source Project
#
# 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 logging
import os
import re
import sys
import shutil
import abc
import docker
from tqdm import tqdm
from emu.utils import mkdir_p
class ProgressTracker(object):
"""Tracks progress using tqdm for a set of layers that are pushed."""
def update(self, entry):
"""Update the progress bars given a an entry.."""
if "id" not in entry:
return
identity = entry["id"]
if identity not in self.progress:
self.idx += 1
self.progress[identity] = {
"tqdm": tqdm(total=0, position=self.idx, unit="B", unit_scale=True), # The progress bar
"total": 0, # Total of bytes we are shipping
"status": "", # Status message.
"current": 0, # Current of total already send.
}
prog = self.progress[identity]
total = int(entry.get("progressDetail", {}).get("total", -1))
current = int(entry.get("progressDetail", {}).get("current", 0))
if prog["total"] != total and total != -1:
prog["total"] = total
prog["tqdm"].reset(total=total)
if prog["status"] != entry["status"]:
prog["tqdm"].set_description("{0} {1}".format(entry.get("status"), identity))
if current != 0:
diff = current - prog["current"]
prog["current"] = current
prog["tqdm"].update(diff)
class DockerContainer(object):
"""A Docker Device is capable of creating and launching docker images.
In order to successfully create and launch a docker image you must either
run this as root, or have enabled sudoless docker.
"""
TAG_REGEX = re.compile(r"[a-zA-Z0-9][a-zA-Z0-9._-]*:?[a-zA-Z0-9._-]*")
def launch(self, port_map):
"""Launches the container with the given sha, publishing abd on port, and gRPC on port 8554
Returns the container.
"""
image = self.docker_image()
client = docker.from_env()
try:
container = client.containers.run(
image=image.id,
privileged=True,
publish_all_ports=True,
detach=True,
ports=port_map,
)
print("Launched {} (id:{})".format(container.name, container.id))
print("docker logs -f {}".format(container.name))
print("docker stop {}".format(container.name))
return container
except:
logging.exception("Unable to run the %s", image_sha)
print("Unable to start the container, try running it as:")
print("./run.sh ", image_sha)
def create_container(self, dest):
"""Creates the docker container, returning the sha of the container, or None in case of failure."""
identity = None
image_tag = self.full_name()
print("docker build {} -t {}".format(dest, image_tag))
try:
api_client = self.get_api_client()
logging.info("build(path=%s, tag=%s, rm=True, decode=True)", dest, image_tag)
result = api_client.build(path=dest, tag=image_tag, rm=True, decode=True)
for entry in result:
if "stream" in entry:
sys.stdout.write(entry["stream"])
if "aux" in entry and "ID" in entry["aux"]:
identity = entry["aux"]["ID"]
client = docker.from_env()
image = client.images.get(identity)
image.tag(self.repo + self.image_name(), "latest")
except:
logging.exception("Failed to create container.", exc_info=True)
logging.warning("You can manually create the container as follows:")
logging.warning("docker build -t %s %s", image_tag, dest)
return identity
def pull(self, image, tag):
"""Tries to retrieve the given image and tag.
Return True if succeeded, False when failed.
"""
client = self.get_api_client()
try:
tracker = ProgressTracker()
result = client.pull(self.repo + image, tag)
for entry in result:
tracker.update(entry)
except:
logging.info("Failed to retrieve image, this is not uncommon.", exc_info=True)
return False
return True
def docker_image(self):
"""The docker local docker image if any
Returns:
{docker.models.images.Image}: A docker image object, or None.
"""
client = self.get_client()
for img in client.images.list():
for tag in img.tags:
if self.image_name() in tag:
return img
return None
def available(self):
"""True if this container image is locally available."""
return self.docker_image() != None
def can_pull(self):
"""True if this container image can be pulled from a registry."""
return self.pull(self.image_name(), self.docker_tag())
@abc.abstractmethod
def write(self, destination):
"""Method responsible for writing the Dockerfile and all necessary files to build a container.
Args:
destination ({string}): A path to a directory where all the container files should reside.
Raises:
NotImplementedError: [description]
"""
raise NotImplementedError()
@abc.abstractmethod
def image_name(self):
"""The image name without the tag used to uniquely identify this image.
Raises:
NotImplementedError: [description]
"""
raise NotImplementedError()
@abc.abstractmethod
@abc.abstractmethod
def depends_on(self):
"""Name of the system image this container is build on."""
raise NotImplementedError()
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] | 2.390398 | 2,687 |
# vi: set shiftwidth=4 tabstop=4 expandtab:
import datetime
import re
rules_re = re.compile(r"(?P<min>\d+)-(?P<max>\d+)")
if __name__ == "__main__":
begin = datetime.datetime.now()
run_tests()
get_solutions()
end = datetime.datetime.now()
print(end - begin)
| [
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8,
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] | 2.291339 | 127 |
#!/usr/bin/python3
from brownie import Pokemon, accounts, network, config
from scripts.helpful_scripts import fund
import asyncio
import json
| [
2,
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] | 3.763158 | 38 |
from tensorflow.keras import backend as K
from tensorflow.keras import initializers
from tensorflow.keras.layers import (Activation, BatchNormalization, Conv2D,
Dense, DepthwiseConv2D, Dropout, Flatten,
PReLU)
from tensorflow.keras.regularizers import l2
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from .__head__ import *
class BaseDataset(torch.utils.data.Dataset):
"""
Base Dataset class to be passed to torch Dataloader so train functions can be unified.
Not particularly designed for end-user, but for pre-training models.
"""
def __len__(self):
"Total number of samples"
return self.N
def __getitem__(self, index):
"Generates one batch of data"
return (
self.X_static[index],
self.X_series[index],
self.X_mask[index],
self.y_series[index],
)
def get_whole_batch(self):
"Returns all data as a single batch"
return self.X_static, self.X_series, self.X_mask, self.y_series
class standard_dataset(BaseDataset):
"""
Dataset to be passed to a torch DataLoader
"""
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] | 2.338109 | 349 |
import tempsensor
import time
import btnlib as btn
import ledlib as led
red = 80
orange = 75
yellow = 70
green = 65
blue = 60
purple = 55
while btn.isOn(btn.switch): #while the switch is on:
led.turn_on(led.white)
temp = tempsensor.tempF() #get the temp
print temp
if temp > red :
led.turn_on_all()
elif temp > orange :
led.turn_off(led.red)
led.turn_on(led.orange)
led.turn_on(led.yellow)
led.turn_on(led.green)
led.turn_on(led.blue)
led.turn_on(led.purple)
elif temp > yellow :
led.turn_off(led.red)
led.turn_off(led.orange)
led.turn_on(led.yellow)
led.turn_on(led.green)
led.turn_on(led.blue)
led.turn_on(led.purple)
elif temp > green :
led.turn_off(led.red)
led.turn_off(led.orange)
led.turn_off(led.yellow)
led.turn_on(led.green)
led.turn_on(led.blue)
led.turn_on(led.purple)
elif temp > blue :
led.turn_off(led.red)
led.turn_off(led.orange)
led.turn_off(led.yellow)
led.turn_off(led.green)
led.turn_on(led.blue)
led.turn_on(led.purple)
elif temp > purple :
led.turn_off(led.red)
led.turn_off(led.orange)
led.turn_off(led.yellow)
led.turn_off(led.green)
led.turn_off(led.blue)
led.turn_on(led.purple)
else :
led.turn_off_all()
led.turn_on(led.white)
time.sleep(0.5)
led.turn_off(led.white)
time.sleep(0.5)
led.GPIO.cleanup()
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929,
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] | 2.110932 | 622 |
import simulator
import numpy as np
'''
TODO Support CNN in the Hardware Evaluation
'''
weights_dir = "./data/mnist-lenet.npz"
image_dir = "./data/dataset/mnist/test.npy"
batch_size = 10 # The number of input picture
weights = np.load(weights_dir)['arr_0'].item()
data = np.load(image_dir)[:batch_size]
images = data[:, 0]
labels = data[:, 1]
params = simulator.Parameterinput() # Read parameters in simconfig
# Define the neural network
net = [
['Conv2d',],
['Conv2d',],
['Linear',],
['Linear',],
['Linear',],
]
# SIM
HWsim = simulator.SystemSim(params)
HWsim.apply(net, weights, images, labels) # Forward computing
HWsim.show() # Show the result in console
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] | 2.467577 | 293 |
#import config
import os
from tweepy import OAuthHandler, Stream
from tweepy.streaming import StreamListener
import json
import logging
import pymongo
import time
client = pymongo.MongoClient("mongodb")
db = client.tweets
time.sleep(10)
api_key = os.getenv('TWITTER_CONSUMER_API_KEY')
api_secret = os.getenv('TWITTER_CONSUMER_API_SECRET')
access_token = os.getenv('TWITTER_ACCESS_TOKEN')
access_secret = os.getenv('TWITTER_ACCESS_TOKEN_SECRET')
def authenticate():
'''Function for handling Twitter Auth. Pulls Env Variables in bash_profile'''
auth = OAuthHandler(api_key, api_secret)
auth.set_access_token(access_token, access_secret)
return auth
if __name__ == '__main__':
auth = authenticate()
listener = TwitterListener()
stream = Stream(auth, listener)
stream.filter(track=['marathon'], languages=['en'])
| [
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7,
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8,
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28,
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6,
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28,
17816,
268,
6,
12962,
198
] | 2.80198 | 303 |
import clr
clr.AddReference("Aurora")
clr.AddReference("System.Drawing")
from Aurora import Global
from Aurora.Settings import KeySequence
from Aurora.Devices import DeviceKeys
from Aurora.EffectsEngine import EffectLayer
from System.Drawing import Color
import System
array_device_keys = System.Array[DeviceKeys] | [
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] | 3.674419 | 86 |
from pymoo.model.mutation import Mutation
from pymoo.operators.mutation.no_mutation import NoMutation
from pymoo.operators.mutation.polynomial_mutation import PolynomialMutation
from pymoo.operators.integer_from_float_operator import IntegerFromFloatMutation
from uninas.utils.args import ArgsInterface, Argument, Namespace
from uninas.register import Register
@Register.hpo_pymoo_crossover()
class NoPymooMutation(AbstractPymooCrossover):
"""
No mutation at all
"""
@classmethod
@Register.hpo_pymoo_mutation()
class PolynomialPymooMutation(AbstractPymooCrossover):
"""
IntegerFromFloatMutation for integer variables, PolynomialMutation for floats
"""
@classmethod
def args_to_add(cls, index=None) -> [Argument]:
""" list arguments to add to argparse when this class (or a child class) is chosen """
return super().args_to_add(index) + [
Argument('type', default='int', type=str, choices=['int', 'real'], help='?'),
Argument('eta', default=30, type=int, help='?'),
]
@classmethod
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6,
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1037,
11639,
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828,
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220,
220,
220,
220,
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220,
220,
220,
45751,
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28,
600,
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220,
220,
220,
2361,
628,
220,
220,
220,
2488,
4871,
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198
] | 2.802083 | 384 |
import os
if __name__ == "__main__":
file_list = FileList()
file_list.list_files()
print(file_list.num_files, "files listed")
for f in file_list.files:
print(f)
| [
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] | 2.292683 | 82 |
# BSD Licence
# Copyright (c) 2009, Science & Technology Facilities Council (STFC)
# All rights reserved.
#
# See the LICENSE file in the source distribution of this software for
# the full license text.
""" This module contains CSML WFS 2.0 stored query implementations.
To add a new stored query, write the functional code here, then describe the functionality as
a new query in:
wfs_csmllayer.CSMLStoredQueries.queries
These query functions should either return a resultset (python list) containing the features,
or 2 lists, one being the resultset, and the other containing string representations of XML addtional
objects - in CSML these are going to be StorageDescriptors.
"""
import wfs_csmllayer
import logging
log = logging.getLogger(__name__)
import csml.parser, csml.csmllibs.csmlextra
from xml.etree import ElementTree as etree
from pylons import request, config
# qualifiedFeatureType='{http://ndg.nerc.ac.uk/csml}' + storagedescriptor.__class__.__name__
# emptyelem=etree.Element(qualifiedFeatureType)
# log.debug(request.environ)
# storagedescriptor.fileName.CONTENT='http://'+request.environ['HTTP_HOST']+'/filestore/' +storagedescriptor.fileName.CONTENT
# csmlelem=storagedescriptor.toXML(emptyelem)
# storagedescXML=etree.tostring(csmlelem)
# return [csmlfi], [storagedescXML]
| [
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8,
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220,
1441,
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273,
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3798,
55,
5805,
60,
628,
220,
198,
220,
220,
220,
220
] | 2.947253 | 455 |
import numpy as np
from numpy.linalg import inv
from scipy.stats import binom
#a1 = failed
#a2 = passed
testPassingProbabilities = [0.7, 0.6, 0.8, 0.9]
noOfSpecialistsCurrent = [100, 70, 50, 60]
wantedSpecialists = 5
wantedWithProbability = 0.9
transitionMatrix = np.zeros((len(testPassingProbabilities) + 2, len(testPassingProbabilities) + 2))
transitionMatrix[0, 0] = 1
transitionMatrix[1, 1] = 1
for i in range(2, len(testPassingProbabilities) + 1):
listIndex = i - 2
transitionMatrix[i, 0] = 1 - testPassingProbabilities[listIndex]
transitionMatrix[i, i+1] = testPassingProbabilities[listIndex]
transitionMatrix[i+1, 0] = 1 - testPassingProbabilities[-1]
transitionMatrix[i+1, 1] = testPassingProbabilities[-1]
Q = transitionMatrix[2:,2:]
I = np.identity(len(testPassingProbabilities))
N = inv(I - Q)
R = transitionMatrix[2:,:2]
B = np.dot(N,R)
passVector = B[:, 1]
noOfSpecialistsFinal = round(sum(passVector * noOfSpecialistsCurrent), 3)
print(f"The expected number of specialists is {noOfSpecialistsFinal}.")
newStudentPassingProbability = float(passVector[0])
for employeesSentToCourse in range(wantedSpecialists, 100):
probability = 1 - binom.cdf(wantedSpecialists - 1, employeesSentToCourse, newStudentPassingProbability)
if probability > wantedWithProbability:
print(f"{employeesSentToCourse} must be sent to the course.")
break | [
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] | 2.733202 | 506 |
import os
import json
import inspect
import eosfactory.core.logger as logger
import eosfactory.core.errors as errors
import eosfactory.core.setup as setup
import eosfactory.core.interface as interface
import eosfactory.core.teos as teos
if setup.node_api == "cleos":
import eosfactory.core.cleos as cleos
elif setup.node_api == "eosjs":
import eosfactory.core.eosjs as cleos
import eosfactory.core.manager as manager
class Wallet(cleos.WalletCreate):
''' Create a new wallet locally and operate it.
- **parameters**::
name: The name of the new wallet, defaults to `default`.
is_verbose: If `0`, do not print unless on error,
default is `1`.
- **attributes**::
name: The name of the wallet.
password: The password returned by wallet create.
error: Whether any error ocurred.
json: The json representation of the object.
is_verbose: Verbosity at the constraction time.
'''
wallet_keys = None
wallet = None
globals = None
setup.node_api
def index(self):
''' Lists opened wallets, * marks unlocked.
Returns `cleos.WalletList` object
'''
result = cleos.WalletList(is_verbose=0)
logger.OUT(result.out_msg)
def open(self):
''' Opens the wallet.
Returns `WalletOpen` object
'''
result = cleos.WalletOpen(self.name, is_verbose=False)
logger.TRACE('''
* Wallet ``{}`` opened.
'''.format(self.name))
def lock(self):
''' Lock the wallet.
Returns `cleos.WalletLock` object.
'''
result = cleos.WalletLock(self.name, is_verbose=False)
logger.TRACE("Wallet `{}` locked.".format(self.name))
def lock_all(self):
''' Lock the wallet.
Returns `cleos.WalletLock` object.
'''
result = cleos.WalletLockAll(is_verbose=False)
logger.TRACE("All wallets locked.")
def unlock(self):
''' Unlock the wallet.
Returns `WalletUnlock` object.
'''
result = cleos.WalletUnlock(
self.name, self.password, is_verbose=False)
logger.TRACE('''
* Wallet ``{}`` unlocked.
'''.format(self.name))
def open_unlock(self):
''' Open&Unlock automatics.
'''
cleos.WalletOpen(self.name, is_verbose=False)
cleos.WalletUnlock(
self.name, self.password, is_verbose=False)
def remove_key(self, account_or_key):
'''
'''
self.open_unlock()
removed_keys = []
account_name = None
if isinstance(account_or_key, interface.Account):
cleos.WalletRemove_key(
interface.key_arg(
account_or_key, is_owner_key=True, is_private_key=True),
self.name, is_verbose=False)
removed_keys.append(interface.key_arg(
account_or_key, is_owner_key=True, is_private_key=False))
cleos.WalletRemove_key(
interface.key_arg(
account_or_key, is_owner_key=False, is_private_key=True),
self.name, is_verbose=False)
removed_keys.append(interface.key_arg(
account_or_key, is_owner_key=False, is_private_key=False))
else:
cleos.WalletRemove_key(
interface.key_arg(
account_or_key, is_private_key=True),
self.name, is_verbose=False)
removed_keys.append(interface.key_arg(
account_or_key, is_private_key=False))
if account_name is None:
if len(removed_keys) > 0:
logger.TRACE('''
Removing key '{}'
from the wallet '{}'
'''.format(removed_keys[0], self.name),
verbosity
)
else:
logger.TRACE('''
Removing keys of the account '{}' from the wallet '{}'
'''.format(account_name, self.name)
)
wallet_keys = cleos.WalletKeys(is_verbose=False)
for key in removed_keys:
if key in wallet_keys.json:
raise errors.Error('''
Failed to remove key '{}' from the wallet '{}'
'''.format(key, self.name))
logger.TRACE('''
* Cross-checked: all listed keys removed from the wallet.
''')
return True
def import_key(self, account_or_key):
''' Imports private keys of an account into wallet.
Returns list of `cleos.WalletImport` objects
'''
self.open_unlock()
imported_keys = []
account_name = None
if isinstance(account_or_key, interface.Account):
account_name = account_or_key.name
wallet_import = cleos.WalletImport(
interface.key_arg(
account_or_key, is_owner_key=True, is_private_key=True),
self.name, is_verbose=False)
imported_keys.append(interface.key_arg(
account_or_key, is_owner_key=True, is_private_key=False))
wallet_import = cleos.WalletImport(
interface.key_arg(
account_or_key, is_owner_key=False, is_private_key=True),
self.name, is_verbose=False)
imported_keys.append(interface.key_arg(
account_or_key, is_owner_key=False, is_private_key=False))
logger.TRACE('''
* Importing keys of the account ``{}`` into the wallet ``{}``
'''.format(account_name, self.name)
)
else:
wallet_import = cleos.WalletImport(
interface.key_arg(account_or_key, is_private_key=True),
self.name, is_verbose=False)
logger.TRACE('''
* Importing keys into the wallet ``{}``
'''.format(self.name)
)
return True
wallet_keys = cleos.WalletKeys(is_verbose=False)
if len(imported_keys) == 0:
raise errors.Error('''
The list of imported keys is empty.
''')
ok = True
for key in imported_keys:
if not key in wallet_keys.json:
ok = False
raise errors.Error('''
Failed to import keys of the account '{}' into the wallet '{}'
'''.format(
account_name if account_name else "n/a", self.name))
if ok:
logger.TRACE('''
* Cross-checked: all account keys are in the wallet.
''')
return True
def restore_accounts(self):
'''
'''
self.open_unlock()
account_map = manager.account_map()
new_map = {}
wallet_keys = cleos.WalletKeys(is_verbose=0)
if len(account_map) > 0:
logger.INFO('''
######### Restore cached account objects:
''')
for name, object_name in account_map.items():
try:
account_ = cleos.GetAccount(
name, is_info=False, is_verbose=False)
if account_.owner_key in wallet_keys.json and \
account_.active_key in wallet_keys.json:
new_map[name] = object_name
from eosfactory.shell.account import create_account
create_account(
object_name, name, restore=True, verbosity=None)
except errors.AccountDoesNotExistError:
pass
manager.save_account_map(new_map)
else:
logger.INFO('''
* The wallet is empty.
''')
def keys(self):
''' Lists public keys from all unlocked wallets.
Returns `cleos.WalletKeys` object.
'''
self.open_unlock()
self.wallet_keys = cleos.WalletKeys(is_verbose=False)
logger.TRACE('''
Keys in all open walets:
{}
'''.format(self.wallet_keys.out_msg))
def private_keys(self):
''' Lists public keys from all unlocked wallets.
Returns `cleos.WalletKeys` object.
