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#!/usr/bin/env python2
# -*- encoding: utf-8 -*-
# Gimp Markup Builder
# author: duangsuse
# date: Thu May 02 2019 CST
from os import linesep
from Util import stream_join
class MarkupBuilder:
''' Gimp Markup SGML builder '''
'''
Indent rules:
when starting new tag, write last spaces, last spaces += indent
if new tag is not text tag start (inner is just text), write newline
when leaving tag, last spaces -= indent
'''
indented = property(useindent)
def wnewline(self):
''' see use_indent'''
self.marks += self.nl
def windent(self):
''' see use_indent'''
wrote = 0
for _ in range(0, self.last_spaces):
self.marks += ' '
wrote += 1 # dummy?
return wrote
def cancel_indent(self):
''' cancel last indent '''
if self.indented: self.marks = self.marks[:-self.revert_last_indent_size]
def do_indent(self, entering = True):
''' Write indent, increase last_spaces, saving wrote spaces and newline to revert_last_indent_size '''
if self.indented: do()
def do_last_indent(self, *args, **kwargs):
''' write indenting for last block '''
self.last_spaces -= self.indent
self.do_indent(*args, **kwargs)
self.last_spaces += self.indent
def begin(self, tag, attrs = {}):
'''
Make a tag with name and attributes
Attribute name, value and tag name is escaped
'''
self.last_is_text = False
attrst = str()
tagscape = self.escape(tag)
ary = list(stream_join(attrs.keys(), attrs.values())) if attrs.__class__ is dict else list(attrs)
if len(attrs) != 0:
for n in range(0, len(ary), 2):
attrst += self.escape(str(ary[n]))
attrst += '='
#print(ary)
#print(n)
attrst += "\"%s\"" % self.escape(str(ary[n+1]))
self.marks += '<' + tagscape
if len(attrs) != 0: self.marks += ' '
self.marks += attrst + '>'
# always write indents for next line
# makes its possible to drop last indent (text tag)
self.do_indent()
self.tag_stack.append(tagscape)
return self
def tag(self, *args, **kwargs):
r'''
EDSL using __close__ with syntax
create nodes like:
with xml.tag('span', {color: '#66ccff'}):
xml % 'Q \w\ Q'
'''
self.last_is_text = False
return TagBuilder(self)
def text(self, content):
''' append text content '''
self.last_is_text = True
if self.indented: self.cancel_indent()
self.marks += self.escape(content)
return self
#@staticmethod
#def test():
# m = MarkupBuilder()
# m > 'html'
# m > 'head'
# m > 'title'
# m < 'Hello World'
# m <= 2
# m > 'body'
# m > 'text'
# with m.tag("b"):
# m < 'String'
# m >= ['a', {'id': 'str'}]
# m < '|sg.'
# m <= 4
# return m
def end(self):
''' delimites last tag '''
if not self.last_is_text: # cancel indentation
#print(self.indent, self.tag_stack)
self.cancel_indent()
self.do_indent(False)
self.marks += '</' + self.tag_stack.pop() + '>'
self.do_indent(False)
self.last_is_text = False
# Not cared by Markup indent emitter
def raw(self, raw):
''' write raw text (unescaped) '''
self.marks += raw
return self
def rawtag(self, rawtext):
''' append unescaped raw <> text '''
self.marks += '<'
self.marks += rawtext
self.marks += '>'
def _escape(self, xml):
'''
Escape XML string
' is replaced with '
" is replaced with "
& is replaced with &
< is replaced with <
> is replaced with >
'''
escapes = frozenset("'\"&<>")
replacement = { '\'': 'apos', '"': 'quot', '&': 'amp', '<': 'lt', '>': 'gt' }
if len(xml) < 1: return
output = str()
for i in range(0, len(xml)):
char = xml[i]
if (char in escapes):
output += '&'
output += replacement[char]
output += ';'
else: output += char
return output
escape = classmethod(_escape)
def __str__(self):
''' M(marks)..[tag stack] '''
return 'M(' + self.marks + ')..' + str(self.tag_stack)
__lt__ = text # chain
__gt__ = begin # chain
__add__ = raw # chain
def __contains__(self, tag):
''' is tag inside enclosing tags ? '''
return tag in self.tag_stack
def __ge__(self, tag_attr):
''' xml >= ['markup', {'name': 'abcs'}] '''
mark = tag_attr[0]
attr = tag_attr[1]
self.begin(mark, attr)
def __le__(self, n = 1):
''' Leave (close) N tags '''
while n > 0:
self.end()
n -= 1
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2116,
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437,
3419,
198,
220,
220,
220,
220,
220,
299,
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352,
628
] | 2.37605 | 1,904 |
from pkg_resources import DistributionNotFound, get_distribution, parse_version
try:
import psycopg2 # noqa: F401
except ImportError:
raise ImportError(
'No module named psycopg2. Please install either '
'psycopg2 or psycopg2-binary package for CPython '
'or psycopg2cffi for Pypy.'
)
for package in ['psycopg2', 'psycopg2-binary', 'psycopg2cffi']:
try:
if get_distribution(package).parsed_version < parse_version('2.5'):
raise ImportError('Minimum required version for psycopg2 is 2.5')
break
except DistributionNotFound:
pass
__version__ = get_distribution('hs-sqlalchemy-redshift').version
from sqlalchemy.dialects import registry
registry.register("redshift", "sqlalchemy_redshift.dialect", "RedshiftDialect")
registry.register(
"redshift.psycopg2", "sqlalchemy_redshift.dialect", "RedshiftDialect"
)
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] | 2.594203 | 345 |
# -*- coding: utf-8 -*-
| [
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] | 1.714286 | 14 |
"""
This module contains the top level API for managing the project/file templates.
"""
import json
import logging
import os
import re
from binaryornot.check import is_binary
from hackedit.app import settings
def create(template, dest_dir, answers):
"""
Creates a file/project from the specified template, at the specified directory.
:param template: Template data.
:param dest_dir: Destination directory where to create the file/project
:param answers: Dict of answers for substitution variables
"""
ret_val = []
if not os.path.exists(dest_dir):
os.makedirs(dest_dir)
src_dir = template['path']
for root, dirs, files in os.walk(src_dir):
for file in files:
if file == 'template.json' or file.endswith('.pyc'):
continue
src, dst = get_paths(root, file, src_dir, dest_dir)
dst = subsitute_vars(dst)
encoding = get_file_encoding(src)
try:
content = open_file(src, encoding)
except OSError:
_logger().exception('failed to open file: %r', src)
if encoding != 'binary':
content = subsitute_vars(content)
if file == 'btpad_btn_img_0.png':
print(len(content), encoding)
try:
open_file(dst, encoding, to_write=content)
except PermissionError:
_logger().exception('failed to write file: %r', dst)
else:
ret_val.append(dst)
assert open_file(dst, encoding) == content
for directory in dirs:
src, dst = get_paths(root, directory, src_dir, dest_dir)
dst = subsitute_vars(dst)
try:
os.mkdir(dst)
except PermissionError:
_logger().exception('failed to create directory: %r', dst)
return ret_val
def get_sources():
"""
Returns the template sources (directory associated with a label).
"""
s = settings.load()
tmpl_sources = s.value('_templates/sources', '[]')
tmpl_sources = json.loads(tmpl_sources)
return sorted(tmpl_sources, key=lambda x: x['label'])
def add_source(label, path):
"""
Adds a template source
:param label: Name of the template source.
:param path: Path of the template source.
"""
tmpl_sources = get_sources()
tmpl_sources.append({'label': label, 'path': path})
s = settings.load()
s.setValue('_templates/sources', json.dumps(tmpl_sources))
def rm_source(label):
"""
Removes the specified template source.
:param label: Name of the template source to remove.
"""
tmpl_sources = get_sources()
for src in tmpl_sources:
if src['label'] == label:
tmpl_sources.remove(src)
s = settings.load()
s.setValue('_templates/sources', json.dumps(tmpl_sources))
def clear_sources():
"""
Clear template sources.
"""
s = settings.load()
s.setValue('_templates/sources', json.dumps([]))
def get_templates(category='', source_filter=''):
"""
Gets the list of templates.
:param category: Template category to retrieve.
- use "Project" to get project templates
- use "File" to get file templates
- use an empty string to retrieve them all (default).
:param source: Label of the source of the templates to retrieve. Use an empty string to retrieve
templates from all sources.
"""
def filtered_sources():
"""
Filter list of sources based on the ``source`` parameter.
"""
tmpl_sources = get_sources()
filtered = []
if source_filter:
# only keep the requested template source
for src in tmpl_sources:
if src['label'] == source_filter:
filtered.append(src)
break
else:
filtered = tmpl_sources
return filtered
def get_template(tdir):
"""
Returns template data for the given template directory.
Returns None if the template is invalid.
:param tdir: Template directory to get data from.
"""
tmpl = None
template_json = os.path.join(tdir, 'template.json')
if not os.path.exists(template_json):
# no template.json -> invalid template
_logger().warn('"template.json" not found in template directory: %r', tdir)
else:
try:
with open(template_json) as f:
tmpl = json.loads(f.read())
except (OSError, json.JSONDecodeError):
# unreadable template.json -> invalid template
_logger().exception('failed to read %r', template_json)
tmpl = None
else:
try:
tmpl_cat = tmpl['category']
except KeyError:
# no metadata or no category in template.json -> invalid template
_logger().exception('failed to read category from template metadata, '
'incomplete template.json?')
tmpl = None
else:
# valid template (finally).
tmpl['source'] = src
if category and category != tmpl_cat:
_logger().debug('rejecting template directory: %r, invalid category', tdir)
tmpl = None
return tmpl
def listdir(directory):
"""
Securely list subdirectories of ``directory``.
Returns an empty list of an OSError occurred.
"""
try:
return os.listdir(directory)
except OSError:
return []
for src in filtered_sources():
for tdir in listdir(src['path']):
tdir = os.path.join(src['path'], tdir)
if os.path.isfile(tdir):
continue
tmpl = get_template(tdir)
if tmpl:
tmpl['path'] = tdir
yield tmpl
def get_template(source, template):
"""
Returns the specified template data.
"""
for t in get_templates(source_filter=source):
if t['name'] == template:
return t
return None
if __name__ == '__main__':
clear_sources()
add_source('COBOL', '/home/colin/Documents/hackedit-cobol/hackedit_cobol/templates')
add_source('Python', '/home/colin/Documents/hackedit-python/hackedit_python/templates')
for tmpl in get_templates():
print(json.dumps(tmpl, indent=4, sort_keys=True))
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14,
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259,
14,
38354,
14,
71,
6021,
270,
12,
66,
672,
349,
14,
71,
6021,
270,
62,
66,
672,
349,
14,
11498,
17041,
11537,
198,
220,
220,
220,
751,
62,
10459,
10786,
37906,
3256,
31051,
11195,
14,
4033,
259,
14,
38354,
14,
71,
6021,
270,
12,
29412,
14,
71,
6021,
270,
62,
29412,
14,
11498,
17041,
11537,
198,
220,
220,
220,
329,
256,
76,
489,
287,
651,
62,
11498,
17041,
33529,
198,
220,
220,
220,
220,
220,
220,
220,
3601,
7,
17752,
13,
67,
8142,
7,
17209,
489,
11,
33793,
28,
19,
11,
3297,
62,
13083,
28,
17821,
4008,
198
] | 2.184029 | 3,043 |
"""
Convert video format x to MP4/H.264.
"""
import os
import sys
import logging
from .videometainfo import VideoMetaInfo
from .utils import sizeof_fmt, time_fmt, find_files, check_dependencies, call, ffmpeg
logger = logging.getLogger(__name__)
class VideoToMP4:
"""To Mp4"""
SUPPORTED_EXTENSIONS = ".wmv, .avi, .mkv, .mov, .flv"
RULES = {
".wmv": "-c:v libx264 -crf 19 ",
".avi":
"-vf yadif=1 -c:v h264_nvenc -preset slow -tune film -crf 17",
".mkv": "-c copy",
".mov": "-vcodec h264 -acodec aac -strict -2 -crf 19 ",
".flv": " -r 20 ",
}
def process(self, video_file: str):
"""Convert video files to MP4 container format."""
name = os.path.splitext(video_file)[0]
ext = os.path.splitext(video_file)[1]
new_name = f"{name}.mp4"
if os.path.exists(new_name):
logger.info(f"Skipping file {new_name} already exists!")
elif ext not in VideoToMP4.RULES:
logger.error(f"Skipping unsupported type {ext}!")
else:
print(f'Convert {ext} to MP4 {new_name} ... ')
meta_info = VideoMetaInfo(video_file)
rule = VideoToMP4.RULES[ext]
flags = "-movflags +faststart -pix_fmt yuv420p"
ffmpeg(
f'-i "{video_file}" {flags} {rule} -metadata date="{meta_info.original_date}" "{new_name}"'
)
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1,
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277,
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62,
7753,
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532,
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90,
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62,
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13,
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62,
4475,
36786,
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62,
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92,
30543,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1267,
198
] | 2.002782 | 719 |
from setuptools import setup
setup(
name='parasol',
dependency_links=[
],
install_requires=[
]
)
| [
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] | 2.4375 | 48 |
# Generated by Django 3.2.8 on 2021-11-02 12:46
from django.db import migrations, models
| [
2,
2980,
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6738,
42625,
14208,
13,
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1330,
15720,
602,
11,
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628
] | 2.84375 | 32 |
import csv
import io
import json
import logging
import os
import warnings
from collections import defaultdict
from ..utils.exceptions import OasisException
from ..utils.log import oasis_log
from .files import GENERAL_SETTINGS_FILE, GUL_SUMMARIES_FILE, IL_SUMMARIES_FILE, MODEL_SETTINGS_FILE
def _get_summaries(summary_file):
"""
Get a list representation of a summary file.
"""
summaries_dict = defaultdict(lambda: {'leccalc': {}})
with io.open(summary_file, 'r', encoding='utf-8') as csvfile:
reader = csv.reader(csvfile)
for row in reader:
id = int(row[0])
if row[1].startswith('leccalc'):
summaries_dict[id]['leccalc'][row[1]] = row[2].lower() == 'true'
else:
summaries_dict[id][row[1]] = row[2].lower() == 'true'
summaries = list()
for id in sorted(summaries_dict):
summaries_dict[id]['id'] = id
summaries.append(summaries_dict[id])
return summaries
@oasis_log
def create_analysis_settings_json(directory):
"""
Generate an analysis settings JSON from a set of
CSV files in a specified directory.
Args:
``directory`` (string): the directory containing the CSV files.
Returns:
The analysis settings JSON.
"""
if not os.path.exists(directory):
error_message = "Directory does not exist: {}".format(directory)
logging.getLogger().error(error_message)
raise OasisException(error_message)
general_settings_file = os.path.join(directory, GENERAL_SETTINGS_FILE)
model_settings_file = os.path.join(directory, MODEL_SETTINGS_FILE)
gul_summaries_file = os.path.join(directory, GUL_SUMMARIES_FILE)
il_summaries_file = os.path.join(directory, IL_SUMMARIES_FILE)
for file in [general_settings_file, model_settings_file, gul_summaries_file, il_summaries_file]:
if not os.path.exists(file):
error_message = "File does not exist: {}".format(directory)
logging.getLogger().error(error_message)
raise OasisException(error_message)
general_settings = dict()
with io.open(general_settings_file, 'r', encoding='utf-8') as csvfile:
reader = csv.reader(csvfile)
for row in reader:
general_settings[row[0]] = eval("{}('{}')".format(row[2], row[1]))
model_settings = dict()
with io.open(model_settings_file, 'r', encoding='utf-8') as csvfile:
reader = csv.reader(csvfile)
for row in reader:
model_settings[row[0]] = eval("{}('{}')".format(row[2], row[1]))
gul_summaries = _get_summaries(gul_summaries_file)
il_summaries = _get_summaries(il_summaries_file)
analysis_settings = general_settings
analysis_settings['model_settings'] = model_settings
analysis_settings['gul_summaries'] = gul_summaries
analysis_settings['il_summaries'] = il_summaries
output_json = json.dumps(analysis_settings)
logging.getLogger().info("Analysis settings json: {}".format(output_json))
return output_json
def read_analysis_settings(analysis_settings_fp, il_files_exist=False,
ri_files_exist=False):
"""Read the analysis settings file"""
# Load analysis_settings file
try:
# Load as a json
with io.open(analysis_settings_fp, 'r', encoding='utf-8') as f:
analysis_settings = json.load(f)
# Extract the analysis_settings part within the json
if analysis_settings.get('analysis_settings'):
analysis_settings = analysis_settings['analysis_settings']
except (IOError, TypeError, ValueError):
raise OasisException('Invalid analysis settings file or file path: {}.'.format(
analysis_settings_fp))
# Reset il_output if the files are not there
if not il_files_exist or 'il_output' not in analysis_settings:
# No insured loss output
analysis_settings['il_output'] = False
analysis_settings['il_summaries'] = []
# Same for ri_output
if not ri_files_exist or 'ri_output' not in analysis_settings:
# No reinsured loss output
analysis_settings['ri_output'] = False
analysis_settings['ri_summaries'] = []
# If we want ri_output, we will need il_output, which needs il_files
if analysis_settings['ri_output'] and not analysis_settings['il_output']:
if not il_files_exist:
warnings.warn("ri_output selected, but il files not found")
analysis_settings['ri_output'] = False
analysis_settings['ri_summaries'] = []
else:
analysis_settings['il_output'] = True
# guard - Check if at least one output type is selected
if not any([
analysis_settings['gul_output'] if 'gul_output' in analysis_settings else False,
analysis_settings['il_output'] if 'il_output' in analysis_settings else False,
analysis_settings['ri_output'] if 'ri_output' in analysis_settings else False,
]):
raise OasisException(
'No valid output settings in: {}'.format(analysis_settings_fp))
return analysis_settings
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] | 2.540594 | 2,020 |
#!/usr/bin/env python
#--------------------------------------------------------------------------------
#Changes the sky coordinates (x,y,z) to the disk coordinates (x_d,y_d,z_d)
#The x axis is the rotation axis
#--------------------------------------------------------------------------------
#Radiative transfer equation
#--------------------------------------------------------------------------------
#Optical depth
#--------------------------------------------------------------------------------
#--------------------------------------------------------------------------------
#Black body radiation
#--------------------------------------------------------------------------------
#--------------------------------------------------------------------------------
#--------------------------------------------------------------------------------
#--------------------------------------------------------------------------------
#Lee las tablas de opacidad DSHARP
#Load opacities
with np.load('default_opacities_smooth.npz') as d:
a_w = d['a']
gsca_w = d['g']
lam_w = d['lam']
k_abs_w = d['k_abs']
k_sca_w = d['k_sca']
lam_avgs = wl
# We split the opacities within the range of frequency to make the calculations faster
k_abs_w = k_abs_w[(0.9*lam_avgs<lam_w) & (1.1*lam_avgs>lam_w),:]
k_sca_w = k_sca_w[(0.9*lam_avgs<lam_w) & (1.1*lam_avgs>lam_w),:]
k_sca_w = k_sca_w*(1. - gsca_w[(0.9*lam_avgs<lam_w) & (1.1*lam_avgs>lam_w),:])
lam_w = lam_w[(0.9*lam_avgs<lam_w) & (1.1*lam_avgs>lam_w)]
opac_grid = opacity.size_average_opacity(lam_avgs, a_w, lam_w, k_abs_w.T, k_sca_w.T, q=3.5, plot=True)
function_ext = interpolate.interp1d(a_w, opac_grid['ka'][:]+opac_grid['ks'][:],kind='cubic')
function_alb = interpolate.interp1d(a_w, opac_grid['ks'][:]/(opac_grid['ka'][:]+opac_grid['ks'][:]),kind='cubic')
if not scattering:
function_alb = interpolate.interp1d(a_w, np.zeros((np.shape(opac_grid['ks'][:]))),kind='cubic')
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] | 3.043411 | 645 |
# -*- coding: utf-8 -*-
"""
Created on Wed Jun 16 18:06:05 2021
@author: jhask
"""
import csv
import pandas as pd
import numpy as np
import re
import scipy.io as sio
import os
# Map MCM names to TUV labels
j_vals_dict= dict({
'O3 -> O2 + O(1D)':'J1',
'O3 -> O2 + O(3P)':'J2',
'H2O2 -> 2 OH':'J3',
'NO2 -> NO + O(3P)':'J4',
'NO3 -> NO + O2':'J5',
'NO3 -> NO2 + O(3P)':'J6',
'HNO2 -> OH + NO':'J7',
'HNO3 -> OH + NO2':'J8',
'CH2O -> H + HCO':'J11',
'CH2O -> H2 + CO':'J12',
'CH3CHO -> CH3 + HCO':'J13',
'C2H5CHO -> C2H5 + HCO':'J14',
'CH2=C(CH3)CHO -> Products':'J18',
'CH3COCH3 -> CH3CO + CH3':'J21',
'CH3COCH2CH3 -> CH3CO + CH2CH3':'J22',
'CH3COCH=CH2 -> Products':'J23',
'CHOCHO -> H2 + 2CO':'J31',
'CHOCHO -> CH2O + CO':'J32',
'CHOCHO -> HCO + HCO':'J33',
'CH3COCHO -> CH3CO + HCO':'J34',
'CH3COCOCH3 -> Products':'J35',
'CH3OOH -> CH3O + OH':'J41',
'CH3ONO2 -> CH3O + NO2':'J51',
'C2H5ONO2 -> C2H5O + NO2':'J52',
'n-C3H7ONO2 -> C3H7O + NO2':'J53',
'CH3CHONO2CH3 -> CH3CHOCH3 + NO2':'J54',
'C(CH3)3(ONO2) -> C(CH3)3(O.) + NO2':'J55',
'CH3COCH2(ONO2) -> CH3COCH2(O.) + NO2':'J56',
'CH2(OH)COCH3 -> CH3CO + CH2(OH)':'Jn10',
'CH2=CHCHO -> Products':'Jn11',
'CH3CO(OONO2) -> CH3CO(OO) + NO2':'Jn14',
'CH3CO(OONO2) -> CH3CO(O) + NO3':'Jn15',
'CH3(OONO2) -> CH3(OO) + NO2':'Jn16',
'CH3(OONO2) -> CH3(OO) + NO2':'Jn17',
'N2O5 -> NO3 + NO2':'Jn19',
'N2O5 -> NO3 + NO + O(3P)':'Jn20',
'HNO4 -> HO2 + NO2':'Jn21'})
#TUV output file.
file= 'C:/Users/jhask/OneDrive/Documents/MATLAB/F0AM/Setups/SOAS_RCIM/foam_6_29_out.txt'
with open(file, "r",errors="ignore") as f: # read line by line.
reader = csv.reader(f, delimiter="\t")
# Initialize vars we fill in reading the file.
ln_num = 0; map_cols=dict({})
in_species_list=False;
pass_go=False
for row in reader:
line = " ".join(row) # read line by line.
hdrs= [key for key in list(j_vals_dict.keys()) if key in line]
if len(hdrs) > 0 :
headers= re.search(r"[\d]*[\=\w]", line)
print(line, hdrs, j_vals_dict[ hdrs[:][0]])
if headers: map_cols[headers.group()]=j_vals_dict[ hdrs[:][0]]
if (pass_go is True) and ('------' not in line ):
# Append the j-values to the dataframe at this point in time.
splt= [float(item) for item in line.split(" ") if item !='']
df.loc[len(df)]=np.array(splt)
if 'time, hrs. sza, deg.' in line:
pass_go=True
df=pd.DataFrame(columns= ['time', 'sza']+ list(map_cols.values()))
to_mat={name: col.values for name, col in df.items()}
filename= os.path.join('C:/Users/jhask/OneDrive/Documents/MATLAB/F0AM/Setups/SOAS_RCIM/'+'F0AM_tuv.mat')
sio.savemat(filename, to_mat)
print(filename)
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] | 1.792441 | 1,614 |
'''
Imports
'''
from config import *
from newspaper import Article
import sys as sys
import pandas as pd
import csv
from collections import defaultdict
import re
'''
URL Extract
'''
columns = defaultdict(list)
with open('SecurityIDRBT.csv') as f:
reader = csv.DictReader(f) # read rows into a dictionary format
for row in reader: # read a row as {column1: value1, column2: value2,...}
for (k,v) in row.items(): # go over each column name and value
columns[k].append(v) # append the value into the appropriate list
url_list = [] # based on column name k
for element in range(len(columns['Body'])):
urls = re.findall('https?://(?:[-\w.]|(?:%[\da-fA-F]{2}))+', columns['Body'][element])
for url in urls:
url_list.append(url)
'''
Find Unique URLs and filter with semantic search results
'''
url_unique = []
for element in url_list:
if element not in url_unique:
if element not in common_urls_http:
if element not in common_urls_https:
url_unique.append(element)
'''
Write it in a new CSV
'''
with open('url.csv', 'w',newline='') as myfile:
wr = csv.writer(myfile, quoting=csv.QUOTE_ALL)
for word in url_unique:
wr.writerow([word])
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201,
198,
220,
220,
220,
220,
220,
220,
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220
] | 2.284014 | 588 |
# -*- coding: utf-8 -*-
# Copyright 2018, IBM.
#
# This source code is licensed under the Apache License, Version 2.0 found in
# the LICENSE.txt file in the root directory of this source tree.
# pylint: disable=missing-docstring
from qiskit.mapper import _coupling
from .common import QiskitTestCase
| [
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] | 3.177083 | 96 |
import os
import re
import k3d
import types
import random
import pytest
import numbers
import tempfile
import itertools
import numpy as np
import discretisedfield as df
import matplotlib.pyplot as plt
from .test_mesh import TestMesh
| [
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] | 3.405797 | 69 |
__all__ = ['get']
import collections
def get(input):
"""return a list with input values or [] if input is None"""
if input is None:
return []
if not _iterable(input) or _string(input):
return [input]
return list(input)
| [
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2019/8/20 0020 16:49
# @Author : Hadrianl
# @File : __init__.py
from .widget import StrategyReviewer | [
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2,
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220,
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220,
198,
2,
2488,
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220,
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220,
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15003,
834,
13,
9078,
198,
198,
6738,
764,
42655,
1330,
20561,
35407
] | 2.323944 | 71 |
import xml.etree.ElementTree as ET
xml_string = '''
<stuff>
<users>
<user x = "2">
<id>001</id>
<name>Chuck</name>
</user>
<user x = "7">
<id>007</id>
<name>Brent</name>
</user>
</users>
</stuff>
'''
root_stuff = ET.fromstring(xml_string)
#don't usually refer to root element
user_elements = root_stuff.findall('users/user')
print ('user count:', len(user_elements))
for user in user_elements:
print('name:', user.find('name').text)
print('id:', user.find('id').text)
print('attribute(x):', user.get('x'))
#to identify attribute use 'get's
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220,
220,
220,
1303,
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779,
705,
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198
] | 2.142384 | 302 |
from multiprocessing.dummy import Pool
pool = Pool(3)
origin_num = [x for x in range(10)]
result = pool.map(calc_power2, origin_num)
print(f'计算1-10的平方分别为：{result}')
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] | 2.060976 | 82 |
from django.apps import AppConfig
| [
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] | 3.888889 | 9 |
from django.core.urlresolvers import reverse
from django.views.generic.detail import DetailView
from django.views.generic.list import ListView
from django.views.generic.edit import CreateView, UpdateView, DeleteView
from django.core.urlresolvers import reverse_lazy
from django.utils.decorators import method_decorator
from django.contrib.auth.decorators import login_required
from .models import Eintrag
from .forms import EintragForm
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764,
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] | 3.5 | 126 |
from typing import Any, Optional, Tuple, Union
import torch
from torch.nn.functional import mse_loss
import pystiche
import pystiche.loss.functional as F
from pystiche import enc, loss
from pystiche_papers.utils import HyperParameters
from ._utils import (
extract_normalized_patches2d,
hyper_parameters as _hyper_parameters,
multi_layer_encoder as _multi_layer_encoder,
target_transforms as _target_transforms,
)
__all__ = [
"FeatureReconstructionLoss",
"content_loss",
"MRFLoss",
"style_loss",
"TotalVariationLoss",
"regularization",
"perceptual_loss",
]
class FeatureReconstructionLoss(loss.FeatureReconstructionLoss):
r"""Feature reconstruction loss from :cite:`LW2016`.
