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pt/003/python/main.py | franciscogomes2020/exercises | 0 | 1610057 | # Crie um programa que leia dois números e mostre a soma entre eles.
| [
1,
396,
315,
2546,
1922,
16914,
712,
454,
423,
19760,
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359,
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2637,
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267,
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13,
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2
] |
export_onnx_group_norm.py | lachinov/brats2019 | 42 | 42224 | <reponame>lachinov/brats2019
import argparse
import torch.onnx
import os
from collections import OrderedDict
from torch.onnx.symbolic_registry import register_op
from torch.onnx.symbolic_helper import parse_args
from model import UNet
parser = argparse.ArgumentParser(description="PyTorch SegTHOR")
parser.add_argument("--name", default="test", type=str, help="experiment name")
parser.add_argument("--models_path", default="./models", type=str, help="path to models folder")
parser.add_argument("--input_size", default=(128, 128, 128), help="Input image size", nargs="+", type=int)
@parse_args('v', 'i', 'v', 'v', 'f', 'i')
def group_norm_symbolic(g, input, num_groups, weight, bias, eps, cudnn_enabled):
return g.op("ExperimentalDetectronGroupNorm", input, weight, bias, num_groups_i=num_groups, eps_f=eps)
def main():
opt = parser.parse_args()
print(torch.__version__)
print(opt)
enc_layers = [1,2,2,4]
dec_layers = [1,1,1,1]
number_of_channels=[int(8*2**i) for i in range(1,1+len(enc_layers))]#[16,32,64,128]
model = UNet(depth=len(enc_layers), encoder_layers=enc_layers, decoder_layers=dec_layers, number_of_channels=number_of_channels, number_of_outputs=3)
s = torch.load(os.path.join(opt.models_path, opt.name, opt.name+'best_model.pth'), map_location='cpu')
new_state_dict = OrderedDict()
for k, v in s['model'].state_dict().items():
name = k[7:] # remove 'module' word in the beginning of keys.
new_state_dict[name] = v
model.load_state_dict(new_state_dict)
model.eval()
x = torch.randn(1, 4, opt.input_size[0], opt.input_size[1], opt.input_size[2], requires_grad=True)
register_op("group_norm", group_norm_symbolic, "", 10)
torch_out = torch.onnx.export(model, # model being run
[x,], # model input (or a tuple for multiple inputs)
os.path.join(opt.models_path, opt.name, opt.name+".onnx"), # where to save the model (can be a file or file-like object)
export_params=True,
verbose=True, # store the trained parameter weights inside the model file
opset_version=10
)
if __name__ == "__main__":
main()
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] |
src/test/resources/scripts/infinity.py | zella/pyass | 1 | 1616437 | <filename>src/test/resources/scripts/infinity.py
import time
while True:
sleep(1)
print('hello', flush = True)
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pyfr/util.py | DengSonic/PyFR | 1 | 153258 | <filename>pyfr/util.py
# -*- coding: utf-8 -*-
from contextlib import contextmanager
from ctypes import c_void_p
import functools as ft
import hashlib
import itertools as it
import os
import pickle
import shutil
from pyfr.ctypesutil import get_libc_function
class memoize(object):
def __init__(self, func):
self.func = func
def __get__(self, instance, owner):
return self.func if instance is None else ft.partial(self, instance)
def __call__(self, *args, **kwargs):
instance = args[0]
try:
cache = instance._memoize_cache
except AttributeError:
cache = instance._memoize_cache = {}
key = (self.func, pickle.dumps(args[1:]), pickle.dumps(kwargs))
try:
res = cache[key]
except KeyError:
res = cache[key] = self.func(*args, **kwargs)
return res
class proxylist(list):
def __getattr__(self, attr):
return proxylist(getattr(x, attr) for x in self)
def __setattr__(self, attr, val):
for x in self:
setattr(x, attr, val)
def __delattr__(self, attr):
for x in self:
delattr(x, attr)
def __call__(self, *args, **kwargs):
return proxylist(x(*args, **kwargs) for x in self)
class silence(object):
def __init__(self, stdout=os.devnull, stderr=os.devnull):
self.outfiles = stdout, stderr
self.combine = (stdout == stderr)
# Acquire a handle to fflush from libc
self.libc_fflush = get_libc_function('fflush')
self.libc_fflush.argtypes = [c_void_p]
def __enter__(self):
import sys
self.sys = sys
# Flush
sys.__stdout__.flush()
sys.__stderr__.flush()
# Save
self.saved_streams = [sys.__stdout__, sys.__stderr__]
self.fds = [s.fileno() for s in self.saved_streams]
self.saved_fds = [os.dup(f) for f in self.fds]
# Open the redirects
if self.combine:
self.new_streams = [open(self.outfiles[0], 'wb', 0)]*2
else:
self.new_streams = [open(f, 'wb', 0) for f in self.outfiles]
self.new_fds = [s.fileno() for s in self.new_streams]
# Replace
os.dup2(self.new_fds[0], self.fds[0])
os.dup2(self.new_fds[1], self.fds[1])
def __exit__(self, *args):
sys = self.sys
# Flush
self.libc_fflush(None)
# Restore
os.dup2(self.saved_fds[0], self.fds[0])
os.dup2(self.saved_fds[1], self.fds[1])
sys.stdout, sys.stderr = self.saved_streams
# Clean up
self.new_streams[0].close()
self.new_streams[1].close()
os.close(self.saved_fds[0])
os.close(self.saved_fds[1])
@contextmanager
def setenv(**kwargs):
_env = os.environ.copy()
os.environ.update(kwargs)
try:
yield
finally:
os.environ.clear()
os.environ.update(_env)
@contextmanager
def chdir(dirname):
cdir = os.getcwd()
try:
if dirname:
os.chdir(dirname)
yield
finally:
os.chdir(cdir)
class lazyprop(object):
def __init__(self, fn):
self.fn = fn
def __get__(self, instance, owner):
if instance is None:
return None
value = self.fn(instance)
setattr(instance, self.fn.__name__, value)
return value
def subclasses(cls, just_leaf=False):
sc = cls.__subclasses__()
ssc = [g for s in sc for g in subclasses(s, just_leaf)]
return [s for s in sc if not just_leaf or not s.__subclasses__()] + ssc
def subclass_where(cls, **kwargs):
for s in subclasses(cls):
for k, v in kwargs.items():
if not hasattr(s, k) or getattr(s, k) != v:
break
else:
return s
attrs = ', '.join('{0} = {1}'.format(k, v) for k, v in kwargs.items())
raise KeyError('No subclasses of {0} with attrs == ({1})'
.format(cls.__name__, attrs))
def ndrange(*args):
return it.product(*map(range, args))
def digest(*args, hash='sha256'):
return getattr(hashlib, hash)(pickle.dumps(args)).hexdigest()
def rm(path):
if os.path.isfile(path) or os.path.islink(path):
os.remove(path)
else:
shutil.rmtree(path)
def mv(src, dst):
shutil.move(src, dst)
def match_paired_paren(delim, n=5):
open, close = delim
ocset = '[^{1}{0}]'.format(open, close)
lft = r'{0}*?(?:\{1}'.format(ocset, open)
mid = r'{0}*?'.format(ocset)
rgt = r'\{1}{0}*?)*?'.format(ocset, close)
return lft*n + mid + rgt*n
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] |
postpatch.py | mr-ma/basic-self-checksumming | 1 | 573 | <reponame>mr-ma/basic-self-checksumming
import argparse
import os
import r2pipe
import struct
import mmap
import base64
from shutil import copyfile
import pprint
pp = pprint.PrettyPrinter(indent=4)
def precompute_hash(r2, offset, size):
print('Precomputing hash')
h = 0
print("r2 command to get the function body in base64:\np6e {}@{}".format(size, offset))
b64_func = r2.cmd("p6e {}@{}".format(size, offset))
func_bytes = bytearray(base64.b64decode(b64_func))
for b in func_bytes:
h = h ^ b
print('Precomuted hash:', hex(h))
return h
def patch_binary(mm, search_value, patch_value):
print("search value:{} patch value:{}".format(search_value, patch_value))
flag = "<I" # little-endian unsigned int
search_bytes = struct.pack(flag, search_value)
address = mm.find(search_bytes)
if address == -1:
mm.seek(0)
address = mm.find(search_bytes)
mm.seek(address, os.SEEK_SET)
patch_bytes = struct.pack(flag, patch_value)
mm.write(patch_bytes)
def get_protected_function_info(r2, function):
# find addresses and sizes of all functions
r2.cmd("aa")
r2.cmd("aac")
function_list = r2.cmdj("aflj")
# print(function_list)
funcs = {}
for func in function_list:
attr = {'size': func['size'], 'offset': func['offset']}
funcs[func['name']] = attr
# Basic search for mangled names
if function == 'main':
# main function is entry0 in the binary
function = 'entry0'
print("Cannot precompute the expected hash for the main function, why is that?")
exit(1)
match = 0
mangledName = ""
for name, attr in funcs.items():
# sometimes r2 prepends sym. to function names
if function in name:
mangledName = name
match += 1
if match != 1:
print("Failed to safely find function in the binary!")
pp.pprint(funcs)
exit(1)
return funcs[mangledName]
def main():
parser = argparse.ArgumentParser(
description='Postpatch protected C program.')
parser.add_argument('-b', action="store", dest="binary",
help="program.out protected program binary", required=True)
parser.add_argument('-f', action="store", dest="function",
help="protected function name", required=True)
parser.add_argument('-p', nargs="+", dest="placeholders",
help="list of used placeholders in the exact order of function, size, expected hash", required=True)
results = parser.parse_args()
print("python protect program", results)
r2 = r2pipe.open(results.binary)
funcInfo = get_protected_function_info(r2, results.function)
funcOffset = funcInfo["offset"]
funcSize = funcInfo["size"]
funcExpectedHash = precompute_hash(r2, funcOffset, funcSize)
print("funcOffset:{} funcSize:{} funcExpectedHash:{}".format(
funcOffset, funcSize, funcExpectedHash))
binaryFile, _ = os.path.splitext(results.binary)
patchedBinary = "{}-patched.out".format(binaryFile)
copyfile(results.binary, patchedBinary)
with open(patchedBinary, 'r+b') as binary:
mm = mmap.mmap(binary.fileno(), 0)
patch_binary(mm, int(results.placeholders[0]), int(funcSize))
patch_binary(mm, int(results.placeholders[1]), int(funcExpectedHash))
print("Successfully stored patched binary {}".format(patchedBinary))
status = os.system(
"chmod +x {}".format(patchedBinary))
if status != 0:
print("Error in setting permission, try:\n sudo chmod +x {}".format(patchedBinary))
exit(1)
if __name__ == '__main__':
main()
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learning_journal/security.py | han8909227/pyramid-learning-journal | 0 | 52407 | <filename>learning_journal/security.py
"""Configure and hold all pertinent security information for the app."""
import os
from pyramid.authentication import AuthTktAuthenticationPolicy
from pyramid.authorization import ACLAuthorizationPolicy
from pyramid.security import Authenticated
from pyramid.security import Allow
from passlib.apps import custom_app_context as pwd_context
from pyramid.session import SignedCookieSessionFactory # <-- include this
class MyRoot(object):
"""My root class."""
def __init__(self, request):
"""Init auth class."""
self.request = request
__acl__ = [
(Allow, Authenticated, 'secret'),
]
def is_authenticated(username, password):
"""Check if the user's username and password are good."""
if username == os.environ.get('AUTH_USERNAME', ''):
if pwd_context.verify(password, os.environ.get('AUTH_PASSWORD', '')):
return True
return False
def includeme(config):
"""."""
# set up authentication
auth_secret = os.environ.get('AUTH_SECRET', '')
authn_policy = AuthTktAuthenticationPolicy(
secret=auth_secret,
hashalg='sha512'
)
config.set_authentication_policy(authn_policy)
# set up authorization
authz_policy = ACLAuthorizationPolicy()
config.set_authorization_policy(authz_policy)
# config.set_default_permission('secret')
# if i want every view by default to be behind a login wall
config.set_root_factory(MyRoot)
# set up CSRF token to prevent CSRF attack
session_secret = os.environ.get('SESSION_SECRET', '')
session_factory = SignedCookieSessionFactory(session_secret)
config.set_session_factory(session_factory)
config.set_default_csrf_options(require_csrf=True)
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backend/main/models.py | hadi-f90/futurecoder | 0 | 111657 | from django.contrib.auth.models import AbstractUser
from django.db import models
from main.text import page_slugs_list, pages
from jsonfield import JSONField
class PagesProgress(dict):
"""Supplies the first step name as the default for missing pages"""
def __missing__(self, key):
result = {"step_name": pages[key].step_names[0]}
self[key] = result
return result
class User(AbstractUser):
page_slug = models.CharField(default=page_slugs_list[0], max_length=128)
json = JSONField(default={"pages_progress": {}})
developer_mode = models.BooleanField(default=False)
@property
def pages_progress(self):
"""
Returns a PagesProgress dict which fills in default values.
Ensures the result is directly in the json field so changes
are written back to the database.
"""
result = self.json["pages_progress"]
if not isinstance(result, PagesProgress):
result = self.json["pages_progress"] = PagesProgress(result)
return result
class ListEmail(models.Model):
email = models.EmailField()
class CodeEntry(models.Model):
timestamp = models.DateTimeField(auto_now=True)
input = models.TextField()
output = models.TextField(null=True)
source = models.CharField(max_length=32)
user = models.ForeignKey(User, on_delete=models.SET_NULL, related_name='code_entries', null=True)
page_slug = models.CharField(max_length=128)
step_name = models.CharField(max_length=128)
| [
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29898,
3317,
29918,
2848,
29922,
29896,
29906,
29947,
29897,
13,
2
] |
pytoolkit/applications/__init__.py | ak110/pytoolk | 26 | 24210 | """Kerasの各種モデル。"""
# pylint: skip-file
# flake8: noqa
from . import darknet53, efficientnet, xception
| [
1,
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232,
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29945,
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29892,
921,
1441,
13,
2
] |
pys/testing.py | nahian-147/my_codes | 0 | 89255 | <reponame>nahian-147/my_codes
import subprocess
p = int(subprocess.check_output(["./testing","5"]).decode())
print(p*p)
| [
1,
529,
276,
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13,
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13,
2158,
29898,
29886,
29930,
29886,
8443,
13,
2
] |
Netmiko/GetInts.py | NetworkNick-US/PythonScripts | 0 | 60907 | import netmiko
import LogUtility
class CiscoBase:
def __init__(self, hostname, ip, device_type=None, username=None, password=<PASSWORD>):
self.hostname = hostname
self.connection_data = {
'ip': ip,
'device_type': device_type,
'username': username,
'password': password
}
def login(self):
return netmiko.ConnectHandler(**self.connection_data)
class CiscoRTR(CiscoBase):
def populate_interface_list(self):
connect = self.login()
sh_ip_int_brief = connect.send_command("show ip int brief", use_textfsm=True)
self.interface_list = []
for interface in sh_ip_int_brief:
self.interface_list.append(interface['intf'])
CoreRTR = CiscoRTR("Core1", "192.168.255.254", "cisco-ios", "localAdmin", "SuperSecure123!!")
CoreRTR.populate_interface_list()
ints = CoreRTR.interface_list
print(ints)
logList = LogUtility.NetworkLogger("interfaces.txt")
logList.log(str(ints))
| [
1,
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613,
376,
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29929,
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29953,
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13,
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13724,
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703,
1639,
8726,
29889,
3945,
1159,
13,
1188,
1293,
29889,
1188,
29898,
710,
29898,
9466,
876,
13,
2
] |
exceptions/ServiceExceptions.py | sachinraina112/flask_app | 0 | 156469 | <gh_stars>0
from enum import Enum
class ExceptionMessages(Enum):
@property
def code(self):
return self.value[0]
@property
def message(self):
return self.value[1]
PREDICT_JOB_FAIL = (10001, "Error occurred in prediction job")
VALIDATE_JOB_FAIL = (10002, "Error occurred in validation job")
DATA_FETCH_ERROR = (10003, "Error in fetching report")
ERROR_IN_WRITING_DATA = (10004, "Error in writing data into report")
ERROR_IN_LOADING_MODULE = (10005, "Error in loading module")
DATA_NOT_PRESENT = (10006, "Data not present")
ERROR_READING_CONFIG = (10007, "Error while reading app-config file, make sure app-config file exists with model_params property")
URL_REQUIRED_FOR_REQUEST = (10008, "URL should not empty")
class ServiceException(Exception):
def __init__(self, ex: ExceptionMessages=None, message: str=None, code: int=None):
if ex is not None:
self.message = ex.message
self.code = ex.code or 100
else:
self.message = message
self.code = code
super().__init__(self.message)
| [
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1550,
5183,
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2917,
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4864,
411,
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13,
13,
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2451,
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13,
13,
1678,
822,
4770,
2344,
12035,
1311,
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429,
29901,
8960,
25510,
29922,
8516,
29892,
2643,
29901,
851,
29922,
8516,
29892,
775,
29901,
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29922,
8516,
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13,
4706,
565,
429,
338,
451,
6213,
29901,
13,
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1583,
29889,
4906,
353,
429,
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13,
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1583,
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401,
353,
429,
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401,
470,
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29900,
13,
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353,
2643,
13,
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1583,
29889,
401,
353,
775,
13,
4706,
2428,
2141,
1649,
2344,
12035,
1311,
29889,
4906,
29897,
13,
2
] |
dali/core/framework/types.py | seetaresearch/Dragon | 81 | 158161 | <reponame>seetaresearch/Dragon
# ------------------------------------------------------------
# Copyright (c) 2017-present, SeetaTech, Co.,Ltd.
#
# Licensed under the BSD 2-Clause License.
# You should have received a copy of the BSD 2-Clause License
# along with the software. If not, See,
#
# <https://opensource.org/licenses/BSD-2-Clause>
#
# ------------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy
try:
from nvidia.dali import types as dali_types
# ConstantWrapper
Constant = dali_types.Constant
ScalarConstant = dali_types.ScalarConstant
# DALIDataType
BOOL = dali_types.BOOL
FLOAT = dali_types.FLOAT
FLOAT16 = dali_types.FLOAT16
FLOAT32 = dali_types.FLOAT
FLOAT64 = dali_types.FLOAT64
INT8 = dali_types.INT8
INT16 = dali_types.INT16
INT32 = dali_types.INT32
INT64 = dali_types.INT64
STRING = dali_types.STRING
UINT8 = dali_types.UINT8
UINT16 = dali_types.UINT16
UINT32 = dali_types.UINT32
UINT64 = dali_types.UINT64
# DALIImageType
BGR = dali_types.BGR
RGB = dali_types.RGB
# DALIInterpType
INTERP_CUBIC = dali_types.INTERP_CUBIC
INTERP_GAUSSIAN = dali_types.INTERP_GAUSSIAN
INTERP_LANCZOS3 = dali_types.INTERP_LANCZOS3
INTERP_LINEAR = dali_types.INTERP_LINEAR
INTERP_NN = dali_types.INTERP_NN
INTERP_TRIANGULAR = dali_types.INTERP_TRIANGULAR
# PipelineAPIType
PIPELINE_API_BASIC = dali_types.PipelineAPIType.BASIC
PIPELINE_API_ITERATOR = dali_types.PipelineAPIType.ITERATOR
PIPELINE_API_SCHEDULED = dali_types.PipelineAPIType.SCHEDULED
# TensorLayout
NCHW = dali_types.NCHW
NHWC = dali_types.NHWC
except ImportError:
dali_types = None
NO_DALI = -1
# ConstantWrapper
Constant = NO_DALI
ScalarConstant = NO_DALI
# DALIDataType
BOOL = NO_DALI
FLOAT = NO_DALI
FLOAT16 = NO_DALI
FLOAT32 = NO_DALI
FLOAT64 = NO_DALI
INT8 = NO_DALI
INT16 = NO_DALI
INT32 = NO_DALI
INT64 = NO_DALI
STRING = NO_DALI
UINT8 = NO_DALI
UINT16 = NO_DALI
UINT32 = NO_DALI
UINT64 = NO_DALI
# DALIImageType
BGR = NO_DALI
RGB = NO_DALI
# DALIInterpType
INTERP_CUBIC = NO_DALI
INTERP_GAUSSIAN = NO_DALI
INTERP_LANCZOS3 = NO_DALI
INTERP_LINEAR = NO_DALI
INTERP_NN = NO_DALI
INTERP_TRIANGULAR = NO_DALI
# PipelineAPIType
PIPELINE_API_BASIC = NO_DALI
PIPELINE_API_ITERATOR = NO_DALI
PIPELINE_API_SCHEDULED = NO_DALI
# TensorLayout
NCHW = NO_DALI
NHWC = NO_DALI
def np_dtype(dali_dtype):
"""Convert the dali dtype into the numpy format."""
return numpy.dtype(dali_dtype)
| [
1,
529,
276,
1112,
420,
29958,
344,
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] |
ExpenseTracker/grocery/models.py | lennyAiko/LifeExpenses | 0 | 1606139 | <gh_stars>0
from django.db import models
from django.contrib.auth.models import User
# Create your models here.
PROGRESS = (
('In progress', 'In progress'),
('Completed', 'Completed')
)
class List(models.Model):
user = models.ForeignKey(User,
on_delete=models.CASCADE,
null=True,
related_name='list')
name = models.CharField(max_length=70,
null=True)
status = models.CharField(choices=PROGRESS,
null=True,
max_length=12,
default="In progress")
date_created = models.DateTimeField(auto_now_add=True)
date_updated = models.DateTimeField(auto_now=True)
def __str__(self):
return f"{self.name}"
class Meta:
ordering = ['-date_created']
verbose_name = 'Shopping List'
verbose_name_plural = 'Shopping Lists'
@property
def total_list(self, value=0):
items = self.item.all()
for i in items:
value += i.total_item
return value
class Item(models.Model):
name = models.CharField(max_length=70,
null=True)
price = models.DecimalField(max_digits=7,
decimal_places=2,
default=0.00,
null=True)
quantity = models.IntegerField(default=1,
null=True)
list = models.ForeignKey(List,
on_delete=models.CASCADE,
null=True,
related_name='item')
date_created = models.DateTimeField(auto_now_add=True)
date_updated = models.DateTimeField(auto_now=True)
def __str__(self):
return f"{self.name}"
class Meta:
ordering = ['-date_created']
verbose_name = 'Shopping Item'
verbose_name_plural = 'Shopping Items'
@property
def total_item(self):
return self.price * self.quantity
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tests/models/test_inv_resourcegroup.py | prorevizor/noc | 84 | 68810 | # ----------------------------------------------------------------------
# inv.ResourceGroup tests
# ----------------------------------------------------------------------
# Copyright (C) 2007-2018 The NOC Project
# See LICENSE for details
# ----------------------------------------------------------------------
# NOC modules
from noc.inv.models.resourcegroup import ResourceGroup
def test_clean_leagacy_id():
left = ResourceGroup._get_collection().find_one({"_legacy_id": {"$exists": True}})
assert left is None, "_legacy_id field has been left by migration"
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qa_tool/tests/test_relevancy_score.py | pg-irc/pathways-backend | 12 | 105485 | import datetime
from test_plus.test import TestCase
from qa_tool.tests.helpers import RelevancyScoreBuilder, AlgorithmBuilder, SearchLocationBuilder
from rest_framework import status
from rest_framework.authtoken.models import Token
from rest_framework.test import APIClient
from human_services.services_at_location.tests.helpers import ServiceAtLocationBuilder
from newcomers_guide.tests.helpers import create_topic
from common.testhelpers.random_test_values import an_integer, a_string
class GETRelevancyScoreTests(TestCase):
def setUp(self):
self.user = self.make_user()
self.token = Token.objects.create(user=self.user)
self.APIClient = APIClient()
self.data = {
'value': an_integer(),
'algorithm': AlgorithmBuilder().create().id,
'search_location': SearchLocationBuilder().create().id,
'service_at_location': ServiceAtLocationBuilder().create().id,
'topic': create_topic(a_string()).id,
}
def test_can_get_one_entity_unauthenticated(self):
score_value = an_integer()
score = RelevancyScoreBuilder(self.user).with_value(score_value).create()
url = '/qa/v1/relevancyscores/{0}/'.format(score.pk)
response = self.APIClient.get(url)
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(response.json()['value'], score_value)
def test_can_get_entities_unauthenticated(self):
RelevancyScoreBuilder(self.user).create()
RelevancyScoreBuilder(self.user).create()
url = '/qa/v1/relevancyscores/'
response = self.APIClient.get(url)
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(len(response.json()), 2)
def test_cannot_get_non_existent_entity_unauthenticated(self):
url = '/qa/v1/relevancyscores/0/'
response = self.APIClient.get(url)
self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)
class DELETERelevancyScoreTests(TestCase):
def setUp(self):
self.user = self.make_user()
self.token = Token.objects.create(user=self.user)
self.APIClient = APIClient()
self.data = {
'value': an_integer(),
'algorithm': AlgorithmBuilder().create().id,
'search_location': SearchLocationBuilder().create().id,
'service_at_location': ServiceAtLocationBuilder().create().id,
'topic': create_topic(a_string()).id,
}
def test_can_delete(self):
self.APIClient.credentials(HTTP_AUTHORIZATION='Token ' + self.token.key)
score = RelevancyScoreBuilder(self.user).create()
url = '/qa/v1/relevancyscores/{0}/'.format(score.pk)
response = self.APIClient.delete(url)
self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT)
def test_cannot_delete_unauthenticated(self):
score = RelevancyScoreBuilder(self.user).create()
url = '/qa/v1/relevancyscores/{0}/'.format(score.pk)
response = self.APIClient.delete(url)
self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)
def test_cannot_delete_non_existent_entity(self):
self.APIClient.credentials(HTTP_AUTHORIZATION='Token ' + self.token.key)
url = '/qa/v1/relevancyscores/0/'
response = self.APIClient.delete(url)
self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)
class PUTRelevancyScoreTests(TestCase):
def setUp(self):
self.user = self.make_user()
self.new_user = self.make_user(username='testuser2')
self.token = Token.objects.create(user=self.user)
self.APIClient = APIClient()
self.sample_score = RelevancyScoreBuilder(self.user).create()
self.data = {
'value': an_integer(),
'algorithm': AlgorithmBuilder().create().id,
'search_location': SearchLocationBuilder().create().id,
'service_at_location': ServiceAtLocationBuilder().create().id,
'topic': create_topic(a_string()).id,
}
def test_can_put_value(self):
self.APIClient.credentials(HTTP_AUTHORIZATION='Token ' + self.token.key)
new_value = an_integer()
self.data['value'] = new_value
url = '/qa/v1/relevancyscores/{0}/'.format(self.sample_score.pk)
response = self.APIClient.put(url, self.data)
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(response.json()['value'], new_value)
def test_can_put_algorithm(self):
self.APIClient.credentials(HTTP_AUTHORIZATION='Token ' + self.token.key)
new_algorithm = AlgorithmBuilder().create()
self.data['algorithm'] = new_algorithm.id
url = '/qa/v1/relevancyscores/{0}/'.format(self.sample_score.pk)
response = self.APIClient.put(url, self.data)
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(response.json()['algorithm'], new_algorithm.id)
def test_can_put_search_location(self):
self.APIClient.credentials(HTTP_AUTHORIZATION='Token ' + self.token.key)
new_search_location = SearchLocationBuilder().create()
self.data['search_location'] = new_search_location.id
url = '/qa/v1/relevancyscores/{0}/'.format(self.sample_score.pk)
response = self.APIClient.put(url, self.data)
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(response.json()['search_location'], new_search_location.id)
def test_can_put_service_at_location(self):
self.APIClient.credentials(HTTP_AUTHORIZATION='Token ' + self.token.key)
new_service_at_location = ServiceAtLocationBuilder().create()
self.data['service_at_location'] = new_service_at_location.id
url = '/qa/v1/relevancyscores/{0}/'.format(self.sample_score.pk)
response = self.APIClient.put(url, self.data)
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(response.json()['service_at_location'], new_service_at_location.id)
def test_can_put_topic(self):
self.APIClient.credentials(HTTP_AUTHORIZATION='Token ' + self.token.key)
new_topic = create_topic(a_string())
self.data['topic'] = new_topic.id
url = '/qa/v1/relevancyscores/{0}/'.format(self.sample_score.pk)
response = self.APIClient.put(url, self.data)
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(response.json()['topic'], new_topic.id)
def test_put_response_has_new_time_stamp(self):
url = '/qa/v1/relevancyscores/{0}/'.format(self.sample_score.pk)
self.APIClient.credentials(HTTP_AUTHORIZATION='Token ' + self.token.key)
response = self.APIClient.put(url, self.data)
self.assertEqual(response.status_code, status.HTTP_200_OK)
recreated_time = datetime.datetime.strptime(response.json()['time_stamp'], '%Y-%m-%dT%H:%M:%S.%fZ')
self.assertLessEqual(recreated_time, datetime.datetime.now())
self.assertGreaterEqual(recreated_time + datetime.timedelta(seconds=1), datetime.datetime.now())
def test_can_put_with_different_credential(self):
new_token = Token.objects.create(user=self.new_user)
self.APIClient.credentials(HTTP_AUTHORIZATION='Token ' + new_token.key)
new_value = an_integer()
self.data['value'] = new_value
url = '/qa/v1/relevancyscores/{0}/'.format(self.sample_score.pk)
response = self.APIClient.put(url, self.data)
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(response.json()['user'], self.new_user.id)
self.assertEqual(response.json()['value'], new_value)
def test_cannot_put_id(self):
self.APIClient.credentials(HTTP_AUTHORIZATION='Token ' + self.token.key)
new_id = an_integer()
self.data['id'] = new_id
url = '/qa/v1/relevancyscores/{0}/'.format(self.sample_score.pk)
response = self.APIClient.put(url, self.data)
self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
self.assertEqual(response.json(), 'id of relevancyscore is immutable')
def test_cannot_put_user(self):
self.APIClient.credentials(HTTP_AUTHORIZATION='Token ' + self.token.key)
self.data['user'] = self.new_user.id
url = '/qa/v1/relevancyscores/{0}/'.format(self.sample_score.pk)
response = self.APIClient.put(url, self.data)
self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
self.assertEqual(response.json(), 'user_id of relevancyscore is immutable')
def test_cannot_put_unauthenticated(self):
url = '/qa/v1/relevancyscores/{0}/'.format(self.sample_score.pk)
self.data['value'] = an_integer()
response = self.APIClient.put(url, self.data)
self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)
def test_cannot_put_non_existent_entity(self):
url = '/qa/v1/relevancyscores/0/'
self.APIClient.credentials(HTTP_AUTHORIZATION='Token ' + self.token.key)
response = self.APIClient.put(url, self.data)
self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)
class POSTRelevancyScoreTests(TestCase):
def setUp(self):
self.user = self.make_user()
self.token = Token.objects.create(user=self.user)
self.APIClient = APIClient()
self.score_value = an_integer()
self.data_without_algorithm_in_body = {
'value': self.score_value,
'search_location': SearchLocationBuilder().create().id,
'service_at_location': ServiceAtLocationBuilder().create().id,
'topic': create_topic('test').id,
}
self.data_with_algorithm_in_body = {
'value': self.score_value,
'algorithm': AlgorithmBuilder().create().id,
'search_location': SearchLocationBuilder().create().id,
'service_at_location': ServiceAtLocationBuilder().create().id,
'topic': create_topic(a_string()).id,
}
def test_can_post_with_algorithm_in_body_not_in_url_short_url(self):
self.APIClient.credentials(HTTP_AUTHORIZATION='Token ' + self.token.key)
url = '/qa/v1/relevancyscores/'
response = self.APIClient.post(url, data=self.data_with_algorithm_in_body)
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
self.assertEqual(response.json()['value'], self.score_value)
def test_can_post_with_algorithm_in_url_not_in_body_long_url(self):
self.APIClient.credentials(HTTP_AUTHORIZATION='Token ' + self.token.key)
algorithm = AlgorithmBuilder().create()
url = '/qa/v1/algorithms/{0}/relevancyscores/'.format(algorithm.pk)
response = self.APIClient.post(url, data=self.data_without_algorithm_in_body)
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
self.assertEqual(response.json()['value'], self.score_value)
def test_post_response_has_correct_algorithm(self):
self.APIClient.credentials(HTTP_AUTHORIZATION='Token ' + self.token.key)
url = '/qa/v1/relevancyscores/'
response = self.APIClient.post(url, self.data_with_algorithm_in_body)
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
self.assertEqual(response.json()['algorithm'], self.data_with_algorithm_in_body['algorithm'])
def test_post_response_has_correct_search_location(self):
self.APIClient.credentials(HTTP_AUTHORIZATION='Token ' + self.token.key)
url = '/qa/v1/relevancyscores/'
response = self.APIClient.post(url, self.data_with_algorithm_in_body)
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
self.assertEqual(response.json()['search_location'], self.data_with_algorithm_in_body['search_location'])
def test_post_response_has_correct_service_at_location(self):
self.APIClient.credentials(HTTP_AUTHORIZATION='Token ' + self.token.key)
url = '/qa/v1/relevancyscores/'
response = self.APIClient.post(url, self.data_with_algorithm_in_body)
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
self.assertEqual(response.json()['service_at_location'],
self.data_with_algorithm_in_body['service_at_location'])
def test_post_response_has_correct_topic(self):
self.APIClient.credentials(HTTP_AUTHORIZATION='Token ' + self.token.key)
url = '/qa/v1/relevancyscores/'
response = self.APIClient.post(url, self.data_with_algorithm_in_body)
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
self.assertEqual(response.json()['topic'], self.data_with_algorithm_in_body['topic'])
def test_post_response_has_correct_user(self):
self.APIClient.credentials(HTTP_AUTHORIZATION='Token ' + self.token.key)
url = '/qa/v1/relevancyscores/'
response = self.APIClient.post(url, self.data_with_algorithm_in_body)
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
self.assertEqual(response.json()['user'], self.user.id)
def test_post_response_has_accurate_time_stamp(self):
self.APIClient.credentials(HTTP_AUTHORIZATION='Token ' + self.token.key)
url = '/qa/v1/relevancyscores/'
response = self.APIClient.post(url, self.data_with_algorithm_in_body)
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
recreated_time = datetime.datetime.strptime(response.json()['time_stamp'], '%Y-%m-%dT%H:%M:%S.%fZ')
self.assertLessEqual(recreated_time, datetime.datetime.now())
self.assertGreaterEqual(recreated_time + datetime.timedelta(seconds=1), datetime.datetime.now())
def test_cannot_post_unauthenticated(self):
url = '/qa/v1/relevancyscores/'
response = self.APIClient.post(url, data=self.data_with_algorithm_in_body)
self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)
def test_cannot_post_with_invalid_algorithm_in_url_valid_algorithm_in_body_long_url(self):
self.APIClient.credentials(HTTP_AUTHORIZATION='Token ' + self.token.key)
url = '/qa/v1/algorithms/0/relevancyscores/'
response = self.APIClient.post(url, data=self.data_with_algorithm_in_body)
self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
def test_cannot_post_without_algorithm_in_body_or_url_short_url(self):
self.APIClient.credentials(HTTP_AUTHORIZATION='Token ' + self.token.key)
url = '/qa/v1/relevancyscores/'
response = self.APIClient.post(url, data=self.data_without_algorithm_in_body)
self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
def test_cannot_post_with_invalid_algorithm_in_url_missing_algorithm_in_body_long_url(self):
self.APIClient.credentials(HTTP_AUTHORIZATION='Token ' + self.token.key)
url = '/qa/v1/algorithms/0/relevancyscores/'
response = self.APIClient.post(url, data=self.data_without_algorithm_in_body)
self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
def test_cannot_post_with_algorithm_in_body_and_url_short_url(self):
self.APIClient.credentials(HTTP_AUTHORIZATION='Token ' + self.token.key)
algorithm_id = self.data_with_algorithm_in_body['algorithm']
url = '/qa/v1/relevancyscores/{0}/'.format(algorithm_id)
response = self.APIClient.post(url, data=self.data_with_algorithm_in_body)
self.assertEqual(response.status_code, status.HTTP_405_METHOD_NOT_ALLOWED)
def test_cannot_post_with_algorithm_in_body_and_url_long_url(self):
self.APIClient.credentials(HTTP_AUTHORIZATION='Token ' + self.token.key)
algorithm_id = self.data_with_algorithm_in_body['algorithm']
url = '/qa/v1/algorithms/{0}/relevancyscores/'.format(algorithm_id)
response = self.APIClient.post(url, data=self.data_with_algorithm_in_body)
self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
def test_cannot_post_with_algorithm_in_url_not_in_body_short_url(self):
self.APIClient.credentials(HTTP_AUTHORIZATION='Token ' + self.token.key)
algorithm_id = AlgorithmBuilder().create().id
url = '/qa/v1/relevancyscores/{0}/'.format(algorithm_id)
response = self.APIClient.post(url, data=self.data_without_algorithm_in_body)
self.assertEqual(response.status_code, status.HTTP_405_METHOD_NOT_ALLOWED)
def test_cannot_post_when_missing_fields(self):
self.APIClient.credentials(HTTP_AUTHORIZATION='Token ' + self.token.key)
bad_data = {
'value': an_integer(),
'algorithm': AlgorithmBuilder().create().id,
# 'search_location': missing search_location
'service_at_location': ServiceAtLocationBuilder().create().id,
'topic': create_topic(a_string()).id,
}
url = '/qa/v1/relevancyscores/'
response = self.APIClient.post(url, bad_data)
self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
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days/month/day5_20.py | sanjaylokula/100dayspython | 0 | 70636 | # Definition for singly-linked list.
class ListNode:
def __init__(self, val=0, next=None):
self.val = val
self.next = next
class Solution:
def removeElements(self, head: ListNode, val: int) -> ListNode:
dummy = ListNode(-1)
dummy.next = head
def t(d):
if d.next:
if d.next.val == val:
d.next = d.next.next
t(d)
else:
t(d.next)
t(dummy)
return dummy.next
if __name__=="__main__":
output=Solution().removeElements([1,2,6,3,4,5,6],6)
print(output) | [
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adafruit_rsa/common.py | sommersoft/Adafruit_CircuitPython_RSA | 0 | 1608976 | # -*- coding: utf-8 -*-
#
# Copyright 2011 <NAME> <<EMAIL>>
#
# 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.
"""Common functionality shared by several modules."""
# pylint: disable=invalid-name
__version__ = "0.0.0-auto.0"
__repo__ = "https://github.com/adafruit/Adafruit_CircuitPython_RSA.git"
def bit_length(int_type):
"""Return the number of bits necessary to represent an integer in binary,
excluding the sign and leading zeros"""
length = 0
while int_type:
int_type >>= 1
length += 1
return length
class NotRelativePrimeError(ValueError):
"""Raises if provided a and b not relatively prime."""
def __init__(self, a, b, d, msg=None):
super(NotRelativePrimeError, self).__init__(
msg or "%d and %d are not relatively prime, divider=%i" % (a, b, d)
)
self.a = a
self.b = b
self.d = d
def bit_size(num):
"""
Number of bits needed to represent a integer excluding any prefix
0 bits.
Usage::
>>> bit_size(1023)
10
>>> bit_size(1024)
11
>>> bit_size(1025)
11
:param num:
Integer value. If num is 0, returns 0. Only the absolute value of the
number is considered. Therefore, signed integers will be abs(num)
before the number's bit length is determined.
:returns:
Returns the number of bits in the integer.
"""
try:
return bit_length(num)
except AttributeError:
raise TypeError("bit_size(num) only supports integers, not %r" % type(num))
def byte_size(number):
"""
Returns the number of bytes required to hold a specific long number.
The number of bytes is rounded up.
Usage::
>>> byte_size(1 << 1023)
128
>>> byte_size((1 << 1024) - 1)
128
>>> byte_size(1 << 1024)
129
:param number:
An unsigned integer
:returns:
The number of bytes required to hold a specific long number.
"""
if number == 0:
return 1
return ceil_div(bit_size(number), 8)
def ceil_div(num, div):
"""
Returns the ceiling function of a division between `num` and `div`.
Usage::
>>> ceil_div(100, 7)
15
>>> ceil_div(100, 10)
10
>>> ceil_div(1, 4)
1
:param num: Division's numerator, a number
:param div: Division's divisor, a number
:return: Rounded up result of the division between the parameters.
"""
quanta, mod = divmod(num, div)
if mod:
quanta += 1
return quanta
def extended_gcd(a, b):
"""Returns a tuple (r, i, j) such that r = gcd(a, b) = ia + jb
"""
# r = gcd(a,b) i = multiplicitive inverse of a mod b
# or j = multiplicitive inverse of b mod a
# Neg return values for i or j are made positive mod b or a respectively
# Iterateive Version is faster and uses much less stack space
x = 0
y = 1
lx = 1
ly = 0
oa = a # Remember original a/b to remove
ob = b # negative values from return results
while b != 0:
q = a // b
(a, b) = (b, a % b)
(x, lx) = ((lx - (q * x)), x)
(y, ly) = ((ly - (q * y)), y)
if lx < 0:
lx += ob # If neg wrap modulo orignal b
if ly < 0:
ly += oa # If neg wrap modulo orignal a
return a, lx, ly # Return only positive values
def inverse(x, n):
"""Returns the inverse of x % n under multiplication, a.k.a x^-1 (mod n)
>>> inverse(7, 4)
3
>>> (inverse(143, 4) * 143) % 4
1
"""
(divider, inv, _) = extended_gcd(x, n)
if divider != 1:
raise NotRelativePrimeError(x, n, divider)
return inv
def crt(a_values, modulo_values):
"""Chinese Remainder Theorem.
Calculates x such that x = a[i] (mod m[i]) for each i.
:param a_values: the a-values of the above equation
:param modulo_values: the m-values of the above equation
:returns: x such that x = a[i] (mod m[i]) for each i
>>> crt([2, 3], [3, 5])
8
>>> crt([2, 3, 2], [3, 5, 7])
23
>>> crt([2, 3, 0], [7, 11, 15])
135
"""
m = 1
x = 0
for modulo in modulo_values:
m *= modulo
for (m_i, a_i) in zip(modulo_values, a_values):
M_i = m // m_i
inv = inverse(M_i, m_i)
x = (x + a_i * M_i * inv) % m
return x
| [
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src/features/build_features.py | JL1829/EmployeeSalaryPrediction | 0 | 63234 | """
Author: <NAME>
Date: 6th/July/2020
Copyright: <NAME>, 2020
email: <EMAIL>
website: https://johdev.com
"""
import numpy as np
import pandas as pd
class FeatureGenerator(object):
"""A feature engineering generator to create additional feature to DataFrame
Group by each label encoded column, compute the:
* mean
* max
* min
* Standard Deviation
* median
of label column
Parameter
-------------
data: object, a dataset object created by `preprocessing.py`
Method
-------------
add_group_stats(self):
group by each label encoded column, and compute the Grouped Statitics.
fill any NaN value with 0
Return: grouped by statitics DataFrame merged with original DataFrame.
