Datasets:

ytzi

/

the-stack-dedup-python-filtered-docstrings

like 0

e7e9653d546ade6c8ce9b53c49b25b1b21568a5c

py

Python

VisualGimp/Markup.py

duangsuse/VisualGimp

79776fded12595ab3c56855b5ae56e2242780b2e

2019-05-07T12:09:11.000Z

2019-05-08T09:31:44.000Z

VisualGimp/Markup.py

duangsuse-valid-projects/VisualGimp

79776fded12595ab3c56855b5ae56e2242780b2e

VisualGimp/Markup.py

duangsuse-valid-projects/VisualGimp

79776fded12595ab3c56855b5ae56e2242780b2e

#!/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 &apos; " is replaced with &quot; & is replaced with &amp; < is replaced with &lt; > is replaced with &gt; ''' 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

#!/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 ''' def __init__(self, indent = -1, nl = linesep, buffer = str): self.marks = buffer() self.tag_stack = list() self.nl = nl self.indent = indent self.last_spaces = 0 self.revert_last_indent_size = 0 self.last_is_text = False ''' 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 ''' def useindent(self): return self.indent != -1 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 ''' def do(): self.wnewline() if (entering): self.last_spaces += self.indent else: self.last_spaces -= self.indent self.revert_last_indent_size = self.windent() +1 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 make(self): return self.marks 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 class TagBuilder: def __init__(self, xml): self.xml = xml def __enter__(self): self.xml.begin(*args, attrs = kwargs) def __exit__(self, *lveinfo): self.xml.end() 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 &apos; " is replaced with &quot; & is replaced with &amp; < is replaced with &lt; > is replaced with &gt; ''' 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

8796a12ade2e6974f6dfc98adc77e755604d7da8

py

Python

sqlalchemy_redshift/__init__.py

Hivestack/sqlalchemy-redshift

6226ffe4c6f3583606016492641e1bd5d351933a

sqlalchemy_redshift/__init__.py

Hivestack/sqlalchemy-redshift

6226ffe4c6f3583606016492641e1bd5d351933a

sqlalchemy_redshift/__init__.py

Hivestack/sqlalchemy-redshift

6226ffe4c6f3583606016492641e1bd5d351933a

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" )

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" )

fdbf1c941811766f3c215aa9700b09effe98e5e6

py

Python

ch2/chapter2_features_of_fastapi_02.py

PacktPublishing/Understanding-How-Web-APIs-Work

63220e7bf6b31315c46650e45c670ca9a01011fc

2021-10-03T09:34:34.000Z

2021-10-04T04:52:48.000Z

ch2/chapter2_features_of_fastapi_02.py

PacktPublishing/Understanding-How-Web-APIs-Work

63220e7bf6b31315c46650e45c670ca9a01011fc

2021-04-25T05:57:34.000Z

2021-04-25T14:49:24.000Z

ch2/chapter2_features_of_fastapi_02.py

PacktPublishing/Understanding-How-Web-APIs-Work

63220e7bf6b31315c46650e45c670ca9a01011fc

2021-05-13T09:39:27.000Z

2021-06-29T05:51:46.000Z

# -*- coding: utf-8 -*-

# -*- coding: utf-8 -*- def message(age: int = 0, name: str = 'stranger') -> str: return f'Hello {name}, you are {age} years old'

515654029ae48e70e4487c739d107ea440403f1d

py

Python

Lib/site-packages/hackedit/app/templates.py

fochoao/cpython

3dc84b260e5bced65ebc2c45c40c8fa65f9b5aa9

Lib/site-packages/hackedit/app/templates.py

fochoao/cpython

3dc84b260e5bced65ebc2c45c40c8fa65f9b5aa9

2021-05-03T18:02:23.000Z

2022-03-12T12:01:04.000Z

Lib/site-packages/hackedit/app/templates.py

fochoao/cpython

3dc84b260e5bced65ebc2c45c40c8fa65f9b5aa9

""" 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))

""" 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 """ def get_paths(root, path, src_dir, dest_dir): src_path = os.path.join(root, path) rel_path = os.path.relpath(src_path, src_dir) dst_path = os.path.join(dest_dir, rel_path) return src_path, dst_path def get_file_encoding(path): if is_binary(path): return 'binary' try: encodings = template['encodings'] except KeyError: encodings = ['utf-8', 'cp1252'] for encoding in encodings: try: with open(path, encoding=encoding) as f: f.read() except UnicodeDecodeError: continue else: return encoding def open_file(path, encoding, to_write=None): if encoding == 'binary': if to_write is None: mode = 'rb' else: mode = 'wb' encoding = None else: if to_write is None: mode = 'r' else: mode = 'w' content = None with open(path, mode, encoding=encoding) as f: if to_write is not None: f.write(to_write) else: content = f.read() return content def subsitute_vars(string): for var, value in answers.items(): string = re.sub('@%s@' % var, value, string) return string 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 def _logger(): return logging.getLogger(__name__) 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))

1a60970d1a4cf3ecc7aacdd16b38eca549a34840

py

Python

src/tubize/videotomp4.py

olivervinn/tubizescripts

8756f322d3e31f76f8b77cb8e084ded5941e29fa

src/tubize/videotomp4.py

olivervinn/tubizescripts

8756f322d3e31f76f8b77cb8e084ded5941e29fa

src/tubize/videotomp4.py

olivervinn/tubizescripts

8756f322d3e31f76f8b77cb8e084ded5941e29fa

""" 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}"' )

""" 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}"' ) def file(self, filename: str) -> None: logger.debug(f"converting file {filename}") self.process(filename) def directory(self, path: str, extension: str) -> int: files = find_files(path, extension) if len(files) < 1: print("No matching files found in directory!", file=sys.stderr) else: for f in files: self.file(f)

8052d0446907259540de210ff2c92410c7342f2e

py

Python

setup.py

snasiriany/parasol

88b99704676fb1253b8bc6402665a3174a00072d

2019-01-07T23:59:26.000Z

2021-12-29T16:51:56.000Z

setup.py

snasiriany/parasol

88b99704676fb1253b8bc6402665a3174a00072d

2019-01-09T01:35:54.000Z

2021-08-23T20:05:03.000Z

setup.py

snasiriany/parasol

88b99704676fb1253b8bc6402665a3174a00072d

2019-03-26T01:02:33.000Z

2022-01-26T20:34:34.000Z

from setuptools import setup setup( name='parasol', dependency_links=[ ], install_requires=[ ] )

from setuptools import setup setup( name='parasol', dependency_links=[ ], install_requires=[ ] )

79299c770a188b579e6412af89f2263960e65f50

py

Python

app/migrations/0007_auto_20211102_1946.py

Rqwannn/Rudemy

fe2d84540f3cc64c0ff6821e5f2fac22675fd381

2021-12-27T06:16:26.000Z

2022-01-20T02:13:03.000Z

app/migrations/0007_auto_20211102_1946.py

Rqwannn/Rudemy

fe2d84540f3cc64c0ff6821e5f2fac22675fd381

app/migrations/0007_auto_20211102_1946.py

Rqwannn/Rudemy

fe2d84540f3cc64c0ff6821e5f2fac22675fd381

# Generated by Django 3.2.8 on 2021-11-02 12:46 from django.db import migrations, models

# Generated by Django 3.2.8 on 2021-11-02 12:46 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('app', '0006_auto_20211102_1928'), ] operations = [ migrations.RemoveField( model_name='profile', name='skill', ), migrations.AddField( model_name='profile', name='tags', field=models.ManyToManyField(blank=True, to='app.Tag'), ), migrations.DeleteModel( name='Skill', ), ]

752ee840202809a32e9848a1a2c9a1828e74e71c

py

Python

oasislmf/model_execution/conf.py

ibailey-SCOR/OasisLMF

966b4de4e1e64851970f4291c5bdfe7edc20cb7a

oasislmf/model_execution/conf.py

ibailey-SCOR/OasisLMF

966b4de4e1e64851970f4291c5bdfe7edc20cb7a

oasislmf/model_execution/conf.py

ibailey-SCOR/OasisLMF

966b4de4e1e64851970f4291c5bdfe7edc20cb7a

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

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

cb8ea6149e57e707c1ee331f670e37c8feb61914

py

Python

codes/functions.py

Wenupi/protoplanetary_disks

51f8decbec5415e1da9893316f03d32ca5ab27de

codes/functions.py

Wenupi/protoplanetary_disks

51f8decbec5415e1da9893316f03d32ca5ab27de

codes/functions.py

Wenupi/protoplanetary_disks

51f8decbec5415e1da9893316f03d32ca5ab27de

#!/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')

#!/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 def FUN_rotation(x,y,z): x_d = x y_d = y*np.cos(inc) - z*np.sin(inc) z_d = y*np.sin(inc) + z*np.cos(inc) return x_d,y_d,z_d #-------------------------------------------------------------------------------- #Radiative transfer equation def FUN_intensity(I,z,x,y,optde): x_d,y_d,z_d = FUN_rotation(x,y,z) density = EQ_density(x_d,y_d,z_d) amax = EQ_amax(x_d,y_d,z_d) opa = function_ext(amax) S = funcion_S([z_d,y_d,x_d]) # print ('x,y,z', x,y,z) # print (S, x_d, y_d, z_d) # print (optde(z)) dIdz = -S*opa*density*np.exp(-optde(z)) #z es la variable de integracion (debe ser evaluada en cualquier punto) return dIdz #-------------------------------------------------------------------------------- #Optical depth def FUN_tau(tt,z,x,y): x_d,y_d,z_d = FUN_rotation(x,y,z) density = EQ_density(x_d,y_d,z_d) amax = EQ_amax(x_d,y_d,z_d) opa = function_ext(amax) dtau = -opa*density return dtau #-------------------------------------------------------------------------------- def FUN_tau_zaxis(tt,z,x,y): x_d,y_d,z_d = x,y,z density = EQ_density(x_d,y_d,z_d) amax = EQ_amax(x_d,y_d,z_d) opa = function_ext(amax) dtau = -opa*density return dtau #-------------------------------------------------------------------------------- #Black body radiation def FUN_BB(nu,T): # B = 2.*hP*nu**3/clight**2/( np.exp(hP*nu/kB/T) - 1.) B = 1./( np.exp(hP*nu/kB/T) - 1.) return B #-------------------------------------------------------------------------------- def FUN_limits_mult(xx,yy): Hout = EQ_Height(Rout) lim_z = Rout*np.sin(inc) + 2.*Hout*np.cos(inc) #Based on the geometry of the disk lim_y = Rout*np.cos(inc) + 2.*Hout*np.sin(inc) #Based on the geometry of the disk z_arr = np.linspace(1.1*lim_z, -1.1*lim_z, 200) z_crit = [] if ((np.abs(xx) <=Rout) and (np.abs(yy) <= lim_y)): xd,yd,zd = FUN_rotation(xx,yy,z_arr) crit = np.zeros((len(z_arr))) ############################################################################### #Funciona pero podria ser optimizado ############################################################################### for ii in range(len(z_arr)): #Crea un vector de densidad en la linea de vision if (EQ_density(xd,yd[ii],zd[ii]) == 0.): crit[ii] = 0 else: crit[ii] = 1 for ii in range(len(z_arr)): #Ve los indices donde cambia de 0 a algun valor, o de algun valor a 0 (fronteras) if ( (ii != 0) and (crit[ii] - crit[ii-1] != 0 )): z_crit.append(z_arr[ii]) elif(ii == 0 and crit[0] == 1): z_crit.append(z_arr[0]) ############################################################################### return z_crit #-------------------------------------------------------------------------------- def FUN_creates_source_function(x_array,y_array): #Arrays and limits Hout = EQ_Height(Rout) z_array = np.linspace(-2.*Hout, 2.*Hout, 200) Sfunction = np.zeros((len(z_array),len(y_array),len(x_array))) Temfunction = np.zeros((len(z_array),len(y_array),len(x_array))) op_depth_p = np.zeros((len(y_array),len(x_array))) #Computes the optical depth (perpendicular to the disk midplane) for j in range(len(y_array)): for i in range(len(x_array)): if(x_array[i] == 0. and y_array[j] == 0.): Sfunction[:,j,i] = 0. Temfunction[:,j,i] = 0. else: rad = np.sqrt(x_array[i]**2 + y_array[j]**2) Hscale = EQ_Height(rad) z_integ = np.linspace(2.*Hscale,-2.*Hscale,200) sol = odeint(FUN_tau_zaxis,0.,z_integ,args=(x_array[i],y_array[j])).T[0] op_depth_p[j][i] = sol[len(z_integ)-1] inter_opt = interpolate.interp1d(z_integ,sol,kind='linear', bounds_error=False,fill_value=0.) for k in range(len(z_array)): amax = EQ_amax(x_array[i],y_array[j],z_array[k]) albedo = function_alb(amax) ##########Temperature########## Omega2 = Ggrav*Mstar/(rad*AU)**3 Teff4 = 3.*Mdot*Omega2/8./np.pi/sigmaB Tacc4 = 3./4.*(7.*inter_opt(abs(z_array[k])) + 2./3.)*Teff4 Tirr4 = Tstar**4./4.*(Rstar/rad/AU)**2*np.exp(-7.*inter_opt(abs(z_array[k]))/phi_angle) Temfunction[k,j,i] = (Tacc4 + Tirr4)**(0.25) #Temfunction[k,j,i] = EQ_temperature(x_array[i],y_array[j],z_array[k]) ############################### Sfunction[k,j,i] = FUN_BB(nu,Temfunction[k,j,i])*(1.+ albedo*FUN_f(inter_opt(z_array[k]),op_depth_p[j][i],albedo)) #Crea funcion fuente y temperatura en 3D funcion_S = RegularGridInterpolator((z_array, y_array, x_array), Sfunction,bounds_error=False,fill_value=None) funcion_T = RegularGridInterpolator((z_array, y_array, x_array), Temfunction,bounds_error=False,fill_value=None) return funcion_S, funcion_T #-------------------------------------------------------------------------------- def FUN_f(t,tau,alb): eps = np.sqrt(1.-alb) fff = np.exp(-np.sqrt(3.)*eps*t) + np.exp(np.sqrt(3.)*eps*(t-tau)) fff = fff/( np.exp(-np.sqrt(3.)*eps*tau)*(eps-1.) - (eps+1.) ) return fff #-------------------------------------------------------------------------------- #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')

d281bf9d519356903906b4ce02f43f84e40f8147

py

Python

F0AM_Tools/TUV_to_mat.py

jdhask/pyMCM

32b65e1dff2e9626df5d52623fd1ac4af29f8c57

2021-11-15T19:24:40.000Z

2021-11-15T19:24:40.000Z

F0AM_Tools/TUV_to_mat.py

jdhask/pyMCM

32b65e1dff2e9626df5d52623fd1ac4af29f8c57

F0AM_Tools/TUV_to_mat.py

jdhask/pyMCM

32b65e1dff2e9626df5d52623fd1ac4af29f8c57

2021-11-15T19:23:46.000Z

2021-11-29T12:42:26.000Z

# -*- 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)

# -*- 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)

1d7b25e9a1db4f378a05b7199423917d7b5b9f81

py

Python

extract_url.py

nickinack/extract_url

d084ca0a791d5c50ab2accaee7cb4d0b981bd132

2022-02-07T05:51:36.000Z

2022-02-07T05:52:11.000Z

extract_url.py

nickinack/extract_url

d084ca0a791d5c50ab2accaee7cb4d0b981bd132

extract_url.py

nickinack/extract_url

d084ca0a791d5c50ab2accaee7cb4d0b981bd132

2020-05-18T08:29:22.000Z

2020-05-18T08:29:22.000Z

''' 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])

''' 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])

56b682792eb61ccb189ac68b9d7a874cbd6c0a60

py

Python

test/python/test_mapper_coupling.py

kifumi/qiskit-terra

203fca6d694a18824a6b12cbabd3dd2c64dd12ae

2018-11-01T01:35:43.000Z

2018-11-01T01:35:43.000Z

test/python/test_mapper_coupling.py

a-amaral/qiskit-terra

e73beba1e68de2617046a7e1e9eeac375b61de81

test/python/test_mapper_coupling.py

a-amaral/qiskit-terra

e73beba1e68de2617046a7e1e9eeac375b61de81

# -*- 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

# -*- 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 class CouplingTest(QiskitTestCase): def test_coupling_dict2list(self): input_dict = {0: [1, 2], 1: [2]} result = _coupling.coupling_dict2list(input_dict) expected = [[0, 1], [0, 2], [1, 2]] self.assertEqual(expected, result) def test_coupling_dict2list_empty_dict(self): self.assertIsNone(_coupling.coupling_dict2list({})) def test_coupling_list2dict(self): input_list = [[0, 1], [0, 2], [1, 2]] result = _coupling.coupling_list2dict(input_list) expected = {0: [1, 2], 1: [2]} self.assertEqual(expected, result) def test_coupling_list2dict_empty_list(self): self.assertIsNone(_coupling.coupling_list2dict([])) def test_empty_coupling_class(self): coupling = _coupling.Coupling() self.assertEqual(0, coupling.size()) self.assertEqual([], coupling.get_qubits()) self.assertEqual([], coupling.get_edges()) self.assertFalse(coupling.connected()) self.assertEqual("", str(coupling)) def test_coupling_str(self): coupling_dict = {0: [1, 2], 1: [2]} coupling = _coupling.Coupling(coupling_dict) expected = ("qubits: q[0] @ 1, q[1] @ 2, q[2] @ 3\n" "edges: q[0]-q[1], q[0]-q[2], q[1]-q[2]") self.assertEqual(expected, str(coupling)) def test_coupling_compute_distance(self): coupling_dict = {0: [1, 2], 1: [2]} coupling = _coupling.Coupling(coupling_dict) self.assertTrue(coupling.connected()) coupling.compute_distance() qubits = coupling.get_qubits() result = coupling.distance(qubits[0], qubits[1]) self.assertEqual(1, result) def test_coupling_compute_distance_coupling_error(self): coupling = _coupling.Coupling() self.assertRaises(_coupling.CouplingError, coupling.compute_distance) def test_add_qubit(self): coupling = _coupling.Coupling() self.assertEqual("", str(coupling)) coupling.add_qubit(('q', 0)) self.assertEqual("qubits: q[0] @ 1", str(coupling)) def test_add_qubit_not_tuple(self): coupling = _coupling.Coupling() self.assertRaises(_coupling.CouplingError, coupling.add_qubit, 'q0') def test_add_qubit_tuple_incorrect_form(self): coupling = _coupling.Coupling() self.assertRaises(_coupling.CouplingError, coupling.add_qubit, ('q', '0')) def test_add_edge(self): coupling = _coupling.Coupling() self.assertEqual("", str(coupling)) coupling.add_edge(("q", 0), ('q', 1)) expected = ("qubits: q[0] @ 1, q[1] @ 2\n" "edges: q[0]-q[1]") self.assertEqual(expected, str(coupling)) def test_distance_error(self): """Test distance method validation.""" graph = _coupling.Coupling({0: [1, 2], 1: [2]}) self.assertRaises(_coupling.CouplingError, graph.distance, ('q0', 0), ('q1', 1))