Args:
encoder: Encoder used to encode the input.
impl_params: If ``False``, calculate the score with the squared error (SE)
instead of the mean squared error (MSE).
**feature_reconstruction_loss_kwargs: Additional parameters of a
:class:`pystiche.loss.FeatureReconstructionLoss`.
.. seealso::
:class:`pystiche.loss.FeatureReconstructionLoss`
"""
def content_loss(
impl_params: bool = True,
multi_layer_encoder: Optional[enc.MultiLayerEncoder] = None,
hyper_parameters: Optional[HyperParameters] = None,
) -> FeatureReconstructionLoss:
r"""Content loss from :cite:`LW2016`.
Args:
impl_params: Switch the behavior and hyper-parameters between the reference
implementation of the original authors and what is described in the paper.
For details see :ref:`here <li_wand_2016-impl_params>`.
multi_layer_encoder: Pretrained multi-layer encoder. If
omitted, :func:`~pystiche_papers.li_wand_2016.multi_layer_encoder` is used.
hyper_parameters: Hyper parameters. If omitted,
:func:`~pystiche_papers.li_wand_2016.hyper_parameters` is used.
.. seealso::
:class:`pystiche_papers.li_wand_2016.FeatureReconstructionLoss`
"""
if multi_layer_encoder is None:
multi_layer_encoder = _multi_layer_encoder()
if hyper_parameters is None:
hyper_parameters = _hyper_parameters(impl_params=impl_params)
return FeatureReconstructionLoss(
multi_layer_encoder.extract_encoder(hyper_parameters.content_loss.layer),
impl_params=impl_params,
score_weight=hyper_parameters.content_loss.score_weight,
)
class MRFLoss(loss.MRFLoss):
r"""MRF loss from :cite:`LW2016`.
Args:
encoder: Encoder used to encode the input.
patch_size: Spatial size of the neural patches.
impl_params: If ``True``, normalize the gradient of the neural patches. If
``False``, use a score correction factor of 1/2.
**mrf_loss_kwargs: Additional parameters of a :class:`pystiche.loss.MRFLoss`.
In contrast to :class:`pystiche.loss.MRFLoss`, the score is calculated with the
squared error (SE) instead of the mean squared error (MSE).
.. seealso::
- :class:`pystiche.loss.MRFLoss`
- :func:`pystiche_papers.li_wand_2016.extract_normalized_patches2d`
"""
def style_loss(
impl_params: bool = True,
multi_layer_encoder: Optional[enc.MultiLayerEncoder] = None,
hyper_parameters: Optional[HyperParameters] = None,
) -> loss.MultiLayerEncodingLoss:
r"""Style loss from :cite:`LW2016`.
Args:
impl_params: Switch the behavior and hyper-parameters between the reference
implementation of the original authors and what is described in the paper.
For details see :ref:`here <li_wand_2016-impl_params>`.
multi_layer_encoder: Pretrained multi-layer encoder. If
omitted, :func:`~pystiche_papers.li_wand_2016.multi_layer_encoder` is used.
hyper_parameters: Hyper parameters. If omitted,
:func:`~pystiche_papers.li_wand_2016.hyper_parameters` is used.
.. seealso::
- :class:`pystiche_papers.li_wand_2016.MRFLoss`
"""
if multi_layer_encoder is None:
multi_layer_encoder = _multi_layer_encoder()
if hyper_parameters is None:
hyper_parameters = _hyper_parameters(impl_params=impl_params)
return loss.MultiLayerEncodingLoss(
multi_layer_encoder,
hyper_parameters.style_loss.layers,
encoding_loss_fn,
layer_weights=hyper_parameters.style_loss.layer_weights,
score_weight=hyper_parameters.style_loss.score_weight,
)
class TotalVariationLoss(loss.TotalVariationLoss):
r"""Total variation loss from :cite:`LW2016`.
Args:
impl_params: If ``False``, use a score correction factor of 1/2.
**total_variation_loss_kwargs: Additional parameters of a
:class:`pystiche.loss.TotalVariationLoss`.
In contrast to :class:`pystiche.loss.TotalVariationLoss`, the the score is
calculated with the squared error (SE) instead of the mean squared error (MSE).
.. seealso::
- :class:`pystiche.loss.TotalVariationLoss`
"""
def regularization(
impl_params: bool = True,
hyper_parameters: Optional[HyperParameters] = None,
) -> TotalVariationLoss:
r"""Regularization from :cite:`LW2016`.
Args:
impl_params: Switch the behavior and hyper-parameters between the reference
implementation of the original authors and what is described in the paper.
For details see :ref:`here <li_wand_2016-impl_params>`.
hyper_parameters: Hyper parameters. If omitted,
:func:`~pystiche_papers.li_wand_2016.hyper_parameters` is used.
.. seealso::
- :class:`pystiche_papers.li_wand_2016.TotalVariationLoss`
"""
if hyper_parameters is None:
hyper_parameters = _hyper_parameters()
return TotalVariationLoss(
impl_params=impl_params,
score_weight=hyper_parameters.regularization.score_weight,
)
def perceptual_loss(
impl_params: bool = True,
multi_layer_encoder: Optional[enc.MultiLayerEncoder] = None,
hyper_parameters: Optional[HyperParameters] = None,
) -> loss.PerceptualLoss:
r"""Perceptual loss from :cite:`LW2016`.
Args:
impl_params: Switch the behavior and hyper-parameters between the reference
implementation of the original authors and what is described in the paper.
For details see :ref:`here <li_wand_2016-impl_params>`.
multi_layer_encoder: Pretrained multi-layer encoder. If
omitted, :func:`~pystiche_papers.li_wand_2016.multi_layer_encoder` is used.
hyper_parameters: Hyper parameters. If omitted,
:func:`~pystiche_papers.li_wand_2016.hyper_parameters` is used.
.. seealso::
- :func:`pystiche_papers.li_wand_2016.content_loss`
- :func:`pystiche_papers.li_wand_2016.style_loss`
- :func:`pystiche_papers.li_wand_2016.regularization`
"""
if multi_layer_encoder is None:
multi_layer_encoder = _multi_layer_encoder()
if hyper_parameters is None:
hyper_parameters = _hyper_parameters()
return loss.PerceptualLoss(
content_loss(
impl_params=impl_params,
multi_layer_encoder=multi_layer_encoder,
hyper_parameters=hyper_parameters,
),
style_loss(
impl_params=impl_params,
multi_layer_encoder=multi_layer_encoder,
hyper_parameters=hyper_parameters,
),
regularization(impl_params=impl_params, hyper_parameters=hyper_parameters),
)
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] | 2.511022 | 2,994 |
# -*- coding: utf-8 -*-
# (c) 2017-2019, ETH Zurich, Institut fuer Theoretische Physik
# Author: Dominik Gresch <[email protected]>
"""
Configuration file for the pytest tests.
"""
import os
import json
import pytest
import numpy as np
import bands_inspect as bi
import parameters # pylint: disable=wrong-import-order
#--------------------------FIXTURES-------------------------------------#
@pytest.fixture
def test_name(request):
"""Returns module_name.function_name for a given test"""
return request.module.__name__ + '/' + request._parent_request._pyfuncitem.name # pylint: disable=protected-access
@pytest.fixture
def compare_data(request, test_name, scope="session"): # pylint: disable=unused-argument,redefined-outer-name
"""Returns a function which either saves some data to a file or (if that file exists already) compares it to pre-existing data using a given comparison function."""
return inner
@pytest.fixture
def compare_equal(compare_data): # pylint: disable=redefined-outer-name
"""
Returns a function which checks that a given data is equal to the stored reference.
"""
return lambda data, tag=None: compare_data(lambda x, y: x == y, data, tag)
@pytest.fixture
def assert_equal():
"""
Returns a function which checks that two bands-inspect object instances are equal.
"""
return inner
@pytest.fixture
def sample():
"""
Returns the absolute path of the sample with a given name.
"""
return inner
| [
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] | 3.191898 | 469 |
import os
# import tensorflow as tf
import tensorrt as trt
from tensorrt.parsers import uffparser
import pycuda.driver as cuda
# import uff
import cv2
import numpy as np
from tqdm import tqdm
TEST_PATH = "/media/andy/Data/DevWorkSpace/Projects/imageClassifier/data/test/"
LABEL = 0
ENGINE_PATH = "/home/andy/caffe/examples/mydata/slot_classifier/engine/px2_classifier.engine"
NET_INPUT_SHAPE = (256, 256)
NET_OUTPUT_SHAPE = 5
class_labels = ['error', 'half', 'invlb', 'invls', 'valid']
# Load Image
imgTestData = test_Loader(TEST_PATH, NET_INPUT_SHAPE)
# Load Engine file
G_LOGGER = trt.infer.ConsoleLogger(trt.infer.LogSeverity.ERROR)
engine = trt.utils.load_engine(G_LOGGER, ENGINE_PATH)
context = engine.create_execution_context()
runtime = trt.infer.create_infer_runtime(G_LOGGER)
# output = np.empty(1, dtype = np.float32)
# # Alocate device memory
# d_input = cuda.mem_alloc(1 * imgTestData[0][0][0].nbytes)
# d_output = cuda.mem_alloc(NET_OUTPUT_SHAPE * output.nbytes)
# bindings = [int(d_input), int(d_output)]
# stream = cuda.Stream()
predicts = []
pair = imgTestData[0]
for img, label in pair:
output = np.empty(NET_OUTPUT_SHAPE, dtype = np.float32)
# Alocate device memory
d_input = cuda.mem_alloc(1 * img.nbytes)
d_output = cuda.mem_alloc(1 * output.nbytes)
bindings = [int(d_input), int(d_output)]
stream = cuda.Stream()
# Transfer input data to device
cuda.memcpy_htod_async(d_input, img, stream)
# Execute model
context.enqueue(1, bindings, stream.handle, None)
# Transfer predictions back
cuda.memcpy_dtoh_async(output, d_output, stream)
# Syncronize threads
stream.synchronize()
softmax = np.exp(output) / np.sum(np.exp(output))
predict = np.argmax(softmax)
predicts.append(predict)
print("True = ",label, ", predict = ", predict, ", softmax = ", softmax)
grandTrue = np.array(imgTestData[1][1])
predicts = np.array(predicts)
error = predicts[predicts!=grandTrue]
print(imgTestData[1][1])
print("-------")
print(predicts)
print("-------")
print(len(error))
print((len(imgTestData[0])-len(error))/len(imgTestData[0])) | [
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7,
9600,
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7,
18224,
4008,
14,
11925,
7,
9600,
14402,
6601,
58,
15,
60,
4008
] | 2.546108 | 835 |
import filters
import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import freqz
from sklearn.neural_network import MLPRegressor
if __name__ == "__main__":
# Create a random dataset
# [fc, bandwidth, gain]
n = 100
filtersNum = 1
X, Y = genXY(n=n, filtersNum=filtersNum)
# Fit regression model
regr = MLPRegressor(hidden_layer_sizes=(10,), max_iter=10000)
regr.fit(X, Y)
print('train loss', regr.loss_)
# Predict
X_test, Y_test = genXY(n=n, filtersNum=filtersNum)
print('test loss', ((Y_test - regr.predict(X_test)) ** 2).mean())
# paras = [(1e4, 2500, 3), (300, 201, 10), (400, 600, 5), (600, 200, 8),
# (2000, 3500, 13), (6000, 4000, 3), (8500, 6000, 2.75),]
paras = [(1e4, 2500, 3),]
f, db = filterModel(paras)
plt.semilogx(f, db, label="target", color='red')
y_pred = regr.predict([db])
f, db = filterModel(y_pred.reshape(filtersNum, 3))
plt.semilogx(f, db, label="NN")
plt.legend()
plt.show() | [
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import torch.distributed as dist
import torch
def synchronize():
"""
Helper function to synchronize (barrier) among all processes when
using distributed training
"""
if not dist.is_available():
return
if not dist.is_initialized():
return
world_size = dist.get_world_size()
if world_size == 1:
return
dist.barrier() | [
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import json
import pymysql
import datetime
from dbutils.pooled_db import PooledDB
import pymysql
from conf.common import *
mysql_client = MysqlClient()
| [
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#!/usr/bin/env python
"""
Script to launch a VDI session (or connect to already running session)
and start a Jupyter server on the VDI
A ssh tunnel from the local machine to the VDI is set up and the local
webbrowser is spawned.
This is a python3 script (uses unicode strings). If you don't have
python3 on your local machine, try installing Miniconda3
The only external module is pexpect which may need to be installed
using conda or pip.
Usage:
- if you use a password, the script will ask for your password when needed
- if you have already set up SSH public key with Strudel, try running
$ ssh-add ~/.ssh/MassiveLauncherKey
to add your public key to the ssh key agent.
Author: James Munroe, 2017
"""
from __future__ import print_function
import re
import sys
import time
import getpass
import pexpect
import os
import configparser
# Requires future module https://pypi.org/project/future/
from builtins import input
import argparse
import logging
logging.basicConfig(format='[%(asctime)s jupyter_vdi.py] %(message)s',
datefmt='%H:%M:%S',
level=logging.INFO)
try:
import appscript
except ImportError:
import webbrowser
is_mac = False
else:
is_mac = True
DEFAULTS = {
'user' : getpass.getuser(),
'JupyterPort' : '8889',
'BokehPort' : '8787',
'execHost' : 'vdi.nci.org.au'
}
verbose = 0
config_path = os.path.expanduser('~/cosima_cookbook.conf')
parser = configparser.ConfigParser(defaults=DEFAULTS)
if os.path.exists(config_path):
logging.info('Using config file: {}'.format(config_path))
parser.read(config_path)
else:
logging.warn('No config file found. Creating default {} file.'.format(config_path))
logging.warn('*** Please edit this file as needed. ***')
while DEFAULTS['user']==getpass.getuser() or DEFAULTS['user']=="":
DEFAULTS['user']=input('What is your NCI username? ')
parser = configparser.ConfigParser(defaults=DEFAULTS)
with open(config_path, 'w') as f:
parser.write(f)
params = parser.defaults()
def ssh(cmd, params, login_timeout=10):
"""
Run a remote command via SSH
"""
clean_params(params)
cmd = ("ssh -x -l {user} {exechost} " + cmd).format(**params)
if verbose > 0: logging.info(cmd)
s = pexpect.spawn(cmd)
# SSH pexpect logic taken from pxshh:
i = s.expect(["(?i)are you sure you want to continue connecting",
"(?i)(?:password)|(?:passphrase for key)",
"(?i)permission denied",
"(?i)connection closed by remote host",
pexpect.EOF, pexpect.TIMEOUT], timeout=login_timeout)
# First phase
if i == 0:
# New certificate -- always accept it.
# This is what you get if SSH does not have the remote host's
# public key stored in the 'known_hosts' cache.
s.sendline("yes")
i = s.expect(["(?i)are you sure you want to continue connecting",
"(?i)(?:password)|(?:passphrase for key)",
"(?i)permission denied",
"(?i)connection closed by remote host",
pexpect.EOF, pexpect.TIMEOUT], timeout=login_timeout)
if i == 1: # password or passphrase
if 'password' not in params:
params['password'] = getpass.getpass('password: ')
s.sendline(params['password'])
i = s.expect(["(?i)are you sure you want to continue connecting",
"(?i)(?:password)|(?:passphrase for key)",
"(?i)permission denied",
"(?i)connection closed by remote host",
pexpect.EOF, pexpect.TIMEOUT], timeout=login_timeout)
# TODO: check if ssh connection is successful
return s
def session(func, *args, **kwargs):
"""wrapper for sending session-ctl commands"""
cmd = '/opt/vdi/bin/session-ctl --configver=20151620513 ' + func
s = ssh(cmd, *args, **kwargs)
s.close()
return s
tunnel_started = False
tunnel = None
if __name__ == "__main__":
main_argv()
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834,
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628,
220,
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220,
1388,
62,
853,
85,
3419,
198
] | 2.474803 | 1,647 |
# -*- coding=utf-8 -*-
__all__ = [
'tiny_imagenet',
'imagewoof2',
'imagenette2'
]
import os
import torch
import torchvision
_default_batch_size = 32
_default_num_workers = 4
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] | 2.270588 | 85 |
from django.db import models
from django.contrib.auth.models import User
from django.db.models import Sum
from datetime import datetime
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] | 3.538462 | 39 |
#!/usr/bin/env pytest
import io
import json
from os import path
from pytest import fixture, mark
from sls import App
import storyscript.hub.Hub as StoryHub
from storyhub.sdk.AutoUpdateThread import AutoUpdateThread
from tests.e2e.utils.features import parse_options
from tests.e2e.utils.fixtures import find_test_files, hub, test_dir
test_files = find_test_files(relative=True)
@fixture
# compile a story and compare its completion with the expected tree
# load a story from the file system and load its expected result file (.json)
@mark.usefixtures("patched_storyhub")
@mark.parametrize("test_file", test_files)
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62,
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1600,
1332,
62,
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8,
198
] | 3.310526 | 190 |
""" Where's My Mouse? """
import tkinter
root = tkinter.Tk()
root.bind('<Button>', mouse_click)
root.mainloop()
| [
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] | 2.320755 | 53 |
from dataclasses import dataclass
from enum import Enum
from typing import Callable, Dict, Final, Optional, Type, Union
from botx import Bot, Collector, Message
from botx.concurrency import callable_to_coroutine
from botx.middlewares.base import BaseMiddleware
from botx.typing import Executor
_default_transition: Final = object()
@dataclass
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] | 3.441176 | 102 |
#!/usr/bin/env python
'''
This program attempts to convert XPLOR Pseudocontact shift restraints in AMBER format
XPLOR:
assign ( resid 200 and name OO ) ( resid 200 and name Z ) ( resid 200 and name X ) (resid 200 and name Y ) ( resid 13 and name C ) 0.2400 0.2000
assign ( resid 200 and name OO ) ( resid 200 and name Z ) ( resid 200 and name X ) ( resid 200 and name Y ) ( resid 13 and name CA ) 0.4300 0.2000
assign ( resid 200 and name OO ) ( resid 200 and name Z ) ( resid 200 and name X ) ( resid 200 and name Y )( resid 13 and name CB ) 0.1000 0.2000
AMBER:
&align
num_datasets=2,
dcut= -1.0, freezemol= .false.,
ndip= 10, dwt= 5*0.1, 5*0.1
gigj= 5*-3.1631,5*-3.1631,
dij= 5*1.041,5*1.041,
s11= -4.236,-4.236
s12= 56.860,56.860
s13= -34.696,-34.696
s22= -27.361,-27.361
s23= -12.867,-12.867
dataset=1,
id(1)=20, jd(1)=19, dobsl(1)=-2.13, dobsu(1)=-2.13,
id(2)=31, jd(2)=30, dobsl(2)= 1.10, dobsu(2)= 1.10,
id(3)=43, jd(3)=42, dobsl(3)=-5.54, dobsu(3)=-5.54,
...
...
&end
'''
import sys
import os
import commands
from optparse import OptionParser
from xml_parser import *
from normalize_tbl import normalize
from constants import convtable
if __name__ == '__main__':
usage = "usage: %prog -w working_directory -p pdb_filename -o out_filename"
parser = OptionParser(usage)
parser.add_option("-w", "--wdir", dest="wd",
help="Working directory", metavar="WORKDIR")
parser.add_option("-p", "--pdbfile", dest="pdbfile",
help="PDB filename", metavar="FILE")
parser.add_option("-o", "--outfile", dest="outfile",
help="Output filename", metavar="FILE")
(options, args) = parser.parse_args()
if not options.wd:
parser.error("Working directory is required")
wd=os.path.abspath(options.wd)+'/'
if options.pdbfile:
pdbfile=os.path.join(wd, options.pdbfile)
else:
parser.error("PDB filename is required")
if options.outfile:
outfile=os.path.join(wd, options.outfile)
else:
parser.error("Output filename is required")
xml_input=os.path.join(wd,'input.xml')
doc = etree.parse(xml_input)
ndoc = etree.tostring(doc)
new=parse_node(etree.fromstring(ndoc))
out=convert(pdbfile, new, wd)
fout=open(outfile,'w')
fout.writelines(out)
fout.close() | [
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] | 2.166078 | 1,132 |
"""Versioned body readers and writers for track message bodies.
Attributes:
LATEST_VERSION (int): Latest version supported by the library.
"""
from typing import Callable, Tuple
from . import TrackInfo, codec
LATEST_VERSION = 2
ReaderType = Callable[[codec.Reader], TrackInfo]
WriterType = Callable[[codec.Writer, TrackInfo], None]
_FORMAT_VERSIONS = {
1: (read_body_v1, write_body_v1),
2: (read_body_v2, write_body_v2),
}
def get_reader(version: int) -> ReaderType:
"""Get a body reader for the given version.
Raises:
ValueError: If the version isn't supported.
"""
return _get_format(version)[0]
def get_writer(version: int) -> WriterType:
"""Get a body writer for the given version.
Raises:
ValueError: If the version isn't supported.
"""
return _get_format(version)[1]
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/home/runner/.cache/pip/pool/f3/de/85/7dca1e096a43e00e6ff1ca900dda1ca91c8c5c3a1d6798e466a9173a00 | [
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"""
The Alarm Extension provides easy access to setting an application alarm to
handle timing out operations. See the
`Python Signal Library <https://docs.python.org/3.5/library/signal.html>`_.
Requirements
------------
* No external dependencies.
* Only available on Unix/Linux
Configuration
-------------
This extension does not honor any application configuration settings.
Usage
-----
.. code-block:: python
import time
from cement.core.foundation import CementApp
from cement.core.exc import CaughtSignal
class MyApp(CementApp):
class Meta:
label = 'myapp'
exit_on_close = True
extensions = ['alarm']
with MyApp() as app:
try:
app.run()
app.alarm.set(3, "The operation timed out after 3 seconds!")
# do something that takes time to operate
time.sleep(5)
app.alarm.stop()
except CaughtSignal as e:
print(e.msg)
app.exit_code = 1
Looks like:
.. code-block:: console
$ python myapp.py
ERROR: The operation timed out after 3 seconds!
Caught signal 14
"""
import signal
from ..utils.misc import minimal_logger
LOG = minimal_logger(__name__)
class AlarmManager(object):
"""
Lets the developer easily set and stop an alarm. If the
alarm exceeds the given time it will raise ``signal.SIGALRM``.
"""
def set(self, time, msg):
"""
Set the application alarm to ``time`` seconds. If the time is
exceeded ``signal.SIGALRM`` is raised.
:param time: The time in seconds to set the alarm to.
:param msg: The message to display if the alarm is triggered.
"""
LOG.debug('setting application alarm for %s seconds' % time)
self.msg = msg
signal.alarm(int(time))
def stop(self):
"""
Stop the application alarm.
"""
LOG.debug('stopping application alarm')
signal.alarm(0)
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220,
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220,
220,
220,
220,
220,
6737,
13,
282,
1670,
7,
15,
8,
628
] | 2.591203 | 773 |
from meross_iot.cloud.abilities import *
from meross_iot.cloud.device import AbstractMerossDevice
from meross_iot.logger import POWER_PLUGS_LOGGER as l
from meross_iot.meross_event import DeviceSwitchStatusEvent
| [
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from django.db.backends.mysql.introspection import *
from django.db.backends.mysql.introspection import DatabaseIntrospection as MYSQLDatabaseIntrospection
from django.utils.functional import cached_property
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import highiq
import numpy as np
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355,
45941,
628,
628,
628
] | 3.166667 | 12 |
# Base imports
import subprocess
from typing import Iterable, Optional
# Project imports
from docker import common
from docker.run import run
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import json
import glob
import numpy as np
import os
path = "data_state_space_v3/"
out_path = "small_data/"
files = glob.glob(path + "*.npy") # ワイルドカードが使用可能
train_data_num = 100
test_data_num = 10
train_data = {}
test_data = {}
for filename in files:
obj = np.load(filename)
if filename.find("_test.npy") >= 0:
test_data[filename] = obj
else:
train_data[filename] = obj
os.makedirs(out_path, exist_ok=True)
for k, v in train_data.items():
b = os.path.basename(k)
print(b, v.shape)
o = v[:train_data_num]
np.save(out_path + b, o)
for k, v in test_data.items():
b = os.path.basename(k)
print(b, v.shape)
o = v[:test_data_num]
np.save(out_path + b, o)
fp = open(path + "pack_selected_info.json")
obj = json.load(fp)
obj["pid_list_train"] = obj["pid_list_train"][:train_data_num]
obj["pid_list_test"] = obj["pid_list_test"][:test_data_num]
fp = open(out_path + "pack_selected_info.json", "w")
json.dump(obj, fp)
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# https://www.hackerrank.com/challenges/three-month-preparation-kit-maxsubarray/problem
#!/bin/python3
import math
import os
import random
import re
import sys
#
# Complete the 'maxSubarray' function below.
#
# The function is expected to return an INTEGER_ARRAY.
# The function accepts INTEGER_ARRAY arr as parameter.
#
if __name__ == '__main__':
fptr = open(os.environ['OUTPUT_PATH'], 'w')
t = int(input().strip())
for t_itr in range(t):
n = int(input().strip())
arr = list(map(int, input().rstrip().split()))
result = maxSubarray(arr)
fptr.write(' '.join(map(str, result)))
fptr.write('\n')
fptr.close() | [
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11537,
628,
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20692,
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] | 2.507463 | 268 |
import torch
from torch.nn.modules.module import Module
from torch.autograd import Function
import correlation_cuda
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from tensorflow.python.ops import data_flow_ops
import tensorflow.contrib.tensorrt as trt
import numpy as np
import time
from tensorflow.python.platform import gfile
from tensorflow.python.client import timeline
import argparse, sys, itertools,datetime
import json
tf.logging.set_verbosity(tf.logging.INFO)
import os
from utils import *
#main
if "__main__" in __name__:
P=argparse.ArgumentParser(prog="trt_convert")
P.add_argument('--FP32',action='store_true')
P.add_argument('--FP16',action='store_true')
P.add_argument('--INT8',action='store_true')
P.add_argument('--input_file',type=str)
P.add_argument('--input_path_calibration',type=str,default='./',help="path to read input files from for calibration mode")
P.add_argument('--output_prefix',type=str)
P.add_argument('--batch_size',type=int, default=32)
P.add_argument('--num_calibration_runs',type=int, default=10)
P.add_argument('--workspace_size',type=int, default=1<<20,help="workspace size in MB")
P.add_argument('--gpu', type=int, default=0)
#P.add_argument('--update_graphdef',action='store_true')
#parse args
f,unparsed=P.parse_known_args()
#select the GPU
os.environ["CUDA_VISIBLE_DEVICES"]=str(f.gpu) #selects a specific device
#create a session just in case
sess = tf.Session()
#print graph
print_graph(f.input_file)
#do the conversion
if f.FP32:
getFP32(input_file=f.input_file, output_prefix=f.output_prefix, output=["Softmax"], batch_size=f.batch_size, workspace_size=f.workspace_size)
if f.FP16:
getFP16(input_file=f.input_file, output_prefix=f.output_prefix, output=["Softmax"], batch_size=f.batch_size, workspace_size=f.workspace_size)
if f.INT8:
calibGraph = getINT8CalibGraph(input_file=f.input_file, output_prefix=f.output_prefix, output=["Softmax"], batch_size=f.batch_size, workspace_size=f.workspace_size)
print('Calibrating Graph...')