Example
-------------
>>> feature_engineering = True
>>> if feature_engineering:
FeatureGenerator(data).add_group_stats()
"""
def __init__(self, data:object):
"""initializes class and creates groupby object for data"""
self.data = data
self.cat_cols = data.cat_cols
self.groups = data.train_df.groupby(self.cat_cols)
def add_group_stats(self):
"""adds group statistics to data stored in data object"""
group_stats_df = self._get_group_stats()
group_stats_df.reset_index(inplace=True)
# merge derived columns to original df
self.data.train_df = self._merge_new_cols(self.data.train_df, group_stats_df, self.cat_cols, fillna=True)
self.data.test_df = self._merge_new_cols(self.data.test_df, group_stats_df, self.cat_cols, fillna=True)
# update column list
group_stats_cols = ['group_mean_salary', 'group_max_salary', 'group_min_salary', 'group_std_salary', 'group_median_salary']
self._extend_col_lists(self.data, cat_cols=group_stats_cols)
def _get_group_stats(self):
"""calculate group statistics"""
target_col = self.data.target_col
group_stats_df = pd.DataFrame({'group_mean_salary': self.groups[target_col].mean()})
group_stats_df['group_max_salary'] = self.groups[target_col].max()
group_stats_df['group_min_salary'] = self.groups[target_col].min()
group_stats_df['group_std_salary'] = self.groups[target_col].std()
group_stats_df['group_median_salary'] = self.groups[target_col].median()
return group_stats_df
def _merge_new_cols(self, df, new_cols_df, keys, fillna=False):
"""Merges engineered features with original df"""
DataFrame = pd.merge(df, new_cols_df, on=keys, how='left')
if fillna:
DataFrame.fillna(0, inplace=True)
return DataFrame
def _extend_col_lists(self, data, cat_cols=[], num_cols=[]):
"""addes engineered features cols to data cols lists"""
data.num_cols.extend(num_cols)
data.cat_cols.extend(cat_cols)
data.feature_cols.extend(num_cols + cat_cols)
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2
] |
code/twostones.py | kkirigaya/Kattis | 1 | 124502 | <filename>code/twostones.py
n = int(input())
print("Alice" if n%2 == 1 else "Bob")
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backend/env_collection/env_creator.py | MU-Software/dodoco | 6 | 47912 | import json
import pathlib
import traceback
import typing
def get_traceback_msg(err):
return ''.join(traceback.format_exception(
etype=type(err),
value=err,
tb=err.__traceback__))
def json_to_envfiles(output_file: pathlib.Path):
try:
output_name: str = output_file.stem
input_env_file_content: str = output_file.read_text()
input_env: dict[str, typing.Union[str, dict[str, str]]] = json.loads(input_env_file_content)
template_launch_json_file: typing.Optional[pathlib.Path] = pathlib.Path('template/launch.json')
if not template_launch_json_file.exists():
print('There\'s no launch.json template file for VS Code.\n'
'Place file on template/launch.json')
template_launch_json_file = None
else:
template_launch_json_file_content: dict[str, object] = json.loads(template_launch_json_file.read_text())
output_docker_file: pathlib.Path = pathlib.Path(output_name+'.env')
output_bash_file: pathlib.Path = pathlib.Path(output_name+'.sh')
output_ps_file: pathlib.Path = pathlib.Path(output_name+'.ps1')
output_launch_json_file: pathlib.Path = pathlib.Path('../.vscode/launch.json')
if output_docker_file.exists():
output_docker_file.unlink()
if output_bash_file.exists():
output_bash_file.unlink()
if output_ps_file.exists():
output_ps_file.unlink()
if template_launch_json_file is not None:
if not output_launch_json_file.parent.exists():
output_launch_json_file.parent.mkdir()
if output_launch_json_file.exists():
output_launch_json_file.unlink()
with output_docker_file.open('w') as docker_fp,\
output_bash_file.open('w') as bash_fp,\
output_ps_file.open('w') as ps_fp:
# Add Shebang
bash_fp.write('#!/usr/bin/env bash\n')
ps_fp.write('#!/usr/bin/env pwsh\n')
for env_name, env_value in input_env.items():
if env_name.startswith('__comment'):
comment_line = f'# {env_value}\n'
docker_fp.write(comment_line)
bash_fp.write(comment_line)
ps_fp.write(comment_line)
continue
elif env_name.startswith('__line_break'):
docker_fp.write('\n')
bash_fp.write('\n')
ps_fp.write('\n')
continue
docker_line = f'{env_name}='
bash_line = f'export {env_name}='
ps_line = f'$env:{env_name}='
if type(env_value) == dict:
bash_line += f'"{env_value["bash"]}"\n'
ps_line += f'"{env_value["powershell"]}"\n'
docker_line += f'"{env_value["vscode_launch"].format(**input_env)}"\n'
else:
bash_line += f'"{env_value}"\n'
ps_line += f'"{env_value}"\n'
docker_line += f'"{env_value}"\n'
docker_fp.write(docker_line)
bash_fp.write(bash_line)
ps_fp.write(ps_line)
if template_launch_json_file is not None:
with output_launch_json_file.open('w') as launch_json_fp:
launch_json_env = dict()
for k, v in input_env.items():
if k.startswith('__comment') or k.startswith('__line_break'):
continue
elif type(v) == str:
launch_json_env[k] = v
else:
launch_json_env[k] = v['vscode_launch'].format(**input_env)
template_launch_json_file_content['configurations'][0]['env'] = launch_json_env
launch_json_fp.write(json.dumps(template_launch_json_file_content, indent=4))
except Exception as e:
print('Exception raised!')
print(get_traceback_msg(e))
if __name__ == '__main__':
import os
import sys
if len(sys.argv) > 1:
target_file = pathlib.Path(sys.argv[1]).absolute()
if not target_file.exists():
print(target_file.as_posix())
# Ignore just now. We'll retry this after changing paths
target_file = None
else:
print('Need to specify target environment variables collection file(.json)')
os._exit(1)
os.chdir(pathlib.Path(__file__).parents[0])
if target_file is None:
target_file = pathlib.Path(sys.argv[1]).absolute()
if not target_file.exists():
raise FileNotFoundError()
json_to_envfiles(target_file)
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] |
data/evaluate_detections.py | Jacqueline121/RetinaNet_pytorch | 1 | 1605425 | <reponame>Jacqueline121/RetinaNet_pytorch
import os
import pickle
import numpy as np
from data.VOC_eval import voc_eval
from config.config import cfg
dataset_dir = os.path.join(os.path.dirname(__file__), 'dataset')
def load_image_set_index(image_set):
image_set_file = os.path.join(dataset_dir, cfg.DATASET_NAME, 'ImageSets', image_set + '.txt')
assert os.path.exists(image_set_file), \
'Path does not exist: {}'.format(image_set_file)
with open(image_set_file) as f:
image_index = [(x.split(' ')[0]).split('.')[0] for x in f.readlines()]
return image_index
def get_results_file_template(image_set):
filename = 'comp4' + '_det_' + image_set + '_{:s}.txt'
filedir = os.path.join(dataset_dir, cfg.DATASET_NAME, 'Results')
if not os.path.exists(filedir):
os.makedirs(filedir)
path = os.path.join(filedir, filename)
return path
def write_results_file(all_boxes):
image_index = load_image_set_index('test') # get val image index(id)
for cls_ind, cls in enumerate(cfg.CLASSES):
print('Writing {} results file'.format(cls))
filename = get_results_file_template('test').format(cls)
with open(filename, 'wt') as f:
for im_ind, index in enumerate(image_index):
dets = all_boxes[cls_ind][im_ind]
if dets == []:
continue
for k in range(dets.shape[0]):
f.write('{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'.
format(index, dets[k, -1],
dets[k, 0] + 1, dets[k, 1] + 1,
dets[k, 2] + 1, dets[k, 3] + 1))
def do_python_eval(output_dir, use_07_metric):
anno_path = os.path.join(dataset_dir, cfg.DATASET_NAME, 'Annotations', '{:s}.xml')
imageset_file = os.path.join(dataset_dir, cfg.DATASET_NAME, 'ImageSets', 'test.txt')
cache_dir = os.path.join(dataset_dir, cfg.DATASET_NAME, 'Cache')
aps = []
if not os.path.isdir(output_dir):
os.mkdir(output_dir)
for i, cls in enumerate(cfg.CLASSES):
file_name = get_results_file_template('test').format(cls)
rec, prec, ap = voc_eval(
anno_path=anno_path,
imageset_file=imageset_file,
cache_dir=cache_dir,
class_name=cls,
det_path=file_name,
ious_thresh=0.5,
use_07_metric=use_07_metric)
aps += [ap]
print('AP for {} = {:.4f}'.format(cls, ap))
with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f:
pickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f)
print('Mean AP = {:.4f}'.format(np.mean(aps)))
print('~~~~~~~~')
print('Results:')
for ap in aps:
print('{:.3f}'.format(ap))
print('{:.3f}'.format(np.mean(aps)))
print('~~~~~~~~')
print('')
print('--------------------------------------------------------------')
print('Results computed with the **unofficial** Python eval code.')
print('Results should be very close to the official MATLAB eval code.')
print('Recompute with `./tools/reval.py --matlab ...` for your paper.')
print('-- Thanks, The Management')
print('--------------------------------------------------------------')
def evaluate_detections(all_boxes, output_dir):
write_results_file(all_boxes)
do_python_eval(output_dir, True)
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clearScreen.py | BlueDragon022/Fruit-Machine | 0 | 114922 | <gh_stars>0
import os
import platform
#Function to clear the screen
def clearScreen():
#This goes through different os's and uses the correct command to clear the terminal/console
if platform.system() == "Windows":
os.system("cls")
elif platform.system() == "Linux":
os.system("clear")
elif platform.system() == "Darwin": #MacOS
os.system("clear")
else:
os.system("clear")
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ch05_GUI/tkhello2.py | Nickhool/core-python | 1 | 168668 | <gh_stars>1-10
__Author__ = "noduez"
'''Button控件演示'''
import tkinter as tk
top = tk.Tk()
quit = tk.Button(top, text='Hello World!',
command=top.quit)
quit.pack()
tk.mainloop() | [
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apostello/migrations/0007_auto_20160315_1213.py | LaudateCorpus1/apostello | 69 | 27234 | # -*- coding: utf-8 -*-
# Generated by Django 1.9.4 on 2016-03-15 12:13
from __future__ import unicode_literals
import django.core.validators
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [("apostello", "0006_userprofile_show_tour")]
operations = [
migrations.AddField(model_name="userprofile", name="can_archive", field=models.BooleanField(default=True)),
migrations.AlterField(
model_name="recipient",
name="first_name",
field=models.CharField(
db_index=True,
max_length=16,
validators=[
django.core.validators.RegexValidator(
"^[\\s\\w@?£!1$\"¥#è?¤é%ù&ì\\ò(Ç)*:Ø+;ÄäøÆ,<LÖlöæ\\-=ÑñÅß.>ÜüåÉ/§à¡¿']+$",
message="You can only use GSM characters.",
)
],
verbose_name="First Name",
),
),
migrations.AlterField(
model_name="recipient",
name="last_name",
field=models.CharField(
db_index=True,
max_length=40,
validators=[
django.core.validators.RegexValidator(
"^[\\s\\w@?£!1$\"¥#è?¤é%ù&ì\\ò(Ç)*:Ø+;ÄäøÆ,<LÖlöæ\\-=ÑñÅß.>ÜüåÉ/§à¡¿']+$",
message="You can only use GSM characters.",
)
],
verbose_name="Last Name",
),
),
]
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src/amuse/community/rebound/interface.py | aatrani/amuse | 0 | 65689 | from amuse.community import *
from amuse.community.interface.gd import GravitationalDynamicsInterface
from amuse.community.interface.gd import GravitationalDynamics
from amuse.community.interface.gd import SinglePointGravityFieldInterface
from amuse.community.interface.gd import GravityFieldCode
class ReboundInterface(CodeInterface,
LiteratureReferencesMixIn,
GravitationalDynamicsInterface,
StoppingConditionInterface,
#SinglePointGravityFieldInterface
):
"""
REBOUND - An open-source multi-purpose N-body code
.. [#] <NAME>., <NAME>., *Astronomy and Astrophysics* , **Volume 537**, A128 (2012)
For different integrators, cite:
... IAS15: <NAME>., <NAME>., *MNRAS* , **Volume 446**, Issue 2, p.1424-1437 (2015)
... WHFast: <NAME>., <NAME>., *MNRAS* , **Volume 452**, Issue 1, p.376-388 (2015)
... Hermes: <NAME>., et al., in prep.
... SEI: <NAME>., <NAME>., *MNRAS* , **Volume 415**, Issue 4, p.3168-3176 (2011)
... JANUS: <NAME>., <NAME>., *arXiv* , 1704.07715 (2017)
"""
include_headers = ['worker_code.h', 'stopcond.h']
__so_module__ = 'rebound_cython'
def __init__(self, **options):
CodeInterface.__init__(self, name_of_the_worker="rebound_worker",
**options)
LiteratureReferencesMixIn.__init__(self)
@legacy_function
def new_particle():
"""
Define a new particle in the stellar dynamics code. The particle is initialized with the provided
mass, radius, position and velocity. This function returns an index that can be used to refer
to this particle.
"""
function = LegacyFunctionSpecification()
function.can_handle_array = True
function.addParameter('index_of_the_particle', dtype='int32', direction=function.OUT, description =
"""
An index assigned to the newly created particle.
This index is supposed to be a local index for the code
(and not valid in other instances of the code or in other codes)
"""
)
function.addParameter('mass', dtype='float64', direction=function.IN, description = "The mass of the particle")
function.addParameter('x', dtype='float64', direction=function.IN, description = "The initial position vector of the particle")
function.addParameter('y', dtype='float64', direction=function.IN, description = "The initial position vector of the particle")
function.addParameter('z', dtype='float64', direction=function.IN, description = "The initial position vector of the particle")
function.addParameter('vx', dtype='float64', direction=function.IN, description = "The initial velocity vector of the particle")
function.addParameter('vy', dtype='float64', direction=function.IN, description = "The initial velocity vector of the particle")
function.addParameter('vz', dtype='float64', direction=function.IN, description = "The initial velocity vector of the particle")
function.addParameter('radius', dtype='float64', direction=function.IN, description = "The radius of the particle", default = 0)
function.addParameter('subset', dtype='int32', direction=function.IN, description = "The subset index of the particle (defaults to 0, use new_subset for higher indices)", default = 0)
function.result_type = 'int32'
function.result_doc = """ 0 - OK
particle was created and added to the model
-1 - ERROR
particle could not be created"""
return function
def delete_particle(self, index_of_the_particle, code_index=0):
return self._delete_particle(index_of_the_particle, code_index)
@legacy_function
def _delete_particle():
"""
Delete a particle.
"""
function = LegacyFunctionSpecification()
function.can_handle_array = True
function.addParameter('index_of_the_particle', dtype='int32', direction=function.IN, description ="Index of the particle")
function.addParameter('code_index', dtype='int32', direction=function.IN, description = "Index of the code in rebound", default = 0)
function.result_type = 'int32'
function.result_doc = """ 0 - OK
particle was deleted
-1 - ERROR
particle not deleted"""
return function
@legacy_function
def _set_integrator():
function = LegacyFunctionSpecification()
function.addParameter('integrator_name', dtype='i', direction=function.IN)
function.addParameter('code_index', dtype='int32', direction=function.IN, description = "Index of the code in rebound", default = 0)
function.result_type = 'int32'
function.can_handle_array = False
return function
@legacy_function
def _get_integrator():
function = LegacyFunctionSpecification()
function.addParameter('code_index', dtype='int32', direction=function.IN, description = "Index of the code in rebound", default = 0)
function.addParameter('integrator_name', dtype='i', direction=function.OUT)
function.result_type = 'int32'
function.can_handle_array = False
return function
INTEGRATORS = {"ias15": 0, "whfast": 1, "sei": 2, "leapfrog": 4, "hermes": 5, "whfast-helio": 6, "none": 7, "janus": 8}
def set_integrator(self, name, code_index = 0 ):
return self._set_integrator(self.INTEGRATORS[name], code_index)
def get_integrator(self, code_index = 0):
value, error = self._get_integrator(code_index)
for key, index in self.INTEGRATORS.items():
if value == index:
return key
return "none"
@legacy_function
def _set_solver():
function = LegacyFunctionSpecification()
function.addParameter('solver_name', dtype='i', direction=function.IN)
function.addParameter('code_index', dtype='int32', direction=function.IN, description = "Index of the code in rebound", default = 0)
function.result_type = 'int32'
function.can_handle_array = False
return function
@legacy_function
def _get_solver():
function = LegacyFunctionSpecification()
function.addParameter('code_index', dtype='int32', direction=function.IN, description = "Index of the code in rebound", default = 0)
function.addParameter('solver_name', dtype='i', direction=function.OUT)
function.result_type = 'int32'
function.can_handle_array = False
return function
SOLVERS = {"none": 0, "basic": 1, "compensated": 2, "tree": 3}
def set_solver(self, name, code_index = 0 ):
return self._set_solver(self.SOLVERS[name], code_index)
def get_solver(self, code_index = 0):
value, error = self._get_solver(code_index)
for key, index in self.SOLVERS.items():
if value == index:
return key
return "none"
@legacy_function
def get_opening_angle2():
function = LegacyFunctionSpecification()
function.addParameter('code_index', dtype='int32', direction=function.IN, description = "Index of the code in rebound", default = 0)
function.addParameter('opening_angle2', dtype='float64', direction=function.OUT,
description = "theta, the opening angle for building the tree: between 0 and 1")
function.result_type = 'int32'
return function
@legacy_function
def set_opening_angle2():
function = LegacyFunctionSpecification()
function.addParameter('opening_angle2', dtype='float64', direction=function.IN,
description = "theta, the opening angle for building the tree: between 0 and 1")
function.addParameter('code_index', dtype='int32', direction=function.IN, description = "Index of the code in rebound", default = 0)
function.result_type = 'int32'
return function
@legacy_function
def get_eps2():
function = LegacyFunctionSpecification()
"""
Get epsilon^2, a softening parameter for gravitational potentials with point particles.
"""
function = LegacyFunctionSpecification()
function.addParameter('code_index', dtype='int32', direction=function.IN, description = "Index of the code in rebound", default = 0)
function.addParameter('epsilon_squared', dtype='float64', direction=function.OUT,
description = "epsilon^2, a softening parameter for gravitational potentials with point particles",
unit = nbody_system.length * nbody_system.length)
function.result_type = 'int32'
function.result_doc = """
0 - OK
the parameter was retrieved
-1 - ERROR
could not retrieve parameter
"""
return function
@legacy_function
def set_eps2():
"""
Set epsilon^2, a softening parameter for gravitational potentials with point particles.
"""
function = LegacyFunctionSpecification()
function.addParameter('epsilon_squared', dtype='float64', direction=function.IN,
description = "epsilon^2, a softening parameter for gravitational potentials with point particles",
unit = nbody_system.length * nbody_system.length)
function.addParameter('code_index', dtype='int32', direction=function.IN, description = "Index of the code in rebound", default = 0)
function.result_type = 'int32'
function.result_doc = """
0 - OK
the parameter was set
-1 - ERROR
could not set parameter
"""
return function
@legacy_function
def _set_boundary():
function = LegacyFunctionSpecification()
function.addParameter('boundary_name', dtype='i', direction=function.IN)
function.addParameter('code_index', dtype='int32', direction=function.IN, description = "Index of the code in rebound", default = 0)
function.result_type = 'int32'
function.can_handle_array = False
return function
@legacy_function
def _get_boundary():
function = LegacyFunctionSpecification()
function.addParameter('code_index', dtype='int32', direction=function.IN, description = "Index of the code in rebound", default = 0)
function.addParameter('boundary_name', dtype='i', direction=function.OUT)
function.result_type = 'int32'
function.can_handle_array = False
return function
BOUNDARIES = {"none": 0, "open": 1, "periodic": 2, "shear": 3}
def set_boundary(self, name, code_index = 0 ):
return self._set_boundary(self.BOUNDARIES[name], code_index)
def get_boundary(self, code_index = 0):
value, error = self._get_boundary(code_index)
for key, index in self.BOUNDARIES.items():
if value == index:
return key
return "none"
@legacy_function
def get_boundary_size():
function = LegacyFunctionSpecification()
"""
Get the size of the boundaries.
"""
function = LegacyFunctionSpecification()
function.addParameter('code_index', dtype='int32', direction=function.IN, description = "Index of the code in rebound", default = 0)
function.addParameter('boundary_size', dtype='float64', direction=function.OUT,
description = "boundary size",
unit = nbody_system.length)
function.result_type = 'int32'
function.result_doc = """
0 - OK
the parameter was retrieved
-1 - ERROR
could not retrieve parameter
"""
return function
@legacy_function
def set_boundary_size():
"""
Set size of the boundaries.
"""
function = LegacyFunctionSpecification()
function.addParameter('boundary_size', dtype='float64', direction=function.IN,
description = "boundary size",
unit = nbody_system.length)
function.addParameter('code_index', dtype='int32', direction=function.IN, description = "Index of the code in rebound", default = 0)
function.result_type = 'int32'
function.result_doc = """
0 - OK
the parameter was set
-1 - ERROR
could not set parameter
"""
return function
@legacy_function
def set_time_step():
"""
Update timestep.
"""
function = LegacyFunctionSpecification()
function.addParameter('timestep', dtype='float64', direction=function.IN,
description = "timestep")
function.addParameter('code_index', dtype='int32', direction=function.IN, description = "Index of the code in rebound", default = 0)
function.result_type = 'int32'
function.result_doc = """
0 - OK
timestep was changed
"""
return function
@legacy_function
def get_potential_energy():
"""
Retrieve the current potential energy of the model
"""
function = LegacyFunctionSpecification()
function.addParameter('code_index', dtype='int32', direction=function.IN, description = "Index of the code in rebound", default = 0)
function.addParameter('potential_energy', dtype='float64', direction=function.OUT,
description = "The potential energy of the model")
function.result_type = 'int32'
function.result_doc = """
0 - OK
Current value of the potential energy was set
-1 - ERROR
Kinetic potential could not be provided
"""
return function
@legacy_function
def get_kinetic_energy():
"""
Retrieve the current kinetic energy of the model
"""
function = LegacyFunctionSpecification()
function.addParameter('code_index', dtype='int32', direction=function.IN, description = "Index of the code in rebound", default = 0)
function.addParameter('kinetic_energy', dtype='float64', direction=function.OUT,
description = "The kinetic energy of the model")
function.result_type = 'int32'
function.result_doc = """
0 - OK
Current value of the kinetic energy was set
-1 - ERROR
Kinetic energy could not be provided
"""
return function
@legacy_function
def evolve_model():
"""
Evolve the model until the given time, or until a stopping condition is set.
"""
function = LegacyFunctionSpecification()
function.addParameter('time', dtype='float64', direction=function.IN,
description = "Model time to evolve the code to. The model will be "
"evolved until this time is reached exactly or just after.")
function.addParameter('code_index', dtype='int32', direction=function.IN, description = "Index of the code in rebound (default -1, evolve all systems)", default = -1)
function.result_type = 'int32'
return function
@legacy_function
def get_time():
"""
Retrieve the model time. This time should be close to the end time specified
in the evolve code. Or, when a collision was detected, it will be the
model time of the collision.
"""
function = LegacyFunctionSpecification()
function.addParameter('code_index', dtype='int32', direction=function.IN, description = "Index of the code in rebound", default = 0)
function.addParameter('time', dtype='float64', direction=function.OUT,
description = "The current model time")
function.result_type = 'int32'
function.result_doc = """
0 - OK
Current value of the time was retrieved
-1 - ERROR
The code does not have support for querying the time
"""
return function
@legacy_function
def get_time_step():
"""
Retrieve the model timestep.
"""
function = LegacyFunctionSpecification()
function.addParameter('code_index', dtype='int32', direction=function.IN, description = "Index of the code in rebound", default = 0)
function.addParameter('time_step', dtype='float64', direction=function.OUT,
description = "The current model timestep")
function.result_type = 'int32'
function.result_doc = """
0 - OK
Current value of the time step was retrieved
-1 - ERROR
The code does not have support for querying the time
"""
return function
@legacy_function
def new_subset():
"""
Create a new particle subset (and corresponding code). This subset will evolve seperately from others.
"""
function = LegacyFunctionSpecification()
function.can_handle_array = True
function.addParameter('index_of_the_subset', dtype='int32', direction=function.OUT, description =
"""
An index assigned to the newly created subset
"""
)
function.addParameter('time_offset', dtype='float64', direction=function.IN, description = "Time of the system (defaults to the current model time)", default = -1)
function.result_type = 'int32'
function.result_doc = """ 0 - OK
code was created
-1 - ERROR
code could not be created"""
return function
@legacy_function
def get_state():
"""
Retrieve the current state of a particle. The *minimal* information of a stellar
dynamics particle (mass, radius, position and velocity) is returned.
"""
function = LegacyFunctionSpecification()
function.can_handle_array = True
function.addParameter('index_of_the_particle', dtype='int32', direction=function.IN,
description = "Index of the particle to get the state from. This index must have been returned by an earlier call to :meth:`new_particle`")
function.addParameter('mass', dtype='float64', direction=function.OUT, description = "The current mass of the particle")
function.addParameter('x', dtype='float64', direction=function.OUT, description = "The current position vector of the particle")
function.addParameter('y', dtype='float64', direction=function.OUT, description = "The current position vector of the particle")
function.addParameter('z', dtype='float64', direction=function.OUT, description = "The current position vector of the particle")
function.addParameter('vx', dtype='float64', direction=function.OUT, description = "The current velocity vector of the particle")
function.addParameter('vy', dtype='float64', direction=function.OUT, description = "The current velocity vector of the particle")
function.addParameter('vz', dtype='float64', direction=function.OUT, description = "The current velocity vector of the particle")
function.addParameter('radius', dtype='float64', direction=function.OUT, description = "The current radius of the particle")
function.addParameter('subset', dtype='int32', direction=function.OUT, description = "The current subset of the particle")
function.result_type = 'int32'
function.result_doc = """
0 - OK
particle was removed from the model
-1 - ERROR
particle could not be found
"""
return function
@legacy_function
def get_subset():
"""
Retrieve the subset index of a particle.
"""
function = LegacyFunctionSpecification()
function.addParameter('index_of_the_particle', dtype='int32', direction=function.IN,
description = "Index of the particle to get the subset of. This index must have been returned by an earlier call to :meth:`new_particle`")
function.addParameter('subset', dtype='int32', direction=function.OUT, description = "The current subset of the particle")
function.result_type = 'int32'
function.can_handle_array = True
function.result_doc = """
0 - OK
particle was found in the model and the information was retreived
-1 - ERROR
particle could not be found
"""
return function
@legacy_function
def stop_subset():
"""
Stop a subset code
"""
function = LegacyFunctionSpecification()
function.can_handle_array = True
function.addParameter('index_of_the_subset', dtype='int32', direction=function.IN, description =
"""
An index assigned to an existing subset
"""
)
function.result_type = 'int32'
function.result_doc = """ 0 - OK
subset evolving was stopped
-1 - ERROR
subset evolving was already stopped"""
return function
@legacy_function
def set_subset():
"""
Retrieve the subset index of a particle.
"""
function = LegacyFunctionSpecification()
function.addParameter('index_of_the_particle', dtype='int32', direction=function.IN,
description = "Index of the particle to get the subset of. This index must have been returned by an earlier call to :meth:`new_particle`")
function.addParameter('subset', dtype='int32', direction=function.IN, description = "The new subset of the particle, as this is actually read only this will fail if changed!")
function.result_type = 'int32'
function.can_handle_array = True
function.result_doc = """
0 - OK
particle was found in the model and the information was retreived
-1 - ERROR
particle could not be found
"""
return function
class Rebound(GravitationalDynamics, GravityFieldCode):
__interface__ = ReboundInterface
def __init__(self, convert_nbody = None, **options):
self.stopping_conditions = StoppingConditions(self)
legacy_interface = self.__interface__(**options)
self.legacy_doc = legacy_interface.__doc__
GravitationalDynamics.__init__(
self,
legacy_interface,
convert_nbody,
**options
)
def define_state(self, handler):
GravitationalDynamics.define_state(self, handler)
#GravityFieldCode.define_state(self, handler)
self.stopping_conditions.define_state(handler)
handler.add_method('EDIT', 'new_subset')
handler.add_method('RUN', 'new_subset')
def define_parameters(self, handler):
self.stopping_conditions.define_parameters(handler)
GravitationalDynamics.define_parameters(self, handler)
handler.add_method_parameter(
"get_time_step",
"set_time_step",
"timestep",
"constant timestep for iteration",
default_value = 0.0001 | nbody_system.time
)
handler.add_method_parameter(
"get_integrator",
"set_integrator",
"integrator",
"name of the integrator to use ({0})".format(sorted(self.INTEGRATORS.keys())),
default_value = "ias15"
)
handler.add_method_parameter(
"get_solver",
"set_solver",
"solver",
"name of the gravity solver to use ({0})".format(sorted(self.SOLVERS.keys())),
default_value = "compensated"
)
handler.add_method_parameter(
"get_eps2",
"set_eps2",
"epsilon_squared",
"smoothing parameter for gravity calculations",
default_value = 0.0 | nbody_system.length * nbody_system.length
)
handler.add_method_parameter(
"get_opening_angle2",
"set_opening_angle2",
"opening_angle2",
"opening angle, theta, for building the tree in case of tree solver: between 0 and 1",
default_value = 0.5
)
handler.add_method_parameter(
"get_boundary",
"set_boundary",
"boundary",
"name of the boundary type to use ({0}) (required for tree solver)".format(sorted(self.BOUNDARIES.keys())),
default_value = "none"
)
handler.add_method_parameter(
"get_boundary_size",
"set_boundary_size",
"boundary_size",
"size of the boundaries, if the type is not none",
default_value = 1.0 | nbody_system.length
)
def define_methods(self, handler):
GravitationalDynamics.define_methods(self, handler)
handler.add_method(
"new_particle",
(
nbody_system.mass,
nbody_system.length,
nbody_system.length,
nbody_system.length,
nbody_system.speed,
nbody_system.speed,
nbody_system.speed,
nbody_system.length,
handler.NO_UNIT,
),
(
handler.INDEX,
handler.ERROR_CODE,
)
)
handler.add_method(
"get_potential_energy",
(handler.INDEX,),
(nbody_system.mass * nbody_system.length ** 2 * nbody_system.time ** -2, handler.ERROR_CODE,)
)
handler.add_method(
"get_kinetic_energy",
(handler.INDEX,),
(nbody_system.mass * nbody_system.length ** 2 * nbody_system.time ** -2, handler.ERROR_CODE,)
)
handler.add_method(
'evolve_model',
(
nbody_system.time,
handler.INDEX
),
(
handler.ERROR_CODE,
)
)
handler.add_method(
'get_time',
(handler.INDEX,),
(nbody_system.time, handler.ERROR_CODE,)
)
handler.add_method(
"get_time_step",
(handler.INDEX,),
(nbody_system.time, handler.ERROR_CODE,)
)
handler.add_method(
"set_time_step",
(nbody_system.time, handler.INDEX,),
(handler.ERROR_CODE,)
)
handler.add_method(
"get_state",
(
handler.NO_UNIT,
),
(
nbody_system.mass,
nbody_system.length,
nbody_system.length,
nbody_system.length,
nbody_system.speed,
nbody_system.speed,
nbody_system.speed,
nbody_system.length,
handler.NO_UNIT,
handler.ERROR_CODE
)
)
handler.add_method(
"get_subset",
(
handler.NO_UNIT,
),
(
handler.NO_UNIT,
handler.ERROR_CODE
)
)
handler.add_method(
"set_subset",
(
handler.NO_UNIT,
handler.NO_UNIT,
),
(
handler.ERROR_CODE,
)
)
handler.add_method(
'new_subset',
(
nbody_system.time,
),
(
handler.INDEX,
handler.ERROR_CODE,
)
)
self.stopping_conditions.define_methods(handler)
def define_particle_sets(self, handler):
GravitationalDynamics.define_particle_sets(self, handler)
self.stopping_conditions.define_particle_set(handler)
handler.add_getter('particles', 'get_subset')
handler.add_setter('particles', 'set_subset')
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ports/esp32/modules/linefinder.py | AITT-VN/micropython | 0 | 135717 | from setting import *
from machine import Pin
S1_IN_S2_OUT = const(0)
S1_OUT_S2_IN = const(1)
S1_OUT_S2_OUT = const(2)
S1_IN_S2_IN = const(3)
class LineFinder:
def __init__(self):
pass
def _reset_port(self, port):
if port < 0 or port > 5:
print('Port not supported')
self.port = port
self.left_pin = Pin(PORTS_DIGITAL[port][0], mode=Pin.IN, pull=None)
self.right_pin = Pin(PORTS_DIGITAL[port][1], mode=Pin.IN, pull=None)
def read(self, port):
if port != self.port:
self._reset_port(port)
if self.left_pin.value() and not self.right_pin.value():
# detect line on left side
return S1_IN_S2_OUT
elif not self.left_pin.value() and self.right_pin.value():
# detect line on right side
return S1_OUT_S2_IN
elif not self.left_pin.value() and not self.right_pin.value():
# detect line on none side
return S1_OUT_S2_OUT
elif self.left_pin.value() and self.right_pin.value():
# detect line on both side
return S1_IN_S2_IN
else:
# detect error
return -1
line_finder = LineFinder() | [
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python/python_challenge/31/31.py | yunyu2019/blog | 0 | 92475 | #!/usr/bin/python
# -*- coding: utf-8 -*-
# @Date : 2016-05-23 18:29:57
# @Author : Yunyu2019 (<EMAIL>)
# @Link : http://www.pythonchallenge.com/pc/ring/grandpa.html
#user:'kohsamui'
#password:'<PASSWORD>'
import Image
import requests
"""
req=requests.get('http://www.pythonchallenge.com/pc/rock/mandelbrot.gif',auth=('kohsamui','thailand'))
fp=open('mandelbrot.gif','wb')
fp.write(req.content)
fp.close()
"""
imgbase = Image.open('mandelbrot.gif')
img = imgbase.copy()
w,h=imgbase.size
left = 0.34
top = 0.57+0.027
width = 0.036
height = 0.027
nums = 128
diff = []
for j in range(h):
for i in range(w):
point0 = complex(left + i*width/w, top - (1+j)*height/h)
point = 0+0j
for k in range(nums):
point = point **2 + point0
if point.imag**2+point.real**2>4:
break
img.putpixel((i,j),k)
s=imgbase.getpixel((i,j))
if k!=s:
diff.append(k - s)
img.save('output.png')
img2 = Image.new('1',(23,73))
data=[i<0 for i in diff]
img2.putdata(data)
img2.save('output1.png')
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app/__init__.py | jimmybutton/moviedb | 0 | 2989 | from flask import Flask
from config import Config
from sqlalchemy import MetaData
from flask_sqlalchemy import SQLAlchemy
from flask_migrate import Migrate
from flask_login import LoginManager
from flask_moment import Moment
from flask_misaka import Misaka
from flask_bootstrap import Bootstrap
import os
import logging
from logging.handlers import RotatingFileHandler
from elasticsearch import Elasticsearch
convention = {
"ix": 'ix_%(column_0_label)s',
"uq": "uq_%(table_name)s_%(column_0_name)s",
"ck": "ck_%(table_name)s_%(constraint_name)s",
"fk": "fk_%(table_name)s_%(column_0_name)s_%(referred_table_name)s",
"pk": "pk_%(table_name)s"
}
metadata = MetaData(naming_convention=convention)
db = SQLAlchemy(metadata=metadata)
migrate = Migrate()
login = LoginManager()
login.login_view = "auth.login"
moment = Moment()
md = Misaka()
bootstrap = Bootstrap()
def create_app(config_class=Config):
app = Flask(__name__)
app.config.from_object(Config)
db.init_app(app)
with app.app_context():
if db.engine.url.drivername == 'sqlite':
migrate.init_app(app, db, render_as_batch=True)
else:
migrate.init_app(app, db)
# migrate.init_app(app, db)
login.init_app(app)
moment.init_app(app)
md.init_app(app)
bootstrap.init_app(app)
from app.errors import bp as errors_bp
app.register_blueprint(errors_bp)
from app.auth import bp as auth_bp
app.register_blueprint(auth_bp, url_prefix='/auth')
from app.main import bp as main_bp
app.register_blueprint(main_bp)
from app.cli import bp as cli_bp
app.register_blueprint(cli_bp)
app.elasticsearch = Elasticsearch([app.config['ELASTICSEARCH_URL']]) \
if app.config['ELASTICSEARCH_URL'] else None
from app import models
if not app.debug and not app.testing:
if not os.path.exists("logs"):
os.mkdir("logs")
file_handler = RotatingFileHandler(
"logs/moviedb.log", maxBytes=10240, backupCount=10
)
file_handler.setFormatter(
logging.Formatter(
"%(asctime)s %(levelname)s: %(message)s [in %(pathname)s:%(lineno)d]"
)
)
file_handler.setLevel(logging.INFO)
app.logger.addHandler(file_handler)
app.logger.setLevel(logging.INFO)
app.logger.info("Moviedb startup")
return app
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run.py | mcarans/hdx-scraper-unesco | 0 | 66563 | <reponame>mcarans/hdx-scraper-unesco<filename>run.py
#!/usr/bin/python
# -*- coding: utf-8 -*-
"""
Top level script. Calls other functions that generate datasets that this script then creates in HDX.