b9bba5bf54a9320d3bc8a8acd4f1c2d5d0aaa83f

py

Python

discretisedfield/tests/test_field.py

StephenPotato/discretisedfield

de49577b47acadd9372854252688194c348844a3

2021-03-13T09:43:52.000Z

2021-03-13T09:43:52.000Z

discretisedfield/tests/test_field.py

StephenPotato/discretisedfield

de49577b47acadd9372854252688194c348844a3

discretisedfield/tests/test_field.py

StephenPotato/discretisedfield

de49577b47acadd9372854252688194c348844a3

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

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 def check_field(field): assert isinstance(field.mesh, df.Mesh) assert isinstance(field.dim, int) assert field.dim > 0 assert isinstance(field.array, np.ndarray) assert field.array.shape == (*field.mesh.n, field.dim) average = field.average assert isinstance(average, (tuple, numbers.Real)) rstr = repr(field) assert isinstance(rstr, str) pattern = (r'^Field\(mesh=Mesh\(region=Region\(p1=\(.+\), ' r'p2=\(.+\)\), .+\), dim=\d+\)$') assert re.search(pattern, rstr) assert isinstance(field.__iter__(), types.GeneratorType) assert len(list(field)) == len(field.mesh) line = field.line(p1=field.mesh.region.pmin, p2=field.mesh.region.pmax, n=5) assert isinstance(line, df.Line) assert line.n == 5 plane = field.plane('z', n=(2, 2)) assert isinstance(plane, df.Field) assert len(plane.mesh) == 4 assert plane.mesh.n == (2, 2, 1) project = field.project('z') assert isinstance(project, df.Field) assert project.mesh.n[2] == 1 assert isinstance(field(field.mesh.region.centre), (tuple, numbers.Real)) assert isinstance(field(field.mesh.region.random_point()), (tuple, numbers.Real)) assert field == field assert not field != field assert +field == field assert -(-field) == field assert field + field == 2*field assert field - (-field) == field + field assert 1*field == field assert -1*field == -field if field.dim == 1: grad = field.grad assert isinstance(grad, df.Field) assert grad.dim == 3 assert all(i not in dir(field) for i in 'xyz') assert isinstance((field * df.dx).integral(), numbers.Real) assert isinstance((field * df.dy).integral(), numbers.Real) assert isinstance((field * df.dz).integral(), numbers.Real) assert isinstance((field * df.dV).integral(), numbers.Real) assert isinstance((field.plane('z') * df.dS).integral(), tuple) assert isinstance((field.plane('z') * abs(df.dS)).integral(), numbers.Real) if field.dim == 3: norm = field.norm assert isinstance(norm, df.Field) assert norm == abs(field) assert norm.dim == 1 assert isinstance(field.x, df.Field) assert field.x.dim == 1 assert isinstance(field.y, df.Field) assert field.y.dim == 1 assert isinstance(field.z, df.Field) assert field.z.dim == 1 div = field.div assert isinstance(div, df.Field) assert div.dim == 1 curl = field.curl assert isinstance(curl, df.Field) assert curl.dim == 3 field_plane = field.plane('z') assert isinstance((field * df.dx).integral(), tuple) assert isinstance((field * df.dy).integral(), tuple) assert isinstance((field * df.dz).integral(), tuple) assert isinstance((field * df.dV).integral(), tuple) assert isinstance((field.plane('z') @ df.dS).integral(), numbers.Real) assert isinstance((field.plane('z') * abs(df.dS)).integral(), tuple) orientation = field.orientation assert isinstance(orientation, df.Field) assert orientation.dim == 3 assert all(i in dir(field) for i in 'xyz') class TestField: def setup(self): # Get meshes using valid arguments from TestMesh. tm = TestMesh() tm.setup() self.meshes = [] for p1, p2, n, cell in tm.valid_args: region = df.Region(p1=p1, p2=p2) mesh = df.Mesh(region=region, n=n, cell=cell) self.meshes.append(mesh) # Create lists of field values. self.consts = [0, -5., np.pi, 1e-15, 1.2e12, random.random()] self.iters = [(0, 0, 1), (0, -5.1, np.pi), [70, 1e15, 2*np.pi], [5, random.random(), np.pi], np.array([4, -1, 3.7]), np.array([2.1, 0.0, -5*random.random()])] self.sfuncs = [lambda c: 1, lambda c: -2.4, lambda c: -6.4e-15, lambda c: c[0] + c[1] + c[2] + 1, lambda c: (c[0]-1)**2 - c[1]+7 + c[2]*0.1, lambda c: np.sin(c[0]) + np.cos(c[1]) - np.sin(2*c[2])] self.vfuncs = [lambda c: (1, 2, 0), lambda c: (-2.4, 1e-3, 9), lambda c: (c[0], c[1], c[2] + 100), lambda c: (c[0]+c[2]+10, c[1], c[2]+1), lambda c: (c[0]-1, c[1]+70, c[2]*0.1), lambda c: (np.sin(c[0]), np.cos(c[1]), -np.sin(2*c[2]))] # Create a field for plotting tests mesh = df.Mesh(p1=(-5e-9, -5e-9, -5e-9), p2=(5e-9, 5e-9, 5e-9), n=(5, 5, 5)) def norm_fun(point): x, y, z = point if x**2 + y**2 <= (5e-9)**2: return 1e5 else: return 0 def value_fun(point): x, y, z = point if x <= 0: return (0, 0, 1) else: return (0, 0, -1) self.pf = df.Field(mesh, dim=3, value=value_fun, norm=norm_fun) def test_init_valid_args(self): for mesh in self.meshes: for value in self.consts + self.sfuncs: f = df.Field(mesh, dim=1, value=value) check_field(f) for value in self.iters + self.vfuncs: f = df.Field(mesh, dim=3, value=value) check_field(f) def test_init_invalid_args(self): with pytest.raises(TypeError): mesh = 'meaningless_mesh_string' f = df.Field(mesh, dim=1) for mesh in self.meshes: for dim in [0, -1, 'dim', (2, 3)]: with pytest.raises((ValueError, TypeError)): f = df.Field(mesh, dim=dim) def test_set_with_ndarray(self): for mesh in self.meshes: f = df.Field(mesh, dim=3) f.value = np.ones((*f.mesh.n, f.dim,)) check_field(f) assert isinstance(f.value, np.ndarray) assert f.average == (1, 1, 1) with pytest.raises(ValueError): f.value = np.ones((2, 2)) def test_set_with_callable(self): for mesh in self.meshes: for func in self.sfuncs: f = df.Field(mesh, dim=1, value=func) check_field(f) rp = f.mesh.region.random_point() # Make sure to be at the centre of the cell rp = f.mesh.index2point(f.mesh.point2index(rp)) assert f(rp) == func(rp) for mesh in self.meshes: for func in self.vfuncs: f = df.Field(mesh, dim=3, value=func) check_field(f) rp = f.mesh.region.random_point() rp = f.mesh.index2point(f.mesh.point2index(rp)) assert np.all(f(rp) == func(rp)) def test_set_with_dict(self): p1 = (0, 0, 0) p2 = (10e-9, 10e-9, 10e-9) n = (5, 5, 5) subregions = {'r1': df.Region(p1=(0, 0, 0), p2=(4e-9, 10e-9, 10e-9)), 'r2': df.Region(p1=(4e-9, 0, 0), p2=(10e-9, 10e-9, 10e-9))} mesh = df.Mesh(p1=p1, p2=p2, n=n, subregions=subregions) field = df.Field(mesh, dim=3, value={'r1': (0, 0, 1), 'r2': (0, 0, 2), 'r1:r2': (0, 0, 5)}) assert np.all(field((3e-9, 7e-9, 9e-9)) == (0, 0, 1)) assert np.all(field((8e-9, 2e-9, 9e-9)) == (0, 0, 2)) def test_set_exception(self): for mesh in self.meshes: with pytest.raises(ValueError): f = df.Field(mesh, dim=3, value='meaningless_string') with pytest.raises(ValueError): f = df.Field(mesh, dim=3, value=5+5j) def test_value(self): p1 = (0, 0, 0) p2 = (10e-9, 10e-9, 10e-9) n = (5, 5, 5) mesh = df.Mesh(p1=p1, p2=p2, n=n) f = df.Field(mesh, dim=3) f.value = (1, 1, 1) assert f.value == (1, 1, 1) f.array[0, 0, 0, 0] = 3 assert isinstance(f.value, np.ndarray) def test_norm(self): mesh = df.Mesh(p1=(0, 0, 0), p2=(10, 10, 10), cell=(5, 5, 5)) f = df.Field(mesh, dim=3, value=(2, 2, 2)) assert np.all(f.norm.value == 2*np.sqrt(3)) assert np.all(f.norm.array == 2*np.sqrt(3)) assert np.all(f.array == 2) f.norm = 1 assert np.all(f.norm.value == 1) assert np.all(f.norm.array == 1) assert np.all(f.array == 1/np.sqrt(3)) f.array[0, 0, 0, 0] = 3 assert isinstance(f.norm.value, np.ndarray) assert not np.all(f.norm.value == 1) for mesh in self.meshes: for value in self.iters + self.vfuncs: for norm_value in [1, 2.1, 50, 1e-3, np.pi]: f = df.Field(mesh, dim=3, value=value, norm=norm_value) # Compute norm. norm = f.array[..., 0]**2 norm += f.array[..., 1]**2 norm += f.array[..., 2]**2 norm = np.sqrt(norm) assert norm.shape == f.mesh.n assert f.norm.array.shape == (*f.mesh.n, 1) assert np.all(abs(norm - norm_value) < 1e-12) # Exception mesh = df.Mesh(p1=(0, 0, 0), p2=(10, 10, 10), cell=(1, 1, 1)) f = df.Field(mesh, dim=1, value=-5) with pytest.raises(ValueError): f.norm = 5 def test_norm_is_not_preserved(self): p1 = (0, 0, 0) p2 = (10e-9, 10e-9, 10e-9) n = (5, 5, 5) mesh = df.Mesh(p1=p1, p2=p2, n=n) f = df.Field(mesh, dim=3) f.value = (0, 3, 0) f.norm = 1 assert np.all(f.norm.array == 1) f.value = (0, 2, 0) assert np.all(f.norm.value != 1) assert np.all(f.norm.array == 2) def test_norm_zero_field_exception(self): p1 = (0, 0, 0) p2 = (10e-9, 10e-9, 10e-9) n = (5, 5, 5) mesh = df.Mesh(p1=p1, p2=p2, n=n) f = df.Field(mesh, dim=3, value=(0, 0, 0)) with pytest.raises(ValueError): f.norm = 1 def test_zero(self): p1 = (0, 0, 0) p2 = (10e-9, 10e-9, 10e-9) n = (5, 5, 5) mesh = df.Mesh(p1=p1, p2=p2, n=n) f = df.Field(mesh, dim=1, value=1e-6) zf = f.zero assert f.mesh == zf.mesh assert f.dim == zf.dim assert not np.any(zf.array) f = df.Field(mesh, dim=3, value=(5, -7, 1e3)) zf = f.zero assert f.mesh == zf.mesh assert f.dim == zf.dim assert not np.any(zf.array) def test_orientation(self): p1 = (-5e-9, -5e-9, -5e-9) p2 = (5e-9, 5e-9, 5e-9) cell = (1e-9, 1e-9, 1e-9) mesh = df.Mesh(p1=p1, p2=p2, cell=cell) # No zero-norm cells f = df.Field(mesh, dim=3, value=(2, 0, 0)) assert f.orientation.average == (1, 0, 0) # With zero-norm cells def value_fun(point): x, y, z = point if x <= 0: return (0, 0, 0) else: return (3, 0, 4) f = df.Field(mesh, dim=3, value=value_fun) assert f.orientation((-1.5e-9, 3e-9, 0)) == (0, 0, 0) assert f.orientation((1.5e-9, 3e-9, 0)) == (0.6, 0, 0.8) f = df.Field(mesh, dim=1, value=0) with pytest.raises(ValueError): of = f.orientation def test_average(self): value = -1e-3 + np.pi tol = 1e-12 p1 = (-5e-9, -5e-9, -5e-9) p2 = (5e-9, 5e-9, 5e-9) cell = (1e-9, 1e-9, 1e-9) mesh = df.Mesh(p1=p1, p2=p2, cell=cell) f = df.Field(mesh, dim=1, value=2) assert abs(f.average - 2) < tol f = df.Field(mesh, dim=3, value=(0, 1, 2)) assert np.allclose(f.average, (0, 1, 2)) def test_field_component(self): for mesh in self.meshes: f = df.Field(mesh, dim=3, value=(1, 2, 3)) assert all(isinstance(getattr(f, i), df.Field) for i in 'xyz') assert all(getattr(f, i).dim == 1 for i in 'xyz') f = df.Field(mesh, dim=2, value=(1, 2)) assert all(isinstance(getattr(f, i), df.Field) for i in 'xy') assert all(getattr(f, i).dim == 1 for i in 'xy') # Exception. f = df.Field(mesh, dim=1, value=1) with pytest.raises(AttributeError): fx = f.x.dim def test_get_attribute_exception(self): for mesh in self.meshes: f = df.Field(mesh, dim=3) with pytest.raises(AttributeError) as excinfo: f.__getattr__('nonexisting_attribute') assert 'has no attribute' in str(excinfo.value) def test_dir(self): for mesh in self.meshes: f = df.Field(mesh, dim=3, value=(5, 6, -9)) assert all(attr in dir(f) for attr in ['x', 'y', 'z', 'div']) assert 'grad' not in dir(f) f = df.Field(mesh, dim=1, value=1) assert all(attr not in dir(f) for attr in ['x', 'y', 'z', 'div']) assert 'grad' in dir(f) def test_eq(self): p1 = (-5e-9, -5e-9, -5e-9) p2 = (15e-9, 5e-9, 5e-9) cell = (5e-9, 1e-9, 2.5e-9) mesh = df.Mesh(p1=p1, p2=p2, cell=cell) f1 = df.Field(mesh, dim=1, value=0.2) f2 = df.Field(mesh, dim=1, value=0.2) f3 = df.Field(mesh, dim=1, value=3.1) f4 = df.Field(mesh, dim=3, value=(1, -6, 0)) f5 = df.Field(mesh, dim=3, value=(1, -6, 0)) assert f1 == f2 assert not f1 != f2 assert not f1 == f3 assert f1 != f3 assert not f2 == f4 assert f2 != f4 assert f4 == f5 assert not f4 != f5 assert not f1 == 0.2 assert f1 != 0.2 def test_allclose(self): p1 = (-5e-9, -5e-9, -5e-9) p2 = (15e-9, 5e-9, 5e-9) cell = (5e-9, 1e-9, 2.5e-9) mesh = df.Mesh(p1=p1, p2=p2, cell=cell) f1 = df.Field(mesh, dim=1, value=0.2) f2 = df.Field(mesh, dim=1, value=0.2+1e-9) f3 = df.Field(mesh, dim=1, value=0.21) f4 = df.Field(mesh, dim=3, value=(1, -6, 0)) f5 = df.Field(mesh, dim=3, value=(1, -6+1e-8, 0)) f6 = df.Field(mesh, dim=3, value=(1, -6.01, 0)) assert f1.allclose(f2) assert not f1.allclose(f3) assert not f1.allclose(f5) assert f4.allclose(f5) assert not f4.allclose(f6) with pytest.raises(TypeError): f1.allclose(2) def test_point_neg(self): p1 = (-5e-9, -5e-9, -5e-9) p2 = (5e-9, 5e-9, 5e-9) cell = (1e-9, 1e-9, 1e-9) mesh = df.Mesh(p1=p1, p2=p2, cell=cell) # Scalar field f = df.Field(mesh, dim=1, value=3) res = -f check_field(res) assert res.average == -3 assert f == +f assert f == -(-f) assert f == +(-(-f)) # Vector field f = df.Field(mesh, dim=3, value=(1, 2, -3)) res = -f check_field(res) assert res.average == (-1, -2, 3) assert f == +f assert f == -(-f) assert f == +(-(-f)) def test_pow(self): p1 = (0, 0, 0) p2 = (15e-9, 6e-9, 6e-9) cell = (3e-9, 3e-9, 3e-9) mesh = df.Mesh(p1=p1, p2=p2, cell=cell) # Scalar field f = df.Field(mesh, dim=1, value=2) res = f**2 assert res.average == 4 res = f**(-1) assert res.average == 0.5 # Attempt vector field f = df.Field(mesh, dim=3, value=(1, 2, -2)) with pytest.raises(ValueError): res = f**2 # Attempt to raise to non numbers.Real f = df.Field(mesh, dim=1, value=2) with pytest.raises(TypeError): res = f**'a' with pytest.raises(TypeError): res = f**f def test_add_subtract(self): p1 = (0, 0, 0) p2 = (5e-9, 10e-9, -5e-9) n = (2, 2, 1) mesh = df.Mesh(p1=p1, p2=p2, n=n) # Scalar fields f1 = df.Field(mesh, dim=1, value=1.2) f2 = df.Field(mesh, dim=1, value=-0.2) res = f1 + f2 assert res.average == 1 res = f1 - f2 assert res.average == 1.4 f1 += f2 assert f1.average == 1 f1 -= f2 assert f1.average == 1.2 # Vector fields f1 = df.Field(mesh, dim=3, value=(1, 2, 3)) f2 = df.Field(mesh, dim=3, value=(-1, -3, -5)) res = f1 + f2 assert res.average == (0, -1, -2) res = f1 - f2 assert res.average == (2, 5, 8) f1 += f2 assert f1.average == (0, -1, -2) f1 -= f2 assert f1.average == (1, 2, 3) # Artithmetic checks assert f1 + f2 + (1, 1, 1) == (1, 1, 1) + f2 + f1 assert f1 - f2 - (0, 0, 0) == (0, 0, 0) - (f2 - f1) assert f1 + (f1 + f2) == (f1 + f1) + f2 assert f1 - (f1 + f2) == f1 - f1 - f2 assert f1 + f2 - f1 == f2 + (0, 0, 0) # Constants f1 = df.Field(mesh, dim=1, value=1.2) f2 = df.Field(mesh, dim=3, value=(-1, -3, -5)) res = f1 + 2 assert res.average == 3.2 res = f1 - 1.2 assert res.average == 0 f1 += 2.5 assert f1.average == 3.7 f1 -= 3.7 assert f1.average == 0 res = f2 + (1, 3, 5) assert res.average == (0, 0, 0) res = f2 - (1, 2, 3) assert res.average == (-2, -5, -8) f2 += (1, 1, 1) assert f2.average == (0, -2, -4) f2 -= (-1, -2, 3) assert f2.average == (1, 0, -7) # Exceptions with pytest.raises(TypeError): res = f1 + '2' # Fields with different dimensions with pytest.raises(ValueError): res = f1 + f2 # Fields defined on different meshes mesh1 = df.Mesh(p1=(0, 0, 0), p2=(5, 5, 5), n=(1, 1, 1)) mesh2 = df.Mesh(p1=(0, 0, 0), p2=(3, 3, 3), n=(1, 1, 1)) f1 = df.Field(mesh1, dim=1, value=1.2) f2 = df.Field(mesh2, dim=1, value=1) with pytest.raises(ValueError): res = f1 + f2 with pytest.raises(ValueError): f1 += f2 with pytest.raises(ValueError): f1 -= f2 def test_mul_truediv(self): p1 = (0, 0, 0) p2 = (5e-9, 5e-9, 5e-9) cell = (1e-9, 5e-9, 1e-9) mesh = df.Mesh(p1=p1, p2=p2, cell=cell) # Scalar fields f1 = df.Field(mesh, dim=1, value=1.2) f2 = df.Field(mesh, dim=1, value=-2) res = f1 * f2 assert res.average == -2.4 res = f1 / f2 assert res.average == -0.6 f1 *= f2 assert f1.average == -2.4 f1 /= f2 assert f1.average == 1.2 # Scalar field with a constant f = df.Field(mesh, dim=1, value=5) res = f * 2 assert res.average == 10 res = 3 * f assert res.average == 15 res = f * (1, 2, 3) assert res.average == (5, 10, 15) res = (1, 2, 3) * f assert res.average == (5, 10, 15) res = f / 2 assert res.average == 2.5 res = 10 / f assert res.average == 2 res = (5, 10, 15) / f assert res.average == (1, 2, 3) f *= 10 assert f.average == 50 f /= 10 assert f.average == 5 # Scalar field with a vector field f1 = df.Field(mesh, dim=1, value=2) f2 = df.Field(mesh, dim=3, value=(-1, -3, 5)) res = f1 * f2 # __mul__ assert res.average == (-2, -6, 10) res = f2 * f1 # __rmul__ assert res.average == (-2, -6, 10) res = f2 / f1 # __truediv__ assert res.average == (-0.5, -1.5, 2.5) f2 *= f1 # __imul__ assert f2.average == (-2, -6, 10) f2 /= f1 # __truediv__ assert f2.average == (-1, -3, 5) with pytest.raises(ValueError): res = f1 / f2 # __rtruediv__ # Vector field with a scalar f = df.Field(mesh, dim=3, value=(1, 2, 0)) res = f * 2 assert res.average == (2, 4, 0) res = 5 * f assert res.average == (5, 10, 0) res = f / 2 assert res.average == (0.5, 1, 0) f *= 2 assert f.average == (2, 4, 0) f /= 2 assert f.average == (1, 2, 0) with pytest.raises(ValueError): res = 10 / f # Further checks f1 = df.Field(mesh, dim=1, value=2) f2 = df.Field(mesh, dim=3, value=(-1, -3, -5)) assert f1 * f2 == f2 * f1 assert 1.3 * f2 == f2 * 1.3 assert -5 * f2 == f2 * (-5) assert (1, 2.2, -1) * f1 == f1 * (1, 2.2, -1) assert f1 * (f1 * f2) == (f1 * f1) * f2 assert f1 * f2 / f1 == f2 # Exceptions f1 = df.Field(mesh, dim=1, value=1.2) f2 = df.Field(mesh, dim=3, value=(-1, -3, -5)) with pytest.raises(TypeError): res = f2 * 'a' with pytest.raises(TypeError): res = 'a' / f1 with pytest.raises(ValueError): res = f2 * f2 with pytest.raises(ValueError): res = f2 / f2 with pytest.raises(ValueError): res = 1 / f2 with pytest.raises(ValueError): res = f1 / f2 with pytest.raises(TypeError): f2 *= 'a' with pytest.raises(TypeError): f2 /= 'a' with pytest.raises(ValueError): f1 /= f2 # Fields defined on different meshes mesh1 = df.Mesh(p1=(0, 0, 0), p2=(5, 5, 5), n=(1, 1, 1)) mesh2 = df.Mesh(p1=(0, 0, 0), p2=(3, 3, 3), n=(1, 1, 1)) f1 = df.Field(mesh1, dim=1, value=1.2) f2 = df.Field(mesh2, dim=1, value=1) with pytest.raises(ValueError): res = f1 * f2 with pytest.raises(ValueError): res = f1 / f2 with pytest.raises(ValueError): f1 *= f2 with pytest.raises(ValueError): f1 /= f2 def test_dot(self): p1 = (0, 0, 0) p2 = (10, 10, 10) cell = (2, 2, 2) mesh = df.Mesh(p1=p1, p2=p2, cell=cell) # Zero vectors f1 = df.Field(mesh, dim=3, value=(0, 0, 0)) res = f1@f1 assert res.dim == 1 assert res.average == 0 # Orthogonal vectors f1 = df.Field(mesh, dim=3, value=(1, 0, 0)) f2 = df.Field(mesh, dim=3, value=(0, 1, 0)) f3 = df.Field(mesh, dim=3, value=(0, 0, 1)) assert (f1 @ f2).average == 0 assert (f1 @ f3).average == 0 assert (f2 @ f3).average == 0 assert (f1 @ f1).average == 1 assert (f2 @ f2).average == 1 assert (f3 @ f3).average == 1 # Check if commutative assert f1 @ f2 == f2 @ f1 assert f1 @ (-1, 3, 2.2) == (-1, 3, 2.2) @ f1 # Vector field with a constant f = df.Field(mesh, dim=3, value=(1, 2, 3)) res = (1, 1, 1) @ f assert res.average == 6 res = f @ [1, 1, 1] assert res.average == 6 # Spatially varying vectors def value_fun1(point): x, y, z = point return (x, y, z) def value_fun2(point): x, y, z = point return (z, x, y) f1 = df.Field(mesh, dim=3, value=value_fun1) f2 = df.Field(mesh, dim=3, value=value_fun2) # Check if commutative assert f1 @ f2 == f2 @ f1 # The dot product should be x*z + y*x + z*y assert (f1 @ f2)((1, 1, 1)) == 3 assert (f1 @ f2)((3, 1, 1)) == 7 assert (f1 @ f2)((5, 7, 1)) == 47 # Check norm computed using dot product assert f1.norm == (f1 @ f1)**(0.5) # Exceptions f1 = df.Field(mesh, dim=1, value=1.2) f2 = df.Field(mesh, dim=3, value=(-1, -3, -5)) with pytest.raises(ValueError): res = f1 @ f2 with pytest.raises(ValueError): res = f1 @ f2 with pytest.raises(TypeError): res = f1 @ 3 # Fields defined on different meshes mesh1 = df.Mesh(p1=(0, 0, 0), p2=(5, 5, 5), n=(1, 1, 1)) mesh2 = df.Mesh(p1=(0, 0, 0), p2=(3, 3, 3), n=(1, 1, 1)) f1 = df.Field(mesh1, dim=3, value=(1, 2, 3)) f2 = df.Field(mesh2, dim=3, value=(3, 2, 1)) with pytest.raises(ValueError): res = f1 @ f2 def test_cross(self): p1 = (0, 0, 0) p2 = (10, 10, 10) cell = (2, 2, 2) mesh = df.Mesh(p1=p1, p2=p2, cell=cell) # Zero vectors f1 = df.Field(mesh, dim=3, value=(0, 0, 0)) res = f1 & f1 assert res.dim == 3 assert res.average == (0, 0, 0) # Orthogonal vectors f1 = df.Field(mesh, dim=3, value=(1, 0, 0)) f2 = df.Field(mesh, dim=3, value=(0, 1, 0)) f3 = df.Field(mesh, dim=3, value=(0, 0, 1)) assert (f1 & f2).average == (0, 0, 1) assert (f1 & f3).average == (0, -1, 0) assert (f2 & f3).average == (1, 0, 0) assert (f1 & f1).average == (0, 0, 0) assert (f2 & f2).average == (0, 0, 0) assert (f3 & f3).average == (0, 0, 0) # Constants assert (f1 & (0, 1, 0)).average == (0, 0, 1) assert ((0, 1, 0) & f1).average == (0, 0, 1) # Check if not comutative assert f1 & f2 == -(f2 & f1) assert f1 & f3 == -(f3 & f1) assert f2 & f3 == -(f3 & f2) f1 = df.Field(mesh, dim=3, value=lambda point: (point[0], point[1], point[2])) f2 = df.Field(mesh, dim=3, value=lambda point: (point[2], point[0], point[1])) # The cross product should be # (y**2-x*z, z**2-x*y, x**2-y*z) assert (f1 & f2)((1, 1, 1)) == (0, 0, 0) assert (f1 & f2)((3, 1, 1)) == (-2, -2, 8) assert (f2 & f1)((3, 1, 1)) == (2, 2, -8) assert (f1 & f2)((5, 7, 1)) == (44, -34, 18) # Exceptions f1 = df.Field(mesh, dim=1, value=1.2) f2 = df.Field(mesh, dim=3, value=(-1, -3, -5)) with pytest.raises(TypeError): res = f1 & 2 with pytest.raises(ValueError): res = f1 & f2 # Fields defined on different meshes mesh1 = df.Mesh(p1=(0, 0, 0), p2=(5, 5, 5), n=(1, 1, 1)) mesh2 = df.Mesh(p1=(0, 0, 0), p2=(3, 3, 3), n=(1, 1, 1)) f1 = df.Field(mesh1, dim=3, value=(1, 2, 3)) f2 = df.Field(mesh2, dim=3, value=(3, 2, 1)) with pytest.raises(ValueError): res = f1 & f2 def test_lshift(self): p1 = (0, 0, 0) p2 = (10e6, 10e6, 10e6) cell = (5e6, 5e6, 5e6) mesh = df.Mesh(p1=p1, p2=p2, cell=cell) f1 = df.Field(mesh, dim=1, value=1) f2 = df.Field(mesh, dim=1, value=-3) f3 = df.Field(mesh, dim=1, value=5) res = f1 << f2 << f3 assert res.dim == 3 assert res.average == (1, -3, 5) # Different dimensions f1 = df.Field(mesh, dim=1, value=1.2) f2 = df.Field(mesh, dim=2, value=(-1, -3)) res = f1 << f2 assert res.average == (1.2, -1, -3) res = f2 << f1 assert res.average == (-1, -3, 1.2) # Constants f1 = df.Field(mesh, dim=1, value=1.2) res = f1 << 2 assert res.average == (1.2, 2) res = f1 << (1, -1) assert res.average == (1.2, 1, -1) res = 3 << f1 assert res.average == (3, 1.2) res = (1.2, 3) << f1 << 3 assert res.average == (1.2, 3, 1.2, 3) # Exceptions with pytest.raises(TypeError): res = 'a' << f1 with pytest.raises(TypeError): res = f1 << 'a' # Fields defined on different meshes mesh1 = df.Mesh(p1=(0, 0, 0), p2=(5, 5, 5), n=(1, 1, 1)) mesh2 = df.Mesh(p1=(0, 0, 0), p2=(3, 3, 3), n=(1, 1, 1)) f1 = df.Field(mesh1, dim=1, value=1.2) f2 = df.Field(mesh2, dim=1, value=1) with pytest.raises(ValueError): res = f1 << f2 def test_all_operators(self): p1 = (0, 0, 0) p2 = (5e-9, 5e-9, 10e-9) n = (2, 2, 1) mesh = df.Mesh(p1=p1, p2=p2, n=n) f1 = df.Field(mesh, dim=1, value=2) f2 = df.Field(mesh, dim=3, value=(-4, 0, 1)) res = ((+f1/2 + f2.x)**2 - 2*f1*3)/(-f2.z) - 2*f2.y + 1/f2.z**2 + f2@f2 assert np.all(res.array == 21) res = 1 + f1 + 0*f2.x - 3*f2.y/3 assert res.average == 3 def test_pad(self): p1 = (0, 0, 0) p2 = (10, 8, 2) cell = (1, 1, 1) mesh = df.Mesh(p1=p1, p2=p2, cell=cell) field = df.Field(mesh, dim=1, value=1) pf = field.pad({'x': (1, 1)}, mode='constant') # zeros padded assert pf.array.shape == (12, 8, 2, 1) def test_derivative(self): p1 = (0, 0, 0) p2 = (10, 10, 10) cell = (2, 2, 2) # f(x, y, z) = 0 -> grad(f) = (0, 0, 0) # No BC mesh = df.Mesh(p1=p1, p2=p2, cell=cell) f = df.Field(mesh, dim=1, value=0) check_field(f.derivative('x')) assert f.derivative('x', n=1).average == 0 assert f.derivative('y', n=1).average == 0 assert f.derivative('z', n=1).average == 0 assert f.derivative('x', n=2).average == 0 assert f.derivative('y', n=2).average == 0 assert f.derivative('z', n=2).average == 0 # f(x, y, z) = x + y + z -> grad(f) = (1, 1, 1) # No BC mesh = df.Mesh(p1=p1, p2=p2, cell=cell) def value_fun(point): x, y, z = point return x + y + z f = df.Field(mesh, dim=1, value=value_fun) assert f.derivative('x', n=1).average == 1 assert f.derivative('y', n=1).average == 1 assert f.derivative('z', n=1).average == 1 assert f.derivative('x', n=2).average == 0 assert f.derivative('y', n=2).average == 0 assert f.derivative('z', n=2).average == 0 # f(x, y, z) = x*y + 2*y + x*y*z -> # grad(f) = (y+y*z, x+2+x*z, x*y) # No BC mesh = df.Mesh(p1=p1, p2=p2, cell=cell) def value_fun(point): x, y, z = point return x*y + 2*y + x*y*z f = df.Field(mesh, dim=1, value=value_fun) assert f.derivative('x')((7, 5, 1)) == 10 assert f.derivative('y')((7, 5, 1)) == 16 assert f.derivative('z')((7, 5, 1)) == 35 assert f.derivative('x', n=2)((1, 1, 1)) == 0 assert f.derivative('y', n=2)((1, 1, 1)) == 0 assert f.derivative('z', n=2)((1, 1, 1)) == 0 # f(x, y, z) = (0, 0, 0) # -> dfdx = (0, 0, 0) # -> dfdy = (0, 0, 0) # -> dfdz = (0, 0, 0) # No BC mesh = df.Mesh(p1=p1, p2=p2, cell=cell) f = df.Field(mesh, dim=3, value=(0, 0, 0)) check_field(f.derivative('y')) assert f.derivative('x').average == (0, 0, 0) assert f.derivative('y').average == (0, 0, 0) assert f.derivative('z').average == (0, 0, 0) # f(x, y, z) = (x, y, z) # -> dfdx = (1, 0, 0) # -> dfdy = (0, 1, 0) # -> dfdz = (0, 0, 