#run graph
runGraph(calibGraph, f.batch_size, f.num_calibration_runs, "Placeholder", ["Softmax"], dtype=np.float32, input_data=f.input_path_calibration)
print('done...')
#get int8 graph
getINT8InferenceGraph(output_prefix=f.output_prefix, calibGraph=calibGraph)
sys.exit(0)
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] | 2.707317 | 861 |
import Adafruit_SSD1306
import Image
import ImageDraw
import ImageFont
# I2C ADDRESS / BITS
SSD1306_ADDRESS = 0x3C
| [
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] | 2.489362 | 47 |
if __name__ == '__main__':
print compute(1, 0.1) # default values
| [
198,
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] | 2.571429 | 28 |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
from ..optimization import discretization
from ..common.decorators import Registry
registry = Registry()
@registry.register
@registry.register
@registry.register
@registry.register
@registry.register
@registry.register
@registry.register
@registry.register
@registry.register
@registry.register
@registry.register
@registry.register
def lunacek(x: np.ndarray) -> float:
""" Based on https://www.cs.unm.edu/~neal.holts/dga/benchmarkFunction/lunacek.html."""
problemDimensions = len(x)
s = 1.0 - (1.0 / (2.0 * np.sqrt(problemDimensions + 20.0) - 8.2))
mu1 = 2.5
mu2 = - np.sqrt(abs((mu1**2 - 1.0) / s))
firstSum = 0.0
secondSum = 0.0
thirdSum = 0.0
for i in range(problemDimensions):
firstSum += (x[i]-mu1)**2
secondSum += (x[i]-mu2)**2
thirdSum += 1.0 - np.cos(2*np.pi*(x[i]-mu1))
return min(firstSum, 1.0*problemDimensions + secondSum)+10*thirdSum
# following functions using discretization should not be used with translation/rotation
@registry.register_with_info(no_transfrom=True)
@registry.register_with_info(no_transfrom=True)
@registry.register_with_info(no_transfrom=True)
@registry.register_with_info(no_transfrom=True)
@registry.register_with_info(no_transfrom=True)
@registry.register_with_info(no_transfrom=True)
@registry.register_with_info(no_transfrom=True)
@registry.register_with_info(no_transfrom=True)
@registry.register_with_info(no_transfrom=True)
@registry.register_with_info(no_transfrom=True)
@registry.register_with_info(no_transfrom=True)
@registry.register_with_info(no_transfrom=True)
@registry.register_with_info(no_transfrom=True)
@registry.register_with_info(no_transfrom=True)
@registry.register
@registry.register
@registry.register
@registry.register
@registry.register
@registry.register
@registry.register
@registry.register
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7,
3919,
62,
7645,
6738,
28,
17821,
8,
628,
198,
31,
2301,
4592,
13,
30238,
62,
4480,
62,
10951,
7,
3919,
62,
7645,
6738,
28,
17821,
8,
628,
198,
31,
2301,
4592,
13,
30238,
62,
4480,
62,
10951,
7,
3919,
62,
7645,
6738,
28,
17821,
8,
628,
198,
31,
2301,
4592,
13,
30238,
628,
198,
31,
2301,
4592,
13,
30238,
628,
198,
31,
2301,
4592,
13,
30238,
628,
198,
31,
2301,
4592,
13,
30238,
628,
198,
31,
2301,
4592,
13,
30238,
628,
198,
31,
2301,
4592,
13,
30238,
628,
198,
31,
2301,
4592,
13,
30238,
628,
198,
31,
2301,
4592,
13,
30238,
198
] | 2.67087 | 793 |
string = input()
d = {}
for i in string:
if i in d:
d[i] += 1
else:
d[i] = 1
s = sorted(sorted(d), key = d.get, reverse = True)
for i in s[:3]:
print(i, d[i])
| [
8841,
796,
5128,
3419,
198,
67,
796,
23884,
198,
1640,
1312,
287,
4731,
25,
198,
220,
220,
220,
611,
1312,
287,
288,
25,
198,
220,
220,
220,
220,
220,
220,
220,
288,
58,
72,
60,
15853,
352,
198,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
288,
58,
72,
60,
796,
352,
198,
82,
796,
23243,
7,
82,
9741,
7,
67,
828,
1994,
796,
288,
13,
1136,
11,
9575,
796,
6407,
8,
198,
1640,
1312,
287,
264,
58,
25,
18,
5974,
198,
220,
220,
220,
3601,
7,
72,
11,
288,
58,
72,
12962,
198
] | 1.888889 | 99 |
for row in solve(10):
print(row)
| [
198,
198,
1640,
5752,
287,
8494,
7,
940,
2599,
198,
220,
220,
220,
3601,
7,
808,
8,
198
] | 2.166667 | 18 |
import random
import string
from subprocess import run
from types import SimpleNamespace
import psycopg2
import versions
from docker_helpers import get_image_name, pull, exec_safely
from service_config import api_db_user
from settings import get_secret
root_user = "vimc"
# these tables should only be modified via sql migrations
protected_tables = ["gavi_support_level", "activity_type",
"burden_outcome",
"gender",
"responsibility_set_status",
"impact_outcome",
"gavi_support_level",
"support_type",
"touchstone_status",
"permission",
"role",
"role_permission"]
def for_each_user(root_password, users, operation):
"""Operation is a callback (function) that takes the connection cursor
and a UserConfig object"""
with connect(root_user, root_password) as conn:
with conn.cursor() as cur:
for user in users:
operation(cur, user)
conn.commit()
# NOTE: it might be worth revisiting this to not run this script
# directly (that requires corresponding changes in montagu-db to move
# the inline sql into a standalone .sql file and then getting psql to
# run it via docker exec - it must run as the vimc user). The
# passwords might move directly under control here using set_password
# (but these are special users so we'd not want to use the rest of the
# user machinery). But I suggest waiting until the restore is done
# VIMC-1560) because that is likely to affect how we deal with users
| [
11748,
4738,
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11748,
4731,
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6738,
850,
14681,
1330,
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220,
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220,
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220,
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220,
220,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
70,
15820,
62,
11284,
62,
5715,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
11284,
62,
4906,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
29332,
6440,
62,
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1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
525,
3411,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
18090,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
18090,
62,
525,
3411,
8973,
628,
628,
628,
628,
628,
628,
628,
628,
198,
198,
4299,
329,
62,
27379,
62,
7220,
7,
15763,
62,
28712,
11,
2985,
11,
4905,
2599,
198,
220,
220,
220,
37227,
32180,
318,
257,
23838,
357,
8818,
8,
326,
2753,
262,
4637,
23493,
198,
220,
220,
220,
290,
257,
11787,
16934,
2134,
37811,
198,
220,
220,
220,
351,
2018,
7,
15763,
62,
7220,
11,
6808,
62,
28712,
8,
355,
48260,
25,
198,
220,
220,
220,
220,
220,
220,
220,
351,
48260,
13,
66,
21471,
3419,
355,
1090,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
2836,
287,
2985,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4905,
7,
22019,
11,
2836,
8,
198,
220,
220,
220,
220,
220,
220,
220,
48260,
13,
41509,
3419,
628,
198,
198,
2,
24550,
25,
340,
1244,
307,
2861,
22124,
1780,
428,
284,
407,
1057,
428,
4226,
198,
2,
3264,
357,
5562,
4433,
11188,
2458,
287,
40689,
11433,
12,
9945,
284,
1445,
198,
2,
262,
26098,
44161,
656,
257,
27669,
764,
25410,
2393,
290,
788,
1972,
279,
25410,
284,
198,
2,
1057,
340,
2884,
36253,
2452,
532,
340,
1276,
1057,
355,
262,
43907,
66,
2836,
737,
220,
383,
198,
2,
21442,
1244,
1445,
3264,
739,
1630,
994,
1262,
900,
62,
28712,
198,
2,
357,
4360,
777,
389,
2041,
2985,
523,
356,
1549,
407,
765,
284,
779,
262,
1334,
286,
262,
198,
2,
2836,
20230,
737,
220,
887,
314,
1950,
4953,
1566,
262,
11169,
318,
1760,
198,
2,
569,
3955,
34,
12,
1314,
1899,
8,
780,
326,
318,
1884,
284,
2689,
703,
356,
1730,
351,
2985,
628
] | 2.615987 | 638 |
#!/usr/bin/env python3
import poplib
import argparse
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='MailBox basic params')
parser.add_argument('--hostname', action="store", dest="hostname")
parser.add_argument('--port', action="store", dest="port")
parser.add_argument('--user', action="store", dest="user")
given_args = parser.parse_args()
hostname = given_args.hostname
port = given_args.port
user = given_args.user
import getpass
password = getpass.getpass(prompt='Enter your password:')
main(hostname,port,user,password)
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
18,
198,
198,
11748,
1461,
8019,
198,
11748,
1822,
29572,
198,
198,
361,
11593,
3672,
834,
6624,
705,
834,
12417,
834,
10354,
198,
220,
220,
220,
220,
220,
220,
220,
30751,
796,
1822,
29572,
13,
28100,
1713,
46677,
7,
11213,
11639,
25804,
14253,
4096,
42287,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
30751,
13,
2860,
62,
49140,
10786,
438,
4774,
3672,
3256,
2223,
2625,
8095,
1600,
2244,
2625,
4774,
3672,
4943,
198,
220,
220,
220,
220,
220,
220,
220,
30751,
13,
2860,
62,
49140,
10786,
438,
634,
3256,
2223,
2625,
8095,
1600,
2244,
2625,
634,
4943,
198,
220,
220,
220,
220,
220,
220,
220,
30751,
13,
2860,
62,
49140,
10786,
438,
7220,
3256,
2223,
2625,
8095,
1600,
2244,
2625,
7220,
4943,
628,
220,
220,
220,
220,
220,
220,
220,
1813,
62,
22046,
796,
30751,
13,
29572,
62,
22046,
3419,
220,
198,
220,
220,
220,
220,
220,
220,
220,
2583,
3672,
796,
1813,
62,
22046,
13,
4774,
3672,
198,
220,
220,
220,
220,
220,
220,
220,
2493,
796,
1813,
62,
22046,
13,
634,
198,
220,
220,
220,
220,
220,
220,
220,
2836,
796,
1813,
62,
22046,
13,
7220,
628,
220,
220,
220,
220,
220,
220,
220,
1330,
651,
6603,
220,
198,
220,
220,
220,
220,
220,
220,
220,
9206,
796,
651,
6603,
13,
1136,
6603,
7,
16963,
457,
11639,
17469,
534,
9206,
25,
11537,
628,
220,
220,
220,
220,
220,
220,
220,
1388,
7,
4774,
3672,
11,
634,
11,
7220,
11,
28712,
8,
628
] | 2.557312 | 253 |
import sys
import time
import redis
from openob.rtp.tx import RTPTransmitter
from openob.rtp.rx import RTPReceiver
import gst
from colorama import Fore, Back, Style
# OpenOB Link Manager
# One of these runs at each end and negotiates everything (RX pushes config info to TX), reconnects when links fail, and so on.
class Manager:
'''OpenOB Manager. Handles management of links, mostly recovery from failures.'''
| [
11748,
25064,
201,
198,
11748,
640,
201,
198,
11748,
2266,
271,
201,
198,
6738,
1280,
672,
13,
17034,
79,
13,
17602,
1330,
371,
7250,
8291,
37974,
201,
198,
6738,
1280,
672,
13,
17034,
79,
13,
40914,
1330,
371,
7250,
3041,
39729,
201,
198,
11748,
308,
301,
201,
198,
6738,
3124,
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4558,
11,
5157,
11,
17738,
201,
198,
2,
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9864,
7502,
9142,
201,
198,
2,
1881,
286,
777,
4539,
379,
1123,
886,
290,
5578,
689,
2279,
357,
49,
55,
20070,
4566,
7508,
284,
15326,
828,
37671,
82,
618,
6117,
2038,
11,
290,
523,
319,
13,
201,
198,
4871,
9142,
25,
201,
198,
220,
705,
7061,
11505,
9864,
9142,
13,
7157,
829,
4542,
286,
6117,
11,
4632,
7628,
422,
15536,
2637,
7061,
201,
198
] | 3.4 | 125 |
# -*- coding: utf-8 -*-
# ------------------------------------------------------------------------------
#
# Copyright 2018-2019 Fetch.AI Limited
#
# 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.
#
# ------------------------------------------------------------------------------
"""This module contains the tests of the strategy class of the confirmation aw2 skill."""
import datetime
import logging
from pathlib import Path
from typing import cast
from unittest.mock import Mock, patch
import pytest
from packages.fetchai.skills.confirmation_aw2.registration_db import RegistrationDB
from packages.fetchai.skills.confirmation_aw2.strategy import Strategy
from tests.conftest import ROOT_DIR
from tests.test_packages.test_skills.test_confirmation_aw2.intermediate_class import (
ConfirmationAW2TestCase,
)
class TestStrategy(ConfirmationAW2TestCase):
"""Test Strategy of confirmation aw2."""
path_to_skill = Path(ROOT_DIR, "packages", "fetchai", "skills", "confirmation_aw2")
@classmethod
def setup(cls):
"""Setup the test class."""
super().setup()
cls.minimum_hours_between_txs = 4
cls.minimum_minutes_since_last_attempt = 2
cls.strategy = Strategy(
aw1_aea="some_aw1_aea",
mininum_hours_between_txs=cls.minimum_hours_between_txs,
minimum_minutes_since_last_attempt=cls.minimum_minutes_since_last_attempt,
name="strategy",
skill_context=cls._skill.skill_context,
)
cls.address = "some_address"
cls.info = {
"ethereum_address": "some_value",
"signature_of_ethereum_address": "some_signature_of_ethereum_address",
"signature_of_fetchai_address": "some_signature_of_fetchai_address",
"developer_handle": "some_developer_handle",
"tweet": "some_tweet",
}
cls.logger = cls._skill.skill_context.logger
cls.db = cast(RegistrationDB, cls._skill.skill_context.registration_db)
cls.counterparty = "couterparty_1"
def test__init__i(self):
"""Test the __init__ of Strategy class."""
assert self.strategy.aw1_aea == self.aw1_aea
assert self.strategy.minimum_hours_between_txs == self.minimum_hours_between_txs
assert (
self.strategy.minimum_minutes_since_last_attempt
== self.minimum_minutes_since_last_attempt
)
def test__init__ii(self):
"""Test the __init__ of Strategy class where aw1_aea is None."""
with pytest.raises(ValueError, match="aw1_aea must be provided!"):
Strategy(
aw1_aea=None,
mininum_hours_between_txs=self.minimum_hours_between_txs,
minimum_minutes_since_last_attempt=self.minimum_minutes_since_last_attempt,
name="strategy",
skill_context=self.skill.skill_context,
)
def test_get_acceptable_counterparties(self):
"""Test the get_acceptable_counterparties method of the Strategy class."""
# setup
couterparties = ("couterparty_1", "couterparty_2", "couterparty_3")
is_valid_counterparty = [True, False, True]
# operation
with patch.object(
self.strategy, "is_valid_counterparty", side_effect=is_valid_counterparty
):
actual_acceptable_counterparties = self.strategy.get_acceptable_counterparties(
couterparties
)
# after
assert actual_acceptable_counterparties == ("couterparty_1", "couterparty_3")
def test_is_enough_time_since_last_attempt_i(self):
"""Test the is_enough_time_since_last_attempt method of the Strategy class where now IS greater than last attempt + min minutes."""
# setup
counterparty_last_attempt_time_str = "2020-12-22 20:30:00.000000"
counterparty_last_attempt_time = datetime.datetime.strptime(
counterparty_last_attempt_time_str, "%Y-%m-%d %H:%M:%S.%f"
)
mocked_now_greater_than_last_plus_minimum = "2020-12-22 20:33:00.000000"
datetime_mock = Mock(wraps=datetime.datetime)
datetime_mock.now.return_value = datetime.datetime.strptime(
mocked_now_greater_than_last_plus_minimum, "%Y-%m-%d %H:%M:%S.%f"
)
self.strategy.last_attempt = {self.counterparty: counterparty_last_attempt_time}
# operation
with patch("datetime.datetime", new=datetime_mock):
is_enough_time = self.strategy.is_enough_time_since_last_attempt(
self.counterparty
)
# after
assert is_enough_time is True
def test_is_enough_time_since_last_attempt_ii(self):
"""Test the is_enough_time_since_last_attempt method of the Strategy class where now is NOT greater than last attempt + min minutes."""
# setup
counterparty_last_attempt_time_str = "2020-12-22 20:30:00.000000"
counterparty_last_attempt_time = datetime.datetime.strptime(
counterparty_last_attempt_time_str, "%Y-%m-%d %H:%M:%S.%f"
)
mocked_now_less_than_last_plus_minimum = "2020-12-22 20:31:00.000000"
datetime_mock = Mock(wraps=datetime.datetime)
datetime_mock.now.return_value = datetime.datetime.strptime(
mocked_now_less_than_last_plus_minimum, "%Y-%m-%d %H:%M:%S.%f"
)
self.strategy.last_attempt = {self.counterparty: counterparty_last_attempt_time}
# operation
with patch("datetime.datetime", new=datetime_mock):
is_enough_time = self.strategy.is_enough_time_since_last_attempt(
self.counterparty
)
# after
assert is_enough_time is False
def test_is_enough_time_since_last_attempt_iii(self):
"""Test the is_enough_time_since_last_attempt method of the Strategy class where now counterparty is NOT in last_attempt."""
# setup
self.strategy.last_attempt = {}
# operation
is_enough_time = self.strategy.is_enough_time_since_last_attempt(
self.counterparty
)
# after
assert is_enough_time is True
def test_is_valid_counterparty_i(self):
"""Test the is_valid_counterparty method of the Strategy class where is_registered is False."""
# operation
with patch.object(self.db, "is_registered", return_value=False):
with patch.object(self.logger, "log") as mock_logger:
is_valid = self.strategy.is_valid_counterparty(self.counterparty)
# after
mock_logger.assert_any_call(
logging.INFO, f"Invalid counterparty={self.counterparty}, not registered!",
)
assert is_valid is False
def test_is_valid_counterparty_ii(self):
"""Test the is_valid_counterparty method of the Strategy class where is_enough_time_since_last_attempt is False."""
# operation
with patch.object(self.db, "is_registered", return_value=True):
with patch.object(
self.strategy, "is_enough_time_since_last_attempt", return_value=False
):
with patch.object(self.logger, "log") as mock_logger:
is_valid = self.strategy.is_valid_counterparty(self.counterparty)
# after
mock_logger.assert_any_call(
logging.DEBUG,
f"Not enough time since last attempt for counterparty={self.counterparty}!",
)
assert is_valid is False
def test_is_valid_counterparty_iii(self):
"""Test the is_valid_counterparty method of the Strategy class where is_allowed_to_trade is False."""
# operation
with patch.object(self.db, "is_registered", return_value=True):
with patch.object(
self.strategy, "is_enough_time_since_last_attempt", return_value=True
):
with patch.object(self.db, "is_allowed_to_trade", return_value=False):
is_valid = self.strategy.is_valid_counterparty(self.counterparty)
# after
assert is_valid is False
def test_is_valid_counterparty_iv(self):
"""Test the is_valid_counterparty method of the Strategy class where it succeeds."""
# operation
with patch.object(self.db, "is_registered", return_value=True):
with patch.object(
self.strategy, "is_enough_time_since_last_attempt", return_value=True
):
with patch.object(self.db, "is_allowed_to_trade", return_value=True):
is_valid = self.strategy.is_valid_counterparty(self.counterparty)
# after
assert is_valid is True
def test_successful_trade_with_counterparty(self):
"""Test the successful_trade_with_counterparty method of the Strategy class."""
# setup
data = {"some_key_1": "some_value_1", "some_key_2": "some_value_2"}
mocked_now_str = "2020-12-22 20:33:00.000000"
mock_now = datetime.datetime.strptime(mocked_now_str, "%Y-%m-%d %H:%M:%S.%f")
datetime_mock = Mock(wraps=datetime.datetime)
datetime_mock.now.return_value = mock_now
# operation
with patch.object(self.db, "set_trade") as mock_set_trade:
with patch("datetime.datetime", new=datetime_mock):
with patch.object(self.logger, "log") as mock_logger:
self.strategy.successful_trade_with_counterparty(
self.counterparty, data
)
# after
mock_set_trade.assert_any_call(self.counterparty, mock_now, data)
mock_logger.assert_any_call(
logging.INFO,
f"Successful trade with={self.counterparty}. Data acquired={data}!",
)
def test_register_counterparty(self):
"""Test the register_counterparty method of the Strategy class."""
# setup
developer_handle = "some_developer_handle"
# operation
with patch.object(self.db, "set_registered") as mock_set_registered:
self.strategy.register_counterparty(self.counterparty, developer_handle)
# after
mock_set_registered.assert_any_call(self.counterparty, developer_handle)
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] | 2.337744 | 4,610 |
try:
from mpi4py import MPI # @UnusedImport @IgnorePep8 This is imported before NEURON to avoid a bug in NEURON
except ImportError:
mpi_comm = DummyMPICom()
else:
mpi_comm = MPI.COMM_WORLD
MPI_ROOT = 0
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] | 2.369565 | 92 |
# -*- coding: utf-8 -*-
"""Unpacks Raw API data from zkillboard into victim files that contain
TEST - 10/02/2019
Params: 10000002201505.csv | 61MB | 28208 rows x 8 columns
Output:
```
(+0.000s|t:0.000s) Importing modules...
(+2.209s|t:2.209s) Loading CSV data from local file...
(+1.132s|t:3.341s) Converting DataFrame column value types...
(+18.746s|t:22.087s) Loading YAML files into memory...
(+3.88m|t:4.25m) Unpacking DataFrame values...
(+2.30m|t:6.55m) Writing results to CSV...
(+8.008s|t:6.68m) Exit
```
Written By: Adam Coscia
Updated On: 11/09/2019
"""
# Start timing
import time
start = time.time()
total = 0
def lap(msg):
"""Records time elapsed."""
global start, total
elapsed = (time.time() - start) - total
total = time.time() - start
if elapsed > 3600:
print(f'(+{elapsed/3600:.2f}h|t:{total/3600:.2f}h) {msg}')
elif elapsed > 60:
if total > 3600:
print(f'(+{elapsed/60:.2f}m|t:{total/3600:.2f}h) {msg}')
else:
print(f'(+{elapsed/60:.2f}m|t:{total/60:.2f}m) {msg}')
else:
if total > 3600:
print(f'(+{elapsed:.3f}s|t:{total/3600:.2f}h) {msg}')
elif total > 60:
print(f'(+{elapsed:.3f}s|t:{total/60:.2f}m) {msg}')
else:
print(f'(+{elapsed:.3f}s|t:{total:.3f}s) {msg}')
lap("Importing modules...")
from ast import literal_eval
import os
import sys
import numpy as np
import pandas as pd
import yaml
def load_yaml(file_loc, encoding='utf-8'):
"""Loads yaml file at file_loc and returns Python object based on yaml
structure.
"""
data = None
with open(file_loc, 'r', encoding=encoding) as stream:
try:
data = yaml.safe_load(stream)
except yaml.YAMLError as exc:
print(exc)
return data
def unpack(data: pd.DataFrame):
"""Operations to unpack nested data, yield row for row in old data.
Iterate over each row of data using generator and unpack each row.
