content
stringlengths 1
1.04M
| input_ids
sequencelengths 1
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int64 1
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|
|---|---|---|---|
import torch
import numpy as np
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import jwt
from django.conf import settings
from rest_framework import authentication, exceptions
from .models import User
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#!/usr/bin/env python
import csv
import sys
from pymongo import Connection, objectid
import config
import utils
connection = Connection()
db = connection[config.CENSUS_DB]
collection = db[config.GEOGRAPHIES_COLLECTION]
collection_2000 = db[config.GEOGRAPHIES_2000_COLLECTION]
row_count = 0
inserts = 0
KEY_MAPPINGS = {}
CROSSWALK_FIELDS_BY_TABLE = {}
# Maps 2010 field names to their 2000 equivalents
with open('field_mappings_2000_2010.csv', 'rU') as f:
reader = csv.DictReader(f)
for row in reader:
# Skip fields that don't map
if not row['field_2000'].strip():
continue
if not row['field_2010'].strip():
continue
# TODO - skipping computed fields
if '-' in row['field_2000'] or '+' in row['field_2000']:
continue
if '-' in row['field_2010'] or '+' in row['field_2010']:
continue
# Fields in 2000 may map to multiple fields in 2010 (e.g. P001001 -> P001001 and P004001)
if row['field_2000'] not in KEY_MAPPINGS:
KEY_MAPPINGS[row['field_2000']] = []
KEY_MAPPINGS[row['field_2000']].append(row['field_2010'])
# Load crosswalk lookup table
with open('sf_crosswalk_key.csv') as f:
reader = csv.reader(f)
for row in reader:
CROSSWALK_FIELDS_BY_TABLE[row[0]] = row[1]
for geography in collection.find({}, fields=['data', 'geoid', 'metadata.NAME', 'sumlev', 'xwalk']):
row_count += 1
data = {}
# TRACTS - require true crosswalk
if geography['sumlev'] == config.SUMLEV_TRACT:
geography_2000s = list(utils.find_geographies_for_xwalk(collection_2000, geography, fields=['data', 'geoid']))
# Tract is new
if not geography_2000s:
continue
for table in geography_2000s[0]['data']['2000']:
crosswalk_field = CROSSWALK_FIELDS_BY_TABLE[table]
# Table contains medians or other values that can't be crosswalked
if not crosswalk_field:
continue
for k, v in geography_2000s[0]['data']['2000'][table].items():
try:
keys_2010 = KEY_MAPPINGS[k]
except KeyError:
# Skip 2000 fields that don't exist in 2010
continue
# Skip 2000 fields that don't have an equivalent in 2010
if not keys_2010:
continue
# Copy value to all 2010 fields which are comparable to this field in 2000
for key_2010 in keys_2010:
table_2010 = utils.parse_table_from_key(key_2010)
if table_2010 not in data:
data[table_2010] = {}
parts = []
for g in geography_2000s:
value = float(g['data']['2000'][table][k])
pct = geography['xwalk'][g['geoid']][crosswalk_field]
parts.append(value * pct)
data[table_2010][key_2010] = int(sum(parts))
# OTHER SUMLEVS - can be directly compared by geoid
else:
geography_2000 = collection_2000.find_one({ 'geoid': geography['geoid'] }, fields=['data'])
if not geography_2000:
print 'Couldn\'t find matching 2000 geography for %s (%s)' % (geography['metadata']['NAME'], geography['geoid'])
continue
for table in geography_2000['data']['2000']:
for k, v in geography_2000['data']['2000'][table].items():
try:
keys_2010 = KEY_MAPPINGS[k]
except KeyError:
# Skip 2000 fields that don't exist in 2010
continue
# Skip 2000 fields that don't have an equivalent in 2010
if not keys_2010:
continue
# Copy value to all 2010 fields which are comparable to this field in 2000
for key_2010 in keys_2010:
table_2010 = utils.parse_table_from_key(key_2010)
if table_2010 not in data:
data[table_2010] = {}
parts = []
data[table_2010][key_2010] = geography_2000['data']['2000'][table][k]
geography['data']['2000'] = data
collection.update({ '_id': objectid.ObjectId(geography['_id']) }, { '$set': { 'data': geography['data'] } }, safe=True)
inserts += 1
print 'Row count: %i' % row_count
print 'Inserted: %i' % inserts
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# -*- coding: utf-8 -*-
"""
ontraportlib.models.charge_now_25_enum
This file was automatically generated by APIMATIC v2.0 ( https://apimatic.io ) on 11/14/2017
"""
class ChargeNow25Enum(object):
"""Implementation of the 'ChargeNow25' enum.
TODO: type enum description here.
Attributes:
CHARGENOW: TODO: type description here.
CHARGELOG: TODO: type description here.
"""
CHARGENOW = 'chargeNow'
CHARGELOG = 'chargeLog'
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] | 2.314815 | 216 |
import datetime
import logging
from eventstudy.eventstudy import *
from eventstudy.ibdatareader import IBDataReader
from examples.airline_crashes.ac_event_matrix import AirlineCrashEventMatrix
if __name__ == "__main__":
main()
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import functools
from collections import defaultdict
from river import utils
class ConfusionMatrix:
"""Confusion Matrix for binary and multi-class classification.
Parameters
----------
classes
The initial set of classes. This is optional and serves only for displaying purposes.
Examples
--------
>>> from river import metrics
>>> y_true = ['cat', 'ant', 'cat', 'cat', 'ant', 'bird']
>>> y_pred = ['ant', 'ant', 'cat', 'cat', 'ant', 'cat']
>>> cm = metrics.ConfusionMatrix()
>>> for yt, yp in zip(y_true, y_pred):
... cm = cm.update(yt, yp)
>>> cm
ant bird cat
ant 2 0 0
bird 0 0 1
cat 1 0 2
>>> cm['bird']['cat']
1.0
Notes
-----
This confusion matrix is a 2D matrix of shape `(n_classes, n_classes)`, corresponding
to a single-target (binary and multi-class) classification task.
Each row represents `true` (actual) class-labels, while each column corresponds
to the `predicted` class-labels. For example, an entry in position `[1, 2]` means
that the true class-label is 1, and the predicted class-label is 2 (incorrect prediction).
This structure is used to keep updated statistics about a single-output classifier's
performance and to compute multiple evaluation metrics.
"""
def __getitem__(self, key):
"""Syntactic sugar for accessing the counts directly."""
return self.data[key]
@property
@property
@property
@property
@property
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] | 2.793961 | 563 |
# Copyright 2022 AI Singapore
#
# 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.
"""
Blurs area bounded by bounding boxes over detected object.
"""
from typing import Any, Dict, List
import cv2
import numpy as np
from peekingduck.pipeline.nodes.abstract_node import AbstractNode
class Node(AbstractNode): # pylint: disable=too-few-public-methods
"""Blurs area bounded by bounding boxes on image.
The :mod:`draw.blur_bbox` node blurs the areas of the image bounded by the
bounding boxes output from an object detection model.
Inputs:
|img_data|
|bboxes_data|
Outputs:
|img_data|
Configs:
blur_kernel_size (:obj:`int`): **default = 50**. |br|
This defines the kernel size used in the blur filter. Larger values
of ``blur_kernel_size`` gives more intense blurring.
"""
@staticmethod
def _blur(
bboxes: List[np.ndarray], image: np.ndarray, blur_kernel_size: int
) -> np.ndarray:
"""Blurs the area bounded by bbox in an image."""
height = image.shape[0]
width = image.shape[1]
for bbox in bboxes:
x_1, y_1, x_2, y_2 = bbox
y_1, y_2 = int(y_1 * height), int(y_2 * height)
x_1, x_2 = int(x_1 * width), int(x_2 * width)
# to get the area bounded by bbox
bbox_image = image[y_1:y_2, x_1:x_2, :]
# apply the blur using blur filter from opencv
blur_bbox_image = cv2.blur(bbox_image, (blur_kernel_size, blur_kernel_size))
image[y_1:y_2, x_1:x_2, :] = blur_bbox_image
return image
def run(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
"""Reads the image input and returns the image, with the areas bounded
by the bboxes blurred.
Args:
inputs (dict): Dictionary of inputs with keys "img", "bboxes"
Returns:
outputs (dict): Output in dictionary format with key "img"
"""
blurred_img = self._blur(inputs["bboxes"], inputs["img"], self.blur_kernel_size)
outputs = {"img": blurred_img}
return outputs
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92,
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220,
220,
220,
220,
220,
220,
220,
1441,
23862,
198
] | 2.496682 | 1,055 |
import subprocess
import os
import platform
def open_via_helper(filepath):
"""Open a file via the system viewer for the specified file.
Returns a handle to a cleanup function. You should call
handle() before exiting."""
if hasattr(os,"startfile"):
# On Windows the os module has a "startfile" function just for this
os.startfile(filepath)
return lambda: None # startfile() fully detaches its subprocess
elif platform.system() == "Darwin":
# Mac OS X has a "open" command
subproc = subprocess.Popen(["open",filepath])
return subproc.communicate # caller should call this to wait for subproc to finish
else:
# Assume Linux/FreeDesktop
subproc = subprocess.Popen(["xdg-open",filepath])
return subproc.communicate # caller should call this to wait for subproc to finish
pass
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198
] | 2.878289 | 304 |
from graphviz import Digraph
def draw(codes):
'''
:param codes:
:return:
'''
dot = Digraph(comment='The Round Table', filename='visible', format='png', strict=False)
idx = 1
for code in codes:
s = code['sign'] + '|' + ''.join(code['affi_sign'])
dot.node(str(idx), s)
idx += 1
for i, code in enumerate(codes):
if 'son' in code:
dot.edge(str(i+1), str(code['son'][0] + 1))
if 'left' in code:
dot.edge(str(i+1), str(code['left'] + 1))
if 'right' in code:
dot.edge(str(i+1), str(code['right'] + 1))
print('yyyyyyy')
dot.render()
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13,
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201,
198,
220,
220,
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3601,
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22556,
88,
11537,
201,
198,
220,
220,
220,
16605,
13,
13287,
3419,
201,
198
] | 1.947977 | 346 |
from .encodings import single, repeat, bernoulli, poisson, rank_order
from .loaders import bernoulli_loader, poisson_loader, rank_order_loader
from .encoders import (
Encoder,
NullEncoder,
SingleEncoder,
RepeatEncoder,
BernoulliEncoder,
PoissonEncoder,
RankOrderEncoder,
)
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] | 2.640351 | 114 |
from selenium import webdriver
from selenium.webdriver.common.keys import Keys
from selenium.common.exceptions import WebDriverException
import os
import sys
import argparse
# adding optional args, url of wog's score and driver of selenium-chrome
parser = argparse.ArgumentParser()
parser.add_argument('--url', action='store', help='Enter URL to test',
required=False, default='127.0.0.1:8777')
parser.add_argument('--driver', action='store', help='Enter selenium driver location',
required=False, default=r"C:\Users\tomer9000\chromedriver.exe")
arg = parser.parse_args()
def test_scores_service(app_url, driver_location):
'''
Test wog web service.
:param app_url: URL where the wog web service runs.
:param driver_location: selenium chromedriver location.
:return: True if the score is between 1 to 1,000, else False
'''
if os.name == 'nt':
# if os that runs e2e is Windows.
driver = webdriver.Chrome(fr"{driver_location}")
return _query_url(driver, app_url)
elif os.name == 'posix':
# If os that runs e2e is Unix-based.
chrome_options = webdriver.ChromeOptions()
chrome_options.add_argument('--no-sandbox')
chrome_options.add_argument('--window-size=1420,1080')
chrome_options.add_argument('--headless')
chrome_options.add_argument('--disable-gpu')
driver = webdriver.Chrome(fr"{driver_location}", chrome_options=chrome_options)
return _query_url(driver, app_url)
def _query_url(driver_to_query, app_url):
"""
Internal usage in order not to run it twice
:param driver_to_query: chromedriver already loaded with the necessary parameters.
:param app_url: URL where the wog web service runs.
:return: True if the score is between 1 to 1,000, else False
"""
try:
driver_to_query.get(f"http://{app_url}/")
q = driver_to_query.find_element_by_id("score")
num_q = int(q.text)
driver_to_query.close()
return (num_q >= 1 and num_q <= 1000)
except WebDriverException:
driver_to_query.close()
print(f"Website {app_url} isn't reachable")
def main_function(app_url, driver_location):
"""
call our tests function.
:param app_url: URL where the wog web service runs.
:param driver_location: selenium chromedriver location.
:return: -1 as an OS exit code if the tests failed and 0 if they passed.
"""
if test_scores_service(app_url, driver_location):
return sys.exit(0)
return sys.exit(-1)
print(main_function(arg.url, arg.driver))
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] | 2.608392 | 1,001 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# File : Ampel-plot/ampel-plot-browse/ampel/plot/util/clipboard.py
# License : BSD-3-Clause
# Author : vb <[email protected]>
# Date : 16.11.2021
# Last Modified Date: 22.11.2021
# Last Modified By : vb <[email protected]>
import platform, base64, json, time, re, gc # type: ignore[import]
from typing import Any, Optional, Union
from ampel.util.recursion import walk_and_process_dict
from ampel.plot.SVGCollection import SVGCollection
from ampel.plot.SVGPlot import SVGPlot
from ampel.model.PlotBrowseOptions import PlotBrowseOptions
from ampel.plot.util.show import show_collection, show_svg_plot
if platform.system() == 'Darwin':
import AppKit # type: ignore[import] # noqa
else:
import pyperclip # type: ignore[import] # noqa
print_func = print
def read_from_clipboard(
pbo: PlotBrowseOptions,
keyboard_callback: Any,
gui_callback: Any = None,
exit_on_interrupt: bool = True
) -> None:
"""
Note: method never returns unless CTRL-C is pressed or gui_callback raises KeyboardInterrupt
"""
recent_value = ""
scol = _new_col(pbo)
pattern = re.compile(r"(?:NumberLong|ObjectId)\((.*?)\)", re.DOTALL)
#from bson.json_util import loads
if gui_callback is None:
gui_callback = lambda: None
gui_callback()
print_func("Monitoring clipboard...")
if platform.system() == 'Darwin':
board = AppKit.NSPasteboard.generalPasteboard()
get_cbc = lambda: board.changeCount()
else:
pyperclip.copy('')
get_cbc = pyperclip.paste()
try:
while True:
tmp_value = get_cbc()
gui_callback()
if tmp_value != recent_value:
recent_value = tmp_value
if platform.system() == 'Darwin':
tmp_value = board.stringForType_(AppKit.NSStringPboardType)
try:
# if pattern.match(tmp_value): # no time to check why it doesn't work
if tmp_value and ("NumberLong" in tmp_value or "ObjectId" in tmp_value):
j = json.loads(
re.sub(pattern, r"\1", tmp_value)
)
else:
j = json.loads(tmp_value)
ctrl_pressed = keyboard_callback()
if pbo.debug and ctrl_pressed:
print_func("CTRL is pressed")
if (
(isinstance(j, dict) and 'svg' not in j) or
(isinstance(j, list) and len(j) > 0 and 'svg' not in j[0])
):
plots: list[dict] = []
walk_and_process_dict(
arg = j,
callback = _gather_plots,
match = ['plot'],
plots = plots,
debug = pbo.debug
)
if plots:
scol = _handle_json(plots, pbo, scol, ctrl_pressed)
else:
scol = _handle_json(j, pbo, scol, ctrl_pressed)
gc.collect()
except KeyboardInterrupt:
raise
except json.decoder.JSONDecodeError:
if pbo.debug:
import traceback
print_func("JSONDecodeError")
print_func(traceback.format_exc())
print_func(tmp_value)
except Exception:
import traceback
with open('error.log', 'w') as f:
traceback.print_exc(file=f)
print_func(traceback.format_exc())
time.sleep(0.1)
except KeyboardInterrupt:
if exit_on_interrupt:
import sys
print_func("\nUntil next time...\n")
sys.exit(0)
def _load_and_add_plot(j: Any, scol: SVGCollection, pbo: PlotBrowseOptions) -> SVGCollection:
""" will display and reset collection if number of plots exceeds pbo.stack """
if (d := _check_adapt(j)):
splot = SVGPlot(d) # type: ignore
print_func("Adding", splot._record['name'])
scol.add_svg_plot(splot)
if (len(scol._svgs) % pbo.stack) == 0:
print_func(f"Displaying plot stack ({len(scol._svgs)} figures)")
show_collection(scol, pbo)
scol = _new_col(pbo)
return scol
def _gather_plots(path, k, d, **kwargs) -> None:
""" Used by walk_and_process_dict(...) """
if isinstance(d[k], dict):
if kwargs.get('debug'):
print_func(f"Found {path}.{k}: {d[k]['name']}")
kwargs['plots'].append(d[k])
elif isinstance(d[k], list):
for i, el in enumerate(d[k]):
if kwargs.get('debug'):
print_func(f"Found {path}.{k}.{i}: {d[k][i]['name']}")
kwargs['plots'].append(el)
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] | 2.309685 | 1,776 |
#
# ElGamal.py : ElGamal encryption/decryption and signatures
#
# Part of the Python Cryptography Toolkit
#
# Originally written by: A.M. Kuchling
#
# ===================================================================
# The contents of this file are dedicated to the public domain. To
# the extent that dedication to the public domain is not available,
# everyone is granted a worldwide, perpetual, royalty-free,
# non-exclusive license to exercise all rights associated with the
# contents of this file for any purpose whatsoever.
# No rights are reserved.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS
# BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN
# ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
# ===================================================================
"""ElGamal public-key algorithm (randomized encryption and signature).
Signature algorithm
-------------------
The security of the ElGamal signature scheme is based (like DSA) on the discrete
logarithm problem (DLP_). Given a cyclic group, a generator *g*,
and an element *h*, it is hard to find an integer *x* such that *g^x = h*.
The group is the largest multiplicative sub-group of the integers modulo *p*,
with *p* prime.
The signer holds a value *x* (*0<x<p-1*) as private key, and its public
key (*y* where *y=g^x mod p*) is distributed.
The ElGamal signature is twice as big as *p*.
Encryption algorithm
--------------------
The security of the ElGamal encryption scheme is based on the computational
Diffie-Hellman problem (CDH_). Given a cyclic group, a generator *g*,
and two integers *a* and *b*, it is difficult to find
the element *g^{ab}* when only *g^a* and *g^b* are known, and not *a* and *b*.
As before, the group is the largest multiplicative sub-group of the integers
modulo *p*, with *p* prime.
The receiver holds a value *a* (*0<a<p-1*) as private key, and its public key
(*b* where *b*=g^a*) is given to the sender.
The ElGamal ciphertext is twice as big as *p*.
Domain parameters
-----------------
For both signature and encryption schemes, the values *(p,g)* are called
*domain parameters*.
They are not sensitive but must be distributed to all parties (senders and
receivers).
Different signers can share the same domain parameters, as can
different recipients of encrypted messages.
Security
--------
Both DLP and CDH problem are believed to be difficult, and they have been proved
such (and therefore secure) for more than 30 years.
The cryptographic strength is linked to the magnitude of *p*.
In 2012, a sufficient size for *p* is deemed to be 2048 bits.
For more information, see the most recent ECRYPT_ report.
Even though ElGamal algorithms are in theory reasonably secure for new designs,
in practice there are no real good reasons for using them.
The signature is four times larger than the equivalent DSA, and the ciphertext
is two times larger than the equivalent RSA.
Functionality
-------------
This module provides facilities for generating new ElGamal keys and for constructing
them from known components. ElGamal keys allows you to perform basic signing,
verification, encryption, and decryption.
>>> from Crypto import Random
>>> from Crypto.Random import random
>>> from Crypto.PublicKey import ElGamal
>>> from Crypto.Util.number import GCD
>>> from Crypto.Hash import SHA
>>>
>>> message = "Hello"
>>> key = ElGamal.generate(1024, Random.new().read)
>>> h = SHA.new(message).digest()
>>> while 1:
>>> k = random.StrongRandom().randint(1,key.p-1)
>>> if GCD(k,key.p-1)==1: break
>>> sig = key.sign(h,k)
>>> ...
>>> if key.verify(h,sig):
>>> print "OK"
>>> else:
>>> print "Incorrect signature"
.. _DLP: http://www.cosic.esat.kuleuven.be/publications/talk-78.pdf
.. _CDH: http://en.wikipedia.org/wiki/Computational_Diffie%E2%80%93Hellman_assumption
.. _ECRYPT: http://www.ecrypt.eu.org/documents/D.SPA.17.pdf
"""
__revision__ = "$Id$"
__all__ = ['generate', 'construct', 'error', 'ElGamalobj']
from Crypto.PublicKey.pubkey import *
from Crypto.Util import number
from Crypto import Random
# Generate an ElGamal key with N bits
def generate(bits, randfunc, progress_func=None):
"""Randomly generate a fresh, new ElGamal key.
The key will be safe for use for both encryption and signature
(although it should be used for **only one** purpose).
:Parameters:
bits : int
Key length, or size (in bits) of the modulus *p*.
Recommended value is 2048.
randfunc : callable
Random number generation function; it should accept
a single integer N and return a string of random data
N bytes long.
progress_func : callable
Optional function that will be called with a short string
containing the key parameter currently being generated;
it's useful for interactive applications where a user is
waiting for a key to be generated.
:attention: You should always use a cryptographically secure random number generator,
such as the one defined in the ``Crypto.Random`` module; **don't** just use the
current time and the ``random`` module.
:Return: An ElGamal key object (`ElGamalobj`).
"""
obj=ElGamalobj()
# Generate a safe prime p
# See Algorithm 4.86 in Handbook of Applied Cryptography
if progress_func:
progress_func('p\n')
while 1:
q = bignum(getPrime(bits-1, randfunc))
obj.p = 2*q+1
if number.isPrime(obj.p, randfunc=randfunc):
break
# Generate generator g
# See Algorithm 4.80 in Handbook of Applied Cryptography
# Note that the order of the group is n=p-1=2q, where q is prime
if progress_func:
progress_func('g\n')
while 1:
# We must avoid g=2 because of Bleichenbacher's attack described
# in "Generating ElGamal signatures without knowning the secret key",
# 1996
#
obj.g = number.getRandomRange(3, obj.p, randfunc)
safe = 1
if pow(obj.g, 2, obj.p)==1:
safe=0
if safe and pow(obj.g, q, obj.p)==1:
safe=0
# Discard g if it divides p-1 because of the attack described
# in Note 11.67 (iii) in HAC
if safe and divmod(obj.p-1, obj.g)[1]==0:
safe=0
# g^{-1} must not divide p-1 because of Khadir's attack
# described in "Conditions of the generator for forging ElGamal
# signature", 2011
ginv = number.inverse(obj.g, obj.p)
if safe and divmod(obj.p-1, ginv)[1]==0:
safe=0
if safe:
break
# Generate private key x
if progress_func:
progress_func('x\n')
obj.x=number.getRandomRange(2, obj.p-1, randfunc)
# Generate public key y
if progress_func:
progress_func('y\n')
obj.y = pow(obj.g, obj.x, obj.p)
return obj
def construct(tup):
"""Construct an ElGamal key from a tuple of valid ElGamal components.
The modulus *p* must be a prime.
The following conditions must apply:
- 1 < g < p-1
- g^{p-1} = 1 mod p
- 1 < x < p-1
- g^x = y mod p
:Parameters:
tup : tuple
A tuple of long integers, with 3 or 4 items
in the following order:
1. Modulus (*p*).
2. Generator (*g*).
3. Public key (*y*).
4. Private key (*x*). Optional.
:Return: An ElGamal key object (`ElGamalobj`).
"""
obj=ElGamalobj()
if len(tup) not in [3,4]:
raise ValueError('argument for construct() wrong length')
for i in range(len(tup)):
field = obj.keydata[i]
setattr(obj, field, tup[i])
return obj
class ElGamalobj(pubkey):
"""Class defining an ElGamal key.
:undocumented: __getstate__, __setstate__, __repr__, __getattr__
"""
#: Dictionary of ElGamal parameters.
#:
#: A public key will only have the following entries:
#:
#: - **y**, the public key.
#: - **g**, the generator.
#: - **p**, the modulus.
