content
stringlengths 1
1.04M
| input_ids
sequencelengths 1
774k
| ratio_char_token
float64 0.38
22.9
| token_count
int64 1
774k
|
|---|---|---|---|
intgr = 11
flt = 11.11
stng = "Eleven"
tple = (1,2)
lst = [1,2,3,4,5]
h_i = hash(intgr)
h_f = hash(flt)
h_s = hash(stng)
h_t = hash(tple)
#h_l = hash(lst)
print("hash of {} is {} ".format(intgr,h_i))
print("hash of {} is {} ".format(flt,h_f))
print("hash of {} is {} ".format(stng,h_s))
print("hash of {} is {} ".format(tple,h_t))
# print(h_l) list hash hoy na | [
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"""
Display a line depicting a noisy signal consisting of a lot of points.
"""
import numpy as np
import pygfx as gfx
from PySide6 import QtWidgets
from wgpu.gui.qt import WgpuCanvas
app = QtWidgets.QApplication([])
canvas = WgpuCanvas()
renderer = gfx.WgpuRenderer(canvas)
scene = gfx.Scene()
# todo: crank this to 1M when wgpu allows it :D
x = np.linspace(0, 100, 10_000, dtype=np.float32)
y = np.sin(x) * 30 + np.random.normal(0, 5, len(x)).astype(np.float32)
positions = np.column_stack([x, y, np.zeros_like(x)])
geometry = gfx.Geometry(positions=positions)
material = gfx.LineMaterial(thickness=2.0, color=(0.0, 0.7, 0.3, 1.0))
line = gfx.Line(geometry, material)
scene.add(line)
camera = gfx.OrthographicCamera(110, 110)
camera.position.set(50, 0, 0)
if __name__ == "__main__":
canvas.request_draw(lambda: renderer.render(scene, camera))
app.exec()
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from rest_framework.serializers import ModelSerializer
from rocket_erp.apps.accounts.models import Account
class AccountSerializer(ModelSerializer):
"""Serializer for account app, with all fields."""
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__description__ = \
"""
Main class for holding fit parameters, including guesses, values, ranges, etc.
"""
__date__ = "2016-09-02"
__author__ = "Michael J. Harms"
import copy
import numpy as np
class FitParameter:
"""
Class for storing and manipulating generic fit parameters.
"""
def __init__(self,name,guess=None,fixed=False,bounds=None,alias=None):
"""
Initialize class. Parameters:
name: name of parameter (string)
guess: parameter guess (float). If None, class will guess intelligently
based on the parameter name. If no intelligent guess is available,
guess will be set to 1.0.
fixed: whether or not the parameter is fixed (bool)
bounds: bounds on fit for parameter (list-like object of 2 floats). If
None, bounds will be set to (None,None). If (None,5), no lower
bound, upper bound of 5.
alias: alias for parameter name, for linking to global paramter names. (str)
If None, no alias is made.
"""
self.name = name
self.guess = guess
self.fixed = fixed
self.bounds = bounds
self.alias = alias
#--------------------------------------------------------------------------
# parameter name
@property
def name(self):
"""
Name of the parameter.
"""
try:
return self._name
except AttributeError:
return None
#--------------------------------------------------------------------------
# parameter value
@property
def value(self):
"""
Value of the parameter.
"""
try:
return self._value
except AttributeError:
return None
@value.setter
def value(self,value=None):
"""
If value is set to None, set value to self.guess value.
"""
if value is None:
self._value = self.guess
else:
try:
value = np.float(value)
except ValueError:
err = f"parameter value '{value}' cannot be interpretable as a float\n"
raise ValueError(err)
self._value = value
#--------------------------------------------------------------------------
# parameter stdev
@property
def stdev(self):
"""
Standard deviation on the parameter.
"""
return self._stdev
@stdev.setter
def stdev(self,s):
"""
Set the standard deviation of the parameter.
"""
self._stdev = s
#--------------------------------------------------------------------------
# parameter 95% confidence
@property
def ninetyfive(self):
"""
95% confidence interval on the parameter.
"""
return self._ninetyfive
@ninetyfive.setter
def ninetyfive(self,value):
"""
Set the 95% confidence interval on the parameter.
"""
if len(value) != 2:
err = "ninetyfive requires a list-like with length 2.\n"
raise ValueError(err)
self._ninetyfive[0] = value[0]
self._ninetyfive[1] = value[1]
#--------------------------------------------------------------------------
# parameter guess
@property
def guess(self):
"""
Guess for the parameter.
"""
return self._guess
@guess.setter
def guess(self,g):
"""
Set the guess. If None, choose intelligently based on the name of the
parameter.
"""
if g != None:
self._guess = g
else:
if self.name.startswith("dH"):
self._guess = 1000.0
elif self.name.startswith("beta") or self.name.startswith("K"):
self._guess = 1e6
elif self.name.startswith("fx"):
self._guess = 1.0
else:
self._guess = 1.0
self._value = self._guess
#--------------------------------------------------------------------------
# parameter fixed-ness.
@property
def fixed(self):
"""
Whether or not the parameter if fixed.
"""
return self._fixed
@fixed.setter
def fixed(self,bool_value):
"""
Fix or unfix the parameter.
"""
self._fixed = bool(bool_value)
self._initialize_fit_results()
#--------------------------------------------------------------------------
# bounds for fit.
@property
def bounds(self):
"""
Fit bounds. Either list of bounds or None.
"""
return self._bounds
@bounds.setter
def bounds(self,b):
"""
Set fit bounds.
"""
if b != None:
try:
if len(b) != 2:
raise TypeError
except TypeError:
err = "Bounds must be list-like object of length 2\n"
raise ValueError(err)
self._bounds = tuple(copy.deepcopy(b))
else:
self._bounds = (-np.inf,np.inf)
#--------------------------------------------------------------------------
# parameter alias
@property
def alias(self):
"""
Parameter alias. Either string or None.
"""
return self._alias
@alias.setter
def alias(self,a):
"""
Set alias.
"""
try:
if self._alias != None and self._alias != a and a != None:
err = "Could not set alias to {:} because it is already set to {:}".format(a,self._alias)
raise ValueError(err)
except AttributeError:
pass
self._alias = a
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] | 2.3232 | 2,500 |
from abc import abstractmethod
from collections import OrderedDict
import os
import pickle
import re
from typing import Tuple, Union
import pandas as pd
import numpy as np
import gym
from gridworld.log import logger
from gridworld import ComponentEnv
from gridworld.utils import to_scaled, to_raw, maybe_rescale_box_space
from gridworld.agents.buildings.obs_space import make_obs_space
from gridworld.agents.buildings import defaults
from gridworld.agents.buildings import five_zone_rom_dynamics as dyn
# Below are control variables' boundary.
MAX_FLOW_RATE = [2.2, 2.2, 2.2, 2.2, 3.2] # Max flow rate for each individual zone
MIN_FLOW_RATE = [.22, .22, .22, .22, .32] # Max flow rate for each individual zone
MAX_TOTAL_FLOW_RATE = 10.0 # Total flow rate for all zones should be lower than 10 kg/sec.
MAX_DISCHARGE_TEMP = 16.0 # Max temp of air leaving chiller
MIN_DISCHARGE_TEMP = 10.0 # Min temp of air leaving chiller
DEFAULT_COMFORT_BOUNDS = (22., 28.) # Temps between these values are considered "comfortable"
def load_data(start_time: str = None, end_time: str = None) -> Tuple[pd.DataFrame, dict]:
"""Returns exogenous data dataframe, and state space model (per-zone) dict."""
THIS_DIR = os.path.dirname(os.path.abspath(__file__))
df = pd.read_csv(os.path.join(THIS_DIR, "data/exogenous_data.csv"), index_col=0)
df.index = pd.DatetimeIndex(df.index)
start_time = pd.Timestamp(start_time) if start_time else df.index[0]
end_time = pd.Timestamp(end_time) if end_time else df.index[-1]
_df = df.loc[start_time:end_time]
if _df is None or len(_df) == 0:
raise ValueError(
f"start and/or end times ({start_time}, {end_time}) " +
"resulted in empty dataframe. First and last indices are " +
f"({df.index[0]}, {df.index[-1]}), choose values in this range.")
with open(os.path.join(THIS_DIR, "data/state_space_model.p"), "rb") as f:
models = pickle.load(f)
return _df, models
def get_col(df, pattern, index=None):
"""Returns a dataframe with columns matching regex pattern."""
return df[[c for c in df.columns if re.match(pattern, c)]].values
class FiveZoneROMThermalEnergyEnv(FiveZoneROMEnv):
"""Subclass with identical physics, but that balances energy and comfort costs."""
def step_reward(self) -> Tuple[float, dict]:
"""Overwriting reward to balance energy and comfort."""
alpha = 0.2
energy_consumption_reward = -self.state["p_consumed"] / 12.0
comfort_error = [
max(self.state["zone_upper_viol_{}".format(i)], self.state["zone_lower_viol_{}".format(i)], 0.0)
for i in range(self.num_zones)
]
comfort_reward = -(sum([x**2 for x in comfort_error]))
reward = alpha * energy_consumption_reward * 0.5 + (1. - alpha) * comfort_reward
meta = {
"comfort_rew": comfort_reward,
"energy_rew": energy_consumption_reward
}
return reward, meta
if __name__ == '__main__':
env = FiveZoneROMThermalEnergyEnv()
obs = env.reset()
print(obs) | [
6738,
450,
66,
1330,
12531,
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] | 2.583609 | 1,208 |
__test__ = {"API_TESTS": r"""
>>> from django.conf import settings
>>> ORIGINAL_TIME_ZONE = settings.TIME_ZONE
>>> settings.TIME_ZONE = "UTC"
>>> from timezones import forms
# the default case where no timezone is given explicitly.
# uses settings.TIME_ZONE.
>>> f = forms.LocalizedDateTimeField()
>>> f.clean("2008-05-30 14:30:00")
datetime.datetime(2008, 5, 30, 14, 30, tzinfo=<UTC>)
# specify a timezone explicity. this may come from a UserProfile for example.
>>> f = forms.LocalizedDateTimeField(timezone="America/Denver")
>>> f.clean("2008-05-30 14:30:00")
datetime.datetime(2008, 5, 30, 20, 30, tzinfo=<UTC>)
>>> f = forms.TimeZoneField()
>>> f.clean('US/Eastern')
<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>
>>> settings.TIME_ZONE = ORIGINAL_TIME_ZONE
""",
"DECORATOR_TESTS": r"""
>>> from timezones import decorators
>>> from datetime import *
>>> class Foo(object):
... datetime = datetime(2008, 6, 20, 23, 58, 17)
... @decorators.localdatetime('datetime')
... def localdatetime(self):
... return 'Australia/Lindeman'
...
>>> foo = Foo()
>>> foo.datetime
datetime.datetime(2008, 6, 20, 23, 58, 17)
>>> foo.localdatetime
datetime.datetime(2008, 6, 21, 9, 58, 17, tzinfo=<DstTzInfo 'Australia/Lindeman' EST+10:00:00 STD>)
>>> foo.localdatetime = datetime(2008, 6, 12, 23, 50, 0)
>>> foo.datetime
datetime.datetime(2008, 6, 12, 13, 50, tzinfo=<UTC>)
>>> foo.localdatetime
datetime.datetime(2008, 6, 12, 23, 50, tzinfo=<DstTzInfo 'Australia/Lindeman' EST+10:00:00 STD>)
"""}
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] | 2.589041 | 584 |
from functions import Profile
from mongodb import MongoDb
from flask import Flask, render_template, request, redirect
app= Flask(__name__)
@app.route('/')
@app.route('/',methods=['POST'])
if __name__=='__main__':
app.run(debug=True) | [
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] | 2.741573 | 89 |
from tkinter import ttk
import tkinter as tk
import tkinter.messagebox
from gui.menu_bar import MenuBar
from gui.stream_frame import StreamFrame
from config_file.fire_stream import ConfigFile
### Represents the main application, which is the combination of multiple frames | [
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# -*- coding: utf-8 -*-
from __future__ import print_function
import abc
# import clu.abstract
import collections.abc
import contextlib
import json
import sys, os
abstract = abc.abstractmethod
from clu.constants import consts
from clu.constants.exceptions import CDBError
from clu.fs.abc import BaseFSName
from clu.fs.filesystem import TemporaryName, Directory, rm_rf
from clu.fs.misc import u8str
# from clu.predicates import tuplize
from clu.repr import strfields
from clu.exporting import Exporter
exporter = Exporter(path=__file__)
export = exporter.decorator()
@export
@export
@export
export(CDBError)
# Assign the modules’ `__all__` and `__dir__` using the exporter:
__all__, __dir__ = exporter.all_and_dir()
if __name__ == '__main__':
sys.exit(test()) | [
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import os
import sys
sys.path.append('../../arl-python')
import numpy as np
import time
import argparse
from arl.image.cleaners import *
from utils import *
if __name__ == '__main__':
np.random.seed(0)
parser = argparse.ArgumentParser()
parser.add_argument('--data_dir', type=str, default='./data')
parser.add_argument('--niter', type=int, default=0)
parser.add_argument('--gain', type=float, default=0.0)
parser.add_argument('--thresh', type=float, default=0.0)
parser.add_argument('--fracthresh', type=float, default=0.0)
parser.add_argument('--nscales', type=int, default=0)
parser.add_argument('--nmoments', type=int, default=0)
parser.add_argument('--nx', type=int, default=0)
parser.add_argument('--ny', type=int, default=0)
args = parser.parse_args()
test_cleaners(args.data_dir, args.niter, args.gain, args.thresh, args.fracthresh, \
args.nscales, args.nmoments, args.nx, args.ny)
| [
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] | 2.501292 | 387 |
l = [1, 2, 3]
id1 = id(l)
print(f"id1: {id1}")
l *= 2
id2 = id(l)
print(f"id2: {id2}")
assert id1 == id2
print(f"id1 == id2: {id1 == id2}")
| [
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# -*- coding: utf-8 -*-
# (C) 2015 Muthiah Annamalai
#
# This file is part of 'open-tamil' package tests
#
# setup the paths
from __future__ import print_function
from opentamiltests import *
if __name__ == "__main__":
unittest.main()
| [
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from django.contrib import admin
from .models import Publication
admin.site.register(Publication)
| [
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"""Async Mopidy Client via JSON/RPC Websocket interface"""
# Fork of https://github.com/ismailof/mopidy-json-client by ismailof
__author__ = 'svinerus ([email protected])'
__version__ = '0.6.4'
from .client import MopidyClient
__all__ = [
'MopidyClient',
]
| [
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from django.utils.translation import ugettext_lazy as _
from i18nfield.forms import I18nFormField, I18nTextarea
from pretix.base.forms import SettingsForm
| [
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def get_dapp_type(dapp):
"""Return the available dapp implementation."""
if hasattr(dapp, "ipfsdapp"):
return "ipfs"
return None
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"""Parse a kraken output file and generate a report and possibly extract reads for selected clades. (Adapted from original kraken-report.pl)
"""
import sys
import gzip
from csv import reader
from Bio import SeqIO
from pysam import AlignmentFile
from collections import defaultdict
import argparse
from pathlib import Path
import os
import json
import contextlib
#grep 'scientific name' names.dmp |cut -d'|' -f 1,2 |gzip -c >names_trimmed.dmp
#cut -d '|' -f 1,2,3 nodes.dmp|gzip -c >nodes_trimmed.dmp
# remember to use filtered nodes.dmp and names.dmp
def load_taxonomy(db_prefix):
"""Create/Read a taxonomy maps into dicts
"""
global name_map
name_map = {}
global rank_map
rank_map = {}
global child_lists
child_lists = defaultdict(list)
global name_clade_map
parent_map = {}
#read the taxonomy .dmp to and create or dict
if not os.path.exists(db_prefix+"/taxonomy/name_map.json") or \
not os.path.exists(db_prefix+"/taxonomy/rank_map.json") or \
not os.path.exists(db_prefix+"/taxonomy/child_lists.json") or \
not os.path.exists(db_prefix+"/taxonomy/parent_map.json"):
print ("Map files don't exist, creating json...", file=sys.stderr)
with gzip.open(db_prefix+"/taxonomy/names_trimmed.dmp.gz", 'rt') as name_file:
for line in name_file:
node_id, name = line.strip().split('|')
node_id = node_id.strip()
name = name.strip()
name_map[node_id] = name
with gzip.open(db_prefix+"/taxonomy/nodes_trimmed.dmp.gz", 'rt') as nodes_file:
for line in nodes_file:
node_id, parent_id, rank = line.strip().split('|')
node_id = node_id.strip()
parent_id = parent_id.strip()
rank = rank.strip()
if node_id == '1':
parent_id = '0'
child_lists[parent_id].append(node_id)
rank_map[node_id] = rank
parent_map[node_id] = parent_id
#save our dicts as json
with open(db_prefix+"/taxonomy/name_map.json",'w') as name_map_file, \
open(db_prefix+"/taxonomy/rank_map.json",'w') as rank_map_file, \
open(db_prefix+"/taxonomy/child_lists.json",'w') as child_lists_file, \
open(db_prefix+"/taxonomy/parent_map.json",'w') as parent_map_file:
json.dump(name_map,name_map_file)
json.dump(rank_map, rank_map_file)
json.dump(child_lists,child_lists_file)
json.dump(parent_map, parent_map_file)
else: #load the json
with open(db_prefix+"/taxonomy/name_map.json",'r') as name_map_file, \
open(db_prefix+"/taxonomy/rank_map.json",'r') as rank_map_file, \
open(db_prefix+"/taxonomy/child_lists.json",'r') as child_lists_file:
name_map = json.load(name_map_file)
rank_map = json.load(rank_map_file)
child_lists = json.load(child_lists_file)
name_clade_map = {v: k for k, v in name_map.items()}
#return (name_map, rank_map, child_lists, name_clade_map)
def rank_code(rank):
"""Translate ranks into single letters code
"""
if rank == "species": return "S"
if rank == "genus": return "G"
if rank == "family": return "F"
if rank == "order": return "O"
if rank == "class": return "C"
if rank == "phylum": return "P"
if rank == "kingdom": return "K"
if rank == "superkingdom": return "D"
return "-"
def get_taxonomy_str(taxid):
"""Generate the full taxonomy from a specific clade
Parameters
----------
taxid: str
Returns
-------
str
"""
taxid_string = known_taxonomy_strings.get(taxid, False)
if not taxid_string:
nodes = []
while (taxid != '0'):
nodes += [name_map[taxid]]
taxid = parent_map[taxid]
taxid_string = ';'.join(nodes[::-1])
known_taxonomy_strings[taxid] = taxid_string
return taxid_string
@contextlib.contextmanager
def extract_fasta_from_id(fileout, id_list, seqfile, min_length):
"""Extract reads assigned to specific taxa.