'''
self.open_unlock()
self.wallet_private_keys = cleos.WalletPrivateKeys(is_verbose=False)
logger.TRACE('''
Keys in all open walets:
{}
'''.format(json.dumps(
self.wallet_private_keys.json, indent=4)))
def map_account(self, account_object_name, account_object):
'''
'''
if not self.is_name_taken(account_object_name, account_object.name):
account_map_json = manager.account_map(self)
if account_map_json is None:
return
account_map_json[account_object.name] = account_object_name
with open(self.wallet_dir + setup.account_map, "w") as out:
out.write(json.dumps(
account_map_json, indent=3, sort_keys=True))
logger.TRACE('''
* Account object ``{}`` stored in the file
``{}`` in the wallet directory:
{}
'''.format(
account_object_name,
setup.account_map,
self.wallet_dir + setup.account_map))
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944,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
705,
7061,
4946,
5,
3118,
5354,
3557,
23372,
13,
198,
220,
220,
220,
220,
220,
220,
220,
705,
7061,
198,
220,
220,
220,
220,
220,
220,
220,
1190,
418,
13,
47152,
11505,
7,
944,
13,
3672,
11,
318,
62,
19011,
577,
28,
25101,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1190,
418,
13,
47152,
3118,
5354,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
3672,
11,
2116,
13,
28712,
11,
318,
62,
19011,
577,
28,
25101,
8,
628,
220,
220,
220,
825,
4781,
62,
2539,
7,
944,
11,
1848,
62,
273,
62,
2539,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
705,
7061,
198,
220,
220,
220,
220,
220,
220,
220,
705,
7061,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
9654,
62,
403,
5354,
3419,
628,
220,
220,
220,
220,
220,
220,
220,
4615,
62,
13083,
796,
17635,
198,
220,
220,
220,
220,
220,
220,
220,
1848,
62,
3672,
796,
6045,
198,
220,
220,
220,
220,
220,
220,
220,
611,
318,
39098,
7,
23317,
62,
273,
62,
2539,
11,
7071,
13,
30116,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1190,
418,
13,
47152,
27914,
62,
2539,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7071,
13,
2539,
62,
853,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1848,
62,
273,
62,
2539,
11,
318,
62,
18403,
62,
2539,
28,
17821,
11,
318,
62,
19734,
62,
2539,
28,
17821,
828,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
3672,
11,
318,
62,
19011,
577,
28,
25101,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4615,
62,
13083,
13,
33295,
7,
39994,
13,
2539,
62,
853,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1848,
62,
273,
62,
2539,
11,
318,
62,
18403,
62,
2539,
28,
17821,
11,
318,
62,
19734,
62,
2539,
28,
25101,
4008,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1190,
418,
13,
47152,
27914,
62,
2539,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7071,
13,
2539,
62,
853,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1848,
62,
273,
62,
2539,
11,
318,
62,
18403,
62,
2539,
28,
25101,
11,
318,
62,
19734,
62,
2539,
28,
17821,
828,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
3672,
11,
318,
62,
19011,
577,
28,
25101,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4615,
62,
13083,
13,
33295,
7,
39994,
13,
2539,
62,
853,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1848,
62,
273,
62,
2539,
11,
318,
62,
18403,
62,
2539,
28,
25101,
11,
318,
62,
19734,
62,
2539,
28,
25101,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1190,
418,
13,
47152,
27914,
62,
2539,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7071,
13,
2539,
62,
853,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1848,
62,
273,
62,
2539,
11,
318,
62,
19734,
62,
2539,
28,
17821,
828,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
3672,
11,
318,
62,
19011,
577,
28,
25101,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4615,
62,
13083,
13,
33295,
7,
39994,
13,
2539,
62,
853,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1848,
62,
273,
62,
2539,
11,
318,
62,
19734,
62,
2539,
28,
25101,
4008,
628,
220,
220,
220,
220,
220,
220,
220,
611,
1848,
62,
3672,
318,
6045,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
18896,
7,
2787,
2668,
62,
13083,
8,
1875,
657,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
49706,
13,
5446,
11598,
7,
7061,
6,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3982,
5165,
1994,
705,
90,
92,
6,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
422,
262,
13008,
705,
90,
92,
6,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
10148,
4458,
18982,
7,
2787,
2668,
62,
13083,
58,
15,
4357,
2116,
13,
3672,
828,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
15942,
16579,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
49706,
13,
5446,
11598,
7,
7061,
6,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3982,
5165,
8251,
286,
262,
1848,
705,
90,
92,
6,
422,
262,
13008,
705,
90,
92,
6,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
10148,
4458,
18982,
7,
23317,
62,
3672,
11,
2116,
13,
3672,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1267,
220,
220,
220,
220,
220,
220,
220,
220,
628,
220,
220,
220,
220,
220,
220,
220,
13008,
62,
13083,
796,
1190,
418,
13,
47152,
40729,
7,
271,
62,
19011,
577,
28,
25101,
8,
628,
220,
220,
220,
220,
220,
220,
220,
329,
1994,
287,
4615,
62,
13083,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
1994,
287,
13008,
62,
13083,
13,
17752,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5298,
8563,
13,
12331,
7,
7061,
6,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
22738,
284,
4781,
1994,
705,
90,
92,
6,
422,
262,
13008,
705,
90,
92,
6,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
10148,
4458,
18982,
7,
2539,
11,
2116,
13,
3672,
4008,
628,
220,
220,
220,
220,
220,
220,
220,
49706,
13,
5446,
11598,
7,
7061,
6,
198,
220,
220,
220,
220,
220,
220,
220,
1635,
6372,
12,
26752,
25,
477,
5610,
8251,
4615,
422,
262,
13008,
13,
198,
220,
220,
220,
220,
220,
220,
220,
705,
7061,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
6407,
628,
198,
220,
220,
220,
825,
1330,
62,
2539,
7,
944,
11,
1848,
62,
273,
62,
2539,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
705,
7061,
1846,
3742,
2839,
8251,
286,
281,
1848,
656,
13008,
13,
198,
220,
220,
220,
220,
220,
220,
220,
16409,
1351,
286,
4600,
2375,
418,
13,
47152,
20939,
63,
5563,
198,
220,
220,
220,
220,
220,
220,
220,
705,
7061,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
9654,
62,
403,
5354,
3419,
628,
220,
220,
220,
220,
220,
220,
220,
17392,
62,
13083,
796,
17635,
198,
220,
220,
220,
220,
220,
220,
220,
1848,
62,
3672,
796,
6045,
198,
220,
220,
220,
220,
220,
220,
220,
611,
318,
39098,
7,
23317,
62,
273,
62,
2539,
11,
7071,
13,
30116,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1848,
62,
3672,
796,
1848,
62,
273,
62,
2539,
13,
3672,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
13008,
62,
11748,
796,
1190,
418,
13,
47152,
20939,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7071,
13,
2539,
62,
853,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1848,
62,
273,
62,
2539,
11,
318,
62,
18403,
62,
2539,
28,
17821,
11,
318,
62,
19734,
62,
2539,
28,
17821,
828,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
3672,
11,
318,
62,
19011,
577,
28,
25101,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
17392,
62,
13083,
13,
33295,
7,
39994,
13,
2539,
62,
853,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1848,
62,
273,
62,
2539,
11,
318,
62,
18403,
62,
2539,
28,
17821,
11,
318,
62,
19734,
62,
2539,
28,
25101,
4008,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
13008,
62,
11748,
796,
1190,
418,
13,
47152,
20939,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7071,
13,
2539,
62,
853,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1848,
62,
273,
62,
2539,
11,
318,
62,
18403,
62,
2539,
28,
25101,
11,
318,
62,
19734,
62,
2539,
28,
17821,
828,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
3672,
11,
318,
62,
19011,
577,
28,
25101,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
17392,
62,
13083,
13,
33295,
7,
39994,
13,
2539,
62,
853,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1848,
62,
273,
62,
2539,
11,
318,
62,
18403,
62,
2539,
28,
25101,
11,
318,
62,
19734,
62,
2539,
28,
25101,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
49706,
13,
5446,
11598,
7,
7061,
6,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1635,
17267,
278,
8251,
286,
262,
1848,
7559,
90,
92,
15506,
656,
262,
13008,
7559,
90,
92,
15506,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
10148,
4458,
18982,
7,
23317,
62,
3672,
11,
2116,
13,
3672,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
13008,
62,
11748,
796,
1190,
418,
13,
47152,
20939,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7071,
13,
2539,
62,
853,
7,
23317,
62,
273,
62,
2539,
11,
318,
62,
19734,
62,
2539,
28,
17821,
828,
220,
198,
220,
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220,
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220,
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220,
220,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
9058,
13,
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62,
15908,
1343,
9058,
13,
23317,
62,
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4008,
628,
198
] | 1.948241 | 4,946 |
from random import randint
x = (randint(1, 10), randint(1, 10), randint(1, 10), randint(1, 10), randint(1, 10), randint(1, 10))
print('sorteei os numeros:', end='')
for n in x:
print(f'{n}', end=", ")
print(f"\nO maior valor na ordem foi {max(x)}")
print(f"\nO menor valor na ordem foi {min(x)}")
| [
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] | 2.20438 | 137 |
#! /usr/bin/python
# Copyright (C) 2005 Dr. Ralf Schlatterbeck Open Source Consulting.
# Reichergasse 131, A-3411 Weidling.
# Web: http://www.runtux.com Email: [email protected]
# All rights reserved
# ****************************************************************************
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software
# Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
# ****************************************************************************
from cStringIO import StringIO
from os.path import splitext
from roundup.cgi.actions import Action, EditItemAction
from roundup.cgi import templating
from roundup import hyperdb
from roundup.date import Date, Interval
from roundup.cgi.exceptions import Redirect
try :
import ooopy.Transforms as Transforms
from ooopy.OOoPy import OOoPy
from ooopy.Transformer import Transformer, autosuper
from ooopy.Transforms import renumber_all, get_meta, set_meta
except ImportError :
from rsclib.autosuper import autosuper
Reject = ValueError
# end class Invoice
# end class Unmark_Invoice
# end class Mark_Invoice
# end class OOoPy_Invoice_Wrapper
# end class Generate_Invoice
# end class _Mark_Invoice
# end class Mark_Invoice_Sent
# end class Mark_Single_Invoice_Sent
# end def create_file
# end def handle
# end class Download_Letter
# end def handle
# end class Personalized_Template
class Edit_Payment_Action (EditItemAction, autosuper) :
""" Remove items that did not change (for which we defined a hidden
attribute in the mask) from the new items. Then proceed as usual
like for EditItemAction.
"""
# end def _editnodes
# end class Edit_Payment_Action
# end def init
| [
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5312,
62,
19197,
434,
62,
12502,
198,
2,
886,
825,
2315,
198
] | 2.953181 | 833 |
# -*- coding: UTF-8 -*-
# **********************************************************************************#
# File:
# **********************************************************************************#
import time
from datetime import datetime
from utils.dict import CompositeDict
from utils.decorator import singleton
from .. configs import logger
from .. data.database_api import load_futures_rt_minute_data
def _get_minute_price_info(universe):
"""
Get the latest info of stocks.
Args:
universe(list): universe list
"""
futures_rt_minute_data = load_futures_rt_minute_data(universe)
info = dict()
for symbol, minute_bar_list in futures_rt_minute_data.iteritems():
if minute_bar_list:
item = minute_bar_list[-1]
item.update({
'secID': symbol
})
info[symbol] = minute_bar_list[-1]
return info
@singleton
| [
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198,
220,
220,
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1441,
7508,
628,
198,
31,
12215,
10565,
198
] | 2.770149 | 335 |
# Generated by Django 2.2.1 on 2019-08-03 05:44
import django.contrib.gis.db.models.fields
from django.db import migrations, models
| [
2,
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] | 2.791667 | 48 |
# encoding=utf8
import requests
import datetime
import pytz
import argparse
import random
import json
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='weixin auto sport script.')
parser.add_argument('user', help='your user name', type=str)
parser.add_argument('password', help='your password', type=str)
args = parser.parse_args()
autorepoter = Report(user=args.user, password=args.password)
count = 3
while count != 0:
ret = autorepoter.report()
if ret != False:
break
print("Sport Failed, retry...")
count = count - 1
if count != 0:
exit(0)
else:
exit(-1) | [
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25,
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220,
220,
220,
220,
220,
220,
2270,
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220,
220,
220,
220,
220,
220,
3601,
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42576,
22738,
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8,
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220,
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220,
220,
954,
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220,
611,
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220,
220,
220,
220,
8420,
7,
15,
8,
198,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
8420,
32590,
16,
8
] | 2.539326 | 267 |
class ObservationsSummaryDewPt:
""" Defines an object for the observations summary period temp data. """
dewpt = {}
def __init__(self, dewpt_json):
"""
Constructor - this takes an individual observations summary period's dewpoint json.
{
maxC": 5,
"maxF": 41,
"minC": -3,
"minF": 26,
"avgC": -0.6,
"avgF": 30.9,
"count": 23
},
"""
self.dewpt = dewpt_json
@property
def maxC(self) -> float:
""" The maximum dew point in Celsius. Null if unavailable. """
return self.dewpt["maxC"]
@property
def maxF(self) -> float:
""" The maximum dew point in Fahrenheit. Null if unavailable. """
return self.dewpt["maxF"]
@property
def minC(self) -> float:
""" The minimum dew point in Celsius. Null if unavailable. """
return self.dewpt["minC"]
@property
def minF(self) -> float:
""" The minimum dew point in Fahrenheit. Null if unavailable. """
return self.dewpt["minF"]
@property
def avgC(self) -> float:
""" The average dew point in Celsius. Null if unavailable. """
return self.dewpt["avgC"]
@property
def avgF(self) -> float:
""" The average dew point in Fahrenheit. Null if unavailable. """
return self.dewpt["avgF"]
@property
def count(self) -> int:
""" The total number of observations that included dew point information. """
return self.dewpt["count"]
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1441,
2116,
13,
67,
413,
457,
14692,
9127,
8973,
198
] | 2.202156 | 742 |
from PyQt5.QtWidgets import QWidget
from forms.ui_receivePage import Ui_ReceivePage
class ReceivePage(QWidget, Ui_ReceivePage):
"""The page to generate bolt11 invoices"""
def clearForm(self):
"""Reset the form to the default values"""
self.spinValue.setValue(1)
self.lineLabel.setText("")
self.lineDescription.setText("")
self.spinExpiry.setValue(604800) # A week
def generateInvoice(self):
"""Generate an invoice and display it"""
amount_msat = self.spinValue.value()
label = self.lineLabel.text()
description = self.lineDescription.text()
expiry = self.spinExpiry.value()
invoice = self.plugin.rpc.invoice(amount_msat, label, description, expiry)
# Condition to prevent RPC error
if invoice:
self.textResultInvoice.setText(invoice["bolt11"])
def initUi(self):
"""Initialize the UI by connecting actions"""
self.buttonGenerate.clicked.connect(self.generateInvoice)
self.buttonClear.clicked.connect(self.clearForm)
| [
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8,
198
] | 2.439189 | 444 |
import http.server, requests, json
username_cache = {}
whitelist = ("current.html", "runner64.png", "flags.min.css", "flags.png")
mimes = {
"html": "text/html",
"css": "text/css",
"png": "image/png",
"json": "application/jsonn"
}
PAGE_404 = """<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<title>404 Not Found</title>
</head>
<body>
nuthin to see here bruh
</body>
</html>
"""
HEADERS = {"User-Agent": "Jobicade's Magnificent PB/WR grabber"}
GAME_ID = "4pd0n31e"
CATEGORY_ID = "wk6pexd1"
USER_ID = "48g0ln2x"
BASE = "http://www.speedrun.com/api/v1/"
PB_QUERY = BASE + "users/" + USER_ID + "/personal-bests"
WR_QUERY = BASE + "leaderboards/" + GAME_ID + "/category/" + CATEGORY_ID
httpd = http.server.HTTPServer(('127.0.0.1', 80), RecordRequest)
sa = httpd.socket.getsockname()
print("Serving HTTP on", sa[0], "port", sa[1], "...")
httpd.serve_forever()
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220,
220,
220,
796,
49688,
1343,
366,
27940,
12821,
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1343,
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62,
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1343,
12813,
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38,
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62,
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7,
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13,
16,
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8,
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58,
15,
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366,
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1600,
473,
58,
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366,
9313,
8,
198,
4023,
67,
13,
2655,
303,
62,
754,
332,
3419,
198
] | 2.239506 | 405 |
from pydantic import BaseModel
from typing import Optional
| [
6738,
279,
5173,
5109,
1330,
7308,
17633,
198,
6738,
19720,
1330,
32233,
628
] | 4.615385 | 13 |
import logging
from typing import Optional
import torch
import tqdm
from torch.utils.data import DataLoader
from phuber.metrics import AccuracyMetric
class Evaluator:
"""Model evaluator
Args:
model: model to be evaluated
device: device on which to evaluate model
loader: dataloader on which to evaluate model
checkpoint_path: path to model checkpoint
"""
def evaluate(self) -> float:
"""Evaluates the model
Returns:
(float) accuracy (on a 0 to 1 scale)
"""
# Progress bar
pbar = tqdm.tqdm(total=len(self.loader), leave=False)
pbar.set_description("Evaluating... ")
# Set to eval
self.model.eval()
# Loop
for data, target in self.loader:
with torch.no_grad():
# To device
data, target = data.to(self.device), target.to(self.device)
# Forward
out = self.model(data)
self.acc_metric.update(out, target)
# Update progress bar
pbar.update()
pbar.close()
accuracy = self.acc_metric.compute()
self.logger.info(f"Accuracy: {accuracy:.4f}\n")
return accuracy
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220,
220,
220,
2116,
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13,
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7,
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1,
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25,
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13,
19,
69,
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77,
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628,
220,
220,
220,
220,
220,
220,
220,
1441,
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] | 2.194444 | 576 |
import functools
import idna
import logging
import re
import requests
import rfc3339
import time
from bottle import Bottle, request, response, static_file, template, redirect
from datetime import timedelta
from publicsuffixlist import PublicSuffixList
from requests_oauthlib import OAuth1
from urllib.parse import urlsplit
from utils.param_parse import (ParamParseError, parse_params,
integer_param, string_param, boolean_param)
from .misc import html_default_error_hander, security_headers, set_headers
log = logging.getLogger(__name__)
# Disable some logging to reduce log spam.
# Elasticsearch logs all requests at (at least) INFO level.
logging.getLogger('elasticsearch').setLevel(logging.WARNING)
PSL_CACHE_SIZE = 10_000
psl = PublicSuffixList()
# Domains are a series of two or more names, separated by periods, with an optional trailing period.
# (Technically one name is allowed, but TLDs aren't usually HTTP sites.)
# (Note that we actually strip any trailing period during normalization - along with lowercasing
# characters - but support has been left in the regex for completeness.)
# Each name can contain latin characters (case insensitive), digits, or dashes.
# Names can't be longer than 63 characters, or start/end with a dash.
# The final name - the TLD - can't be numeric (only digits).
DOMAIN_REGEX = re.compile(r'^(?:[a-z\d](?:[a-z\d-]{0,61}[a-z\d])?\.)+(?!\d+\.?$)[a-z\d](?:[a-z\d-]{0,61}[a-z\d])?\.?$')
DOMAIN_MAX_LENGTH = 253
REQUEST_TIMEOUT_INDIVIDUAL = 5
SCAN_START_TIMEOUT = 20
SCAN_TIMEOUT = 30
SCAN_AGENT = 'GpcSupBot'
SCAN_HEADERS = {'User-Agent': f'{SCAN_AGENT}/0.1 (https://gpcsup.com)'}
ROBOTS_MAX_CONTENT_LENGTH = 512 * 1024 # 512kB
GPC_PATH = '/.well-known/gpc.json'
GPC_MAX_CONTENT_LENGTH = 1024 # 1kB
SCAN_TTL = timedelta(minutes=10)
NEXT_SCAN_OFFSET = timedelta(days=7)
SCAN_FAIL_OFFSETS = [
timedelta(days=1),
timedelta(days=7),
timedelta(days=30),
]
SCAN_RESULT_MAX_AGE_SECS = SCAN_TTL.seconds
SCAN_RESULT_HEADERS = {'Cache-Control': f'max-age={SCAN_RESULT_MAX_AGE_SECS}'}
STATIC_FILE_MAX_AGE_SECS = timedelta(hours=1).seconds
STATIC_FILE_HEADERS = {'Cache-Control': f'max-age={STATIC_FILE_MAX_AGE_SECS}'}
SITES_PAGE_SIZE = 8
SERVER_READY = True
class ScanError(Exception):
"""The scan has failed, and the user should be shown the specified template."""
@functools.lru_cache(maxsize=PSL_CACHE_SIZE)
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2246,
13909,
62,
33489,
8,
628,
628,
628,
198
] | 2.679245 | 901 |
import myhdl
from myhdl import delay
| [
198,
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198,
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616,
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1330,
5711,
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] | 3 | 13 |
import operator
import re
from typing import Any, Dict, List, Tuple, Union
import frappe
from frappe import _
from frappe.query_builder import Criterion, Field, Order
def like(key: str, value: str) -> frappe.qb:
"""Wrapper method for `LIKE`
Args:
key (str): field
value (str): criterion
Returns:
frappe.qb: `frappe.qb object with `LIKE`
"""
return Field(key).like(value)
def func_in(key: str, value: Union[List, Tuple]) -> frappe.qb:
"""Wrapper method for `IN`
Args:
key (str): field
value (Union[int, str]): criterion
Returns:
frappe.qb: `frappe.qb object with `IN`
"""
return Field(key).isin(value)
def not_like(key: str, value: str) -> frappe.qb:
"""Wrapper method for `NOT LIKE`
Args:
key (str): field
value (str): criterion
Returns:
frappe.qb: `frappe.qb object with `NOT LIKE`
"""
return Field(key).not_like(value)
def func_not_in(key: str, value: Union[List, Tuple]):
"""Wrapper method for `NOT IN`
Args:
key (str): field
value (Union[int, str]): criterion
Returns:
frappe.qb: `frappe.qb object with `NOT IN`
"""
return Field(key).notin(value)
def func_regex(key: str, value: str) -> frappe.qb:
"""Wrapper method for `REGEX`
Args:
key (str): field
value (str): criterion
Returns:
frappe.qb: `frappe.qb object with `REGEX`
"""
return Field(key).regex(value)
def func_between(key: str, value: Union[List, Tuple]) -> frappe.qb:
"""Wrapper method for `BETWEEN`
Args:
key (str): field
value (Union[int, str]): criterion
Returns:
frappe.qb: `frappe.qb object with `BETWEEN`
"""
return Field(key)[slice(*value)]
def make_function(key: Any, value: Union[int, str]):
"""returns fucntion query
Args:
key (Any): field
value (Union[int, str]): criterion
Returns:
frappe.qb: frappe.qb object
"""
return OPERATOR_MAP[value[0]](key, value[1])
def change_orderby(order: str):
"""Convert orderby to standart Order object
Args:
order (str): Field, order
Returns:
tuple: field, order
"""
order = order.split()
if order[1].lower() == "asc":
orderby, order = order[0], Order.asc
return orderby, order
orderby, order = order[0], Order.desc
return orderby, order
OPERATOR_MAP = {
"+": operator.add,
"=": operator.eq,
"-": operator.sub,
"!=": operator.ne,
"<": operator.lt,
">": operator.gt,
"<=": operator.le,
">=": operator.ge,
"in": func_in,
"not in": func_not_in,
"like": like,
"not like": not_like,
"regex": func_regex,
"between": func_between
}
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23395,
1298,
25439,
62,
23395,
198,
197,
197,
197,
197,
92,
628
] | 2.401515 | 1,056 |
import json
from decimal import Decimal
from typing import Optional, Dict
from lib.exchange.upbit.model.post_orders_response import PostOrdersResponse
from lib.order.order import Order
from lib.order.order_type import OrderType
from lib.type import JsonString
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] | 3.742857 | 70 |
import pytest
from lhotse.cut import make_windowed_cuts_from_features
from lhotse.features import Features, FeatureSet
def features(rec_id, start, duration):
"""Helper method for fixture readability (specify only relevant attributes)."""
return Features(
recording_id=rec_id,
channels=0,
start=start,
duration=duration,
sampling_rate=16000,
type="irrelevant",
num_frames=round(duration / 0.01),
num_features=23,
storage_type="irrelevant",
storage_path="irrelevant",
storage_key="irrelevant",
frame_shift=0.01,
)
@pytest.fixture
# noinspection PyMethodMayBeStatic
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2,
645,
1040,
14978,
9485,
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] | 2.483516 | 273 |
import builtins
import collections
import copy
import functools
import itertools
import math
import operator
import types
import warnings
from functools import lru_cache
import numpy
import torch
from . import config
@lru_cache(None)
@lru_cache(None)
def _allowed_function_ids():
"""
Walk torch.* and get the ids of all the stuff in it
"""
warnings.filterwarnings("ignore", category=UserWarning, module="torch.distributed")
torch.distributions.Distribution.set_default_validate_args(False)
torch_object_ids = dict()
_find_torch_objects(torch)
_find_torch_objects(math)
for idx in _disallowed_function_ids():
if idx in torch_object_ids:
del torch_object_ids[idx]
return torch_object_ids
def is_allowed(obj):
"""Is this safe to trace like torch.add ?"""
return id(obj) in _allowed_function_ids()
def is_disallowed(obj):
"""Is this safe to trace like torch.add ?"""
return id(obj) in _disallowed_function_ids()
@lru_cache(None)
@lru_cache(None)
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] | 2.743386 | 378 |
from transformers import BertConfig, BertForSequenceClassification, BertTokenizer
import torch
import os
import json
import logging
MAX_CONTEXT_LEN = 120
if __name__ == "__main__":
cloze_model = ClozeMCModel("cuda:0")
split_dir="/mnt/sharedfolder/hpchan/datasets/cased-cnn-dailymail_coref/train"
with open(os.path.join(split_dir, "{}.json".format(0))) as f:
js = json.loads(f.read())
"""
masked_questions_ids_list = js["masked_question_ids_list"]
multiple_choices_ids_list = js["multiple_choices_ids_list"]
answer_id_list = js["answer_idx_list"]
summary_sent_list = js["abstract"]
summary_str = ' '.join(summary_sent_list)
context_str_list = [summary_str for i in range(len(answer_id_list))]
context_str_list[-1] = "Mentally ill inmates in Miami are housed on the `` forgotten floor '' Judge Steven Leifman says most are there as a result of `` avoidable felonies ''"
confidence_score = cloze_model.compute_confidence_score(masked_questions_ids_list, multiple_choices_ids_list, answer_id_list, context_str_list)
print(masked_questions_ids_list)
print(multiple_choices_ids_list)
print(context_str_list)
print(answer_id_list)
print(len(answer_id_list))
print(confidence_score)
"""
masked_questions_ids_2dlist = []
multiple_choices_ids_2dlist = []
answer_id_2dlist = []
context_str_2dlist = []
for i in range(3):
with open(os.path.join(split_dir, "{}.json".format(i))) as f:
js = json.loads(f.read())
masked_questions_ids_list = js["masked_question_ids_list"]
multiple_choices_ids_list = js["multiple_choices_ids_list"]
answer_id_list = js["answer_idx_list"]
summary_sent_list = js["abstract"]
summary_str = ' '.join(summary_sent_list)
context_str_list = [summary_str for i in range(len(answer_id_list))]
masked_questions_ids_2dlist.append(masked_questions_ids_list)
multiple_choices_ids_2dlist.append(multiple_choices_ids_list)
answer_id_2dlist.append(answer_id_list)
context_str_2dlist.append(context_str_list)
num_questions_per_sample = [len(questions) for questions in masked_questions_ids_2dlist]
print(num_questions_per_sample)
flattened_masked_questions_ids_list = []
flattened_answer_id_list = []
flattened_multiple_choices_ids_list = []
flattened_context_str_list = []
for masked_question_ids_list, answer_id_list, multiple_choices_ids_list, context_str_list in zip(masked_questions_ids_2dlist, answer_id_2dlist,
multiple_choices_ids_2dlist, context_str_2dlist):
flattened_masked_questions_ids_list += masked_question_ids_list
flattened_answer_id_list += answer_id_list
flattened_multiple_choices_ids_list += multiple_choices_ids_list
flattened_context_str_list += context_str_list
print(flattened_context_str_list)
print(len(flattened_context_str_list))
print(len(flattened_masked_questions_ids_list))
print(len(flattened_answer_id_list))
print(len(flattened_multiple_choices_ids_list))
confidence_score = cloze_model.compute_confidence_score(flattened_masked_questions_ids_list, flattened_multiple_choices_ids_list, flattened_answer_id_list, flattened_context_str_list)
print(confidence_score)
num_processed_samples = 0
score_for_each_batch = []
for i in range(len(num_questions_per_sample)):
# average for each batch
avg_score = confidence_score[num_processed_samples:num_processed_samples+num_questions_per_sample[i]].mean(dim=0)
score_for_each_batch.append(avg_score)
print(num_processed_samples)
print(num_processed_samples+num_questions_per_sample[i])
num_processed_samples += num_questions_per_sample[i]
print(score_for_each_batch)
score_for_each_batch = torch.cat(score_for_each_batch, dim=0)
print(score_for_each_batch)
print(score_for_each_batch.size())
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] | 2.431942 | 1,653 |
from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
from builtins import range
from past.utils import old_div
from .tesisfunctions import Plotim,overlay,padVH
import cv2
import numpy as np
#from invariantMoments import centroid,invmoments,normalizedinvariantmoment,bwmoment
from .tesisfunctions import sigmoid,histogram,brightness,getthresh,threshold,pad,graphpolygontest, polygontest
#http://stackoverflow.com/questions/14725181/speed-up-iteration-over-numpy-arrays-opencv-cv2-image
#http://opencv-python-tutroals.readthedocs.org/en/latest/py_tutorials/py_imgproc/py_contours/py_contour_features/py_contour_features.html
fn1 = r'im1_2.jpg'
#fn1 = r"asift2Result_with_alfa1.png"
#fn1 = r"im_completer_Result2.png"
fore = cv2.imread(fn1)
fore = cv2.resize(fore,(300,300))
name = fn1.split('\\')[-1].split(".")[0]
fore2 = fore.copy()
"""
fore = fore.astype("float")
fb = fore[:,:,0]
fg = fore[:,:,1]
fr = fore[:,:,2]
# threshold retinal area
alfa = -1
beta = 50 # if alfa >0 :if beta = 50 with noise, if beta = 200 without noise
th = 1
kernel = np.ones((100,100),np.uint8)
enhanced = sigmoid(fr,alfa,beta)
thresh = cv2.threshold(enhanced.astype("uint8"),th,1,cv2.THRESH_BINARY_INV)[1]
#dilation = cv2.dilate(thresh,kernel,iterations = 1)
#erosion = cv2.erode(dilation,kernel,iterations = 1)
#closing = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
#dilation = cv2.dilate(opening,kernel,iterations = 1)
lastthresh = opening
"""
P = brightness(fore)
thresh = getthresh(cv2.resize(P,(300,300)))
print(thresh)
lastthresh=threshold(P,thresh,1,0)
thresh,lastthresh = cv2.threshold(P,0,1,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
#lastthresh = pad(lastthresh,1)
plotc = Plotim(name + " overlayed lastthresh", overlay(fore.copy(), lastthresh * 255, alpha=lastthresh))
plotc.show()
# find biggest area
contours,hierarchy = cv2.findContours(lastthresh.copy(),cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
print("objects: ",len(contours))
index = 0
maxarea = 0
#objectarea = np.sum(lastthresh)
for i in range(len(contours)):
area = cv2.contourArea(contours[i])
if area>maxarea:
index = i
maxarea = area
print("area contour:",maxarea,"index: ",index)
cnt = contours[index]
print("optaining polygon test...")