"""
for row in data.itertuples():
# Some killmails are npcs, don't include their items and values
if 'character_id' in row.victim:
# These values are guaranteed in every killmail
victim_row = [row.killmail_time,
row.solar_system_id,
row.victim['character_id']]
# Try to add ship_type_id to victim values if exists
if 'ship_type_id' in row.victim:
victim_row.append(row.victim['ship_type_id'])
else:
victim_row.append(np.nan)
# Try to add item info to victim values if exists
if 'items' in row.victim and row.victim['items']:
victim_row.append(parse_items(row.victim['items']))
else:
victim_row.append([]) # keep empty array
else:
victim_row = None
if 'npc' in row.zkb:
npc = row.zkb['npc']
else:
npc = False # Assume there are attackers
attacker_rows = []
if not npc:
attacker_rows.extend(
[attacker for attacker in parse_attackers(row.attackers)]
)
yield victim_row, attacker_rows, row.killmail_id
# Specify S3 parameters and SQL query
bucket='dilabevetrajectorymining'
key='eve-trajectory-mining/Killmail_Fetching/killmail_scrapes/byregion/10000002/10000002201505.csv'
query="""
SELECT *
FROM s3Object s
LIMIT 5
"""
# Let amazon do the api calls
# print('Querying s3 bucket...')
# df = select(bucket, key, query)
#
# Open YAML file of typeIDs to get names of items
# typeIDs.yaml -> dictionary of typeID keys which contain attributes
# ex. typeIDs[11317] -> {'description': {'en': 'blah', ...}, ...}
# typeIDs[11317]['name']['en'] == '800mm Rolled Tungsten Compact Plates'
# typeIDs[11317]['groupID'] == 329
# groupIDs[329] -> {'name': {'en': 'blah', ...}, ...}
# groupIDs[329]['name']['en'] == 'Armor Reinforcer'
#
lap("Loading YAML files into memory...")
root = "../Trajectory_Mining/docs/eve files" # YAML file location
typeIDs = load_yaml(os.path.join(root, 'typeIDs.yaml'))
groupIDs = load_yaml(os.path.join(root, 'groupIDs.yaml'))
# invFlags = load_yaml(os.path.join(root, 'invFlags.yaml'))
# invMarketGroups = load_yaml(os.path.join(root, 'invMarketGroups.yaml'))
# categoryIDs = load_yaml(os.path.join(root, 'categoryIDs.yaml'))
# Sequentially load CSV's from file
lap("Loading CSV data from killmail_scrapes...")
victims = [] # list of victim dataframes generated from CSV's
attackers = [] # list of victim dataframes generated from CSV's
for root, dirs, files in os.walk("../Killmail_Fetching/killmail_scrapes/byregion", topdown=False):
count = 0
num_files = len(files) # number of CSV files
for file in sorted(files):
print(f"Progress {count/num_files:2.1%} ", end="\r")
df = pd.read_csv(os.path.join(root, file), encoding='utf-8')
# Convert all timestamp strings to numpy.datetime64
# print("> Converting DataFrame column value types ", end="")
df['killmail_time'] = pd.to_datetime(df['killmail_time'],
# Turn errors into NaT
errors='coerce',
# Use this format to parse str
format='%Y-%m-%dT%H:%M:%SZ')
# Convert all numeric values in 'solar_system_id' to smallest int type
# Convert all non-numeric values in 'solar_system_id' to NaN
df['solar_system_id'] = pd.to_numeric(df['solar_system_id'],
# Turn errors into NaN
errors='coerce',
# Convert to smallest int type
downcast='integer')
# Convert values in columns to python objects
df['victim'] = df['victim'].apply(literal_eval)
df['attackers'] = df['attackers'].apply(literal_eval)
df['zkb'] = df['zkb'].apply(literal_eval)
# Unpack DataFrame subset containing lists and dicts
# print("> Unpacking DataFrame values ", end="")
victim_rows = []
attacker_rows = []
a_col = ['final_blow', 'damage_done', 'ship_type_id']
v_col = ['killmail_time', 'solar_system_id', 'character_id',
'ship_type_id', 'items']
for v_row, a_rows, k_id in unpack(df):
if v_row is not None: # If no character ID, don't append victim
victim_rows.append(pd.DataFrame(
[v_row],
columns=v_col,
index=pd.Index([k_id], name='killmail_id')
))
if a_rows:
attacker_rows.extend([pd.DataFrame(
[a_row],
columns=a_col,
index=pd.MultiIndex.from_tuples(
[(k_id, a_id)],
names=('killmail_id',
'character_id')
)
) for a_id, a_row in a_rows])
# Concat victim_rows together
# print("> Concating victim rows ", end="\r")
victims.append(pd.concat(victim_rows, sort=False))
# attackers.append(pd.concat(attacker_rows, sort=False))
count += 1
# Save victim and attacker info to CSV
lap("Writing results to CSV...")
df_victims = pd.concat(victims)
df_victims.to_csv('data/all_victims_items.csv')
# df_attackers = pd.concat(attackers)
# df_attackers.to_csv('data/all_attackers.csv')
lap("Exit")
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62,
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364,
13,
40664,
11537,
198,
198,
37796,
7203,
30337,
4943,
198
] | 1.991009 | 4,004 |
from django.shortcuts import render, get_object_or_404
from django.contrib import messages
from .forms import ReportForm
from data.models import Paper
| [
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#!/usr/bin/python
#stats.py
'''
Classes and functons to implement calculation and output of statistics
'''
#geonomics imports
from geonomics.utils.io import (_append_array2d_to_array_stack,
_append_row_to_csv, _write_dict_to_csv)
from geonomics.ops.selection import _calc_fitness
from geonomics.utils.viz import _check_display
#other imports
import numpy as np
from scipy.stats.stats import pearsonr
from collections import Counter as C
import os
import matplotlib as mpl
_check_display()
import matplotlib.pyplot as plt
######################################
# -----------------------------------#
# CLASSES ---------------------------#
# -----------------------------------#
######################################
#a StatsCollector class, to parameterize and manage calculation
#and collection of stats, then write them to file at the end of
#each model iteration
#create a master method, to be called each timestep, which will make a list
#of all stats that need to be calculated that timestep (based on the
#calculation-frequencies provided in the params dicts), and then calls the
#functions to calculate them all and adds the results to self.stats
#a method to make the filenames for all of the stats to be saved
#wrapper around io.append_array2d_to_array_stack
#TODO WHAT TO DO WITH t IN THIS CASE?? CAN'T ADD TO txt 3D ARRAY FILE
#wrapper around io.append_row_to_csv
#use io._write_dict_to_csv to write to disk all "other stats", i.e.
#all stats that collect only a single value per species per timestep
#TODO: CHANGE THE 'OTHER STATS' NAMING CONVENTION TO SOMETING MORE
#DESCRIPTIVE
#method to write stats to files, in the appropriate directory (by model
#and iteration number), and with the appropriate spp names in the filenames
#method to plot whichever stat as a function of runtime
######################################
# -----------------------------------#
# FUNCTIONS -------------------------#
# -----------------------------------#
######################################
#method to get pop size (NOTE: not actually calculating it)
#function to calculate the locus-wise (if mean == False) or mean (if
#mean == True) heterozygosity of the species
#function to calculate the locus-wise minor allele frequency of the species
#function to calculate the mean fitness of the species
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1612,
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198
] | 3.520408 | 686 |
from eve import Eve
from .db_domains import db_domains
import os
SETTINGS = {
'DOMAIN': db_domains,
'MONGO_HOST': 'localhost',
'MONGO_PORT': 27017,
# MONGO_USERNAME': os.environ.get(...),
# MONGO_PASSWORD': os.environ.get(...),
'MONGO_DBNAME': 'quantz',
'RENDERERS': [
'eve.render.JSONRenderer'
# 'eve.render.XMLRenderer'
],
'ALLOW_UNKNOWN': True,
# 'X_DOMAINS_RE': r'.*',
'X_DOMAINS': [r'*.zhangyuzheng.com'],
'IF_MATCH': False,
'ENFORCE_IF_MATCH': False,
'HATEOAS': False,
# 修改数据域名称,从 _items 改为 items，避免前端语法检查严格不能使用_开头的变量
# 'ITEMS': 'items',
# 'META': 'meta',
# 'DATE_CREATED': 'created',
# 'ID_FIELD': 'id', # FIXME: not working, Y?
# 'LAST_UPDATED': 'updated',
# 'ETAG': 'etag',
'PAGINATION_DEFAULT': 10000,
'PAGINATION_LIMIT': 99999999,
# 'OPTIMIZE_PAGINATION_FOR_SPEED': True,
'RESOURCE_METHODS': ['GET'],
'ITEM_METHODS': ['GET']
}
app = Eve(settings=SETTINGS)
app.on_fetched_resource += on_fetched_resource
@app.route('/mnt')
if __name__ == '__main__':
app.run(host='0.0.0.0', port=80)
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198,
220,
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11639,
15,
13,
15,
13,
15,
13,
15,
3256,
2493,
28,
1795,
8,
198
] | 1.883527 | 601 |
import datetime as dt
import calendar
import time
def iter_dates(start, end, period):
"""Yield dates from `start` to `end` with step equalt to `period`."""
current = start
while current <= end:
yield current
current += period
def month_last_day(date):
"""Return the last date of the month for the month containing date."""
_, last_day = calendar.monthrange(date.year, date.month)
return dt.datetime(date.year, date.month, last_day)
def month_first_day(date):
"""Return the first date of the month (always 01) for the month containing date."""
return dt.datetime(date.year, date.month, 1)
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8079,
7,
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13,
1941,
11,
3128,
13,
8424,
11,
352,
8,
628,
198
] | 2.90991 | 222 |
# https://leetcode.com/problems/remove-all-adjacent-duplicates-in-string | [
2,
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] | 2.769231 | 26 |
#-*- coding: utf-8 -*-
from py3wirecard.entities.lib.wireentity import *
from py3wirecard.entities.taxdocument import TaxDocument
| [
2,
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chinese_strings = [
'京', '津', '冀', '晋', '蒙', '辽', '吉', '黑', '沪', '苏',
'浙', '皖', '闽', '赣', '鲁', '豫', '鄂', '湘', '粤', '桂',
'琼', '渝', '川', '贵', '云', '藏', '陕', '甘', '青', '宁',
'新', '港', '澳', '台', '警', 'WJ']
# 36
chinese = [
'Beijing',
'Tianjin',
'Hebei',
'Shanxi',
'InnerMongolia',
'Liaoning',
'Jilin',
'Heilongjiang',
'Shanghai',
'Jiangsu',
'Zhejiang',
'Anhui',
'Fujian',
'Jiangxi',
'Shandong',
'Henan',
'Hubei',
'Hunan',
'Guangdong',
'Guangxi',
'Hainan',
'Chongqing',
'Sichuan',
'Guizhou',
'Yunnan',
'Xizang',
'Shaanxi',
'Gansu',
'Qinghai',
'Ningxia',
'Xinjiang',
'HongKong',
'Macau',
'Tibet',
'police',
'WJ']
# 26
alphabet = [
'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'J', 'K',
'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'U', 'V',
'W', 'X', 'Y', 'Z', 'I', 'O']
# 10
number = [
'0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
blank = ['-']
CHARS = blank + chinese + alphabet + number
SHOW_CHARS = blank + chinese_strings + alphabet + number | [
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] | 1.596291 | 701 |
# Generated by Django 3.1.7 on 2021-04-05 14:35
from django.db import migrations, models
import django.db.models.deletion
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] | 2.818182 | 44 |
"""Main definitions for Virtool Workflows."""
from typing import Any, Callable, Coroutine, Iterable, Optional, Sequence
from virtool_workflow.utils import coerce_to_coroutine_function
from fixtures import FixtureScope
WorkflowStep = Callable[..., Coroutine[Any, Any, None]]
class Workflow:
"""
A Workflow is a step-wise, long-running operation.
A workflow is comprised of:
1. a set of functions to be executed on startup (.on_startup)
2. a set of step functions which will be executed in order (.steps)
3. a set of functions to be executed once all steps are completed (.on_cleanup)
"""
on_startup: Sequence[WorkflowStep]
on_cleanup: Sequence[WorkflowStep]
steps: Sequence[WorkflowStep]
def __new__(
cls,
*args,
startup: Optional[Iterable[WorkflowStep]] = None,
cleanup: Optional[Iterable[WorkflowStep]] = None,
steps: Optional[Iterable[WorkflowStep]] = None,
**kwargs
):
"""
:param startup: An initial set of startup steps.
:param cleanup: An initial set of cleanup steps.
:param steps: An initial set of steps.
"""
obj = super().__new__(cls)
obj.on_startup = []
obj.on_cleanup = []
obj.steps = []
if startup:
obj.on_startup.extend(startup)
if cleanup:
obj.on_cleanup.extend(cleanup)
if steps:
obj.steps.extend(steps)
return obj
def startup(self, action: Callable) -> Callable:
"""Decorator for adding a step to workflow startup."""
self.on_startup.append(coerce_to_coroutine_function(action))
return action
def cleanup(self, action: Callable) -> Callable:
"""Decorator for adding a step to workflow cleanup."""
self.on_cleanup.append(coerce_to_coroutine_function(action))
return action
def step(self, step: Callable) -> Callable:
"""Decorator for adding a step to the workflow."""
self.steps.append(coerce_to_coroutine_function(step))
return step
def merge(self, *workflows: "Workflow"):
"""Merge steps from other workflows into this workflow."""
self.steps.extend(step for w in workflows for step in w.steps)
self.on_startup.extend(
step for w in workflows for step in w.on_startup)
self.on_cleanup.extend(
step for w in workflows for step in w.on_cleanup)
return self
async def bind_to_fixtures(self, scope: FixtureScope):
"""
Bind a workflow to fixtures.
This is a convenience method for binding a workflow to a set of fixtures.
"""
self.on_startup = [await scope.bind(f) for f in self.on_startup]
self.on_cleanup = [await scope.bind(f) for f in self.on_cleanup]
self.steps = [await scope.bind(f) for f in self.steps]
return self
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8,
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62,
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62,
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7,
69,
8,
329,
277,
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261,
62,
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220,
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220,
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2116,
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4548,
8354,
13,
21653,
7,
69,
8,
329,
277,
287,
2116,
13,
20214,
60,
628,
220,
220,
220,
220,
220,
220,
220,
1441,
2116,
198
] | 2.454163 | 1,189 |
#coding:utf-8
import threading
import time
import pytest
from .connect import DataClient, DataBase
from ..psrc import ConnPool
from concurrent.futures import ProcessPoolExecutor,ThreadPoolExecutor,as_completed
app = None
DATABASECONFIG = {
"test":{
"host": "localhost",
"port": 3306,
"username": "root",
"password": "",
"schema" : "test"
}
}
| [
2,
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220,
220,
220,
220,
220,
1782,
198,
92,
198,
220,
220,
220,
220,
628,
198
] | 2.348837 | 172 |
from . import parseutil
| [
6738,
764,
1330,
21136,
22602,
198
] | 4 | 6 |
# package org.apache.helix
#from org.apache.helix import *
#from java.lang.reflect import Constructor
#from java.util import ArrayList
#from java.util import Collection
#from java.util import Collections
#from java.util import HashMap
#from java.util import List
#from java.util import Map
from org.apache.helix.util.misc import enum
from org.apache.helix.ZNRecord import ZNRecord
import traceback
HelixPropertyAttribute = enum('BUCKET_SIZE', 'GROUP_MESSAGE_MODE')
| [
2,
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62,
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11537,
628,
628
] | 3.309859 | 142 |
# Copyright (c) 2019 fortiss GmbH
#
# This software is released under the MIT License.
# https://opensource.org/licenses/MIT
import unittest
import matplotlib.pyplot as plt
from bark.world.agent import *
from bark.models.behavior import *
from bark.world import *
from bark.geometry import *
from bark.models.dynamic import *
from bark.models.execution import *
from bark.geometry import *
from bark.geometry.standard_shapes import *
from modules.runtime.commons.parameters import ParameterServer
from bark.world.opendrive import *
from bark.world.map import *
from modules.runtime.commons.xodr_parser import XodrParser
if __name__ == '__main__':
unittest.main()
| [
2,
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10354,
198,
220,
220,
220,
555,
715,
395,
13,
12417,
3419,
198
] | 3.17757 | 214 |
if sm.hasQuest(1666):
sm.warpInstanceIn(931050429)
sm.createClock(6*60)
sm.invokeAfterDelay(6*60*1000, "warpInstanceOut", 230040410, 0)
else:
map = 230040420
portal = 2
sm.warp(map, portal)
| [
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7,
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11,
17898,
8,
198
] | 2.087379 | 103 |
from typing import List, Union
from pysbr.queries.query import Query
import pysbr.utils as utils
class MarketsByMarketIds(Query):
"""Get information about a number of leagues from their league ids.
Market name, description, and market type id are included in the response.
Args:
market_ids: SBR market id or list of market ids.
sport_id: SBR sport id.
"""
@Query.typecheck
| [
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628,
220,
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2488,
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13,
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9122,
198
] | 2.985612 | 139 |
from py_uci import UCIEngine
e = UCIEngine()
e.new_game()
e.set_position(moves=["e2e4"])
e.find_best_move()
| [
6738,
12972,
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] | 2.204082 | 49 |
name = "branch_bound"
if __name__ == "__main__":
print("branch bound installed!")
| [
3672,
796,
366,
1671,
3702,
62,
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1,
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220,
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5421,
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8,
198
] | 2.636364 | 33 |
# coding:utf-8
# tensorflow version1.13.1
import tensorflow as tf
saver = tf.train.import_meta_graph('models/model.ckpt-667589.meta', clear_devices=True)
with tf.Session() as sess:
chpt_state = tf.train.get_checkpoint_state('models/model.ckpt-667589')
# if chpt_state:
# last_model = chpt_state.model_checkpoint_path
last_model = "models/model.ckpt-667589"
saver.restore(sess,last_model)
print ("model was loaded",last_model)
# else:
# print ("model cannot loaded")
# exit(1)
graph = tf.get_default_graph()
graph_def = graph.as_graph_def()
x = graph.get_tensor_by_name('x:0')
out = graph.get_tensor_by_name('reduce/out:0')
tf.saved_model.simple_save(sess, './models', inputs={"x": x}, outputs={"reduce/out": out})
| [
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445,
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] | 2.491694 | 301 |
'''Running subprocesses asynchronously.'''
from __future__ import print_function
import fcntl
import os
import os.path
import subprocess
import functools
import datetime
import pty
import signal
import atexit
import tornado.ioloop
from tornado.ioloop import IOLoop, PeriodicCallback
import tornado.process
from seesaw.event import Event
from seesaw.task import Task
from seesaw.config import realize
import time
_all_procs = set()
@atexit.register
class AsyncPopen(object):
'''Asynchronous version of :class:`subprocess.Popen`.
Deprecated.
'''
@classmethod
class AsyncPopen2(object):
'''Adapter for the legacy AsyncPopen'''
@property
class ExternalProcess(Task):
'''External subprocess runner.'''
class WgetDownload(ExternalProcess):
'''Download with Wget process runner.'''
class RsyncUpload(ExternalProcess):
'''Upload with Rsync process runner.'''
class CurlUpload(ExternalProcess):
'''Upload with Curl process runner.'''
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] | 3.22549 | 306 |
from fastai.vision.all import *
import gc
import torch
from typing import Callable
__all__ = ['EMAAverager', 'EMACallback', 'add_ema_to_gan_learner', 'custom_save_model',
'custom_load_model', 'SaveCheckpointsCallback', 'clean_mem']
class EMAAverager():
"""Callable class that calculates the EMA of a parameter.
It can be used as the `avg_fn` parameter of `torch.optim.swa_utils.AveragedModel`
Args:
decay (float): weight of averaged value. The new value of the parameter is
multiplied by 1 - decay.
"""
class FullyAveragedModel(torch.optim.swa_utils.AveragedModel):
"""Extension of AveragedModel that also averages the buffers.
To update both the parameters and the buffers, the method `update_all` should be
called instead of `update_parameters`."""
@torch.no_grad()
def _update_bn(loader, model, device=None, forward_batch:Callable=None):
r"""Updates BatchNorm running_mean, running_var buffers in the model.
It performs one pass over data in `loader` to estimate the activation
statistics for BatchNorm layers in the model.
Args:
loader (torch.utils.data.DataLoader): dataset loader to compute the
activation statistics on. Each data batch should be either a
tensor, or a list/tuple whose first element is a tensor
containing data.
model (torch.nn.Module): model for which we seek to update BatchNorm
statistics.
device (torch.device, optional): If set, data will be transferred to
:attr:`device` before being passed into :attr:`model`.
forward_batch: method that chooses how to extract the input from every
element of :attr:`loader`, transfers it to :attr:`device` and
finally makes a forward pass on :attr:`model`.
Example:
>>> loader, model = ...
>>> _update_bn(loader, model)
"""
momenta = {}
for module in model.modules():
if isinstance(module, torch.nn.modules.batchnorm._BatchNorm):
module.running_mean = torch.zeros_like(module.running_mean)
module.running_var = torch.ones_like(module.running_var)
momenta[module] = module.momentum
if not momenta:
return
was_training = model.training
model.train()
for module in momenta.keys():
module.momentum = None
module.num_batches_tracked *= 0
if forward_batch is None: forward_batch = _default_forward_batch
for batch in loader:
forward_batch(model, batch, device)
for bn_module in momenta.keys():
bn_module.momentum = momenta[bn_module]
model.train(was_training)
class EMACallback(Callback):
"""Updates the averaged weights of the generator of a GAN after every opt step.
It's meant to be used only with a GANLearner; i.e., an instance of this callback
is assumed to be attached to a GANLearner.
Args:
ema_model: AveragedModel that wraps the averaged generator module.
orig_model: active (not averaged) generator module, the one that's included
in learner.model and updated by the optimizer.
dl: dataloader needed to iterate over all data and make forward passes over the
ema_model in order to update the running statistic of BN layers.
update_buffers: if True, not only parameters, but also buffers, of ema_model are
averaged and updated,
forward_batch (Callable): Method with params (model, batch, device) that chooses
how to extract the input from every element of `dl`, transfers it to the proper
device and finally makes a forward pass on the model (here `ema_model`).
It's needed for updating the running statistics of BN layers.
"""
def add_ema_to_gan_learner(gan_learner, dblock, decay=0.999, update_bn_dl_bs=64,
forward_batch=None):
""""Creates and setups everything needed to update an alternative EMA generator.
It stores the EMA generator in `ema_model` attribute of `gan_learner`.
Args:
gan_learner (GANLearner): the learner to add the EMA generator to.
dblock (DataBlock): needed to create dataloaders that are independent of those
of `gan_learner`, used after fit to update BN running stats of the EMA G.
decay: weight that multiplies averaged parameter every update.
update_bn_dl_bs: batch size used to update BN running stats.
forward_batch (Callable): Method with params (model, batch, device) that chooses
how to extract the input from every element of the dataloader, transfers it
to the proper device and finally makes a forward pass on the ema model.
It's needed for updating the running statistics of BN layers.
"""
generator = gan_learner.model.generator
ema_avg_fn = EMAAverager(decay=decay)
gan_learner.ema_model = FullyAveragedModel(generator, avg_fn=ema_avg_fn)
ds_path = gan_learner.dls.path
clean_dls = dblock.dataloaders(ds_path, path=ds_path, bs=update_bn_dl_bs)
gan_learner.ema_model.eval().to(clean_dls.device)
gan_learner.add_cb(EMACallback(gan_learner.ema_model, generator, clean_dls.train,
forward_batch=forward_batch))
def custom_save_model(learner, filename, base_path='.'):
"""Saves the model and optimizer state of the learner.
The path of the generated file is base_path/learner.model_dir/filename
with ".pth" extension. If the learner has an EMA G model attached too,
a similar file with the suffix "_ema" is generated too.
"""
if isinstance(base_path, str): base_path = Path(base_path)
if not isinstance(base_path, Path): raise Exception('Invalid base_path')
file = join_path_file(filename, base_path/learner.model_dir, ext='.pth')
save_model(file, learner.model, learner.opt)
if getattr(learner, 'ema_model', None) is not None:
_save_ema_model(learner, base_path, filename)
def custom_load_model(learner, filename, with_opt=True, device=None,
base_path='./models',
with_ema=False, **kwargs):
"""Loads the model and optimizer state of the learner.
The file is expected to be placed in `base_path/filename` with ".pth"
extension. `kwargs` are forwarded to fastai's `load_model` method.
"""
if isinstance(base_path, str): base_path = Path(base_path)
if not isinstance(base_path, Path): raise Exception('Invalid base_path')
if device is None and hasattr(learner.dls, 'device'): device = learner.dls.device
if learner.opt is None: learner.create_opt()
#file = join_path_file(filename, base_path/learner.model_dir, ext='.pth')
file = base_path/f'{filename}.pth'
load_model(file, learner.model, learner.opt, with_opt=with_opt, device=device, **kwargs)
if with_ema:
_load_ema_model(learner, base_path, filename)
class SaveCheckpointsCallback(Callback):
"Callback that saves the model at the end of each epoch."
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] | 2.660038 | 2,665 |
"""A set of sudoku puzzles to experiment with the spinnaker_csp package.
the puzzles are containned on the dictionary puzzles, keys are the name of the puzzle and values are tuples with the
puzzle as first element and solution as second element.
"""
puzzles={
#---------------------------------------------------------------------
'Dream': ("dream",
#---------------------------------------------------------------------
[[0 for x in range(9)] for y in range(9)],
None),
#---------------------------------------------------------------------
'easy':("easy", # easy from doi:10.1038/srep00725
#---------------------------------------
[[0, 4, 0, 8, 0, 5, 2, 0, 0],
[0, 2, 0, 0, 4, 0, 0, 5, 0],
[5, 0, 0, 0, 0, 0, 0, 0, 4],
[0, 9, 0, 0, 0, 3, 1, 2, 0],
[1, 0, 6, 0, 7, 8, 0, 0, 3],
[3, 7, 0, 9, 0, 4, 0, 8, 0],
[0, 0, 0, 0, 0, 6, 7, 0, 0],
[0, 0, 8, 3, 5, 9, 0, 1, 0],
[0, 1, 9, 0, 0, 7, 6, 0, 0]],
#---------------------------------------
[[9, 4, 7, 8, 3, 5, 2, 6, 1],
[6, 2, 3, 7, 4, 1, 8, 5, 9],
[5, 8, 1, 6, 9, 2, 3, 7, 4],
[8, 9, 4, 5, 6, 3, 1, 2, 7],
[1, 5, 6, 2, 7, 8, 9, 4, 3],
[3, 7, 2, 9, 1, 4, 5, 8, 6],
[4, 3, 5, 1, 2, 6, 7, 9, 8],
[7, 6, 8, 3, 5, 9, 4, 1, 2],
[2, 1, 9, 4, 8, 7, 6, 3, 5]]),
#---------------------------------------------------------------------
'hard':('hard', # hard puzzle from https://doi.org/10.1371/journal.pcbi.1003311
#---------------------------------------------------------------------
[[8, 0, 5, 0, 0, 0, 0, 3, 0],
[0, 3, 0, 9, 0, 0, 0, 0, 0],
[4, 0, 6, 0, 3, 0, 0, 0, 0],
[6, 0, 0, 0, 1, 0, 9, 0, 0],
[0, 5, 0, 3, 0, 8, 0, 7, 0],
[0, 0, 9, 0, 4, 0, 0, 0, 1],
[0, 0, 0, 0, 2, 0, 3, 0, 8],
[0, 0, 0, 0, 0, 9, 0, 2, 0],
[0, 7, 0, 0, 0, 0, 5, 0, 4]],
#---------------------------------------------------------------------
[[8, 1, 5, 6, 7, 4, 2, 3, 9],
[7, 3, 2, 9, 5, 1, 4, 8, 6],
[4, 9, 6, 8, 3, 2, 7, 1, 5],
[6, 8, 7, 2, 1, 5, 9, 4, 3],
[1, 5, 4, 3, 9, 8, 6, 7, 2],
[3, 2, 9, 7, 4, 6, 8, 5, 1],
[9, 4, 1, 5, 2, 7, 3, 6, 8],
[5, 6, 3, 4, 8, 9, 1, 2, 7],
[2, 7, 8, 1, 6, 3, 5, 9, 4]]),
#---------------------------------------------------------------------
'AI_escargot': ('AI_escargot',
#---------------------------------------------------------------------
[[1, 0, 0, 0, 0, 7, 0, 9, 0],
[0, 3, 0, 0, 2, 0, 0, 0, 8],
[0, 0, 9, 6, 0, 0, 5, 0, 0],
[0, 0, 5, 3, 0, 0, 9, 0, 0],
[0, 1, 0, 0, 8, 0, 0, 0, 2],
[6, 0, 0, 0, 0, 4, 0, 0, 0],
[3, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 4, 0, 0, 0, 0, 0, 0, 7],
[0, 0, 7, 0, 0, 0, 3, 0, 0]],
#---------------------------------------------------------------------
[[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0]]),
#---------------------------------------------------------------------
'platinum_blonde':('platinum_blonde', # hard from doi:10.1038/srep00725
#---------------------------------------------------------------------
[[0, 0, 0, 0, 0, 0, 0, 1, 2],
[0, 0, 0, 0, 0, 0, 0, 0, 3],
[0, 0, 2, 3, 0, 0, 4, 0, 0],
[0, 0, 1, 8, 0, 0, 0, 0, 5],
[0, 6, 0, 0, 7, 0, 8, 0, 0],
[0, 0, 0, 0, 0, 9, 0, 0, 0],
[0, 0, 8, 5, 0, 0, 0, 0, 0],
[9, 0, 0, 0, 4, 0, 5, 0, 0],
[4, 7, 0, 0, 0, 6, 0, 0, 0]],
#---------------------------------------------------------------------
[[8, 3, 9, 4, 6, 5, 7, 1, 2],
[1, 4, 6, 7, 8, 2, 9, 5, 3],
[7, 5, 2, 3, 9, 1, 4, 8, 6],
[3, 9, 1, 8, 2, 4, 6, 7, 5],
[5, 6, 4, 1, 7, 3, 8, 2, 9],
[2, 8, 7, 6, 5, 9, 3, 4, 1],
[6, 2, 8, 5, 3, 7, 1, 9, 4],
[9, 1, 3, 2, 4, 8, 5, 6, 7],
[4, 7, 5, 9, 1, 6, 2, 3, 8]])
}
#-----------------TEMPLATE---------------------------------------------
##---------------------------------------------------------------------
# [[0, 0, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 0, 0],
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# [0, 0, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 0, 0]]
#-----------------TEMPLATE 16X16----------------------------------------
# #---------------------------------------------------------------------
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11907
] | 2.015638 | 2,494 |
#!/usr/bin/env python
from __future__ import print_function
import os
print(
"""[busco]
out_path = {0}
tmp_path = {0}/tmp
[tblastn]
# path to tblastn
path = /usr/bin/
[makeblastdb]
# path to makeblastdb
path = /usr/bin/
[augustus]
# path to augustus
path = /opt/augustus/bin/
[etraining]
# path to augustus etraining
path = /opt/augustus/bin/
# path to augustus perl scripts, redeclare it for each new script
[gff2gbSmallDNA.pl]
path = /usr/bin/
[new_species.pl]
path = /usr/bin/
[optimize_augustus.pl]
path = /usr/bin/
[hmmsearch]
# path to HMMsearch executable
path = /usr/local/bin/
[Rscript]
# path to Rscript, if you wish to use the plot tool
path = /usr/bin/""".format(os.environ['PWD'])
)
| [
2,
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49,
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198
] | 2.429553 | 291 |
from flask import Flask, current_app
import jwt
from disasterpets import db
from disasterpets.Pets.models import Pets
| [
6738,
42903,
1330,
46947,
11,
1459,
62,
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198,
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474,
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198,
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198,
6738,
9336,
79,
1039,
13,
47,
1039,
13,
27530,
1330,
43578,
198
] | 3.6875 | 32 |
# Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree. An additional grant
# of patent rights can be found in the PATENTS file in the same directory.
"""
Utilities for downloading and building data.