Parameters
----------
fileout: str
Filename to write into
id_list: list of
"""
if seqfile.endswith('a') or seqfile.endswith('a.gz'):
file_type = "fasta"
file_suffix = '.fa'
elif seqfile.endswith('q') or seqfile.endswith('q.gz'):
file_type = "fastq"
file_suffix = '.fq'
with open(fileout+file_suffix, 'w') as fout, \
gzip.open(seqfile, "rt") if seqfile.endswith("gz") else _ret_file(seqfile) as seqfile:
# working with a generator expression, may be better memory-wise
input_seq_iterator = SeqIO.parse(seqfile, file_type)
fasta_seq_iterator = (rec for rec in input_seq_iterator if rec.id in id_list and len(rec) >= min_length)
count = SeqIO.write(fasta_seq_iterator, fout, file_type)
if len(id_list) != count: # sanity check you may want to extract from a demultiplexed file
print("Warning, EOF reached but", len(id_list) - count, "sequences remained, is extractFile the original source?", file=sys.stderr)
#this function will discard child clades in order to have a proper summation
if __name__ == "__main__":
name_map = rank_map = child_lists = node_name_map = clade_counts = taxo_counts = seq_count = extract_ids = seq_ids = None
_main()
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] | 2.238757 | 2,446 |
from pythonforandroid.toolchain import Recipe, shprint, shutil, current_directory
from pythonforandroid.toolchain import CompiledComponentsPythonRecipe
from pythonforandroid.util import current_directory, ensure_dir
from pythonforandroid.logger import debug, shprint, info
from os.path import exists, join, dirname
import sh
import glob
recipe = LXMLRecipe()
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] | 3.913043 | 92 |
#!/usr/bin/python
import argparse
import subprocess
import json
import re
import config
import collections
from datetime import datetime, timedelta
import time
from pprint import pprint
from slacker import Slacker
slack = Slacker(config.token)
option_age = ""
option_owner = None
option_protocol = 'slack'
option_ssm = None
option_stat = None
query_cache = {}
HOST="openbmc.gerrit"
username_map = {
'irc': {
'jenkins-openbmc': "Jenkins",
'williamspatrick': "stwcx",
},
'slack': {
'amboar': "@arj",
'anoo1': "@anoo",
'bradbishop': "@bradleyb",
'bjwyman': "@v2cib530",
'cbostic': "@cbostic",
'dhruvibm': "@dhruvaraj",
'dkodihal': "@dkodihal",
'devenrao': "@devenrao",
'geissonator': "@andrewg",
'eddiejames': "@eajames",
'gtmills': "@gmills",
'jenkins-openbmc': "Jenkins",
'jk-ozlabs' : "@jk",
'mine260309': "@shyulei",
'msbarth': "@msbarth",
'mtritz': "@mtritz",
'ngorugan': "@ngorugan",
'navrathi' : "@navrathi",
'ojayanth': "@ojayanth",
'ratagupt': "@ratagupt",
'shenki': "@jms",
'spinler': "@spinler",
'tomjoseph83': "@tomjoseph",
},
}
project_map = {
'openbmc/witherspoon-pfault-analysis': ('spinler','Matt Spinler'),
'openbmc/phosphor-mrw-tools':('spinler','Matt Spinler'),
'openbmc/mboxbridge': ('amboar','Andrew Jeffery'),
'openbmc/obmc-console': ('jk-ozlabs','Jeremy Kerr'),
'openbmc/btbridge': ('jk-ozlabs','Jeremy Kerr'),
'openbmc/inarp': ('jk-ozlabs','Jeremy Kerr'),
'openbmc/phosphor-settingsd' :('dkodihal','Deepak Kodihalli'),
'openbmc/phosphor-logging' :('dkodihal','Deepak Kodihalli'),
'openbmc/openpower-vpd-parser': ('dkodihal','Deepak Kodihalli'),
'openbmc/phosphor-mboxd': ('amboar','Andrew Jeffery'),
'openbmc/openbmc': ('bradbishop','Brad Bishop'),
'openbmc/phosphor-host-ipmid': ('tomjoseph83','Tom Joseph')
}
send_to_slack = ['@andrewg',
'@anoo',
'@arj',
'@bradleyb',
'@cbostic',
'@devenrao',
'@dkodihal',
'@dhruvaraj',
'@eajames',
'@gmills',
'@jms',
'@jk',
'@msbarth',
'@mtritz',
'@navrathi',
'@ngorugan',
'@ojayanth',
'@ratagupt',
'@spinler',
'@tomjoseph',
'@v2cib530']
# print "sending stats to openbmcdev channel"
# slack.chat.post_message('#openbmcdev',message)
parser = argparse.ArgumentParser()
parser.add_argument('--owner', help='Change owner', type=str,
action='append')
parser.add_argument('--protocol', help='Protocol for username conversion',
type=str, choices=(username_map.keys()))
parser.add_argument('-sm', action='store_true',help='send slack message flag')
parser.add_argument('-stat', action='store_true',help='send statistics to slack flag')
subparsers = parser.add_subparsers()
report = subparsers.add_parser('report', help='Generate report')
report.set_defaults(func=do_report)
args = parser.parse_args()
if ('owner' in args) and args.owner:
option_owner = " OR ".join(map(lambda x: "owner:" + x,
args.owner))
if 'protocol' in args and args.protocol:
option_protocol = args.protocol
if args.sm:
option_ssm = 'True'
print("will send messages to slack")
else:
print("no slack messges will be sent")
if args.stat:
option_stat = 'True'
if 'func' in args:
args.func(args)
else:
parser.print_help()
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] | 2.0138 | 1,884 |
import numpy as np
import unittest
from rlscore.measure.cindex_measure import cindex
from rlscore.measure.measure_utilities import UndefinedPerformance
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"""The window_event_manager module implements the WindowEventManager class."""
from typing import Callable, Dict, List, Set, Union
from pyglet.window import Window as PygletWindow
from pycat.base.event.key_event import KeyEvent
from pycat.base.event.mouse_event import MouseEvent
from pycat.geometry.point import Point
from pycat.debug.print import print_failure
from pycat.base.event.publisher import Subscriber, Publisher
from pycat.base.event.window_event_subscriber import WindowEventSubscriber
class WindowEventManager:
"""Manage pyglet window events.
- Adds support for multiple callbacks on window events.
- Tracks currently pressed keys and mouse position
- Simplifies window event callback function signatures
"""
def __init__(self, window: PygletWindow):
"""Instantiate new instance of WindowEventManager class.
:param window: the window whose events are to be managed
:type window: `pyglet.window.Window`
"""
self.__mouse_position = Point()
self.__mouse_delta = Point()
self.__mouse_scroll_delta = Point()
self.__active_keys: Set[Union[int, str]] = set()
self.__active_key: Union[int, str] = ""
self.__publishers: Dict[str, Publisher] = {
"on_key_press": Publisher[Callable[[KeyEvent], None]](),
"on_key_release": Publisher[Callable[[KeyEvent], None]](),
"on_mouse_drag": Publisher[Callable[[MouseEvent], None]](),
"on_mouse_enter": Publisher[Callable[[MouseEvent], None]](),
"on_mouse_leave": Publisher[Callable[[MouseEvent], None]](),
"on_mouse_motion": Publisher[Callable[[MouseEvent], None]](),
"on_mouse_press": Publisher[Callable[[MouseEvent], None]](),
"on_mouse_release": Publisher[Callable[[MouseEvent], None]](),
"on_mouse_scroll": Publisher[Callable[[MouseEvent], None]](),
}
window.on_key_press = self.__on_key_press
window.on_key_release = self.__on_key_release
window.on_mouse_drag = self.__on_mouse_drag
window.on_mouse_enter = self.__on_mouse_enter
window.on_mouse_leave = self.__on_mouse_leave
window.on_mouse_motion = self.__on_mouse_motion
window.on_mouse_press = self.__on_mouse_press
window.on_mouse_release = self.__on_mouse_release
window.on_mouse_scroll = self.__on_mouse_scroll
@property
def mouse_position(self) -> Point:
"""Return the current mouse position.
If the mouse has exited the window,
will return the last mouse position before exiting
:return: the current mouse position
:rtype: Point
"""
return self.__mouse_position
@property
def mouse_delta(self) -> Point:
"""Return the current mouse position.
If the mouse has exited the window,
will return the last mouse position before exiting
:return: the current mouse position
:rtype: Point
"""
return self.__mouse_delta
@property
@property
def active_keys(self) -> Set[Union[int, str]]:
"""Return a set of the currently pressed keys.
Key codes constants are defined in `pycat.keyboard.KEY`
:return: set of currently pressed keys
:rtype: Set[int]
"""
return self.__active_keys
def add_subscribers(self, **kwargs: Union[Subscriber, List[Subscriber]]):
"""Add subscribers by event keyword."""
for key in kwargs:
if key in self.__publishers:
self.__publishers[key].add_subscribers(kwargs[key])
else:
self.__invalid_event_name(key)
def remove_subscribers(self, **kwargs: Union[Subscriber,
List[Subscriber]]):
"""Remove subscribers by event keyword."""
for key in kwargs:
if key in self.__publishers:
self.__publishers[key].remove_subscribers(kwargs[key])
else:
self.__invalid_event_name(key)
# Key Events
# ------------------------------------------------------------------------
# Mouse Events
# ------------------------------------------------------------------------
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] | 2.53436 | 1,688 |
'''
this module contains all the urls for the whole project
'''
from django.contrib import admin
from django.urls import path,include
from article import views as articleViews
from django.contrib.staticfiles.urls import staticfiles_urlpatterns
from django.conf import settings
from django.conf.urls.static import static
# from django.views.generic import RedirectView
urlpatterns = [
path('admin/', admin.site.urls),
path('article/', include('article.urls')),
path('auth/', include('social_django.urls', namespace='social')),
path('watch/', include('watch_course.urls', namespace='watch')),
# path('', RedirectView.as_view(url='/questionnaire/')),
path('', include('questionnaire.urls',namespace='questionnaire')),
path('users/', include('users.urls',namespace='user')),
]
urlpatterns += staticfiles_urlpatterns()
urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)
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] | 2.92 | 325 |
#!/usr/bin/env python3
# (c) https://t.me/TelethonChat/37677
# This Source Code Form is subject to the terms of the GNU
# MIT TeamDragons If a copy of the developer was not distributed with this
# file, You can obtain one at https://www.gnu.org/licenses/MIT/TeamDragons
from telethon.sessions import StringSession
from telethon.sync import TelegramClient
print(
"""Please go-to my.telegram.org
Login using your Telegram account
Click on API Development Tools
Create a new application, by entering the required details"""
)
APP_ID = int(input("MASUKAN API KEY : "))
API_HASH = input("MASUKAN API HASH : ")
with TelegramClient(StringSession(), APP_ID, API_HASH) as client:
print(client.session.save())
client.send_message("me", client.session.save())
| [
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node=[1,2,3]
key=0
current_level={}
node_store=[]
main(node,key,current_level,node_store)
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#!/usr/bin/env python
import rospy
from ar_track_alvar_msgs.msg import AlvarMarkers
import tf
from tf.transformations import (
translation_matrix,
quaternion_matrix,
translation_from_matrix,
quaternion_from_matrix,
)
import threading
import copy
import numpy
import ipdb
comfortable_pick_frame = numpy.matrix([[0.9270661704094123, 0.37483076461918924, 0.007086153923013515, 0.746], [0.37479879099742724, -0.9270901506257341, 0.0054515025104288975, -0.089], [0.008612894362151227, -0.002398021632153474, -0.9999600329727978, 0.115], [0.0, 0.0, 0.0, 1.0]])
to_confortable_pick_frame = comfortable_pick_frame[:3, :3].I
handcoded_marker_compensation = {
0: numpy.array(
[[0.04258225549353267, -0.9976699860199164, -0.05330431982591899, -0.003], [0.9979499876999266, 0.045024270145620604, -0.045482272893637384, 0.0], [0.04777628665771998, -0.05125830754984532, 0.9975419852519115, -0.033], [0.0, 0.0, 0.0, 1.0]]
, dtype=numpy.float64),
4: numpy.array(
((1.0, 0.0, 0.0, -0.0),
(0.0, 1.0, 0.0, -0.0),
(0.0, 0.0, 1.0, 0.05),
(0.0, 0.0, 0.0, 1.0))
, dtype=numpy.float64),
6: numpy.array(
((1.0, 0.0, 0.0, -0.0),
(0.0, 1.0, 0.0, -0.0),
(0.0, 0.0, 1.0, 0.05),
(0.0, 0.0, 0.0, 1.0))
, dtype=numpy.float64),
8: numpy.array(
[[0.04157746411335095, -0.9972777195581611, -0.06089716373343029, 0.0], [0.9963386576809764, 0.04594110483626879, -0.07210197013183828, -0.011], [0.07470337133203928, -0.0576763812950862, 0.9955364590773818, -0.028], [0.0, 0.0, 0.0, 1.0]]
, dtype=numpy.float64),
11: numpy.array((
((1.0, 0.0, 0.0, 0.016),
(0.0, 1.0, 0.0, -0.021),
(0.0, 0.0, 1.0, 0.029),
(0.0, 0.0, 0.0, 1.0))
), dtype=numpy.float64),
13: numpy.array((
[[0.09622387309747282, -0.9940084038491953, -0.05184842643028589, 0.004], [0.9940084038491953, 0.09867616353674313, -0.04701391099286735, 0.0], [0.05184842643028589, -0.04701391099286735, 0.9975477095607297, -0.017], [0.0, 0.0, 0.0, 1.0]]
), dtype=numpy.float64),
17: numpy.array((
((1.0, 0.0, 0.0, 0.0),
(0.0, 1.0, 0.0, -0.0),
(0.0, 0.0, 1.0, 0.075),
(0.0, 0.0, 0.0, 1.0))
), dtype=numpy.float64),
18: numpy.array((
[[-0.07792074091621037, 0.9962448437817034, -0.037743467957233004, 0.009],
[-0.9936061393855619, -0.07450042029363613, 0.08483234731746714, -0.027],
[0.081701884374772, 0.044112340840647246, 0.9956801210605593, -0.020],
[0.0, 0.0, 0.0, 1.0]]
), dtype=numpy.float64),
20: numpy.array((
[[0.06595220199306795, -0.9974408579981445, -0.02760510547328114, 0.009],
[0.9958555310795135, 0.06406061873788338, 0.06456003675076039, 0.016],
[-0.06262641831212726, -0.031748573556066424, 0.9975319342289496, -0.040],
[0.0, 0.0, 0.0, 1.0]]
), dtype=numpy.float64),
22: numpy.array((
((1.0,0.0,0.0,0.0),
(0.0, 1.0, 0.0, 0.016),
(0.0 , 0.0 ,1.0, 0.016),
(0.0, 0.0, 0.0, 1.0))
), dtype=numpy.float64),
24: numpy.array((
[[0.06595220199306795, -0.9974408579981445, -0.02760510547328114, 0.009],
[0.9958555310795135, 0.06406061873788338, 0.06456003675076039, 0.016],
[-0.06262641831212726, -0.031748573556066424, 0.9975319342289496, -0.06],
[0.0, 0.0, 0.0, 1.0]]
), dtype=numpy.float64),
}
writable = threading.Event()
writable.clear()
shared_msg = None
if __name__ == '__main__':
rospy.init_node("alvar_marker_to_baxter_picking_pose_py")
rospy.Subscriber("ar_pose_marker", AlvarMarkers, cb)
writable.set()
listener = tf.TransformListener()
broadcaster = tf.TransformBroadcaster()
pub = rospy.Publisher("baxter_available_picking_pose", AlvarMarkers, queue_size=10)
r = rospy.Rate(10)
while not rospy.is_shutdown():
writable.clear()
msg = copy.deepcopy(shared_msg)
writable.set()
if msg is not None:
look_up_t = rospy.Time(0)
listener.waitForTransform('base', 'left_hand_camera', look_up_t, rospy.Duration(3))
base_to_cam = listener.lookupTransform('base', 'left_hand_camera', look_up_t)
base_to_cam_mat = listener.fromTranslationRotation(*base_to_cam)
for marker in msg.markers:
pose = marker.pose.pose
pos = pose.position
ori = pose.orientation
cam_to_marker_mat = numpy.dot(translation_matrix((pos.x, pos.y, pos.z)), quaternion_matrix((ori.x, ori.y, ori.z, ori.w)))
base_to_marker = numpy.dot(base_to_cam_mat, cam_to_marker_mat)
broadcaster.sendTransform(
translation_from_matrix(base_to_marker),
quaternion_from_matrix(base_to_marker),
rospy.Time.now(),
'raw_marker_%s'%marker.id,
'base',
)
flipped_mat = transform_into_baxter_picking_space(base_to_marker)
trans = translation_from_matrix(flipped_mat)
quat = quaternion_from_matrix(flipped_mat)
broadcaster.sendTransform(
trans,
quat,
rospy.Time.now(),
'flipped_%s'%marker.id,
'base',
)
if marker.id in handcoded_marker_compensation:
compensated_mat = numpy.dot(flipped_mat, handcoded_marker_compensation[marker.id])
trans = translation_from_matrix(compensated_mat)
quat = quaternion_from_matrix(compensated_mat)
broadcaster.sendTransform(
trans,
quat,
rospy.Time.now(),
'compensated_%s'%marker.id,
'base',
)
noisy_mat = add_noise(compensated_mat)
trans = translation_from_matrix(noisy_mat)
quat = quaternion_from_matrix(noisy_mat)
broadcaster.sendTransform(
trans,
quat,
rospy.Time.now(),
'baxter_picking_pose_%s'%marker.id,
'base',
)
marker.pose.pose.position.x = trans[0]
marker.pose.pose.position.y = trans[1]
marker.pose.pose.position.z = trans[2]
marker.pose.pose.orientation.x = quat[0]
marker.pose.pose.orientation.y = quat[1]
marker.pose.pose.orientation.z = quat[2]
marker.pose.pose.orientation.w = quat[3]
if len(msg.markers) != 0:
pub.publish(msg)
try:
r.sleep()
except rospy.ROSInterruptException:
break
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2270,
198
] | 1.743558 | 3,997 |
# Copyright 2017 Huawei Technologies Co.,LTD.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
from nova.api.validation import parameter_types
list_query_schema_v253 = {
'type': 'object',
'properties': {
# The 2.33 microversion added support for paging by limit and marker.
'limit': parameter_types.single_param(
parameter_types.non_negative_integer),
'marker': parameter_types.single_param({'type': 'string'}),
# The 2.53 microversion adds support for filtering by hostname pattern
# and requesting hosted servers in the GET /os-hypervisors and
# GET /os-hypervisors/detail response.
'hypervisor_hostname_pattern': parameter_types.single_param(
parameter_types.hostname),
'with_servers': parameter_types.single_param(
parameter_types.boolean)
},
'additionalProperties': False
}
show_query_schema_v253 = {
'type': 'object',
'properties': {
'with_servers': parameter_types.single_param(
parameter_types.boolean)
},
'additionalProperties': False
}
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] | 2.909091 | 550 |
"""
Scenarios for ODD generation/restriction of a base graph.
Steps to create scenarios:
1. Define your reference graph:
Add/choose graph fixture
-> This is your base/reference graph, analogous to our OSM base map
2. Define your final odd graph (expected output)
Restrict base graph using/adding `restriction` functions
-> This will create an ODD subgraph, which is the final result expected by the method under test
3. Generate synthetic scenarios
Use/add `transformation` functions
-> This will create synthetic inputs to the methods under test
4. Make sure these are fed to `generate_scenarios`
"""
from copy import deepcopy
import networkx as nx
from locintel.graphs.datamodel.jurbey import Node
from allpairspy import AllPairs
from tests.synthetic.graphs import (
urban_grid_no_geometry,
urban_grid_node_geometry,
urban_grid_node_and_edge_geometry,
)
from tests.synthetic.utils import (
interpolated_geometry,
create_edge,
requires,
find_midpoint,
)
########################################################
# ODD restrictions #
# #
# Restriction to apply to base graph, to generate #
# synthetic ODDs #
# #
########################################################
########################################################
# Graph transformations #
# #
# Transformations to apply to the ODD graph, in order #
# to emulate real world use cases on arbitrary #
# provider maps #
# #
########################################################
@requires("edge_geometry")
def generate_scenarios(
base_graphs, restrictions, transformations, combination_function=None
):
"""
Generates scenarios consisting of combinations of graphs, restrictions and transformations, according to logic
defined on combination_function. By default applies all pairs combinatorial method to keep it efficient, see more
info here: https://www.tutorialspoint.com/software_testing_dictionary/all_pairs_testing.htm
"""
combination_function = combination_function or AllPairs
for base_graph, restriction, transformation in combination_function(
[base_graphs, restrictions, transformations], filter_func=is_valid_combination
):
odd_graph = restriction(base_graph)
transformed_graph = transformation(odd_graph)
name = f"{base_graph.metadata['version']}_{restriction.__name__}_{transformation.__name__}"
yield GraphTestScenario(name, base_graph, odd_graph, transformed_graph)
odd_restrictions = [no_restrictions, remove_node, remove_edge]
graph_transformations = [no_transformations, change_node_ids, split_edges]
graphs = [
urban_grid_no_geometry,
urban_grid_node_geometry,
urban_grid_node_and_edge_geometry,
]
scenarios = generate_scenarios(graphs, odd_restrictions, graph_transformations)
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] | 2.654198 | 1,203 |
#-*- coding=utf-8 -*-
#author: [email protected]
import os
from tmtc_ut import *
from time import gmtime, strftime
from lib.report import *
from lib.htmlgenerator import *
from lib.jinjagenerator import *
if __name__ == '__main__':
sdk = Sdkut(casedir="./cases", bindir='./bin')
sdk.run()
sdk.dumpreport() | [
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] | 2.308219 | 146 |
import csv
import os
lnr_genomes_aa = [] # aa = assembly_accession
with open("bacteria_as_comref_genomes_wo_ecoli",'r') as f:
next(f) # skip headings
reader=csv.reader(f,delimiter='\t')
tmp_size = None
tmp_aa = None
tmp_s_id = None
for line in reader:
filepath = line[20].replace("/cygdrive/h/","H:/")
print filepath
statinfo = os.stat(filepath)
# Check whether there is a duplicate
if tmp_s_id != line[6]: # sid = species_id
tmp_aa = line[0]
lnr_genomes_aa.append(tmp_aa)
tmp_size = statinfo.st_size
else:
print ">",tmp_s_id
# Keep the largest genomes in a same clade
if tmp_size < statinfo.st_size:
tmp_size = statinfo.st_size
tmp_aa = line[0]
tmp_s_id = line[6]
lnr_genomes_aa.append(tmp_aa)
print lnr_genomes_aa
print "TOTAL:",len(lnr_genomes_aa)
with open("bacteria_as_comref_genomes_wo_ecoli",'r') as f:
lines = f.readlines()
# lnr = Largest Non-Redundant
nf = open("bacteria_as_comref_genomes_wo_ecoli_lnr","w")
for line in lines:
aa = line.split("\t")[0]
if aa in lnr_genomes_aa:
nf.write(line)
lnr_genomes_aa.remove(aa)
else:
print "NOT IN: ",aa
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220,
220,
197,
197,
4798,
366,
11929,
3268,
25,
33172,
7252,
198
] | 2.155598 | 527 |
from functools import wraps
@cache
fn(1)
fn(1)
fn(3)
| [
6738,
1257,
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10141,
1330,
27521,
628,
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] | 2.074074 | 27 |
# -*- coding: utf-8 -*-
#
# || ____ _ __
# +------+ / __ )(_) /_______________ _____ ___
# | 0xBC | / __ / / __/ ___/ ___/ __ `/_ / / _ \
# +------+ / /_/ / / /_/ /__/ / / /_/ / / /_/ __/
# || || /_____/_/\__/\___/_/ \__,_/ /___/\___/
#
# Copyright (C) 2019 Bitcraze AB
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License
# as published by the Free Software Foundation; either version 2
# of the License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software
# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston,
# MA 02110-1301, USA.
import unittest
import cflib.crtp
from cflib.crazyflie import Crazyflie
from cflib.crazyflie.log import LogConfig
from cflib.crazyflie.swarm import CachedCfFactory
from cflib.crazyflie.swarm import Swarm
from cflib.crazyflie.syncCrazyflie import SyncCrazyflie
from cflib.crazyflie.syncLogger import SyncLogger
from sys_test.swarm_test_rig.rig_support import RigSupport
| [
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1430,
26,
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407,
11,
3551,
284,
262,
3232,
10442,
198,
2,
220,
5693,
11,
3457,
1539,
6885,
14021,
3530,
11,
19383,
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11,
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11,
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2,
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8779,
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657,
2481,
940,
12,
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11,
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269,
2704,
571,
13,
50112,
2704,
494,
13,
2032,
1670,
1330,
327,
2317,
34,
69,
22810,
198,
6738,
269,
2704,
571,
13,
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2704,
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13,
2032,
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1330,
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6738,
269,
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13,
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2704,
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13,
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34,
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2704,
494,
1330,
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34,
5918,
2704,
494,
198,
6738,
269,
2704,
571,
13,
50112,
2704,
494,
13,
27261,
11187,
1362,
1330,
35908,
11187,
1362,
198,
6738,
25064,
62,
9288,
13,
2032,
1670,
62,
9288,
62,
4359,
13,
4359,
62,
11284,
1330,
24666,
15514,
628
] | 2.830645 | 496 |
from flask import Flask, render_template, request
app = Flask(__name__)
@app.route('/upload', methods=['GET', 'POST'])
@app.route('/upload/progress')
@app.route('/progress')
if __name__ == '__main__':
app.run(debug=True)
| [
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7,
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28,
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8,
201,
198
] | 2.43 | 100 |
'''
Visualize steps of the calibration process to ensure everything went according to plan
'''
from matplotlib import pyplot as plt
from astropy.io import fits
from visualization import zscale #https://github.com/abostroem/utilities
overscan_size = 32 #pixels
unusable_bottom = 48//2 #pixels
| [
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] | 3.141414 | 99 |
import time
class log:
"""
This module is used to track the progress of events
and write it into a log file.