polygontest = graphpolygontest((P,cnt)).show()
#DEFECTS
pallet = [[0,0,0],[255,255,255]]
pallet = np.array(pallet,np.uint8)
imdefects = pallet[lastthresh]
hull = cv2.convexHull(cnt,returnPoints = False)
defects = cv2.convexityDefects(cnt,hull)
distances = defects[:,0,3]
two_max = np.argpartition(distances, -2)[-2:] # get indeces of two maximum values
for i in range(defects.shape[0]):
s,e,f,d = defects[i,0]
start = tuple(cnt[s][0])
end = tuple(cnt[e][0])
far = tuple(cnt[f][0])
cv2.line(imdefects,start,end,[0,255,0],2)
cv2.circle(imdefects,far,5,[0,0,255],-1)
#SEPARATING LINE
points = defects[:,0,2]
x1,y1 = tuple(cnt[points[two_max[0]]][0])
x2,y2 = tuple(cnt[points[two_max[1]]][0])
m = old_div((y2-y1),float(x2-x1))
b = int(y1-x1*m)
# find interception with xf and yf axis
if b>imdefects.shape[0]: # if start outside yf
start = int(old_div((imdefects.shape[0]-b),m)),imdefects.shape[0] # (yf-b)/m, yf
else: # if start inside yf
start = 0,b # 0,y
y = int(m*imdefects.shape[1]+b) # m*xf+b
if y<0: # if end outside yf
end = int(old_div(-b,m)),0# x,0
else: # if end inside yf
end = imdefects.shape[1],y # xf, y
cv2.line(imdefects,start,end,[0,0,100],2)
plotc = Plotim(name + " defects", imdefects)
plotc.show()
#ROI
ROI = np.zeros(P.shape,dtype=np.uint8)
cv2.drawContours(ROI,[cnt],0,1,-1)
plotc = Plotim(name + " ROI", ROI)
plotc.show()
M = cv2.moments(cnt) # find moments
#M2 = invmoments(ROI,Area=None,center=None)
#cx = int(M['m10']/M['m00'])
#cy = int(M['m01']/M['m00'])
#x,y = centroid(ROI,maxarea)
#normalizedinvariantmoment(ROI,maxarea,0,0,x,y)
#n00 = bwmoment(ROI,0,0,cx,cy)
#print "(cx,cy)",(cx,cy)
#print "x,y",x,y
#cv2.circle(fore, (cx,cy), 10, (0, 0, 255), -1, 8)
#cv2.circle(fore, (int(x),int(y)), 10, (0, 255, 255), -1, 8)
cv2.drawContours(fore,[cnt],0,(0, 0, 255),2)
ellipse = cv2.fitEllipse(cnt)
cv2.ellipse(fore,ellipse,(0,255,0),2)
plotc = Plotim(name + " description", fore)
plotc.show()
mask = np.ones(P.shape,dtype=np.uint8)
cv2.ellipse(mask,ellipse,0,-1)
fore2[mask>0]=0
plotc = Plotim(name + " result", fore2)
plotc.show()
cv2.imwrite("mask_"+name+".png",fore2)
"""
# Saving the objects:
import pickle
data = {"thresh":thresh,"lastthresh":lastthresh,"cnt":cnt,"ellipse":ellipse,"polygontest":polygontest}
with open("masks_"+name+'.pickle', 'w') as f:
pickle.dump(data, f)""" | [
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] | 2.211992 | 2,118 |
import email, smtplib, ssl, sys, imaplib, email.header
import pandas as pd
from config import *
from email import encoders
from email.mime.base import MIMEBase
from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText
# Loads the csv file given by the CSV_FILE variable
# @return -> list of email addresses contained in the column Email
# Loads the email body from the TEXT_BODY_FILE
body = open(TEXT_BODY_FILE,'r').read()
# Removes the given email from the loaded CSV file containing the emails
# @return -> True is the emailing list changed, False otherwise
# will use the IMAP protocol to check the EMAIL_FOLDER for unseen messages with the subject given by CANCEL_SUBJECT_KEYWORD
# @return -> the list of emails to be removed
# will handle all the unsubcribing, getting the emails to unsubscribe using the getUnsubscribers() function
# and remove them using the removeSubscriber(email_to_remove) function
# @return -> list of email adresses removed from the mailing list
# builds the message object for the emails given the email adress of the receiver (email_receiver)
# @return -> message object "stringified"
# builds the message object for the unsubscribed emails given the email adress of the receiver (email_receiver) and the path to the file containing the
# message to be sent to unsubscribers (UNSUB_BODY_FILE)
# @return -> message object "stringified"
# Will import the email adresses, handle unsubscribing emails using the previously defined functions,
# then it will send the unsub message to emails that asked to be removed if SEND_UNSUB_MESSAGE is set to True
# and the newsletter to the emails present in the emailing list
pyNewsletter() | [
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] | 3.681128 | 461 |
import sys
from stuff import *
import heapq as hq
ls = lines()#chunks()#nums()
cs = {}
d = {}
for y,l in enumerate(ls):
for x,n in enumerate(l):
n = eval(n)
for Y in range(5):
for X in range(5):
c=complex(x+len(l)*X,y+len(ls)*Y)
cs[c] = (n-1+X+Y)%9+1
d[c] = float('inf')
#u.add(complex(x,y))
g = c #thank fuck for python's scoping being shit
d[0] = 0
u = {*cs.keys()}
h = []
hq.heappush(h,(0,(0,0)))
while g in u:
_,c = hq.heappop(h)#min(u,key=d.__getitem__)
c = complex(*c)
u.remove(c)
for o in von_neumann:
if c+o not in cs: continue
if d[c+o] == float('inf'):
hq.heappush(h,(d[c]+cs[c+o],c_t(c+o))) #uh?
d[c+o] = min(d[c+o], d[c]+cs[c+o])
print(d[g])
| [
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66,
10,
78,
12962,
198,
198,
4798,
7,
67,
58,
70,
12962,
198
] | 1.690377 | 478 |
import json
import urllib.parse
from unittest.mock import patch
from uuid import uuid4
from freezegun import freeze_time
from ee.clickhouse.models.event import create_event
from ee.clickhouse.util import ClickhouseTestMixin
from posthog.client import sync_execute
from posthog.models.cohort import Cohort
from posthog.models.person import Person
from posthog.tasks.calculate_cohort import insert_cohort_from_insight_filter
from posthog.tasks.test.test_calculate_cohort import calculate_cohort_test_factory
| [
11748,
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419,
62,
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62,
69,
9548,
628,
628
] | 3.154321 | 162 |
from django.urls import path
import core.views as core_views
urlpatterns = (
path('dataset-snippet/<int:study_id>',
core_views.DatasetSnippetView.as_view(), name='dataset-snippet'),
path('analysis-type-snippet/<int:dataset_id>',
core_views.AnalysisTypeSnippetView.as_view(), name='analysis-type-snippet'),
path('filter-snippet/<int:dataset_id>',
core_views.FilterSnippetView.as_view(), name='filter-snippet'),
path('attribute-snippet/<int:dataset_id>',
core_views.AttributeSnippetView.as_view(), name='attribute-snippet'),
path('search-router/', core_views.SearchRouterView.as_view(), name='search-router'),
path('download-router/<int:search_log_id>', core_views.DownloadRouterView.as_view(), name='download-router'),
path('additional-form-router/<int:dataset_id>/<int:analysis_type_id>', core_views.AdditionalFormRouterView.as_view(), name='additional-form-router'),
path('base-search/', core_views.BaseSearchView.as_view(), name='base-search'),
path('base-download/<int:search_log_id>', core_views.BaseDownloadView.as_view(), name='base-download'),
path('save-search/', core_views.save_search, name='save-search'),
path('saved-search-list/', core_views.SavedSearchListView.as_view(), name='saved-search-list'),
path('retrieve-saved-search/<int:saved_search_id>', core_views.RetrieveSavedSearchView.as_view(), name='retrieve-saved-search'),
path('core-document-view/<int:dataset_id>/<document_es_id>/', core_views.BaseDocumentView.as_view(), name='core-document-view'),
path('core-document-review-create/<int:dataset_id>/<document_es_id>/', core_views.DocumentReviewCreateView.as_view(), name='core-document-review-create'),
path('core-document-review-update/<int:dataset_id>/<int:document_review_id>/', core_views.DocumentReviewUpdateView.as_view(), name='core-document-review-update'),
path('core-document-list/', core_views.DocumentReviewListView.as_view(), name='core-document-list'),
)
| [
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12,
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14,
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62,
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13,
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62,
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22784,
1438,
11639,
7295,
12,
22897,
12,
4868,
33809,
198,
8,
198
] | 2.630607 | 758 |
disordered = {(10, 5): 'b', (3, 10): 'a', (5, 2): 'c'}
# sort keys, then get values from original - fast
sorted_list = sorted(disordered.items(), key=lambda x: x[0][1], reverse=True)
print(sorted_list) | [
6381,
24071,
796,
1391,
7,
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11,
642,
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705,
65,
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357,
18,
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28,
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8,
198,
198,
4798,
7,
82,
9741,
62,
4868,
8
] | 2.56962 | 79 |
from ichnaea.api.locate.constants import DataAccuracy, DataSource
from ichnaea.api.locate.query import Query
from ichnaea.api.locate.result import (
Position,
PositionResultList,
Region,
RegionResultList,
Result,
ResultList,
)
from ichnaea.models import encode_mac
from ichnaea.tests.factories import WifiShardFactory
| [
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1749,
1330,
370,
22238,
2484,
446,
22810,
628,
628,
628,
198
] | 2.761905 | 126 |
import os
import sys
import numpy as np
import math
#MWA scripts
from mwa_pb import primary_beam
from vcstools.pointing_utils import sex2deg
from vcstools.metadb_utils import mwa_alt_az_za, get_common_obs_metadata, getmeta
import logging
logger = logging.getLogger(__name__)
def pixel_area(ra_min, ra_max, dec_min, dec_max):
"""
Calculate the area of a pixel on the sky from the pixel borders
Parameters:
-----------
ra_min: float
The Right Acension minimum in degrees
ra_max: float
The Right Acension maximum in degrees
dec_min: float
The Declination minimum in degrees
dec_max: float
The Declination maximum in degrees
Returns:
--------
area: float
Area of the pixel in square degrees
"""
return (180./math.pi) * (ra_max - ra_min) * (math.sin(math.radians(dec_max)) - math.sin(math.radians(dec_min)))
def field_of_view(obsid,
beam_meta_data=None, dur=None):
"""
Will find the field-of-view of the observation (including the drift) in square degrees.
Parameters:
-----------
obsid: int
The observation ID
beam_meta_data: list
OPTIONAL - the list of common metadata from vcstools.metadb_utils.get_common_obs_metadata.
By default will download the metadata for you
dur: int
OPTIONAL - Duration of observation to calculate for in seconds
By default will use the entire observation duration
Returns:
--------
area: float
The field-of-view of the observation in square degrees
"""
if beam_meta_data is None:
beam_meta_data = get_common_obs_metadata(obsid)
if dur is None:
dt = 296
else:
dt = 100
# Change the dur to the inpur dur
obsid, ra, dec, _, delays, centrefreq, channels = beam_meta_data
beam_meta_data = [obsid, ra, dec, dur, delays, centrefreq, channels]
# Make a pixel for each degree on the sky
names_ra_dec = []
for ra in range(0,360):
for dec in range(-90,90):
names_ra_dec.append(["sky_pos", ra+0.5, dec+0.5])
# Get tile beam power for all pixels
sky_powers = get_beam_power_over_time(beam_meta_data, names_ra_dec,
degrees=True, dt=dt)
# Find the maximum power over all time
max_sky_powers = []
for pixel_power in sky_powers:
temp_power = 0.
for time_power in pixel_power:
if time_power[0] > temp_power:
temp_power = time_power[0]
max_sky_powers.append(temp_power)
# Find all pixels greater than the half power point and sum their area
half_power_point = max(max_sky_powers) / 2
i = 0
area_sum = 0
for ra in range(0,360):
for dec in range(-90,90):
if max_sky_powers[i] > half_power_point:
area_sum = area_sum + pixel_area(ra, ra+1, dec, dec+1)
i = i + 1
return area_sum
def beam_enter_exit(powers, duration, dt=296, min_power=0.3):
"""
Calculates when the source enters and exits the beam
beam_enter_exit(min_power, powers, imax, dt):
powers: list of powers fo the duration every dt and freq powers[times][freqs]
dt: the time interval of how often powers are calculated
duration: duration of the observation according to the metadata in seconds
min_power: zenith normalised power cut off
"""
from scipy.interpolate import UnivariateSpline
time_steps = np.array(range(0, duration, dt), dtype=float)
#For each time step record the min power so even if the source is in
#one freq channel it's recorded
powers_freq_min = []
for p in powers:
powers_freq_min.append(float(min(p) - min_power))
if min(powers_freq_min) > 0.:
enter_beam = 0.
exit_beam = 1.
else:
powers_freq_min = np.array(powers_freq_min)
logger.debug("time_steps: {}".format(time_steps))
logger.debug("powers: {}".format(powers_freq_min))
try:
spline = UnivariateSpline(time_steps, powers_freq_min , s=0.)
except:
return None, None
if len(spline.roots()) == 2:
enter_beam, exit_beam = spline.roots()
enter_beam /= duration
exit_beam /= duration
elif len(spline.roots()) == 1:
if powers_freq_min[0] > powers_freq_min[-1]:
#power declines so starts in beem then exits
enter_beam = 0.
exit_beam = spline.roots()[0]/duration
else:
enter_beam = spline.roots()[0]/duration
exit_beam = 1.
else:
enter_beam = 0.
exit_beam = 1.
return enter_beam, exit_beam
def get_beam_power_over_time(beam_meta_data, names_ra_dec,
dt=296, centeronly=True, verbose=False,
option='analytic', degrees=False,
start_time=0):
"""
Calulates the power (gain at coordinate/gain at zenith) for each source over time.
get_beam_power_over_time(beam_meta_data, names_ra_dec,
dt=296, centeronly=True, verbose=False,
option = 'analytic')
Args:
beam_meta_data: [obsid,ra, dec, time, delays,centrefreq, channels]
obsid metadata obtained from meta.get_common_obs_metadata
names_ra_dec: and array in the format [[source_name, RAJ, DecJ]]
dt: time step in seconds for power calculations (default 296)
centeronly: only calculates for the centre frequency (default True)
verbose: prints extra data to (default False)
option: primary beam model [analytic, advanced, full_EE]
start_time: the time in seconds from the begining of the observation to
start calculating at
"""
obsid, _, _, time, delays, centrefreq, channels = beam_meta_data
names_ra_dec = np.array(names_ra_dec)
logger.info("Calculating beam power for OBS ID: {0}".format(obsid))
starttimes=np.arange(start_time,time+start_time,dt)
stoptimes=starttimes+dt
stoptimes[stoptimes>time]=time
Ntimes=len(starttimes)
midtimes=float(obsid)+0.5*(starttimes+stoptimes)
if not centeronly:
PowersX=np.zeros((len(names_ra_dec),
Ntimes,
len(channels)))
PowersY=np.zeros((len(names_ra_dec),
Ntimes,
len(channels)))
# in Hz
frequencies=np.array(channels)*1.28e6
else:
PowersX=np.zeros((len(names_ra_dec),
Ntimes,1))
PowersY=np.zeros((len(names_ra_dec),
Ntimes,1))
if centrefreq > 1e6:
logger.warning("centrefreq is greater than 1e6, assuming input with units of Hz.")
frequencies=np.array([centrefreq])
else:
frequencies=np.array([centrefreq])*1e6
if degrees:
RAs = np.array(names_ra_dec[:,1],dtype=float)
Decs = np.array(names_ra_dec[:,2],dtype=float)
else:
RAs, Decs = sex2deg(names_ra_dec[:,1],names_ra_dec[:,2])
if len(RAs)==0:
sys.stderr.write('Must supply >=1 source positions\n')
return None
if not len(RAs)==len(Decs):
sys.stderr.write('Must supply equal numbers of RAs and Decs\n')
return None
if verbose is False:
#Supress print statements of the primary beam model functions
sys.stdout = open(os.devnull, 'w')
for itime in range(Ntimes):
# this differ's from the previous ephem_utils method by 0.1 degrees
_, Azs, Zas = mwa_alt_az_za(midtimes[itime], ra=RAs, dec=Decs, degrees=True)
# go from altitude to zenith angle
theta = np.radians(Zas)
phi = np.radians(Azs)
for ifreq in range(len(frequencies)):
#Decide on beam model
if option == 'analytic':
rX,rY=primary_beam.MWA_Tile_analytic(theta, phi,
freq=frequencies[ifreq], delays=delays,
zenithnorm=True,
power=True)
elif option == 'advanced':
rX,rY=primary_beam.MWA_Tile_advanced(theta, phi,
freq=frequencies[ifreq], delays=delays,
zenithnorm=True,
power=True)
elif option == 'full_EE':
rX,rY=primary_beam.MWA_Tile_full_EE(theta, phi,
freq=frequencies[ifreq], delays=delays,
zenithnorm=True,
power=True)
PowersX[:,itime,ifreq]=rX
PowersY[:,itime,ifreq]=rY
if verbose is False:
sys.stdout = sys.__stdout__
Powers=0.5*(PowersX+PowersY)
return Powers
def find_sources_in_obs(obsid_list, names_ra_dec,
obs_for_source=False, dt_input=100, beam='analytic',
min_power=0.3, cal_check=False, all_volt=False,
degrees_check=False, metadata_list=None):
"""
Either creates text files for each MWA obs ID of each source within it or a text
file for each source with each MWA obs is that the source is in.
Args:
obsid_list: list of MWA obs IDs
names_ra_dec: [[source_name, ra, dec]]
dt: the time step in seconds to do power calculations
beam: beam simulation type ['analytic', 'advanced', 'full_EE']
min_power: if above the minium power assumes it's in the beam
cal_check: checks the MWA pulsar database if there is a calibration for the obsid
all_volt: Use all voltages observations including some inital test data
with incorrect formats
degrees_check: if false ra and dec is in hms, if true in degrees
Output [output_data, obsid_meta]:
output_data: The format of output_data is dependant on obs_for_source.
If obs_for_source is True:
output_data = {jname:[[obsid, duration, enter, exit, max_power],
[obsid, duration, enter, exit, max_power]]}
If obs_for_source is False:
ouput_data = {obsid:[[jname, enter, exit, max_power],
[jname, enter, exit, max_power]]}
obsid_meta: a list of the output of get_common_obs_metadata for each obsid
"""
#prepares metadata calls and calculates power
powers = []
#powers[obsid][source][time][freq]
obsid_meta = []
obsid_to_remove = []
for i, obsid in enumerate(obsid_list):
if metadata_list:
beam_meta_data, _ = metadata_list[i]
else:
beam_meta_data = get_common_obs_metadata(obsid)
#beam_meta_data = obsid,ra_obs,dec_obs,time_obs,delays,centrefreq,channels
if dt_input * 4 > beam_meta_data[3]:
# If the observation time is very short then a smaller dt time is required
# to get enough ower imformation
dt = int(beam_meta_data[3] / 4.)