These can be replaced if your particular file system does not support them.
"""
import datetime
import os
import requests
import shutil
import wget
def built(path):
"""Checks if '.built' flag has been set for that task."""
return os.path.isfile(os.path.join(path, '.built'))
def download(path, url, redownload=True):
"""Downloads file using `wget`. If redownload is set to false, then will not
download tar file again if it is present (default true).
"""
if redownload or not os.path.isfile(path):
filename = wget.download(url, out=path)
print() # wget prints download status, without newline
def download_request(url, path, fname):
"""Downloads file using `requests`."""
with requests.Session() as session:
response = session.get(url, stream=True)
CHUNK_SIZE = 32768
with open(os.path.join(path, fname), 'wb') as f:
for chunk in response.iter_content(CHUNK_SIZE):
if chunk: # filter out keep-alive new chunks
f.write(chunk)
response.close()
def make_dir(path):
"""Makes the directory and any nonexistent parent directories."""
os.makedirs(path, exist_ok=True)
def mark_done(path):
"""Marks the path as done by adding a '.built' file with the current
timestamp.
"""
with open(os.path.join(path, '.built'), 'w') as write:
write.write(str(datetime.datetime.today()))
def move(path1, path2):
"""Renames the given file."""
shutil.move(path1, path2)
def remove_dir(path):
"""Removes the given directory, if it exists."""
shutil.rmtree(path, ignore_errors=True)
def untar(path, fname, deleteTar=True):
"""Unpacks the given archive file to the same directory, then (by default)
deletes the archive file.
"""
print('unpacking ' + fname)
fullpath = os.path.join(path, fname)
shutil.unpack_archive(fullpath, path)
if deleteTar:
os.remove(fullpath)
def download_from_google_drive(gd_id, destination):
"""Uses the requests package to download a file from Google Drive."""
URL = 'https://docs.google.com/uc?export=download'
with requests.Session() as session:
response = session.get(URL, params={'id': gd_id}, stream=True)
token = _get_confirm_token(response)
if token:
response.close()
params = {'id': gd_id, 'confirm': token}
response = session.get(URL, params=params, stream=True)
CHUNK_SIZE = 32768
with open(destination, 'wb') as f:
for chunk in response.iter_content(CHUNK_SIZE):
if chunk: # filter out keep-alive new chunks
f.write(chunk)
response.close()
| [
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] | 2.638243 | 1,161 |
from datetime import datetime
import pytz
import firebase_admin
from firebase_admin import credentials
from firebase_admin import firestore
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] | 4.117647 | 34 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from bacpypes.iocb import IOCB
from bacpypes.pdu import Address, GlobalBroadcast
from bacpypes.apdu import WhoIsRequest, ReadPropertyRequest, ReadPropertyACK
from bacpypes.object import get_object_class, get_datatype
from bacpypes.object import ObjectType, registered_object_types
from bacpypes.basetypes import PropertyIdentifier
from eyed.driver.bacnet import definition
#
# BACnet Client
#
#
# BACnetClient 初期化処理
#
#
# getAddressByDeviceID
#
#
# WhoIsRequest
#
#
# IamRequest の 受信待ち
# - 例外: Empty (タイムアウト時)
#
#
# ReadProperty
#
#
# ReadProperty
#
#
# ReadDeviceProperty (デバイス関連の情報読み出し)
#
#
# addObject (オブジェクト の 登録)
#
#
# addProperty (プロパティ の 登録)
#
#
# getProperty (プロパティ の 登録)
#
#
# getObjectByID (オブジェクト の 取得)
#
#
# getObjectByID (オブジェクト の 取得 [ID 検索])
#
#
# getObjectByName (オブジェクト の 取得 [名前で検索])
#
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] | 1.889121 | 478 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from bson.objectid import ObjectId
from pymongo import MongoClient
from validate_email import validate_email
from views.base import base
import config
import hashlib
'''
forms constructor.
* Es necesario crear una variable tipo dict() que debe llevar la siguiente estructura.
{
'config(requerido)':{
'method(requerido)': 'valores POST o GET',
'action(requerido)': 'url para enviar la data',
'class' : 'Clases de css',
'error-class': 'Clase para el error'
},
fields(requerido): [
{
'name(requerido)': 'nombre del campo',
'widget(requerido)': 'Tipo de input',
'class': 'Clases de css',
'id': 'Valor del ID',
'label'(*Requiere que el ID del campo este seteado.): {
'attributes': 'Cualquier otro valor que no este disponible. ejemplo: data-*= "" ',
'class': 'Clases de css'
}
'placeholder': 'Valor del placeholder',
'required': 'Valores True o False',
'value': 'valor default del campo.'
}
]
}
'''
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6,
198,
197,
197,
197,
197,
92,
198,
197,
197,
197,
197,
6,
5372,
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10354,
705,
7762,
273,
1619,
46076,
3256,
198,
197,
197,
197,
197,
821,
421,
1202,
10354,
705,
7762,
2850,
6407,
267,
10352,
3256,
198,
197,
197,
197,
197,
6,
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10354,
705,
2100,
273,
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1619,
1413,
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198,
197,
197,
197,
92,
198,
197,
197,
60,
628,
197,
92,
198,
7061,
6,
628
] | 2.40566 | 424 |
import traceback
from twisted.internet import reactor
reactor.callWhenRunning(stack)
reactor.run()
| [
11748,
12854,
1891,
198,
6738,
19074,
13,
37675,
1330,
21905,
628,
198,
198,
260,
11218,
13,
13345,
2215,
28768,
7,
25558,
8,
198,
260,
11218,
13,
5143,
3419,
198
] | 3.517241 | 29 |
# -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import logging
from ralph.util import plugin
from ralph.util.api_scrooge import get_blade_servers
from ralph_scrooge.models import (
AssetInfo,
DailyAssetInfo,
DailyUsage,
UsageType,
)
logger = logging.getLogger(__name__)
def update_usage(daily_asset_info, date, value, usage_type):
"""
Saves single record to model
"""
usage, created = DailyUsage.objects.get_or_create(
date=date,
type=usage_type,
daily_pricing_object=daily_asset_info,
defaults=dict(
service_environment=daily_asset_info.service_environment,
)
)
usage.service_environment = daily_asset_info.service_environment
usage.value = value
usage.save()
return created
def update_blade_server(data, date, usage_type):
"""
Updates single Blade Server usage type record
"""
try:
asset_info = AssetInfo.objects.get(device_id=data['device_id'])
daily_asset_info = asset_info.dailyassetinfo_set.get(date=date)
return update_usage(
daily_asset_info,
date,
1,
usage_type,
)
except AssetInfo.DoesNotExist:
raise AssetInfoNotFoundError()
except DailyAssetInfo.DoesNotExist:
raise DailyAssetInfoNotFoundError()
def get_usage_type():
"""
Returns Blade Server usage type
"""
return UsageType.objects.get_or_create(
symbol='blade_server',
defaults=dict(
name='Blade server',
)
)[0]
@plugin.register(chain='scrooge', requires=['asset', 'service'])
def blade_server(today, **kwargs):
"""
Updates Blade Servers usages from Ralph
"""
usage_type = get_usage_type()
new_blades = updated = total = 0
for data in get_blade_servers():
try:
if update_blade_server(data, today, usage_type):
new_blades += 1
else:
updated += 1
except AssetInfoNotFoundError:
logger.warning('Device {} not found'.format(data['device_id']))
except DailyAssetInfoNotFoundError:
logger.warning(
'DailyAssetInfo for id {} and date {} not found'.format(
data['device_id'],
today,
)
)
total += 1
return (
True,
'{} new Blade Servers usages, {} updated, {} total'.format(
new_blades,
updated,
total,
)
)
| [
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220,
220,
220,
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220,
220,
220,
220,
220,
220,
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352,
198,
220,
220,
220,
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220,
220,
220,
220,
220,
220,
220,
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220,
220,
220,
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649,
62,
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220,
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220,
220,
220,
220,
220,
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220,
6153,
11,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2472,
11,
198,
220,
220,
220,
220,
220,
220,
220,
1267,
198,
220,
220,
220,
1267,
198
] | 2.242017 | 1,190 |
from django.db import models
from django.contrib.auth.models import (
AbstractUser,
BaseUserManager
)
| [
6738,
42625,
14208,
13,
9945,
1330,
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198,
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] | 2.897436 | 39 |
#!/usr/bin/env python3
#
# Convert Pavlick's dictionary to hunalign
#
import argparse
import re
if __name__ == "__main__":
main()
| [
2,
48443,
14629,
14,
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14,
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21015,
18,
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2,
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2,
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] | 2.72 | 50 |
# Standard Library
from typing import List, Union, Optional
from ipaddress import IPv4Network, IPv6Network
# Third Party
from pydantic import StrictStr
# Project
from rp.models._common import Flag, RPModel
class RouteFilterEntry(RPModel):
"""JunOS route-filter-list item JSON model."""
address: Union[IPv4Network, IPv6Network]
longer: Flag
orlonger: Flag
exact: Flag
prefix_length_range: Optional[StrictStr]
through: Optional[StrictStr]
upto: Optional[StrictStr]
class Config:
"""Pydantic config overrides."""
fields = {"prefix_length_range": "prefix-length-range"}
class RouteFilterList(RPModel):
"""JunOS route-filter-list JSON model."""
name: StrictStr
rf_list: List[RouteFilterEntry]
| [
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220,
220,
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220,
220,
374,
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62,
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25,
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58,
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22417,
30150,
60,
198
] | 2.908397 | 262 |
# -*- coding: utf-8 -*-
"""
Tencent is pleased to support the open source community by making 蓝鲸智云PaaS平台社区版 (BlueKing PaaS Community
Edition) available.
Copyright (C) 2017-2021 THL A29 Limited, a Tencent company. All rights reserved.
Licensed under the MIT License (the "License"); you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://opensource.org/licenses/MIT
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 json
from django.utils.translation import ugettext_lazy as _
from jsonschema import SchemaError
from jsonschema import ValidationError as JsonValidationError
from jsonschema import validate as json_validate
from rest_framework.exceptions import ValidationError
from .constants import KEY_PATTERN, NUM_VAR_ERROR_MSG, REAL_NUM_VAR_PATTERN
from .models import VersionedEntity, get_model_class_by_resource_name
def is_name_duplicate(resource_name, resource_id, name, version_id):
"""同一类资源的名称不能重复"""
# 判断新名称与老名称是否一致，如果一致，则不会重复
model_class = get_model_class_by_resource_name(resource_name)
try:
resource = model_class.objects.get(id=resource_id)
if name == resource.name:
return False
except model_class.DoesNotExist:
pass
# 只校验当前版本内是否重复
try:
version_entity = VersionedEntity.objects.get(id=version_id)
except VersionedEntity.DoesNotExist:
return False
else:
entity = version_entity.get_entity()
resource_ids = entity.get(resource_name, '')
if not resource_ids:
return False
if model_class.objects.filter(name=name, id__in=resource_ids.split(',')):
return True
return False
def validate_variable_inconfig(config):
"""校验配置文件中的变量名是否合法"""
search_list = KEY_PATTERN.findall(json.dumps(config))
search_keys = set(search_list)
for ikey in search_keys:
if not REAL_NUM_VAR_PATTERN.match(ikey):
raise ValidationError(_('变量[{}]不合法, {}').format(ikey, NUM_VAR_ERROR_MSG))
| [
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8,
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220,
220,
329,
220,
522,
88,
287,
2989,
62,
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25,
198,
220,
220,
220,
220,
220,
220,
220,
611,
407,
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62,
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62,
53,
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62,
47,
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7,
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88,
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198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5298,
3254,
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20998,
246,
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237,
58,
90,
92,
60,
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230,
37345,
243,
11,
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7,
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88,
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62,
53,
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62,
24908,
62,
5653,
38,
4008,
628,
198
] | 2.43949 | 942 |
'''
DeviceManager:
a Component that manages different device families e.g. Telescope, Camera, FilterWheel
via a GUI element that permits selection/connection/disconnection
DeviceFamily:
superclass of e.g. Camera, Telescope, FilterWheel
handles communication with devices for generic functions such as
select, connect, disconnect as well as common error handling
Device:
superclass of device instances e.g. SXCamera, ASCOMFilterWheel
'''
import json
import importlib
from functools import partial
from kivy.app import App
from loguru import logger
from kivy.metrics import dp
from kivy.uix.spinner import Spinner
from kivy.uix.button import Button
from kivy.uix.label import Label
from kivy.uix.boxlayout import BoxLayout
from kivy.event import EventDispatcher
from kivy.core.window import Window
from kivy.properties import (
ObjectProperty,
StringProperty, BooleanProperty, DictProperty
)
from kivy.clock import Clock
from jocular.component import Component
from jocular.settingsmanager import SettingsBase
from jocular.widgets import jicon, LabelL
from jocular.formwidgets import configurable_to_widget
from kivy.lang import Builder
Builder.load_string('''
<DeviceManager>:
canvas:
Color:
rgba: .2, .2, .2, .7
Ellipse:
pos: self.x + dp(58) + (self.width - self.height) / 2, dp(58)
size: self.height - dp(116), self.height - dp(116)
orientation: 'vertical'
pos_hint: {'center_x': 10, 'center_y': .5}
''')
''' Each actual device e.g. ASCOMTelescope, ManualFilterwheel etc is a subclass of this
'''
| [
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7,
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25,
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6,
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62,
87,
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838,
11,
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62,
88,
10354,
764,
20,
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628,
628,
198,
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6,
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304,
13,
70,
13,
25400,
2662,
33317,
3798,
3008,
11,
17969,
22417,
22001,
3503,
318,
257,
47611,
286,
428,
198,
7061,
6,
220,
628
] | 3.067327 | 505 |
import numpy as np
import cv2
import time
#Test unit
if __name__ == '__main__':
while True:
img = capture_image(0,10)
print(img)
time.sleep(2)
cv2.imshow("c",img)
cv2.waitKey(0)
cv2.destroyAllWindows()
| [
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220,
220,
220,
220,
220,
220,
3601,
7,
9600,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
640,
13,
42832,
7,
17,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
220,
220,
220,
220,
269,
85,
17,
13,
320,
12860,
7203,
66,
1600,
9600,
8,
198,
220,
220,
220,
220,
220,
220,
220,
269,
85,
17,
13,
17077,
9218,
7,
15,
8,
198,
220,
220,
220,
220,
220,
220,
220,
269,
85,
17,
13,
41659,
3237,
11209,
3419,
198
] | 1.681564 | 179 |
from ._Encode import *
| [
6738,
47540,
4834,
8189,
1330,
1635,
198
] | 3.285714 | 7 |
a = float(2)
b = int(2.0)
c = bool(a)
d = float(None) # fails
e = int(None) # fails
f = bool(None) # fails
# show_store()
| [
64,
796,
12178,
7,
17,
8,
198,
65,
796,
493,
7,
17,
13,
15,
8,
198,
66,
796,
20512,
7,
64,
8,
198,
67,
796,
12178,
7,
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8,
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2,
905,
62,
8095,
3419,
198
] | 2.047619 | 63 |
from db import nova_conexao
from mysql.connector.errors import ProgrammingError
sql = '''
SELECT A.NOME,
A.TEL,
B.DESCRICAO
FROM CONTATOS A
INNER JOIN GRUPOS B ON A.IDGRUPO = B.ID
ORDER BY B.DESCRICAO, A.NOME
'''
with nova_conexao() as conexao:
try:
cursor = conexao.cursor()
cursor.execute(sql)
contatos = cursor.fetchall()
except ProgrammingError as e:
print(f'Erro: {e.msg}')
else:
for contato in contatos:
print(f'Nome: {contato[0]:10s} tel: {contato[1]:15s} grupo: {contato[2]}') | [
6738,
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25,
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220,
220,
220,
220,
220,
220,
220,
329,
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5549,
287,
542,
35492,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3601,
7,
69,
6,
45,
462,
25,
1391,
3642,
5549,
58,
15,
5974,
940,
82,
92,
13632,
25,
1391,
3642,
5549,
58,
16,
5974,
1314,
82,
92,
22848,
7501,
25,
1391,
3642,
5549,
58,
17,
48999,
11537
] | 1.964286 | 308 |
#!/usr/bin/env python3
import json
import logging
import socket
import sys
from os.path import dirname, realpath; sys.path.append(dirname(dirname(dirname(realpath(__file__)))))
from logger.utils.formats import Text
from logger.readers.reader import Reader
# Don't barf if they don't have redis installed. Only complain if
# they actually try to use it, below
try:
import paho.mqtt.client as mqtt # import the client | $ pip installing paho-mqtt is necessary
PAHO_ENABLED = True
except ModuleNotFoundError:
PAHO_ENABLED = False
################################################################################
class MQTTReader(Reader):
"""
Read messages from an mqtt broker
"""
def __init__(self, broker, channel, client_name):
"""
Read text records from the channel subscription.
```
broker MQTT broker to connect, broker format[###.###.#.#]
channel MQTT channel to read from, channel format[@broker/path_of_subscripton]
```
Instructions on how to start an MQTT broker:
1. First install the Mosquitto Broker :
```
sudo apt-get update
sudo apt-get install mosquitto
sudo apt-get install mosquitto-clients
```
2. The mosquitto service starts automatically when downloaded but use :
```
sudo service mosquitto start
sudo service mosquitto stop
```
to start and stop the service.
3. To test the install use:
```
netstat -at
```
and you should see the MQTT broker which is the port 1883
4. In order to manually subscribe to a client use :
```
mosquitto_sub -t "example/topic"
```
and publish a message by using
```
mosquitto_pub -m "published message" -t "certain/channel"
```
5. Mosquitto uses a configuration file "mosquitto.conf" which you can find in /etc/mosquitto folder
```
"""
super().__init__(output_format=Text)
if not PAHO_ENABLED:
raise ModuleNotFoundError('MQTTReader(): paho-mqtt is not installed. Please '
'try "pip install paho-mqtt" prior to use.')
self.broker = broker
self.channel = channel
self.client_name = client_name
try:
self.paho = mqtt.Client(client_name)
self.paho.connect(broker)
self.paho.subscribe(channel)
while paho.loop() == 0:
pass
except mqtt.WebsocketConnectionError as e:
logging.error('Unable to connect to broker at %s:%s',
self.broker, self.channel)
raise e
############################
################################################################################
| [
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220,
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220,
220,
220,
2845,
285,
80,
926,
13,
1135,
1443,
5459,
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355,
304,
25,
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220,
220,
220,
220,
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540,
284,
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284,
20426,
379,
4064,
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25,
4,
82,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
7957,
6122,
11,
2116,
13,
17620,
8,
198,
220,
220,
220,
220,
220,
5298,
304,
628,
220,
1303,
14468,
7804,
21017,
198,
198,
29113,
29113,
14468,
198
] | 2.852679 | 896 |
import logging
from synch.enums import ClickHouseEngine
from synch.factory import Global
from synch.replication.etl import etl_full
from synch.writer.collapsing_merge_tree import ClickHouseCollapsingMergeTree
from synch.writer.merge_tree import ClickHouseMergeTree
logger = logging.getLogger("synch.replication.consumer")
| [
11748,
18931,
198,
198,
6738,
6171,
354,
13,
268,
5700,
1330,
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469,
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1136,
11187,
1362,
7203,
28869,
354,
13,
35666,
3299,
13,
49827,
4943,
628
] | 3.282828 | 99 |
# Authors: Lukas Breuer <[email protected]>
"""
----------------------------------------------------------------------
--- jumeg.decompose.fourier_ica_plot ---------------------------------
----------------------------------------------------------------------
autor : Lukas Breuer
email : [email protected]
last update: 17.11.2016
version : 1.1
----------------------------------------------------------------------
This is a simple implementation to plot the results achieved by
applying FourierICA
----------------------------------------------------------------------
"""
#######################################################
# #
# plotting functions for FourierICA #
# #
#######################################################
# +++++++++++++++++++++++++++++++++++++++++++++++++++++++
# Simple function to adjust axis in plots
# +++++++++++++++++++++++++++++++++++++++++++++++++++++++
def adjust_spines(ax, spines, labelsize=10):
"""
Simple function to adjust axis in plots
Parameters
----------
ax: axis object
Plot object which should be adjusted
spines: list of strings ['bottom', 'left']
Name of the axis which should be adjusted
labelsize: integer
Font size for the x- and y-axis labels
"""
for loc, spine in list(ax.spines.items()):
if loc in spines:
spine.set_position(('outward', 4)) # outward by 4 points
# spine.set_smart_bounds(True)
else:
spine.set_color('none') # don't draw spine
# turn off ticks where there is no spine
if 'left' in spines:
ax.yaxis.set_ticks_position('left')
else:
# no yaxis ticks
ax.yaxis.set_ticks([])
if 'bottom' in spines:
ax.xaxis.set_ticks_position('bottom')
else:
# no xaxis ticks
ax.xaxis.set_ticks([])
ax.tick_params(axis='x', labelsize=labelsize)
ax.tick_params(axis='y', labelsize=labelsize)
# +++++++++++++++++++++++++++++++++++++++++++++++++++++++
# function to generate automatically combined labels
# +++++++++++++++++++++++++++++++++++++++++++++++++++++++
def get_combined_labels(subject='fsaverage', subjects_dir=None,
parc='aparc.a2009s'):
"""
Helper function to combine labels automatically
according to previous studies.