"""
| [
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{
"targets": [
{
"target_name": "forbidden-point-finder",
"sources": [
"src/binding/finder.cpp",
"forbidden-point-finder/ForbiddenPointFinder.cpp"
]
}
]
} | [
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] | 1.662162 | 148 |
# Copyright 2020 The MuLT Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
from model import Model
import tensorflow as tf
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2746,
1330,
9104,
198,
11748,
11192,
273,
11125,
355,
48700,
628
] | 4.236994 | 173 |
from datetime import datetime
import json
from collections import OrderedDict
import os.path
from math import log
from math import e
from sklearn.preprocessing import minmax_scale
from sklearn.preprocessing import MinMaxScaler
import numpy as np
from math import sqrt
import pandas as pd
data = OrderedDict()
weights = []
lat = []
long = []
num_points = 500
if os.path.isfile('maps.json') :
with open ('maps.json', 'r+') as fp:
data = json.load(fp, object_pairs_hook=OrderedDict)
for key in range(len(list(data.keys()))):
if key > num_points:
break
stored = data[list(data.keys())[len(list(data.keys())) - 1 - key]]
month = stored['Month']
day = stored['Day']
year = stored['Year']
lat.append(stored['Latitude'])
long.append(stored['Longitude'])
date = month + " " + str(day) + ", " + str(year)
date_format = "%B %d, %Y"
now = datetime.now()
date_object = datetime.strptime(date, date_format)
delta = now - date_object
num_hours = delta.days*24
if num_hours != 0:
weights.append(sqrt(1.0/num_hours) * 1000)
else:
weights.append(25)
weights = np.array(weights)
weights = weights.reshape(-1, 1)
min_max_scaler = MinMaxScaler(feature_range=(0, 2))
weights = min_max_scaler.fit_transform(np.float32(weights))
weights = weights.tolist()
points = OrderedDict()
long_shit = []
lat_shit = []
weight_shit = []
for i in range(num_points):
long_shit.append(long[i])
lat_shit.append(lat[i])
weight_shit.append(weights[i][0])
df = pd.DataFrame()
df["lng"] = np.array(long_shit)
df['lat'] = np.array(lat_shit)
df ['weight'] = np.array(weight_shit)
df.to_csv('heat_map.csv', index=False)
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] | 2.390476 | 735 |
"""一个纯 Python 实现的 Python 字节码解释器"""
# 改编自
# 1. pyvm2 作者：Paul Swartz，来自 http://www.twistedmatrix.com/users/z3p/
# 2. byterun 作者：Ned Batchelder，github.com/nedbat/byterun
import dis, operator, sys, collections, inspect, types
Block = collections.namedtuple("Block", "type, handler, stack_height")
class Function(object):
"""
创建一个真实的函数对象，定义解释器期望的东西。
"""
# 去掉 '__doc__'
__slots__ = [
'func_code', 'func_name', 'func_defaults', 'func_globals',
'func_locals', 'func_dict', 'func_closure',
'__name__', '__dict__',
'_vm', '_func',
]
def __init__(self, name, code, globs, defaults, closure, vm):
"""你不需要按照这个来理解解释器。"""
self._vm = vm
self.func_code = code
self.func_name = self.__name__ = name or code.co_name
self.func_defaults = tuple(defaults)
self.func_globals = globs
self.func_locals = self._vm.frame.local_names
self.__dict__ = {}
self.func_closure = closure
self.__doc__ = code.co_consts[0] if code.co_consts else None
# 有时我们需要一个真正的 Python 函数，这里就是
kw = {
'argdefs': self.func_defaults,
}
if closure:
kw['closure'] = tuple(make_cell(0) for _ in closure)
# 利用 types 模块的 FunctionType 生成方法
self._func = types.FunctionType(code, globs, **kw)
def __call__(self, *args, **kwargs):
"""调用函数时，创建一个新帧并运行它。"""
# Python 3.6.1更新（bpo-19611）：
# 作用域和生成器表达式作用域生成的隐式 .0 参数会变为 implicit0
# 处理的时候需要注意(在 byte_LOAD_FAST 中)
callargs = inspect.getcallargs(self._func, *args, **kwargs)
# 使用 callargs 提供参数的映射：传递到新帧
frame = self._vm.make_frame(
self.func_code, callargs, self.func_globals, {}
)
return self._vm.run_frame(frame)
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] | 1.592048 | 1,157 |
from abc import ABC
from jax.ops import index_update, index_add, index
from typing import List, Union, Any
from spdc_inv.utils.defaults import QUBIT
import scipy.special as sp
import jax.numpy as np
import math
# Constants:
pi = np.pi
c = 2.99792458e8 # speed of light [meter/sec]
eps0 = 8.854187817e-12 # vacuum permittivity [Farad/meter]
h_bar = 1.054571800e-34 # [m^2 kg / s], taken from http://physics.nist.gov/cgi-bin/cuu/Value?hbar|search_for=planck
# lambda functions:
G1_Normalization = lambda w: h_bar * w / (2 * eps0 * c)
I = lambda A, n: 2 * n * eps0 * c * np.abs(A) ** 2 # Intensity
Power2D = lambda A, n, dx, dy: np.sum(I(A, n)) * dx * dy
# Compute the idler wavelength given pump and signal
SFG_idler_wavelength = lambda lambda_p, lambda_s: lambda_p * lambda_s / (lambda_s - lambda_p)
def PP_crystal_slab(
delta_k,
z,
crystal_profile,
inference=None
):
"""
Periodically poled crystal slab.
create the crystal slab at point z in the crystal, for poling period 2pi/delta_k
Parameters
----------
delta_k: k mismatch
z: longitudinal point for generating poling pattern
crystal_profile: Crystal 3D hologram (if None, ignore)
inference: (True/False) if in inference mode, we include more coefficients in the poling
description for better validation
Returns Periodically poled crystal slab at point z
-------
"""
if crystal_profile is None:
return np.sign(np.cos(np.abs(delta_k) * z))
else:
magnitude = np.abs(crystal_profile)
phase = np.angle(crystal_profile)
if inference is not None:
max_order_fourier = 20
poling = 0
magnitude = magnitude / magnitude.max()
DutyCycle = np.arcsin(magnitude) / np.pi
for m in range(max_order_fourier):
if m == 0:
poling = poling + 2 * DutyCycle - 1
else:
poling = poling + (2 / (m * np.pi)) * \
np.sin(m * pi * DutyCycle) * 2 * np.cos(m * phase + m * np.abs(delta_k) * z)
return poling
else:
return (2 / np.pi) * np.exp(1j * (np.abs(delta_k) * z)) * magnitude * np.exp(1j * phase)
def HermiteBank(
lam,
refractive_index,
W0,
max_mode_x,
max_mode_y,
x,
y,
z=0
):
"""
generates a dictionary of Hermite Gauss basis functions
Parameters
----------
lam; wavelength
refractive_index: refractive index
W0: beam waist
max_mode_x: maximum projection mode 1st axis
max_mode_y: maximum projection mode 2nd axis
x: transverse points, x axis
y: transverse points, y axis
z: projection longitudinal position
Returns
-------
dictionary of Hermite Gauss basis functions
"""
Hermite_dict = {}
for nx in range(max_mode_x):
for ny in range(max_mode_y):
Hermite_dict[f'|HG{nx}{ny}>'] = Hermite_gauss(lam, refractive_index, W0, nx, ny, z, x, y)
return np.array(list(Hermite_dict.values())), [*Hermite_dict]
def LaguerreBank(
lam,
refractive_index,
W0,
max_mode_p,
max_mode_l,
x,
y,
z=0,
get_dict: bool = False,
):
"""
generates a dictionary of Laguerre Gauss basis functions
Parameters
----------
lam; wavelength
refractive_index: refractive index
W0: beam waist
max_mode_p: maximum projection mode 1st axis
max_mode_l: maximum projection mode 2nd axis
x: transverse points, x axis
y: transverse points, y axis
z: projection longitudinal position
get_dict: (True/False) if True, the function will return a dictionary,
else the dictionary is splitted to basis functions np.array and list of dictionary keys.
Returns
-------
dictionary of Laguerre Gauss basis functions
"""
Laguerre_dict = {}
for p in range(max_mode_p):
for l in range(-max_mode_l, max_mode_l + 1):
Laguerre_dict[f'|LG{p}{l}>'] = Laguerre_gauss(lam, refractive_index, W0, l, p, z, x, y)
if get_dict:
return Laguerre_dict
return np.array(list(Laguerre_dict.values())), [*Laguerre_dict]
def TomographyBankLG(
lam,
refractive_index,
W0,
max_mode_p,
max_mode_l,
x,
y,
z=0,
relative_phase: List[Union[Union[int, float], Any]] = None,
tomography_quantum_state: str = None,
):
"""
generates a dictionary of basis function with projections into two orthogonal LG bases and mutually unbiased
bases (MUBs). The MUBs are constructed from superpositions of the two orthogonal LG bases.
according to: https://doi.org/10.1364/AOP.11.000067
Parameters
----------
lam; wavelength
refractive_index: refractive index
W0: beam waist
max_mode_p: maximum projection mode 1st axis
max_mode_l: maximum projection mode 2nd axis
x: transverse points, x axis
y: transverse points, y axis
z: projection longitudinal position
relative_phase: The relative phase between the mutually unbiased bases (MUBs) states
tomography_quantum_state: the current quantum state we calculate it tomography matrix.
currently we support: qubit/qutrit
Returns
-------
dictionary of bases functions used for constructing the tomography matrix
"""
TOMO_dict = \
LaguerreBank(
lam,
refractive_index,
W0,
max_mode_p,
max_mode_l,
x, y, z,
get_dict=True)
if tomography_quantum_state is QUBIT:
del TOMO_dict['|LG00>']
LG_modes, LG_string = np.array(list(TOMO_dict.values())), [*TOMO_dict]
for m in range(len(TOMO_dict) - 1, -1, -1):
for n in range(m - 1, -1, -1):
for k in range(len(relative_phase)):
TOMO_dict[f'{LG_string[m]}+e^j{str(relative_phase[k]/np.pi)}π{LG_string[n]}'] = \
(1 / np.sqrt(2)) * (LG_modes[m] + np.exp(1j * relative_phase[k]) * LG_modes[n])
return np.array(list(TOMO_dict.values())), [*TOMO_dict]
def TomographyBankHG(
lam,
refractive_index,
W0,
max_mode_x,
max_mode_y,
x,
y,
z=0,
relative_phase: List[Union[Union[int, float], Any]] = None,
tomography_quantum_state: str = None,
):
"""
generates a dictionary of basis function with projections into two orthogonal HG bases and mutually unbiased
bases (MUBs). The MUBs are constructed from superpositions of the two orthogonal HG bases.
according to: https://doi.org/10.1364/AOP.11.000067
Parameters
----------
lam; wavelength
refractive_index: refractive index
W0: beam waist
max_mode_x: maximum projection mode 1st axis
max_mode_y: maximum projection mode 2nd axis
x: transverse points, x axis
y: transverse points, y axis
z: projection longitudinal position
relative_phase: The relative phase between the mutually unbiased bases (MUBs) states
tomography_quantum_state: the current quantum state we calculate it tomography matrix.
currently we support: qubit
Returns
-------
dictionary of bases functions used for constructing the tomography matrix
"""
TOMO_dict = \
HermiteBank(
lam,
refractive_index,
W0,
max_mode_x,
max_mode_y,
x, y, z,
get_dict=True)
if tomography_quantum_state is QUBIT:
del TOMO_dict['|HG00>']
del TOMO_dict['|HG11>']
HG_modes, HG_string = np.array(list(TOMO_dict.values())), [*TOMO_dict]
for m in range(len(TOMO_dict) - 1, -1, -1):
for n in range(m - 1, -1, -1):
for k in range(len(relative_phase)):
TOMO_dict[f'{HG_string[m]}+e^j{str(relative_phase[k]/np.pi)}π{HG_string[n]}'] = \
(1 / np.sqrt(2)) * (HG_modes[m] + np.exp(1j * relative_phase[k]) * HG_modes[n])
return np.array(list(TOMO_dict.values())), [*TOMO_dict]
def Hermite_gauss(lam, refractive_index, W0, nx, ny, z, X, Y, coef=None):
"""
Hermite Gauss in 2D
Parameters
----------
lam: wavelength
refractive_index: refractive index
W0: beam waists
n, m: order of the HG beam
z: the place in z to calculate for
x,y: matrices of x and y
coef
Returns
-------
Hermite-Gaussian beam of order n,m in 2D
"""
k = 2 * np.pi * refractive_index / lam
z0 = np.pi * W0 ** 2 * refractive_index / lam # Rayleigh range
Wz = W0 * np.sqrt(1 + (z / z0) ** 2) # w(z), the variation of the spot size
invR = z / ((z ** 2) + (z0 ** 2)) # radius of curvature
gouy = (nx + ny + 1)*np.arctan(z/z0)
if coef is None:
coefx = np.sqrt(np.sqrt(2/pi) / (2**nx * math.factorial(nx)))
coefy = np.sqrt(np.sqrt(2/pi) / (2**ny * math.factorial(ny)))
coef = coefx * coefy
U = coef * \
(W0/Wz) * np.exp(-(X**2 + Y**2) / Wz**2) * \
HermiteP(nx, np.sqrt(2) * X / Wz) * \
HermiteP(ny, np.sqrt(2) * Y / Wz) * \
np.exp(-1j * (k * (X**2 + Y**2) / 2) * invR) * \
np.exp(1j * gouy)
return U
def Laguerre_gauss(lam, refractive_index, W0, l, p, z, x, y, coef=None):
"""
Laguerre Gauss in 2D
Parameters
----------
lam: wavelength
refractive_index: refractive index
W0: beam waists
l, p: order of the LG beam
z: the place in z to calculate for
x,y: matrices of x and y
coef
Returns
-------
Laguerre-Gaussian beam of order l,p in 2D
"""
k = 2 * np.pi * refractive_index / lam
z0 = np.pi * W0 ** 2 * refractive_index / lam # Rayleigh range
Wz = W0 * np.sqrt(1 + (z / z0) ** 2) # w(z), the variation of the spot size
r = np.sqrt(x**2 + y**2)
phi = np.arctan2(y, x)
invR = z / ((z ** 2) + (z0 ** 2)) # radius of curvature
gouy = (np.abs(l)+2*p+1)*np.arctan(z/z0)
if coef is None:
coef = np.sqrt(2*math.factorial(p)/(np.pi * math.factorial(p + np.abs(l))))
U = coef * \
(W0/Wz)*(r*np.sqrt(2)/Wz)**(np.abs(l)) * \
np.exp(-r**2 / Wz**2) * \
LaguerreP(p, l, 2 * r**2 / Wz**2) * \
np.exp(-1j * (k * r**2 / 2) * invR) * \
np.exp(-1j * l * phi) * \
np.exp(1j * gouy)
return U
def HermiteP(n, x):
"""
Hermite polynomial of rank n Hn(x)
Parameters
----------
n: order of the LG beam
x: matrix of x
Returns
-------
Hermite polynomial
"""
if n == 0:
return 1
elif n == 1:
return 2 * x
else:
return 2 * x * HermiteP(n - 1, x) - 2 * (n - 1) * HermiteP(n - 2, x)
def LaguerreP(p, l, x):
"""
Generalized Laguerre polynomial of rank p,l L_p^|l|(x)
Parameters
----------
l, p: order of the LG beam
x: matrix of x
Returns
-------
Generalized Laguerre polynomial
"""
if p == 0:
return 1
elif p == 1:
return 1 + np.abs(l)-x
else:
return ((2*p-1+np.abs(l)-x)*LaguerreP(p-1, l, x) - (p-1+np.abs(l))*LaguerreP(p-2, l, x))/p
class Beam(ABC):
"""
A class that holds everything to do with a beam
"""
def __init__(self,
lam: float,
ctype,
polarization: str,
T: float,
power: float = 0):
"""
Parameters
----------
lam: beam's wavelength
ctype: function that holds crystal type fo calculating refractive index
polarization: Polarization of the beam
T: crystal's temperature [Celsius Degrees]
power: beam power [watt]
"""
self.lam = lam
self.n = ctype(lam * 1e6, T, polarization) # refractive index
self.w = 2 * np.pi * c / lam # frequency
self.k = 2 * np.pi * ctype(lam * 1e6, T, polarization) / lam # wave vector
self.power = power # beam power
def fix_power(
A,
power,
n,
dx,
dy
):
"""
The function takes a field A and normalizes in to have the power indicated
Parameters
----------
A
power
n
dx
dy
Returns
-------
"""
output = A * np.sqrt(power) / np.sqrt(Power2D(A, n, dx, dy))
return output
class DensMat(ABC):
"""
A class that holds tomography dimensions and
tensors used for calculating qubit and qutrit tomography
"""
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] | 2.136486 | 5,964 |
"""
Testing WCS projections on LSST simulation files
"""
import os, sys
sys.path.append(os.path.abspath('..'))