else:
dt = dt_input
logger.debug("obsid: {0}, time_obs {1} s, dt {2} s".format(obsid, beam_meta_data[3], dt))
# Perform the file meta data call
files_meta_data = getmeta(service='data_files', params={'obs_id':obsid, 'nocache':1})
if files_meta_data is None:
logger.warning("No file metadata data found for obsid {}. Skipping".format(obsid))
obsid_to_remove.append(obsid)
continue
# Check raw voltage files
raw_available = False
raw_deleted = False
for file_name in files_meta_data.keys():
if file_name.endswith('dat'):
deleted = files_meta_data[file_name]['deleted']
if deleted:
raw_deleted = True
else:
raw_available = True
# Check combined voltage tar files
comb_available = False
comb_deleted = False
for file_name in files_meta_data.keys():
if file_name.endswith('tar'):
deleted = files_meta_data[file_name]['deleted']
if deleted:
comb_deleted = True
else:
comb_available = True
if raw_available or comb_available or all_volt:
powers.append(get_beam_power_over_time(beam_meta_data, names_ra_dec,
dt=dt, centeronly=True, verbose=False,
option=beam, degrees=degrees_check))
obsid_meta.append(beam_meta_data)
elif raw_deleted and comb_deleted:
logger.warning('Raw and combined voltage files deleted for {}'.format(obsid))
obsid_to_remove.append(obsid)
elif raw_deleted:
logger.warning('Raw voltage files deleted for {}'.format(obsid))
obsid_to_remove.append(obsid)
elif comb_deleted:
logger.warning('Combined voltage files deleted for {}'.format(obsid))
obsid_to_remove.append(obsid)
else:
logger.warning('No raw or combined voltage files for {}'.format(obsid))
obsid_to_remove.append(obsid)
for otr in obsid_to_remove:
obsid_list.remove(otr)
#chooses whether to list the source in each obs or the obs for each source
output_data = {}
if obs_for_source:
for sn, source in enumerate(names_ra_dec):
source_data = []
for on, obsid in enumerate(obsid_list):
source_ob_power = powers[on][sn]
if max(source_ob_power) > min_power:
duration = obsid_meta[on][3]
centre_freq = obsid_meta[on][5] #MHz
channels = obsid_meta[on][6]
bandwidth = (channels[-1] - channels[0] + 1.)*1.28 #MHz
logger.debug("Running beam_enter_exit on obsid: {}".format(obsid))
enter_beam, exit_beam = beam_enter_exit(source_ob_power,duration,
dt=dt, min_power=min_power)
if enter_beam is not None:
source_data.append([obsid, duration, enter_beam, exit_beam,
max(source_ob_power)[0],
centre_freq, bandwidth])
# For each source make a dictionary key that contains a list of
# lists of the data for each obsid
output_data[source[0]] = source_data
else:
#output a list of sorces for each obs
for on, obsid in enumerate(obsid_list):
duration = obsid_meta[on][3]
obsid_data = []
for sn, source in enumerate(names_ra_dec):
source_ob_power = powers[on][sn]
if max(source_ob_power) > min_power:
enter_beam, exit_beam = beam_enter_exit(source_ob_power, duration,
dt=dt, min_power=min_power)
obsid_data.append([source[0], enter_beam, exit_beam, max(source_ob_power)[0]])
# For each obsid make a dictionary key that contains a list of
# lists of the data for each source/pulsar
output_data[obsid] = obsid_data
return output_data, obsid_meta | [
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220,
220,
220,
220,
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640,
62,
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287,
17465,
62,
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25,
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220,
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62,
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25,
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268,
342,
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1417,
1176,
2005,
572,
198,
220,
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220,
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198,
220,
220,
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422,
629,
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88,
13,
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378,
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28,
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11,
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60,
14,
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40045,
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62,
40045,
62,
6477,
62,
2502,
62,
2435,
7,
40045,
62,
28961,
62,
7890,
11,
3891,
62,
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62,
12501,
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220,
220,
220,
220,
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220,
220,
220,
220,
220,
220,
220,
288,
83,
28,
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11,
3641,
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28,
17821,
11,
15942,
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28,
25101,
11,
198,
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220,
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220,
220,
220,
220,
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220,
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220,
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220,
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220,
220,
220,
220,
220,
220,
220,
3038,
11639,
38200,
13370,
3256,
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28,
25101,
11,
198,
220,
220,
220,
220,
220,
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220,
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220,
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220,
220,
220,
220,
220,
220,
220,
923,
62,
2435,
28,
15,
2599,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
2199,
15968,
262,
1176,
357,
48544,
379,
20435,
14,
48544,
379,
1976,
268,
342,
8,
329,
1123,
2723,
625,
640,
13,
628,
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220,
220,
651,
62,
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62,
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2502,
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3038,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
18896,
7,
354,
8961,
22305,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
287,
26109,
198,
220,
220,
220,
220,
220,
220,
220,
19998,
28,
37659,
13,
18747,
7,
354,
8961,
27493,
16,
13,
2078,
68,
21,
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220,
220,
220,
2073,
25,
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220,
220,
220,
220,
220,
220,
20668,
55,
28,
37659,
13,
9107,
418,
19510,
11925,
7,
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62,
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62,
12501,
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399,
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20668,
56,
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62,
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220,
220,
220,
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220,
220,
220,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
220,
399,
22355,
11,
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220,
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611,
1247,
5420,
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25,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
49706,
13,
43917,
7203,
1087,
5420,
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318,
3744,
621,
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68,
21,
11,
13148,
5128,
351,
4991,
286,
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198,
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220,
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220,
220,
220,
220,
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19998,
28,
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2073,
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19998,
28,
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13,
18747,
26933,
1087,
5420,
42180,
12962,
9,
16,
68,
21,
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220,
220,
611,
7370,
25,
198,
220,
220,
220,
220,
220,
220,
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371,
1722,
796,
45941,
13,
18747,
7,
14933,
62,
430,
62,
12501,
58,
45299,
16,
4357,
67,
4906,
28,
22468,
8,
198,
220,
220,
220,
220,
220,
220,
220,
4280,
82,
796,
45941,
13,
18747,
7,
14933,
62,
430,
62,
12501,
58,
45299,
17,
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67,
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28,
22468,
8,
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2073,
25,
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220,
220,
220,
220,
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17,
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7,
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62,
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62,
12501,
58,
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17,
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628,
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220,
220,
611,
18896,
7,
49,
1722,
8,
855,
15,
25,
198,
220,
220,
220,
220,
220,
220,
220,
25064,
13,
301,
1082,
81,
13,
13564,
10786,
34320,
5127,
18189,
16,
2723,
6116,
59,
77,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
6045,
198,
220,
220,
220,
611,
407,
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7,
49,
1722,
8,
855,
11925,
7,
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82,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
25064,
13,
301,
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13,
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34320,
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4961,
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286,
371,
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290,
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82,
59,
77,
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198,
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220,
220,
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220,
1441,
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220,
220,
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318,
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25,
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220,
220,
220,
220,
220,
220,
1303,
40784,
601,
3601,
6299,
286,
262,
4165,
15584,
2746,
5499,
198,
220,
220,
220,
220,
220,
220,
220,
25064,
13,
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448,
796,
1280,
7,
418,
13,
7959,
8423,
11,
705,
86,
11537,
198,
220,
220,
220,
329,
340,
524,
287,
2837,
7,
45,
22355,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
428,
13238,
338,
422,
262,
2180,
2462,
4411,
62,
26791,
2446,
416,
657,
13,
16,
7370,
198,
220,
220,
220,
220,
220,
220,
220,
4808,
11,
7578,
82,
11,
1168,
292,
796,
285,
10247,
62,
2501,
62,
1031,
62,
4496,
7,
21184,
999,
58,
22552,
4357,
2179,
28,
49,
1722,
11,
875,
28,
10707,
82,
11,
7370,
28,
17821,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
467,
422,
20334,
284,
1976,
268,
342,
9848,
198,
220,
220,
220,
220,
220,
220,
220,
262,
8326,
796,
45941,
13,
6335,
1547,
7,
57,
292,
8,
198,
220,
220,
220,
220,
220,
220,
220,
872,
72,
796,
45941,
13,
6335,
1547,
7,
26903,
82,
8,
198,
220,
220,
220,
220,
220,
220,
220,
329,
611,
42180,
287,
2837,
7,
11925,
7,
69,
8897,
3976,
8,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
10707,
485,
319,
15584,
2746,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
3038,
6624,
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13370,
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198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
374,
55,
11,
81,
56,
28,
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62,
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13,
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32,
62,
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62,
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13370,
7,
1169,
8326,
11,
872,
72,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2030,
80,
28,
69,
8897,
3976,
58,
361,
42180,
4357,
16119,
28,
12381,
592,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1976,
268,
342,
27237,
28,
17821,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1176,
28,
17821,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
3038,
6624,
705,
32225,
2903,
10354,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
374,
55,
11,
81,
56,
28,
39754,
62,
40045,
13,
14326,
32,
62,
35103,
62,
32225,
2903,
7,
1169,
8326,
11,
872,
72,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2030,
80,
28,
69,
8897,
3976,
58,
361,
42180,
4357,
16119,
28,
12381,
592,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1976,
268,
342,
27237,
28,
17821,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1176,
28,
17821,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
3038,
6624,
705,
12853,
62,
6500,
10354,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
374,
55,
11,
81,
56,
28,
39754,
62,
40045,
13,
14326,
32,
62,
35103,
62,
12853,
62,
6500,
7,
1169,
8326,
11,
872,
72,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2030,
80,
28,
69,
8897,
3976,
58,
361,
42180,
4357,
16119,
28,
12381,
592,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1976,
268,
342,
27237,
28,
17821,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1176,
28,
17821,
8,
198,
220,
220,
220,
220,
220,
220,
220,
20668,
55,
58,
45299,
22552,
11,
361,
42180,
22241,
81,
55,
198,
220,
220,
220,
220,
220,
220,
220,
20668,
56,
58,
45299,
22552,
11,
361,
42180,
22241,
81,
56,
198,
220,
220,
220,
611,
15942,
577,
318,
10352,
25,
198,
220,
220,
220,
220,
220,
220,
220,
25064,
13,
19282,
448,
796,
25064,
13,
834,
19282,
448,
834,
198,
220,
220,
220,
20668,
28,
15,
13,
20,
9,
7,
47,
3618,
55,
10,
47,
3618,
56,
8,
198,
220,
220,
220,
1441,
20668,
628,
198,
4299,
1064,
62,
82,
2203,
62,
259,
62,
8158,
7,
8158,
312,
62,
4868,
11,
3891,
62,
430,
62,
12501,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
10201,
62,
1640,
62,
10459,
28,
25101,
11,
288,
83,
62,
15414,
28,
3064,
11,
15584,
11639,
38200,
13370,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
949,
62,
6477,
28,
15,
13,
18,
11,
2386,
62,
9122,
28,
25101,
11,
477,
62,
37764,
28,
25101,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7370,
62,
9122,
28,
25101,
11,
20150,
62,
4868,
28,
14202,
2599,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
15467,
8075,
2420,
3696,
329,
1123,
337,
15543,
10201,
4522,
286,
1123,
2723,
1626,
340,
393,
257,
2420,
198,
220,
220,
220,
2393,
329,
1123,
2723,
351,
1123,
337,
15543,
10201,
318,
326,
262,
2723,
318,
287,
13,
198,
220,
220,
220,
943,
14542,
25,
198,
220,
220,
220,
220,
220,
220,
220,
10201,
312,
62,
4868,
25,
1351,
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337,
15543,
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32373,
198,
220,
220,
220,
220,
220,
220,
220,
3891,
62,
430,
62,
12501,
25,
16410,
10459,
62,
3672,
11,
2179,
11,
875,
11907,
198,
220,
220,
220,
220,
220,
220,
220,
288,
83,
25,
262,
640,
2239,
287,
4201,
284,
466,
1176,
16765,
198,
220,
220,
220,
220,
220,
220,
220,
15584,
25,
15584,
18640,
2099,
37250,
38200,
13370,
3256,
705,
32225,
2903,
3256,
705,
12853,
62,
6500,
20520,
198,
220,
220,
220,
220,
220,
220,
220,
949,
62,
6477,
25,
611,
2029,
262,
949,
1505,
1176,
18533,
340,
338,
287,
262,
15584,
198,
220,
220,
220,
220,
220,
220,
220,
2386,
62,
9122,
25,
8794,
262,
337,
15543,
22271,
283,
6831,
611,
612,
318,
257,
36537,
329,
262,
10201,
312,
198,
220,
220,
220,
220,
220,
220,
220,
477,
62,
37764,
25,
5765,
477,
13161,
1095,
13050,
1390,
617,
287,
1287,
1332,
1366,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
351,
11491,
17519,
198,
220,
220,
220,
220,
220,
220,
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] | 2.041022 | 7,752 |
# -*- coding=utf-8 -*-
import os
if __name__ == '__main__':
# ================================================================== #
# Select Sample List #
# ================================================================== #
output = open('ALL.txt', 'w')
with open('Path_Xmls.txt', 'r') as fp:
for oneFile in fp:
xmlname = oneFile.strip()
jpgname = xmlname.replace('.xml', '.jpg').replace('Annotations', 'JPEGImages')
output.write(jpgname)
output.write('\t')
output.write(xmlname)
output.write('\n')
output.close()
| [
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] | 2.272109 | 294 |
"""superb URL Configuration
The `urlpatterns` list routes URLs to views. For more information please see:
https://docs.djangoproject.com/en/3.2/topics/http/urls/
Examples:
Function views
1. Add an import: from my_app import views
2. Add a URL to urlpatterns: path('', views.home, name='home')
Class-based views
1. Add an import: from other_app.views import Home
2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')
Including another URLconf
1. Import the include() function: from django.urls import include, path
2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))
"""
from django.contrib import admin
from django.urls import path, include
from django.conf.urls.static import static
from django.conf import settings
from rest_framework_simplejwt.views import (
TokenObtainPairView,
TokenRefreshView,
)
urlpatterns = [
path ('admin/', admin.site.urls),
path('ckeditor/', include('ckeditor_uploader.urls')),
path ('', include("core.urls")),
path ('', include("blog.urls")),
path ('', include("accounts.urls", namespace = "accounts")),
path ('', include("order.urls", namespace="order")),
path ('', include("products.urls")),
path('api-auth/', include('rest_framework.urls')),
path('api/', include('api.urls')),
path('api/token/', TokenObtainPairView.as_view(), name='token_obtain_pair'),
path('api/token/refresh/', TokenRefreshView.as_view(), name='token_refresh'),
]
if settings.DEBUG:
urlpatterns += static(settings.STATIC_URL, document_root = settings.STATIC_ROOT)
urlpatterns += static(settings.MEDIA_URL, document_root = settings.MEDIA_ROOT) | [
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8
] | 2.819865 | 594 |
from classes.Shell import Shell
from classes.FileSystem import FileSystem
main() | [
6738,
6097,
13,
23248,
1330,
17537,
198,
6738,
6097,
13,
8979,
11964,
1330,
9220,
11964,
198,
12417,
3419
] | 4.444444 | 18 |
version_info = (5, 3, 1)
version = '.'.join(str(v) for v in version_info)
| [
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] | 2.387097 | 31 |
from fontTools.ttLib.tables import otTables
from fontTools.otlLib.builder import buildStatTable
from fontTools.varLib import instancer
import pytest
@pytest.mark.parametrize(
"limits, expected, isNonRIBBI",
[
# Regular
(
{"wght": 400},
{
(1, 3, 1, 0x409): "Test Variable Font",
(2, 3, 1, 0x409): "Regular",
(3, 3, 1, 0x409): "2.001;GOOG;TestVariableFont-Regular",
(6, 3, 1, 0x409): "TestVariableFont-Regular",
},
False,
),
# Regular Normal (width axis Normal isn't included since it is elided)
(
{"wght": 400, "wdth": 100},
{
(1, 3, 1, 0x409): "Test Variable Font",
(2, 3, 1, 0x409): "Regular",
(3, 3, 1, 0x409): "2.001;GOOG;TestVariableFont-Regular",
(6, 3, 1, 0x409): "TestVariableFont-Regular",
},
False,
),
# Black
(
{"wght": 900},
{
(1, 3, 1, 0x409): "Test Variable Font Black",
(2, 3, 1, 0x409): "Regular",
(3, 3, 1, 0x409): "2.001;GOOG;TestVariableFont-Black",
(6, 3, 1, 0x409): "TestVariableFont-Black",
(16, 3, 1, 0x409): "Test Variable Font",
(17, 3, 1, 0x409): "Black",
},
True,
),
# Thin
(
{"wght": 100},
{
(1, 3, 1, 0x409): "Test Variable Font Thin",
(2, 3, 1, 0x409): "Regular",
(3, 3, 1, 0x409): "2.001;GOOG;TestVariableFont-Thin",
(6, 3, 1, 0x409): "TestVariableFont-Thin",
(16, 3, 1, 0x409): "Test Variable Font",
(17, 3, 1, 0x409): "Thin",
},
True,
),
# Thin Condensed
(
{"wght": 100, "wdth": 79},
{
(1, 3, 1, 0x409): "Test Variable Font Thin Condensed",
(2, 3, 1, 0x409): "Regular",
(3, 3, 1, 0x409): "2.001;GOOG;TestVariableFont-ThinCondensed",
(6, 3, 1, 0x409): "TestVariableFont-ThinCondensed",
(16, 3, 1, 0x409): "Test Variable Font",
(17, 3, 1, 0x409): "Thin Condensed",
},
True,
),
# Condensed with unpinned weights
(
{"wdth": 79, "wght": instancer.AxisRange(400, 900)},
{
(1, 3, 1, 0x409): "Test Variable Font Condensed",
(2, 3, 1, 0x409): "Regular",
(3, 3, 1, 0x409): "2.001;GOOG;TestVariableFont-Condensed",
(6, 3, 1, 0x409): "TestVariableFont-Condensed",
(16, 3, 1, 0x409): "Test Variable Font",
(17, 3, 1, 0x409): "Condensed",
},
True,
),
],
)
@pytest.mark.parametrize(
"limits, expected, isNonRIBBI",
[
# Regular | Normal
(
{"wght": 400},
{
(1, 3, 1, 0x409): "Test Variable Font",
(2, 3, 1, 0x409): "Normal",
},
False,
),
# Black | Negreta
(
{"wght": 900},
{
(1, 3, 1, 0x409): "Test Variable Font Negreta",
(2, 3, 1, 0x409): "Normal",
(16, 3, 1, 0x409): "Test Variable Font",
(17, 3, 1, 0x409): "Negreta",
},
True,
),
# Black Condensed | Negreta Zhuštěné
(
{"wght": 900, "wdth": 79},
{
(1, 3, 1, 0x409): "Test Variable Font Negreta Zhuštěné",
(2, 3, 1, 0x409): "Normal",
(16, 3, 1, 0x409): "Test Variable Font",
(17, 3, 1, 0x409): "Negreta Zhuštěné",
},
True,
),
],
)
@pytest.mark.parametrize(
"limits, expected, isNonRIBBI",
[
# Regular | Normal
(
{"wght": 400},
{
(1, 3, 1, 0x409): "Test Variable Font",
(2, 3, 1, 0x409): "Italic",
(6, 3, 1, 0x409): "TestVariableFont-Italic",
},
False,
),
# Black Condensed Italic
(
{"wght": 900, "wdth": 79},
{
(1, 3, 1, 0x409): "Test Variable Font Black Condensed",
(2, 3, 1, 0x409): "Italic",
(6, 3, 1, 0x409): "TestVariableFont-BlackCondensedItalic",
(16, 3, 1, 0x409): "Test Variable Font",
(17, 3, 1, 0x409): "Black Condensed Italic",
},
True,
),
],
)
| [
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# Generated by Django 2.2.1 on 2019-05-17 19:15
from django.db import migrations, models
import django.db.models.deletion
import wagtailstreamforms.fields
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] | 2.962264 | 53 |
# Velocity Verlet Integrator as a Functor containing the integrator specs
# Initializes Integrator with time step & force field | [
2,
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import contextlib
import hashlib
import json
import logging
import sys
import time
import threading
from ecdsa import NIST256p
from ecdsa import VerifyingKey
import requests
import utils
MINING_DIFFICULTY = 3
MINING_SENDER = 'THE BLOCKCHAIN'
MINING_REWARD = 1.0
MINING_TIMER_SEC = 20
BLOCKCHAIN_PORT_RANGE = (5000, 5003)
NEIGHBOURS_IP_RANGE_NUM = (0, 1)
BLOCKCHAIN_NEIGHBOURS_SYNC_TIME_SEC = 20
logging.basicConfig(level=logging.INFO, stream=sys.stdout)
logger = logging.getLogger(__name__)
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] | 2.57513 | 193 |
#!/home/ocean/anaconda3/bin/python3
from numpy import cos, arccos, sin, arctan, tan, pi, sqrt, e; from numpy import array as ary; import numpy as np; tau = 2*pi
from matplotlib import pyplot as plt
import seaborn as sns
#uses arrows to plot where each point lands after being transformed by the function
LongComputationalTime = False
x = np.linspace(-5,5, 50)
y = np.linspace(-5,5, 40)
startingPts = mymeshgrid(x,y)
#Oh yeah computer memory problem. Now I've got a wobble in my grid.
#startingPts = np.concatenate(np.transpose(np.meshgrid(x,y)), axis=0)
cycleLen=len(startingPts)
cycleLen=6
uncleanLandingPts = [complexFunc(z) for z in startingPts]
landingPts = np.array([z if np.isfinite(z).all() else [0,0] for z in uncleanLandingPts])
sortedLandingPoints = np.reshape(landingPts,[len(x),len(y),2])
colorcycle = iter([rotateColorSpace(theta) for theta in np.linspace(0,tau, cycleLen)])
#Order=FABCDEF
if LongComputationalTime:
for n in range(len(startingPts)):
plt.annotate("", xy=startingPts[n], xytext=landingPts[n],arrowprops=dict(color = next(colorcycle), arrowstyle= '<-', alpha=0.5,),)#use arrowheads
#pass
#plot the y direction gridlines landing location
for n in range(len(x)):
plt.plot(sortedLandingPoints[n,:,0],sortedLandingPoints[n,:,1], lw=0.8, alpha=1, color= "green")#next(colorcycle))
#for n in range (int(len(y)/2)) :next(colorcycle)
#for n in range (int(cycleLen/3)): next(colorcycle) #shift the color cycle foward by 1/3
#plot the x direction gridlines landing location
for n in range(len(y)):
plt.plot(sortedLandingPoints[:,n,0],sortedLandingPoints[:,n,1], lw=0.8, alpha=1, color= "red")#next(colorcycle))
xmin = [min(startingPts[:,0]),min(landingPts[:,0])]
xmax = [max(startingPts[:,0]),max(landingPts[:,0])]
ymin = [min(startingPts[:,1]),min(landingPts[:,1])]
ymax = [max(startingPts[:,1]),max(landingPts[:,1])]
plt.xlim( min (xmin) , max ( xmax ) )
plt.ylim( min (ymin) , max ( ymax ) )
plt.show()
plt.plot(landingPts[:,:]-np.ones(np.shape(landingPts[:,:])))
plt.show()
Radius = 100
small = 0.001
st_line = np.linspace(0,0,100)-1j*np.logspace(np.log10(small),np.log10(Radius),100)
theta=np.linspace(-pi/2,pi/2,200)
theta_2 = theta[:int(len(theta)/2)]
theta_3 = theta[int(len(theta)/2):]
semiCirc= Radius*cos(theta)+1j*Radius*sin(theta)
smallCirc =small*cos(-theta_2)+1j*small*sin(-theta_2)
smallCirc2=small*cos(-theta_3)+1j*small*sin(-theta_3)
reverse_st = -st_line[::-1]
Dee = np.concatenate([smallCirc2,st_line,semiCirc,reverse_st,smallCirc])
color = sns.palettes.hls_palette(n_colors=6)
for n in range(0,6):
segment=complexInput(Dee)[100*n:100*(n+1)]
plt.plot(segment.real,segment.imag,color=color[n])
#plt.xlim(-1,1)
#plt.ylim(-1,1)
#plt.scatter(complexInput(Dee).real[::20],complexInput(Dee.imag)[::20])
plt.show() | [
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] | 2.328308 | 1,194 |
from core import settings
from modules.podcast.utils import episode_process_hook
from modules.podcast.models import EpisodeStatus
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from .RFEExtraTrees import RFEExtraTrees, load_RFEExtraTrees
| [
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"""
Pseudo Python command line interface.
It tries to mimic a subset of Python CLI:
https://docs.python.org/3/using/cmdline.html
"""
from __future__ import absolute_import, print_function
import code
import copy
import runpy
import sys
import traceback
from collections import namedtuple
try:
from types import SimpleNamespace
except ImportError:
from argparse import Namespace as SimpleNamespace
ARGUMENT_HELP = """
positional arguments:
script path to file (default: None)
args arguments passed to program in sys.argv[1:]
optional arguments:
-h, --help show this help message and exit
-i inspect interactively after running script.
--version, -V Print the Python version number and exit.
-VV is not supported.
-c COMMAND Execute the Python code in COMMAND.
-m MODULE Search sys.path for the named MODULE and execute its contents
as the __main__ module.
"""
ArgDest = namedtuple("ArgDest", "dest names default")
Optional = namedtuple("Optional", "name is_long argdest nargs action terminal")
Result = namedtuple("Result", "option values")
class PyArgumentParser(object):
"""
`ArgumentParser`-like parser with "terminal option" support.
Major differences:
* Formatted help has to be provided to `description`.
* Many options for `.add_argument` are not supported.
* Especially, there is no positional argument support: all positional
arguments go into `ns.args`.
* `.add_argument` can take boolean option `terminal` (default: `False`)
to stop parsing after consuming the given option.
"""
# Once we drop Python 2, we can do:
"""
def add_argument(self, name, *alt, dest=None, nargs=None, action=None,
default=None, terminal=False):
"""
# fmt: off
def _find_matches(self, arg):
"""
Return a list of `.Result`.
If value presents in `arg` (i.e., ``--long-option=value``), it
becomes the element of `.Result.values` (a list). Otherwise,
this list has to be filled by the caller (`_parse_until_terminal`).
"""
for opt in self._options:
if arg == opt.name:
return [Result(opt, [])]
elif arg.startswith(opt.name):
# i.e., len(arg) > len(opt.name):
if opt.is_long and arg[len(opt.name)] == "=":
return [Result(opt, [arg[len(opt.name) + 1:]])]
elif not opt.is_long:
if opt.nargs != 0:
return [Result(opt, [arg[len(opt.name):]])]
else:
results = [Result(opt, [])]
rest = "-" + arg[len(opt.name):]
results.extend(self._find_matches(rest))
return results
# arg="-ih" -> rest="-h"
return []
# fmt: on
if __name__ == "__main__":
sys.exit(main())
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] | 2.362637 | 1,274 |
#!/usr/bin/env python
# -*- coding: UTF-8 -*-
# ---------------------------------------------------------------------------
# ___ __ __ __ ___
# / | \ | \ | \ / Automatic
# \__ |__/ |__/ |___| \__ Annotation
# \ | | | | \ of
# ___/ | | | | ___/ Speech
#
#
# http://www.sppas.org/
#
# ---------------------------------------------------------------------------
# Laboratoire Parole et Langage, Aix-en-Provence, France
# Copyright (C) 2011-2016 Brigitte Bigi
#
# This banner notice must not be removed
# ---------------------------------------------------------------------------
# Use of this software is governed by the GNU Public License, version 3.
#
# SPPAS is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# SPPAS is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with SPPAS. If not, see <http://www.gnu.org/licenses/>.
#
# ---------------------------------------------------------------------------
# File: trsinfodialog.py
# ----------------------------------------------------------------------------
__docformat__ = """epytext"""
__authors__ = """Brigitte Bigi"""
__copyright__ = """Copyright (C) 2011-2015 Brigitte Bigi"""
import wx
from sppas.src.ui.wxgui.panels.trslist import TrsList
from sppas.src.ui.wxgui.cutils.imageutils import spBitmap
from sppas.src.ui.wxgui.cutils.ctrlutils import CreateGenButton
from sppas.src.ui.wxgui.sp_icons import APP_ICON
from sppas.src.ui.wxgui.sp_icons import CANCEL_ICON
from sppas.src.ui.wxgui.sp_icons import INFO_ICON
from sppas.src.ui.wxgui.sp_consts import FRAME_STYLE
from sppas.src.ui.wxgui.sp_consts import FRAME_TITLE
# ----------------------------------------------------------------------------
class TrsInfoDialog( wx.Dialog ):
"""
@author: Brigitte Bigi
@contact: [email protected]
@license: GPL, v3
@summary: Open a dialog with information about a transcription.