#:
#: A private key will also have:
#:
#: - **x**, the private key.
keydata=['p', 'g', 'y', 'x']
def encrypt(self, plaintext, K):
"""Encrypt a piece of data with ElGamal.
:Parameter plaintext: The piece of data to encrypt with ElGamal.
It must be numerically smaller than the module (*p*).
:Type plaintext: byte string or long
:Parameter K: A secret number, chosen randomly in the closed
range *[1,p-2]*.
:Type K: long (recommended) or byte string (not recommended)
:Return: A tuple with two items. Each item is of the same type as the
plaintext (string or long).
:attention: selection of *K* is crucial for security. Generating a
random number larger than *p-1* and taking the modulus by *p-1* is
**not** secure, since smaller values will occur more frequently.
Generating a random number systematically smaller than *p-1*
(e.g. *floor((p-1)/8)* random bytes) is also **not** secure.
In general, it shall not be possible for an attacker to know
the value of any bit of K.
:attention: The number *K* shall not be reused for any other
operation and shall be discarded immediately.
"""
return pubkey.encrypt(self, plaintext, K)
def decrypt(self, ciphertext):
"""Decrypt a piece of data with ElGamal.
:Parameter ciphertext: The piece of data to decrypt with ElGamal.
:Type ciphertext: byte string, long or a 2-item tuple as returned
by `encrypt`
:Return: A byte string if ciphertext was a byte string or a tuple
of byte strings. A long otherwise.
"""
return pubkey.decrypt(self, ciphertext)
def sign(self, M, K):
"""Sign a piece of data with ElGamal.
:Parameter M: The piece of data to sign with ElGamal. It may
not be longer in bit size than *p-1*.
:Type M: byte string or long
:Parameter K: A secret number, chosen randomly in the closed
range *[1,p-2]* and such that *gcd(k,p-1)=1*.
:Type K: long (recommended) or byte string (not recommended)
:attention: selection of *K* is crucial for security. Generating a
random number larger than *p-1* and taking the modulus by *p-1* is
**not** secure, since smaller values will occur more frequently.
Generating a random number systematically smaller than *p-1*
(e.g. *floor((p-1)/8)* random bytes) is also **not** secure.
In general, it shall not be possible for an attacker to know
the value of any bit of K.
:attention: The number *K* shall not be reused for any other
operation and shall be discarded immediately.
:attention: M must be be a cryptographic hash, otherwise an
attacker may mount an existential forgery attack.
:Return: A tuple with 2 longs.
"""
return pubkey.sign(self, M, K)
def verify(self, M, signature):
"""Verify the validity of an ElGamal signature.
:Parameter M: The expected message.
:Type M: byte string or long
:Parameter signature: The ElGamal signature to verify.
:Type signature: A tuple with 2 longs as return by `sign`
:Return: True if the signature is correct, False otherwise.
"""
return pubkey.verify(self, M, signature)
object=ElGamalobj
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220,
220,
32233,
2163,
326,
481,
307,
1444,
351,
257,
1790,
4731,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7268,
262,
1994,
11507,
3058,
852,
7560,
26,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
340,
338,
4465,
329,
14333,
5479,
810,
257,
2836,
318,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4953,
329,
257,
1994,
284,
307,
7560,
13,
628,
220,
220,
220,
1058,
1078,
1463,
25,
921,
815,
1464,
779,
257,
8194,
33145,
5713,
4738,
1271,
17301,
11,
198,
220,
220,
220,
220,
220,
220,
220,
884,
355,
262,
530,
5447,
287,
262,
7559,
23919,
78,
13,
29531,
15506,
8265,
26,
12429,
9099,
470,
1174,
655,
779,
262,
198,
220,
220,
220,
220,
220,
220,
220,
1459,
640,
290,
262,
7559,
25120,
15506,
8265,
13,
628,
220,
220,
220,
1058,
13615,
25,
1052,
2574,
34777,
282,
1994,
2134,
357,
63,
9527,
34777,
282,
26801,
63,
737,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
26181,
28,
9527,
34777,
282,
26801,
3419,
198,
220,
220,
220,
1303,
2980,
378,
257,
3338,
6994,
279,
198,
220,
220,
220,
1303,
4091,
978,
42289,
604,
13,
4521,
287,
30579,
286,
27684,
15126,
4867,
198,
220,
220,
220,
611,
4371,
62,
20786,
25,
198,
220,
220,
220,
220,
220,
220,
220,
4371,
62,
20786,
10786,
79,
59,
77,
11537,
198,
220,
220,
220,
981,
352,
25,
198,
220,
220,
220,
220,
220,
220,
220,
10662,
796,
275,
570,
388,
7,
1136,
26405,
7,
9895,
12,
16,
11,
43720,
20786,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
26181,
13,
79,
796,
362,
9,
80,
10,
16,
198,
220,
220,
220,
220,
220,
220,
220,
611,
1271,
13,
271,
26405,
7,
26801,
13,
79,
11,
43720,
20786,
28,
25192,
20786,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2270,
198,
220,
220,
220,
1303,
2980,
378,
17301,
308,
198,
220,
220,
220,
1303,
4091,
978,
42289,
604,
13,
1795,
287,
30579,
286,
27684,
15126,
4867,
198,
220,
220,
220,
1303,
5740,
326,
262,
1502,
286,
262,
1448,
318,
299,
28,
79,
12,
16,
28,
17,
80,
11,
810,
10662,
318,
6994,
198,
220,
220,
220,
611,
4371,
62,
20786,
25,
198,
220,
220,
220,
220,
220,
220,
220,
4371,
62,
20786,
10786,
70,
59,
77,
11537,
198,
220,
220,
220,
981,
352,
25,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
775,
1276,
3368,
308,
28,
17,
780,
286,
17175,
41437,
65,
3493,
338,
1368,
3417,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
287,
366,
8645,
803,
2574,
34777,
282,
17239,
1231,
1900,
278,
262,
3200,
1994,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
8235,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
198,
220,
220,
220,
220,
220,
220,
220,
26181,
13,
70,
796,
1271,
13,
1136,
29531,
17257,
7,
18,
11,
26181,
13,
79,
11,
43720,
20786,
8,
198,
220,
220,
220,
220,
220,
220,
220,
3338,
796,
352,
198,
220,
220,
220,
220,
220,
220,
220,
611,
7182,
7,
26801,
13,
70,
11,
362,
11,
26181,
13,
79,
8,
855,
16,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3338,
28,
15,
198,
220,
220,
220,
220,
220,
220,
220,
611,
3338,
290,
7182,
7,
26801,
13,
70,
11,
10662,
11,
26181,
13,
79,
8,
855,
16,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3338,
28,
15,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
8444,
446,
308,
611,
340,
36319,
279,
12,
16,
780,
286,
262,
1368,
3417,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
287,
5740,
1367,
13,
3134,
357,
15479,
8,
287,
367,
2246,
198,
220,
220,
220,
220,
220,
220,
220,
611,
3338,
290,
2659,
4666,
7,
26801,
13,
79,
12,
16,
11,
26181,
13,
70,
38381,
16,
60,
855,
15,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3338,
28,
15,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
308,
36796,
12,
16,
92,
1276,
407,
14083,
279,
12,
16,
780,
286,
5311,
324,
343,
338,
1368,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
3417,
287,
366,
25559,
1756,
286,
262,
17301,
329,
44096,
2574,
34777,
282,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
9877,
1600,
2813,
198,
220,
220,
220,
220,
220,
220,
220,
308,
16340,
796,
1271,
13,
259,
4399,
7,
26801,
13,
70,
11,
26181,
13,
79,
8,
198,
220,
220,
220,
220,
220,
220,
220,
611,
3338,
290,
2659,
4666,
7,
26801,
13,
79,
12,
16,
11,
308,
16340,
38381,
16,
60,
855,
15,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3338,
28,
15,
198,
220,
220,
220,
220,
220,
220,
220,
611,
3338,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2270,
198,
220,
220,
220,
1303,
2980,
378,
2839,
1994,
2124,
198,
220,
220,
220,
611,
4371,
62,
20786,
25,
198,
220,
220,
220,
220,
220,
220,
220,
4371,
62,
20786,
10786,
87,
59,
77,
11537,
198,
220,
220,
220,
26181,
13,
87,
28,
17618,
13,
1136,
29531,
17257,
7,
17,
11,
26181,
13,
79,
12,
16,
11,
43720,
20786,
8,
198,
220,
220,
220,
1303,
2980,
378,
1171,
1994,
331,
198,
220,
220,
220,
611,
4371,
62,
20786,
25,
198,
220,
220,
220,
220,
220,
220,
220,
4371,
62,
20786,
10786,
88,
59,
77,
11537,
198,
220,
220,
220,
26181,
13,
88,
796,
7182,
7,
26801,
13,
70,
11,
26181,
13,
87,
11,
26181,
13,
79,
8,
198,
220,
220,
220,
1441,
26181,
198,
198,
4299,
5678,
7,
83,
929,
2599,
198,
220,
220,
220,
37227,
42316,
281,
2574,
34777,
282,
1994,
422,
257,
46545,
286,
4938,
2574,
34777,
282,
6805,
13,
628,
220,
220,
220,
383,
953,
23515,
1635,
79,
9,
1276,
307,
257,
6994,
13,
628,
220,
220,
220,
383,
1708,
3403,
1276,
4174,
25,
628,
220,
220,
220,
532,
352,
1279,
308,
1279,
279,
12,
16,
198,
220,
220,
220,
532,
308,
36796,
79,
12,
16,
92,
796,
352,
953,
279,
198,
220,
220,
220,
532,
352,
1279,
2124,
1279,
279,
12,
16,
198,
220,
220,
220,
532,
308,
61,
87,
796,
331,
953,
279,
628,
220,
220,
220,
1058,
48944,
25,
198,
220,
220,
220,
220,
220,
220,
220,
256,
929,
1058,
46545,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
317,
46545,
286,
890,
37014,
11,
351,
513,
393,
604,
3709,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
287,
262,
1708,
1502,
25,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
352,
13,
3401,
23515,
20789,
79,
9,
737,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
362,
13,
35986,
20789,
70,
9,
737,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
513,
13,
5094,
1994,
20789,
88,
9,
737,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
604,
13,
15348,
1994,
20789,
87,
9,
737,
32233,
13,
628,
220,
220,
220,
1058,
13615,
25,
1052,
2574,
34777,
282,
1994,
2134,
357,
63,
9527,
34777,
282,
26801,
63,
737,
198,
220,
220,
220,
37227,
628,
220,
220,
220,
26181,
28,
9527,
34777,
282,
26801,
3419,
198,
220,
220,
220,
611,
18896,
7,
83,
929,
8,
407,
287,
685,
18,
11,
19,
5974,
198,
220,
220,
220,
220,
220,
220,
220,
5298,
11052,
12331,
10786,
49140,
329,
5678,
3419,
2642,
4129,
11537,
198,
220,
220,
220,
329,
1312,
287,
2837,
7,
11925,
7,
83,
929,
8,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
2214,
796,
26181,
13,
2539,
7890,
58,
72,
60,
198,
220,
220,
220,
220,
220,
220,
220,
900,
35226,
7,
26801,
11,
2214,
11,
256,
929,
58,
72,
12962,
198,
220,
220,
220,
1441,
26181,
198,
198,
4871,
2574,
34777,
282,
26801,
7,
12984,
2539,
2599,
198,
220,
220,
220,
37227,
9487,
16215,
281,
2574,
34777,
282,
1994,
13,
628,
220,
220,
220,
1058,
917,
17664,
25,
11593,
1136,
5219,
834,
11,
11593,
2617,
5219,
834,
11,
11593,
260,
1050,
834,
11,
11593,
1136,
35226,
834,
198,
220,
220,
220,
37227,
628,
220,
220,
220,
1303,
25,
28261,
286,
2574,
34777,
282,
10007,
13,
198,
220,
220,
220,
1303,
25,
198,
220,
220,
220,
1303,
25,
317,
1171,
1994,
481,
691,
423,
262,
1708,
12784,
25,
198,
220,
220,
220,
1303,
25,
198,
220,
220,
220,
1303,
25,
220,
532,
12429,
88,
1174,
11,
262,
1171,
1994,
13,
198,
220,
220,
220,
1303,
25,
220,
532,
12429,
70,
1174,
11,
262,
17301,
13,
198,
220,
220,
220,
1303,
25,
220,
532,
12429,
79,
1174,
11,
262,
953,
23515,
13,
198,
220,
220,
220,
1303,
25,
198,
220,
220,
220,
1303,
25,
317,
2839,
1994,
481,
635,
423,
25,
198,
220,
220,
220,
1303,
25,
198,
220,
220,
220,
1303,
25,
220,
532,
12429,
87,
1174,
11,
262,
2839,
1994,
13,
198,
220,
220,
220,
1994,
7890,
28,
17816,
79,
3256,
705,
70,
3256,
705,
88,
3256,
705,
87,
20520,
628,
220,
220,
220,
825,
34117,
7,
944,
11,
8631,
5239,
11,
509,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
27195,
6012,
257,
3704,
286,
1366,
351,
2574,
34777,
282,
13,
628,
220,
220,
220,
220,
220,
220,
220,
1058,
36301,
8631,
5239,
25,
383,
3704,
286,
1366,
284,
34117,
351,
2574,
34777,
282,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
632,
1276,
307,
5470,
1146,
4833,
621,
262,
8265,
20789,
79,
9,
737,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
6030,
8631,
5239,
25,
18022,
4731,
393,
890,
628,
220,
220,
220,
220,
220,
220,
220,
1058,
36301,
509,
25,
317,
3200,
1271,
11,
7147,
15456,
287,
262,
4838,
198,
220,
220,
220,
220,
220,
220,
220,
220,
2837,
1635,
58,
16,
11,
79,
12,
17,
60,
24620,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
6030,
509,
25,
890,
357,
47335,
1631,
8,
393,
18022,
4731,
357,
1662,
7151,
8,
628,
220,
220,
220,
220,
220,
220,
220,
1058,
13615,
25,
317,
46545,
351,
734,
3709,
13,
5501,
2378,
318,
286,
262,
976,
2099,
355,
262,
198,
220,
220,
220,
220,
220,
220,
220,
220,
8631,
5239,
357,
8841,
393,
890,
737,
628,
220,
220,
220,
220,
220,
220,
220,
1058,
1078,
1463,
25,
6356,
286,
1635,
42,
9,
318,
8780,
329,
2324,
13,
2980,
803,
257,
198,
220,
220,
220,
220,
220,
220,
220,
220,
4738,
1271,
4025,
621,
1635,
79,
12,
16,
9,
290,
2263,
262,
953,
23515,
416,
1635,
79,
12,
16,
9,
318,
198,
220,
220,
220,
220,
220,
220,
220,
220,
12429,
1662,
1174,
5713,
11,
1201,
4833,
3815,
481,
3051,
517,
6777,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
2980,
803,
257,
4738,
1271,
25735,
4833,
621,
1635,
79,
12,
16,
9,
198,
220,
220,
220,
220,
220,
220,
220,
220,
357,
68,
13,
70,
13,
1635,
28300,
19510,
79,
12,
16,
20679,
23,
27493,
4738,
9881,
8,
318,
635,
12429,
1662,
1174,
5713,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
554,
2276,
11,
340,
2236,
407,
307,
1744,
329,
281,
15250,
284,
760,
198,
220,
220,
220,
220,
220,
220,
220,
220,
262,
1988,
286,
597,
1643,
286,
509,
13,
628,
220,
220,
220,
220,
220,
220,
220,
1058,
1078,
1463,
25,
383,
1271,
1635,
42,
9,
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] | 2.751793 | 4,323 |
# Copyright 2017 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 gapit_test_framework import gapit_test, require, require_equal
from gapit_test_framework import require_not_equal, little_endian_bytes_to_int
from gapit_test_framework import GapitTest
from vulkan_constants import *
@gapit_test("BeginAndEndCommandBuffer_test")
@gapit_test("BeginAndEndCommandBuffer_test")
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] | 3.607287 | 247 |
from classes import Player
abilities = {
'strength': 16,
'dexterity': 14,
'constitution': 14,
'intelligence': 10,
'wisdom': 12,
'charisma': 10,
'armorclass': 16,
'hitpoints':28,
'maxhitpoints': 28,
'hitdice': '3d10',
'speed': 30,
'language': ['Common','Orc','Deep Speech','Elvish'],
'skills': ['Animal Handling','Athletics','Insight','Intimidation','Perception','Survival']
}
playerinfo = {
'name': 'Grog',
'class': 'Ranger',
'level': 3,
'race': 'Half-Orc',
'alignment': 'Neutral Good',
'experience': 900,
'background': 'Outlander',
'weapons': {'Longbow': {'Bonus': 6, 'Damage': '1d8+2', 'Type': 'Piercing'},
'Shortsword': {'Bonus': 5, 'Damage': '1d6+3', 'Type': 'Piercing'}},
'features': ('Favored Enemy (Monstrosities)','Natural Explorer (Forest)', 'Fighting Style (Archery)', 'Spellcasting', 'Prieval Awareness',
'Relentless Endurance', 'Savage Attacks'),
'attacks': ['Colossus Slayer'],
'spellslots': {'1': 3},
'currentslots': {'1': 3},
'spellsknown': {'1':['Cure Wounds', 'hunter\'s mark', 'longstride']},
'gear':['Scale Mail','Shortsword','Longbow','Backpack','Traveler\'s Clothes','Hunting Trap','Mess Kit', 'Hempen Rope (50ft)',
'Tinderbox','Waterskin','Staff'],
'consumables': {'Arrows': 20, 'Torch': 10,'Rations - 1 day': 10},
'proficiency': 2
}
player = Player(abilities, **playerinfo)
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220,
220,
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92,
198,
198,
7829,
796,
7853,
7,
5738,
11,
12429,
7829,
10951,
8,
198
] | 2.405272 | 607 |
from mongoengine import signals
from flask import url_for
import os
from application import db
| [
6738,
285,
25162,
18392,
1330,
10425,
198,
6738,
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] | 4.173913 | 23 |
# Exercise 2.6
# Author: Noah Waterfield Price
v0 = 1
g = 9.81
n = 11
dt = 2 * v0 / g / (n - 1)
print '%6s %6s' % ('t', 'y')
for i in range(0, n):
t = i * dt
y = v0 * t - 0.5 * g * t ** 2
print '%6.3f %6.3f' % (t, y)
"""
Sample run:
python ball_table1.py
t y
0.000 0.000
0.020 0.018
0.041 0.033
0.061 0.043
0.082 0.049
0.102 0.051
0.122 0.049
0.143 0.043
0.163 0.033
0.183 0.018
0.204 0.000
"""
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13,
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220,
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13,
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16,
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657,
13,
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220,
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13,
15,
2920,
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657,
13,
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657,
13,
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13,
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220,
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13,
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830,
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198
] | 1.782258 | 248 |
import os
import re
import random
import readJSON
from docx import Document
data = readJSON.read_json("data.json")
idiom = data["famous"] # a 代表前面垫话，b代表后面垫话
before = data["before"] # 在名人名言前面弄点废话
after = data['after'] # 在名人名言后面弄点废话
shit_start = data['bosh_start']
shit = data['bosh'] # 代表文章主要废话来源
title = "废话"
repeat = 2
next_shit = random_loop(shit)
next_idiom = random_loop(idiom)
next_shit_start = random_loop(shit_start)
if __name__ == "__main__":
xx = input("请输入文章主题: ")
length = int(input("字數: "))
fn = input('文件名: ')
eles = input('具备元素，支持多个，半角空格隔开: ').split()
total = 0
# tmp = str(" ")
tmp = Document()
tmp.add_heading(xx, 0)
p = tmp.add_paragraph()
while (total < length):
conditiion = random.randint(0, 100)
conditiion1 = random.randint(0, 100)
if conditiion < 20:
# tmp += another_paragraph()
if len(p.text) > 0:
p = tmp.add_paragraph()
elif conditiion < 40:
s = ''
if conditiion1 < 40:
s += next(next_shit_start)
s += add_idiom()
if eles:
s = s.replace("x", random.choice(eles))
else:
s = s.replace("x", xx)
total += len(s)
# tmp += s
p.add_run(s)
else:
s = ''
if conditiion1 < 60:
s += next(next_shit_start)
s += next(next_shit)
if eles:
s = s.replace("x", random.choice(eles))
else:
s = s.replace("x", xx)
total += len(s)
# tmp += s
p.add_run(s)
#tmp = tmp.replace("x", xx)
#print(tmp)
tmp.save('{}.docx'.format(fn))
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10786,
90,
27422,
15390,
87,
4458,
18982,
7,
22184,
4008,
198
] | 1.638104 | 1,097 |
import matplotlib.ticker as ticker
from matplotlib import pyplot as plt
import seaborn as sns
import json
import argparse
import itertools
import pandas as pd
from ab_utils import load_ab_data
from utils import VALUES, is_successful_swap, get_max_value, format_value
sns.set(color_codes=True)
sns.set_palette('muted')
args = get_program_args()
data = [d for d in load_ab_data(args.path) if len(list(d.keys())) > 1]
print(f'Loaded {len(data)} data items')
urls = sorted(set(itertools.chain(*(d.keys() for d in data))))
sns.catplot(
x='swap value',
y='response time',
hue='url',
data=pd.DataFrame(
[
[
url,
d[url]['metadata']['responseTime'],
get_max_value(d[url]),
] for url, d in itertools.product(urls, data)
],
columns=['url', 'response time', 'swap value'],
),
kind='bar',
errcolor='black',
errwidth=1,
capsize=.1,
order=[max_value for min_value, max_value in VALUES],
legend=True,
legend_out=False,
)
for t, url in zip(plt.gca().get_legend().texts, urls):
t.set_text(url)
plt.xticks(list(range(len(VALUES))), [f'< {format_value(max_value)}' for min_value, max_value in VALUES])
plt.title(f'A-B response time by swap value ({len(data)} swaps)')
plt.subplots_adjust(top=0.9, right=0.95, left=0.05)
plt.show()
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] | 2.223127 | 614 |
__author__ = 'avraham'
from xmlrpclib import ServerProxy
port = 9876
| [
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] | 2.84 | 25 |
import detectron2
from detectron2.data import transforms as T
from detectron2.data import detection_utils as utils
import torch
import numpy as np
import cv2
import copy
from .augment import get_transforms
class PersonalMapper (detectron2.data.DatasetMapper):
"""
Define a detectron2 personal mapper in order to be able to use albumentation augmentations
"""
def __call__(self, dataset_dict):
"""
Args:
dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.