Parameters
----------
subject: string containing the subjects name
default: subject='fsaverage'
subjects_dir: Subjects directory. If not given the
system variable SUBJECTS_DIR is used
default: subjects_dir=None
parc: name of the parcellation to use for reading
in the labels
default: parc='aparc.a2009s'
Return
------
label_keys: names of the new labels
labels: list containing the combined labels
"""
# ------------------------------------------
# import necessary modules
# ------------------------------------------
from mne import read_labels_from_annot
import numpy as np
from os.path import join
# ------------------------------------------
# define labels based on previous studies
# ------------------------------------------
# to get more information about the label names and their
# locations check the following publication:
# Destrieux et al. (2010), Automatic parcellation of human
# cortical gyri and sulci using standard anatomical nomenclature,
# NeuroImage, DOI: 10.1016/j.neuroimage.2010.06.010
label_combinations = {
'auditory': ['G_temp_sup-G_T_transv', 'G_temp_sup-Plan_polar',
'Lat_Fis-post'],
'broca': ['G_front_inf-Opercular', 'G_front_inf-Triangul',
'Lat_Fis-ant-Vertical'],
'cingulate': ['G_cingul-Post-dorsal', 'G_cingul-Post-ventral',
'G_and_S_cingul-Ant', 'G_and_S_cingul-Mid-Ant',
'G_and_S_cingul-Mid-Post', 'S_pericallosal',
'cingul-Post-ventral'],
'frontal': ['G_and_S_frontomargin', 'G_and_S_transv_frontopol',
'G_front_inf-Orbital', 'G_front_middle',
'G_front_sup', 'G_orbital',
'G_rectus', 'G_subcallosal',
'Lat_Fis-ant-Horizont', 'S_front_inf',
'S_front_middle', 'S_front_sup',
'S_orbital_lateral', 'S_orbital-H_Shaped',
'S_suborbital'],
'gustatory': ['G_and_S_subcentral'],
'insula': ['S_circular_insula_ant', 'S_circular_insula_inf',
'S_circular_insula_sup', 'G_Ins_lg_and_S_cent_ins',
'G_insular_short'],
'motor': ['G_precentral', 'S_precentral-sup-part',
'S_precentral-inf-part', 'S_central'],
'olfactory': ['S_temporal_transverse'],
'somatosensory': ['G_postcentral', 'S_postcentral'],
'somatosensory associated': ['G_and_S_paracentral', 'G_pariet_inf-Angular',
'G_parietal_sup', 'S_cingul-Marginalis',
'S_intrapariet_and_P_trans'],
'temporal': ['G_oc-temp_lat-fusifor', 'G_oc-temp_med-Parahip',
'G_temp_sup-Plan_polar', 'G_temporal_inf',
'G_temporal_middle', 'G_temp_sup-Lateral',
'Pole_temporal', 'S_collat_transv_ant',
'S_oc-temp_lat', 'S_oc-temp_med_and_Lingual',
'S_temporal_inf', 'S_temporal_sup'],
'vision': ['G_and_S_occipital_inf', 'G_occipital_middle',
'G_oc-temp_med-Lingual', 'S_collat_transv_post',
'S_oc_sup_and_transversal', 'S_occipital_ant',
'S_oc_middle_and_Lunatus'],
'visual': ['G_cuneus', 'G_precuneus',
'S_calcarine', 'S_parieto_occipital',
'G_occipital_sup', 'Pole_occipital',
'S_subparietal'],
'wernicke': ['G_pariet_inf-Supramar', 'G_temp_sup-Plan_tempo',
'S_interm_prim-Jensen']
}
label_keys = list(label_combinations.keys())
labels = []
# ------------------------------------------
# combine labels
# ------------------------------------------
# loop over both hemispheres
for hemi in ['lh', 'rh']:
# read all labels in
labels_all = read_labels_from_annot(subject, parc=parc, hemi=hemi,
surf_name='inflated',
subjects_dir=subjects_dir,
verbose=False)
# loop over all labels to extract label names
label_names = []
for label in labels_all:
label_names.append(label.name)
# ------------------------------------------
# now generate labels based on previous
# studies
# ------------------------------------------
# loop over all previously defined labels
for label_key in label_keys:
# get name of all labels related to the current one
label_members = label_combinations[label_key]
label_members = [x+'-'+hemi for x in label_members]
# check which labels we need for the current one
idx_labels_want = np.where(np.in1d(label_names, label_members))[0]
labels_want = [labels_all[i] for i in idx_labels_want]
# combine labels
label_new = np.sum(labels_want)
label_new.name = label_key + '-' + hemi
# fill the surface between sources
label_new.values.fill(1.0)
label_new.smooth(subject=subject, subjects_dir=subjects_dir)
# save new label
fnout = join(subjects_dir, subject, 'label',
hemi + '.' + label_key + '.label')
label_new.save(fnout)
labels.append(label_new)
return label_keys, labels
# +++++++++++++++++++++++++++++++++++++++++++++++++++++++
# function to get the anatomical label to a given vertex
# +++++++++++++++++++++++++++++++++++++++++++++++++++++++
def get_anat_label_name(vertex, hemi, labels=None, subject='fsaverage',
subjects_dir=None, parc='aparc.a2009s'):
"""
Helper function to get to a given vertex the
name of the anatomical label
Parameters
----------
vertex: integer containing the vertex number
hemi: string containing the information in which
hemisphere the vertex is located. Should be
either 'lh' or 'rh'
labels: labels to use for checking. If not given
the labels are read from the subjects directory
default: labels=None
subject: string containing the subjects name
default: subject='fsaverage'
subjects_dir: Subjects directory. If not given the
system variable SUBJECTS_DIR is used
default: subjects_dir=None
parc: name of the parcellation to use for reading
in the labels
default: parc='aparc.a2009s'
Return
------
name: string containing the name of the anatomical
label related to the given vertex
"""
# ------------------------------------------
# import necessary modules
# ------------------------------------------
from mne import read_labels_from_annot
import numpy as np
# ------------------------------------------
# check input parameter
# ------------------------------------------
# check if labels are given or must be read
if not labels:
labels = read_labels_from_annot(subject, parc=parc, hemi=hemi,
surf_name='inflated',
subjects_dir=subjects_dir,
verbose=False)
# ------------------------------------------
# loop over labels to find corresponding
# label
# ------------------------------------------
name = ''
for label in labels:
if label.hemi == hemi:
# get vertices of current label
label_vert = np.in1d(np.array(vertex), label.vertices)
if label_vert:
name = label.name
break
if name == '':
name = 'unknown-' + hemi
return name
# +++++++++++++++++++++++++++++++++++++++++++++++++++++++
# function to get the MNI-coordinate(s) to a given
# FourierICA component
# +++++++++++++++++++++++++++++++++++++++++++++++++++++++
def get_mni_coordinates(A_orig,
subject='fsaverage', subjects_dir=None,
parc='aparc.a2009s', percentile=97,
combine_labels=True):
"""
Helper function to get the MNI-coordinate(s) to a given
FourierICA component. The selection if a component has
activation in both hemispheres or only in one is made
like follows: estimate for each component an activation
threshold based on the given percentile. Next, estimate
the total number of voxels in the component which are
above the estimated threshold. Now check if at least 20%
of the total number of voxels above threshold are in each
hemisphere. If yes both hemispheres are marked as active,
otherwise only one.
Parameters
----------
A_orig: array
2D-mixing-array (nvoxel, ncomp) estimated
when applying FourierICA
subject: string containing the subjects name
default: subject='fsaverage'
subjects_dir: Subjects directory. If not given the
system variable SUBJECTS_DIR is used
default: subjects_dir=None
parc: name of the parcellation to use for reading
in the labels
default: parc='aparc.a2009s'
percentile: integer
value between 0 and 100 used to set a lower
limit for the shown intensity range of the
spatial plots
combine_labels: if set labels are combined automatically
according to previous studies
default: combine_labels=True
Return
------
mni_coords: dictionary
The dictionary contains two elements: 'rh' and 'lh',
each of which containing a list with the MNI
coordinates as string.
Note, each list contains the same number of
elements as components are given. If there is no MNI
coordinate for a component an empty string is used, e.g.
for two components
{'rh': ['(37.55, 1.58, -21.71)', '(44.78, -10.41, 27.89)'],
'lh': ['(-39.43, 5.60, -27.80)', '']}
hemi_loc_txt: list
containing for each FourierICA component to which region
it spatially belongs ('left', 'right' or 'both')
classification: dictionary
classification object. It is a dictionary containing
two sub-dictionaries 'lh' and 'rh' (for left and
right hemisphere). In both sub-dictionaries the
information about the groups is stored, i.e. a
group/region name + the information which components
are stored in this group (as indices). An example
for 6 components might look like this:
{'rh': {'somatosensory': [1, 3], 'cingulate': [4, 5]},
'lh': {'somatosensory': [1, 2], 'cingulate': [0, 5]}}
labels: list of strings
names of the labels which are involved in this data set
"""
# ------------------------------------------
# import necessary modules
# ------------------------------------------
from mne import vertex_to_mni
import numpy as np
from os import environ
import types
# -------------------------------------------
# check input parameter
# -------------------------------------------
if not subjects_dir:
subjects_dir = environ.get('SUBJECTS_DIR')
# -------------------------------------------
# generate spatial profiles
# (using magnitude and phase)
# -------------------------------------------
if isinstance(A_orig[0, 0], complex):
A_orig_mag = np.abs(A_orig)
else:
A_orig_mag = A_orig
# -------------------------------------------
# set some parameters
# -------------------------------------------
nvoxel, ncomp = A_orig_mag.shape
nvoxel_half = int(nvoxel / 2)
hemi = ['lh', 'rh']
hemi_names = ['left ', 'right', 'both ']
hemi_indices = [[0, nvoxel_half], [nvoxel_half, -1]]
hemi_loc_txt = np.array([' '] * ncomp)
hemi_loc = np.zeros(ncomp)
# -------------------------------------------
# generate structures to save results
# -------------------------------------------
# generate dictionary to save MNI coordinates
mni_coords = {'rh': [''] * ncomp, 'lh': [''] * ncomp}
# ------------------------------------------
# check if labels should be combined
# automatically
# ------------------------------------------
if combine_labels:
label_names, labels = get_combined_labels(subject=subject,
subjects_dir=subjects_dir,
parc=parc)
# generate empty classification dictionary
class_keys = label_names[:]
class_keys.append('unknown')
classification = {'lh': {key: [] for key in class_keys},
'rh': {key: [] for key in class_keys}}
# if not generate empty variables
else:
label_names, labels = None, None
classification = {}
# ------------------------------------------
# loop over all components
# ------------------------------------------
for icomp in range(ncomp):
# ------------------------------------------
# extract maxima in the spatial profile of
# the current component separately for both
# hemispheres
# ------------------------------------------
idx_ver_max_lh = np.argmax(A_orig_mag[:nvoxel_half, icomp])
idx_ver_max_rh = np.argmax(A_orig_mag[nvoxel_half:, icomp])
# ------------------------------------------
# check for both maxima if they are
# significant
# ------------------------------------------
# set some paremeter
threshold = np.percentile(A_orig_mag[:, icomp], percentile)
nidx_above = len(np.where(A_orig_mag[:, icomp] > threshold)[0])
cur_label_name = []
# loop over both hemispheres
for idx_hemi, idx_vertex_max in enumerate([idx_ver_max_lh, idx_ver_max_rh]):
# get the number of vertices above the threshold
# in the current hemisphere
nidx_above_hemi = len(np.where(A_orig_mag[hemi_indices[idx_hemi][0]:hemi_indices[idx_hemi][1],
icomp] > threshold)[0])
# check if at least 20% of all vertices above the threshold
# are in the current hemisphere
if nidx_above_hemi * 5 > nidx_above:
# get MNI-coordinate
mni_coord = vertex_to_mni(idx_vertex_max, idx_hemi, subject,
subjects_dir=subjects_dir)[0]
# store results in structures
mni_coords[hemi[idx_hemi]][icomp] = \
'(' + ', '.join(["%2.2f" % x for x in mni_coord]) + ')'
# store hemisphere information
hemi_loc[icomp] += idx_hemi + 1.0
# ------------------------------------------
# get MNI-coordinate to vertex as well as
# the name of the corresponding anatomical
# label
# ------------------------------------------
anat_name = get_anat_label_name(idx_vertex_max, hemi[idx_hemi],
subject=subject, subjects_dir=subjects_dir,
parc=parc, labels=labels)
cur_label_name.append(anat_name[:-3])
else:
cur_label_name.append(' ')
# ------------------------------------------
# check which results must be saved
# ------------------------------------------
if combine_labels:
# check if activation was found in both hemispheres
# --> if not we can directly save the results
if ' ' in cur_label_name:
# adjust classification dictionary
if cur_label_name[0] == ' ':
classification[hemi[1]][cur_label_name[1]].append(icomp)
else:
classification[hemi[0]][cur_label_name[0]].append(icomp)
# --> otherwise we have to make sure that we group the
# component only into one region
else:
# check if both vertices are in the same anatomical location
# --> then we have no problem
if cur_label_name[0] == cur_label_name[1]:
classification[hemi[0]][cur_label_name[0]].append(icomp)
classification[hemi[1]][cur_label_name[1]].append(icomp)
else:
# check if we have an unknown region being involved
# --> if yes chose the other one
if cur_label_name[0] == 'unknown':
classification[hemi[1]][cur_label_name[1]].append(icomp)
hemi_loc[icomp], mni_coords[hemi[0]][icomp] = 2, ''
elif cur_label_name[1] == 'unknown':
classification[hemi[0]][cur_label_name[0]].append(icomp)
hemi_loc[icomp], mni_coords[hemi[1]][icomp] = 1, ''
# otherwise chose the region with the strongest vertex
else:
if A_orig_mag[idx_ver_max_lh, icomp] > A_orig_mag[idx_ver_max_rh, icomp]:
classification[hemi[0]][cur_label_name[0]].append(icomp)
hemi_loc[icomp], mni_coords[hemi[1]][icomp] = 1, ''
else:
classification[hemi[1]][cur_label_name[1]].append(icomp)
hemi_loc[icomp], mni_coords[hemi[0]][icomp] = 2, ''
# ------------------------------------------
# adjust hemi_loc_txt if activity was found
# in both hemispheres
# ------------------------------------------
for idx, hemi_name in enumerate(hemi_names):
idx_change = np.where(hemi_loc == (idx + 1.0))[0]
hemi_loc_txt[idx_change] = hemi_name
# ------------------------------------------
# adjust label_names to only contain regions
# being involved in processing the current
# data
# ------------------------------------------
labels = []
for cur_hemi in hemi:
for key in label_names:
if classification[cur_hemi][key]:
labels.append(key)
labels = np.unique(labels).tolist()
return mni_coords, hemi_loc_txt, classification, labels
# +++++++++++++++++++++++++++++++++++++++++++++++++++++++
# helper function to check if classification was
# performed prior to plotting
# +++++++++++++++++++++++++++++++++++++++++++++++++++++++
def _check_classification(classification, ncomp):
"""
Helper function to check if classification was
performed prior to plotting
Parameters
----------
classification: dictionary
classification object from the group_ica_object.
It is a dictionary containing two sub-dictionaries
'lh' and 'rh' (for left and right hemisphere). In
both sub-dictionaries the information about the
groups is stored, i.e. a group/region name + the
information which components are stored in this
group
ncomp: integer
number of components
Return
------
keys: list containing the group names
key_borders: list containing the group borders, i.e.
the information where to plot a new group name
idx_sort: array containing the plotting order of
the components, i.e. components beloning to one
group are plotted together
"""
# ------------------------------------------
# import necessary modules
# ------------------------------------------
import numpy as np
# ------------------------------------------
# check if classification was done
# ------------------------------------------
key_borders = []
if np.any(classification):
# initialize empty lists
idx_sort = []
keys_hemi = list(classification.keys())
# sort keys
keys = list(classification[keys_hemi[0]].keys())
keys.sort(key=lambda v: v.upper())
# set 'unknown' variables to the end
keys.remove('unknown')
keys.append('unknown')
# remove keys with empty entries
keys_want = []
for key in keys:
if classification[keys_hemi[0]][key] or\
classification[keys_hemi[1]][key]:
keys_want.append(key)
# loop over all keys
for key in keys_want:
# get indices to each class
idx_lh = classification[keys_hemi[0]][key]
idx_rh = classification[keys_hemi[1]][key]
# get indices of components in both hemispheres
idx_both = np.intersect1d(idx_lh, idx_rh)
# get indices of components only in right hemisphere
idx_only_rh = np.setdiff1d(idx_rh, idx_lh)
# get indices of components only in left hemisphere
idx_only_lh = np.setdiff1d(idx_lh, idx_rh)
# add components to list of sorted indices
idx_all = np.concatenate((idx_both, idx_only_rh, idx_only_lh))
idx_sort += idx_all.tolist()
key_borders.append(len(idx_all))
# add first border and estimate cumulative sum to
# have the right borders
key_borders = np.insert(key_borders, 0, 1)
key_borders = np.cumsum(key_borders)[:-1]
# ------------------------------------------
# if classification was not performed set
# some default values
# ------------------------------------------
else:
idx_sort = np.arange(ncomp)
keys_want = []
return keys_want, key_borders, idx_sort
# +++++++++++++++++++++++++++++++++++++++++++++++++++++++
# helper function to handle time courses for plotting
# +++++++++++++++++++++++++++++++++++++++++++++++++++++++
def _get_temporal_envelopes(fourier_ica_obj, W_orig, temporal_envelope=[],
src_loc_data=[], tICA=False, global_scaling=True,
win_length_sec=None, tpre=None, flow=None):
"""
Helper function to check if classification was
performed prior to plotting
Parameters
----------
fourier_ica_obj: FourierICA object generated
when applying jumeg.decompose.fourier_ica
W_orig: array
2D-demixing-array (ncomp x nvoxel) estimated
when applying FourierICA
temporal_envelope: list of arrays containing
the temporal envelopes. If the temporal
envelopes are already given here z-scoring
and mean estimation is performed
src_loc_data: array
3D array containing the source localization
data used for FourierICA estimation
(nfreq x nepochs x nvoxel). Only necessary
if temporal_envelope is not given.
tICA: bool
If set we know that temporal ICA was applied
when estimating the FourierICA, i.e. when
generating the temporal-envelopes the data
must not be transformed from the Fourier
domain to the time-domain
global_scaling: bool
If set all temporal-envelopes are globally
scaled. Otherwise each component is scaled
individually
win_length_sec: float or None
Length of the epoch window in seconds
tpre: float or None
Lower border (in seconds) of the time-window
used for generating/showing the epochs. If
'None' the value stored in 'fourier_ica_obj'
is used
flow: float, integer or None
Lower frequency border for generating the
temporal-envelope. If 'None' the frequency
border stored in 'fourier_ica_obj' is used
Return
------
temporal_envelope_mean: list containing the 2D
arrays of the mean temporal envelopes
of the components
temporal_envelope: list containing the 3D
arrays of the temporal envelopes of the
components. Necessary for estimating the
spectral profiles
"""
# ------------------------------------------
# import necessary modules
# ------------------------------------------
from mne.baseline import rescale
import numpy as np
from scipy import fftpack
# -------------------------------------------
# check input parameter
# -------------------------------------------
if tpre == None:
tpre = fourier_ica_obj.tpre
if flow == None:
flow = fourier_ica_obj.flow
if not win_length_sec:
win_length_sec = fourier_ica_obj.win_length_sec
# estimate some simple parameter
sfreq = fourier_ica_obj.sfreq
ncomp, nvoxel = W_orig.shape
win_ntsl = int(np.floor(sfreq * win_length_sec))
startfftind = int(np.floor(flow * win_length_sec))
# -------------------------------------------
# check if temporal envelope is already
# given or should be estimated
# -------------------------------------------
if temporal_envelope == []:
# -------------------------------------------
# check if 'src_loc_data' is given...
# if not throw an error
# -------------------------------------------
if src_loc_data == []:
print(">>> ERROR: You have to provide either the 'temporal_envelope' or")
print(">>> 'src_loc_data'. Otherwise no temporal information can be plotted!")
import pdb
pdb.set_trace()
# -------------------------------------------
# get independent components
# -------------------------------------------
nfreq, nepochs, nvoxel = src_loc_data.shape
act = np.zeros((ncomp, nepochs, nfreq), dtype=np.complex)
if tICA:
win_ntsl = nfreq
temporal_envelope = np.zeros((nepochs, ncomp, win_ntsl))
fft_act = np.zeros((ncomp, win_ntsl), dtype=np.complex)
# loop over all epochs to get time-courses from
# source localized data by inverse FFT
for iepoch in range(nepochs):
# normalize data
src_loc_zero_mean = (src_loc_data[:, iepoch, :] - np.dot(np.ones((nfreq, 1)), fourier_ica_obj.dmean)) / \
np.dot(np.ones((nfreq, 1)), fourier_ica_obj.dstd)
act[:ncomp, iepoch, :] = np.dot(W_orig, src_loc_zero_mean.transpose())
#act[ncomp:, iepoch, :] = np.dot(W_orig, src_loc_zero_mean.transpose())
if tICA:
temporal_envelope[iepoch, :, :] = act[:, iepoch, :].real
else:
# -------------------------------------------
# generate temporal profiles
# -------------------------------------------
# apply inverse STFT to get temporal envelope
fft_act[:, startfftind:(startfftind + nfreq)] = act[:, iepoch, :]
temporal_envelope[iepoch, :, :] = fftpack.ifft(fft_act, n=win_ntsl, axis=1).real
# -------------------------------------------
# average temporal envelope
# -------------------------------------------
if not isinstance(temporal_envelope, list):
temporal_envelope = [[temporal_envelope]]
ntemp = len(temporal_envelope)
temporal_envelope_mean = np.empty((ntemp, 0)).tolist()
times = (np.arange(win_ntsl) / sfreq + tpre)
# -------------------------------------------
# perform baseline correction
# -------------------------------------------
for itemp in range(ntemp):
for icomp in range(ncomp):
temporal_envelope[itemp][0][:, icomp, :] = rescale(temporal_envelope[itemp][0][:, icomp, :],
times, (None, 0), 'zscore')
# -------------------------------------------
# estimate mean from temporal envelopes
# -------------------------------------------
for itemp in range(ntemp):
temporal_envelope_mean[itemp].append(np.mean(temporal_envelope[itemp][0], axis=0)[:, 5:-5])
# -------------------------------------------
# check if global scaling should be used
# -------------------------------------------
# if not scale each component separately between -0.5 and 0.5
if not global_scaling:
for icomp in range(ncomp):
min_val = np.min([temporal_envelope_mean[0][0][icomp, :], temporal_envelope_mean[1][0][icomp, :]])
max_val = np.max([temporal_envelope_mean[0][0][icomp, :], temporal_envelope_mean[1][0][icomp, :]])
scale_fact = 1.0 / (max_val - min_val)
for itemp in range(ntemp):
temporal_envelope_mean[itemp][0][icomp, :] = np.clip(
scale_fact * temporal_envelope_mean[itemp][0][icomp, :]
- scale_fact * min_val - 0.5, -0.5, 0.5)
# if global scaling should be used, scale all
# data between -0.5 and 0.5
else:
# scale temporal envelope between -0.5 and 0.5
min_val = np.min(temporal_envelope_mean)
max_val = np.max(temporal_envelope_mean)
scale_fact = 1.0 / (max_val - min_val)
for itemp in range(ntemp):
temporal_envelope_mean[itemp][0] = np.clip(scale_fact * temporal_envelope_mean[itemp][0]
- scale_fact * min_val - 0.5, -0.5, 0.5)
return temporal_envelope_mean, temporal_envelope
# +++++++++++++++++++++++++++++++++++++++++++++++++++++++
# helper function to handle spatial profiles for plotting
# +++++++++++++++++++++++++++++++++++++++++++++++++++++++
def _get_spatial_profiles(A_orig, keys, idx_text, vertno=[],
subject='fsaverage', subjects_dir=None,
labels=None, classification={}, percentile=97,
mni_coord=[], add_foci=False, fnout=None):
"""
Helper function to get/generate the spatial
profiles of the FourierICA components for
plotting
Parameters
----------
A_orig: array
2D-mixing-array (nvoxel, ncomp) estimated
when applying FourierICA
keys: list containing the group names
idx_text: list containing the information in which
brain hemisphere a component is mainly
located (could be either 'both', 'left', 'right'
or ' ' if no classification was performed before
plotting)
vertno: list
list containing two arrays with the order
of the vertices. If not given it will be
generated in this routine
subject: string
string containing the subjects ID
subjects_dir: string
string containing the subjects directory
path
labels: list of strings
names of the labels which should be plotted.
Note, the prefix 'lh.' and the suffix '.label'
are automatically added
classification: dictionary
classification object from the group_ica_object.
It is a dictionary containing two sub-dictionaries
'lh' and 'rh' (for left and right hemisphere). In
both sub-dictionaries the information about the
groups is stored, i.e. a group/region name + the
information which components are stored in this
group
percentile: integer
value between 0 and 100 used to set a lower
limit for the shown intensity range of the
spatial plots
mni_coord: list of strings
if given the MNI coordinates are plotted
beneath the spatial profiles
add_foci: bool
if True and the MNI coordinates are given
a foci is plotted at the position of the
MNI coordinate
fnout: string or None
if labels and classification is given the
output filename of the brain plot containing
all labels. If 'None' the results are not stored
Return
------
temp_plot_dir: string
directory where the spatial profiles are
stored
"""
# ------------------------------------------
# import necessary modules
# ------------------------------------------
from matplotlib import gridspec as grd
from matplotlib import pyplot as plt
from mayavi import mlab
from mne.source_estimate import _make_stc
import numpy as np
from os import environ, makedirs
from os.path import exists, join
import re
from scipy import misc
from surfer import set_log_level
import types
# set log level to 'WARNING'
set_log_level('CRITICAL')
import mayavi
mayavi.mlab.options.offscreen = True
# -------------------------------------------
# create temporary directory to save plots
# of spatial profiles
# -------------------------------------------
temp_plot_dir = join(subjects_dir, subject, 'temp_plots')
if not exists(temp_plot_dir):
makedirs(temp_plot_dir)
# -------------------------------------------
# generate spatial profiles
# (using magnitude and phase)
# -------------------------------------------
if not subjects_dir:
subjects_dir = environ.get('SUBJECTS_DIR')
if isinstance(A_orig[0, 0], complex):
A_orig_mag = np.abs(A_orig)
else:
A_orig_mag = A_orig
nvoxel, ncomp = A_orig_mag.shape
# -------------------------------------------
# check if vertno is given, otherwise
# generate it
# -------------------------------------------
if not np.any(vertno):
vertno = [np.arange(nvoxel/2), np.arange(nvoxel/2)]
# -------------------------------------------
# check if labels should be plotted and if
# classification was already performed
# --> if yes define some colors for the
# labels
# -------------------------------------------
if labels and classification:
colors = ['green', 'red', 'cyan', 'yellow', 'mediumblue',
'magenta', 'chartreuse', 'indigo', 'sandybrown',
'slateblue', 'purple', 'lightpink', 'springgreen',
'orange', 'sienna', 'cadetblue', 'crimson',
'maroon', 'powderblue', 'deepskyblue', 'olive']
# -------------------------------------------
# loop over all components to generate
# spatial profiles
# -------------------------------------------
for icomp in range(ncomp):
# -------------------------------------------
# plot spatial profile
# -------------------------------------------
# generate stc-object from current component
A_cur = A_orig_mag[:, icomp]
src_loc = _make_stc(A_cur[:, np.newaxis], vertices=vertno, tmin=0, tstep=1,
subject=subject)
# define current range (Xth percentile)
fmin = np.percentile(A_cur, percentile)
fmax = np.max(A_cur)
fmid = 0.5 * (fmin + fmax)
clim = {'kind': 'value',
'lims': [fmin, fmid, fmax]}
# plot spatial profiles
brain = src_loc.plot(surface='inflated', hemi='split', subjects_dir=subjects_dir,
config_opts={'cortex': 'bone'}, views=['lateral', 'medial'],
time_label=' ', colorbar=False, clim=clim)
# check if foci should be added to the plot
if add_foci and np.any(mni_coord):
for i_hemi in ['lh', 'rh']:
mni_string = mni_coord[i_hemi][icomp]
# if 'mni_string' is not empty (it might be empty if activity
# can only be found in one hemisphere) plot a foci
if mni_string != "":
mni_float = list(map(float, re.findall("[-+]?\d*\.\d+|\d+", mni_string)))
brain.add_foci(mni_float, coords_as_verts=False, hemi=i_hemi, color='chartreuse',
scale_factor=1.5, map_surface='white')
# -------------------------------------------
# check if labels should be plotted
# -------------------------------------------
if labels and classification:
# import module to read in labels
from mne import read_label
# get path to labels
dir_labels = join(subjects_dir, subject, 'label')
# identify in which group the IC is classified
hemi = 'rh' if idx_text[icomp] == 'right' else 'lh'
# read in the corresponding label
for idx_key, key in enumerate(keys):
if icomp in classification[hemi][key]:
label_name = ".%s.label" % key
color = colors[idx_key]
break
# loop over both hemispheres to read the label in and plot it
hemi = ['lh', 'rh'] if idx_text[icomp] == 'both ' else [hemi]
for hemi_cur in hemi:
label = read_label(join(dir_labels, hemi_cur + label_name), subject=subject)
brain.add_label(label, borders=False, hemi=hemi_cur, color=color, alpha=0.1)
brain.add_label(label, borders=True, hemi=hemi_cur, color=color)
# save results
fn_base = "IC%02d_spatial_profile.png" % (icomp+1)
fnout_img = join(temp_plot_dir, fn_base)
brain.save_image(fnout_img)
# close mlab figure
mlab.close(all=True)
# -------------------------------------------
# also generate one plot with all labels
# -------------------------------------------
if labels and classification:
# set clim in a way that no activity can be seen
# (Note: we only want to see the labels)
clim = {'kind': 'value',
'lims': [fmax, 1.5 * fmax, 2.0 * fmax]}
# generate plot
brain = src_loc.plot(surface='inflated', hemi='split', subjects_dir=subjects_dir,
config_opts={'cortex': 'bone'}, views=['lateral', 'medial'],
time_label=' ', colorbar=False, clim=clim, background='white')
# loop over all labels
for idx_key, key in enumerate(keys):
label_name = ".%s.label" % key
color = colors[idx_key]
# loop over both hemispheres in order to plotting the labels
for hemi in ['lh', 'rh']:
label = read_label(join(dir_labels, hemi + label_name), subject=subject)
brain.add_label(label, borders=False, hemi=hemi, color=color, alpha=0.6)
# save results
if fnout:
fnout_img = '%s_labels.png' % fnout
brain.save_image(fnout_img)
# close mlab figure
mlab.close(all=True)
# -------------------------------------------
# now adjust the label plot appropriate
# -------------------------------------------
# read spatial profile image
spat_tmp = misc.imread(fnout_img)
# rearrange image
x_size, y_size, _ = spat_tmp.shape
x_half, y_half = x_size / 2, y_size / 2
x_frame, y_frame = int(0.11 * x_half), int(0.01 * y_half)
spatial_profile = np.concatenate((spat_tmp[x_frame:(x_half - x_frame), y_frame:(y_half - y_frame), :],
spat_tmp[(x_half + x_frame):-x_frame, y_frame:(y_half - y_frame), :],
spat_tmp[(x_half + x_frame):-x_frame, (y_half + y_frame):-y_frame, :],
spat_tmp[x_frame:(x_half - x_frame), (y_half + y_frame):-y_frame, :]),
axis=1)
# plot image
plt.ioff()
fig = plt.figure('Labels plots', figsize=(17, 3))
gs = grd.GridSpec(1, 30, wspace=0.00001, hspace=0.00001,
left=0.0, right=1.0, bottom=0.0, top=1.0)
# set plot position and plot image
p1 = fig.add_subplot(gs[0, 0:26])
p1.imshow(spatial_profile)
adjust_spines(p1, [])
# add label names
keys_fac = 0.8/len(keys)
keys_split = 0
p_text = fig.add_subplot(gs[0, 26:30])
keys_sort_idx = np.argsort(keys)
for idx_key in range(len(keys)):
key = keys[keys_sort_idx[idx_key]]
# check if string should be split
if len(key) > 21 and ' ' in key:
p_text.text(0.0, 0.9-keys_fac*(idx_key+keys_split), key.split()[0]+'-',
fontsize=13, color=colors[keys_sort_idx[idx_key]])
keys_split += 1
p_text.text(0.0, 0.9-keys_fac*(idx_key+keys_split), key.split()[1],
fontsize=13, color=colors[keys_sort_idx[idx_key]])
else:
p_text.text(0.0, 0.9-keys_fac*(idx_key+keys_split), key, fontsize=13,
color=colors[keys_sort_idx[idx_key]])
adjust_spines(p_text, [])
plt.savefig(fnout_img, dpi=300)
# close plot and set plotting back to screen
plt.close('FourierICA plots')
plt.ion()
mayavi.mlab.options.offscreen = False
return temp_plot_dir
# +++++++++++++++++++++++++++++++++++++++++++++++++++++++
# helper function to get spectral profiles for plotting
# +++++++++++++++++++++++++++++++++++++++++++++++++++++++
def _get_spectral_profile(temporal_envelope, tpre,
sfreq, flow, fhigh,
bar_plot=False,
use_multitaper=False):
"""
Helper function to get the spectral-profile of the
temporal-envelopes of the FourierICA components
for plotting
Parameters
----------
temporal_envelope: list of arrays containing
the temporal envelopes.