"""
# End __init__
# ------------------------------------------------------------------------
# ------------------------------------------------------------------------
# Create the GUI
# ------------------------------------------------------------------------
# ------------------------------------------------------------------------------
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] | 3.076271 | 944 |
"""
A collection of utilities for MF-GP Bandits.
-- [email protected]
"""
# pylint: disable=import-error
# pylint: disable=no-member
# pylint: disable=invalid-name
# pylint: disable=relative-import
# pylint: disable=super-on-old-class
import numpy as np
# Local imports
from ..opt.function_caller import get_euc_function_caller_from_function
# Hartmann Functions ---------------------------------------------------------------------
def hartmann(x, alpha, A, P, max_val=np.inf):
""" Computes the hartmann function for any given A and P. """
log_sum_terms = (A * (P - x)**2).sum(axis=1)
return min(max_val, alpha.dot(np.exp(-log_sum_terms)))
def _get_hartmann_data(domain_dim):
""" Returns A and P for the 3D hartmann function. """
# pylint: disable=bad-whitespace
if domain_dim == 3:
A = np.array([[3.0, 10, 30],
[0.1, 10, 35],
[3.0, 10, 30],
[0.1, 10, 35]], dtype=np.float64)
P = 1e-4 * np.array([[3689, 1170, 2673],
[4699, 4387, 7470],
[1091, 8732, 5547],
[ 381, 5743, 8828]], dtype=np.float64)
alpha = np.array([1.0, 1.2, 3.0, 3.2])
domain = [[0, 1]] * 3
opt_pt = np.array([0.114614, 0.555649, 0.852547])
max_val = 3.86278
elif domain_dim == 6:
A = np.array([[ 10, 3, 17, 3.5, 1.7, 8],
[0.05, 10, 17, 0.1, 8, 14],
[ 3, 3.5, 1.7, 10, 17, 8],
[ 17, 8, 0.05, 10, 0.1, 14]], dtype=np.float64)
P = 1e-4 * np.array([[1312, 1696, 5569, 124, 8283, 5886],
[2329, 4135, 8307, 3736, 1004, 9991],
[2348, 1451, 3522, 2883, 3047, 6650],
[4047, 8828, 8732, 5743, 1091, 381]], dtype=np.float64)
alpha = np.array([1.0, 1.2, 3.0, 3.2])
domain = [[0, 1]] * 6
opt_pt = np.array([0.20169, 0.150011, 0.476874, 0.275332, 0.311652, 0.6573])
max_val = 3.322368
else:
raise NotImplementedError('Only implemented in 3 and 6 dimensions.')
return A, P, alpha, opt_pt, domain, max_val
def get_hartmann_high_d_function_caller(domain_dim, **kwargs):
""" Constructs a higher dimensional Hartmann function. """
group_dim = 6
num_groups = int(domain_dim/group_dim)
A, P, alpha, l_opt_pt, l_domain_bounds, l_max_val = _get_hartmann_data(group_dim)
hartmann_func = lambda x: hartmann(x, alpha, A, P, l_max_val)
def _eval_highd_hartmann_func(x):
""" Evaluates the higher dimensional hartmann function. """
ret = 0
for j in range(num_groups):
ret += hartmann_func(x[j*group_dim:(j+1)*group_dim])
return ret
opt_pt = np.tile(l_opt_pt, num_groups+1)[0:domain_dim]
opt_val = num_groups * l_max_val
domain_bounds = np.tile(np.array(l_domain_bounds).T, num_groups+1).T[0:domain_dim]
return get_euc_function_caller_from_function(_eval_highd_hartmann_func, domain_bounds,
vectorised=False, opt_pt=opt_pt, opt_val=opt_val, **kwargs)
def get_hartmann_high_d_function_caller_from_descr(descr, **kwargs):
""" Constructs a high dimensional hartmann function from a string. """
domain_dim = int(descr[8:])
return get_hartmann_high_d_function_caller(domain_dim, **kwargs)
def get_hartmann_function_caller(domain_dim, **kwargs):
""" Returns a FunctionCaller object for the hartmann function. """
A, P, alpha, opt_pt, domain_bounds, max_val = _get_hartmann_data(domain_dim)
hartmann_func = lambda x: hartmann(x, alpha, A, P, max_val)
return get_euc_function_caller_from_function(hartmann_func, domain_bounds,
vectorised=False,
opt_pt=opt_pt, opt_val=max_val, **kwargs)
# Hartmann Functions end here ------------------------------------------------------------
# Shekel Function ------------------------------------------------------------------------
def shekel(x, C, beta, max_val=np.inf):
""" Computes the Shekel function for the given C and beta. """
inv_terms = ((C.T - x)**2).sum(axis=1) + beta
return min(max_val, (1/inv_terms).sum())
def _get_shekel_data():
""" Returns the C, beta parameters and optimal values for the shekel function. """
C = [[4, 1, 8, 6, 3, 2, 5, 8, 6, 7],
[4, 1, 8, 6, 7, 9, 3, 1, 2, 3],
[4, 1, 8, 6, 3, 2, 5, 8, 6, 7],
[4, 1, 8, 6, 7, 9, 3, 1, 2, 3]]
C = np.array(C, dtype=np.double)
beta = 0.1 * np.array([1, 2, 2, 4, 4, 6, 3, 7, 5, 5], dtype=np.double)
opt_pt = np.array([4, 4, 4, 4], dtype=np.double)
opt_val = shekel(opt_pt, C, beta)
domain_bounds = [[0, 10]] * 4
return C, beta, opt_pt, domain_bounds, opt_val
def get_shekel_function_caller(**kwargs):
""" Returns a FunctionCaller object for the hartmann function. """
C, beta, opt_pt, domain_bounds, opt_val = _get_shekel_data()
shekel_func = lambda x: shekel(x, C, beta, opt_val)
return get_euc_function_caller_from_function(shekel_func, domain_bounds,
vectorised=False, opt_pt=opt_pt, opt_val=opt_val, **kwargs)
# Shekel function ends here --------------------------------------------------------------
# Currin Exponential Function ------------------------------------------------------------
def currin_exp(x, alpha):
""" Computes the currin exponential function. """
x1 = x[0]
x2 = x[1]
val_1 = 1 - alpha * np.exp(-1/(2 * x2))
val_2 = (2300*x1**3 + 1900*x1**2 + 2092*x1 + 60) / (100*x1**3 + 500*x1**2 + 4*x1 + 20)
return val_1 * val_2
def get_currin_exp_function_caller(**kwargs):
""" Returns a FunctionCaller object for the Currin Exponential function. """
currin_exp_func = lambda x: currin_exp(x, 1)
opt_val = 13.798650
opt_pt = None
domain_bounds = np.array([[0, 1], [0, 1]])
return get_euc_function_caller_from_function(currin_exp_func,
domain_bounds=domain_bounds, vectorised=False, opt_val=opt_val,
opt_pt=opt_pt, **kwargs)
# Branin Function ------------------------------------------------------------------------
def branin(x, a, b, c, r, s, t):
""" Computes the Branin function. """
x1 = x[0]
x2 = x[1]
neg_ret = a * (x2 - b*x1**2 + c*x1 - r)**2 + s*(1-t)*np.cos(x1) + s
return -neg_ret
def _get_branin_data():
""" Gets the constants for the branin function. """
a = 1
b = 5.1/(4*np.pi**2)
c = 5/np.pi
r = 6
s = 10
t = 1/(8*np.pi)
opt_pt = np.array([np.pi, 2.275])
domain_bounds = np.array([[-5, 10], [0, 15]])
return a, b, c, r, s, t, opt_pt, domain_bounds
def get_branin_function_caller(**kwargs):
""" Returns a FunctionCaller object for the Branin function. """
a, b, c, r, s, t, opt_pt, domain_bounds = _get_branin_data()
branin_func = lambda x: branin(x, a, b, c, r, s, t)
opt_val = branin_func(opt_pt)
return get_euc_function_caller_from_function(branin_func, domain_bounds=domain_bounds,
vectorised=False, opt_val=opt_val, opt_pt=opt_pt, **kwargs)
def get_branin_high_d_function_caller(domain_dim, **kwargs):
""" Constructs a higher dimensional Hartmann function. """
group_dim = 2
num_groups = int(domain_dim/group_dim)
a, b, c, r, s, t, opt_pt_2d, domain_bounds_2d = _get_branin_data()
branin_func = lambda x: branin(x, a, b, c, r, s, t)
def _eval_highd_branin_func(x):
""" Evaluates higher dimensional branin function. """
ret = 0
for j in range(num_groups):
ret += branin_func(x[j*group_dim:(j+1)*group_dim])
return ret
opt_pt = np.tile(opt_pt_2d, num_groups+1)[0:domain_dim]
opt_val = _eval_highd_branin_func(opt_pt)
domain_bounds = np.tile(np.array(domain_bounds_2d).T, num_groups+1).T[0:domain_dim]
return get_euc_function_caller_from_function(_eval_highd_branin_func, domain_bounds,
vectorised=False, opt_pt=opt_pt, opt_val=opt_val, **kwargs)
def get_branin_high_d_function_caller_from_descr(descr, **kwargs):
""" Constructs a high dimensional hartmann function from a string. """
domain_dim = int(descr[7:])
return get_branin_high_d_function_caller(domain_dim, **kwargs)
# Borehole Function ----------------------------------------------------------------------
def borehole(x, z, max_val):
""" Computes the Bore Hole function. """
# pylint: disable=bad-whitespace
rw = x[0]
r = x[1]
Tu = x[2]
Hu = x[3]
Tl = x[4]
Hl = x[5]
L = x[6]
Kw = x[7]
# Compute high fidelity function
frac2 = 2*L*Tu/(np.log(r/rw) * rw**2 * Kw)
f2 = min(max_val, 2 * np.pi * Tu * (Hu - Hl)/(np.log(r/rw) * (1 + frac2 + Tu/Tl)))
# Compute low fidelity function
f1 = 5 * Tu * (Hu - Hl)/(np.log(r/rw) * (1.5 + frac2 + Tu/Tl))
# Compute final output
return f2*z + f1*(1-z)
def get_borehole_function_caller(**kwargs):
""" Returns a FunctionCaller object for the Borehold function. """
opt_val = 309.523221
opt_pt = None
borehole_func = lambda x: borehole(x, 1, opt_val)
domain_bounds = [[0.05, 0.15],
[100, 50000],
[63070, 115600],
[990, 1110],
[63.1, 116],
[700, 820],
[1120, 1680],
[9855, 12045]]
return get_euc_function_caller_from_function(borehole_func, domain_bounds,
vectorised=False, opt_val=opt_val, opt_pt=opt_pt, **kwargs)
# Park1 function ==================================================================
def park1(x, max_val=np.inf):
""" Computes the park1 function. """
x1 = x[0]
x2 = x[1]
x3 = x[2]
x4 = x[3]
ret1 = (x1/2) * (np.sqrt(1 + (x2 + x3**2)*x4/(x1**2)) - 1)
ret2 = (x1 + 3*x4) * np.exp(1 + np.sin(x3))
return min(ret1 + ret2, max_val)
def get_park1_function_caller(**kwargs):
""" Returns the park1 function caller. """
opt_val = 25.5872304
opt_pt = None
func = lambda x: park1(x, opt_val)
domain_bounds = [[0, 1]] * 4
return get_euc_function_caller_from_function(func, domain_bounds,
vectorised=False, opt_val=opt_val, opt_pt=opt_pt, **kwargs)
# Park2 function ==================================================================
def park2(x, max_val=np.inf):
""" Comutes the park2 function """
x1 = x[0]
x2 = x[1]
x3 = x[2]
x4 = x[3]
ret = (2.0/3.0) * np.exp(x1 + x2) - x4*np.sin(x3) + x3
return min(ret, max_val)
def get_park2_function_caller(**kwargs):
""" Returns function caller for park2. """
opt_val = 5.925698
opt_pt = None
func = lambda x: park2(x, opt_val)
domain_bounds = [[0, 1]] * 4
return get_euc_function_caller_from_function(func, domain_bounds,
vectorised=False, opt_val=opt_val, opt_pt=opt_pt, **kwargs)
def get_high_d_function_from_low_d(domain_dim, group_dim, low_d_func):
""" Constructs a higher dimensional Hartmann function. """
num_groups = int(domain_dim/group_dim)
def _eval_highd_func(x):
""" Evaluates the higher dimensional function. """
ret = 0
for j in range(num_groups):
ret += low_d_func(x[j*group_dim: (j+1)*group_dim])
return ret
return _eval_highd_func, num_groups
def get_high_d_function_caller_from_low_d_func(domain_dim, low_d_func,
low_d_domain_bounds, low_d_opt_val, low_d_opt_pt, **kwargs):
""" Gets a low dimensional function caller from a high dimensional one. """
group_dim = len(low_d_domain_bounds)
high_d_func, num_groups = get_high_d_function_from_low_d(domain_dim, group_dim,
low_d_func)
high_d_domain_bounds = np.tile(np.array(low_d_domain_bounds).T,
num_groups+1).T[0:domain_dim]
high_d_opt_pt = None
high_d_opt_val = None
if low_d_opt_pt is not None:
high_d_opt_pt = np.tile(low_d_opt_pt, num_groups+1)[0:domain_dim]
high_d_opt_val = high_d_func(high_d_opt_pt)
elif low_d_opt_val is not None:
high_d_opt_val = num_groups * low_d_opt_val
# Return
func_caller = get_euc_function_caller_from_function(high_d_func,
high_d_domain_bounds,
vectorised=False,
opt_val=high_d_opt_val,
opt_pt=high_d_opt_pt,
**kwargs)
return func_caller
def get_high_d_function_caller_from_low_d_func_caller(domain_dim,
low_d_func_caller, **kwargs):
""" Gets a high dimensional function caller from a low dimensional one. """
return get_high_d_function_caller_from_low_d_func(domain_dim, low_d_func_caller.func,
low_d_func_caller.domain.bounds, low_d_func_caller.opt_val,
low_d_func_caller.raw_opt_pt, **kwargs)
def get_high_d_function_caller_from_description(descr, **kwargs):
""" Gets a high dimensional function caller from the description. """
segments = descr.split('-')
domain_dim = int(segments[1])
descr_to_func_dict = {'hartmann': lambda: get_hartmann_function_caller(6,),
'branin': get_branin_function_caller,
'currinexp': get_currin_exp_function_caller,
'park1': get_park1_function_caller,
'park2': get_park2_function_caller,
'borehole': get_borehole_function_caller,
'shekel': get_shekel_function_caller}
low_d_func_caller = descr_to_func_dict[segments[0].lower()](**kwargs)
return get_high_d_function_caller_from_low_d_func_caller(domain_dim,
low_d_func_caller, **kwargs)
# Finally, one very convenient wrapper.
def get_syn_function_caller_from_name(function_name, **kwargs):
""" A very convenient wrapper so that you can just get the function from the name. """
#pylint: disable=too-many-return-statements
if function_name.lower() == 'hartmann3':
return get_hartmann_function_caller(3, descr=function_name, **kwargs)
elif function_name.lower() == 'hartmann6':
return get_hartmann_function_caller(6, descr=function_name, **kwargs)
elif function_name.lower() == 'currinexp':
return get_currin_exp_function_caller(descr=function_name, **kwargs)
elif function_name.lower() == 'branin':
return get_branin_function_caller(descr=function_name, **kwargs)
elif function_name.lower() == 'borehole':
return get_borehole_function_caller(descr=function_name, **kwargs)
elif function_name.lower() == 'shekel':
return get_shekel_function_caller(descr=function_name, **kwargs)
elif function_name.lower() == 'park1':
return get_park1_function_caller(descr=function_name, **kwargs)
elif function_name.lower() == 'park2':
return get_park2_function_caller(descr=function_name, **kwargs)
else:
return get_high_d_function_caller_from_description(descr=function_name, **kwargs)
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8,
628
] | 2.211703 | 6,665 |
import dairlib
import drake
import numpy as np
# Class to easily convert list of lcmt_osc_tracking_data_t to numpy arrays
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import RPi.GPIO as GPIO
from drive import Drive
from dribbler import Dribbler
from field import Field
from enum import Enum
from kicker import Kicker
import time
import numpy as np
#########
# ENUMS #
#
############
# FUNCTIONS
# these should be very generic
###########
# VARIABLES
# used for condition checking or generic functions within navigation class
BALL_IN_DRIBBLER_DIST = 9 # min distance when ball is in the dribbler
BALL_IN_DRIBBLER_ANGLE = 15 # ball must be within +-28 deg of fov to be in dribbler (32 is max)
GOAL_DIST = 90 # distance to stop at to shoot for goal
GOAL_ANGLE = 3 # goal must be within +-3 deg of fov to be in dribbler
OBSTACLE_DIST = 30 # min dist to begin circ,ing object
MIN_SPEED = 35 # the speed the robot will be at when its target is very close
#############
# NAV CLASS #
#
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] | 2.87 | 300 |
import simplekml
import json
from polycircles import polycircles
import time
from subprocess import call
'''
https://github.com/LiquidGalaxyLAB/ViewYourData/blob/master/VYD_Project/VYD/Utils/PresentationManager/cylinder_generator.py
https://developers.google.com/maps/documentation/geocoding/intro?hl=es-419
'''
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"""
来点小强+{可选小强数量}
可以艾特一个人使用
"""
import base64
import random
from io import BytesIO
import httpx
from botoy import GroupMsg, S
from botoy import decorators as deco
from botoy.parser import group as gp
from PIL import Image
小强 = Image.open(
BytesIO(
base64.b64decode(
"""
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"""
)
)
)
if 小强.mode != "RGBA":
小强 = 小强.convert("RGBA")
@deco.ignore_botself
@deco.on_regexp(r"来点小强(\d{0,})")
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28156,
118,
38016,
67,
90,
15,
11,
30072,
4943,
198
] | 1.29674 | 33,895 |
import abc
| [
11748,
450,
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628,
198
] | 2.6 | 5 |
"""Smooth a mesh.
"""
from compas.datastructures import Mesh
from compas.geometry import smooth_area
import compas_rhino
__author__ = ['Tom Van Mele', 'Matthias Rippmann']
__copyright__ = 'Copyright 2017, BRG - ETH Zurich',
__license__ = 'MIT'
__email__ = '[email protected]'
# select a Rhino mesh
# and make it into a mesh datastructure
guid = compas_rhino.select_mesh()
mesh = compas_rhino.mesh_from_guid(Mesh, guid)
# extract the data needed by the smoothing algorithm
# identify the boundary as fixed
vertices = mesh.get_vertices_attributes('xyz')
faces = [mesh.face_vertices(fkey) for fkey in mesh.faces()]
adjacency = [mesh.vertex_faces(key, ordered=True) for key in mesh.vertices()]
fixed = mesh.vertices_on_boundary()
# run the smoothing algorithm
# update the mesh
# display the result in Rhino
smooth_area(vertices, faces, adjacency, fixed=fixed, kmax=100)
for key, attr in mesh.vertices(True):
attr['x'] = vertices[key][0]
attr['y'] = vertices[key][1]
attr['z'] = vertices[key][2]
compas_rhino.mesh_draw(mesh)
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76,
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62,
19334,
7,
76,
5069,
8,
201,
198
] | 2.456954 | 453 |
"""
SoftLayer.tests.managers.event_log_tests
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
:license: MIT, see LICENSE for more details.