Returns:
dict: a format that builtin models in detectron2 accept
"""
dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below
# USER: Write your own image loading if it's not from a file
image = utils.read_image(dataset_dict["file_name"], format=self.image_format)
#utils.check_image_size(dataset_dict, image)
##### ADDED PART
#print("dataset dict : ",dataset_dict)
annos = [
obj for obj in dataset_dict["annotations"]
]
annos_bbox = [
obj["bbox"] for obj in dataset_dict["annotations"]
]
annos_categroy_id = [
obj["category_id"] for obj in dataset_dict.pop("annotations")
]
transform_list=get_transforms(self.is_train)
image=cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
transform_result=transform_list(image=image,bboxes=annos_bbox,category_id=annos_categroy_id)
image=cv2.cvtColor(transform_result["image"], cv2.COLOR_RGB2BGR)
annos=[annos[i] for i in range(len(transform_result["bboxes"]))]
for i in range(len(annos)):
annos[i]["bbox"]=list(transform_result["bboxes"][i])
annos[i]["category_id"]=transform_result["category_id"][i]
dataset_dict["annotations"]=annos
##### ADDED PART
# USER: Remove if you don't do semantic/panoptic segmentation.
if "sem_seg_file_name" in dataset_dict:
sem_seg_gt = utils.read_image(dataset_dict.pop("sem_seg_file_name"), "L").squeeze(2)
else:
sem_seg_gt = None
aug_input = T.AugInput(image, sem_seg=sem_seg_gt)
transforms = self.augmentations(aug_input)
image, sem_seg_gt = aug_input.image, aug_input.sem_seg
image_shape = image.shape[:2] # h, w
# Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,
# but not efficient on large generic data structures due to the use of pickle & mp.Queue.
# Therefore it's important to use torch.Tensor.
dataset_dict["image"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))
if sem_seg_gt is not None:
dataset_dict["sem_seg"] = torch.as_tensor(sem_seg_gt.astype("long"))
# USER: Remove if you don't use pre-computed proposals.
# Most users would not need this feature.
if self.proposal_topk is not None:
utils.transform_proposals(
dataset_dict, image_shape, transforms, proposal_topk=self.proposal_topk
)
if not self.is_train:
# USER: Modify this if you want to keep them for some reason.
dataset_dict.pop("annotations", None)
dataset_dict.pop("sem_seg_file_name", None)
return dataset_dict
if "annotations" in dataset_dict:
# USER: Modify this if you want to keep them for some reason.
for anno in dataset_dict["annotations"]:
if not self.use_instance_mask:
anno.pop("segmentation", None)
if not self.use_keypoint:
anno.pop("keypoints", None)
# USER: Implement additional transformations if you have other types of data
annos = [
utils.transform_instance_annotations(
obj, transforms, image_shape, keypoint_hflip_indices=self.keypoint_hflip_indices
)
for obj in dataset_dict.pop("annotations")
if obj.get("iscrowd", 0) == 0
]
instances = utils.annotations_to_instances(
annos, image_shape, mask_format=self.instance_mask_format
)
# After transforms such as cropping are applied, the bounding box may no longer
# tightly bound the object. As an example, imagine a triangle object
# [(0,0), (2,0), (0,2)] cropped by a box [(1,0),(2,2)] (XYXY format). The tight
# bounding box of the cropped triangle should be [(1,0),(2,1)], which is not equal to
# the intersection of original bounding box and the cropping box.
if self.recompute_boxes:
instances.gt_boxes = instances.gt_masks.get_bounding_boxes()
dataset_dict["instances"] = utils.filter_empty_instances(instances)
return dataset_dict | [
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] | 2.202476 | 2,262 |
"""The GraphUpdate layer and its pieces."""
import sys
from typing import Any, Callable, Mapping, Optional, Sequence
import tensorflow as tf
from tensorflow_gnn.graph import graph_constants as const
from tensorflow_gnn.graph import graph_tensor as gt
from tensorflow_gnn.graph import graph_tensor_ops as ops
from tensorflow_gnn.graph.keras.layers import next_state as next_state_lib
# pylint:disable=g-import-not-at-top
if sys.version_info >= (3, 8):
from typing import Protocol
else:
from typing_extensions import Protocol
# pylint:enable=g-import-not-at-top
# This file defines the canonical implementations of EdgeSetUpdate,
# NodeSetUpdate, and ContextUpdate. However, users are free to pass objects
# of their own compatible reimplementations as long as they:
# 1. subclass tf.keras.layers.Layer,
# 2. provide the signatures in call() (and hence __call__()) given below.
# We use the following type names to express that, and we want some docstrings
# attached to them, but we do not want to require users to subclass an abstract
# interface. Hence we resort to Protocols to check item 2 and leave it to
# runtime checks to check item 1 (which is the less surprising one in a Keras
# environment).
# NOTE: Item 2 is not checked yet, https://github.com/google/pytype/issues/81.
class EdgeSetUpdateLayer(Protocol):
"""A Keras layer that can be called like the standard EdgeSetUpdate."""
def call(self, graph: gt.GraphTensor, *,
edge_set_name: const.EdgeSetName) -> const.FieldOrFields:
"""Returns field(s) shaped like edge features."""
...
class NodeSetUpdateLayer(Protocol):
"""A Keras layer that can be called like the standard NodeSetUpdate."""
def call(self, graph: gt.GraphTensor, *,
node_set_name: const.NodeSetName) -> const.FieldOrFields:
"""Returns field(s) shaped like node features."""
...
class ContextUpdateLayer(Protocol):
"""A Keras layer that can be called like the standard ContextUpdate."""
def call(self, graph: gt.GraphTensor) -> const.FieldOrFields:
"""Returns field(s) shaped like context features."""
...
# The NodeSetUpdate and ContextUpdate layers are initialized with maps of
# input layers from those graph pieces that are in a many-to-one relation
# with the updated graph piece (e.g., many incoming edges per node, many
# nodes per graph component). There is a variety of such input layers,
# including user-defined ones, and they are required to
# 1. subclass tf.keras.layers.Layer,
# 2. provide the signatures in call() (and hence __call__()) listed below.
# We use the following type names to express that, with Protocols as above.
class EdgesToNodePoolingLayer(Protocol):
"""A Keras layer for input from an EdgeSet into a NodeSetUpdate.
Typical implementations of this protocol are:
* Convolutions, which propagate state from adjacent nodes along the edge set
and pool it for the receiver node. They may use edge features,
but do not update them (that is, the edge set has no evolving state).
* Edge state poolings, which pool already-computed states from incident
edges of the edge set for the receiver node. Using these in a
NodeSetUpdate typically requires a corresponding EdgeSetUpdate
in the same GraphUpdate.
A typical implementation accepts an initializer argument with an
IncidentNodeTag (like tfgnn.SOURCE or tfgnn.TARGET) to select which
incident node of each edge is the receiver. The conventional terms
source and target distinguish between the endpoints of the edge,
but the data can flow in either direction.
"""
def call(self, graph: gt.GraphTensor, *,
edge_set_name: const.EdgeSetName) -> const.FieldOrFields:
"""Returns field(s) shaped like node features."""
...
class NodesToContextPoolingLayer(Protocol):
"""A Keras layer for input from a NodeSet into a ContextUpdate."""
def call(self, graph: gt.GraphTensor, *,
node_set_name: const.NodeSetName) -> const.FieldOrFields:
"""Returns field(s) shaped like context features."""
...
class EdgesToContextPoolingLayer(Protocol):
"""A Keras layer for input from an EdgeSet into a ContextUpdate."""
def call(self, graph: gt.GraphTensor, *,
edge_set_name: const.EdgeSetName) -> const.FieldOrFields:
"""Returns field(s) shaped like context features."""
...
@tf.keras.utils.register_keras_serializable(package="GNN")
class GraphUpdate(tf.keras.layers.Layer):
"""Applies one round of updates to EdgeSets, NodeSets and Context.
The updates of EdgeSets, NodeSets and Context can either be passed as
init arguments, or constructed later by passing a deferred_init_callback,
which allows advanced users to adjust the updates to the GraphTensorSpec
of the input (which EdgeSets and NodeSets even exist).
Init args:
edge_sets: A dict `{edge_set_name: edge_set_update, ...}` of EdgeSetUpdate
layers (or custom reimplementations). They are run on the input graph
tensor as `edge_set_update(graph, edge_set_name=edge_set_name)`.
Their results are merged into the feature map of the respective edge set.
This argument can be omitted, which is common in models with node set
updates that use convolutions (i.e., read from adjacent nodes without
computing explicit edge states).
node_sets: A dict `{node_set_name: node_set_update, ...}` of NodeSetUpdate
layers (or custom reimplementations). They are run on the graph tensor
with edge set updates (if any) as
`node_set_update(graph, node_set_name=node_set_name)`,
Their results are merged into the feature map of the respective node set.
This argument can be omitted (but that is uncommon).
context: A ContextUpdate that is run on the graph tensor with edge set and
node set updates (if any). Its results are merged back into the context
feature map. This argument can be omitted, which is common in models
without a context state.
deferred_init_callback: Can be set to a function that accepts a
GraphTensorSpec and returns a dictionary with the kwargs
edge_sets=..., node_sets=... and context=... that would otherwise be
passed directly at initialization time. If this argument is set,
edge_sets, node_sets and context must all be unset.
The object is initialized upon its first call from the results of
the callback on the spec of the input. Before that, the object cannot
be saved.
Call result:
A graph tensor with feature maps that have all configured updates merged in:
If an update returns a str-keyed dict, it gets merged into respective
feature map with the given names. If an update returns a single tensor,
the tfgnn.DEFAULT_STATE_NAME is used.
"""
@tf.keras.utils.register_keras_serializable(package="GNN")
class EdgeSetUpdate(tf.keras.layers.Layer):
"""Computes the new state of an EdgeSet from select input features.
Init args:
next_state: The NextState layer to apply.
edge_input_feature: The feature name(s) of inputs from the edge set to
`next_state`, defaults to `tfgnn.DEFAULT_STATE_NAME`.
If set to a single feature name, a single tensor is passed.
If set to `None` or an empty sequence, an empty dict is passed.
Otherwise, a dict of tensors keyed by feature names is passed.
node_input_tags: The incident nodes of each edge whose states are used
as an input, specified as IncidentNodeTags (tfgnn.SOURCE and tfgnn.TARGET
by default).
node_input_feature: The feature name of the input from node sets to
`next_state`, defaults to `tfgnn.DEFAULT_STATE_NAME`.
Setting this to `None` passes an empty dict of node inputs.
This class supports only a single input feature from nodes. For more
complex settings, you need to write your own, or start a design discussion
about a node_input_map from tags to the respective features for each.
context_input_feature: The feature name(s) of inputs from the context to
`next_state`. Defaults to `None`, which passes an empty dict.
If set to a single feature name, a single tensor is passed.
Otherwise, a dict of tensors keyed by feature names is passed.
To pass the default state tensor of the context, set this to
`tfgnn.DEFAULT_STATE_NAME`.
Call returns:
The result of next_state called on the configured inputs.
"""
@tf.keras.utils.register_keras_serializable(package="GNN")
class NodeSetUpdate(tf.keras.layers.Layer):
"""A node state update with input from convolutions or other edge set inputs.
Init args:
edge_set_inputs: A dict `{edge_set_name: edge_set_input, ...}` of Keras
layers (such as convolutions) that return values shaped like node features
with information aggregated from the given edge set.
They are run in parallel on the input graph tensor as
`edge_set_input(graph, edge_set_name=edge_set_name)`.
next_state: A Keras layer to compute the new node state from a tuple of
inputs that contains, in this order:
- the `node_input_feature` (see there),
- a dict `{edge_set_name: input}` with the results of `edge_set_inputs`,
in which each result is a tensor or dict of tensors,
- if context_input_feature is not None, those feature(s).
node_input_feature: The feature name(s) of inputs from the node set to
`next_state`, defaults to `tfgnn.DEFAULT_STATE_NAME`.
If set to a single feature name, a single tensor is passed.
If set to `None` or an empty sequence, an empty dict is passed.
Otherwise, a dict of tensors keyed by feature names is passed.
context_input_feature: The feature name(s) of inputs from the context to
`next_state`. Defaults to `None`, which passes an empty dict.
If set to a single feature name, a single tensor is passed.
Otherwise, a dict of tensors keyed by feature names is passed.
To pass the default state tensor of the context, set this to
`tfgnn.DEFAULT_STATE_NAME`.
Call result:
The tensor or dict of tensors with the new node state, as returned by
next_state.
"""
@tf.keras.utils.register_keras_serializable(package="GNN")
class ContextUpdate(tf.keras.layers.Layer):
"""A context update with input from node sets and/or edge sets.
Init args:
node_set_inputs: A dict `{node_set_name: node_set_input, ...}` of Keras
layers that return values shaped like context features with information
aggregated from the given edge set. They are run on the input graph tensor
as `node_set_input(graph, node_set_name=node_set_name)`.
edge_set_inputs: A dict `{edge_set_name: edge_set_input, ...}` of Keras
layers that return values shaped like context features with information
aggregated from the given edge set. They are run on the input graph tensor
as `edge_set_input(graph, edge_set_name=edge_set_name)`.
next_state: A Keras layer to compute the new node state from a tuple of
inputs that contains, in this order:
- the `context_input_feature` (see there),
- a dict `{node_set_name: input}` with the results of `node_set_inputs`,
in which each result is a tensor or dict of tensors,
- a dict `{edge_set_name: input}` with the results of `edge_set_inputs`,
in which each result is a tensor or dict of tensors, if there are any.
context_input_feature: The feature name(s) of inputs from the context to
`next_state`, defaults to `tfgnn.DEFAULT_STATE_NAME`.
If set to a single feature name, a single tensor is passed.
If set to `None` or an empty sequence, an empty dict is passed.
Otherwise, a dict of tensors keyed by feature names is passed.
Call result:
The tensor or dict of tensors with the new node state, as returned by
next_state.
"""
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] | 3.181794 | 3,735 |
from PositionIndex import PositionIndex
from GDL import *
import pdb
# def unary_constrain(self, vp):
# # remove all models that have different values for all the positions one variable appears in
# for g in self.__grounds[:]:
# values = vp.get_values(g)
# if not values.uniform():
# self.__grounds.remove(g)
# @staticmethod
# def binary_constrain(m1, m2, vp1, vp2, constraint):
# # for each ground in the first model, make sure there exists a matching value in the
# # second model
# for g1 in m1.__grounds.copy():
# found_match = False
# val1 = vp1.get_values(g1)
# for g2 in m2.__grounds:
# val2 = vp2.get_values(g2)
# if constraint.check(val1, val2):
# found_match = True
# m2_matched.add(g2)
# break
# if not found_match:
# m1.__grounds.remove(g1)
#
# # do the same for the second model
# for g2 in m2.__grounds.copy():
# found_match = False
# val2 = vp2.get_values(g2)
# for g1 in m1.__grounds:
# val1 = vp1.get_values(g1)
# if constraint.check(val1, val2)
# found_match = True
# break
# if not found_match:
# m2.__grounds.remove(g2)
# check that the ground g1 that models the bi1th body condition
# and the ground g2 that models the bi2th body condition fulfill
# all constraints between the two body conditions
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] | 2.405204 | 538 |
/home/runner/.cache/pip/pool/6d/36/f7/4340a87af18a62fe5d5f596cfbe2e7f2d941d3e5043ac8bd070ce567eb | [
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] | 1.684211 | 57 |
# Authors: Gianni Barlacchi <[email protected]>
import argparse
import sys
import logging
import os
from geol.utils import utils
from geol.geometry.squaregrid import SquareGrid
from geol.crawler import foursquare_crawler
os.environ['NO_PROXY'] = "nominatim.openstreetmap.org"
logger = logging.getLogger(__name__)
if __name__ == "__main__":
main(sys.argv[1:])
| [
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] | 2.726619 | 139 |
#!/usr/bin/env python
import os
import argparse
import json
import shutil
import numpy as np
import torch
import skvideo.io
import pandas as pd
import time
from .io import IO
import tools
import tools.utils as utils
import matplotlib.pyplot as plt
import subprocess
from tools.utils.file_util import verify_directory
from tools.utils.openpose import openpose
from tools.utils import video as video_util
from tools.utils import file_util
from scipy.special import softmax
from tools.utils.number_util import normalize, round_traditional
class Predict(IO):
"""
Demo for Skeleton-based Action Recgnition
"""
@staticmethod
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from unittest.mock import Mock
import pytest
from cashews.backends.memory import Memory
from cashews.decorators.bloom import _counting_bloom as counting_bloom
from cashews.decorators.bloom import bloom, dual_bloom
pytestmark = pytest.mark.asyncio
@pytest.fixture(
name="backend",
params=[
"memory",
pytest.param("redis", marks=pytest.mark.redis),
pytest.param("diskcache", marks=pytest.mark.diskcache),
],
)
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828,
198,
220,
220,
220,
16589,
198,
8,
628,
628,
198
] | 2.544944 | 178 |
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import models, migrations
import cpro.models
| [
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] | 2.930233 | 43 |
import copy
import functools
from typing import Dict, List
import pandas
from geopandas import GeoDataFrame
from othello.gis import io
def add_weighted_columns_to_dataframe(df: GeoDataFrame, criteria_information: List[Dict], join_on: str) -> GeoDataFrame:
"""Add the weighted criteria to the 'df' dataframe by joining data on the join_on column name.
Args:
df: Base GeoDataFrame
criteria_information: Criteria data that allows to retrieve the exact column from a table from a layer from aGDB file
join_on: Column name on which the table join will be based
Returns:
GeoDataFrame with the added criteria and the final score
"""
weighted_columns = []
for criterion_information in criteria_information:
field = criterion_information['field']
filepath = criterion_information['filepath']
layer = criterion_information['layer']
weight = criterion_information['weight']
criterion_name = criterion_information['criterion_name']
criterion_df = io.read(filepath, layer=layer)[[join_on, field]]
criterion_df.columns = [join_on, criterion_name + '_np']
criterion_df[criterion_name + '_p'] = weight * criterion_df[criterion_name + '_np']
df = pandas.merge(left=df, right=criterion_df, on=join_on)
weighted_columns.append(criterion_name + '_p')
# df[criterion_name + '_np'] = criterion_df
# df[criterion_name + '_p'] = weight * criterion_df
# Final score in a new series
df['FinalScore'] = [0 for _ in range(len(df))]
for column in weighted_columns:
df['FinalScore'] += df[column]
return df
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] | 2.787625 | 598 |
from .BaseDataset import BaseDataset
from .data_utils import _process_array_argument, _return_first_element_of_list, _process_transform_argument, _process_co_transform_argument, _pass_through
class TensorDataset(BaseDataset):
"""
Dataset class for loading in-memory data.
:param inputs: (numpy array)
:param targets: (numpy array)
:param input_transform: (transform):
transform to apply to input sample individually
:param target_transform: (transform):
transform to apply to target sample individually
:param co_transform: (transform):
transform to apply to both input and target sample simultaneously
"""
def __getitem__(self, index):
"""
Index the dataset and return the input + target
"""
input_sample = [self.input_transform[i](self.inputs[i][index]) for i in range(self.num_inputs)]
if self.has_target:
target_sample = [self.target_transform[i](self.targets[i][index]) for i in range(self.num_targets)]
#for i in range(self.min_inputs_or_targets):
# input_sample[i], target_sample[i] = self.co_transform[i](input_sample[i], target_sample[i])
return self.input_return_processor(input_sample), self.target_return_processor(target_sample)
else:
return self.input_return_processor(input_sample) | [
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] | 2.680934 | 514 |
import PIL.Image
from multiprocessing.pool import ThreadPool
from uncertify.utils.xiaoran.IndexFlow_camcan import *
import numpy as np
import pickle
import os
import cv2
import math
import matplotlib.pyplot as plt
n_boxes = 8
class BufferedWrapper(object):
"""Fetch next batch asynchronuously to avoid bottleneck during GPU
training."""
#
def tile(X, rows, cols):
"""Tile images for display."""
tiling = np.zeros((rows * X.shape[1], cols * X.shape[2], X.shape[3]), dtype=X.dtype)
for i in range(rows):
for j in range(cols):
idx = i * cols + j
if idx < X.shape[0]:
img = X[idx, ...]
tiling[
i * X.shape[1]:(i + 1) * X.shape[1],
j * X.shape[2]:(j + 1) * X.shape[2],
:] = img
return tiling
def plot_batch(X, out_path, vmax=0, vmin=0):
"""Save batch of images tiled."""
X = np.stack(X)
n_channels = X.shape[3]
if n_channels > 3:
X = X[:, :, :, np.random.choice(n_channels, size=3)]
# X = postprocess(X)
rc = math.sqrt(X.shape[0])
rows = cols = math.ceil(rc)
canvas = tile(X, rows, cols)
canvas = np.squeeze(canvas)
# plt.imsave(out_path, canvas)
if vmax == vmin:
plt.imsave(out_path, canvas)
else:
plt.imsave(out_path, canvas, vmin=vmin, vmax=vmax)
def get_camcan_batches(
shape,
index_path,
train,
box_factor,
len_consecutive=64,
fill_batches=True,
shuffle=True,
consecutive=False):
"""Buffered IndexFlow."""
if not consecutive:
flow = IndexFlowCamCAN(shape, index_path, train, box_factor, fill_batches, shuffle)
# print(flow.n)
else:
# flow = IndexFlowCamCAN_consecutive(shape, index_path, train, box_factor, len_consecutive, fill_batches, shuffle)
flow = IndexFlowCamCAN_3D(shape, index_path, train, box_factor, fill_batches, shuffle)
return BufferedWrapper(flow)
def get_brats_batches(
shape,
index_path,
train,
box_factor,
fill_batches=True,
shuffle=True):
"""Buffered IndexFlow."""
flow = IndexFlowBrats(shape, index_path, train, box_factor, fill_batches, shuffle)
return BufferedWrapper(flow)
def get_brats_batch_subject(
shape,
index_path,
train,
box_factor,
fill_batches=True,
shuffle=True):
"""Buffered IndexFlow."""
flow = IndexFlowBrats(shape, index_path, train, box_factor, fill_batches, shuffle)
return BufferedWrapper(flow)
def get_multichannel_batches(
shape,
index_path,
train,
box_factor,
len_consecutive=64,
fill_batches=True,
shuffle=True):
"""Buffered IndexFlow."""
flow = IndexFlow_consecutive(shape=shape,
index_path=index_path,
train=train,
number=len_consecutive,
box_factor=box_factor,
fill_batches=fill_batches,
shuffle=shuffle)
return BufferedWrapper(flow)
def get_ucsd_batches(
shape,
index_path,
fill_batches=True,
shuffle=True):
"""Buffered IndexFlow."""
flow = IndexFlowUCSD(shape=shape, index_path=index_path, fill_batches=fill_batches,
shuffle=shuffle)
return BufferedWrapper(flow)
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] | 2.024291 | 1,729 |
#############################################
# #
# Carlos Santos #
# ECE 351-51 #
# Lab 7 #
# 03/10/2020 #
# #
# #
#############################################
import numpy as np
import matplotlib.pyplot as plt
import scipy.signal as sig
#%% 3.3.1 , 3.3.2, 3.3.3, 3.3.4, 3.3.5
# Equation G(s) Transfer Function
Gnum = [1,9] # numerator
Gden = [1,-2,-40,-64] # denomerator
# Equation A(s)
Anum = [1,4] # numerator
Aden = [1,4,3] # denomerator
# Equation B(s)
Bnum = [1,26,168] # numerator
# Equation G(s) Transfer Function
rG,pG,kG = sig.tf2zpk(Gnum,Gden)
print('Equation G(s)')
print('rG:',rG)
print('pG:',pG)
print('kG:',kG)
print('\n')
# Equation A(s)
rA,pA,kA = sig.tf2zpk(Anum,Aden)
print('Equation A(s)')
print('rA:',rA)
print('pA:',pA)
print('kA:',kA)
print('\n')
# Equation B(s)
rB = np.roots(Bnum)
print('Equation B(s)')
print('rB:',rB)
print('\n')
#open loop function H(s) = A(s) * G(s)
print('Equation H(s) = A(s) * G(s)')
Hnum = sig.convolve(Anum,Gnum)
print('Hnum = ',Hnum)
Hden = sig.convolve(Aden,Gden)
print('Hden = ',Hden)
print('\n')
#open loop step response X(s) = H(s) * (1/s)
print('Open Loop Equation X(s) = H(s) * 1/s')
Xnum = sig.convolve(Hnum,[1])
print('Xnum = ',Xnum)
Xden = sig.convolve(Hden,[1,0])
print('Xden = ',Xden)
print('\n')
#plot step response
steps = 1e-3
t = np.arange(0,2 + steps, steps)
tout, yout = sig.step((Xnum,Xden), T = t)
plt.figure(figsize = (10,7))
plt.plot(tout,yout)
plt.xlabel('t')
plt.grid()
plt.title('Open Loop Step Response:')
#%% 4.3.1, 4.3.2, 4.3.3, 4.3.4, 4.3.5
#closed loop function H(s) = G(s)A(s) / 1 + G(s)B(s)
print('Closed Loop Equation X(s) = G(s)*A(s) / 1 + G(s)*B(s)')
Hnum = sig.convolve(Gnum,Anum)
print('Hnum = ',Hnum)
Hden = sig.convolve(Gden + sig.convolve(Bnum,Gnum),Aden)
print('Hden = ',Hden)
tout, yout = sig.step((Hnum,Hden), T = t)
plt.figure(figsize = (10,7))
plt.plot(tout,yout)
plt.xlabel('t')
plt.grid()
plt.title('Closed Loop Step Response')
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628,
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628,
628,
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198
] | 1.835673 | 1,211 |
# Event: LCCS Python Fundamental Skills Workshop
# Date: May 2018
# Author: Joe English, PDST
# eMail: [email protected]
# Purpose: A program to demonstrate basic string slicing
# Initialise the string
pangram = "The quick brown fox jumps over the lazy dog!"