1) def value_fun(point): x, y, z = point return (x, y, z) f = df.Field(mesh, dim=3, value=value_fun) assert f.derivative('x').average == (1, 0, 0) assert f.derivative('y').average == (0, 1, 0) assert f.derivative('z').average == (0, 0, 1) # f(x, y, z) = (x*y, y*z, x*y*z) # -> dfdx = (y, 0, y*z) # -> dfdy = (x, z, x*z) # -> dfdz = (0, y, x*y) def value_fun(point): x, y, z = point return (x*y, y*z, x*y*z) f = df.Field(mesh, dim=3, value=value_fun) assert f.derivative('x')((3, 1, 3)) == (1, 0, 3) assert f.derivative('y')((3, 1, 3)) == (3, 3, 9) assert f.derivative('z')((3, 1, 3)) == (0, 1, 3) assert f.derivative('x')((5, 3, 5)) == (3, 0, 15) assert f.derivative('y')((5, 3, 5)) == (5, 5, 25) assert f.derivative('z')((5, 3, 5)) == (0, 3, 15) # f(x, y, z) = (3+x*y, x-2*y, x*y*z) # -> dfdx = (y, 1, y*z) # -> dfdy = (x, -2, x*z) # -> dfdz = (0, 0, x*y) def value_fun(point): x, y, z = point return (3+x*y, x-2*y, x*y*z) f = df.Field(mesh, dim=3, value=value_fun) assert f.derivative('x')((7, 5, 1)) == (5, 1, 5) assert f.derivative('y')((7, 5, 1)) == (7, -2, 7) assert f.derivative('z')((7, 5, 1)) == (0, 0, 35) # f(x, y, z) = 2*x*x + 2*y*y + 3*z*z # -> grad(f) = (4, 4, 6) def value_fun(point): x, y, z = point return 2*x*x + 2*y*y + 3*z*z f = df.Field(mesh, dim=1, value=value_fun) assert f.derivative('x', n=2).average == 4 assert f.derivative('y', n=2).average == 4 assert f.derivative('z', n=2).average == 6 # f(x, y, z) = (2*x*x, 2*y*y, 3*z*z) def value_fun(point): x, y, z = point return (2*x*x, 2*y*y, 3*z*z) f = df.Field(mesh, dim=3, value=value_fun) assert f.derivative('x', n=2).average == (4, 0, 0) assert f.derivative('y', n=2).average == (0, 4, 0) assert f.derivative('z', n=2).average == (0, 0, 6) with pytest.raises(NotImplementedError): res = f.derivative('x', n=3) def test_derivative_pbc(self): p1 = (0, 0, 0) p2 = (10, 8, 6) cell = (2, 2, 2) mesh_nopbc = df.Mesh(p1=p1, p2=p2, cell=cell) mesh_pbc = df.Mesh(p1=p1, p2=p2, cell=cell, bc='xyz') # Scalar field def value_fun(point): return point[0]*point[1]*point[2] # No PBC f = df.Field(mesh_nopbc, dim=1, value=value_fun) assert f.derivative('x')((9, 1, 1)) == 1 assert f.derivative('y')((1, 7, 1)) == 1 assert f.derivative('z')((1, 1, 5)) == 1 # PBC f = df.Field(mesh_pbc, dim=1, value=value_fun) assert f.derivative('x')((9, 1, 1)) == -1.5 assert f.derivative('y')((1, 7, 1)) == -1 assert f.derivative('z')((1, 1, 5)) == -0.5 # Vector field def value_fun(point): return (point[0]*point[1]*point[2],) * 3 # No PBC f = df.Field(mesh_nopbc, dim=3, value=value_fun) assert f.derivative('x')((9, 1, 1)) == (1, 1, 1) assert f.derivative('y')((1, 7, 1)) == (1, 1, 1) assert f.derivative('z')((1, 1, 5)) == (1, 1, 1) # PBC f = df.Field(mesh_pbc, dim=3, value=value_fun) assert f.derivative('x')((9, 1, 1)) == (-1.5, -1.5, -1.5) assert f.derivative('y')((1, 7, 1)) == (-1, -1, -1) assert f.derivative('z')((1, 1, 5)) == (-0.5, -0.5, -0.5) def test_derivative_neumann(self): p1 = (0, 0, 0) p2 = (10, 8, 6) cell = (2, 2, 2) mesh_noneumann = df.Mesh(p1=p1, p2=p2, cell=cell) mesh_neumann = df.Mesh(p1=p1, p2=p2, cell=cell, bc='neumann') # Scalar field def value_fun(point): return point[0]*point[1]*point[2] # No Neumann f1 = df.Field(mesh_noneumann, dim=1, value=value_fun) assert f1.derivative('x')((9, 1, 1)) == 1 assert f1.derivative('y')((1, 7, 1)) == 1 assert f1.derivative('z')((1, 1, 5)) == 1 # Neumann f2 = df.Field(mesh_neumann, dim=1, value=value_fun) assert (f1.derivative('x')(f1.mesh.region.centre) == f2.derivative('x')(f2.mesh.region.centre)) assert (f1.derivative('x')((1, 7, 1)) != f2.derivative('x')((1, 7, 1))) def test_derivative_single_cell(self): p1 = (0, 0, 0) p2 = (10, 10, 2) cell = (2, 2, 2) mesh = df.Mesh(p1=p1, p2=p2, cell=cell) # Scalar field: f(x, y, z) = x + y + z # -> grad(f) = (1, 1, 1) def value_fun(point): x, y, z = point return x + y + z f = df.Field(mesh, dim=1, value=value_fun) # only one cell in the z-direction assert f.plane('x').derivative('x').average == 0 assert f.plane('y').derivative('y').average == 0 assert f.derivative('z').average == 0 # Vector field: f(x, y, z) = (x, y, z) # -> grad(f) = (1, 1, 1) def value_fun(point): x, y, z = point return (x, y, z) f = df.Field(mesh, dim=3, value=value_fun) # only one cell in the z-direction assert f.plane('x').derivative('x').average == (0, 0, 0) assert f.plane('y').derivative('y').average == (0, 0, 0) assert f.derivative('z').average == (0, 0, 0) def test_grad(self): p1 = (0, 0, 0) p2 = (10, 10, 10) cell = (2, 2, 2) mesh = df.Mesh(p1=p1, p2=p2, cell=cell) # f(x, y, z) = 0 -> grad(f) = (0, 0, 0) f = df.Field(mesh, dim=1, value=0) check_field(f.grad) assert f.grad.average == (0, 0, 0) # f(x, y, z) = x + y + z -> grad(f) = (1, 1, 1) def value_fun(point): x, y, z = point return x + y + z f = df.Field(mesh, dim=1, value=value_fun) assert f.grad.average == (1, 1, 1) # f(x, y, z) = x*y + y + z -> grad(f) = (y, x+1, 1) def value_fun(point): x, y, z = point return x*y + y + z f = df.Field(mesh, dim=1, value=value_fun) assert f.grad((3, 1, 3)) == (1, 4, 1) assert f.grad((5, 3, 5)) == (3, 6, 1) # f(x, y, z) = x*y + 2*y + x*y*z -> # grad(f) = (y+y*z, x+2+x*z, x*y) def value_fun(point): x, y, z = point return x*y + 2*y + x*y*z f = df.Field(mesh, dim=1, value=value_fun) assert f.grad((7, 5, 1)) == (10, 16, 35) assert f.grad.x == f.derivative('x') assert f.grad.y == f.derivative('y') assert f.grad.z == f.derivative('z') # Exception f = df.Field(mesh, dim=3, value=(1, 2, 3)) with pytest.raises(ValueError): res = f.grad def test_div_curl(self): p1 = (0, 0, 0) p2 = (10, 10, 10) cell = (2, 2, 2) mesh = df.Mesh(p1=p1, p2=p2, cell=cell) # f(x, y, z) = (0, 0, 0) # -> div(f) = 0 # -> curl(f) = (0, 0, 0) f = df.Field(mesh, dim=3, value=(0, 0, 0)) check_field(f.div) assert f.div.dim == 1 assert f.div.average == 0 check_field(f.curl) assert f.curl.dim == 3 assert f.curl.average == (0, 0, 0) # f(x, y, z) = (x, y, z) # -> div(f) = 3 # -> curl(f) = (0, 0, 0) def value_fun(point): x, y, z = point return (x, y, z) f = df.Field(mesh, dim=3, value=value_fun) assert f.div.average == 3 assert f.curl.average == (0, 0, 0) # f(x, y, z) = (x*y, y*z, x*y*z) # -> div(f) = y + z + x*y # -> curl(f) = (x*z-y, -y*z, -x) def value_fun(point): x, y, z = point return (x*y, y*z, x*y*z) f = df.Field(mesh, dim=3, value=value_fun) assert f.div((3, 1, 3)) == 7 assert f.div((5, 3, 5)) == 23 assert f.curl((3, 1, 3)) == (8, -3, -3) assert f.curl((5, 3, 5)) == (22, -15, -5) # f(x, y, z) = (3+x*y, x-2*y, x*y*z) # -> div(f) = y - 2 + x*y # -> curl(f) = (x*z, -y*z, 1-x) def value_fun(point): x, y, z = point return (3+x*y, x-2*y, x*y*z) f = df.Field(mesh, dim=3, value=value_fun) assert f.div((7, 5, 1)) == 38 assert f.curl((7, 5, 1)) == (7, -5, -6) # Exception f = df.Field(mesh, dim=1, value=3.11) with pytest.raises(ValueError): res = f.div with pytest.raises(ValueError): res = f.curl def test_laplace(self): p1 = (0, 0, 0) p2 = (10, 10, 10) cell = (2, 2, 2) mesh = df.Mesh(p1=p1, p2=p2, cell=cell) # f(x, y, z) = (0, 0, 0) # -> laplace(f) = 0 f = df.Field(mesh, dim=3, value=(0, 0, 0)) check_field(f.laplace) assert f.laplace.dim == 3 assert f.laplace.average == (0, 0, 0) # f(x, y, z) = x + y + z # -> laplace(f) = 0 def value_fun(point): x, y, z = point return x + y + z f = df.Field(mesh, dim=1, value=value_fun) check_field(f.laplace) assert f.laplace.average == 0 # f(x, y, z) = 2*x*x + 2*y*y + 3*z*z # -> laplace(f) = 4 + 4 + 6 = 14 def value_fun(point): x, y, z = point return 2*x*x + 2*y*y + 3*z*z f = df.Field(mesh, dim=1, value=value_fun) assert f.laplace.average == 14 # f(x, y, z) = (2*x*x, 2*y*y, 3*z*z) # -> laplace(f) = (4, 4, 6) def value_fun(point): x, y, z = point return (2*x*x, 2*y*y, 3*z*z) f = df.Field(mesh, dim=3, value=value_fun) assert f.laplace.average == (4, 4, 6) def test_integral(self): # Volume integral. p1 = (0, 0, 0) p2 = (10, 10, 10) cell = (1, 1, 1) mesh = df.Mesh(p1=p1, p2=p2, cell=cell) f = df.Field(mesh, dim=1, value=0) assert (f * df.dV).integral() == 0 assert (f * df.dx*df.dy*df.dz).integral() == 0 f = df.Field(mesh, dim=1, value=2) assert (f * df.dV).integral() == 2000 assert (f * df.dx*df.dy*df.dz).integral() == 2000 f = df.Field(mesh, dim=3, value=(-1, 0, 3)) assert (f * df.dV).integral() == (-1000, 0, 3000) assert (f * df.dx*df.dy*df.dz).integral() == (-1000, 0, 3000) def value_fun(point): x, y, z = point if x <= 5: return (-1, -2, -3) else: return (1, 2, 3) f = df.Field(mesh, dim=3, value=value_fun) assert (f * df.dV).integral() == (0, 0, 0) assert (f * df.dx*df.dy*df.dz).integral() == (0, 0, 0) # Surface integral. p1 = (0, 0, 0) p2 = (10, 5, 3) cell = (1, 1, 1) mesh = df.Mesh(p1=p1, p2=p2, cell=cell) f = df.Field(mesh, dim=1, value=0) assert (f.plane('x') * abs(df.dS)).integral() == 0 assert (f.plane('x') * df.dy*df.dz).integral() == 0 f = df.Field(mesh, dim=1, value=2) assert (f.plane('x') * abs(df.dS)).integral() == 30 assert (f.plane('x') * df.dy*df.dz).integral() == 30 assert (f.plane('y') * abs(df.dS)).integral() == 60 assert (f.plane('y') * df.dx*df.dz).integral() == 60 assert (f.plane('z') * abs(df.dS)).integral() == 100 assert (f.plane('z') * df.dx*df.dy).integral() == 100 f = df.Field(mesh, dim=3, value=(-1, 0, 3)) assert (f.plane('x') * abs(df.dS)).integral() == (-15, 0, 45) assert (f.plane('y') * abs(df.dS)).integral() == (-30, 0, 90) assert (f.plane('z') * abs(df.dS)).integral() == (-50, 0, 150) f = df.Field(mesh, dim=3, value=(-1, 0, 3)) assert df.integral(f.plane('x') @ df.dS) == -15 assert df.integral(f.plane('y') @ df.dS) == 0 assert df.integral(f.plane('z') @ df.dS) == 150 # Directional integral p1 = (0, 0, 0) p2 = (10, 10, 10) cell = (1, 1, 1) mesh = df.Mesh(p1=p1, p2=p2, cell=cell) f = df.Field(mesh, dim=3, value=(1, 1, 1)) f = f.integral(direction='x') assert isinstance(f, df.Field) assert f.dim == 3 assert f.mesh.n == (1, 10, 10) assert f.average == (10, 10, 10) f = f.integral(direction='x').integral(direction='y') assert isinstance(f, df.Field) assert f.dim == 3 assert f.mesh.n == (1, 1, 10) assert f.average == (100, 100, 100) f = f.integral('x').integral('y').integral('z') assert f.dim == 3 assert f.mesh.n == (1, 1, 1) assert f.average == (1000, 1000, 1000) assert (f.integral('x').integral('y').integral('z').average == f.integral()) # Improper integral p1 = (0, 0, 0) p2 = (10, 10, 10) cell = (1, 1, 1) mesh = df.Mesh(p1=p1, p2=p2, cell=cell) f = df.Field(mesh, dim=3, value=(1, 1, 1)) f = f.integral(direction='x', improper=True) assert isinstance(f, df.Field) assert f.dim == 3 assert f.mesh.n == (10, 10, 10) assert f.average == (5.5, 5.5, 5.5) assert f((0, 0, 0)) == (1, 1, 1) assert f((10, 10, 10)) == (10, 10, 10) # Exceptions with pytest.raises(ValueError): res = f.integral(direction='xy', improper=True) def test_line(self): mesh = df.Mesh(p1=(0, 0, 0), p2=(10, 10, 10), n=(10, 10, 10)) f = df.Field(mesh, dim=3, value=(1, 2, 3)) check_field(f) line = f.line(p1=(0, 0, 0), p2=(5, 5, 5), n=20) assert isinstance(line, df.Line) assert line.n == 20 assert line.dim == 3 def test_plane(self): for mesh, direction in itertools.product(self.meshes, ['x', 'y', 'z']): f = df.Field(mesh, dim=1, value=3) check_field(f) plane = f.plane(direction, n=(3, 3)) assert isinstance(plane, df.Field) p, v = zip(*list(plane)) assert len(p) == 9 assert len(v) == 9 def test_getitem(self): p1 = (0, 0, 0) p2 = (90, 50, 10) cell = (5, 5, 5) subregions = {'r1': df.Region(p1=(0, 0, 0), p2=(30, 50, 10)), 'r2': df.Region(p1=(30, 0, 0), p2=(90, 50, 10))} mesh = df.Mesh(p1=p1, p2=p2, cell=cell, subregions=subregions) def value_fun(point): x, y, z = point if x <= 60: return (-1, -2, -3) else: return (1, 2, 3) f = df.Field(mesh, dim=3, value=value_fun) check_field(f) check_field(f['r1']) check_field(f['r2']) check_field(f[subregions['r1']]) check_field(f[subregions['r2']]) assert f['r1'].average == (-1, -2, -3) assert f['r2'].average == (0, 0, 0) assert f[subregions['r1']].average == (-1, -2, -3) assert f[subregions['r2']].average == (0, 0, 0) assert len(f['r1'].mesh) + len(f['r2'].mesh) == len(f.mesh) # Meshes are not aligned subregion = df.Region(p1=(1.1, 0, 0), p2=(9.9, 15, 5)) assert f[subregion].array.shape == (2, 3, 1, 3) def test_project(self): p1 = (-5, -5, -5) p2 = (5, 5, 5) cell = (1, 1, 1) mesh = df.Mesh(p1=p1, p2=p2, cell=cell) # Constant scalar field f = df.Field(mesh, dim=1, value=5) check_field(f) assert f.project('x').array.shape == (1, 10, 10, 1) assert f.project('y').array.shape == (10, 1, 10, 1) assert f.project('z').array.shape == (10, 10, 1, 1) # Constant vector field f = df.Field(mesh, dim=3, value=(1, 2, 3)) assert f.project('x').array.shape == (1, 10, 10, 3) assert f.project('y').array.shape == (10, 1, 10, 3) assert f.project('z').array.shape == (10, 10, 1, 3) # Spatially varying scalar field def value_fun(point): x, y, z = point if z <= 0: return 1 else: return -1 f = df.Field(mesh, dim=1, value=value_fun) sf = f.project('z') assert sf.array.shape == (10, 10, 1, 1) assert sf.average == 0 # Spatially varying vector field def value_fun(point): x, y, z = point if z <= 0: return (3, 2, 1) else: return (3, 2, -1) f = df.Field(mesh, dim=3, value=value_fun) sf = f.project('z') assert sf.array.shape == (10, 10, 1, 3) assert sf.average == (3, 2, 0) def test_angle(self): p1 = (0, 0, 0) p2 = (8e-9, 2e-9, 2e-9) cell = (2e-9, 2e-9, 2e-9) mesh = df.Mesh(region=df.Region(p1=p1, p2=p2), cell=cell) def value_fun(point): x, y, z = point if x < 2e-9: return (1, 1, 1) elif 2e-9 <= x < 4e-9: return (1, -1, 0) elif 4e-9 <= x < 6e-9: return (-1, -1, 0) elif 6e-9 <= x < 8e-9: return (-1, 1, 0) f = df.Field(mesh, dim=3, value=value_fun) assert abs(f.plane('z').angle((1e-9, 2e-9, 2e-9)) - np.pi/4) < 1e-3 assert abs(f.plane('z').angle((3e-9, 2e-9, 2e-9)) - 7*np.pi/4) < 1e-3 assert abs(f.plane('z').angle((5e-9, 2e-9, 2e-9)) - 5*np.pi/4) < 1e-3 assert abs(f.plane('z').angle((7e-9, 2e-9, 2e-9)) - 3*np.pi/4) < 1e-3 # Exception with pytest.raises(ValueError): res = f.angle # the field is not sliced def test_write_read_ovf(self): representations = ['txt', 'bin4', 'bin8'] filename = 'testfile.ovf' p1 = (0, 0, 0) p2 = (8e-9, 5e-9, 3e-9) cell = (1e-9, 1e-9, 1e-9) mesh = df.Mesh(region=df.Region(p1=p1, p2=p2), cell=cell) # Write/read for dim, value in [(1, lambda point: point[0] + point[1] + point[2]), (3, lambda point: (point[0], point[1], point[2]))]: f = df.Field(mesh, dim=dim, value=value) for rep in representations: with tempfile.TemporaryDirectory() as tmpdir: tmpfilename = os.path.join(tmpdir, filename) f.write(tmpfilename, representation=rep) f_read = df.Field.fromfile(tmpfilename) assert f.allclose(f_read) # Extend scalar for rep in representations: f = df.Field(mesh, dim=1, value=lambda point: point[0]+point[1]+point[2]) with tempfile.TemporaryDirectory() as tmpdir: tmpfilename = os.path.join(tmpdir, filename) f.write(tmpfilename, extend_scalar=True) f_read = df.Field.fromfile(tmpfilename) assert f.allclose(f_read.x) # Read different OOMMF representations # (OVF1, OVF2) x (txt, bin4, bin8) filenames = ['oommf-ovf2-txt.omf', 'oommf-ovf2-bin4.omf', 'oommf-ovf2-bin8.omf', 'oommf-ovf1-txt.omf', 'oommf-ovf1-bin4.omf', 'oommf-ovf1-bin8.omf'] dirname = os.path.join(os.path.dirname(__file__), 'test_sample') for filename in filenames: omffilename = os.path.join(dirname, filename) f_read = df.Field.fromfile(omffilename) if 'ovf2' in filename: # The magnetisation is in the x-direction in OVF2 files. assert abs(f_read.orientation.x.average - 1) < 1e-2 else: # The norm of magnetisation is known. assert abs(f_read.norm.average - 1261566.2610100) < 1e-3 # Read different mumax3 bin4 files (made on linux and windows) filenames = ['mumax-bin4-linux.ovf', 'mumax-bin4-windows.ovf'] dirname = os.path.join(os.path.dirname(__file__), 'test_sample') for filename in filenames: omffilename = os.path.join(dirname, filename) f_read = df.Field.fromfile(omffilename) # We know the saved magentisation. f_saved = df.Field(f_read.mesh, dim=3, value=(1, 0.1, 0), norm=1) assert f_saved.allclose(f_read) # Exception (dim=2) f = df.Field(mesh, dim=2, value=(1, 2)) with pytest.raises(TypeError) as excinfo: f.write(filename) def test_write_read_vtk(self): filename = 'testfile.vtk' p1 = (0, 0, 0) p2 = (1e-9, 2e-9, 1e-9) cell = (1e-9, 1e-9, 1e-9) mesh = df.Mesh(region=df.Region(p1=p1, p2=p2), cell=cell) for dim, value in [(1, -1.2), (3, (1e-3, -5e6, 5e6))]: f = df.Field(mesh, dim=dim, value=value) with tempfile.TemporaryDirectory() as tmpdir: tmpfilename = os.path.join(tmpdir, filename) f.write(tmpfilename) f_read = df.Field.fromfile(tmpfilename) assert np.allclose(f.array, f_read.array) assert np.allclose(f.mesh.region.pmin, f_read.mesh.region.pmin) assert np.allclose(f.mesh.region.pmax, f_read.mesh.region.pmax) assert np.allclose(f.mesh.cell, f_read.mesh.cell) assert f.mesh.n == f_read.mesh.n def test_write_read_hdf5(self): filenames = ['testfile.hdf5', 'testfile.h5'] p1 = (0, 0, 0) p2 = (10e-12, 5e-12, 5e-12) cell = (1e-12, 1e-12, 1e-12) mesh = df.Mesh(region=df.Region(p1=p1, p2=p2), cell=cell) for dim, value in [(1, -1.23), (3, (1e-3 + np.pi, -5e6, 6e6))]: f = df.Field(mesh, dim=dim, value=value) for filename in filenames: with tempfile.TemporaryDirectory() as tmpdir: tmpfilename = os.path.join(tmpdir, filename) f.write(tmpfilename) f_read = df.Field.fromfile(tmpfilename) assert f == f_read def test_read_write_invalid_extension(self): filename = 'testfile.jpg' p1 = (0, 0, 0) p2 = (10e-12, 5e-12, 3e-12) cell = (1e-12, 1e-12, 1e-12) mesh = df.Mesh(region=df.Region(p1=p1, p2=p2), cell=cell) f = df.Field(mesh, dim=1, value=5e-12) with pytest.raises(ValueError) as excinfo: f.write(filename) with pytest.raises(ValueError) as excinfo: f = df.Field.fromfile(filename) def test_mpl_scalar(self): # No axes self.pf.x.plane('x', n=(3, 4)).mpl_scalar() # Axes fig = plt.figure() ax = fig.add_subplot(111) self.pf.x.plane('x', n=(3, 4)).mpl_scalar(ax=ax) # All arguments self.pf.x.plane('x').mpl_scalar(figsize=(10, 10), filter_field=self.pf.norm, colorbar=True, colorbar_label='something', multiplier=1e-6, cmap='hsv', clim=(-1, 1)) # Lightness field filenames = ['skyrmion.omf', 'skyrmion-disk.omf'] for i in filenames: filename = os.path.join(os.path.dirname(__file__), 'test_sample', i) field = df.Field.fromfile(filename) field.plane('z').angle.mpl_scalar(lightness_field=field.z) field.plane('z').angle.mpl_scalar(lightness_field=-field.z, filter_field=field.norm) field.plane('z').mpl(scalar_lightness_field=-field.z) # Saving plot filename = 'testfigure.pdf' with tempfile.TemporaryDirectory() as tmpdir: tmpfilename = os.path.join(tmpdir, filename) self.pf.x.plane('x', n=(3, 4)).mpl_scalar(filename=tmpfilename) # Exceptions with pytest.raises(ValueError): self.pf.x.mpl_scalar() # not sliced with pytest.raises(ValueError): self.pf.plane('z').mpl_scalar() # vector field with pytest.raises(ValueError): # wrong filter field self.pf.x.plane('z').mpl_scalar(filter_field=self.pf) with pytest.raises(ValueError): # wrong filter field self.pf.x.plane('z').mpl_scalar(lightness_field=self.pf) plt.close('all') def test_mpl_vector(self): # No axes self.pf.plane('x', n=(3, 4)).mpl_vector() # Axes fig = plt.figure() ax = fig.add_subplot(111) self.pf.plane('x', n=(3, 4)).mpl_vector(ax=ax) # All arguments self.pf.plane('x').mpl_vector(figsize=(10, 10), color_field=self.pf.y, colorbar=True, colorbar_label='something', multiplier=1e-6, cmap='hsv', clim=(-1, 1)) # Saving plot filename = 'testfigure.pdf' with tempfile.TemporaryDirectory() as tmpdir: tmpfilename = os.path.join(tmpdir, filename) self.pf.plane('x', n=(3, 4)).mpl_vector(filename=tmpfilename) # Exceptions with pytest.raises(ValueError) as excinfo: self.pf.mpl_vector() # not sliced with pytest.raises(ValueError) as excinfo: self.pf.y.plane('z').mpl_vector() # scalar field with pytest.raises(ValueError) as excinfo: # wrong color field self.pf.plane('z').mpl_vector(color_field=self.pf) plt.close('all') def test_mpl(self): # No axes self.pf.plane('x', n=(3, 4)).mpl() # Axes fig = plt.figure() ax = fig.add_subplot(111) self.pf.x.plane('x', n=(3, 4)).mpl(ax=ax) # All arguments for a vector field self.pf.plane('x').mpl(figsize=(12, 6), scalar_field=self.pf.plane('x').angle, scalar_filter_field=self.pf.norm, scalar_colorbar_label='something', scalar_cmap='twilight', vector_field=self.pf, vector_color_field=self.pf.y, vector_color=True, vector_colorbar=True, vector_colorbar_label='vector', vector_cmap='hsv', vector_clim=(0, 1e6), multiplier=1e-12) # All arguments for a scalar field self.pf.z.plane('x').mpl(figsize=(12, 6), scalar_field=self.pf.x, scalar_filter_field=self.pf.norm, scalar_colorbar_label='something', scalar_cmap='twilight', vector_field=self.pf, vector_color_field=self.pf.y, vector_color=True, vector_colorbar=True, vector_colorbar_label='vector', vector_cmap='hsv', vector_clim=(0, 1e6), multiplier=1e-12) # Saving plot filename = 'testfigure.pdf' with tempfile.TemporaryDirectory() as tmpdir: tmpfilename = os.path.join(tmpdir, filename) self.pf.plane('x', n=(3, 4)).mpl(filename=tmpfilename) # Exception with pytest.raises(ValueError): self.pf.mpl() plt.close('all') def test_k3d_nonzero(self): # Default self.pf.norm.k3d_nonzero() # Color self.pf.x.k3d_nonzero(color=0xff00ff) # Multiplier self.pf.x.k3d_nonzero(color=0xff00ff, multiplier=1e-6) # Interactive field self.pf.x.plane('z').k3d_nonzero(color=0xff00ff, multiplier=1e-6, interactive_field=self.pf) # kwargs self.pf.x.plane('z').k3d_nonzero(color=0xff00ff, multiplier=1e-6, interactive_field=self.pf, wireframe=True) # Plot plot = k3d.plot() plot.display() self.pf.x.plane(z=0).k3d_nonzero(plot=plot, color=0xff00ff, multiplier=1e-6, interactive_field=self.pf) # Continuation for interactive plot testing. self.pf.x.plane(z=1e-9).k3d_nonzero(plot=plot, color=0xff00ff, multiplier=1e-6, interactive_field=self.pf) assert len(plot.objects) == 2 with pytest.raises(ValueError) as excinfo: self.pf.k3d_nonzero() def test_k3d_scalar(self): # Default self.pf.y.k3d_scalar() # Filter field self.pf.y.k3d_scalar(filter_field=self.pf.norm) # Colormap self.pf.x.k3d_scalar(filter_field=self.pf.norm, cmap='hsv', color=0xff00ff) # Multiplier self.pf.y.k3d_scalar(filter_field=self.pf.norm, color=0xff00ff, multiplier=1e-6) # Interactive field self.pf.y.k3d_scalar(filter_field=self.pf.norm, color=0xff00ff, multiplier=1e-6, interactive_field=self.pf) # kwargs self.pf.y.k3d_scalar(filter_field=self.pf.norm, color=0xff00ff, multiplier=1e-6, interactive_field=self.pf, wireframe=True) # Plot plot = k3d.plot() plot.display() self.pf.y.plane(z=0).k3d_scalar(plot=plot, filter_field=self.pf.norm, color=0xff00ff, multiplier=1e-6, interactive_field=self.pf) # Continuation for interactive plot testing. self.pf.y.plane(z=1e-9).k3d_scalar(plot=plot, filter_field=self.pf.norm, color=0xff00ff, multiplier=1e-6, interactive_field=self.pf) assert len(plot.objects) == 2 # Exceptions with pytest.raises(ValueError) as excinfo: self.pf.k3d_scalar() with pytest.raises(ValueError): self.pf.x.k3d_scalar(filter_field=self.pf) # filter field dim=3 def test_k3d_vector(self): # Default self.pf.k3d_vector() # Color field self.pf.k3d_vector(color_field=self.pf.x) # Colormap self.pf.k3d_vector(color_field=self.pf.norm, cmap='hsv') # Head size self.pf.k3d_vector(color_field=self.pf.norm, cmap='hsv', head_size=3) # Points self.pf.k3d_vector(color_field=self.pf.norm, cmap='hsv', head_size=3, points=False) # Point size self.pf.k3d_vector(color_field=self.pf.norm, cmap='hsv', head_size=3, points=False, point_size=1) # Vector multiplier self.pf.k3d_vector(color_field=self.pf.norm, cmap='hsv', head_size=3, points=False, point_size=1, vector_multiplier=1) # Multiplier self.pf.k3d_vector(color_field=self.pf.norm, cmap='hsv', head_size=3, points=False, point_size=1, vector_multiplier=1, multiplier=1e-6) # Interactive field self.pf.plane('z').k3d_vector(color_field=self.pf.norm, cmap='hsv', head_size=3, points=False, point_size=1, vector_multiplier=1, multiplier=1e-6, interactive_field=self.pf) # Plot plot = k3d.plot() plot.display() self.pf.plane(z=0).k3d_vector(plot=plot, interactive_field=self.pf) # Continuation for interactive plot testing. self.pf.plane(z=1e-9).k3d_vector(plot=plot, interactive_field=self.pf) assert len(plot.objects) == 3 # Exceptions with pytest.raises(ValueError) as excinfo: self.pf.x.k3d_vector() with pytest.raises(ValueError): self.pf.k3d_vector(color_field=self.pf) # filter field dim=3 def test_plot_large_sample(self): p1 = (0, 0, 0) p2 = (50e9, 50e9, 50e9) cell = (25e9, 25e9, 25e9) mesh = df.Mesh(p1=p1, p2=p2, cell=cell) value = (1e6, 1e6, 1e6) field = df.Field(mesh, dim=3, value=value) field.plane('z').mpl() field.norm.k3d_nonzero() field.x.k3d_scalar() field.k3d_vector()