"""
import SoftLayer
from SoftLayer import fixtures
from SoftLayer import testing
| [
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198,
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] | 3.629032 | 62 |
import RPi.GPIO as GPIO
import time
#When the mercury is contact, we get a signal on 21
pin = 21
light = 20
GPIO.setmode(GPIO.BCM)
GPIO.setup(pin, GPIO.IN)
GPIO.setup(light, GPIO.OUT)
while True:
if GPIO.input(pin):
print("contact")
GPIO.output(light, GPIO.HIGH)
else:
print("no contact")
GPIO.output(light, GPIO.LOW)
time.sleep(0.3)
GPIO.cleanup() | [
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7,
15,
13,
18,
8,
198,
198,
16960,
9399,
13,
27773,
929,
3419
] | 2.230337 | 178 |
from .atomic_node import AtomicNode
| [
6738,
764,
47116,
62,
17440,
1330,
28976,
19667,
628
] | 4.111111 | 9 |
_default_store = "dir"
_default_clevel = 3
_default_codec = "zstd"
_default_workers_backend = "threading"
| [
62,
12286,
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62,
22896,
62,
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437,
796,
366,
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278,
1,
198
] | 2.585366 | 41 |
import izi
@izi.get("/birthday")
| [
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528,
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] | 2.058824 | 17 |
# coding: utf-8
"""
FeersumNLU API
This is the HTTP API for Feersum NLU. See https://github.com/praekelt/feersum-nlu-api-wrappers for examples of how to use the API. # noqa: E501
OpenAPI spec version: 2.0.54.dev2
Contact: [email protected]
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from __future__ import absolute_import
import re # noqa: F401
# python 2 and python 3 compatibility library
import six
from feersum_nlu.api_client import ApiClient
class RegexEntityExtractorsApi(object):
"""NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
Ref: https://github.com/swagger-api/swagger-codegen
"""
def regex_entity_extractor_create(self, create_details, **kwargs): # noqa: E501
"""Create a regular expression entity extractor. # noqa: E501
Create a new regular expression entity extractor or reload one from the trash. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.regex_entity_extractor_create(create_details, async_req=True)
>>> result = thread.get()
:param async_req bool
:param RegexEntityExtractorCreateDetails create_details: The details of the instance to create. (required)
:param str x_caller:
:return: RegexEntityExtractorInstanceDetail
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.regex_entity_extractor_create_with_http_info(create_details, **kwargs) # noqa: E501
else:
(data) = self.regex_entity_extractor_create_with_http_info(create_details, **kwargs) # noqa: E501
return data
def regex_entity_extractor_create_with_http_info(self, create_details, **kwargs): # noqa: E501
"""Create a regular expression entity extractor. # noqa: E501
Create a new regular expression entity extractor or reload one from the trash. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.regex_entity_extractor_create_with_http_info(create_details, async_req=True)
>>> result = thread.get()
:param async_req bool
:param RegexEntityExtractorCreateDetails create_details: The details of the instance to create. (required)
:param str x_caller:
:return: RegexEntityExtractorInstanceDetail
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['create_details', 'x_caller'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method regex_entity_extractor_create" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'create_details' is set
if ('create_details' not in params or
params['create_details'] is None):
raise ValueError("Missing the required parameter `create_details` when calling `regex_entity_extractor_create`") # noqa: E501
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
if 'x_caller' in params:
header_params['X-CALLER'] = params['x_caller'] # noqa: E501
form_params = []
local_var_files = {}
body_params = None
if 'create_details' in params:
body_params = params['create_details']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['APIKeyHeader', 'APIKeyHeader_old'] # noqa: E501
return self.api_client.call_api(
'/nlu/v2/regex_entity_extractors', 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='RegexEntityExtractorInstanceDetail', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def regex_entity_extractor_del(self, instance_name, **kwargs): # noqa: E501
"""Delete named instance. # noqa: E501
Delete and get the details of the named regular expression entity extractor instance. Deleted models can be reloaded from the trash with the create operation. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.regex_entity_extractor_del(instance_name, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str instance_name: The name of the instance. (required)
:param str x_caller:
:return: RegexEntityExtractorInstanceDetail
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.regex_entity_extractor_del_with_http_info(instance_name, **kwargs) # noqa: E501
else:
(data) = self.regex_entity_extractor_del_with_http_info(instance_name, **kwargs) # noqa: E501
return data
def regex_entity_extractor_del_with_http_info(self, instance_name, **kwargs): # noqa: E501
"""Delete named instance. # noqa: E501
Delete and get the details of the named regular expression entity extractor instance. Deleted models can be reloaded from the trash with the create operation. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.regex_entity_extractor_del_with_http_info(instance_name, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str instance_name: The name of the instance. (required)
:param str x_caller:
:return: RegexEntityExtractorInstanceDetail
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['instance_name', 'x_caller'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method regex_entity_extractor_del" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'instance_name' is set
if ('instance_name' not in params or
params['instance_name'] is None):
raise ValueError("Missing the required parameter `instance_name` when calling `regex_entity_extractor_del`") # noqa: E501
collection_formats = {}
path_params = {}
if 'instance_name' in params:
path_params['instance_name'] = params['instance_name'] # noqa: E501
query_params = []
header_params = {}
if 'x_caller' in params:
header_params['X-CALLER'] = params['x_caller'] # noqa: E501
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['APIKeyHeader', 'APIKeyHeader_old'] # noqa: E501
return self.api_client.call_api(
'/nlu/v2/regex_entity_extractors/{instance_name}', 'DELETE',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='RegexEntityExtractorInstanceDetail', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def regex_entity_extractor_get_details(self, instance_name, **kwargs): # noqa: E501
"""Get details of named instance. # noqa: E501
Get the details of the named regular expression entity extractor instance. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.regex_entity_extractor_get_details(instance_name, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str instance_name: The name of the instance. (required)
:param str x_caller:
:return: RegexEntityExtractorInstanceDetail
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.regex_entity_extractor_get_details_with_http_info(instance_name, **kwargs) # noqa: E501
else:
(data) = self.regex_entity_extractor_get_details_with_http_info(instance_name, **kwargs) # noqa: E501
return data
def regex_entity_extractor_get_details_with_http_info(self, instance_name, **kwargs): # noqa: E501
"""Get details of named instance. # noqa: E501
Get the details of the named regular expression entity extractor instance. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.regex_entity_extractor_get_details_with_http_info(instance_name, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str instance_name: The name of the instance. (required)
:param str x_caller:
:return: RegexEntityExtractorInstanceDetail
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['instance_name', 'x_caller'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method regex_entity_extractor_get_details" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'instance_name' is set
if ('instance_name' not in params or
params['instance_name'] is None):
raise ValueError("Missing the required parameter `instance_name` when calling `regex_entity_extractor_get_details`") # noqa: E501
collection_formats = {}
path_params = {}
if 'instance_name' in params:
path_params['instance_name'] = params['instance_name'] # noqa: E501
query_params = []
header_params = {}
if 'x_caller' in params:
header_params['X-CALLER'] = params['x_caller'] # noqa: E501
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['APIKeyHeader', 'APIKeyHeader_old'] # noqa: E501
return self.api_client.call_api(
'/nlu/v2/regex_entity_extractors/{instance_name}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='RegexEntityExtractorInstanceDetail', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def regex_entity_extractor_get_details_all(self, **kwargs): # noqa: E501
"""Get list of regular expression entity extractors. # noqa: E501
Get the list of regular expression entity extractors. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.regex_entity_extractor_get_details_all(async_req=True)
>>> result = thread.get()
:param async_req bool
:param str x_caller:
:return: list[RegexEntityExtractorInstanceDetail]
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.regex_entity_extractor_get_details_all_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.regex_entity_extractor_get_details_all_with_http_info(**kwargs) # noqa: E501
return data
def regex_entity_extractor_get_details_all_with_http_info(self, **kwargs): # noqa: E501
"""Get list of regular expression entity extractors. # noqa: E501
Get the list of regular expression entity extractors. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.regex_entity_extractor_get_details_all_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:param str x_caller:
:return: list[RegexEntityExtractorInstanceDetail]
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['x_caller'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method regex_entity_extractor_get_details_all" % key
)
params[key] = val
del params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
if 'x_caller' in params:
header_params['X-CALLER'] = params['x_caller'] # noqa: E501
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['APIKeyHeader', 'APIKeyHeader_old'] # noqa: E501
return self.api_client.call_api(
'/nlu/v2/regex_entity_extractors', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='list[RegexEntityExtractorInstanceDetail]', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def regex_entity_extractor_get_params(self, instance_name, **kwargs): # noqa: E501
"""Get the editable model parameters of named regex entity extractor. # noqa: E501
Get the editable model parameters of named regex entity extractor. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.regex_entity_extractor_get_params(instance_name, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str instance_name: The name of the instance. (required)
:param str x_caller:
:return: ModelParams
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.regex_entity_extractor_get_params_with_http_info(instance_name, **kwargs) # noqa: E501
else:
(data) = self.regex_entity_extractor_get_params_with_http_info(instance_name, **kwargs) # noqa: E501
return data
def regex_entity_extractor_get_params_with_http_info(self, instance_name, **kwargs): # noqa: E501
"""Get the editable model parameters of named regex entity extractor. # noqa: E501
Get the editable model parameters of named regex entity extractor. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.regex_entity_extractor_get_params_with_http_info(instance_name, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str instance_name: The name of the instance. (required)
:param str x_caller:
:return: ModelParams
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['instance_name', 'x_caller'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method regex_entity_extractor_get_params" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'instance_name' is set
if ('instance_name' not in params or
params['instance_name'] is None):
raise ValueError("Missing the required parameter `instance_name` when calling `regex_entity_extractor_get_params`") # noqa: E501
collection_formats = {}
path_params = {}
if 'instance_name' in params:
path_params['instance_name'] = params['instance_name'] # noqa: E501
query_params = []
header_params = {}
if 'x_caller' in params:
header_params['X-CALLER'] = params['x_caller'] # noqa: E501
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['APIKeyHeader', 'APIKeyHeader_old'] # noqa: E501
return self.api_client.call_api(
'/nlu/v2/regex_entity_extractors/{instance_name}/params', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ModelParams', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def regex_entity_extractor_retrieve(self, instance_name, text_input, **kwargs): # noqa: E501
"""Extract information based on the regular expression. # noqa: E501
Extract the entities matching the regular expression. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.regex_entity_extractor_retrieve(instance_name, text_input, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str instance_name: The name of the instance. (required)
:param TextInput text_input: The input text. (required)
:param str x_caller:
:return: list[RegexEntity]
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.regex_entity_extractor_retrieve_with_http_info(instance_name, text_input, **kwargs) # noqa: E501
else:
(data) = self.regex_entity_extractor_retrieve_with_http_info(instance_name, text_input, **kwargs) # noqa: E501
return data
def regex_entity_extractor_retrieve_with_http_info(self, instance_name, text_input, **kwargs): # noqa: E501
"""Extract information based on the regular expression. # noqa: E501
Extract the entities matching the regular expression. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.regex_entity_extractor_retrieve_with_http_info(instance_name, text_input, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str instance_name: The name of the instance. (required)
:param TextInput text_input: The input text. (required)
:param str x_caller:
:return: list[RegexEntity]
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['instance_name', 'text_input', 'x_caller'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method regex_entity_extractor_retrieve" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'instance_name' is set
if ('instance_name' not in params or
params['instance_name'] is None):
raise ValueError("Missing the required parameter `instance_name` when calling `regex_entity_extractor_retrieve`") # noqa: E501
# verify the required parameter 'text_input' is set
if ('text_input' not in params or
params['text_input'] is None):
raise ValueError("Missing the required parameter `text_input` when calling `regex_entity_extractor_retrieve`") # noqa: E501
collection_formats = {}
path_params = {}
if 'instance_name' in params:
path_params['instance_name'] = params['instance_name'] # noqa: E501
query_params = []
header_params = {}
if 'x_caller' in params:
header_params['X-CALLER'] = params['x_caller'] # noqa: E501
form_params = []
local_var_files = {}
body_params = None
if 'text_input' in params:
body_params = params['text_input']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['APIKeyHeader', 'APIKeyHeader_old'] # noqa: E501
return self.api_client.call_api(
'/nlu/v2/regex_entity_extractors/{instance_name}/retrieve', 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='list[RegexEntity]', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def regex_entity_extractor_set_params(self, instance_name, model_params, **kwargs): # noqa: E501
"""Set the model parameters of named regex entity extractor. # noqa: E501
Set the model parameters of named regex entity extractor. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.regex_entity_extractor_set_params(instance_name, model_params, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str instance_name: The name of the instance. (required)
:param ModelParams model_params: The model parameters. (required)
:param str x_caller:
:return: RegexEntityExtractorInstanceDetail
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.regex_entity_extractor_set_params_with_http_info(instance_name, model_params, **kwargs) # noqa: E501
else:
(data) = self.regex_entity_extractor_set_params_with_http_info(instance_name, model_params, **kwargs) # noqa: E501
return data
def regex_entity_extractor_set_params_with_http_info(self, instance_name, model_params, **kwargs): # noqa: E501
"""Set the model parameters of named regex entity extractor. # noqa: E501
Set the model parameters of named regex entity extractor. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.regex_entity_extractor_set_params_with_http_info(instance_name, model_params, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str instance_name: The name of the instance. (required)
:param ModelParams model_params: The model parameters. (required)
:param str x_caller:
:return: RegexEntityExtractorInstanceDetail
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['instance_name', 'model_params', 'x_caller'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method regex_entity_extractor_set_params" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'instance_name' is set
if ('instance_name' not in params or
params['instance_name'] is None):
raise ValueError("Missing the required parameter `instance_name` when calling `regex_entity_extractor_set_params`") # noqa: E501
# verify the required parameter 'model_params' is set
if ('model_params' not in params or
params['model_params'] is None):
raise ValueError("Missing the required parameter `model_params` when calling `regex_entity_extractor_set_params`") # noqa: E501
collection_formats = {}
path_params = {}
if 'instance_name' in params:
path_params['instance_name'] = params['instance_name'] # noqa: E501
query_params = []
header_params = {}
if 'x_caller' in params:
header_params['X-CALLER'] = params['x_caller'] # noqa: E501
form_params = []
local_var_files = {}
body_params = None
if 'model_params' in params:
body_params = params['model_params']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['APIKeyHeader', 'APIKeyHeader_old'] # noqa: E501
return self.api_client.call_api(
'/nlu/v2/regex_entity_extractors/{instance_name}/params', 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='RegexEntityExtractorInstanceDetail', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def regex_entity_extractor_vaporise(self, instance_name, **kwargs): # noqa: E501
"""Vaporise the named model. # noqa: E501
Permanently vaporises a model even if not trashed. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.regex_entity_extractor_vaporise(instance_name, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str instance_name: The name of the instance. (required)
:param str x_caller:
:return: RegexEntityExtractorInstanceDetail
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.regex_entity_extractor_vaporise_with_http_info(instance_name, **kwargs) # noqa: E501
else:
(data) = self.regex_entity_extractor_vaporise_with_http_info(instance_name, **kwargs) # noqa: E501
return data
def regex_entity_extractor_vaporise_with_http_info(self, instance_name, **kwargs): # noqa: E501
"""Vaporise the named model. # noqa: E501
Permanently vaporises a model even if not trashed. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.regex_entity_extractor_vaporise_with_http_info(instance_name, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str instance_name: The name of the instance. (required)
:param str x_caller:
:return: RegexEntityExtractorInstanceDetail
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['instance_name', 'x_caller'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method regex_entity_extractor_vaporise" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'instance_name' is set
if ('instance_name' not in params or
params['instance_name'] is None):
raise ValueError("Missing the required parameter `instance_name` when calling `regex_entity_extractor_vaporise`") # noqa: E501
collection_formats = {}
path_params = {}
if 'instance_name' in params:
path_params['instance_name'] = params['instance_name'] # noqa: E501
query_params = []
header_params = {}
if 'x_caller' in params:
header_params['X-CALLER'] = params['x_caller'] # noqa: E501
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['APIKeyHeader', 'APIKeyHeader_old'] # noqa: E501
return self.api_client.call_api(
'/nlu/v2/regex_entity_extractors/{instance_name}/vaporise', 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='RegexEntityExtractorInstanceDetail', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
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220,
220,
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1303,
14626,
13639,
4600,
38855,
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13639,
62,
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17816,
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13,
19738,
62,
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62,
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7,
198,
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37250,
31438,
14,
17752,
6,
12962,
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25,
412,
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628,
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63,
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13639,
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62,
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6284,
62,
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62,
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13,
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13,
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62,
15042,
7,
198,
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220,
220,
220,
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220,
220,
220,
220,
220,
220,
31051,
77,
2290,
14,
85,
17,
14,
260,
25636,
62,
26858,
62,
2302,
974,
669,
3256,
705,
32782,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3108,
62,
37266,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
12405,
62,
37266,
11,
198,
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220,
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62,
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198,
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1767,
28,
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1281,
62,
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28,
687,
62,
37266,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3696,
28,
12001,
62,
7785,
62,
16624,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2882,
62,
4906,
11639,
3041,
25636,
32398,
11627,
40450,
33384,
11242,
603,
3256,
220,
1303,
645,
20402,
25,
412,
33548,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6284,
62,
33692,
28,
18439,
62,
33692,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
30351,
62,
42180,
28,
37266,
13,
1136,
10786,
292,
13361,
62,
42180,
33809,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4808,
7783,
62,
4023,
62,
7890,
62,
8807,
28,
37266,
13,
1136,
10786,
62,
7783,
62,
4023,
62,
7890,
62,
8807,
33809,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4808,
3866,
2220,
62,
11299,
28,
37266,
13,
1136,
10786,
62,
3866,
2220,
62,
11299,
3256,
6407,
828,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4808,
25927,
62,
48678,
28,
37266,
13,
1136,
10786,
62,
25927,
62,
48678,
33809,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4947,
62,
687,
1381,
28,
43681,
62,
687,
1381,
8,
628,
220,
220,
220,
825,
40364,
62,
26858,
62,
2302,
40450,
62,
12381,
7,
944,
11,
4554,
62,
3672,
11,
12429,
46265,
22046,
2599,
220,
1303,
645,
20402,
25,
412,
33548,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
38727,
3706,
4554,
13,
220,
1303,
645,
20402,
25,
412,
33548,
628,
220,
220,
220,
220,
220,
220,
220,
23520,
290,
651,
262,
3307,
286,
262,
3706,
3218,
5408,
9312,
7925,
273,
4554,
13,
1024,
33342,
4981,
460,
307,
18126,
276,
422,
262,
13913,
351,
262,
2251,
4905,
13,
220,
1303,
645,
20402,
25,
412,
33548,
198,
220,
220,
220,
220,
220,
220,
220,
770,
2446,
1838,
257,
18305,
516,
14626,
2581,
416,
4277,
13,
1675,
787,
281,
198,
220,
220,
220,
220,
220,
220,
220,
39354,
14626,
2581,
11,
3387,
1208,
30351,
62,
42180,
28,
17821,
198,
220,
220,
220,
220,
220,
220,
220,
13163,
4704,
796,
40391,
13,
260,
25636,
62,
26858,
62,
2302,
40450,
62,
12381,
7,
39098,
62,
3672,
11,
30351,
62,
42180,
28,
17821,
8,
198,
220,
220,
220,
220,
220,
220,
220,
13163,
1255,
796,
4704,
13,
1136,
3419,
628,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
30351,
62,
42180,
20512,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
965,
4554,
62,
3672,
25,
383,
1438,
286,
262,
4554,
13,
357,
35827,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
965,
2124,
62,
13345,
263,
25,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
7783,
25,
797,
25636,
32398,
11627,
40450,
33384,
11242,
603,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1002,
262,
2446,
318,
1444,
355,
24871,
3481,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5860,
262,
2581,
4704,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
479,
86,
22046,
17816,
62,
7783,
62,
4023,
62,
7890,
62,
8807,
20520,
796,
6407,
198,
220,
220,
220,
220,
220,
220,
220,
611,
479,
86,
22046,
13,
1136,
10786,
292,
13361,
62,
42180,
6,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
2116,
13,
260,
25636,
62,
26858,
62,
2302,
40450,
62,
12381,
62,
4480,
62,
4023,
62,
10951,
7,
39098,
62,
3672,
11,
12429,
46265,
22046,
8,
220,
1303,
645,
20402,
25,
412,
33548,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
357,
7890,
8,
796,
2116,
13,
260,
25636,
62,
26858,
62,
2302,
40450,
62,
12381,
62,
4480,
62,
4023,
62,
10951,
7,
39098,
62,
3672,
11,
12429,
46265,
22046,
8,
220,
1303,
645,
20402,
25,
412,
33548,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
1366,
628,
220,
220,
220,
825,
40364,
62,
26858,
62,
2302,
40450,
62,
12381,
62,
4480,
62,
4023,
62,
10951,
7,
944,
11,
4554,
62,
3672,
11,
12429,
46265,
22046,
2599,
220,
1303,
645,
20402,
25,
412,
33548,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
38727,
3706,
4554,
13,
220,
1303,
645,
20402,
25,
412,
33548,
628,
220,
220,
220,
220,
220,
220,
220,
23520,
290,
651,
262,
3307,
286,
262,
3706,
3218,
5408,
9312,
7925,
273,
4554,
13,
1024,
33342,
4981,
460,
307,
18126,
276,
422,
262,
13913,
351,
262,
2251,
4905,
13,
220,
1303,
645,
20402,
25,
412,
33548,
198,
220,
220,
220,
220,
220,
220,
220,
770,
2446,
1838,
257,
18305,
516,
14626,
2581,
416,
4277,
13,
1675,
787,
281,
198,
220,
220,
220,
220,
220,
220,
220,
39354,
14626,
2581,
11,
3387,
1208,
30351,
62,
42180,
28,
17821,
198,
220,
220,
220,
220,
220,
220,
220,
13163,
4704,
796,
40391,
13,
260,
25636,
62,
26858,
62,
2302,
40450,
62,
12381,
62,
4480,
62,
4023,
62,
10951,
7,
39098,
62,
3672,
11,
30351,
62,
42180,
28,
17821,
8,
198,
220,
220,
220,
220,
220,
220,
220,
13163,
1255,
796,
4704,
13,
1136,
3419,
628,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
30351,
62,
42180,
20512,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
965,
4554,
62,
3672,
25,
383,
1438,
286,
262,
4554,
13,
357,
35827,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
965,
2124,
62,
13345,
263,
25,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
7783,
25,
797,
25636,
32398,
11627,
40450,
33384,
11242,
603,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1002,
262,
2446,
318,
1444,
355,
24871,
3481,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5860,
262,
2581,
4704,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
628,
220,
220,
220,
220,
220,
220,
220,
477,
62,
37266,
796,
37250,
39098,
62,
3672,
3256,
705,
87,
62,
13345,
263,
20520,
220,
1303,
645,
20402,
25,
412,
33548,
198,
220,
220,
220,
220,
220,
220,
220,
477,
62,
37266,
13,
33295,
10786,
292,
13361,
62,
42180,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
477,
62,
37266,
13,
33295,
10786,
62,
7783,
62,
4023,
62,
7890,
62,
8807,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
477,
62,
37266,
13,
33295,
10786,
62,
3866,
2220,
62,
11299,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
477,
62,
37266,
13,
33295,
10786,
62,
25927,
62,
48678,
11537,
628,
220,
220,
220,
220,
220,
220,
220,
42287,
796,
17205,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
329,
1994,
11,
1188,
287,
2237,
13,
2676,
23814,
7,
37266,
17816,
46265,
22046,
20520,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