# 01234567890123456789012345678901234567890123
# Extract from index 1 up to but NOT including index 5
print(pangram[1:5]) # "he q"
# Extract from index 17 up to but NOT including index 19
print(pangram[17:19]) # ox
print(pangram[:19]) # "The quick brown fox"
print(pangram[20:26]) # "jumps"
print(pangram[26:]) # "over the lazy dog!"
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# Generated by Django 3.1.4 on 2021-01-05 18:14
from django.db import migrations, models
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import os
import numpy as np
from tensorflow import keras
from PIL import Image
try:
M=keras.models.load_model("./models/Unet_1_16.h5")
C=keras.models.load_model("./models/compteur2.h5")
except:
pass
#input: a picture
#return a list of 512*512*1 array croped from the picture X and Y
#input: a array of croped pictures 512*512
#output: a array of pictures with cells anotated
#draw points where the cells are
#input: a array of croped picture 512*512
#put the picture back in one piece
#input: a array of croped pictures 512*512
# count the cells in a anotated picture array
#alternative version for count, need the full anotated picture as a input
# call the methode above
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] | 2.779923 | 259 |
import mail
import plotten
plotten.temp()
files=['templog.txt', 'templog.png']
mail.send_attachment(files)
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import numpy as np
from PIL import Image
import pandas as pd
import argparse
from pathlib import Path
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="preprocess")
parser.add_argument("--image_source", type=str, help="画像ファイルのディレクトリ")
parser.add_argument("--scale", type=int, help="何分の1にするかを決める")
parser.add_argument("--output", type=str, help="どこのディレクトリに保存するか")
parser.add_argument("--annotation", type=str, help="annotation csv file")
parser.add_argument("--out_ano_name", type=str, help="annotation csv file")
args = parser.parse_args()
main(args)
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3419,
198,
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7,
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8,
198
] | 2.48374 | 246 |
from __future__ import print_function
import sys
import gdb.printing
class SmallStringPrinter:
"""Print an llvm::SmallString object."""
class StringRefPrinter:
"""Print an llvm::StringRef object."""
class SmallVectorPrinter(Iterator):
"""Print an llvm::SmallVector object."""
class ArrayRefPrinter:
"""Print an llvm::ArrayRef object."""
class ExpectedPrinter(Iterator):
"""Print an llvm::Expected object."""
class OptionalPrinter(Iterator):
"""Print an llvm::Optional object."""
class DenseMapPrinter:
"Print a DenseMap"
class TwinePrinter:
"Print a Twine"
def string_from_pretty_printer_lookup(self, val):
'''Lookup the default pretty-printer for val and use it.
If no pretty-printer is defined for the type of val, print an error and
return a placeholder string.'''
pp = gdb.default_visualizer(val)
if pp:
s = pp.to_string()
# The pretty-printer may return a LazyString instead of an actual Python
# string. Convert it to a Python string. However, GDB doesn't seem to
# register the LazyString type, so we can't check
# "type(s) == gdb.LazyString".
if 'LazyString' in type(s).__name__:
s = s.value().address.string()
else:
print(('No pretty printer for {} found. The resulting Twine ' +
'representation will be incomplete.').format(val.type.name))
s = '(missing {})'.format(val.type.name)
return s
def string_from_child(self, child, kind):
'''Return the string representation of the Twine::Child child.'''
if self.is_twine_kind(kind, 'EmptyKind') or self.is_twine_kind(kind, 'NullKind'):
return ''
if self.is_twine_kind(kind, 'TwineKind'):
return self.string_from_twine_object(child['twine'].dereference())
if self.is_twine_kind(kind, 'CStringKind'):
return child['cString'].string()
if self.is_twine_kind(kind, 'StdStringKind'):
val = child['stdString'].dereference()
return self.string_from_pretty_printer_lookup(val)
if self.is_twine_kind(kind, 'StringRefKind'):
val = child['stringRef'].dereference()
pp = StringRefPrinter(val)
return pp.to_string()
if self.is_twine_kind(kind, 'SmallStringKind'):
val = child['smallString'].dereference()
pp = SmallStringPrinter(val)
return pp.to_string()
if self.is_twine_kind(kind, 'CharKind'):
return chr(child['character'])
if self.is_twine_kind(kind, 'DecUIKind'):
return str(child['decUI'])
if self.is_twine_kind(kind, 'DecIKind'):
return str(child['decI'])
if self.is_twine_kind(kind, 'DecULKind'):
return str(child['decUL'].dereference())
if self.is_twine_kind(kind, 'DecLKind'):
return str(child['decL'].dereference())
if self.is_twine_kind(kind, 'DecULLKind'):
return str(child['decULL'].dereference())
if self.is_twine_kind(kind, 'DecLLKind'):
return str(child['decLL'].dereference())
if self.is_twine_kind(kind, 'UHexKind'):
val = child['uHex'].dereference()
return hex(int(val))
print(('Unhandled NodeKind {} in Twine pretty-printer. The result will be '
'incomplete.').format(kind))
return '(unhandled {})'.format(kind)
def string_from_twine_object(self, twine):
'''Return the string representation of the Twine object twine.'''
lhs_str = ''
rhs_str = ''
lhs = twine['LHS']
rhs = twine['RHS']
lhs_kind = str(twine['LHSKind'])
rhs_kind = str(twine['RHSKind'])
lhs_str = self.string_from_child(lhs, lhs_kind)
rhs_str = self.string_from_child(rhs, rhs_kind)
return lhs_str + rhs_str
pp = gdb.printing.RegexpCollectionPrettyPrinter("LLVMSupport")
pp.add_printer('llvm::SmallString', '^llvm::SmallString<.*>$', SmallStringPrinter)
pp.add_printer('llvm::StringRef', '^llvm::StringRef$', StringRefPrinter)
pp.add_printer('llvm::SmallVectorImpl', '^llvm::SmallVector(Impl)?<.*>$', SmallVectorPrinter)
pp.add_printer('llvm::ArrayRef', '^llvm::(Mutable)?ArrayRef<.*>$', ArrayRefPrinter)
pp.add_printer('llvm::Expected', '^llvm::Expected<.*>$', ExpectedPrinter)
pp.add_printer('llvm::Optional', '^llvm::Optional<.*>$', OptionalPrinter)
pp.add_printer('llvm::DenseMap', '^llvm::DenseMap<.*>$', DenseMapPrinter)
pp.add_printer('llvm::Twine', '^llvm::Twine$', TwinePrinter)
gdb.printing.register_pretty_printer(gdb.current_objfile(), pp)
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] | 2.517837 | 1,738 |
# Copyright 2020 Google LLC.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#
# 1. Redistributions of source code must retain the above copyright notice,
# this list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from this
# software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
"""Tests for deepvariant.allele_frequency."""
from absl.testing import absltest
from absl.testing import parameterized
from third_party.nucleus.io import fasta
from third_party.nucleus.io import vcf
from third_party.nucleus.protos import variants_pb2
from deepvariant import allele_frequency
from deepvariant import testdata
from deepvariant.protos import deepvariant_pb2
if __name__ == '__main__':
absltest.main()
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] | 3.473214 | 560 |
##########################################################################
# mriWorks - Copyright (C) IRMAGE/INSERM, 2020
# Distributed under the terms of the CeCILL-B license, as published by
# the CEA-CNRS-INRIA. Refer to the LICENSE file or to
# https://cecill.info/licences/Licence_CeCILL_V2-en.html
# for details.
##########################################################################
import yaml
import re
from PyQt5.QtWidgets import QDialog, QCheckBox, QVBoxLayout, QHBoxLayout, \
QPushButton, QScrollArea, QWidget, QMenuBar, QAction, \
QTextEdit
from PyQt5 import QtCore
from PyQt5.QtGui import QFontMetrics
import time
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] | 3.046083 | 217 |
import nose.tools as nt
import numpy as np
import theano
import theano.tensor as T
import treeano
import treeano.nodes as tn
from treeano.sandbox.nodes import paired_conv
fX = theano.config.floatX
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] | 2.913043 | 69 |
import configparser
import os
try:
config = configparser.ConfigParser()
config.read('bot.ini')
common = config['Common']
except (IOError, KeyError):
common = {}
TOKEN = _get('TOKEN', common)
DATABASE_URL = _get('DATABASE_URL', common)
USE_WEBHOOK = _get('USE_WEBHOOK', common, True)
if USE_WEBHOOK:
WEBHOOK_PORT = int(_get('PORT', common, '5000'))
WEBHOOK_URL = _get('WEBHOOK_URL', common)
IMAGE_BLUR = _get('IMAGE_BLUR', common, True)
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] | 2.409326 | 193 |
import pytest
from fcgiproto.records import (
encode_name_value_pairs, decode_name_value_pairs, decode_record, FCGIStdin, FCGIBeginRequest,
FCGIEndRequest, FCGIUnknownType, FCGIStdout, FCGIGetValues, FCGIGetValuesResult,
FCGIAbortRequest)
from fcgiproto.exceptions import ProtocolError
@pytest.mark.parametrize('data, expected', [
(b'\x03\x06foobarbar\x01\x03Xxyz', [(u'foo', u'barbar'), ('X', 'xyz')]),
(b'\x03\x80\x01\x00\x00foo' + b'x' * 65536, [(u'foo', u'x' * 65536)]),
(b'\x80\x01\x00\x00\x03' + b'x' * 65536 + b'foo', [(u'x' * 65536, 'foo')]),
(b'\x80\x01\x00\x00\x80\x01\x00\x00' + b'x' * 65536 + b'y' * 65536,
[('x' * 65536, 'y' * 65536)])
], ids=['short_both', 'long_value', 'long_name', 'long_both'])
@pytest.mark.parametrize('data, message', [
(b'\x80\x00\x00', 'not enough data to decode name length in name-value pair'),
(b'\x03', 'not enough data to decode value length in name-value pair'),
(b'\x03\x06foo', 'name/value data missing from buffer')
], ids=['name_missing', 'value_missing', 'content_missing'])
@pytest.mark.parametrize('pairs, expected', [
([(u'foo', u'barbar'), (u'X', u'xyz')], b'\x03\x06foobarbar\x01\x03Xxyz'),
([(u'foo', u'x' * 65536)], b'\x03\x80\x01\x00\x00foo' + b'x' * 65536),
([(u'x' * 65536, u'foo')], b'\x80\x01\x00\x00\x03' + b'x' * 65536 + b'foo'),
([(u'x' * 65536, u'y' * 65536)],
b'\x80\x01\x00\x00\x80\x01\x00\x00' + b'x' * 65536 + b'y' * 65536)
], ids=['short_both', 'long_value', 'long_name', 'long_both'])
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198,
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84,
6,
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6,
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62,
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3256,
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62,
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3256,
705,
6511,
62,
3672,
3256,
705,
6511,
62,
16885,
6,
12962,
628,
628,
198
] | 2.040951 | 757 |
from datetime import datetime
from pathlib import Path
from attr import dataclass
"""
Defines a dataclass for passing all configuration information between
the worker classes
"""
@dataclass
| [
6738,
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] | 3.938776 | 49 |
import logger
import testutil
import test_engine
from test_engine import Table, Column
log = logger.Logger(__name__, logger.INFO)
| [
11748,
49706,
198,
198,
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] | 3.384615 | 39 |
from django.db import models | [
6738,
42625,
14208,
13,
9945,
1330,
4981
] | 4 | 7 |
"""Middleware to communicate with PubSub Message Broker."""
from collections.abc import Callable
from enum import Enum
from queue import LifoQueue, Empty
from typing import Any
import socket, json, pickle
import xml.etree.ElementTree as ET
class MiddlewareType(Enum):
"""Middleware Type."""
CONSUMER = 1
PRODUCER = 2
class Queue:
"""Representation of Queue interface for both Consumers and Producers."""
def __init__(self, topic, _type=MiddlewareType.CONSUMER):
"""Create Queue."""
self.topic = topic
self._type = _type
self.sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
self.sock.connect(('localhost', 5000))
def push(self, value): # usado pelo produtor
"""Sends data to broker. """
msg = self.format_protocol("publish", self.topic, value)
msg = self.encode(msg)
self.sock.send(len(msg).to_bytes(2, 'big') + msg)
def pull(self) -> (str, tuple): # usado pelo consumer
"""Receives (topic, data) from broker.
recv_msg(self.sock)
Should BLOCK the consumer!"""
header = self.sock.recv(2) # get first two bytes to know how many bytes to get after
header = int.from_bytes(header, "big")
body = self.sock.recv(header) # return the exact bytes needed
if len(body) != 0:
data = self.decode(body)
return data["topic"], data["value"] # enviou uma mensagem do publisher de um topico ao qual o cliente esta subscrito
def list_topics(self, callback: Callable): # callback é um arg do tipo funcao, essa funcao vai
# o callback vai ser usado pelo pool para
"""Lists all topics available in the broker."""
msg = self.format_protocol("topics")
msg = self.encode(msg)
self.sock.send(len(msg).to_bytes(2, 'big') + msg) # ask broker for list
def cancel(self):
"""Cancel subscription."""
msg = self.format_protocol("cancel", str(self.topic))
msg = self.encode(msg)
self.sock.send(len(msg).to_bytes(2, 'big') + msg)
class JSONQueue(Queue):
"""Queue implementation with JSON based serialization."""
class XMLQueue(Queue):
"""Queue implementation with XML based serialization."""
class PickleQueue(Queue):
"""Queue implementation with Pickle based serialization."""
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220,
37227,
34991,
7822,
351,
12346,
293,
1912,
11389,
1634,
526,
15931,
198
] | 2.583058 | 909 |
from helpers import read_data, get_settings
import api
settings = get_settings()
src_root = settings['src_root']
locale = settings['locale']
article_map = read_data('article_map')
for src_article in article_map:
print('Archiving {}...'.format(src_article))
# DELETE / api / v2 / help_center / articles / {id}.json
url = '{}/articles/{}.json'.format(src_root, src_article)
api.delete_resource(url)
| [
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] | 2.875862 | 145 |
#!/usr/bin/python3
# -*- coding: utf-8 -*-
import sys
from PySide2.QtWidgets import QApplication
from PySide2.QtGui import QFont
from utils.main_ui import MainWindow
if __name__ == '__main__':
qss = ''
try:
with open('utils/qdark.qss', 'r') as f:
qss = f.read()
except:
print('警告！找不到QSS文件！请从github项目地址下载完整文件。')
app = QApplication(sys.argv)
app.setStyleSheet(qss)
app.setFont(QFont('微软雅黑', 9))
mainWindow = MainWindow()
sys.exit(app.exec_())
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] | 1.806452 | 279 |
# ===========================================================================
#
# Filename: sipify.py
#
# Copyright (c) 2015 Lamond Lab
#
# 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.txt
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# ===========================================================================
import os
from sys import exit
from io import BytesIO, StringIO
from datetime import datetime
from CppHeaderParser import CppHeader, CppParseError
QT_VERSION=0x54000
if __name__=="__main__":
from argparse import ArgumentParser
parser=ArgumentParser(description="Generate SIP files from C++ headers.")
parser.add_argument(
'-i','--input',
dest="input",
type=str,
default=".",
help="Directory containing C++ header input files."
)
parser.add_argument(
'-o','--output',
dest="output",
type=str,
default='.',
help="Directory to contain SIP output files."
)
parser.add_argument(
'--header',
dest="header",
type=str,
default=None,
help="Name of file containing header template."
)
parser.add_argument(
'--lib_name',
dest="lib_name",
type=str,
default="",
help="Library name."
)
parser.add_argument(
'--year',
dest="year",
type=str,
default=str(datetime.now().year),
help="Copyright year."
)
parser.add_argument(
'--name',
dest="name",
type=str,
default="",
help="Copyright name."
)
args=parser.parse_args()
headerTemplate=None
if args.header:
headerTemplateFile=os.path.abspath(args.header)
if os.path.exists(headerTemplateFile):
headerTemplate=open(headerTemplateFile,'r').read()
headerTemplate=headerTemplate.format(
lib_name=args.lib_name,
copy_year=args.year,
copy_name=args.name,
file_name='{file_name}'
)
input_dir=os.path.abspath(args.input)
if not (os.path.exists(input_dir) and os.path.isdir(input_dir)): raise Exception
output_dir=os.path.abspath(args.output)
if not (os.path.exists(output_dir)):
os.mkdir(output_dir)
for f in os.listdir(input_dir):
name,ext=os.path.splitext(f)
if ext!='.h' or name.endswith('_p'): continue
if f=="ctkWidgets.h": continue
createSIP(os.path.join(input_dir,f), output_dir, headerTemplate)
print("Removing empty files:")
for f in os.listdir(output_dir):
fpath=os.path.join(output_dir,f)
if os.stat(fpath).st_size==0:
print("\t{}".format(f))
os.remove(fpath)
for f in os.listdir(output_dir):
name,ext=os.path.splitext(f)
if ext!='.sip': continue
print("%Include {}".format(f)) | [
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"""Deployment file to facilitate releases.
"""
import os
import json
import webbrowser
import requests
from invoke import task
from automatminer import __version__
from monty.os import cd
__author__ = ["Alex Dunn", "Shyue Ping Ong", "Anubhav Jain"]
# Making and updatig documentation
@task
@task
# Consistuent tasks
@task
@task
@task
def publish(ctx):
"""
Do this last
"""
ctx.run("rm dist/*.*", warn=True)
ctx.run("python3 setup.py sdist bdist_wheel")
ctx.run("twine upload dist/* --verbose")
@task
@task | [
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198,
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31,
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628,
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] | 2.78866 | 194 |
import logging
from datetime import datetime
from rdr_service import config
from rdr_service.dao.hpro_consent_dao import HealthProConsentDao
from rdr_service.storage import GoogleCloudStorageProvider
class HealthProConsentFile:
"""
Service used for transferring Consent records
from RDR to HealthPro consent bucket
"""
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from pypy.module.cpyext.test.test_cpyext import AppTestCpythonExtensionBase
| [
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from typing import Optional, Callable
from functools import wraps
from .request import Request
from .response import Response
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import sys
from setuptools import setup
setup_args = dict(
name='tikatko',
version='0.1',
py_modules=['tikatko'],
description="""A lightning talk timer""",
author='Petr Viktorin',
author_email='[email protected]',
install_requires=['gillcup_graphics==0.2.0-alpha.1'],
classifiers=[
'License :: OSI Approved :: MIT License',
'Operating System :: OS Independent',
'Programming Language :: Python',
'Programming Language :: Python :: 2',
'Programming Language :: Python :: 2.6',
'Programming Language :: Python :: 2.7',
],
entry_points={
'console_scripts': ['tikatko = tikatko:script_entry_point'],
},
)
if __name__ == '__main__':
setup(**setup_args)
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] | 2.454545 | 308 |
from django.conf import settings
from django.db import models
from django.utils.translation import ugettext as _
from wagtail.wagtailcore.models import Page as WagtailPage
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] | 3.604167 | 48 |
import pprint
import re # noqa: F401
import six
class TaskDef(object):
"""NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
"""
"""
Attributes:
swagger_types (dict): The key is attribute name
and the value is attribute type.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
"""
swagger_types = {
'owner_app': 'str',
'create_time': 'int',
'update_time': 'int',
'created_by': 'str',
'updated_by': 'str',
'name': 'str',
'description': 'str',
'retry_count': 'int',
'timeout_seconds': 'int',
'input_keys': 'list[str]',
'output_keys': 'list[str]',
'timeout_policy': 'str',
'retry_logic': 'str',
'retry_delay_seconds': 'int',
'response_timeout_seconds': 'int',
'concurrent_exec_limit': 'int',
'input_template': 'dict(str, object)',
'rate_limit_per_frequency': 'int',
'rate_limit_frequency_in_seconds': 'int',
'isolation_group_id': 'str',
'execution_name_space': 'str',
'owner_email': 'str',
'poll_timeout_seconds': 'int'
}
attribute_map = {
'owner_app': 'ownerApp',
'create_time': 'createTime',
'update_time': 'updateTime',
'created_by': 'createdBy',
'updated_by': 'updatedBy',
'name': 'name',
'description': 'description',
'retry_count': 'retryCount',
'timeout_seconds': 'timeoutSeconds',
'input_keys': 'inputKeys',
'output_keys': 'outputKeys',
'timeout_policy': 'timeoutPolicy',
'retry_logic': 'retryLogic',
'retry_delay_seconds': 'retryDelaySeconds',
'response_timeout_seconds': 'responseTimeoutSeconds',
'concurrent_exec_limit': 'concurrentExecLimit',
'input_template': 'inputTemplate',
'rate_limit_per_frequency': 'rateLimitPerFrequency',
'rate_limit_frequency_in_seconds': 'rateLimitFrequencyInSeconds',
'isolation_group_id': 'isolationGroupId',
'execution_name_space': 'executionNameSpace',
'owner_email': 'ownerEmail',
'poll_timeout_seconds': 'pollTimeoutSeconds'
}
def __init__(self, owner_app=None, create_time=None, update_time=None, created_by=None, updated_by=None, name=None, description=None, retry_count=None, timeout_seconds=None, input_keys=None, output_keys=None, timeout_policy=None, retry_logic=None, retry_delay_seconds=None, response_timeout_seconds=None, concurrent_exec_limit=None, input_template=None, rate_limit_per_frequency=None, rate_limit_frequency_in_seconds=None, isolation_group_id=None, execution_name_space=None, owner_email=None, poll_timeout_seconds=None): # noqa: E501
"""TaskDef - a model defined in Swagger""" # noqa: E501
self._owner_app = None
self._create_time = None
self._update_time = None
self._created_by = None
self._updated_by = None
self._name = None
self._description = None
self._retry_count = None
self._timeout_seconds = None
self._input_keys = None
self._output_keys = None
self._timeout_policy = None
self._retry_logic = None
self._retry_delay_seconds = None
self._response_timeout_seconds = None
self._concurrent_exec_limit = None
self._input_template = None
self._rate_limit_per_frequency = None
self._rate_limit_frequency_in_seconds = None
self._isolation_group_id = None
self._execution_name_space = None
self._owner_email = None
self._poll_timeout_seconds = None
self.discriminator = None
if owner_app is not None:
self.owner_app = owner_app
if create_time is not None:
self.create_time = create_time
if update_time is not None:
self.update_time = update_time
if created_by is not None:
self.created_by = created_by
if updated_by is not None:
self.updated_by = updated_by
self.name = name
if description is not None:
self.description = description
if retry_count is not None:
self.retry_count = retry_count
self.timeout_seconds = timeout_seconds
if input_keys is not None:
self.input_keys = input_keys
if output_keys is not None:
self.output_keys = output_keys
if timeout_policy is not None:
self.timeout_policy = timeout_policy
if retry_logic is not None:
self.retry_logic = retry_logic
if retry_delay_seconds is not None:
self.retry_delay_seconds = retry_delay_seconds
if response_timeout_seconds is not None:
self.response_timeout_seconds = response_timeout_seconds
if concurrent_exec_limit is not None:
self.concurrent_exec_limit = concurrent_exec_limit
if input_template is not None:
self.input_template = input_template
if rate_limit_per_frequency is not None:
self.rate_limit_per_frequency = rate_limit_per_frequency
if rate_limit_frequency_in_seconds is not None:
self.rate_limit_frequency_in_seconds = rate_limit_frequency_in_seconds
if isolation_group_id is not None:
self.isolation_group_id = isolation_group_id
if execution_name_space is not None:
self.execution_name_space = execution_name_space
if owner_email is not None:
self.owner_email = owner_email
if poll_timeout_seconds is not None:
self.poll_timeout_seconds = poll_timeout_seconds
@property
def owner_app(self):
"""Gets the owner_app of this TaskDef. # noqa: E501
:return: The owner_app of this TaskDef. # noqa: E501
:rtype: str
"""
return self._owner_app
@owner_app.setter
def owner_app(self, owner_app):
"""Sets the owner_app of this TaskDef.
:param owner_app: The owner_app of this TaskDef. # noqa: E501
:type: str
"""
self._owner_app = owner_app
@property
def create_time(self):
"""Gets the create_time of this TaskDef. # noqa: E501
:return: The create_time of this TaskDef. # noqa: E501
:rtype: int
"""
return self._create_time
@create_time.setter
def create_time(self, create_time):
"""Sets the create_time of this TaskDef.
