content
stringlengths 1
1.04M
| input_ids
sequencelengths 1
774k
| ratio_char_token
float64 0.38
22.9
| token_count
int64 1
774k
|
|---|---|---|---|
s = "a)))KkFmQ*wFz)TixK*||"
flag = ''
for i in s:
flag += chr(ord(i) ^ 25)
print flag
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# -*- coding: utf-8 -*-
"""Tests for Signals"""
from ELDAmwl.bases.factory import BaseOperation
from ELDAmwl.bases.factory import BaseOperationFactory
from ELDAmwl.component.registry import Registry
from unittest.mock import patch
import unittest
DB_DATA = [
('TestA', OperationA),
('TestB', OperationB),
]
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#!/usr/bin/env python
# example label.py
import pygtk
pygtk.require('2.0')
import gtk
if __name__ == "__main__":
Labels()
main()
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# for10.py
# [์
๋ ฅ๋ณ์ for ์ถ์ถ๋ณ์ in ๋์ if ์กฐ๊ฑด ]
# a ์ 1 ~ 10 ๊น์ง ์ซ์
a = [ i for i in range(1,11) ]
print(a)
# b ์ 1 ~ 10 ๊น์ง ์ซ์
b = [ i+1 for i in range(10) ]
print(b)
print(id(a) == id(b))
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__author__ = 'BorisMirage'
# --- coding:utf-8 ---
'''
Create by BorisMirage
File Name: plot
Create Time: 2018-12-02 14:45
'''
from time import time
import numpy as np
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
if __name__ == '__main__':
pass
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import subprocess, os
print("Building Rust component...")
# "cargo build" the bridge project
root_dir = os.path.dirname(os.path.realpath(__file__))
bridge_dir = os.path.join(root_dir, "rust")
subprocess.check_output(["cargo", "build", "--release"], cwd=bridge_dir)
print("Done!")
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#!/usr/bin/env python3
# coding: utf-8
"""
@author: Ping Qiu [email protected]
@last modified by: Ping Qiu
@file:__init__.py.py
@time:2021/03/05
"""
from .filter import filter_cells, filter_genes, filter_coordinates
from .normalize import Normalizer, normalize_total, normalize_zscore_disksmooth, quantile_norm
from .qc import cal_qc
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] | 2.661417 | 127 |
# Copyright 2021-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""
Test USPS dataset operators
"""
import os
from typing import cast
import matplotlib.pyplot as plt
import numpy as np
import pytest
import mindspore.dataset as ds
import mindspore.dataset.vision.transforms as vision
from mindspore import log as logger
DATA_DIR = "../data/dataset/testUSPSDataset"
WRONG_DIR = "../data/dataset/testMnistData"
def load_usps(path, usage):
"""
load USPS data
"""
assert usage in ["train", "test"]
if usage == "train":
data_path = os.path.realpath(os.path.join(path, "usps"))
elif usage == "test":
data_path = os.path.realpath(os.path.join(path, "usps.t"))
with open(data_path, 'r') as f:
raw_data = [line.split() for line in f.readlines()]
tmp_list = [[x.split(':')[-1] for x in data[1:]] for data in raw_data]
images = np.asarray(tmp_list, dtype=np.float32).reshape((-1, 16, 16, 1))
images = ((cast(np.ndarray, images) + 1) / 2 * 255).astype(dtype=np.uint8)
labels = [int(d[0]) - 1 for d in raw_data]
return images, labels
def visualize_dataset(images, labels):
"""
Helper function to visualize the dataset samples
"""
num_samples = len(images)
for i in range(num_samples):
plt.subplot(1, num_samples, i + 1)
plt.imshow(images[i].squeeze(), cmap=plt.cm.gray)
plt.title(labels[i])
plt.show()
def test_usps_content_check():
"""
Validate USPSDataset image readings
"""
logger.info("Test USPSDataset Op with content check")
train_data = ds.USPSDataset(DATA_DIR, "train", num_samples=10, shuffle=False)
images, labels = load_usps(DATA_DIR, "train")
num_iter = 0
# in this example, each dictionary has keys "image" and "label"
for i, data in enumerate(train_data.create_dict_iterator(num_epochs=1, output_numpy=True)):
for m in range(16):
for n in range(16):
assert (data["image"][m, n, 0] != 0 or images[i][m, n, 0] != 255) and \
(data["image"][m, n, 0] != 255 or images[i][m, n, 0] != 0)
assert (data["image"][m, n, 0] == images[i][m, n, 0]) or\
(data["image"][m, n, 0] == images[i][m, n, 0] + 1) or\
(data["image"][m, n, 0] + 1 == images[i][m, n, 0])
np.testing.assert_array_equal(data["label"], labels[i])
num_iter += 1
assert num_iter == 3
test_data = ds.USPSDataset(DATA_DIR, "test", num_samples=3, shuffle=False)
images, labels = load_usps(DATA_DIR, "test")
num_iter = 0
# in this example, each dictionary has keys "image" and "label"
for i, data in enumerate(test_data.create_dict_iterator(num_epochs=1, output_numpy=True)):
for m in range(16):
for n in range(16):
if (data["image"][m, n, 0] == 0 and images[i][m, n, 0] == 255) or\
(data["image"][m, n, 0] == 255 and images[i][m, n, 0] == 0):
assert False
if (data["image"][m, n, 0] != images[i][m, n, 0]) and\
(data["image"][m, n, 0] != images[i][m, n, 0] + 1) and\
(data["image"][m, n, 0] + 1 != images[i][m, n, 0]):
assert False
np.testing.assert_array_equal(data["label"], labels[i])
num_iter += 1
assert num_iter == 3
def test_usps_basic():
"""
Validate USPSDataset
"""
logger.info("Test USPSDataset Op")
# case 1: test loading whole dataset
train_data = ds.USPSDataset(DATA_DIR, "train")
num_iter = 0
for _ in train_data.create_dict_iterator(num_epochs=1):
num_iter += 1
assert num_iter == 3
test_data = ds.USPSDataset(DATA_DIR, "test")
num_iter = 0
for _ in test_data.create_dict_iterator(num_epochs=1):
num_iter += 1
assert num_iter == 3
# case 2: test num_samples
train_data = ds.USPSDataset(DATA_DIR, "train", num_samples=2)
num_iter = 0
for _ in train_data.create_dict_iterator(num_epochs=1):
num_iter += 1
assert num_iter == 2
# case 3: test repeat
train_data = ds.USPSDataset(DATA_DIR, "train", num_samples=2)
train_data = train_data.repeat(5)
num_iter = 0
for _ in train_data.create_dict_iterator(num_epochs=1):
num_iter += 1
assert num_iter == 10
# case 4: test batch with drop_remainder=False
train_data = ds.USPSDataset(DATA_DIR, "train", num_samples=3)
assert train_data.get_dataset_size() == 3
assert train_data.get_batch_size() == 1
train_data = train_data.batch(batch_size=2) # drop_remainder is default to be False
assert train_data.get_batch_size() == 2
assert train_data.get_dataset_size() == 2
num_iter = 0
for _ in train_data.create_dict_iterator(num_epochs=1):
num_iter += 1
assert num_iter == 2
# case 5: test batch with drop_remainder=True
train_data = ds.USPSDataset(DATA_DIR, "train", num_samples=3)
assert train_data.get_dataset_size() == 3
assert train_data.get_batch_size() == 1
train_data = train_data.batch(batch_size=2, drop_remainder=True) # the rest of incomplete batch will be dropped
assert train_data.get_dataset_size() == 1
assert train_data.get_batch_size() == 2
num_iter = 0
for _ in train_data.create_dict_iterator(num_epochs=1):
num_iter += 1
assert num_iter == 1
def test_usps_exception():
"""
Test error cases for USPSDataset
"""
error_msg_3 = "num_shards is specified and currently requires shard_id as well"
with pytest.raises(RuntimeError, match=error_msg_3):
ds.USPSDataset(DATA_DIR, "train", num_shards=10)
ds.USPSDataset(DATA_DIR, "test", num_shards=10)
error_msg_4 = "shard_id is specified but num_shards is not"
with pytest.raises(RuntimeError, match=error_msg_4):
ds.USPSDataset(DATA_DIR, "train", shard_id=0)
ds.USPSDataset(DATA_DIR, "test", shard_id=0)
error_msg_5 = "Input shard_id is not within the required interval"
with pytest.raises(ValueError, match=error_msg_5):
ds.USPSDataset(DATA_DIR, "train", num_shards=5, shard_id=-1)
ds.USPSDataset(DATA_DIR, "test", num_shards=5, shard_id=-1)
with pytest.raises(ValueError, match=error_msg_5):
ds.USPSDataset(DATA_DIR, "train", num_shards=5, shard_id=5)
ds.USPSDataset(DATA_DIR, "test", num_shards=5, shard_id=5)
with pytest.raises(ValueError, match=error_msg_5):
ds.USPSDataset(DATA_DIR, "train", num_shards=2, shard_id=5)
ds.USPSDataset(DATA_DIR, "test", num_shards=2, shard_id=5)
error_msg_6 = "num_parallel_workers exceeds"
with pytest.raises(ValueError, match=error_msg_6):
ds.USPSDataset(DATA_DIR, "train", shuffle=False, num_parallel_workers=0)
ds.USPSDataset(DATA_DIR, "test", shuffle=False, num_parallel_workers=0)
with pytest.raises(ValueError, match=error_msg_6):
ds.USPSDataset(DATA_DIR, "train", shuffle=False, num_parallel_workers=256)
ds.USPSDataset(DATA_DIR, "test", shuffle=False, num_parallel_workers=256)
with pytest.raises(ValueError, match=error_msg_6):
ds.USPSDataset(DATA_DIR, "train", shuffle=False, num_parallel_workers=-2)
ds.USPSDataset(DATA_DIR, "test", shuffle=False, num_parallel_workers=-2)
error_msg_7 = "Argument shard_id"
with pytest.raises(TypeError, match=error_msg_7):
ds.USPSDataset(DATA_DIR, "train", num_shards=2, shard_id="0")
ds.USPSDataset(DATA_DIR, "test", num_shards=2, shard_id="0")
error_msg_8 = "invalid input shape"
with pytest.raises(RuntimeError, match=error_msg_8):
train_data = ds.USPSDataset(DATA_DIR, "train")
train_data = train_data.map(operations=vision.Decode(), input_columns=["image"], num_parallel_workers=1)
for _ in train_data.__iter__():
pass
test_data = ds.USPSDataset(DATA_DIR, "test")
test_data = test_data.map(operations=vision.Decode(), input_columns=["image"], num_parallel_workers=1)
for _ in test_data.__iter__():
pass
error_msg_9 = "usps does not exist or is a directory"
with pytest.raises(RuntimeError, match=error_msg_9):
train_data = ds.USPSDataset(WRONG_DIR, "train")
for _ in train_data.__iter__():
pass
error_msg_10 = "usps.t does not exist or is a directory"
with pytest.raises(RuntimeError, match=error_msg_10):
test_data = ds.USPSDataset(WRONG_DIR, "test")
for _ in test_data.__iter__():
pass
def test_usps_visualize(plot=False):
"""
Visualize USPSDataset results
"""
logger.info("Test USPSDataset visualization")
train_data = ds.USPSDataset(DATA_DIR, "train", num_samples=3, shuffle=False)
num_iter = 0
image_list, label_list = [], []
for item in train_data.create_dict_iterator(num_epochs=1, output_numpy=True):
image = item["image"]
label = item["label"]
image_list.append(image)
label_list.append("label {}".format(label))
assert isinstance(image, np.ndarray)
assert image.shape == (16, 16, 1)
assert image.dtype == np.uint8
assert label.dtype == np.uint32
num_iter += 1
assert num_iter == 3
if plot:
visualize_dataset(image_list, label_list)
test_data = ds.USPSDataset(DATA_DIR, "test", num_samples=3, shuffle=False)
num_iter = 0
image_list, label_list = [], []
for item in test_data.create_dict_iterator(num_epochs=1, output_numpy=True):
image = item["image"]
label = item["label"]
image_list.append(image)
label_list.append("label {}".format(label))
assert isinstance(image, np.ndarray)
assert image.shape == (16, 16, 1)
assert image.dtype == np.uint8
assert label.dtype == np.uint32
num_iter += 1
assert num_iter == 3
if plot:
visualize_dataset(image_list, label_list)
def test_usps_usage():
"""
Validate USPSDataset image readings
"""
logger.info("Test USPSDataset usage flag")
assert test_config("train") == 3
assert test_config("test") == 3
assert "usage is not within the valid set of ['train', 'test', 'all']" in test_config("invalid")
assert "Argument usage with value ['list'] is not of type [<class 'str'>]" in test_config(["list"])
# change this directory to the folder that contains all USPS files
all_files_path = None
# the following tests on the entire datasets
if all_files_path is not None:
assert test_config("train", all_files_path) == 3
assert test_config("test", all_files_path) == 3
assert ds.USPSDataset(all_files_path, usage="train").get_dataset_size() == 3
assert ds.USPSDataset(all_files_path, usage="test").get_dataset_size() == 3
if __name__ == '__main__':
test_usps_content_check()
test_usps_basic()
test_usps_exception()
test_usps_visualize(plot=True)
test_usps_usage()
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import tkinter as tk
from pandas import DataFrame
import matplotlib.pyplot as plt
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
data = {'Raman Shift': [-464, -460, -455, -450, -445],
'Intensity1': [745, 752, 746, 740, 750], 'Intensity2': [734, 745, 768, 763, 755]
} # ๋ฐ์ดํฐ ์งค๋ผ์จ๊ฒ ๋ฐ์์ค๊ธฐ
df = DataFrame(data) # data ๋ก๋ถํฐ ๋ฐ์ดํฐ ํ๋ ์ ๋ง๋ฆ
df.set_index('Raman Shift', inplace=True) # ๋ง๋ค์ด์ง ๋ฐ์ดํฐ ํ๋ ์ ์ค, Raman_Shift ํญ๋ชฉ์ x ์ถ์ผ๋ก ์ง์
root= tk.Tk() # tkinter ๋ก ์ฐฝ ๋์ฐ๊ธฐ
figure = plt.Figure(figsize=(5,4), dpi=100) # ๊ทธ๋ํ ๋์ธ ์ฐฝ ์ฌ์ด์ฆ
ax = figure.add_subplot(111) # ๊ทธ๋ํ plot ๋ฐ x,y์ถ ๋ฒ์ ์กฐ์ (๋ฒ์ ์ง์ ์๋ตํ๋ฉด auto)
ax.set_title('Raman spectrum at selected point')
line = FigureCanvasTkAgg(figure, root) # Figure ๊ทธ๋ ค์ root์ ํ์
line.get_tk_widget().pack() # pack ์์ ๊ทธ๋ํ๋ฅผ ์ข์ธก์ ๋ ฌ/์ฑ์ฐ๊ธฐ ๋ฑ ์ค์
#df.plot(~~~) # df.ํ์ด๋ฆ ์ผ๋ก ํน์ ํ ์ ํ ๊ฐ๋ฅ
df.Intensity2.plot(kind='line', ax=ax, color='r', marker='o', fontsize=10)
root.mainloop() #์๋ก๊ณ ์นจ
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] | 1.409509 | 652 |
# encoding: utf-8
import json
import numpy as np
import matplotlib.pyplot as plt
import os
import queue
import _thread
import traceback
point_name = [
"Nose",
"Neck",
"RShoulder",
"RElbow",
"RWrist",
"LShoulder",
"LElbow",
"LWrist",
"MidHip",
"RHip",
"RKnee",
"RAnkle",
"LHip",
"LKnee",
"LAnkle",
"REye",
"LEye",
"REar",
"LEar",
"LBigToe",
"LSmallToe",
"LHeel",
"RBigToe",
"RSmallToe",
"RHeel",
"Background"
]
if __name__ == '__main__':
while 1:
for i in OpenposeJsonParser().stream_update_point_change_data_in_the_dir("G:\openpose\output",sum=True):
print(i)
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] | 2.066225 | 302 |
#
# This script develops the script 'variabledNLsimulation_v1.py' (Yury Eidelman)
#
# Started at June 28, 2019
#
# The three laws to change the strengths 't' of all nonlinear lens are implemented.
# From initial value t_i to final value t_f during N stepsthese laws are follows.
# 1) Linear: for step number n
# t(n) = t_0 + (t_f-t_0)*n/(N-1) for n = 0,1,...,N-1 .
# 2) Parabolic: for step number n
# t(n) = t_0 + (t_f-t_0)*n^2/(N-1)^2 for n = 0,1,...,N-1 .
# 3) Smooth sign-function: for step number n
# t(n) = .5*(t_0+t_f) + .5*(t_f-t_0)*tanh(x(n)), where
# x(n) = (6*n-3*(N-1))/(N-1) for n=0,1,...,N-1 .
# In this approach x(0) = -3., x(N-1) = 3.; so, tanh(3.) = - tanh(-3.) = .9951
#
import synergia
import os, sys
import inspect
import math
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
from matplotlib import gridspec
import rssynergia
from rssynergia.base_diagnostics import lfplot
from rssynergia.base_diagnostics import plotbeam
from rssynergia.base_diagnostics import pltbunch
#
# Output attributes of 'generate_lens' method:
#
# same as output of 'NonlinearInsertion'class and as well:
# s_vals (ndArray): coordinates of the center of each nonlinear lens (float ndArray, m);
# knll (ndArray): "strength" of each nonlinear lens (float ndArray, m);
# cnll (ndArray): aperture parameters for each nonlinear lens (float ndArray, m^1/2).
#
# Pickle helper is not necessary but is retained for this example
#
# Definition of class to ramp nonlinear lens
#
# Args of 'Ramp_actions' method are:
# 'type' - type of magnification (1 - relative, 2 - absolute),
# 'stepNumber' - current step of magnification,
# 'strengthLens' - set of strengthes 't' of central lens of the nonlinear insertion for all steps of
# magnification (relative magnification) or set of strengthes 't' of all lenses for
# current step (absolute magnification),
# 'updateOutputFlag' - flag to output the strength of one of nonlinear lens after it's magnification
# for current step,
# controlName - name of lens with maximal strength to use in output for checking of process
# of magnification.
#
#
# The arguments to __init__ are what the Ramp_actions instance is initialized with:
#
# Main method 'simulation'
#
#
# End of main method 'simulation'
#
#========================================================
fileIOTA = ".../ioptics/ioptics/lattices/Iota8-2/lattice_1IO_nll_center.madx"
# fileIOTA = ".../ioptics/ioptics/lattices/Iota8-4/lattice_8-4_1IO_nll_forTest.madx"
print "\nIOTA Nonlinear lattice: {} \n".format(fileIOTA)
lattice = synergia.lattice.MadX_reader().get_lattice("iota", \
"../ioptics/ioptics/lattices/Iota8-2/lattice_1IO_nll_center.madx")
# --------- Games -----------------------------
# indices = np.argsort(knllLenses)
# print "indices = ",indices
# for n in range(nLenses+1):
# print n,") name after sorting is ",nameLenses[indices[n]]
# for n in range(nLenses+1):
# print n,") knll after sorting is ",knllLenses[indices[n]]
# for n in range(nLenses+1):
# print n,") place after sorting is ",placeLenses[indices[n]]
# ----------- End of games --------------------
stepperCrrnt = synergia.simulation.Independent_stepper_elements(lattice,2,3)
lattice_simulator_Crrnt = stepperCrrnt.get_lattice_simulator()
# To recognize attributes of 'bunchParticles':
# printAttributes(lattice_simulator_Crrnt,'lattice_simulator_Crrnt','stepperCrrnt.get_lattice_simulator()')
# slicesHelp = lattice_simulator_Crrnt.get_slices()
# To recognize attributes of 'slicesHelp':
# printAttributes(slicesHelp,'slicesHelp','lattice_simulator_Crrnt.get_slices()')
# Bunch:
bunch_origin = synergia.optics.generate_matched_bunch_transverse(lattice_simulator_Crrnt, 1e-6, \
1e-6, 1e-3, 1e-4, 1e9, 1000, seed=1234)
#
# To compare two methods for drawing of the particles distributions:
#
loclTitle = "\nThese distributions were constructed using \
'synergia.optics.generated_matched_bunch_transverse' method"
loclTitle += "\nand plotted using two methods - 'pltbunch.plot_bunch' from the code synergia"
loclTitle += "\nand 'plotcoordDistr' from this script (to verify method 'plotcoordDistr'):"
print loclTitle
pltbunch.plot_bunch(bunch_origin)
# Distributions X-Y, X-X', Y-Y' using method 'plotcoordDistr':
bunchParticles = bunch_origin.get_local_particles()
# To recognize attributes of 'bunchParticles':
# printAttributes(bunchParticles,'bunchParticles', 'bunch.get_local_particles()')
plotcoordDistr(bunchParticles)
selection = 'loop'
while selection == 'loop':
simulation()
selection = raw_input("\nTo continue the simulation ('yes' or 'no'):")
print'Your selection is ',selection
if selection == 'yes':
selection = 'loop'
# if selection == 'no':
# exit(0)
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] | 2.547256 | 1,968 |
import time
connect("127.0.0.1", "10039", "weblab")
#test_me("hello")
start_experiment()
send_file("script.py", "A script file")
response = send_command("Test Command")
print "The response is: %s" % response
msg_box("Test Message", "test")
time.sleep(2)
dispose()
disconnect()
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] | 2.338346 | 133 |
# Copyright 2014 Intel Corporation, All Rights Reserved.
# Licensed under the Apache License, Version 2.0 (the"License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
from django.core import validators
from django.core.urlresolvers import reverse
from django.utils.translation import ugettext_lazy as _
from horizon import exceptions
from horizon import forms
from horizon import messages
from horizon.utils.validators import validate_port_range
# from horizon.utils import fields
import logging
from vsm_dashboard.api import vsm as vsm_api
from vsm_dashboard.utils.validators import validate_pool_name
LOG = logging.getLogger(__name__)
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] | 3.728522 | 291 |
import arrow
import requests
from arrow import Arrow
from bs4 import BeautifulSoup
from collections import defaultdict
from icalendar import Calendar, Event, vText, vCalAddress
from hashlib import md5
import json
| [
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] | 3.683333 | 60 |
import os
import sys
import pyproj
from pyproj import Proj
Proj(init="epsg:4269")
# Test pyproj_datadir.
if not os.path.isdir(pyproj.datadir.get_data_dir()):
sys.exit(1)
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"""Completers for pip."""
import contextlib
import os
import subprocess
from xonsh.built_ins import XSH
from xonsh.completers.tools import RichCompletion, contextual_command_completer
from xonsh.parsers.completion_context import CommandContext
def generate_completions_from_string(output: str):
"""Rich completion from multi-line string, each line representing a completion."""
if output:
lines = output.strip().splitlines(keepends=False)
# if there is a single completion candidate then maybe it is over
append_space = len(lines) == 1
for line in lines:
comp = create_rich_completion(line, append_space)
yield comp
@contextual_command_completer
def xonsh_complete(ctx: CommandContext):
"""Completes python's package manager pip."""
if not ctx.completing_command("kitty"):
return None
# like fish's
# commandline --tokenize --cut-at-cursor --current-process
tokens = [arg.raw_value for arg in ctx.args[: ctx.arg_index]]
# it already filters by prefix, just return it
return get_completions(*tokens, ctx.prefix)
if __name__ == "__main__":
# small testing won't hurt
from xonsh.main import setup
setup()
print(list(get_completions("kitty", "-")))
print(list(get_completions("kitty", "--")))
print(list(get_completions("kitty", "--d")))
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] | 2.769697 | 495 |
# -*- coding: utf-8 -*-
"""
Contains the definition of the ArgParser class.