1994,
407,
287,
477,
62,
37266,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5298,
5994,
12331,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
30074,
281,
10059,
21179,
4578,
705,
4,
82,
29653,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
284,
2446,
40364,
62,
26858,
62,
2302,
40450,
62,
12381,
1,
4064,
1994,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
42287,
58,
2539,
60,
796,
1188,
198,
220,
220,
220,
220,
220,
220,
220,
1619,
42287,
17816,
46265,
22046,
20520,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
11767,
262,
2672,
11507,
705,
39098,
62,
3672,
6,
318,
900,
198,
220,
220,
220,
220,
220,
220,
220,
611,
19203,
39098,
62,
3672,
6,
407,
287,
42287,
393,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
42287,
17816,
39098,
62,
3672,
20520,
318,
6045,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5298,
11052,
12331,
7203,
43730,
262,
2672,
11507,
4600,
39098,
62,
3672,
63,
618,
4585,
4600,
260,
25636,
62,
26858,
62,
2302,
40450,
62,
12381,
63,
4943,
220,
1303,
645,
20402,
25,
412,
33548,
628,
220,
220,
220,
220,
220,
220,
220,
4947,
62,
687,
1381,
796,
23884,
628,
220,
220,
220,
220,
220,
220,
220,
3108,
62,
37266,
796,
23884,
198,
220,
220,
220,
220,
220,
220,
220,
611,
705,
39098,
62,
3672,
6,
287,
42287,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3108,
62,
37266,
17816,
39098,
62,
3672,
20520,
796,
42287,
17816,
39098,
62,
3672,
20520,
220,
1303,
645,
20402,
25,
412,
33548,
628,
220,
220,
220,
220,
220,
220,
220,
12405,
62,
37266,
796,
17635,
628,
220,
220,
220,
220,
220,
220,
220,
13639,
62,
37266,
796,
23884,
198,
220,
220,
220,
220,
220,
220,
220,
611,
705,
87,
62,
13345,
263,
6,
287,
42287,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
13639,
62,
37266,
17816,
55,
12,
34,
7036,
1137,
20520,
796,
42287,
17816,
87,
62,
13345,
263,
20520,
220,
1303,
645,
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62,
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28,
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13,
1136,
10786,
292,
13361,
62,
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33809,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4808,
7783,
62,
4023,
62,
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62,
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28,
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13,
1136,
10786,
62,
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62,
4023,
62,
7890,
62,
8807,
33809,
198,
220,
220,
220,
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220,
220,
220,
220,
220,
4808,
3866,
2220,
62,
11299,
28,
37266,
13,
1136,
10786,
62,
3866,
2220,
62,
11299,
3256,
6407,
828,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4808,
25927,
62,
48678,
28,
37266,
13,
1136,
10786,
62,
25927,
62,
48678,
33809,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4947,
62,
687,
1381,
28,
43681,
62,
687,
1381,
8,
628,
220,
220,
220,
825,
40364,
62,
26858,
62,
2302,
40450,
62,
1136,
62,
36604,
7,
944,
11,
4554,
62,
3672,
11,
12429,
46265,
22046,
2599,
220,
1303,
645,
20402,
25,
412,
33548,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
3855,
3307,
286,
3706,
4554,
13,
220,
1303,
645,
20402,
25,
412,
33548,
628,
220,
220,
220,
220,
220,
220,
220,
3497,
262,
3307,
286,
262,
3706,
3218,
5408,
9312,
7925,
273,
4554,
13,
220,
1303,
645,
20402,
25,
412,
33548,
198,
220,
220,
220,
220,
220,
220,
220,
770,
2446,
1838,
257,
18305,
516,
14626,
2581,
416,
4277,
13,
1675,
787,
281,
198,
220,
220,
220,
220,
220,
220,
220,
39354,
14626,
2581,
11,
3387,
1208,
30351,
62,
42180,
28,
17821,
198,
220,
220,
220,
220,
220,
220,
220,
13163,
4704,
796,
40391,
13,
260,
25636,
62,
26858,
62,
2302,
40450,
62,
1136,
62,
36604,
7,
39098,
62,
3672,
11,
30351,
62,
42180,
28,
17821,
8,
198,
220,
220,
220,
220,
220,
220,
220,
13163,
1255,
796,
4704,
13,
1136,
3419,
628,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
30351,
62,
42180,
20512,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
965,
4554,
62,
3672,
25,
383,
1438,
286,
262,
4554,
13,
357,
35827,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
965,
2124,
62,
13345,
263,
25,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
7783,
25,
797,
25636,
32398,
11627,
40450,
33384,
11242,
603,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1002,
262,
2446,
318,
1444,
355,
24871,
3481,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5860,
262,
2581,
4704,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
479,
86,
22046,
17816,
62,
7783,
62,
4023,
62,
7890,
62,
8807,
20520,
796,
6407,
198,
220,
220,
220,
220,
220,
220,
220,
611,
479,
86,
22046,
13,
1136,
10786,
292,
13361,
62,
42180,
6,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
2116,
13,
260,
25636,
62,
26858,
62,
2302,
40450,
62,
1136,
62,
36604,
62,
4480,
62,
4023,
62,
10951,
7,
39098,
62,
3672,
11,
12429,
46265,
22046,
8,
220,
1303,
645,
20402,
25,
412,
33548,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
357,
7890,
8,
796,
2116,
13,
260,
25636,
62,
26858,
62,
2302,
40450,
62,
1136,
62,
36604,
62,
4480,
62,
4023,
62,
10951,
7,
39098,
62,
3672,
11,
12429,
46265,
22046,
8,
220,
1303,
645,
20402,
25,
412,
33548,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
1366,
628,
220,
220,
220,
825,
40364,
62,
26858,
62,
2302,
40450,
62,
1136,
62,
36604,
62,
4480,
62,
4023,
62,
10951,
7,
944,
11,
4554,
62,
3672,
11,
12429,
46265,
22046,
2599,
220,
1303,
645,
20402,
25,
412,
33548,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
3855,
3307,
286,
3706,
4554,
13,
220,
1303,
645,
20402,
25,
412,
33548,
628,
220,
220,
220,
220,
220,
220,
220,
3497,
262,
3307,
286,
262,
3706,
3218,
5408,
9312,
7925,
273,
4554,
13,
220,
1303,
645,
20402,
25,
412,
33548,
198,
220,
220,
220,
220,
220,
220,
220,
770,
2446,
1838,
257,
18305,
516,
14626,
2581,
416,
4277,
13,
1675,
787,
281,
198,
220,
220,
220,
220,
220,
220,
220,
39354,
14626,
2581,
11,
3387,
1208,
30351,
62,
42180,
28,
17821,
198,
220,
220,
220,
220,
220,
220,
220,
13163,
4704,
796,
40391,
13,
260,
25636,
62,
26858,
62,
2302,
40450,
62,
1136,
62,
36604,
62,
4480,
62,
4023,
62,
10951,
7,
39098,
62,
3672,
11,
30351,
62,
42180,
28,
17821,
8,
198,
220,
220,
220,
220,
220,
220,
220,
13163,
1255,
796,
4704,
13,
1136,
3419,
628,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
30351,
62,
42180,
20512,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
965,
4554,
62,
3672,
25,
383,
1438,
286,
262,
4554,
13,
357,
35827,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
965,
2124,
62,
13345,
263,
25,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
7783,
25,
797,
25636,
32398,
11627,
40450,
33384,
11242,
603,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1002,
262,
2446,
318,
1444,
355,
24871,
3481,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5860,
262,
2581,
4704,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
628,
220,
220,
220,
220,
220,
220,
220,
477,
62,
37266,
796,
37250,
39098,
62,
3672,
3256,
705,
87,
62,
13345,
263,
20520,
220,
1303,
645,
20402,
25,
412,
33548,
198,
220,
220,
220,
220,
220,
220,
220,
477,
62,
37266,
13,
33295,
10786,
292,
13361,
62,
42180,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
477,
62,
37266,
13,
33295,
10786,
62,
7783,
62,
4023,
62,
7890,
62,
8807,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
477,
62,
37266,
13,
33295,
10786,
62,
3866,
2220,
62,
11299,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
477,
62,
37266,
13,
33295,
10786,
62,
25927,
62,
48678,
11537,
628,
220,
220,
220,
220,
220,
220,
220,
42287,
796,
17205,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
329,
1994,
11,
1188,
287,
2237,
13,
2676,
23814,
7,
37266,
17816,
46265,
22046,
20520,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
1994,
407,
287,
477,
62,
37266,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5298,
5994,
12331,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
30074,
281,
10059,
21179,
4578,
705,
4,
82,
29653,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
284,
2446,
40364,
62,
26858,
62,
2302,
40450,
62,
1136,
62,
36604,
1,
4064,
1994,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
42287,
58,
2539,
60,
796,
1188,
198,
220,
220,
220,
220,
220,
220,
220,
1619,
42287,
17816,
46265,
22046,
20520,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
11767,
262,
2672,
11507,
705,
39098,
62,
3672,
6,
318,
900,
198,
220,
220,
220,
220,
220,
220,
220,
611,
19203,
39098,
62,
3672,
6,
407,
287,
42287,
393,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
42287,
17816,
39098,
62,
3672,
20520,
318,
6045,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5298,
11052,
12331,
7203,
43730,
262,
2672,
11507,
4600,
39098,
62,
3672,
63,
618,
4585,
4600,
260,
25636,
62,
26858,
62,
2302,
40450,
62,
1136,
62,
36604,
63,
4943,
220,
1303,
645,
20402,
25,
412,
33548,
628,
220,
220,
220,
220,
220,
220,
220,
4947,
62,
687,
1381,
796,
23884,
628,
220,
220,
220,
220,
220,
220,
220,
3108,
62,
37266,
796,
23884,
198,
220,
220,
220,
220,
220,
220,
220,
611,
705,
39098,
62,
3672,
6,
287,
42287,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3108,
62,
37266,
17816,
39098,
62,
3672,
20520,
796,
42287,
17816,
39098,
62,
3672,
20520,
220,
1303,
645,
20402,
25,
412,
33548,
628,
220,
220,
220,
220,
220,
220,
220,
12405,
62,
37266,
796,
17635,
628,
220,
220,
220,
220,
220,
220,
220,
13639,
62,
37266,
796,
23884,
198,
220,
220,
220,
220,
220,
220,
220,
611,
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87,
62,
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6,
287,
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25,
198,
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13639,
62,
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17816,
55,
12,
34,
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1296,
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1957,
62,
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62,
16624,
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628,
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220,
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220,
220,
220,
1767,
62,
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6045,
198,
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220,
220,
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1303,
14626,
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4600,
38855,
63,
198,
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220,
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220,
13639,
62,
37266,
17816,
38855,
20520,
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13,
15042,
62,
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13,
19738,
62,
25677,
62,
13635,
7,
198,
220,
220,
220,
220,
220,
220,
220,
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220,
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220,
37250,
31438,
14,
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6,
12962,
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62,
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62,
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7,
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25,
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220,
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220,
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220,
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37250,
31438,
14,
17752,
6,
12962,
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25,
412,
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220,
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220,
220,
220,
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220,
1303,
48191,
4634,
198,
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220,
220,
220,
220,
220,
6284,
62,
33692,
796,
37250,
17614,
9218,
39681,
3256,
705,
17614,
9218,
39681,
62,
727,
20520,
220,
1303,
645,
20402,
25,
412,
33548,
628,
220,
220,
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220,
220,
220,
220,
1441,
2116,
13,
15042,
62,
16366,
13,
13345,
62,
15042,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
31051,
77,
2290,
14,
85,
17,
14,
260,
25636,
62,
26858,
62,
2302,
974,
669,
14,
90,
39098,
62,
3672,
92,
3256,
705,
18851,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3108,
62,
37266,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
12405,
62,
37266,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
13639,
62,
37266,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1767,
28,
2618,
62,
37266,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1281,
62,
37266,
28,
687,
62,
37266,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3696,
28,
12001,
62,
7785,
62,
16624,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2882,
62,
4906,
11639,
3041,
25636,
32398,
11627,
40450,
33384,
11242,
603,
3256,
220,
1303,
645,
20402,
25,
412,
33548,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6284,
62,
33692,
28,
18439,
62,
33692,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
30351,
62,
42180,
28,
37266,
13,
1136,
10786,
292,
13361,
62,
42180,
33809,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4808,
7783,
62,
4023,
62,
7890,
62,
8807,
28,
37266,
13,
1136,
10786,
62,
7783,
62,
4023,
62,
7890,
62,
8807,
33809,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4808,
3866,
2220,
62,
11299,
28,
37266,
13,
1136,
10786,
62,
3866,
2220,
62,
11299,
3256,
6407,
828,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4808,
25927,
62,
48678,
28,
37266,
13,
1136,
10786,
62,
25927,
62,
48678,
33809,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4947,
62,
687,
1381,
28,
43681,
62,
687,
1381,
8,
628,
220,
220,
220,
825,
40364,
62,
26858,
62,
2302,
40450,
62,
1136,
62,
36604,
62,
439,
7,
944,
11,
12429,
46265,
22046,
2599,
220,
1303,
645,
20402,
25,
412,
33548,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
3855,
1351,
286,
3218,
5408,
9312,
7925,
669,
13,
220,
1303,
645,
20402,
25,
412,
33548,
628,
220,
220,
220,
220,
220,
220,
220,
3497,
262,
1351,
286,
3218,
5408,
9312,
7925,
669,
13,
220,
1303,
645,
20402,
25,
412,
33548,
198,
220,
220,
220,
220,
220,
220,
220,
770,
2446,
1838,
257,
18305,
516,
14626,
2581,
416,
4277,
13,
1675,
787,
281,
198,
220,
220,
220,
220,
220,
220,
220,
39354,
14626,
2581,
11,
3387,
1208,
30351,
62,
42180,
28,
17821,
198,
220,
220,
220,
220,
220,
220,
220,
13163,
4704,
796,
40391,
13,
260,
25636,
62,
26858,
62,
2302,
40450,
62,
1136,
62,
36604,
62,
439,
7,
292,
13361,
62,
42180,
28,
17821,
8,
198,
220,
220,
220,
220,
220,
220,
220,
13163,
1255,
796,
4704,
13,
1136,
3419,
628,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
30351,
62,
42180,
20512,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
965,
2124,
62,
13345,
263,
25,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
7783,
25,
1351,
58,
3041,
25636,
32398,
11627,
40450,
33384,
11242,
603,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1002,
262,
2446,
318,
1444,
355,
24871,
3481,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5860,
262,
2581,
4704,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
479,
86,
22046,
17816,
62,
7783,
62,
4023,
62,
7890,
62,
8807,
20520,
796,
6407,
198,
220,
220,
220,
220,
220,
220,
220,
611,
479,
86,
22046,
13,
1136,
10786,
292,
13361,
62,
42180,
6,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
2116,
13,
260,
25636,
62,
26858,
62,
2302,
40450,
62,
1136,
62,
36604,
62,
439,
62,
4480,
62,
4023,
62,
10951,
7,
1174,
46265,
22046,
8,
220,
1303,
645,
20402,
25,
412,
33548,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
357,
7890,
8,
796,
2116,
13,
260,
25636,
62,
26858,
62,
2302,
40450,
62,
1136,
62,
36604,
62,
439,
62,
4480,
62,
4023,
62,
10951,
7,
1174,
46265,
22046,
8,
220,
1303,
645,
20402,
25,
412,
33548,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
1366,
628,
220,
220,
220,
825,
40364,
62,
26858,
62,
2302,
40450,
62,
1136,
62,
36604,
62,
439,
62,
4480,
62,
4023,
62,
10951,
7,
944,
11,
12429,
46265,
22046,
2599,
220,
1303,
645,
20402,
25,
412,
33548,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
3855,
1351,
286,
3218,
5408,
9312,
7925,
669,
13,
220,
1303,
645,
20402,
25,
412,
33548,
628,
220,
220,
220,
220,
220,
220,
220,
3497,
262,
1351,
286,
3218,
5408,
9312,
7925,
669,
13,
220,
1303,
645,
20402,
25,
412,
33548,
198,
220,
220,
220,
220,
220,
220,
220,
770,
2446,
1838,
257,
18305,
516,
14626,
2581,
416,
4277,
13,
1675,
787,
281,
198,
220,
220,
220,
220,
220,
220,
220,
39354,
14626,
2581,
11,
3387,
1208,
30351,
62,
42180,
28,
17821,
198,
220,
220,
220,
220,
220,
220,
220,
13163,
4704,
796,
40391,
13,
260,
25636,
62,
26858,
62,
2302,
40450,
62,
1136,
62,
36604,
62,
439,
62,
4480,
62,
4023,
62,
10951,
7,
292,
13361,
62,
42180,
28,
17821,
8,
198,
220,
220,
220,
220,
220,
220,
220,
13163,
1255,
796,
4704,
13,
1136,
3419,
628,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
30351,
62,
42180,
20512,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
965,
2124,
62,
13345,
263,
25,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
7783,
25,
1351,
58,
3041,
25636,
32398,
11627,
40450,
33384,
11242,
603,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1002,
262,
2446,
318,
1444,
355,
24871,
3481,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5860,
262,
2581,
4704,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
628,
220,
220,
220,
220,
220,
220,
220,
477,
62,
37266,
796,
37250,
87,
62,
13345,
263,
20520,
220,
1303,
645,
20402,
25,
412,
33548,
198,
220,
220,
220,
220,
220,
220,
220,
477,
62,
37266,
13,
33295,
10786,
292,
13361,
62,
42180,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
477,
62,
37266,
13,
33295,
10786,
62,
7783,
62,
4023,
62,
7890,
62,
8807,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
477,
62,
37266,
13,
33295,
10786,
62,
3866,
2220,
62,
11299,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
477,
62,
37266,
13,
33295,
10786,
62,
25927,
62,
48678,
11537,
628,
220,
220,
220,
220,
220,
220,
220,
42287,
796,
17205,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
329,
1994,
11,
1188,
287,
2237,
13,
2676,
23814,
7,
37266,
17816,
46265,
22046,
20520,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
1994,
407,
287,
477,
62,
37266,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5298,
5994,
12331,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
30074,
281,
10059,
21179,
4578,
705,
4,
82,
29653,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
284,
2446,
40364,
62,
26858,
62,
2302,
40450,
62,
1136,
62,
36604,
62,
439,
1,
4064,
1994,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
42287,
58,
2539,
60,
796,
1188,
198,
220,
220,
220,
220,
220,
220,
220,
1619,
42287,
17816,
46265,
22046,
20520,
628,
220,
220,
220,
220,
220,
220,
220,
4947,
62,
687,
1381,
796,
23884,
628,
220,
220,
220,
220,
220,
220,
220,
3108,
62,
37266,
796,
23884,
628,
220,
220,
220,
220,
220,
220,
220,
12405,
62,
37266,
796,
17635,
628,
220,
220,
220,
220,
220,
220,
220,
13639,
62,
37266,
796,
23884,
198,
220,
220,
220,
220,
220,
220,
220,
611,
705,
87,
62,
13345,
263,
6,
287,
42287,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
13639,
62,
37266,
17816,
55,
12,
34,
7036,
1137,
20520,
796,
42287,
17816,
87,
62,
13345,
263,
20520,
220,
1303,
645,
20402,
25,
412,
33548,
628,
220,
220,
220,
220,
220,
220,
220,
1296,
62,
37266,
796,
17635,
198,
220,
220,
220,
220,
220,
220,
220,
1957,
62,
7785,
62,
16624,
796,
23884,
628,
220,
220,
220,
220,
220,
220,
220,
1767,
62,
37266,
796,
6045,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
14626,
13639,
4600,
38855,
63,
198,
220,
220,
220,
220,
220,
220,
220,
13639,
62,
37266,
17816,
38855,
20520,
796,
2116,
13,
15042,
62,
16366,
13,
19738,
62,
25677,
62,
13635,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
37250,
31438,
14,
17752,
6,
12962,
220,
1303,
645,
20402,
25,
412,
33548,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
14626,
13639,
4600,
19746,
12,
6030,
63,
198,
220,
220,
220,
220,
220,
220,
220,
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62,
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62,
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4808,
3866,
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62,
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28,
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13,
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62,
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2220,
62,
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828,
198,
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4808,
25927,
62,
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28,
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13,
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62,
25927,
62,
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33809,
198,
220,
220,
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220,
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4947,
62,
687,
1381,
28,
43681,
62,
687,
1381,
8,
628,
220,
220,
220,
825,
40364,
62,
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62,
2302,
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62,
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7,
944,
11,
4554,
62,
3672,
11,
12429,
46265,
22046,
2599,
220,
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645,
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25,
412,
33548,
198,
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220,
220,
220,
220,
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220,
37227,
3855,
262,
4370,
540,
2746,
10007,
286,
3706,
40364,
9312,
7925,
273,
13,
220,
1303,
645,
20402,
25,
412,
33548,
628,
220,
220,
220,
220,
220,
220,
220,
3497,
262,
4370,
540,
2746,
10007,
286,
3706,
40364,
9312,
7925,
273,
13,
220,
1303,
645,
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25,
412,
33548,
198,
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220,
220,
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220,
220,
220,
770,
2446,
1838,
257,
18305,
516,
14626,
2581,
416,
4277,
13,
1675,
787,
281,
198,
220,
220,
220,
220,
220,
220,
220,
39354,
14626,
2581,
11,
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1208,
30351,
62,
42180,
28,
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198,
220,
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220,
220,
220,
220,
220,
13163,
4704,
796,
40391,
13,
260,
25636,
62,
26858,
62,
2302,
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62,
1136,
62,
37266,
7,
39098,
62,
3672,
11,
30351,
62,
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28,
17821,
8,
198,
220,
220,
220,
220,
220,
220,
220,
13163,
1255,
796,
4704,
13,
1136,
3419,
628,
220,
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220,
220,
220,
220,
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1058,
17143,
30351,
62,
42180,
20512,
198,
220,
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220,
220,
220,
220,
220,
1058,
17143,
965,
4554,
62,
3672,
25,
383,
1438,
286,
262,
4554,
13,
357,
35827,
8,
198,
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220,
220,
220,
220,
220,
220,
1058,
17143,
965,
2124,
62,
13345,
263,
25,
198,
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220,
220,
220,
220,
220,
220,
1058,
7783,
25,
9104,
10044,
4105,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1002,
262,
2446,
318,
1444,
355,
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3481,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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5860,
262,
2581,
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13,
198,
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37227,
198,
220,
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220,
220,
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220,
479,
86,
22046,
17816,
62,
7783,
62,
4023,
62,
7890,
62,
8807,
20520,
796,
6407,
198,
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220,
220,
220,
611,
479,
86,
22046,
13,
1136,
10786,
292,
13361,
62,
42180,
6,
2599,
198,
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220,
220,
220,
220,
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1441,
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13,
260,
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2302,
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1136,
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37266,
62,
4480,
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4023,
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10951,
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25,
198,
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357,
7890,
8,
796,
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13,
260,
25636,
62,
26858,
62,
2302,
40450,
62,
1136,
62,
37266,
62,
4480,
62,
4023,
62,
10951,
7,
39098,
62,
3672,
11,
12429,
46265,
22046,
8,
220,
1303,
645,
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25,
412,
33548,
198,
220,
220,
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220,
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1441,
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825,
40364,
62,
26858,
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2302,
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62,
1136,
62,
37266,
62,
4480,
62,
4023,
62,
10951,
7,
944,
11,
4554,
62,
3672,
11,
12429,
46265,
22046,
2599,
220,
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645,
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25,
412,
33548,
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220,
220,
220,
220,
220,
220,
220,
37227,
3855,
262,
4370,
540,
2746,
10007,
286,
3706,
40364,
9312,
7925,
273,
13,
220,
1303,
645,
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25,
412,
33548,
628,
220,
220,
220,
220,
220,
220,
220,
3497,
262,
4370,
540,
2746,
10007,
286,
3706,
40364,
9312,
7925,
273,
13,
220,
1303,
645,
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25,
412,
33548,
198,
220,
220,
220,
220,
220,
220,
220,
770,
2446,
1838,
257,
18305,
516,
14626,
2581,
416,
4277,
13,
1675,
787,
281,
198,
220,
220,
220,
220,
220,
220,
220,
39354,
14626,
2581,
11,
3387,
1208,
30351,
62,
42180,
28,
17821,
198,
220,
220,
220,
220,
220,
220,
220,
13163,
4704,
796,
40391,
13,
260,
25636,
62,
26858,
62,
2302,
40450,
62,
1136,
62,
37266,
62,
4480,
62,
4023,
62,
10951,
7,
39098,
62,
3672,
11,
30351,
62,
42180,
28,
17821,
8,
198,
220,
220,
220,
220,
220,
220,
220,
13163,
1255,
796,
4704,
13,
1136,
3419,
628,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
30351,
62,
42180,
20512,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
965,
4554,
62,
3672,
25,
383,
1438,
286,
262,
4554,
13,
357,
35827,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
965,
2124,
62,
13345,
263,
25,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
7783,
25,
9104,
10044,
4105,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1002,
262,
2446,
318,
1444,
355,
24871,
3481,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5860,
262,
2581,
4704,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
628,
220,
220,
220,
220,
220,
220,
220,
477,
62,
37266,
796,
37250,
39098,
62,
3672,
3256,
705,
87,
62,
13345,
263,
20520,
220,
1303,
645,
20402,
25,
412,
33548,
198,
220,
220,
220,
220,
220,
220,
220,
477,
62,
37266,
13,
33295,
10786,
292,
13361,
62,
42180,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
477,
62,
37266,
13,
33295,
10786,
62,
7783,
62,
4023,
62,
7890,
62,
8807,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
477,
62,
37266,
13,
33295,
10786,
62,
3866,
2220,
62,
11299,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
477,
62,
37266,
13,
33295,
10786,
62,
25927,
62,
48678,
11537,
628,
220,
220,
220,
220,
220,
220,
220,
42287,
796,
17205,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
329,
1994,
11,
1188,
287,
2237,
13,
2676,
23814,
7,
37266,
17816,
46265,
22046,
20520,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
1994,
407,
287,
477,
62,
37266,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5298,
5994,
12331,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
30074,
281,
10059,
21179,
4578,
705,
4,
82,
29653,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
284,
2446,
40364,
62,
26858,
62,
2302,
40450,
62,
1136,
62,
37266,
1,
4064,
1994,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
42287,
58,
2539,
60,
796,
1188,
198,
220,
220,
220,
220,
220,
220,
220,
1619,
42287,
17816,
46265,
22046,
20520,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
11767,
262,
2672,
11507,
705,
39098,
62,
3672,
6,
318,
900,
198,
220,
220,
220,
220,
220,
220,
220,
611,
19203,
39098,
62,
3672,
6,
407,
287,
42287,
393,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
42287,
17816,
39098,
62,
3672,
20520,
318,
6045,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5298,
11052,
12331,
7203,
43730,
262,
2672,
11507,
4600,
39098,
62,
3672,
63,
618,
4585,
4600,
260,
25636,
62,
26858,
62,
2302,
40450,
62,