d991aedad470b351e70cf5b10b085c74cc95e474

py

Python

env/Lib/site-packages/values/__init__.py

KaceyHirth/Library-DBMS-System

40b425ed5c7b46627b7c48724b2d20e7a64cf025

2022-02-06T00:54:58.000Z

2022-02-25T12:44:43.000Z

env/Lib/site-packages/values/__init__.py

KaceyHirth/Library-DBMS-System

40b425ed5c7b46627b7c48724b2d20e7a64cf025

2021-03-23T04:58:47.000Z

2021-04-02T02:40:54.000Z

env/Lib/site-packages/values/__init__.py

KaceyHirth/Library-DBMS-System

40b425ed5c7b46627b7c48724b2d20e7a64cf025

2022-02-08T13:43:20.000Z

2022-02-08T13:43:20.000Z

__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)

__all__ = ['get'] import collections def _iterable(obj): return isinstance(obj, collections.Iterable) def _string(value): try: return isinstance(value, basestring) except NameError: return isinstance(value, str) 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)

d0e19b396bd5c3861e79601ace321dbbd96d9384

py

Python

vnpy/app/strategy_reviewer/ui/__init__.py

xyh888/vnpy

7b51716928ab9574f171a2eda190b37b4f393bb1

2019-05-24T05:19:55.000Z

2020-07-29T13:21:49.000Z

vnpy/app/strategy_reviewer/ui/__init__.py

xyh888/vnpy

7b51716928ab9574f171a2eda190b37b4f393bb1

vnpy/app/strategy_reviewer/ui/__init__.py

xyh888/vnpy

7b51716928ab9574f171a2eda190b37b4f393bb1

2019-07-01T02:14:04.000Z

2020-07-29T13:21:53.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- # @Time : 2019/8/20 0020 16:49 # @Author : Hadrianl # @File : __init__.py from .widget import StrategyReviewer

#!/usr/bin/env python # -*- coding: utf-8 -*- # @Time : 2019/8/20 0020 16:49 # @Author : Hadrianl # @File : __init__.py from .widget import StrategyReviewer

4a04e22adafbd1373a9d9fc82325fd3d15005b8b

py

Python

Lesson 13.gf/xml_Leader2.py

gfoo003/programming-together

225e0a2255dd8da1f1ef32d2a88deea27c050f10

Lesson 13.gf/xml_Leader2.py

gfoo003/programming-together

225e0a2255dd8da1f1ef32d2a88deea27c050f10

Lesson 13.gf/xml_Leader2.py

gfoo003/programming-together

225e0a2255dd8da1f1ef32d2a88deea27c050f10

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

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

bebc974c59298f013c68b5d5e434ba4b2d82a0a8

py

Python

第4章/program/Chapter_4_dummy.py

kingname/SourceCodeOfBook

ab7275108994dca564905818b678bbd2f771c18e

2018-10-01T11:07:25.000Z

2022-03-17T13:48:45.000Z

第4章/program/Chapter_4_dummy.py

kingname/SourceCodeOfBook

ab7275108994dca564905818b678bbd2f771c18e

2019-02-28T14:18:21.000Z

2022-03-02T14:57:39.000Z

第4章/program/Chapter_4_dummy.py

kingname/SourceCodeOfBook

ab7275108994dca564905818b678bbd2f771c18e

2018-10-16T06:08:37.000Z

2022-03-16T08:19:29.000Z

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}')

from multiprocessing.dummy import Pool def calc_power2(num): return num * num 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}')

9c1b437a67fd15632bb77976584935abcfb546e4

py

Python

traf_stat/apps.py

bashmak/djing

8cc0c670600254d288178acd47965f7b3db6856e

2017-04-27T20:13:22.000Z

2022-03-16T12:47:29.000Z

traf_stat/apps.py

bashmak/djing

8cc0c670600254d288178acd47965f7b3db6856e

2017-04-04T15:03:12.000Z

2021-01-26T15:30:57.000Z

traf_stat/apps.py

bashmak/djing

8cc0c670600254d288178acd47965f7b3db6856e

2017-08-22T16:00:03.000Z

2022-03-20T03:12:15.000Z

from django.apps import AppConfig

from django.apps import AppConfig class TrafStatConfig(AppConfig): name = 'traf_stat'

6996b5b815f2d10dc544bc52eb21ec8c9cd0c496

py

Python

entries/views.py

acdh-oeaw/vhioe

83c8bce83d7cb21150f404409477d2cd1c7ee240

entries/views.py

acdh-oeaw/vhioe

83c8bce83d7cb21150f404409477d2cd1c7ee240

2020-02-11T23:56:16.000Z

2021-12-13T19:45:38.000Z

entries/views.py

acdh-oeaw/vhioe

83c8bce83d7cb21150f404409477d2cd1c7ee240

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

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 class EintragDetailView(DetailView): model = Eintrag class EintragListView(ListView): model = Eintrag class EintragCreate(CreateView): model = Eintrag template_name_suffix = '_create' form_class = EintragForm success_url = '.' @method_decorator(login_required) def dispatch(self, *args, **kwargs): return super(EintragCreate, self).dispatch(*args, **kwargs) class EintragUpdate(UpdateView): model = Eintrag form_class = EintragForm template_name_suffix = '_create' @method_decorator(login_required) def dispatch(self, *args, **kwargs): return super(EintragUpdate, self).dispatch(*args, **kwargs) class EintragDelete(DeleteView): model = Eintrag template_name = 'vocabs/confirm_delete.html' success_url = reverse_lazy('browsing:browse_entries') @method_decorator(login_required) def dispatch(self, *args, **kwargs): return super(EintragDelete, self).dispatch(*args, **kwargs)

cee8341ee37a27bddc6bb669594ab3c522880752

py

Python

pystiche_papers/li_wand_2016/_loss.py

pystiche/papers

0d8179dc51f6eda0b27fa525dc0b86b866bc88e1

2021-09-30T09:30:07.000Z

2021-09-30T09:30:07.000Z

pystiche_papers/li_wand_2016/_loss.py

pystiche/papers

0d8179dc51f6eda0b27fa525dc0b86b866bc88e1

2021-10-10T13:37:25.000Z

2022-03-31T07:31:45.000Z

pystiche_papers/li_wand_2016/_loss.py

pystiche/papers

0d8179dc51f6eda0b27fa525dc0b86b866bc88e1

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), )