"""
import logging
from argparse import ArgumentParser, ArgumentError, Namespace
import shlex
from enhterm.command import Command
from enhterm.command.error import ErrorCommand
from enhterm.command.noop import NoOpCommand
from enhterm.command.text import TextCommand
from enhterm.impl.p2p.p2p_provider import RemoteProvider
from enhterm.provider import Provider
from enhterm.provider.parser import Parser
from enhterm.provider.queue_provider import QueueProvider
from enhterm.provider.text_provider import TextProvider
logger = logging.getLogger('et.argparser')
class ArgParseCommand(Command):
"""
A command returned by our parser.
"""
def __init__(self, parsed=None, *args, **kwargs):
""" Constructor. """
super().__init__(*args, **kwargs)
self.parsed = parsed
if parsed is not None:
self.call_me = parsed.func
del parsed.__dict__['func']
if hasattr(parsed, 'command'):
# Because we set the dest parameter to 'command' a
# command attribute is set, with the value of the
# name of the subparser.
self.command_name = parsed.command
del parsed.__dict__['command']
else:
# When a subparser was not set or was set but without
# dest argument.
self.command_name = None
else:
self.command_name = None
self.call_me = None
def __str__(self):
""" Represent this object as a human-readable string. """
return 'ArgParseCommand()'
def __repr__(self):
""" Represent this object as a python constructor. """
return 'ArgParseCommand()'
def execute(self):
"""
Called by the command loop to do some work.
The return value will be deposited by the command loop it into
the `result` member.
"""
return self.call_me(command=self, **self.parsed.__dict__)
def encode(self):
"""
Called when a class instance needs to be serialized.
.. note:
The `result` and `uuid` members should not be serialized
in case of :class:`~Command`.
"""
return self.command_name, self.parsed.__dict__
def decode(self, raw_data):
"""
Apply raw data to this instance.
It is asserted that correct class has already been constructed
and that it has `result` and `uuid` members set in case of
:class:`~Command`..
Raises:
DecodeError:
The implementation should raise this class or a
subclass of it.
Arguments:
raw_data (bytes):
The data to apply.
"""
assert len(raw_data) == 2
self.command_name, self.parsed = raw_data
self.parsed = Namespace(**self.parsed)
@classmethod
def class_id(cls):
"""
A unique identifier of the class.
This value is used as a key when a constructor needs to
be associated with a string
(see :class:`enhterm.ser_deser.dsds.DictSerDeSer`).
"""
return "argparse"
class ParserError(Exception):
""" Hops the exceptions back to :meth:`~parse`."""
pass
class NoOpError(Exception):
""" :meth:`~parse` should return a :class:`~NoOpCommand`."""
pass
class ArgParser(ArgumentParser, Parser):
"""
Parser that uses argparse library to interpret the text.
Note the two functions of this class: an `enhterm` parser
and :class:`argparse.ArgumentParser`.
The usual use of this parser is through subparsers that implement commands.
>>> from enhterm.provider.parser.argparser import ArgParser
>>> testee = ArgParser()
>>> subparsers = testee.add_subparsers(
>>> title="commands", dest="command", help="commands")
>>> def do_add(command, arguments):
>>> return sum(arguments.integers)
>>> parser_add = subparsers.add_parser('add')
>>> parser_add.add_argument(
>>> 'integers', metavar='int', nargs='+', type=int,
>>> help='an integer to be summed')
>>> parser_add.set_defaults(func=do_add)
>>> testee.parse('add -h')
>>> result = testee.parse('add 1 2 3')
>>> exec_result = result.execute()
A simpler variant is:
>>> from enhterm.provider.parser.argparser import ArgParser
>>> testee = ArgParser()
Attributes:
"""
def __init__(self, *args, **kwargs):
"""
Constructor.
Arguments:
"""
provider = kwargs.pop('provider', None)
super().__init__(*args, **kwargs)
assert provider is not None, "The provider must be set and kept " \
"the same for the lifetime of the parser"
self.provider = provider
self.prog = ''
self._subparser_action = None
self.prefix = ''
self.suffix = ''
def add_subparsers(self, **kwargs):
"""
Monkey-patch add_parser method.
Parsers created by the sub-parser have same class as
the main parser (in our case the class:`~ArgParser` class).
Because we want messages printed by the argparse library
to go through our watchers, we want to set the parser
so it is available in :meth:`~_print_message`.
This is because we don't want to ask the user to place
this argument themselves each time they create the parser.
"""
result = super().add_subparsers(**kwargs)
previous_method = result.add_parser
result.add_parser = monkey_patch
return result
def __str__(self):
""" Represent this object as a human-readable string. """
return 'ArgParser()'
def __repr__(self):
""" Represent this object as a python constructor. """
return 'ArgParser()'
@property
def parse(self, text):
"""
Convert a text into a command.
Arguments:
text (str):
The text to parse. This should be a full command.
Returns:
Command
The command that resulted from parsing the text.
If the parsing was unsuccessful the method may return either
:class:`~NoOpCommand' to keep using the provider or `None` to
uninstall it.
"""
try:
if text.startswith('wrap-commands') or text.startswith('wcs ') or text == 'wcs':
args = self.parse_args(shlex.split(text))
else:
args = self.parse_args(shlex.split(f'{self.prefix}{text}{self.suffix}'))
return ArgParseCommand(parsed=args)
except ParserError as exc:
message = str(exc)
self.provider.term.error(message)
return ErrorCommand(message=message)
except NoOpError:
return NoOpCommand()
def error(self, message):
"""
The parser has encountered an error while interpreting the input.
This method, according to argparse specs, should not return.
We raise a custom exception that is caught in :meth:`~parse`
and we pass along the error message.
"""
raise ParserError(message)
def exit(self, status=0, message=None):
""" Trap any exits left out by other code (help, version). """
raise NoOpError
class ArgparseRemoteProvider(RemoteProvider):
"""
A provider that simply takes the text and creates a text command for it.
"""
def __init__(self, parser=None, *args, **kwargs):
"""
Constructor.
"""
super().__init__(*args, **kwargs)
if parser:
self.parser = parser
parser.provider = self
else:
self.parser = ArgParser(provider=self)
def __str__(self):
""" Represent this object as a human-readable string. """
return 'ArgparseRemoteProvider()'
def __repr__(self):
""" Represent this object as a python constructor. """
return 'ArgparseRemoteProvider()'
def enqueue_command(self, command):
""" Adds a command to the internal list. """
assert isinstance(command, TextCommand)
new_command = self.parser.parse(command.content)
new_command.provider = self
new_command.uuid = command.uuid
self.queue.put(new_command)
return new_command
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] | 2.422499 | 3,529 |
"""LinkNet
Paper: https://arxiv.org/pdf/1707.03718
Adapted from: https://github.com/qubvel/segmentation_models.pytorch/blob/master/segmentation_models_pytorch/linknet/model.py
Copyright 2021 | farabio
"""
from typing import List, Optional, Union, Any
import torch
import torch.nn as nn
import torch.nn.functional as F
from farabio.models.segmentation.base import SegModel, SegmentationHead
from farabio.models.segmentation.backbones._backbones import get_backbone
from farabio.models.segmentation.blocks import Conv2dReLU
from farabio.utils.helpers import get_num_parameters
__all__ = [
'Linknet', 'linknet_vgg11', 'linknet_vgg11_bn', 'linknet_vgg13', 'linknet_vgg13_bn',
'linknet_vgg16', 'linknet_vgg16_bn', 'linknet_vgg19', 'linknet_vgg19_bn', 'linknet_mobilenetv2',
'linknet_resnet18', 'linknet_resnet34', 'linknet_resnet50', 'linknet_resnet101', 'linknet_resnet152'
]
# test() | [
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62,
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6,
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60,
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220,
220,
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220,
628,
628,
628,
628,
628,
628,
628,
628,
628,
198,
198,
2,
1332,
3419
] | 2.545205 | 365 |
# Generated by Django 3.0.5 on 2020-06-13 19:56
from django.db import migrations, models
| [
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] | 2.567568 | 37 |
#!/usr/bin/env python3
# coding: utf-8
# Import built-in packages
# Import external packages | [
2,
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] | 3.032258 | 31 |
import os
import glob
from chwall.utils import get_logger
import gettext
# Uncomment the following line during development.
# Please, be cautious to NOT commit the following line uncommented.
# gettext.bindtextdomain("chwall", "./locale")
gettext.textdomain("chwall")
_ = gettext.gettext
logger = get_logger(__name__)
| [
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198
] | 3.295918 | 98 |
#!/usr/bin/env python3
TOKEN = ""
TIMER = 30
__all__ = ["SimpleHTTPRequestHandler"]
import os
import posixpath
import http.server
import urllib.request, urllib.parse, urllib.error
import cgi
import shutil
import mimetypes
import re
from io import BytesIO
import time
from AWSRekognition import AWSRekognition
import re
import np
import cv2
if __name__ == '__main__':
run()
| [
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197,
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] | 2.737589 | 141 |
import os
import numpy as np
from skmultiflow.data.generator.sea_generator import SEAGenerator
| [
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13,
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62,
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1352,
1330,
7946,
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] | 3.2 | 30 |
import json
import flask
import os
app = flask.Flask(__name__)
@app.route("/")
if __name__ == "__main__":
begin()
| [
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198
] | 2.469388 | 49 |
from flask import Flask
import os
basedir = os.path.abspath(os.path.dirname(__file__))
# `flask run` - runs application on local server
app = Flask(__name__, static_url_path='', static_folder='static',
instance_relative_config=True)
DATABASE_URL = os.environ.get('DATABASE_URL')
if os.environ.get('TESTING') == 'True':
DATABASE_URL = os.environ.get('TEST_DATABASE_URL')
app.config.from_mapping(
SECRET_KEY=os.environ.get('SECRET_KEY'),
SQLALCHEMY_DATABASE_URI=DATABASE_URL,
SQLALCHEMY_TRACK_MODIFICATIONS=False,
)
from . import routes, models, exceptions, auth
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] | 2.426829 | 246 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Author: gexiao
# Created on 2018-05-07 22:04
import logging
import requests
import base64
SERVER_HOST = 'https://v2-api.jsdama.com/upload'
SOFTWARE_ID = 9487
SOFTWARE_SECRET = 'nb4GHmdsPxzbcB7iIrU36JPI73HOjUyUEnq3pkob'
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from __future__ import print_function
PLEVEL = 0
def parity4(data):
'''
Thanks to http://www.dalkescientific.com/writings/diary/archive/2016/08/15/fragment_parity_calculation.html
'''
if data[0] < data[1]:
if data[2] < data[3]:
if data[0] < data[2]:
if data[1] < data[2]:
return 0 # (0, 1, 2, 3)
else:
if data[1] < data[3]:
return 1 # (0, 2, 1, 3)
else:
return 0 # (0, 3, 1, 2)
else:
if data[0] < data[3]:
if data[1] < data[3]:
return 0 # (1, 2, 0, 3)
else:
return 1 # (1, 3, 0, 2)
else:
return 0 # (2, 3, 0, 1)
else:
if data[0] < data[3]:
if data[1] < data[2]:
if data[1] < data[3]:
return 1 # (0, 1, 3, 2)
else:
return 0 # (0, 2, 3, 1)
else:
return 1 # (0, 3, 2, 1)
else:
if data[0] < data[2]:
if data[1] < data[2]:
return 1 # (1, 2, 3, 0)
else:
return 0 # (1, 3, 2, 0)
else:
return 1 # (2, 3, 1, 0)
else:
if data[2] < data[3]:
if data[0] < data[3]:
if data[0] < data[2]:
return 1 # (1, 0, 2, 3)
else:
if data[1] < data[2]:
return 0 # (2, 0, 1, 3)
else:
return 1 # (2, 1, 0, 3)
else:
if data[1] < data[2]:
return 1 # (3, 0, 1, 2)
else:
if data[1] < data[3]:
return 0 # (3, 1, 0, 2)
else:
return 1 # (3, 2, 0, 1)
else:
if data[0] < data[2]:
if data[0] < data[3]:
return 0 # (1, 0, 3, 2)
else:
if data[1] < data[3]:
return 1 # (2, 0, 3, 1)
else:
return 0 # (2, 1, 3, 0)
else:
if data[1] < data[2]:
if data[1] < data[3]:
return 0 # (3, 0, 2, 1)
else:
return 1 # (3, 1, 2, 0)
else:
return 0 # (3, 2, 1, 0)
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] | 1.403746 | 1,922 |
from sort import *
import time
import random
n1 = int(input("Size\nFrom: "))
n2 = int(input("To: "))
h = int(input("Step:"))
if n1 > n2 or n2 == n1 or h == 0:
print("Wrong input")
exit()
else:
result = measure_time(get_best_array, get_best_array, mysort_quick_middle, n1, n2 + 1, h, 100)
print("\n", result, "\n")
result = measure_time(get_worst_array, get_best_array, mysort_quick_end, n1, n2 + 1, h, 100)
print("\n", result, "\n")
result = measure_time(get_random_array, get_random_array, mysort_quick_middle, n1, n2 + 1, h, 100)
print("\n", result, "\n")
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] | 2.214286 | 280 |
# Copyright ClusterHQ Inc. See LICENSE file for details.
"""
Era information for Flocker nodes.
Every time a node reboots it gets a new, globally unique era.
"""
import sys
from uuid import UUID
from zope.interface import implementer
from twisted.internet.defer import succeed
from twisted.python.filepath import FilePath
from twisted.python.usage import Options
from twisted.python.runtime import platform
from ..common.script import (
ICommandLineScript, flocker_standard_options, FlockerScriptRunner,
)
_BOOT_ID = FilePath(b"/proc/sys/kernel/random/boot_id")
def get_era():
"""
:return UUID: A node- and boot-specific globally unique id.
"""
return UUID(hex=_BOOT_ID.getContent().strip())
@flocker_standard_options
class EraOptions(Options):
"""
Command line options for ``flocker-node-era``.
"""
longdesc = (
"Print the current node's era to stdout. The era is a unique"
"identifier per reboot per node, and can be used to discover the"
"current node's state safely using Flocker's REST API.\n"
)
synopsis = "Usage: flocker-node-era"
@implementer(ICommandLineScript)
class EraScript(object):
"""
Output the era to stdout.
"""
def era_main():
"""
Entry point for ``flocker-node-era`` command-line tool.
"""
return FlockerScriptRunner(
script=EraScript(),
options=EraOptions(),
logging=False).main()
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] | 2.891566 | 498 |
# Distributions in Pandas
import pandas as pd
import numpy as np
np.random.binomial(1, 0.5)
np.random.binomial(1000, 0.5)/1000
chance_of_tornado = 0.01/100
np.random.binomial(100000, chance_of_tornado)
chance_of_tornado = 0.01
tornado_events = np.random.binomial(1, chance_of_tornado, 1000000)
two_days_in_a_row = 0
for j in range(1,len(tornado_events)-1):
if tornado_events[j]==1 and tornado_events[j-1]==1:
two_days_in_a_row+=1
print('{} tornadoes back to back in {} years'.format(two_days_in_a_row, 1000000/365))
np.random.uniform(0, 1)
np.random.normal(0.75)
Formula for standard deviation
$$\sqrt{\frac{1}{N} \sum_{i=1}^N (x_i - \overline{x})^2}$$
distribution = np.random.normal(0.75,size=1000)
np.sqrt(np.sum((np.mean(distribution)-distribution)**2)/len(distribution))
np.std(distribution)
import scipy.stats as stats
stats.kurtosis(distribution)
stats.skew(distribution)
chi_squared_df2 = np.random.chisquare(2, size=10000)
stats.skew(chi_squared_df2)
chi_squared_df5 = np.random.chisquare(5, size=10000)
stats.skew(chi_squared_df5)
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
output = plt.hist([chi_squared_df2,chi_squared_df5], bins=50, histtype='step',
label=['2 degrees of freedom','5 degrees of freedom'])
plt.legend(loc='upper right')
# Hypothesis Testing
df = pd.read_csv('grades.csv')
df.head()
len(df)
early = df[df['assignment1_submission'] <= '2015-12-31']
late = df[df['assignment1_submission'] > '2015-12-31']
early.mean()
late.mean()
from scipy import stats
stats.ttest_ind?
stats.ttest_ind(early['assignment1_grade'], late['assignment1_grade'])
stats.ttest_ind(early['assignment2_grade'], late['assignment2_grade'])
stats.ttest_ind(early['assignment3_grade'], late['assignment3_grade']) | [
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18,
62,
9526,
6,
4357,
2739,
17816,
562,
16747,
18,
62,
9526,
6,
12962
] | 2.379128 | 757 |
from jarbas.settings import *
MIGRATION_MODULES = DisableMigrations()
| [
6738,
17379,
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] | 2.92 | 25 |
# coding: utf-8
# -------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
# --------------------------------------------------------------------------
"""
FILE: identity_sample.py
DESCRIPTION:
These samples demonstrate creating a user, issuing a token, revoking a token and deleting a user.