1136,
62,
37266,
63,
4943,
220,
1303,
645,
20402,
25,
412,
33548,
628,
220,
220,
220,
220,
220,
220,
220,
4947,
62,
687,
1381,
796,
23884,
628,
220,
220,
220,
220,
220,
220,
220,
3108,
62,
37266,
796,
23884,
198,
220,
220,
220,
220,
220,
220,
220,
611,
705,
39098,
62,
3672,
6,
287,
42287,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3108,
62,
37266,
17816,
39098,
62,
3672,
20520,
796,
42287,
17816,
39098,
62,
3672,
20520,
220,
1303,
645,
20402,
25,
412,
33548,
628,
220,
220,
220,
220,
220,
220,
220,
12405,
62,
37266,
796,
17635,
628,
220,
220,
220,
220,
220,
220,
220,
13639,
62,
37266,
796,
23884,
198,
220,
220,
220,
220,
220,
220,
220,
611,
705,
87,
62,
13345,
263,
6,
287,
42287,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
13639,
62,
37266,
17816,
55,
12,
34,
7036,
1137,
20520,
796,
42287,
17816,
87,
62,
13345,
263,
20520,
220,
1303,
645,
20402,
25,
412,
33548,
628,
220,
220,
220,
220,
220,
220,
220,
1296,
62,
37266,
796,
17635,
198,
220,
220,
220,
220,
220,
220,
220,
1957,
62,
7785,
62,
16624,
796,
23884,
628,
220,
220,
220,
220,
220,
220,
220,
1767,
62,
37266,
796,
6045,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
14626,
13639,
4600,
38855,
63,
198,
220,
220,
220,
220,
220,
220,
220,
13639,
62,
37266,
17816,
38855,
20520,
796,
2116,
13,
15042,
62,
16366,
13,
19738,
62,
25677,
62,
13635,
7,
198,
220,
220,
220,
220,
220,
220,
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37250,
31438,
14,
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6,
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1767,
28,
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3696,
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30351,
62,
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28,
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292,
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62,
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198,
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4808,
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62,
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62,
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28,
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62,
4023,
62,
7890,
62,
8807,
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198,
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220,
220,
220,
220,
220,
220,
220,
4808,
3866,
2220,
62,
11299,
28,
37266,
13,
1136,
10786,
62,
3866,
2220,
62,
11299,
3256,
6407,
828,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4808,
25927,
62,
48678,
28,
37266,
13,
1136,
10786,
62,
25927,
62,
48678,
33809,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4947,
62,
687,
1381,
28,
43681,
62,
687,
1381,
8,
628,
220,
220,
220,
825,
40364,
62,
26858,
62,
2302,
40450,
62,
1186,
30227,
7,
944,
11,
4554,
62,
3672,
11,
2420,
62,
15414,
11,
12429,
46265,
22046,
2599,
220,
1303,
645,
20402,
25,
412,
33548,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
11627,
974,
1321,
1912,
319,
262,
3218,
5408,
13,
220,
1303,
645,
20402,
25,
412,
33548,
628,
220,
220,
220,
220,
220,
220,
220,
29677,
262,
12066,
12336,
262,
3218,
5408,
13,
220,
1303,
645,
20402,
25,
412,
33548,
198,
220,
220,
220,
220,
220,
220,
220,
770,
2446,
1838,
257,
18305,
516,
14626,
2581,
416,
4277,
13,
1675,
787,
281,
198,
220,
220,
220,
220,
220,
220,
220,
39354,
14626,
2581,
11,
3387,
1208,
30351,
62,
42180,
28,
17821,
198,
220,
220,
220,
220,
220,
220,
220,
13163,
4704,
796,
40391,
13,
260,
25636,
62,
26858,
62,
2302,
40450,
62,
1186,
30227,
7,
39098,
62,
3672,
11,
2420,
62,
15414,
11,
30351,
62,
42180,
28,
17821,
8,
198,
220,
220,
220,
220,
220,
220,
220,
13163,
1255,
796,
4704,
13,
1136,
3419,
628,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
30351,
62,
42180,
20512,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
965,
4554,
62,
3672,
25,
383,
1438,
286,
262,
4554,
13,
357,
35827,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
8255,
20560,
2420,
62,
15414,
25,
383,
5128,
2420,
13,
357,
35827,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
965,
2124,
62,
13345,
263,
25,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
7783,
25,
1351,
58,
3041,
25636,
32398,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1002,
262,
2446,
318,
1444,
355,
24871,
3481,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5860,
262,
2581,
4704,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
479,
86,
22046,
17816,
62,
7783,
62,
4023,
62,
7890,
62,
8807,
20520,
796,
6407,
198,
220,
220,
220,
220,
220,
220,
220,
611,
479,
86,
22046,
13,
1136,
10786,
292,
13361,
62,
42180,
6,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
2116,
13,
260,
25636,
62,
26858,
62,
2302,
40450,
62,
1186,
30227,
62,
4480,
62,
4023,
62,
10951,
7,
39098,
62,
3672,
11,
2420,
62,
15414,
11,
12429,
46265,
22046,
8,
220,
1303,
645,
20402,
25,
412,
33548,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
357,
7890,
8,
796,
2116,
13,
260,
25636,
62,
26858,
62,
2302,
40450,
62,
1186,
30227,
62,
4480,
62,
4023,
62,
10951,
7,
39098,
62,
3672,
11,
2420,
62,
15414,
11,
12429,
46265,
22046,
8,
220,
1303,
645,
20402,
25,
412,
33548,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
1366,
628,
220,
220,
220,
825,
40364,
62,
26858,
62,
2302,
40450,
62,
1186,
30227,
62,
4480,
62,
4023,
62,
10951,
7,
944,
11,
4554,
62,
3672,
11,
2420,
62,
15414,
11,
12429,
46265,
22046,
2599,
220,
1303,
645,
20402,
25,
412,
33548,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
11627,
974,
1321,
1912,
319,
262,
3218,
5408,
13,
220,
1303,
645,
20402,
25,
412,
33548,
628,
220,
220,
220,
220,
220,
220,
220,
29677,
262,
12066,
12336,
262,
3218,
5408,
13,
220,
1303,
645,
20402,
25,
412,
33548,
198,
220,
220,
220,
220,
220,
220,
220,
770,
2446,
1838,
257,
18305,
516,
14626,
2581,
416,
4277,
13,
1675,
787,
281,
198,
220,
220,
220,
220,
220,
220,
220,
39354,
14626,
2581,
11,
3387,
1208,
30351,
62,
42180,
28,
17821,
198,
220,
220,
220,
220,
220,
220,
220,
13163,
4704,
796,
40391,
13,
260,
25636,
62,
26858,
62,
2302,
40450,
62,
1186,
30227,
62,
4480,
62,
4023,
62,
10951,
7,
39098,
62,
3672,
11,
2420,
62,
15414,
11,
30351,
62,
42180,
28,
17821,
8,
198,
220,
220,
220,
220,
220,
220,
220,
13163,
1255,
796,
4704,
13,
1136,
3419,
628,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
30351,
62,
42180,
20512,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
965,
4554,
62,
3672,
25,
383,
1438,
286,
262,
4554,
13,
357,
35827,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
8255,
20560,
2420,
62,
15414,
25,
383,
5128,
2420,
13,
357,
35827,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
965,
2124,
62,
13345,
263,
25,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
7783,
25,
1351,
58,
3041,
25636,
32398,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1002,
262,
2446,
318,
1444,
355,
24871,
3481,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5860,
262,
2581,
4704,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
628,
220,
220,
220,
220,
220,
220,
220,
477,
62,
37266,
796,
37250,
39098,
62,
3672,
3256,
705,
5239,
62,
15414,
3256,
705,
87,
62,
13345,
263,
20520,
220,
1303,
645,
20402,
25,
412,
33548,
198,
220,
220,
220,
220,
220,
220,
220,
477,
62,
37266,
13,
33295,
10786,
292,
13361,
62,
42180,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
477,
62,
37266,
13,
33295,
10786,
62,
7783,
62,
4023,
62,
7890,
62,
8807,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
477,
62,
37266,
13,
33295,
10786,
62,
3866,
2220,
62,
11299,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
477,
62,
37266,
13,
33295,
10786,
62,
25927,
62,
48678,
11537,
628,
220,
220,
220,
220,
220,
220,
220,
42287,
796,
17205,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
329,
1994,
11,
1188,
287,
2237,
13,
2676,
23814,
7,
37266,
17816,
46265,
22046,
20520,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
1994,
407,
287,
477,
62,
37266,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5298,
5994,
12331,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
30074,
281,
10059,
21179,
4578,
705,
4,
82,
29653,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
284,
2446,
40364,
62,
26858,
62,
2302,
40450,
62,
1186,
30227,
1,
4064,
1994,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
42287,
58,
2539,
60,
796,
1188,
198,
220,
220,
220,
220,
220,
220,
220,
1619,
42287,
17816,
46265,
22046,
20520,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
11767,
262,
2672,
11507,
705,
39098,
62,
3672,
6,
318,
900,
198,
220,
220,
220,
220,
220,
220,
220,
611,
19203,
39098,
62,
3672,
6,
407,
287,
42287,
393,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
42287,
17816,
39098,
62,
3672,
20520,
318,
6045,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5298,
11052,
12331,
7203,
43730,
262,
2672,
11507,
4600,
39098,
62,
3672,
63,
618,
4585,
4600,
260,
25636,
62,
26858,
62,
2302,
40450,
62,
1186,
30227,
63,
4943,
220,
1303,
645,
20402,
25,
412,
33548,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
11767,
262,
2672,
11507,
705,
5239,
62,
15414,
6,
318,
900,
198,
220,
220,
220,
220,
220,
220,
220,
611,
19203,
5239,
62,
15414,
6,
407,
287,
42287,
393,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
42287,
17816,
5239,
62,
15414,
20520,
318,
6045,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5298,
11052,
12331,
7203,
43730,
262,
2672,
11507,
4600,
5239,
62,
15414,
63,
618,
4585,
4600,
260,
25636,
62,
26858,
62,
2302,
40450,
62,
1186,
30227,
63,
4943,
220,
1303,
645,
20402,
25,
412,
33548,
628,
220,
220,
220,
220,
220,
220,
220,
4947,
62,
687,
1381,
796,
23884,
628,
220,
220,
220,
220,
220,
220,
220,
3108,
62,
37266,
796,
23884,
198,
220,
220,
220,
220,
220,
220,
220,
611,
705,
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62,
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220,
220,
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220,
220,
220,
220,
220,
4808,
25927,
62,
48678,
28,
37266,
13,
1136,
10786,
62,
25927,
62,
48678,
33809,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4947,
62,
687,
1381,
28,
43681,
62,
687,
1381,
8,
628,
220,
220,
220,
825,
40364,
62,
26858,
62,
2302,
40450,
62,
2617,
62,
37266,
7,
944,
11,
4554,
62,
3672,
11,
2746,
62,
37266,
11,
12429,
46265,
22046,
2599,
220,
1303,
645,
20402,
25,
412,
33548,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
7248,
262,
2746,
10007,
286,
3706,
40364,
9312,
7925,
273,
13,
220,
1303,
645,
20402,
25,
412,
33548,
628,
220,
220,
220,
220,
220,
220,
220,
5345,
262,
2746,
10007,
286,
3706,
40364,
9312,
7925,
273,
13,
220,
1303,
645,
20402,
25,
412,
33548,
198,
220,
220,
220,
220,
220,
220,
220,
770,
2446,
1838,
257,
18305,
516,
14626,
2581,
416,
4277,
13,
1675,
787,
281,
198,
220,
220,
220,
220,
220,
220,
220,
39354,
14626,
2581,
11,
3387,
1208,
30351,
62,
42180,
28,
17821,
198,
220,
220,
220,
220,
220,
220,
220,
13163,
4704,
796,
40391,
13,
260,
25636,
62,
26858,
62,
2302,
40450,
62,
2617,
62,
37266,
7,
39098,
62,
3672,
11,
2746,
62,
37266,
11,
30351,
62,
42180,
28,
17821,
8,
198,
220,
220,
220,
220,
220,
220,
220,
13163,
1255,
796,
4704,
13,
1136,
3419,
628,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
30351,
62,
42180,
20512,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
965,
4554,
62,
3672,
25,
383,
1438,
286,
262,
4554,
13,
357,
35827,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
9104,
10044,
4105,
2746,
62,
37266,
25,
383,
2746,
10007,
13,
357,
35827,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
965,
2124,
62,
13345,
263,
25,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
7783,
25,
797,
25636,
32398,
11627,
40450,
33384,
11242,
603,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1002,
262,
2446,
318,
1444,
355,
24871,
3481,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5860,
262,
2581,
4704,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
479,
86,
22046,
17816,
62,
7783,
62,
4023,
62,
7890,
62,
8807,
20520,
796,
6407,
198,
220,
220,
220,
220,
220,
220,
220,
611,
479,
86,
22046,
13,
1136,
10786,
292,
13361,
62,
42180,
6,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
2116,
13,
260,
25636,
62,
26858,
62,
2302,
40450,
62,
2617,
62,
37266,
62,
4480,
62,
4023,
62,
10951,
7,
39098,
62,
3672,
11,
2746,
62,
37266,
11,
12429,
46265,
22046,
8,
220,
1303,
645,
20402,
25,
412,
33548,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
357,
7890,
8,
796,
2116,
13,
260,
25636,
62,
26858,
62,
2302,
40450,
62,
2617,
62,
37266,
62,
4480,
62,
4023,
62,
10951,
7,
39098,
62,
3672,
11,
2746,
62,
37266,
11,
12429,
46265,
22046,
8,
220,
1303,
645,
20402,
25,
412,
33548,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
1366,
628,
220,
220,
220,
825,
40364,
62,
26858,
62,
2302,
40450,
62,
2617,
62,
37266,
62,
4480,
62,
4023,
62,
10951,
7,
944,
11,
4554,
62,
3672,
11,
2746,
62,
37266,
11,
12429,
46265,
22046,
2599,
220,
1303,
645,
20402,
25,
412,
33548,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
7248,
262,
2746,
10007,
286,
3706,
40364,
9312,
7925,
273,
13,
220,
1303,
645,
20402,
25,
412,
33548,
628,
220,
220,
220,
220,
220,
220,
220,
5345,
262,
2746,
10007,
286,
3706,
40364,
9312,
7925,
273,
13,
220,
1303,
645,
20402,
25,
412,
33548,
198,
220,
220,
220,
220,
220,
220,
220,
770,
2446,
1838,
257,
18305,
516,
14626,
2581,
416,
4277,
13,
1675,
787,
281,
198,
220,
220,
220,
220,
220,
220,
220,
39354,
14626,
2581,
11,
3387,
1208,
30351,
62,
42180,
28,
17821,
198,
220,
220,
220,
220,
220,
220,
220,
13163,
4704,
796,
40391,
13,
260,
25636,
62,
26858,
62,
2302,
40450,
62,
2617,
62,
37266,
62,
4480,
62,
4023,
62,
10951,
7,
39098,
62,
3672,
11,
2746,
62,
37266,
11,
30351,
62,
42180,
28,
17821,
8,
198,
220,
220,
220,
220,
220,
220,
220,
13163,
1255,
796,
4704,
13,
1136,
3419,
628,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
30351,
62,
42180,
20512,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
965,
4554,
62,
3672,
25,
383,
1438,
286,
262,
4554,
13,
357,
35827,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
9104,
10044,
4105,
2746,
62,
37266,
25,
383,
2746,
10007,
13,
357,
35827,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
965,
2124,
62,
13345,
263,
25,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
7783,
25,
797,
25636,
32398,
11627,
40450,
33384,
11242,
603,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1002,
262,
2446,
318,
1444,
355,
24871,
3481,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5860,
262,
2581,
4704,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
628,
220,
220,
220,
220,
220,
220,
220,
477,
62,
37266,
796,
37250,
39098,
62,
3672,
3256,
705,
19849,
62,
37266,
3256,
705,
87,
62,
13345,
263,
20520,
220,
1303,
645,
20402,
25,
412,
33548,
198,
220,
220,
220,
220,
220,
220,
220,
477,
62,
37266,
13,
33295,
10786,
292,
13361,
62,
42180,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
477,
62,
37266,
13,
33295,
10786,
62,
7783,
62,
4023,
62,
7890,
62,
8807,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
477,
62,
37266,
13,
33295,
10786,
62,
3866,
2220,
62,
11299,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
477,
62,
37266,
13,
33295,
10786,
62,
25927,
62,
48678,
11537,
628,
220,
220,
220,
220,
220,
220,
220,
42287,
796,
17205,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
329,
1994,
11,
1188,
287,
2237,
13,
2676,
23814,
7,
37266,
17816,
46265,
22046,
20520,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
1994,
407,
287,
477,
62,
37266,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5298,
5994,
12331,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
30074,
281,
10059,
21179,
4578,
705,
4,
82,
29653,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
284,
2446,
40364,
62,
26858,
62,
2302,
40450,
62,
2617,
62,
37266,
1,
4064,
1994,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
42287,
58,
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198
] | 2.32275 | 16,000 |
try:
from setuptools import setup, find_packages
except ImportError:
from distutils.core import setup, find_packages
setup(
name = 'django_fabv',
version = '0.1.1',
url = 'https://pypi.python.org/pypi/django_fabv/0.1.0',
include_package_data=True,
packages=find_packages(),
license = '3-clause BSD',
author = 'Bob Jansen',
author_email = '[email protected]',
description = 'A/B module for Django based on ABingo',
long_description=open('README.txt').read(),
)
| [
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] | 2.517073 | 205 |
programmer = Programmer()
tester = Tester()
project = ProjectManagement()
print(project.process(programmer))
print(project.process(tester))
#La clase ProjectManagment es la que se puede extender
#por eso la dejamos asi, ya que cumple con el principio
#Abierto/Cerrado
#(no le entendi muy bien)
| [
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] | 2.839623 | 106 |
# -*- encoding: utf-8 -*-
'''
Current module: mobile.monkeyrunner.driver
Rough version history:
v1.0 Original version to use
********************************************************************
@AUTHOR: Administrator-Bruce Luo(罗科峰)
MAIL: [email protected]
RCS: rock4.softtest.pad.hierarchy.driver,v 2.0 2017年2月7日
FROM: 2015年12月23日
********************************************************************
======================================================================
Android UI automation frame for python.
Jython-monkeyrunner--need use jython to compileall
monkeyrunner.jar & chimpchat.jar & hierarchyviewer2lib.jar
'''
from com.android.monkeyrunner import MonkeyRunner,MonkeyDevice
from com.android.monkeyrunner.easy import By,EasyMonkeyDevice
from com.android.chimpchat.hierarchyviewer import HierarchyViewer
import os,re,time
import p_m_env
class MobileApp():
''' Mobile App Test.(need MonkeyRunner + Hierarchy)'''
@classmethod
@classmethod
@classmethod
def CloseApp(cls,app_package):
''' only close app . keep the session'''
result = re.search(".*%s\r\n" %app_package,os.popen("adb shell ps").read())
if result:
pid = re.findall("\w+",result.group())[1]
f=open("tmp",'w')
f.write("su\r\nkill -9 %s\r\nexit\r\nexit\r\n" %pid)
f.close()
os.system("adb shell <tmp")
os.remove("tmp")
@classmethod
def IsAppInstalled(cls,app_package):
'''
app_package
app_component
apk_path
'''
if re.search(app_package,os.popen("adb shell pm list packages").read()):
result = True
else:
result = False
time.sleep(0.5)
return result
@classmethod
def InstallApp(cls,apk_path):
''' install the app to mobile
apk_path=r"c:\test.apk"
'''
getattr(p_m_env.DEVICE,"installPackage")(apk_path)
time.sleep(0.5)
@classmethod
@classmethod
@classmethod
@classmethod
@classmethod
@classmethod
@classmethod
@classmethod
@classmethod
@classmethod
@classmethod
@classmethod
@classmethod
@classmethod
@classmethod
@classmethod
@classmethod
@classmethod
@classmethod
@classmethod
@classmethod
class MobileElement():
''' Mobile App Element Test.(need MonkeyRunner + Hierarchy)'''
(by,value,parent,timeout) = (None,None,None,30)
@classmethod
@classmethod
@classmethod
def Swipe(cls,duration=2,steps=100):
''' 模拟用户滑动
cls.by = "positon"
cls.value = [100,100,200,200]
duration --> 设置拖动过程的耗时
steps --> 设置拖动过程的步长， 步长较短的话，效果就是长按
'''
if cls.by == "position":
if not isinstance(cls.value, list):
raise Exception("Need list type:",cls.value)
getattr(p_m_env.DEVICE,"drag'")((cls.value[0],cls.value[1]),(cls.value[2],cls.value[3]),duration,steps)
@classmethod
@classmethod
@classmethod
@classmethod
@classmethod
@classmethod
@classmethod
@classmethod
@classmethod
@classmethod
@classmethod
@classmethod
@classmethod
@classmethod
@classmethod
@classmethod
@classmethod
@classmethod
@classmethod
if __name__ == "__main__":
# monkeyrunner driver.py
#等待连接
MobileApp.WaitForConnection()
#登录的示例
component = "com.tianwen.aischool/.ui.publics.login.LoginActivity"
MobileApp.LaunchApp(component)
print "launch app ok"
# 截图
MobileElement.by = "id"
MobileElement.value = "id/login_user_img"
MobileElement.SaveCurrentImageToFile(r"d:\auto\buffer\test.png")
print "Saving a picture to a file. ok"
#用户名
MobileElement.by = "id"
MobileElement.value = "id/login_account_input";#使用android sdk中的工具-->hierarchyviewer.bat查看。还有一个辅助工具 uiautomatorviewer.bat
MobileElement.TypeInClear("brucestudent1")
print "Typing username(brucestudent1) ok"
#密码
(MobileElement.by,MobileElement.value) = ("id","id/login_password_input")
MobileElement.TypeInClear("123456")
print "Typing password(123456) ok"
#登录按钮
(MobileElement.by,MobileElement.value) = ("id","id/login_start_button")
MobileElement.Touch()
print "Tap login button ok"
#验证信息
(MobileElement.by,MobileElement.value) = ("id","id/login_error_tip_text")
result = MobileElement.GetText()
print "verify login info"
print "-->",result
if "正在登录" in result:
print "Login Test Pass"
else:
print "Login Test Fail"
# 点击 练习作业
print "->click link_text"
(MobileElement.by,MobileElement.value,MobileElement.parent,MobileElement.timeout) = ("link_text",u"练习作业","id/homepage_exercise",30)
MobileElement.Touch()
print "Link_text is ok"
# 点击 练习作业
print "->click index"
(MobileElement.by,MobileElement.value,MobileElement.parent) = ("index",[0,0,1,0],"id/grid")
MobileElement.Touch()
print "Index is ok"
| [
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users = []
| [
18417,
796,
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628,
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] | 2.3 | 10 |
# -*- coding: utf-8 -*-
from bs4 import BeautifulSoup
import requests
import urllib.request
from moviepy.video.io.VideoFileClip import VideoFileClip
import os
if __name__ == '__main__':
#main('https://arca.live/e/56?target=title&keyword=%EB%9D%BC%EC%98%A4&sort=rank&p=1')
con_download() | [
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] | 2.225352 | 142 |
# ============================================================================
#
# Copyright (c) 2007-2010 Integral Technology Solutions Pty Ltd,
# All Rights Reserved.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT OF THIRD PARTY RIGHTS.
# IN NO EVENT SHALL THE COPYRIGHT HOLDER OR HOLDERS INCLUDED IN THIS NOTICE BE
# LIABLE FOR ANY CLAIM, OR ANY SPECIAL INDIRECT OR CONSEQUENTIAL DAMAGES, OR
# ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER
# IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT
# OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
#
# FOR FURTHER INFORMATION PLEASE SEE THE INTEGRAL TECHNOLOGY SOLUTIONS
# END USER LICENSE AGREEMENT (ELUA).
#
# ============================================================================
import common.assertions as assertions
import common.logredirect as logredirect
from java.io import File
execfile('wlst/common.py')
execfile('wlst/server.py')
execfile('wlst/workmgr.py')
execfile('wlst/deployment.py')
execfile('wlst/createDomain.py')
execfile('wlst/jdbc_offline.py')
def run(cfg):
"""Create WebLogic Domain"""
assertions.sanityCheckInstall(cfg)
assertions.sanityCheckDomainConfig(cfg)
if wlst_support:
logredirect.setup()
create_domain(cfg)
else:
raise Exception('WLST support required for this command')
| [
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] | 3.194215 | 484 |
def unquote(string):
"""Decode and replace URL percent-escapes in string.
unquote('abc%20def') -> b'abc def'.
Note: If a string, not a bytes object, is passed, it is encoded as UTF-8.
This is only an issue if it contains unescaped non-ASCII characters, which
URIs should not.
"""
if not string:
return b''
if isinstance(string, str):
string = string.encode('utf-8')
bits = string.split(b'%')
if len(bits) == 1:
return string
res = bytearray(bits[0])
append = res.append
extend = res.extend
for item in bits[1:]:
try:
append(int(item[:2], 16))
extend(item[2:])
except KeyError:
append(b'%')
extend(item)
return bytes(res)
| [
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220,
220,
220,
220,
220,
220,
220,
220,
24443,
7,
600,
7,
9186,
58,
25,
17,
4357,
1467,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
9117,
7,
9186,
58,
17,
25,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
2845,
7383,
12331,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
24443,
7,
65,
6,
4,
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198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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9117,
7,
9186,
8,
628,
220,
220,
220,
1441,
9881,
7,
411,
8,
198
] | 2.25072 | 347 |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
from mongoengine import EmbeddedDocument, StringField, Document, EmailField, EmbeddedDocumentField, DateTimeField
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
18,
198,
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
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9,
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11,
9570,
15878,
11,
13302,
47238,
24941,
15878,
11,
7536,
7575,
15878,
198
] | 3.22 | 50 |
from app.schemas.appointment import Appointment
import pickle
from redis import Redis
from typing import Any
from datetime import timedelta
from app.models.user import User as UserModel
from .config import settings
| [
6738,
598,
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1416,
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355,
11787,
17633,
198,
6738,
764,
11250,
1330,
6460,
198
] | 3.927273 | 55 |
import spidertools.common.nano as nano
import os
| [
198,
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861,
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13,
11321,
13,
77,
5733,
355,
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198,
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28686,
628
] | 3 | 17 |
from shapely.geometry import *
from utils import geomerremovertools
class PrecisionRedux(object):
"""Class PrecisionRedux for managing the precision of geometries
"""
class PrecisionReduxPoint(PrecisionRedux):
"""Derived class for Point"""
class PrecisionReduxMultiPoint(PrecisionRedux):
"""Derived class for MultiPoint"""
class PrecisionReduxLineString(PrecisionRedux):
"""Derived class for LineString"""
class PrecisionReduxMultiLineString(PrecisionRedux):
"""Derived class for MultiLineString"""
| [
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329,
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] | 3.573427 | 143 |
import json, os, sys
from openpyxl import load_workbook
"""
This script is for creating receipts for Smop coders using data given from mongo
"""
if __name__ == '__main__':
main() | [
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] | 3.172414 | 58 |
# -*- coding: utf-8 -*-
# try something like
if not session.met_index:
session.met_index = 0
if not session.met_sort:
session.met_sort = 'ID'
if not session.met_display_flash:
session.met_display_flash = 'No'
if not session.met_filter_cat:
session.met_filter_cat = 'ID'
session.met_filter_text = ''
| [
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] | 2.477273 | 132 |
import argparse
import gzip
import logging
import os
import re
import subprocess
from pathlib import Path
try:
from checks.drc_checks.magic.converters import magic_drc_to_rdb, magic_drc_to_tcl, magic_drc_to_tr_drc, tr2klayout
except ImportError:
from converters import magic_drc_to_rdb, magic_drc_to_tcl, magic_drc_to_tr_drc, tr2klayout
def violations_count(drc_content):
"""
design name
violation message
list of violations
Total Count:
"""
split_line = '----------------------------------------'
drc_sections = drc_content.split(split_line)
if len(drc_sections) == 2:
return 0
else:
vio_dict = dict()
for i in range(1, len(drc_sections) - 1, 2):
vio_dict[drc_sections[i]] = len(drc_sections[i + 1].split("\n")) - 2
count = 0
for key in vio_dict:
val = vio_dict[key]
count += val
logging.error(f"Violation Message \"{str(key.strip())} \"found {str(val)} Times.")
return count
if __name__ == "__main__":
logging.basicConfig(level=logging.DEBUG, format=f"%(asctime)s | %(levelname)-7s | %(message)s", datefmt='%d-%b-%Y %H:%M:%S')
parser = argparse.ArgumentParser(description='Runs magic and klayout drc checks on a given GDS.')
parser.add_argument('--gds_input_file_path', '-g', required=True, help='GDS File to apply DRC checks on')
parser.add_argument('--output_directory', '-o', required=True, help='Output Directory')
parser.add_argument('--pdk_root', '-p', required=True, help='PDK Path')
parser.add_argument('--design_name', '-d', required=True, help='Design Name')
args = parser.parse_args()
gds_input_file_path = Path(args.gds_input_file_path)
output_directory = Path(args.output_directory)
pdk_root = Path(args.pdk_root)
design_name = args.design_name
if gds_input_file_path.exists() and gds_input_file_path.suffix == ".gds":
if output_directory.exists() and output_directory.is_dir():
if magic_gds_drc_check(gds_input_file_path, args.design_name, pdk_root, output_directory):
logging.info("Magic GDS DRC Clean")
else:
logging.info("Magic GDS DRC Dirty")
else:
logging.error(f"{output_directory} is not valid")
else:
logging.error(f"{gds_input_file_path} is not valid")
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92,
318,
407,
4938,
4943,
198
] | 2.340509 | 1,022 |
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