:param create_time: The create_time of this TaskDef. # noqa: E501
:type: int
"""
self._create_time = create_time
@property
def update_time(self):
"""Gets the update_time of this TaskDef. # noqa: E501
:return: The update_time of this TaskDef. # noqa: E501
:rtype: int
"""
return self._update_time
@update_time.setter
def update_time(self, update_time):
"""Sets the update_time of this TaskDef.
:param update_time: The update_time of this TaskDef. # noqa: E501
:type: int
"""
self._update_time = update_time
@property
def created_by(self):
"""Gets the created_by of this TaskDef. # noqa: E501
:return: The created_by of this TaskDef. # noqa: E501
:rtype: str
"""
return self._created_by
@created_by.setter
def created_by(self, created_by):
"""Sets the created_by of this TaskDef.
:param created_by: The created_by of this TaskDef. # noqa: E501
:type: str
"""
self._created_by = created_by
@property
def updated_by(self):
"""Gets the updated_by of this TaskDef. # noqa: E501
:return: The updated_by of this TaskDef. # noqa: E501
:rtype: str
"""
return self._updated_by
@updated_by.setter
def updated_by(self, updated_by):
"""Sets the updated_by of this TaskDef.
:param updated_by: The updated_by of this TaskDef. # noqa: E501
:type: str
"""
self._updated_by = updated_by
@property
def name(self):
"""Gets the name of this TaskDef. # noqa: E501
:return: The name of this TaskDef. # noqa: E501
:rtype: str
"""
return self._name
@name.setter
def name(self, name):
"""Sets the name of this TaskDef.
:param name: The name of this TaskDef. # noqa: E501
:type: str
"""
if name is None:
raise ValueError("Invalid value for `name`, must not be `None`") # noqa: E501
self._name = name
@property
def description(self):
"""Gets the description of this TaskDef. # noqa: E501
:return: The description of this TaskDef. # noqa: E501
:rtype: str
"""
return self._description
@description.setter
def description(self, description):
"""Sets the description of this TaskDef.
:param description: The description of this TaskDef. # noqa: E501
:type: str
"""
self._description = description
@property
def retry_count(self):
"""Gets the retry_count of this TaskDef. # noqa: E501
:return: The retry_count of this TaskDef. # noqa: E501
:rtype: int
"""
return self._retry_count
@retry_count.setter
def retry_count(self, retry_count):
"""Sets the retry_count of this TaskDef.
:param retry_count: The retry_count of this TaskDef. # noqa: E501
:type: int
"""
self._retry_count = retry_count
@property
def timeout_seconds(self):
"""Gets the timeout_seconds of this TaskDef. # noqa: E501
:return: The timeout_seconds of this TaskDef. # noqa: E501
:rtype: int
"""
return self._timeout_seconds
@timeout_seconds.setter
def timeout_seconds(self, timeout_seconds):
"""Sets the timeout_seconds of this TaskDef.
:param timeout_seconds: The timeout_seconds of this TaskDef. # noqa: E501
:type: int
"""
if timeout_seconds is None:
raise ValueError("Invalid value for `timeout_seconds`, must not be `None`") # noqa: E501
self._timeout_seconds = timeout_seconds
@property
def input_keys(self):
"""Gets the input_keys of this TaskDef. # noqa: E501
:return: The input_keys of this TaskDef. # noqa: E501
:rtype: list[str]
"""
return self._input_keys
@input_keys.setter
def input_keys(self, input_keys):
"""Sets the input_keys of this TaskDef.
:param input_keys: The input_keys of this TaskDef. # noqa: E501
:type: list[str]
"""
self._input_keys = input_keys
@property
def output_keys(self):
"""Gets the output_keys of this TaskDef. # noqa: E501
:return: The output_keys of this TaskDef. # noqa: E501
:rtype: list[str]
"""
return self._output_keys
@output_keys.setter
def output_keys(self, output_keys):
"""Sets the output_keys of this TaskDef.
:param output_keys: The output_keys of this TaskDef. # noqa: E501
:type: list[str]
"""
self._output_keys = output_keys
@property
def timeout_policy(self):
"""Gets the timeout_policy of this TaskDef. # noqa: E501
:return: The timeout_policy of this TaskDef. # noqa: E501
:rtype: str
"""
return self._timeout_policy
@timeout_policy.setter
def timeout_policy(self, timeout_policy):
"""Sets the timeout_policy of this TaskDef.
:param timeout_policy: The timeout_policy of this TaskDef. # noqa: E501
:type: str
"""
allowed_values = ["RETRY", "TIME_OUT_WF", "ALERT_ONLY"] # noqa: E501
if timeout_policy not in allowed_values:
raise ValueError(
"Invalid value for `timeout_policy` ({0}), must be one of {1}" # noqa: E501
.format(timeout_policy, allowed_values)
)
self._timeout_policy = timeout_policy
@property
def retry_logic(self):
"""Gets the retry_logic of this TaskDef. # noqa: E501
:return: The retry_logic of this TaskDef. # noqa: E501
:rtype: str
"""
return self._retry_logic
@retry_logic.setter
def retry_logic(self, retry_logic):
"""Sets the retry_logic of this TaskDef.
:param retry_logic: The retry_logic of this TaskDef. # noqa: E501
:type: str
"""
allowed_values = ["FIXED", "EXPONENTIAL_BACKOFF"] # noqa: E501
if retry_logic not in allowed_values:
raise ValueError(
"Invalid value for `retry_logic` ({0}), must be one of {1}" # noqa: E501
.format(retry_logic, allowed_values)
)
self._retry_logic = retry_logic
@property
def retry_delay_seconds(self):
"""Gets the retry_delay_seconds of this TaskDef. # noqa: E501
:return: The retry_delay_seconds of this TaskDef. # noqa: E501
:rtype: int
"""
return self._retry_delay_seconds
@retry_delay_seconds.setter
def retry_delay_seconds(self, retry_delay_seconds):
"""Sets the retry_delay_seconds of this TaskDef.
:param retry_delay_seconds: The retry_delay_seconds of this TaskDef. # noqa: E501
:type: int
"""
self._retry_delay_seconds = retry_delay_seconds
@property
def response_timeout_seconds(self):
"""Gets the response_timeout_seconds of this TaskDef. # noqa: E501
:return: The response_timeout_seconds of this TaskDef. # noqa: E501
:rtype: int
"""
return self._response_timeout_seconds
@response_timeout_seconds.setter
def response_timeout_seconds(self, response_timeout_seconds):
"""Sets the response_timeout_seconds of this TaskDef.
:param response_timeout_seconds: The response_timeout_seconds of this TaskDef. # noqa: E501
:type: int
"""
self._response_timeout_seconds = response_timeout_seconds
@property
def concurrent_exec_limit(self):
"""Gets the concurrent_exec_limit of this TaskDef. # noqa: E501
:return: The concurrent_exec_limit of this TaskDef. # noqa: E501
:rtype: int
"""
return self._concurrent_exec_limit
@concurrent_exec_limit.setter
def concurrent_exec_limit(self, concurrent_exec_limit):
"""Sets the concurrent_exec_limit of this TaskDef.
:param concurrent_exec_limit: The concurrent_exec_limit of this TaskDef. # noqa: E501
:type: int
"""
self._concurrent_exec_limit = concurrent_exec_limit
@property
def input_template(self):
"""Gets the input_template of this TaskDef. # noqa: E501
:return: The input_template of this TaskDef. # noqa: E501
:rtype: dict(str, object)
"""
return self._input_template
@input_template.setter
def input_template(self, input_template):
"""Sets the input_template of this TaskDef.
:param input_template: The input_template of this TaskDef. # noqa: E501
:type: dict(str, object)
"""
self._input_template = input_template
@property
def rate_limit_per_frequency(self):
"""Gets the rate_limit_per_frequency of this TaskDef. # noqa: E501
:return: The rate_limit_per_frequency of this TaskDef. # noqa: E501
:rtype: int
"""
return self._rate_limit_per_frequency
@rate_limit_per_frequency.setter
def rate_limit_per_frequency(self, rate_limit_per_frequency):
"""Sets the rate_limit_per_frequency of this TaskDef.
:param rate_limit_per_frequency: The rate_limit_per_frequency of this TaskDef. # noqa: E501
:type: int
"""
self._rate_limit_per_frequency = rate_limit_per_frequency
@property
def rate_limit_frequency_in_seconds(self):
"""Gets the rate_limit_frequency_in_seconds of this TaskDef. # noqa: E501
:return: The rate_limit_frequency_in_seconds of this TaskDef. # noqa: E501
:rtype: int
"""
return self._rate_limit_frequency_in_seconds
@rate_limit_frequency_in_seconds.setter
def rate_limit_frequency_in_seconds(self, rate_limit_frequency_in_seconds):
"""Sets the rate_limit_frequency_in_seconds of this TaskDef.
:param rate_limit_frequency_in_seconds: The rate_limit_frequency_in_seconds of this TaskDef. # noqa: E501
:type: int
"""
self._rate_limit_frequency_in_seconds = rate_limit_frequency_in_seconds
@property
def isolation_group_id(self):
"""Gets the isolation_group_id of this TaskDef. # noqa: E501
:return: The isolation_group_id of this TaskDef. # noqa: E501
:rtype: str
"""
return self._isolation_group_id
@isolation_group_id.setter
def isolation_group_id(self, isolation_group_id):
"""Sets the isolation_group_id of this TaskDef.
:param isolation_group_id: The isolation_group_id of this TaskDef. # noqa: E501
:type: str
"""
self._isolation_group_id = isolation_group_id
@property
def execution_name_space(self):
"""Gets the execution_name_space of this TaskDef. # noqa: E501
:return: The execution_name_space of this TaskDef. # noqa: E501
:rtype: str
"""
return self._execution_name_space
@execution_name_space.setter
def execution_name_space(self, execution_name_space):
"""Sets the execution_name_space of this TaskDef.
:param execution_name_space: The execution_name_space of this TaskDef. # noqa: E501
:type: str
"""
self._execution_name_space = execution_name_space
@property
def owner_email(self):
"""Gets the owner_email of this TaskDef. # noqa: E501
:return: The owner_email of this TaskDef. # noqa: E501
:rtype: str
"""
return self._owner_email
@owner_email.setter
def owner_email(self, owner_email):
"""Sets the owner_email of this TaskDef.
:param owner_email: The owner_email of this TaskDef. # noqa: E501
:type: str
"""
self._owner_email = owner_email
@property
def poll_timeout_seconds(self):
"""Gets the poll_timeout_seconds of this TaskDef. # noqa: E501
:return: The poll_timeout_seconds of this TaskDef. # noqa: E501
:rtype: int
"""
return self._poll_timeout_seconds
@poll_timeout_seconds.setter
def poll_timeout_seconds(self, poll_timeout_seconds):
"""Sets the poll_timeout_seconds of this TaskDef.
:param poll_timeout_seconds: The poll_timeout_seconds of this TaskDef. # noqa: E501
:type: int
"""
self._poll_timeout_seconds = poll_timeout_seconds
def to_dict(self):
"""Returns the model properties as a dict"""
result = {}
for attr, _ in six.iteritems(self.swagger_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
))
elif hasattr(value, "to_dict"):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map(
lambda item: (item[0], item[1].to_dict())
if hasattr(item[1], "to_dict") else item,
value.items()
))
else:
result[attr] = value
if issubclass(TaskDef, dict):
for key, value in self.items():
result[key] = value
return result
def to_str(self):
"""Returns the string representation of the model"""
return pprint.pformat(self.to_dict())
def __repr__(self):
"""For `print` and `pprint`"""
return self.to_str()
def __eq__(self, other):
"""Returns true if both objects are equal"""
if not isinstance(other, TaskDef):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""Returns true if both objects are not equal"""
return not self == other
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] | 2.278192 | 9,116 |
import os
from setuptools import setup, find_packages
from setuptools.command.sdist import sdist
from setuptools.command.install import install
from setuptools.command.develop import develop
# Versions
antlr_version = '4.8'
file_dir = os.path.dirname(os.path.realpath(__file__))
zkay_version = _read_file(os.path.join(file_dir, 'zkay', 'VERSION'))
zkay_libsnark_commit_hash = '4e3c7a53ec333f52fe27ff45ff836102bcdb8e28'
packages = find_packages()
setup(
# Metadata
name='zkay',
version=zkay_version,
author='Nick Baumann, SRI Lab ETH Zurich',
url='https://github.com/eth-sri/zkay',
license='MIT',
description='Zkay is a programming language which enables automatic compilation of intuitive data privacy specifications to Ethereum smart contracts leveraging encryption and non-interactive zero-knowledge (NIZK) proofs. The zkay package provides a toolchain for compiling, deploying and using zkay contracts.',
# Dependencies
python_requires='>=3.7,<4',
install_requires=[
'Cython>=0.29,<0.30',
'web3[tester]>=v5.11,<v5.13',
f'antlr4-python3-runtime=={antlr_version}',
'parameterized>=0.7,<0.8',
'py-solc-x>=1.0.0,<1.1.0',
'pycryptodome>=3.9,<4',
'appdirs>=1.4,<1.5',
'argcomplete>=1,<2',
'semantic-version>=2.8.4,<2.9',
'pysha3>=1.0.2,<1.1', # Console script doesn't work without this even though it is not required
],
# Contents
packages=packages,
include_package_data=True,
entry_points={
"console_scripts": [
"zkay=zkay.__main__:main"
]
},
# Build steps
cmdclass={
'sdist': CustomSdist,
'install': CustomInstall,
'develop': CustomDevelop
}
)
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220,
705,
16244,
10354,
8562,
19246,
198,
220,
220,
220,
1782,
198,
8,
198
] | 2.359146 | 749 |
import resotolib.logger
import multiprocessing
import resotolib.proc
from concurrent import futures
from typing import Optional, Dict
from resotolib.baseplugin import BaseCollectorPlugin
from resotolib.args import ArgumentParser
from argparse import Namespace
from resotolib.config import Config, RunningConfig
from resotolib.graph import Graph
from kubernetes import client
from .config import K8sConfig
from .utils import k8s_config
from .collector import KubernetesCollector
from .resources.cluster import KubernetesCluster
log = resotolib.logger.getLogger("resoto." + __name__)
| [
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] | 3.482143 | 168 |
from codestat_token import Token
from token_builders import TokenBuilder
# token reader for brace comment
| [
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] | 4.28 | 25 |
import pytest
from widgetastic.widget import View
from widgetastic_patternfly4.ouia import Card
TESTING_PAGE_URL = "https://patternfly-docs-ouia.netlify.app/documentation/react/components/card"
@pytest.fixture
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] | 2.986111 | 72 |
import operator
from functools import reduce
import sys
import os
import importlib
import json
import base64
from pathlib import PurePath, Path
from urllib.parse import urljoin
import ecdsa
import hashlib
import grpc
import collections
import web3
from web3.gas_strategies.rpc import rpc_gas_price_strategy
from eth_account.messages import defunct_hash_message
from rfc3986 import urlparse
import ipfsapi
from web3.utils.datastructures import AttributeDict, MutableAttributeDict
import snet_sdk.generic_client_interceptor as generic_client_interceptor
__version__ = "0.0.1"
main_dir_path = PurePath(os.path.abspath(sys.modules['__main__'].__file__)).parent
cur_dir = PurePath(os.path.realpath(__file__)).parent
class TransactionError(Exception):
"""Raised when an Ethereum transaction receipt has a status of 0. Can provide a custom message. Optionally includes receipt"""
snet_sdk_defaults = {
"libraries_base_path": "grpc",
"eth_rpc_endpoint": "https://kovan.infura.io",
"ipfs_rpc_endpoint": "http://ipfs.singularitynet.io:80",
"private_key": None,
"signer_private_key": None,
"account_index": 0,
"default_gas": 1000000,
"mpe_address": None,
"token_address": None,
"allow_transactions": False
}
class Snet:
"""Base Snet SDK"""
# Generic Eth transaction functions
# Contract functions
# Generic utility functions
# Service client
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] | 2.922449 | 490 |
#!/usr/bin/env python
'''
This script subscribes to data notifications from a desired mote and
reports the latency statistics.
'''
#============================ adjust path =====================================
import sys
import os
if __name__ == "__main__":
here = sys.path[0]
sys.path.insert(0, os.path.join(here, '..', 'libs'))
sys.path.insert(0, os.path.join(here, '..', 'external_libs'))
#============================ imports =========================================
import urllib3
import traceback
import certifi
# generic SmartMeshSDK imports
from SmartMeshSDK import sdk_version
# VManager-specific imports
from VManagerSDK.vmanager import Configuration
from VManagerSDK.vmgrapi import VManagerApi
from VManagerSDK.vmanager.rest import ApiException
#============================ defines =========================================
DFLT_VMGR_HOST = "127.0.0.1"
DFLT_MOTE_MAC = "00-17-0D-00-00-60-08-DC"
urllib3.disable_warnings() # disable warnings that show up about self-signed certificates
#============================ variables =======================================
notif_count = 0
latency_total = 0
latency_max = 0
#============================ helpers =========================================
def process_data(mydata):
'''Process data notifications
Checks whether the notification came from the desired MAC address,
then prints latency data and calculates average and max latencies.
'''
global notif_count, latency_total, latency_max
if macaddr == mydata.mac_address:
mylatency = mydata.latency
notif_count += 1
latency_total += mylatency
average = latency_total / notif_count
if mylatency > latency_max:
latency_max = mylatency
print(' Mote {0} --> Latency {1} -- Average {2} -- Max {3}\n'.format(mydata.mac_address, mylatency, average, latency_max))
def process_notif(notif):
'''
Dispatch notifications to specific processing functions
'''
if notif.type in (
'dataPacketReceived',
'ipPacketReceived',
):
# handle data notifications
process_data(notif)
elif notif.type in (
'deviceHealthReport',
'discoveryHealthReport',
'neighborHealthReport',
):
# handle health reports
pass
elif notif.type in (
'configChanged',
'configDeleted',
'configLoaded',
'configRestored',
):
# handle config notifications
pass
else:
# handle other event notifications
pass
#============================ main ============================================
try:
# print banner
print '\nVMgr_LatencyMote (c) Dust Networks'
print 'SmartMesh SDK {0}\n'.format('.'.join([str(i) for i in sdk_version.VERSION]))
# ask the user for VManager host
mgrhost = raw_input('Enter the IP address of the manager (e.g. {0} ): '.format(DFLT_VMGR_HOST))
if mgrhost == "":
mgrhost = DFLT_VMGR_HOST
# ask the user for mote's MAC address
macaddr = raw_input('Enter MAC address of mote to Ping (e.g. {0}): '.format(DFLT_MOTE_MAC))
if macaddr == "":
macaddr = DFLT_MOTE_MAC
macaddr = macaddr.upper() # make sure all letters are upper case
# log-in as user "dust"
config = Configuration()
config.username = 'dust'
config.password = 'dust'
config.verify_ssl = False
if os.path.isfile(certifi.where()):
config.ssl_ca_cert = certifi.where()
else:
config.ssl_ca_cert = os.path.join(os.path.dirname(sys.executable), "cacert.pem")
# initialize the VManager Python library
voyager = VManagerApi(host=mgrhost)
# Start listening for data notifications
voyager.get_notifications('data', notif_callback=process_notif)
print '\n==== Subscribing to data notifications'
reply = raw_input ('\n Waiting for notifications from mote, Press any key to stop\n')
voyager.stop_notifications()
print 'Script ended normally'
except:
traceback.print_exc()
print ('Script ended with an error.')
sys.exit()
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] | 2.493166 | 1,756 |
#!/usr/bin/env python3
# Usage:
# go one level above emmer/
# python3 -m emmer.test.test_viewer
from ..main.basic.math import NonDesityMatrix
from ..posthoc.visual.viewer import Projection
from pandas.util.testing import assert_frame_equal
import numpy.testing
import unittest
import pandas
import numpy
if __name__ == '__main__':
unittest.main()
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] | 2.879032 | 124 |
#!/usr/bin/env python3
import argparse
import json
import os
import sys
from collections import defaultdict
import itertools
import requests
REPOS_API = 'https://api.github.com/repos/'
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('repo', help='e.g. gittenburg/instant-issues')
args = parser.parse_args()
print(json.dumps(aggregate_issues(args.repo, os.environ['TOKEN'], requests.Session())))
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] | 2.841772 | 158 |
from PyQt4 import QtGui, QtCore
| [
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83,
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33734,
8205,
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33734,
14055,
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] | 2.538462 | 13 |
# Copyright (C) 2015 Atsushi Togo
# All rights reserved.
#
# This file is part of phonopy.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
#
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in
# the documentation and/or other materials provided with the
# distribution.
#
# * Neither the name of the phonopy project nor the names of its
# contributors may be used to endorse or promote products derived
# from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
# COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
import numpy as np
from phonopy import Phonopy
from phonopy.harmonic.force_constants import compact_fc_to_full_fc
from phonopy.harmonic.dynmat_to_fc import get_commensurate_points
from phonopy.structure.atoms import PhonopyAtoms
from phonopy.structure.cells import get_supercell
class Unfolding(object):
"""Calculation of a phonon unfolding method
Implementation of an unfolding method by
P. B. Allen et al., Phys. Rev. B 87, 085322 (2013)
T(r_i) in this implementation is defined as
T(r_i) f(x) = f(x - r_i).
The sign is opposite from that written in the Allen's paper.
Bloch wave is defined in the same way for phase convention
Psi_k(x + r) = exp(ikr) Psi_k(x).
By these, sign of phase in Eq.(3) (Eq.(7) as well) is opposite.
Vacancies are treated as atomic sites where no atoms exist.
Interstitials are treated as atomic sites with the interstitial
atoms in a specific virtual primitive cell, but the respective
atomic sites in the other virtual
primitive cells are set as vacancies.
The unfolded band structure may be plotted as
x (wave vector): qpoints
y (frequency): frequencies
z (intencity) : unfolding_weights
Attributes
----------
unfolding_weights : ndarray
Unfolding weights.
shape=(qpoints, supercell atoms * 3), dtype='double', order='C'
frequencies : ndarray
Phonon frequencies at Gamma point of the supercell phonon. By
unfolding, these are considered at the phonon frequencies at
the specified qpoints but with the unfolding weights.
shape=(qpoints, supercell atoms * 3), dtype='double', order='C'
commensurate_points : ndarray
Commensurate points corresponding to ``supercell_matrix``.
shape=(N, 3), dtype='double', order='C'
where N = det(supercell_matrix)
"""
def __init__(self,
phonon,
supercell_matrix,
ideal_positions,
atom_mapping,
qpoints):
"""
Parameters
----------
phonon : Phonopy
Phonopy object to be unfolded.
supercell_matrix : array_like
Matrix that represents the virtual primitive translation enforced
inside the supercell. This works like an inverse primitive matrix.
shape=(3, 3), dtype='intc'
ideal_positions : array_like
Positions of atomic sites in supercell. This corresponds to those
in a set of virtual primitive cells in the supercell.
shape=(3, 3), dtype='intc'
atom_mapping : list
Atomic index mapping from ``ideal_positions`` to supercell atoms
in ``phonon``. The elements of this list are intergers for atoms
and None for vacancies.
qpoints : array_like
q-points in reciprocal virtual-primitive-cell coordinates
shape=(num_qpoints, 3), dtype='double'
"""
self._phonon = self._get_supercell_phonon(phonon)
self._supercell_matrix = np.array(supercell_matrix, dtype='intc')
self._ideal_positions = np.array(ideal_positions, dtype='double')
self._qpoints_p = qpoints # in PBZ
self._qpoints_s = self._get_qpoints_in_SBZ() # in SBZ
self._symprec = self._phonon.symmetry.get_symmetry_tolerance()
self._frequencies = None
self._eigvecs = None # This may have anormal array shape.
self._unfolding_weights = None
self._q_count = None # As counter for iterator
# Commensurate q-vectors in PBZ
self._comm_points = get_commensurate_points(self._supercell_matrix)
self._trans_s = None # in SC (see docstring in _set_translations)
self._trans_p = None # in PC (see docstring in _set_translations)
self._N = None
self._set_translations()
self._atom_mapping = None
self._index_map_inv = None
self._set_index_map(atom_mapping)
@property
@property
@property
def _set_translations(self):
"""Set primitive translations in supercell
_trans_s
Translations with respect to supercell basis vectors
_trans_p
Translations with respect to primitive cell basis vectors
_N
Number of the translations = det(supercel_matrix)
"""
pcell = PhonopyAtoms(numbers=[1],
scaled_positions=[[0, 0, 0]],
cell=np.diag([1, 1, 1]))
smat = self._supercell_matrix
self._trans_s = get_supercell(pcell, smat).scaled_positions
self._trans_p = np.dot(self._trans_s, self._supercell_matrix.T)
self._N = len(self._trans_s)
def _set_index_map(self, atom_mapping):
"""T(r_i) in Eq.(3) is given as permutation of atom indices.