///authenticating a client via a connection string
USAGE:
python identity_samples.py
Set the environment variables with your own values before running the sample:
1) AZURE_COMMUNICATION_SERVICE_ENDPOINT - Communication Service endpoint url
"""
import os
if __name__ == '__main__':
sample = CommunicationIdentityClientSamples()
sample.create_user()
sample.create_user_with_token()
sample.get_token()
sample.revoke_tokens()
sample.delete_user()
| [
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62,
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482,
641,
3419,
198,
220,
220,
220,
6291,
13,
33678,
62,
7220,
3419,
198
] | 3.828 | 250 |
'''
Created on 1 Dec 2012
@author: Jeremy
'''
import serial
import sys
import rt
import time
s = serial.Serial(sys.argv[1],115200,timeout=15)
t = time.time()
c = 0
RT = rt.RaceTech(s)
RT.run(decode)
| [
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] | 2.156863 | 102 |
from django.contrib import admin
from django.urls import path, include
from django.conf import settings
from django.conf.urls.static import static
from pages import views
urlpatterns = [
path('admin/', admin.site.urls),
path('accounts/', include('allauth.urls')), # allauth
path('', include('pages.urls')), # Home and tools pages
path('db/', include('db.urls')), # Oil field and well database
path('accounts/', include('users.urls')),
] + static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)
handler404 = views.handler404
handler500 = views.handler500
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26429,
198,
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796,
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13,
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198
] | 2.401434 | 279 |
#!/usr/bin/python
import numpy as np
import pandas as pd
import sys
df = pd.read_csv('https://raw.githubusercontent.com/ChrisFodor333/early_warning/main/assets/machine.csv',header = 0);
df = df.dropna();
#df.head(20);
from sklearn.model_selection import train_test_split
data = df
X = data[['altman', 'in05', 'quicktest','bonity','taffler','binkert']]
y = data['result']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)
pd.options.mode.chained_assignment = None
from sklearn.preprocessing import LabelEncoder
labelencoder = LabelEncoder()
data["result"] = labelencoder.fit_transform(data["result"])
type = pd.DataFrame({'result': ['No Financial Distress', 'First Degree Financial Distress ', 'Second Degree Financial Distress', 'Third Degree Financial Distress']})
data = create_dummies(data,"result")
# Aby nevypรญsal warningy
import warnings
from sklearn.exceptions import DataConversionWarning
warnings.filterwarnings(action='ignore', category=DataConversionWarning)
# Vlastnosti pred strednou normalizรกciou
vlastnosti_pred = X_train
# Strednรก normalizรกcia pre rรฝchlejลกรญ classifier
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
#Transformรกcia dรกt
X_train_array = sc.fit_transform(X_train.values)
# Priradรญm ลกkรกlovanรฉ รบdaje do DataFrame a pouลพijem argumenty indexu a stฤบpcov, aby som zachoval svoje pรดvodnรฉ indexy a nรกzvy stฤบpcov:
X_train = pd.DataFrame(X_train_array, index=X_train.index, columns=X_train.columns)
# Vycentrovanรฉ testovacie dรกta na trรฉnovacรญch dรกtach
X_test_array = sc.transform(X_test.values)
X_test = pd.DataFrame(X_test_array, index=X_test.index, columns=X_test.columns)
# import modelu MLP
from sklearn.neural_network import MLPClassifier
# Inicializovanie perceptrรณnu
mlp = MLPClassifier(hidden_layer_sizes =(100,),solver='adam',learning_rate_init= 0.01, max_iter=500)
# Natrรฉnovaลฅ model
mlp.fit(X_train, y_train)
# Vรฝstupy
MLPClassifier (activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=10, learning_rate='constant',
learning_rate_init=0.01, max_iter=1000, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='adam', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
altman = sys.argv[1]
in05 = sys.argv[2]
qt = sys.argv[3]
bonity = sys.argv[4]
taffler = sys.argv[5]
binkert = sys.argv[6]
X_test = [[altman, in05, qt, bonity, taffler, binkert]];
X_test = np.array(X_test);
X_test.reshape(1, -1);
mlp.predict(X_test)
mlp.predict_proba(X_test)*100
print(mlp.predict(X_test),mlp.predict_proba(X_test)*100);
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7,
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62,
9288,
27493,
3064,
1776,
198
] | 2.508523 | 1,056 |
#!/usr/bin/python
"""Sample program."""
def hello_world():
"""Print a message to stdout."""
print("Hello, world!")
def return_true():
"""You can rent this space for only $5 a week."""
return True
| [
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from django.contrib.auth import authenticate
from .base import AllAccessTestCase
class AuthBackendTestCase(AllAccessTestCase):
"Custom contrib.auth backend tests."
def test_successful_authenticate(self):
"User successfully authenticated."
provider = self.access.provider
identifier = self.access.identifier
user = authenticate(provider=provider, identifier=identifier)
self.assertEqual(user, self.user, "Correct user was not returned.")
def test_provider_name(self):
"Match on provider name as a string."
provider = self.access.provider.name
identifier = self.access.identifier
user = authenticate(provider=provider, identifier=identifier)
self.assertEqual(user, self.user, "Correct user was not returned.")
def test_failed_authentication(self):
"No matches found for the provider/id pair."
provider = self.access.provider
identifier = self.access.identifier
self.access.delete()
user = authenticate(provider=provider, identifier=identifier)
self.assertEqual(user, None, "No user should be returned.")
def test_match_no_user(self):
"Matched access is not associated with a user."
self.access.user = None
self.access.save()
user = authenticate(provider=self.access.provider, identifier=self.access.identifier)
self.assertEqual(user, None, "No user should be returned.")
def test_performance(self):
"Only one query should be required to get the user."
with self.assertNumQueries(1):
authenticate(provider=self.access.provider, identifier=self.access.identifier) | [
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1304,
11,
27421,
28,
944,
13,
15526,
13,
738,
7483,
8
] | 3.194093 | 474 |
from _stories.mounted import ClassMountedStory
| [
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276,
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628,
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] | 4.083333 | 12 |
from os import system, name
system('cls' if name == 'nt' else 'clear')
dsc = ('''DESAFIO 017:
Faรงa um programa que leia o comprimento do cateto oposto e do cateto adjacente de um
triรขngulo retรขngulo, calcule e mostre o comprimento da hipotenusa.
''')
from math import hypot
n1 = float(input('Cateto oposto: '))
n2 = float(input('Cateto adjacente: '))
#print('A hipotenusa รฉ {}'.format((n1 ** 2 + n2 ** 2) ** 0.5))
print('A hipotenusa รฉ {}'.format(hypot(n1, n2)))
| [
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198
] | 2.574586 | 181 |
from __future__ import unicode_literals
import biplist
import os.path
app = defines.get('app', './dmg/JoyfulPlayer.app')
appname = os.path.basename(app)
# Basics
format = defines.get('format', 'UDZO')
size = defines.get('size', None)
files = [ app ]
icon_locations = {
appname: (160, 160),
}
# Window configuration
show_status_bar = False
show_tab_view = False
show_toolbar = False
show_pathbar = False
show_sidebar = False
sidebar_width = 180
window_rect = ((322, 331), (320, 362))
defaullt_view = 'icon_view'
# Icon view configuration
arrange_by = None
grid_offset = (0, 0)
grid_spacing = 100
scrolll_position = (0, 0)
label_pos = 'bottom'
text_size = 12
icon_size = 164
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62,
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11,
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11,
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15,
11,
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8,
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62,
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75,
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15,
11,
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8,
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62,
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705,
22487,
6,
198,
5239,
62,
7857,
796,
1105,
198,
4749,
62,
7857,
796,
25307,
628
] | 2.636364 | 264 |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# @Filename: mf_lqr.py
# @Date: 2019-06-16-18-38
# @Author: Hany Abdulsamad
# @Contact: [email protected]
import gym
from trajopt.gps import MFGPS
# lqr task
env = gym.make('LQR-TO-v0')
env._max_episode_steps = 100
alg = MFGPS(env, nb_steps=100,
kl_bound=10.,
init_ctl_sigma=50.,
activation=range(100))
# run gps
trace = alg.run(nb_episodes=10, nb_iter=5)
# plot dists
alg.plot()
# execute and plot
nb_episodes = 25
data = alg.sample(nb_episodes, stoch=False)
import matplotlib.pyplot as plt
plt.figure()
for k in range(alg.nb_xdim):
plt.subplot(alg.nb_xdim + alg.nb_udim, 1, k + 1)
plt.plot(data['x'][k, ...])
for k in range(alg.nb_udim):
plt.subplot(alg.nb_xdim + alg.nb_udim, 1, alg.nb_xdim + k + 1)
plt.plot(data['u'][k, ...])
plt.show()
# plot objective
plt.figure()
plt.plot(trace)
plt.show()
| [
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7,
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198
] | 2.017621 | 454 |
import time
import sys
import uuid
import argparse
import ibmiotf.device
import wiotp.sdk.device
from configparser import ConfigParser
| [
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] | 3.425 | 40 |
from django.urls import path
from django.contrib.auth import views as auth_views
from . import editorviews
from . import userviews
urlpatterns = [
# editor paths
path("project", editorviews.render_all_projects, name="projects"),
path("project/create", editorviews.parse_new_project_request, name="new"),
path("project/<str:project_name>", editorviews.paint, name="paint"),
path("project/<str:project_name>/save", editorviews.parse_save_request, name="save"),
path("project/<str:project_name>/render", editorviews.parse_render_request, name="render"),
path("project/<str:project_name>/view", editorviews.parse_view_request, name="view"),
path("project/<str:project_name>/publish", editorviews.parse_post_request, name="publish"),
path("project/<str:project_name>/load", editorviews.parse_image_request, name="images"),
path("project/<str:user>/<str:project_name>/detail", userviews.detail, name="project-detail"),
path("project/<str:user>/<str:project_name>/comment", userviews.submit_comment, name="submit-comment"),
path("", userviews.home, name="home"),
# user authentication paths
path("login/", auth_views.LoginView.as_view(template_name='login.html'), name="login"),
path("logout/", auth_views.LogoutView.as_view(template_name='logout.html'), name="logout"),
path("register/", userviews.register, name="register"),
# password reset paths
path("password_reset/", auth_views.PasswordResetView.as_view(template_name='password_reset.html'),
name='password_reset'),
path("password_reset/done", auth_views.PasswordResetDoneView.as_view(template_name='password_reset_done.html'),
name='password_reset_done'),
path("password_reset/confirm",
auth_views.PasswordResetConfirmView.as_view(template_name='password_reset_confirm.html'),
name='password_reset_confirm'),
path("password_reset/complete",
auth_views.PasswordResetCompleteView.as_view(template_name='password_reset_complete.html'),
name='password_reset_complete'),
]
| [
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6284,
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13,
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62,
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7,
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62,
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62,
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62,
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13,
6494,
33809,
198,
220,
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220,
220,
220,
220,
220,
220,
1438,
11639,
28712,
62,
42503,
62,
20751,
33809,
198,
60,
198
] | 2.867872 | 719 |
from PyQt5 import QtWidgets
from .pages import IntroPage, EMPage, SimTypePage, IntegratorPage, \
NeighbourSearchPage, FreqControlPage, CoulombPage, \
VdWPage, EwaldPage, ThermostatPage, EndPage
| [
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] | 2.873239 | 71 |
# -*- coding: utf-8 -*-
"""
Utilities for transforming and validating data types
Given that many of the data transformations involve copying the data, they should
ideally happen in a lazy manner to avoid memory issues.
Created on Tue Nov 3 21:14:25 2015
@author: Suhas Somnath, Chris Smith
"""
from __future__ import division, absolute_import, unicode_literals, print_function
import sys
from warnings import warn
import h5py
import numpy as np
import dask.array as da
__all__ = ['flatten_complex_to_real', 'get_compound_sub_dtypes', 'flatten_compound_to_real', 'check_dtype',
'stack_real_to_complex', 'validate_dtype', 'is_complex_dtype',
'stack_real_to_compound', 'stack_real_to_target_dtype', 'flatten_to_real']
from sidpy.hdf.hdf_utils import lazy_load_array
if sys.version_info.major == 3:
unicode = str
def flatten_complex_to_real(dataset, lazy=False):
"""
Stacks the real values followed by the imaginary values in the last dimension of the given N dimensional matrix.
Thus a complex matrix of shape (2, 3, 5) will turn into a matrix of shape (2, 3, 10)
Parameters
----------
dataset : array-like or :class:`numpy.ndarray`, or :class:`h5py.Dataset`, or :class:`dask.array.core.Array`
Dataset of complex data type
lazy : bool, optional. Default = False
If set to True, will use lazy Dask arrays instead of in-memory numpy arrays
Returns
-------
retval : :class:`numpy.ndarray`, or :class:`dask.array.core.Array`
real valued dataset
"""
if not isinstance(dataset, (h5py.Dataset, np.ndarray, da.core.Array)):
raise TypeError('dataset should either be a h5py.Dataset or numpy / dask array')
if not is_complex_dtype(dataset.dtype):
raise TypeError("Expected a complex valued dataset")
if isinstance(dataset, da.core.Array):
lazy = True
xp = np
if lazy:
dataset = lazy_load_array(dataset)
xp = da
axis = xp.array(dataset).ndim - 1
if axis == -1:
return xp.hstack([xp.real(dataset), xp.imag(dataset)])
else: # along the last axis
return xp.concatenate([xp.real(dataset), xp.imag(dataset)], axis=axis)
def flatten_compound_to_real(dataset, lazy=False):
"""
Flattens the individual components in a structured array or compound valued hdf5 dataset along the last axis to form
a real valued array. Thus a compound h5py.Dataset or structured numpy matrix of shape (2, 3, 5) having 3 components
will turn into a real valued matrix of shape (2, 3, 15), assuming that all the sub-dtypes of the matrix are real
valued. ie - this function does not handle structured dtypes having complex values
Parameters
----------
dataset : :class:`numpy.ndarray`, or :class:`h5py.Dataset`, or :class:`dask.array.core.Array`
Numpy array that is a structured array or a :class:`h5py.Dataset` of compound dtype
lazy : bool, optional. Default = False
If set to True, will use lazy Dask arrays instead of in-memory numpy arrays
Returns
-------
retval : :class:`numpy.ndarray`, or :class:`dask.array.core.Array`
real valued dataset
"""
if isinstance(dataset, h5py.Dataset):
if len(dataset.dtype) == 0:
raise TypeError("Expected compound h5py dataset")
if lazy:
xp = da
dataset = lazy_load_array(dataset)
else:
xp = np
warn('HDF5 datasets will be loaded as Dask arrays in the future. ie - kwarg lazy will default to True in future releases of sidpy')
return xp.concatenate([xp.array(dataset[name]) for name in dataset.dtype.names], axis=len(dataset.shape) - 1)
elif isinstance(dataset, (np.ndarray, da.core.Array)):
if isinstance(dataset, da.core.Array):
lazy = True
xp = np
if lazy:
dataset = lazy_load_array(dataset)
xp = da
if len(dataset.dtype) == 0:
raise TypeError("Expected structured array")
if dataset.ndim > 0:
return xp.concatenate([dataset[name] for name in dataset.dtype.names], axis=dataset.ndim - 1)
else:
return xp.hstack([dataset[name] for name in dataset.dtype.names])
elif isinstance(dataset, np.void):
return np.hstack([dataset[name] for name in dataset.dtype.names])
else:
raise TypeError('Datatype {} not supported'.format(type(dataset)))
def flatten_to_real(ds_main, lazy=False):
"""
Flattens complex / compound / real valued arrays to real valued arrays
Parameters
----------
ds_main : :class:`numpy.ndarray`, or :class:`h5py.Dataset`, or :class:`dask.array.core.Array`
Compound, complex or real valued numpy array or HDF5 dataset
lazy : bool, optional. Default = False
If set to True, will use lazy Dask arrays instead of in-memory numpy arrays
Returns
----------
ds_main : :class:`numpy.ndarray`, or :class:`dask.array.core.Array`
Array raveled to a float data type
"""
if not isinstance(ds_main, (h5py.Dataset, np.ndarray, da.core.Array)):
ds_main = np.array(ds_main)
if is_complex_dtype(ds_main.dtype):
return flatten_complex_to_real(ds_main, lazy=lazy)
elif len(ds_main.dtype) > 0:
return flatten_compound_to_real(ds_main, lazy=lazy)
else:
return ds_main
def get_compound_sub_dtypes(struct_dtype):
"""
Returns a dictionary of the dtypes of each of the fields in the given structured array dtype
Parameters
----------
struct_dtype : :class:`numpy.dtype`
dtype of a structured array
Returns
-------
dtypes : dict
Dictionary whose keys are the field names and values are the corresponding dtypes
"""
if not isinstance(struct_dtype, np.dtype):
raise TypeError('Provided object must be a structured array dtype')
dtypes = dict()
for field_name in struct_dtype.fields:
dtypes[field_name] = struct_dtype.fields[field_name][0]
return dtypes
def check_dtype(h5_dset):
"""
Checks the datatype of the input HDF5 dataset and provides the appropriate
function calls to convert it to a float
Parameters
----------
h5_dset : :class:`h5py.Dataset`
Dataset of interest
Returns
-------
func : callable
function that will convert the dataset to a float
is_complex : bool
is the input dataset complex?
is_compound : bool
is the input dataset compound?
n_features : Unsigned int
Unsigned integer - the length of the 2nd dimension of the data after `func` is called on it
type_mult : Unsigned int
multiplier that converts from the typesize of the input :class:`~numpy.dtype` to the
typesize of the data after func is run on it
"""
if not isinstance(h5_dset, h5py.Dataset):
raise TypeError('h5_dset should be a h5py.Dataset object')
is_complex = False
is_compound = False
in_dtype = h5_dset.dtype
# TODO: avoid assuming 2d shape - why does one even need n_samples!? We only care about the last dimension!
n_features = h5_dset.shape[-1]
if is_complex_dtype(h5_dset.dtype):
is_complex = True
new_dtype = np.real(h5_dset[0, 0]).dtype
type_mult = new_dtype.itemsize * 2
func = flatten_complex_to_real
n_features *= 2
elif len(h5_dset.dtype) > 0:
"""
Some form of structured numpy is in use
We only support real scalars for the component types at the current time
"""
is_compound = True
# TODO: Avoid hard-coding to float32
new_dtype = np.float32
type_mult = len(in_dtype) * new_dtype(0).itemsize
func = flatten_compound_to_real
n_features *= len(in_dtype)
else:
if h5_dset.dtype not in [np.float32, np.float64]:
new_dtype = np.float32
else:
new_dtype = h5_dset.dtype.type
type_mult = new_dtype(0).itemsize
func = new_dtype
return func, is_complex, is_compound, n_features, type_mult
def stack_real_to_complex(ds_real, lazy=False):
"""
Puts the real and imaginary sections of the provided matrix (in the last axis) together to make complex matrix
Parameters
------------
ds_real : :class:`numpy.ndarray`, :class:`dask.array.core.Array`, or :class:`h5py.Dataset`
n dimensional real-valued numpy array or HDF5 dataset where data arranged as [instance, 2 x features],
where the first half of the features are the real component and the
second half contains the imaginary components
lazy : bool, optional. Default = False
If set to True, will use lazy Dask arrays instead of in-memory numpy arrays
Returns
----------
ds_compound : :class:`numpy.ndarray` or :class:`dask.array.core.Array`
2D complex array arranged as [sample, features]
"""
if not isinstance(ds_real, (np.ndarray, da.core.Array, h5py.Dataset)):
if not isinstance(ds_real, (tuple, list)):
raise TypeError("Expected at least an iterable like a list or tuple")
ds_real = np.array(ds_real)
if len(ds_real.dtype) > 0:
raise TypeError("Array cannot have a compound dtype")
if is_complex_dtype(ds_real.dtype):
raise TypeError("Array cannot have complex dtype")
if ds_real.shape[-1] / 2 != ds_real.shape[-1] // 2:
raise ValueError("Last dimension must be even sized")
half_point = ds_real.shape[-1] // 2
if isinstance(ds_real, da.core.Array):
lazy = True
if lazy and not isinstance(ds_real, da.core.Array):
ds_real = lazy_load_array(ds_real)
return ds_real[..., :half_point] + 1j * ds_real[..., half_point:]
def stack_real_to_compound(ds_real, compound_type, lazy=False):
"""
Converts a real-valued dataset to a compound dataset (along the last axis) of the provided compound d-type
Parameters
------------
ds_real : :class:`numpy.ndarray`, :class:`dask.array.core.Array`, or :class:`h5py.Dataset`
n dimensional real-valued numpy array or HDF5 dataset where data arranged as [instance, features]
compound_type : :class:`numpy.dtype`
Target complex data-type
lazy : bool, optional. Default = False
If set to True, will use lazy Dask arrays instead of in-memory numpy arrays
Returns
----------
ds_compound : :class:`numpy.ndarray` or :class:`dask.array.core.Array`
N-dimensional complex-valued array arranged as [sample, features]
"""
if lazy or isinstance(ds_real, da.core.Array):
raise NotImplementedError('Lazy operation not available due to absence of Dask support')
if not isinstance(ds_real, (np.ndarray, h5py.Dataset)):
if not isinstance(ds_real, (list, tuple)):
raise TypeError("Expected at least an iterable like a list or tuple")
ds_real = np.array(ds_real)
if len(ds_real.dtype) > 0:
raise TypeError("Array cannot have a compound dtype")
elif is_complex_dtype(ds_real.dtype):
raise TypeError("Array cannot have complex dtype")
if not isinstance(compound_type, np.dtype):
raise TypeError('Provided object must be a structured array dtype')
new_spec_length = ds_real.shape[-1] / len(compound_type)
if new_spec_length % 1:
raise ValueError('Provided compound type was not compatible by number of elements')
new_spec_length = int(new_spec_length)
new_shape = list(ds_real.shape) # Make mutable
new_shape[-1] = new_spec_length
xp = np
kwargs = {}
"""
if isinstance(ds_real, h5py.Dataset) and not lazy:
warn('HDF5 datasets will be loaded as Dask arrays in the future. ie - kwarg lazy will default to True in future releases of sidpy')
if isinstance(ds_real, da.core.Array):
lazy = True
if lazy:
xp = da
ds_real = lazy_load_array(ds_real)
kwargs = {'chunks': 'auto'}
"""
ds_compound = xp.empty(new_shape, dtype=compound_type, **kwargs)
for name_ind, name in enumerate(compound_type.names):
i_start = name_ind * new_spec_length
i_end = (name_ind + 1) * new_spec_length
ds_compound[name] = ds_real[..., i_start:i_end]
return ds_compound.squeeze()
def stack_real_to_target_dtype(ds_real, new_dtype, lazy=False):
"""
Transforms real data into the target dtype
Parameters
----------
ds_real : :class:`numpy.ndarray`, :class:`dask.array.core.Array` or :class:`h5py.Dataset`
n dimensional real-valued numpy array or HDF5 dataset
new_dtype : :class:`numpy.dtype`
Target data-type
Returns
----------
ret_val : :class:`numpy.ndarray` or :class:`dask.array.core.Array`
N-dimensional array of the target data-type
"""
if is_complex_dtype(new_dtype):
return stack_real_to_complex(ds_real, lazy=lazy)
try:
if len(new_dtype) > 0:
return stack_real_to_compound(ds_real, new_dtype, lazy=lazy)
except TypeError:
return new_dtype(ds_real)
# catching all other cases, such as np.dtype('<f4')
return new_dtype.type(ds_real)
def validate_dtype(dtype):
"""
Checks the provided object to ensure that it is a valid dtype that can be written to an HDF5 file.