"""
import logging
from os.path import join, expanduser
from timeit import default_timer
from hdx.hdx_configuration import Configuration
from hdx.utilities.downloader import Download
from hdx.utilities.path import temp_dir
from unesco import generate_dataset_and_showcase, get_countriesdata, get_endpoints_metadata
from hdx.facades.simple import facade
logger = logging.getLogger(__name__)
lookup = 'hdx-scraper-unesco'
def main():
"""Generate dataset and create it in HDX"""
base_url = Configuration.read()['base_url']
with temp_dir('UNESCO') as folder:
with Download(extra_params_yaml=join(expanduser('~'), '.extraparams.yml'), extra_params_lookup=lookup) as downloader:
endpoints = Configuration.read()['endpoints']
endpoints_metadata = get_endpoints_metadata(base_url, downloader, endpoints)
countriesdata = get_countriesdata(base_url, downloader)
logger.info('Number of datasets to upload: %d' % len(countriesdata))
for countrydata in countriesdata:
for dataset, showcase in generate_dataset_and_showcase(downloader, countrydata, endpoints_metadata, folder=folder, merge_resources=True, single_dataset=False): # TODO: fix folder
if dataset:
dataset.update_from_yaml()
start = default_timer()
dataset.create_in_hdx(remove_additional_resources=True, hxl_update=False)
print("total time = %d" % (default_timer() - start))
resources = dataset.get_resources()
resource_ids = [x['id'] for x in sorted(resources, key=lambda x: x['name'], reverse=False)]
dataset.reorder_resources(resource_ids, hxl_update=False)
showcase.create_in_hdx()
showcase.add_dataset(dataset)
if __name__ == '__main__':
facade(main, user_agent_config_yaml=join(expanduser('~'), '.useragents.yml'), user_agent_lookup=lookup, project_config_yaml=join('config', 'project_configuration.yml'))
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dist/assets/rename.py | shubham-shinde/Apstract_poker | 0 | 57870 | <filename>dist/assets/rename.py<gh_stars>0
# Pythono3 code to rename multiple
# files in a directory or folder
# importing os module
import os
# Function to rename multiple files
def main():
i = 0
for filename in os.listdir("Cards"):
# dst ="Hostel" + str(i) + ".jpg"
src ='Cards/'+ filename
dst ='Cards/'+ filename[6: ]
print(src)
# rename() function will
# rename all the files
os.rename(src, dst)
i += 1
# Driver Code
if __name__ == '__main__':
# Calling main() function
main() | [
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ckanext/ckanext-basiccharts/ckanext/basiccharts/tests/test_view.py | cascaoSDC/ckan | 0 | 142337 | <filename>ckanext/ckanext-basiccharts/ckanext/basiccharts/tests/test_view.py
import os
import inspect
import mock
import nose.tools
import pylons.config as config
import ckan.plugins as p
import ckanext.basiccharts
class TestBaseChart(object):
@classmethod
def setup_class(cls):
cls.plugin = ckanext.basiccharts.plugin.BaseChart()
@nose.tools.raises(p.PluginNotFoundException)
def test_plugin_cant_be_loaded(self):
p.load('basechart')
def test_chart_type(self):
assert self.plugin.CHART_TYPE == 'base', '"CHART_TYPE" should be "base"'
def test_group_by_is_required(self):
assert self.plugin.GROUP_BY_IS_REQUIRED == False, '"GROUP_BY_IS_REQUIRED" should be false'
def test_plugin_templates_path_is_added_to_config(self):
filename = inspect.getfile(inspect.currentframe())
path = os.path.dirname(filename)
templates_path = os.path.abspath(path + "/../theme/templates")
assert templates_path in config['extra_template_paths'], templates_path
def test_can_view_only_if_datastore_is_active(self):
active_datastore_data_dict = {
'resource': { 'datastore_active': True }
}
inactive_datastore_data_dict = {
'resource': { 'datastore_active': False }
}
assert self.plugin.can_view(active_datastore_data_dict)
assert not self.plugin.can_view(inactive_datastore_data_dict)
def test_schema_exists(self):
schema = self.plugin.info()['schema']
assert schema is not None, 'Plugin should define schema'
def test_schema_has_y_axis(self):
schema = self.plugin.info()['schema']
assert schema.get('y_axis') is not None, 'Schema should define "y_axis"'
def test_schema_y_axis_is_required(self):
schema = self.plugin.info()['schema']
not_empty = p.toolkit.get_validator('not_empty')
assert not_empty in schema['y_axis'], '"y_axis" should be required'
def test_schema_has_group_by(self):
schema = self.plugin.info()['schema']
assert schema.get('group_by') is not None, 'Schema should define "group_by"'
def test_schema_group_by_doesnt_validate_if_group_by_isnt_required(self):
original_value = self.plugin.GROUP_BY_IS_REQUIRED
self.plugin.GROUP_BY_IS_REQUIRED = False
schema = self.plugin.info()['schema']
self.plugin.GROUP_BY_IS_REQUIRED = original_value
ignore_missing = p.toolkit.get_validator('ignore_missing')
assert ignore_missing in schema['group_by'], '"group_by" should ignore missing'
def test_schema_group_by_is_required_if_group_by_is_required(self):
original_value = self.plugin.GROUP_BY_IS_REQUIRED
self.plugin.GROUP_BY_IS_REQUIRED = True
schema = self.plugin.info()['schema']
self.plugin.GROUP_BY_IS_REQUIRED = original_value
not_empty = p.toolkit.get_validator('not_empty')
assert not_empty in schema['group_by'], '"group_by" should be required'
def test_schema_has_show_legends(self):
schema = self.plugin.info()['schema']
assert schema.get('show_legends') is not None, 'Schema should define "show_legends"'
def test_schema_show_legends_doesnt_validate(self):
schema = self.plugin.info()['schema']
ignore_missing = p.toolkit.get_validator('ignore_missing')
assert ignore_missing in schema['show_legends'], '"show_legends" should ignore missing'
def test_plugin_isnt_iframed(self):
iframed = self.plugin.info().get('iframed', True)
assert not iframed, 'Plugin should not be iframed'
def test_plugin_is_filterable(self):
filterable = self.plugin.info().get('filterable', False)
assert filterable, 'Plugin should be filterable'
@mock.patch('ckan.plugins.toolkit.get_action')
def test_setup_template_variables_adds_resource(self, _):
resource = {
'id': 'resource_id',
'other_attribute': 'value'
}
template_variables = self._setup_template_variables(resource)
assert 'resource' in template_variables
assert template_variables['resource'] == resource
@mock.patch('ckan.plugins.toolkit.get_action')
def test_setup_template_variables_adds_resource_view(self, _):
resource_view = {
'id': 'resource_id',
'other_attribute': 'value'
}
template_variables = \
self._setup_template_variables(resource_view=resource_view)
assert 'resource_view' in template_variables
assert template_variables['resource_view'] == resource_view
@mock.patch('ckan.plugins.toolkit.get_action')
def test_setup_template_variables_adds_show_legends_as_true_if_it_was_defined(self, _):
template_variables = self._setup_template_variables(resource_view={'show_legends': 'True'})
show_legends = template_variables['resource_view']['show_legends']
assert show_legends is True, show_legends
@mock.patch('ckan.plugins.toolkit.get_action')
def test_setup_template_variables_adds_show_legends_as_false_if_it_was_undefined(self, _):
template_variables = self._setup_template_variables(resource_view={})
show_legends = template_variables['resource_view']['show_legends']
assert show_legends is False, show_legends
@mock.patch('ckan.plugins.toolkit.get_action')
def test_setup_template_variables_adds_fields_without_the_id(self, get_action):
fields = [
{'id': '_id', 'type': 'int4'},
{'id': 'price', 'type': 'numeric'},
]
expected_fields = [{'value': 'price'}]
get_action.return_value.return_value = {
'fields': fields,
'records': {}
}
template_variables = self._setup_template_variables()
returned_fields = template_variables.get('fields')
assert returned_fields is not None
assert returned_fields == expected_fields
@mock.patch('ckan.plugins.toolkit.get_action')
def test_setup_template_variables_adds_chart_type(self, _):
template_variables = self._setup_template_variables()
assert 'chart_type' in template_variables
assert template_variables['chart_type'] == self.plugin.CHART_TYPE
@mock.patch('ckan.plugins.toolkit.get_action')
def test_setup_template_variables_defines_group_by_is_required_correctly(self, _):
template_variables = self._setup_template_variables()
assert 'group_by_is_required' in template_variables
assert template_variables['group_by_is_required'] == self.plugin.GROUP_BY_IS_REQUIRED
def _setup_template_variables(self, resource={'id': 'id'}, resource_view={}):
context = {}
data_dict = {
'resource': resource,
'resource_view': resource_view
}
return self.plugin.setup_template_variables(context, data_dict)
class TestLineChart(TestBaseChart):
@classmethod
def setup_class(cls):
p.load('linechart')
cls.plugin = p.get_plugin('linechart')
@classmethod
def teardown_class(cls):
p.unload('linechart')
def test_schema_has_x_axis(self):
schema = self.plugin.info()['schema']
assert schema.get('x_axis') is not None, 'Schema should define "x_axis"'
def test_schema_x_axis_is_required(self):
schema = self.plugin.info()['schema']
not_empty = p.toolkit.get_validator('not_empty')
assert not_empty in schema['x_axis'], '"x_axis" should be required'
def test_chart_type(self):
assert self.plugin.CHART_TYPE == 'lines', '"CHART_TYPE" should be "lines"'
class TestBarChart(TestLineChart):
@classmethod
def setup_class(cls):
p.load('barchart')
cls.plugin = p.get_plugin('barchart')
@classmethod
def teardown_class(cls):
p.unload('barchart')
def test_schema_has_horizontal(self):
schema = self.plugin.info()['schema']
assert schema.get('horizontal') is not None, 'Schema should define "horizontal"'
def test_schema_horizontal_doesnt_validate(self):
schema = self.plugin.info()['schema']
ignore_missing = p.toolkit.get_validator('ignore_missing')
assert ignore_missing in schema['horizontal'], '"horizontal" should ignore missing'
@mock.patch('ckan.plugins.toolkit.get_action')
def test_setup_template_variables_adds_horizontal_as_true_if_it_was_defined(self, _):
template_variables = self._setup_template_variables(resource_view={'horizontal': 'True'})
horizontal = template_variables['resource_view']['horizontal']
assert horizontal is True, horizontal
@mock.patch('ckan.plugins.toolkit.get_action')
def test_setup_template_variables_adds_horizontal_as_false_if_it_was_undefined(self, _):
template_variables = self._setup_template_variables(resource_view={})
horizontal = template_variables['resource_view']['horizontal']
assert horizontal is False, horizontal
def test_chart_type(self):
assert self.plugin.CHART_TYPE == 'bars', '"CHART_TYPE" should be "bars"'
class TestPieChart(TestBaseChart):
@classmethod
def setup_class(cls):
p.load('piechart')
cls.plugin = p.get_plugin('piechart')
@classmethod
def teardown_class(cls):
p.unload('piechart')
def test_chart_type(self):
assert self.plugin.CHART_TYPE == 'pie', '"CHART_TYPE" should be "pie"'
def test_group_by_is_required(self):
assert self.plugin.GROUP_BY_IS_REQUIRED == True, '"GROUP_BY_IS_REQUIRED" should be true'
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3235,
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1525,
29984,
3120,
19386,
29908,
881,
367,
1565,
29915,
13,
2
] |
at/abc119/a.py | xsthunder/a | 1 | 103439 | <filename>at/abc119/a.py
s = input()
y, m, d = map(int, s.split('/'))
f = False
(
Heisei,
TBD,
)= (
'Heisei',
'TBD',
)
if y < 2019:
print(Heisei)
elif y == 2019:
if(m < 4):
print(Heisei)
elif m == 4:
if(d <= 30):
print(Heisei)
else :
print(TBD)
else :
print(TBD)
else :
print(TBD)
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1596,
29898,
24895,
29928,
29897,
13,
2
] |
snypy/core/rest/permissions.py | snypy/snypy-backend | 0 | 154911 | <reponame>snypy/snypy-backend
from rest_framework.permissions import DjangoModelPermissions
class BaseModelPermissions(DjangoModelPermissions):
perms_map = {
"GET": ["%(app_label)s.view_%(model_name)s"],
"OPTIONS": [],
"HEAD": [],
"POST": ["%(app_label)s.add_%(model_name)s"],
"PUT": ["%(app_label)s.change_%(model_name)s"],
"PATCH": ["%(app_label)s.change_%(model_name)s"],
"DELETE": ["%(app_label)s.delete_%(model_name)s"],
}
def has_object_permission(self, request, view, obj):
has_permission = super().has_permission(request, view)
if has_permission and view.action == "retrieve":
return self._queryset(view).viewable().filter(pk=obj.pk).exists()
if has_permission and view.action == "update":
return self._queryset(view).editable().filter(pk=obj.pk).exists()
if has_permission and view.action == "partial_update":
return self._queryset(view).editable().filter(pk=obj.pk).exists()
if has_permission and view.action == "destroy":
return self._queryset(view).deletable().filter(pk=obj.pk).exists()
return False
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] |
insertion_sort.py | undersonrs/PYTHON_Projetos | 0 | 171752 | <reponame>undersonrs/PYTHON_Projetos<filename>insertion_sort.py
def insertion_sort(v):
for i in range (1, len(v)):
x = v[i]
j = i-1
while j>=0 and x< v[j]:
v[j+1] = v[j]
j -= 1
v[j+1] = x
print()
b = [8,3,9,2,1,10,7,5,4,6]
print(b)
print()
a = insertion_sort(b)
print()
a = b
print(a)
print()
| [
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Numbers/from e to the nth digit/e.py | Shetty073/Solutions-for-Mega-Project-List | 0 | 120257 | from math import e
def pi_dec(n):
if n > 15 or n < 1:
print("\nPlease provide a valid number in range of 1 to 15...")
else:
print(f"E = {str(e)[0:n+2]}")
pi_dec(n=int(input("\nUp to how many decimal places do you want the value of 'e'? \n> "))) | [
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project/starter_code/student_utils.py | nihaagarwalla/nd320-c1-emr-data-starter | 0 | 6935 | <reponame>nihaagarwalla/nd320-c1-emr-data-starter<filename>project/starter_code/student_utils.py
import pandas as pd
import numpy as np
import os
import tensorflow as tf
import functools
####### STUDENTS FILL THIS OUT ######
#Question 3
def reduce_dimension_ndc(df, ndc_df):
'''
df: pandas dataframe, input dataset
ndc_df: pandas dataframe, drug code dataset used for mapping in generic names
return:
df: pandas dataframe, output dataframe with joined generic drug name
'''
ndc_df["Non-proprietary Name"]= ndc_df["Non-proprietary Name"].str.replace("Hcl", "Hydrochloride")
ndc_df["Non-proprietary Name"]= ndc_df["Non-proprietary Name"].str.replace(" And ", "-")
ndc_df["Non-proprietary Name"]= (ndc_df["Non-proprietary Name"].str.strip()).str.upper()
# ndc_df["Dosage Form"]= ndc_df["Dosage Form"].str.replace("Tablet, Film Coated", "TABLET")
# ndc_df["Dosage Form"]= ndc_df["Dosage Form"].str.replace("Tablet, Coated", "TABLET")
# ndc_df["Dosage Form"]= ndc_df["Dosage Form"].str.replace("Tablet, Film Coated, Extended Release", "Tablet Extended Release")
# ndc_df["Dosage Form"]= ndc_df["Dosage Form"].str.replace("Tablet, Extended Release", "Tablet Extended Release")
# ndc_df["Dosage Form"]= ndc_df["Dosage Form"].str.replace("For Suspension, Extended Release", "For Suspension Extended Release")
# ndc_df["Dosage Form"]= ndc_df["Dosage Form"].str.replace("Powder, Metered", "Powder Metered")
# ndc_df["Dosage Form"]= (ndc_df["Dosage Form"].str.strip()).str.upper()
# ndc_df["generic_drug_name"]= ndc_df["Non-proprietary Name"]+"_"+ndc_df["Dosage Form"]
ndc_df["generic_drug_name"]= ndc_df["Non-proprietary Name"]
df_reduce_dimension = pd.merge(df, ndc_df, on=['ndc_code'], how='inner')
df_reduce_dimension['LABEL'] = 0
reduce_dim_df= df_reduce_dimension.drop(columns=['Proprietary Name', 'Non-proprietary Name', 'Dosage Form', 'Route Name', 'Company Name', 'Product Type'])
return reduce_dim_df
#Question 4
def select_first_encounter(df):
'''
df: pandas dataframe, dataframe with all encounters
return:
- first_encounter_df: pandas dataframe, dataframe with only the first encounter for a given patient
'''
first_encounter_df = df.sort_values('encounter_id').groupby('patient_nbr').first()
first_encounter_df = first_encounter_df.reset_index()
return first_encounter_df
#Question 6
def patient_dataset_splitter(df, key='patient_nbr'):
'''
df: pandas dataframe, input dataset that will be split
patient_key: string, column that is the patient id
return:
- train: pandas dataframe,
- validation: pandas dataframe,
- test: pandas dataframe,
'''
df = df.iloc[np.random.permutation(len(df))]
unique_values = df[key].unique()
total_values = len(unique_values)
train_size = round(total_values * (1 - 0.4 ))
train = df[df[key].isin(unique_values[:train_size])].reset_index(drop=True)
left_size = len(unique_values[train_size:])
validation_size = round(left_size*0.5)
validation = df[df[key].isin(unique_values[train_size:train_size+validation_size])].reset_index(drop=True)
test = df[df[key].isin(unique_values[validation_size+train_size:])].reset_index(drop=True)
return train, validation, test
#Question 7
def create_tf_categorical_feature_cols(categorical_col_list,
vocab_dir='./diabetes_vocab/'):
'''
categorical_col_list: list, categorical field list that will be transformed with TF feature column
vocab_dir: string, the path where the vocabulary text files are located
return:
output_tf_list: list of TF feature columns
'''
output_tf_list = []
for c in categorical_col_list:
vocab_file_path = os.path.join(vocab_dir, c + "_vocab.txt")
'''
Which TF function allows you to read from a text file and create a categorical feature
You can use a pattern like this below...
tf_categorical_feature_column = tf.feature_column.......
'''
tf_categorical_feature_column = tf.feature_column.categorical_column_with_vocabulary_file(
key=c, vocabulary_file = vocab_file_path, num_oov_buckets=1)
one_hot_origin_feature = tf.feature_column.indicator_column(tf_categorical_feature_column)
output_tf_list.append(one_hot_origin_feature)
return output_tf_list
#Question 8
def normalize_numeric_with_zscore(col, mean, std):
'''
This function can be used in conjunction with the tf feature column for normalization
'''
return (col - mean)/std
def create_tf_numeric_feature(col, MEAN, STD, default_value=0):
'''
col: string, input numerical column name
MEAN: the mean for the column in the training data
STD: the standard deviation for the column in the training data
default_value: the value that will be used for imputing the field
return:
tf_numeric_feature: tf feature column representation of the input field
'''
normalizer = functools.partial(normalize_numeric_with_zscore, mean=MEAN, std=STD)
tf_numeric_feature= tf.feature_column.numeric_column(
key=col, default_value = default_value, normalizer_fn=normalizer, dtype=tf.float64)
return tf_numeric_feature
#Question 9
def get_mean_std_from_preds(diabetes_yhat):
'''
diabetes_yhat: TF Probability prediction object
'''
m = diabetes_yhat.mean()
s = diabetes_yhat.stddev()
return m, s
# Question 10
def get_student_binary_prediction(df, col):
'''
df: pandas dataframe prediction output dataframe
col: str, probability mean prediction field
return:
student_binary_prediction: pandas dataframe converting input to flattened numpy array and binary labels
def convert_to_binary(df, pred_field, actual_field):
df['score'] = df[pred_field].apply(lambda x: 1 if x>=25 else 0 )
df['label_value'] = df[actual_field].apply(lambda x: 1 if x>=25 else 0)
return df
binary_df = convert_to_binary(model_output_df, 'pred', 'actual_value')
binary_df.head()
'''
return student_binary_prediction
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] |
autofit/graphical/factor_graphs/factor.py | vishalbelsare/PyAutoFit | 0 | 72424 | <filename>autofit/graphical/factor_graphs/factor.py<gh_stars>0
from abc import ABC
from functools import lru_cache
from inspect import getfullargspec
from itertools import chain, repeat
from typing import \
(
Tuple, Dict, Union, Set, Callable, List
)
import numpy as np
from autofit.graphical.factor_graphs.abstract import \
(
AbstractNode, FactorValue
)
from autofit.graphical.utils import \
(
aggregate, Axis, cached_property
)
from autofit.mapper.variable import Variable
class AbstractFactor(AbstractNode, ABC):
def __init__(
self,
name="",
**kwargs: Variable,
):
super().__init__(**kwargs)
self._name = name or f"factor_{self.id}"
self._deterministic_variables = set()
@property
def deterministic_variables(self) -> Set[Variable]:
return self._deterministic_variables
@property
def name(self) -> str:
return self._name
def __mul__(self, other):
"""
When two factors are multiplied together this creates a graph
"""
from autofit.graphical.factor_graphs.graph import FactorGraph
return FactorGraph([self]) * other
@property
def variables(self) -> Set[Variable]:
"""
Dictionary mapping the names of variables to those variables
"""
return set(self._kwargs.values())
@property
def _kwargs_dims(self) -> Dict[str, int]:
"""
The number of plates for each keyword argument variable
"""
return {
key: len(value)
for key, value
in self._kwargs.items()
}
@cached_property
def _variable_plates(self) -> Dict[str, np.ndarray]:
"""
Maps the name of each variable to the indices of its plates
within this node
"""
return {
variable: self._match_plates(
variable.plates
)
for variable
in self.all_variables
}
@property
def n_deterministic(self) -> int:
"""
How many deterministic variables are there associated with this node?
"""
return len(self._deterministic_variables)
def __hash__(self):
return hash((type(self), self.id))
def _resolve_args(
self,
**kwargs: np.ndarray
) -> dict:
"""
Transforms in the input arguments to match the arguments
specified for the factor.
Parameters
----------
args
kwargs
Returns
-------
"""
return {n: kwargs[v.name] for n, v in self._kwargs.items()}
class Factor(AbstractFactor):
"""
A node in a graph representing a factor with analytic evaluation
of its Jacobian
Parameters
----------
factor
the function being wrapped, must accept calls through keyword
argument
name: optional, str
the name of the factor, if not passed then uses the name
of the function passed
vectorised: optional, bool
if true the factor will call the function directly over multiple
inputs. If false the factor will call the function iteratively
over each argument.
is_scalar: optional, bool
if true the factor returns a scalar value. Note if multiple arguments
are passed then a vector will still be returned
kwargs: Variables
Variables for each keyword argument for the function
Methods
-------
__call__({x: x0}, axis=axis)
calls the factor, the passed input must be a dictionary with
where the keys are the Variable objects that the function takes
as input. The Variable keys only have to match the _names_
of the variables of the function.
`axis` controls the shape of the output if the variables and factor have
plates associated with them, when axis=False then no reduction is performed,
otherwise it is equivalent to calling np.sum(log_val, axis=axis) on the
returned value
returns a FactorValue object which behaves like an np.ndarray
func_jacobian({x: x0}, variables=(x,), axis=axis)
calls the factor and returns it value and the jacobian of its value
with respect to the `variables` passed. if variables is None then
it returns the jacobian with respect to all variables.
returns fval, {x: d fval / dx}
"""
def __init__(
self,
factor: Callable,
name="",
vectorised=False,
is_scalar=False,
**kwargs: Variable
):
"""
A node in a graph representing a factor
Parameters
----------
factor
A wrapper around some callable
args
Variables representing positional arguments for the function
kwargs
Variables representing keyword arguments for the function
"""
self.vectorised = vectorised
self.is_scalar = is_scalar
self._set_factor(factor)
args = getfullargspec(self._factor).args
kwargs = {
**kwargs,
**{
arg: Variable(arg)
for arg
in args
if arg not in kwargs and arg != "self"
}
}
super().__init__(
**kwargs,
name=name or factor.__name__
)
def _set_factor(self, factor):
self._factor = factor
self._has_exact_projection = getattr(
factor, 'has_exact_projection', None)
self._calc_exact_projection = getattr(
factor, 'calc_exact_projection', None)
self._calc_exact_update = getattr(
factor, 'calc_exact_update', None)
def has_exact_projection(self, mean_field) -> bool:
if self._has_exact_projection:
return self._has_exact_projection(
**self.resolve_variable_dict(mean_field))
else:
return False
def calc_exact_projection(self, mean_field) -> 'MeanField':
if self._calc_exact_projection:
from autofit.graphical.mean_field import MeanField
projection = self._calc_exact_projection(
**self.resolve_variable_dict(mean_field))
return MeanField({
self._kwargs[v]: dist for v, dist in projection.items()
})
else:
return NotImplementedError
def calc_exact_update(self, mean_field) -> 'MeanField':
if self._calc_exact_update:
from autofit.graphical.mean_field import MeanField
projection = self._calc_exact_update(
**self.resolve_variable_dict(mean_field))
return MeanField({
self._kwargs[v]: dist for v, dist in projection.items()
})
else:
return NotImplementedError
def safe_exact_update(self, mean_field) -> Tuple[bool, 'MeanField']:
if self._has_exact_projection:
from autofit.graphical.mean_field import MeanField
_mean_field = self.resolve_variable_dict(mean_field)
if self._has_exact_projection(**_mean_field):
projection = self._calc_exact_update(**_mean_field)
return True, MeanField({
self._kwargs[v]: dist for v, dist in projection.items()
})
return False, mean_field
def __hash__(self) -> int:
# TODO: might this break factor repetition somewhere?
return hash(self._factor)
def _reshape_factor(
self, factor_val, values
):
shift, shape = self._function_shape(**values)
if self.is_scalar:
if shift:
return np.sum(
factor_val, axis=np.arange(1, np.ndim(factor_val)))
return np.sum(factor_val)
return np.reshape(factor_val, shape)
def _function_shape(
self,
**kwargs: np.ndarray) -> Tuple[int, ...]:
"""
Calculates the expected function shape based on the variables
"""
var_shapes = {
k: np.shape(x) for k, x in kwargs.items()}
return self._var_shape(**var_shapes)
@lru_cache()
def _var_shape(self, **kwargs: Tuple[int, ...]) -> Tuple[int, ...]:
"""This is called by _function_shape
caches result so that does not have to be recalculated each call
lru_cache caches f(x=1, y=2) to f(y=2, x=1), but in this case
it should be find as the order of kwargs is set by self._kwargs
which should be stable
"""
var_shapes = {self._kwargs[k]: v for k, v in kwargs.items()}
var_dims_diffs = {
v: len(s) - v.ndim
for v, s in var_shapes.items()
}
"""
If all the passed variables have an extra dimension then
we assume we're evaluating multiple instances of the function at the
same time
otherwise an error is raised
"""
if set(var_dims_diffs.values()) == {1}:
# Check if we're passing multiple values e.g. for sampling
shift = 1
elif set(var_dims_diffs.values()) == {0}:
shift = 0
else:
raise ValueError("dimensions of passed inputs do not match")
"""
Updating shape of output array to match input arrays
singleton dimensions are always assumed to match as in
standard array broadcasting
e.g. (1, 2, 3) == (3, 2, 1)
"""
shape = np.ones(self.ndim + shift, dtype=int)
for v, vs in var_shapes.items():
ind = self._variable_plates[v] + shift
vshape = vs[shift:]
if shift:
ind = np.r_[0, ind]
vshape = (vs[0],) + vshape
if shape.size:
if not (
np.equal(shape[ind], 1) |
np.equal(shape[ind], vshape) |
np.equal(vshape, 1)).all():
raise AssertionError(
"Shapes do not match"
)
shape[ind] = np.maximum(shape[ind], vshape)
return shift, tuple(shape)
def _call_factor(
self,
**kwargs: np.ndarray
) -> np.ndarray:
"""
Call the underlying function
Parameters
----------
args
Positional arguments for the function
kwargs
Keyword arguments for the function
Returns
-------
Value returned by the factor
"""
# kws = self._resolve_args(
# **kwargs
# )
if self.vectorised:
return self._factor(**kwargs)
"""Some factors may not be vectorised to broadcast over
multiple inputs
this method checks whether multiple input values have been
passed, and if so automatically loops over the inputs.
If any of the inputs have initial dimension one, it repeats
that value to match the length of the other inputs
If the other inputs do not match then it raises ValueError
"""
kwargs_dims = {k: np.ndim(a) for k, a in kwargs.items()}
# Check dimensions of inputs directly match plates
direct_call = (
all(dim == kwargs_dims[k] for k, dim in self._kwargs_dims.items()))
if direct_call:
return self._factor(**kwargs)
# Check dimensions of inputs match plates + 1
vectorised = (
all(dim + 1 == kwargs_dims[k]
for k, dim in self._kwargs_dims.items()))
if not vectorised:
raise ValueError(
"input dimensions do not match required dims"
f"input: **kwargs={kwargs_dims}"
f"required: "
f"**kwargs={self._kwargs_dims}")
kw_lens = {k: len(a) for k, a in kwargs.items()}
# checking 1st dimensions match
sizes = set(kw_lens.values())
dim0 = max(sizes)
if sizes.difference({1, dim0}):
raise ValueError(
f"size mismatch first dimensions passed: {sizes}")
iter_kws = {
k: iter(a) if kw_lens[k] == dim0 else iter(repeat(a[0]))
for k, a in kwargs.items()}
# iterator to generate keyword arguments
def gen_kwargs():
for _ in range(dim0):
yield {
k: next(a) for k, a in iter_kws.items()}
# TODO this loop can also be parallelised for increased performance
res = np.array([
self._factor(**kws)
for kws in gen_kwargs()])
return res
def __call__(
self,
variable_dict: Dict[
Variable,
Union[
np.ndarray,
float,
List[float]
]
],
axis: Axis = False,
) -> FactorValue:
"""
Call the underlying factor
Parameters
----------
args
Positional arguments for the factor
kwargs
Keyword arguments for the factor
Returns
-------
Object encapsulating the result of the function call
"""
kwargs = self.resolve_variable_dict(variable_dict)
val = self._call_factor(**kwargs)
val = aggregate(self._reshape_factor(val, kwargs), axis)
return FactorValue(val, {})
def broadcast_variable(
self,
variable: str,
value: np.ndarray
) -> np.ndarray:
"""
broadcasts the value of a variable to match the specific shape
of the factor
if the number of dimensions passed of the variable is 1
greater than the dimensions of the variable then it's assumed
that that dimension corresponds to multiple samples of that variable
"""
return self._broadcast(
self._variable_plates[variable],
value
)
def collapse(
self,
variable: str,
value: np.ndarray,
agg_func=np.sum
) -> np.ndarray:
"""
broadcasts `value` to match the specific shape of the factor,
where `value` has the shape of the factor
if the number of dimensions passed of the variable is 1
greater than the dimensions of the variable then it's assumed
that that dimension corresponds to multiple samples of that variable
"""
ndim = np.ndim(value)
shift = ndim - self.ndim
assert shift in {0, 1}
inds = self._variable_plates[variable] + shift
dropaxes = tuple(np.setdiff1d(
np.arange(shift, ndim), inds))
# to ensured axes of returned array is in the correct order
moved = np.moveaxis(value, inds, np.sort(inds))
return agg_func(moved, axis=dropaxes)
def __eq__(self, other: Union["Factor", Variable]):
"""
If set equal to a variable that variable is taken to be deterministic and
so a DeterministicFactorNode is generated.
"""
if isinstance(other, Factor):
if isinstance(other, type(self)):
return (
(self._factor == other._factor)
and (frozenset(self._kwargs.items())
== frozenset(other._kwargs.items()))
and (frozenset(self.variables)
== frozenset(other.variables))
and (frozenset(self.deterministic_variables)
== frozenset(self.deterministic_variables)))
else:
return False
return DeterministicFactor(
self._factor,
other,
name=self.name,
**self._kwargs
)
def __repr__(self) -> str:
args = ", ".join(chain(
map("{0[0]}={0[1]}".format, self._kwargs.items())))
return f"Factor({self.name}, {args})"
class DeterministicFactor(Factor):
"""
A deterministic factor is used to convert a function f(g(x)) to f(y)g(x) (integrating over y wit
a delta function) so that it can be represented in a factor graph.
Parameters
----------
factor
The original factor to which the deterministic factor is associated
variable
The deterministic variable that is returned by the factor, so
to represent the case f(g(x)), we would define,
```
>>> x = Variable('x')
>>> y = Variable('y')
>>> g_ = Factor(g, x) == y
>>> f_ = Factor(f, y)
```
Alternatively g could be directly defined,
```
>>> g_ = DeterministicFactor(g, y, x=x)
```
kwargs
Variables for the original factor
"""
def __init__(
self,
factor: Callable,
variable: Variable,
*args: Variable,
name: str = '',
**kwargs: Variable
):
"""
A deterministic factor is used to convert a function f(g(x)) to f(y)g(x) (integrating over y wit
a delta function) so that it can be represented in a factor graph.
Parameters
----------
factor
The original factor to which the deterministic factor is associated
variable
The deterministic factor used
args
Variables for the original factor
kwargs
Variables for the original factor
"""
super().__init__(
factor,
*args,
name=name or factor.__name__,
**kwargs
)
self._deterministic_variables = {
variable
}
def __call__(
self,
variable_dict: Dict[Variable, np.ndarray],
axis: Axis = False,
# **kwargs: np.ndarray
) -> FactorValue:
"""
Call this factor with a set of arguments
Parameters
----------
args
Positional arguments for the underlying factor
kwargs
Keyword arguments for the underlying factor
Returns
-------
An object encapsulating the value for the factor
"""
kwargs = self.resolve_variable_dict(variable_dict)
res = self._call_factor(**kwargs)
shift, shape = self._function_shape(**kwargs)
plate_dim = dict(zip(self.plates, shape[shift:]))
det_shapes = {
v: shape[:shift] + tuple(
plate_dim[p] for p in v.plates)
for v in self.deterministic_variables
}
if not (isinstance(res, tuple) or self.n_deterministic > 1):
res = res,
log_val = (
0. if (shape == () or axis is None) else
aggregate(np.zeros(tuple(1 for _ in shape)), axis))
det_vals = {
k: np.reshape(val, det_shapes[k])
if det_shapes[k]
else val
for k, val
in zip(self._deterministic_variables, res)
}
return FactorValue(log_val, det_vals)
def __repr__(self) -> str:
factor_str = super().__repr__()
var_str = ", ".join(sorted(variable.name for variable in self._deterministic_variables))
return f"({factor_str} == ({var_str}))"
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] |
third_party/gsutil/third_party/protorpc/protorpc/google_imports.py | ravitejavalluri/catapult | 76 | 156574 | <reponame>ravitejavalluri/catapult
"""Dynamically decide from where to import other SDK modules.
All other protorpc code should import other SDK modules from
this module. If necessary, add new imports here (in both places).
"""
__author__ = '<EMAIL> (<NAME>)'
# pylint: disable=g-import-not-at-top
# pylint: disable=unused-import
try:
from google.net.proto import ProtocolBuffer
except ImportError:
pass
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tests/api/base.py | davidlmorton/webapp-skeleton | 0 | 128436 | from pprint import pprint
from webapp.api.wsgi import app
import abc
import json
import requests
import unittest
__all__ = ['BaseAPITest']
def get_endpoint_name(url):
with app.test_request_context(url) as request_ctx:
url_rule = request_ctx.request.url_rule
if url_rule is None:
return 'Unknown Endpoint'
else:
return url_rule.endpoint.split('.')[-1]
class BaseAPITest(unittest.TestCase):
__metaclass__ = abc.ABCMeta
session = requests.Session()
session.verify = True
def get(self, url, **kwargs):
response = self.deserialize_response(self.session.get(url, **kwargs))
self.print_response(response, **kwargs)
return response
def patch(self, url, data, **kwargs):
response = self.deserialize_response(
self.session.patch(url,
headers={'content-type': 'application/json'},
data=json.dumps(data),
**kwargs))
self.print_response(response)
return response
def post(self, url, data, **kwargs):
response = self.deserialize_response(
self.session.post(url,
allow_redirects=False,
headers={'content-type': 'application/json'},
data=json.dumps(data), **kwargs))
self.print_response(response, **kwargs)
return response
def put(self, url, data, **kwargs):
response = self.deserialize_response(
self.session.put(url,
allow_redirects=False,
headers={'content-type': 'application/json'},
data=json.dumps(data), **kwargs))
self.print_response(response, **kwargs)
return response
def delete(self, url, **kwargs):
response = self.deserialize_response(
self.session.delete(url,
allow_redirects=False,
**kwargs))
self.print_response(response, **kwargs)
return response
@staticmethod
def print_response(response, auth=None, **kwargs):
pad = '==========================='
dpad = pad.replace('=', '-')
request = response.request
print("\n#===================================================" + pad)
print("# %s %s %s" % (response.status_code,
request.method, get_endpoint_name(request.url)))
print("# Url: %s" % request.url)
print("#---------------------------------------------------" + dpad)
if 'Authorization' in request.headers:
print("# Authorization: %s" % request.headers['Authorization'])
if auth:
print("# BasicAuth: USER=%s PASS=%s" % auth)
if request.body:
print("#----------[ Request ]------------------------------" + dpad)
pprint(json.loads(request.body))
print("#----------[ Response: %s ]------------------------" %
response.status_code + dpad)
pprint(response.DATA)
@staticmethod
def deserialize_response(response):
response.DATA = response.json()
return response
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313,
5327,
29889,
4882,
29918,
401,
29892,
13,
462,
1678,
2009,
29889,
5696,
29892,
679,
29918,
29734,
29918,
978,
29898,
3827,
29889,
2271,
4961,
13,
4706,
1596,
14822,
501,
2096,
29901,
1273,
29879,
29908,
1273,
2009,
29889,
2271,
29897,
13,
4706,
1596,
14822,
2683,
2683,
2683,
5634,
29908,
718,
270,
8305,
29897,
13,
4706,
565,
525,
25471,
29915,
297,
2009,
29889,
13662,
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13,
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14822,
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1273,
29879,
4514,
2683,
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29908,
1273,
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2933,
29889,
4882,
29918,
401,
718,
270,
8305,
29897,
13,
4706,
282,
2158,
29898,
5327,
29889,
14573,
29897,
13,
13,
1678,
732,
7959,
5696,
13,
1678,
822,
16964,
6646,
29918,
5327,
29898,
5327,
1125,
13,
4706,
2933,
29889,
14573,
353,
2933,
29889,
3126,
580,
13,
4706,
736,
2933,
13,
2
] |
bst.py | kentsommer/bst-amortized-analysis | 0 | 95159 | import random
from tabulate import tabulate
class Node:
# Parent pointer used for easier in-order traversal function
def __init__(self, value, parent=None):
self.value = value
self.left = None
self.right = None
self.parent = parent
class BST:
def __init__(self):
self.root = None
# Standard BST insert implementation
def insert(self, value):
if self.root is None:
self.root = Node(value)
current = self.root
while current:
if value == current.value:
return
elif value < current.value:
if current.left:
current = current.left
else:
current.left = Node(value, current)
return
else:
if current.right:
current = current.right
else:
current.right = Node(value, current)
return
# Select random number N between 50:100 for use as tree size.
# Sample N unique random numbers between 0:10,000
# For each i'th unique number insert value into tree.
def gen_random_tree(self):
num_nodes = random.randint(50, 100)
unique_list = random.sample(range(0,10000), num_nodes)
for i in range(0, num_nodes):
self.insert(unique_list[i])
# If current node has left child
# Set current to left child
# Continue until no more left children
def findmostleft(node, stepcount=0):
stepcount = stepcount
current = node
while True:
if current.left != None:
current = current.left
stepcount += 1
continue
break
return current, stepcount
# If the current node has a right child, return leftmost child of right child.
# Else, continue going up parent pointers until parent.left equals current
# Return parent
def findnext(node):
stepcount = 0
if node.right != None:
return findmostleft(node.right, 1)
else:
parent = node.parent
stepcount += 1
while parent != None:
if parent.left == node:
break
node = parent
parent = node.parent
stepcount += 1
return parent, stepcount
# Get first node by calling findmostleft on root
# Make N-1 calls to findnext (where N is the size of the tree)
def inorder(node):
stepcount = 0
first = findmostleft(node)
stepcount += first[1]
current = first[0]
for x in range(1, bst_size(node)):
next = findnext(current)
stepcount += next[1]
current = next[0]
return stepcount
def bst_size(root, count=0):
if root is None:
return count
return bst_size(root.left, bst_size(root.right, count + 1))
# Main function to allow for quick comparison and testing
# Generates 50 random trees and displays actual steps, estimated steps, and size of tree
if __name__ == '__main__':
table = []
for i in range(1, 51):
cTree = BST()
cTree.gen_random_tree()
count = inorder(cTree.root)
size = bst_size(cTree.root)
table.append(["Tree " + str(i), str(size), str(count), str(2 * (size-1))])
print tabulate(table, headers=["Tree Number", "Number of Nodes", "Number of Steps", "2(N-1)"])
| [
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289,
303,
29918,
2311,
29898,
29883,
9643,
29889,
4632,
29897,
13,
4706,
1591,
29889,
4397,
29898,
3366,
9643,
376,
718,
851,
29898,
29875,
511,
851,
29898,
2311,
511,
851,
29898,
2798,
511,
851,
29898,
29906,
334,
313,
2311,
29899,
29896,
876,
2314,
13,
1678,
1596,
4434,
5987,
29898,
2371,
29892,
9066,
29922,
3366,
9643,
9681,
613,
376,
4557,
310,
405,
2631,
613,
376,
4557,
310,
2443,
567,
613,
376,
29906,
29898,
29940,
29899,
29896,
5513,
2314,
13,
2
] |
coPrime.py | prateekgulati/numberTheory | 0 | 179520 | <reponame>prateekgulati/numberTheory
__author__ = 'Prateek'
from sympy import gcd
def coPrime(n):
num = set()
for i in range(1, n):
if (gcd(i, n) == 1):
num.add(i)
return num
#if __author__ == 'Prateek':
# print coPrime(8)
| [
1,
529,
276,
1112,
420,
29958,
558,
403,
1416,
29887,
352,
2219,
29914,
4537,
1576,
706,
13,
1649,
8921,
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353,
525,
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403,
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13,
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330,
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13,
13,
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603,
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13,
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13,
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13,
13,
13,
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361,
4770,
8921,
1649,
1275,
525,
4040,
403,
1416,
2396,
13,
29937,
1678,
1596,
1302,
4040,
603,
29898,
29947,
29897,
13,
2
] |
stanford/sms-tools/lectures/02-DFT/plots-code/idft.py | phunc20/dsp | 1 | 4979 | import matplotlib.pyplot as plt
import numpy as np
import sys
sys.path.append('../../../software/models/')
import dftModel as DFT
import math
k0 = 8.5
N = 64
w = np.ones(N)
x = np.cos(2*np.pi*k0/N*np.arange(-N/2,N/2))
mX, pX = DFT.dftAnal(x, w, N)
y = DFT.dftSynth(mX, pX, N)
plt.figure(1, figsize=(9.5, 5))
plt.subplot(311)
plt.title('positive freq. magnitude spectrum in dB: mX')
plt.plot(np.arange(mX.size), mX, 'r', lw=1.5)
plt.axis([0,mX.size, min(mX), max(mX)+1])
plt.subplot(312)
plt.title('positive freq. phase spectrum: pX')
plt.plot(np.arange(pX.size), pX, 'c', lw=1.5)
plt.axis([0, pX.size,-np.pi,np.pi])
plt.subplot(313)
plt.title('inverse spectrum: IDFT(X)')
plt.plot(np.arange(-N/2, N/2), y,'b', lw=1.5)
plt.axis([-N/2,N/2-1,min(y), max(y)])
plt.tight_layout()
plt.savefig('idft.png')
plt.show()
| [
1,
1053,
22889,
29889,
2272,
5317,
408,
14770,
13,
5215,
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408,
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13,
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13,
13,
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281,
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405,
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13,
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353,
360,
7818,
29889,
29881,
615,
29216,
386,
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29885,
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smart_alarm_clock/news_api.py | kfb19/Smart-Alarm-Clock | 0 | 50518 | <gh_stars>0
"""this module collects current news headlines from the API"""
import logging
import requests
from extract_json import get_key
from extract_json import get_location
from extract_json import get_urls
logging.basicConfig(filename='pysys.log',level=logging.INFO, format='%(asctime)s %(levelname)-8s %(message)s', datefmt='%Y-%m-%d %H:%M:%S')
BASE_URL = get_urls("news")
API_KEY = get_key("news")
COUNTRY = get_location("country")
COMPLETE_URL = BASE_URL + "country=" + COUNTRY + "&apiKey=" + API_KEY
response = requests.get(COMPLETE_URL)
def get_news() -> list:
"""this function gets the current news information using an API"""
logging.info("Articles returned by get_news()")
news_dict = response.json()
articles = news_dict["articles"]
news_articles = []
for article in articles:
news_articles.append({'name':article['title'], 'url':article['url']})
return news_articles
def check_news_version() -> bool:
"""this function checks for the correct setup of the API"""
try:
news_dict = response.json()
return True
except:
return False
if __name__ == '__main__':
logging.info("News API Module Tested")
print(get_news())#tests the function
print(check_news_version())#tests the function
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opteryx/utils/__init__.py | mabel-dev/mabel-sql | 1 | 144235 | <filename>opteryx/utils/__init__.py
from functools import lru_cache
@lru_cache(1)
def is_running_from_ipython():
"""
True when running in Jupyter
"""
try:
from IPython import get_ipython # type:ignore
return get_ipython() is not None
except:
return False
def safe_field_name(field_name):
"""strip all the non-alphanums from a field name"""
import re
pattern = re.compile(r"[^a-zA-Z0-9\_\-]+")
return pattern.sub("", field_name)
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fixture/application.py | turovod/Python_Addressbook_New_Test_By_Charm | 0 | 144488 | from selenium import webdriver
from selenium.common.exceptions import NoSuchElementException
from fixture.contact import ContactHelper
from fixture.group import GroupHelper
from fixture.session import SessionHelper
class Application:
def __init__(self, browser, base_url):
if browser == "firefox":
# launch from IDE
# self.driver = webdriver.Firefox(executable_path="../Drivers/geckodriver.exe")
# launch from console
self.driver = webdriver.Firefox(executable_path=self.adapted_url_to_webdriver("Drivers\geckodriver.exe"))
elif browser == "chrome":
self.driver = webdriver.Chrome(executable_path=self.adapted_url_to_webdriver("Drivers\chromedriver.exe"))
elif browser == "edge":
self.driver = webdriver.Edge(executable_path=self.adapted_url_to_webdriver("Drivers\msedgedriver.exe"))
else:
raise ValueError(f"Unrecognised browser {browser}")
# self.driver.implicitly_wait(1) # waiting in seconds
# self.base_url = "http://localhost/addressbook/"
self.base_url = base_url
self.session = SessionHelper(self)
self.group = GroupHelper(self)
self.contact = ContactHelper(self)
self.base_url = base_url
def is_valid(self):
try:
self.driver.current_url
return True
except:
return False
def destroy(self):
self.driver.quit()
def open_home_page(self):
driver = self.driver
driver.get(self.base_url)
def is_element_present(self, how, what):
try:
self.driver.find_element(by=how, value=what)
except NoSuchElementException as e:
return False
return True
def adapted_url_to_webdriver(self, local_driver_url):
wd_url = __file__
wd_url = wd_url.strip("fixture\\application.py")
wd_url = "\\".join([wd_url, local_driver_url])
return wd_url | [
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] |
tests/test_higherorder.py | hsadeghidw/dimod | 0 | 129221 | <reponame>hsadeghidw/dimod