import numpy as np
import matplotlib.pyplot as plt
from spheredb.get_data import\
get_stripe82_file, all_lsst_exposures, get_LSST_file
from spheredb.conversions import FITS_to_HPX, HPX_grid_step
from spheredb.util import regrid
import os
import pyfits
import re
import datetime
# Note: USE INSERT NOT MERGE!!!!
if 1:
from scidbpy import interface
sdb = interface.SciDBShimInterface('http://vega.cs.washington.edu:8080')
Nside = 2 ** 16 #19
hdulist = get_LSST_file()
output = FITS_to_HPX(hdulist[1].header, hdulist[1].data, Nside,
return_sparse=True)
print output.shape
RA_range = (output.row.min(), output.row.max())
DEC_range = (output.col.min(), output.col.max())
dRA = RA_range[1] - RA_range[0]
dDEC = DEC_range[1] - DEC_range[0]
RA_range = (RA_range[0] - 1 * dRA, RA_range[1] + 1 * dRA)
DEC_range = (DEC_range[0] - 1 * dDEC, DEC_range[1] + 1 * dDEC)
arr = sdb.from_sparse(output)
subarr = arr[RA_range[0]:RA_range[1],
DEC_range[0]:DEC_range[1]]
plt.imshow(np.log(subarr.toarray()), cmap=plt.cm.binary)
plt.show()
elif 1:
times = [hdulist[1].header['TAI'] for hdulist in all_lsst_exposures()]
times = np.asarray(times)
times.sort()
print times.min()
print times.max()
plt.plot(24 * (times - 50095), '.k')
plt.show()
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] | 2.231579 | 665 |
import logging
import shutil
import pytest
import salt.features
import salt.loader
import salt.pillar
log = logging.getLogger(__name__)
@pytest.fixture(scope="package")
@pytest.fixture(scope="module")
@pytest.fixture(scope="module")
@pytest.fixture(scope="module")
@pytest.fixture(scope="module")
@pytest.fixture(autouse=True)
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9078,
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7,
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28,
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8,
198
] | 2.826446 | 121 |
import csv
import numpy as np
### I had only used numpy , as this is when I had just dived in
### this universe
### So , for beginners go search for pandas you will reduce the lines of code and it
### is awesome !!
### read data
with open('../Data/train.csv') as f:
reader = csv.reader(f, delimiter=',')
data = []
for row in reader:
data.append(row)
### labels
data_headers = data[0]
### get some fields only
for i in ["Name", "PassengerId", "Survived", "Ticket", "Fare", "Cabin"]:
data_headers.remove(i)
### preprocessing and encoding
data = np.array(data[1:])
data = np.delete(data, [0, 3], 1)
order = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Embarked']
data[data == ''] = '01111110'
train_result = data[:, 0]
data = np.delete(data, [0, 6], 1)
data = np.delete(data, 5, 1)
data = np.delete(data, 5, 1)
print(data_headers, data[0])
data[data == "male"] = 0
data[data == "female"] = 1
data[data == "S"] = 1
data[data == "Q"] = 0
data[data == "C"] = 2
### using various classifiers
# from sklearn.naive_bayes import GaussianNB
# clf=GaussianNB()
# from sklearn.tree import DecisionTreeClassifier
# clf=DecisionTreeClassifier()
# from sklearn.svm import SVC
# clf=SVC()
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
clf = RandomForestClassifier(max_depth=5, n_estimators=10, max_features=2)
# clf=AdaBoostClassifier()
# from sklearn.neighbors import KNeighborsClassifier
# clf=KNeighborsClassifier()
# print(np.array(['','1']).astype(np.float),"jsbd")
# print(len(data.astype(np.float)),"############",len(train_result.astype(np.float)))
### fit data to classifier
clf.fit(data.astype(np.float), train_result.astype(np.float))
# Testing data
data = []
with open('../Data/test.csv') as f:
reader = csv.reader(f, delimiter=',')
data = []
for row in reader:
data.append(row)
# print(len(data))
data_headers = data[0]
### preprocessing for test data
for i in ["Name", "PassengerId", "Ticket", "Fare", "Cabin"]:
data = np.delete(data, data_headers.index(i), 1)
data_headers.remove(i)
data = np.array(data[1:])
data[data == ''] = '01111110'
data[data == "male"] = 0
data[data == "female"] = 1
data[data == "S"] = 1
data[data == "Q"] = 0
data[data == "C"] = 2
# print(len(data),len(order),data,"end data")
test_data = np.array(data[:, data_headers.index(order[0])])
for i in order[1:]:
test_data = np.vstack((test_data, data[:, data_headers.index(i)]))
# print(data_headers,"jdbfue",test_data,"jdbueb")
with open('../Data/gender_submission.csv') as f:
reader = csv.reader(f, delimiter=',')
test_labels = []
for row in reader:
test_labels.append(row[1])
print(len(test_labels))
test_labels = np.array(test_labels[1:])
ans = clf.predict(test_data.astype(np.float).T)
ans1 = np.array([range(892, 1310)])
ans = np.vstack((ans1.astype(np.int), ans.astype(np.int))).T
np.savetxt("fo1o.csv", ans, delimiter=",", fmt='%d')
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from __future__ import absolute_import
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] | 4.1 | 10 |
import logging
from typing import Optional
import qcodes as qc
from qcodes import Instrument, InstrumentChannel, Parameter
from qcodes import validators as vals
from qcodes.instrument.base import InstrumentBase
from qcodes.utils.validators import Validator
import nanotune as nt
logger = logging.getLogger(__name__)
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] | 3.72093 | 86 |
import argparse
import os
import sys
from sys import stdout
import mdtraj as md
import numpy as np
import parmed
import simtk.openmm as mm
import simtk.openmm.app as app
import simtk.unit as unit
from openforcefield.topology import Molecule, Topology
from openmmforcefields.generators import SystemGenerator
from perses.utils.openeye import OEMol_to_omm_ff, createOEMolFromSDF
from simtk.openmm import MonteCarloBarostat, XmlSerializer
from simtk.openmm.app import CheckpointReporter, ForceField, PDBFile
from simtk.openmm.app.pdbreporter import PDBReporter
from simtk.openmm.app.statedatareporter import StateDataReporter
# Read arguments to get ligand
parser = argparse.ArgumentParser()
parser.add_argument(
"-ligand",
help="the docked ligand to be prepared for simulation",
choices=["larotrectinib", "selitrectinib", "repotrectinib"],
type=str,
)
args = parser.parse_args()
chosen_ligand = args.ligand
# Parameters
print("--> Reading parameters")
pressure = 1.0 * unit.bar
temperature = 300 * unit.kelvin
nonbonded_method = app.PME
constraints = app.HBonds
remove_cm_motion = True
collision_rate = 1.0 / unit.picoseconds
timestep = 0.002 * unit.picoseconds
solvent_padding = 10.0 * unit.angstrom
ionic_strength = 150 * unit.millimolar
# Forcefield
protein_forcefield = "amber14/protein.ff14SB.xml"
small_molecule_forcefield = "openff-1.1.0"
solvation_forcefield = "amber14/tip3p.xml"
forcefield = ForceField(protein_forcefield, solvation_forcefield)
# Set steps and frequencies
nsteps = 2500000 # 5 ns
report_freq = 100
chk_freq = 500
traj_freq = 1000 # 2500 frames
# Set the input file names
input_pdb = "6KZD_prepped.pdb"
input_ligands_sdf = "../../structures_from_docking/6KZD_chemgauss_docking.sdf"
# Create output directory
output_prefix = "./output/" + chosen_ligand
os.makedirs(output_prefix, exist_ok=True)
print("--> Directory ", output_prefix, " created ")
# Set file names
integrator_xml_filename = "integrator_2fs.xml"
state_xml_filename = "equilibrated_state_5ns.xml"
state_pdb_filename = "equilibrated_state_5ns.pdb"
system_xml_filename = "equilibrated_system_5ns.xml"
checkpoint_filename = "equilibrated_checkpoint_5ns.chk"
traj_output_filename = "equilibrated_traj_5ns.xtc"
# Define the barostat for the system
barostat = mm.MonteCarloBarostat(pressure, temperature)
# Load and sort ligands
molecules = Molecule.from_file(input_ligands_sdf)
ligand_names = ["larotrectinib", "selitrectinib", "repotrectinib"]
ligand_dict = dict(zip(ligand_names, molecules)) # Create dict for easy access later
# Make the SystemGenerator
system_generator = SystemGenerator(
forcefields=[protein_forcefield, solvation_forcefield],
barostat=barostat,
periodic_forcefield_kwargs={"nonbondedMethod": app.PME},
small_molecule_forcefield=small_molecule_forcefield,
molecules=ligand_dict[chosen_ligand],
)
# Read in the PDB and create an OpenMM topology
pdbfile = app.PDBFile(input_pdb)
protein_topology, protein_positions = pdbfile.topology, pdbfile.positions
# Add ligand to topology - credit to @hannahbrucemacdonald for help here
print("--> Combining protein and ligand topologies")
off_ligand_topology = Topology.from_molecules(ligand_dict[chosen_ligand])
ligand_topology = off_ligand_topology.to_openmm()
ligand_positions = ligand_dict[chosen_ligand].conformers[0]
md_protein_topology = md.Topology.from_openmm(
protein_topology
) # using mdtraj for protein top
md_ligand_topology = md.Topology.from_openmm(
ligand_topology
) # using mdtraj for ligand top
md_complex_topology = md_protein_topology.join(md_ligand_topology) # add them together
complex_topology = md_complex_topology.to_openmm() # now back to openmm
total_atoms = len(protein_positions) + len(ligand_positions)
complex_positions = unit.Quantity(np.zeros([total_atoms, 3]), unit=unit.nanometers)
complex_positions[0 : len(protein_positions)] = protein_positions
for index, atom in enumerate(ligand_positions, len(protein_positions)):
coords = atom / atom.unit
complex_positions[index] = (
coords / 10.0
) * unit.nanometers # since openmm works in nm
# Add hydrogens and solvate the system
modeller = app.Modeller(complex_topology, complex_positions)
print("Adding hydrogens to the system...")
modeller.addHydrogens(system_generator.forcefield)
print("Solvating the system...")
modeller.addSolvent(
forcefield=system_generator.forcefield,
model="tip3p",
ionicStrength=ionic_strength,
padding=solvent_padding,
)
# Create an OpenMM system
print("--> Creating an OpenMM system")
system = system_generator.create_system(modeller.topology)
# Make and serialize integrator - Langevin dynamics
print(
"Serializing integrator to %s"
% os.path.join(output_prefix, integrator_xml_filename)
)
integrator = mm.LangevinIntegrator(
temperature, collision_rate, timestep # Friction coefficient
)
with open(os.path.join(output_prefix, integrator_xml_filename), "w") as outfile:
xml = mm.XmlSerializer.serialize(integrator)
outfile.write(xml)
# Define the platform to use; CUDA, OpenCL, CPU, or Reference. Or do not specify
# the platform to use the default (fastest) platform
# platform = mm.Platform.getPlatformByName("OpenCL")
# prop = dict(OpenCLPrecision="mixed") # Use mixed single/double precision
# Create the Simulation object
sim = app.Simulation(modeller.topology, system, integrator) # , platform, prop)
# Set the particle positions
sim.context.setPositions(modeller.positions)
# Minimize the energy
print("--> Minimising energy with docked ligand: " + chosen_ligand)
print(
" initial : %8.3f kcal/mol"
% (
sim.context.getState(getEnergy=True).getPotentialEnergy()
/ unit.kilocalories_per_mole
)
)
sim.minimizeEnergy()
print(
" final : %8.3f kcal/mol"
% (
sim.context.getState(getEnergy=True).getPotentialEnergy()
/ unit.kilocalories_per_mole
)
)
# set starting velocities:
print("--> Generating random starting velocities")
sim.context.setVelocitiesToTemperature(temperature * unit.kelvin)
# write limited state information to standard out:
sim.reporters.append(
StateDataReporter(
stdout,
reportInterval=report_freq,
step=True,
time=True,
potentialEnergy=True,
kineticEnergy=True,
temperature=True,
speed=True,
progress=True,
remainingTime=True,
totalSteps=nsteps,
separator="\t",
)
)
# Write to checkpoint files regularly:
sim.reporters.append(
CheckpointReporter(
file=os.path.join(output_prefix, checkpoint_filename), reportInterval=chk_freq
)
)
# Write out the trajectory
sim.reporters.append(
md.reporters.XTCReporter(
file=os.path.join(output_prefix, traj_output_filename), reportInterval=traj_freq
)
)
# Run NPT dynamics
print("--> Running dynamics in the NPT ensemble for the 6KZD:" + chosen_ligand + " complex")
sim.step(nsteps)
# Save and serialize the final state
print("--> Serializing state to %s" % os.path.join(output_prefix, state_xml_filename))
state = sim.context.getState(
getPositions=True, getVelocities=True, getEnergy=True, getForces=True
)
with open(os.path.join(output_prefix, state_xml_filename), "w") as outfile:
xml = mm.XmlSerializer.serialize(state)
outfile.write(xml)
# Save the final state as a PDB
print("--> Saving final state as %s" % os.path.join(output_prefix, state_pdb_filename))
with open(os.path.join(output_prefix, state_pdb_filename), "w") as outfile:
PDBFile.writeFile(
sim.topology,
sim.context.getState(getPositions=True, enforcePeriodicBox=True).getPositions(),
file=outfile,
keepIds=True,
)
# Save and serialize system
print("--> Serializing system to %s" % os.path.join(output_prefix, system_xml_filename))
system.setDefaultPeriodicBoxVectors(*state.getPeriodicBoxVectors())
with open(os.path.join(output_prefix, system_xml_filename), "w") as outfile:
xml = mm.XmlSerializer.serialize(system)
outfile.write(xml) | [
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] | 2.76284 | 2,901 |
"""Holds AnswerSelect model."""
import datetime
from .. import db
class AnswerSelect(db.Model):
"""Model to hold a users selected answers."""
__tablename__ = "answerselects"
id = db.Column(db.Integer, primary_key=True)
user_id = db.Column(db.Integer, db.ForeignKey("users.id"))
quiz_id = db.Column(db.Integer, db.ForeignKey("quizzes.id"))
question_id = db.Column(db.Integer, db.ForeignKey("questions.id"))
answer_id = db.Column(db.Integer, db.ForeignKey("answers.id"))
created_date = db.Column(db.DateTime, default=datetime.datetime.utcnow)
user = db.relationship("User", backref="answerselects", lazy=True)
answer = db.relationship("Answer", backref="answerselects", lazy=True)
def __repr__(self):
"""Return string readable version of model."""
return "<AnswerSelect {}:{}>".format(
self.user.name,
self.answer.text)
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] | 2.637681 | 345 |
import typer
from .app import run_as_service
from .sensitivity_ua_services import (
sensitivity_ua_linear_regression,
sensitivity_ua_test_func,
)
from .demo_services import demo_func
main = typer.Typer()
@main.command()
@main.command()
@main.command()
if __name__ == "__main__":
main()
| [
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834,
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834,
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220,
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220,
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] | 2.725664 | 113 |
from mongodb_statedb import StateDb
| [
6738,
285,
506,
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21989,
65,
1330,
1812,
43832,
628
] | 3.083333 | 12 |
# -*- encoding: utf-8 -*-
"""
DeepQ Learning is a Reinforcement Learning Platform where AI Learns to Play Games
In this project, I'm trying to develop some 'Q-Learning Algorithms' where the
neural network will learn to play various games. The `engine` is specifically
designed to build games that can be used to train and test the models. The game
engines are also built such that an user can self play without any overhead.
List of Games Available:
1. Classic Snake Game (`snake.py`)
@author: Debmalya Pramanik
@Contact: [email protected]
"""
# init-time options registrations
from .snake import * # noqa: F403
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539,
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] | 3.486188 | 181 |
"""
Constants for ocrd_utils.
"""
from pkg_resources import get_distribution
__all__ = [
'VERSION',
'MIMETYPE_PAGE',
'EXT_TO_MIME',
'MIME_TO_EXT'
]
VERSION = get_distribution('ocrd_utils').version
MIMETYPE_PAGE = 'application/vnd.prima.page+xml'
EXT_TO_MIME = {
'.tif': 'image/tiff',
'.tiff': 'image/tiff',
'.png': 'image/png',
'.jpg': 'image/jpg',
'.jpeg': 'image/jpg',
'.xml': MIMETYPE_PAGE
}
MIME_TO_EXT = {
'image/tiff': '.tif',
'image/png': '.png',
'image/jpg': '.jpg',
'image/jpeg': '.jpg',
MIMETYPE_PAGE: '.xml',
'application/alto+xml': '.xml',
}
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220,
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73,
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9479,
3256,
198,
220,
220,
220,
337,
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25,
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14,
282,
1462,
10,
19875,
10354,
45302,
19875,
3256,
198,
92,
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] | 2.062914 | 302 |
from logging import exception
from math import sqrt
from random import uniform
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1330,
19862,
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] | 4.9375 | 16 |
"""UI Templates: For reusable ui pieces built from components."""
from zygrader import ui
from zygrader.zybooks import Zybooks
def filename_input(purpose, text=""):
"""Get a valid filename from the user"""
window = ui.get_window()
path_input = ui.layers.PathInputLayer("Filepath Entry")
path_input.set_prompt(
[f"Enter the path and filename for {purpose} [~ is supported]"])
path_input.set_text(text)
window.run_layer(path_input)
if path_input.canceled:
return None
return path_input.get_path()
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62,
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13,
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62,
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198
] | 2.704433 | 203 |
if __name__ == "__main__":
print(selection_sort([]))
print(selection_sort([1]))
print(selection_sort([0, 100000000, 20000]))
print(selection_sort([1, 0]))
print(selection_sort([1.5, -2.6, 2, 1.1]))
print(selection_sort([3, 6, 8, 1, 2, 5, 3, 9, 3, 5, 9, 2]))
print(selection_sort([3, 6, -45, 1, 2, 5, 3, -9, 3, 0, 9, 2]))
print(selection_sort([3, 6, -45, 1, 2, 5, 3, -9,
3, 0, 9, 2], comparison=lambda a, b: a > b))
print(selection_sort(["hello", "apple", "cat", "zebra", "goat", ""]))
| [
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25,
257,
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275,
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198,
220,
220,
220,
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7,
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62,
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7,
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31373,
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366,
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1600,
366,
9246,
1600,
366,
89,
37052,
1600,
366,
2188,
265,
1600,
366,
8973,
4008,
198
] | 2.010989 | 273 |
#//
#//------------------------------------------------------------------------------
#// Copyright 2007-2011 Mentor Graphics Corporation
#// Copyright 2007-2011 Cadence Design Systems, Inc.
#// Copyright 2010-2011 Synopsys, Inc.
#// Copyright 2013 NVIDIA Corporation
#// All Rights Reserved Worldwide
#//
#// Licensed under the Apache License, Version 2.0 (the
#// "License"); you may not use this file except in
#// compliance with the License. You may obtain a copy of
#// the License at
#//
#// http://www.apache.org/licenses/LICENSE-2.0
#//
#// Unless required by applicable law or agreed to in
#// writing, software distributed under the License is
#// distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
#// CONDITIONS OF ANY KIND, either express or implied. See
#// the License for the specific language governing
#// permissions and limitations under the License.
#//------------------------------------------------------------------------------
#
#`ifndef UVM_DAP_SVH
# `define UVM_DAP_SVH
#
#// Set/Get DAPS
# `include "dap/uvm_set_get_dap_base.svh"
# `include "dap/uvm_simple_lock_dap.svh"
# `include "dap/uvm_get_to_lock_dap.svh"
# `include "dap/uvm_set_before_get_dap.svh"
#
#`endif // UVM_DAP_SVH
#
| [
2,
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62,
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2,
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366,
67,
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62,
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62,
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62,
5354,
62,
67,
499,
13,
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71,
1,
198,
2,
4600,
17256,
366,
67,
499,
14,
84,
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62,
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62,
19052,
62,
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62,
67,
499,
13,
21370,
71,
1,
198,
2,
198,
2,
63,
32088,
3373,
471,
15996,
62,
35,
2969,
62,
50,
53,
39,
198,
2,
198
] | 3.053269 | 413 |
# Houston
# Connects a serial device on Windows this would COMx
# Sets the board type
# Then starts polling analog pin 17 which is Analog pin 3
# You will need MRLComm.ino loaded on the Arduino
# If all goes well - you should have 2 traces running
# in the arduino->oscope tab - you can at this point connect
# input - for example a 5v line to the lines and see them change
from time import sleep
from org.myrobotlab.service import Arduino
from org.myrobotlab.service import Servo
from org.myrobotlab.service import Motor
# variables dependent on your setup
boardType = "atmega2560" # atmega168 | atmega328p | atmega2560 | atmega1280 | atmega32u4
#comPort = "/dev/ttyACM0"
#comPort = "COM9"
lfaencoder = 38
analogSensorPin = 67
# create service for Houston
arduino = runtime.createAndStart("arduino","Arduino")
lshoulder = runtime.createAndStart("lshoulder","Servo")
lbicep = runtime.createAndStart("lbicep","Servo")
lelbow = runtime.createAndStart("lelbow","Servo")
rshoulder = runtime.createAndStart("rshoulder","Servo")
rbicep = runtime.createAndStart("rbicep","Servo")
relbow = runtime.createAndStart("relbow","Servo")
# 4 motors
lfmotor = runtime.createAndStart("lfmotor","Motor") # left front
rfmotor = runtime.createAndStart("rfmotor","Motor") # right front
lbmotor = runtime.createAndStart("lbmotor","Motor") # left back
rbmotor = runtime.createAndStart("rbmotor","Motor") # right back
# set config for the services
arduino.setBoard(boardType) # atmega168 | mega2560 | etc
arduino.connect(comPort,57600,8,1,0)
sleep(1) # give it a second for the serial device to get ready
# attach Servos & Motors to arduino
arduino.servoAttach(lshoulder.getName(), 46)
arduino.servoAttach(lbicep.getName(), 47)
arduino.servoAttach(lelbow.getName(), 48)
arduino.servoAttach(rshoulder.getName(), 50)
arduino.servoAttach(rbicep.getName(), 51)
arduino.servoAttach(relbow.getName(), 52)
arduino.motorAttach(lfmotor.getName(), 4, 30)
arduino.motorAttach(rfmotor.getName(), 5, 31)
arduino.motorAttach(lbmotor.getName(), 6, 32)
arduino.motorAttach(rbmotor.getName(), 7, 33)
# update the gui with configuration changes
arduino.publishState()
lshoulder.publishState()
lbicep.publishState()
lelbow.publishState()
rshoulder.publishState()
rbicep.publishState()
relbow.publishState()
lfmotor.publishState()
rfmotor.publishState()
lbmotor.publishState()
rbmotor.publishState()