_index_set : ndarray
For each translation (shift), atomic indices of the positions
(_ideal_positions - shift) are searched and stored. The indices
are used to select eigenvectors, by which T(r_i)|KJ> is
represented.
shape=(num_trans, num_sites), dtype='intc'
"""
lattice = self._phonon.supercell.cell
natom = len(self._ideal_positions)
index_map_inv = np.zeros((self._N, natom), dtype='intc')
for i, shift in enumerate(self._trans_s):
for j, p in enumerate(self._ideal_positions - shift): # minus r_i
diff = self._ideal_positions - p
diff -= np.rint(diff)
dist = np.sqrt((np.dot(diff, lattice) ** 2).sum(axis=1))
# k is index in _ideal_positions.
k = np.where(dist < self._symprec)[0][0]
index_map_inv[i, j] = k
self._index_map_inv = index_map_inv
self._atom_mapping = np.zeros(len(atom_mapping), dtype='int')
for i, idx in enumerate(atom_mapping):
if idx is None:
self._atom_mapping[i] = -1
else:
self._atom_mapping[i] = idx
def _get_unfolding_weights(self):
"""Calculate Eq. (7)
k = K + G + g
K -> _qpoints_s[_q_index] (in SBZ)
k -> _qpoints_p[_q_index] (in PBZ)
G -> _comm_points (in PBZ)
g -> a reciprocal lattice point in PBZ (unused explicitly)
j -> Primitive translations in supercell (_trans_p)
J -> Band indices of supercell phonon modes (axis=1 or eigvecs)
The phase factor corresponding to K is not included in eigvecs
with our choice of dynamical matrix.
"""
eigvecs = self._eigvecs
dtype = "c%d" % (np.dtype('double').itemsize * 2)
q_p = self._qpoints_p[self._q_count] # k
q_s = self._qpoints_s[self._q_count] # K
diff = q_p - np.dot(q_s, np.linalg.inv(self._supercell_matrix))
# Search G points corresponding to k = G + K
for G in self._comm_points:
d = diff - G
d -= np.rint(d)
if (np.abs(d) < 1e-5).all():
break
e = np.zeros((len(self._atom_mapping) * 3, eigvecs.shape[1]),
dtype=dtype)
phases = np.exp(2j * np.pi * np.dot(self._trans_p, G))
for phase, indices in zip(
phases, self._atom_mapping[self._index_map_inv]):
eig_indices = (
np.c_[indices * 3, indices * 3 + 1, indices * 3 + 2]).ravel()
e += eigvecs[eig_indices, :] * phase
e /= self._N
weights = (e.conj() * e).sum(axis=0)
# indices = self._atom_mapping
# eig_indices_r = (
# np.c_[indices * 3, indices * 3 + 1, indices * 3 + 2]).ravel()
# for phase, indices in zip(
# phases, self._atom_mapping[self._index_map_inv]):
# eig_indices_l = (
# np.c_[indices * 3, indices * 3 + 1, indices * 3 + 2]).ravel()
# e += eigvecs[eig_indices_l, :] * eigvecs[eig_indices_r, :].conj() * phase
# e /= self._N
# weights = e.sum(axis=0)
if (weights.imag > 1e-5).any():
print("Phonopy warning: Encountered imaginary values.")
return weights.real
def _get_supercell_phonon(self, ph_in):
"""Returns Phonopy instance of supercell as the primitive"""
ph = Phonopy(ph_in.supercell,
supercell_matrix=[1, 1, 1],
primitive_matrix='P')
fc_shape = ph_in.force_constants.shape
if fc_shape[0] == fc_shape[1]: # assume full fc
ph.force_constants = ph_in.force_constants.copy()
else:
ph.force_constants = compact_fc_to_full_fc(
ph_in, ph_in.force_constants)
if ph_in.nac_params:
p2p = ph_in.primitive.p2p_map
s2p = ph_in.primitive.s2p_map
s2pp = [p2p[i] for i in s2p]
born_in = ph_in.nac_params['born']
born = [born_in[i] for i in s2pp]
nac_params = {'born': np.array(born, dtype='double', order='C'),
'factor': ph_in.nac_params['factor'],
'dielectric': ph_in.nac_params['dielectric'].copy()}
ph.nac_params = nac_params
return ph
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] | 2.274244 | 4,795 |
from django.db import models
from django.utils import timezone
# Student user | [
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] | 3.714286 | 21 |
#-------------------------------------------------------------------------------
# Subs
#-------------------------------------------------------------------------------
#-------------------------------------------------------------------------------
# Fin
#-------------------------------------------------------------------------------
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# 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.
#
# Code generated by Microsoft (R) AutoRest Code Generator 2.3.33.0
# Changes may cause incorrect behavior and will be lost if the code is
# regenerated.
# --------------------------------------------------------------------------
from .create_endpoint_variant_request import CreateEndpointVariantRequest
class AKSServiceCreateRequest(CreateEndpointVariantRequest):
"""The request to create an AKS service.
:param name: The service name.
:type name: str
:param description: The description of the service.
:type description: str
:param kv_tags: The service tag dictionary. Tags are mutable.
:type kv_tags: dict[str, str]
:param properties: The service properties dictionary. Properties are
immutable.
:type properties: dict[str, str]
:param keys: The authentication keys.
:type keys: ~_restclient.models.CreateServiceRequestKeys
:param image_id: The Image Id.
:type image_id: str
:param environment_image_request: The Environment, models and assets
needed for inferencing.
:type environment_image_request:
~_restclient.models.CreateServiceRequestEnvironmentImageRequest
:param location: The location of the service.
:type location: str
:param overwrite: Whether to overwrite existing service if exists
:type overwrite: bool
:param deployed_api_options: Options for the API that the package exposes.
Defaults to the standard AzureML REST server.
:type deployed_api_options:
~_restclient.models.CreateServiceRequestDeployedApiOptions
:param compute_type: Constant filled by server.
:type compute_type: str
:param is_default: Is this the default variant.
:type is_default: bool
:param traffic_percentile: The amount of traffic variant receives.
:type traffic_percentile: float
:param type: The type of the variant. Possible values include: 'Control',
'Treatment'
:type type: str or ~_restclient.models.VariantType
:param num_replicas: The number of replicas on the cluster.
:type num_replicas: int
:param data_collection: Details of the data collection options specified.
:type data_collection:
~_restclient.models.AKSServiceCreateRequestDataCollection
:param compute_name: The name of the compute resource.
:type compute_name: str
:param app_insights_enabled: Whether or not Application Insights is
enabled.
:type app_insights_enabled: bool
:param auto_scaler: The auto scaler properties.
:type auto_scaler: ~_restclient.models.AKSServiceCreateRequestAutoScaler
:param container_resource_requirements: The container resource
requirements.
:type container_resource_requirements:
~_restclient.models.AKSServiceCreateRequestContainerResourceRequirements
:param max_concurrent_requests_per_container: The maximum number of
concurrent requests per container.
:type max_concurrent_requests_per_container: int
:param max_queue_wait_ms: Maximum time a request will wait in the queue
(in milliseconds). After this time, the service will return 503 (Service
Unavailable)
:type max_queue_wait_ms: int
:param namespace: Kubernetes namespace for the service.
:type namespace: str
:param scoring_timeout_ms: The scoring timeout in milliseconds.
:type scoring_timeout_ms: int
:param auth_enabled: Whether or not authentication is enabled.
:type auth_enabled: bool
:param liveness_probe_requirements: The liveness probe requirements.
:type liveness_probe_requirements:
~_restclient.models.AKSServiceCreateRequestLivenessProbeRequirements
:param aad_auth_enabled: Whether or not AAD authentication is enabled.
:type aad_auth_enabled: bool
"""
_validation = {
'name': {'required': True},
'compute_type': {'required': True},
}
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'kv_tags': {'key': 'kvTags', 'type': '{str}'},
'properties': {'key': 'properties', 'type': '{str}'},
'keys': {'key': 'keys', 'type': 'CreateServiceRequestKeys'},
'image_id': {'key': 'imageId', 'type': 'str'},
'environment_image_request': {'key': 'environmentImageRequest', 'type': 'CreateServiceRequestEnvironmentImageRequest'},
'location': {'key': 'location', 'type': 'str'},
'overwrite': {'key': 'overwrite', 'type': 'bool'},
'deployed_api_options': {'key': 'deployedApiOptions', 'type': 'CreateServiceRequestDeployedApiOptions'},
'compute_type': {'key': 'computeType', 'type': 'str'},
'is_default': {'key': 'isDefault', 'type': 'bool'},
'traffic_percentile': {'key': 'trafficPercentile', 'type': 'float'},
'type': {'key': 'type', 'type': 'VariantType'},
'num_replicas': {'key': 'numReplicas', 'type': 'int'},
'data_collection': {'key': 'dataCollection', 'type': 'AKSServiceCreateRequestDataCollection'},
'compute_name': {'key': 'computeName', 'type': 'str'},
'app_insights_enabled': {'key': 'appInsightsEnabled', 'type': 'bool'},
'auto_scaler': {'key': 'autoScaler', 'type': 'AKSServiceCreateRequestAutoScaler'},
'container_resource_requirements': {'key': 'containerResourceRequirements', 'type': 'AKSServiceCreateRequestContainerResourceRequirements'},
'max_concurrent_requests_per_container': {'key': 'maxConcurrentRequestsPerContainer', 'type': 'int'},
'max_queue_wait_ms': {'key': 'maxQueueWaitMs', 'type': 'int'},
'namespace': {'key': 'namespace', 'type': 'str'},
'scoring_timeout_ms': {'key': 'scoringTimeoutMs', 'type': 'int'},
'auth_enabled': {'key': 'authEnabled', 'type': 'bool'},
'liveness_probe_requirements': {'key': 'livenessProbeRequirements', 'type': 'AKSServiceCreateRequestLivenessProbeRequirements'},
'aad_auth_enabled': {'key': 'aadAuthEnabled', 'type': 'bool'},
}
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] | 2.841869 | 2,226 |
# Generated by Django 2.0.2 on 2018-02-19 12:53
from django.db import migrations, models
import django.db.models.deletion
import yamlfield.fields
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# -*- coding: utf-8 -*
'''!
@file BC20_Serial.py
@brief Send AT commands to the BC20 module via USB Serial
@copyright Copyright (c) 2010 DFRobot Co.Ltd (http://www.dfrobot.com)
@license The MIT License (MIT)
@author [PengKaixing]([email protected])
@version V1.0.0
@date 2019-07-16
@url https://github.com/DFRobot/DFRobot_BC20_Gravity
'''
import sys
import os
import time
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.realpath(__file__)))))
from DFRobot_BC20_Gravity import *
RED = 'R'
BLUE = 'B'
GREEN = 'G'
YELLOW = 'Y'
PURPLE = 'P'
CYAN = 'C'
WHITE = 'W'
'''For general controllers. Communicate by IIC
Connect Instructions
Raspberry Pi | Module(BC20)
SDA(GPIO3) | D/T
SCL(GPIO5) | C/R
GND(GPIO6) | GND
VCC(GPIO2) | VCC
IIC address(A0,A1)
0x30:(A0=0,A1=0)
0x31:(A0=0,A1=1)
0x32:(A0=1,A1=0)
0x33:(A0=1,A1=1) default'''
'''For general controllers. Communicate by Serial
Connect Instructions
Raspberry Pi | Module(BC20)
RXD(GPIO10) | D/T
TXD(GPIO8) | C/R
GND(GPIO6) | GND
VCC(GPIO2) | VCC'''
BC20=DFRobot_BC20_IIC(0x33)
#BC20=DFRobot_BC20_Serial()
#Initialize BC20
print("Starting the BC20.Please wait. . . ")
BC20.change_color(RED)
while BC20.power_on()==False:
BC20.led_on()
time.sleep(0.5)
BC20.led_off()
time.sleep(0.5)
print("BC20 started successfully!\r\n")
#Deep Sleep Mode is automatically enable every time upon power up.
#When this mode is entered,
#BC20 will not respond any AT commands from the controller
#Disable sleep mode to ensure BC20 always responding AT commands
#BC20.config_sleep_mode(0)
while True:
#Each AT command should begin with "AT" or "at" and end with "Carriage return".
#The commands can be upper-case or lower-case. ex. "AT+CSQ" or "at+csq".
BC20.send_cmd(raw_input("please input:\r\n"))
time.sleep(0.5)
if BC20.available():
print("read_data from BC20:\r\n")
print(BC20.read_data(1000)) | [
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] | 2.129555 | 988 |
from collections import namedtuple
from collections import defaultdict
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from mpl_toolkits.mplot3d import axes3d
# Source: https://www.blackjackinfo.com/dealer-outcome-probabilities/#TIDS17
dealer_prob = [[.3536, .3739, .3945, .4164, .4232, .2623, .2447, .2284, .2298, .1665],
[.1398, .1350, .1305, .1223, .1654, .3686, .1286, .1200, .1207, .1889],
[.1349, .1305, .1259, .1223, .1063, .1378, .3593, .1200, .1207, .1889],
[.1297, .1256, .1213, .1177, .1063, .0786, .1286, .3508, .1207, .1889],
[.1240, .1203, .1165, .1131, .1017, .0786, .0694, .1200, .3707, .1889],
[.1180, .1147, .1112, .1082, .0972, .0741, .0694, .0608, .0374, .0778]]
draw_prob = {"num": 1/13, "face": 3/13, "ace": 1/13}
State = namedtuple('State', ['X', 'Y'])
states = []
for x in range(4,21+1):
for y in range(2,11+1):
states.append(State(x, y))
V_curr = defaultdict(int)
V_prev = defaultdict(int)
policy = defaultdict(bool)
epochs = 100
for epoch in range(epochs):
for state in states:
# Action = Hit
hit_value = 0
for card in range(2,11+1):
if card < 10:
p = draw_prob["num"]
elif card == 10:
p = draw_prob["face"]
else:
p = draw_prob["ace"]
if state.X + card <= 21:
hit_value += p * V_prev[State(state.X + card, state.Y)]
else:
hit_value += p * (-1)
# Action = Stick
p_win = dealer_prob[0][state.Y - 2]
for i in range(state.X - 17):
p_win += dealer_prob[i + 1][state.Y - 2]
if state.X >= 17:
p_draw = dealer_prob[state.X - 17 + 1][state.Y - 2]
else:
p_draw = 0
p_lose = 1 - p_win - p_draw
stick_value = 0
stick_value += p_win * 1 # Win
stick_value += p_draw * 0 # Draw
stick_value += p_lose * (-1) # Lose
if hit_value > stick_value:
policy[state] = True
else:
policy[state] = False
V_curr[state] = max(hit_value, stick_value)
V_prev = V_curr
# Heat Map Plot
plt.figure()
Z = np.zeros([18, 10])
for x in range(4,21+1):
for y in range(2,11+1):
Z[x-4, y-2] = policy[State(x, y)]
ax = sns.heatmap(Z, xticklabels=[i for i in range(2,11+1)], yticklabels=[i for i in range(4,21+1)], cmap=sns.color_palette("hls", 2))
ax.invert_yaxis()
# Surface Plot
plt.figure()
Z2 = np.zeros([18, 10])
X = np.array([i for i in range(4,21+1)])
Y = np.array([i for i in range(2,11+1)])
for x in range(4,21+1):
for y in range(2,11+1):
Z2[x-4, y-11] = V_curr[State(x, y)]
X, Y = np.meshgrid(Y, X)
ax2 = plt.axes(projection='3d')
ax2.plot_surface(X, Y, Z2, rstride=1, cstride=1, cmap='viridis', edgecolor='none')
plt.show()
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# -*- coding: utf-8 -*-
from __future__ import division
import datetime, os
from django.utils import timezone
from django.utils.translation import ugettext_lazy as _, string_concat
from django.conf import settings as django_settings
from django.utils.deconstruct import deconstructible
from django.core.validators import MaxValueValidator, MinValueValidator
from django.db import models
from django.db.models import Q
from magi.item_model import MagiModel, get_image_url, get_http_image_url
from magi.utils import split_data, join_data, AttrDict, tourldash, randomString
from magi.models import User
from cpro.model_choices import *
from cpro.django_translated import t
############################################################
# Utils
@deconstructible
############################################################
# Idol
############################################################
# Event
############################################################
# Account
############################################################
# Card
############################################################
# Owned Card
############################################################
# Favorite Card
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"""
Author :- Manas Kumar mishra
Task :- Test for Series connection. This is not a part of the network. it is for Testing algos
Begin DATE :- 01- APRIL- 2021
"""
from socket import*
from random import randint
# CIF Customer Information file
# It maaps between the Card info to the Bank information
global CIF_number
CIF_number={
"1001 0110 2002 0011":"98765432011",
"1001 0110 2002 0026":"98765432026",
"1001 0110 2002 0006":"98765432006"
}
global accountDetails
accountDetails={
"98765432011":["00000000011", "RBIS0PFMS01"],
"98765432026":["00000000026", "RBIS0PFMS01"],
"98765432006":["00000000006", "RBIS0PFMS01"],
}
# function for converting the binary message into list
# Input is receved message from payment gateway
# output is full message in list
TpsPortNumber = 9988
TpsServer = socket(AF_INET, SOCK_STREAM)
TpsServer.bind(('', TpsPortNumber))
TpsServer.listen(1)
print("Server is connected...")
while 1:
ppInstance, ppAddress = TpsServer.accept()
print("Connection established...")
recvMsgFromPP = ppInstance.recv(2048)
recvInfo = give_list(recvMsgFromPP)
print("Received message...")
print(recvInfo)
cardNumber = recvInfo[0]
print(cardNumber)
print(CIF_number[str(cardNumber)])
OTP = otp_gen()
receivedOtp=ppInstance.recv(2048)
recvotp = receivedOtp.decode()
print("RECEIVED OTP from the user :- ",recvotp)
if recvotp == str(OTP):
ppInstance.send("True".encode())
else:
ppInstance.send("False".encode())
ppInstance.close()
# ppInstance.send("1".encode())
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from .combinatorics import *
from .files import *
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from plugin.core.environment import Environment
from plugin.models.m_plex.account import PlexAccount
from plugin.models.core import db
from exception_wrappers.libraries.playhouse.apsw_ext import *
from xml.etree import ElementTree
import logging
import requests
log = logging.getLogger(__name__)
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# =============================================================================
# PROJECT CHRONO - http://projectchrono.org
#
# Copyright (c) 2014 projectchrono.org
# All right reserved.
#
# Use of this source code is governed by a BSD-style license that can be found
# in the LICENSE file at the top level of the distribution and at
# http://projectchrono.org/license-chrono.txt.
#
# =============================================================================
# Authors: Han Wang
# =============================================================================
#
# Demo program for Gator simulation.
#
# The vehicle reference frame has Z up, X towards the front of the vehicle, and
# Y pointing to the left.
# All units SI.
#
# =============================================================================
import pychrono.core as chrono
import pychrono.irrlicht as irr
import pychrono.vehicle as veh
import pychrono.sensor as sens
import math
import os
# =============================================================================
# The path to the Chrono data directory containing various assets (meshes, textures, data files)
# is automatically set, relative to the default location of this demo.
# If running from a different directory, you must change the path to the data directory with:
#chrono.SetChronoDataPath('path/to/data')
veh.SetDataPath(chrono.GetChronoDataPath() + 'vehicle/')
print(chrono.GetChronoDataPath() + 'vehicle/')
# Initial vehicle location and orientation
initLoc = chrono.ChVectorD(0, 0, 0.4)
initRot = chrono.ChQuaternionD(1, 0, 0, 0)
# Visualization type for vehicle parts (PRIMITIVES, MESH, or NONE)
chassis_vis_type = veh.VisualizationType_MESH
suspension_vis_type = veh.VisualizationType_PRIMITIVES
steering_vis_type = veh.VisualizationType_PRIMITIVES
wheel_vis_type = veh.VisualizationType_NONE
tire_vis_type = veh.VisualizationType_MESH
# Poon chassis tracked by the camera
trackPoint = chrono.ChVectorD(0.0, 0.0, 1.75)
# Simulation step sizes
step_size = 1e-3
tire_step_size = step_size
# Simulation end time
tend = 1000
# Time interval between two render frames
render_step_size = 1.0 / 50 # FPS = 50
noise_model="NONE" # No noise model
# Update rate in Hz
update_rate = 30
# Image width and height
image_width = 1280
image_height = 720
# Camera's horizontal field of view
fov = 1.408
# Lag (in seconds) between sensing and when data becomes accessible
lag = 0
# Exposure (in seconds) of each image
exposure_time = 0
# ---------------------
# Simulation parameters
# ---------------------
# view camera images
vis = True
# =============================================================================
print( "Copyright (c) 2017 projectchrono.org\n")
# --------------
# Create systems
# --------------
# Create the vehicle, set parameters, and initialize
gator = veh.Gator()
gator.SetContactMethod(chrono.ChContactMethod_NSC)
gator.SetChassisFixed(False)
gator.SetInitPosition(chrono.ChCoordsysD(initLoc, initRot))
gator.SetBrakeType(veh.BrakeType_SHAFTS)
gator.SetTireType(veh.TireModelType_TMEASY)
gator.SetTireStepSize(tire_step_size)
gator.SetInitFwdVel(0.0)
gator.Initialize()
gator.SetChassisVisualizationType(chassis_vis_type)
gator.SetSuspensionVisualizationType(suspension_vis_type)
gator.SetSteeringVisualizationType(steering_vis_type)
gator.SetWheelVisualizationType(wheel_vis_type)
gator.SetTireVisualizationType(tire_vis_type)
print("Vehicle mass: " + str(gator.GetVehicle().GetVehicleMass()))
print("Vehicle mass (with tires): " + str(gator.GetTotalMass()))
print("Driveline type: " + gator.GetVehicle().GetDriveline().GetTemplateName())
print("Brake type: " + gator.GetVehicle().GetBrake(1, veh.LEFT).GetTemplateName())
print("Tire type: " + gator.GetVehicle().GetTire(1, veh.LEFT).GetTemplateName())
print("\n")
# ------------------
# Create the terrain
# ------------------
terrain = veh.RigidTerrain(gator.GetSystem())
patch_mat = chrono.ChMaterialSurfaceNSC()
patch_mat.SetFriction(0.9)
patch_mat.SetRestitution(0.01)
patch = terrain.AddPatch(patch_mat,
chrono.ChVectorD(0, 0, 0), chrono.ChVectorD(0, 0, 1),
600, 600)
patch.SetColor(chrono.ChColor(0.8, 0.8, 1.0))
patch.SetTexture(veh.GetDataFile("terrain/textures/tile4.jpg"), 1200, 1200)
terrain.Initialize()
# -------------------------------------
# Create the vehicle Irrlicht interface
# Create the driver system
# -------------------------------------
vis = veh.ChWheeledVehicleVisualSystemIrrlicht()
gator.GetVehicle().SetVisualSystem(vis)
vis.SetWindowTitle('Gator')
vis.SetWindowSize(1280, 1024)
vis.SetChaseCamera(trackPoint, 6.0, 0.5)
vis.Initialize()
vis.AddLogo(chrono.GetChronoDataFile('logo_pychrono_alpha.png'))
vis.AddTypicalLights()
vis.AddSkyBox()
# Create the interactive driver system
driver = veh.ChIrrGuiDriver(vis)
# Set the time response for steering and throttle keyboard inputs.
steering_time = 1.0 # time to go from 0 to +1 (or from 0 to -1)
throttle_time = 1.0 # time to go from 0 to +1
braking_time = 0.3 # time to go from 0 to +1
driver.SetSteeringDelta(render_step_size / steering_time)
driver.SetThrottleDelta(render_step_size / throttle_time)
driver.SetBrakingDelta(render_step_size / braking_time)
driver.Initialize()
# -----------------------
# Create a sensor manager
# -----------------------
manager = sens.ChSensorManager(gator.GetSystem())
intensity = 1.0
manager.scene.AddPointLight(chrono.ChVectorF(2, 2.5, 100), chrono.ChColor(intensity, intensity, intensity), 500.0)
manager.scene.AddPointLight(chrono.ChVectorF(9, 2.5, 100), chrono.ChColor(intensity, intensity, intensity), 500.0)
manager.scene.AddPointLight(chrono.ChVectorF(16, 2.5, 100), chrono.ChColor(intensity, intensity, intensity), 500.0)
manager.scene.AddPointLight(chrono.ChVectorF(23, 2.5, 100), chrono.ChColor(intensity, intensity, intensity), 500.0)
# ------------------------------------------------
# Create two camera and add it to the sensor manager
# ------------------------------------------------
offset_pose = chrono.ChFrameD(chrono.ChVectorD(.1, 0, 1.45), chrono.Q_from_AngAxis(.2, chrono.ChVectorD(0, 1, 0)))
cam = sens.ChCameraSensor(
gator.GetChassisBody(),
update_rate,
offset_pose,
image_width,
image_height,
fov
)
cam.SetName("First Person POV")
# Renders the image at current point in the filter graph
if vis:
cam.PushFilter(sens.ChFilterVisualize(image_width, image_height, "Before Grayscale Filter"))
manager.AddSensor(cam)
offset_pose1 = chrono.ChFrameD(chrono.ChVectorD(-8, 0, 3), chrono.Q_from_AngAxis(.2, chrono.ChVectorD(0, 1, 0)))
cam1 = sens.ChCameraSensor(
gator.GetChassisBody(),
update_rate,
offset_pose,
image_width,
image_height,
fov
)
cam1.SetName("Third Person POV")
# Renders the image at current point in the filter graph
if vis:
cam1.PushFilter(sens.ChFilterVisualize(image_width, image_height, "Before Grayscale Filter"))
manager.AddSensor(cam1)
# ---------------
# Simulation loop
# ---------------
orbit_radius = 15
orbit_rate = 1
realtime_timer = chrono.ChRealtimeStepTimer()
while vis.Run() :
time = gator.GetSystem().GetChTime()
# Render scene
vis.BeginScene()
vis.DrawAll()
vis.EndScene()
# Collect output data from modules (for inter-module communication)
driver_inputs = driver.GetInputs()
# Update modules (process inputs from other modules)
driver.Synchronize(time)
terrain.Synchronize(time)
gator.Synchronize(time, driver_inputs, terrain)
vis.Synchronize(driver.GetInputModeAsString(), driver_inputs)
# Update sensor manager
cam1.SetOffsetPose(chrono.ChFrameD(
chrono.ChVectorD(-orbit_radius * math.cos(time * orbit_rate), -orbit_radius * math.sin(time * orbit_rate), 1),
chrono.Q_from_AngAxis(time * orbit_rate, chrono.ChVectorD(0, 0, 1))))
manager.Update()
# Advance simulation for one timestep for all modules
driver.Advance(step_size)
terrain.Advance(step_size)
gator.Advance(step_size)
vis.Advance(step_size)
# Spin in place for real time to catch up
realtime_timer.Spin(step_size) | [
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] | 3.001104 | 2,717 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright (c) 2016 dotzero <[email protected]>
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included
# in all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
"""This module contains a object that represents Tests for TildaError"""
import unittest
import sys
from tilda import TildaError
from tests.base import BaseTest
sys.path.append('.')