Raises a type error if invalid. Returns True if the object passed the tests
Parameters
----------
dtype : object
Object that is hopefully a :class:`h5py.Datatype`, or :class:`numpy.dtype` object
Returns
-------
status : bool
True if the object was a valid data-type
"""
if isinstance(dtype, (h5py.Datatype, np.dtype)):
pass
elif isinstance(np.dtype(dtype), np.dtype):
# This should catch all those instances when dtype is something familiar like - np.float32
pass
else:
raise TypeError('dtype should either be a numpy or h5py dtype')
return True
def is_complex_dtype(dtype):
"""
Checks if the provided dtype is a complex dtype
Parameters
----------
dtype : object
Object that is a class:`h5py.Datatype`, or :class:`numpy.dtype` object
Returns
-------
is_complex : bool
True if the dtype was a complex dtype. Else returns False
"""
validate_dtype(dtype)
if dtype in [np.complex, np.complex64, np.complex128]:
return True
return False
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] | 2.504848 | 5,776 |
#! /usr/bin/env python
# -*- coding: utf-8 -*-
"""
Module that contains functions related with Pixar USD usdview application
"""
from __future__ import print_function, division, absolute_import
__author__ = "Tomas Poveda"
__license__ = "MIT"
__maintainer__ = "Tomas Poveda"
__email__ = "[email protected]"
import os
import sys
import logging
import subprocess
from artellapipe.libs.usd.core import usdpaths
LOGGER = logging.getLogger('artellapipe-libs-usd')
def get_usd_view_path():
"""
Returns path to USD view executable
:return: str
"""
platform_path = usdpaths.get_platform_path()
usd_view_path = os.path.join(platform_path, 'pixar', 'bin', 'usdview')
return usd_view_path
def open_usd_file(usd_file_path):
"""
Opens given USD file in USD viewer (usdview)
:param usd_file_path: str
:return: bool
"""
if not usd_file_path or not os.path.isfile(usd_file_path):
LOGGER.warning('Given USD file path does not exists: {}!'.format(usd_file_path))
return False
usd_view_path = get_usd_view_path()
if not os.path.exists(usd_view_path):
LOGGER.warning(
'usdview path does not exists: {}. Impossible to open USD file!'.format(usd_view_path))
return False
usd_view_python_libs_path = get_usd_view_python_libs_path()
if not os.path.isdir(usd_view_python_libs_path):
LOGGER.warning(
'No usdview Pythyon libs directory found. usdview cannot be opened or usdview OpenGL can be disabled')
usd_view_python_libs_path = None
pixar_usd_binaries_path = usdpaths.get_pixar_usd_binaries_path()
if not pixar_usd_binaries_path:
LOGGER.warning(
'No Pixar USD binaries path found: "{}". Impossible to launch usdview'.format(pixar_usd_binaries_path))
return False
pixar_usd_libraries_path = usdpaths.get_pixar_usd_libraries_path()
if not pixar_usd_libraries_path:
LOGGER.warning(
'No Pixar USD libraries path found: "{}". Impossible to launch usdview'.format(pixar_usd_libraries_path))
return False
# Dictionary that contains the environment configuration that will be used by usdview instance
usd_view_env = dict()
usd_view_env['PATH'] = r'{}{}{}'.format(pixar_usd_binaries_path, os.pathsep, pixar_usd_libraries_path)
pixar_usd_python_libs_path = usdpaths.get_pixar_usd_python_libs_path()
if pixar_usd_python_libs_path and os.path.isdir(pixar_usd_python_libs_path):
if usd_view_python_libs_path and os.path.isdir(usd_view_python_libs_path):
usd_view_env['PYTHONPATH'] = r'{}{}{}'.format(
pixar_usd_python_libs_path, os.pathsep, usd_view_python_libs_path)
else:
usd_view_env['PYTHONPATH'] = r'{}'.format(pixar_usd_python_libs_path)
else:
if usd_view_python_libs_path and os.path.isdir(usd_view_python_libs_path):
usd_view_env['PYTHONPATH'] = r'{}'.format(usd_view_python_libs_path)
usd_view_plugins_path = get_usd_view_plugins_path()
if usd_view_plugins_path and os.path.isdir(usd_view_python_libs_path):
usd_view_env['PYTHONPATH'] += r'{}{}'.format(os.pathsep, usd_view_plugins_path)
for name in os.listdir(usd_view_plugins_path):
plugin_path = os.path.join(usd_view_plugins_path, name)
if not os.path.isdir(plugin_path):
continue
if usd_view_env.get('PXR_PLUGINPATH_NAME', None):
usd_view_env['PXR_PLUGINPATH_NAME'] += r'{}{}'.format(os.pathsep, plugin_path)
else:
usd_view_env['PXR_PLUGINPATH_NAME'] = r'{}'.format(plugin_path)
p = subprocess.Popen(
['python.exe', usd_view_path, usd_file_path], env=usd_view_env)
# output, error = p.communicate()
# if error:
# LOGGER.error('>>> usdview: {}'.format(error))
return True
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6407,
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] | 2.206466 | 1,763 |
# Copyright (c) 2019-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.
#
#TOFILL
if __name__ == '__main__':
param = [
(4,'aggayxysdfa','aggajxaaasdfa',),
(2,'55571659965107','390286654154',),
(3,'01011011100','0000110001000',),
(5,'aggasdfa','aggajasdfaxy',),
(2,'5710246551','79032504084062',),
(3,'0100010','10100000',),
(3,'aabcaaaa','baaabcd',),
(1,'1219','3337119582',),
(2,'111000011','011',),
(2,'wiC oD','csiuGOUwE',)
]
n_success = 0
for i, parameters_set in enumerate(param):
if f_filled(*parameters_set) == f_gold(*parameters_set):
n_success+=1
print("#Results: %i, %i" % (n_success, len(param))) | [
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72,
1,
4064,
357,
77,
62,
13138,
11,
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7,
17143,
22305
] | 2.236915 | 363 |
import numpy as np
import os
import copy
import torch
import torch.nn as nn
from torch.optim import Adam
import torch.nn.functional as FF
os.environ['CUDA_VISIBLE_DEVICES'] = '1'
USE_CUDA = torch.cuda.is_available()
FloatTensor = torch.cuda.FloatTensor if USE_CUDA else torch.FloatTensor
Device = torch.device("cuda" if USE_CUDA else "cpu")
| [
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36166,
4943,
628,
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628,
628,
198
] | 2.892562 | 121 |
from QuickPotato.database.queries import Crud
| [
6738,
12029,
25396,
5549,
13,
48806,
13,
421,
10640,
1330,
3864,
463,
628,
628
] | 3.5 | 14 |
#
# example from CHiLL manual page 13
#
# peel 4 statements from the END of innermost loop
#
from chill import *
source('peel9101112.c')
destination('peel12modified.c')
procedure('mm')
loop(0)
peel(1,2,-4) # statement 1, loop 2 (middle, for j), 4 statements from END
| [
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262,
23578,
286,
8434,
1712,
9052,
198,
2,
198,
198,
6738,
20493,
1330,
1635,
198,
198,
10459,
10786,
431,
417,
6420,
486,
14686,
13,
66,
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198,
16520,
1883,
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431,
417,
1065,
41771,
13,
66,
11537,
198,
198,
1676,
771,
495,
10786,
3020,
11537,
198,
198,
26268,
7,
15,
8,
198,
198,
431,
417,
7,
16,
11,
17,
12095,
19,
8,
220,
1303,
2643,
352,
11,
9052,
362,
357,
27171,
11,
329,
474,
828,
604,
6299,
422,
23578,
628
] | 2.732673 | 101 |
import scrapy
from opencc import OpenCC
import os
all = [[]]
del(all[0]) | [
11748,
15881,
88,
198,
6738,
1280,
535,
1330,
4946,
4093,
198,
11748,
28686,
198,
198,
439,
796,
16410,
11907,
198,
12381,
7,
439,
58,
15,
12962
] | 2.807692 | 26 |
from django.conf.urls import patterns, include, url
from django.contrib import admin
admin.autodiscover()
urlpatterns = patterns('',
url(r'^$', 'example.app.views.home'),
url(r'^admin/', include(admin.site.urls)),
url(r'^signup-email/', 'example.app.views.signup_email'),
url(r'^email-sent/', 'example.app.views.validation_sent'),
url(r'^login/$', 'example.app.views.home'),
url(r'^logout/$', 'example.app.views.logout'),
url(r'^done/$', 'example.app.views.done', name='done'),
url(r'^email/$', 'example.app.views.require_email', name='require_email'),
url(r'', include('social.apps.django_app.urls', namespace='social'))
)
| [
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13,
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13,
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62,
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62,
12888,
33809,
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7,
81,
6,
3256,
2291,
10786,
14557,
13,
18211,
13,
28241,
14208,
62,
1324,
13,
6371,
82,
3256,
25745,
11639,
14557,
6,
4008,
198,
8,
198
] | 2.520913 | 263 |
if __name__ == "__main__":
main()
| [
198,
198,
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366,
834,
12417,
834,
1298,
198,
220,
220,
220,
1388,
3419,
198
] | 2.105263 | 19 |
from functools import wraps
from http import HTTPStatus
from django.utils.translation import gettext as _
from apps.api.errors import ApiException
def signature_exempt(view_func):
"""Mark a view function as being exempt from signature and apikey check."""
wrapped_view.signature_exempt = True
return wraps(view_func)(wrapped_view)
| [
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] | 3.421569 | 102 |
from otree.api import Currency as c, currency_range
from ._builtin import Page, WaitPage
from .models import Constants
page_sequence = [
QV
]
| [
6738,
267,
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1195,
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60,
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] | 3.170213 | 47 |
#!/usr/bin/env python3
import os
import sys
import random
from pprint import pprint
import yaml
import raffle
# ------------------------
# Command-line interface
# ------------------------
USAGE = f"""
Usage: {os.path.basename(__file__)} config_file [random_seed]
config_file (required):
Raffle configuration file in YAML format. See config.sample.yaml
for an example.
random_seed (optional):
An optional seed value to use for the underlying random number
generator. Use this parameter for greater control and repeatable
results. If not specified, the random number generator will use
cryptographic random values provided by the operating system.
"""
try:
with open(sys.argv[1], 'r') as config_file:
configuration = yaml.safe_load(config_file)
random_seed = sys.argv[2] if len(sys.argv) > 2 else None
except (IndexError, IOError, yaml.parser.ParserError) as e:
sys.stderr.write(USAGE)
raise e
try:
prizes = configuration['prizes']
entries = configuration['entries']
preferences = configuration['preferences']
except KeyError as e:
sys.stderr.write(f"Invalid configuration file: {repr(e)}\n")
sys.exit(1)
if random_seed:
print(f"Using random seed: {random_seed}")
random_source = random.Random(random_seed)
else:
print("Using system random number generator")
random_source = random.SystemRandom()
print("Running raffle with configuration:")
pprint(configuration)
results = raffle.raffle(prizes, entries, preferences, random_source)
leftover_prizes = list(prizes)
print("=" * 78)
print("Results:\n")
for i, (participant, prize) in enumerate(results):
print(f"{i + 1}: {participant} -> {prize}")
leftover_prizes.remove(prize)
print("=" * 78)
print("Leftover prizes:\n")
pprint(leftover_prizes)
| [
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13,
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62,
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8,
198,
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25,
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220,
220,
220,
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1080,
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220,
220,
220,
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8,
198,
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1635,
8699,
8,
198,
4798,
7203,
25468,
7479,
77,
4943,
198,
1640,
1312,
11,
357,
48013,
415,
11,
11596,
8,
287,
27056,
378,
7,
43420,
2599,
198,
220,
220,
220,
3601,
7,
69,
1,
90,
72,
1343,
352,
38362,
1391,
48013,
415,
92,
4613,
1391,
3448,
2736,
92,
4943,
198,
220,
220,
220,
39191,
62,
3448,
12271,
13,
28956,
7,
3448,
2736,
8,
198,
198,
4798,
7203,
2625,
1635,
8699,
8,
198,
4798,
7203,
18819,
2502,
21740,
7479,
77,
4943,
198,
381,
22272,
7,
9464,
2502,
62,
3448,
12271,
8,
198
] | 2.898734 | 632 |
from django.contrib import admin
from .models import Comment
admin.site.register(Comment, CommentAdmin)
| [
6738,
42625,
14208,
13,
3642,
822,
1330,
13169,
198,
198,
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198,
198,
28482,
13,
15654,
13,
30238,
7,
21357,
11,
18957,
46787,
8,
198
] | 3.6 | 30 |
#!/home/ssericksen/anaconda2/bin/python2.7
import pandas as pd
import numpy as np
# load Ching-Pei's compound scores for BGLF4 with PKIS1
df1 = pd.read_csv('bglf4_pkis1', sep=" ")
df1.set_index('fid', inplace=True)
df1.columns = ['BGLF4']
df1.index.rename('molid', inplace=True)
df1.index = df1.index.map(str)
# load informer list as dataframe
df2 = pd.read_csv('new_pkis1_informers_CP.csv', header=None)
df2.set_index(0, inplace=True)
df2.index.rename('molid', inplace=True)
df2.columns = ['BGLF4']
df2.index = df2.index.map(str)
# merge dataframes
df3 = pd.concat( [df1, df2], axis=0 )
print("duplicated indices: {}").format( df3.duplicated().sum() )
# check duplicates for PKIS1 molid '11959682'
print( df3.loc['11959682'] )
| [
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20520,
1267,
628
] | 2.29375 | 320 |
import pandas as pd
import numpy as np
| [
11748,
19798,
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355,
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67,
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11748,
299,
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355,
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628,
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] | 2.875 | 16 |
letters = 'abcdefghijklmnopqrstuvwxyz'
numbers = '0123456789'
"""Teste
Main criado para testar as funรงรตes.
"""
if __name__ == '__main__':
print(er_to_afd('[J-M1-9]abc'))
# er_to_afd('a(a|b)*a')
# er_to_afd('aa*(bb*aa*b)*')
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9,
7,
11848,
9,
7252,
9,
65,
27493,
11537,
628
] | 1.825758 | 132 |
# MIT License
# Copyright (c) 2017 MassChallenge, Inc.
from datetime import datetime
from decimal import Decimal
from pytz import utc
import swapper
from django.conf import settings
from django.core.validators import (
RegexValidator,
MaxLengthValidator,
)
from django.db import models
from django.db.models import Q
from sorl.thumbnail import ImageField
from django.utils.safestring import mark_safe
from accelerator_abstract.models.accelerator_model import AcceleratorModel
from accelerator_abstract.models.base_user_role import (
BaseUserRole,
)
from accelerator_abstract.models.base_base_profile import (
EXPERT_USER_TYPE,
)
from accelerator_abstract.models.base_user_utils import (
has_staff_clearance,
)
from accelerator_abstract.models.base_program import (
ACTIVE_PROGRAM_STATUS,
ENDED_PROGRAM_STATUS,
)
INVITED_JUDGE_ALERT = (
"<h4>{first_name}, we would like to invite you to be a judge at "
"MassChallenge!</h4>"
"<p> </p>"
"<p>{round_name} judging occurs from {start_date} to {end_date}! "
"Of all our potential judges, we would like you, {first_name}, "
"to take part."
"</p><p> </p>"
'<p><a class="btn btn-primary" href="/expert/commitments/">Click '
"here to tell us your availability"
"</a></p> <p> </p>"
)
MENTOR_TYPE_HELPTEXT = (
"Allowed Values: "
"F - Functional Expert, "
"P - Partner, "
"T - Technical, "
"E - Entrepreneur, "
"N - Once accepted, now rejected, "
"X - Not Accepted as a Mentor (may still be a judge)")
JUDGE_TYPE_HELPTEXT = (
"Allowed Values: "
"1 - Round 1 Judge, "
"2 - Round 2 Judge, "
"3 - Pre-final Judge, "
"4 - Final Judge, "
"0 - Once Accepted, now rejected, "
"X - Not Accepted as a Judge (May still be a mentor)")
IDENTITY_HELP_TEXT_VALUE = (mark_safe(
'Select as many options as you feel best represent your identity. '
'Please press and hold Control (CTRL) on PCs or '
'Command (⌘) on Macs to select multiple options'))
JUDGE_FIELDS_TO_LABELS = {'desired_judge_label': 'Desired Judge',
'confirmed_judge_label': 'Judge'}
BIO_MAX_LENGTH = 7500
PRIVACY_CHOICES = (("staff", "MC Staff Only"),
("finalists and staff", "Finalists and MC Staff"),
("public", "All Users"),)
BASE_INTEREST = "I would like to participate in MassChallenge %s"
BASE_TOPIC = ("Please describe the topic(s) you would be available "
"to speak%s about%s")
REF_BY_TEXT = ("If someone referred you to MassChallenge, please provide "
"their name (and organization if relevant). Otherwise, please "
"indicate how you learned about the opportunity to participate "
"at MassChallenge (helps us understand the effectiveness of "
"our outreach programs).")
OTHER_EXPERTS_TEXT = ("We're always looking for more great experts to join "
"the MassChallenge community and program. We welcome "
"the names and contact info (email) of individuals you "
"think could be great additions to the program, as well "
"as how you think they might want to be involved "
"(Judge, Mentor, etc.) Also, please encourage these "
"individuals to fill out their own Expert Profile.")
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628
] | 2.530973 | 1,356 |
import re
import asyncio
import pexpect as px
import sys
from glulxe.interface import i7Game
from avatar import Avatar
GAME_FILE_NAME = "rooms.gblorb"
game = None
current_location = None
EXIT_COMMANDS = ["quit", "exit"]
ROOM_SELECTION_PATTERN = 'You entered (.*) room'
MESSAGE_PARAMS_PATTERN = '@([^\s]+) (.*)'
agent = None
if __name__ == "__main__":
if len(sys.argv) == 3:
jid = sys.argv[1]
password = sys.argv[2]
loop = asyncio.get_event_loop()
loop.run_until_complete(main(jid, password))
| [
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12417,
7,
73,
312,
11,
9206,
4008,
198,
220,
220,
220,
220
] | 2.317597 | 233 |
from bricks_modeling.file_IO.model_writer import write_bricks_to_file_with_steps, write_model_to_file
from util.debugger import MyDebugger
from bricks_modeling.file_IO.model_reader import read_model_from_file, read_bricks_from_file
'''
We assume the following information is provided:
1) assembly order
2) grouping
3) default camera view
'''
if __name__ == "__main__":
debugger = MyDebugger("brick_heads")
file_path = r"data/full_models/steped_talor.ldr"
model = read_model_from_file(file_path, read_fake_bricks=True)
write_model_to_file(model, debugger.file_path(f"complete_full.ldr")) | [
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1,
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12853,
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81,
48774
] | 2.827103 | 214 |
from sweeper.cloud import resource_config_combinations
class CloudProvider:
"""
A CloudProvider object represents a Cloud Computing service
that sweeper can manage in order to execute a workflow in this
cloud base
"""
def __init__(self):
"""
Default constructor. You should overwrite all of this
class for creating a new Cloud base
"""
self.name = "Base Cloud Provider"
"""Name of the cloud base"""
def create_vm(self, name, config, **kwargs):
"""
Creates a virtual machine in the cloud base service
"""
raise NotImplementedError("You must implement create_vm")
def delete_vm(self, name):
"""
Deletes the named virtual machine provided by this CloudProvider
:param name: Name of the cloud resource to delete from this cloud base
:return: None
"""
raise NotImplementedError("You must implement delete_vm")
def get_config(self, config_name):
"""
Get a configuration name provided
:param config_name: Name of the Configuration Name provided by this cloud base
:return: as ResourceConfig object
"""
raise NotImplementedError("You must implement get_config")
def list_configs(self):
"""
List all available configurations provided by this cloud base
:return: A list of ResourceConfig Objects
"""
raise NotImplementedError("You must implement list_configs")
# NOTE: We assume Method create_instance is implemented in each Cloud Provider Class
# but, I can't find a way to create an interface for such static method
def possible_configs(self, num):
"""
Returns all possible combinations of VM resources
that has the number of :num: resources required.