# system check - need to do checks to see all systems are go !
# start the analog pin sample to display
# in the oscope
arduino.analogReadPollingStart(analogSensorPin)
# change the pinMode of digital pin 13
arduino.pinMode(lfaencoder, Arduino.OUTPUT)
# begin tracing the digital pin 13
arduino.digitalReadPollStart(lfaencoder)
# turn off the trace
# arduino.digitalReadPollStop(lfaencoder)
# turn off the analog sampling
# arduino.analogReadPollingStop(analogSensorPin)
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] | 2.963542 | 960 |
from datetime import datetime
from typing import Dict
from flask import Flask, request, send_file
from music import BeatTimestamp, FileOffsetRecording
from room import Room
app = Flask(__name__)
rooms: Dict[str, Room] = {}
# Accepts datetime in milliseconds and converts to microseconds
@app.route("/")
# --------------------------------------------------------
# Rooms
# --------------------------------------------------------
# Adds a new room to the rooms dictionary and returns it's ID
# The room ID is necessary for all future interactions
@app.route("/create-room", methods=['POST'])
# Allows a new user to validate their room ID
@app.route("/<string:room_id>/is-valid-room-id", methods=['POST'])
# --------------------------------------------------------
# Recording
# --------------------------------------------------------
# Informs the server that a user has begun recording
# All users who start recording in a room must stop recording for
# a composition to be produced
@app.route("/<string:room_id>/start-recording", methods=['POST'])
# Informs the server that a user is finished recording and provides
# the FileOffsetRecordings as a json in the following format:
# {
# 'start_time': "%D:%H:%M:%S.%f" (f is milliseconds)
# 'end_time': "%D:%H:%M:%S.%f"
# 'events' : [
# {
# filename: string, -- name of the audio file (uploaded and default)
# time: "%D:%H:%M:%S.%f"
# loopable: bool
# },
# ...
# ]
# }
# Returns whether the recording session is complete
@app.route("/<string:room_id>/stop-recording", methods=['POST'])
# Upload a sound file to current recording session
# Must be in .mp4 format and with the filename that will be used to
# reference the file in the offsets of the FileOffsetRecordings
@app.route("/<string:room_id>/upload-sound", methods=['PUT'])
# --------------------------------------------------------
# Getting Composition
# --------------------------------------------------------
# Returns whether a is recording complete, meaning the same number of users
# who started recording have stopped
# Allows users to poll when they should call get-composition
@app.route("/<string:room_id>/is-recording-complete")
# Returns the generated composition as an mp3 file
# Produces composition if necessary with the FileOffsetRecordings
@app.route("/<string:room_id>/get-composition")
# --------------------------------------------------------
# Misc.
# --------------------------------------------------------
# Simple health check to test connection to server
@app.route("/health-check")
# Can be called as an hourly chron job to clean expired data
@app.route("/cleanup", methods=['POST'])
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] | 3.581794 | 758 |
"""
Created on 16 Apr 2017
@author: Bruno Beloff ([email protected])
"""
from scs_dfe.interface.component.pca8574 import PCA8574State
# --------------------------------------------------------------------------------------------------------------------
PCA8574State.init()
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#!/usr/bin/env python3
import argparse
import datetime
import os
import shutil
import sys
import time
from subprocess import check_output
import geopandas as gpd
import numpy as np
from colorama import Fore, Style, init
from basin_setup import __version__
# Initialize colors
init()
DEBUG = False
out = Messages()
def check_path(filename, outfile=False):
"""
Checks whether an file has been provided exists.
If outfile is true then we assume we are making a file and there fore we
should only check if the directory exists.
Args:
filename: path to a file
outfile: Boolean indicating whether to check for a file (outfile=False)
or a directory (outfile==True)
"""
folder = os.path.dirname(filename)
if outfile and not os.path.isdir(folder):
out.error("Directory provided for output location does not exist!"
"\nMissing----->{}".format(filename))
sys.exit()
if not outfile and not os.path.isfile(filename):
out.error("Input file does not exist!\nMissing----->{}"
"".format(filename))
sys.exit()
def run_cmd(cmd, nthreads=None):
"""
Executes the command and pipes the output to the console.
Args:
cmd: String command to be entered in the the command prompt
"""
out.dbg('Running {}'.format(cmd))
if nthreads is not None:
cmd = 'mpiexec -n {0} '.format(nthreads) + cmd
s = check_output(cmd, shell=True, universal_newlines=True)
out.dbg(s)
def pitremove(demfile, outfile=None, nthreads=None):
"""
STEP #1
Builds the command to pit fill the DEM and executes it.
Args:
demfile: Path to tif of the DEM.
outfile: Path to write the pit filled DEM.
nthreads: Number of cores to use for mpiexec
"""
out.msg("Removing Pits from DEM...")
if outfile is None:
outfile = 'filled.tif'
check_path(demfile)
check_path(outfile, outfile=True)
CMD = "pitremove -z {0} -fel {1}".format(demfile, outfile)
run_cmd(CMD, nthreads=nthreads)
def calcD8Flow(filled_dem, d8dir_file=None, d8slope_file=None, nthreads=None):
"""
STEP #2
Builds the command to calculate the D8 flow for the flow direction and
executes it.
Args:
filled_dem: Path to tif of the pit filled DEM.
d8dir_file: Path to write the D8 flow direction.
d8slope_file: Path to write the D8 flow slope.
nthreads: Number of cores to use for mpiexec
"""
out.msg("Calculating D8 flow direction...")
# Check paths
check_path(filled_dem)
check_path(d8dir_file, outfile=True)
check_path(d8slope_file, outfile=True)
CMD = "d8flowdir -fel {0} -p {1} -sd8 {2}".format(filled_dem,
d8dir_file,
d8slope_file)
run_cmd(CMD, nthreads=nthreads)
def calcD8DrainageArea(d8flowdir, areaD8_out=None, nthreads=None):
"""
STEP #3
Calculates D8 Contributing area to each cell in the DEM.
Args:
d8flowdir: Path to the D8 Flow direction image
areaD8_out: Path to output the Drainage area image
nthreads: Number of cores to use for mpiexec
"""
check_path(d8flowdir)
check_path(areaD8_out, outfile=True)
CMD = "aread8 -p {0} -ad8 {1}".format(d8flowdir, areaD8_out)
run_cmd(CMD, nthreads=nthreads)
def defineStreamsByThreshold(areaD8, threshold_streams_out=None, threshold=100,
nthreads=None):
"""
STEP #4
Stream definition by threshold in order to extract a first version of the
stream network
Args:
areaD8: Path to the D8 Drainage area image
threshold_streams_out: Path to output the thresholded image
threshold: threshold value to recategorize the data
nthreads: Number of cores to use for mpiexec
"""
out.msg(
"Performing stream estimation using threshold of {0}".format(
threshold))
check_path(areaD8)
check_path(threshold_streams_out, outfile=True)
CMD = "threshold -ssa {0} -src {1} -thresh {2}".format(
areaD8,
threshold_streams_out,
threshold)
run_cmd(CMD, nthreads=nthreads)
def outlets_2_streams(d8flowdir, threshold_streams, pour_points,
new_pour_points=None,
nthreads=None):
"""
STEP #5 Move Outlets to Streams, so as to move the catchment outlet point
on one of the DEM cells identified by TauDEM as belonging to the
stream network
Args:
d8flowdir: Path to the D8 Flow direction image
threshold_streams: Path to output the thresholded stream image
pour_points: Path to pour point locations in a list
new_pour_points: Path to output the new list of points
nthreads: Number of cores to use for mpiexec
"""
check_path(d8flowdir)
check_path(threshold_streams)
check_path(pour_points)
check_path(new_pour_points, outfile=True)
CMD = 'moveoutletstostrm -p {0} -src {1} -o {2} -om {3}'.format(
d8flowdir,
threshold_streams,
pour_points,
new_pour_points)
run_cmd(CMD, nthreads=nthreads)
def calcD8DrainageAreaBasin(d8flowdir, basin_outlets_moved, areaD8_out=None,
nthreads=None):
"""
STEP #6
D8 Contributing Area again, but with the catchment outlet point as
additional input data
Args:
d8flowdir: Path to the D8 Flow direction image
basin_outlets_moved: all pour points that have been moved to the stream
areaD8_out: Path to output the Drainage area image that utilize all the
points
nthreads: Number of cores to use for mpiexec
"""
out.msg("Calculating drainage area using pour points...")
check_path(d8flowdir)
check_path(basin_outlets_moved)
check_path(areaD8_out, outfile=True)
CMD = 'aread8 -p {0} -o {1} -ad8 {2}'.format(d8flowdir,
basin_outlets_moved,
areaD8_out)
run_cmd(CMD, nthreads=nthreads)
def delineate_streams(dem, d8flowdir, basin_drain_area, threshold_streams,
basin_outlets_moved, stream_orderfile=None,
treefile=None, coordfile=None, netfile=None,
wfile=None, nthreads=None):
"""
STEP #8 Stream Reach And Watershed
Args:
dem: path to a filled dem image
d8flowdir: path to the flow direction image
basin_drain_area: path to the flow accumulation image for the basin
threshold_streams: streams defintion image defined by a threshold
basin_outlets_moved: Path to a .bna of the pour points corrected to be
on the streams.
stream_orderfile: Name of the file to output the stream segment order
treefile: Name of the file to output the subbasin flow order.
coordfile: Not sure what this file is
netfile: Name of the images to output the stream definitions.
wfile: Name of the image to output subbasin definitions.
nthreads: Number of cores to use for mpiexec
"""
out.msg("Creating watersheds and stream files...")
# Check path validity
inputs = [dem, d8flowdir, basin_drain_area, threshold_streams,
basin_outlets_moved]
outputs = [stream_orderfile, treefile, coordfile, netfile, wfile]
for f in inputs:
check_path(f)
for f in outputs:
check_path(f, outfile=True)
CMD = ('streamnet -fel {0} -p {1} -ad8 {2} -src {3} -ord {4} -tree {5}'
' -coord {6} -net {7} -o {8} -w {9}').format(
dem,
d8flowdir,
basin_drain_area,
threshold_streams,
stream_orderfile,
treefile,
coordfile,
netfile,
basin_outlets_moved,
wfile)
run_cmd(CMD, nthreads=nthreads)
def convert2ascii(infile, outfile=None):
"""
Convert to ascii
"""
check_path(infile)
check_path(outfile, outfile=True)
# convert wfile files to ascii
CMD = 'gdal_translate -of AAIGrid {0} {1}'.format(infile, outfile)
run_cmd(CMD)
def produce_shapefiles(watershed_tif, corrected_points,
output_dir=None, streamflow=False):
"""
Outputs the polygons of the individual subbasins to a shapfile.
Args:
watershed_tif: Path to a geotiff of the watersheds
corrected_points: Path to the corrected points used for delineation
output_dir: Output location used for producing shapefiles
"""
# Check files
check_path(watershed_tif)
check_path(corrected_points)
wfname = os.path.basename(watershed_tif).split('.')[0] + '.shp'
# Polygonize creates a raster with all subbasins
watershed_shp = os.path.join(output_dir, wfname)
CMD = 'gdal_polygonize.py -f "ESRI SHAPEFILE" {} {}'.format(watershed_tif,
watershed_shp)
run_cmd(CMD)
# Read in and identify the names of the pour points with the subbasins
ptdf = gpd.read_file(corrected_points)
wdf = gpd.read_file(watershed_shp)
# Identify the name and output the individual basins
for nm, pt in zip(ptdf['Primary ID'].values, ptdf['geometry'].values):
for pol, idx in zip(wdf['geometry'].values, wdf.index):
if pt.within(pol):
# Create a new dataframe and output it
df = gpd.GeoDataFrame(columns=wdf.columns, crs=wdf.crs)
df = df.append(wdf.loc[idx])
out.msg("Creating the subbasin outline for {}...".format(nm))
df.to_file(os.path.join(output_dir, '{}_subbasin.shp'
''.format(
(nm.lower()).replace(' ', '_'))
))
# Output the full basin outline
out.msg("Creating the entire basin outline...")
same = np.ones(len(wdf.index))
wdf['all'] = same
basin_outline = wdf.dissolve(by='all')
basin_outline.to_file(os.path.join(output_dir, 'basin_outline.shp'))
return watershed_shp
def create_readme(sysargs, output_dir):
"""
Creates a readme with all the details for creating the files
Args:
sysargs: command used for generating files
"""
dt = ((datetime.datetime.today()).isoformat()).split('T')[0]
out_str = (
"###################################################################\n"
"# BASIN DELINEATION TOOL V{0}\n"
"###################################################################\n"
"\n The files in this folder were generated on {1}.\n"
"This was accomplished using the following command:\n"
"\n$ {2}\n"
"\nTo get access to the source code please visit:\n"
"https://github.com/USDA-ARS-NWRC/basin_setup")
out_str = out_str.format(__version__, dt, ' '.join(sys.argv))
with open(os.path.join(output_dir, 'README.txt'), 'w') as fp:
fp.write(out_str)
fp.close()
def cleanup(output_dir, at_start=False):
"""
Removes the temp folder and removes the following files:
* output/watersheds.shp
* output/*_subbasin.shp
* output/basin_outline.shp
* output/corrected_points.shp
Args:
output_dir: folder to lookin for cleanup
at_start: If at the beginning we cleanup a lot more files versus
than at the end of a run.
"""
out.msg("Cleaning up files...")
# Always cleanup the temp folder
temp = os.path.join(output_dir, 'temp')
if os.path.isdir(temp):
shutil.rmtree(temp)
if at_start:
# Remove any potential streamflow folders
streamflow = os.path.join(output_dir, 'streamflow')
if os.path.isdir(streamflow):
shutil.rmtree(streamflow)
fnames = os.listdir(output_dir)
for f in fnames:
fn = os.path.join(output_dir, f)
if ("_subbasin." in f or "thresh" in f or "basin_outline." in f
or 'watersheds_' in f or 'out.' in f
or "corrected_points_" in f):
out.dbg("Removing {}".format(f))
os.remove(fn)
def confirm_norerun(non_thresholdkeys, imgs):
"""
Checks if the non-thresholded files exist, if so confirm the user wants
to overwrite them.
Args:
non-thresholdedkeys: keys to check in the imgs dictionary of paths
imgs: Dictionary of paths to images
Returns
bool: Indicating whether we continue or not
"""
out.dbg("Checking if important delineation images pre-exist...")
# Quickly check if the user wants to over write a possible rerun
move_forward = False
any_file_exists = False
for f in non_thresholdkeys:
if os.path.isfile(imgs[f]):
out.dbg("{} image exists!".format(f))
any_file_exists = True
out.warn("You are about to overwrite the delineation files that"
" take the longest to make. \n\nAre you sure you want to"
" do this? (y/n)\n")
answer = input()
acceptable_answer = False
while not acceptable_answer:
if answer.lower() == 'y':
acceptable_answer = True
move_forward = True
elif answer.lower() == 'n':
acceptable_answer = True
else:
acceptable_answer = False
break
# If there weren't any files then move ahead
if not any_file_exists:
move_forward = True
out.dbg("No pre-existing files, moving forward...")
return move_forward
def create_ars_streamflow_files(treefile, coordfile, threshold, wshp, netdir,
output='basin_catchments.csv'):
"""
Takes in the Tree file and the Coordinates file to produce a csv of the
downstream catchment, the elevation of a catchment, and contributing area
"""
today = (datetime.datetime.today().date()).isoformat()
header = ("#############################################################\n"
" Basin Catchment File for USDA-ARS-NWRC Streamflow modeling. \n"
" Delineatation Threshold: {}\n"
" Date Created: {}\n"
" Created using basin_setup v{}\n"
"#############################################################\n"
"\n".format(threshold, today,
__version__)
)
with open(output, 'w+') as fp:
fp.write(header)
fp.close()
# tree_names = ['link', 'start number', 'end number', 'downstream',
# 'upstream',
# 'strahler',
# 'monitor point',
# 'network magnitude']
# coord_names = ['dummy', 'x', 'y', 'distance', 'elevation', 'area']
# dftree = pd.read_csv(treefile, delimiter='\t', names=tree_names)
# dfcoord = pd.read_csv(coordfile, delimiter='\t', names=coord_names)
dfwshp = gpd.read_file(wshp)
# Get the network shpapefile which lives under a folder named after the
# tif.
name = os.path.split(netdir)[-1].split('.')[0] + '.shp'
netshp = os.path.join(netdir, name)
dfnet = gpd.read_file(netshp)
dfnet = dfnet.set_index('WSNO')
# Collect the area of each basin
dfwshp['area'] = dfwshp.area
# handle individual cells acting as subbasins
dfwshp = dfwshp.groupby('DN').sum()
# Collect down stream info.
dfwshp['downstream'] = dfnet['DSLINKNO']
dfwshp.to_csv(output, mode='a')
def output_streamflow(imgs, threshold, wshp, temp="temp",
output_dir='streamflow'):
"""
Outputs files necessary for streamflow modeling. This will create a file
structure under a folder defined by output_dir and the threshold.
E.g. streamflow/thresh_10000000
Args:
imgs: Dictionary containing a files to be outputted.
threshold: threshold used for creating subbasins
wshp: Watershed shapefile
output_dir: Location to output files
"""
# Dictionary to grab filenames for ARS streamflow
dat = {}
out.msg("Creating streamflow files...")
final_output = os.path.join(output_dir, "thresh_{}".format(threshold))
if not os.path.isdir(output_dir):
out.msg("Making streamflow directory")
os.mkdir(output_dir)
if not os.path.isdir(final_output):
out.msg("Making streamflow threshold directory...")
os.mkdir(final_output)
# Convert the watersheds to ascii and move files to streamflow folder for
# SLF streamflow
for k in ['corrected_points', 'watersheds', 'coord', 'tree']:
name = os.path.basename(imgs[k])
outfile = os.path.join(final_output, k + "." + name.split('.')[-1])
# Handle grabbing data for outputing ARS streamflow
if k in ['tree', 'coord']:
dat[k] = outfile
if k == 'watersheds':
outfile = os.path.join(final_output, k + '.asc')
convert2ascii(imgs[k], outfile)
else:
shutil.copy(imgs[k], outfile)
# Copy over threshold files
for f in os.listdir(imgs['net']):
to_f = os.path.join(final_output, os.path.basename(f))
shutil.copy(os.path.join(imgs["net"], f), to_f)
# Create the files for ARS Streamflow
create_ars_streamflow_files(dat['tree'],
dat['coord'],
threshold,
wshp,
imgs['net'],
output=os.path.join(final_output,
'basin_catchments.csv'))
def ernestafy(demfile, pour_points, output=None, temp=None, threshold=100,
rerun=False,
nthreads=None,
out_streams=False):
"""
Run TauDEM using the script Ernesto Made.... therefore we will
ernestafy this basin.