class TildaErrorTest(BaseTest, unittest.TestCase):
"""This object represents Tests for TildaError."""
def test_page_init(self):
"""Test TildaError.__init__() method"""
print('Testing TildaError.__init__()')
try:
raise TildaError()
except TildaError as e:
self.assertEqual(e.status, '')
self.assertEqual(e.message, '')
self.assertEqual(e.errorside, '')
def test_page_str(self):
"""Test TildaError.__str__() method"""
print('Testing TildaError.__str__()')
try:
raise TildaError({
'status': 200,
'message': 'OK',
})
except TildaError as e:
self.assertEqual(e.status, 200)
self.assertEqual(e.message, 'OK')
self.assertEqual(str(e), 'OK')
if __name__ == '__main__':
unittest.main()
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] | 2.757688 | 813 |
import matplotlib.pyplot as plt
DEFAULT_LABEL_FS = 12
DEFAULT_AXIS_FS = 12
DEFAULT_FIGSIZE = (12,8)
def plot_single_ed(wide_df, hosp_id, figsize=(12,3),
fontsize=DEFAULT_LABEL_FS, line_width=2):
'''
Plot a single ED's data
Assumes data are passed in wide format.
Params:
-------
wide_df: pandas.Dataframe
ED time series data in wide format
hosp_id: str
name of hospital column to plot e.g. 'hosp_1'
figsize: tuple(int, int), optional (default=(12,3))
`matplotlib` figure size
fontsize: int, optional (default=DEFAULT_LABEL_FS)
Size of label font
line_width: int
Width of the line plot
Returns:
-------
matplotlib fig, ax
'''
fig = plt.figure(figsize=figsize)
ax = fig.add_subplot()
ax.set_xlabel("Date", fontsize=fontsize)
ax.set_ylabel("Attendances", fontsize=fontsize)
_ = ax.plot(wide_df[hosp_id], lw=line_width)
# include x, y grid
_ = ax.grid(ls='--')
# set size of x, y ticks
_ = ax.tick_params(axis='both', labelsize=fontsize)
# return the figure
return fig, ax
def plot_eds(wide_df, figsize=DEFAULT_FIGSIZE, label_font_size=DEFAULT_LABEL_FS,
axis_font_size=DEFAULT_AXIS_FS):
'''
Plot all ED's attendances in a 1x4 grid layout.
Params:
------
wide_df: pandas.Dataframe
ED time series data in wide format
figsize: tuple(int, int), optional (default=(12,3))
`matplotlib` figure size
label_font_size: int, optional (default=DEFAULT_LABEL_FS)
Size of label font
axis_font_size: int, optional (default=DEFAULT_AXIS_FS)
Size of axis tick font
Returns:
--------
matplotlib fig
'''
fig, axs = plt.subplots(nrows=4, ncols=1, tight_layout=True, figsize=(12,8),
sharex=True)
# note that axs is a 2D array
for hosp_idx in range(0, 4):
_ = axs[hosp_idx].plot(wide_df[f'hosp_{hosp_idx+1}'])
_ = axs[hosp_idx].set_title(f'Hospital {hosp_idx+1}',
fontsize=label_font_size)
_ = axs[hosp_idx].grid(ls='--')
# axis labels matplotlib >=3.4
AXIS_LABEL_SIZE = 12
_ = fig.supylabel('ED Attendances', fontsize=axis_font_size)
_ = fig.supxlabel('Date', fontsize=axis_font_size)
return fig | [
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] | 2.049628 | 1,209 |
# ######################################################################################################################
# Copyright 2020 TRIXTER GmbH #
# #
# Redistribution and use in source and binary forms, with or without modification, are permitted provided #
# that the following conditions are met: #
# #
# 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following #
# disclaimer. #
# #
# 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the #
# following disclaimer in the documentation and/or other materials provided with the distribution. #
# #
# 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote #
# products derived from this software without specific prior written permission. #
# #
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, #
# INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE #
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, #
# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS #
# OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF #
# LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY #
# OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. #
# ######################################################################################################################
from tractor.api.author.base import *
class Command(KeyValueElement):
"""Command Patch"""
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] | 1.955193 | 1,473 |
import discord
from discord.ext import commands
import os
import random
@commands.command(aliases = ['8ball'])
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] | 3.484848 | 33 |
import datetime
import addict
import boto3
import botocore
import pytest
from bai_kafka_utils.events import (
FetcherBenchmarkEvent,
FetcherPayload,
FileSystemObject,
DownloadableContent,
ContentSizeInfo,
FetchedType,
BenchmarkDoc,
BenchmarkJob,
)
from bai_kafka_utils.executors.descriptor import BenchmarkDescriptor, DescriptorError
from bai_kafka_utils.executors.execution_callback import ExecutionEngineException, NoResourcesFoundException
from botocore.stub import Stubber
from pytest import fixture
from sagemaker import Session
from sagemaker.estimator import EstimatorBase, Framework
from sm_executor import sm_execution_engine
from sm_executor.args import SageMakerExecutorConfig
from sm_executor.estimator_factory import create_estimator, EstimatorFactory
from sm_executor.sm_execution_engine import SageMakerExecutionEngine
from unittest.mock import create_autospec, PropertyMock
HUGE_DATASET_GB = 100
EXPECTED_HUGE_VOLUME = 110
HUGE_DATASET_SIZE_BYTES = HUGE_DATASET_GB * 1024 ** 3
SCRIPTS = [FileSystemObject(dst="s3://exchange/script.tar")]
ACTION_ID = "ACTION_ID"
CLIENT_ID = "CLIENT_ID"
DATASET_ID = "some_data"
DATASET_S3_URI = "s3://datapull/somedata.zip"
DEFAULT_SM_VOLUME_GB = 30
CREATED_JOB_ID = "MAY_BE_ALSO_ACTION_ID"
TMP_DIR = "/tmp/dir/random"
MOCK_ERROR_RESPONSE = {"Error": {"Message": "Something is wrong"}}
@fixture
@fixture
@fixture
@fixture
@fixture
MOCKED_REGION = "us-east-1"
@fixture
@fixture
@fixture
@fixture(autouse=True)
@fixture(autouse=True)
@pytest.mark.parametrize(
["file_size", "expected_train_volume_size"],
[(42, DEFAULT_SM_VOLUME_GB), (HUGE_DATASET_SIZE_BYTES, EXPECTED_HUGE_VOLUME)],
)
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] | 2.586466 | 665 |
import os
import sys
import numpy as np
from mtuq import read, open_db, download_greens_tensors
from mtuq.event import Origin
from mtuq.graphics import plot_data_greens2, plot_misfit_depth, plot_misfit_dc, plot_variance_reduction_lune
#from mtuq.graphics.uq.hypocenter import _get_depths, _get_sources, _min_dataarray
from mtuq.grid import DoubleCoupleGridRegular
from mtuq.grid_search import grid_search
from mtuq.misfit import Misfit
from mtuq.misfit.waveform import calculate_norm_data
from mtuq.process_data import ProcessData
from mtuq.util import fullpath, save_json
from mtuq.util.cap import parse_station_codes, Trapezoid
from mpi4py import MPI
# Tester script just trying to get the information I need
# Event is the July 2021 event that capuaf has been run for
# Utility functions from previous version of mtuq
if len(sys.argv) != 2:
raise Exception('proper useage: python run_mtuq.py eid')
else:
eid = sys.argv[1]
eid = str(eid)
print('event id: ', eid)
path_data = fullpath('%s/*.[zrt]' % eid)
path_weights = fullpath('%s/weight.dat' % eid)
#model = 'ak135'
#CHANGE THIS APPROPRIATELY
db = open_db('/store/wf/FK_synthetics/scak',format='FK')
model='scak'
# Process the body and surface waves separately
process_bw = ProcessData(
filter_type='Bandpass',
freq_min= 0.20,
freq_max= 0.6667,
pick_type='FK_metadata',
FK_database='/store/wf/FK_synthetics/scak',
window_type='body_wave',
window_length=15.,
capuaf_file=path_weights,
)
process_sw = ProcessData(
filter_type='Bandpass',
freq_min=0.0333,
freq_max=0.0625,
pick_type='FK_metadata',
FK_database='/store/wf/FK_synthetics/scak',
window_type='surface_wave',
window_length=120.,
capuaf_file=path_weights,
)
# Objective function
misfit_bw = Misfit(
norm='L2',
time_shift_min=-2.,
time_shift_max=+2.,
time_shift_groups=['ZR'],
)
misfit_rayleigh = Misfit(
norm='L2',
time_shift_min=-10.,
time_shift_max=+10.,
time_shift_groups=['ZR'],
)
misfit_love = Misfit(
norm='L2',
time_shift_min=-10.,
time_shift_max=+10.,
time_shift_groups=['T'],
)
misfit_sw = Misfit(
norm='L2',
time_shift_min=-10.,
time_shift_max=+10.,
time_shift_groups=['ZR','T'],
)
# Weight file
station_id_list = parse_station_codes(path_weights)
# Begin setup for depth search
catalog_origin = Origin({
'time': '2020-04-17T00:22:45.356000Z',
'latitude': 55.9717,
'longitude': -153.3077,
'depth_in_m': 7340.7,
'id': str(eid)
})
# Depths (in meters) to search over
depths = np.array([1000., 2000., 3000., 5000., 6000., 7000., 8000., 10000., 11000., 12000., 13000., 15000., 16000., 17000., 18000., 20000.])
#tactmod has boundary at 3,11,24,31,76km
#scak has bounday at 4,9,14,19,24,33,49,66km
origins = []
for depth in depths:
origins += [catalog_origin.copy()]
setattr(origins[-1], 'depth_in_m', depth)
# Magnitudes to search over
magnitudes = np.array([4.9, 5.0, 5.1, 5.2, 5.3, 5.4, 5.5, 5.6, 5.7, 5.8, 5.9])
grid = DoubleCoupleGridRegular(
npts_per_axis=30,
magnitudes=magnitudes)
# Define source-time function
wavelet = Trapezoid(magnitude=4.7)
comm = MPI.COMM_WORLD
# Main I/O starts here
if comm.rank==0:
print('Reading data...\n')
data = read(path_data, format='sac',
event_id=eid,
station_id_list=station_id_list,
tags=['units:cm', 'type:velocity'])
data.sort_by_distance()
stations = data.get_stations()
print('Processing data...\n')
data_bw = data.map(process_bw)
data_sw = data.map(process_sw)
data_all = data_bw + data_sw
print('Reading Greens functions...\n')
#greens = download_greens_tensors(stations, origins, model)
greens = db.get_greens_tensors(stations, origins)
print('Processing Greens functions...\n')
greens.convolve(wavelet)
greens_bw = greens.map(process_bw)
greens_sw = greens.map(process_sw)
greens_all = greens_bw + greens_sw
else:
stations = None
data_bw = None
data_sw = None
greens_bw = None
greens_sw = None
stations = comm.bcast(stations, root=0)
data_all = comm.bcast(data, root=0)
data_bw = comm.bcast(data_bw, root=0)
data_sw = comm.bcast(data_sw, root=0)
greens_bw = comm.bcast(greens_bw, root=0)
greens_sw = comm.bcast(greens_sw, root=0)
# Main computational work
if comm.rank==0:
print('Evaluating body wave misfit...\n')
results_bw = grid_search(
data_bw, greens_bw, misfit_bw, origins, grid)
if comm.rank==0:
print('Evaluating rayleigh wave misfit...\n')
results_rayleigh = grid_search(
data_sw, greens_sw, misfit_rayleigh, origins, grid)
if comm.rank==0:
print('Evaluating love wave misfit...\n')
results_love = grid_search(
data_sw, greens_sw, misfit_love, origins, grid)
# Generate figures and save results
if comm.rank == 0:
results = results_bw + results_rayleigh + results_love
print(results)
# Source corresponding to minimum misfit
idx = results.idxmin('source')
best_source = grid.get(idx)
lune_dict = grid.get_dict(idx)
mt_dict = grid.get(idx).as_dict()
# Calculate norms for variance reduction
norm_bw = calculate_norm_data(data_bw,
misfit_bw.norm, ['Z', 'R'])
norm_rayleigh = calculate_norm_data(data_sw,
misfit_rayleigh.norm, ['Z', 'R'])
norm_love = calculate_norm_data(data_sw,
misfit_love.norm, ['T'])
data_norm = norm_bw + norm_rayleigh + norm_love
norms = {misfit_bw.norm+'_bw': norm_bw,
misfit_rayleigh.norm+'_rayleigh': norm_rayleigh,
misfit_love.norm+'_love': norm_love,
'L2__data_norm': data_norm}
save_json(eid+'_data_norms.json',norms)
# origin corresponding to minimum misfit
best_origin = origins[results.idxmin('origin')]
origin_dict = best_origin.as_dict()
# Best sources for each depth
depths = _get_depths(origins)
values, indices = _min_dataarray(results)
best_sources = _get_sources(grid,indices)
source_depth = {}
for ii in range(len(best_sources)):
temp = best_sources[ii].as_dict()
temp['misfit'] = values[ii]
vr_temp = 100*(1-values[ii]/data_norm)
temp['vr'] = vr_temp
km = int(depths[ii]/1000)
source_depth[km] = temp
save_json(eid+'_depths_source.json',source_depth)
# only generate components present in the data
components_bw = data_bw.get_components()
components_sw = data_sw.get_components()
greens_bw = greens_bw.select(best_origin)
greens_sw = greens_sw.select(best_origin)
# synthetics corresponding to minimum misfit
synthetics_bw = greens_bw.get_synthetics(
best_source, components_bw, mode='map')
synthetics_sw = greens_sw.get_synthetics(
best_source, components_sw, mode='map')
# time shifts and other attributes corresponding to minimum misfit
list_bw = misfit_bw.collect_attributes(
data_bw, greens_bw, best_source)
list_rayleigh = misfit_rayleigh.collect_attributes(
data_sw, greens_sw, best_source)
list_love = misfit_love.collect_attributes(
data_sw, greens_sw, best_source)
list_sw = [{**list_rayleigh[_i], **list_love[_i]}
for _i in range(len(stations))]
dict_bw = {station.id: list_bw[_i]
for _i,station in enumerate(stations)}
dict_rayleigh = {station.id: list_rayleigh[_i]
for _i,station in enumerate(stations)}
dict_love = {station.id: list_love[_i]
for _i,station in enumerate(stations)}
dict_sw = {station.id: list_sw[_i]
for _i,station in enumerate(stations)}
print('Generating figures...\n')
plot_data_greens2(eid+'DC+_waveforms.png',
data_bw, data_sw, greens_bw, greens_sw, process_bw, process_sw,
misfit_bw, misfit_sw, stations, best_origin, best_source, lune_dict)
plot_misfit_depth(eid+'DC+_misfit_depth.png',
results, origins, title=eid)
plot_variance_reduction_lune(eid+'DC_vr_bw.png',
results_bw, norm_bw, title='Body waves',
colorbar_label='Variance reduction (percent)')
plot_variance_reduction_lune(eid+'DC_vr_rayleigh.png',
results_rayleigh, norm_rayleigh, title='Rayleigh waves',
colorbar_label='Variance reduction (percent)')
plot_variance_reduction_lune(eid+'DC_vr_love.png',
results_love, norm_love, title='Love waves',
colorbar_label='Variance reduction (percent)')
plot_variance_reduction_lune(eid+'DC_vr_data.png',results,data_norm,title='data norm',colorbar_label='Variance reduction (percent)')
# save stations and origins
stations_dict = {station.id: station
for _i,station in enumerate(stations)}
save_json(eid+'DC_stations.json', stations_dict)
save_json(eid+'DC_origin.json', {0: catalog_origin})
# save best fit origin
save_json(eid+'DC_best_origin.json', origin_dict)
# save best fit source
save_json(eid+'DC_mt.json', mt_dict)
save_json(eid+'DC_lune.json',lune_dict)
# save time shifts and amplitude ratios
save_json(eid+'_bw.json', dict_bw)
save_json(eid+'_rayleigh.json', dict_rayleigh)
save_json(eid+'_love.json', dict_love)
#save misfit values
results.save(eid+'misfit.nc')
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] | 2.322532 | 4,012 |
from setuptools import setup, find_packages
version = '0.1'
setup(
name='ckanext-googleanalytics',
version=version,
description="Add GA tracking and reporting to CKAN instance",
long_description="""\
""",
classifiers=[], # Get strings from http://pypi.python.org/pypi?%3Aaction=list_classifiers
keywords='',
author='Seb Bacon',
author_email='[email protected]',
url='',
license='',
packages=find_packages(exclude=['ez_setup', 'examples', 'tests']),
namespace_packages=['ckanext', 'ckanext.googleanalytics'],
include_package_data=True,
zip_safe=False,
install_requires=[
],
entry_points=\
"""
[ckan.plugins]
# Add plugins here, eg
googleanalytics=ckanext.googleanalytics.plugin:GoogleAnalyticsPlugin
[paste.paster_command]
loadanalytics = ckanext.googleanalytics.commands:LoadAnalytics
initdb = ckanext.googleanalytics.commands:InitDB
""",
)
| [
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198,
197,
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1600,
198,
8,
198
] | 2.65 | 340 |
# Copyright (c) 2021 by Cisco Systems, Inc.
# All rights reserved.
expected_output = {
'mac_addr': 'ffff.ffff.fffd',
'reference_count': 1,
'epoch': 0,
'producer': 'L2FIB',
'flags': ['None'],
'adjacency': {
'type':'olist',
'desc': 'Olist: 3, Ports: 1',
'olist': {
'olist': 3,
'port_count': 1
}
},
'pd_adjacency': {
'type':'olist',
'desc': 'Olist: 3, Ports: 1',
'olist': {
'olist': 3,
'port_count': 1
}
},
'packet_count': 0,
'bytes': 0
} | [
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] | 1.618824 | 425 |
# import itertools
#
# import numpy as np
#
# import holoviews as hv
#
#
# def _create_paths(count_array: np.ndarray):
# """
# Returns an iterable containing coordinates for a single path, where the genes are integer-indexed
# along the x-axis, and the mass of the gene (length * counts), is along the y-axis.
#
# This function should not need to be called directly.
#
# :param count_array:
#
# :return:
# An iterable containing coordinate tuples (x, y) for the gene mass path.
# """
# index_range = np.arange(count_array.shape[0])
# starting_coords = list(zip(index_range, np.zeros(index_range.shape)))
# ending_coords = list(zip(index_range, count_array))
# return itertools.chain.from_iterable(zip(starting_coords, ending_coords, starting_coords))
#
#
# def plot_sample(sample: np.ndarray):
# """
# Plots the gene counts for the given sample array as a bar-plot like single-path element.
#
# :param np.ndarray sample:
# An array containing the count values.
#
# :return:
# """
# paths = _create_paths(sample)
# plot = hv.Path(paths)
# return plot.opts(width=400, height=400, padding=0.05)
#
#
# def plot_sample_mass(sample: np.ndarray, lengths: np.ndarray):
# """
# Plots gene masses (counts * lengths) for the given sample array.
#
# :param np.ndarray sample:
# An array containing the count values.
#
# :param np.ndarray lengths:
# An array containing the length values.
#
# :return:
# """
#
# masses = sample * lengths
# paths = _create_paths(masses)
# plot = hv.Path(paths)
# return plot.opts(width=400, height=400, padding=0.05)
#
#
# def plot_count_sum_mass(counts: np.ndarray, lengths: np.ndarray):
# """
# Plots the sum of the gene masses (along the sample axis) for the given count array.
#
# :param counts:
# :param lengths:
# :return:
# """
# sample_counts_sum = counts.sum(axis=0)
# mass_sums = sample_counts_sum * lengths
# paths = _create_paths(mass_sums)
# plot = hv.Path(paths)
# return plot.opts(width=400, height=400, padding=0.05)
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] | 2.451429 | 875 |
import imp
import os.path
from amazonproduct import HOSTS
from amazonproduct.processors import objectify, etree, minidom
from amazonproduct.utils import load_config
_here = os.path.abspath(os.path.dirname(__file__))
try:
fp, path, desc = imp.find_module('config', [_here])
_config = imp.load_module('config', fp, _here, desc)
except ImportError:
_config = None
#: Directory containing XML responses for API versions (one directory for each
#: API version)
XML_TEST_DIR = _here
#: Versions of Amazon API to be tested against
TESTABLE_API_VERSIONS = [
'2011-08-01',
'2010-12-01', '2010-11-01', '2010-10-01', '2010-09-01', '2010-06-01',
'2009-11-01', '2009-10-01'
]
#: Locales to test against.