You should call this method from the implementation classes
"""
configs = self.list_configs()
combs = resource_config_combinations(num, configs)
return combs
| [
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628,
220,
220,
220,
220,
220,
220,
220,
1441,
401,
1443,
198
] | 2.870968 | 713 |
import re
import glob
import os
path = r".\data\log"
os.chdir(path)
t = []
logs = glob.glob("log*.txt")
Nbrun = len(logs)
for log in logs:
l = open(log,'r')
m = re.findall("(?<=Elapsed: )(.*?)(?=s)",l.read())
if float(m[-1]) > 0:
t.append(float(m[-1]))
l.close()
if t:
t = float(sum(t)/len(t))
print("Average time of execution:",t,"seconds")
path = r"..\tracking"
os.chdir(path)
TipsFile = glob.glob("Number*.txt")
NbModule = 0
for file in TipsFile:
NbTips = 0
Nbrun = 0
f = open(file,'r')
for line in f.readlines():
NbTips += int(line)
Nbrun += 1
NbTips = NbTips/Nbrun
print("Average number of tips for NodeModule[" + str(NbModule) + "]:",NbTips)
NbModule += 1
f.close()
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] | 1.975309 | 405 |
# -*- coding: utf-8 -*- #
# Copyright 2014 Google LLC. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Resource filters supplementary help."""
from __future__ import absolute_import
from __future__ import division
from __future__ import unicode_literals
import textwrap
from googlecloudsdk.calliope import base
from googlecloudsdk.core.resource import resource_topics
class Filters(base.TopicCommand):
"""Resource filters supplementary help."""
detailed_help = {
'DESCRIPTION':
textwrap.dedent("""\
{description}
+
Note: Depending on the specific server API, filtering may be done
entirely by the client, entirely by the server, or by a combination
of both.
### Filter Expressions
A filter expression is a Boolean function that selects the resources
to print from a list of resources. Expressions are composed
of terms connected by logic operators.
*LogicOperator*::
Logic operators must be in uppercase: *AND*, *OR*, *NOT*.
Additionally, expressions containing both *AND* and *OR* must be
parenthesized to disambiguate precedence.
*NOT* _term-1_:::
True if _term-1_ is False, otherwise False.
_term-1_ *AND* _term-2_:::
True if both _term-1_ and _term-2_ are true.
_term-1_ *OR* _term-2_:::
True if at least one of _term-1_ or _term-2_ is true.
_term-1_ _term-2_:::
Term conjunction (implicit *AND*) is True if both _term-1_
and _term-2_ are true. Conjunction has lower precedence than *OR*.
*Terms*::
A term is a _key_ _operator_ _value_ tuple, where _key_ is a dotted
name that evaluates to the value of a resource attribute, and _value_
may be:
*number*::: integer or floating point numeric constant
*unquoted literal*::: character sequence terminated by space, ( or )
*quoted literal*::: _"..."_ or _'...'_
Most filter expressions need to be quoted in shell commands. If you
use _'...'_ shell quotes then use _"..."_ filter string literal quotes
and vice versa.
Quoted literals will be interpreted as string values, even when the
value could also be a valid number. For example, 'key:1e9' will be
interpreted as a key named 'key' with the string value '1e9', rather
than with the float value of one billion expressed in scientific
notation.
*Operator Terms*::
_key_ *:* _simple-pattern_:::
*:* operator evaluation is changing for consistency across Google
APIs. The current default is deprecated and will be dropped shortly.
A warning will be displayed when a --filter expression would return
different matches using both the deprecated and new implementations.
+
The current deprecated default is True if _key_ contains
_simple-pattern_. The match is case insensitive. It allows one
```*``` that matches any sequence of 0 or more characters.
If ```*``` is specified then the match is anchored, meaning all
characters from the beginning and end of the value must match.
+
The new implementation is True if _simple-pattern_ matches any
_word_ in _key_. Words are locale specific but typically consist of
alpha-numeric characters. Non-word characters that do not appear in
_simple-pattern_ are ignored. The matching is anchored and case
insensitive. An optional trailing ```*``` does a word prefix match.
+
Use _key_```:*``` to test if _key_ is defined and
```-```_key_```:*``` to test if _key_ is undefined.
_key_ *:(* _simple-pattern_ ... *)*:::
True if _key_ matches any _simple-pattern_ in the
(space, tab, newline, comma) separated list.
_key_ *=* _value_:::
True if _key_ is equal to _value_, or [deprecated] equivalent to *:*
with the exception that the trailing ```*``` prefix match is not
supported.
+
For historical reasons, this operation currently behaves differently
for different Google APIs. For many APIs, this is True if key is
equal to value. For a few APIs, this is currently equivalent to *:*,
with the exception that the trailing ```*``` prefix match is not
supported. However, this behaviour is being phased out, and use of
```=``` for those APIs is deprecated; for those APIs, if you want
matching, you should use ```:``` instead of ```=```, and if you want
to test for equality, you can use
_key_ <= _value_ AND _key_ >= _value_.
_key_ *=(* _value_ ... *)*:::
True if _key_ is equal to any _value_ in the
(space, tab, newline, *,*) separated list.
_key_ *!=* _value_:::
True if _key_ is not _value_. Equivalent to
-_key_=_value_ and NOT _key_=_value_.
_key_ *<* _value_:::
True if _key_ is less than _value_. If both _key_ and
_value_ are numeric then numeric comparison is used, otherwise
lexicographic string comparison is used.
_key_ *<=* _value_:::
True if _key_ is less than or equal to _value_. If both
_key_ and _value_ are numeric then numeric comparison is used,
otherwise lexicographic string comparison is used.
_key_ *>=* _value_:::
True if _key_ is greater than or equal to _value_. If
both _key_ and _value_ are numeric then numeric comparison is used,
otherwise lexicographic string comparison is used.
_key_ *>* _value_:::
True if _key_ is greater than _value_. If both _key_ and
_value_ are numeric then numeric comparison is used, otherwise
lexicographic string comparison is used.
_key_ *~* _value_:::
True if _key_ contains a match for the RE (regular expression) pattern
_value_.
_key_ *!*~ _value_:::
True if _key_ does not contain a match for the RE (regular expression)
pattern _value_.
For more about regular expression syntax, see:
https://docs.python.org/3/library/re.html#re-syntax which follows the
PCRE dialect.
### Determine which fields are available for filtering
In order to build filters, it is often helpful to review some
representative fields returned from commands. One simple way to do
this is to add `--format=yaml --limit=1` to a command. With these
flags, a single record is returned and its full contents are displayed
as a YAML document. For example, a list of project fields could be
generated by running:
$ gcloud projects list --format=yaml --limit=1
This might display the following data:
```yaml
createTime: '2021-02-10T19:19:49.242Z'
lifecycleState: ACTIVE
name: MyProject
parent:
id: '123'
type: folder
projectId: my-project
projectNumber: '456'
```
Using this data, one way of filtering projects is by their parent's ID
by specifying ``parent.id'' as the _key_.
### Filter on a custom or nested list in response
By default the filter exprespression operates on root level resources.
In order to filter on a nested list(not at the root level of the json)
, one can use the `--flatten` flag to provide a the `resource-key` to
list. For example, To list members under `my-project` that have an
editor role, one can run:
$ gcloud projects get-iam-policy cloudsdktest --flatten=bindings --filter=bindings.role:roles/editor --format='value(bindings.members)'
""").format(
description=resource_topics.ResourceDescription('filter')),
'EXAMPLES':
textwrap.dedent("""\
List all Google Compute Engine instance resources:
$ gcloud compute instances list
List Compute Engine instance resources that have machineType
*f1-micro*:
$ gcloud compute instances list --filter="machineType:f1-micro"
List Compute Engine instance resources using a regular expression for
zone *us* and not MachineType *f1-micro*:
$ gcloud compute instances list --filter="zone ~ us AND -machineType:f1-micro"
List Compute Engine instance resources with tag *my-tag*:
$ gcloud compute instances list --filter="tags.items=my-tag"
List Compute Engine instance resources with tag *my-tag* or
*my-other-tag*:
$ gcloud compute instances list --filter="tags.items=(my-tag,my-other-tag)"
List Compute Engine instance resources with tag *my-tag* and
*my-other-tag*:
$ gcloud compute instances list --filter="tags.items=my-tag AND tags.items=my-other-tag"
List Compute Engine instance resources which either have tag *my-tag*
but not *my-other-tag* or have tag *alternative-tag*:
$ gcloud compute instances list --filter="(tags.items=my-tag AND -tags.items=my-other-tag) OR tags.items=alternative-tag"
List Compute Engine instance resources which contain the key *fingerprint*
in the *metadata* object:
$ gcloud compute instances list --limit=1 --filter="metadata.list(show="keys"):fingerprint"
List Compute Engine instance resources with label *my-label* with any
value:
$ gcloud compute instances list --filter="labels.my-label:*"
List Container Registry images that have a tag with the value
'30e5504145':
$ gcloud container images list-tags --filter="'tags:30e5504145'"
The last example encloses the filter expression in single quotes
because the value '30e5504145' could be interpreted as a number in
scientific notation.
List in JSON format those projects where the labels match specific
values (e.g. label.env is 'test' and label.version is alpha):
$ gcloud projects list --format="json" --filter="labels.env=test AND labels.version=alpha"
List projects that were created on and after a specific date:
$ gcloud projects list --format="table(projectNumber,projectId,createTime)" --filter="createTime>=2018-01-15"
List projects that were created on and after a specific date and time
and sort from oldest to newest (with dates and times listed according
to the local timezone):
$ gcloud projects list --format="table(projectNumber,projectId,createTime.date(tz=LOCAL))" --filter="createTime>=2018-01-15T12:00:00" --sort-by=createTime
List projects that were created within the last two weeks, using
ISO8601 durations:
$ gcloud projects list --format="table(projectNumber,projectId,createTime)" --filter="createTime>-P2W"
For more about ISO8601 durations, see: https://en.wikipedia.org/wiki/ISO_8601
+
The table below shows examples of pattern matching if used with
the `:` operator:
PATTERN | VALUE | MATCHES | DEPRECATED_MATCHES
--- | --- | --- | ---
abc* | abcpdqxyz | True | True
abc | abcpdqxyz | False | True
pdq* | abcpdqxyz | False | False
pdq | abcpdqxyz | False | True
xyz* | abcpdqxyz | False | False
xyz | abcpdqxyz | False | True
* | abcpdqxyz | True | True
* | (None) | False | False
* | ('') | False | False
* | (otherwise) | True | True
abc* | abc.pdq.xyz | True | True
abc | abc.pdq.xyz | True | True
abc.pdq | abc.pdq.xyz | True | True
pdq* | abc.pdq.xyz | True | False
pdq | abc.pdq.xyz | True | True
pdq.xyz | abc.pdq.xyz | True | True
xyz* | abc.pdq.xyz | True | False
xyz | abc.pdq.xyz | True | True
"""),
}
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198
] | 2.55325 | 5,061 |
from bq import create_client, read_sql, query
DATASET = 'ai2_replication'
client = create_client()
make_table('institutions')
make_table('paper_authors_w_countries')
make_table('language')
make_table('ai_papers_any_author')
make_table('paper_author_institution')
make_table('oecd_comparison')
| [
6738,
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] | 2.855769 | 104 |
from django.db import models
from datetime import datetime
# Create your models here.
| [
6738,
42625,
14208,
13,
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1330,
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198,
6738,
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1330,
4818,
8079,
198,
2,
13610,
534,
4981,
994,
13,
198
] | 3.909091 | 22 |
import os
import argparse
import logging
role = os.getenv('DMLC_ROLE').upper()
if role == 'WORKER':
role = 'Worker' # backward compatibility
rank = os.getenv('DMLC_{}_ID'.format(role.upper()))
logging.basicConfig(level=logging.INFO, format='%(asctime)s {0}[{1}] %(message)s'.format(role, rank))
from common import find_mxnet, data, fit
import mxnet as mx
if __name__ == '__main__':
# parse args
parser = argparse.ArgumentParser(description="train imagenet",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
fit.add_fit_args(parser)
data.add_data_args(parser)
data.add_data_aug_args(parser)
# use a large aug level
data.set_data_aug_level(parser, 3)
parser.set_defaults(
# network
network = 'resnet',
num_layers = 18,
# data
data_train = '/home/ubuntu/ILSVRC2012/ILSVRC2012_dataset_train.rec', # ALL DATA MUST BE PLACED IN A FOLDER
data_val = '/home/ubuntu/ILSVRC2012/ILSVRC2012_dataset_val.rec', # INSTEAD OF A BUCKET
num_classes = 1000,
num_examples = 281167,
image_shape = '3,224,224',
min_random_scale = 1, # if input image has min size k, suggest to use
# 256.0/x, e.g. 0.533 for 480
# train
lr = 0.03,
num_epochs = 80,
lr_step_epochs = '30,60',
disp_batches = 1
)
args = parser.parse_args()
# load network
from importlib import import_module
net = import_module('symbols.'+args.network)
sym = net.get_symbol(**vars(args))
# train
fit.fit(args, sym, data.get_rec_iter)
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11,
1366,
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1136,
62,
8344,
62,
2676,
8,
198
] | 2.085366 | 820 |
#!/usr/bin/python2.5
# Copyright (C) 2007 Google Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
import codecs
import csv
import os
import re
import zipfile
from . import gtfsfactoryuser
from . import problems
from . import util
from .compat import StringIO
| [
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764,
1330,
7736,
198,
6738,
764,
5589,
265,
1330,
10903,
9399,
628
] | 3.626126 | 222 |
import pytest
from pytest_djangoapp import configure_djangoapp_plugin
pytest_plugins = configure_djangoapp_plugin(
extend_INSTALLED_APPS=[
'django.contrib.admin',
],
)
@pytest.fixture
def build_tree():
"""Builds a sitetree from dict definition.
Returns items indexed by urls.
Example:
items_map = build_tree(
{'alias': 'mytree'},
[{
'title': 'one', 'url': '/one/', 'children': [
{'title': 'subone', 'url': '/subone/'}
]
}]
)
"""
from sitetree.models import Tree, TreeItem
from django.contrib.auth.models import Permission
return build
@pytest.fixture
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13,
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220,
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220,
1441,
1382,
628,
198,
31,
9078,
9288,
13,
69,
9602,
198
] | 2.138973 | 331 |
import numpy as np | [
11748,
299,
32152,
355,
45941
] | 3.6 | 5 |
import pandas as pd
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
iowa_file_path = 'rain.csv'
home_data = pd.read_csv(iowa_file_path)
# Create target object and call it y
y = home_data.SalePrice
# Create X
features = ['LotArea', 'YearBuilt', '1stFlrSF', '2ndFlrSF', 'FullBath', 'BedroomAbvGr', 'TotRmsAbvGrd']
X = home_data[features]
# Split into validation and training data
train_X, val_X, train_y, val_y = train_test_split(X, y, random_state=1)
# Specify Model
iowa_model = DecisionTreeRegressor(random_state=1)
# Fit Model
iowa_model.fit(train_X, train_y)
# Make validation predictions and calculate mean absolute error
val_predictions = iowa_model.predict(val_X)
val_mae = mean_absolute_error(val_predictions, val_y)
print("Validation MAE: {:,.0f}".format(val_mae))
# Find best tree dept to reduce overfitting and underfitting
candidate_max_leaf_nodes = [5, 25, 50, 100, 250, 500]
# Write loop to find the ideal tree size from candidate_max_leaf_nodes
candidate = 0
min_mae = get_mae(candidate_max_leaf_nodes[0], train_X, val_X, train_y, val_y)
for i in range(len(candidate_max_leaf_nodes)):
n = candidate_max_leaf_nodes[i]
mae = get_mae(n, train_X, val_X, train_y, val_y)
if mae < min_mae:
min_mae = mae
candidate = i
# Store the best value of max_leaf_nodes (it will be either 5, 25, 50, 100, 250 or 500)
best_tree_size = candidate_max_leaf_nodes[candidate]
print(candidate)
# Final optimized model
final_model = DecisionTreeRegressor(max_leaf_nodes = 100, random_state = 0)
final_model.fit(X, y) | [
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] | 2.621236 | 631 |
from turtle import *
# Fractals
if __name__ == '__main__':
draw_fractal(5, 90, 10, 'FX', 'X', 'X+YF+', 'Y', '-FX-Y')
| [
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from .command_conversion import CommandConversion
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2020/1/10 3:23 PM
# @Author : Slade
# @File : datamake.py
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import tensorflow as tf
import numpy as np
flags = tf.app.flags
flags.DEFINE_string("input_dir", "./data/", "input dir")
flags.DEFINE_string("output_dir", "./text/data/", "output dir")
FLAGS = flags.FLAGS
# ่ฟ ไธค่ฝฆ ่ฅฟ็ ๅฐ ๅไบฌ ๅฐไป
# 23 1023 94 782 4234 10304
if __name__ == "__main__":
tf.logging.set_verbosity(tf.logging.INFO)
tf.app.run()
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8,
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220,
220,
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13,
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] | 2.326772 | 254 |
from math import sqrt
from pathlib import Path
import ase.io
import numpy as np
from numpy.linalg import norm
from numpy.testing import assert_equal as np_assert_equal
import pytest
from pytest import approx
import tests
from mofun import Atoms
from mofun.helpers import typekey
sqrt2_2 = sqrt(2) / 2
sqrt3_2 = sqrt(3) / 2
@pytest.fixture
@pytest.fixture
@pytest.fixture
@pytest.fixture
@pytest.fixture
@pytest.fixture
@pytest.fixture
@pytest.fixture
@pytest.fixture
@pytest.fixture
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"""
Created on 31 Dec 2016
@author: Bruno Beloff ([email protected])
A helper class for validating and preparing GPS module output strings.
https://www.nmea.org
https://en.wikipedia.org/wiki/NMEA_0183
"""
# --------------------------------------------------------------------------------------------------------------------
class NMEAReport(object):
"""
classdocs
"""
# ----------------------------------------------------------------------------------------------------------------
@classmethod
# ----------------------------------------------------------------------------------------------------------------
@classmethod
# ----------------------------------------------------------------------------------------------------------------
def __init__(self, fields):
"""
Constructor
"""
self.__fields = fields
# ----------------------------------------------------------------------------------------------------------------
@property
# ----------------------------------------------------------------------------------------------------------------
# ----------------------------------------------------------------------------------------------------------------
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628,
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198,
220,
220,
220,
1303,
16529,
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198
] | 5.608696 | 230 |
# Generated by Django 2.2.16 on 2020-11-07 11:31
from django.db import migrations, models
import django.db.models.deletion
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# -*- coding: utf-8 -*-
# Define here the models for your scraped items
#
# See documentation in:
# http://doc.scrapy.org/en/latest/topics/items.html
import scrapy
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] | 2.666667 | 63 |
from conftest import app
from model.User import User | [
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import unittest
from busco import BuscoConfig
import shutil
import os
from unittest.mock import Mock
from unittest.mock import patch, call
| [
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"""Add driver switch activity status
Revision ID: 8fde055f9d29
Revises: 8fe63e4276dc
Create Date: 2020-02-15 16:46:48.890628
"""
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision = "8fde055f9d29"
down_revision = "8fe63e4276dc"
branch_labels = None
depends_on = None
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"""UWEC Language Tools manager module
Provides functions for defining and managing a corpus.
"""
# Python 3 forward compatability imports.
from __future__ import print_function
from __future__ import division
from __future__ import absolute_import
from __future__ import unicode_literals
import sys
import os
import hashlib
import uweclang.batch
from itertools import chain
# Import async module.
import trollius as asyncio
from trollius import From
# Setup logger.
import logging
logging.getLogger(__name__).addHandler(logging.NullHandler())
def _default_filter(meta_data):
"""The default meta data filter which accepts all files. """
return True
def default_metadata_function(filename):
"""A function producing a dictionary of metadata for a given file. This
is the default implementation producing the file name, location, extension,
and file size.
Arguments:
filename (str): The name of the file.
Returns:
None: If the file path is invalid.
(dict): A dictionary containing the metadata.
"""
if not (filename and os.path.isfile(filename)):
return None
metadata = dict()
# Collect basic metadata:
metadata['filename'] = os.path.basename(filename)
metadata['location'] = os.path.abspath(filename)
ext = uweclang.split_ext(filename)
metadata['base'] = ext[0]
metadata['extension'] = ext[1]
metadata['size'] = os.path.getsize(filename)
# Get word count:
# with open(os.path.abspath(filename), 'r') as f:
# words = 0
# buf_size = 1024 * 1024
# read_f = f.read # loop optimization
# buf = read_f(buf_size)
# while buf:
# try:
# words += buf.count('/')
# buf = read_f(buf_size)
# except UnicodeDecodeError as e:
# pass # Skip decode error?
metadata['word_count'] = 0#words
return metadata
def get_file_md5(filename):
"""Returns the MD5 hash of the given file.
"""
block_size = 65536
hasher = hashlib.md5()
with open(filename, 'rb') as f:
buf = f.read(block_size)
while len(buf) > 0:
hasher.update(buf)
buf = f.read(block_size)
return hasher.hexdigest()
class Corpus(object):
"""A corpus object for managing a collection of tagged text files.
Attributes:
file_metadata (dict): A dictionary containing corpus meta data for
files indexed by ID.
"""
def add_files(self,
search_locations,
extensions=None,
recursive=False):
"""Searches for files in the given locations and adds them to the
corpus.
Arguments:
search_locations ([str]): A list of files and directories to search.
extensions ([str]): The file extensions to find in directories.
Defaults to None, which will find all files.
recursive: (bool): Whether to search directories recursively.
Note: Files given in search_locations that do not have the specified
extensions will be included in the output. The extensions argument
only effects files in the directories given.