Args:
demfile: Original DEM tif.
pour_points: Locations of the pour_points in a .bna file format
output: Output folder location, default is ./delineation
threshold: Threshold to use, can be a list or a single value
rerun: boolean indicating whether to avoid re-doing steps 1-3
out_streams: Boolean determining whether to output the files for
streamflow modeling
"""
create_readme(sys.argv, output)
# Output File keys without a threshold in the filename
non_thresholdkeys = ['filled', 'flow_dir', 'slope', 'drain_area',
'basin_drain_area']
# Output File keys WITH a threshold in the filename
thresholdkeys = ['thresh_streams', 'thresh_basin_streams', 'order', 'tree',
'coord', 'net', 'watersheds', 'basin_outline',
'corrected_points']
filekeys = non_thresholdkeys + thresholdkeys
# Create file paths for the output file management
imgs = {}
for k in filekeys:
base = os.path.join(output, k)
# Add the threshold to the filename if need be
if k in thresholdkeys:
base = os.path.join(temp, k)
base += '_thresh_{}'.format(threshold)
# Watchout for shapefiles
if 'points' in k:
imgs[k] = base + '.shp'
# Files we need for streamflow
elif k in ['coord', 'tree']:
imgs[k] = base + '.dat'
else:
imgs[k] = base + '.tif'
# This file if it already exists causes problems
if os.path.isfile(imgs['net']):
out.msg("Removing pre-existing stream network file...")
os.remove(imgs['net'])
# If we rerun we don't want to run steps 1-3 again
if rerun:
out.warn("Performing a rerun, assuming files for flow direction and"
" accumulation exist...")
else:
move_forward = confirm_norerun(non_thresholdkeys, imgs)
if move_forward:
# 1. Pit Remove in order to fill the pits in the DEM
pitremove(demfile, outfile=imgs['filled'], nthreads=nthreads)
# 2. D8 Flow Directions in order to compute the flow direction in
# each DEM cell
calcD8Flow(imgs['filled'], d8dir_file=imgs['flow_dir'],
d8slope_file=imgs['slope'],
nthreads=nthreads)
# 3. D8 Contributing Area so as to compute the drainage area in
# each DEM cell
calcD8DrainageArea(imgs['flow_dir'], areaD8_out=imgs['drain_area'],
nthreads=nthreads)
else:
out.msg("Please use the '--rerun' flag to perform a rerun.\n")
sys.exit()
##########################################################################
# This section and below gets run every call. (STEPS 4-8)
##########################################################################
# 4. Stream Definition by Threshold, in order to extract a first version of
# the stream network
defineStreamsByThreshold(imgs['drain_area'],
threshold_streams_out=imgs['thresh_streams'],
threshold=threshold, nthreads=nthreads)
# 5. Move Outlets to Streams, so as to move the catchment outlet point on
# one of the DEM cells identified by TauDEM as belonging to the stream
# network
outlets_2_streams(imgs['flow_dir'], imgs['thresh_streams'], pour_points,
new_pour_points=imgs['corrected_points'],
nthreads=nthreads)
# 6. D8 Contributing Area again, but with the catchment outlet point as
# additional input data
calcD8DrainageAreaBasin(imgs['flow_dir'], imgs['corrected_points'],
areaD8_out=imgs['basin_drain_area'],
nthreads=nthreads)
# 7. Stream Definition by Threshold again, but with the catchment outlet
# point as additional input data
defineStreamsByThreshold(imgs['basin_drain_area'],
threshold_streams_out=imgs['thresh_basin_streams'], # noqa
threshold=threshold,
nthreads=nthreads)
# 8. Stream Reach And Watershed
delineate_streams(demfile, imgs['flow_dir'], imgs['basin_drain_area'],
imgs['thresh_basin_streams'], imgs['corrected_points'],
stream_orderfile=imgs['order'], treefile=imgs['tree'],
coordfile=imgs['coord'], netfile=imgs['net'],
wfile=imgs['watersheds'], nthreads=nthreads)
# Output the shapefiles of the watershed
wshp = produce_shapefiles(imgs['watersheds'], imgs['corrected_points'],
output_dir=output)
if out_streams:
output_streamflow(imgs, threshold, wshp, temp=temp,
output_dir=os.path.join(output, 'streamflow'))
if __name__ == '__main__':
main()
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319,
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416,
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284,
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220,
220,
4269,
3127,
628,
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220,
220,
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25,
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220,
220,
220,
220,
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220,
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62,
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82,
25,
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276,
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220,
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220,
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12797,
62,
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25,
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287,
257,
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220,
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262,
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286,
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198,
220,
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25,
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285,
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87,
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198,
220,
220,
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628,
220,
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220,
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67,
23,
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8,
198,
220,
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400,
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8,
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8,
198,
220,
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11,
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28,
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327,
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15,
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92,
532,
296,
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18,
92,
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198,
220,
220,
220,
220,
220,
220,
220,
288,
23,
11125,
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11,
198,
220,
220,
220,
220,
220,
220,
220,
11387,
62,
5532,
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11,
198,
220,
220,
220,
220,
220,
220,
220,
12797,
62,
13033,
11,
198,
220,
220,
220,
220,
220,
220,
220,
649,
62,
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62,
13033,
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220,
1057,
62,
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34,
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299,
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28,
77,
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8,
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35,
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259,
7,
67,
23,
11125,
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11,
34164,
62,
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5289,
62,
76,
2668,
11,
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35,
23,
62,
448,
28,
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11,
198,
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220,
220,
220,
220,
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220,
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220,
49154,
1303,
21,
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220,
220,
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23,
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278,
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757,
11,
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351,
262,
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434,
16615,
966,
355,
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220,
220,
3224,
5128,
1366,
628,
220,
220,
220,
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14542,
25,
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220,
220,
220,
220,
220,
220,
220,
288,
23,
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25,
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23,
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4571,
2939,
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220,
220,
220,
220,
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220,
34164,
62,
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2668,
25,
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2173,
326,
423,
587,
3888,
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220,
220,
220,
220,
220,
220,
1989,
35,
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25,
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5072,
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36024,
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1989,
2939,
326,
17624,
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220,
220,
220,
220,
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25,
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329,
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21749,
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220,
220,
37227,
198,
220,
220,
220,
503,
13,
19662,
7203,
9771,
3129,
803,
37664,
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12797,
2173,
9313,
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198,
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220,
220,
2198,
62,
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67,
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220,
220,
327,
12740,
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533,
324,
23,
532,
79,
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15,
92,
532,
78,
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16,
92,
532,
324,
23,
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17,
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23,
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220,
220,
220,
220,
220,
220,
220,
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220,
220,
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34164,
62,
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76,
2668,
11,
198,
220,
220,
220,
220,
220,
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220,
220,
220,
220,
220,
220,
220,
1989,
35,
23,
62,
448,
8,
628,
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220,
220,
1057,
62,
28758,
7,
34,
12740,
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299,
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82,
28,
77,
16663,
82,
8,
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4299,
46925,
378,
62,
5532,
82,
7,
9536,
11,
288,
23,
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15908,
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34164,
62,
67,
3201,
62,
20337,
11,
11387,
62,
5532,
82,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
220,
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220,
220,
220,
220,
220,
220,
34164,
62,
448,
5289,
62,
76,
2668,
11,
4269,
62,
2875,
7753,
28,
14202,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5509,
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28,
14202,
11,
6349,
7753,
28,
14202,
11,
2010,
7753,
28,
14202,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
266,
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28,
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299,
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28,
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2599,
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220,
37227,
198,
220,
220,
220,
49154,
1303,
23,
13860,
25146,
843,
21827,
704,
198,
220,
220,
220,
943,
14542,
25,
198,
220,
220,
220,
220,
220,
220,
220,
1357,
25,
3108,
284,
257,
5901,
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2939,
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220,
220,
220,
220,
220,
220,
288,
23,
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25,
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34164,
62,
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25,
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220,
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220,
11387,
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25,
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600,
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34164,
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307,
198,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
220,
220,
319,
262,
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13,
198,
220,
220,
220,
220,
220,
220,
220,
4269,
62,
2875,
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5072,
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220,
220,
220,
220,
220,
220,
6349,
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25,
1892,
1654,
644,
428,
2393,
318,
198,
220,
220,
220,
220,
220,
220,
220,
2010,
7753,
25,
6530,
286,
262,
4263,
284,
5072,
262,
4269,
17336,
13,
198,
220,
220,
220,
220,
220,
220,
220,
266,
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25,
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286,
262,
2939,
284,
5072,
850,
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259,
17336,
13,
198,
220,
220,
220,
220,
220,
220,
220,
299,
16663,
82,
25,
7913,
286,
21758,
284,
779,
329,
285,
21749,
87,
721,
198,
220,
220,
220,
37227,
628,
220,
220,
220,
503,
13,
19662,
7203,
32071,
42640,
82,
290,
4269,
3696,
9313,
8,
628,
220,
220,
220,
1303,
6822,
3108,
19648,
198,
220,
220,
220,
17311,
796,
685,
9536,
11,
288,
23,
11125,
15908,
11,
34164,
62,
67,
3201,
62,
20337,
11,
11387,
62,
5532,
82,
11,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
34164,
62,
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62,
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2668,
60,
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220,
220,
23862,
796,
685,
5532,
62,
2875,
7753,
11,
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11,
6349,
7753,
11,
2010,
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11,
266,
7753,
60,
198,
220,
220,
220,
329,
277,
287,
17311,
25,
198,
220,
220,
220,
220,
220,
220,
220,
2198,
62,
6978,
7,
69,
8,
628,
220,
220,
220,
329,
277,
287,
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25,
198,
220,
220,
220,
220,
220,
220,
220,
2198,
62,
6978,
7,
69,
11,
503,
7753,
28,
17821,
8,
628,
220,
220,
220,
327,
12740,
796,
19203,
5532,
3262,
532,
69,
417,
1391,
15,
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532,
79,
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16,
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532,
324,
23,
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17,
92,
532,
10677,
1391,
18,
92,
532,
585,
1391,
19,
92,
532,
21048,
1391,
20,
92,
6,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
532,
37652,
1391,
21,
92,
532,
3262,
1391,
22,
92,
532,
78,
1391,
23,
92,
532,
86,
1391,
24,
92,
27691,
18982,
7,
198,
220,
220,
220,
220,
220,
220,
220,
1357,
11,
198,
220,
220,
220,
220,
220,
220,
220,
288,
23,
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11,
198,
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220,
220,
220,
220,
220,
220,
34164,
62,
67,
3201,
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220,
220,
220,
220,
220,
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11387,
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82,
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62,
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220,
220,
220,
220,
220,
220,
5509,
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11,
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220,
220,
220,
220,
220,
220,
220,
6349,
7753,
11,
198,
220,
220,
220,
220,
220,
220,
220,
2010,
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198,
220,
220,
220,
220,
220,
220,
220,
34164,
62,
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62,
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34,
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28,
77,
16663,
82,
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628,
198,
4299,
10385,
17,
292,
979,
72,
7,
259,
7753,
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7753,
28,
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2599,
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220,
220,
37227,
198,
220,
220,
220,
38240,
284,
355,
979,
72,
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220,
220,
37227,
198,
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2198,
62,
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4439,
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16624,
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62,
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5072,
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37227,
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25235,
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684,
286,
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427,
499,
7753,
13,
628,
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943,
14542,
25,
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220,
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42640,
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2173,
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341,
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220,
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5072,
62,
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25,
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4067,
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5485,
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37227,
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1303,
6822,
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62,
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276,
62,
13033,
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220,
220,
266,
69,
3672,
796,
28686,
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6624,
705,
834,
12417,
834,
10354,
198,
220,
220,
220,
1388,
3419,
198
] | 2.232222 | 10,434 |
"""Compute entropy
"""
from typing import Union
import numpy
from mlxtk.log import get_logger
def compute_entropy(
natpop: numpy.ndarray, normalize: bool = False
) -> Union[numpy.ndarray, numpy.float64]:
"""Compute the Boltzmann entropy from natural populations.
The entropy is computed using the formula
:math:`S_\\mathrm{B}=-\\sum\\limits_{i}\\lambda_i\\ln(\\lambda_i)`.
Arguments:
natpop (numpy.ndarray): one- or two-dimensional array containing
natural populations
Returns:
Boltzmann entropy
"""
if len(natpop.shape) == 1:
result = 0.0
for lam in natpop:
if lam != 0.0:
result -= lam * numpy.log(lam)
if normalize:
m = natpop.shape[0]
if m == 1:
raise ZeroDivisionError("cannot normalize entropy for m=1")
else:
S_max = numpy.log(m)
result = result / S_max
return result
if len(natpop.shape) == 2:
result = numpy.zeros(natpop.shape[0])
for i in range(natpop.shape[0]):
for lam in natpop[i]:
if lam != 0.0:
result[i] -= lam * numpy.log(lam)
if normalize:
m = natpop.shape[1]
if m == 1:
raise ZeroDivisionError("cannot normalize entropy for m=1")
else:
S_max = numpy.log(m)
result = result / S_max
return result
raise ValueError("natpop must be either 1- or 2-dimensional")
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12924,
1276,
307,
2035,
352,
12,
393,
362,
12,
19577,
4943,
198
] | 2.051114 | 763 |
# Author:
# Romain Bentz (pixis - @hackanddo)
# Website:
# https://beta.hackndo.com
| [
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] | 2.351351 | 37 |
class FreinerConfigurationError(Exception):
"""
Raised when a configuration problem is encountered.
"""
| [
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4848,
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] | 3.625 | 32 |
"""
Script to run all the produce_results scripts in the
validation_tests/xxx/xxx/ directories
"""
import os
import time
import anuga
from anuga import indent
#from anuga.validation_utilities.parameters import alg
#from anuga.validation_utilities.parameters import cfl
args = anuga.get_args()
alg = args.alg
np = args.np
verbose = args.verbose
#---------------------------------
# Get the current svn revision
#---------------------------------
timestamp = time.asctime()
major_revision = anuga.get_version()
try:
# This fails if using git for version control
minor_revision = anuga.get_revision_number()
except:
try:
# This works when using git on unix
minor_revision = os.popen("git show-ref --head -s | head -n1").read().strip()
except:
# This is a fallback position
minor_revision = 'unknown'
#----------------------------------
# Now it is ok to create the latex
# macro file with run parameters
#
# FIXME: THis is a little dangerous as
# this is changed before all the tests
# are run.
#----------------------------------
f = open('saved_parameters.tex', 'w')
#f.write('\\newcommand{\\cfl}{\\UScore{%s}}\n' % str(cfl))
f.write('\\newcommand{\\alg}{\\UScore{%s}}\n' % str(alg))
f.write('\\newcommand{\\majorR}{\\UScore{%s}}\n' % str(major_revision))
f.write('\\newcommand{\\minorR}{\\UScore{%s}}\n' % str(minor_revision))
f.write('\\newcommand{\\timeR}{{%s}}\n' % str(timestamp))
f.close()
#---------------------------------
# Run the tests
#---------------------------------
os.chdir('..')
buildroot = os.getcwd()
Upper_dirs = os.listdir('.')
dir = '.'
Upper_dirs = [name for name in os.listdir(dir) if os.path.isdir(os.path.join(dir, name))]
try:
Upper_dirs.remove('.svn')
except ValueError:
pass
try:
Upper_dirs.remove('reports')
except ValueError:
pass
try:
Upper_dirs.remove('case_studies')
except ValueError:
pass
#print Upper_dirs
#os.chdir('./Tests')
#print 'Tests'
print(Upper_dirs)
time_total = 0.0
test_number = 1
for dir in Upper_dirs:
os.chdir(dir)
print(72 * '=')
print('Directory: ' + dir)
print(72 * '=')
#print 'Changing to', os.getcwd()
dir = '.'
Lower_dirs = [name for name in os.listdir(dir) if os.path.isdir(os.path.join(dir, name))]
try:
Lower_dirs.remove('.svn')
except ValueError:
pass
#print Lower_dirs
for l_dir in Lower_dirs:
os.chdir(l_dir)
#print os.getcwd()
print(60 * '=')
print('Subdirectory %g: '% (test_number) + l_dir)
test_number += 1
print(60 * '=')
try:
t0 = time.time()
if verbose:
cmd = 'python produce_results.py -alg %s -np %s -v '% (str(alg),str(np))
else:
cmd = 'python produce_results.py -alg %s -np %s '% (str(alg),str(np))
print(2 * indent + 'Running: ' + cmd)
os.system(cmd)
t1 = time.time() - t0
time_total += t1
print(2 * indent + 'That took ' + str(t1) + ' secs')
except:
print(2 * indent + 'Failed running produce_results in ' + os.getcwd())
pass
os.chdir('..')
#print 'Changing to', os.getcwd()
os.chdir('..')
#print 'Changing to', os.getcwd()
os.chdir(buildroot)
print(72 * '=')
print('That took ' + str(time_total) + ' secs')
print(72 * '=')
# go back to reports directory to typeset report
os.chdir('reports')
os.system('python validations_typeset_report.py')
import subprocess
cmd = 'mv validations_report.pdf validations_report_alg_%s.pdf' % (str(alg))
print(cmd)
subprocess.call([cmd], shell=True)
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26933,
28758,
4357,
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8,
628,
628,
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] | 2.36457 | 1,558 |
from . import track
from . import webserver
from . import tracksocketserver
from . import globalconfig
| [
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] | 4.291667 | 24 |
'''
https://www.codeeval.com/open_challenges/113/
'''
import sys
if __name__ == '__main__':
main() | [
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198,
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220,
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3419
] | 2.386364 | 44 |
_ntypes = 0
TYPE_VOID = add_type()
TYPE_INT = add_type()
TYPE_FLOAT = add_type()
TYPE_STRING = add_type()
TYPE_LVALUE = add_type()
TYPE_FUNCTION = add_type()
_funs = dict()
| [
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82,
796,
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628,
628,
198
] | 2.26506 | 83 |
from django.apps import AppConfig
| [
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13,
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1330,
2034,
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628
] | 3.888889 | 9 |
from mojo.UI import CurrentGlyphWindow
from mojo.events import addObserver, removeObserver
import vanilla
class PrevNextGlyph:
"""
ControlBoard
"PrevNextGlyph" demo
Use a Rotary Encoder component to swtich the Current Glyph Window to the previous or next glyphs.
After removing the code for the sample window, this script could be used as a Startup Script
"""
PrevNextGlyph()
| [
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220,
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220,
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220,
220,
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220,
220
] | 2.291667 | 216 |
import pprint
import argparse
from src.knowledgehub.api import KnowledgeHubAPI
if __name__ == "__main__":
main()
| [
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220,
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1388,
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] | 3.025 | 40 |
num1 = int( input() )
num2 = int( input() )
if num1 > 0:
if num2 > 0:
print(num1, num2)
| [
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] | 1.844828 | 58 |
from PyQt5.QtCore import QDate, Qt
p1 = QDate(1996, 4, 2)
p2 = QDate(1994, 6, 13)
dayspassed = p1.daysTo(p2)
print("{0} days have passed since {1} to {2}".format(dayspassed, p1.toString(Qt.ISODate), p2.toString(Qt.ISODate))) | [
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] | 2.121495 | 107 |
import torch
from qtorch import Number, FixedPoint, BlockFloatingPoint, FloatingPoint
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from torch.utils.cpp_extension import load
import os
current_path = os.path.dirname(os.path.realpath(__file__))
quant_cpu = load(
name='quant_cpu',
sources=[
os.path.join(current_path, "quant_cpu/quant_cpu.cpp"),
os.path.join(current_path, "quant_cpu/bit_helper.cpp"),
os.path.join(current_path, "quant_cpu/sim_helper.cpp"),
]
)
if torch.cuda.is_available():
quant_cuda = load(
name='quant_cuda',
sources=[
os.path.join(current_path, "quant_cuda/quant_cuda.cpp"),
os.path.join(current_path, "quant_cuda/bit_helper.cu"),
os.path.join(current_path, "quant_cuda/sim_helper.cu"),
os.path.join(current_path, "quant_cuda/block_kernel.cu"),
os.path.join(current_path, "quant_cuda/float_kernel.cu"),
os.path.join(current_path, "quant_cuda/fixed_point_kernel.cu"),
os.path.join(current_path, "quant_cuda/quant.cu"),
]
)
else:
quant_cuda = quant_cpu
__all__ = ['fixed_point_quantize', 'block_quantize', 'float_quantize', "quantizer"]
def quantizer(forward_number=None, backward_number=None,
forward_rounding="stochastic", backward_rounding="stochastic",
clamping_grad_zero=False, backward_hooks=[]):
"""
Creates a quantization function to support quantizing forward and backward process differently.
Args:
- :param: forward_number (qtorch.Number, optional) : the number format used for forward quantization.
if is None, the quantization would be a identity mapping.
- :param: backward_number (qtorch.Number, optional) : the number format used for backward quantization.
if is None, the quantization would be a identity mapping.
- :param: forward_rounding (string) : rounding mode, \"stochastic\" or \"nearest\" (default: \"stochastic\")
- :param: backward_rounding (string) : rounding mode, \"stochastic\" or \"nearest\" (default: \"stochastic\")
- :param: clamping_grad_zero (bool) : zero out the gradient of numbers that are being clamped during forward propagation.
currently requires forward_number to be a fixed point number.
- :param: backward_hooks (iterable) : iterable of functions that will be applied to gradients before backward quantization.
For example, this can be used to support custom scaling.