# Copyright 2018 D-Wave Systems 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.
import itertools
import unittest
import dimod
from dimod import make_quadratic, poly_energy, poly_energies
class TestMakeQuadratic(unittest.TestCase):
def test__spin_prod(self):
bqm = dimod.higherorder.utils._spin_product(['a', 'b', 'p', 'aux'])
for v in ['a', 'b', 'p', 'aux']:
self.assertIn(v, bqm.variables)
self.assertEqual(len(bqm), 4) # has an auxiliary variable
seen = set()
samples = dimod.ExactSolver().sample(bqm)
for sample, energy in samples.data(['sample', 'energy']):
if energy == 0:
self.assertEqual(sample['a'] * sample['b'], sample['p'])
seen.add((sample['a'], sample['b'], sample['p']))
if sample['a'] * sample['b'] != sample['p']:
self.assertGreaterEqual(energy, 1) # gap 1
self.assertEqual(seen, {(-1, -1, +1),
(-1, +1, -1),
(+1, -1, -1),
(+1, +1, +1)})
def test__binary_prod(self):
variables = ['a', 'b', 'p']
bqm = dimod.higherorder.utils._binary_product(variables)
for v in variables:
self.assertIn(v, bqm.variables)
self.assertEqual(len(bqm), 3)
seen = set()
samples = dimod.ExactSolver().sample(bqm)
for sample, energy in samples.data(['sample', 'energy']):
if energy == 0:
self.assertEqual(sample['a'] * sample['b'], sample['p'])
seen.add((sample['a'], sample['b'], sample['p']))
if sample['a'] * sample['b'] != sample['p']:
self.assertGreaterEqual(energy, 1) # gap 1
self.assertEqual(seen, {(0, 0, 0), (0, 1, 0), (1, 0, 0), (1, 1, 1)})
def test_empty(self):
bqm = make_quadratic({}, 1.0, dimod.SPIN)
self.assertEqual(bqm, dimod.BinaryQuadraticModel.empty(dimod.SPIN))
def test_no_higher_order(self):
poly = {(0, 1): -1, (1, 2): 1}
bqm = make_quadratic(poly, 1.0, dimod.SPIN)
variables = set().union(*poly)
aux_variables = tuple(set(bqm.linear) - variables)
variables = tuple(variables)
for config in itertools.product((-1, 1), repeat=len(variables)):
sample = dict(zip(variables, config))
energy = poly_energy(sample, poly)
reduced_energies = []
for aux_config in itertools.product((-1, 1),
repeat=len(aux_variables)):
aux_sample = dict(zip(aux_variables, aux_config))
aux_sample.update(sample)
reduced_energies.append(bqm.energy(aux_sample))
self.assertAlmostEqual(energy, min(reduced_energies))
def test_simple(self):
poly = {(0, 1, 2): -1}
bqm = make_quadratic(poly, 5.0, dimod.SPIN)
variables = set().union(*poly)
aux_variables = tuple(set(bqm.linear) - variables)
variables = tuple(variables)
self.assertTrue(aux_variables)
for config in itertools.product((-1, 1), repeat=len(variables)):
sample = dict(zip(variables, config))
energy = poly_energy(sample, poly)
reduced_energies = []
for aux_config in itertools.product((-1, 1),
repeat=len(aux_variables)):
aux_sample = dict(zip(aux_variables, aux_config))
aux_sample.update(sample)
reduced_energies.append(bqm.energy(aux_sample))
self.assertAlmostEqual(energy, min(reduced_energies))
def test_several_terms(self):
poly = {(0, 1, 2): -1, (1, 2, 3): 1, (0, 2, 3): .5,
(0,): .4,
(): .5}
bqm = make_quadratic(poly, 5.0,
bqm=dimod.BinaryQuadraticModel.empty(dimod.SPIN))
variables = set().union(*poly)
aux_variables = tuple(set(bqm.linear) - variables)
variables = tuple(variables)
self.assertTrue(aux_variables)
for config in itertools.product((-1, 1), repeat=len(variables)):
sample = dict(zip(variables, config))
energy = poly_energy(sample, poly)
reduced_energies = []
for aux_config in itertools.product((-1, 1),
repeat=len(aux_variables)):
aux_sample = dict(zip(aux_variables, aux_config))
aux_sample.update(sample)
reduced_energies.append(bqm.energy(aux_sample))
self.assertAlmostEqual(energy, min(reduced_energies))
def test_bug(self):
# from a bug report
# https://support.dwavesys.com/hc/en-us/community/posts/360035719954-dimod-make-quadratic-returns-error
H = {(0, 1, 0, 1): -4.61898,
(0, 1, 1, 0): 4.61898,
(0, 2, 0, 2): -5.18353,
(0, 2, 2, 0): 5.18353,
(1, 0, 0, 1): 4.61898,
(1, 0, 1, 0): -4.61898,
(1, 2, 2, 1): 4.97017,
(2, 0, 0, 2): 5.18353,
(2, 0, 2, 0): -5.18353,
(2, 1, 1, 2): 4.97017,
}
bqm = make_quadratic(H, 1, 'BINARY')
# should be no aux variables
self.assertEqual(set(bqm.variables), {0, 1, 2})
def test_another(self):
J = {(0, 1, 2): -1, (0, 1, 3): -1, (2, 3, 0): 1, (3, 2, 0): -1}
h = {0: 0, 1: 0, 2: 0, 3: 0}
off = .5
poly = J.copy()
poly.update({(v,): bias for v, bias in h.items()})
poly[()] = off
bqm = make_quadratic(J, 5.0,
bqm=dimod.BinaryQuadraticModel.from_ising(h, {},
off))
variables = set(h).union(*J)
aux_variables = tuple(set(bqm.linear) - variables)
variables = tuple(variables)
self.assertTrue(aux_variables)
for config in itertools.product((-1, 1), repeat=len(variables)):
sample = dict(zip(variables, config))
energy = poly_energy(sample, poly)
reduced_energies = []
for aux_config in itertools.product((-1, 1),
repeat=len(aux_variables)):
aux_sample = dict(zip(aux_variables, aux_config))
aux_sample.update(sample)
reduced_energies.append(bqm.energy(aux_sample))
self.assertAlmostEqual(energy, min(reduced_energies))
def test_vartype(self):
poly = {(0, 1, 2): 10, (0, 1): 5} # make sure (0, 1) is most common
self.assertEqual(dimod.make_quadratic(poly, 1.0, 'SPIN'),
dimod.make_quadratic(poly, 1.0, dimod.SPIN))
self.assertEqual(dimod.make_quadratic(poly, 1.0, 'BINARY'),
dimod.make_quadratic(poly, 1.0, dimod.BINARY))
def test_quad_to_linear(self):
J = {(0, 1): -1, (0, 1, 2): 1, (0, 1, 3): 1}
h = {}
off = .5
poly = J.copy()
poly.update({(v,): bias for v, bias in h.items()})
poly[()] = off
bqm = make_quadratic(J, 10.0,
bqm=dimod.BinaryQuadraticModel.from_ising(h, {},
off))
variables = set(h).union(*J)
aux_variables = tuple(set(bqm.linear) - variables)
variables = tuple(variables)
self.assertTrue(aux_variables)
for config in itertools.product((-1, 1), repeat=len(variables)):
sample = dict(zip(variables, config))
energy = poly_energy(sample, poly)
reduced_energies = []
for aux_config in itertools.product((-1, 1),
repeat=len(aux_variables)):
aux_sample = dict(zip(aux_variables, aux_config))
aux_sample.update(sample)
reduced_energies.append(bqm.energy(aux_sample))
self.assertAlmostEqual(energy, min(reduced_energies))
def test_linear_and_offset(self):
poly = {(0,): .5, tuple(): 1.3}
bqm = dimod.make_quadratic(poly, 10.0, dimod.BINARY)
self.assertEqual(bqm, dimod.BinaryQuadraticModel({0: .5}, {}, 1.3,
dimod.BINARY))
def test_binary_polynomial(self):
HUBO = {(0, 1, 2): .5, (0, 1): 1.3, (2, 4, 1): -1, (3, 2): -1}
bqm = make_quadratic(HUBO, 1000.0, dimod.BINARY)
variables = set().union(*HUBO)
aux_variables = tuple(set(bqm.linear) - variables)
variables = tuple(variables)
self.assertTrue(aux_variables)
for config in itertools.product((0, 1), repeat=len(variables)):
sample = dict(zip(variables, config))
energy = poly_energy(sample, HUBO)
reduced_energies = []
for aux_config in itertools.product((0, 1),
repeat=len(aux_variables)):
aux_sample = dict(zip(aux_variables, aux_config))
aux_sample.update(sample)
reduced_energies.append(bqm.energy(aux_sample))
self.assertAlmostEqual(energy, min(reduced_energies))
def test_poly_energies(self):
linear = {0: 1.0, 1: 1.0}
j = {(0, 1, 2): 0.5}
poly = dimod.BinaryPolynomial.from_hising(linear, j)
samples = [[1, 1, -1], [1, -1, 1], [1, 1, 1], [-1, 1, -1]]
en = poly_energies(samples, poly)
self.assertListEqual(list(en), [1.5, -0.5, 2.5, 0.5])
en = poly_energy(samples[0], poly)
self.assertAlmostEqual(en, 1.5)
with self.assertRaises(ValueError):
poly_energy(samples, poly)
poly = {('a',): 1.0, ('b',): 1.0, ('a', 'b', 'c'): 0.5}
samples = [{'a': 1, 'b': 1, 'c': -1},
{'a': 1, 'b': -1, 'c': 1},
{'a': 1, 'b': 1, 'c': 1},
{'a': -1, 'b': 1, 'c': -1}]
en = poly_energies(samples, poly)
self.assertListEqual(list(en), [1.5, -0.5, 2.5, 0.5])
en = poly_energy(samples[0], poly)
self.assertAlmostEqual(en, 1.5)
with self.assertRaises(ValueError):
poly_energy(samples, poly)
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] |
src/python-bakeoff/setup_shared.py | dhermes/de-casteljau-bakeoff | 0 | 111570 | # 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.
# NOTE: This file is copied (rather than symlinked) since a symlink **outside**
# of the package tree won't get copied during a ``pip install``.
import os
import pathlib
import subprocess
import sys
import numpy as np
import setuptools
FORTRAN_LIBRARY_PREFIX = "libraries: ="
GFORTRAN_MISSING_LIBS = """\
``gfortran`` default library path not found via:
$ gfortran -print-search-dirs
{}"""
GFORTRAN_BAD_PATH = "``gfortran`` library path {} is not a directory."
def gfortran_search_path():
"""Get the library directory paths for ``gfortran``.
Looks for ``libraries: =`` in the output of ``gfortran -print-search-dirs``
and then parses the paths. If this fails for any reason, this method will
print an error and return ``library_dirs``.
Returns:
List[str]: The library directories for ``gfortran``.
"""
cmd = ("gfortran", "-print-search-dirs")
process = subprocess.Popen(cmd, stdout=subprocess.PIPE)
return_code = process.wait()
# Bail out if the command failed.
if return_code != 0:
return []
cmd_output = process.stdout.read().decode("utf-8")
# Find single line starting with ``libraries: ``.
search_lines = cmd_output.strip().split("\n")
library_lines = [
line[len(FORTRAN_LIBRARY_PREFIX) :]
for line in search_lines
if line.startswith(FORTRAN_LIBRARY_PREFIX)
]
if len(library_lines) != 1:
msg = GFORTRAN_MISSING_LIBS.format(cmd_output)
print(msg, file=sys.stderr)
return []
# Go through each library in the ``libraries: = ...`` line.
library_line = library_lines[0]
accepted = set()
for part in library_line.split(os.pathsep):
full_path = os.path.abspath(part.strip())
if os.path.isdir(full_path):
accepted.add(full_path)
else:
# Ignore anything that isn't a directory.
msg = GFORTRAN_BAD_PATH.format(full_path)
print(msg, file=sys.stderr)
return sorted(accepted)
def get_extra_objects(here):
return (
os.path.join(here, "object_files", "types.o"),
os.path.join(here, "object_files", "forall_.o"),
os.path.join(here, "object_files", "do_.o"),
os.path.join(here, "object_files", "spread_.o"),
os.path.join(here, "object_files", "serial_.o"),
os.path.join(here, "object_files", "vs_algorithm.o"),
)
def extension_modules(here, name):
extra_objects = get_extra_objects(here)
missing = [path for path in extra_objects if not os.path.isfile(path)]
if missing:
parts = ["Missing object file(s):"]
parts.extend(f"- {path}" for path in missing)
parts.extend(
[
"",
f"here: {here}",
f"__file__: {__file__}",
"",
"files in `here`:",
]
)
files_here = pathlib.Path(here).glob("*")
parts.extend(f"- {path}" for path in files_here)
msg = "\n".join(parts)
raise RuntimeError(msg)
extension = setuptools.Extension(
f"{name}._binary",
[os.path.join(name, "_binary.c")],
extra_objects=extra_objects,
include_dirs=[np.get_include()],
libraries=["gfortran"],
library_dirs=gfortran_search_path(),
)
return [extension]
def do_setup(here, name):
ext_modules = extension_modules(here, name)
setuptools.setup(
name=name,
packages=[name],
install_requires=["numpy"],
ext_modules=ext_modules,
)
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] |
conftest.py | BodenmillerGroup/readimc | 0 | 152409 | <filename>conftest.py
import pytest
import requests
import shutil
from pathlib import Path
from typing import Generator
from readimc import MCDFile, TXTFile
_imc_test_data_asset_url = (
"https://github.com/BodenmillerGroup/TestData"
"/releases/download/v1.0.7/210308_ImcTestData_raw.tar.gz"
)
_imc_test_data_raw_dir = "datasets/210308_ImcTestData/raw"
_imc_test_data_mcd_file = "20210305_NE_mockData1/20210305_NE_mockData1.mcd"
_imc_test_data_txt_file = (
"20210305_NE_mockData1/20210305_NE_mockData1_ROI_001_1.txt"
)
def _download_and_extract_asset(tmp_dir_path: Path, asset_url: str):
asset_file_path = tmp_dir_path / "asset.tar.gz"
response = requests.get(asset_url, stream=True)
if response.status_code == 200:
with asset_file_path.open(mode="wb") as f:
f.write(response.raw.read())
shutil.unpack_archive(asset_file_path, tmp_dir_path)
@pytest.fixture(scope="session")
def imc_test_data_raw_path(tmp_path_factory) -> Generator[Path, None, None]:
tmp_dir_path = tmp_path_factory.mktemp("raw")
_download_and_extract_asset(tmp_dir_path, _imc_test_data_asset_url)
yield tmp_dir_path / Path(_imc_test_data_raw_dir)
shutil.rmtree(tmp_dir_path)
@pytest.fixture
def imc_test_data_mcd_file(
imc_test_data_raw_path: Path,
) -> Generator[MCDFile, None, None]:
path = imc_test_data_raw_path / Path(_imc_test_data_mcd_file)
with MCDFile(path) as f:
yield f
@pytest.fixture
def imc_test_data_txt_file(
imc_test_data_raw_path: Path,
) -> Generator[TXTFile, None, None]:
path = imc_test_data_raw_path / Path(_imc_test_data_txt_file)
with TXTFile(path) as f:
yield f
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Dynamic Programming/32_chef_monocarp.py | Szymon-Budziak/ASD_exercises_solutions | 7 | 139903 | <gh_stars>1-10
# <NAME> has just put n dishes into an oven. He knows that the i-th dish has its optimal
# cooking time equal to ti minutes. At any positive integer minute T Monocarp can put no more than
# one dish out of the oven. If the i-th dish is put out at some minute T, then its unpleasant value
# is |T − t[i]| — the absolute difference between T and t[i]. Once the dish is out of the oven, it
# can't go back in. Monocarp should put all the dishes out of the oven. What is the minimum total
# unpleasant value Monocarp can obtain?
from math import inf
def chef_monocarp(n, T):
T.sort()
DP = [[inf] * (2 * n + 1) for _ in range(n + 1)]
for i in range(2 * n + 1):
DP[0][i] = 0
for i in range(1, n + 1):
for j in range(1, 2 * n + 1):
DP[i][j] = min(DP[i][j - 1], DP[i - 1][j - 1] + abs(T[i - 1] - j))
return DP[-1][-1]
n = 12
T = [4, 11, 12, 4, 10, 12, 9, 3, 4, 11, 10, 10]
print(chef_monocarp(n, T))
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gallery/mode/photo_manager.py | bob-chen/gallery | 0 | 49667 | <filename>gallery/mode/photo_manager.py
# -*- coding: utf-8 -*-
'''
Created on Mar 19, 2015
@author: Bob.Chen
'''
import sys
import urllib2
import time
from PIL import Image
from constant import PHOTO_DATA_ROOT
from common import Logger, RandomID, getPhotoUrl, getPhotoPath
from error_api import Err
from gallery.models import Photo
from StringIO import StringIO
class PhotoManager(object):
@staticmethod
def addPhoto(content, title = "", comment = "", isUrl=False):
Logger.LogParameters(funcname = sys._getframe().f_code.co_name, func_vars = vars(), module = "PhotoManager")
try:
imageName = RandomID.gen()
if not isUrl:
fileObj = StringIO(content.read())
else:
#url = "http://www.didao8.com:8080/static/44/thumb/nvLcHMu1JS3mepZPkQBqriG4ANthz2s5.jpg"
fileObj = StringIO(urllib2.urlopen(content).read())
img = Image.open(fileObj)
width, height = img.size
fileName = "%s%s%s" % (imageName, ".", img.format)
filePath = (PHOTO_DATA_ROOT + "%s") % fileName
img.save(filePath)
photo = Photo(title=title, comment=comment, datetime = time.time(),
imageName=fileName, width = width, height = height)
photo.save()
return Err.genOK(photo.id)
except Exception, ex:
print ex
Logger.SaveLogDebug(ex, level=Logger.LEVEL_ERROR, module = "PhotoManager")
return Err.genErr(Err.ERR_ADD_PHOTO_FAIL)
@staticmethod
def getPhotos(page=1, prePage=5):
Logger.LogParameters(funcname = sys._getframe().f_code.co_name, func_vars = vars(), module = "PhotoManager")
try:
photos = []
intPage = int(page)
intPrePage = int(prePage)
photoItems = Photo.objects.all()[(intPage-1)*intPrePage:intPage*intPrePage]
attrList = ["id", "title", "comment", "datetime", "width", "height", "imageName"]
for item in photoItems:
photo = {key:getattr(item, key) for key in attrList}
photo["imageUrl"] = getPhotoUrl(item.imageName)
photos.append(photo)
return Err.genOK(photos)
except Exception, ex:
print ex
Logger.SaveLogDebug(ex, level=Logger.LEVEL_ERROR, module = "PhotoManager")
return Err.genErr(Err.ERR_GET_PHOTO_LIST_FAIL)
@staticmethod
def getPhoto(imageName, pointX=0, pointY=0, needWidth=0):
'''
Return a StringIO obj, use StringIO.getvalue() to get the content
'''
Logger.LogParameters(funcname = sys._getframe().f_code.co_name, func_vars = vars(), module = "PhotoManager")
try:
imagePath = getPhotoPath(imageName)
needWidth, pointX, pointY = int(needWidth), int(pointX), int(pointY)
im = Image.open(imagePath)
width, height = im.size
imgFormat = im.format
scale = float(height)/width
box = (0, 0, width, height)
if needWidth > 0 and pointX < width and pointY < height:
if needWidth + pointX > width:
needWidth = width - pointX
needHeight = needWidth * scale
if needHeight + pointY > height:
needHeight = height - pointY
needWidth = needHeight / scale
if needHeight < 1:
needHeight = 1
if needWidth < 1:
needWidth = 1
print needHeight, needWidth
#print needWidth, needHeight, scale
box = (pointX, pointY, int(pointX + needWidth), int(pointY + needHeight))
region = im.crop(box)
container = StringIO()
region.save(container, imgFormat)
return Err.genOK([container, imgFormat])
except Exception, ex:
print ex
Logger.SaveLogDebug(ex, level=Logger.LEVEL_ERROR, module = "PhotoManager")
return Err.genErr(Err.ERR_GET_PHOTO_DATA_FAIL)
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core/verify-installation/selenium.py | bfosterscripps/utilities | 0 | 101684 | <filename>core/verify-installation/selenium.py
# Author: <NAME>
# Date: 20150821
# Purpose:
# test to see if selenium for python is installed
import sys
try:
# attempt to import relevant selenium pieces
from selenium import webdriver
from selenium.common.exceptions import TimeoutException
from selenium.webdriver.support.ui import WebDriverWait # available since 2.4.0
from selenium.webdriver.support import expected_conditions as EC # available since 2.26.0
except ImportError:
# exit with status of 1 to indicate there was an import error
sys.exit(1)
else:
# exit with a status of 0 to indicate all is well
sys.exit(0)
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Data/__init__.py | himammz/GpTest | 1 | 48326 | __all__ = ['Database'] | [
1,
4770,
497,
1649,
353,
6024,
9112,
2033,
2
] |
plotDiff_log.py | kmoskovtsev/Electrons-on-Helium-Scripts | 0 | 14311 | from __future__ import division
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
import gsd
import gsd.fl
import numpy as np
import os
import sys
import datetime
import time
import pickle
from shutil import copyfile
import inspect
import md_tools27 as md_tools
from multiprocessing import Pool
"""
This script plots diffusion vs Gamma in log(D)-log(Gamma) or log(D)-gamma format. The data from a .dat file is used, must be precalculated by plotDiff_pG_parallel.py.
Arguments: --cmfree, --cmfixed for the free-moving center of mass regime, and v_cm subtracted respectively.
--sf <fubfolder>: subfolder to process (e.g. p32)
--NP <number>: number of subprocesses to use for parallelization. Very efficient acceleration by a factor of <number>.
"""
#Use LaTex for text
from matplotlib import rc
rc('font',**{'family':'serif','serif':['Computer Modern Roman']})
rc('text', usetex=True)
def read_log(path):
coulomb_status = ''
with open(path + '/log.txt', 'r') as f:
for i, line in enumerate(f.readlines()):
if i == 0:
timestamp = line.rstrip()
if line[:10] == '# Periodic':
words = line.split(' ')
p = int(words[9])
A = float(words[6])
if line[:4] == '# a ':
words = line.split(' ')
repeat_x = int(words[6])
repeat_y = int(words[9])
Np = 2*repeat_x*repeat_y
if line[:7] == '# Gamma':
words = line.split(' ')
dt = float(words[9])
if line[:9] == '# Coulomb':
words = line.split(' ')
coulomb_status = words[-1]
if line[:9] == '# N_therm':
words = line.split(' ')
snap_period = int(float(words[5]))
# T_gamma = 31.8265130646
if line[:9] == '# T_gamma':
words = line.split(' ')
T_gamma = float(words[3])
return {'timestamp': timestamp,'A':A, 'p':p, 'Np': Np, 'coulomb_status':coulomb_status, 'snap_period':snap_period,\
'dt':dt, 'T_gamma':T_gamma}
def OLS(x, y):
'''OLS: x must be a vertical two-dimensional array'''
X = np.hstack((np.reshape(np.ones(x.shape[0]), (-1,1)), x))#.transpose()
Xpr = X.transpose()
beta = np.dot(np.dot(np.linalg.inv(np.dot(Xpr, X)), Xpr), y)
#Estimate errors
sigma_sq = np.dot(y - np.dot(X, beta), y - np.dot(X, beta))/(len(y) - 1.)
sigma_beta_sq = sigma_sq*np.linalg.inv(np.dot(Xpr, X))
return beta, sigma_beta_sq # = [f_0, df/d(A^2)]
def diffusion_from_transport_gsd(folder_path, f_name, center_fixed = True, useframes = -1):
"""
Diffusion constant D is calculated from 4Dt = <(r(t) - r(0))^2>, or 2D_x*t = <(x(t) - x(0))^2>.
The average is calculated over all particles and over different time origins.
Time origins go from 0 to n_frames/2, and t goes from 0 to n_frames/2. This way,
the data are always within the trajectory.
center_fixed = True: eliminate oveall motion of center of mass
return D_x, D_y
D_x, D_y diffusion for x- and y-coordinates;
"""
params = read_log(folder_path)
if folder_path[-1] != '/':
folder_path = folder_path + '/'
with gsd.fl.GSDFile(folder_path + f_name, 'rb') as f:
n_frames = f.nframes
box = f.read_chunk(frame=0, name='configuration/box')
half_frames = int(n_frames/2) - 1 #sligtly less than half to avoid out of bound i
if useframes < 1 or useframes > half_frames:
useframes = half_frames
t_step = f.read_chunk(frame=0, name='configuration/step')
n_p = f.read_chunk(frame=0, name='particles/N')
x_sq_av = np.zeros(useframes)
y_sq_av = np.zeros(useframes)
for t_origin in range(n_frames - useframes - 1):
pos_0 = f.read_chunk(frame=t_origin, name='particles/position')
mean_pos_0 = np.mean(pos_0, axis = 0)
pos = pos_0
pos_raw = pos_0
for j_frame in range(useframes):
pos_m1 = pos
pos_m1_raw = pos_raw
pos_raw = f.read_chunk(frame=j_frame + t_origin, name='particles/position') - pos_0
pos = md_tools.correct_jumps(pos_raw, pos_m1, pos_m1_raw, box[0], box[1])
if center_fixed:
pos -= np.mean(pos, axis = 0) - mean_pos_0 #correct for center of mass movement
x_sq_av[j_frame] += np.mean(pos[:,0]**2)
y_sq_av[j_frame] += np.mean(pos[:,1]**2)
x_sq_av /= (n_frames - useframes - 1)
y_sq_av /= (n_frames - useframes - 1)
# OLS estimate for beta_x[0] + beta_x[1]*t = <|x_i(t) - x_i(0)|^2>
a = np.ones((useframes, 2)) # matrix a = ones(half_frames) | (0; dt; 2dt; 3dt; ...)
a[:,1] = params['snap_period']*params['dt']*np.cumsum(np.ones(useframes), axis = 0) - params['dt']
b_cutoff = int(useframes/10) #cutoff to get only linear part of x_sq_av, makes results a bit more clean
beta_x = np.linalg.lstsq(a[b_cutoff:, :], x_sq_av[b_cutoff:], rcond=-1)
beta_y = np.linalg.lstsq(a[b_cutoff:, :], y_sq_av[b_cutoff:], rcond=-1)
fig, ax = plt.subplots(1,1, figsize=(7,5))
ax.scatter(a[:,1], x_sq_av, label='$\\langle x^2\\rangle$')
ax.scatter(a[:,1], y_sq_av, label='$\\langle y^2\\rangle$')
ax.legend(loc=7)
ax.set_xlabel('$t$')
ax.set_ylabel('$\\langle r_i^2 \\rangle$')
if center_fixed:
center_fixed_str = 'cm_fixed'
else:
center_fixed_str = 'cm_free'
fig.savefig(folder_path + 'r2_diff_' + f_name +'_' + center_fixed_str + '.png')
plt.close('all')
D_x = beta_x[0][1]/2
D_y = beta_y[0][1]/2
print('D_x = {}'.format(D_x))
print('D_y = {}'.format(D_y))
return (D_x, D_y)
def diffusion_helper(arg_dict):
return diffusion_from_transport_gsd(arg_dict['sf'], arg_dict['fname'], center_fixed=arg_dict['center_fixed'], useframes = arg_dict['useframes'])
def Teff_from_gsd(args):
fpath = args['sf'] + '/' + args['fname']
with gsd.fl.GSDFile(fpath, 'rb') as f:
n_frames = f.nframes
N = f.read_chunk(frame=0, name='particles/N')
v = np.zeros((n_frames, int(N), 2))
for t in range(n_frames):
v_t = f.read_chunk(frame=t, name='particles/velocity')
v[t, :, 0] = v_t[:,0]
v[t, :, 1] = v_t[:,1]
#v_cm = np.mean(v, axis=1)
#mean_v_cmx = np.mean(v_cm[:,0])
#print("mean v_cm = {}".format(mean_v_cmx))
#sigma_v_cmx = np.sqrt(np.mean((v_cm[:,0] - mean_v_cmx)**2))/np.sqrt(n_frames)
#print("error = {}".format(sigma_v_cmx))
#mean_v_cmy = np.mean(v_cm[:,1])
#print("mean v_cm_y = {}".format(mean_v_cmy))
#sigma_v_cmy = np.sqrt(np.mean((v_cm[:,1] - mean_v_cmy)**2))/np.sqrt(n_frames)
#print("error_y = {}".format(sigma_v_cmy))
#v_rel = np.swapaxes(v, 0,1) - v_cm
v_swap = np.swapaxes(v, 0,1)
#T_eff = 0.5*np.mean(v_rel[:,:,0]**2 + v_rel[:,:,1]**2, axis = 0)
T_eff = 0.5*np.mean(v_swap[:,:,0]**2 + v_swap[:,:,1]**2, axis = 0)
print('T_eff = {}'.format(np.mean(T_eff)))
return np.mean(T_eff)
def print_help():
print('This script plots diffusion vs Gamma for data taken in diffusion measurements.')
print('===========================================================')
print('Usage: python plotDiff_pG.py diffusion_data/a32x32_* [--options]')
print('This will process all folders that match mobility_data/a32x32_*')
print('===========================================================')
print('Options:')
print('\t--cmfixed will subtract the displacement of the center of mass in diffusion calculation (default behavior)')
print('\t--cmfree will NOT subtract the displacement of the center of mass in diffusion calculation (default behavior)')
print('\t--showtext will print text info on the plots')
print('\t--NP N - will use N parallel processes in the calculations')
print('\t--sf [subfolder] - will only process the specified subfolder in all folders')
print('\t--help or -h will print this help')
## =======================================================================
# Units
unit_M = 9.10938356e-31 # kg, electron mass
unit_D = 1e-6 # m, micron
unit_E = 1.38064852e-23 # m^2*kg/s^2
unit_t = np.sqrt(unit_M*unit_D**2/unit_E) # = 2.568638150515e-10 s
epsilon_0 = 8.854187817e-12 # F/m = C^2/(J*m), vacuum permittivity
hbar = 1.0545726e-27/(unit_E*1e7)/unit_t
m_e = 9.10938356e-31/unit_M
unit_Q = np.sqrt(unit_E*1e7*unit_D*1e2) # Coulombs
unit_Qe = unit_Q/4.8032068e-10 # e, unit charge in units of elementary charge e
e_charge = 1/unit_Qe # electron charge in units of unit_Q
curr_fname = inspect.getfile(inspect.currentframe())
curr_path = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
##=======================================================================
# Make a list of folders we want to process
cm_fixed = True #default that can be changed by --cmfree
cm_fixed_str = 'cm_fixed'
show_text = False
Nproc = 1
selected_subfolders = []
folder_list = []
for i in range(len(sys.argv)):
if os.path.isdir(sys.argv[i]):
folder_list.append(sys.argv[i])
elif sys.argv[i] == '--sf':
try:
selected_subfolders.append(sys.argv[i+1])
except:
raise RuntimeError('Could not recognize the value of --sf. argv={}'.format(argv))
elif sys.argv[i] == '--showtext':
show_text = True
elif sys.argv[i] == '--GC':
gamma_c = float(sys.argv[i+1])
elif sys.argv[i] == '--help' or sys.argv[i] == '-h':
print_help()
exit()
try:
print('Gamma_c = {}'.format(gamma_c))
except:
raise RuntimeError('Gamma_c not specified. Use --GC argument.')
print('Selected subfolders: {}'.format(selected_subfolders))
# Make a list of subfolders p### in each folders
subfolder_lists = []
for folder in folder_list:
sf_list = []
for item in os.walk(folder):
# subfolder name and contained files
sf_list.append((item[0], item[2]))
sf_list = sf_list[1:]
subfolder_lists.append(sf_list)
##=======================================================================
for ifold, folder in enumerate(folder_list):
print('==========================================================')
print(folder)
print('==========================================================')
# Keep only selected subfolders in the list is there is selection
if len(selected_subfolders) > 0:
sf_lists_to_go = []
for isf, sf in enumerate(subfolder_lists[ifold]):
sf_words = sf[0].split('/')
if sf_words[-1] in selected_subfolders:
sf_lists_to_go.append(sf)
else:
sf_lists_to_go = subfolder_lists[ifold]
for isf, sf in enumerate(sf_lists_to_go):
sf_words = sf[0].split('/')
print(sf_words[-1])
if sf_words[-1][0] != 'p':
raise ValueError("Expected subfolder name to start with `p`, in {}".format(fname))
log_data = read_log(sf[0])
folder_name = folder.split('/')[-1]
if sf[0][-1] == '/':
sf[0] = sf[0][:-1]
sf_name = sf[0].split('/')[-1]
#Read Dx Dy vs Gamma from the .dat file
#DxDy_data = {'Dx_arr':Dx_arr, 'Dy_arr':Dy_arr, 'Dx_arr_gauss': Dx_arr*cm2s_convert, 'Dy_arr_gauss':Dy_arr*cm2s_convert, \
# 'gamma_arr':gamma_arr, 'gamma_eff_arr':gamma_eff_arr}
cm_fixed_str = 'cm_fixed'
with open(sf[0] + '/DxDy_data_' + cm_fixed_str + '_' + sf_name + '_' + folder_name + '.dat', 'r') as ff:
DxDy_data = pickle.load(ff)
Dx_arr = DxDy_data['Dx_arr']
Dy_arr = DxDy_data['Dy_arr']
gamma_eff_arr = DxDy_data['gamma_eff_arr']
# Remove points where gamma > gamma_c
clip_ind = np.where(gamma_eff_arr < gamma_c)[0]
Dx_arr_clip = Dx_arr[clip_ind]
Dy_arr_clip = Dy_arr[clip_ind]
gamma_arr_clip = gamma_eff_arr[clip_ind]
print('Dx_arr = {}'.format(Dx_arr_clip))
print('Dy_arr = {}'.format(Dy_arr_clip))
## ======================================================================
## Plot Dx,Dy vs effective G (calculated from data rather then read from the log)
# in Gaussian units
labelfont = 28
tickfont = labelfont - 4
legendfont = labelfont - 4
cm2s_convert = unit_D**2/unit_t*1e4
fig, ax1 = plt.subplots(1,1, figsize=(7,6))
scatter1 = ax1.scatter(gamma_arr_clip, np.log(Dx_arr_clip*cm2s_convert), label='$D_\\perp$', color = 'green', marker='o')
ax1.set_xlabel('$\\Gamma$', fontsize=labelfont)
ax1.set_ylabel('$\\log(D/D_0)$', fontsize=labelfont)
scatter2 = ax1.scatter(gamma_arr_clip, np.log(Dy_arr_clip*cm2s_convert), label='$D_\\parallel$', color = 'red', marker='s')
#ax1.set_xlim([np.min(gamma_eff_arr) - 2, np.max(gamma_eff_arr) + 2])
ax1.legend(loc=1, fontsize=legendfont)
ax1.tick_params(labelsize= tickfont)
ax1.locator_params(nbins=6, axis='y')
formatter = mticker.ScalarFormatter(useMathText=True)
formatter.set_powerlimits((-3,2))
ax1.yaxis.set_major_formatter(formatter)
#Place text
if show_text:
text_list = ['$\\Gamma_c = {:.1f}$'.format(gamma_c)]
y_lim = ax1.get_ylim()
x_lim = ax1.get_xlim()
h = y_lim[1] - y_lim[0]
w = x_lim[1] - x_lim[0]
text_x = x_lim[0] + 0.5*w
text_y = y_lim[1] - 0.05*h
if type(text_list) == list:
n_str = len(text_list)
for i_fig in range(n_str):
ax1.text(text_x, text_y - 0.05*h*i_fig, text_list[i_fig])
elif type(text_list) == str:
ax1.text(text_x, text_y, text_list)
else:
raise TypeError('text_list must be a list of strings or a string')
#fig.patch.set_alpha(alpha=1)
plt.tight_layout()
fig.savefig(folder + '/' + 'DxDy_G_log_' + sf_name + '_' + folder_name + '_{:.2f}'.format(gamma_c) + '.pdf')
#fig.savefig(sf[0] + '/' + 'DxDy_Geff_' + cm_fixed_str + '_' + sf_name + '_' + folder_name + '.png')
#fig.savefig(sf[0] + '/' + 'DxDy_Geff_' + cm_fixed_str + '_' + sf_name + '_' + folder_name + '.eps')
#fig.savefig(sf[0] + '/' + 'DxDy_Geff_' + cm_fixed_str + '_' + sf_name + '_' + folder_name + '.pdf')
plt.close('all')
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Week 8_activity/controller/__init__.py | Kenny-WilliamsStockdale/PythonPlay | 0 | 104926 | __all__ = ["DES"]
| [
1,
4770,
497,
1649,
353,
6796,
2287,
29903,
3108,
30004,
13,
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] |
rpython/translator/platform/posix.py | wdv4758h/mu-client-pypy | 0 | 4850 | """Base support for POSIX-like platforms."""
import py, os, sys
from rpython.translator.platform import Platform, log, _run_subprocess
import rpython
rpydir = str(py.path.local(rpython.__file__).join('..'))
class BasePosix(Platform):
exe_ext = ''
make_cmd = 'make'
relevant_environ = ('CPATH', 'LIBRARY_PATH', 'C_INCLUDE_PATH')
DEFAULT_CC = 'gcc'
rpath_flags = ['-Wl,-rpath=\'$$ORIGIN/\'']
def __init__(self, cc=None):
self.cc = cc or os.environ.get('CC', self.DEFAULT_CC)
def _libs(self, libraries):
return ['-l%s' % lib for lib in libraries]
def _libdirs(self, library_dirs):
assert '' not in library_dirs
return ['-L%s' % ldir for ldir in library_dirs]
def _includedirs(self, include_dirs):
assert '' not in include_dirs
return ['-I%s' % idir for idir in include_dirs]
def _linkfiles(self, link_files):
return list(link_files)
def _compile_c_file(self, cc, cfile, compile_args):
oname = self._make_o_file(cfile, ext='o')
args = ['-c'] + compile_args + [str(cfile), '-o', str(oname)]
self._execute_c_compiler(cc, args, oname,
cwd=str(cfile.dirpath()))
return oname
def _link_args_from_eci(self, eci, standalone):
return Platform._link_args_from_eci(self, eci, standalone)
def _exportsymbols_link_flags(self):
if (self.cc == 'mingw32' or (self.cc== 'gcc' and os.name=='nt')
or sys.platform == 'cygwin'):
return ["-Wl,--export-all-symbols"]
return ["-Wl,--export-dynamic"]
def _link(self, cc, ofiles, link_args, standalone, exe_name):
args = [str(ofile) for ofile in ofiles] + link_args
args += ['-o', str(exe_name)]
if not standalone:
args = self._args_for_shared(args)
self._execute_c_compiler(cc, args, exe_name,
cwd=str(exe_name.dirpath()))
return exe_name
def _pkg_config(self, lib, opt, default, check_result_dir=False):
try:
ret, out, err = _run_subprocess("pkg-config", [lib, opt])
except OSError, e:
err = str(e)
ret = 1
if ret:
result = default
else:
# strip compiler flags
result = [entry[2:] for entry in out.split()]
#
if not result:
pass # if pkg-config explicitly returned nothing, then
# we assume it means no options are needed
elif check_result_dir:
# check that at least one of the results is a valid dir
for check in result:
if os.path.isdir(check):
break
else:
if ret:
msg = ("running 'pkg-config %s %s' failed:\n%s\n"
"and the default %r is not a valid directory" % (
lib, opt, err.rstrip(), default))
else:
msg = ("'pkg-config %s %s' returned no valid directory:\n"
"%s\n%s" % (lib, opt, out.rstrip(), err.rstrip()))
raise ValueError(msg)
return result
def get_rpath_flags(self, rel_libdirs):
# needed for cross-compilation i.e. ARM
return self.rpath_flags + ['-Wl,-rpath-link=\'%s\'' % ldir
for ldir in rel_libdirs]
def get_shared_only_compile_flags(self):
return tuple(self.shared_only) + ('-fvisibility=hidden',)
def gen_makefile(self, cfiles, eci, exe_name=None, path=None,
shared=False, headers_to_precompile=[],
no_precompile_cfiles = [], icon=None):
cfiles = self._all_cfiles(cfiles, eci)
if path is None:
path = cfiles[0].dirpath()
rpypath = py.path.local(rpydir)
if exe_name is None:
exe_name = cfiles[0].new(ext=self.exe_ext)
else:
exe_name = exe_name.new(ext=self.exe_ext)
linkflags = list(self.link_flags)
if shared:
linkflags = self._args_for_shared(linkflags)
linkflags += self._exportsymbols_link_flags()
if shared:
libname = exe_name.new(ext='').basename
target_name = 'lib' + exe_name.new(ext=self.so_ext).basename
else:
target_name = exe_name.basename
if shared:
cflags = tuple(self.cflags) + self.get_shared_only_compile_flags()
else:
cflags = tuple(self.cflags) + tuple(self.standalone_only)
m = GnuMakefile(path)
m.exe_name = path.join(exe_name.basename)
m.eci = eci
def rpyrel(fpath):
lpath = py.path.local(fpath)
rel = lpath.relto(rpypath)
if rel:
return os.path.join('$(RPYDIR)', rel)
# Hack: also relativize from the path '$RPYDIR/..'.
# Otherwise, when translating pypy, we get the paths in
# pypy/module/* that are kept as absolute, which makes the
# whole purpose of $RPYDIR rather pointless.
rel = lpath.relto(rpypath.join('..'))
if rel:
return os.path.join('$(RPYDIR)', '..', rel)
m_dir = m.makefile_dir
if m_dir == lpath:
return '.'
if m_dir.dirpath() == lpath:
return '..'