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 __init__( self, encoder: enc.Encoder, impl_params: bool = True, **feature_reconstruction_loss_kwargs: Any, ): super().__init__(encoder, **feature_reconstruction_loss_kwargs) # https://github.com/pmeier/CNNMRF/blob/fddcf4d01e2a6ce201059d8bc38597f74a09ba3f/mylib/content.lua#L15 # nn.MSECriterion() was used as criterion to calculate the content loss, which # by default uses reduction="mean" self.loss_reduction = "mean" if impl_params else "sum" def calculate_score( self, input_repr: torch.Tensor, target_repr: torch.Tensor, ctx: Optional[torch.Tensor], ) -> torch.Tensor: return mse_loss(input_repr, target_repr, reduction=self.loss_reduction) 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 __init__( self, encoder: enc.Encoder, patch_size: Union[int, Tuple[int, int]], impl_params: bool = True, **mrf_loss_kwargs: Any, ): super().__init__(encoder, patch_size, **mrf_loss_kwargs) # https://github.com/pmeier/CNNMRF/blob/fddcf4d01e2a6ce201059d8bc38597f74a09ba3f/mylib/mrf.lua#L221 # https://github.com/pmeier/CNNMRF/blob/fddcf4d01e2a6ce201059d8bc38597f74a09ba3f/mylib/mrf.lua#L224 # They use normalized patches instead of the unnormalized patches described in # the paper. self.normalize_patches_grad = impl_params self.loss_reduction = "sum" # The score correction factor is not visible in the reference implementation # of the original authors, since the calculation is performed with respect to # the gradient and not the score. Roughly speaking, since the calculation # comprises a *squared* distance, we need a factor of 1/2 in the forward pass. # https://github.com/pmeier/CNNMRF/blob/fddcf4d01e2a6ce201059d8bc38597f74a09ba3f/mylib/mrf.lua#L220 self.score_correction_factor = 1.0 / 2.0 if impl_params else 1.0 def enc_to_repr(self, enc: torch.Tensor, is_guided: bool) -> torch.Tensor: if self.normalize_patches_grad: repr = extract_normalized_patches2d(enc, self.patch_size, self.stride) else: repr = pystiche.extract_patches2d(enc, self.patch_size, self.stride) if not is_guided: return repr return self._guide_repr(repr) def calculate_score( self, input_repr: torch.Tensor, target_repr: torch.Tensor, ctx: Optional[torch.Tensor], ) -> torch.Tensor: score = F.mrf_loss( input_repr, target_repr, reduction=self.loss_reduction, batched_input=True ) return score * self.score_correction_factor 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) def encoding_loss_fn(encoder: enc.Encoder, layer_weight: float) -> MRFLoss: return MRFLoss( encoder, hyper_parameters.style_loss.patch_size, # type: ignore[union-attr] impl_params=impl_params, stride=hyper_parameters.style_loss.stride, # type: ignore[union-attr] target_transforms=_target_transforms( impl_params=impl_params, hyper_parameters=hyper_parameters ), score_weight=layer_weight, ) 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 __init__(self, impl_params: bool = True, **total_variation_loss_kwargs: Any): super().__init__(**total_variation_loss_kwargs) self.loss_reduction = "sum" # The score correction factor is not visible in the reference implementation # of the original authors, since the calculation is performed with respect to # the gradient and not the score. Roughly speaking, since the calculation # comprises a *squared* distance, we need a factor of 1/2 in the forward pass. # https://github.com/pmeier/CNNMRF/blob/fddcf4d01e2a6ce201059d8bc38597f74a09ba3f/mylib/tv.lua#L20-L30 self.score_correction_factor = 1.0 / 2.0 if impl_params else 1.0 def calculate_score(self, input_repr: torch.Tensor) -> torch.Tensor: score = F.total_variation_loss( input_repr, exponent=self.exponent, reduction=self.loss_reduction ) return score * self.score_correction_factor 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), )

1b3186c99a60818dc9d24b438538877520aa1347

py

Python

tests/conftest.py

Z2PackDev/bands_inspect

76fdb0130d9ff64c738365a1911bc61f035927f2

2017-12-19T07:21:56.000Z

2017-12-19T07:21:56.000Z

tests/conftest.py

Z2PackDev/bands-inspect

76fdb0130d9ff64c738365a1911bc61f035927f2

2018-02-27T09:07:46.000Z

2018-03-06T12:26:04.000Z

tests/conftest.py

Z2PackDev/bands_inspect

76fdb0130d9ff64c738365a1911bc61f035927f2

2017-12-19T07:21:55.000Z

2017-12-19T07:21:55.000Z

# -*- coding: utf-8 -*- # (c) 2017-2019, ETH Zurich, Institut fuer Theoretische Physik # Author: Dominik Gresch <greschd@gmx.ch> """ 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

# -*- coding: utf-8 -*- # (c) 2017-2019, ETH Zurich, Institut fuer Theoretische Physik # Author: Dominik Gresch <greschd@gmx.ch> """ 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.""" def inner(compare_fct, data, tag=None): full_name = test_name + (tag or '') # get rid of json-specific quirks # store as string because I cannot add the decoder to the pytest cache data_str = json.dumps(data) data = json.loads(data_str) val = json.loads(request.config.cache.get(full_name, 'null')) if val is None: request.config.cache.set(full_name, data_str) raise ValueError('Reference data does not exist.') assert compare_fct(val, data) 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. """ def inner(obj1, obj2): if isinstance(obj1, bi.kpoints.KpointsBase): np.testing.assert_equal( obj1.kpoints_explicit, obj2.kpoints_explicit ) elif isinstance(obj1, bi.eigenvals.EigenvalsData): np.testing.assert_equal( obj1.kpoints.kpoints_explicit, obj2.kpoints.kpoints_explicit ) np.testing.assert_equal(obj1.eigenvals, obj2.eigenvals) else: raise ValueError("Unknown type {}".format(type(obj1))) return inner @pytest.fixture def sample(): """ Returns the absolute path of the sample with a given name. """ def inner(name): return os.path.join(parameters.SAMPLES_DIR, name) return inner

a365ad738e2f0d42460bbe15195bfcc181ad7c09

py

Python

src/tensorrt/tools/caffe_engine/call_engine_to_infer_all.py

aimuch/AIEnvConfig

4ccd54e9c601e8c91efebcec1a50115d75d0cf96

2019-06-14T16:12:20.000Z

2022-03-27T09:56:26.000Z

src/tensorrt/tools/caffe_engine/call_engine_to_infer_all.py

aimuch/AIEnvConfig

4ccd54e9c601e8c91efebcec1a50115d75d0cf96

2018-08-10T07:15:39.000Z

2018-10-23T01:51:17.000Z

src/tensorrt/tools/caffe_engine/call_engine_to_infer_all.py

aimuch/AIEnvConfig

4ccd54e9c601e8c91efebcec1a50115d75d0cf96

2019-08-16T13:42:13.000Z

2022-02-23T03:38:09.000Z

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]))

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 def load_image(img_path, net_input_shape): img = cv2.resize(cv2.imread(img_path), net_input_shape) # BGR -> RGB #img = img[:,:, (2, 1, 0)] ## Method 1 # imgT = np.transpose(img, (2, 0, 1)) # c,w,h # imgF = np.asarray(imgT, dtype=np.float32) # mean = [[[88.159309]], [[97.966286]], [[103.66106]]] # Caffe image mean # imgS = np.subtract(imgF,mean) ## Method 2 imgF = np.asarray(img, dtype=np.float32) mean = [88.159309, 97.966286, 103.66106] # Caffe image mean imgSS = np.subtract(imgF, mean) imgS = np.transpose(imgSS, (2, 0, 1)) # CHW # RGB_MEAN_PIXELS = np.array([88.159309, 97.966286, 103.66106]).reshape((1,1,1,3)).astype(np.float32) return np.ascontiguousarray(imgS, dtype=np.float32) # avoid error: ndarray is not contiguous def test_Loader(TEST_PATH, net_input_shape): label_list = [] img_list = [] pair = [] folders = os.listdir(TEST_PATH) for folder in folders: folder_path = os.path.join(TEST_PATH, folder) imgs = os.listdir(folder_path) for img in tqdm(imgs): img_path = os.path.join(folder_path, img) img = load_image(img_path, net_input_shape) label = class_labels.index(folder) img_list.append(img) label_list.append(label) pair.append((img, label)) return pair, (img_list, label_list) 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]))

3db8ec872b628c2d5573b83d71f828295df1aa7e

py

Python

machineLearning.py

z-Wind/EQOptimum

c046daec2c6218277a3fec9fa0c87bea0b30ff2f

machineLearning.py

z-Wind/EQOptimum

c046daec2c6218277a3fec9fa0c87bea0b30ff2f

machineLearning.py

z-Wind/EQOptimum

c046daec2c6218277a3fec9fa0c87bea0b30ff2f

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()

import filters import numpy as np import matplotlib.pyplot as plt from scipy.signal import freqz from sklearn.neural_network import MLPRegressor def filterModel(x): # [fc, bandwidth, gain] w_final = None db_final = 0 fs = 44100 for fc, BW, gain in x: b, a = filters.bandpass_peaking(fc=fc, gain=gain, BW=BW) w, h = freqz(b, a, worN=np.linspace(np.pi*2/fs*20, np.pi*2/fs*20e3, 500)) db = 20 * np.log10(abs(h)) w_final = w db_final += db # plt.semilogx(w_final * fs / (2*np.pi), db_final) return w_final*fs/(2*np.pi), db_final def genXY(n, filtersNum): total = n * filtersNum fc = np.random.uniform(20, 20e3, size=(total,1)) bw = np.random.uniform(100, 10000, size=(total,1)) gain = np.random.uniform(0, 20, size=(total,1)) Y = np.concatenate((fc,bw,gain), axis=1) Y = Y.reshape(n, filtersNum, 3) X = [] for paras in Y: f, db = filterModel(paras) X.append(db) X = np.array(X) Y = Y.reshape(n, filtersNum*3) return X, Y 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()

6f7dc504b463999eb2e9b24300c31ee083334da5

py

Python

src/utils/dist.py

shaoeric/torch-atom

7688fc38c0d19fe4d13a9773115df911ffe6eaaa

2022-03-06T06:04:54.000Z

2022-03-27T04:14:33.000Z

src/utils/dist.py

shaoeric/torch-atom

7688fc38c0d19fe4d13a9773115df911ffe6eaaa

src/utils/dist.py

shaoeric/torch-atom

7688fc38c0d19fe4d13a9773115df911ffe6eaaa

2022-03-11T07:01:58.000Z

2022-03-17T05:34:41.000Z

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()

import torch.distributed as dist import torch def get_world_size(): if not dist.is_available(): return 1 if not dist.is_initialized(): return 1 return dist.get_world_size() def get_rank(): if not dist.is_available(): return 0 if not dist.is_initialized(): return 0 return dist.get_rank() def is_main_process(): return get_rank() == 0 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() def reduce_value(value, average=True): world_size = get_world_size() if world_size < 2: return value with torch.no_grad(): dist.all_reduce(value) if average: value /= world_size return value

7d4f4e96803718430d878ca088bcaed92b3079cc

py

Python

base_pool/mysql_pool/mysql_views.py

zhanzhangwei/kafka-study

6be4167319b855c9560e92932aae628f87a5e680

base_pool/mysql_pool/mysql_views.py

zhanzhangwei/kafka-study

6be4167319b855c9560e92932aae628f87a5e680

base_pool/mysql_pool/mysql_views.py

zhanzhangwei/kafka-study

6be4167319b855c9560e92932aae628f87a5e680

import json import pymysql import datetime from dbutils.pooled_db import PooledDB import pymysql from conf.common import * mysql_client = MysqlClient()

import json import pymysql import datetime from dbutils.pooled_db import PooledDB import pymysql from conf.common import * class MysqlClient(object): __pool = None def __init__(self): """ :param mincached:连接池中空闲连接的初始数量 :param maxcached:连接池中空闲连接的最大数量 :param maxshared:共享连接的最大数量 :param maxconnections:创建连接池的最大数量 :param blocking:超过最大连接数量时候的表现，为True等待连接数量下降，为false直接报错处理 :param maxusage:单个连接的最大重复使用次数 :param setsession:optional list of SQL commands that may serve to prepare the session, e.g. ["set datestyle to ...", "set time zone ..."] :param reset:how connections should be reset when returned to the pool (False or None to rollback transcations started with begin(), True to always issue a rollback for safety's sake) :param host:数据库ip地址 :param port:数据库端口 :param db:库名 :param user:用户名 :param passwd:密码 :param charset:字符编码 """ mincached = 10 maxcached = 20 maxshared = 10 maxconnections = 200 blocking = True maxusage = 100 setsession = None reset = True host = MYSQL_HOST port = MYSQL_PORT db = DATABASE user = USER passwd = PASSWORD charset = 'utf8mb4' if not self.__pool: self.__class__.__pool = PooledDB(pymysql, mincached, maxcached, maxshared, maxconnections, blocking, maxusage, setsession, reset, host=host, port=port, db=db, user=user, passwd=passwd, charset=charset, cursorclass=pymysql.cursors.DictCursor ) self._conn = None self._cursor = None self.__get_conn() def __get_conn(self): self._conn = self.__pool.connection() self._cursor = self._conn.cursor() def close(self): try: self._cursor.close() self._conn.close() except Exception as e: print(e) def __execute(self, sql, param=()): count = self._cursor.execute(sql, param) print(count) return count @staticmethod def __dict_datetime_obj_to_str(result_dict): """把字典里面的datatime对象转成字符串，使json转换不出错""" if result_dict: result_replace = {k: v.__str__() for k, v in result_dict.items() if isinstance(v, datetime.datetime)} result_dict.update(result_replace) return result_dict def select_one(self, sql, param=()): """查询单个结果""" count = self.__execute(sql, param) result = self._cursor.fetchone() """:type result:dict""" result = self.__dict_datetime_obj_to_str(result) return count, result def select_many(self, sql, param=()): """ 查询多个结果 :param sql: qsl语句 :param param: sql参数 :return: 结果数量和查询结果集 """ count = self.__execute(sql, param) result = self._cursor.fetchall() """:type result:list""" [self.__dict_datetime_obj_to_str(row_dict) for row_dict in result] return count, result def execute(self, sql, param=()): count = self.__execute(sql, param) return count def begin(self): """开启事务""" self._conn.autocommit(0) def end(self, option='commit'): """结束事务""" if option == 'commit': self._conn.autocommit() else: self._conn.rollback() mysql_client = MysqlClient()

fbbdf9d38ba25ab279b3c1a4de1e0e092ad03325

py

Python

scripts/jupyter_vdi.py

ScottWales/cosima-cookbook

0ed83e2165efe5badfca59e2dccf835ab7acecca

scripts/jupyter_vdi.py

ScottWales/cosima-cookbook

0ed83e2165efe5badfca59e2dccf835ab7acecca

scripts/jupyter_vdi.py

ScottWales/cosima-cookbook

0ed83e2165efe5badfca59e2dccf835ab7acecca

2020-01-30T05:36:08.000Z

2020-01-30T05:36:08.000Z

#!/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()

#!/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 parse_args(args): parser = argparse.ArgumentParser(description="Log into the VDI, start a jupyter notebook session and ssh tunnel to local machine") parser.add_argument("-v","--verbose", help="Increase verbosity", action='count', default=0) return parser.parse_args(args) def clean_params(params): for key, value in params.items(): try: params[key] = value.decode() except AttributeError: pass 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 def open_jupyter_url(params): # Open browser locally status = '' url = 'http://localhost:{jupyterport}/?token={token}'.format(**params) if is_mac: status = "Using appscript to open {}".format(url) safari = appscript.app("Safari") safari.make(new=appscript.k.document, with_properties={appscript.k.URL: url}) else: status = "Opening {}".format(url) webbrowser.open(url) return status tunnel_started = False tunnel = None def start_tunnel(params): # Create ssh tunnel for local access to jupyter notebook cmd = ' '.join(['-N -f -L {jupyterport}:localhost:{jupyterport}', '-L {bokehport}:localhost:{bokehport}']) # This print statement is needed as there are /r/n line endings from # the jupyter notebook output that are difficult to suppress logging.info("Starting ssh tunnel...") tunnel = ssh(cmd, params, login_timeout=2) tunnel.expect (pexpect.EOF) # Open web browser and log result logging.info(open_jupyter_url(params)) def main(args): # global verbose means it doesn't need to be passed to every routine global verbose verbose = args.verbose logging.info("Checking SSH keys to VDI are configured...") r = session('hello --partition main', params) if r.exitstatus != 0: # suggest setting up SSH keys logging.error("Error with ssh keys/password and VDI.") logging.error(" Incorrect user name in ~/cosima_cookbook.conf file?") logging.error(" Edit ~/cosima_cookbook.conf before continuing.") sys.exit(1) logging.info("SSH keys configured OK") logging.info("Determine if VDI session is already running...") r = session('list-avail --partition main', params) m = re.search('#~#id=(?P<jobid>(?P<jobidNumber>.*?))#~#state=(?P<state>.*?)(?:#~#time_rem=(?P<remainingWalltime>.*?))?#~#', r.before.decode()) if m is not None: params.update(m.groupdict()) w = int(params['remainingWalltime']) remainingWalltime = '{:02}:{:02}:{:02}'.format( w // 3600, w % 3600 // 60, w % 60) logging.info('Time remaining: %s', remainingWalltime) # TODO: should give user option of starting a new session if the remaining walltime is short else: logging.info('No VDI session found') logging.info("Launching a new VDI session...") r = session('launch --partition main', params) m = re.search('#~#id=(?P<jobid>(?P<jobidNumber>.*?))#~#', r.before.decode()) if m is None: logging.info('Unable to launch new VDI session:\n'+r.before.decode()) params.update(m.groupdict()) time.sleep(2) # TODO: instead of waiting, should check for confirmation # use has-started logging.info("Determine jobid for VDI session...{jobid}".format(**params)) logging.info("Get exechost for VDI session...") r = session('get-host --jobid {jobid}', params) m = re.search('#~#host=(?P<exechost>.*?)#~#', r.before.decode()) params.update(m.groupdict()) logging.info('exechost: {exechost}'.format(**params)) logging.info("Running Jupyter on VDI...") setupconda = params.get('setupconda', """module use /g/data3/hh5/public/modules && module load conda/analysis3 """.replace('\n', ' ')) jupyterapp = params.get('jupyterapp', "notebook") run_jupyter = "jupyter %s --no-browser --port {jupyterport}" % jupyterapp run_jupyter = setupconda + ' && ' + run_jupyter cmd = ' '.join(['-t', """'bash -l -c "%s"'""" % run_jupyter]) logging.info("Waiting for Jupyter to start...") # Launch jupyter on VDI s = ssh(cmd, params, login_timeout=2) ret = s.expect('http://\S*:(?P<jupyterport>\d+)/\?token=(?P<token>[a-zA-Z0-9]+)') if s.match: params.update(s.match.groupdict()) start_tunnel(params) else: logging.info("Could not find url information in jupyter output") sys.exit(1) # Grab all the output up to the incorrect URL -- uses the token twice, which is unhelpful ret = s.expect('http://.*') logging.info("Use Control-C to stop the Notebook server and shut down all kernels (twice to skip confirmation)\n\n") # give control over to user s.interact() logging.info('end of script') # optional: terminate to close the vdi session? def main_argv(): args = parse_args(sys.argv[1:]) main(args) if __name__ == "__main__": main_argv()

1fa6873ff966dcc647833979508b75f9d44bd7bd

py

Python

utils/data.py

YOUSIKI/PyTorch-FBS

5e94c3183f064ef5ed7f4b7d82b076056200b368

2020-09-14T02:40:37.000Z

2022-01-13T11:13:36.000Z

utils/data.py

YOUSIKI/PyTorch-FBS

5e94c3183f064ef5ed7f4b7d82b076056200b368

2020-11-28T05:48:45.000Z

2022-03-11T13:44:50.000Z

utils/data.py

YOUSIKI/PyTorch-FBS

5e94c3183f064ef5ed7f4b7d82b076056200b368

2020-11-28T02:27:08.000Z

2021-11-24T03:10:10.000Z

# -*- coding=utf-8 -*- __all__ = [ 'tiny_imagenet', 'imagewoof2', 'imagenette2' ] import os import torch import torchvision _default_batch_size = 32 _default_num_workers = 4

# -*- coding=utf-8 -*- __all__ = [ 'tiny_imagenet', 'imagewoof2', 'imagenette2' ] import os import torch import torchvision _default_batch_size = 32 _default_num_workers = 4 def _transform(train=True): mean = [0.485, 0.456, 0.406] std = [0.229, 0.224, 0.225] if train: return torchvision.transforms.Compose([ torchvision.transforms.RandomResizedCrop(224), torchvision.transforms.RandomHorizontalFlip(), torchvision.transforms.ToTensor(), torchvision.transforms.Normalize(mean, std) ]) else: return torchvision.transforms.Compose([ torchvision.transforms.CenterCrop(224), torchvision.transforms.ToTensor(), torchvision.transforms.Normalize(mean, std) ]) def tiny_imagenet(name='train', batch_size=_default_batch_size, num_workers=_default_num_workers): dataset = torchvision.datasets.ImageFolder( os.path.join('datasets', 'tiny-imagenet-200', name), transform=_transform(name == 'train') ) dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, drop_last=True, shuffle=name == 'train') return dataloader def imagewoof2(name='train', batch_size=_default_batch_size, num_workers=_default_num_workers): dataset = torchvision.datasets.ImageFolder( os.path.join('datasets', 'imagewoof2', name), transform=_transform(name == 'train') ) dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, drop_last=True, shuffle=name == 'train') return dataloader def imagenette2(name='train', batch_size=_default_batch_size, num_workers=_default_num_workers): dataset = torchvision.datasets.ImageFolder( os.path.join('datasets', 'imagenette2', name), transform=_transform(name == 'train') ) dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, drop_last=True, shuffle=name == 'train') return dataloader