tpre: float
Lower border (in seconds) of the time-window
used for generating/showing the epochs. If
'None' the value stored in 'fourier_ica_obj'
is used
sfreq: float
Sampling frequency of the data
flow: float or integer
Lower frequency range for time frequency analysis
fhigh: float or integer
Upper frequency range for time frequency analysis
bar_plot: boolean
if set the number of time points for time-frequency
estimation is reduced in order to save memory and
computing-time
use_multitaper: boolean
If set 'multitaper' is usewd for time frequency
analysis, otherwise 'stockwell'
Return
------
average_power_all: list containing the averaged
frequency power of all components
freqs: array containing the frequencies used to
calculate the frequency power
vmin: lower frequency range for plotting
vmax: upper frequency range for plotting
"""
# ------------------------------------------
# import necessary modules
# ------------------------------------------
from mne.baseline import rescale
from mne.time_frequency._stockwell import _induced_power_stockwell
import numpy as np
# ------------------------------------------
# define some parameter
# ------------------------------------------
ntemp = len(temporal_envelope)
ncomp = temporal_envelope[0][0].shape[1]
win_ntsl = temporal_envelope[0][0].shape[-1]
average_power_all = np.empty((ntemp, 0)).tolist()
vmin = np.zeros(ncomp)
vmax = np.zeros(ncomp)
# define some time parameter
times = np.arange(win_ntsl) / sfreq + tpre
idx_start = np.argmin(np.abs(times - tpre))
idx_end = np.argmin(np.abs(times - (tpre + win_ntsl/sfreq)))
if bar_plot:
decim = 10
else:
decim = 1
# ------------------------------------------
# loop over all time courses, i.e.
# conditions, and all components
# ------------------------------------------
for itemp in range(ntemp):
for icomp in range(ncomp):
# extract some information from the temporal_envelope
nepochs = temporal_envelope[itemp][0].shape[0]
# ------------------------------------------
# perform time frequency analysis
# ------------------------------------------
# prepare data for frequency analysis
data_stockwell = temporal_envelope[itemp][0][:, icomp, idx_start:idx_end].\
reshape((nepochs, 1, idx_end-idx_start))
data_stockwell = data_stockwell.transpose([1, 0, 2])
# mirror data to reduce transient frequencies
data_stockwell = np.concatenate((data_stockwell[:, :, 50:0:-1],
data_stockwell, data_stockwell[:, :, -1:-51:-1]), axis=-1)
n_fft = data_stockwell.shape[-1]
# check if 'multitaper' or 'stockwell' should be
# used for time-frequency analysis
if use_multitaper:
from mne.time_frequency.tfr import _compute_tfr
n_cycle = 3.0
if (10.0 * n_cycle*sfreq)/(2.0 * np.pi * flow) > n_fft:
flow *= ((10.0 * n_cycle*sfreq)/(2.0 * np.pi * flow))/n_fft
flow = np.ceil(flow)
freqs = np.arange(flow, fhigh)
power_data = _compute_tfr(data_stockwell, freqs, sfreq=sfreq, use_fft=True,
n_cycles=n_cycle, zero_mean=True, decim=decim,
output='power', method='multitaper',
time_bandwidth=10)
else:
power_data, _, freqs = _induced_power_stockwell(data_stockwell, sfreq=sfreq, fmin=flow,
fmax=fhigh, width=0.6, decim=1, n_fft=n_fft,
return_itc=False, n_jobs=4)
# perform baseline correction (and remove mirrored parts from data)
power_data = rescale(power_data[:, :, int(50/decim):-int(50/decim)],
times[idx_start:idx_end][0:-1:decim], (None, 0), 'mean')
average_power = np.mean(power_data, axis=0)
# ------------------------------------------
# store all frequency data in one list
# ------------------------------------------
average_power_all[itemp].append(average_power)
# ------------------------------------------
# estimate frequency thresholds for plotting
# ------------------------------------------
vmax[icomp] = np.max((np.percentile(average_power, 98), vmax[icomp]))
vmin[icomp] = np.min((np.percentile(average_power, 2), vmin[icomp]))
if np.abs(vmax[icomp]) > np.abs(vmin[icomp]):
vmin[icomp] = - np.abs(vmax[icomp])
else:
vmax[icomp] = np.abs(vmin[icomp])
return average_power_all, freqs, vmin, vmax
# +++++++++++++++++++++++++++++++++++++++++++++++++++++++
# plot results when Fourier ICA was applied in the
# source space
# +++++++++++++++++++++++++++++++++++++++++++++++++++++++
def plot_results_src_space(fourier_ica_obj, W_orig, A_orig,
src_loc_data=[], temporal_envelope=[], # parameter for temporal profiles
tpre=None, win_length_sec=None, tICA=False,
vertno=[], subject='fsaverage', subjects_dir=None, # parameter for spatial profiles
percentile=97, add_foci=True, classification={},
mni_coords=[], labels=None,
flow=None, fhigh=None, bar_plot=False, # parameter for spectral profiles
global_scaling=True, ncomp_per_plot=13, fnout=None, # general plotting parameter
temp_profile_names=[]):
"""
Generate plot containing all results achieved by
applying FourierICA in source space, i.e., plot
spatial and spectral profiles.
Parameters
----------
fourier_ica_obj: FourierICA object generated
when applying jumeg.decompose.fourier_ica
W_orig: array
2D-demixing-array (ncomp x nvoxel) estimated
when applying FourierICA
A_orig: array
2D-mixing-array (nvoxel, ncomp) estimated
when applying FourierICA
**** parameter for temporal profiles ****
src_loc_data: array
3D array containing the source localization
data used for FourierICA estimation
(nfreq x nepochs x nvoxel). Only necessary
if temporal_envelope is not given.
default: src_loc_data=[]
temporal_envelope: list of arrays containing
the temporal envelopes. If not given the
temporal envelopes are estimated here based
on the 'src_loc_data'
default: temporal_envelope=[]
tpre: float
Lower border (in seconds) of the time-window
used for generating/showing the epochs. If
'None' the value stored in 'fourier_ica_obj'
is used
win_length_sec: float or None
Length of the epoch window in seconds. If
'None' the value stored in 'fourier_ica_obj'
is used
tICA: boolean
should be True if temporal ICA was applied
default: tICA=False
**** parameter for spatial profiles ****
vertno: list
list containing two arrays with the order
of the vertices. If list is empty it will be
automatically generated
default: vertno=[]
subject: string
subjects ID
default: subject='fsaverage'
subjects_dir: string or None
string containing the subjects directory
path
default: subjects_dir=None --> system variable
SUBJETCS_DIR is used
percentile: integer
value between 0 and 100 used to set a lower
limit for the shown intensity range of the
spatial plots
default: percentile=97
add_foci: bool
if True and the MNI coordinates are given
a foci is plotted at the position of the
MNI coordinate
default: add_foci=True
classification: dictionary
classification object from the group_ica_object.
It is a dictionary containing two sub-dictionaries
'lh' and 'rh' (for left and right hemisphere). In
both sub-dictionaries the information about the
groups is stored, i.e. a group/region name + the
information which components are stored in this
group
default: classification={}
mni_coords: list of strings
if given the MNI coordinates are plotted
beneath the spatial profiles
default: mni_coords=[]
labels: list of strings
names of the labels which should be plotted.
Note, the prefix 'lh.' and the suffix '.label'
are automatically added
default: labels=None
**** parameter for spectral profiles ****
flow: float or integer
Lower frequency range for time frequency analysis
fhigh: float or integer
Upper frequency range for time frequency analysis
bar_plot: boolean
If set the results of the time-frequency analysis
are shown as bar plot. This option is recommended
when FourierICA was applied to resting-state data
default: bar_plot=False
**** general plotting parameter ****
global_scaling: bool
If set spatial, spectral and temporal profiles
are globally scaled. Otherwise each component
is scaled individually
default: global_scaling=True
ncomp_per_plot: integer
number of components per plot
fnout: string
default: fnout=None
temp_profile_names: list of string
The list should have the same number of elements
as conditions were used to generate the temporal
envelopes. The names given here are used as headline
for the temporal profiles in the plot
default: temp_profile_name=[]
"""
# ------------------------------------------
# import necessary modules
# ------------------------------------------
from matplotlib import pyplot as plt
from matplotlib import gridspec as grd
from matplotlib.colors import Normalize
import numpy as np
from os import remove, rmdir
from os.path import exists, join
from scipy import misc
# -------------------------------------------
# check input parameter
# -------------------------------------------
if tpre == None:
tpre = fourier_ica_obj.tpre
if flow == None:
flow = fourier_ica_obj.flow
if not fhigh:
fhigh = fourier_ica_obj.fhigh
if not win_length_sec:
win_length_sec = fourier_ica_obj.win_length_sec
# check if either 'src_loc_data' or
# 'temporal_envelope' is given, otherwise stop
if src_loc_data == [] and temporal_envelope == []:
print(">>> ERROR: you have either to provide the variable")
print(">>> 'src_loc_data' or 'temporal_envelope'.")
import pdb
pdb.set_trace()
# estimate/set some simple parameter
sfreq = fourier_ica_obj.sfreq
win_ntsl = int(np.floor(sfreq * win_length_sec))
ncomp, nvoxel = W_orig.shape
ylim_temp = [-0.55, 0.55]
time_range = [tpre, tpre + win_length_sec]
# -------------------------------------------
# get temporal envelopes, or rather check if
# temporal envelopes already exist or must
# be calculated
# -------------------------------------------
temporal_envelope_mean, temporal_envelope = \
_get_temporal_envelopes(fourier_ica_obj, W_orig, temporal_envelope=temporal_envelope,
src_loc_data=src_loc_data, tICA=tICA, global_scaling=global_scaling,
win_length_sec=win_length_sec, tpre=tpre, flow=flow)
ntemp = len(temporal_envelope)
# -------------------------------------------
# get MNI-coordinates of the FourierICA
# components
# -------------------------------------------
if not classification and not mni_coords and not labels:
mni_coords, hemi_loc_txt, classification, labels = \
get_mni_coordinates(A_orig, subject=subject, subjects_dir=subjects_dir,
percentile=percentile)
# otherwise we only have to get the 'hemi_loc_txt' variable
else:
hemi_loc = np.array([int(i != '') for i in mni_coords['lh']])
hemi_loc += np.array([2*int(i != '') for i in mni_coords['rh']])
hemi_loc_txt = np.array([' '] * len(hemi_loc))
for idx, hemi_name in enumerate(['left ', 'right', 'both ']):
idx_change = np.where(hemi_loc == (idx + 1.0))[0]
hemi_loc_txt[idx_change] = hemi_name
# check if classification was performed prior to plotting
keys, key_borders, idx_sort = _check_classification(classification, ncomp)
# -------------------------------------------
# get spatial profiles of all components
# Note: This will take a while
# -------------------------------------------
temp_plot_dir = _get_spatial_profiles(A_orig, keys, hemi_loc_txt, vertno=vertno,
subject=subject, subjects_dir=subjects_dir,
labels=labels, classification=classification,
percentile=percentile, mni_coord=mni_coords,
add_foci=add_foci, fnout=fnout)
# -------------------------------------------
# get spectral profiles of all components
# Note: This will take a while
# -------------------------------------------
average_power_all, freqs, vmin, vmax = \
_get_spectral_profile(temporal_envelope, tpre, sfreq, flow, fhigh, bar_plot=bar_plot)
# check if bar plot should be used
# --> if yes estimate histogram data and normalize results
if bar_plot:
# generate an array to store the results
freq_heights = np.zeros((ntemp, ncomp, len(freqs)))
# loop over all conditions
for i_power, average_power in enumerate(average_power_all):
freq_heights[i_power, :, :] = np.sum(np.abs(average_power), axis=2)
# normalize to a range between 0 and 1
freq_heights /= np.max(freq_heights)
# ------------------------------------------
# now generate plot containing spatial,
# spectral and temporal profiles
# ------------------------------------------
# set some general parameter
plt.ioff()
nimg = int(np.ceil(ncomp/(1.0*ncomp_per_plot)))
idx_key = 0
nplot = list(range(ncomp_per_plot, nimg*ncomp_per_plot, ncomp_per_plot))
nplot.append(ncomp)
# generate image and its layout for plotting
fig = plt.figure('FourierICA plots', figsize=(14 + ntemp * 8, 34))
n_keys = len(key_borders) if len(key_borders) > 0 else 1
gs = grd.GridSpec(ncomp_per_plot * 20 + n_keys * 10, 10 + ntemp * 8, wspace=0.1, hspace=0.05,
left=0.04, right=0.96, bottom=0.04, top=0.96)
# ------------------------------------------
# loop over the estimated number of images
# ------------------------------------------
for iimg in range(nimg):
# clear figure (to start with a white image in each loop)
plt.clf()
# estimate how many plots on current image
istart_plot = int(ncomp_per_plot * iimg)
# set idx_class parameter
idx_class = 1 if key_borders == [] else 0
# ------------------------------------------
# loop over all components which should be
# plotted on the current image
# ------------------------------------------
for icomp in range(istart_plot, nplot[iimg]):
# ----------------------------------------------
# check if key_boarders is set and should be
# written on the image
# ----------------------------------------------
if (icomp == istart_plot and key_borders != []) or \
((icomp + 1) in key_borders):
# adjust key-index
if (icomp + 1) in key_borders:
idx_key += 1
# add sub-plot with 'key_text'
p_text = fig.add_subplot(gs[20 * (icomp - istart_plot) + idx_class * 10: \
20 * (icomp - istart_plot) + 8 + idx_class * 10, 0:10])
p_text.text(0, 0, keys[idx_key-1], fontsize=25)
adjust_spines(p_text, [])
# adjust idx_class parameter
idx_class += 1
# ----------------------------------------------
# plot spatial profiles
# ----------------------------------------------
# read spatial profile image
fn_base = "IC%02d_spatial_profile.png" % (idx_sort[icomp] + 1)
fnin_img = join(temp_plot_dir, fn_base)
spat_tmp = misc.imread(fnin_img)
remove(fnin_img)
# rearrange image
x_size, y_size, _ = spat_tmp.shape
x_half, y_half = x_size / 2, y_size / 2
x_frame, y_frame = int(0.11 * x_half), int(0.01 * y_half)
spatial_profile = np.concatenate((spat_tmp[x_frame:(x_half - x_frame), y_frame:(y_half - y_frame), :],
spat_tmp[(x_half + x_frame):-x_frame, y_frame:(y_half - y_frame), :],
spat_tmp[(x_half + x_frame):-x_frame, (y_half + y_frame):-y_frame, :],
spat_tmp[x_frame:(x_half - x_frame), (y_half + y_frame):-y_frame, :]),
axis=1)
# set plot position and plot image
p1 = fig.add_subplot(
gs[20 * (icomp - istart_plot) + idx_class * 10:20 * (icomp - istart_plot) + 15 + idx_class * 10, 0:10])
p1.imshow(spatial_profile)
# set some plotting options
p1.yaxis.set_ticks([])
p1.xaxis.set_ticks([])
y_name = "IC#%02d" % (idx_sort[icomp] + 1)
p1.set_ylabel(y_name, fontsize=18)
# ----------------------------------------------
# if given write MNI coordinates under the image
# ----------------------------------------------
if np.any(mni_coords):
# left hemisphere
plt.text(120, 360, mni_coords['lh'][int(idx_sort[int(icomp)])], color="black",
fontsize=18)
# right hemisphere
plt.text(850, 360, mni_coords['rh'][int(idx_sort[int(icomp)])], color="black",
fontsize=18)
# add location information of the component
# --> if located in 'both', 'left' or 'right' hemisphere
plt.text(-220, 100, hemi_loc_txt[int(idx_sort[int(icomp)])], color="red",
fontsize=25, rotation=90)
# ----------------------------------------------
# temporal/spectral profiles
# ----------------------------------------------
# loop over all time courses
for itemp in range(ntemp):
# ----------------------------------------------
# if given plot a headline above the time
# courses of each condition
# ----------------------------------------------
if icomp == istart_plot and len(temp_profile_names):
# add a sub-plot for the text
p_text = fig.add_subplot(gs[(idx_class - 1) * 10: 6 + (idx_class - 1) * 12,
(itemp) * 8 + 11:(itemp + 1) * 8 + 9])
# plot the text and adjust spines
p_text.text(0, 0, " " + temp_profile_names[itemp], fontsize=30)
adjust_spines(p_text, [])
# set plot position
if bar_plot:
p2 = plt.subplot(
gs[20 * (icomp - istart_plot) + idx_class * 11:20 * (icomp - istart_plot) + 13 + idx_class * 10,
itemp * 8 + 11:(itemp + 1) * 8 + 9])
else:
p2 = plt.subplot(
gs[20 * (icomp - istart_plot) + idx_class * 10:20 * (icomp - istart_plot) + 15 + idx_class * 10,
itemp * 8 + 11:(itemp + 1) * 8 + 9])
# extract temporal plotting information
times = (np.arange(win_ntsl) / sfreq + tpre)[5:-5]
idx_start = np.argmin(np.abs(times - time_range[0]))
idx_end = np.argmin(np.abs(times - time_range[1]))
# ----------------------------------------------
# plot spectral profile
# ----------------------------------------------
# check if global scaling should be used
if global_scaling:
vmin_cur, vmax_cur = np.min(vmin), np.max(vmax)
else:
vmin_cur, vmax_cur = vmin[icomp], vmax[icomp]
# show spectral profile
if bar_plot:
plt.bar(freqs, freq_heights[itemp, int(idx_sort[icomp]), :], width=1.0, color='cornflowerblue')
plt.xlim(flow, fhigh)
plt.ylim(0.0, 1.0)
# set some parameter
p2.set_xlabel("freq. [Hz]")
p2.set_ylabel("ampl. [a.u.]")
# ----------------------------------------------
# plot temporal profile on the some spot
# ----------------------------------------------
ax = plt.twiny()
ax.set_xlabel("time [s]")
ax.plot(times[idx_start:idx_end], 0.5+temporal_envelope_mean[itemp][0][int(idx_sort[icomp]), idx_start:idx_end],
color='red', linewidth=3.0)
ax.set_xlim(times[idx_start], times[idx_end])
ax.set_ylim(0.0, 1.0)
else:
average_power = average_power_all[itemp][int(idx_sort[icomp])]
extent = (times[idx_start], times[idx_end], freqs[0], freqs[-1])
p2.imshow(average_power, extent=extent, aspect="auto", origin="lower",
picker=False, cmap='RdBu_r', vmin=vmin_cur,
vmax=vmax_cur)
# set some parameter
p2.set_xlabel("time [s]")
p2.set_ylabel("freq. [Hz]")
# ----------------------------------------------
# plot temporal profile on the some spot
# ----------------------------------------------
ax = plt.twinx()
ax.set_xlim(times[idx_start], times[idx_end])
ax.set_ylim(ylim_temp)
ax.set_ylabel("ampl. [a.u.]")