Returns:
A quantization function as specified (torch.Tensor -> torch.Tensor)
"""
for rounding in [forward_rounding, backward_rounding]:
assert rounding in ["stochastic", "nearest"], "invalid rounding type {:s}".format(rounding)
for num in [forward_number, backward_number]:
if num != None: assert isinstance(num, Number)
if clamping_grad_zero==False:
if forward_rounding=="nearest":
if type(forward_number)==BlockFloatingPoint:
forward_quant = lambda x, quant_module: quant_module.block_quantize_nearest(x, forward_number.wl, forward_number.dim)
elif type(forward_number)==FixedPoint:
forward_quant = lambda x, quant_module: quant_module.fixed_point_quantize_nearest(x, forward_number.wl,
forward_number.fl, forward_number.clamp,
forward_number.symmetric)
elif type(forward_number)==FloatingPoint:
forward_quant = lambda x, quant_module: quant_module.float_quantize_nearest(x, forward_number.man, forward_number.exp)
elif forward_rounding=="stochastic":
if type(forward_number)==BlockFloatingPoint:
forward_quant = lambda x, quant_module: quant_module.block_quantize_stochastic(x, forward_number.wl, forward_number.dim)
elif type(forward_number)==FixedPoint:
forward_quant = lambda x, quant_module: quant_module.fixed_point_quantize_stochastic(x, forward_number.wl, forward_number.fl,
forward_number.clamp, forward_number.symmetric)
elif type(forward_number)==FloatingPoint:
forward_quant = lambda x, quant_module: quant_module.float_quantize_stochastic(x, forward_number.man, forward_number.exp)
else:
if type(forward_number)==FixedPoint or forward_number==None:
assert forward_number==None or forward_number.clamp == True, "must use clamping if zeroing out clamped gradient"
if forward_rounding=="nearest":
forward_quant = lambda x, quant_module: quant_module.fixed_point_quantize_nearest_mask(x, forward_number.wl, forward_number.fl, forward_number.symmetric)
elif forward_rounding=="stochastic":
forward_quant = lambda x, quant_module: quant_module.fixed_point_quantize_stochastic_mask(x, forward_number.wl, forward_number.fl, forward_number.symmetric)
else:
raise ValueError("zeroing clamping gradient only support fixed point.")
if backward_rounding=="nearest":
if type(backward_number)==BlockFloatingPoint:
backward_quant = lambda a, quant_module: quant_module.block_quantize_nearest(a, backward_number.wl, backward_number.dim)
elif type(backward_number)==FixedPoint:
backward_quant = lambda a, quant_module: quant_module.fixed_point_quantize_nearest(a, backward_number.wl, backward_number.fl,
backward_number.clamp, backward_number.symmetric)
elif type(backward_number)==FloatingPoint:
backward_quant = lambda a, quant_module: quant_module.float_quantize_nearest(a, backward_number.man, backward_number.exp)
elif backward_rounding=="stochastic":
if type(backward_number)==BlockFloatingPoint:
backward_quant = lambda a, quant_module: quant_module.block_quantize_stochastic(a, backward_number.wl, backward_number.dim)
elif type(backward_number)==FixedPoint:
backward_quant = lambda a, quant_module: quant_module.fixed_point_quantize_stochastic(a, backward_number.wl, backward_number.fl,
backward_number.clamp, backward_number.symmetric)
elif type(backward_number)==FloatingPoint:
backward_quant = lambda a, quant_module: quant_module.float_quantize_stochastic(a, backward_number.man, backward_number.exp)
if clamping_grad_zero == False:
else:
return Rounding.apply
def fixed_point_quantize(x, wl, fl, clamp=True, symmetric=False, rounding="stochastic"):
"""
Quantize a single precision Floating Point into low-precision Fixed Point
Args:
- :param: `x` (torch.Tensor) : the single precision number to be quantized
- :param: `wl` (int) : word length of the fixed point number being simulated
- :param: `fl` (int) : fractional length of the fixed point number being simulated
- :param: `clamp` (bool, optional) : clamp input numbers into representable range. if false,
the quantization will only simulate the effect on precision
- :param: `symmetric` (bool, optional) : discard the minimum representable number to make the representable
range symmetric
- :param: `rounding` (string) : rounding mode, \"stochastic\" or \"nearest\" (default: \"stochastic\")
Returns:
- a quantized low-precision block floating point number (torch.Tensor)
"""
assert isinstance(x, torch.Tensor)
assert rounding in ["stochastic", "nearest"]
assert_wl_fl(wl, fl)
quant_module = get_module(x)
if rounding == "nearest":
out = quant_module.fixed_point_quantize_nearest(x.contiguous(), wl, fl, clamp, symmetric)
elif rounding == "stochastic":
out = quant_module.fixed_point_quantize_stochastic(x.contiguous(), wl, fl, clamp, symmetric)
return out
def block_quantize(x, wl, dim=-1, rounding="stochastic"):
"""
Quantize a single precision Floating Point into low-precision Block Floating Point
Args:
- :param: `x` (torch.Tensor) : the single precision number to be quantized
- :param: `wl` (int) : word length of the block floating point number being simulated
- :param: `rounding` (string) : rounding mode, \"stochastic\" or \"nearest\"
Returns:
- a quantized low-precision block floating point number (torch.Tensor)
"""
assert isinstance(x, torch.Tensor), "x is not a single precision Floating Point Tensor"
assert rounding in ["stochastic", "nearest"], "invalid rounding mode, {}".format(rounding)
quant_module = get_module(x)
if rounding=="nearest":
out = quant_module.block_quantize_nearest(x.contiguous(), wl, dim)
elif rounding=="stochastic":
out = quant_module.block_quantize_stochastic(x.contiguous(), wl, dim)
return out
def float_quantize(x, exp, man, rounding="stochastic"):
"""
Quantize a single precision Floating Point into low-precision Floating Point
Args:
- :attr: `x` (torch.Tensor) : the single precision number(torch.Tensor) to be quantized
- :attr: `exp` (int) : number of bits allocated for exponent
- :attr: `man` (int) : number of bits allocated for mantissa, not counting the virtual bit
- :attr: `rounding` (string) : rounding mode, \"stochastic\" or \"nearest\"
Returns:
- a quantized low-precision floating point number (torch.Tensor)
"""
assert isinstance(x, torch.Tensor), "x is not a single precision Floating Point Tensor"
assert rounding in ["stochastic", "nearest"], "invalid rounding mode, {}".format(rounding)
quant_module = get_module(x)
if rounding=="nearest":
out = quant_module.float_quantize_nearest(x.contiguous(), man, exp)
elif rounding=="stochastic":
out = quant_module.float_quantize_stochastic(x.contiguous(), man, exp)
return out
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] | 2.414916 | 4,331 |
"""
Base class and common constants needed for pylmod tests
"""
import os
from unittest import TestCase
class BaseTest(TestCase):
"""
Base class with convenient constants and URL endpoints for pylmod testing.
"""
# This should be removed if we end up with common methods, but for
# now they are just common attributes.
# pylint: disable=too-few-public-methods
DATA_ROOT = os.path.join(
os.path.dirname(os.path.realpath(__file__)),
'data'
)
CERT = os.path.join(DATA_ROOT, 'certs', 'test_cert.pem')
URLBASE = 'https://testingstuff/'
GRADEBOOK_REGISTER_BASE = URLBASE + 'service/gradebook/'
MEMBERSHIP_REGISTER_BASE = URLBASE + 'service/membership/'
GBUUID = 'STELLAR:/project/testingstuff'
CUUID = '/project/testingstuff'
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] | 2.656667 | 300 |
#!/usr/bin/env python3
from taptaptap3.proc import plan, ok, out
plan(tests=10)
ok("Starting the program")
ok("Starting the engine")
ok("Find the object")
ok("Transport object to target")
ok("Check for existing fire")
ok("Place it beneath the desk")
ok("Search for fire extinguisher")
ok("Extinguish fire")
ok("Put fire extinguisher back")
ok("Terminate")
out()
## validity: 0
## ok testcases: 10 / 10
## bailout: no
## stderr: Find the object
| [
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] | 2.881988 | 161 |
import unittest
from classes.nag import Nag
if __name__ == '__main__':
unittest.main() | [
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] | 2.676471 | 34 |
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
import unittest
import torch
from hydra.experimental import compose, initialize_config_module
from vissl.utils.hydra_config import convert_to_attrdict
from vissl.utils.misc import merge_features
from vissl.utils.test_utils import (
gpu_test,
in_temporary_directory,
run_integration_test,
)
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] | 3.302013 | 149 |
from src.PCA import *
from src.generate_data import *
from src.generate_picture import *
from src.dimensionality_reduction_image import *
if __name__ == '__main__':
main()
| [
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] | 2.983607 | 61 |
import pybullet
import time
import numpy as np
import random
# np.random.seed(5)
# random.seed(5)
import sys
import os
import argparse
import csv
from scipy.spatial.transform import Rotation
from collect_pose_data import PoseDataCollector
sys.path.insert(1, '../utils/')
from coord_helper import *
from data_helper import *
from collision_helper import *
import bullet_client as bc
sys.path.insert(1, '../lin_my/')
from classifier_dataset_torch import ClassifierDataset
from scipy.spatial import KDTree
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("--home_dir_data", default="../data")
parser.add_argument("--hook_name", default='')
parser.add_argument("--sherlock", action='store_true')
parser.add_argument("--obj_cat_split_id", type=int, default=-1)
args = parser.parse_args()
obj_cat_split_id = int(args.obj_cat_split_id)
if args.sherlock:
args.home_dir_data = '/scratch/groups/bohg/hang'
assert args.hook_name != ''
assert obj_cat_split_id >= 0
data_dir = os.path.join(args.home_dir_data, 'geo_data')
labels_folder_dir = os.path.join(args.home_dir_data, 'geo_data/labels/')
exclude_dir = os.path.join(args.home_dir_data, 'exclude')
pos_collection_result_folder_dir = os.path.join(args.home_dir_data, 'collection_result_pene_big_pos_new')
collection_result_folder_dir = os.path.join(args.home_dir_data, 'collection_result')
neg_collection_result_folder_dir = os.path.join(args.home_dir_data, 'collection_result_pene_big_neg_new')
pos_labels_dir = os.path.join(pos_collection_result_folder_dir, 'labels')
neg_labels_dir = os.path.join(neg_collection_result_folder_dir, 'labels')
mkdir_if_not(pos_collection_result_folder_dir)
mkdir_if_not(neg_collection_result_folder_dir)
mkdir_if_not(pos_labels_dir)
mkdir_if_not(neg_labels_dir)
all_hook_name, all_hook_urdf, all_object_name, all_object_urdf = load_all_hooks_object_w_split_id(obj_cat_split_id, data_dir, exclude_dir, labels_folder_dir, True, True, with_wall=False)
p_id = bc.BulletClient(connection_mode=pybullet.DIRECT)
cp_result_folder_dir = os.path.join(args.home_dir_data, 'dataset_cp')
train_list_dir = os.path.join(cp_result_folder_dir, 'labels', 'train_list.txt')
test_list_dir = os.path.join(cp_result_folder_dir, 'labels', 'test_list.txt')
train_set = ClassifierDataset(args.home_dir_data, train_list_dir, False, split='train', with_wall=False, one_per_pair=True)
test_set = ClassifierDataset(args.home_dir_data, test_list_dir, False, split='test', with_wall=False, one_per_pair=True)
if not os.path.exists(neg_collection_result_folder_dir):
os.mkdir(neg_collection_result_folder_dir)
collector = PeneDataCollector(p_id)
ct = 0
print('result file names', len(train_set.all_result_file_names), len(test_set.all_result_file_names))
for i, hook_name in enumerate(all_hook_name):
if args.hook_name != '' and args.hook_name != hook_name:
continue
out_pos_labels_dir = os.path.join(pos_labels_dir, '{}.txt'.format(hook_name))
out_neg_labels_dir = os.path.join(neg_labels_dir, '{}.txt'.format(hook_name))
# if os.path.exists(out_pos_labels_dir) and os.path.exists(out_neg_labels_dir):
# print('skip', hook_name)
# continue
hook_urdf = all_hook_urdf[i]
hook_bullet_id, hook_scaling = collector.init_hook(hook_urdf)
hook_world_pos_offset = get_hook_wall_offset(hook_urdf)
hook_pc_dir = get_numpy_dir_from_urdf(hook_urdf)
hook_world_pos = collector.get_hook_world_pos(hook_bullet_id, hook_world_pos_offset)
hook_pc = np.load(hook_pc_dir)
hook_tree = KDTree(hook_pc[:, :3], leafsize=1000)
num_pos_dict = {}
num_neg_dict = {}
for j, object_name in enumerate(all_object_name):
# if not 'daily_object' in object_name:
# continue
result_file_name = hook_name + '_' + object_name
if (not result_file_name in train_set.all_result_file_names) \
and (not result_file_name in test_set.all_result_file_names):
continue
object_urdf = all_object_urdf[j]
object_pc_dir = get_numpy_dir_from_urdf(object_urdf)
object_pc = np.load(object_pc_dir)
print(result_file_name)
neg_out_dir = os.path.join(neg_collection_result_folder_dir, result_file_name + '.txt')
pos_out_dir = os.path.join(pos_collection_result_folder_dir, result_file_name + '.txt')
# result_dir = os.path.join(collection_result_folder_dir, result_file_name+ '.txt')
# if not os.path.isfile(result_dir):
# continue
# result_file_poses = load_result_file(result_dir)
# if result_file_poses.shape[0] == 0:
# continue
# if os.path.isfile(out_dir):
# continue
ct += 1
object_bullet_id = collector.p.loadURDF(object_urdf, basePosition=[0, 0, 2], baseOrientation=[0, 0, 0, 1], globalScaling=1, useFixedBase=False)
object_scaling = collector.p.getCollisionShapeData(object_bullet_id, -1)[0][3][0]
pos_result_arr, neg_result_arr = collector.collect_pene_data_one_hook_object(hook_bullet_id, object_bullet_id, hook_urdf, object_urdf, hook_scaling, object_scaling, hook_world_pos,
hook_pc, object_pc, hook_tree, None)
num_pos_dict[result_file_name] = len(pos_result_arr)
num_neg_dict[result_file_name] = len(neg_result_arr)
print(len(pos_result_arr), len(neg_result_arr), result_file_name)
with open(pos_out_dir, 'w+') as f:
for result in pos_result_arr:
f.write(comma_separated(result) + '\n')
with open(neg_out_dir, 'w+') as f:
for result in neg_result_arr:
f.write(comma_separated(result) + '\n')
# print(pos_out_dir, neg_out_dir)
collector.p.removeBody(object_bullet_id)
if (ct + 1) % 30 == 0:
print('reset')
collector.p.disconnect()
p_id = bc.BulletClient(connection_mode=pybullet.DIRECT)
collector = PeneDataCollector(p_id)
hook_bullet_id, hook_scaling = collector.init_hook(hook_urdf)
out_pos_labels_dir = os.path.join(pos_labels_dir, '{}.txt'.format(hook_name))
out_neg_labels_dir = os.path.join(neg_labels_dir, '{}.txt'.format(hook_name))
collector.p.removeBody(hook_bullet_id)
dict_to_csv(out_pos_labels_dir, num_pos_dict)
dict_to_csv(out_neg_labels_dir, num_neg_dict)
# for j in range(20000):
# collector.p.stepSimulation()
# time.sleep(1./240.) | [
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197,
197,
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62,
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62,
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7,
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1424,
62,
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11,
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62,
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8,
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7,
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62,
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62,
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62,
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11,
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62,
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62,
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8,
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198,
197,
197,
2,
640,
13,
42832,
7,
16,
19571,
16102,
2014
] | 2.422729 | 2,543 |
# Software License Agreement (BSD License)
#
# Copyright (c) 2013, Open Source Robotics Foundation, Inc.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above
# copyright notice, this list of conditions and the following
# disclaimer in the documentation and/or other materials provided
# with the distribution.
# * Neither the name of Open Source Robotics Foundation, Inc. nor
# the names of its contributors may be used to endorse or promote
# products derived from this software without specific prior
# written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
# COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
from __future__ import print_function
from .release_file import ReleaseFile
| [
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62,
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198,
198,
6738,
764,
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62,
7753,
1330,
13868,
8979,
628
] | 3.569358 | 483 |
import avanza
| [
11748,
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628
] | 3.75 | 4 |
import unittest
from rsgis import Metadata
import os
if __name__ == '__main__':
unittest.main()
| [
11748,
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220,
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555,
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] | 2.675 | 40 |
from ma import ma
from models.entities import Todo
| [
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871,
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] | 3.714286 | 14 |
from ulauncher_virtualbox.VirtualboxExtension import VirtualboxExtension
if __name__ == '__main__':
VirtualboxExtension().run()
| [
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] | 3.243902 | 41 |
#! -*- coding:utf8 -*-
# This file used to calculate avg temperature of everyday
import sys
import numpy as np
import pandas as pd
from filter import check_threshold, check_data_integrity, check_column_name
def calculate_average(data, column_names):
"""计算温度相关列的平均值
:param data:
:param column_names:
:return:
"""
temperatures = []
grouped_data = data.groupby('day')
for name, group in grouped_data:
temperature = {}
temperature['date'] = name
for cn in column_names:
_cal_ave(temperature, group, cn)
temperatures.append(temperature.copy())
temperatures_dataframe = pd.DataFrame(temperatures)
return temperatures_dataframe
def _cal_ave(temperature, df, column_name):
""" 计算指定列的平均值
:return:
"""
# check today temperature is valid
if check_column_name(column_name) and check_threshold(df, 50, column_name) and check_data_integrity(df, None):
temperature[column_name] = df[column_name].mean()
else:
temperature[column_name] = np.NaN
if __name__ == '__main__':
input, output = sys.argv[1:]
data, column_names = read(input)
# 日期不参与计算
column_names.remove('day')
tem_df = calculate_average(data, column_names)
tem_df.to_csv(output)
| [
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106,
245,
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220,
220,
220,
5721,
62,
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13,
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820,
11537,
198,
220,
220,
220,
2169,
62,
7568,
796,
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62,
23913,
7,
7890,
11,
5721,
62,
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8,
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220,
220,
220,
2169,
62,
7568,
13,
1462,
62,
40664,
7,
22915,
8,
198
] | 2.345521 | 547 |
import os
import logging
from dataclasses import dataclass, field
from typing import Optional
import pandas as pd
import torch
from tqdm import tqdm
from transformers import T5Tokenizer, BartTokenizer, HfArgumentParser
logger = logging.getLogger(__name__)
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
"""
task: str = field(
metadata={"help":
"Which task 'qa', 'qg', 'e2e_qg', 'ans_ext', 'multi'. 'multi' means 'qa', 'qg', 'ans_ext' tasks"},
)
model_type: str = field(metadata={"help": "One of 't5', 'bart'"})
dataset_path: Optional[str] = field(
default='~/Desktop/aqa/data/squad',
metadata={'help': 'data directory for train and validation csv files.'}
)
train_file_name: Optional[str] = field(
default=None,
metadata={"help": "name for cached train dataset"},
)
valid_file_name: Optional[str] = field(
default=None,
metadata={"help": "name for cached valid dataset"},
)
valid_for_qg_only: bool = field(
default=False,
metadata={"help": "For multitask dataset valid split should contain only qg task or all tasks."}
)
qg_format: Optional[str] = field(
default='highlight_qg_format',
metadata={'help': "How to format inputs for que generation, 'highlight_qg_format' or 'prepend_qg_format'"},
)
max_source_length: Optional[int] = field(
default=512,
metadata={'help': 'Max input length for the source text'},
)
max_target_length: Optional[int] = field(
default=64,
metadata={'help': 'Max input length for the target text'},
)
if __name__ == "__main__":
main()
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] | 2.549356 | 699 |
# -*- coding: UTF-8 -*-
import os
from flask import Flask, redirect, url_for
from app.config import DevelopmentConfig, ProductionConfig
__author__ = 'lpe234'
# create application
bms_app = Flask(__name__)
if os.environ.get('BMS_ENV') == 'PRODUCTION':
bms_app.config.from_object(ProductionConfig)
else:
bms_app.config.from_object(DevelopmentConfig)
# db
from app.models import db
db.init_app(bms_app)
# login
from app.login_utils import login_manager
login_manager.init_app(bms_app)
# main blueprint
from app.main_views import main
bms_app.register_blueprint(main, url_prefix='/main/')
# api blueprint
from app.api import api
bms_app.register_blueprint(api, url_prefix='/api/')
@bms_app.route('/')
if __name__ == '__main__':
bms_app.run()
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] | 2.687719 | 285 |
#!/usr/bin/env python3
import base64
import yaml
import sys
import re
import os
import random
import string
import subprocess
import json
from d3des import encrypt as d3des
try:
from passlib.hash import md5_crypt, sha256_crypt, sha512_crypt
from cryptography.hazmat.primitives import serialization as crypto_serialization
from cryptography.hazmat.primitives.asymmetric import rsa
from cryptography.hazmat.backends import default_backend as crypto_default_backend
except ImportError:
pass
###################################################################
#
# YAML related utilities
#
###################################################################
yaml_include_path = []
secrets_file = '_secrets_file_'
key_store = '_ssh_key_store_'
yaml_pp_vars = dict(os.environ)
valid_re = re.compile(r'^[_A-Za-z][_A-Za-z0-9]*$')
include_res = [ re.compile(r'^(\s*)#\s*include\s+') , re.compile(r'^(\s*-\s*)#\s*include\s+')]
include_type = re.compile(r'\s*--(raw|bin)\s+')
keygen_re = re.compile(r'(.*)\$KEYGEN:([A-Za-z][A-Za-z0-9]*)(:[^\$]*|)\$')
pwgen_re = re.compile(r'(.*)\$PWGEN:([A-Za-z][A-Za-z0-9]*)(:[^\$]*|)\$')
define_re = re.compile(r'^\s*#\s*define\s+([_A-Za-z][_A-Za-z0-9]*)\s*')
ifdef_re = re.compile(r'^\s*#\s*ifdef\s+([_A-Za-z][_A-Za-z0-9]*)\s*')
ifndef_re = re.compile(r'^\s*#\s*ifndef\s+([_A-Za-z][_A-Za-z0-9]*)\s*')
else_re = re.compile(r'^\s*#\s*else\s*')
endif_re = re.compile(r'^\s*#\s*endif\s*')
exec_re = re.compile(r'^(\s*)#\s*exec\s+(.*)$')
###################################################################
#
# Main command line
#
###################################################################
if __name__ == '__main__':
from argparse import ArgumentParser, Action
cli = ArgumentParser(prog='ypp',description='YAML file pre-processor')
cli.add_argument('-I','--include', help='Add Include path', action='append')
cli.add_argument('-D','--define', help='Add constant', action='append')
cli.add_argument('-y','--yaml', help='Parse YAML',action='store_true')
cli.add_argument('-p','--preproc', help='Use pre-processor when parsing yaml',action='store_true')
cli.add_argument('file', help='YAML file to parse')
args = cli.parse_args()
yparse_cmd(args)
sys.exit()
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62,
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537,
72,
13,
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537,
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331,
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62,
28758,
7,
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8,
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220,
25064,
13,
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198
] | 2.5 | 890 |
"""empty message
Revision ID: 1b5bbc75de44
Revises: 1531599e3534
Create Date: 2014-01-02 20:52:28.389571
"""
# revision identifiers, used by Alembic.
revision = '1b5bbc75de44'
down_revision = '1531599e3534'
from alembic import op
import sqlalchemy as sa
from kozmic.models import db
| [
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1330,
20613,
628,
198
] | 2.54386 | 114 |
import os
import sys
import attrdict
import ssl
import json
import zipfile
import random
import traceback
import gnupg
import base64
from Crypto.Cipher import AES, PKCS1_OAEP
from Crypto.PublicKey import RSA
from web3.auto import w3
from eth_account.messages import defunct_hash_message
from shutil import copyfile
keccak256 = w3.soliditySha3
debug = True
if __name__ == '__main__':
sconeDir = '/scone'
iexecOutDir = '/iexec_out'
determinismFile = 'determinism.iexec'
callbackFile = 'callback.iexec'
WriteEnclaveSign(sconeDir + '/' + determinismFile)
copyfile(sconeDir + '/' + callbackFile, iexecOutDir + '/' + callbackFile)
| [
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67,
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220,
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87,
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6,
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220,
220,
220,
19430,
4834,
44281,
11712,
7,
1416,
505,
35277,
1343,
31051,
6,
1343,
3416,
1042,
8979,
8,
628,
220,
220,
220,
4866,
7753,
7,
1416,
505,
35277,
1343,
31051,
6,
1343,
23838,
8979,
11,
37941,
87,
721,
7975,
35277,
1343,
31051,
6,
1343,
23838,
8979,
8,
198
] | 2.811966 | 234 |
from lightning_baselines3.off_policy_models.off_policy_model import OffPolicyModel
from lightning_baselines3.off_policy_models.dqn import DQN
from lightning_baselines3.off_policy_models.td3 import TD3
from lightning_baselines3.off_policy_models.ddpg import DDPG
from lightning_baselines3.off_policy_models.sac import SAC
| [
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62,
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62,
27530,
13,
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1330,
311,
2246,
198
] | 3.21 | 100 |
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import train_test_split
from tensorflow.keras.utils import to_categorical
default_folds = 5
default_test_size = 0.2 | [
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] | 3.229508 | 61 |
"""
Airmap package init code
AirMapSDK
Created by AirMap Team on 6/28/16.