24a125f749e07df3306878e5eb148d1afc6b30c4

py

Python

NewsPaper/NewsPaper/NewsPaper/news/models.py

PavelPopkov/D3.4.-Practice-Popkov

46de6209bad81c17882520397fbb358c0834e753

NewsPaper/NewsPaper/NewsPaper/news/models.py

PavelPopkov/D3.4.-Practice-Popkov

46de6209bad81c17882520397fbb358c0834e753

NewsPaper/NewsPaper/NewsPaper/news/models.py

PavelPopkov/D3.4.-Practice-Popkov

46de6209bad81c17882520397fbb358c0834e753

from django.db import models from django.contrib.auth.models import User from django.db.models import Sum from datetime import datetime

from django.db import models from django.contrib.auth.models import User from django.db.models import Sum from datetime import datetime class Author(models.Model): author = models.CharField(max_length=100) rating = models.IntegerField(default=0) user = models.OneToOneField(User, on_delete=models.CASCADE) def __str__(self): return self.author # .aggregate(Sum("rating")) def update_rating(self): auth = Author.objects.get(author=self.author) sum_rat_post = 0 posts = auth.post_set.all() for post in posts: sum_rat_post += post.rating_post * 3 usr = auth.one_to_one_rel sum_rat_comm = 0 comments = usr.comment_set.all() for comm in comments: sum_rat_comm += comm.rating_comm sum_rat_auth = 0 # comments_posts = auth.post_set.comment_set.all() for post in posts: comm_posts = post.comment_set.all() for comm_post in comm_posts: sum_rat_auth += comm_post.rating_comm self.rating = sum_rat_post + sum_rat_comm + sum_rat_auth self.save() class Category(models.Model): category = models.CharField(max_length=100, unique=True) class Post(models.Model): article = 'AR' new = 'NE' POSITIONS = [ (article, 'Статья'), (new, 'Новость') ] ar_or_new = models.CharField(max_length=2, choices=POSITIONS, default=article) created = models.DateTimeField(auto_now_add=True) post_name = models.CharField(max_length=250) content = models.TextField() rating = models.IntegerField(default=0) author = models.ForeignKey(Author, on_delete=models.CASCADE) category = models.ManyToManyField(Category, through='PostCategory') def like(self): self.rating += 1 self.save() def dislike(self): self.rating -= 1 if self.rating_comm < 0: self.rating_comm = 0 self.save() def preview(self): prev = self.content[:124] + '...' return prev class PostCategory(models.Model): post = models.ForeignKey(Post, on_delete=models.CASCADE) category = models.ForeignKey(Category, on_delete=models.CASCADE) class Comment(models.Model): comment = models.TextField() created = models.DateTimeField(auto_now_add=True) rating = models.IntegerField(default=0) post = models.ForeignKey(Post, on_delete=models.CASCADE) user = models.ForeignKey(User, on_delete=models.CASCADE) def like(self): self.rating += 1 self.save() def dislike(self): self.rating -= 1 if self.rating < 0: self.rating = 0 self.save()

838d22d0dea3f0cea788de6ba72e416ad4ef2add

py

Python

tests/e2e/runner.py

wilzbach/storyscript-sls

d71d74a53852ebae54bdaab341678b04f2775411

tests/e2e/runner.py

wilzbach/storyscript-sls

d71d74a53852ebae54bdaab341678b04f2775411

tests/e2e/runner.py

wilzbach/storyscript-sls

d71d74a53852ebae54bdaab341678b04f2775411

#!/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)

#!/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 def patched_storyhub(mocker, scope="module"): mocker.patch.object(StoryHub, "StoryscriptHub", return_value=hub) mocker.patch.object(AutoUpdateThread, "dispatch_update") # compile a story and compare its completion with the expected tree def run_test_completion(uri, source, expected, patch, options): action = options.pop("action", "complete") if action == "complete": result = App(hub=hub).complete(uri=uri, text=source, **options) else: assert action == "click" result = App(hub=hub).click(uri=uri, text=source, **options) assert result == expected # load a story from the file system and load its expected result file (.json) def run_test(story_path, patch): story_string = None with io.open(story_path, "r") as f: story_string = f.read() expected_path = path.splitext(story_path)[0] assert path.isfile( expected_path + ".json" ), f"Path: `{expected_path}.json` does not exist." expected_completion = None with io.open(expected_path + ".json", "r") as f: expected_completion = f.read() # deserialize the expected completion expected = json.loads(expected_completion) options = parse_options(story_string) return run_test_completion( story_path, story_string, expected, patch, options ) @mark.usefixtures("patched_storyhub") @mark.parametrize("test_file", test_files) def test_story(test_file, patch): test_file = path.join(test_dir, test_file) run_test(test_file, patch)

a028f9eab21f99b975a3ac640714e3b636189bcc

py

Python

Misc/Become_a_Python_Developer/2_Programming Fundamentals in the Real World/Ex_Files_Programming_Realworld/Exercise Files/Ch05/05_03/start_05_03_coordinates.py

specter01wj/LAB-Lynda

1915ada66f4498cdf15a0e2a068c938e325e9ba3

Misc/Become_a_Python_Developer/2_Programming Fundamentals in the Real World/Ex_Files_Programming_Realworld/Exercise Files/Ch05/05_03/start_05_03_coordinates.py

specter01wj/LAB-Lynda

1915ada66f4498cdf15a0e2a068c938e325e9ba3

2020-07-08T06:20:03.000Z

2022-03-02T10:05:06.000Z

Misc/Become_a_Python_Developer/2_Programming Fundamentals in the Real World/Ex_Files_Programming_Realworld/Exercise Files/Ch05/05_03/start_05_03_coordinates.py

specter01wj/LAB-Lynda

1915ada66f4498cdf15a0e2a068c938e325e9ba3

""" Where's My Mouse? """ import tkinter root = tkinter.Tk() root.bind('<Button>', mouse_click) root.mainloop()

""" Where's My Mouse? """ import tkinter def mouse_click(event): # retrieve XY coords as a tuple coords = root.winfo_pointerxy() print('coords: {}'.format(coords)) print('X: {}'.format(coords[0])) print('Y: {}'.format(coords[1])) root = tkinter.Tk() root.bind('<Button>', mouse_click) root.mainloop()

b485f685ca90029c0dd0acd04f32bc0b55820f14

py

Python

examples/fsm/bot/middleware.py

ExpressApp/pybotx

97c8b1ce5d45a05567ed01d545cb43174a2dcbb9

2021-01-21T12:43:10.000Z

2022-03-23T11:11:59.000Z

examples/fsm/bot/middleware.py

ExpressApp/pybotx

97c8b1ce5d45a05567ed01d545cb43174a2dcbb9

2020-02-26T08:51:03.000Z

2022-03-23T11:08:36.000Z

examples/fsm/bot/middleware.py

ExpressApp/pybotx

97c8b1ce5d45a05567ed01d545cb43174a2dcbb9

2019-12-02T16:19:22.000Z

2021-11-22T20:33:34.000Z

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

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 class Transition: on_failure: Optional[Union[Enum, object]] = _default_transition on_success: Optional[Union[Enum, object]] = _default_transition class FlowError(Exception): pass class FSM: def __init__(self, states: Type[Enum]) -> None: self.transitions: Dict[Enum, Transition] = {} self.collector = Collector() self.states = states def handler( self, on_state: Enum, next_state: Optional[Union[Enum, object]] = _default_transition, on_failure: Optional[Union[Enum, object]] = _default_transition, ) -> Callable: def decorator(handler: Callable) -> Callable: self.collector.add_handler( handler, body=on_state.name, name=on_state.name, include_in_status=False, ) self.transitions[on_state] = Transition( on_success=next_state, on_failure=on_failure, ) return handler return decorator def change_state(message: Message, new_state: Optional[Enum]) -> None: message.bot.state.fsm_state[(message.user_huid, message.group_chat_id)] = new_state class FSMMiddleware(BaseMiddleware): def __init__( self, executor: Executor, bot: Bot, fsm: FSM, initial_state: Optional[Enum] = None, ) -> None: super().__init__(executor) bot.state.fsm_state = {} self.fsm = fsm self.initial_state = initial_state for state in self.fsm.states: # check that for each state there is registered handler assert state in self.fsm.transitions async def dispatch(self, message: Message, call_next: Executor) -> None: current_state: Enum = message.bot.state.fsm_state.setdefault( (message.user_huid, message.group_chat_id), self.initial_state, ) if current_state is not None: transition = self.fsm.transitions[current_state] handler = self.fsm.collector.handler_for(current_state.name) try: await handler(message) except Exception as exc: if transition.on_failure is not _default_transition: change_state(message, transition.on_failure) raise exc else: if transition.on_success is not _default_transition: change_state(message, transition.on_success) else: await callable_to_coroutine(call_next, message)

42f3981074dbd8b6458eb716c4608442ffca1db6

py

Python

webenmr/lib/convrdc.py

andreagia/WEBNMR

512a8cc04cf69300796585feae722614501389a9

webenmr/lib/convrdc.py

andreagia/WEBNMR

512a8cc04cf69300796585feae722614501389a9

webenmr/lib/convrdc.py

andreagia/WEBNMR

512a8cc04cf69300796585feae722614501389a9

#!/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()

#!/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 def searchres(nres, lpdb): for l in lpdb: if l.strip().lower().startswith('atom'): s=l.split() if int(nres)==int(s[4]): return s[3] def searchC(outx): i=0 c=[] while i<len(outx): if outx[i].strip().startswith('XDIPO_RDC>frun'): while i<len(outx): i+=1 if i>=len(outx): break if outx[i].strip().startswith('C1='): t=[] l=outx[i].split() for x in range(1,len(l),2): t.append(l[x]) c.append(t) break i+=1 return c def convert(pdb, new, wd): if new.calculation.protocol.xrdc: xfiles=[] if len(new.calculation.protocol.xrdc)==1: xfiles.append(new.calculation.protocol.xrdc.attrib_.xrdc_file) else: for i in range(len(new.calculation.protocol.xrdc)): xfiles.append(new.calculation.protocol.xrdc[i].attrib_.xrdc_file) else: sys.exit('%s: RDC not found\n' % sys.argv[0]) try: lpdb=open(pdb, 'r').readlines() except IOError, (errno, strerror): sys.exit('%s: IOError(%s): %s %s\n' % (sys.argv[0], errno, pdb, strerror)) numMap = {} for l in lpdb: if l.strip().lower().startswith('atom'): ls=l.split() k='%s:%s' % (ls[4],ls[2]) numMap[k]=ls[1] cmd=' /opt/local_prog/xplor-nih-2.22/bin/xplor tensor.inp' outx=commands.getoutput(cmd) outx=outx.split('\n') #outx=open('xplor.outx').readlines() c=searchC(outx) out=[' &align\n'] out.append(' num_datasets=%d,\n' % len(xfiles)) out.append(' dcut=-1.0, freezemol=.false.,\n') out.append(' ndip=10,') out.append(' dcut=-1.0,dwt=92*0.1,\n') out.append(' gigj=92*-3.163,\n') out.append(' dij=92*1.01,\n') s11=' s11=' s12=' s12=' s13=' s13=' s22=' s22=' s23=' s23=' for i in range(len(c)): s11='%s%s,' % (s11, c[i][0]) s12='%s%s,' % (s12, c[i][1]) s13='%s%s,' % (s13, c[i][2]) s22='%s%s,' % (s22, c[i][3]) s23='%s%s,' % (s23, c[i][4]) out.append('%s\n' % s11) out.append('%s\n' % s12) out.append('%s\n' % s13) out.append('%s\n' % s22) out.append('%s\n' % s23) counter=0 nrdc=0 for xfile in xfiles: counter+=1 nxfile=os.path.join(wd, 'rdc_%d_web_enmr_normalized.tbl' % counter) xfile=os.path.join(wd, xfile) try: normalize(xfile, nxfile, new, wd) except: sys.exit('%s: unable to normalize %s tbl file\n' % (sys.argv[0], xfile)) try: xp=open(nxfile,'r').readlines() except IOError, (errno, strerror): sys.exit('%s: IOError(%s): %s %s\n' % (sys.argv[0], errno, nxfile, strerror)) out.append(' dataset=%d,\n' % counter) for l in xp: if l.strip().startswith('assign'): nrdc+=1 ls=l.split() res=searchres(ls[31], lpdb) kk='%s:%s' % (res, ls[34]) if convtable.has_key(kk): ls[34]=convtable[kk].split(':')[1] k='%s:%s' % (ls[31], ls[34]) natm1=numMap[k] res=searchres(ls[38], lpdb) kk='%s:%s' % (res, ls[41]) if convtable.has_key(kk): ls[41]=convtable[kk].split(':')[1] k='%s:%s' % (ls[38], ls[41]) natm2=numMap[k] out.append(' id(%s)=%s, jd(%s)=%s, dobsl(%s)=%s, dobsu(%s)=%s, \n' % (nrdc, natm1, nrdc, natm2, nrdc, ls[43], nrdc, ls[43])) out[3]=' ndip=%d,' % nrdc out.append(' &end') return out 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()

1441c3ed71c2dc67d784d782e0dab2d91d827d06

py

Python

lptrack/versions.py

gieseladev/lptrack

fb4c64021c23522f96733db41ceb69f0ccb9b713

lptrack/versions.py

gieseladev/lptrack

fb4c64021c23522f96733db41ceb69f0ccb9b713

lptrack/versions.py

gieseladev/lptrack

fb4c64021c23522f96733db41ceb69f0ccb9b713

"""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]

"""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 def _read_body_v1_2(stream: codec.Reader, version: int) -> TrackInfo: return TrackInfo( title=stream.read_utf(), author=stream.read_utf(), duration=stream.read_long() / 1000, identifier=stream.read_utf(), is_stream=stream.read_bool(), uri=stream.read_optional_utf() if version >= 2 else None, ) def read_body_v1(stream: codec.Reader) -> TrackInfo: return _read_body_v1_2(stream, 1) def read_body_v2(stream: codec.Reader) -> TrackInfo: return _read_body_v1_2(stream, 2) def _write_body_v1_2(stream: codec.Writer, track: TrackInfo, version: int) -> None: stream.write_utf(track.title) stream.write_utf(track.author) stream.write_long(int(track.duration * 1000)) stream.write_utf(track.identifier) stream.write_bool(track.is_stream) if version >= 2: stream.write_optional_utf(track.uri) def write_body_v1(stream: codec.Writer, track: TrackInfo) -> None: _write_body_v1_2(stream, track, 1) def write_body_v2(stream: codec.Writer, track: TrackInfo) -> None: _write_body_v1_2(stream, track, 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_format(version: int) -> Tuple: try: return _FORMAT_VERSIONS[version] except KeyError: raise ValueError(f"Unsupported version: {version}") from None 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]

45b20d04060d1b766f35010e3ce9fedfd6a34eba

py

Python

venv/lib/python3.8/site-packages/poetry/core/toml/__init__.py

Retraces/UkraineBot

3d5d7f8aaa58fa0cb8b98733b8808e5dfbdb8b71

2022-03-13T01:58:52.000Z

2022-03-31T06:07:54.000Z

venv/lib/python3.8/site-packages/poetry/core/toml/__init__.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

2021-11-20T04:09:18.000Z

2022-03-23T15:05:55.000Z

venv/lib/python3.8/site-packages/poetry/core/toml/__init__.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

/home/runner/.cache/pip/pool/f3/de/85/7dca1e096a43e00e6ff1ca900dda1ca91c8c5c3a1d6798e466a9173a00

/home/runner/.cache/pip/pool/f3/de/85/7dca1e096a43e00e6ff1ca900dda1ca91c8c5c3a1d6798e466a9173a00

4053282fdcb4c61c6094cfb3f6a832822c2a096c

py

Python

venv/lib/python2.7/site-packages/cement/ext/ext_alarm.py

zwachtel11/fruitful-backend

45b8994917182e7b684b9e25944cc79c9494c9f3

2018-06-05T10:49:32.000Z

2019-12-31T11:07:36.000Z

v_env/lib/python3.7/site-packages/cement/ext/ext_alarm.py

buds-lab/expanded-psychrometric-chart

e7267f57584d8ba645507189ea4a8e474c67e0de

2019-10-08T01:53:02.000Z

2021-06-04T22:02:21.000Z

v_env/lib/python3.7/site-packages/cement/ext/ext_alarm.py

buds-lab/expanded-psychrometric-chart

e7267f57584d8ba645507189ea4a8e474c67e0de

2018-09-17T06:10:32.000Z

2022-03-19T13:15:30.000Z

""" 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)

""" 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__) def alarm_handler(app, signum, frame): if signum == signal.SIGALRM: app.log.error(app.alarm.msg) 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 __init__(self, *args, **kw): super(AlarmManager, self).__init__(*args, **kw) self.msg = None 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) def load(app): app.catch_signal(signal.SIGALRM) app.extend('alarm', AlarmManager()) app.hook.register('signal', alarm_handler)

020f0bdb4147d07bfcd522f1fe1fb911459c901a

py

Python

meross_iot/cloud/devices/power_plugs.py

robertodormepoco/MerossIot

95f7c235d0db7d07823cab7f8daed693fe35af96

meross_iot/cloud/devices/power_plugs.py

robertodormepoco/MerossIot

95f7c235d0db7d07823cab7f8daed693fe35af96

meross_iot/cloud/devices/power_plugs.py

robertodormepoco/MerossIot

95f7c235d0db7d07823cab7f8daed693fe35af96

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

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 class GenericPlug(AbstractMerossDevice): # Channels _channels = [] # Dictionary {channel->status} _state = {} def __init__(self, cloud_client, device_uuid, **kwords): super(GenericPlug, self).__init__(cloud_client, device_uuid, **kwords) def _get_consumptionx(self): return self.execute_command("GET", CONSUMPTIONX, {}) def _get_electricity(self): return self.execute_command("GET", ELECTRICITY, {}) def _toggle(self, status, callback=None): payload = {"channel": 0, "toggle": {"onoff": status}} return self.execute_command("SET", TOGGLE, payload, callback=callback) def _togglex(self, channel, status, callback=None): payload = {'togglex': {"onoff": status, "channel": channel}} return self.execute_command("SET", TOGGLEX, payload, callback=callback) def _channel_control_impl(self, channel, status, callback=None): if TOGGLE in self.get_abilities(): return self._toggle(status, callback=callback) elif TOGGLEX in self.get_abilities(): return self._togglex(channel, status, callback=callback) else: raise Exception("The current device does not support neither TOGGLE nor TOGGLEX.") def _handle_push_notification(self, namespace, payload, from_myself=False): def fire_switch_state_change(dev, channel_id, o_state, n_state, f_myself): if o_state != n_state: evt = DeviceSwitchStatusEvent(dev=dev, channel_id=channel_id, switch_state=n_state, generated_by_myself=f_myself) self.fire_event(evt) with self._state_lock: if namespace == TOGGLE: # Update the local state and fire the event only if the state actually changed channel_index = 0 old_switch_state = self._state.get(channel_index) switch_state = payload['toggle']['onoff'] == 1 self._state[channel_index] = switch_state fire_switch_state_change(self, channel_index, old_switch_state, switch_state, from_myself) elif namespace == TOGGLEX: if isinstance(payload['togglex'], list): for c in payload['togglex']: # Update the local state and fire the event only if the state actually changed channel_index = c['channel'] old_switch_state = self._state.get(channel_index) switch_state = c['onoff'] == 1 self._state[channel_index] = switch_state fire_switch_state_change(self, channel_index, old_switch_state, switch_state, from_myself) elif isinstance(payload['togglex'], dict): # Update the local state and fire the event only if the state actually changed channel_index = payload['togglex']['channel'] old_switch_state = self._state.get(channel_index) switch_state = payload['togglex']['onoff'] == 1 self._state[channel_index] = switch_state fire_switch_state_change(self, channel_index, old_switch_state, switch_state, from_myself) elif namespace == REPORT or namespace == CONSUMPTIONX: # For now, we simply ignore push notification of these kind. # In the future, we might think of handling such notification by caching them # and avoid the network round-trip when asking for power consumption (if the latest report is # recent enough) pass else: l.error("Unknown/Unsupported namespace/command: %s" % namespace) def _get_status_impl(self): res = {} data = self.get_sys_data()['all'] if 'digest' in data: for c in data['digest']['togglex']: res[c['channel']] = c['onoff'] == 1 elif 'control' in data: res[0] = data['control']['toggle']['onoff'] == 1 return res def _get_channel_id(self, channel): # Otherwise, if the passed channel looks like the channel spec, lookup its array indexindex if channel in self._channels: return self._channels.index(channel) # if a channel name is given, lookup the channel id from the name if isinstance(channel, str): for i, c in enumerate(self.get_channels()): if c['devName'] == channel: return c['channel'] # If an integer is given assume that is the channel ID elif isinstance(channel, int): return channel # In other cases return an error raise Exception("Invalid channel specified.") def get_status(self, channel=0): # In order to optimize the network traffic, we don't call the get_status() api at every request. # On the contrary, we only call it the first time. Then, the rest of the API will silently listen # for state changes and will automatically update the self._state structure listening for # messages of the device. # Such approach, however, has a side effect. If we call TOGGLE/TOGGLEX and immediately after we call # get_status(), the reported status will be still the old one. This is a race condition because the # "status" RESPONSE will be delivered some time after the TOGGLE REQUEST. It's not a big issue for now, # and synchronizing the two things would be inefficient and probably not very useful. # Just remember to wait some time before testing the status of the item after a toggle. with self._state_lock: c = self._get_channel_id(channel) if self._state == {}: self._state = self._get_status_impl() return self._state[c] def get_power_consumption(self): if CONSUMPTIONX in self.get_abilities(): return self._get_consumptionx()['consumptionx'] else: # Not supported! return None def get_electricity(self): if ELECTRICITY in self.get_abilities(): return self._get_electricity()['electricity'] else: # Not supported! return None def get_channels(self): return self._channels def get_channel_status(self, channel): c = self._get_channel_id(channel) return self.get_status(c) def turn_on_channel(self, channel, callback=None): c = self._get_channel_id(channel) return self._channel_control_impl(c, 1, callback=callback) def turn_off_channel(self, channel, callback=None): c = self._get_channel_id(channel) return self._channel_control_impl(c, 0, callback=callback) def turn_on(self, channel=0, callback=None): c = self._get_channel_id(channel) return self._channel_control_impl(c, 1, callback=callback) def turn_off(self, channel=0, callback=None): c = self._get_channel_id(channel) return self._channel_control_impl(c, 0, callback=callback) def get_usb_channel_index(self): # Look for the usb channel for i, c in enumerate(self.get_channels()): if 'type' in c and c['type'] == 'USB': return i return None def enable_usb(self, callback=None): c = self.get_usb_channel_index() if c is None: return else: return self.turn_on_channel(c, callback=callback) def disable_usb(self, callback=None): c = self.get_usb_channel_index() if c is None: return else: return self.turn_off_channel(c, callback=callback) def get_usb_status(self): c = self.get_usb_channel_index() if c is None: return else: return self.get_channel_status(c) def __str__(self): base_str = super().__str__() with self._state_lock: if not self.online: return base_str channels = "Channels: " channels += ",".join(["%d = %s" % (k, "ON" if v else "OFF") for k, v in enumerate(self._state)]) return base_str + "\n" + "\n" + channels