return fpath
rel_cfiles = [m.pathrel(cfile) for cfile in cfiles]
rel_ofiles = [rel_cfile[:rel_cfile.rfind('.')]+'.o' for rel_cfile in rel_cfiles]
m.cfiles = rel_cfiles
rel_includedirs = [rpyrel(incldir) for incldir in
self.preprocess_include_dirs(eci.include_dirs)]
rel_libdirs = [rpyrel(libdir) for libdir in
self.preprocess_library_dirs(eci.library_dirs)]
m.comment('automatically generated makefile')
definitions = [
('RPYDIR', '"%s"' % rpydir),
('TARGET', target_name),
('DEFAULT_TARGET', exe_name.basename),
('SOURCES', rel_cfiles),
('OBJECTS', rel_ofiles),
('LIBS', self._libs(eci.libraries) + list(self.extra_libs)),
('LIBDIRS', self._libdirs(rel_libdirs)),
('INCLUDEDIRS', self._includedirs(rel_includedirs)),
('CFLAGS', cflags),
('CFLAGSEXTRA', list(eci.compile_extra)),
('LDFLAGS', linkflags),
('LDFLAGS_LINK', list(self.link_flags)),
('LDFLAGSEXTRA', list(eci.link_extra)),
('CC', self.cc),
('CC_LINK', eci.use_cpp_linker and 'g++' or '$(CC)'),
('LINKFILES', eci.link_files),
('RPATH_FLAGS', self.get_rpath_flags(rel_libdirs)),
]
for args in definitions:
m.definition(*args)
rules = [
('all', '$(DEFAULT_TARGET)', []),
('$(TARGET)', '$(OBJECTS)', '$(CC_LINK) $(LDFLAGSEXTRA) -o $@ $(OBJECTS) $(LIBDIRS) $(LIBS) $(LINKFILES) $(LDFLAGS)'),
('%.o', '%.c', '$(CC) $(CFLAGS) $(CFLAGSEXTRA) -o $@ -c $< $(INCLUDEDIRS)'),
('%.o', '%.s', '$(CC) $(CFLAGS) $(CFLAGSEXTRA) -o $@ -c $< $(INCLUDEDIRS)'),
('%.o', '%.cxx', '$(CXX) $(CFLAGS) $(CFLAGSEXTRA) -o $@ -c $< $(INCLUDEDIRS)'),
]
for rule in rules:
m.rule(*rule)
if shared:
m.definition('SHARED_IMPORT_LIB', libname),
m.definition('PYPY_MAIN_FUNCTION', "pypy_main_startup")
m.rule('main.c', '',
'echo "'
'int $(PYPY_MAIN_FUNCTION)(int, char*[]); '
'int main(int argc, char* argv[]) '
'{ return $(PYPY_MAIN_FUNCTION)(argc, argv); }" > $@')
m.rule('$(DEFAULT_TARGET)', ['$(TARGET)', 'main.o'],
'$(CC_LINK) $(LDFLAGS_LINK) main.o -L. -l$(SHARED_IMPORT_LIB) -o $@ $(RPATH_FLAGS)')
return m
def execute_makefile(self, path_to_makefile, extra_opts=[]):
if isinstance(path_to_makefile, GnuMakefile):
path = path_to_makefile.makefile_dir
else:
path = path_to_makefile
log.execute('make %s in %s' % (" ".join(extra_opts), path))
returncode, stdout, stderr = _run_subprocess(
self.make_cmd, ['-C', str(path)] + extra_opts)
self._handle_error(returncode, stdout, stderr, path.join('make'))
class Definition(object):
def __init__(self, name, value):
self.name = name
self.value = value
def write(self, f):
def write_list(prefix, lst):
lst = lst or ['']
for i, fn in enumerate(lst):
fn = fn.replace('\\', '\\\\')
print >> f, prefix, fn,
if i < len(lst)-1:
print >> f, '\\'
else:
print >> f
prefix = ' ' * len(prefix)
name, value = self.name, self.value
if isinstance(value, str):
f.write('%s = %s\n' % (name, value.replace('\\', '\\\\')))
else:
write_list('%s =' % (name,), value)
f.write('\n')
class Rule(object):
def __init__(self, target, deps, body):
self.target = target
self.deps = deps
self.body = body
def write(self, f):
target, deps, body = self.target, self.deps, self.body
if isinstance(deps, str):
dep_s = deps
else:
dep_s = ' '.join(deps)
f.write('%s: %s\n' % (target, dep_s))
if isinstance(body, str):
f.write('\t%s\n' % body)
elif body:
f.write('\t%s\n' % '\n\t'.join(body))
f.write('\n')
class Comment(object):
def __init__(self, body):
self.body = body
def write(self, f):
f.write('# %s\n' % (self.body,))
class GnuMakefile(object):
def __init__(self, path=None):
self.defs = {}
self.lines = []
self.makefile_dir = py.path.local(path)
def pathrel(self, fpath):
if fpath.dirpath() == self.makefile_dir:
return fpath.basename
elif fpath.dirpath().dirpath() == self.makefile_dir.dirpath():
assert fpath.relto(self.makefile_dir.dirpath()), (
"%r should be relative to %r" % (
fpath, self.makefile_dir.dirpath()))
path = '../' + fpath.relto(self.makefile_dir.dirpath())
return path.replace('\\', '/')
else:
return str(fpath)
def definition(self, name, value):
defs = self.defs
defn = Definition(name, value)
if name in defs:
self.lines[defs[name]] = defn
else:
defs[name] = len(self.lines)
self.lines.append(defn)
def rule(self, target, deps, body):
self.lines.append(Rule(target, deps, body))
def comment(self, body):
self.lines.append(Comment(body))
def write(self, out=None):
if out is None:
f = self.makefile_dir.join('Makefile').open('w')
else:
f = out
for line in self.lines:
line.write(f)
f.flush()
if out is None:
f.close()
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] |
src/cobald/daemon/runners/trio_runner.py | thoto/cobald | 7 | 51972 | import trio
from functools import partial
from .base_runner import BaseRunner
from .async_tools import raise_return, AsyncExecution
class TrioRunner(BaseRunner):
"""Runner for coroutines with :py:mod:`trio`"""
flavour = trio
def __init__(self):
self._nursery = None
super().__init__()
def register_payload(self, payload):
super().register_payload(partial(raise_return, payload))
def run_payload(self, payload):
execution = AsyncExecution(payload)
super().register_payload(execution.coroutine)
return execution.wait()
def _run(self):
return trio.run(self._await_all)
async def _await_all(self):
"""Async component of _run"""
delay = 0.0
# we run a top-level nursery that automatically reaps/cancels for us
async with trio.open_nursery() as nursery:
while self.running.is_set():
await self._start_payloads(nursery=nursery)
await trio.sleep(delay)
delay = min(delay + 0.1, 1.0)
# cancel the scope to cancel all payloads
nursery.cancel_scope.cancel()
async def _start_payloads(self, nursery):
"""Start all queued payloads"""
with self._lock:
for coroutine in self._payloads:
nursery.start_soon(coroutine)
self._payloads.clear()
await trio.sleep(0)
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] |
test/validators/test_string_validator.py | jmmease/ipyplotly | 8 | 145114 | import pytest
from ipyplotly.basevalidators import StringValidator
import numpy as np
# Fixtures
# --------
@pytest.fixture()
def validator():
return StringValidator('prop', 'parent')
@pytest.fixture()
def validator_values():
return StringValidator('prop', 'parent', values=['foo', 'BAR', ''])
@pytest.fixture()
def validator_no_blanks():
return StringValidator('prop', 'parent', no_blank=True)
@pytest.fixture
def validator_aok():
return StringValidator('prop', 'parent', array_ok=True)
@pytest.fixture
def validator_aok_values():
return StringValidator('prop', 'parent', values=['foo', 'BAR', '', 'baz'], array_ok=True)
@pytest.fixture()
def validator_no_blanks_aok():
return StringValidator('prop', 'parent', no_blank=True, array_ok=True)
# Array not ok
# ------------
# ### Acceptance ###
@pytest.mark.parametrize('val',
['bar', 'HELLO!!!', 'world!@#$%^&*()', ''])
def test_acceptance(val, validator: StringValidator):
assert validator.validate_coerce(val) == val
# ### Rejection by value ###
@pytest.mark.parametrize('val',
[(), [], [1, 2, 3], set(), np.nan, np.pi])
def test_rejection(val, validator: StringValidator):
with pytest.raises(ValueError) as validation_failure:
validator.validate_coerce(val)
assert 'Invalid value' in str(validation_failure.value)
# Valid values
# ------------
@pytest.mark.parametrize('val',
['foo', 'BAR', ''])
def test_acceptance_values(val, validator_values: StringValidator):
assert validator_values.validate_coerce(val) == val
@pytest.mark.parametrize('val',
['FOO', 'bar', 'other', '1234'])
def test_rejection_values(val, validator_values: StringValidator):
with pytest.raises(ValueError) as validation_failure:
validator_values.validate_coerce(val)
assert 'Invalid value'.format(val=val) in str(validation_failure.value)
assert "['foo', 'BAR', '']" in str(validation_failure.value)
# ### No blanks ###
@pytest.mark.parametrize('val',
['bar', 'HELLO!!!', 'world!@#$%^&*()'])
def test_acceptance_no_blanks(val, validator_no_blanks: StringValidator):
assert validator_no_blanks.validate_coerce(val) == val
@pytest.mark.parametrize('val',
[''])
def test_rejection_no_blanks(val, validator_no_blanks: StringValidator):
with pytest.raises(ValueError) as validation_failure:
validator_no_blanks.validate_coerce(val)
assert 'A non-empty string' in str(validation_failure.value)
# Array ok
# --------
# ### Acceptance ###
@pytest.mark.parametrize('val',
['foo', 'BAR', '', 'baz'])
def test_acceptance_aok_scalars(val, validator_aok: StringValidator):
assert validator_aok.validate_coerce(val) == val
@pytest.mark.parametrize('val',
['foo', ['foo'], np.array(['BAR', ''], dtype='object'), ['baz', 'baz', 'baz']])
def test_acceptance_aok_list(val, validator_aok: StringValidator):
coerce_val = validator_aok.validate_coerce(val)
if isinstance(val, (list, np.ndarray)):
assert np.array_equal(coerce_val, np.array(val, dtype=coerce_val.dtype))
else:
assert coerce_val == val
# ### Rejection by type ###
@pytest.mark.parametrize('val',
[['foo', ()], ['foo', 3, 4], [3, 2, 1]])
def test_rejection_aok(val, validator_aok: StringValidator):
with pytest.raises(ValueError) as validation_failure:
validator_aok.validate_coerce(val)
assert 'Invalid element(s)' in str(validation_failure.value)
# ### Rejection by value ###
@pytest.mark.parametrize('val',
[['foo', 'bar'], ['3', '4'], ['BAR', 'BAR', 'hello!']])
def test_rejection_aok_values(val, validator_aok_values: StringValidator):
with pytest.raises(ValueError) as validation_failure:
validator_aok_values.validate_coerce(val)
assert 'Invalid element(s)' in str(validation_failure.value)
# ### No blanks ###
@pytest.mark.parametrize('val',
['123', ['bar', 'HELLO!!!'], ['world!@#$%^&*()']])
def test_acceptance_no_blanks_aok(val, validator_no_blanks_aok: StringValidator):
coerce_val = validator_no_blanks_aok.validate_coerce(val)
if isinstance(val, (list, np.ndarray)):
assert np.array_equal(coerce_val, np.array(val, dtype=coerce_val.dtype))
else:
assert coerce_val == val
@pytest.mark.parametrize('val',
['', ['foo', 'bar', ''], ['']])
def test_rejection_no_blanks_aok(val, validator_no_blanks_aok: StringValidator):
with pytest.raises(ValueError) as validation_failure:
validator_no_blanks_aok.validate_coerce(val)
assert 'A non-empty string' in str(validation_failure.value)
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envi/archs/msp430/__init__.py | vEpiphyte/vivisect | 1 | 98744 | <filename>envi/archs/msp430/__init__.py
"""
msp430 module
"""
############
# Author: <NAME>
# Started: 05/23/2009
#
import envi
from envi.archs.msp430.regs import *
from envi.archs.msp430.disasm import *
from envi.archs.msp430.const import *
class Msp430Module(envi.ArchitectureModule):
def __init__(self):
envi.ArchitectureModule.__init__(self, "msp430", maxinst=4)
self._arch_dis = Msp430Disasm()
def archGetRegCtx(self):
return Msp430RegisterContext()
def archGetNopInstr(self):
return '\x03\x43' # NOP is emulated with: MOV #0, R3
def archGetRegisterGroups(self):
groups = envi.ArchitectureModule.archGetRegisterGroups(self)
general= ('general', registers, )
groups.append(general)
return groups
def getPointerSize(self):
return 2
def pointerString(self, va):
return '0x{:04x}'.format(va)
def archParseOpcode(self, bytes, offset=0, va=0):
return self._arch_dis.disasm(bytes, offset, va)
def getEmulator(self):
return Msp430Emulator()
def getArchDefaultCall(self):
return 'msp430call'
# NOTE: This one must be after the definition of Msp430Module
from envi.archs.msp430.emu import *
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method/pruning_googlenet.py | Nuctech-AI/LBS_pruning | 6 | 49687 | <reponame>Nuctech-AI/LBS_pruning
import torch.nn as nn
import torch
import copy
import numpy as np
import collections
def pruning_grad_densent_layer(namex,layer,index=None,groups_y=None,re_num=None):
if isinstance(layer, nn.Conv2d):
if index is not None:
layer.weight.data = layer.weight.data[:, index]
layer.in_channels = index.shape[0]
weight = layer.weight.data
a = np.linspace(0, weight.shape[0]-1, num=weight.shape[0])
if re_num is None:
dsa=a
else:
dsa_ori=re_num
dsa=np.setdiff1d(a,dsa_ori)
dsa.sort()
layer.weight.data = layer.weight.data[dsa]
if layer.bias is not None:
layer.bias.data = layer.bias.data[dsa]
layer.out_channels = dsa.shape[0]
return layer, dsa
elif isinstance(layer, nn.BatchNorm2d):
if layer.weight is not None:
layer.weight.data=layer.weight.data[index]
if layer.bias is not None:
layer.bias.data=layer.bias.data[index]
layer.running_mean.data = layer.running_mean.data[index]
layer.running_var.data = layer.running_var.data[index]
layer.num_features = index.shape[0]
return layer, index
elif isinstance(layer, torch.nn.Linear):
layer.weight.data = layer.weight.data[:, index]
layer.in_features = index.shape[0]
return layer,index
else:
print(layer)
def get_ori_shape(model):
ori_shape = collections.OrderedDict()
for name, layer in model.named_modules():
if isinstance(layer, torch.nn.Conv2d) or isinstance(layer, torch.nn.BatchNorm2d) or isinstance(layer,torch.nn.Linear):
namex = name + '.weight'
if layer.weight is not None:
ori_shape[namex] = layer.weight.data.shape[0]
else:
ori_shape[namex] = layer.running_mean.data.shape[0]
return ori_shape
def get_ori_channels(model):
channels = collections.OrderedDict()
for name, layer in model.named_modules():
if isinstance(layer, torch.nn.Conv2d) or isinstance(layer,torch.nn.Linear):
namex = name + '.weight'
channels[namex] = layer.weight.data.shape[1]
return channels
def pruning_googlenet_grad(model,config_prune=None,re_ind=None):
ori_shape = get_ori_shape(model)
ori_channels=get_ori_channels(model)
index=None
layer_index = collections.OrderedDict()
ori_model=copy.deepcopy(model)
#ori_model.cuda()
concat_fig=config_prune['concat']
layer_list = list(model.state_dict().keys())
for name, m in model.named_modules():
if isinstance(m, torch.nn.Conv2d):
ne=name+'.weight'
if ne not in re_ind:
#print(ne)
re_ind[ne]=None
offset=collections.OrderedDict()
for index_i,i in enumerate(concat_fig):
if index_i==0:
continue
for index_j,j in enumerate(i):
offset[j]=concat_fig[index_i-1]
for name_cnv,name_x in offset.items():
for ll,each_x in enumerate(name_x):
if 'branch3x3' in each_x:
name_x[ll]=name_x[ll][:-8]+'3'+name_x[ll][-7:]
if 'branch5x5' in each_x:
name_x[ll] =name_x[ll][:-8]+'6'+name_x[ll][-7:]
cov_index=collections.OrderedDict()
for name_cnv, name_x in offset.items():
offset1=0
cov_index1=None
for ll, each_x in enumerate(name_x):
if ll==0:
offset1=0
cov_index1=copy.deepcopy(re_ind[each_x])
else:
offset1=offset1+ori_shape[name_x[ll-1]]
if re_ind[each_x] is None:
continue
if cov_index1 is None:
cov_index1=copy.deepcopy(re_ind[each_x])+offset1
else:
cov_index1=np.hstack((cov_index1,(re_ind[each_x]+offset1)))
if cov_index1 is not None:
cov_index1.sort()
cov_index[name_cnv]=cov_index1
index = None
for name,layer in model.named_modules():
if isinstance(layer,torch.nn.Conv2d) or isinstance(layer,torch.nn.BatchNorm2d) or isinstance(layer,torch.nn.Linear):
namex = name + '.weight'
#print(namex)
if isinstance(layer,torch.nn.Conv2d):
#ori_shape[namex]=layer.weight.data.shape[0]
if namex in cov_index:
a = np.linspace(0, ori_channels[namex] - 1, num=ori_channels[namex])
if cov_index[namex] is not None:
index = np.setdiff1d(a, cov_index[namex])
else:
index=a
layer, index = pruning_grad_densent_layer(namex, layer, index,re_num=re_ind[namex])
layer_index[namex]=index
elif isinstance(layer,torch.nn.BatchNorm2d):
layer,index=pruning_grad_densent_layer(namex,layer,index)
layer_index[namex]=index
elif isinstance(layer,torch.nn.Linear):
#ori_shape[namex] = layer.weight.data.shape[0]
if namex in cov_index:
a = np.linspace(0, ori_channels[namex] - 1, num=ori_channels[namex])
if cov_index[namex] is not None:
index = np.setdiff1d(a, cov_index[namex])
else:
index=a
layer,index=pruning_grad_densent_layer(namex,layer,index)
layer_index[namex]=index
else:
continue
else:
continue
return model | [
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tests/test_anonymous.py | psentinelli/filebrowser-safe | 25 | 180710 | from django.test import TestCase
from django.urls import reverse
class FilebrowserAnonymousTestCase(TestCase):
def test_browse(self):
url = reverse("fb_browse")
response = self.client.get(url)
self.assertEqual(302, response.status_code)
self.assertEqual("/admin/login/?next=" + url, response.url)
def test_mkdir(self):
url = reverse("fb_browse")
response = self.client.get(url)
self.assertEqual(302, response.status_code)
self.assertEqual("/admin/login/?next=" + url, response.url)
def test_rename(self):
url = reverse("fb_rename")
response = self.client.get(url)
self.assertEqual(302, response.status_code)
self.assertEqual("/admin/login/?next=" + url, response.url)
def test_delete(self):
url = reverse("fb_delete")
response = self.client.get(url)
self.assertEqual(302, response.status_code)
self.assertEqual("/admin/login/?next=" + url, response.url)
def test_upload(self):
url = reverse("fb_upload")
response = self.client.get(url)
self.assertEqual(302, response.status_code)
self.assertEqual("/admin/login/?next=" + url, response.url)
def test_do_upload(self):
url = reverse("fb_do_upload")
response = self.client.get(url)
self.assertEqual(302, response.status_code)
self.assertEqual("/admin/login/?next=" + url, response.url)
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GdbPublisherNode.py | larics/gdb_ros_publisher | 10 | 106503 | import rospy
from rospy.msg import AnyMsg
class GdbPublisher(object):
def __init__(self, topic, msgtype, queue_size = 1):
self.topic = topic
self.msgtype = msgtype
# Construct publisher lazily, since we don't know whether to latch or not yet.
self.publisher = None
self.queue_size = queue_size
def publish(self, serialized_message, latch):
message = AnyMsg()
message.deserialize(serialized_message)
if self.publisher == None:
self.publisher = rospy.Publisher(self.topic, self.msgtype, queue_size=self.queue_size, latch = latch)
self.publisher.publish(message)
class GdbPublisherDictionary(dict):
def __init__(self, publishers = None):
super(GdbPublisherDictionary, self).__init__()
if(publishers != None):
self.insert(publishers)
def insert(self, publishers):
try:
iterator = iter(publishers)
for publisher in publishers:
self[publisher.topic] = publisher
except TypeError:
# add the single publisher that we received
self[publishers.topic] = publishers
def publish(self, topic, serialized_message, latch):
publisher = self.get(topic, None)
if publisher != None:
publisher.publish(serialized_message, latch)
return True
return False
class GdbMessageBreakpoint(gdb.Breakpoint):
# the default context extractors
default_serialized_message_extractor = lambda self: gdb.selected_frame().read_var('m')
default_topic_extractor = lambda self: str(gdb.selected_frame().read_var('this').dereference()\
['name_']['_M_dataplus']['_M_p'].string())
default_latched_extractor = lambda self: str(gdb.selected_frame().read_var('this').dereference()\
['latch_'])
def __init__(self, location, context_extractor = lambda: {}):
super(GdbMessageBreakpoint, self).__init__(location, internal = True)
self.context_extractor = context_extractor
self.silent = True
def stop(self):
if(not hasattr(self, 'enclosing_node')):
raise Exception('Missing node reference')
context = self.context_extractor()
if 'serialized_variable' not in context:
context['serialized_variable'] = self.default_serialized_message_extractor()
if 'topic' not in context:
context['topic'] = self.default_topic_extractor()
if 'latch' not in context:
context['latch'] = self.default_latched_extractor()
self.enclosing_node.handle_message(**context)
return False
class GdbPublisherNode(object):
def __init__ (self, publisher_dictionary, breakpoints, log_publisher = False, log_publisher_state = False):
self.ros_handle = rospy.init_node('gdb_publisher')
self.publisher_dictionary = publisher_dictionary
self.breakpoints = []
self.add_breakpoints(breakpoints)
self.log_publisher = log_publisher
self.log_publisher_state = log_publisher_state
self.enabled_states_saved = False
print "Initialized ROS publisher."
def add_breakpoint(self, breakpoint):
breakpoint.enclosing_node = self
self.breakpoints.append(breakpoint)
def add_breakpoints(self, breakpoints):
try:
iterator = iter(breakpoints)
for breakpoint in breakpoints:
self.add_breakpoint(breakpoint)
except TypeError:
self.add_breakpoint(breakpoint)
def handle_message(self, serialized_variable, topic, latch):
if topic[0] == '/':
topic = topic[1:]
if topic not in self.publisher_dictionary:
if self.log_publisher:
print "didn't publish message in {}".format(topic)
# don't bother with executing the rest since we are not publishing this message
return
message_start = serialized_variable['message_start']
# in ros/serialization.h, serializeMessage(), num_bytes = len + 4
serialized_length = serialized_variable['num_bytes'] - 4
serialized_message = self.convert_serialized_to_python(message_start, serialized_length)
self.publisher_dictionary.publish(topic, serialized_message, latch)
if self.log_publisher:
print 'published {}, {}'.format(topic, serialized_length)
def enable_breakpoints(self, print_notice = True):
for b in self.breakpoints:
b.enabled = True
if print_notice:
print "Enabled ROS publisher."
def disable_breakpoints(self, print_notice = True):
for b in self.breakpoints:
b.enabled = False
if print_notice:
print "Disabled ROS publisher."
def save_state_and_disable_ros_publisher(self):
if self.enabled_states_saved == True:
raise Exception('This should not have happened, ROS publisher state has already been saved')
self.breakpoint_enabled_states = [ b.enabled for b in self.breakpoints ]
self.enabled_states_saved = True
self.disable_breakpoints(False)
if self.breakpoint_enabled_states[0] and self.log_publisher_state:
print "Temporarily disabled ROS publisher."
def restore_ros_publisher_state(self):
if self.enabled_states_saved == True:
for b in zip(self.breakpoints, self.breakpoint_enabled_states):
b[0].enabled = b[1]
if self.breakpoint_enabled_states[0] and self.log_publisher_state:
print "Re-enabled ROS publisher."
self.enabled_states_saved = False
@staticmethod
def convert_serialized_to_python(c_array, length):
return ''.join([chr(c_array[char]) for char in range(length)])
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] |
scripts/pulp_test.py | rajiv256/cs531_proj_2 | 0 | 27582 | import pulp as pl
def create_affine_expression(coeffs, var_names):
assert len(coeffs) == len(var_names)
n = len(coeffs)
X = [pl.LpVariable(var_names[i]) for i in range(n)]
affine = pl.LpAffineExpression([(X[i], coeffs[i]) for i in range(n)])
return affine
def create_constraint(coeffs, var_names, sense, rhs):
"""Creates a constraint based on the args
Args:
coeffs: coefficients of the constraints
vars: Names of the vars
sense: +1, 0, -1 based on >=, ==, <= respectively. Or we can use pl.LpConstraintLE
rhs: numerical value of the rhs
Returns:
"""
assert len(coeffs) == len(var_names)
lhs = create_affine_expression(coeffs, var_names)
constr = pl.LpConstraint(lhs, sense=sense, rhs=rhs)
return constr
def test_affine_expression(coeffs=[1, 2, 3], var_names=['x_0', 'x_1', 'x_2']):
print(f'coeffs: {coeffs} | var_names: {var_names}')
affine = create_affine_expression(coeffs, var_names)
print(affine)
def test_constraint(coeffs=[1, 2, 3], var_names=['x_0', 'x_1', 'x_2'], sense=pl.LpConstraintLE, rhs=1):
print(f'coeffs: {coeffs} | var_names: {var_names} | sense: {sense} | rhs: {rhs}')
constraint = create_constraint(coeffs, var_names, sense, rhs)
print(constraint)
def test_0(solver_type):
model = pl.LpProblem("Example", pl.LpMaximize)
print(solver_type)
solver = pl.getSolver(solver_type)
_var = pl.LpVariable('a', 0, 1)
_var2 = pl.LpVariable('a2', 0, 2)
model += _var + _var2 <= 3
model += _var + _var2
x = _var + _var2
status = model.solve(solver)
print(pl.value(_var), pl.value(_var2))
print(pl.value(x))
return status
if __name__ == "__main__":
# Get the available solvers
av = pl.listSolvers(onlyAvailable=True)
print(av)
# Take the first available solver
status = test_0(av[0])
print(status)
test_affine_expression()
test_constraint()
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tests/contrib/epidemiology/test_seir.py | johannespitz/pyro | 0 | 88734 | # Copyright Contributors to the Pyro project.
# SPDX-License-Identifier: Apache-2.0
import pytest
import torch
import pyro.distributions as dist
from pyro.contrib.epidemiology import OverdispersedSEIRModel, SimpleSEIRModel
@pytest.mark.parametrize("duration", [3, 7])
@pytest.mark.parametrize("forecast", [0, 7])
@pytest.mark.parametrize("options", [
{},
{"dct": 1.},
{"num_quant_bins": 8},
], ids=str)
def test_simple_smoke(duration, forecast, options):
population = 100
incubation_time = 2.0
recovery_time = 7.0
# Generate data.
model = SimpleSEIRModel(population, incubation_time, recovery_time,
[None] * duration)
for attempt in range(100):
data = model.generate({"R0": 1.5, "rho": 0.5})["obs"]
if data.sum():
break
assert data.sum() > 0, "failed to generate positive data"
# Infer.
model = SimpleSEIRModel(population, incubation_time, recovery_time, data)
num_samples = 5
model.fit(warmup_steps=2, num_samples=num_samples, max_tree_depth=2,
**options)
# Predict and forecast.
samples = model.predict(forecast=forecast)
assert samples["S"].shape == (num_samples, duration + forecast)
assert samples["E"].shape == (num_samples, duration + forecast)
assert samples["I"].shape == (num_samples, duration + forecast)
@pytest.mark.parametrize("duration", [3, 7])
@pytest.mark.parametrize("forecast", [0, 7])
@pytest.mark.parametrize("options", [
{},
{"dct": 1.},
{"num_quant_bins": 8},
], ids=str)
def test_overdispersed_smoke(duration, forecast, options):
population = 100
incubation_time = 2.0
recovery_time = 7.0
# Generate data.
model = OverdispersedSEIRModel(
population, incubation_time, recovery_time, [None] * duration)
for attempt in range(100):
data = model.generate({"R0": 1.5, "rho": 0.5, "k": 1.0})["obs"]
if data.sum():
break
assert data.sum() > 0, "failed to generate positive data"
# Infer.
model = OverdispersedSEIRModel(
population, incubation_time, recovery_time, data)
num_samples = 5
model.fit(warmup_steps=2, num_samples=num_samples, max_tree_depth=2,
**options)
# Predict and forecast.
samples = model.predict(forecast=forecast)
assert samples["S"].shape == (num_samples, duration + forecast)
assert samples["E"].shape == (num_samples, duration + forecast)
assert samples["I"].shape == (num_samples, duration + forecast)
@pytest.mark.parametrize("duration", [3, 7])
@pytest.mark.parametrize("forecast", [0, 7])
def test_coalescent_likelihood_smoke(duration, forecast):
population = 100
incubation_time = 2.0
recovery_time = 7.0
# Generate data.
model = OverdispersedSEIRModel(
population, incubation_time, recovery_time, [None] * duration)
for attempt in range(100):
data = model.generate({"R0": 1.5, "rho": 0.5, "k": 1.0})["obs"]
if data.sum():
break
assert data.sum() > 0, "failed to generate positive data"
leaf_times = torch.rand(5).pow(0.5) * duration
coal_times = dist.CoalescentTimes(leaf_times).sample()
coal_times = coal_times[..., torch.randperm(coal_times.size(-1))]
# Infer.
model = OverdispersedSEIRModel(
population, incubation_time, recovery_time, data,
leaf_times=leaf_times, coal_times=coal_times)
num_samples = 5
model.fit(warmup_steps=2, num_samples=num_samples, max_tree_depth=2)
# Predict and forecast.
samples = model.predict(forecast=forecast)
assert samples["S"].shape == (num_samples, duration + forecast)
assert samples["E"].shape == (num_samples, duration + forecast)
assert samples["I"].shape == (num_samples, duration + forecast)
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16862,
12181,
1275,
313,
1949,
29918,
27736,
29892,
14385,
718,
29821,
579,
29897,
13,
2
] |
psy/utils/__init__.py | Alias-Alan/pypsy | 169 | 173869 | from psy.utils.probs import inverse_logistic, get_log_beta_pd, get_log_lognormal_pd, get_log_normal_pd, get_nodes_weights, \
r4beta
from psy.utils.tools import cached_property
from psy.utils.randoms import gen_item_bank
| [
1,
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] |
python/lib/Lib/site-packages/django/views/static.py | truthiswill/intellij-community | 2 | 91201 | <reponame>truthiswill/intellij-community<filename>python/lib/Lib/site-packages/django/views/static.py
"""
Views and functions for serving static files. These are only to be used
during development, and SHOULD NOT be used in a production setting.
"""
import mimetypes
import os
import posixpath
import re
import stat
import urllib
import warnings
from email.Utils import parsedate_tz, mktime_tz
from django.template import loader
from django.http import Http404, HttpResponse, HttpResponseRedirect, HttpResponseNotModified
from django.template import Template, Context, TemplateDoesNotExist
from django.utils.http import http_date
from django.contrib.staticfiles.views import (directory_index,
was_modified_since, serve as staticfiles_serve)
def serve(request, path, document_root=None, show_indexes=False, insecure=False):
"""
Serve static files below a given point in the directory structure.
To use, put a URL pattern such as::
(r'^(?P<path>.*)$', 'django.views.static.serve', {'document_root' : '/path/to/my/files/'})
in your URLconf. You must provide the ``document_root`` param. You may
also set ``show_indexes`` to ``True`` if you'd like to serve a basic index
of the directory. This index view will use the template hardcoded below,
but if you'd like to override it, you can create a template called
``static/directory_index.html``.
"""
warnings.warn("The view at `django.views.static.serve` is deprecated; "
"use the path `django.contrib.staticfiles.views.serve` "
"instead.", PendingDeprecationWarning)
return staticfiles_serve(request, path, document_root, show_indexes, insecure)
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PyWeiboSpy/spy.py | veikai/PyWeiboSpy | 1 | 73824 | from .exceptions import LoginError
import base64
import binascii
import json
import math
import random
import re
import requests
import rsa
import time
SPY_HEADERS = {
"Accept": "*/*",
"Accept-Encoding": "gzip, deflate, br",
"Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6",
"Connection": "keep-alive",
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
"Chrome/84.0.4147.89 Safari/537.36 Edg/84.0.522.40 "
}
SPY_LOGIN_DATA = {
"entry": "weibo",
"gateway": 1,
"from": "",
"savestate": 7,
"qrcode_flag": False,
"useticket": 1,
"pagerefer": "",
"vsnf": 1,
"service": "miniblog",
"pwencode": "rsa2",
"sr": "1920*1080",
"encoding": "UTF-8",
"prelt": 43,
"url": "https://weibo.com/ajaxlogin.php?framelogin=1&callback=parent.sinaSSOController.feedBackUrlCallBack",
"returntype": "META"
}
class WeiboSpy:
def __init__(self):
self.prelogin_url = "https://login.sina.com.cn/sso/prelogin.php?entry=weibo&callback=sinaSSOController" \
".preloginCallBack&su={}&rsakt=mod&checkpin=1&client=ssologin.js(v1.4.19)&_={} "
self.login_url = "https://login.sina.com.cn/sso/login.php?client=ssologin.js(v1.4.19)"
self.pin_url = "https://login.sina.com.cn/cgi/pin.php?r={}&s=0&p={}"
self.post_blog_url = "https://weibo.com/aj/mblog/add?ajwvr=6&__rnd={}"
self.login_data = SPY_LOGIN_DATA
self.__session = requests.Session()
self.headers = SPY_HEADERS
def login(self, username: str, password: str):
su = base64.b64encode(username.encode("utf8")).decode("utf8")
self.login_data["su"] = su
resp = self.__session.get(self.prelogin_url.format(su, int(time.time() * 1000)), headers=self.headers)
if resp.status_code != 200:
raise LoginError(f"prelogin status code: {resp.status_code}")
resp_str = resp.content.decode("utf8")[len("sinaSSOController.preloginCallBack("):-1]
prelogin_data = json.loads(resp_str)
self.login_data["nonce"] = prelogin_data["nonce"]
self.login_data["rsakv"] = prelogin_data["rsakv"]
self.login_data["servertime"] = prelogin_data["servertime"]
self.login_data["pcid"] = prelogin_data["pcid"]
rsa_publickey = int(prelogin_data["pubkey"], 16)
key = rsa.PublicKey(rsa_publickey, 65537)
data = f"{prelogin_data['servertime']}\t{prelogin_data['nonce']}\n{password}".encode("utf8")
sp = binascii.b2a_hex(rsa.encrypt(data, key)).decode("utf8")
self.login_data["sp"] = sp
resp = self.__session.get(
self.pin_url.format(math.floor(random.random() * 100000000), prelogin_data["pcid"]), headers=self.headers)
if resp.status_code != 200:
raise LoginError(f"pin status code: {resp.status_code}")
with open("pin.png", "wb") as wf:
wf.write(resp.content)
door = input("请输入验证码:")
self.login_data["door"] = door
resp = self.__session.post(self.login_url, data=self.login_data, headers=self.headers)
pa = r'location\.replace\([\'"](.*?)[\'"]\)'
redirect_url = re.findall(pa, resp.content.decode("GBK"))[0]
resp = self.__session.get(redirect_url, headers=self.headers)
pa = r'setCrossDomainUrlList\((.*?)\)'
redirect_str = re.findall(pa, resp.content.decode("GBK"))[0]
redirect_json = json.loads(redirect_str)
redirect_url = f"{redirect_json['arrURL'][0]}&callback=sinaSSOController.doCrossDomainCallBack&scriptId" \
f"=ssoscript0&client=ssologin.js(v1.4.19)&_={int(time.time()*1000)}"
resp = self.__session.get(redirect_url, headers=self.headers)
print(resp.text)
def __upload_image(self, image_path):
with open(image_path, "rb") as rf:
image_data = base64.b64encode(rf.read())
my_headers = {
"Accept": "*/*",
"Accept-Encoding": "gzip, deflate",
"Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6",
"Connection": "keep-alive",
"Content-Length": str(len(image_data)),
"Content-Type": "multipart/form-data",
"Host": "picupload.weibo.com",
"Origin": "https://weibo.com",
"Referer": "https://weibo.com/u/7459826781/home",
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
"Chrome/84.0.4147.89 Safari/537.36 Edg/84.0.522.40"
}
post_data = {
"b64_data": image_data
}
url = "http://picupload.service.weibo.com/interface/pic_upload.php?ori=1&mime=image%2Fjpeg&data=base64&url=0&markpos=1&logo=&nick=0&marks=1&app=miniblog"
resp = self.__session.post(url, data=post_data, headers=my_headers)
print(resp.text)
def post_blog(self, text: str):
my_headers = {
"Accept": "*/*",
"Accept-Encoding": "gzip, deflate",
"Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6",
"Connection": "keep-alive",
"Content-Length": "600",
"Content-Type": "application/x-www-form-urlencoded",
"Origin": "http://www.weibo.com",
"Referer": "http://www.weibo.com/u/7459826781/home?wvr=5",
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.89 Safari/537.36 Edg/84.0.522.40",
"X-Requested-With": "XMLHttpRequest"
}
url = f"http://www.weibo.com/aj/mblog/add?ajwvr=6&__rnd={int(time.time() * 1000)}"
post_data = {
"location": "v6_content_home",
"text": text,
"appkey": "",
"stype_type": 1,
"pic_id": "0088QFINgy1ggwavrqa7ij31c00u0keo|0088QFINgy1ggwavrk6ctj31z40u0e81"
"|0088QFINgy1ggwavriwj5j31c00u0qjv|0088QFINgy1ggwavreljhj31hc0u0tm8"
"|0088QFINgy1ggwavuqkacj31hc0u04qz|0088QFINgy1ggwavt15zij31hc0u0b2a"
"|0088QFINgy1ggwavtezjbj31hc0u0hdw|0088QFINgy1ggwavsm519j31hc0u04qp"
"|0088QFINgy1ggwavsearjj31z40u0h12",
"tid": "",
"pdetail": "",
"mid": "",
"isReEdit": False,
"rank": 0,
"rankid": "",
"module": "stissue",
"pub_source": "main_",
"updata_img_num": 9,
"pub_type": "dialog",
"isPri": 0,
"_t": 0
}
resp = self.__session.post(url, data=post_data, headers=my_headers) | [
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] |
yolov3/coco_metric.py | LiuChiachi/hapi | 92 | 142116 | # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import sys
import json
from pycocotools.cocoeval import COCOeval
from pycocotools.coco import COCO
import logging
FORMAT = '%(asctime)s-%(levelname)s: %(message)s'
logging.basicConfig(level=logging.INFO, format=FORMAT)
logger = logging.getLogger(__name__)
__all__ = ['COCOMetric']
OUTFILE = './bbox.json'
# COCOMetric behavior is different from Metric defined in high
# level API, COCOMetric will and con only accumulate on the epoch
# end, so we impliment COCOMetric as not a high level API Metric
class COCOMetric():
"""
Metrci for MS-COCO dataset, only support update with batch
size as 1.