ax.plot(times[idx_start:idx_end], temporal_envelope_mean[itemp][0][int(idx_sort[icomp]), idx_start:idx_end],
color='black', linewidth=3.0)
# ----------------------------------------------
# finally plot a color bar
# ----------------------------------------------
if not bar_plot:
# first normalize the color table
norm = Normalize(vmin=np.round(vmin_cur, 2), vmax=np.round(vmax_cur, 2))
sm = plt.cm.ScalarMappable(cmap='RdBu_r', norm=norm)
sm.set_array(np.linspace(vmin_cur, 1.0))
# estimate position of the color bar
xpos = 0.405 + 0.5/(ntemp + 1.0)
if n_keys > 1:
cbaxes = fig.add_axes([xpos, 0.135, 0.2, 0.006])
else:
cbaxes = fig.add_axes([xpos, 0.03, 0.2, 0.006])
ticks_fac = (vmax_cur - vmin_cur) * 0.3333
ticks = np.round([vmin_cur, vmin_cur + ticks_fac, vmax_cur - ticks_fac, vmax_cur], 2)
# ticks = [-1.0, -0.5, 0.0, 0.5, 1.0]
# now plot color bar
cb = plt.colorbar(sm, ax=p2, cax=cbaxes, use_gridspec=False,
orientation='horizontal', ticks=ticks,
format='%1.2g')
cb.ax.tick_params(labelsize=18)
# ----------------------------------------------
# save image
# ----------------------------------------------
if fnout:
fnout_complete = '%s_%02d.png' % (fnout, iimg + 1)
plt.savefig(fnout_complete, format='png', dpi=300)
# close plot and set plotting back to screen
plt.close('FourierICA plots')
plt.ion()
# remove temporary directory for
# spatial profile plots
if exists(temp_plot_dir):
rmdir(temp_plot_dir)
return mni_coords, classification, labels
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220,
220,
1303,
4532,
6167,
62,
14933,
284,
691,
3994,
7652,
198,
220,
220,
220,
1303,
852,
2950,
287,
7587,
262,
1459,
198,
220,
220,
220,
1303,
1366,
198,
220,
220,
220,
1303,
20368,
35937,
198,
220,
220,
220,
14722,
796,
17635,
198,
220,
220,
220,
329,
1090,
62,
4411,
72,
287,
339,
11632,
25,
198,
220,
220,
220,
220,
220,
220,
220,
329,
1994,
287,
6167,
62,
14933,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
17923,
58,
22019,
62,
4411,
72,
7131,
2539,
5974,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
14722,
13,
33295,
7,
2539,
8,
628,
220,
220,
220,
14722,
796,
45941,
13,
34642,
7,
23912,
1424,
737,
83,
349,
396,
3419,
628,
220,
220,
220,
1441,
285,
8461,
62,
1073,
3669,
11,
339,
11632,
62,
17946,
62,
14116,
11,
17923,
11,
14722,
628,
628,
198,
2,
1343,
44627,
44627,
44627,
14030,
4880,
198,
2,
31904,
2163,
284,
2198,
611,
17923,
373,
198,
2,
6157,
3161,
284,
29353,
198,
2,
1343,
44627,
44627,
44627,
14030,
4880,
198,
4299,
4808,
9122,
62,
4871,
2649,
7,
4871,
2649,
11,
299,
5589,
2599,
628,
220,
220,
220,
37227,
198,
220,
220,
220,
5053,
525,
2163,
284,
2198,
611,
17923,
373,
198,
220,
220,
220,
6157,
3161,
284,
29353,
628,
220,
220,
220,
220,
220,
220,
220,
40117,
198,
220,
220,
220,
220,
220,
220,
220,
24200,
438,
198,
220,
220,
220,
220,
220,
220,
220,
17923,
25,
22155,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
17923,
2134,
422,
262,
1448,
62,
3970,
62,
15252,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
632,
318,
257,
22155,
7268,
734,
850,
12,
67,
2867,
3166,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
75,
71,
6,
290,
705,
17179,
6,
357,
1640,
1364,
290,
826,
33169,
737,
554,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1111,
850,
12,
67,
2867,
3166,
262,
1321,
546,
262,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2628,
318,
8574,
11,
1312,
13,
68,
13,
257,
1448,
14,
36996,
1438,
1343,
262,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1321,
543,
6805,
389,
8574,
287,
428,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1448,
198,
220,
220,
220,
220,
220,
220,
220,
299,
5589,
25,
18253,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1271,
286,
6805,
628,
220,
220,
220,
220,
220,
220,
220,
8229,
198,
220,
220,
220,
220,
220,
220,
220,
40103,
198,
220,
220,
220,
220,
220,
220,
220,
8251,
25,
1351,
7268,
262,
1448,
3891,
198,
220,
220,
220,
220,
220,
220,
220,
1994,
62,
65,
6361,
25,
1351,
7268,
262,
1448,
11637,
11,
1312,
13,
68,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
262,
1321,
810,
284,
7110,
257,
649,
1448,
1438,
198,
220,
220,
220,
220,
220,
220,
220,
4686,
87,
62,
30619,
25,
7177,
7268,
262,
29353,
1502,
286,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
262,
6805,
11,
1312,
13,
68,
13,
6805,
894,
12484,
284,
530,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1448,
389,
37515,
1978,
198,
220,
220,
220,
37227,
628,
220,
220,
220,
1303,
20368,
35937,
198,
220,
220,
220,
1303,
1330,
3306,
13103,
198,
220,
220,
220,
1303,
20368,
35937,
198,
220,
220,
220,
1330,
299,
32152,
355,
45941,
628,
198,
220,
220,
220,
1303,
20368,
35937,
198,
220,
220,
220,
1303,
2198,
611,
17923,
373,
1760,
198,
220,
220,
220,
1303,
20368,
35937,
198,
220,
220,
220,
1994,
62,
65,
6361,
796,
17635,
198,
220,
220,
220,
611,
45941,
13,
1092,
7,
4871,
2649,
2599,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
41216,
6565,
8341,
198,
220,
220,
220,
220,
220,
220,
220,
4686,
87,
62,
30619,
796,
17635,
198,
220,
220,
220,
220,
220,
220,
220,
8251,
62,
4411,
72,
796,
1351,
7,
4871,
2649,
13,
13083,
28955,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
3297,
8251,
198,
220,
220,
220,
220,
220,
220,
220,
8251,
796,
1351,
7,
4871,
2649,
58,
13083,
62,
4411,
72,
58,
15,
60,
4083,
13083,
28955,
198,
220,
220,
220,
220,
220,
220,
220,
8251,
13,
30619,
7,
2539,
28,
50033,
410,
25,
410,
13,
45828,
28955,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
900,
705,
34680,
6,
9633,
284,
262,
886,
198,
220,
220,
220,
220,
220,
220,
220,
8251,
13,
28956,
10786,
34680,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
8251,
13,
33295,
10786,
34680,
11537,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
4781,
8251,
351,
6565,
12784,
198,
220,
220,
220,
220,
220,
220,
220,
8251,
62,
42949,
796,
17635,
198,
220,
220,
220,
220,
220,
220,
220,
329,
1994,
287,
8251,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
17923,
58,
13083,
62,
4411,
72,
58,
15,
60,
7131,
2539,
60,
393,
59,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
17923,
58,
13083,
62,
4411,
72,
58,
16,
60,
7131,
2539,
5974,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
8251,
62,
42949,
13,
33295,
7,
2539,
8,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
9052,
625,
477,
8251,
198,
220,
220,
220,
220,
220,
220,
220,
329,
1994,
287,
8251,
62,
42949,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
651,
36525,
284,
1123,
1398,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4686,
87,
62,
75,
71,
796,
17923,
58,
13083,
62,
4411,
72,
58,
15,
60,
7131,
2539,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4686,
87,
62,
17179,
796,
17923,
58,
13083,
62,
4411,
72,
58,
16,
60,
7131,
2539,
60,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
651,
36525,
286,
6805,
287,
1111,
16869,
8802,
19079,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4686,
87,
62,
16885,
796,
45941,
13,
3849,
8831,
16,
67,
7,
312,
87,
62,
75,
71,
11,
4686,
87,
62,
17179,
8,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
651,
36525,
286,
6805,
691,
287,
826,
33169,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4686,
87,
62,
8807,
62,
17179,
796,
45941,
13,
2617,
26069,
16,
67,
7,
312,
87,
62,
17179,
11,
4686,
87,
62,
75,
71,
8,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
651,
36525,
286,
6805,
691,
287,
1364,
33169,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4686,
87,
62,
8807,
62,
75,
71,
796,
45941,
13,
2617,
26069,
16,
67,
7,
312,
87,
62,
75,
71,
11,
4686,
87,
62,
17179,
8,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
751,
6805,
284,
1351,
286,
23243,
36525,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4686,
87,
62,
439,
796,
45941,
13,
1102,
9246,
268,
378,
19510,
312,
87,
62,
16885,
11,
4686,
87,
62,
8807,
62,
17179,
11,
4686,
87,
62,
8807,
62,
75,
71,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4686,
87,
62,
30619,
15853,
4686,
87,
62,
439,
13,
83,
349,
396,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1994,
62,
65,
6361,
13,
33295,
7,
11925,
7,
312,
87,
62,
439,
4008,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
751,
717,
4865,
290,
8636,
23818,
2160,
284,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
423,
262,
826,
11637,
198,
220,
220,
220,
220,
220,
220,
220,
1994,
62,
65,
6361,
796,
45941,
13,
28463,
7,
2539,
62,
65,
6361,
11,
657,
11,
352,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1994,
62,
65,
6361,
796,
45941,
13,
66,
5700,
388,
7,
2539,
62,
65,
6361,
38381,
21912,
16,
60,
628,
198,
220,
220,
220,
1303,
20368,
35937,
198,
220,
220,
220,
1303,
611,
17923,
373,
407,
6157,
900,
198,
220,
220,
220,
1303,
617,
4277,
3815,
198,
220,
220,
220,
1303,
20368,
35937,
198,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
4686,
87,
62,
30619,
796,
45941,
13,
283,
858,
7,
77,
5589,
8,
198,
220,
220,
220,
220,
220,
220,
220,
8251,
62,
42949,
796,
17635,
628,
198,
220,
220,
220,
1441,
8251,
62,
42949,
11,
1994,
62,
65,
6361,
11,
4686,
87,
62,
30619,
628,
628,
198,
2,
1343,
44627,
44627,
44627,
14030,
4880,
198,
2,
31904,
2163,
284,
5412,
640,
10902,
329,
29353,
198,
2,
1343,
44627,
44627,
44627,
14030,
4880,
198,
4299,
4808,
1136,
62,
11498,
35738,
62,
268,
1091,
274,
7,
69,
280,
5277,
62,
3970,
62,
26801,
11,
370,
62,
11612,
11,
21964,
62,
268,
1091,
68,
41888,
4357,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
12351,
62,
17946,
62,
7890,
41888,
4357,
256,
25241,
28,
25101,
11,
3298,
62,
1416,
4272,
28,
17821,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1592,
62,
13664,
62,
2363,
28,
14202,
11,
256,
3866,
28,
14202,
11,
5202,
28,
14202,
2599,
628,
220,
220,
220,
37227,
198,
220,
220,
220,
5053,
525,
2163,
284,
2198,
611,
17923,
373,
198,
220,
220,
220,
6157,
3161,
284,
29353,
628,
220,
220,
220,
220,
220,
220,
220,
40117,
198,
220,
220,
220,
220,
220,
220,
220,
24200,
438,
198,
220,
220,
220,
220,
220,
220,
220,
46287,
5277,
62,
3970,
62,
26801,
25,
34296,
5277,
25241,
2134,
7560,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
618,
11524,
474,
388,
1533,
13,
12501,
3361,
577,
13,
69,
280,
5277,
62,
3970,
198,
220,
220,
220,
220,
220,
220,
220,
370,
62,
11612,
25,
7177,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
362,
35,
12,
9536,
844,
278,
12,
18747,
357,
77,
5589,
2124,
299,
85,
1140,
417,
8,
6108,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
618,
11524,
34296,
5277,
25241,
198,
220,
220,
220,
220,
220,
220,
220,
21964,
62,
268,
1091,
68,
25,
1351,
286,
26515,
7268,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
262,
21964,
16441,
274,
13,
1002,
262,
21964,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
16441,
274,
389,
1541,
1813,
994,
1976,
12,
46536,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
290,
1612,
31850,
318,
6157,
198,
220,
220,
220,
220,
220,
220,
220,
12351,
62,
17946,
62,
7890,
25,
7177,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
513,
35,
7177,
7268,
262,
2723,
42842,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1366,
973,
329,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
2198,
611,
4731,
815,
307,
6626,
198,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
18896,
7,
2539,
8,
1875,
2310,
290,
705,
705,
287,
1994,
25,
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220,
220,
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62,
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5239,
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15,
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12,
13083,
62,
38942,
9,
7,
312,
87,
62,
2539,
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13083,
62,
35312,
828,
1994,
13,
35312,
3419,
58,
15,
48688,
29001,
3256,
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220,
220,
220,
220,
220,
220,
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220,
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220,
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220,
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8251,
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220,
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220,
220,
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62,
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13,
5239,
7,
15,
13,
15,
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13083,
62,
38942,
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7,
312,
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62,
2539,
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62,
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828,
1994,
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3419,
58,
16,
4357,
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220,
220,
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220,
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220,
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220,
220,
220,
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2073,
25,
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220,
220,
220,
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220,
220,
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220,
220,
220,
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13,
5239,
7,
15,
13,
15,
11,
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24,
12,
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62,
38942,
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7,
312,
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62,
2539,
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13083,
62,
35312,
828,
1994,
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10369,
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28,
1485,
11,
198,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3124,
28,
4033,
669,
58,
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62,
30619,
62,
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58,
312,
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62,
2539,
11907,
8,
198,
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220,
220,
220,
220,
220,
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220,
220,
220,
4532,
62,
2777,
1127,
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79,
62,
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12962,
628,
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220,
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220,
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220,
220,
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13,
21928,
5647,
7,
22184,
448,
62,
9600,
11,
288,
14415,
28,
6200,
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628,
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220,
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220,
220,
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1303,
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7110,
290,
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29353,
736,
284,
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21528,
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13,
25811,
13,
2364,
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2,
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44627,
44627,
14030,
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2,
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35738,
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198,
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40117,
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24200,
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21964,
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329,
1312,
9600,
287,
2837,
7,
77,
9600,
2599,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
1598,
3785,
357,
1462,
923,
351,
257,
2330,
2939,
287,
1123,
9052,
8,
198,
220,
220,
220,
220,
220,
220,
220,
458,
83,
13,
565,
69,
3419,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
8636,
703,
867,
21528,
319,
1459,
2939,
198,
220,
220,
220,
220,
220,
220,
220,
318,
83,
433,
62,
29487,
796,
493,
7,
77,
5589,
62,
525,
62,
29487,
1635,
1312,
9600,
8,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
900,
4686,
87,
62,
4871,
11507,
198,
220,
220,
220,
220,
220,
220,
220,
4686,
87,
62,
4871,
796,
352,
611,
1994,
62,
65,
6361,
6624,
17635,
2073,
657,
628,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
20368,
35937,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
9052,
625,
477,
6805,
543,
815,
307,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
37515,
319,
262,
1459,
2939,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
20368,
35937,
198,
220,
220,
220,
220,
220,
220,
220,
329,
14158,
3361,
287,
2837,
7,
396,
433,
62,
29487,
11,
299,
29487,
58,
72,
9600,
60,
2599,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
20368,
26171,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
2198,
611,
1994,
62,
3526,
364,
318,
900,
290,
815,
307,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
3194,
319,
262,
2939,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
20368,
26171,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
357,
291,
3361,
6624,
318,
83,
433,
62,
29487,
290,
1994,
62,
65,
6361,
14512,
685,
12962,
393,
3467,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
14808,
291,
3361,
1343,
352,
8,
287,
1994,
62,
65,
6361,
2599,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
4532,
1994,
12,
9630,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
357,
291,
3361,
1343,
352,
8,
287,
1994,
62,
65,
6361,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4686,
87,
62,
2539,
15853,
352,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
751,
850,
12,
29487,
351,
705,
2539,
62,
5239,
6,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
279,
62,
5239,
796,
2336,
13,
2860,
62,
7266,
29487,
7,
14542,
58,
1238,
1635,
357,
291,
3361,
532,
318,
83,
433,
62,
29487,
8,
1343,
4686,
87,
62,
4871,
1635,
838,
25,
3467,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1160,
1635,
357,
291,
3361,
532,
318,
83,
433,
62,
29487,
8,
1343,
807,
1343,
4686,
87,
62,
4871,
1635,
838,
11,
657,
25,
940,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
279,
62,
5239,
13,
5239,
7,
15,
11,
657,
11,
8251,
58,
312,
87,
62,
2539,
12,
16,
4357,
10369,
7857,
28,
1495,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4532,
62,
2777,
1127,
7,
79,
62,
5239,
11,
685,
12962,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
4532,
4686,
87,
62,
4871,
11507,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4686,
87,
62,
4871,
15853,
352,
628,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
20368,
26171,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
7110,
21739,
16545,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
20368,
26171,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
1100,
21739,
7034,
2939,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
24714,
62,
8692,
796,
366,
2149,
4,
2999,
67,
62,
2777,
34961,
62,
13317,
13,
11134,
1,
4064,
357,
312,
87,
62,
30619,
58,
291,
3361,
60,
1343,
352,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
24714,
259,
62,
9600,
796,
4654,
7,
29510,
62,
29487,
62,
15908,
11,
24714,
62,
8692,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
15246,
62,
22065,
796,
12747,
13,
320,
961,
7,
22184,
259,
62,
9600,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4781,
7,
22184,
259,
62,
9600,
8,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
37825,
858,
2939,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2124,
62,
7857,
11,
331,
62,
7857,
11,
4808,
796,
15246,
62,
22065,
13,
43358,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2124,
62,
13959,
11,
331,
62,
13959,
796,
2124,
62,
7857,
1220,
362,
11,
331,
62,
7857,
1220,
362,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2124,
62,
14535,
11,
331,
62,
14535,
796,
493,
7,
15,
13,
1157,
1635,
2124,
62,
13959,
828,
493,
7,
15,
13,
486,
1635,
331,
62,
13959,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
21739,
62,
13317,
796,
45941,
13,
1102,
9246,
268,
378,
19510,
2777,
265,
62,
22065,
58,
87,
62,
14535,
37498,
87,
62,
13959,
532,
2124,
62,
14535,
828,
331,
62,
14535,
37498,
88,
62,
13959,
532,
331,
62,
14535,
828,
1058,
4357,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
15246,
62,
22065,
58,
7,
87,
62,
13959,
1343,
2124,
62,
14535,
2599,
12,
87,
62,
14535,
11,
331,
62,
14535,
37498,
88,
62,
13959,
532,
331,
62,
14535,
828,
1058,
4357,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
15246,
62,
22065,
58,
7,
87,
62,
13959,
1343,
2124,
62,
14535,
2599,
12,
87,
62,
14535,
11,
357,
88,
62,
13959,
1343,
331,
62,
14535,
2599,
12,
88,
62,
14535,
11,
1058,
4357,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
15246,
62,
22065,
58,
87,
62,
14535,
37498,
87,
62,
13959,
532,
2124,
62,
14535,
828,
357,
88,
62,
13959,
1343,
331,
62,
14535,
2599,
12,
88,
62,
14535,
11,
1058,
46570,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
16488,
28,
16,
8,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
900,
7110,
2292,
290,
7110,
2939,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
279,
16,
796,
2336,
13,
2860,
62,
7266,
29487,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
308,
82,
58,
1238,
1635,
357,
291,
3361,
532,
318,
83,
433,
62,
29487,
8,
1343,
4686,
87,
62,
4871,
1635,
838,
25,
1238,
1635,
357,
291,
3361,
532,
318,
83,
433,
62,
29487,
8,
1343,
1315,
1343,
4686,
87,
62,
4871,
1635,
838,
11,
657,
25,
940,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
279,
16,
13,
320,
12860,
7,
2777,
34961,
62,
13317,
8,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
900,
617,
29353,
3689,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
279,
16,
13,
88,
22704,
13,
2617,
62,
83,
3378,
26933,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
279,
16,
13,
87,
22704,
13,
2617,
62,
83,
3378,
26933,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
331,
62,
3672,
796,
366,
2149,
2,
4,
2999,
67,
1,
4064,
357,
312,
87,
62,
30619,
58,
291,
3361,
60,
1343,
352,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
279,
16,
13,
2617,
62,
2645,
9608,
7,
88,
62,
3672,
11,
10369,
7857,
28,
1507,
8,
628,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
20368,
26171,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
611,
1813,
3551,
337,
22125,
22715,
739,
262,
2939,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
20368,
26171,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
45941,
13,
1092,
7,
76,
8461,
62,
1073,
3669,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
1364,
33169,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
458,
83,
13,
5239,
7,
10232,
11,
11470,
11,
285,
8461,
62,
1073,
3669,
17816,
75,
71,
6,
7131,
600,
7,
312,
87,
62,
30619,
58,
600,
7,
291,
3361,
8,
12962,
4357,
3124,
2625,
13424,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
10369,
7857,
28,
1507,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
826,
33169,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
458,
83,
13,
5239,
7,
25764,
11,
11470,
11,
285,
8461,
62,
1073,
3669,
17816,
17179,
6,
7131,
600,
7,
312,
87,
62,
30619,
58,
600,
7,
291,
3361,
8,
12962,
4357,
3124,
2625,
13424,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
10369,
7857,
28,
1507,
8,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
751,
4067,
1321,
286,
262,
7515,
198,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
14610,
611,
5140,
287,
705,
16885,
3256,
705,
9464,
6,
393,
705,
3506,
6,
33169,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
458,
83,
13,
5239,
32590,
17572,
11,
1802,
11,
339,
11632,
62,
17946,
62,
14116,
58,
600,
7,
312,
87,
62,
30619,
58,
600,
7,
291,
3361,
8,
12962,
4357,
3124,
2625,
445,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
10369,
7857,
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] | 2.235067 | 31,825 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Author : qichun tang
# @Contact : [email protected]
import json
import os
from pathlib import Path
import pandas as pd
from joblib import Parallel, delayed
from joblib import dump
info = {
"bohb": ("HpBandSter-BOHB", "r",),
"ultraopt_BOHB": ("UltraOpt-BOHB", "g",),
"ultraopt_HyperBand": ("HyperBand", "b",),
"tpe": ("HyperOpt-TPE", "r",),
"ultraopt_ETPE": ("UltraOpt-ETPE", "g",),
"ultraopt_Random": ("Random", "b",),
}
benchmarks = [
"protein_structure",
"slice_localization",
"naval_propulsion",
"parkinsons_telemonitoring"
]
args_list = []
for _, benchmark in enumerate(benchmarks):
for fname in info.keys():
args_list.append((benchmark, fname))
Parallel(n_jobs=10)(
delayed(process)(*args) for args in args_list
)
| [
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] | 2.385714 | 350 |
# coding=utf-8
# Copyright 2021 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# -*- coding: utf-8 -*-
#
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sphinx-doc.org/en/master/config
import os
import sys
sys.path.insert(0, os.path.abspath('..'))
# -- Project information -----------------------------------------------------
project = 'learned_optimization'
copyright = '2021, Google LLC.'
author = 'The learned_optimization authors'
# The short X.Y version
version = ''
# The full version, including alpha/beta/rc tags
release = ''
# -- General configuration ---------------------------------------------------
# If your documentation needs a minimal Sphinx version, state it here.
#
needs_sphinx = '2.1'
extensions = [
'sphinx.ext.autodoc',
'sphinx.ext.autosummary',
'sphinx.ext.intersphinx',
'sphinx.ext.mathjax',
'sphinx.ext.napoleon',
'sphinx.ext.viewcode',
'matplotlib.sphinxext.plot_directive',
'sphinx_autodoc_typehints',
'myst_nb',
]
intersphinx_mapping = {
'python': ('https://docs.python.org/3/', None),
'numpy': ('https://numpy.org/doc/stable/', None),
'scipy': ('https://docs.scipy.org/doc/scipy/reference/', None),
}
suppress_warnings = [
'ref.citation', # Many duplicated citations in numpy/scipy docstrings.
'ref.footnote', # Many unreferenced footnotes in numpy/scipy docstrings
]
# Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates']
# The suffix(es) of source filenames.
source_suffix = ['.rst', '.ipynb', '.md']
# The main toctree document.
main_doc = 'index'
# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
#
# This is also used if you do content translation via gettext catalogs.
# Usually you set "language" from the command line for these cases.
language = None
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
# This pattern also affects html_static_path and html_extra_path.
exclude_patterns = [
# Sometimes sphinx reads its own outputs as inputs!
'_build/html',
'_build/',
'_build/jupyter_execute',
'notebooks/README.md',
'README.md',
# Ignore markdown source for notebooks; myst-nb builds from the ipynb
# These are kept in sync via jupytext --sync
'notebooks/*.md',
]
# The name of the Pygments (syntax highlighting) style to use.
pygments_style = None
autosummary_generate = True
# -- Options for HTML output -------------------------------------------------
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
#
html_theme = 'sphinx_rtd_theme'
# Theme options are theme-specific and customize the look and feel of a theme
# further. For a list of options available for each theme, see the
# documentation.
html_theme_options = {
'logo_only': True,
}
# The name of an image file (relative to this directory) to place at the top
# of the sidebar.
# TODO(lmetz) add logos!
# html_logo = '_static/logo_250px.png'
# html_favicon = '_static/favicon.png'
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ['_static']
# -- Options for myst ----------------------------------------------
jupyter_execute_notebooks = 'force'
execution_allow_errors = False
# Notebook cell execution timeout; defaults to 30.
execution_timeout = 100
# List of patterns, relative to source directory, that match notebook
# files that will not be executed.
execution_excludepatterns = ['*']
# -- Extension configuration -------------------------------------------------
# Tell sphinx-autodoc-typehints to generate stub parameter annotations including
# types, even if the parameters aren't explicitly documented.
always_document_param_types = True
# force clear docs every rebuild.
import shutil
if os.path.exists('_build/'):
shutil.rmtree('_build/')
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] | 3.257844 | 1,466 |
import tensorflow as tf
from tensorflow.contrib.framework import add_arg_scope
from tfsnippet.utils import (add_name_and_scope_arg_doc, get_static_shape,
get_default_scope_name)
from .conv2d_ import conv2d
from .utils import validate_conv2d_size_tuple, validate_conv2d_input
__all__ = ['shifted_conv2d']
@add_arg_scope
@add_name_and_scope_arg_doc
def shifted_conv2d(input,
out_channels,
kernel_size,
spatial_shift,
strides=(1, 1),
channels_last=True,
conv_fn=conv2d,
name=None,
scope=None,
**kwargs):
"""
2D convolution with shifted input.
This method first pads `input` according to the `kernel_size` and
`spatial_shift` arguments, then do 2D convolution (using `conv_fn`)
with "VALID" padding.
Args:
input (Tensor): The input tensor, at least 4-d.
out_channels (int): The channel numbers of the output.
kernel_size (int or (int, int)): Kernel size over spatial dimensions.
spatial_shift: The `spatial_shift` should be a tuple with two elements
(corresponding to height and width spatial axes), and the elements
can only be -1, 0 or 1.
If the shift for a specific axis is `-1`, then `kernel_size - 1`
zeros will be padded at the end of that axis.
If the shift is `0`, then `(kernel_size - 1) // 2` zeros will be
padded at the front, and `kernel_size // 2` zeros will be padded
at the end that axis.
Otherwise if the shift is `1`, then `kernel_size + 1` zeros will
be padded at the front of that axis.
strides (int or (int, int)): Strides over spatial dimensions.
channels_last (bool): Whether or not the channel axis is the last
axis in `input`? (i.e., the data format is "NHWC")
conv_fn: The 2D convolution function. (default :func:`conv2d`)
\\**kwargs: Other named parameters passed to `conv_fn`.
Returns:
tf.Tensor: The output tensor.
"""
spatial_shift = tuple(spatial_shift)
if len(spatial_shift) != 2 or \
any(s not in (-1, 0, 1) for s in spatial_shift):
raise TypeError('`spatial_shift` must be a tuple with two elements, '
'and the elements can only be -1, 0 or 1.')
kernel_size = validate_conv2d_size_tuple('kernel_size', kernel_size)
if 'padding' in kwargs:
raise ValueError('`padding` argument is not supported.')
input, _, _ = validate_conv2d_input(input, channels_last=channels_last)
rank = len(get_static_shape(input))
pads = [(0, 0)] * rank
is_shifted_conv2d = False
spatial_start = -3 if channels_last else -2
for i, (ksize, shift) in enumerate(zip(kernel_size, spatial_shift)):
axis = i + spatial_start
if shift == 0:
pads[axis] = ((ksize - 1) // 2, ksize // 2)
elif shift == -1:
pads[axis] = (0, ksize - 1)
is_shifted_conv2d = True
else:
assert(shift == 1)
pads[axis] = (ksize - 1, 0)
is_shifted_conv2d = True
# fast routine: no shift, use ordinary conv_fn with padding == 'SAME'
if not is_shifted_conv2d:
return conv_fn(
input=input,
out_channels=out_channels,
kernel_size=kernel_size,
strides=strides,
channels_last=channels_last,
padding='SAME',
scope=scope,
name=name,
**kwargs
)
# slow routine: pad and use conv_fn with padding == 'VALID'
with tf.variable_scope(scope, default_name=name or 'shifted_conv2d'):
output = tf.pad(input, pads)
output = conv_fn(
input=output,
out_channels=out_channels,
kernel_size=kernel_size,
strides=strides,
channels_last=channels_last,
padding='VALID',
scope=get_default_scope_name(
getattr(conv_fn, '__name__', None) or 'conv_fn'),
**kwargs
)
return output
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# Copyright The IETF Trust 2007-2019, All Rights Reserved
# -*- coding: utf-8 -*-
from __future__ import absolute_import, print_function, unicode_literals
import os
from django.contrib.syndication.views import Feed
from django.utils.feedgenerator import Atom1Feed
from django.conf import settings
from django.utils.html import escape
from ietf.doc.models import Document
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"""
Zachary Cook
Manages calls to the databases.
"""
import sqlite3
from Controller import ConfigurationManager
from Model import Time,User
"""
Class representing the database.
"""
class DatabaseManager:
"""
Creates a database manager.
"""
"""
Initializes the tables if they aren't defined.
"""
"""
Marks open sessions with a finish time of -1. This
should only happen if there was power-lose during the operation
of the system.
"""
"""
Returns the type of user.
"""
"""
Sets the access type of a user.
"""
"""
Logs the session starting.
"""
"""
Logs the session ending.
"""
staticDatabaseManager = None
"""
Returns the static database instance.
"""
"""
Returns the User for the given id (non-hash). If
there is no registered User, None is returned.
"""
"""
Sets the access type of a user.
"""
"""
Registers a session being started.
"""
"""
Registers a session ended.
""" | [
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