65badce87f001e1fa721ea024ae15e4646615075

py

Python

django_sphinx_db/backend/sphinx/introspection.py

petekalo/django-sphinx-db

b9190fafab62e69f84b4474f65c1d77f04c313f1

django_sphinx_db/backend/sphinx/introspection.py

petekalo/django-sphinx-db

b9190fafab62e69f84b4474f65c1d77f04c313f1

django_sphinx_db/backend/sphinx/introspection.py

petekalo/django-sphinx-db

b9190fafab62e69f84b4474f65c1d77f04c313f1

from django.db.backends.mysql.introspection import * from django.db.backends.mysql.introspection import DatabaseIntrospection as MYSQLDatabaseIntrospection from django.utils.functional import cached_property

from django.db.backends.mysql.introspection import * from django.db.backends.mysql.introspection import DatabaseIntrospection as MYSQLDatabaseIntrospection from django.utils.functional import cached_property class DatabaseIntrospection(MYSQLDatabaseIntrospection): def get_table_list(self, cursor): """ Returns a list of table and view names in the current database. """ cursor.execute("SHOW TABLES") return [TableInfo(row[0], {'BASE TABLE': 't', 'VIEW': 'v'}.get(row[1])) for row in cursor.fetchall()]

c4fb2fe7e75e47425721a49f845719bb9e6c655f

py

Python

tests/test_highiq.py

ClariNerd617/HighIQ

0305902f889da869535834620bb4fb15ac54b11d

2020-03-16T14:14:45.000Z

2021-09-21T06:39:57.000Z

tests/test_highiq.py

ClariNerd617/HighIQ

0305902f889da869535834620bb4fb15ac54b11d

tests/test_highiq.py

ClariNerd617/HighIQ

0305902f889da869535834620bb4fb15ac54b11d

2019-12-16T19:56:35.000Z

2021-06-09T14:14:47.000Z

import highiq import numpy as np

import highiq import numpy as np def test_io(): my_ds = highiq.io.load_arm_netcdf(highiq.testing.TEST_FILE) assert 'acf' in my_ds.variables.keys() assert 'acf_bkg' in my_ds.variables.keys() my_ds.close() def test_spectra(): my_ds = highiq.io.load_arm_netcdf(highiq.testing.TEST_FILE) my_spectra = highiq.calc.get_psd(my_ds) assert 'power_spectral_density_interp' in my_spectra.variables.keys() assert 'power_spectral_density' in my_spectra.variables.keys() psd = my_spectra['power_spectra_normed'].sel(range=400, method='nearest') vel_bins = my_spectra['vel_bins'] dV = vel_bins[1] - vel_bins[0] np.testing.assert_almost_equal(psd.sum()*dV.values, 100) my_ds.close() my_spectra.close() def test_moments(): my_ds = highiq.io.load_arm_netcdf(highiq.testing.TEST_FILE) my_spectra = highiq.calc.get_psd(my_ds) my_moments = highiq.calc.get_lidar_moments(my_spectra) intensity = my_moments['intensity'].values velocity = my_moments['doppler_velocity'].values assert np.nanmin(intensity) > 1. assert np.nanmin(velocity) < -2. my_ds.close() my_spectra.close() def test_peaks(): my_ds = highiq.io.load_arm_netcdf(highiq.testing.TEST_FILE) my_spectra = highiq.calc.get_psd(my_ds) my_peaks = highiq.calc.calc_num_peaks(my_spectra) my_moments = highiq.calc.get_lidar_moments(my_spectra) my_peaks['npeaks'] = my_peaks['npeaks'].where(my_moments.intensity > 1.1) num_peaks = my_peaks['npeaks'].values assert np.nanmax(num_peaks) == 3 my_ds.close() my_spectra.close() my_peaks.close() my_moments.close()

77ab3b36a849175fa4c24f12a76941077ea58584

py

Python

scripts/docker/migrate.py

guligon90/uac-registry

cb5afe941919c2d9ceffa8d8bf220613b7a20613

scripts/docker/migrate.py

guligon90/uac-registry

cb5afe941919c2d9ceffa8d8bf220613b7a20613

scripts/docker/migrate.py

guligon90/uac-registry

cb5afe941919c2d9ceffa8d8bf220613b7a20613

# Base imports import subprocess from typing import Iterable, Optional # Project imports from docker import common from docker.run import run

# Base imports import subprocess from typing import Iterable, Optional # Project imports from docker import common from docker.run import run def migrate(arguments: Iterable[str], deps: Optional[bool] = True) -> int: print(">>>>>>>>>> Running database migration <<<<<<<<<<") run(['backend', 'python3', common.MANAGE_PY, 'migrate'], deps) def make_migrations(arguments: Iterable[str], deps: Optional[bool] = True) -> int: print(">>>>>>>>>> Running database migration <<<<<<<<<<") run(['backend', 'python3', common.MANAGE_PY, 'makemigrations'], deps)

f979d82751598eba221d7677df764b4451b8c896

py

Python

adw_test/make_small_dataset.py

clinfo/DeepKF

ee4f1be28e5f3bfa46bb47dbdc4d5f678eed36c1

2019-12-19T13:33:36.000Z

2021-06-01T06:08:16.000Z

adw_test/make_small_dataset.py

clinfo/DeepKF

ee4f1be28e5f3bfa46bb47dbdc4d5f678eed36c1

2020-03-03T19:40:55.000Z

2021-05-26T15:27:38.000Z

adw_test/make_small_dataset.py

clinfo/DeepKF

ee4f1be28e5f3bfa46bb47dbdc4d5f678eed36c1

2019-12-19T13:35:07.000Z

2019-12-19T13:35:07.000Z

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)

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)

991fa516fb5524187777ee16359f8b1f0cb6ad59

py

Python

3M/W9/7.py

allenalvin333/Hackerrank_Prep

26ed5b874daba4775d006824d36f9e82ea5ff1ea

2021-11-25T13:38:36.000Z

2021-11-25T13:42:56.000Z

3M/W9/7.py

allenalvin333/Hackerrank_Prep

26ed5b874daba4775d006824d36f9e82ea5ff1ea

3M/W9/7.py

allenalvin333/Hackerrank_Prep

26ed5b874daba4775d006824d36f9e82ea5ff1ea

2021-11-25T13:38:43.000Z

2021-11-25T13:38:43.000Z

# 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()

# 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. # def maxSubarray(arr): p = max(0,arr[0]) l = e = m = arr[0] for z in arr[1:]: e,m,l,p = max(z,e+z),max(m,max(z,e+z)),max(l,z),max(0,z)+p return m,l if(l<0) else p 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()

8c69cd5831be940ef0acbf79e3188bc3d1996e08

py

Python

models/correlation_package/correlation.py

HimankSehgal/DOCS-pytorch

b870babf2bbe2673f68e9134f071d990fb6f4693

2018-11-27T13:00:55.000Z

2022-03-22T15:08:20.000Z

models/correlation_package/correlation.py

HimankSehgal/DOCS-pytorch

b870babf2bbe2673f68e9134f071d990fb6f4693

2018-12-27T07:41:16.000Z

2020-08-14T01:37:46.000Z

models/correlation_package/correlation.py

HimankSehgal/DOCS-pytorch

b870babf2bbe2673f68e9134f071d990fb6f4693

2019-01-17T08:10:08.000Z

2021-12-21T09:44:20.000Z

import torch from torch.nn.modules.module import Module from torch.autograd import Function import correlation_cuda

import torch from torch.nn.modules.module import Module from torch.autograd import Function import correlation_cuda class CorrelationFunction(Function): @staticmethod def forward(ctx, input1, input2, param_dict): ctx.save_for_backward(input1, input2) ctx.pad_size = param_dict["pad_size"] ctx.kernel_size = param_dict["kernel_size"] ctx.max_disp = param_dict["max_disp"] ctx.stride1 = param_dict["stride1"] ctx.stride2 = param_dict["stride2"] ctx.corr_multiply = param_dict["corr_multiply"] with torch.cuda.device_of(input1): rbot1 = input1.new() rbot2 = input2.new() output = input1.new() correlation_cuda.forward(input1, input2, rbot1, rbot2, output, ctx.pad_size, ctx.kernel_size, ctx.max_disp, ctx.stride1, ctx.stride2, ctx.corr_multiply) return output @staticmethod def backward(ctx, grad_output): input1, input2 = ctx.saved_tensors with torch.cuda.device_of(input1): rbot1 = input1.new() rbot2 = input2.new() grad_input1 = input1.new() grad_input2 = input2.new() correlation_cuda.backward(input1, input2, rbot1, rbot2, grad_output, grad_input1, grad_input2, ctx.pad_size, ctx.kernel_size, ctx.max_disp, ctx.stride1, ctx.stride2, ctx.corr_multiply) return grad_input1, grad_input2, None class Correlation(Module): def __init__(self, pad_size=0, kernel_size=0, max_displacement=0, stride1=1, stride2=2, corr_multiply=1): super(Correlation, self).__init__() self.pad_size = pad_size self.kernel_size = kernel_size self.max_displacement = max_displacement self.stride1 = stride1 self.stride2 = stride2 self.corr_multiply = corr_multiply self.out_channels = ((max_displacement/stride2)*2 + 1) * ((max_displacement/stride2)*2 + 1) def forward(self, input1, input2): param_dict = {'pad_size':self.pad_size, 'kernel_size':self.kernel_size, 'max_disp':self.max_displacement, 'stride1':self.stride1, 'stride2':self.stride2, 'corr_multiply':self.corr_multiply} result = CorrelationFunction.apply(input1, input2, param_dict) return result

be789e67d9aef4a43064ec6b0aac240e98f4e74f

py

Python

hep_cnn/tensorrt/convert_tensorrt_tf_integrated.py

NERSC/inference_benchmarks

e51453a755aaece91f7e08e92453a4050722071a

2019-08-29T03:33:58.000Z

2019-08-29T03:33:58.000Z

hep_cnn/tensorrt/convert_tensorrt_tf_integrated.py

NERSC/inference_benchmarks

e51453a755aaece91f7e08e92453a4050722071a

hep_cnn/tensorrt/convert_tensorrt_tf_integrated.py

NERSC/inference_benchmarks

e51453a755aaece91f7e08e92453a4050722071a

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)

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 * def getGraph(filename): with gfile.FastGFile(filename, 'rb') as f: graph_def = tf.GraphDef() graph_def.ParseFromString(f.read()) return graph_def def getFP32(input_file, output_prefix, output, batch_size=128, workspace_size=1<<20): trt_graph = trt.create_inference_graph(getGraph(input_file), output, max_batch_size=batch_size, max_workspace_size_bytes=workspace_size, precision_mode="FP32") # Get optimized graph with gfile.FastGFile(output_prefix+'.FP32.pb', 'wb') as f: f.write(trt_graph.SerializeToString()) return trt_graph def getFP16(input_file, output_prefix, output, batch_size=128, workspace_size=1<<20): trt_graph = trt.create_inference_graph(getGraph(input_file), output, max_batch_size=batch_size, max_workspace_size_bytes=workspace_size, precision_mode="FP16") # Get optimized graph with gfile.FastGFile(output_prefix+'.FP16.pb','wb') as f: f.write(trt_graph.SerializeToString()) return trt_graph def getINT8CalibGraph(input_file, output_prefix, output, batch_size=128, workspace_size=1<<20): trt_graph = trt.create_inference_graph(getGraph(input_file), output, max_batch_size=batch_size, max_workspace_size_bytes=workspace_size, precision_mode="INT8") # calibration with gfile.FastGFile(output_prefix+'.INT8Calib.pb','wb') as f: f.write(trt_graph.SerializeToString()) return trt_graph def getINT8InferenceGraph(output_prefix, calibGraph): trt_graph=trt.calib_graph_to_infer_graph(calibGraph) with gfile.FastGFile(output_prefix+'.INT8.pb','wb') as f: f.write(trt_graph.SerializeToString()) return trt_graph #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)

745b720c7aee2c3450c7326ecfc4595bc580fc48

py

Python

SSD1306.py

krandor/weather_station

06a0c88d92893a95aaabd5bbc2892a99ae4be8e1

SSD1306.py

krandor/weather_station

06a0c88d92893a95aaabd5bbc2892a99ae4be8e1

SSD1306.py

krandor/weather_station

06a0c88d92893a95aaabd5bbc2892a99ae4be8e1

import Adafruit_SSD1306 import Image import ImageDraw import ImageFont # I2C ADDRESS / BITS SSD1306_ADDRESS = 0x3C

import Adafruit_SSD1306 import Image import ImageDraw import ImageFont # I2C ADDRESS / BITS SSD1306_ADDRESS = 0x3C class Ssd1306(object): _display = None _draw = None _image = None _font = None _height = 0 _width = 0 def __init__(self, i2c_bus = 0, ssd1306_rst = "22"): """ :type i2c_bus: int specifying i2c bus number :type ssd1306_rst: string specifying GPIO pin for RST """ # 128x32 display with hardware I2C: self._display = Adafruit_SSD1306.SSD1306_128_32(rst=ssd1306_rst, i2c_bus=i2c_bus) # Initialize library. self._display.begin() # Clear display. self._display.clear() self._display.display() # Create blank image for drawing. # Make sure to create image with mode '1' for 1-bit color. self._width = self._display.width self._height = self._display.height self._image = Image.new('1', (self._width, self._height)) # Get drawing object to draw on image. self._draw = ImageDraw.Draw(self._image) # Load default font. self._font = ImageFont.load_default() @property def height(self): return self._height @property def width(self): return self._width def clear_display(self): self._draw.rectangle((0, 0, self._width, self._height), outline=0, fill=0) def draw_text(self, texttowrite, x, y): self._draw.text((x, y), texttowrite, font=self._font, fill=255) def display_image(self): self._display.image(self._image) self._display.display() def image_width(self): width, height = self._image.size return width def get_text_width(self, text): width, height = self._font.getsize(text) return width

9235c6c3f07aa312f105c296304b0e62256a9961

py

Python

sandbox/flask/multi_page_form/compute.py

carlosal1015/proofofconcept

579873aff082e6fa497a387e0d0a5f8e5ec3ecd2

2015-01-02T19:39:36.000Z

2022-03-09T06:08:10.000Z

sandbox/flask/multi_page_form/compute.py

carlosal1015/proofofconcept

579873aff082e6fa497a387e0d0a5f8e5ec3ecd2

2015-01-02T13:59:58.000Z

2022-03-27T17:22:21.000Z

sandbox/flask/multi_page_form/compute.py

carlosal1015/proofofconcept

579873aff082e6fa497a387e0d0a5f8e5ec3ecd2

2015-02-13T16:00:25.000Z

2020-08-05T17:51:26.000Z

if __name__ == '__main__': print compute(1, 0.1) # default values

def arg_count(inference_rule): # these should be determined based on string match in CSV file num_input=2 num_feed=1 num_output=1 return num_input,num_feed,num_output if __name__ == '__main__': print compute(1, 0.1) # default values

53fad1f9197d87945dd3f90bc49bebbc3ce82648

py

Python

nevergrad/functions/corefuncs.py

se4u/nevergrad

38924bc7b0bff834316ccf974922db2c22be1606

2021-04-21T09:19:44.000Z

2021-04-21T09:19:44.000Z

nevergrad/functions/corefuncs.py

se4u/nevergrad

38924bc7b0bff834316ccf974922db2c22be1606

nevergrad/functions/corefuncs.py

se4u/nevergrad

38924bc7b0bff834316ccf974922db2c22be1606

2019-12-12T10:36:54.000Z

2019-12-12T10:36:54.000Z

# 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

# 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() def _onemax(x: np.ndarray) -> float: return len(x) - sum(1 if int(round(w)) == 1 else 0 for w in x) def _leadingones(x: np.ndarray) -> float: for i, x_ in enumerate(list(x)): if int(round(x_)) != 1: return len(x) - i return 0 def _jump(x: np.ndarray) -> float: # TODO: docstring? n = len(x) m = n // 4 o = n - _onemax(x) if o == n or o <= n - m: return n - m - o return o # Deceptive part. def _styblinksitang(x: np.ndarray, noise: float) -> float: x = np.asarray(x) val = np.sum(np.power(x, 4) - 16 * np.power(x, 2) + 5 * x) # return a positive value for maximization return float(39.16599 * len(x) + 1 * 0.5 * val + noise * np.random.normal(size=val.shape)) @registry.register def sphere(x: np.ndarray) -> float: return float(np.sum(x**2)) @registry.register def sphere1(x: np.ndarray) -> float: return float(np.sum((x - 1.)**2)) @registry.register def sphere2(x: np.ndarray) -> float: return float(np.sum((x - 2.)**2)) @registry.register def sphere4(x: np.ndarray) -> float: return float(np.sum((x - 4.)**2)) @registry.register def maxdeceptive(x: np.ndarray) -> float: dec = 3 * x**2 - (2 / (3**(x - 2)**2 + .1)) return float(np.max(dec)) @registry.register def sumdeceptive(x: np.ndarray) -> float: dec = 3 * x**2 - (2 / (3**(x - 2)**2 + .1)) return float(np.sum(dec)) @registry.register def cigar(x: np.ndarray) -> float: return float(x[0]**2 + 1000000. * np.sum(x[1:]**2)) @registry.register def ellipsoid(x: np.ndarray) -> float: return sum((10**(6 * (i - 1) / float(len(x) - 1))) * (x[i]**2) for i in range(len(x))) @registry.register def rastrigin(x: np.ndarray) -> float: cosi = float(np.sum(np.cos(2 * np.pi * x))) return float(10 * (len(x) - cosi) + sphere(x)) @registry.register def hm(x: np.ndarray) -> float: return float(np.sum((x**2) * (1.1 + np.cos(1. / x)))) @registry.register def rosenbrock(x: np.ndarray) -> float: return sum(100.0*(x[1:] - x[:-1]**2.0)**2.0 + (1 - x[:-1])**2.0) @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) def hardonemax(y: np.ndarray) -> float: return _onemax(discretization.threshold_discretization(y)) @registry.register_with_info(no_transfrom=True) def hardjump(y: np.ndarray) -> float: return _jump(discretization.threshold_discretization(y)) @registry.register_with_info(no_transfrom=True) def hardleadingones(y: np.ndarray) -> float: return _leadingones(discretization.threshold_discretization(y)) @registry.register_with_info(no_transfrom=True) def hardonemax5(y: np.ndarray) -> float: return _onemax(discretization.threshold_discretization(y, 5)) @registry.register_with_info(no_transfrom=True) def hardjump5(y: np.ndarray) -> float: return _jump(discretization.threshold_discretization(y, 5)) @registry.register_with_info(no_transfrom=True) def hardleadingones5(y: np.ndarray) -> float: return _leadingones(discretization.threshold_discretization(y, 5)) @registry.register_with_info(no_transfrom=True) def onemax(y: np.ndarray) -> float: return _onemax(discretization.softmax_discretization(y)) @registry.register_with_info(no_transfrom=True) def jump(y: np.ndarray) -> float: return _jump(discretization.softmax_discretization(y)) @registry.register_with_info(no_transfrom=True) def leadingones(y: np.ndarray) -> float: return _leadingones(discretization.softmax_discretization(y)) @registry.register_with_info(no_transfrom=True) def onemax5(y: np.ndarray) -> float: return _onemax(discretization.softmax_discretization(y, 5)) @registry.register_with_info(no_transfrom=True) def jump5(y: np.ndarray) -> float: return _jump(discretization.softmax_discretization(y, 5)) @registry.register_with_info(no_transfrom=True) def leadingones5(y: np.ndarray) -> float: return _leadingones(discretization.softmax_discretization(y, 5)) @registry.register_with_info(no_transfrom=True) def genzcornerpeak(y: np.ndarray) -> float: value = float(1 + np.mean(np.tanh(y))) if value == 0: return float("inf") return value**(-len(y) - 1) @registry.register_with_info(no_transfrom=True) def minusgenzcornerpeak(y: np.ndarray) -> float: return -float(genzcornerpeak(y)) @registry.register def genzgaussianpeakintegral(x: np.ndarray) -> float: return float(np.exp(-np.sum(x**2 / 4.))) @registry.register def minusgenzgaussianpeakintegral(x: np.ndarray) -> float: return -float(np.exp(-sum(x**2 / 4.))) @registry.register def slope(x: np.ndarray) -> float: return sum(x) @registry.register def linear(x: np.ndarray) -> float: return float(np.tanh(x[0])) @registry.register def st0(x: np.ndarray) -> float: return _styblinksitang(x, 0) @registry.register def st1(x: np.ndarray) -> float: return _styblinksitang(x, 1) @registry.register def st10(x: np.ndarray) -> float: return _styblinksitang(x, 10) @registry.register def st100(x: np.ndarray) -> float: return _styblinksitang(x, 100)

13c3bb6deea1739d745d8f14fbc2523bef0ec1cb

py

Python

HackerRank/Python/Collections/CompanyLogo.py

AdityaChirravuri/CompetitiveProgramming

642550e8916b3f7939a1fdd52d10f5f8ae43f161

2021-07-13T01:49:25.000Z

2021-07-13T01:49:25.000Z

HackerRank/Python/Collections/CompanyLogo.py

AdityaChirravuri/CompetitiveProgramming

642550e8916b3f7939a1fdd52d10f5f8ae43f161

HackerRank/Python/Collections/CompanyLogo.py

AdityaChirravuri/CompetitiveProgramming

642550e8916b3f7939a1fdd52d10f5f8ae43f161

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])

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])

559efba2e8c036ea8426a51546bc4e2aa0ff4a87

py

Python

src/arrays/spiral-order-matrix.py

vighnesh153/ds-algo

79c401dad2d2e575ce1913184ca8665f2712a5b8

src/arrays/spiral-order-matrix.py

vighnesh153/ds-algo

79c401dad2d2e575ce1913184ca8665f2712a5b8

src/arrays/spiral-order-matrix.py

vighnesh153/ds-algo

79c401dad2d2e575ce1913184ca8665f2712a5b8

2020-08-09T06:37:21.000Z

2020-08-09T06:37:21.000Z

for row in solve(10): print(row)

def solve(n): result = [[0 for _ in range(n)] for __ in range(n)] counter = 1 iteration = 0 while counter <= n * n: i = j = iteration while j < n - iteration: result[i][j] = counter counter += 1 j += 1 j -= 1 i += 1 while i < n - iteration: result[i][j] = counter counter += 1 i += 1 i -= 1 j -= 1 while j >= iteration: result[i][j] = counter counter += 1 j -= 1 j += 1 i -= 1 while i > iteration: result[i][j] = counter counter += 1 i -= 1 iteration += 1 return result for row in solve(10): print(row)