Args:
anno_path(str): path to COCO annotation json file
with_background(bool): whether load category id with
background as 0, default True
"""
def __init__(self, anno_path, with_background=True, **kwargs):
self.anno_path = anno_path
self.with_background = with_background
self.bbox_results = []
self.coco_gt = COCO(anno_path)
cat_ids = self.coco_gt.getCatIds()
self.clsid2catid = dict({
i + int(with_background): catid
for i, catid in enumerate(cat_ids)
})
def update(self, img_id, bboxes):
assert img_id.shape[0] == 1, \
"COCOMetric can only update with batch size = 1"
if bboxes.shape[1] != 6:
# no bbox detected in this batch
return
img_id = int(img_id)
for i in range(bboxes.shape[0]):
dt = bboxes[i, :]
clsid, score, xmin, ymin, xmax, ymax = dt.tolist()
catid = (self.clsid2catid[int(clsid)])
w = xmax - xmin + 1
h = ymax - ymin + 1
bbox = [xmin, ymin, w, h]
coco_res = {
'image_id': img_id,
'category_id': catid,
'bbox': bbox,
'score': score
}
self.bbox_results.append(coco_res)
def reset(self):
self.bbox_results = []
def accumulate(self):
if len(self.bbox_results) == 0:
logger.warning("The number of valid bbox detected is zero.\n \
Please use reasonable model and check input data.\n \
stop COCOMetric accumulate!")
return [0.0]
with open(OUTFILE, 'w') as f:
json.dump(self.bbox_results, f)
map_stats = self.cocoapi_eval(OUTFILE, 'bbox', coco_gt=self.coco_gt)
# flush coco evaluation result
sys.stdout.flush()
self.result = map_stats[0]
return [self.result]
def cocoapi_eval(self, jsonfile, style, coco_gt=None, anno_file=None):
assert coco_gt != None or anno_file != None
if coco_gt == None:
coco_gt = COCO(anno_file)
logger.info("Start evaluate...")
coco_dt = coco_gt.loadRes(jsonfile)
coco_eval = COCOeval(coco_gt, coco_dt, style)
coco_eval.evaluate()
coco_eval.accumulate()
coco_eval.summarize()
return coco_eval.stats
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a2ml/api/utils/decorators.py | augerai/a2ml | 30 | 175086 | def error_handler(decorated):
def wrapper(self, *args, **kwargs):
try:
return decorated(self, *args, **kwargs)
except Exception as exc:
if self.ctx.debug:
import traceback
traceback.print_exc()
if "ServiceException" in str(type(exc)):
exc = Exception(exc.message)
self.ctx.log(str(exc))
if not hasattr(self.ctx, 'not_reraise_exceptions')\
or not self.ctx.not_reraise_exceptions:
raise
return wrapper
def authenticated(decorated):
def wrapper(self, *args, **kwargs):
self.credentials.verify()
return decorated(self, *args, **kwargs)
return wrapper
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gputools/core/oclmultireduction.py | gmazzamuto/gputools | 89 | 9208 | <reponame>gmazzamuto/gputools
"""
an adaptation of pyopencl's reduction kernel for weighted avarages
like sum(a*b)
<EMAIL>
"""
from __future__ import print_function, unicode_literals, absolute_import, division
from six.moves import zip
import pyopencl as cl
from pyopencl.tools import (
context_dependent_memoize,
dtype_to_ctype, KernelTemplateBase,
_process_code_for_macro)
import numpy as np
from gputools import get_device
import sys
# {{{ kernel source
KERNEL = r"""//CL//
<%
inds = range(len(map_exprs))
%>
#define GROUP_SIZE ${group_size}
% for i,m in enumerate(map_exprs):
#define READ_AND_MAP_${i}(i) (${m})
% endfor
#define REDUCE(a, b) (${reduce_expr})
% if double_support:
#if __OPENCL_C_VERSION__ < 120
#pragma OPENCL EXTENSION cl_khr_fp64: enable
#endif
#define PYOPENCL_DEFINE_CDOUBLE
% endif
#include <pyopencl-complex.h>
${preamble}
typedef ${out_type} out_type;
__kernel void ${name}(
% for i in inds:
__global out_type *out__base_${i},
% endfor
long out__offset, ${arguments},
unsigned int seq_count, unsigned int n)
{
% for i in inds:
__global out_type *out_${i} = (__global out_type *) (
(__global char *) out__base_${i} + out__offset);
% endfor
${arg_prep}
% for i in inds:
__local out_type ldata_${i}[GROUP_SIZE];
out_type acc_${i} = ${neutral};
% endfor
unsigned int lid = get_local_id(0);
unsigned int i = get_group_id(0)*GROUP_SIZE*seq_count + lid;
//printf("seq: %d\tlid = %d\ti=%d\n",seq_count,lid,i);
for (unsigned s = 0; s < seq_count; ++s)
{
if (i >= n)
break;
% for i in inds:
acc_${i} = REDUCE(acc_${i}, READ_AND_MAP_${i}(i));
% endfor
i += GROUP_SIZE;
}
% for i in inds:
ldata_${i}[lid] = acc_${i};
% endfor
<%
cur_size = group_size
%>
% while cur_size > 1:
barrier(CLK_LOCAL_MEM_FENCE);
<%
new_size = cur_size // 2
assert new_size * 2 == cur_size
%>
if (lid < ${new_size})
{
% for i in inds:
ldata_${i}[lid] = REDUCE(
ldata_${i}[lid],
ldata_${i}[lid + ${new_size}]);
% endfor
}
<% cur_size = new_size %>
% endwhile
if (lid == 0) {
% for i in inds:
out_${i}[get_group_id(0)] = ldata_${i}[0];
% endfor
//printf("result: %.4f\n",out_0[get_group_id(0)] );
}
}
"""
def _get_reduction_source(
ctx, out_type, out_type_size,
neutral, reduce_expr, map_exprs, parsed_args,
name="reduce_kernel", preamble="", arg_prep="",
device=None, max_group_size=None):
if device is not None:
devices = [device]
else:
devices = ctx.devices
# {{{ compute group size
def get_dev_group_size(device):
# dirty fix for the RV770 boards
max_work_group_size = device.max_work_group_size
if "RV770" in device.name:
max_work_group_size = 64
# compute lmem limit
from pytools import div_ceil
lmem_wg_size = div_ceil(max_work_group_size, out_type_size)
result = min(max_work_group_size, lmem_wg_size)
# round down to power of 2
from pyopencl.tools import bitlog2
return 2**bitlog2(result)
group_size = min(get_dev_group_size(dev) for dev in devices)
if max_group_size is not None:
group_size = min(max_group_size, group_size)
# }}}
from mako.template import Template
from pytools import all
from pyopencl.characterize import has_double_support
src = str(Template(KERNEL).render(
out_type=out_type,
arguments=", ".join(arg.declarator() for arg in parsed_args),
group_size=group_size,
neutral=neutral,
reduce_expr=_process_code_for_macro(reduce_expr),
map_exprs=[_process_code_for_macro(m) for m in map_exprs],
name=name,
preamble=preamble,
arg_prep=arg_prep,
double_support=all(has_double_support(dev) for dev in devices),
))
# sys.exit()
from pytools import Record
class ReductionInfo(Record):
pass
return ReductionInfo(
context=ctx,
source=src,
group_size=group_size)
def get_reduction_kernel(stage,
ctx, dtype_out,
neutral, reduce_expr, arguments=None,
name="reduce_kernel", preamble="",
map_exprs = None,
device=None, options=[], max_group_size=None):
if map_exprs is None:
raise ValueError("map_exprs has to be given!")
for i, m in enumerate(map_exprs):
if m is None:
if stage==2:
map_exprs[i] = "pyopencl_reduction_inp_%i[i]"%i
else:
map_exprs[i] = "in[i]"
from pyopencl.tools import (
parse_arg_list, get_arg_list_scalar_arg_dtypes,
get_arg_offset_adjuster_code, VectorArg)
arg_prep = ""
if stage==1 and arguments is not None:
arguments = parse_arg_list(arguments, with_offset=True)
arg_prep = get_arg_offset_adjuster_code(arguments)
if stage==2 and arguments is not None:
arguments = parse_arg_list(arguments)
arguments = (
[VectorArg(dtype_out, "pyopencl_reduction_inp_%i"%i) for i in range(len(map_exprs))]
+arguments)
inf = _get_reduction_source(
ctx, dtype_to_ctype(dtype_out), dtype_out.itemsize,
neutral, reduce_expr, map_exprs, arguments,
name, preamble, arg_prep, device, max_group_size)
inf.program = cl.Program(ctx, inf.source)
inf.program.build(options)
inf.kernel = getattr(inf.program, name)
inf.arg_types = arguments
inf.kernel.set_scalar_arg_dtypes(
[None, ]*len(map_exprs)+[np.int64]
+get_arg_list_scalar_arg_dtypes(inf.arg_types)
+[np.uint32]*2)
return inf
# }}}
# {{{ main reduction kernel
class OCLMultiReductionKernel:
"""
simultanous reduction of a weighted sum of severalbuffers
example:
k = OCLMultiReduction(np.float32,
neutral="0",reduce_expr="a+b",
map_exprs = ["x[i]", "x[i]*y[i]"],
arguments="__global float *x,__global float *y")
k(a,b, out1 = out1, out2 = out2)
"""
def __init__(self, dtype_out,
neutral, reduce_expr, arguments=None,
map_exprs=[None],
name="reduce_kernel", options=[], preamble=""):
ctx = get_device().context
dtype_out = self.dtype_out = np.dtype(dtype_out)
max_group_size = None
trip_count = 0
self.n_exprs = len(map_exprs)
assert self.n_exprs>0
while True:
self.stage_1_inf = get_reduction_kernel(1, ctx,
dtype_out,
neutral, reduce_expr, arguments,
name=name+"_stage1", options=options, preamble=preamble,
map_exprs=map_exprs,
max_group_size=max_group_size)
kernel_max_wg_size = self.stage_1_inf.kernel.get_work_group_info(
cl.kernel_work_group_info.WORK_GROUP_SIZE,
ctx.devices[0])
if self.stage_1_inf.group_size<=kernel_max_wg_size:
break
else:
max_group_size = kernel_max_wg_size
trip_count += 1
assert trip_count<=2
self.stage_2_inf = get_reduction_kernel(2, ctx,
dtype_out,
neutral, reduce_expr, arguments=arguments,
name=name+"_stage2", options=options,
map_exprs = [None]*self.n_exprs,
preamble=preamble,
max_group_size=max_group_size)
from pytools import any
from pyopencl.tools import VectorArg
assert any(
isinstance(arg_tp, VectorArg)
for arg_tp in self.stage_1_inf.arg_types), \
"ReductionKernel can only be used with functions " \
"that have at least one vector argument"
def __call__(self, *args, **kwargs):
MAX_GROUP_COUNT = 1024 # noqa
SMALL_SEQ_COUNT = 4 # noqa
from pyopencl.array import empty
stage_inf = self.stage_1_inf
queue = kwargs.pop("queue", None)
wait_for = kwargs.pop("wait_for", None)
return_event = kwargs.pop("return_event", False)
outs = kwargs.pop("outs", [None]*self.n_exprs)
if kwargs:
raise TypeError("invalid keyword argument to reduction kernel")
stage1_args = args
while True:
invocation_args = []
vectors = []
from pyopencl.tools import VectorArg
for arg, arg_tp in zip(args, stage_inf.arg_types):
if isinstance(arg_tp, VectorArg):
if not arg.flags.forc:
raise RuntimeError("ReductionKernel cannot "
"deal with non-contiguous arrays")
vectors.append(arg)
invocation_args.append(arg.base_data)
if arg_tp.with_offset:
invocation_args.append(arg.offset)
else:
invocation_args.append(arg)
repr_vec = vectors[0]
sz = repr_vec.size
if queue is not None:
use_queue = queue
else:
use_queue = repr_vec.queue
if sz<=stage_inf.group_size*SMALL_SEQ_COUNT*MAX_GROUP_COUNT:
total_group_size = SMALL_SEQ_COUNT*stage_inf.group_size
group_count = (sz+total_group_size-1)//total_group_size
seq_count = SMALL_SEQ_COUNT
else:
group_count = MAX_GROUP_COUNT
macrogroup_size = group_count*stage_inf.group_size
seq_count = (sz+macrogroup_size-1)//macrogroup_size
if group_count==1:
results = [empty(use_queue,
(), self.dtype_out,
allocator=repr_vec.allocator) if out is None else out for out in outs]
else:
results = [empty(use_queue,
(group_count,), self.dtype_out,
allocator=repr_vec.allocator) for out in outs]
last_evt = stage_inf.kernel(
use_queue,
(group_count*stage_inf.group_size,),
(stage_inf.group_size,),
*([r.base_data for r in results]+[results[0].offset,]
+invocation_args+[seq_count, sz]),
**dict(wait_for=wait_for))
wait_for = [last_evt]
#print "ooooo ", group_count, len(args)
if group_count==1:
if return_event:
return results, last_evt
else:
return results
else:
stage_inf = self.stage_2_inf
args = tuple(results)+stage1_args
#args = (results[0],)+stage1_args
if __name__=='__main__':
from gputools import OCLArray, OCLReductionKernel
k1 = OCLReductionKernel(np.float32,
neutral="0", reduce_expr="a+b",
map_expr="x[i]",
arguments="__global float *x")
k2 = OCLMultiReductionKernel(np.float32,
neutral="0", reduce_expr="a+b",
map_exprs=["y[i]*x[i]","x[i]"],
arguments="__global float *x, __global float *y")
N = 512
a = OCLArray.from_array(np.ones((N,N),np.float32))
b = OCLArray.from_array(2.*np.ones((N,N),np.float32))
o1 = OCLArray.empty((),np.float32)
o2 = OCLArray.empty((),np.float32)
from time import time
t = time()
for _ in range(400):
k1(a)
k1(b)
k1(a).get()
k1(b).get()
print(time()-t)
t = time()
#print k2(a,b, outs = [o1,o2])
for _ in range(400):
k2(a[0],b[0], outs = [o1,o2])
o1.get()
print(time()-t)
# open("kern_new_1.txt","w").write(("%s"%k2.stage_1_inf).replace("\\n","\n"))
# open("kern_new_2.txt","w").write(("%s"%k2.stage_2_inf).replace("\\n","\n"))
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Final Class Test/Final Exam - Boundary Detection .py | Ciaran-OBrien/Image-Processing | 2 | 52430 | <reponame>Ciaran-OBrien/Image-Processing
# coding: utf-8
# In[2]:
# Author: <NAME>
# Lecture: Jane Courtney
# Submitted: 13/12/18
# This code is in response to CA Class Test: Boundary Detection
# N.B. This code orignated as a Jupyter file, thus relavent code and lineNumbers remain
# Boiler plate imports
import os
import numpy as np
import cv2
from matplotlib import pyplot as plt
import easygui
import imutils
import math
import operator
# # Setup
#
# Import all the necessary boiler plate code; import statements, header text.
# Obtain important data; Flesh detection in different colour spaces. As the assignment was regrading skin colours, I needed to research what
# colour space and thresholding parameters were necessary here. For this, I took the given image of the skin mole, and used a colour picker tool,
# like Photoshop's colour picker tool, in order to obtain the RGB colours of the skin. I took 10 sample across the image and took an average of
# these values.
# Once I had the RGB vales, I used colorizer.org in order to obtain the HSV values for my chosen skin colour. The algorithm these values were used
# in will be discussed below, along with the code used afterwards.
#
# # Requirements:
#
# - Convert the image to an appropriate colour space;
# - Enhance the image to maximise the contrast between the mole and the skin;
# - Create a binary mask of the mole from the image using thresholding or edge detection;
# - Use morphology to clean up this mask;
# - Extract the contours in the binary image;
# - Sort the contours by area to find the largest;
# - Draw this contour on the original image as a thick red line;
#
# # Method:
#
# My method included 3 steps:
#
# 1. Masking
# 2. Morphological operation
# 3. FindContours
#
# ## 1. Masking:
#
# As mentioned in my setup, I knew that I was dealing with skin colours, so decided to make use of the HSV colour space. Using the values researched
# form above, min_HSV = [20, 70, 0] and max_HSV = [120, 200, 255], I was able to create a binary mask by using the inRange() fnx.
# The inRange() fnx take two parameters, the min and max values for your colour threshold. When we pass in the HSV image, the fnx filters out any
# other colours outside the parameter ranges, and returns a binary mask of the chosen range.This mask was returned from the getHSVMask fxn().
# I did look at trying to use the YCrCb range, as that has proven useful in research for skin thresholding. However, the results I obtained from HSV
# masking we satisfactory. Further work on the mask is shown below.
#
# ## 2. Morphological operation:
#
# The resulting mask from getHSVMask() fnx needed some cleaning up. For this, I made use of OpenCV's morphological operations. These operations
# alter the structure of a binary image, in our case a binary mask. I made use of two operations, closing and opening, with kernel sizes of (7,7)
# and (3,3) respectively.
# Closing is a Dilation followed by an Erosion operation. Here missing holes in the mask, as per the result of getHSVMask(), are filled in relative
# to the background, black.
# Opening an erosion followed by a dilation operation. Here background noise is removed relative to the kernel neighbours. The method implemented
# in the background here is when at least one kernel neighbour has a value of 1, then the pixel element being compared as the central pixel of the
# kernel, is set to a value of 1.
#
# ## 3. FindContours :
#
# The final step in my method concluded with finding the contours of the binary mask, and using a contour to draw a red line around the found mole.
# Firstly I made use of OpenCV's findContour() fnx with three parameters, the binary mask, the contour retrieval mode, and the contour approximation
# method.
# The Binary mask has been defined as per the previous two steps in my method.
# The contour retrieval mode is defined by a set of 5 modes as per OpenCV RetrievalModes documentation. I chose to use the RETR_LIST mode as it
# simply retrieves all the available contours and can be easily accessed just like a regular list. This will be useful, as I am required to sort
# the contours too.
# The contour approximation mode is defined by a set of 5 modes as per OpenCV ContourApproximationModes documentation. I chose to use the
# CHAIN_APPROX_SIMPLE mode as it attempts to filter out false positive contours by compressing horizontal, vertical, and diagonal segments and
# leaves only their end points. However CHAIN_APPROX_NONE, which simply returns all contours found, returns the same number of contours, so either
# mode works.
#
# As the contours are returned as a list, I can easily perform the required task of sorting them according to area size. I did this through the use
# of python's sorted fnx, which takes the list, the sorting key, and order as parameter.
# The lists has been defined as the contours lits earlier
# The sorting key, I use the area of the contours themselves, using cv2.contourArea.
# The order, I set the reversOrder to True, so simply the first element in the list is the largest contours, therefore the contour of the mole.
#
# # Testing:
#
# ## Error checking:
# Error checking occurs when we try to convert an image into HSV. As we can only safely say if an image has 2/3 channels, I built the error
# checking based around that. If the user selects a 2 channle input, an error will be raised.
#
# ## Image Testing:
# Of the two images provided, my current method unfortunately works with one of the images. This is due to the fine tunig required for the
# getMorphologyMask() fnx. Hower, I have added an extra image found online that works with my method.
#
#
# In[533]:
# Converting the Colour image into the HSV range
# The HSV range is more usefull than RGB, as it allows for capturing colours of diffrent Brightness and Saturation
def getHSVImage(colourImage):
try:
if(len(colourImage.shape) == 2):
raise Exception("Potential Gray Image passed. Not allowed!")
elif(len(colourImage.shape) == 3):
return cv2.cvtColor(colourImage.copy(), cv2.COLOR_BGR2HSV)
else:
raise Exception("Unsupported Image")
except Exception as error:
print("An error has occured\n",error)
# In[532]:
def getHSVMask(HSVimage):
min_HSV = np.array([20, 70, 0], dtype=np.uint8)
max_HSV = np.array([120, 190, 255], dtype=np.uint8)
skinMask = cv2.inRange(HSVimage, min_HSV, max_HSV)
return skinMask
# In[283]:
def getYCrCbMask(image):
converted = cv2.cvtColor(image, cv2.COLOR_BGR2YCrCb)
min_YCrCb = np.array([0,133,77],dtype=np.uint8)
max_YCrCb = np.array([255,173,127],dtype=np.uint8)
skinMask = cv2.inRange(converted, min_YCrCb, max_YCrCb)
skinMask[skinMask < 128] = 0
skinMask[skinMask >= 128] = 1
return skinMask
# In[513]:
def getMorphologyMask(hsvMask):
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (7, 7))
mask = cv2.morphologyEx(hsvMask, cv2.MORPH_CLOSE, kernel)
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
mask = cv2.morphologyEx(hsvMask, cv2.MORPH_OPEN, kernel, iterations=2)
return mask
# In[577]:
# Finding contours for the thresholded image
def getDrawContourImage(morphMask,originalImage):
copy = originalImage.copy()
_, contours, _ = cv2.findContours(morphMask, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
cntsSorted = sorted(contours, key=lambda x: cv2.contourArea(x))
cnt = sorted(contours, key=cv2.contourArea, reverse=True)
cv2.drawContours(copy,cnt[1],-1,(255,0,0),6)
if(cnt[1][0][0][0] > 200):
foundLargeContour = True
else:foundLargeContour = False
return copy,foundLargeContour
# In[580]:
# Inform the user if they should get the mole looked at or not
def plotInformation(foundMole):
if foundMole:
return "Better get that checked out !"
else:
return "Sure looks grand !"
# In[578]:
moleImage = plt.imread("mole.jpg")
moleImage2 = plt.imread("mole2.jpg")
skinCancerImage = plt.imread("skincancer.jpg")
# In[585]:
hsvImage = getHSVImage(moleImage)
hsvMask = getHSVMask(hsvImage)
morphMask = getMorphologyMask(hsvMask)
drawnImage,foundMole = getDrawContourImage(morphMask,moleImage)
plt.suptitle(plotInformation(foundMole))
plt.imshow(drawnImage)
plt.show()
# In[538]:
import unittest
class TestNotebook(unittest.TestCase):
def test_nonGrayImageReturned(self):
grayColourImage = cv2.cvtColor(moleImage,cv2.COLOR_BGR2GRAY)
try:
getHSVImage(grayColourImage)
except Exception:
pass
unittest.main(argv=[''], verbosity=2, exit=False)
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13,
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2
] |
Assignment 5/assignment52.py | ziaridoy20/Data-Science-with-Python-and-R | 0 | 115862 | import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import poisson
from scipy.stats import uniform
from scipy.stats import norm
# Data
data = np.array([0.3120639, 0.5550930, 0.2493114, 0.9785842])
# Grid.
mus = np.linspace(0, 1, num=100)
sigmas = np.linspace(0, 1, num=100)
x = []
y = []
z = []
# Grid and computation of the posterior.
for mu in mus:
for sigma in sigmas:
posterior = np.prod(norm.pdf(x=data, loc=mu, scale=sigma)) * uniform.pdf(mu, 0, 1) * uniform.pdf(sigma, 0, 1)
x.append(mu)
y.append(sigma)
z.append(posterior)
# Plot using colors.
plt.scatter(x, y, c=z)
plt.show()
| [
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] |
solutions/1362-Closest-Divisors/1362.py | leetcode-notebook/wonz | 12 | 78818 | <reponame>leetcode-notebook/wonz<gh_stars>10-100
import math
from typing import List
class Solution:
def closestDivisors(self, num: int) -> List[int]:
num1, num2 = num + 1, num + 2
ans1 = self.crack(num1)
ans2 = self.crack(num2)
res = []
if abs(ans1 - int(num1 / ans1)) < abs(ans2 - int(num2 / ans2)): # int
res.append(ans1)
res.append(int(num1 / ans1))
return res
else:
res.append(ans2)
res.append(int(num2 / ans2))
return res
# calculate factor
def crack(self, integer):
factor1, factor2 = 1, integer
for i in range(1, int(math.sqrt(integer)) + 1): # range: [1, sqrt(x) + 1)
if int(integer / i) == integer / i: # i is factor
if integer / i - i < factor2 - factor1:
factor1, factor2 = i, integer / i
return factor1
if __name__ == "__main__":
num = 123
print(Solution().closestDivisors(num)) | [
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] |
Configuration/StandardSequences/python/Eras.py | AndrissP/cmssw | 0 | 129214 | from __future__ import print_function
import FWCore.ParameterSet.Config as cms
from FWCore.ParameterSet.Config import ModifierChain,Modifier
class Eras (object):
"""
Dummy container for all the cms.Modifier instances that config fragments
can use to selectively configure depending on what scenario is active.
"""
def __init__(self):
allEras=['Run1_pA',
'Run1_peripheralPbPb',
'Run2_50ns',
'Run2_50ns_HIPM',
'Run2_25ns',
'Run2_25ns_HIPM',
'Run2_25ns_peripheralPbPb',
'Run2_HI',
'Run2_2016',
'Run2_2016_HIPM',
'Run2_2016_trackingLowPU',
'Run2_2016_pA',
'Run2_2017',
'Run2_2017_FastSim', #new modifier for Phase1 FastSim, skips the muon GEM sequence
'Run2_2017_trackingRun2',
'Run2_2017_trackingLowPU',
'Run2_2017_pp_on_XeXe',
'Run2_2017_ppRef',
'Run2_2018',
'Run2_2018_FastSim', #new modifier for Phase1 FastSim, skips the muon GEM sequence
'Run2_2018_pp_on_AA',
'Run2_2018_pp_on_AA_noHCALmitigation',
'Run2_2018_highBetaStar',
'Run3',
'Phase2',
'Phase2_timing',
'Phase2_timing_layer',
'Phase2_timing_layer_tile',
'Phase2_timing_layer_bar',
'Phase2C4',
'Phase2C4_timing',
'Phase2C6',
'Phase2C6_timing',
'Phase2C4_timing_layer_bar',
'Phase2C8',
'Phase2C8_timing',
'Phase2C8_timing_layer_bar',
]
internalUseMods = ['run2_common', 'run2_25ns_specific',
'run2_50ns_specific', 'run2_HI_specific',
'stage1L1Trigger', 'fastSim',
'peripheralPbPb', 'pA_2016',
'run2_HE_2017', 'stage2L1Trigger', 'stage2L1Trigger_2017',
'run2_HF_2017', 'run2_HCAL_2017', 'run2_HEPlan1_2017', 'run2_HB_2018','run2_HE_2018',
'run3_HB', 'run3_common',
'phase1Pixel', 'run3_GEM', 'run2_GEM_2017',
'run2_CSC_2018',
'phase2_common', 'phase2_tracker',
'phase2_hgcal', 'phase2_muon', 'phase2_timing', 'phase2_hgcalV9', 'phase2_hfnose', 'phase2_hgcalV10',
'phase2_timing_layer','phase2_timing_layer_tile','phase2_timing_layer_bar','phase2_hcal',
'trackingLowPU', 'trackingPhase1', 'ctpps_2016', 'trackingPhase2PU140','highBetaStar_2018',
'tracker_apv_vfp30_2016', 'pf_badHcalMitigation', 'run2_miniAOD_80XLegacy','run2_miniAOD_94XFall17', 'run2_nanoAOD_92X',
'run2_nanoAOD_94XMiniAODv1', 'run2_nanoAOD_94XMiniAODv2', 'run2_nanoAOD_94X2016',
'run2_miniAOD_devel', 'run2_nanoAOD_102Xv1', 'run2_nanoAOD_106Xv1', 'run2_nanoAOD_106Xv2', 'run2_nanoAOD_devel',
'run2_tau_ul_2016', 'run2_tau_ul_2018',
'hcalHardcodeConditions', 'hcalSkipPacker',
'run2_HLTconditions_2016','run2_HLTconditions_2017','run2_HLTconditions_2018',
'bParking']
internalUseModChains = ['run2_2017_noTrackingModifier']
self.pythonCfgLines = {}
for e in allEras:
eObj=getattr(__import__('Configuration.Eras.Era_'+e+'_cff',globals(),locals(),[e],0),e)
self.addEra(e,eObj)
self.pythonCfgLines[e] = 'from Configuration.Eras.Era_'+e+'_cff import '+e
for e in internalUseMods:
eObj=getattr(__import__('Configuration.Eras.Modifier_'+e+'_cff',globals(),locals(),[e],0),e)
self.addEra(e,eObj)
self.pythonCfgLines[e] = 'from Configuration.Eras.Modifier_'+e+'_cff import '+e
for e in internalUseModChains:
eObj=getattr(__import__('Configuration.Eras.ModifierChain_'+e+'_cff',globals(),locals(),[e],0),e)
self.addEra(e,eObj)
self.pythonCfgLines[e] = 'from Configuration.Eras.ModifierChain_'+e+'_cff import '+e
def addEra(self,name,obj):
setattr(self,name,obj)
def inspectModifier(self,m,details):
print(' ',m.__dict__ ['_Modifier__processModifiers'])
def inspectEra(self,e,details):
print('\nEra:',e)
print(' isChosen:',getattr(self,e)._isChosen())
if details: print(' Modifiers:')
nmod=0
for value in getattr(self,e).__dict__['_ModifierChain__chain']:
if isinstance(value, Modifier):
nmod=nmod+1
if details: self.inspectModifier(value,details)
print(' ',nmod,'modifiers defined')
def inspect(self,name=None,onlyChosen=False,details=True):
if name==None:
print('Inspecting the known eras', end=' ')
if onlyChosen: print(' (all active)')
else: print('(all eras defined)')
else:
print('Inspecting the '+name+' era', end=' ')
allEras=[]
for key, value in self.__dict__.items():
if isinstance(value, ModifierChain): allEras.append(key)
for e in allEras:
if name is not None and name==e:
self.inspectEra(e,details)
if name is None:
if not onlyChosen or getattr(self,e).isChosen():
self.inspectEra(e,details)
eras=Eras()
#eras.inspect()
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] |
toga/genetic_algorithm/mutate/float.py | JPLMLIA/TOGA | 0 | 40993 | <gh_stars>0
"""
Author: <NAME>
Date : 1/23/19
Brief :
Notes :
Copyright 2019 California Institute of Technology. ALL RIGHTS RESERVED.
U.S. Government Sponsorship acknowledged.
"""
import numpy as np
import random
from toga.genetic_algorithm.genetype import Mutator
from toga.genetic_algorithm.mutate.genemutate import GeneMutate
class FloatGene(GeneMutate):
def __init__(self, dictionary=None, parents=None, mutator=Mutator.Crossover, mutator_params={}):
self.dictionary = dictionary
self.parents = parents
self.mutator_params = mutator_params if mutator_params is not None else {}
self.percentage = self.mutator_params.get('percentage') if self.mutator_params.get(
'percentage') is not None else 1.0
self.frequency = self.mutator_params.get('frequency')
super().__init__(dictionary, parents, mutator)
def mutate(self):
"""
:return:
"""
return super().mutate()
def crossover(self):
"""
:return:
"""
if self.parents:
value = np.random.choice([x for x in self.parents], size=1, replace=False).tolist()[0]
return float(value)
else:
return self.random()
def random(self):
"""
:return:
"""
values = self.dictionary.get('range')
val = np.random.uniform(min(values), max(values))
return float(val)
def gaussian_step(self):
"""
:return:
"""
if self.parents:
parent = self.parents[0]
original = parent
values = self.dictionary.get('range')
scale = np.abs((max(values) - min(values)))
new_var = np.random.normal(loc=original, scale=scale/4)
new_var = max(min(values), new_var)
new_var = min(max(values), new_var)
return float(new_var)
else:
return self.random()
def gaussian_random(self):
"""
:return:
"""
values = self.dictionary.get('range')
dist = np.arange(min(values), max(values))
return float(min(max(values), max(min(values), random.gauss(np.mean(dist), sigma=3))))
def scaled(self):
values = self.dictionary.get('range')
return float(((max(values) - min(values)) * self.percentage) + min(values))
def minimum(self):
values = self.dictionary.get('range')
return min(values)
def maximum(self):
values = self.dictionary.get('range')
return max(values)
if __name__ == '__main__':
import doctest
doctest.testmod(optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE)
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] |
util/file_processing.py | Valzavator/YouTubeTrendingVideosAnalysis | 2 | 153272 | import pandas as pd
import os
import time
from processing_tool.data_preprocessing import prepare_feature_for_csv
from util.args import Args
def get_data_from_file(file_path: str) -> list:
with open(file_path) as file:
lines = [line.strip() for line in file]
return lines
def write_to_file(output_dir, file_name, data):
if output_dir is None:
raise NotADirectoryError('Invalid output directory name!')
if not os.path.exists(output_dir):
os.makedirs(output_dir)
with open(os.path.join(output_dir, file_name), "w+",
encoding='utf-8') as file:
for row in data:
file.write(f"{row}\n")
def get_videos_data_from_csv(*files) -> list:
data_frames = []
for file in files:
try:
if file is not None:
data_frames.append(pd.read_csv(file))
except FileNotFoundError as e:
print(e.strerror)
if len(data_frames) == 0:
return []
videos_data = pd.concat(data_frames).to_dict('records')
return videos_data
def save_videos_data_into_csv(
videos_data: list,
file_name=f"{time.strftime('%d-%m-%y_%H.%M.%S')}_videos.csv",
output_dir=Args.raw_data_dir()):
if videos_data is None:
raise ValueError('Videos data can`t be None!')
csv_data = [','.join(videos_data[0].keys())]
for video in videos_data:
if video is None:
continue
csv_data.append(','.join([prepare_feature_for_csv(val) for val in video.values()]))
write_to_file(
output_dir,
file_name,
csv_data)
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11799,
29918,
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13,
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2
] |
q5.py | fairoz-ahmed/Python_Practice | 0 | 29755 | class Input_data():
def __init__(self):
self.s=''
def getString(self):
self.s = input()
def printString(self):
print(self.s.upper())
strobj=Input_data()
strobj.getString()
strobj.printString() | [
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odoo/base-addons/website_event/__manifest__.py | LucasBorges-Santos/docker-odoo | 0 | 74582 | # -*- coding: utf-8 -*-
{
'name': 'Events',
'category': 'Website/Website',
'sequence': 166,
'summary': 'Publish events, sell tickets',
'website': 'https://www.odoo.com/page/events',
'description': "",
'depends': ['website', 'website_partner', 'website_mail', 'event'],
'data': [
'data/event_data.xml',
'views/res_config_settings_views.xml',
'views/event_snippets.xml',
'views/event_templates.xml',
'views/event_views.xml',
'security/ir.model.access.csv',
'security/event_security.xml',
],
'demo': [
'data/event_demo.xml'
],
'application': True,
'license': 'LGPL-3',
}
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bacpypes/link/station.py | cbergmiller/bacpypes | 1 | 96162 |
import struct
from .address import Address
__all__ = ['LocalStation', 'RemoteStation']
class LocalStation(Address):
"""
LocalStation
"""
def __init__(self, addr):
self.addrType = Address.localStationAddr
self.addrNet = None
if isinstance(addr, int):
if (addr < 0) or (addr >= 256):
raise ValueError('address out of range')
self.addrAddr = struct.pack('B', addr)
self.addrLen = 1
elif isinstance(addr, (bytes, bytearray)):
self.addrAddr = bytes(addr)
self.addrLen = len(addr)
else:
raise TypeError('integer, bytes or bytearray required')
class RemoteStation(Address):
"""
RemoteStation
"""
def __init__(self, net, addr):
if not isinstance(net, int):
raise TypeError('integer network required')
if (net < 0) or (net >= 65535):
raise ValueError('network out of range')
self.addrType = Address.remoteStationAddr
self.addrNet = net
if isinstance(addr, int):
if (addr < 0) or (addr >= 256):
raise ValueError('address out of range')
self.addrAddr = struct.pack('B', addr)
self.addrLen = 1
elif isinstance(addr, (bytes, bytearray)):
self.addrAddr = bytes(addr)
self.addrLen = len(addr)
else:
raise TypeError('integer, bytes or bytearray required') | [
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tests/test_superconductor_losses.py | tamasorosz/TrafoCalc | 0 | 97837 | from unittest import TestCase
from src.superconductor_losses import parallel_loss, perp_loss, norris_equation, cryostat_losses, cryo_surface, \
thermal_incomes, cooler_cost, sc_load_loss, magnusson_ac_loss
from math import pi
class TestLosses(TestCase):
def test_losses(self):
# BSCCO cable from Magnussons' paper
# f = 50.0
# c = 0.75
# bp = 0.033
# Ac = 0.31 * 4.29 * 1e-6
bpar = 66.0 * 1e-3 # T
# parallel losses
# 0.133 W/m in 10.1109/TASC.2003.813123 but this value seems ok
self.assertAlmostEqual(parallel_loss(50, bpar), 0.177, 3)
# perpendicular losses
self.assertAlmostEqual(perp_loss(50, 68. * 1e-3), 2.009, 3)
# norris equation
self.assertAlmostEqual(norris_equation(50, 50, 115.), 0.0047, 4)
def test_sc_transformer_losses(self):
# Th example is coming from DOI: 10.1109/TASC.2003.813123
# BSCCO cable from Magnussons' paper
# f = 50.0
# c = 0.75
# bp = 0.033
# Ac = 0.31 * 4.29 * 1e-6
bpar = 0.066 # T
bperp = 0.068 # T
self.assertAlmostEqual(parallel_loss(50, bpar), 0.179, 2)
self.assertAlmostEqual(perp_loss(50, bperp), 2.009, 2) # 1.960
# magnusson-formula based loss - 2 W/m
self.assertAlmostEqual(magnusson_ac_loss(bpar, bperp, 50, 25), 2.125, 2)
def test_45kVA_transformer_loss(self):
"""
Source: DOI: 10.1109/TASC.2005.869712
45 kVA transformer
:return:
"""
# The maximum axial and radial fields 30 mT, 23.7 mT
bax = 30. * 1e-3
brad = 23.7 * 1e-3
i1 = 18.75 # A
i2 = 281.25 # A
ac = magnusson_ac_loss(bax, brad, 50, 18.75)
self.assertAlmostEqual(ac, 0.279, 2)
# hv length - 14 turns, 13 pancakes
l_hv = 14 * 13 * (365 + 375.5) / 2 * 3.14 * 1e-3 # m
l_lv = 210 * (286 + 304.5) / 2 * 3.14 * 1e-3 # m
l = l_lv + l_hv
self.assertAlmostEqual(ac * l, 113.5, 1)
def test_losses_second_case_small_field(self):
bpar = 20.0 * 1e-3 # mT
# parallel losses
self.assertAlmostEqual(parallel_loss(50, bpar), 0.0535, 3)
# perpendicular losses
self.assertAlmostEqual(perp_loss(50, 20. * 1e-3), 0.1625, 1)
def test_cryostat_losses(self):
r_in = 0.5 / pi
r_ou = 1.0 / pi
h = 1.0
a_cs = cryo_surface(r_in, r_ou, h) # [m2]
P_cryo = cryostat_losses(a_cs)
P_thermal = thermal_incomes(100, 100)
sc_ll = sc_load_loss(100, 100, 100)
self.assertAlmostEqual(a_cs, 3.238, 2)
# self.assertAlmostEqual(P_cryo, 0.029537, 2)
self.assertAlmostEqual(P_thermal, 54, 1)
self.assertAlmostEqual(sc_ll, 5400, 1)
# tco = modified_tco_evaluation(1, 1, 1, 0, 0, 1, 0)
# self.assertAlmostEqual(tco, 3439.615, 1)
def test_cooler_cost(self):
p_loss_1 = 100. # W
p_loss_2 = 1000. # W
c1 = cooler_cost(p_loss_1)
c2 = cooler_cost(p_loss_2)
self.assertAlmostEqual(c1, 24984.95, 1)
self.assertAlmostEqual(c2, 92827.91, 1)
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] |
psp/gcp_utils.py | amckenna41/DCBLSTM_PSP | 1 | 32285 | <gh_stars>1-10
################################################################################
######## Google Cloud Platform Utilities ########
################################################################################
#Importing required libraries and dependancies
import numpy as np
import pandas as pd
import os
import glob
import subprocess
from google.cloud import storage, exceptions
from googleapiclient import errors
from googleapiclient import discovery
from google.cloud import pubsub_v1
from google.oauth2 import service_account
from oauth2client.client import GoogleCredentials
import tensorflow as tf
from tensorflow.keras.utils import plot_model
import pickle
import json
from subprocess import Popen, PIPE
try:
from _globals import *
except:
from . _globals import *
#initialise storage bucket object
BUCKET = None
def initialise_bucket(bucket):
"""
Description:
Initialise GCP storage bucket and client.
Args:
:bucket (str): name of GCP Storage bucket.
Returns:
None
"""
global BUCKET
storage_client = storage.Client()
bucketName = bucket.replace('gs://','')
bucket = storage_client.get_bucket(bucketName)
BUCKET = bucket
def save_history(history, model_output_folder):
"""
Description:
Save and upload model history to GCP Storage.
Args:
:history (dict): model history.
:model_output_folder (str): output folder where all models assets and results are stored.
Returns:
None
"""
#open history pickle file for writing
try:
f = open('history.pckl', 'wb')
pickle.dump(history.history, f)
f.close()
except pickle.UnpicklingError as e:
print('Error', e)
except (AttributeError, EOFError, ImportError, IndexError) as e:
print(traceback.format_exc(e))
except Exception as e:
print(traceback.format_exc(e))
print('Error creating history pickle.')
#create history blob path
blob_path = os.path.join(model_output_folder, 'history.pckl')
#upload history to bucket
upload_file(blob_path,'history.pckl')
def upload_directory(local_path, gcs_folder_path):
"""
Description:
Upload directory recursively to GCP Storage.
Args:
:local_path (str): local path to directory.
:gcs_folder_path (str): blob folder path on GCP.
Returns:
None
Reference:
https://stackoverflow.com/questions/25599503/how-to-upload-folder-on-google-cloud-storage-using-python-api
"""
if not (os.path.isdir(local_path)):
raise OSError('Path to local directory not found.')
#recursively iterate through directory, uploading each file individually to GCP bucket.
for local_file in glob.glob(local_path + '/**'):
if not os.path.isfile(local_file):
upload_directory(local_file, gcs_folder_path + "/" + os.path.basename(local_file))
else:
remote_path = os.path.join(gcs_folder_path, local_file[1 + len(local_path):])
upload_file(remote_path, local_file)
def upload_file(blob_path, filepath):
"""
Description:
Upload blob to bucket.
Args:
:blob_path (str): path of blob object in bucket.
:filepath (str): local filepath of object.
Returns:
None
"""
#initialise blob in bucket
blob = BUCKET.blob(blob_path)
#upload blob to specified bucket
try:
blob.upload_from_filename(filepath)
except Exception as e:
print("Error uploading blob {} to storage bucket {} .".format(blob_path, e.message))
def download_file(blob_path, filepath):
"""
Description:
Download blob object from GCP Storage bucket to local dir.
Args:
:blob_path (str): path of blob object in bucket.
:filepath (str): local filepath for downloaded blob.
Returns:
None
"""
#initialise blob in bucket
blob = BUCKET.blob(blob_path)
#download blob from GCP Storage bucket to local filepath
try:
blob.download_to_filename(filepath)
print('Blob {} downloaded to {}.'.format(blob_path, filepath))
except Exception as e:
print("Error downloading blob {} from storage bucket {}.".format(blob_path, e.message))
def get_job_hyperparmeters(project_id, job_name):
"""
Description:
Output results from hyperparameter tuning process to csv.
Args:
:project_id (str): name of GCP project.
:job_name (str): name of GCP Ai-Platform job.
Returns:
:df (pandas DataFrame): dataframe of hyperparameters and their associated results from training.
"""
#get GCP credentials for making request
job_id = '{}/jobs/{}'.format(project_id, job_name)
credentials = GoogleCredentials.get_application_default()
ml = discovery.build('ml', 'v1', credentials=credentials)
#make request to hyperparameter job using Google API Client
try:
request = ml.projects().jobs().get(name=job_id).execute()
if request['state'] != "SUCCEEDED": #check that job has completed
print('Hyperparameter tuning job not completed.')
return
except errors.HttpError as err:
print('Error getting job details')
print(err._get_reason())
col = []
row = []
#set the columns of the dataframe to the hyperparameter variables
for column in request['trainingOutput']['trials'][0]['hyperparameters']:
col.append(column)
#for each successful trial, append each hyperparameter metric to the row array
for cols in col:
for trial in range(0, len(request['trainingOutput']['trials'])):
#check hyperparameter has SUCCEEDED
if request['trainingOutput']['trials'][trial]['state'] == "SUCCEEDED":
row.append(request['trainingOutput']['trials'][trial]['hyperparameters'][cols])
#transform row list into a numpy array
row_np = np.asarray(row)
num_params = len(request['trainingOutput']['trials'][0]['hyperparameters'])
#horizontally split numpy array into each of the different columns
row_np = np.split(row_np, num_params)
#create dataframe from hyperparameter metrics
df = pd.DataFrame(row_np)
#transpose dataframe
df = df.T
#set columns of dataframe to metric names
df.columns = col
#append evaluation score and trial ID to dataframe
eval_score = []
trial_id = []
for trial in range(0, len(request['trainingOutput']['trials'])):
eval_score.append(request['trainingOutput']['trials'][trial]['finalMetric']['objectiveValue'])
trial_id.append(request['trainingOutput']['trials'][trial]['trialId'])
df['eval_score'] = eval_score
df['trial_id'] = trial_id
#sort dataframe by the evaluation score
df.sort_values('eval_score')
#put evaluation score and trial ID to beginning of dataframe columns
eval = df['eval_score']
df.drop(labels=['eval_score'], axis=1,inplace = True)
df.insert(0, 'eval_score', eval)
trial_id = df['trial_id']
df.drop(labels=['trial_id'], axis=1,inplace = True)
df.insert(0, 'trial_id', trial_id)
#export dataframe to a csv
df_filename = job_name + "_hyperparameter_tuning"
df.to_csv(df_file_name, encoding='utf-8', index=False)
#upload csv to GCP Storage
blob_path = job_name + ".csv"
upload_file(blob_path,df_filename)
return df
def list_bucket_objects():
"""
Description:
List all objects in bucket.
Args:
None
Returns:
None
"""
#use Google Storage API to get and print all objects in bucket
try:
blobs = storage_client.list_blobs(BUCKET_NAME) #global BUCKET_NAME variable
except Exception as e:
print("Error listing blobs from {} bucket: {}".format(BUCKET_NAME, e.message))
#print bucket blobs
for blob in blobs:
print(blob.name)
def delete_blob(blob_name):
"""
Description:
Delete blob from GCP storage bucket.
Args:
:blob_name (str): name of blob to delete.
Returns:
None
"""
#get blob from bucket using GCP storage API
blob = BUCKET.blob(blob_name)
#delete blob
try:
blob.delete()
print("Blob {} deleted from {} bucket.".format(blob_name, BUCKET_NAME))
except Exception as e:
print("Error deleting blob {} from {} bucket: {}.".format(blob_name, BUCKET_NAME, e.message))
def get_model_output(job_name):
"""
Description:
Output results from metrics captured when training model. Output to CSV.
Args:
:job_name (str): name of AI Platform training job.
Returns:
None
"""
#local filepath and GCP blob name
model_output_csv_blob = os.path.join(job_name, 'output_results.csv')
#converting model_output dictionary to pandas Dataframe
model_output_df = pd.DataFrame(model_output, index=[0])
#exporting Dataframe to CSV
model_output_df.to_csv('output_results.csv',index=False)
#uploading blob to cloud storage
upload_file(os.path.join(job_name, 'output_results.csv'),'output_results.csv')
return model_output_df
def visualise_model(model, save_folder):
"""
Description:
Visualise Keras TF model, including its layers, connections and data types.