TESTABLE_LOCALES = HOSTS.keys()
ELEMENTTREE_IMPLEMENTATIONS = [
'lxml.etree',
'xml.etree.cElementTree',
'xml.etree.ElementTree',
'cElementTree',
'elementtree.ElementTree',
'elementtree.ElementTree'
]
#: Result processors to test with.
TESTABLE_PROCESSORS = {
'objectify': objectify.Processor,
# 'minidom': minidom.Processor,
}
# add ElementTree implementations
for mod in ELEMENTTREE_IMPLEMENTATIONS:
TESTABLE_PROCESSORS[mod] = etree.Processor(module=mod)
def get_config_value(key, default=None):
"""
Loads value from config.py or from environment variable or return default
(in that order).
"""
try:
return getattr(_config, key)
except AttributeError:
return os.environ.get(key, default)
_config = load_config()
AWS_KEY = _config.get('access_key', '')
SECRET_KEY = _config.get('secret_key', '')
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] | 2.572115 | 624 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import sys
try:
from setuptools import setup
except ImportError:
from distutils.core import setup
import TripMapR
version = TripMapR.__version__
setup(
name='TripMapR',
version=version,
author="Rahul Verma",
author_email='[email protected]',
packages=[
'TripMapR',
],
include_package_data=True,
install_requires=[
'Django>=1.7.4',
],
zip_safe=False,
scripts=['TripMapR/manage.py'],
)
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#!/usr/bin/env python
#import OpenImageIO as oiio
import shutil
## This testsuite entry tests oiiotool features related to image
## transformations (moving pixels around and resampling).
# helper function
# Create some test images we need
# No need to do this every time, we stashed it in src
#make_test_pattern1 ("src/target1.exr", 288, 216)
shutil.copy ("../oiiotool/src/tahoe-tiny.tif", "./tahoe-tiny.tif")
shutil.copy ("../oiiotool/src/tahoe-small.tif", "./tahoe-small.tif")
shutil.copy ("../oiiotool/src/image.tif", "./image.tif")
# test resample
command += oiiotool (parent + "/oiio-images/grid.tif --resample 128x128 -o resample.tif")
# test resize
command += oiiotool (parent + "/oiio-images/grid.tif --resize 256x256 -o resize.tif")
command += oiiotool (parent + "/oiio-images/grid.tif --resize 25% -o resize2.tif")
# test extreme resize
command += oiiotool (parent + "/oiio-images/grid.tif --resize 64x64 -o resize64.tif")
command += oiiotool ("resize64.tif --resize 512x512 -o resize512.tif")
# test resize with nonzero origin. Save to exr to make extra sure we have
# the display and data windows correct.
command += oiiotool ("--pattern fill:topleft=1,0,0:topright=0,1,0:bottomleft=0,0,1:bottomright=0,1,1 64x64 3 " +
"--origin +100+100 --fullsize 256x256+0+0 " +
"--resize 128x128 -d half -o resized-offset.exr")
# test fit
command += oiiotool (parent + "/oiio-images/grid.tif --fit 360x240 -d uint8 -o fit.tif")
command += oiiotool (parent + "/oiio-images/grid.tif --fit 240x360 -d uint8 -o fit2.tif")
# regression test: --fit without needing resize used to be problematic
command += oiiotool ("tahoe-tiny.tif --fit 128x128 -d uint8 -o fit3.tif")
# test --fit:exact=1 when we can't get a precise whole-pixel fit of aspect
command += oiiotool ("src/target1.exr --fit:exact=1:filter=blackman-harris 216x162 -o fit4.exr")
# test --pixelaspect
command += oiiotool ("tahoe-small.tif -resize 256x192 --pixelaspect 2.0 -d uint8 -o pixelaspect.tif")
# test rotate
command += oiiotool ("resize.tif --rotate 45 -o rotated.tif")
command += oiiotool ("resize.tif --rotate:center=50,50 45 -o rotated-offcenter.tif")
command += oiiotool ("resize.tif --rotate 45 --rotate 90 --rotate 90 --rotate 90 --rotate 45 -o rotated360.tif")
# test warp
command += oiiotool ("resize.tif --warp 0.7071068,0.7071068,0,-0.7071068,0.7071068,0,128,-53.01933,1 -o warped.tif")
# test flip
command += oiiotool ("image.tif --flip -o flip.tif")
command += oiiotool ("image.tif --crop 180x140+30+30 --flip -o flip-crop.tif")
# test flop
command += oiiotool ("image.tif --flop -o flop.tif")
command += oiiotool ("image.tif --crop 180x140+30+30 --flop -o flop-crop.tif")
# test rotate90
command += oiiotool ("image.tif --rotate90 -o rotate90.tif")
command += oiiotool ("image.tif --crop 180x140+30+30 --rotate90 -o rotate90-crop.tif")
# test rotate270
command += oiiotool ("image.tif --rotate270 -o rotate270.tif")
command += oiiotool ("image.tif --crop 180x140+30+30 --rotate270 -o rotate270-crop.tif")
# test rotate180
command += oiiotool ("image.tif --rotate180 -o flipflop.tif")
command += oiiotool ("image.tif --crop 160x120+30+30 --rotate180 -o flipflop-crop.tif")
# Tricky: make image, rotate, set Orientation, and then re-orient.
# Make it half size so it can't accidentally match to another test image
# for the rotation tests.
command += oiiotool ("image.tif --resample 160x120 --rotate90 --orientccw --reorient -o reorient1.tif")
command += oiiotool ("image.tif --resample 160x120 --rotate180 --orient180 --reorient -o reorient2.tif")
command += oiiotool ("image.tif --resample 160x120 --rotate270 --orientcw --reorient -o reorient3.tif")
# test transpose
command += oiiotool ("image.tif --transpose -o transpose.tif")
command += oiiotool ("image.tif --crop 160x120+30+30 --transpose -o transpose-crop.tif")
# test cshift
command += oiiotool ("image.tif --cshift +100+50 -o cshift.tif")
# To add more tests, just append more lines like the above and also add
# the new 'feature.tif' (or whatever you call it) to the outputs list,
# below.
# Outputs to check against references
outputs = [
"resample.tif", "resize.tif", "resize2.tif",
"resize64.tif", "resize512.tif",
"resized-offset.exr",
"fit.tif", "fit2.tif", "fit3.tif", "fit4.exr",
"pixelaspect.tif",
"warped.tif",
"rotated.tif", "rotated-offcenter.tif", "rotated360.tif",
"flip.tif", "flip-crop.tif",
"flop.tif", "flop-crop.tif",
"flipflop.tif", "flipflop-crop.tif",
"rotate90.tif", "rotate90-crop.tif",
"rotate270.tif", "rotate270-crop.tif",
"reorient1.tif", "reorient2.tif", "reorient3.tif",
"transpose.tif", "transpose-crop.tif",
"cshift.tif",
"out.txt" ]
#print "Running this command:\n" + command + "\n"
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] | 2.525694 | 1,946 |
#
# This file is part of pretix (Community Edition).
#
# Copyright (C) 2014-2020 Raphael Michel and contributors
# Copyright (C) 2020-2021 rami.io GmbH and contributors
#
# This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General
# Public License as published by the Free Software Foundation in version 3 of the License.
#
# ADDITIONAL TERMS APPLY: Pursuant to Section 7 of the GNU Affero General Public License, additional terms are
# applicable granting you additional permissions and placing additional restrictions on your usage of this software.
# Please refer to the pretix LICENSE file to obtain the full terms applicable to this work. If you did not receive
# this file, see <https://pretix.eu/about/en/license>.
#
# This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied
# warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more
# details.
#
# You should have received a copy of the GNU Affero General Public License along with this program. If not, see
# <https://www.gnu.org/licenses/>.
#
from django.utils.encoding import force_str
from django.utils.functional import keep_lazy
from django.utils.safestring import SafeText, mark_safe
_json_escapes = {
ord('>'): '\\u003E',
ord('<'): '\\u003C',
ord('&'): '\\u0026',
}
_json_escapes_attr = {
ord('>'): '\\u003E',
ord('<'): '\\u003C',
ord('&'): '\\u0026',
ord('"'): '"',
ord("'"): ''',
ord("="): '=',
}
@keep_lazy(str, SafeText)
def escapejson(value):
"""Hex encodes characters for use in a application/json type script."""
return mark_safe(force_str(value).translate(_json_escapes))
@keep_lazy(str, SafeText)
def escapejson_attr(value):
"""Hex encodes characters for use in a html attributw script."""
return mark_safe(force_str(value).translate(_json_escapes_attr))
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] | 3.146497 | 628 |
from deap import base
from deap import creator
from deap import tools
import random
import math
#Luke Grantham
#python 3 compatible
#variables
IND_SIZE = 5 #number of key presses
POP_SIZE = 1 #number of individuals
T_SIZE = 3 #tournament size
generations = 1000 #number of generations
selb = 1 #how many individuals to select when you call toolbox.selectBest
selw = 5 #how many individuals to select whe nyou call toolbox.selectWorst
toolbox = base.Toolbox()
creator.create("FitnessMax", base.Fitness, weights=(1.0,))
creator.create("Individual", list, fitness = creator.FitnessMax)
toolbox.register("individual", tools.initRepeat, creator.Individual, generateGene, n=IND_SIZE)
toolbox.register("select", tools.selTournament, k=2, tournsize=T_SIZE)
toolbox.register("onePoint", tools.cxOnePoint)
toolbox.register("twoPoint", tools.cxTwoPoint)
toolbox.register("selectBest", tools.selBest, k=selb)
toolbox.register("selectWorst", tools.selWorst, k=selw)
population = [toolbox.individual() for i in range(POP_SIZE)] #generate population
#individuals are lists of chars A through P
for i in range(len(population)):
#evaluate populations
population[i].fitness.values = evaluate(population[i])
for i in range(generations):
selected = toolbox.select(population) #select
parent1 = toolbox.clone(selected[0])
parent2 = toolbox.clone(selected[1])
child = toolbox.onePoint(parent1, parent2)[0] #crossover
child = mutate(child)
child.fitness.values = evaluate(child) #evaluate child
population.remove(random.choice(toolbox.selectWorst(population))) #survivor select
population.append(child) #replacement
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import os
import importlib
from service import config
from flask import Flask
from flask_sqlalchemy import SQLAlchemy
app = Flask(__name__)
app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False
app.config['SQLALCHEMY_DATABASE_URI'] = config.connection_string \
if not config.get_boolean_param('debug') \
else config.get_param('connection_string_debug', 'DATABASE')
db = SQLAlchemy(app)
__models = 'models'
__controllers = 'controllers'
__models_folder = os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir, __models))
__controllers_folder = os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir, __controllers))
for __imports in [__models_folder, __controllers_folder]:
for __imported in [__import
for __import in os.listdir(__imports)
if __import.endswith('.py') and not __import.startswith('__')]:
importlib.import_module(f'{os.path.basename(__imports)}.{__imported[0:-3]}') | [
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# -*- coding: utf-8 -*-
__doc__="返回选择物体的类型"
import rpw
from rpw import revit, DB, UI,db,doc
from System.Collections.Generic import List
import json
#from pyrevit import script as this_script
#from scriptutils.userinput import CommandSwitchWindow
import subprocess as sp
#Change Selected Grid From 3D to 2D
#selection = rpw.ui.Selection().elements
#Grid=selection[0]
#Grid= rpw.db.Element(Grid).parameters.all
#print(Grid)
#Change Grid buble visibility
selection = rpw.ui.Selection()
Grid=selection
endpoint=DB.DatumEnds.End0
startpoint=DB.DatumEnds.End1
CurrentView=doc.ActiveView
@rpw.db.Transaction.ensure('Hide_Grid_Bubble')
for i in Grid:
Grid_Bubble_Reverse(i,[endpoint,startpoint],CurrentView)
print("Good")
| [
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] | 2.574468 | 282 |
from typing import Dict, Optional, Union
import numpy as np
from pydantic import validator
from src.biota_models.coral.model.coral_constants import CoralConstants
from src.biota_models.coral.model.coral_only import CoralOnly
from src.core import RESHAPE
from src.core.base_model import ExtraModel
from src.core.biota.biota_model import Biota
from src.core.common.space_time import DataReshape
CoralAttribute = Union[float, list, tuple, np.ndarray]
class Coral(Biota):
"""
Implements the `CoralProtocol`.
Coral object, representing one coral type.
"""
constants: CoralConstants = CoralConstants()
dc: CoralAttribute # diameter coral plate [m]
hc: CoralAttribute # coral height [m]
bc: CoralAttribute # diameter coral base [m]
tc: CoralAttribute # thickness coral plate [m]
ac: CoralAttribute # axial distance corals [m]
Csp: Optional[float] = 1 # species constant [-]
# other attributes.
_cover: Optional[CoralAttribute] = None
# light micro-environment
light: Optional[CoralAttribute] = None
light_bc: Optional[CoralAttribute] = None
# flow micro environment
ucm: Optional[CoralAttribute] = None
um: Optional[CoralAttribute] = None
delta_t: Optional[CoralAttribute] = None
# thermal micro-environment
dTc: Optional[CoralAttribute] = None
temp: Optional[CoralAttribute] = None
# photosynthesis
photo_rate: Optional[CoralAttribute] = None
Tlo: Optional[CoralAttribute] = None
Thi: Optional[CoralAttribute] = None
# population states
pop_states: Optional[CoralAttribute] = None
p0: Optional[CoralAttribute] = None
# calcification
calc: Optional[CoralAttribute] = None
@validator("dc", "hc", "bc", "tc", "ac")
@classmethod
def __repr__(self):
"""Development representation."""
return f"Morphology({self.dc}, {self.hc}, {self.bc}, {self.bc}, {self.ac})"
def __str__(self):
"""Print representation."""
return (
f"Coral morphology with: dc = {self.dc} m; hc = {self.hc} ;"
f"bc = {self.bc} m; tc = {self.tc} m; ac = {self.ac} m"
)
@property
def dc_rep(self):
"""Representative coral diameter; weighted average of base and plate diameters."""
return (self.bc * (self.hc - self.tc) + self.dc * self.tc) / self.hc
@property
def rf(self):
"""Form ratio: height-to-diameter ratio."""
return self.hc / self.dc
@property
def rp(self):
"""Plate ratio: base-to-diameter ratio."""
return self.bc / self.dc
@property
def rs(self):
"""Spacing ratio: diameter-to-axial distance ratio."""
return self.dc / self.ac
@property
# changed the volume function - assigned the output coral_volume
def volume(self):
"""Coral volume."""
coral_volume = (
0.25 * np.pi * ((self.hc - self.tc) * self.bc ** 2 + self.tc * self.dc ** 2)
)
return coral_volume
@property
def dc_matrix(self):
"""self.RESHAPEd coral plate diameter."""
return RESHAPE().variable2matrix(self.dc, "space")
@property
def hc_matrix(self):
"""self.RESHAPEd coral height."""
return RESHAPE().variable2matrix(self.hc, "space")
@property
def bc_matrix(self):
"""self.RESHAPEd coral base diameter."""
return RESHAPE().variable2matrix(self.bc, "space")
@property
def tc_matrix(self):
"""self.RESHAPEd coral plate thickness."""
return RESHAPE().variable2matrix(self.tc, "space")
@property
def ac_matrix(self):
"""self.RESHAPEd axial distance."""
return RESHAPE().variable2matrix(self.ac, "space")
@property
def dc_rep_matrix(self):
"""self.RESHAPEd representative coral diameter."""
return RESHAPE().variable2matrix(self.dc_rep, "space")
@property
def as_vegetation_density(self):
"""Translation from coral morphology to (vegetation) density."""
return CoralOnly().in_space(
coral=self, function=function, args=(self.dc_rep, self.ac)
)
@property
def cover(self):
"""Carrying capacity."""
if self._cover is None:
cover = np.ones(np.array(self.volume).shape)
cover[self.volume == 0.0] = 0.0 # 21.09 made 0. instead of just zero
return cover
return self._cover
@property
def living_cover(self):
"""Living coral cover based on population states."""
if self.pop_states is not None:
return self.pop_states.sum(axis=2)
def update_coral_volume(self, coral_volume: CoralAttribute):
"""
Updates the coral morphology based on the given coral volume.
Args:
coral_volume (CoralAttribute): New coral volume.
"""
# TODO what is the difference? And which volume does it call then?
# TODO also function update morphology does not update coral volume
self.update_coral_morphology(
coral_volume, dict(rf=self.rf, rp=self.rp, rs=self.rs)
)
def update_cover(self, carrying_capacity: CoralAttribute):
"""
Update cover value based on given parameters.
Args:
carrying_capacity (CoralAttribute): Carrying capacity [m2 m-2].
"""
carrying_capacity = RESHAPE().variable2array(carrying_capacity)
if not self.volume.shape == carrying_capacity.shape:
raise ValueError(
f"Shapes do not match: "
f"{self.volume.shape} =/= {carrying_capacity.shape}"
)
if sum(self.volume[carrying_capacity == 0.0]) > 0.0:
print(
f"WARNING: Coral volume present where the carrying capacity is zero. This is unrealistic."
)
self._cover = carrying_capacity
def initiate_coral_morphology(self, cover: Optional[np.ndarray] = None):
"""
Initiate the morphology based on the on set of morphological dimensions and the coral cover. This method
contains a catch that it can only be used to initiate the morphology, and cannot overwrite existing spatial
heterogeneous morphology definitions.
Args:
cover (Optional[np.ndarray]): Custom coral definition.
"""
_reshape = RESHAPE()
if cover is not None:
cover = _reshape.variable2array(cover)
if not cover.shape[0] == _reshape.space:
msg = f"Spatial dimension of cover does not match: {cover.shape} =/= {_reshape.space}."
raise ValueError(msg)
else:
cover = np.ones(_reshape.space)
self.p0 = np.array(
[
cover,
np.zeros(cover.shape),
np.zeros(cover.shape),
np.zeros(cover.shape),
]
).transpose()
self.dc = cover * self.dc
self.hc = cover * self.hc
self.bc = cover * self.bc
self.tc = cover * self.tc
self.ac = cover * self.ac
def update_coral_morphology(
self,
coral_volume: Union[float, np.ndarray],
morphology_ratios: Dict[str, Union[float, np.ndarray]],
):
"""
Update the coral morphology based on updated coral volume and morphology ratios.
Args:
coral_volume (Union[float, np.ndarray]): Coral volume
morphology_ratios (Dict[str, Union[float, np.ndarray]]): Morphology ratios (rf, rp, rs, ..)
"""
rf = morphology_ratios["rf"]
rp = morphology_ratios["rp"]
rs = morphology_ratios["rs"]
def vc2dc(coral_volume, rf, rp):
"""Coral volume to coral plate diameter."""
dc = ((4.0 * coral_volume) / (np.pi * rf * rp * (1.0 + rp - rp ** 2))) ** (
1.0 / 3.0
)
return dc
def vc2hc(coral_volume, rf, rp):
"""Coral volume to coral height."""
hc = (
(4.0 * coral_volume * rf ** 2) / (np.pi * rp * (1.0 + rp - rp ** 2))
) ** (1.0 / 3.0)
return hc
def vc2bc(coral_volume, rf, rp):
"""Coral volume > diameter of the base."""
bc = (
(4.0 * coral_volume * rp ** 2) / (np.pi * rf * (1.0 + rp - rp ** 2))
) ** (1.0 / 3.0)
return bc
def vc2tc(coral_volume, rf, rp):
"""Coral volume > thickness of the plate."""
tc = (
(4.0 * coral_volume * rf ** 2 * rp ** 2)
/ (np.pi * (1.0 + rp - rp ** 2))
) ** (1.0 / 3.0)
return tc
def vc2ac(coral_volume, rf, rp, rs):
"""Coral volume > axial distance."""
ac = (1.0 / rs) * (
(4.0 * coral_volume) / (np.pi * rf * rp * (1.0 + rp - rp ** 2))
) ** (1.0 / 3.0)
return ac
# # update morphology
self.dc = vc2dc(coral_volume, rf, rp)
self.hc = vc2hc(coral_volume, rf, rp)
self.bc = vc2bc(coral_volume, rf, rp)
self.tc = vc2tc(coral_volume, rf, rp)
self.ac = vc2ac(coral_volume, rf, rp, rs)
| [
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62,
29048,
11,
374,
69,
11,
374,
79,
11,
44608,
8,
198
] | 2.225573 | 4,145 |
# Generated by Django 3.0.7 on 2020-07-01 06:13
from django.db import migrations, models
import uuid
| [
2,
2980,
515,
416,
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513,
13,
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13,
22,
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12,
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12,
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13,
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1330,
15720,
602,
11,
4981,
198,
11748,
334,
27112,
628
] | 2.861111 | 36 |
from pylab import linspace, xlabel, ylabel, plot, show, grid
def simple(funkjson, start=float, slutt=float, x_label="x", y_label="y"):
'''
Lager enkel graf av funksjonen
din. Passer fint for alle type grafer
'''
x = linspace(start, slutt, 1001)
y = funkjson(x)
xlabel(x_label)
ylabel(y_label)
grid()
plot(x, y)
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220,
10706,
3419,
198,
220,
220,
220,
7110,
7,
87,
11,
331,
8,
628,
198
] | 2.127907 | 172 |
import os
from copy import deepcopy
import json
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import pandas as pd
from functools import partial
import data
import model
import metric
def get_partial_sampler(mode, class_weights):
"""Creates the sampling partial function to apply to the training labels"""
if mode is not None:
sampler = partial(
data.utils.frequency_weighted_sampler,
class_weights=class_weights,
mode=mode.lower(),
)
else:
sampler = None
return sampler
def get_weights(
labels, scaling, min_clip, max_clip, damping_r, device, sample_weights=None
):
"""Computes class weights given the type scaling for frequency balancing."""
if scaling is not None:
weights = data.utils.frequency_balancing(
labels,
scaling=scaling.lower(),
sample_weights=sample_weights,
min_clip=min_clip,
max_clip=max_clip,
damping_r=damping_r,
)
weights = torch.tensor(weights, dtype=torch.float, device=device)
else:
weights = None
return weights
def get_metric_list(dataset):
"""Initialize metrics
The main metric is the macro F1 score. Additionally, the F1 score for each class,
and the overall accuracy are also computed.
"""
metrics = [metric.FBetaScore(1, name="f1_macro")]
for label in sorted(dataset.label_to_name):
name = "f1_" + dataset.label_to_name[label]
metrics.append(metric.FBetaScore(1, labels=label, name=name))
metrics.append(metric.Accuracy())
return metric.MetricList(metrics)
def fill_empty_predictions(predictions, class_idx):
"""Change empty predictions to output the specified class
Arguments:
predictions (numpy.ndarray): the model predictions in binary format.
Shape: (N, K), where N is the number of samples and K is the number of
classes.
class_idx (int): the class to output when the model makes no
prediction. Expects an integer in the range [0, K-1].
Returns:
numpy.ndarray: the predictions
"""
if not 0 <= class_idx < predictions.shape[1]:
raise ValueError(
"class_idx is not in the expected range: [{}, {}], got {}".format(
0, predictions.shape[1], class_idx
)
)
empty_mask = np.sum(predictions, axis=1) == 0
predictions[empty_mask, class_idx] = 1
return predictions
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1267,
198,
220,
220,
220,
220,
220,
220,
220,
1267,
198,
220,
220,
220,
6565,
62,
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796,
45941,
13,
16345,
7,
28764,
9278,
11,
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28,
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8,
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657,
198,
220,
220,
220,
16277,
58,
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62,
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11,
1398,
62,
312,
87,
60,
796,
352,
628,
220,
220,
220,
1441,
16277,
198
] | 2.514822 | 1,012 |
# Generated by Django 2.1.2 on 2018-10-16 08:29
from django.conf import settings
from django.db import migrations, models
| [
2,
2980,
515,
416,
37770,
362,
13,
16,
13,
17,
319,
2864,
12,
940,
12,
1433,
8487,
25,
1959,
198,
198,
6738,
42625,
14208,
13,
10414,
1330,
6460,
198,
6738,
42625,
14208,
13,
9945,
1330,
15720,
602,
11,
4981,
628
] | 3.1 | 40 |
import numpy as np
import cv2
| [
11748,
299,
32152,
355,
45941,
198,
11748,
269,
85,
17,
198,
197,
198
] | 2.461538 | 13 |
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