"""
log = logging.getLogger('uweclang.corpus.manager')
files = uweclang.get_files(search_locations,
extensions=extensions,
recursive=recursive)
self._file_count += files[1]
for f in files[0]:
log.debug('Adding file %s', str(f))
# Get file meta data:
self.file_metadata[self._current_id] = self._meta_op(f)
meta = self.file_metadata[self._current_id]
meta['corpus_id'] = self._current_id
# meta['MD5'] = get_file_md5(f)
# Get file count:
self._word_count += meta['word_count']
# Set next file ID:
self._current_id += 1
# Log File add.
log.info('Adding %s files to corpus.', self._file_count)
@property
@property
def get_file_ids(self, predicate=None):
"""Returns a list of file ids in the corpus.
Arguments:
predicate (dict -> bool): A predicate for selecting files based on
metadata. Only file ids satisfying the predicate will be
returned.
"""
if predicate:
return (k for k in self.file_metadata.keys()
if predicate(file_metadata[k]))
else:
return self.file_metadata.keys()
def get_file_text(self, file_id):
"""Returns the tagged text of the file given by its ID."""
if not self.file_metadata.get(file_id):
return None
with open(self.file_metadata[file_id]['location'], 'r') as f:
return f.read()
def file_modified(self, file_id):
"""Returns true if the file's MD5 hash has changes since it was added
to the corpus.
"""
if not self.file_metadata.get(file_id):
return None
md5 = get_file_md5(self.file_metadata[file_id]['location'])
return md5 != self.file_metadata[file_id]['MD5']
def get_file_metadata(self, file_id):
"""Returns the text of the file associated with the given file_id."""
return self.file_metadata.get(file_id)
def get_id_for_file(self, filename):
"""Returns the id of the given file in the corpus or None if it is not
present.
"""
for k, v in self.file_metadata.items():
if v['location'] == os.path.abspath(filename):
return k
return None
def files(self, meta_filter=None, exclude_modified=False):
"""Returns an iterator over the metadata and text of each file in the
corpus.
"""
meta_filter = meta_filter or _default_filter
for x in self.get_file_ids():
if (meta_filter(self.get_file_metadata(x))
and not (exclude_modified and self.file_modified(x))):
yield (self.get_file_metadata(x), self.get_file_text(x))
def execute_queries(
self,
queries,
definitions=None,
meta_filter=None,
exclude_modified=False):
"""Runs the given queries on the corpus asynchronously.
Arguments:
queries ([Query]): The queries to run.
definitions (dict): A dictionary defining query terms.
meta_filter (dict -> bool): A function taking file meta data and
returning whether the file should be queried.
exclude_modified (bool): Whether to exclude modified files from
the query.
Returns:
[Result]: An iterator producing the results of the query.
"""
log = logging.getLogger('uweclang.corpus.manager')
results = []
# Get filtered files from corpus.
try:
files = self.files(
meta_filter=meta_filter,
exclude_modified=exclude_modified)
except Exception as e:
raise CorpusException(e)
try:
log.debug('Executing query batch.')
for index, (meta, tagged) in enumerate(files):
# Extract TaggedToken list from file.
text = list(chain.from_iterable(uweclang.read_tagged_string(tagged)))
# Execute search.
for i, query in enumerate(queries):
log.debug('Running query #%d on file #%d', i, index)
res = query.match(text, source_id=index, definitions=definitions)
if res:
results.append(res)
return chain.from_iterable(results)
except Exception as e:
raise QueryExecutionError(e)
def execute_queries_async(
self,
queries,
definitions=None,
meta_filter=None,
exclude_modified=False):
"""Runs the given queries on the corpus asynchronously.
Arguments:
queries ([Query]): The queries to run.
definitions (dict): A dictionary defining query terms.
meta_filter (dict -> bool): A function taking file meta data and
returning whether the file should be queried.
exclude_modified (bool): Whether to exclude modified files from
the query.
Returns:
[Result]: An iterator producing the results of the query.
"""
log = logging.getLogger('uweclang.corpus.manager')
results = []
# Get filtered files from corpus.
try:
files = self.files(
meta_filter=meta_filter,
exclude_modified=exclude_modified)
except Exception as e:
raise CorpusException(e)
status = {
'completed' : 0,
'total': 0,
} # Dictionary needed since `nonlocal` is not in Python 2.7.
log.debug('Executing query batch (async.)')
# Function for searching a single file.
# Worker function for running a file search.
@asyncio.coroutine
# Create asynchronous task list.
loop = asyncio.get_event_loop()
tasks = []
for index, (meta, tagged) in enumerate(files):
log.debug('Added task %d', index)
tasks.append(asyncio.ensure_future(worker(meta, tagged, index)))
# Run tasks.
status['total'] = len(tasks)
log.info('Starting %d tasks.', status['total'])
data = loop.run_until_complete(asyncio.gather(*tuple(tasks)))
# Shutdown event loop and logger.
loop.close()
logging.shutdown()
results = (task.result() for task in tasks if task.result())
return chain.from_iterable(results)
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] | 2.320056 | 4,268 |
"""
.. class:: GalaxySpectrumVVDS
.. moduleauthor:: Johan Comparat <johan.comparat__at__gmail.com>
The class GalaxySpectrumVVDS is dedicated to handling VVDS spectra
"""
from os.path import join
import os
import numpy as n
import astropy.io.fits as fits
import glob
import matplotlib
matplotlib.use('pdf')
import matplotlib.pyplot as p
from LineFittingLibrary import *
lfl = LineFittingLibrary()
from filterList import *
from lineListAir import *
class GalaxySpectrumVVDS:
"""
Loads the environement proper to the vvds survey.
Two modes of operation : flux calibration or line fitting
:param catalog_entry: an entry of the vvds catalog
:param calibration: if the class is loaded with intention of flux calibrating the vvds data.
:param lineFits: if the class is loaded with intention of fitting line fluxes on the vvds spectra.
"""
def openObservedSpectrum(self):
"""
reads a VVDS pectrum
returns the wavelength, the flux and the error on the flux and two arrays for masking purpose
"""
spL=glob.glob(join(self.vvds_spectra_dir,"sc_*" + str(self.catalog_entry['NUM']) + "*atm_clean.fits"))
#print spL
if len(spL)==1 :
specFileName=spL[0]
spectraHDU=fits.open(specFileName)
wl=spectraHDU[0].header['CRVAL1'] + spectraHDU[0].header['CDELT1'] * n.arange(2,spectraHDU[0].header['NAXIS1']+2)
fl=spectraHDU[0].data[0]
noiseFileName=glob.glob(join(self.vvds_spectra_dir,"sc_*"+str(self.catalog_entry['NUM'])+"*noise.fits"))[0]
noiseHDU=fits.open(noiseFileName)
flErr=noiseHDU[0].data[0]
self.wavelength,self.fluxl,self.fluxlErr=wl,fl,flErr
else :
self.wavelength,self.fluxl,self.fluxlErr= [-1,-1.],[-1,-1.],[-1,-1.]
def plotFit(self, outputFigureNameRoot, ymin = 1e-19, ymax = 1e-17):
"""
Plots the spectrum and the line fits in a few figures
"""
ok = (self.fluxl >0 ) & (self.fluxl > 1.2* self.fluxlErr)
p.figure(1,(12,4))
p.axes([0.1,0.2,0.85,0.75])
p.errorbar(self.wavelength[ok],self.fluxl[ok]/self.catalog_entry['fo'],yerr = self.fluxlErr[ok]/self.catalog_entry['fo'], linewidth=1, alpha= 0.4, label='spectrum')
p.xlabel('wavelength [A]')
p.ylabel(r'f$_\lambda$ [erg cm$^{-2}$ s$^{-1}$ A$^{-1}$]')
p.yscale('log')
p.ylim((ymin, ymax))
gl = p.legend(loc=0,fontsize=12)
gl.set_frame_on(False)
p.savefig( outputFigureNameRoot + "-all.png" )
p.clf()
a0_1 = (1+self.catalog_entry['Z'])*O2_3727
a0_2 = (1+self.catalog_entry['Z'])*O2_3729
continu= self.catalog_entry['O2_3728_continu']
aas =n.arange(self.catalog_entry['O2_3728_a0']-70, self.catalog_entry['O2_3728_a0']+70,0.1)
flMod=lambda aa,sigma,F0,sh :continu+ lfl.gaussianLineNC(aa,sigma,(1-sh)*F0,a0_1)+lfl.gaussianLineNC(aa,sigma,sh*F0,a0_2)
model = flMod(aas, self.catalog_entry['O2_3728_sigma'], self.catalog_entry['O2_3728_flux'],0.58 )# self.catalog_entry['O2_3728_share'])
p.figure(2,(4,4))
p.axes([0.21,0.2,0.78,0.7])
p.errorbar(self.wavelength,self.fluxl/self.catalog_entry['fo'],yerr = self.fluxlErr/self.catalog_entry['fo'])
p.plot(aas, model/self.catalog_entry['fo'],'g',label='model', lw=2)
p.xlabel('wavelength [A]')
p.ylabel(r'f$_\lambda$ [erg cm$^{-2}$ s$^{-1}$ A$^{-1}$]')
p.yscale('log')
p.ylim((ymin, ymax))
p.xlim(( self.catalog_entry['O2_3728_a0']-100, self.catalog_entry['O2_3728_a0']+100))
gl = p.legend(loc=0,fontsize=12)
gl.set_frame_on(False)
p.title('[OII] 3727')
p.savefig( outputFigureNameRoot + "-O2_3728.png")
p.clf()
a0 = self.catalog_entry['O3_5007_a0']
continu= self.catalog_entry['O3_5007_continu']
aas =n.arange(self.catalog_entry['O3_5007_a0']-70, self.catalog_entry['O3_5007_a0']+70,0.1)
flMod=lambda aa,sigma,F0: lfl.gaussianLine(aa,sigma,F0,a0,continu)
model = flMod(aas, self.catalog_entry['O3_5007_sigma'], self.catalog_entry['O3_5007_flux'])
p.figure(2,(4,4))
p.axes([0.21,0.2,0.78,0.7])
p.errorbar(self.wavelength,self.fluxl/self.catalog_entry['fo'],yerr = self.fluxlErr/self.catalog_entry['fo'])
p.plot(aas, model/self.catalog_entry['fo'],'g',label='model', lw =2)
p.xlabel('wavelength [A]')
p.ylabel(r'f$_\lambda$ [erg cm$^{-2}$ s$^{-1}$ A$^{-1}$]')
p.yscale('log')
p.ylim((ymin, ymax))
p.xlim(( self.catalog_entry['O3_5007_a0']-100, self.catalog_entry['O3_5007_a0']+100))
gl = p.legend(loc=0,fontsize=12)
gl.set_frame_on(False)
p.title('[OIII] 5007')
p.savefig( outputFigureNameRoot + "-O3_5007.png")
p.clf()
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198
] | 2.081106 | 2,133 |
import setuptools
setuptools.setup(
name="RAscore", # Replace with your own username
version="2020.9",
author="Reymond Group/Molecular AI AstraZeneca",
author_email="[email protected]",
license="MIT",
description="Computation of retrosynthetic accessibility from machine learening of CASP predictions",
url="https://github.com/reymond-group/RAscore",
packages=setuptools.find_packages(),
python_requires='>=3.7',
)
| [
11748,
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29,
28,
18,
13,
22,
3256,
198,
8,
198
] | 2.804878 | 164 |
# pylint: disable=missing-function-docstring, missing-module-docstring/
import numpy as np
import pytest
from numpy.random import rand, randint
import modules.complex_func as mod
from pyccel.epyccel import epyccel
@pytest.mark.parametrize("f", [ mod.create_complex_literal__int_int,
mod.create_complex_literal__int_float,
mod.create_complex_literal__int_complex,
mod.create_complex_literal__float_int,
mod.create_complex_literal__float_float,
mod.create_complex_literal__float_complex,
mod.create_complex_literal__complex_int,
mod.create_complex_literal__complex_float,
mod.create_complex_literal__complex_complex,
mod.cast_complex_literal] )
| [
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62,
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62,
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1691,
60,
1267,
198
] | 1.953846 | 455 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from setuptools import setup, find_packages
from sweeperbot._version import __version__
with open("README.rst") as readme_file:
readme = readme_file.read()
with open("HISTORY.rst") as history_file:
history = history_file.read()
setup(
name="sweeperbot",
version=__version__,
description="Test",
long_description=readme + "\n\n" + history,
author="Glanyx",
author_email="[email protected]",
url="https://github.com/glanyx/segachan/",
entry_points={"console_scripts": ["sweeperbot=sweeperbot.launch:main"]},
include_package_data=True,
license="GNU General Public License v3",
zip_safe=False,
keywords=[
"sweeperbot",
"sweeper",
"bot",
"discord",
"benedict",
"benedict 9940",
"segachan",
],
classifiers=[
"Development Status :: 2- Beta",
"Intended Audience :: Developers",
"License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
"Natural Language :: English",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.7",
],
)
| [
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513,
13,
22,
1600,
198,
220,
220,
220,
16589,
198,
8,
198
] | 2.379032 | 496 |
import requests
import os
from datetime import datetime
import json
from bs4 import BeautifulSoup as bs
import time
import random
import string
| [
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# -*- coding: utf-8 -*-
"""
-------------------------------------------------
File Name๏ผ test1
Description : ๅค็บฟ็จๅฎ็ฐ
Author : pengsheng
date๏ผ 2019-04-20
-------------------------------------------------
"""
import threading
new_thread = threading.Thread(target=worker, name='new_thread')
new_thread.start()
# ๆดๅ ๅ
ๅๅฉ็จCPU็ๆง่ฝไผๅฟ(็บฟ็จๆง่กๆฏๅผๆญฅ็)
# ๅผๆญฅ็ผ็จๅค็จไบ่งฃๅณๆง่ฝ้ฎ้ข,ไธ่ฌ้ฎ้ข่ฝๅค็จๅๆญฅๅฐฑ็จๅๆญฅ
t = threading.current_thread()
print(t.getName()) | [
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62,
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3419,
198,
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7,
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13,
1136,
5376,
28955
] | 1.802419 | 248 |
# fix absolute imports on ST3
# TODO: remove
#import sys
#import os
#sys.path.insert(0, os.path.abspath(os.path.dirname(__file__)))
try:
from sublime_jedi import *
except ImportError:
from .sublime_jedi import *
| [
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] | 2.630952 | 84 |
__author__ = "Vanessa Sochat"
__copyright__ = "Copyright 2021, Vanessa Sochat"
__license__ = "MPL 2.0"
import os
import time
import tarfile
import tempfile
import time
from oras.logger import logger
import oras.utils as utils
import oras.defaults as defaults
from .const import TempFilePattern, AnnotationUnpack, AnnotationDigest
from .utils import resolve_name, tar_directory
from .readerat import sizeReaderAt
from .utils import tar_directory
from .opts import CdWriterOpts, WithOutputHash
from .iowriter import IoContentWriter
import opencontainers.image.v1.annotations as annotations
import opencontainers.image.v1.descriptor as descriptor
class FileStore:
"""
A FileStore provides content from the file system
"""
def map_path(self, name, path):
"""
Map a name to a path
"""
path = self.resolve_path(path)
self.path_map[name] = path
return path
def resolve_path(self, name):
"""
Return the path by name
"""
path = self.path_map.get(name)
if path or (path and os.path.isabs(path)):
return path
return os.path.join(self.root, path)
def set(self, desc):
"""
Save a descriptor to the map.
"""
self.descriptor[desc.Digest.value] = desc
def add(self, name, media_type, path):
"""
Add a file reference
"""
path = path or name
path = self.map_path(name, path)
if os.path.isdir(path):
desc = self.descriptor_from_dir(name, media_type, path)
elif os.path.isfile(path):
desc = self.descriptor_from_file(media_type, path)
else:
logger.exit("%s is not a valid path." % path)
desc.Annotations[annotations.AnnotationTitle] = name
self.set(desc)
return desc
def descriptor_from_file(self, media_type, path):
"""
Get a descriptor from file.
"""
if not os.path.exists(path):
logger.exit("%s does not exist." % path)
try:
digest = utils.get_file_hash(path)
except:
logger.exit("Cannot calculate digest for %s" % path)
if not media_type:
media_type = defaults.DefaultBlobMediaType
stat = os.stat(path)
return descriptor.Descriptor(mediaType=media_type, digest=digest, size=stat.st_size)
def descriptor_from_dir(self, name, media_type, root):
"""
Get a descriptor from a director
"""
name = self.map_path(name, tmpfie)
# Compress directory to tmpfile
tar = tar_directory(root, name, strip_times=self.reproducible)
# Get digest
digest = "sha256:%s" % utils.get_file_hash(tar)
# generate descriptor
if not media_type:
media_type = defaults.DefaultBlobMediaType
info = os.stat(tar)
# Question: what is the difference between AnnotationDigest and digest?
annotations = {"AnnotationDigest": digest, "AnnotationUnpack": True}
return descriptor.Descriptor(mediaType=media_type, digest=digest,size=info.st_size, annotations=annotations)
def temp_file(self):
"""
Create and store a temporary file
"""
filen = tempfile.NamedTemporaryFile(prefix=TempFilePattern)
self.tmp_files[filen.name] = filen
return filen
def close(self):
"""Close frees up resources used by the file store
"""
for name, filen in self.tmp_files.items():
filen.close()
if os.path.exists(name):
os.remove(name)
def set(self, desc):
"""
Set an OCI descriptor
"""
self.descriptor[desc.Digest] = desc
def get(desc):
"""
Get an OCI descriptor
"""
value = self.descriptor.get(desc.Digest)
if not value:
return descriptor.Descriptor()
return value
def reader_at(self, desc):
"""ReaderAt provides contents
"""
desc = self.get(desc)
if not desc:
sys.exit("Could not find descriptor.")
name = resolve_name(desc)
if not name:
sys.exit("Cannot resolve name for %s" % desc)
path = self.resolve_path(name)
fileo = open(path, 'r')
return sizeReaderAt(fileo, desc.size)
def writer(self, opts):
"""Writer begins or resumes the active writer identified by desc
"""
wopts = CdWriterOpts()
wopts.update(opts)
desc = wopts.Desc
name = resolve_name(desc)
# if we were not told to ignore NoName, then return an error
if not name and not self.ignore_no_name:
sys.exit("Cannot resolve name for %s" % desc)
elif not name and self.ignore_no_name:
# just return a nil writer - we do not want to calculate the hash, so just use
# whatever was passed in the descriptor
return IoContentWriter(WithOutputHash(desc.Digest)
path = self.resolve_write_path(name)
filen, after_commit = self.create_write_path(path, desc, name)
now = time.time()
# STOPPED HERE need to find content.Status
status =
status: content.Status{
Ref: name,
Total: desc.Size,
StartedAt: now,
UpdatedAt: now,
},
return FileWriter(store=self, fileh=filen, desc=desc, status=status, after_commit=after_commit)
def resolve_write_path(self, name):
"""Resolve the write path
"""
path = self.resolve_path(name)
if not self.allow_path_traversal_on_write:
base = os.path.abspath(self.root)
target = os.path.abspath(path)
rel = os.path.relpath(base, target)
if rel.startswith("../") or rel == "..":
return ""
if self.disable_overwrite:
print("NEED TO CHECK OVERWRITE")
# TODO what do we want to check here, if writable?
#if os.stat(path)
# if _, err := os.Stat(path); err == nil {
# return "", ErrOverwriteDisallowed
# } else if !os.IsNotExist(err) {
# return "", err
return path
def create_write_path(self, path, desc, prefix):
"""
Create a write path?
"""
value = desc.Annotations.get(AnnotationUnpack)
if not value:
os.makedirs(os.path.dirname(path))
with open(path, 'w') as fd:
pass
return filen, None
os.makedirs(path)
filen = tempfile.mkstemp()[1]
checksum = desc.Annotations.get(AnnotationDigest)
return filen, after_commit
class FileWriter:
def __init__(self, store, fileh, desc, status, after_commit, digester=None):
self.store = store # *FileStore
self.file = fileh # *os.File
self.desc = desc # ocispec.Descriptor
self.status = status # content.Status
self.after_commit = after_commit # func()
self.digester = digester or digest.Canonical.Digester() # TODO what is this?
func (w *fileWriter) Status() (content.Status, error) {
return w.status, nil
}
// Digest returns the current digest of the content, up to the current write.