Args:
:model (Keras.model): Keras model to visualise.
:save_folder (str): filepath for model directory to store model img in.
Returns:
None
"""
plot_model(model, to_file=os.path.join(save_folder,'model.png'),
show_shapes=True, show_dtype=True)
def get_trainable_parameters(model):
"""
Description:
Calculate the number of trainable and non-trainable parameters in Keras model.
Args:
:model (Keras.model): Keras model to calculate parameters for.
Returns:
:trainable_params (int): number of trainable parameters.
:non_trainable_params (int): number of non-trainable parameters.
:total_params (int): total number of trainable + non-trainable parameters.
"""
trainable_params = count_params(model.trainable_weights)
non_trainable_params = count_params(model.non_trainable_weights)
total_params = trainable_params + non_trainable_params
return trainable_params, non_trainable_params, total_params
def append_all_output(output_results_df, all_results="all_results.csv"):
"""
Description:
Append training results/parameters of current job to CSV containing
results/parameters of all previous jobs.
Args:
:all_results (str): filepath to csv containing results of previous jobs.
:all_results (str): filepath to csv containing results of current job.
Returns:
None
"""
#check if results file exists in bucket, if so then download locally
if (BUCKET.blob(all_results).exists()):
download_file(all_results, all_results)
else:
return
#read csv results file
all_results_df = pd.read_csv(all_results)
#append results of current training job to all_results file
all_results_df = all_results_df.append(output_results_df)
#export results to csv
all_results_df.to_csv(all_results, index=False)
#upload updated results file to bucket
upload_file(all_results, all_results)
def parse_json_arch(arch_json):
"""
Description:
Parse model architecture JSON.
Args:
:arch_json (str): filepath to model json.
Returns:
None
"""
pass
def get_job_status(job_name):
"""
Description:
Get training status of GCP Ai-Platform training job.
Args:
:job_name (str): name of training job.
Returns:
:status (str): training status of job.
:err_message (str): error message of job.
"""
# job_logs = subprocess.check_output(["gcloud", "ai-platform","jobs","describe",job_name])
job_logs = subprocess.Popen(["gcloud", "ai-platform","jobs","describe",job_name], stdin=PIPE, stdout=PIPE, stderr=PIPE)
output, err = job_logs.communicate(b"input data that is passed to subprocess' stdin")
#parse job status from command-line output
status=""
for item in (output.decode('UTF-8')).split("\n"):
if ("state:" in item):
status = item.strip()
status = (status[status.find(':')+1:]).strip()
#parse error message from command-line output
err_message=""
if (status=="FAILED"):
for item in (output.decode('UTF-8')).split("\n"):
if ("errorMessage:" in item):
err_message = item.strip()
#get err_message down to etag
return status, err_message
def setup_tpu():
"""
Description:
Initialize TPU for training.
Args:
None
Returns:
:strategy (TPUStrategy): TPU training strategy object.
References:
[1]: https://www.tensorflow.org/guide/tpu
[2]: https://cloud.google.com/tpu/docs/tpus
"""
resolver = tf.distribute.cluster_resolver.TPUClusterResolver()
tf.config.experimental_connect_to_cluster(resolver)
tf.tpu.experimental.initialize_tpu_system(resolver)
strategy = tf.distribute.experimental.TPUStrategy(resolver)
return strategy
class StepDecay():
"""
Description:
Step Decay Learning rate scheduler.
Args:
:initAlpha (float): initial learning rate (default=0.0005).
:factor (float): drop factor (default=0.8).
:dropEvery (int): number of epochs learning rate is dropped (default=40).
Returns:
Result from step decay function.
"""
def __init__(self, initAlpha=0.0005, factor=0.8, dropEvery=40):
self.initAlpha = initAlpha
self.factor = factor
self.dropEvery = dropEvery
def __call__(self, epoch):
exp = np.floor((epoch + 1) / self.dropEvery)
alpha = self.initAlpha * (self.factor ** exp)
return float(alpha)
class ExponentialDecay():
"""
Description:
Exponential Decay Learning rate scheduler.
Args:
:initAlpha (float): initial learning rate (default=0.0005).
:k (float): power/exponent of the exponential (default=0.8).
Returns:
Result from exponential decay function.
"""
def __init__(self, initAlpha=0.0005, k=0.8):
self.initAlpha = initAlpha
self.k = k
def __call__(self, epoch):
return (self.initAlpha * math.exp(-k*epoch))
class TimedBased():
"""
Description:
Timed based Decay Learning rate scheduler.
Args:
:initAlpha (float): initial learning rate (default=0.0005).
:epochs (int): number of epochs.
Returns:
Result from timed based decay function.
"""
def __init__(self, initAlpha=0.01, epochs=100):
self.initAlpha = initAlpha
self.epochs = epochs
decay = self.initAlpha / self.epochs
def __call__(self, lr, epochs):
decay = self.initAlpha / self.epochs
return ((lr *1) / (1 + decay * self.epochs))
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research/02_TrafficSignClassification/02_CarNDTrafficSignClassifier/source/path/run.py | LewisCollum/frostAV | 5 | 32638 | import os
from datetime import datetime
directory = "../runs"
current = os.path.join(directory, ".current")
class Run:
def __init__(self, runName):
run = os.path.join(directory, runName)
self.model = os.path.join(run, "model.h5")
self.log = os.path.join(run, "log.csv")
self.accuracy = os.path.join(run, "accuracy.png")
self.modelDiagram = os.path.join(run, "model.png")
self.modelSummary = os.path.join(run, "modelSummary")
def make():
runName = f"{datetime.now():%m-%d_%H%M}"
newRun = os.path.join(directory, runName)
os.mkdir(newRun)
with open(current, 'w') as f:
f.write(runName)
def loadFromName(runName):
return Run(runName)
def loadCurrent():
with open(current) as f:
return loadFromName(f.readline())
def has(path):
return os.path.isfile(path)
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2
] |
vgazer/version/custom_checker/lua.py | edomin/vgazer | 2 | 161879 | <reponame>edomin/vgazer<filename>vgazer/version/custom_checker/lua.py
import requests
from bs4 import BeautifulSoup
from vgazer.version.utils import GetVersionFromFilename
def Check(auth, mirrors):
response = requests.get("http://www.lua.org/download.html")
html = response.content
parsedHtml = BeautifulSoup(html, "html.parser")
links = parsedHtml.find_all("a")
for link in links:
if "ftp/lua-" in link["href"]:
return GetVersionFromFilename(link["href"])
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EPMA Microsegregation Processing Grid.py | smo216/EPMA-Microsegregation-Processing | 0 | 168566 | # -*- coding: utf-8 -*-
"""
Created on Fri Sep 27 15:04:09 2019
@author: EMG
EPMA Microsegregation Analysis
"""
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn import metrics
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
# curve-fit() function imported from scipy
from scipy.optimize import curve_fit
import statsmodels.api as sm
from matplotlib.pyplot import figure
import math
# %% Import EPMA data
""" Paste Filename Here"""
filename='10_1_19 DataBase W Mo Ti disabled new filament _Un 29 Duraloy M10 CCnew Mn.xlsx'
#filename='10_1_19 DataBase W Mo Ti disabled new filament _Un 33 Duraloy M10 DTA.xlsx'
#filename='9_27_19 DataBase W Ti disabled_Un 25 MT418 DTA Grid.xlsx'
#filename='9_24_19 DataBase W Ti disabled_Un 20 418 DTA Linetrace Core.xlsx'
#filename='10_1_19 DataBase W Mo Ti disabled new filament _Un 32 HP-2 DTA.xlsx'
data = pd.read_excel(filename)
data.head
[Len,Wid]=data.shape
data.info
data.describe()
# %% Pull EPMA data
Total=data['Elemental Totals']
Si=data['Si Elemental Percents']
Cr=data["Cr Elemental Percents"]
Fe=data["Fe Elemental Percents"]
Ni=data["Ni Elemental Percents"]
W=data["W Elemental Percents"]
Nb=data["Nb Elemental Percents"]
Mo=data["Mo Elemental Percents"]
Mn=data["Mn Elemental Percents"]
Ti=data["Ti Elemental Percents"]
#data['Total'] = data.sum(axis=1)-distance
#Total=data.sum(axis=1)-distance #totals the composition
# errprs
Si_er=data['Si Percent Errors']
Cr_er=data["Cr Percent Errors"]
Fe_er=data["Fe Percent Errors"]
Ni_er=data["Ni Percent Errors"]
W_er=data["W Percent Errors"]
Nb_er=data["Nb Percent Errors"]
Mo_er=data["Mo Percent Errors"]
Mn_er=data["Mn Percent Errors"]
Ti_er=data["Ti Percent Errors"]
# %%Lets get Plotting
# make subplots?
plt.scatter(Fe,Si,label="Si")
plt.scatter(Fe,Cr,label="Cr")
#plt.plot(Fe,Fe,label="Fe")
plt.scatter(Fe,Ni,label="Ni")
#plt.scatter(Fe,W,label="W")
plt.scatter(Fe,Nb,label="Nb")
#plt.scatter(Fe,Mo,label="Mo")
plt.scatter(Fe,W,label="Mn")
#plt.plot(Fe,Ti,label="Ti")
#
#
#plt.xlabel('Distance (um)')
plt.xlabel('Concentration Fe (wt.%)')
#
plt.ylabel('Concentration (wt.%)')
#
plt.title("Concentration of Elements")
#
plt.legend()
#plt.xlim(30,35)
#plt.ylim(0,40)
#
plt.show()
# %%Lets get Plotting Function of Cr
# make subplots?
plt.scatter(Cr,Si,label="Si")
plt.scatter(Cr,Fe,label="Fe")
#plt.plot(Fe,Fe,label="Fe")
plt.scatter(Cr,Ni,label="Ni")
#plt.scatter(Fe,W,label="W")
plt.scatter(Cr,Nb,label="Nb")
#plt.scatter(Fe,Mo,label="Mo")
plt.scatter(Cr,W,label="Mn")
#plt.plot(Fe,Ti,label="Ti")
#
#
#plt.xlabel('Distance (um)')
plt.xlabel('Concentration Cr (wt.%)')
#
plt.ylabel('Concentration (wt.%)')
#
plt.title("Concentration of Elements")
#
plt.legend()
#plt.xlim(30,35)
#plt.ylim(0,40)
#
plt.show()
# %% Subplots
fig, axs = plt.subplots(6, sharex=True)
fig.suptitle('Concentration wt% as a function of Fe')
axs[0].scatter(Fe,Si)
#axs[0].set_title('Si')
axs[1].scatter(Fe,Cr)
#axs[1].set_title('Cr')
axs[2].scatter(Fe,Ni)
axs[3].scatter(Fe,Nb)
axs[4].scatter(Fe,Mo)
axs[5].scatter(Fe,Mn)
#plt.legend()
#plt.xlim(30,35)
#plt.ylim(0,40)
# %% Filter for Carbides
totalwtcarbide = 95 #max comp for filtering for carbides
M7_filter = (data['Elemental Totals']<totalwtcarbide) & (data["Cr Elemental Percents"] > 70)
M7_comp=data[M7_filter]
AM7_comp=M7_comp.loc[:,"Si Elemental Percents":"V Elemental Percents"].mean(axis=0)
print(AM7_comp)
#M7_comp1=M7_comp.loc['Element Totals':'V Elemental Percents']#.mean(axis=1)
#print(M7_comp1)
MC_filter = (data['Elemental Totals']<totalwtcarbide) & (data["Nb Elemental Percents"] > 80)
MC_comp=data[MC_filter]
AMC_comp=MC_comp.loc[:,"Si Elemental Percents":"V Elemental Percents"].mean(axis=0)
print(AMC_comp)
Avg_comp=data.loc[:,"Si Elemental Percents":"V Elemental Percents"].mean(axis=0)
print(Avg_comp)
# %% WIRS
#filter dataset to remove interdendritic regions
totalwtlow=97 #threshold for filtering interdendritic regions may need to be tweaked
totalwthigh=103
crmax=26
nbmax=1
nimin=30
nimax=36.5
""" This value will influence the kline"""
maxfs=1#0.96#0.899#82.19485515/100 HP-2 #0.96 for M10 #0.899 for HP
max_filter = (data['Elemental Totals']>totalwtlow) & (data["Cr Elemental Percents"] < crmax) & (data["Nb Elemental Percents"] < nbmax) & (data['Elemental Totals']<totalwthigh) & (data["Ni Elemental Percents"] > nimin) & (data["Ni Elemental Percents"] < nimax)
primary_y=data#[max_filter]
#print(primary_y)
#plt.plot(primary_y['Relative Microns'],primary_y[' "Si Elemental Percents"'],label="Si")
#plt.plot(primary_y['Relative Microns'],primary_y["Cr Elemental Percents"],label="Cr")
#plt.show()
#for negatively segregating elements
primary_y['Si_bar']=(primary_y['Si Elemental Percents'] - primary_y['Si Elemental Percents'].min())/primary_y['Si Percent Errors']
primary_y['Cr_bar']=(primary_y["Cr Elemental Percents"] - primary_y["Cr Elemental Percents"].min())/primary_y["Cr Percent Errors"]
primary_y['Ni_bar']=(primary_y["Ni Elemental Percents"] - primary_y["Ni Elemental Percents"].min())/primary_y["Ni Percent Errors"] #if Ni negatively segregates
primary_y['Nb_bar']=(primary_y["Nb Elemental Percents"] - primary_y["Nb Elemental Percents"].min())/primary_y["Nb Percent Errors"]
#primary_y['Mo_bar']=(primary_y["Mo Elemental Percents"] - primary_y["Mo Elemental Percents"].min())/primary_y["Mo Percent Errors"]
primary_y['Mn_bar']=(primary_y["Mn Elemental Percents"] - primary_y["Mn Elemental Percents"].min())/primary_y["Mn Percent Errors"]
#W_bar=(primary_y["W Elemental Percents"] - primary_y["W Elemental Percents"].min())/primary_y["W Percent Errors"]
#for positively segregating elements
primary_y['Fe_bar']=(primary_y["Fe Elemental Percents"].max() - primary_y["Fe Elemental Percents"])/primary_y["Fe Percent Errors"]
#primary_y['Ni_bar']=(primary_y["Ni Elemental Percents"].max() - primary_y["Ni Elemental Percents"])/primary_y["Ni Percent Errors"]
#Ti_bar=(primary_y["Ti Elemental Percents"].max() - primary_y["Ti Elemental Percents"])/primary_y["Ti Percent Errors"]
#Aggregate Values into a new dataframe
#Cbar=pd.DataFrame(data=[Si_bar,Cr_bar,Fe_bar,Ni_bar,W_bar,Nb_bar,Mo_bar,Ti_bar]).T
#Cbar.columns=['Sibar', 'Crbar', 'Febar', 'Nibar', 'Wbar', 'Nbbar', 'Mobar', 'Tibar']
#Cbar=pd.DataFrame(data=[Si_bar,Cr_bar,Fe_bar,Ni_bar,Nb_bar,Mo_bar]).T
#Cbar.columns=['Sibar', 'Crbar', 'Febar', 'Nibar', 'Nbbar', 'Mobar']
#primary_y['Avgbar'] = primary_y[['Si_bar', 'Cr_bar', 'Fe_bar', 'Ni_bar', 'Nb_bar', 'Mo_bar', 'Mn_bar']].mean(axis=1)
primary_y['Avgbar'] = primary_y[['Si_bar', 'Cr_bar', 'Fe_bar', 'Ni_bar', 'Nb_bar', 'Mn_bar']].mean(axis=1)
#print(primary_y)
#sort according to Cbar min to max
primary_y_sort=primary_y.sort_values(by=['Avgbar'])
#print(primary_y_sort)
primary_y_sort['Rank'] = primary_y_sort['Avgbar'].rank(ascending = 1)
#print(primary_y_sort)
primary_y_sort['Fsolid']=(primary_y_sort['Rank'] - 0.5)/primary_y_sort['Rank'].max()*maxfs
#print(primary_y_sort['Fsolid'])
f_solid=primary_y_sort['Fsolid']
# %%Lets get Plotting
plt.plot(primary_y_sort['Fsolid'],primary_y_sort['Si Elemental Percents'],label="Si")
plt.plot(primary_y_sort['Fsolid'],primary_y_sort['Cr Elemental Percents'],label="Cr")
plt.plot(primary_y_sort['Fsolid'],primary_y_sort['Fe Elemental Percents'],label="Fe")
plt.plot(primary_y_sort['Fsolid'],primary_y_sort['Ni Elemental Percents'],label="Ni")
plt.plot(primary_y_sort['Fsolid'],primary_y_sort['Nb Elemental Percents'],label="Nb")
plt.plot(primary_y_sort['Fsolid'],primary_y_sort['Mo Elemental Percents'],label="Mo")
plt.plot(primary_y_sort['Fsolid'],primary_y_sort['Mn Elemental Percents'],label="Mn")
#plt.plot(Csort['Fsolid'],Csort['Fe'],label="Fe")
#plt.plot(Csort['Fsolid'],Csort['Ni'],label="Ni")
##plt.plot(Csort['Fsolid'],Csort['W'],label="W")
#plt.plot(Csort['Fsolid'],Csort['Nb'],label="Nb")
#plt.plot(Csort['Fsolid'],Csort['Mo'],label="Mo")
#plt.plot(Csort['Fsolid'],Csort['Ti'],label="Ti")
plt.xlabel('Fraction Solid')
plt.ylabel('Concentration (wt.%)')
plt.title("Concentration of Elements")
#plt.legend()
#loc='best'
plt.show()
# %% Calculate k from Core
#Nominal Composition
C0={'Si':1.929,'Cr':24.571,'Fe':37.695,'Ni':33.206,'Nb':1.28,'Mn':0.837,'Mo':0.07} #M10
#C0={'Si':1.14,'Cr':25.2,'Fe':36.66,'Ni':35,'Nb':0.418,'Mn':0.899,'Mo':0.06} #HP-2
#C0={'Si':1.929,'Cr':24.571,'Fe':37.695,'Ni':33.206,'Nb':1.28,'Mn':0.837,'Mo':0.07}
acore=10 #points to average from start of sorted data
#pull C0 estimates from grid
C0Si=data['Si Elemental Percents'].mean()
C0Cr=data["Cr Elemental Percents"].mean()
C0Fe=data["Fe Elemental Percents"].mean()
C0Ni=primary_y_sort["Ni Elemental Percents"].mean()
C0W=data["W Elemental Percents"].mean()
C0Nb=data["Nb Elemental Percents"].mean()
#C0Mo=data["Mo Elemental Percents"].mean()
C0Mn=data["Mn Elemental Percents"].mean()
C0Ti=data["Ti Elemental Percents"].mean()
#Average of the first 6 points to solidify to the total average composition
KSi=primary_y_sort['Si Elemental Percents'].iloc[0:acore].mean(axis=0) / C0Si
#primary_y_sort['Si Elemental Percents'].iloc[0:5].mean(axis=0)
print(KSi)
KSic0=primary_y_sort['Si Elemental Percents'].iloc[0:acore].mean(axis=0) / C0['Si']
#primary_y_sort['Si Elemental Percents'].iloc[0:5].mean(axis=0)
print(KSic0)
KCr=primary_y_sort['Cr Elemental Percents'].iloc[0:acore].mean(axis=0) / C0Cr
print(KCr)
KCrc0=primary_y_sort['Cr Elemental Percents'].iloc[0:acore].mean(axis=0) / C0['Cr']
print(KCrc0)
KFe=primary_y_sort['Fe Elemental Percents'].iloc[0:acore].mean(axis=0) / C0Fe
print(KFe)
KFec0=primary_y_sort['Fe Elemental Percents'].iloc[0:acore].mean(axis=0) / C0['Fe']
print(KFec0)
KNi=primary_y_sort['Ni Elemental Percents'].iloc[0:acore].mean(axis=0) / C0Ni
print(KNi)
KNic0=primary_y_sort['Ni Elemental Percents'].iloc[0:acore].mean(axis=0) / C0['Ni']
print(KNic0)
#KW=primary_y_sort['W Elemental Percents'].iloc[0:5].mean(axis=0) / data["W Elemental Percents"].mean()
KNb=primary_y_sort['Nb Elemental Percents'].iloc[0:acore].mean(axis=0) / C0Nb
print(KNb)
KNbc0=primary_y_sort['Nb Elemental Percents'].iloc[0:acore].mean(axis=0) / C0['Nb']
print(KNbc0)
#KMo=primary_y_sort['Mo Elemental Percents'].iloc[0:acore].mean(axis=0) / C0Mo
#print(KMo)
#KMoc0=primary_y_sort['Mo Elemental Percents'].iloc[0:acore].mean(axis=0) / C0['Mo']
#print(KMoc0)
KMn=primary_y_sort['Mn Elemental Percents'].iloc[0:acore].mean(axis=0) / C0Mn
print(KMn)
KMnc0=primary_y_sort['Mn Elemental Percents'].iloc[0:acore].mean(axis=0) / C0['Mn']
print(KMnc0)
#KTi=primary_y_sort['Ti Elemental Percents'].iloc[0:5].mean(axis=0) / data["Ti Elemental Percents"].mean()
# %% Calc from Curve
#CsSi=primary_y_sort['Si Elemental Percents'].div(C0['Si'])
#print(CsSi)
#lnCsSi=CsSi.div(C0['Si'])
#print(lnCsSi)
#lnCsNi=np.log(primary_y_sort['Ni Elemental Percents'].div(C0['Ni']))
lnCsSi=np.log(primary_y_sort['Si Elemental Percents'].div(data["Si Elemental Percents"].mean()))
lnCsCr=np.log(primary_y_sort['Cr Elemental Percents'].div(data["Cr Elemental Percents"].mean()))
lnCsFe=np.log(primary_y_sort['Fe Elemental Percents'].div(data["Fe Elemental Percents"].mean()))
lnCsNi=np.log(primary_y_sort['Ni Elemental Percents'].div(data["Ni Elemental Percents"].mean()))
lnCsNb=np.log(primary_y_sort['Nb Elemental Percents'].div(data["Nb Elemental Percents"].mean()))
#lnCsMo=np.log(primary_y_sort['Mo Elemental Percents'].div(data["Mo Elemental Percents"].mean()))
lnCsMn=np.log(primary_y_sort['Mn Elemental Percents'].div(data["Mn Elemental Percents"].mean()))
lnFL=np.log(1-primary_y_sort['Fsolid'])
FL=1-primary_y_sort['Fsolid']
FS=primary_y_sort['Fsolid']
#loglog(lnFL,lnCsSi)
plt.plot(lnFL,lnCsNi,label="Ni")
plt.xlabel('Ln(Fraction Liquid)')
plt.ylabel('Ln(Cs/C0)')
plt.title("Concentration of Elements")
plt.show()
def test(F,a,b):
# return math.log(a)+(1-a)*F+b
return (a-1)*np.log(1-F)+(b)
Siparam, Siparam_cov = curve_fit(test, FS, lnCsSi)
Crparam, Crparam_cov = curve_fit(test, FS, lnCsCr)
Feparam, Feparam_cov = curve_fit(test, FS, lnCsFe)
Niparam, Niparam_cov = curve_fit(test, FS, lnCsNi)
Nbparam, Nbparam_cov = curve_fit(test, FS, lnCsNb)
#Moparam, Moparam_cov = curve_fit(test, FS, lnCsMo)
Mnparam, Mnparam_cov = curve_fit(test, FS, lnCsMn)
print("Sine funcion coefficients:")
print(Niparam)
print("Covariance of coefficients:")
print(Niparam_cov)
# ans stores the new y-data according to
# the coefficients given by curve-fit() function
ansCr = test(FS,Crparam[0],Crparam[1])#((Crparam[0]-1)*lnFL+Crparam[1])
ansSi = ((Siparam[0]-1)*lnFL+Siparam[1])
ansNi = ((Niparam[0]-1)*lnFL+Niparam[1])
ansFe = ((Feparam[0]-1)*lnFL+Feparam[1])
ansNb = ((Nbparam[0]-1)*lnFL+Nbparam[1])
#ansMo = ((Moparam[0]-1)*lnFL+Moparam[1])
ansMn = ((Mnparam[0]-1)*lnFL+Mnparam[1])
'''Below 4 lines can be un-commented for plotting results
using matplotlib as shown in the first example. '''
plt.plot(lnFL, lnCsCr, 'o', color ='red', label ="data")
plt.plot(lnFL, ansCr, '--', color ='blue', label ="optimized data")
plt.legend()
plt.title("Cr")
plt.xlabel('Ln(Fraction Solid)')
plt.ylabel('Ln(Cs/C0)')
plt.show()
plt.plot(lnFL, lnCsSi, 'o', color ='red', label ="data")
plt.plot(lnFL, ansSi, '--', color ='blue', label ="optimized data")
plt.legend()
plt.title("Si")
plt.xlabel('Ln(Fraction Solid)')
plt.ylabel('Ln(Cs/C0)')
plt.show()
plt.plot(lnFL, lnCsNi, 'o', color ='red', label ="data")
plt.plot(lnFL, ansNi, '--', color ='blue', label ="optimized data")
plt.legend()
plt.title("Ni")
plt.xlabel('Ln(Fraction Solid)')
plt.ylabel('Ln(Cs/C0)')
plt.show()
plt.plot(lnFL, lnCsNb, 'o', color ='red', label ="data")
plt.plot(lnFL, ansNb, '--', color ='blue', label ="optimized data")
plt.legend()
plt.title("Nb")
plt.xlabel('Ln(Fraction Solid)')
plt.ylabel('Ln(Cs/C0)')
plt.show()
plt.plot(lnFL, lnCsFe, 'o', color ='red', label ="data")
plt.plot(lnFL, ansFe, '--', color ='blue', label ="optimized data")
plt.legend()
plt.title("Fe")
plt.xlabel('Ln(Fraction Solid)')
plt.ylabel('Ln(Cs/C0)')
plt.show()
#define new k values
#K["Si"]=??
KSi_line=Siparam[0] #abs(1-Siparam[0])
print(KSi_line)
KCr_line=Crparam[0] #abs(Crparam[0]-2) #Crparam[0]
print(KCr_line)
KFe_line=Feparam[0] #klineFe=abs(Feparam[0]-2)
print(KFe_line)
KNi_line=Niparam[0] #abs(Niparam[0]-2) #Niparam[0]
print(KNi_line)
KNb_line=Nbparam[0] #abs(Nbparam[0]-2) #Nbparam[0]
print(KNb_line)
#KMo_line=Moparam[0] #abs(Moparam[0]-2) #Moparam[0]
#print(KMo_line)
KMn_line=Mnparam[0] #abs(Mnparam[0]-2) #Mnparam[0]
print(KMn_line)
# %%K fit with linear regression
X=lnFL
X=sm.add_constant(X)
Simodel=sm.OLS(lnCsSi,X).fit()
Simodel.summary()
klSi=1-0.3637
print(klSi)
Crmodel=sm.OLS(lnCsCr,X).fit()
Crmodel.summary()
Nbmodel=sm.OLS(lnCsNb,X).fit()
Nbmodel.summary()
Mnmodel=sm.OLS(lnCsMn,X).fit()
Mnmodel.summary()
Nimodel=sm.OLS(lnCsNi,X).fit()
Nimodel.summary()
Femodel=sm.OLS(lnCsFe,X).fit()
Femodel.summary()
# %% Scheil Calculation
def scheil(k,Cnom,fs):
return k*Cnom*(1-fs)**(k-1)
#from dendrite core k values
NEQ_Si=scheil(KSi,C0Si,f_solid)
NEQ_Cr=scheil(KCr,C0Cr,f_solid)
NEQ_Fe=scheil(KFe,C0Fe,f_solid)
NEQ_Ni=scheil(KNi,C0Ni,f_solid)
NEQ_Mn=scheil(KMn,C0Mn,f_solid)
NEQ_Nb=scheil(KNb,C0Nb,f_solid)
#NEQ_Mo=scheil(KMo,C0Mo,f_solid)
# %% Equlibrium Calculation
def equil(k,Cnom,fs):
return k*Cnom/((1-fs)+k*fs)
EQ_Si=equil(KSi,C0Si,f_solid)
EQ_Cr=equil(KCr,C0Cr,f_solid)
EQ_Fe=equil(KFe,C0Fe,f_solid)
EQ_Ni=equil(KNi,C0Ni,f_solid)
EQ_Mn=equil(KMn,C0Mn,f_solid)
EQ_Nb=equil(KNb,C0Nb,f_solid)
#EQ_Mo=equil(KMo,C0Mo,f_solid)
# %% <NAME> Calculation-work in progress
def BF(k,Cnom,fs,alpha):
return k*Cnom*(1-(1-2*alpha*k)*fs)**((k-1)/(1-2*alpha*k))
# %% Plot solidification path
figure(num=None, figsize=(6, 4), dpi=100, facecolor='w', edgecolor='k')
#plt.plot(primary_y_sort['Fsolid'],primary_y_sort['Si Elemental Percents'],label="Si", color='blue')
plt.plot(primary_y_sort['Fsolid'],primary_y_sort['Cr Elemental Percents'],label="Cr", color='green')
plt.plot(primary_y_sort['Fsolid'],primary_y_sort['Fe Elemental Percents'],label="Fe", color='red')
plt.plot(primary_y_sort['Fsolid'],primary_y_sort['Ni Elemental Percents'],label="Ni", color='magenta')
#plt.plot(primary_y_sort['Fsolid'],primary_y_sort['Nb Elemental Percents'],label="Nb", color='cyan')
#plt.plot(primary_y_sort['Fsolid'],primary_y_sort['Mo Elemental Percents'],label="Mo")
#plt.plot(primary_y_sort['Fsolid'],primary_y_sort['Mn Elemental Percents'],label="Mn", color='black')
#plt.plot(primary_y_sort['Fsolid'],NEQ_Si,label="NESi", color='blue')
plt.plot(primary_y_sort['Fsolid'],NEQ_Cr,label="NECr", color='green')
plt.plot(primary_y_sort['Fsolid'],NEQ_Fe,label="NEFe", color='red')
plt.plot(primary_y_sort['Fsolid'],NEQ_Ni,label="NENi", color='magenta')
#plt.plot(primary_y_sort['Fsolid'],NEQ_Nb,label="NENb", color='cyan')
#plt.plot(primary_y_sort['Fsolid'],NEQ_Mo,label="NEMo")
#plt.plot(primary_y_sort['Fsolid'],NEQ_Mn,label="NEMn", color='black')
#plt.plot(primary_y_sort['Fsolid'],EQ_Si,label="ESi", color='blue', linestyle='dashed')
plt.plot(primary_y_sort['Fsolid'],EQ_Cr,label="ECr", color='green', linestyle='dashed')
plt.plot(primary_y_sort['Fsolid'],EQ_Fe,label="EFe", color='red', linestyle='dashed')
plt.plot(primary_y_sort['Fsolid'],EQ_Ni,label="ENi", color='magenta', linestyle='dashed')
#plt.plot(primary_y_sort['Fsolid'],EQ_Nb,label="ENb", color='cyan', linestyle='dashed')
#plt.plot(primary_y_sort['Fsolid'],EQ_Mo,label="EMo")
#plt.plot(primary_y_sort['Fsolid'],EQ_Mn,label="EMn", color='black', linestyle='dashed')
plt.xlabel('Fraction Solid')
plt.ylabel('Concentration (wt.%)')
#plt.title("Solidification Path Solidification")
plt.xlim(0,1.0)
plt.ylim(20,45)
#plt.legend()
#loc='best'
plt.show()
# %% Plot solidification path Major Elements
figure(num=None, figsize=(6, 4), dpi=100, facecolor='w', edgecolor='k')
#plt.plot(primary_y_sort['Fsolid'],primary_y_sort['Si Elemental Percents'],label="Si", color='blue')
plt.plot(primary_y_sort['Fsolid'],primary_y_sort['Cr Elemental Percents'],label="Cr", color='green')
plt.plot(primary_y_sort['Fsolid'],primary_y_sort['Fe Elemental Percents'],label="Fe", color='red')
plt.plot(primary_y_sort['Fsolid'],primary_y_sort['Ni Elemental Percents'],label="Ni", color='magenta')
#plt.plot(primary_y_sort['Fsolid'],primary_y_sort['Nb Elemental Percents'],label="Nb", color='cyan')
#plt.plot(primary_y_sort['Fsolid'],primary_y_sort['Mo Elemental Percents'],label="Mo")
#plt.plot(primary_y_sort['Fsolid'],primary_y_sort['Mn Elemental Percents'],label="Mn", color='black')
#plt.plot(primary_y_sort['Fsolid'],NEQ_Si,label="NESi", color='blue')
plt.plot(primary_y_sort['Fsolid'],NEQ_Cr,label="NECr", color='green')
plt.plot(primary_y_sort['Fsolid'],NEQ_Fe,label="NEFe", color='red')
plt.plot(primary_y_sort['Fsolid'],NEQ_Ni,label="NENi", color='magenta')
#plt.plot(primary_y_sort['Fsolid'],NEQ_Nb,label="NENb", color='cyan')
#plt.plot(primary_y_sort['Fsolid'],NEQ_Mo,label="NEMo")
#plt.plot(primary_y_sort['Fsolid'],NEQ_Mn,label="NEMn", color='black')
#plt.plot(primary_y_sort['Fsolid'],EQ_Si,label="ESi", color='blue', linestyle='dashed')
plt.plot(primary_y_sort['Fsolid'],EQ_Cr,label="ECr", color='green', linestyle='dashed')
plt.plot(primary_y_sort['Fsolid'],EQ_Fe,label="EFe", color='red', linestyle='dashed')
plt.plot(primary_y_sort['Fsolid'],EQ_Ni,label="ENi", color='magenta', linestyle='dashed')
#plt.plot(primary_y_sort['Fsolid'],EQ_Nb,label="ENb", color='cyan', linestyle='dashed')
#plt.plot(primary_y_sort['Fsolid'],EQ_Mo,label="EMo")
#plt.plot(primary_y_sort['Fsolid'],EQ_Mn,label="EMn", color='black', linestyle='dashed')
plt.xlabel('Fraction Solid')
plt.ylabel('Concentration (wt.%)')
#plt.title("Solidification Path Solidification")
plt.xlim(0,1.0)
plt.ylim(20,45)
#plt.legend()
#loc='best'
plt.show()
# %% Minor Elements
figure(num=None, figsize=(6, 4), dpi=100, facecolor='w', edgecolor='k')
plt.plot(primary_y_sort['Fsolid'],primary_y_sort['Si Elemental Percents'],label="Si", color='blue')
plt.plot(primary_y_sort['Fsolid'],primary_y_sort['Nb Elemental Percents'],label="Nb", color='cyan')
#plt.plot(primary_y_sort['Fsolid'],primary_y_sort['Mo Elemental Percents'],label="Mo")
plt.plot(primary_y_sort['Fsolid'],primary_y_sort['Mn Elemental Percents'],label="Mn", color='black')
plt.plot(primary_y_sort['Fsolid'],NEQ_Si,label="NESi", color='blue')
plt.plot(primary_y_sort['Fsolid'],NEQ_Nb,label="NENb", color='cyan')
#plt.plot(primary_y_sort['Fsolid'],NEQ_Mo,label="NEMo")
plt.plot(primary_y_sort['Fsolid'],NEQ_Mn,label="NEMn", color='black')
plt.plot(primary_y_sort['Fsolid'],EQ_Si,label="ESi", color='blue', linestyle='dashed')
plt.plot(primary_y_sort['Fsolid'],EQ_Nb,label="ENb", color='cyan', linestyle='dashed')
#plt.plot(primary_y_sort['Fsolid'],EQ_Mo,label="EMo")
plt.plot(primary_y_sort['Fsolid'],EQ_Mn,label="EMn", color='black', linestyle='dashed')
plt.xlabel('Fraction Solid')
plt.ylabel('Concentration (wt.%)')
#plt.title("Solidification Path Solidification")
plt.xlim(0,1.0)
#plt.ylim(20,45)
#plt.legend()
#loc='best'
plt.show()
# %% ??????
#print(primary_y_sort['Si Elemental Percents'])
#print(lnCs)
#C=data['C']
#Nb=data['Nb']
#Si=data['Si']
#Ti=data['Ti']
#W=data['W']
#F_Y_MC=data['Fraction Y E1']
#F_MC=data['Fraction MC E1']
#F_Y_M7=data['Fraction Y E2']
#F_M7=data['Fraction M7C3 E2']
#
##print(F_Y_MC)
##print(F_MC)
##print(F_Y_M7)
##print(F_M7)
#
##set x and y
#y=F_MC
#X=Si
#X = sm.add_constant(X) ## let's add an intercept (beta_0) to our model
#
#
## To get statistics of the dataset
## Note the difference in argument order
#model = sm.OLS(y, X).fit()
#predictions = model.predict(X) # make the predictions by the model
#
## Print out the statistics
#model.summary()
# %% Output table?
from prettytable import PrettyTable
x = PrettyTable()
x.field_names = ["City name", "Area", "Population", "Annual Rainfall"]
#['Si', 'Cr', 'Fe', 'Ni', 'Nb', 'Mn']
x.add_row(["Adelaide", 1295, 1158259, 600.5])
x.add_row(["Brisbane", 5905, 1857594, 1146.4])
x.add_row(["Darwin", 112, 120900, 1714.7])
x.add_row(["Hobart", 1357, 205556, 619.5])
x.add_row(["Sydney", 2058, 4336374, 1214.8])
x.add_row(["Melbourne", 1566, 3806092, 646.9])
x.add_row(["Perth", 5386, 1554769, 869.4])
print(x)
# %% Bar Charts
# libraries
import numpy as np
import matplotlib.pyplot as plt
# set width of bar
barWidth = 0.25
# set height of bar
bars1 = [KSi, KCr, KFe, KNi, KNb, KMn]
bars2 = [KSic0, KCrc0, KFec0, KNic0, KNbc0, KMnc0]
bars3 = [KSi_line, KCr_line, KFe_line, KNi_line, KNb_line, KMn_line]
# Set position of bar on X axis
r1 = np.arange(len(bars1))
r2 = [x + barWidth for x in r1]
r3 = [x + barWidth for x in r2]
# Make the plot
plt.bar(r1, bars1, color='#7f6d5f', width=barWidth, edgecolor='white', label='Kmean')
plt.bar(r2, bars2, color='#557f2d', width=barWidth, edgecolor='white', label='KC0')
plt.bar(r3, bars3, color='#2d7f5e', width=barWidth, edgecolor='white', label='K_line')
# Add xticks on the middle of the group bars
plt.xlabel('Element', fontweight='bold')
plt.ylabel('Partition Coefficient (k)', fontweight='bold')
plt.xticks([r + barWidth for r in range(len(bars1))], ['Si', 'Cr', 'Fe', 'Ni', 'Nb', 'Mn'])
plt.axhline(1, color="black")#.plot(["Si","Mn"], [1,1], "k--")
# Create legend & Show graphic
plt.ylim(0,1.2)
plt.legend()
plt.show()
#plt.savefig('M10 DTA K-values.png')
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] |
orix/tests/test_neoeuler.py | JoonatanL/orix | 1 | 1601308 | import pytest
import numpy as np
from orix.vector.neo_euler import Rodrigues, Homochoric
from orix.quaternion.rotation import Rotation
""" Rodrigues """
@pytest.mark.parametrize(
"rotation, expected",
[
(Rotation([1, 0, 0, 0]), [0, 0, 0]),
(Rotation([0.9239, 0.2209, 0.2209, 0.2209]), [0.2391, 0.2391, 0.2391]),
],
)
def test_from_rotation(rotation, expected):
rodrigues = Rodrigues.from_rotation(rotation)
assert np.allclose(rodrigues.data, expected, atol=1e-4)
@pytest.mark.parametrize(
"rodrigues, expected", [(Rodrigues([0.2391, 0.2391, 0.2391]), np.pi / 4),]
)
def test_angle(rodrigues, expected):
angle = rodrigues.angle
assert np.allclose(angle.data, expected, atol=1e-3)
""" Homochoric"""
@pytest.mark.parametrize(
"rotation", [Rotation([1, 0, 0, 0]), Rotation([0.9239, 0.2209, 0.2209, 0.2209])]
)
def test_Homochoric_from_rotation(rotation):
h = Homochoric.from_rotation(rotation)
return None
@pytest.mark.parametrize(
"rotation", [Rotation([1, 0, 0, 0]), Rotation([0.9239, 0.2209, 0.2209, 0.2209])]
)
@pytest.mark.xfail(strict=True, reason=AttributeError)
def test_Homochoric_angle(rotation):
h = Homochoric.from_rotation(rotation)
h.angle
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] |
testServer.py | SIshikawa1106/tcp-communication | 0 | 59246 | import tcp
import random
class ServerData(object):
def __init__(self):
self._data = ""
def receive(self, data):
print("input data = {}".format(data))
self._data = data
def send(self):
data = self._data + "{}".format(random.randint(0, 10))
print("make data = {}".format(data))
return data
server_data = ServerData()
server = tcp.TcpServer("localhost", 999,
server_data.receive,
server_data.send,
send_first=False
)
server.daemon = True
server.start()
import time
time.sleep(100)
server.exit() | [
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demos/09-sqlite/orm-peewee/source/models/__init__.py | sk-dwtoulouse/python-initiation-training | 1 | 1604667 | from .person import Person
__all__ = ["Person"]
| [
1,
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] |
test/unit/test_unpack.py | timmartin/skulpt | 2,671 | 113939 | # Unpack tests from CPython converted from doctest to unittest
import unittest
class UnpackTest(unittest.TestCase):
def test_basic(self):
t = (1, 2, 3)
a, b, c = t
self.assertEqual(a, 1)
self.assertEqual(b, 2)
self.assertEqual(c, 3)
l = [4, 5, 6]
a, b, c = l
self.assertEqual(a, 4)
self.assertEqual(b, 5)
self.assertEqual(c, 6)
a, b, c = 7, 8, 9
self.assertEqual(a, 7)
self.assertEqual(b, 8)
self.assertEqual(c, 9)
s = 'one'
a, b, c = s
self.assertEqual(a, 'o')
self.assertEqual(b, 'n')
self.assertEqual(c, 'e')
def test_single(self):
st = (99,)
sl = [100]
a, = st
self.assertEqual(a, 99)
b, = sl
self.assertEqual(b, 100)
def test_non_sequence(self):
def unpack():
a, b, c = 7
# Currently has incorrect message
self.assertRaises(TypeError, unpack)
def test_wrong_size(self):
def tup_too_big():
t = (1, 2, 3)
a, b = t
def list_too_big():
l = [4, 5, 6]
a, b = l
def tup_too_small():
t = (1, 2, 3)
a, b, c, d = t
def list_too_small():
l = [4, 5, 6]
a, b, c, d = l
self.assertRaises(ValueError, tup_too_big)
self.assertRaises(ValueError, list_too_big)
self.assertRaises(ValueError, tup_too_small)
self.assertRaises(ValueError, list_too_small)
def test_class(self):
class Seq:
def __getitem__(self, i):
if i >= 0 and i < 3: return i
raise IndexError
a, b, c = Seq()
self.assertEqual(a, 0)
self.assertEqual(b, 1)
self.assertEqual(c, 2)
def test_class_fail(self):
class Seq:
def __getitem__(self, i):
if i >= 0 and i < 3: return i
raise IndexError
def too_small():
a, b, c, d = Seq()
def too_big():
a, b = Seq()
self.assertRaises(ValueError, too_small)
self.assertRaises(ValueError, too_big)
def test_bad_class(self):
class BadSeq:
def __getitem__(self, i):
if i >=0 and i < 3:
return i
elif i ==3:
raise NameError
else:
raise IndexError
def raise_bad_error1():
a, b, c, d, e = BadSeq()
def raise_bad_error2():
a, b, c = BadSeq()
self.assertRaises(NameError, raise_bad_error1)
self.assertRaises(NameError, raise_bad_error2)
if __name__ == "__main__":
unittest.main()
| [
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venv/Lib/site-packages/h5py/tests/test_h5p.py | Westlanderz/AI-Plat1 | 1 | 1611052 | <reponame>Westlanderz/AI-Plat1