Copyright (c) 2016 AirMap, Inc. All rights reserved.
"""
import airdefs
import connect
import statusAPI
import flightAPI
import drone
import log
import telemetryAPI
import alertsAPI
| [
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] | 3.283951 | 81 |
import subprocess
from re import search
from .methods import config_read
| [
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] | 3.8 | 20 |
'''input
3
1 1 1
2 2 2
3 3 3
27
6
3 14 159 2 6 53
58 9 79 323 84 6
2643 383 2 79 50 288
87
2
1 5
2 4
3 6
3
'''
# -*- coding: utf-8 -*-
# AtCoder Beginner Contest
# Problem C
if __name__ == '__main__':
n = int(input())
a = sorted(list(map(int, input().split())))
b = list(map(int, input().split()))
c = sorted(list(map(int, input().split())))
count = 0
# See:
# https://img.atcoder.jp/arc084/editorial.pdf
# https://docs.python.jp/3/library/bisect.html
# https://beta.atcoder.jp/contests/abc077/submissions/1740764
from bisect import bisect_left
from bisect import bisect_right
for number in b:
a_count = bisect_left(a, number)
c_count = n - bisect_right(c, number)
count += a_count * c_count
print(count)
| [
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220,
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13,
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66,
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14,
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19,
14,
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498,
13,
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220,
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13,
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18,
14,
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14,
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2998,
22,
14,
7266,
8481,
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1558,
30120,
2414,
201,
198,
220,
220,
220,
422,
47457,
478,
1330,
47457,
478,
62,
9464,
201,
198,
220,
220,
220,
422,
47457,
478,
1330,
47457,
478,
62,
3506,
201,
198,
201,
198,
220,
220,
220,
329,
1271,
287,
275,
25,
201,
198,
220,
220,
220,
220,
220,
220,
220,
257,
62,
9127,
796,
47457,
478,
62,
9464,
7,
64,
11,
1271,
8,
201,
198,
220,
220,
220,
220,
220,
220,
220,
269,
62,
9127,
796,
299,
532,
47457,
478,
62,
3506,
7,
66,
11,
1271,
8,
201,
198,
220,
220,
220,
220,
220,
220,
220,
954,
15853,
257,
62,
9127,
1635,
269,
62,
9127,
201,
198,
201,
198,
220,
220,
220,
3601,
7,
9127,
8,
201,
198
] | 2.054054 | 407 |
#! python3
# __author__ = "YangJiaHao"
# date: 2018/3/13
if __name__ == '__main__':
so = Solution()
res = so.restoreIpAddresses('010010')
print(res)
| [
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] | 2.232877 | 73 |
# Generated by Django 3.2.7 on 2021-09-27 16:18
from django.db import migrations, models
| [
2,
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319,
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6738,
42625,
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13,
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602,
11,
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628
] | 2.84375 | 32 |
import requests
from bs4 import BeautifulSoup | [
11748,
7007,
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6738,
275,
82,
19,
1330,
23762,
50,
10486
] | 4.090909 | 11 |
#!/usr/bin/python3
import sys
diction={}
for line in sys.stdin:
line=line.strip()
line_val=line.split(',')
bowl,bat,runs,balls=line_val
runs=int(runs)
print(runs)
balls=int(balls)
key=(bowl,bat)
if key in diction:
diction[key][0].append(runs)
diction[key][1].append(balls)
else:
diction[key]=[[],[]]
diction[key][0].append(runs)
diction[key][1].append(balls)
for key in diction.keys();
diction[key][0]=sum(diction[key][0])
diction[key][1]=sum(diction[key][1])
s=sorted(diction,key=func3)
s=sorted(s,key=func2)
s=sorted(s,key=func1,reverse=True)
for k in s:
if diction[k][1]>5:
print('%s,%s,%d,%d' % (k[0],k[1],diction[k][0],diction[k][1]))
| [
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201,
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28,
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28,
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82,
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74,
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60,
29,
20,
25,
201,
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197,
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10786,
4,
82,
11,
4,
82,
11,
4,
67,
11,
4,
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from django.shortcuts import render, redirect, reverse
from django.template import RequestContext
from django.views import View
from django import http
from django.contrib.auth import login, logout, mixins
from django.db import DatabaseError
from django_redis import get_redis_connection
from django.contrib.auth import authenticate
import json, re
from django.conf import settings
from django.core.paginator import Paginator
import logging
from random import randint
from itsdangerous import TimedJSONWebSignatureSerializer as TOKEN
from .models import User, Address
from meiduo_mall.utils.views import LoginRequiredView
from .utils import check_token_to_user, generate_verify_email_url
from celery_tasks.email.tasks import send_verify_email
from celery_tasks.sms.tasks import send_sms_code
from meiduo_mall.utils.response_code import RETCODE
from goods.models import SKU
from carts.utils import merge_cart_cookie_to_redis
from orders.models import OrderInfo
logger = logging.getLogger('django') # 创建日志输出器
# Create your views here.
class UsernameCountView(View):
'''判断用户名是否已经注册'''
class MobileCountView(View):
'''判断手机号是否重复'''
class LogoutView(View):
'''退出登录'''
class UserInfoView(mixins.LoginRequiredMixin, View):
'''用户中心界面'''
class EmailView(mixins.LoginRequiredMixin, View):
'''添加用户邮箱'''
class VerifyEmailView(View):
'''激活邮箱'''
class AddressView(mixins.LoginRequiredMixin, View):
'''用户收获地址'''
def get(self, request):
'''显示用户收货地址界面'''
user = request.user
# 获取当前用户所有的收货地址
address_qs = Address.objects.filter(is_deleted=False, user=user)
address_list = []
for address in address_qs:
address_dict = {
'id': address.id,
'title': address.title,
'receiver': address.receiver,
'province_id': address.province_id,
'province': address.province.name,
'city_id': address.city_id,
'city': address.city.name,
'district_id': address.district_id,
'district': address.district.name,
'place': address.place,
'mobile': address.mobile,
'tel': address.tel,
'email': address.email,
}
address_list.append(address_dict)
context = {
'addresses': address_list,
'default_address_id': user.default_address_id
}
return render(request, 'user_center_site.html', context)
class CreateAddressView(LoginRequiredView):
'''新增收货地址'''
class UpdateDestroyAddressView(LoginRequiredView):
"""修改和删除"""
def put(self, request, address_id):
"""修改地址逻辑"""
# 查询要修改的地址对象
try:
address = Address.objects.get(id=address_id)
except Address.DoesNotExist:
return http.HttpResponseForbidden('要修改的地址不存在')
# 接收
json_dict = json.loads(request.body.decode())
title = json_dict.get('title')
receiver = json_dict.get('receiver')
province_id = json_dict.get('province_id')
city_id = json_dict.get('city_id')
district_id = json_dict.get('district_id')
place = json_dict.get('place')
mobile = json_dict.get('mobile')
tel = json_dict.get('tel')
email = json_dict.get('email')
# 校验
if all([title, receiver, province_id, city_id, district_id, place, mobile]) is False:
return http.HttpResponseForbidden('缺少必传参数')
if not re.match(r'^1[3-9]\d{9}$', mobile):
return http.HttpResponseForbidden('参数mobile有误')
if tel:
if not re.match(r'^(0[0-9]{2,3}-)?([2-9][0-9]{6,7})+(-[0-9]{1,4})?$', tel):
return http.HttpResponseForbidden('参数tel有误')
if email:
if not re.match(r'^[a-z0-9][\w\.\-]*@[a-z0-9\-]+(\.[a-z]{2,5}){1,2}$', email):
return http.HttpResponseForbidden('参数email有误')
# 修改
Address.objects.filter(id=address_id).update(
title=title,
receiver=receiver,
province_id=province_id,
city_id=city_id,
district_id=district_id,
place=place,
mobile=mobile,
tel=tel,
email=email
)
address = Address.objects.get(id=address_id) # 要重新查询一次新数据
# 把新增的地址数据响应回去
address_dict = {
'id': address.id,
'title': address.title,
'receiver': address.receiver,
'province_id': address.province_id,
'province': address.province.name,
'city_id': address.city_id,
'city': address.city.name,
'district_id': address.district_id,
'district': address.district.name,
'place': address.place,
'mobile': address.mobile,
'tel': address.tel,
'email': address.email,
}
return http.JsonResponse({'code': RETCODE.OK, 'errmsg': 'OK', 'address': address_dict})
# 响应
def delete(self, request, address_id):
"""对收货地址逻辑删除"""
try:
address = Address.objects.get(id=address_id)
except Address.DoesNotExist:
return http.HttpResponseForbidden('要删除的地址不存在')
address.is_deleted = True
# address.delete()
address.save()
return http.JsonResponse({'code': RETCODE.OK, 'errmsg': 'OK'})
class DefaultAddressView(LoginRequiredView):
"""设置默认地址"""
def put(self, request, address_id):
"""实现默认地址"""
try:
address = Address.objects.get(id=address_id)
except Address.DoesNotExist:
return http.HttpResponseForbidden('要修改的地址不存在')
user = request.user
user.default_address = address
user.save()
return http.JsonResponse({'code': RETCODE.OK, 'errmsg': 'OK'})
class UpdateTitleAddressView(LoginRequiredView):
"""修改用户收货地址标题"""
class ChangePasswordView(LoginRequiredView):
"""修改密码"""
class UserBrowseHistory(LoginRequiredView):
'''记录商品浏览记录'''
class FindPasswordView(View):
'''找回密码'''
def get(self, request):
'''渲染找回密码界面'''
return render(request, 'find_password.html')
class UsernameExistView(View):
'''验证用户名是否存在'''
class GenerateSmsCodeView(View):
'''发送短信验证码'''
class SMSVerifyView(View):
'''验证短信验证码'''
class InputPasswordView(View):
'''覆盖原密码'''
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13,
1136,
7,
312,
28,
21975,
62,
312,
8,
198,
220,
220,
220,
220,
220,
220,
220,
2845,
17917,
13,
13921,
3673,
3109,
396,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
2638,
13,
43481,
31077,
1890,
37978,
10786,
17358,
223,
46479,
106,
162,
242,
117,
21410,
28839,
108,
161,
251,
222,
38834,
27764,
246,
28839,
101,
11537,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
10545,
236,
98,
162,
242,
114,
198,
220,
220,
220,
220,
220,
220,
220,
33918,
62,
11600,
796,
33918,
13,
46030,
7,
25927,
13,
2618,
13,
12501,
1098,
28955,
198,
220,
220,
220,
220,
220,
220,
220,
3670,
796,
33918,
62,
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198,
220,
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9733,
796,
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312,
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220,
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62,
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628,
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220,
220,
220,
220,
220,
220,
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254,
94,
165,
103,
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198,
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220,
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220,
220,
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477,
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7839,
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62,
312,
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318,
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220,
220,
220,
220,
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220,
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2638,
13,
43481,
31077,
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37978,
10786,
163,
120,
118,
22887,
239,
33232,
227,
27670,
254,
20998,
224,
46763,
108,
11537,
628,
220,
220,
220,
220,
220,
220,
220,
611,
407,
302,
13,
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61,
16,
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18,
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60,
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67,
90,
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92,
3,
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198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
2638,
13,
43481,
31077,
1890,
37978,
10786,
20998,
224,
46763,
108,
24896,
17312,
231,
46237,
107,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
611,
13632,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
407,
302,
13,
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15,
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17,
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18,
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12,
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21,
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22,
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10,
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58,
15,
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24,
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16,
11,
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92,
19427,
3,
3256,
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198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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1441,
2638,
13,
43481,
31077,
1890,
37978,
10786,
20998,
224,
46763,
108,
37524,
17312,
231,
46237,
107,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
611,
3053,
25,
198,
220,
220,
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407,
302,
13,
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60,
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60,
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89,
60,
90,
17,
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19953,
16,
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17,
92,
3,
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198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
2638,
13,
43481,
31077,
1890,
37978,
10786,
20998,
224,
46763,
108,
12888,
17312,
231,
46237,
107,
11537,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
220,
46479,
106,
162,
242,
117,
198,
220,
220,
220,
220,
220,
220,
220,
17917,
13,
48205,
13,
24455,
7,
312,
28,
21975,
62,
312,
737,
19119,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3670,
28,
7839,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
9733,
28,
260,
39729,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
8473,
62,
312,
28,
15234,
924,
62,
312,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1748,
62,
312,
28,
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62,
312,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4783,
62,
312,
28,
17080,
2012,
62,
312,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1295,
28,
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11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5175,
28,
24896,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
13632,
28,
37524,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3053,
28,
12888,
198,
220,
220,
220,
220,
220,
220,
220,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
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796,
17917,
13,
48205,
13,
1136,
7,
312,
28,
21975,
62,
312,
8,
220,
1303,
5525,
99,
223,
34932,
235,
23877,
108,
162,
253,
98,
46237,
95,
31660,
162,
105,
94,
23877,
108,
46763,
108,
162,
235,
106,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
10545,
232,
232,
23877,
108,
161,
95,
252,
21410,
28839,
108,
161,
251,
222,
46763,
108,
162,
235,
106,
161,
241,
235,
41753,
242,
32368,
252,
43889,
119,
198,
220,
220,
220,
220,
220,
220,
220,
2209,
62,
11600,
796,
1391,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
312,
10354,
2209,
13,
312,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
7839,
10354,
2209,
13,
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198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
260,
39729,
10354,
2209,
13,
260,
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11,
198,
220,
220,
220,
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220,
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220,
220,
705,
15234,
924,
62,
312,
10354,
2209,
13,
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924,
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312,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
15234,
924,
10354,
2209,
13,
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924,
13,
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198,
220,
220,
220,
220,
220,
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220,
220,
220,
220,
220,
705,
19205,
62,
312,
10354,
2209,
13,
19205,
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312,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
19205,
10354,
2209,
13,
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13,
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11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
17080,
2012,
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312,
10354,
2209,
13,
17080,
2012,
62,
312,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
17080,
2012,
10354,
2209,
13,
17080,
2012,
13,
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11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
5372,
10354,
2209,
13,
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11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
24896,
10354,
2209,
13,
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198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
37524,
10354,
2209,
13,
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198,
220,
220,
220,
220,
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220,
220,
220,
220,
220,
705,
12888,
10354,
2209,
13,
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198,
220,
220,
220,
220,
220,
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1782,
198,
220,
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220,
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1441,
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13,
41,
1559,
31077,
15090,
6,
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10354,
30826,
34,
16820,
13,
11380,
11,
705,
8056,
19662,
10354,
705,
11380,
3256,
705,
21975,
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2209,
62,
11600,
30072,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
10263,
241,
235,
41753,
242,
628,
220,
220,
220,
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944,
11,
2581,
11,
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62,
312,
2599,
198,
220,
220,
220,
220,
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220,
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37227,
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117,
162,
242,
114,
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112,
100,
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108,
161,
251,
222,
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119,
164,
122,
239,
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254,
165,
247,
97,
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198,
220,
220,
220,
220,
220,
220,
220,
1949,
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198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2209,
796,
17917,
13,
48205,
13,
1136,
7,
312,
28,
21975,
62,
312,
8,
198,
220,
220,
220,
220,
220,
220,
220,
2845,
17917,
13,
13921,
3673,
3109,
396,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
2638,
13,
43481,
31077,
1890,
37978,
10786,
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223,
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254,
165,
247,
97,
21410,
28839,
108,
161,
251,
222,
38834,
27764,
246,
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101,
11537,
628,
220,
220,
220,
220,
220,
220,
220,
2209,
13,
271,
62,
2934,
33342,
796,
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198,
220,
220,
220,
220,
220,
220,
220,
1303,
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33678,
3419,
198,
220,
220,
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220,
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220,
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13,
21928,
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628,
220,
220,
220,
220,
220,
220,
220,
1441,
2638,
13,
41,
1559,
31077,
15090,
6,
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10354,
30826,
34,
16820,
13,
11380,
11,
705,
8056,
19662,
10354,
705,
11380,
6,
30072,
628,
198,
4871,
15161,
20231,
7680,
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47790,
37374,
7680,
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198,
220,
220,
220,
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164,
106,
122,
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165,
119,
246,
164,
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108,
161,
251,
222,
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628,
220,
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165,
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108,
161,
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198,
220,
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1949,
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220,
220,
220,
2209,
796,
17917,
13,
48205,
13,
1136,
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312,
28,
21975,
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312,
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198,
220,
220,
220,
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220,
2845,
17917,
13,
13921,
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25,
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220,
220,
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1441,
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13,
43481,
31077,
1890,
37978,
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223,
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106,
162,
242,
117,
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28839,
108,
161,
251,
222,
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246,
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101,
11537,
628,
220,
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220,
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2836,
796,
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13,
7220,
198,
220,
220,
220,
220,
220,
220,
220,
2836,
13,
12286,
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21975,
796,
2209,
198,
220,
220,
220,
220,
220,
220,
220,
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13,
21928,
3419,
628,
220,
220,
220,
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220,
220,
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1441,
2638,
13,
41,
1559,
31077,
15090,
6,
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10354,
30826,
34,
16820,
13,
11380,
11,
705,
8056,
19662,
10354,
705,
11380,
6,
30072,
628,
198,
4871,
10133,
19160,
20231,
7680,
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198,
220,
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106,
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242,
117,
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101,
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115,
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108,
161,
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229,
165,
95,
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628,
198,
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198,
220,
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106,
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117,
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228,
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628,
198,
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198,
220,
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198,
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220,
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122,
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228,
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234,
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220,
220,
220,
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33566,
244,
43889,
253,
43380,
228,
163,
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6,
198
] | 1.890415 | 3,422 |
import Rhino.Geometry as rg
import math
# grasshoppper variables
# geometric variables
base_points
normals
layer_count
layer_height
# pattern variables
min_max_val
period
phase_shift
base_layer_set = []
for i, pt in enumerate(base_points):
local_pt = rg.Point3d(pt.X, pt.Y, 0.0)
base_layer_set.append(PointWithNormal(local_pt, normals[i]))
print(base_layer_set)
curve_list = []
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] | 2.651007 | 149 |
"""
Copyright 2020 The OneFlow Authors. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import os
import sys
import unittest
import numpy as np
import oneflow as flow
import oneflow.unittest
rank = flow.env.get_rank()
@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases")
@flow.unittest.skip_unless_1n4d()
if __name__ == "__main__":
unittest.main()
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