//
// Cannot be called concurrently with `Write`.
func (w *fileWriter) Digest() digest.Digest {
return w.digester.Digest()
}
// Write p to the transaction.
func (w *fileWriter) Write(p []byte) (n int, err error) {
n, err = w.file.Write(p)
w.digester.Hash().Write(p[:n])
w.status.Offset += int64(len(p))
w.status.UpdatedAt = time.Now()
return n, err
}
func (w *fileWriter) Commit(ctx context.Context, size int64, expected digest.Digest, opts ...content.Opt) error {
var base content.Info
for _, opt := range opts {
if err := opt(&base); err != nil {
return err
}
}
if w.file == nil {
return errors.Wrap(errdefs.ErrFailedPrecondition, "cannot commit on closed writer")
}
file := w.file
w.file = nil
if err := file.Sync(); err != nil {
file.Close()
return errors.Wrap(err, "sync failed")
}
fileInfo, err := file.Stat()
if err != nil {
file.Close()
return errors.Wrap(err, "stat failed")
}
if err := file.Close(); err != nil {
return errors.Wrap(err, "failed to close file")
}
if size > 0 && size != fileInfo.Size() {
return errors.Wrapf(errdefs.ErrFailedPrecondition, "unexpected commit size %d, expected %d", fileInfo.Size(), size)
}
if dgst := w.digester.Digest(); expected != "" && expected != dgst {
return errors.Wrapf(errdefs.ErrFailedPrecondition, "unexpected commit digest %s, expected %s", dgst, expected)
}
w.store.set(w.desc)
if w.afterCommit != nil {
return w.afterCommit()
}
return nil
}
// Close the writer, flushing any unwritten data and leaving the progress in
// tact.
func (w *fileWriter) Close() error {
if w.file == nil {
return nil
}
w.file.Sync()
err := w.file.Close()
w.file = nil
return err
}
func (w *fileWriter) Truncate(size int64) error {
if size != 0 {
return ErrUnsupportedSize
}
w.status.Offset = 0
w.digester.Hash().Reset()
if _, err := w.file.Seek(0, io.SeekStart); err != nil {
return err
}
return w.file.Truncate(0)
}
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220,
13610,
290,
3650,
257,
8584,
2393,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
1226,
268,
796,
20218,
7753,
13,
45,
2434,
12966,
5551,
8979,
7,
40290,
28,
30782,
8979,
47546,
8,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
22065,
62,
16624,
58,
10379,
268,
13,
3672,
60,
796,
1226,
268,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
1226,
268,
628,
220,
220,
220,
825,
1969,
7,
944,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
26125,
2030,
274,
510,
4133,
973,
416,
262,
2393,
3650,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
329,
1438,
11,
1226,
268,
287,
2116,
13,
22065,
62,
16624,
13,
23814,
33529,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1226,
268,
13,
19836,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
28686,
13,
6978,
13,
1069,
1023,
7,
3672,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
28686,
13,
28956,
7,
3672,
8,
628,
220,
220,
220,
825,
900,
7,
944,
11,
1715,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
5345,
281,
24775,
40,
43087,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
20147,
1968,
273,
58,
20147,
13,
19511,
395,
60,
796,
1715,
220,
220,
220,
220,
220,
220,
220,
220,
628,
220,
220,
220,
825,
651,
7,
20147,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
3497,
281,
24775,
40,
43087,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
1988,
796,
2116,
13,
20147,
1968,
273,
13,
1136,
7,
20147,
13,
19511,
395,
8,
198,
220,
220,
220,
220,
220,
220,
220,
611,
407,
1988,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
43087,
13,
24564,
1968,
273,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
1988,
628,
220,
220,
220,
825,
9173,
62,
265,
7,
944,
11,
1715,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
33634,
2953,
3769,
10154,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
1715,
796,
2116,
13,
1136,
7,
20147,
8,
198,
220,
220,
220,
220,
220,
220,
220,
611,
407,
1715,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
25064,
13,
37023,
7203,
23722,
407,
1064,
43087,
19570,
628,
220,
220,
220,
220,
220,
220,
220,
1438,
796,
10568,
62,
3672,
7,
20147,
8,
198,
220,
220,
220,
220,
220,
220,
220,
611,
407,
1438,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
25064,
13,
37023,
7203,
34,
34574,
10568,
1438,
329,
4064,
82,
1,
4064,
1715,
8,
220,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
220,
220,
220,
220,
3108,
796,
2116,
13,
411,
6442,
62,
6978,
7,
3672,
8,
198,
220,
220,
220,
220,
220,
220,
220,
2393,
78,
796,
1280,
7,
6978,
11,
705,
81,
11537,
220,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
2546,
33634,
2953,
7,
7753,
78,
11,
1715,
13,
7857,
8,
628,
198,
220,
220,
220,
825,
6260,
7,
944,
11,
2172,
82,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
34379,
6140,
393,
42626,
262,
4075,
6260,
5174,
416,
1715,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
266,
404,
912,
796,
327,
67,
34379,
27871,
82,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
266,
404,
912,
13,
19119,
7,
404,
912,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1715,
796,
266,
404,
912,
13,
24564,
198,
220,
220,
220,
220,
220,
220,
220,
1438,
796,
10568,
62,
3672,
7,
20147,
8,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
611,
356,
547,
407,
1297,
284,
8856,
1400,
5376,
11,
788,
1441,
281,
4049,
198,
220,
220,
220,
220,
220,
220,
220,
611,
407,
1438,
290,
407,
2116,
13,
46430,
62,
3919,
62,
3672,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
25064,
13,
37023,
7203,
34,
34574,
10568,
1438,
329,
4064,
82,
1,
4064,
1715,
8,
220,
220,
198,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
407,
1438,
290,
2116,
13,
46430,
62,
3919,
62,
3672,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
655,
1441,
257,
18038,
6260,
532,
356,
466,
407,
765,
284,
15284,
262,
12234,
11,
523,
655,
779,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
4232,
373,
3804,
287,
262,
43087,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
27853,
19746,
34379,
7,
3152,
26410,
26257,
7,
20147,
13,
19511,
395,
8,
628,
220,
220,
220,
220,
220,
220,
220,
3108,
796,
2116,
13,
411,
6442,
62,
13564,
62,
6978,
7,
3672,
8,
628,
220,
220,
220,
220,
220,
220,
220,
1226,
268,
11,
706,
62,
41509,
796,
2116,
13,
17953,
62,
13564,
62,
6978,
7,
6978,
11,
1715,
11,
1438,
8,
198,
220,
220,
220,
220,
220,
220,
220,
783,
796,
640,
13,
2435,
3419,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
44934,
47,
1961,
15698,
761,
284,
1064,
2695,
13,
19580,
198,
220,
220,
220,
220,
220,
220,
220,
3722,
796,
198,
197,
197,
13376,
25,
2695,
13,
19580,
90,
198,
197,
197,
197,
8134,
25,
220,
220,
220,
220,
220,
220,
1438,
11,
198,
197,
197,
197,
14957,
25,
220,
220,
220,
220,
1715,
13,
10699,
11,
198,
197,
197,
197,
10434,
276,
2953,
25,
783,
11,
198,
197,
197,
197,
17354,
2953,
25,
783,
11,
198,
197,
197,
5512,
628,
220,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
9220,
34379,
7,
8095,
28,
944,
11,
2393,
71,
28,
10379,
268,
11,
1715,
28,
20147,
11,
3722,
28,
13376,
11,
706,
62,
41509,
28,
8499,
62,
41509,
8,
628,
198,
220,
220,
220,
825,
10568,
62,
13564,
62,
6978,
7,
944,
11,
1438,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
4965,
6442,
262,
3551,
3108,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
3108,
796,
2116,
13,
411,
6442,
62,
6978,
7,
3672,
8,
198,
220,
220,
220,
220,
220,
220,
220,
611,
407,
2116,
13,
12154,
62,
6978,
62,
9535,
690,
282,
62,
261,
62,
13564,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2779,
796,
28686,
13,
6978,
13,
397,
2777,
776,
7,
944,
13,
15763,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2496,
796,
28686,
13,
6978,
13,
397,
2777,
776,
7,
6978,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
823,
796,
28686,
13,
6978,
13,
2411,
6978,
7,
8692,
11,
2496,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
823,
13,
9688,
2032,
342,
7203,
40720,
4943,
393,
823,
6624,
366,
492,
1298,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
13538,
628,
220,
220,
220,
220,
220,
220,
220,
611,
2116,
13,
40223,
62,
2502,
13564,
25,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3601,
7203,
12161,
1961,
5390,
5870,
25171,
28729,
18564,
12709,
4943,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
16926,
46,
644,
466,
356,
765,
284,
2198,
994,
11,
611,
1991,
540,
30,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
361,
28686,
13,
14269,
7,
6978,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
611,
4808,
11,
11454,
19039,
28686,
13,
17126,
7,
6978,
1776,
11454,
6624,
18038,
1391,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
1441,
366,
1600,
41512,
5886,
13564,
7279,
40845,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
1782,
2073,
611,
5145,
418,
13,
3792,
3673,
3109,
396,
7,
8056,
8,
1391,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
1441,
366,
1600,
11454,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
3108,
628,
220,
220,
220,
825,
2251,
62,
13564,
62,
6978,
7,
944,
11,
3108,
11,
1715,
11,
21231,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
13610,
257,
3551,
3108,
30,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
1988,
796,
1715,
13,
2025,
30078,
13,
1136,
7,
2025,
38983,
3118,
8002,
8,
198,
220,
220,
220,
220,
220,
220,
220,
611,
407,
1988,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
28686,
13,
76,
4335,
17062,
7,
418,
13,
6978,
13,
15908,
3672,
7,
6978,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
351,
1280,
7,
6978,
11,
705,
86,
11537,
355,
277,
67,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1208,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
1226,
268,
11,
6045,
628,
220,
220,
220,
220,
220,
220,
220,
28686,
13,
76,
4335,
17062,
7,
6978,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1226,
268,
796,
20218,
7753,
13,
28015,
927,
79,
3419,
58,
16,
60,
220,
198,
220,
220,
220,
220,
220,
220,
220,
8794,
388,
796,
1715,
13,
2025,
30078,
13,
1136,
7,
2025,
38983,
19511,
395,
8,
220,
198,
197,
7783,
1226,
268,
11,
706,
62,
41509,
628,
198,
4871,
9220,
34379,
25,
628,
220,
220,
220,
825,
11593,
15003,
834,
7,
944,
11,
3650,
11,
2393,
71,
11,
1715,
11,
3722,
11,
706,
62,
41509,
11,
3100,
7834,
28,
14202,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
8095,
796,
3650,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
1635,
8979,
22658,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
7753,
796,
2393,
71,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
1635,
418,
13,
8979,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
20147,
796,
1715,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
267,
66,
271,
43106,
13,
24564,
1968,
273,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
13376,
796,
3722,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
2695,
13,
19580,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
8499,
62,
41509,
796,
706,
62,
41509,
220,
1303,
25439,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
12894,
7834,
796,
3100,
7834,
393,
16274,
13,
6090,
261,
605,
13,
19511,
7834,
3419,
1303,
16926,
46,
644,
318,
428,
30,
198,
198,
20786,
357,
86,
1635,
7753,
34379,
8,
12678,
3419,
357,
11299,
13,
19580,
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] | 2.289919 | 4,077 |
from tensorflow import keras
from tensorflow.keras import backend as K
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] | 3.6 | 20 |
import numpy as np
import torch
import torch.nn as nn
import time
from tqdm import tqdm
from .buffer import Buffer
from .algo.base import Expert
from .env import NormalizedEnv
def soft_update(target, source, tau):
"""Soft update for SAC"""
for t, s in zip(target.parameters(), source.parameters()):
t.data.mul_(1.0 - tau)
t.data.add_(tau * s.data)
def disable_gradient(network: nn.Module):
"""Disable the gradients of parameters in the network"""
for param in network.parameters():
param.requires_grad = False
def add_random_noise(action, std):
"""Add random noise to the action"""
action += np.random.randn(*action.shape) * std
return action.clip(-1.0, 1.0)
def collect_demo(
env: NormalizedEnv,
algo: Expert,
buffer_size: int,
device: torch.device,
std: float,
p_rand: float,
seed: int = 0
):
"""
Collect demonstrations using the well-trained policy
Parameters
----------
env: NormalizedEnv
environment to collect demonstrations
algo: Expert
well-trained algorithm used to collect demonstrations
buffer_size: int
size of the buffer, also the number of s-a pairs in the demonstrations
device: torch.device
cpu or cuda
std: float
standard deviation add to the policy
p_rand: float
with probability of p_rand, the policy will act randomly
seed: int
random seed
Returns
-------
buffer: Buffer
buffer of demonstrations
mean_return: float
average episode reward
"""
env.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
buffer = Buffer(
buffer_size=buffer_size,
state_shape=env.observation_space.shape,
action_shape=env.action_space.shape,
device=device
)
total_return = 0.0
num_steps = []
num_episodes = 0
state = env.reset()
t = 0
episode_return = 0.0
episode_steps = 0
for _ in tqdm(range(1, buffer_size + 1)):
t += 1
if np.random.rand() < p_rand:
action = env.action_space.sample()
else:
action = algo.exploit(state)
action = add_random_noise(action, std)
next_state, reward, done, _ = env.step(action)
mask = True if t == env.max_episode_steps else done
buffer.append(state, action, reward, mask, next_state)
episode_return += reward
episode_steps += 1
if done or t == env.max_episode_steps:
num_episodes += 1
total_return += episode_return
state = env.reset()
t = 0
episode_return = 0.0
num_steps.append(episode_steps)
episode_steps = 0
state = next_state
mean_return = total_return / num_episodes
print(f'Mean return of the expert is {mean_return}')
print(f'Max episode steps is {np.max(num_steps)}')
print(f'Min episode steps is {np.min(num_steps)}')
return buffer, mean_return
def evaluation(
env: NormalizedEnv,
algo: Expert,
episodes: int,
render: bool,
seed: int = 0,
delay: float = 0.03
):
"""
Evaluate the well-trained policy
Parameters
----------
env: NormalizedEnv
environment to evaluate the policy
algo: Expert
well-trained policy to be evaluated
episodes: int
number of episodes used in evaluation
render: bool
render the environment or not
seed: int
random seed
delay: float
number of seconds to delay while rendering, in case the agent moves too fast
Returns
-------
mean_return: float
average episode reward
"""
env.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
total_return = 0.0
num_episodes = 0
num_steps = []
state = env.reset()
t = 0
episode_return = 0.0
episode_steps = 0
while num_episodes < episodes:
t += 1
action = algo.exploit(state)
next_state, reward, done, _ = env.step(action)
episode_return += reward
episode_steps += 1
state = next_state
if render:
env.render()
time.sleep(delay)
if done or t == env.max_episode_steps:
num_episodes += 1
total_return += episode_return
state = env.reset()
t = 0
episode_return = 0.0
num_steps.append(episode_steps)
episode_steps = 0
mean_return = total_return / num_episodes
print(f'Mean return of the policy is {mean_return}')
print(f'Max episode steps is {np.max(num_steps)}')
print(f'Min episode steps is {np.min(num_steps)}')
return mean_return
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] | 2.197226 | 2,307 |
# Lint as: python3
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for forward rate agreement."""
import numpy as np
import tensorflow.compat.v2 as tf
import tf_quant_finance as tff
from tensorflow.python.framework import test_util # pylint: disable=g-direct-tensorflow-import
framework = tff.experimental.pricing_platform.framework
business_days = framework.core.business_days
currencies = framework.core.currencies
daycount_conventions = framework.core.daycount_conventions
interpolation_method = framework.core.interpolation_method
instrument_protos = tff.experimental.pricing_platform.instrument_protos
date_pb2 = instrument_protos.date
decimal_pb2 = instrument_protos.decimal
period_pb2 = instrument_protos.period
fra_pb2 = instrument_protos.forward_rate_agreement
rate_instruments = tff.experimental.pricing_platform.framework.rate_instruments
forward_rate_agreement = rate_instruments.forward_rate_agreement
market_data = tff.experimental.pricing_platform.framework.market_data
DayCountConventions = daycount_conventions.DayCountConventions
BusinessDayConvention = business_days.BusinessDayConvention
RateIndex = instrument_protos.rate_indices.RateIndex
Currency = currencies.Currency
@test_util.run_all_in_graph_and_eager_modes
if __name__ == "__main__":
tf.test.main()
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] | 3.364641 | 543 |
""" Extract charges obtained via HORTON and Bader.
Copyright 2019 Simulation Lab
University of Freiburg
Author: Lukas Elflein <[email protected]>
"""
import os
import pandas as pd
def create_dir(path='./plotting'):
"""Create new folder for pictures if it does not exist yet."""
if os.path.isdir(path):
return path
os.makedirs(path)
return path
def collect_bader():
"""Find charges and put them in one dataframe."""
# Initialize collection data frame
coll_df = None
# Crawl the directory structure
for subdir, dirs, files in sorted(os.walk('./')):
# Exclude template folders from search
if 'template' in subdir or 'exclude' in subdir:
continue
# Select the folders with cost function
if 'bader_charges' in subdir:
print('Moving to {}'.format(subdir))
# Extract timestamp
time = os.path.split(subdir)[0].replace('./', '').replace('_ps_snapshot', '')
time = int(time)
# Use the first charge file to come across as a template
df = pd.read_csv(os.path.join(subdir, 'bader_charges.csv'), sep=r',\s*',
engine='python')
df['timestamp'] = time
if coll_df is None:
coll_df = df
else:
coll_df = coll_df.append(df)
# The table still contains redundant hydrogen atoms: 1CD3... 2CB3
# Delete everything containing '1C' or '2C'
# print(coll_df[coll_df.atom.str.contains(r'[1-2]C')])
coll_df = coll_df.drop(coll_df[coll_df.atom.str.contains(r'[1-2]C')].index)
print('All collected. Transforming wide to long format ...')
# Transform the wide format into a long format version (for easier plotting)
coll_df = coll_df.rename({'q': 'bader'}, axis=1)
coll_df = pd.melt(coll_df, id_vars=['atom', 'residue', 'timestamp'],
value_vars=['bader'])
coll_df = coll_df.rename({'value': 'charge', 'variable': 'Calculation Variant'},
axis=1)
return coll_df
def collect_horton():
"""Find charges and put them in one dataframe."""
# Initialize collection data frame
coll_df = None
# Crawl the directory structure
for subdir, dirs, files in sorted(os.walk('./')):
# Exclude template folders from search
if 'template' in subdir or 'exclude' in subdir or 'sweep' in subdir:
continue
# Select the folders with cost function
if 'horton_cost_function' in subdir:
print('Moving to {}'.format(subdir))
# Extract timestamp
time = os.path.split(subdir)[0].replace('./', '').replace('_ps_snapshot', '')
time = time.replace('/4_horton_cost_function', '')
time = int(time)
# Use the first charge file to come across as a template
df = pd.read_csv(os.path.join(subdir, 'fitted_point_charges.csv'))
df['timestamp'] = time
if coll_df is None:
coll_df = df
else:
coll_df = coll_df.append(df)
print('All collected. Transforming wide to long format ...')
# Transform the wide format into a long format version (for easier plotting)
coll_df = coll_df.rename({'q': 'constrained', 'q_unconstrained': 'unconstrained'}, axis=1)
coll_df = pd.melt(coll_df, id_vars=['atom', 'residue', 'timestamp'],
value_vars=['constrained', 'unconstrained'])
coll_df = coll_df.rename({'value': 'charge', 'variable': 'Calculation Variant'}, axis=1)
return coll_df
def extract_init_charges(rtp_path, df):
"""Extract charges from rtp file"""
atom_names = df.atom.unique()
residuum_names = df.residue.unique()
charges = pd.DataFrame()
with open(rtp_path, 'r') as rtp_file:
print('Successfully loaded topolgy file {}'.format(rtp_path))
rtp_text = rtp_file.readlines()
current_residuum = None
for line in rtp_text:
# atom names are only unique inside one residuum
# Thus, specify which res we are currently in
for residuum in residuum_names:
if residuum in line:
current_residuum = residuum
break
# Now, we can look up the atom name in the charge table.
# First, select the lines with exactly one atom name
for atom_name in atom_names:
# Select lines with at least one atom name
if atom_name in line[0:7]:
second_entry = line[8:18].replace('+', '')
second_entry = second_entry.replace('-', '').strip()
# Select lines with no atom name in second column
if not second_entry in atom_names:
q_value = float(line[24:34].strip(' '))
charges = charges.append({'atom': atom_name,
'residue': current_residuum,
'q_init': q_value},
ignore_index=True)
return charges
def collect_average():
"""Put averaged charegs in a dataframe."""