# This file is part of h5py, a Python interface to the HDF5 library.
#
# http://www.h5py.org
#
# Copyright 2008-2013 <NAME> and contributors
#
# License: Standard 3-clause BSD; see "license.txt" for full license terms
# and contributor agreement.
import unittest as ut
from h5py import h5p, h5f, version
from .common import TestCase
class TestLibver(TestCase):
"""
Feature: Setting/getting lib ver bounds
"""
def test_libver(self):
""" Test libver bounds set/get """
plist = h5p.create(h5p.FILE_ACCESS)
plist.set_libver_bounds(h5f.LIBVER_EARLIEST, h5f.LIBVER_LATEST)
self.assertEqual((h5f.LIBVER_EARLIEST, h5f.LIBVER_LATEST),
plist.get_libver_bounds())
@ut.skipIf(version.hdf5_version_tuple < (1, 10, 2),
'Requires HDF5 1.10.2 or later')
def test_libver_v18(self):
""" Test libver bounds set/get for H5F_LIBVER_V18"""
plist = h5p.create(h5p.FILE_ACCESS)
plist.set_libver_bounds(h5f.LIBVER_EARLIEST, h5f.LIBVER_V18)
self.assertEqual((h5f.LIBVER_EARLIEST, h5f.LIBVER_V18),
plist.get_libver_bounds())
@ut.skipIf(version.hdf5_version_tuple < (1, 10, 2),
'Requires HDF5 1.10.2 or later')
def test_libver_v110(self):
""" Test libver bounds set/get for H5F_LIBVER_V110"""
plist = h5p.create(h5p.FILE_ACCESS)
plist.set_libver_bounds(h5f.LIBVER_V18, h5f.LIBVER_V110)
self.assertEqual((h5f.LIBVER_V18, h5f.LIBVER_V110),
plist.get_libver_bounds())
@ut.skipIf(version.hdf5_version_tuple < (1, 11, 4),
'Requires HDF5 1.11.4 or later')
def test_libver_v112(self):
""" Test libver bounds set/get for H5F_LIBVER_V112"""
plist = h5p.create(h5p.FILE_ACCESS)
plist.set_libver_bounds(h5f.LIBVER_V18, h5f.LIBVER_V112)
self.assertEqual((h5f.LIBVER_V18, h5f.LIBVER_V112),
plist.get_libver_bounds())
class TestDA(TestCase):
'''
Feature: setting/getting chunk cache size on a dataset access property list
'''
def test_chunk_cache(self):
'''test get/set chunk cache '''
dalist = h5p.create(h5p.DATASET_ACCESS)
nslots = 10000 # 40kb hash table
nbytes = 1000000 # 1MB cache size
w0 = .5 # even blend of eviction strategy
dalist.set_chunk_cache(nslots, nbytes, w0)
self.assertEqual((nslots, nbytes, w0),
dalist.get_chunk_cache())
class TestFA(TestCase):
'''
Feature: setting/getting mdc config on a file access property list
'''
def test_mdc_config(self):
'''test get/set mdc config '''
falist = h5p.create(h5p.FILE_ACCESS)
config = falist.get_mdc_config()
falist.set_mdc_config(config)
def test_set_alignment(self):
'''test get/set chunk cache '''
falist = h5p.create(h5p.FILE_ACCESS)
threshold = 10 * 1024 # threshold of 10kiB
alignment = 1024 * 1024 # threshold of 1kiB
falist.set_alignment(threshold, alignment)
self.assertEqual((threshold, alignment),
falist.get_alignment())
@ut.skipUnless(
version.hdf5_version_tuple >= (1, 12, 1) or
(version.hdf5_version_tuple[:2] == (1, 10) and version.hdf5_version_tuple[2] >= 7),
'Requires HDF5 1.12.1 or later or 1.10.x >= 1.10.7')
def test_set_file_locking(self):
'''test get/set file locking'''
falist = h5p.create(h5p.FILE_ACCESS)
use_file_locking = False
ignore_when_disabled = False
falist.set_file_locking(use_file_locking, ignore_when_disabled)
self.assertEqual((use_file_locking, ignore_when_disabled),
falist.get_file_locking())
class TestPL(TestCase):
def test_obj_track_times(self):
"""
tests if the object track times set/get
"""
# test for groups
gcid = h5p.create(h5p.GROUP_CREATE)
gcid.set_obj_track_times(False)
self.assertEqual(False, gcid.get_obj_track_times())
gcid.set_obj_track_times(True)
self.assertEqual(True, gcid.get_obj_track_times())
# test for datasets
dcid = h5p.create(h5p.DATASET_CREATE)
dcid.set_obj_track_times(False)
self.assertEqual(False, dcid.get_obj_track_times())
dcid.set_obj_track_times(True)
self.assertEqual(True, dcid.get_obj_track_times())
# test for generic objects
ocid = h5p.create(h5p.OBJECT_CREATE)
ocid.set_obj_track_times(False)
self.assertEqual(False, ocid.get_obj_track_times())
ocid.set_obj_track_times(True)
self.assertEqual(True, ocid.get_obj_track_times())
def test_link_creation_tracking(self):
"""
tests the link creation order set/get
"""
gcid = h5p.create(h5p.GROUP_CREATE)
gcid.set_link_creation_order(0)
self.assertEqual(0, gcid.get_link_creation_order())
flags = h5p.CRT_ORDER_TRACKED | h5p.CRT_ORDER_INDEXED
gcid.set_link_creation_order(flags)
self.assertEqual(flags, gcid.get_link_creation_order())
# test for file creation
fcpl = h5p.create(h5p.FILE_CREATE)
fcpl.set_link_creation_order(flags)
self.assertEqual(flags, fcpl.get_link_creation_order())
def test_attr_phase_change(self):
"""
test the attribute phase change
"""
cid = h5p.create(h5p.OBJECT_CREATE)
# test default value
ret = cid.get_attr_phase_change()
self.assertEqual((8,6), ret)
# max_compact must < 65536 (64kb)
with self.assertRaises(ValueError):
cid.set_attr_phase_change(65536, 6)
# Using dense attributes storage to avoid 64kb size limitation
# for a single attribute in compact attribute storage.
cid.set_attr_phase_change(0, 0)
self.assertEqual((0,0), cid.get_attr_phase_change())
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] |
weichi.py | AlekseySP/Project-102 | 0 | 65544 | <filename>weichi.py
# Load and initialize Modules here
import pygame
import random
pygame.init()
# Window Information
displayw = 700
displayh = 700
screen = pygame.display.set_mode((displayw,displayh))
surface = pygame.display.get_surface()
W,H = size = surface.get_width(), surface.get_height()
# Clock
windowclock = pygame.time.Clock()
# Load other things such as images and sound files here
#tileset = pygame.image.load("tileset.png").convert_alpha()
# Use convert_alpha() for images with transparency
# Main Class
class MainRun(object):
def __init__(self, width, height):
self.w = width
self.h = height
self.board_size = 19
self.WHITE = (255,255,255)
self.BLACK = (0,0,0)
self.GRAY = (128, 128, 128)
self.BROWN = (139, 69, 19)
self.TEAL = (0,128,128)
self.RED = (255, 0 , 0)
self.side = self.w
if self.h < self.w:
self.side = self.h
self.step = self.side/41
self.z = self.step*2
self.b_step = (self.side - (self.step+self.z)*2) / (self.board_size - 1)
self.coord_list = {}
self.move_history = []
self.accupied_dots = []
self.Main()
def draw_board(self):
r1 = pygame.Rect((self.step, self.step, self.side-self.step*2, self.side-self.step*2))
pygame.draw.rect(screen, self.GRAY, r1, 3)
x1 = self.step + self.z
y1 = self.step + self.z
x2 = self.side - (self.step+self.z)
y2 = self.side - (self.step + self.z)
# coord_list = {}
for v in range(self.board_size):
pygame.draw.line(screen, self.GRAY, [x1, y1], [x1, y2], 1)
self.coord_list[str(v)] = [x1]
x1 += self.b_step
#coord_list[str(board_size)] = [x1]
x1 = self.step + self.z
for h in range(self.board_size):
pygame.draw.line(screen, self.GRAY, [x1, y1], [x2, y1], 1)
self.coord_list[str(h)].append(y1)
y1 += self.b_step
pygame.draw.circle(screen, self.GRAY, self.coord_list["3"], 5)
pygame.draw.circle(screen, self.GRAY, self.coord_list["9"], 5)
pygame.draw.circle(screen, self.GRAY, self.coord_list["15"], 5)
pygame.draw.circle(screen, self.GRAY, [self.coord_list["3"][0], self.coord_list["15"][1]], 5)
pygame.draw.circle(screen, self.GRAY, [self.coord_list["15"][0], self.coord_list["3"][1]], 5)
def make_move(self):
color = random.random()
rx = self.coord_list[str(random.randrange(self.board_size))][0]
ry = self.coord_list[str(random.randrange(self.board_size))][1]
coords = [rx, ry]
if coords in self.accupied_dots:
pass
else:
if color >= 0.5:
col = self.WHITE
else:
col = self.BLACK
self.accupied_dots.append([rx, ry])
self.move_history.append([col, [rx, ry]])
def draw_moves(self):
if self.accupied_dots:
for i in self.move_history:
pygame.draw.circle(screen, i[0], i[1], self.b_step*0.45)
pygame.draw.circle(screen, self.GRAY, i[1], self.b_step*0.45, 1)
def Main(self):
#Put all variables up here
stopped = False
while stopped == False:
screen.fill(self.BLACK) #Tuple for filling display... Current is white
self.draw_board()
#Event Tasking
#Add all your event tasking things here
for event in pygame.event.get():
if event.type == pygame.QUIT:
pygame.quit()
quit()
elif event.type == pygame.MOUSEBUTTONDOWN:
if event.button == 1:
self.make_move()
elif event.type == pygame.KEYDOWN:
stopped = True
#Add things like player updates here
# self.make_move()
self.draw_moves()
#Also things like score updates or drawing additional items
# Remember things on top get done first so they will update in the order yours is set at
# Remember to update your clock and display at the end
pygame.display.update()
windowclock.tick(20)
# If you need to reset variables here
# This includes things like score resets
# After your main loop throw in extra things such as a main menu or a pause menu
# Make sure you throw them in your main loop somewhere where they can be activated by the user
# All player classes and object classes should be made outside of the main class and called inside the class
#The end of your code should look something like this
if __name__ == "__main__":
MainRun(W, H) | [
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DiretoryBrute.py | Moleey/DiretoryBrute.py | 1 | 47123 | from os import system
from requests import get
from pyfiglet import figlet_format
from colored import fore, back, style, attr
attr(0)
print(back.BLACK)
print(fore.BLUE_VIOLET + style.BOLD)
system("clear")
print(figlet_format("DIRETORY BRUTE\nBY MOLEEY", width=58, justify="center", font="smslant"))
site = input("Link Do Site: ")
txt = open("wordlist.txt", "r")
line = txt.readlines()
for diretory in line:
req = get(site+diretory)
status = req.status_code
if status != 404:
print(f"URL: {site+diretory} Status: {status}")
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] |
backend/app/literature/schemas/cross_reference_schemas.py | alliance-genome/agr_literature_service | 0 | 14651 | from typing import List, Optional
from pydantic import BaseModel
from pydantic import validator
class CrossReferenceSchemaRelated(BaseModel):
curie: str
pages: Optional[List[str]] = None
is_obsolete: Optional[bool] = None
@validator('curie')
def name_must_contain_space(cls, v):
if v.count(":") != 1 and not v.startswith("DOI:"):
raise ValueError('must contain a single colon')
return v
class Config():
orm_mode = True
extra = "forbid"
schema_extra = {
"example": {
"curie": "MOD:curie",
"pages": [
"reference"
]
}
}
class CrossReferenceSchemaPost(CrossReferenceSchemaRelated):
resource_curie: Optional[str] = None
reference_curie: Optional[str] = None
class Config():
orm_mod = True
extra = "forbid"
schema_extra = {
"example": {
"curie": "MOD:curie",
"pages": [
"reference"
],
"reference_curie": "AGR:AGRReference<number>"
}
}
class CrossReferencePageSchemaShow(BaseModel):
name: Optional[str] = None
url: Optional[str] = None
class Config():
orm_mode = True
extra = "forbid"
class CrossReferenceSchemaShow(BaseModel):
curie: str
url: Optional[str] = None
pages: Optional[List[CrossReferencePageSchemaShow]] = None
is_obsolete: bool
class CrossReferenceSchema(BaseModel):
curie: str
pages: Optional[List[CrossReferencePageSchemaShow]] = None
url: Optional[str] = None
is_obsolete: Optional[bool] = False
resource_curie: Optional[str] = None
reference_curie: Optional[str] = None
author_ids: Optional[List[int]] = None
editor_ids: Optional[List[int]] = None
class Config():
orm_mode = True
extra = "forbid"
class CrossReferenceSchemaUpdate(BaseModel):
pages: Optional[List[str]] = None
resource_curie: Optional[str] = None
reference_curie: Optional[str] = None
is_obsolete: Optional[bool] = None
class Config():
orm_mode = True
extra = "forbid"
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] |
attic/_targetdatacli.py | KSchopmeyer/smipyping | 0 | 166193 | <filename>attic/_targetdatacli.py
# (C) Copyright 2017 Inova Development Inc.
# All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Argparse CLi for the temporary target script.
TODO: Remove this completely
"""
import os
import sys as _sys
import csv
import argparse as _argparse
from ._cliutils import SmartFormatter as _SmartFormatter
from ._targetdata import TargetsData
from .config import DEFAULT_CONFIG_FILE
from ._configfile import read_config
__all__ = ['ProcessTargetDataCli']
class ProcessTargetDataCli(object):
"""
This class is part of the cli for Target data.
It creates the argparse list
and parses input in two separate functions.
TODO: This should probably be in the cli itself but we are trying to
create test tools for the cli so put here.
Also split into separate functions to allow testing each.
"""
def __init__(self, prog):
argparser = _argparse.ArgumentParser(
prog=prog,
description='Manage Target data',
formatter_class=_SmartFormatter,
usage="""targetdata <command> [<args>]
The commands are:
display Display entries in the Targets data repository
disable Disable an record in the table
fields list the fields in the target data repostiory
record Display the record for a particular target_id
disabled list all records disabled (i.e. ScanEnabled = False)
""")
# a = RunTargetData()
# subcommands = a.__dict__.keys()
argparser.add_argument(
'command', metavar='cmd', help='Subcommand to run')
argparser.add_argument(
'-f', '--config_file', metavar='CONFIG_FILE',
default=DEFAULT_CONFIG_FILE,
help=('Configuration file to use for config information. '
'Default=%s' % DEFAULT_CONFIG_FILE))
argparser.add_argument(
'-v', '--verbose', action='store_true', default=False)
argparser.add_argument(
'-d', '--dbtype', choices=('csv', 'sql'), default=csv,
help='Choice of database type')
args = argparser.parse_args(_sys.argv[1:2])
if not hasattr(self, args.command):
print('Unrecognized command %s ' % args.command)
argparser.print_help()
exit(1)
DBTYPE = 'csv'
db_config = read_config(args.config_file, DBTYPE)
db_config['directory'] = os.path.dirname(args.config_file)
target_data = TargetsData.factory(db_config, args, DBTYPE)
# use dispatch pattern to invoke method with same name
getattr(self, args.command)(target_data)
def display(self, target_data):
"""
Function to display table data.
gets the arguments for the display and display the Table data.
"""
disp_parser = _argparse.ArgumentParser(
description='Display target data repository')
disp_parser.add_argument('-s', '--sort',
default='IP',
help='Sort by field')
# now that we're inside a subcommand, ignore the first
# TWO argvs, ie the command and the subcommand
disp_parser.parse_args(_sys.argv[2:])
print('keys %s' % list(target_data.table_format_dict))
print('\n')
target_data.display_all()
def disable(self, target_data):
"""Function to mark a particular targetdata record disabled."""
disable_parser = _argparse.ArgumentParser(
description='Disable record in the repository')
# prefixing the argument with -- means it's optional
disable_parser.add_argument(
'Id',
help='Set or reset the disable flag for the defined record ')
# prefixing the argument with -- means it's optional
disable_parser.add_argument(
'-f', '--file',
default='targetdata_example.csv',
help='Filename to display')
disable_parser.add_argument(
'-l', '--list',
action='store_true', default=False,
help='List all the disabled entries')
disable_parser.add_argument(
'-e', '--enable',
action='store_true', default=False,
help='Set the disable flag to enable the '
'record rather than disable it')
# now that we're inside a subcommand, ignore the first
# TWO argvs, ie the command and the subcommand
args = disable_parser.parse_args(_sys.argv[2:])
host_record = target_data.get_target(args.Id)
# TODO add test to see if already in correct state
if host_record is not None:
host_record['EnableScan'] = False if args.enable is True else True
target_data.write_updated()
else:
print('Id %s invalid or not in table' % args.Id)
if args.list is True:
target_data.display_disabled()
def disabled(self, target_data):
# ~ disp_parser = _argparse.ArgumentParser(
# ~ description='Display disabled')
# ~ # prefixing the argument with -- means it's optional
# ~ disp_parser.add_argument('-f', '--file',
# ~ default='targetdata_example.csv',
# ~ help='Filename to display')
# ~ args = disp_parser.parse_args(_sys.argv[2:])
target_data.display_disabled()
def fields(self, target_data):
"""Display the fields in the targetdata file."""
# prefixing the argument with -- means it's optional
print('\n'.join(target_data.get_field_list()))
def find(self, target_data):
parser = _argparse.ArgumentParser(
description='Find a particular host')
parser.add_argument('server',
help='Server IP address to find')
parser.add_argument('-f', '--file',
default='targetdata_example.csv',
help='Filename to display')
args = parser.parse_args(_sys.argv[2:])
record = target_data.get_target(args.server)
# TODO separate get record from display record .
if record is None:
print('%s not found' % args.server)
else:
print('%s found\n%s' % (args.server, record))
def record(self, target_data):
"""
Parse an input cli for hosts.
"""
parser = _argparse.ArgumentParser(
description='List info on single record_id')
# prefixing the argument with -- means it's optional
parser.add_argument('record_id', type=int,
help='Record Id from the database. Integers')
parser.add_argument('-f', '--file',
default='targetdata_example.csv',
help='Filename to display')
args = parser.parse_args(_sys.argv[2:])
record_id = "%2.2s" % args.record_id
# TODO get host ids from table
record = target_data.get_target(record_id)
# TODO separate get record from display record .
if record is None:
print('Record %s not found in Targets data.' % record_id)
else:
max_len = 0
for key in record:
if max_len < len(key):
max_len = len(key)
for key in record:
print('%*s : %s' % (max_len, key, record[key]))
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loadlib.py | mit-dci/litloadtest | 0 | 49041 | <reponame>mit-dci/litloadtest<gh_stars>0
import time
import random
import testlib
def square_pos(size, x, y):
return size * (y % size) + (x % size)
# Makes a big grid of nodes, all connected to their neighbors.
def make_grid_env(size):
print('Making grid env of size', size)
env = testlib.TestEnv(size ** 2)
for l in env.lits:
l.rpc.SetFee(Fee=5, CoinType=testlib.REGTEST_COINTYPE)
a = l.make_new_addr()
res = env.bitcoind.rpc.sendtoaddress(a, 10)
print('Funded', a, 'with', res)
env.generate_block()
for x in range(size):
for y in range(size):
pos = square_pos(size, x, y)
litat = env.lits[pos]
print(pos, ':', x, y, '->', str(litat.get_balance_info()))
if x > 0:
litl = env.lits[square_pos(size, x - 1, y)] # l = left
litat.connect_to_peer(litl)
litat.open_channel(litl, 100000000, 200000)
env.generate_block()
if y > 0:
litu = env.lits[square_pos(size, x, y - 1)] # u = up
litat.connect_to_peer(litu)
litat.open_channel(litu, 100000000, 200000)
env.generate_block()
env.generate_block(count=5)
return env
def random_node_pair(env):
x = random.randint(0, len(env.lits) - 1)
y = random.randint(0, len(env.lits) - 2)
if y >= x:
y += 1
return env.lits[x], env.lits[y]
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] |
sunpy/map/sources/yohkoh.py | fluxtransport/sunpy | 0 | 1608553 | """Yohkoh SXT Map subclass definitions"""
__author__ = "<NAME>"
__email__ = "<EMAIL>"
import numpy as np
from astropy.visualization import PowerStretch
from astropy.visualization.mpl_normalize import ImageNormalize
from sunpy.map import GenericMap
from sunpy.map.sources.source_type import source_stretch
from sunpy.sun import constants
__all__ = ['SXTMap']
class SXTMap(GenericMap):
"""Yohkoh SXT Image Map
The Yohkoh Soft X-ray Telescope (SXT) the full solar disk
(42 x 42 arcminutes)in the 0.25 - 4.0 keV range.
It consists of a glancing incidence mirror and a CCD sensor and
used thin metallic filters to acquire images in restricted
portions of its energy range. SXT could resolve features down to 2.5
arcseconds. Information about the temperature and density of the plasma
emitting the observed x-rays was obtained by comparing images acquired with
the different filters. Images could be obtained every 2 to 8 seconds.
Smaller images with a single filter could be obtained as frequently as
once every 0.5 seconds.
Yohkoh was launched on 30 August 1991 and ceased operations on
14 December 2001.
References
----------
* `Yohkoh Mission Page <http://solar.physics.montana.edu/sxt/>`_
* `Fits header reference <http://proba2.oma.be/data/SWAP/level0>`_
* `Yohkoh Analysis Guide <http://ylstone.physics.montana.edu/ylegacy/yag.html>`_
"""
def __init__(self, data, header, **kwargs):
GenericMap.__init__(self, data, header, **kwargs)
self.meta['detector'] = "SXT"
self.meta['telescop'] = "Yohkoh"
self.plot_settings['cmap'] = 'yohkohsxt' + self.measurement[0:2].lower()
self.plot_settings['norm'] = ImageNormalize(
stretch=source_stretch(self.meta, PowerStretch(0.5)), clip=False)
# 2012/12/19 - the SXT headers do not have a value of the distance from
# the spacecraft to the center of the Sun. The FITS keyword 'DSUN_OBS'
# appears to refer to the observed diameter of the Sun. Until such
# time as that is calculated and properly included in the file, we will
# use simple trigonometry to calculate the distance of the center of
# the Sun from the spacecraft. Note that the small angle approximation
# is used, and the solar radius stored in SXT FITS files is in arcseconds.
self.meta['dsun_apparent'] = self.meta.get('dsun_apparent', constants.au)
if 'solar_r' in self.meta:
self.meta['dsun_apparent'] = constants.radius/(np.deg2rad(self.meta['solar_r']/3600.0))
@property
def dsun(self):
""" For Yohkoh Maps, dsun_obs is not always defined. Uses approximation
defined above it is not defined."""
return self.meta.get('dsun_obs', self.meta['dsun_apparent'])
@property
def measurement(self):
"""
Returns the type of data observed.
"""
s = self.meta.get('wavelnth', '')
if s == 'Al.1':
s = 'Al01'
elif s.lower() == 'open':
s = 'white-light'
return s
@classmethod
def is_datasource_for(cls, data, header, **kwargs):
"""Determines if header corresponds to an SXT image"""
return header.get('instrume') == 'SXT'
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13,
2
] |
Vini/Image/views.py | deeppudasaini/vini | 1 | 1610608 | <reponame>deeppudasaini/vini
from django.shortcuts import render
from django.http import JsonResponse
from .models import Image
from rest_framework.decorators import api_view
from rest_framework.response import Response
from .serializers import ImageSerializer
@api_view(["GET"])
def ImageList(request):
images = Image.objects.all()
serializer = ImageSerializer(images, many=True)
return Response(serializer.data)
@api_view(["GET"])
def ImageDetails(request, pk):
images = Image.objects.filter(id=pk)
serializer = ImageSerializer(images, many=True)
return Response(serializer.data)
@api_view(["POST"])
def ImageAdd(request):
serializer = ImageSerializer(data=request.data)
if serializer.is_valid():
serializer.save()
return Response(serializer.data)
@api_view(["POST"])
def ImageUpdate(request, pk):
updatingImage = Image.objects.get(id=pk)
serializer = ImageSerializer(instance=updatingImage, data=request.data)
if serializer.is_valid():
serializer.save()
return Response(serializer.data)
@api_view(["DELETE"])
def ImageDelete(request, pk):
deletingImage = Image.objects.get(id=pk)
deletingImage.delete()
return Response("Image Deleted")
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slackchannel2pdf/locales.py | ErikKalkoken/slackchannel2pdf | 52 | 3128 | import datetime as dt
import logging
from babel import Locale, UnknownLocaleError
from babel.dates import format_datetime, format_time, format_date
import pytz
from tzlocal import get_localzone
from . import settings
logger = logging.getLogger(__name__)
class LocaleHelper:
"""Helpers for converting date & time according to current locale and timezone"""
def __init__(
self,
my_locale: Locale = None,
my_tz: pytz.BaseTzInfo = None,
author_info: dict = None,
) -> None:
"""
Args:
- my_locale: Primary locale to use
- my_tz: Primary timezone to use
- author_info: locale and timezone to use from this Slack response
if my_locale and/or my_tz are not given
"""
self._locale = self._determine_locale(my_locale, author_info)
self._timezone = self._determine_timezone(my_tz, author_info)
@staticmethod
def _determine_locale(my_locale: Locale = None, author_info: dict = None) -> Locale:
if my_locale:
if not isinstance(my_locale, Locale):
raise TypeError("my_locale must be a babel Locale object")
else:
if author_info:
try:
my_locale = Locale.parse(author_info["locale"], sep="-")
except UnknownLocaleError:
logger.warning("Could not use locale info from Slack")
my_locale = Locale.default()
else:
my_locale = Locale.default()
if not my_locale:
my_locale = Locale.parse(settings.FALLBACK_LOCALE)
return my_locale
@staticmethod
def _determine_timezone(
my_tz: pytz.BaseTzInfo = None, author_info: dict = None
) -> pytz.BaseTzInfo:
if my_tz:
if not isinstance(my_tz, pytz.BaseTzInfo):
raise TypeError("my_tz must be of type pytz")
else:
if author_info:
try:
my_tz = pytz.timezone(author_info["tz"])
except pytz.exceptions.UnknownTimeZoneError:
logger.warning("Could not use timezone info from Slack")
my_tz = get_localzone()
else:
my_tz = get_localzone()
if not my_tz:
my_tz = pytz.UTC
return my_tz
@property
def locale(self) -> Locale:
return self._locale
@property
def timezone(self) -> pytz.BaseTzInfo:
return self._timezone
def format_date_full_str(self, my_datetime: dt.datetime) -> str:
return format_date(my_datetime, format="full", locale=self.locale)
def format_datetime_str(self, my_datetime: dt.datetime) -> str:
"""returns formated datetime string for given dt using locale"""
return format_datetime(my_datetime, format="short", locale=self.locale)
def get_datetime_formatted_str(self, ts: int) -> str:
"""return given timestamp as formated datetime string using locale"""
my_datetime = self.get_datetime_from_ts(ts)
return format_datetime(my_datetime, format="short", locale=self.locale)
def get_time_formatted_str(self, ts: int) -> str:
"""return given timestamp as formated datetime string using locale"""
my_datetime = self.get_datetime_from_ts(ts)
return format_time(my_datetime, format="short", locale=self.locale)
def get_datetime_from_ts(self, ts: int) -> dt.datetime:
"""returns datetime object of a unix timestamp with local timezone"""
my_datetime = dt.datetime.fromtimestamp(float(ts), pytz.UTC)
return my_datetime.astimezone(self.timezone)
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2
] |
Wattpad download.py | jbzdarkid/Random | 1 | 70267 | <filename>Wattpad download.py
import urllib2, re
output = '\n\n'
url = '76273009-wereling-wounded-on-hold-prologue'
page = 1
title = None
while(True):
text = urllib2.urlopen('http://www.wattpad.com/'+url+'/page/'+str(page)).read()
new_title = re.search('<title>(.*?)</title>', text).group(1)
if new_title == title: # Reached page limit
page = 1
next_url = re.search('<a class="on-navigate next-up.*?href="(.*?)"', text)
if next_url is None:
break
url = next_url.group(1)
output += '\n\n\t'+title+'\n'
continue
else: # New page
print new_title, len(text)
page += 1
title = new_title
for i in range(len(text)):
if text[i:i+7] == '<p data':
i += 48
while (text[i:i+4] != '</p>'):
output += text[i]
i += 1
output += '\n'
f = open('out.txt', 'wb')
f.write(output)
f.close() | [
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engine.py | anazalea/zestlife | 0 | 161254 | <gh_stars>0
from entities.environment import GameEnvironment
class Engine():
def draw_background():
"""draw any non-interactive object"""
pass
def tick(e: GameEnvironment):
"""Update the state every entity in the game"""
for o in e.game_objects:
o.update()
o.move()
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wsnsims/tocs/energy.py | king4arabs/wsnsims2 | 2 | 174779 | <reponame>king4arabs/wsnsims2
import itertools
import numpy as np
from wsnsims.core.data import segment_volume
class ToCSEnergyModelError(Exception):
pass
class ToCSEnergyModel(object):
def __init__(self, simulation_data, environment):
"""
:param simulation_data:
:type simulation_data: tocs.tocs_sim.TOCS
:param environment:
:type environment: core.environment.Environment
"""
self.sim = simulation_data
self.env = environment
self._ids_to_clusters = {}
self._ids_to_movement_energy = {}
self._ids_to_comms_energy = {}
def cluster_data_volume(self, cluster_id, intercluster_only=False):
"""
:param cluster_id:
:param intercluster_only:
:return:
:rtype: pq.bit
"""
cluster = self._find_cluster(cluster_id)
if not intercluster_only:
# Handle the intra-cluster data volume
segment_pairs = itertools.permutations(cluster.segments, 2)
internal_volume = np.sum(
[segment_volume(s, d, self.env) for s, d in segment_pairs])
else:
internal_volume = 0
# Handle the inter-cluster data volume
external_segments = list(
set(self.sim.segments) - set(cluster.segments))
# Outgoing data ...
segment_pairs = list(
itertools.product(cluster.segments, external_segments))
# Incoming data ...
segment_pairs += list(
itertools.product(external_segments, cluster.segments))
external_volume = np.sum(
[segment_volume(s, d, self.env) for s, d in segment_pairs])
total_volume = internal_volume + external_volume
return total_volume
def centroid_data_volume(self, cluster_id):
"""
:param cluster_id:
:return:
:rtype: pq.bit
"""
centroid = self._find_cluster(cluster_id)
# Handle the intra-centroid data volume
segment_pairs = itertools.permutations(centroid.segments, 2)
volume = np.sum(
[segment_volume(s, d, self.env) for s, d in segment_pairs])
cluster_pairs = list()
# Handle the incoming volume from each cluster
for cluster in self.sim.clusters:
other_clusters = list(self.sim.clusters)
for other_cluster in other_clusters:
# Cluster -> Other Cluster
cluster_pairs.append((cluster, other_cluster))
# Other Cluster -> Cluster
cluster_pairs.append((other_cluster, cluster))
for src_cluster, dst_cluster in cluster_pairs:
src_segments = src_cluster.segments
dst_segments = dst_cluster.segments
segment_pairs = itertools.product(src_segments, dst_segments)
volume += np.sum(
[segment_volume(s, d, self.env) for s, d in segment_pairs])
return volume
def total_comms_energy(self, cluster_id):
if cluster_id in self._ids_to_comms_energy:
return self._ids_to_comms_energy[cluster_id]
if cluster_id == self.sim.centroid.cluster_id:
data_volume = self.centroid_data_volume(cluster_id)
else:
data_volume = self.cluster_data_volume(cluster_id)
energy = data_volume * self.env.comms_cost
self._ids_to_comms_energy[cluster_id] = energy
return energy
def _find_cluster(self, cluster_id):
"""
:param cluster_id:
:return:
:rtype: tocs.cluster.ToCSCluster
"""
if cluster_id in self._ids_to_clusters:
return self._ids_to_clusters[cluster_id]
found_cluster = None
for clust in self.sim.clusters + [self.sim.centroid]:
if clust.cluster_id == cluster_id:
found_cluster = clust
break
if not found_cluster:
raise ToCSEnergyModelError(
"Could not find cluster {}".format(cluster_id))
self._ids_to_clusters[cluster_id] = found_cluster
return found_cluster
def total_movement_energy(self, cluster_id):
"""
Return the amount of energy required to complete a single tour of the
specified cluster.
:param cluster_id: The numeric identifier of the cluster.
:return: The amount of energy required.
:rtype: pq.J
"""
if cluster_id in self._ids_to_movement_energy:
return self._ids_to_movement_energy[cluster_id]
current_cluster = self._find_cluster(cluster_id)
energy = current_cluster.tour_length * self.env.move_cost
self._ids_to_movement_energy[cluster_id] = energy
return energy
def total_energy(self, cluster_id):
"""
Get the sum of communication and movement energy for the given cluster.
:param cluster_id:
:return:
:rtype: pq.J
"""
energy = self.total_comms_energy(cluster_id)
energy += self.total_movement_energy(cluster_id)
return energy
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horo_M5/ap_httpd_constel.py | kcfkwok2003/m5_star_navigator | 0 | 148420 | <filename>horo_M5/ap_httpd_constel.py<gh_stars>0
# ap_httpd_constel.py
version="1.0"
from color import *
import time
import gc
from httpd_util import *
from g_stars_1 import ct
USE_THREAD = False
def main(vs):
global var_store
var_store=vs
start_httpd()
def start_httpd():
global var_store
horo_main = var_store['horo_main']
tft = var_store['tft']
info = horo_main.info
apname = info['apname']
ip = info['ip']
tft.fill_rect(20,100,200,100,NAVY)
tft.rect(20,100,200,100,LIME)
tft.text(apname, 25,110, color=WHITE)
tft.text(ip, 25,120,color=WHITE)
tft.text('starting httpd constel',25,130,color=WHITE)
if USE_THREAD:
import _thread
_thread.start_new_thread(_httpd,())
else:
_httpd()
HTML_CONSTELS="""
<html>
<head><title>Constellations</title></head>
<body>
<h2>Select Constellation</h2>
<form action="/constels" method="get">
<table>
<tr><td>%(constels)s</td></tr>
<tr>
<td>
<input type="Submit" value="submit"
</td>
</tr>
</table>
</form>
<br>
<br>
<br>
<a href="/">Refresh</a>
<br>
</body>
</html>
"""
HTML_CONSTEL="""
<html>
<head><title>Constellation</title></head>
<body>
<h2>Constellation</h2>
<table>
<tr><td>%(ct)s</td></tr>
<tr>
<td>
%(tbs)s
</td>
</tr>
<tr>
<td>
</td>
</tr>
</table>
<br>
<br>
<br>
<a href="/">Refresh</a>
<br>
</body>
</html>
"""
def add_select(dictx):
global ct
ct_keys = list(ct.keys())
ct_keys.sort()
cts='<select name="ct">'
for item in ct_keys:
cts+='<option value="%s">%s</option>' % (item,item)
cts+="</select>"
dictx['constels']=cts
return dictx
def add_table(dictx):
global ct
ctx = dictx['ct']
stars = ct[ctx]['stars']
tb ='<table border="1"><tr><th>star</th><th>RA</th><th>Dec</th></tr>'
for stx in stars:
ra, dec = stars[stx]
tb+="<tr><td>%s</td><td>%s</td><td>%s</td></tr>" % (stx,ra,dec)
tb +='</table><br>'
lines = ct[ctx]['lines']
max_len=0
for line in lines:
if len(line) > max_len:
max_len = len(line)
tb +='<table border="1"><tr><th></th>'
for i in range(max_len):
tb +='<th>S%d</th>' % i
tb+='<th><form action="/constels" method="get"><input type="hidden" name="ct" value="%s"><input type="Submit" value="render"></form></th>' % ctx
tb+="</tr>"
lx=0
for line in lines:
tb+="<tr><td>L%d</td>" % lx
for i in range(max_len):
if i < len(line):
tb +="<td>%s</td>" % line[i]
else:
tb +="<td></td>"
tb+='<td><form action="/ln" method="get"><input type="hidden" name="ct" value="%s"><input type="hidden" name="ln" value="%s"><input type="Submit" value="render"></form></tr>' % (ctx,lx)
lx +=1
tb+="</table>"
dictx['tbs']=tb
return dictx
def draw_ct(ctx):
global var_store, ct, tft
tft = var_store['tft']
stars = ct[ctx]['stars']
lines = ct[ctx]['lines']
min_max = [128,64,0,-90] # xmin, ymin, xmax, ymax
plines=[]
for line in lines:
plines.append([])
for stx in line:
ra, dec = stars[stx]
chk_min_max(ra,dec,min_max)
plines[-1].append((ra,dec))
if min_max[2] - min_max[0] >20:
# it seems wrap around in 24hr, retry
plines =[]
min_max=[128,64,0,-90] # xmin,ymin,xmax,ymax
for line in lines:
plines.append([])
for starx in line:
ra,dec=stars[starx]
if ra <10:
ra+=24 # fix wrap around value
chk_min_max(ra,dec,min_max)
plines[-1].append((ra,dec))
tft.fill_rect(0,0,128,64, NAVY)
tft.rect(0,0,128,64,LIME)
for points in plines:
tft_plot_line(points,min_max)
def draw_ct_line(ctx, ix):
global tft, var_store, ct
tft = var_store['tft']
stars = ct[ctx]['stars']
lines = ct[ctx]['lines']
min_max=[128,64,0,-90] # xmin,ymin,xmax,ymax
plines=[]
for line in lines:
plines.append([])
for starx in line:
ra,dec = stars[starx]
chk_min_max(ra,dec,min_max)
plines[-1].append((ra,dec))
if min_max[2] - min_max[0] >20:
# it seems wrap around in 24hr, retry
plines=[]
min_max=[128,64,0,-90] # xmin,ymin,xmax,ymax
for line in lines:
plines.append([])
for starx in line:
ra,dec=stars[starx]
if ra <10:
ra+=24 # fix wrap around value
chk_min_max(ra,dec,min_max)
plines[-1].append((ra,dec))
tft.fill_rect(0,0,128,64, NAVY)
tft.rect(0,0,128,64,LIME)
tft_plot_line(plines[ix],min_max)
def chk_min_max(x,y,min_max):
if x< min_max[0]:
min_max[0]=x
if y< min_max[1]:
min_max[1]=y
if x > min_max[2]:
min_max[2]=x
if y > min_max[3]:
min_max[3]=y
def tft_plot_line(points,min_max):
global tft
#scale to 2 pixel for 1 deg
XSCALE=15 * 2 # 1hr=15 deg
YSCALE= 2
xr=min_max[0] * XSCALE
yr=min_max[1] * YSCALE
xrng = min_max[2] *XSCALE - xr
yrng = min_max[3] *YSCALE - yr
print('xrng %s yrng:%s' % (xrng,yrng))
XOFS=int((128 - xrng)/2)
YOFS=int((64-yrng)/2)
xp = 128 -int(points[0][0] * XSCALE -xr + XOFS)
yp = 64 -int(points[0][1] * YSCALE -yr + YOFS)
tft_plot_star(xp,yp)
for x,y in points[1:]:
xv = 128 -int(x * XSCALE - xr + XOFS)
yv = 64 - int(y * YSCALE - yr + YOFS)
tft.line(xp,yp,xv,yv,BLUE)
tft_plot_star(xv,yv)
xp=xv
yp=yv
def tft_plot_star(x,y):
global tft
tft.pixel(x-1,y,WHITE)
tft.pixel(x, y-1,WHITE)
tft.pixel(x+1,y,WHITE)
tft.pixel(x, y+1,WHITE)
def _httpd():
global var_store
import socket
addr = socket.getaddrinfo('0.0.0.0',80)[0][-1]
s=socket.socket()
s.bind(addr)
s.listen(1)
print('listening on',addr)
tft = var_store['tft']
tft.text('httpd constel listening',25,130,color=WHITE)
while True:
conn,addr = s.accept()
print("connected from",addr)
req = conn.recv(1024)
print("content=%s" % str(req))
if req==b'':
continue
if req[:4]==b'GET ':
pos = req.find(b' HTTP/')
cmd = req[4:pos]
res = handle_cmd(cmd)
if type(res)==type(()):
resp=res[1]
conn.send(resp)
conn.close()
continue
if res is not None:
resp="""<html><body>Result: %s<br><br><a href="/">Home</a></body></html>""" % res
conn.send(resp)
conn.close()
continue
dictx={}
dictx = add_select(dictx)
resp = HTML_CONSTELS % dictx
conn.send(resp)
conn.close()
def handle_c_constels(parms):
print('set constels %s' % str(parms))
dictx = get_html_parms(parms)
dictx = add_table(dictx)
resp = HTML_CONSTEL % dictx
draw_ct(dictx['ct'])
return ('OK',resp)
def handle_c_ln(parms):
print('set constel %s' % str(parms))
dictx = get_html_parms(parms)
dictx = add_table(dictx)
resp = HTML_CONSTEL % dictx
ix = int(dictx['ln'])
draw_ct_line(dictx['ct'], ix)
return ('OK',resp)
def handle_cmd(cmd):
print('cmd:%s' % str(cmd))
pos=cmd.find(b'?')
cmdx = cmd[:pos]
if cmdx==b'/constels':
return handle_c_constels(cmd[pos+1:])
if cmdx==b'/ln':
return handle_c_ln(cmd[pos+1:])
return None
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] |
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