# Read charges from averaged cost function
input_path = './horton_charges/fitted_point_charges.csv'
avg_df = pd.read_csv(input_path)
# Rename columns for consistency
avg_df = avg_df.rename({'q': 'averaged cost function'}, axis=1)
# Transform to long format
avg_df = pd.melt(avg_df, id_vars=['atom', 'residue'], value_vars=['averaged cost function'])
avg_df = avg_df.rename({'value': 'charge', 'variable': 'Calculation Variant'}, axis=1)
return avg_df
def main():
"""Collect charges and save them to .csv file"""
# Collect averaged charges
avg_df = collect_average()
print(avg_df.loc[avg_df.atom == 'NA2'])
# Collect all horton charges
print('Collecting HORTON charges ...')
horton_df = collect_horton()
print(horton_df.loc[horton_df.atom == 'NA2'])
# Collect all bader charges
print('Collecting Bader charges ...')
bader_df = collect_bader()
# Paste everything into single dataframe
print('Combining different charges into one table ... ')
constr_df = horton_df.loc[horton_df['Calculation Variant'] == 'constrained']
unconstr_df = horton_df.loc[horton_df['Calculation Variant'] == 'unconstrained']
collect_df = avg_df
collect_df = collect_df.append(constr_df, sort=False)
collect_df = collect_df.append(unconstr_df, sort=False)
collect_df = collect_df.append(bader_df, sort=False)
create_dir(path='./plotting')
collect_df.to_csv('./plotting/all_charges.csv')
if __name__ == '__main__':
main()
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] | 2.173309 | 3,237 |
import os
import time
import json
import datetime
import subprocess
from pythonping import ping
from influxdb_client import InfluxDBClient
from influxdb_client.client.write_api import SYNCHRONOUS
from multiprocessing import Process
# InfluxDB Settings
NAMESPACE = os.getenv('NAMESPACE', 'None')
DB_URL = os.getenv('INFLUX_DB_URL', 'http://localhost')
DB_TOKEN = os.getenv('INFLUX_DB_TOKEN', 'my-token')
DB_ORG = os.getenv('INFLUX_DB_ORG', 'my-org')
DB_BUCKET = os.getenv('INFLUX_DB_BUCKET', 'my-bucket')
DB_TAGS = os.getenv('INFLUX_DB_TAGS', None)
PING_TARGETS = os.getenv('PING_TARGETS', '1.1.1.1, 8.8.8.8')
# Speedtest Settings
# Time between tests (in minutes, converts to seconds).
TEST_INTERVAL = int(os.getenv('SPEEDTEST_INTERVAL', '5')) * 60
# Time before retrying a failed Speedtest (in minutes, converts to seconds).
TEST_FAIL_INTERVAL = int(os.getenv('SPEEDTEST_FAIL_INTERVAL', '5')) * 60
# Specific server ID
SERVER_ID = os.getenv('SPEEDTEST_SERVER_ID', '')
# Time between ping tests (in seconds).
PING_INTERVAL = int(os.getenv('PING_INTERVAL', '5'))
with InfluxDBClient(url=DB_URL, token=DB_TOKEN, org=DB_ORG) as client:
write_api = client.write_api(write_options=SYNCHRONOUS)
pass
# time.sleep(TEST_FAIL_INTERVAL)
if __name__ == '__main__':
print('Speedtest CLI data logger to InfluxDB started...')
main()
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] | 2.415517 | 580 |
""" This module contains the RA calculator """
from kivy.uix.gridlayout import GridLayout
from kivy.lang import Builder
from kivy.properties import StringProperty
from Moduler.customwidgets import MyLabel
from Moduler.customwidgets import MyTextInput
from Moduler.datasaving import SurfaceRaData
Builder.load_string(
"""
<BoxLayout>:
orientation: 'horizontal'
<MyTextInput>:
<Ra>:
feed: feed
nr: nr
cols: 1
padding: 10
spacing: 10
BoxLayout:
size_hint_y: None
height: "40dp"
Label:
text: "Feedrate: "
MyTextInput:
id: feed
hint_text: "mm/o"
multiline: False
write_tab: False
on_text_validate: root.calc()
BoxLayout:
size_hint_y: None
height: "40dp"
Label:
text: "Nose Radius: "
MyTextInput:
id: nr
hint_text: "mm"
multiline: False
write_tab: False
on_text_validate: root.calc()
BoxLayout:
size_hint_y: None
height: "40dp"
Button:
text: "Calculate!"
on_press: root.calc()
BoxLayout:
#size_hint_y: None
#height: "200dp"
Label:
BoxLayout:
size_hint_y: None
height: "40dp"
MyLabel:
text: "Ra: "
bcolor: [1, 1, 1, 0.15]
MyLabel:
text: root.surface_ra
bcolor: [1, 1, 1, 0.15]
"""
)
class Ra(GridLayout):
""" Main class for the RA module """
surface_ra = StringProperty()
def calc(self):
""" Calculating RA """
try:
feed = self.feed.text
feed = feed.replace(',', '.')
feed = float(feed)
except ValueError:
pass
try:
nose_radius = self.nr.text
nose_radius = nose_radius.replace(',', '.')
nose_radius = float(nose_radius)
except ValueError:
pass
try:
result = ((feed**2) / (nose_radius*24)) * 1000
result = round(result, 2)
except(TypeError, ZeroDivisionError):
result = "Please input values"
self.surface_ra = str(result)
SurfaceRaData("Database.xlsx").filesave(self.feed.text,
self.nr.text,
result)
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"""Top-level package for nhdpy."""
__author__ = """Jemma Stachelek"""
__email__ = '[email protected]'
__version__ = '0.1.0'
from .nhdpy import nhd_get
from .nhdpy import nhd_list
from .nhdpy import nhd_load
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# TODO merge naive and weighted loss.
import numpy as np
import torch
import torch.nn.functional as F
from ..bbox import bbox_overlaps
from ...ops import sigmoid_focal_loss
from ..bbox.transforms import delta2bbox
# added by Shengkai Wu
# implement iou_balanced cross entropy loss.
def iou_balanced_cross_entropy(pred, label, weight, iou, eta = 1.5, avg_factor=None, reduce=True):
"""
iou_balanced cross entropy loss to make the training process to focus more on positives with higher
iou.
:param pred: tesnor of shape (batch*num_samples, num_class)
:param label: tensor of shape (batch*num_samples), store gt labels such as
0, 1, 2, 80 for corresponding class(0 represent background).
:param weight: tensor of shape (batch*num_samples), 1 for all the elements;
:param iou: tensor of shape (batch*num_samples), iou between predicted boxes and corresponding ground
truth boxes for positives and 0 for negatives.
:param eta: control to which extent the training process focuses on the positives with high iou.
:param avg_factor:
:param reduce:
:return:
"""
# avg_factor = batch*num_samples
# if avg_factor is None:
# avg_factor = max(torch.sum(weight > 0).float().item(), 1.)
raw1 = F.cross_entropy(pred, label, reduction='none')
target = iou.new_zeros(iou.size(0))
# target_1 = iou.new_zeros(iou.size(0))
# the way to get the indexes of positive example may be wrong; is it right?
# pos_inds_1 = label > 0
# target_1[pos_inds_1] = 1
# modify the way to get the indexes
pos_inds = (label > 0).nonzero().view(-1)
# pos_inds = (label >= 1).nonzero().view(-1)
target[pos_inds] = 1.0
# pos_inds_test = target.nonzero().view(-1)
method_1 = True
normalization = True
method_2 = False
threshold = 0.66
# threshold = torch.min(iou[pos_inds]).item()
method_3 = False
target = target.type_as(pred)
if method_1:
if normalization:
iou_weights = (1 - target) + (target * iou).pow(eta)
# normalized to keep the sum of loss for positive examples unchanged;
raw2 = raw1*iou_weights
normalizer = (raw1 * target).sum() / ((raw2 * target).sum() + 1e-6)
normalized_iou_weights = (1 - target) + (target * iou).pow(eta) * normalizer
normalized_iou_weights = normalized_iou_weights.detach()
raw = raw1*normalized_iou_weights
else:
weight_pos = 1.8
iou_weights = (1 - target) + (target * iou).pow(eta)*weight_pos
iou_weights = iou_weights.detach()
raw = raw1*iou_weights
elif method_2:
iou_weights = (1 - target) + (target*(1 + (iou - threshold))).pow(eta)
iou_weights = iou_weights.detach()
raw = raw1 * iou_weights
elif method_3:
ones_weight = iou.new_ones(iou.size(0))
iou_weights_1 = torch.where(iou > threshold, 1.0 + (iou - threshold), ones_weight)
# iou_weights = (1 - target) + (target * iou_weights_1).pow(eta)
iou_weights = (1 - target) + target * iou_weights_1
iou_weights = iou_weights.detach()
raw = raw1 * iou_weights
# raw = (raw1 * iou_weights +raw1)/2
# print('test_loss')
if avg_factor is None:
# avg_factor = max(torch.sum(normalized_iou_weights).float().item(), 1.)
avg_factor = max(torch.sum(weight > 0).float().item(), 1.)
if reduce:
return torch.sum(raw * weight)[None] / avg_factor
else:
return raw * weight / avg_factor
def consistent_loss(pred, label, weight, iou, avg_factor=None, reduce=True):
"""
:param pred: tesnor of shape (batch*num_samples, num_class)
:param label: tensor of shape (batch*num_samples), store gt labels such as
0, 1, 2, 80 for corresponding class(0 represent background).
:param weight: tensor of shape (batch*num_samples), 1 for all the elements;
:param iou: tensor of shape (batch*num_samples), iou between proposals and corresponding ground
truth boxes for positives and 0 for negatives.
:param avg_factor:
:param reduce:
:return:
"""
if avg_factor is None:
avg_factor = max(torch.sum(weight > 0).float().item(), 1.)
raw1 = F.cross_entropy(pred, label, reduction='none')
target = iou.new_zeros(iou.size(0))
pos_inds = (label > 0).nonzero().view(-1)
target[pos_inds] = 1.0
threshold = 0.5
ones_weight = iou.new_ones(iou.size(0))
iou_weights_1 = torch.where(iou > threshold, 1.0 + (iou - threshold), ones_weight)
iou_weights = (1 - target) + target * iou_weights_1
iou_weights = iou_weights.detach()
raw = raw1 * iou_weights
if reduce:
return torch.sum(raw * weight)[None] / avg_factor
else:
return raw * weight / avg_factor
def iou_balanced_binary_cross_entropy(pred, label, weight, iou, eta = 1.5, avg_factor=None, reduce=True):
"""
:param pred: tensor of shape (num_examples, 1)
:param label: tensor of shape (num_examples, 1)
:param weight: tensor of shape (num_examples, 1)
:param iou: tensor of shape (num_examples), containing the iou for all the regressed
positive examples.
:param eta:
:param avg_factor:
:return:
"""
if pred.dim() != label.dim():
label, weight = _expand_binary_labels(label, weight, pred.size(-1))
if avg_factor is None:
avg_factor = max(torch.sum(weight > 0).float().item(), 1.)
raw1 = F.binary_cross_entropy_with_logits(pred, label.float(),reduction='none')
target = label.new_zeros(label.size())
# target_1 = iou.new_zeros(iou.size(0))
# the way to get the indexes of positive example may be wrong; is it wright?
# pos_inds_1 = label > 0
# target_1[pos_inds_1] = 1
# modify the way to get the indexes
# label_squeeze = torch.squeeze(label)
# pos_inds = (label > 0).nonzero().view(-1)
# print('the size of label is ', label.size())
pos_inds = (label > 0).nonzero()
# print('the size of label_squeeze is ', label_squeeze.size())
target[pos_inds] = 1
# print('the num of positive examples is', torch.sum(target))
# print('the num of positive examples for target_1 is', torch.sum(target_1))
normalization = True
if normalization:
target = target.type_as(pred)
iou = iou.unsqueeze(-1)
# print('the size of target is ', target.size())
# print('the size of iou is ', iou.size())
# print('the size of iou_1 is ', iou_1.size())
iou_weights = (1 - target) + (target * iou).pow(eta)
# print('the size of iou_weights is ', iou_weights.size())
# print('the size of raw1 is ', raw1.size())
# iou_weights.unsqueeze(1)
# normalized to keep the sum of loss for positive examples unchanged;
raw2 = raw1 * iou_weights
normalizer = (raw1 * target).sum() / ((raw2 * target).sum() + 1e-6)
normalized_iou_weights = (1 - target) + (target * iou).pow(eta) * normalizer
normalized_iou_weights = normalized_iou_weights.detach()
raw = raw1 * normalized_iou_weights
else:
target = target.type_as(pred)
weight_pos = 1.8
iou_weights = (1 - target) + (target * iou).pow(eta) * weight_pos
iou_weights = iou_weights.detach()
raw = raw1 * iou_weights
if reduce:
return torch.sum(raw * weight)[None] / avg_factor
else:
return raw * weight / avg_factor
# return F.binary_cross_entropy_with_logits(
# pred, label.float(), weight.float(),
# reduction='sum')[None] / avg_factor
# Known from the definition of weight in file anchor_target.py,
# all the elements of tensor 'weight' are 1.
# added by Shengkai Wu
# The focal loss is only computed for negative examples, and the standard binary cross
# entropy loss is computed for the positive examples. This is designed to investigate
# whether hard example mining for positive examples is beneficial for the performance.
def weighted_sigmoid_focal_loss(pred,
target,
weight,
gamma=2.0,
alpha=0.25,
avg_factor=None,
num_classes=80):
"""
note that
:param pred: tensor of shape (batch*A*width*height, num_class)
:param target: tensor of shape (batch*A*width*height, num_class), only the element for the
positive labels are 1.
:param weight: tensor of shape (batch*A*width*height, num_class), 1 for pos and neg, 0 for the others
:param gamma:
:param alpha:
:param avg_factor:
:param num_classes:
:return:
"""
if avg_factor is None:
avg_factor = torch.sum(weight > 0).float().item() / num_classes + 1e-6
return py_sigmoid_focal_loss(
pred, target, weight, gamma=gamma, alpha=alpha,
reduction='sum')[None] / avg_factor
# added by Shengkai Wu
# iou-balanced classification loss is designed to strengthen the correlation between classificaiton and
# localization task. The goal is to make that the detections with high IOU with the ground truth boxes also have
# high classification scores.
def iou_balanced_sigmoid_focal_loss(pred,
target,
weight,
iou,
gamma=2.0,
alpha=0.25,
eta=1.5,
avg_factor=None,
num_classes=80):
"""
:param pred: tensor of shape (batch*A*width*height, num_class)
:param target: tensor of shape (batch*A*width*height, num_class), only the positive label is
assigned 1, 0 for others.
:param weight: tensor of shape (batch*A*width*height, num_class), 1 for pos and neg, 0 for the others.
:param iou: tensor of shape (batch*A*width*height), store the iou between predicted boxes and its
corresponding ground truth boxes for the positives and the iou between the predicted boxes and
anchors for negatives.
:param gamma:
:param alpha:
:param eta: control the suppression for the positives of low iou.
:param avg_factor: num_positive_samples. If None,
:param num_classes:
:return:
"""
# if avg_factor is None:
# avg_factor = torch.sum(target).float().item() + 1e-6
# use_diff_thr = True
# pred_sigmoid = pred.sigmoid()
target = target.type_as(pred)
loss1 = py_sigmoid_focal_loss(
pred, target, weight, gamma=gamma, alpha=alpha,
reduction='none')
IoU_balanced_Cls = True
threshold = 0.5
if IoU_balanced_Cls:
# compute the normalized weights so that the loss produced by the positive examples
# doesn't change.
iou_expanded = iou.view(-1, 1).expand(-1, target.size()[1])
iou_weights = (1 - target) + (target * iou_expanded).pow(eta)
# iou_weights = iou_weights.detach()
loss2 = loss1*iou_weights
normalizer = (loss1*target).sum()/((loss2*target).sum()+1e-6)
# normalizer = 2.1
normalized_iou_weights = (1-target) + (target*iou_expanded).pow(eta)*normalizer
normalized_iou_weights = normalized_iou_weights.detach()
loss = loss1*normalized_iou_weights
# print('test')
else:
# consistent loss
iou_expanded = iou.view(-1, 1).expand(-1, target.size()[1])
ones_weight = iou_expanded.new_ones(iou_expanded.size())
# print('ones_weight.size() is ', ones_weight.size())
iou_weights_1 = torch.where(iou_expanded > threshold, 1.0 + (iou_expanded - threshold), ones_weight)
# iou_weights = (1 - target) + (target * iou_weights_1).pow(eta)
iou_weights = (1 - target) + target * iou_weights_1
iou_weights = iou_weights.detach()
# loss = loss1 * iou_weights
balance_factor = 0.6
loss = loss1*balance_factor + loss1 * iou_weights*(1-balance_factor)
return torch.sum(loss)[None] / avg_factor
# Known from the definition of weight in file anchor_target.py,
# the elements of tensor 'weight' for positive proposals are one.
# added by Shengkai Wu
# implement the focal loss for localization task.
def weighted_iou_balanced_smoothl1(pred, target, iou, weight, beta=1.0, delta=1.5, avg_factor=None):
"""
:param pred: tensor of shape (batch*A*width*height, 4) or (batch*num_pos, 4)
:param target: tensor of shape (batch*A*width*height, 4), store the parametrized coordinates of target boxes
for the positive anchors and other values are set to be 0. Or tensor of shape (batch*num_pos, 4)
:param iou: tensor of shape (batch*A*width*height)Or tensor of shape (batch*num_pos), store the iou between
predicted boxes and its corresponding groundtruth boxes for the positives and the iou between the predicted
boxes and anchors for negatives.
:param weight: tensor of shape (batch*A*width*height, 4), only the weights for positive anchors are set to
be 1 and other values are set to be 0. Or tensor of shape (batch*num_pos, 4), all the elements are 1.
:param beta:
:param delta: control the suppression for the outliers.
:param avg_factor:
:return:
"""
# the pred and target are transformed to image domain and represented by top-left and bottom-right corners.
assert pred.size() == target.size() and target.numel() > 0
# ignore the positive examples of which the iou after regression is smaller
# than 0.5;
ignore_outliers = False
iou_threshold = 0.5
if ignore_outliers:
filter = iou.new_zeros(iou.size())
filter_extend = filter.view(-1, 1).expand(-1, 4)
ind = (iou >= iou_threshold).nonzero()
filter[ind] = 1
iou = iou * filter
iou_expanded = iou.view(-1, 1).expand(-1, 4)
iou_weight = weight * iou_expanded.pow(delta)
iou_weight = iou_weight.detach()
if avg_factor is None:
avg_factor = torch.sum(weight > 0).float().item() / 4 + 1e-6
loss1 = smooth_l1_loss(pred, target, beta, reduction='none')
loss2 = loss1*iou_weight
# loss2 = loss1 *filter_extend
return torch.sum(loss2)[None] / avg_factor
def weighted_iou_regression_loss(iou_pred, iou_target, weight, avg_factor=None):
"""
:param iou_pred: tensor of shape (batch*A*width*height) or (batch*num_pos)
:param iou_target: tensor of shape (batch*A*width*height)Or tensor of shape (batch*num_pos), store the iou between
predicted boxes and its corresponding groundtruth boxes for the positives and the iou between the predicted
boxes and anchors for negatives.
:param weight: tensor of shape (batch*A*width*height) or (batch*num_pos), 1 for positives and 0 for negatives and neutrals.
:param avg_factor:
:return:
"""
# iou_pred_sigmoid = iou_pred.sigmoid()
# iou_target = iou_target.detach()
# L2 loss.
# loss = torch.pow((iou_pred_sigmoid - iou_target), 2)*weight
# Binary cross-entropy loss for the positive examples
loss = F.binary_cross_entropy_with_logits(iou_pred, iou_target, reduction='none')* weight
return torch.sum(loss)[None] / avg_factor
def bounded_iou_loss(pred, target, beta=0.2, eps=1e-3, reduction='mean'):
"""Improving Object Localization with Fitness NMS and Bounded IoU Loss,
https://arxiv.org/abs/1711.00164.
Args:
pred (tensor): Predicted bboxes.
target (tensor): Target bboxes.
beta (float): beta parameter in smoothl1.
eps (float): eps to avoid NaN.
reduction (str): Reduction type.
"""
pred_ctrx = (pred[:, 0] + pred[:, 2]) * 0.5
pred_ctry = (pred[:, 1] + pred[:, 3]) * 0.5
pred_w = pred[:, 2] - pred[:, 0] + 1
pred_h = pred[:, 3] - pred[:, 1] + 1
with torch.no_grad():
target_ctrx = (target[:, 0] + target[:, 2]) * 0.5
target_ctry = (target[:, 1] + target[:, 3]) * 0.5
target_w = target[:, 2] - target[:, 0] + 1
target_h = target[:, 3] - target[:, 1] + 1
dx = target_ctrx - pred_ctrx
dy = target_ctry - pred_ctry
loss_dx = 1 - torch.max(
(target_w - 2 * dx.abs()) /
(target_w + 2 * dx.abs() + eps), torch.zeros_like(dx))
loss_dy = 1 - torch.max(
(target_h - 2 * dy.abs()) /
(target_h + 2 * dy.abs() + eps), torch.zeros_like(dy))
loss_dw = 1 - torch.min(target_w / (pred_w + eps), pred_w /
(target_w + eps))
loss_dh = 1 - torch.min(target_h / (pred_h + eps), pred_h /
(target_h + eps))
loss_comb = torch.stack([loss_dx, loss_dy, loss_dw, loss_dh],
dim=-1).view(loss_dx.size(0), -1)
loss = torch.where(loss_comb < beta, 0.5 * loss_comb * loss_comb / beta,
loss_comb - 0.5 * beta)
reduction_enum = F._Reduction.get_enum(reduction)
# none: 0, mean:1, sum: 2
if reduction_enum == 0:
return loss
elif reduction_enum == 1:
return loss.sum() / pred.numel()
elif reduction_enum == 2:
return loss.sum()
def accuracy(pred, target, topk=1):
"""
:param pred: (batch*num_sample, C)
:param target: (batch*num_sample)
:param topk:
:return:
"""
if isinstance(topk, int):
topk = (topk, )
return_single = True
else:
return_single = False
maxk = max(topk)
_, pred_label = pred.topk(maxk, 1, True, True) # (batch*num_sample, 1)
pred_label = pred_label.t() # (1, batch*num_sample)
correct = pred_label.eq(target.view(1, -1).expand_as(pred_label)) # (1, batch*num_sample)
res = []
for k in topk:
correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
res.append(correct_k.mul_(100.0 / pred.size(0)))
return res[0] if return_single else res
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220,
220,
581,
13,
33295,
7,
30283,
62,
74,
13,
76,
377,
41052,
3064,
13,
15,
1220,
2747,
13,
7857,
7,
15,
22305,
198,
220,
220,
220,
1441,
581,
58,
15,
60,
611,
1441,
62,
29762,
2073,
581,
628,
198
] | 2.372826 | 7,588 |
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