521e8eaf2200d791a592c600d965d7937ff762c4

py

Python

src/database.py

vimc/montagu

c9682b3e57cf25e75b5b7688f748c8dbe882666d

src/database.py

vimc/montagu

c9682b3e57cf25e75b5b7688f748c8dbe882666d

2017-07-28T09:27:15.000Z

2021-11-01T17:16:59.000Z

src/database.py

vimc/montagu

c9682b3e57cf25e75b5b7688f748c8dbe882666d

2020-09-23T11:08:34.000Z

2020-09-23T11:08:34.000Z

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

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 user_configs(password_group): # Later, read these from a yml file? return [ UserConfig(api_db_user, 'all', VaultPassword(password_group, api_db_user)), UserConfig('import', 'all', VaultPassword(password_group, 'import')), UserConfig('orderly', 'all', VaultPassword(password_group, 'orderly')), UserConfig('readonly', 'readonly', VaultPassword(password_group, 'readonly')), ] class GeneratePassword: def get(self): return ''.join(random.SystemRandom().choice( string.ascii_uppercase + string.digits) for _ in range(50)) def __str__(self): return "Generated" class VaultPassword: def __init__(self, password_group, username): self.password_group = password_group self.username = username def get(self): if self.password_group is None: return "changeme" if self.username == "vimc" else self.username else: return get_secret(self._path(), field="password") def _path(self): if self.password_group is None: raise Exception("_path() is not defined without a password group") else: return "database/{password_group}/users/{username}".format( password_group=self.password_group, username=self.username) def __str__(self): if self.password_group is None: return "Using default password value" else: return "From vault at " + self._path() class UserConfig: def __init__(self, name, permissions, password_source, option=None): self.name = name self.permissions = permissions # Currently, this can only be 'all', but the idea is to extend this config later self.password_source = password_source self.option = option.upper() if option else "" self._password = None # Lazy password resolution @property def password(self): if self._password is None: self._password = self.password_source.get() return self._password @classmethod def create(self, name, permissions, password_group, option): password = VaultPassword(password_group, name) return UserConfig(name, permissions, password, option) def set_root_password(service, password): query = "ALTER USER {user} WITH PASSWORD '{password}'".format( user=root_user, password=password) service.db.exec_run( 'psql -U {user} -d postgres -c "{query}"'.format(user=root_user, query=query)) def connect(user, password, host="localhost", port=5432): conn_settings = { "host": host, "port": port, "name": "montagu", "user": user, "password": password } conn_string_template = "host='{host}' port='{port}' dbname='{name}' user='{user}' password='{password}'" conn_string = conn_string_template.format(**conn_settings) return psycopg2.connect(conn_string) def create_user(db, user): sql = """DO $body$ BEGIN IF NOT EXISTS (SELECT FROM pg_catalog.pg_user WHERE usename = '{name}') THEN CREATE ROLE {name} {option} LOGIN PASSWORD '{password}'; END IF; END $body$""".format(name=user.name, password=user.password, option=user.option) db.execute(sql) def set_password(db, user): db.execute( "ALTER USER {name} WITH PASSWORD '{password}'".format(name=user.name, password=user.password)) def revoke_all(db, user): def revoke_all_on(what): db.execute( "REVOKE ALL PRIVILEGES ON ALL {what} IN SCHEMA public FROM {name}".format( name=user.name, what=what)) revoke_all_on("tables") revoke_all_on("sequences") revoke_all_on("functions") def revoke_write_on_protected_tables(db, user): def revoke_specific_on(what): db.execute( "REVOKE INSERT, UPDATE, DELETE ON {what} FROM {name}".format( name=user.name, what=what)) for table in protected_tables: revoke_specific_on(table) def grant_all(db, user): def grant_all_on(what): db.execute( "GRANT ALL PRIVILEGES ON ALL {what} IN SCHEMA public TO {name}".format( name=user.name, what=what)) print(" - Granting all permissions to {name}".format(name=user.name)) grant_all_on("tables") grant_all_on("sequences") grant_all_on("functions") def grant_readonly(db, user): print(" - Granting readonly permissions to {name}".format(name=user.name)) db.execute("GRANT SELECT ON ALL TABLES IN SCHEMA public TO {name}".format( name=user.name)) def set_permissions(db, user): revoke_all(db, user) if user.permissions == 'all': grant_all(db, user) elif user.permissions == 'readonly': grant_readonly(db, user) elif user.permissions == 'pass': pass else: template = "Unhandled permission type '{permissions}' for user '{name}'" raise Exception( template.format(name=user.name, permissions=user.permissions)) def migrate_schema_core(service, root_password): network_name = service.network_name print("- migrating schema") image = "vimc/{name}:{version}".format(name="montagu-migrate", version=versions.db) pull(image) cmd = ["docker", "run", "--rm", "--network=" + network_name, image] + \ ["-user=vimc", "-password=" + root_password, "migrate"] run(cmd, check=True) def setup_user(db, user): print(" - " + user.name) create_user(db, user) set_password(db, user) set_permissions(db, user) 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() def setup(service): print("Waiting for the database to accept connections") exec_safely(service.db, ["montagu-wait.sh", "7200"], check=True) password_group = service.settings["password_group"] print("Setting up database users") print("- Scrambling root password") if password_group is not None: root_password = GeneratePassword().get() else: root_password = 'changeme' set_root_password(service, root_password) print("- Getting user configurations") users = user_configs(password_group) print("- Getting user passwords") passwords = {} for user in users: print(" - {name}: {source}".format(name=user.name, source=user.password_source)) passwords[user.name] = user.password # NOTE: As I work through this - why not set up users *after* the # schema migration? This is because the migration user needs to # exist, though in practice we don't use them so this could be # reordered later. print("- Updating database users") for_each_user(root_password, users, setup_user) print("- Migrating database schema") migrate_schema_core(service, root_password) print("- Refreshing permissions") # The migrations may have added new tables, so we should set the permissions # again, in case users need to have permissions on these new tables for_each_user(root_password, users, set_permissions) # Revoke specific permissions now that all tables have been created. for_each_user(root_password, users, revoke_write_on_protected_tables) setup_streaming_replication(root_password, service) return passwords # 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 def setup_streaming_replication(root_password, service): if service.settings['enable_db_replication']: print("Setting up streaming replication") password_group = service.settings['password_group'] barman = UserConfig.create("barman", "pass", password_group, "superuser") streaming_barman = UserConfig.create("streaming_barman", "pass", password_group, "replication") with connect(root_user, root_password) as conn: with conn.cursor() as db: create_user(db, barman) create_user(db, streaming_barman) pw_barman = VaultPassword(password_group, "barman").get() pw_stream = VaultPassword(password_group, "streaming_barman").get() cmd = ["enable-replication.sh", pw_barman, pw_stream] exec_safely(service.db, cmd, check=True) def prepare_db_for_import(service): print("Preparing databse for import") ## NOTE: this could otherwise be done by connecting using the ## connection function, but that that requires further changes to ## the connect function to allow connection to the postgres ## maintenance database. This way works for now. This also ## allows us to avoid working out what the root password will be ## because we're interating without passwords over exec. db = service.db print("- deleting and recreating database") db.exec_run(["dropdb", "-U", "vimc", "--if-exists", "montagu"]) db.exec_run(["createdb", "-U", "vimc", "montagu"]) print("- configuring users") users = user_configs(service.settings["password_group"]) for user in users: db.exec_run(["createuser", "-U", "vimc", user.name])

d81293ab153a2b3620035047792f5df592c73a94

py

Python

Chapter05/poplib/mailbox_basic_params.py

yangwawa0323/Learning-Python-Networking-Second-Edition

5460fe4fb6acc5d0df19bf36e52ac09e9a11eb8b

2018-12-17T19:33:06.000Z

2022-03-25T18:14:02.000Z

Chapter05/poplib/mailbox_basic_params.py

barretthugh/Learning-Python-Networking-Second-Edition

0f00b8b20c1c85e76754e47113dff8ca9e99d5ca

Chapter05/poplib/mailbox_basic_params.py

barretthugh/Learning-Python-Networking-Second-Edition

0f00b8b20c1c85e76754e47113dff8ca9e99d5ca

2018-12-18T09:08:43.000Z

2022-02-06T02:53:05.000Z

#!/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)

#!/usr/bin/env python3 import poplib import argparse def main(hostname,port,user,password): mailbox = poplib.POP3_SSL(hostname,port) try: mailbox.user(user) mailbox.pass_(password) response, listings, octet_count = mailbox.list() for listing in listings: number, size = listing.decode('ascii').split() print("Message %s has %s bytes" % (number, size)) except poplib.error_proto as exception: print("Login failed:", exception) finally: mailbox.quit() 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)

9b34dbdf2931cc53d5622eda4c91b6c6fb2da69b

py

Python

openob/manager.py

sreimers/openob

8b82e38c37f0a7d748c076a0dc14fc8994bb5998

2018-04-26T11:27:10.000Z

2021-06-01T03:59:41.000Z

openob/manager.py

sreimers/openob

8b82e38c37f0a7d748c076a0dc14fc8994bb5998

openob/manager.py

sreimers/openob

8b82e38c37f0a7d748c076a0dc14fc8994bb5998

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.'''

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.''' def run(self, opts): print("-- OpenOB Audio Link") print(" -- Starting Up") print(" -- Parameters: %s" % opts) # We're now entering the realm where we should desperately try and maintain a link under all circumstances forever. while True: try: # Set up redis and connect config = None while True: try: config = redis.Redis(opts.config_host) print(" -- Connected to configuration server") break except Exception, e: print(Fore.BLACK + Back.RED + " -- Couldn't connect to Redis! Ensure your configuration host is set properly, and you can connect to the default Redis port on that host from here (%s)." % e) print(" Waiting half a second and attempting to connect again." + Fore.RESET + Back.RESET) time.sleep(0.5) # So if we're a transmitter, let's set the options the receiver needs to know about link_key = "openob2:"+opts.link_name+":" if opts.mode == 'tx': if opts.encoding == 'celt' and int(opts.bitrate) > 192: print(Fore.BLACK + Back.YELLOW + " -- WARNING: Can't use bitrates higher than 192kbps for CELT, limiting" + Fore.RESET + Back.RESET) opts.bitrate = 192 # We're a transmitter! config.set(link_key+"port", opts.port) config.set(link_key+"ipv6", opts.ipv6) config.set(link_key+"jitter_buffer", opts.jitter_buffer) config.set(link_key+"encoding", opts.encoding) config.set(link_key+"bitrate", opts.bitrate) print(" -- Configured receiver with:") print(" - Base Port: %s" % config.get(link_key+"port")) print(" - Jitter Buffer: %s ms" % config.get(link_key+"jitter_buffer")) print(" - Encoding: %s" % config.get(link_key+"encoding")) print(" - Bitrate: %s kbit/s" % config.get(link_key+"bitrate")) # Okay, we can't set caps yet - we need to configure ourselves first. opus_opts = {'audio': True, 'bandwidth': -1000, 'frame-size': opts.framesize, 'complexity': opts.complexity, 'constrained-vbr': True, 'inband-fec': opts.fec, 'packet-loss-percentage': opts.loss, 'dtx': opts.dtx} try: transmitter = RTPTransmitter(audio_input=opts.audio_input, audio_device=opts.device, base_port=opts.port, ipv6=opts.ipv6, encoding=opts.encoding, bitrate=opts.bitrate, jack_name=("openob_tx_%s" % opts.link_name), receiver_address=opts.receiver_host, opus_options=opus_opts) # Set it up, get caps try: transmitter.run() config.set(link_key+"caps", transmitter.get_caps()) print(" - Caps: %s" % config.get(link_key+"caps")) transmitter.loop() except Exception, e: print(Fore.BLACK + Back.RED + " -- Lost connection or otherwise had the transmitter fail on us, restarting (%s)" % e) time.sleep(0.5) except gst.ElementNotFoundError, e: print(Fore.BLACK + Back.RED + (" -- Couldn't fulfill our gstreamer module dependencies! You don't have the following element available: %s" % e) + Fore.RESET + Back.RESET) sys.exit(1) else: # We're a receiver! # Default values. port = 3000 caps = '' jitter_buffer = 150 encoding = 'opus' bitrate = '96' while True: try: if config.get(link_key+"port") == None: print(Fore.BLACK + Back.YELLOW + " -- Unable to configure myself from the configuration host; has the transmitter been started yet, and have you got the same link name on each end?") print(" Waiting half a second and attempting to reconfigure myself." + Fore.RESET + Back.RESET) time.sleep(0.5) port = int(config.get(link_key+"port")) ipv6 = int(config.get(link_key+"ipv6")) caps = config.get(link_key+"caps") jitter_buffer = int(config.get(link_key+"jitter_buffer")) encoding = config.get(link_key+"encoding") bitrate = int(config.get(link_key+"bitrate")) print(" -- Configured from transmitter with:") print(" - Base Port: %s" % port) print(" - Jitter Buffer: %s ms" % caps) print(" - Encoding: %s" % encoding) print(" - Bitrate: %s kbit/s" % bitrate) print(" - Caps: %s" % caps) break except Exception, e: print(Fore.BLACK + Back.YELLOW + " -- Unable to configure myself from the configuration host; has the transmitter been started yet? (%s)" % e) print(" Waiting half a second and attempting to reconfigure myself." + Fore.RESET + Back.RESET) time.sleep(0.5) #raise # Okay, we can now configure ourself receiver = RTPReceiver(audio_output=opts.audio_output, audio_device=opts.device, base_port=port, ipv6=ipv6, encoding=encoding, caps=caps, bitrate=bitrate, jitter_buffer=jitter_buffer, jack_name=("openob_tx_%s" % opts.link_name) ) try: receiver.run() receiver.loop() except Exception, e: print(Fore.BLACK + Back.RED + (" -- Lost connection or otherwise had the receiver fail on us, restarting (%s)" % e) + Fore.RESET + Back.RESET) time.sleep(0.5) except Exception, e: print(Fore.BLACK + Back.RED + " -- Unhandled exception occured, please report this as a bug!" + Fore.RESET + Back.RESET) raise

ad9efb2e6f4829da3b80bcc3b918afaea610d7d7

py

Python

tests/test_packages/test_skills/test_confirmation_aw2/test_strategy.py

bryanchriswhite/agents-aea

d3f177a963eb855d9528555167255bf2b478f4ba

2019-09-07T09:32:44.000Z

2022-03-29T14:28:41.000Z

tests/test_packages/test_skills/test_confirmation_aw2/test_strategy.py

salman6049/agents-aea

d3f177a963eb855d9528555167255bf2b478f4ba

2019-08-24T10:08:07.000Z

2022-03-31T14:28:36.000Z

tests/test_packages/test_skills/test_confirmation_aw2/test_strategy.py

salman6049/agents-aea

d3f177a963eb855d9528555167255bf2b478f4ba

2019-09-03T22:13:58.000Z

2022-03-22T01:25:16.000Z

# -*- 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)

# -*- 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)

4a05ac857f23cb032431dca22e9f9dc234c173f4

py

Python

pype9/utils/mpi.py

tclose/Pype9

23f96c0885fd9df12d9d11ff800f816520e4b17a

pype9/utils/mpi.py

tclose/Pype9

23f96c0885fd9df12d9d11ff800f816520e4b17a

pype9/utils/mpi.py

tclose/Pype9

23f96c0885fd9df12d9d11ff800f816520e4b17a

2021-04-08T12:46:21.000Z

2021-04-08T12:46:21.000Z

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

class DummyMPICom(object): rank = 0 size = 1 def barrier(self): pass 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 def is_mpi_master(): return (mpi_comm.rank == MPI_ROOT)

ab0bdc4d6adc83eb5ef6b8b6083e23fb449db951

py

Python

Trajectory_Mining/Bag_of_Words/unpack_items_all_victims.py

AdamCoscia/eve-trajectory-mining

134f142a5665f66fbf92aada8dd6252fab64ddff

Trajectory_Mining/Bag_of_Words/unpack_items_all_victims.py

AdamCoscia/eve-trajectory-mining

134f142a5665f66fbf92aada8dd6252fab64ddff

Trajectory_Mining/Bag_of_Words/unpack_items_all_victims.py

AdamCoscia/eve-trajectory-mining

134f142a5665f66fbf92aada8dd6252fab64ddff

# -*- 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")

# -*- 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. """ def parse_items(items): # Use sets for faster look-up time (no order needed to preserve) # lo_flags = {11, 12, 13, 14, 15, 16, 17, 18} # mi_flags = {19, 20, 21, 22, 23, 24, 25, 26} # hi_flags = {27, 28, 29, 30, 31, 32, 33, 34} itemList=[] for item in items: itemName, groupName = 'Missing', 'Missing' try: item_type_id = item['item_type_id'] try: item_group_id = typeIDs[item_type_id]['groupID'] try: itemName = typeIDs[item_type_id]['name']['en'] try: groupName = groupIDs[item_group_id]['name']['en'] except: pass except: pass except: pass except: pass finally: itemList.append((itemName, groupName)) return itemList def parse_attackers(attackers): attacker_keys = ('final_blow', 'damage_done', 'ship_type_id') for attacker in attackers: if 'character_id' in attacker: a = (attacker['character_id'], []) for a_key in attacker_keys: if a_key in attacker: a[1].append(attacker[a_key]) else: a[1].append(np.nan) yield a 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")

6a00d837f8f2733589c99f7342b4ddb14ce14281

py

Python

iqps/report/views.py

thealphadollar/iqps

187f6b134d82e2dce951b356cb0c7151994ca3ab

iqps/report/views.py

thealphadollar/iqps

187f6b134d82e2dce951b356cb0c7151994ca3ab

iqps/report/views.py

thealphadollar/iqps

187f6b134d82e2dce951b356cb0c7151994ca3ab

from django.shortcuts import render, get_object_or_404 from django.contrib import messages from .forms import ReportForm from data.models import Paper

from django.shortcuts import render, get_object_or_404 from django.contrib import messages from .forms import ReportForm from data.models import Paper def reportPaper(request, paperId): paper = get_object_or_404(Paper, pk=paperId) form = None try: assert request.method == "POST" form = ReportForm(request.POST) assert form.is_valid() report = form.save(commit=False) report.paper = paper report.save() messages.add_message(request, messages.INFO, "Report Successful!") return render(request, "reportform.html", { "form": ReportForm(), "paper": paper }) except: if form is None: form = ReportForm() return render(request, "reportform.html", { "form": form, "paper": paper })

72598642d175089036d9345be8dbcafd77a05743

py

Python

build/lib/geonomics/sim/stats.py

AnushaPB/geonomics-1

deee0c377e81f509463eaf6f9d0b2f0809f2ddc3

build/lib/geonomics/sim/stats.py

AnushaPB/geonomics-1

deee0c377e81f509463eaf6f9d0b2f0809f2ddc3

build/lib/geonomics/sim/stats.py

AnushaPB/geonomics-1

deee0c377e81f509463eaf6f9d0b2f0809f2ddc3

#!/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

#!/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 class _StatsCollector: def __init__(self, model_name, params): #set model_name self.model_name = model_name #set total model time self.T = params.model.T #grab the stats parameters stats_params = params.model.stats #a dictionary to link the stats' names in the params dict #to the functions to be called to calculate them self.calc_fn_dict = {'Nt': _calc_Nt, 'ld': _calc_ld, 'het': _calc_het, 'maf': _calc_maf, 'mean_fit': _calc_mean_fitness, } #a dictionary to link the stats' names in the params dict #to the functions to be called to write them to disk self.write_fn_dict = {'ld': self._write_array_to_stack, 'het': self._write_row_to_csv, 'maf': self._write_row_to_csv, } #a dictionary to link stats to the file extensions that #should be used to write them to disk self.file_suffix_dict = {'Nt': 'OTHER_STATS.csv', 'ld': 'LD.txt', 'het': 'HET.csv', 'maf': 'MAF.csv', 'mean_fit': 'OTHER_STATS.csv', } #get the species names spps_with_wout_genomes = {str(k):('gen_arch' in v.keys()) for k, v in params.comm.species.items()} #list stats that cannot be calculated for species without genomes stats_invalid_wout_genomes = ['ld', 'het', 'maf', 'mean_fit'] #create a stats attribute, to store all stats calculated self.stats = {} for spp_name, genome in spps_with_wout_genomes.items(): self.stats[spp_name] = {} for stat, stat_params in stats_params.items(): #skip species without genomes for stats that need genomes if not genome and stat in stats_invalid_wout_genomes: break #each spp gets a subdict else: #each subdict gets a key for each stat to be calculated if stat_params.calc: #create a subdictionary for each stat, with a list of #NaNs self.T items long, which will be filled in for #each whenever it is sampled (NOTE: this forces all #stats to have the same length so that they all fit #into one pd.DataFrame at the end, and so that plots #easily line up on the same timeframe) self.stats[spp_name][stat]= { 'vals': [np.nan]*self.T, 'freq': stat_params.freq, #add a 'filepath' key, whose value will be updated #to contain to correct filepaths for each stat 'filepath': None, #create tuple of other, stat-specific parameters, #to later be unpacked as arguments to #the appropriate stat function 'other_params': dict([(k,v) for k,v in stat_params.items() if k not in ['calc', 'freq']]) } #if the freq value is 0, change it to self.T -1, so #that it collects only on the first and last timestep if self.stats[spp_name][stat]['freq'] == 0: self.stats[spp_name][stat]['freq'] = self.T-1 #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 def _calc_stats(self, community, t, iteration): #set the filepaths, if this is the first timestep of the model #iteration if t == 0: self._set_filepaths(iteration) #for each species for spp in community.values(): #list the stats to be calculated this timestep if t == self.T-1: #calculate all, if it's the last timestep calc_list = [*self.stats[spp.name]] else: #or else only calculate based on the parameterized frequencies #for each stat calc_list = [k for k,v in self.stats[spp.name].items() if ( t % v['freq'] == 0)] #then calculate each stat for stat in calc_list: vals = self.calc_fn_dict[stat](spp, **self.stats[spp.name][stat]['other_params']) #and add each stat to the right location (by timestep) #in its list try: self.stats[spp.name][stat]['vals'][t] = vals #unless the list isn't long enough (which happens if mod.walk #has been used to run the model past its initially stipulated #length of time), in which case make it long enough and make #the last value the stat just calculated except IndexError: stats_list = self.stats[spp.name][stat]['vals'] stats_list.extend([np.nan] * (t-len(stats_list)) + [vals]) #and write whichever stats are necessary to file self._write_stats(t) #a method to make the filenames for all of the stats to be saved def _set_filepaths(self, iteration): #get the directory name for this model and iteration dirname = os.path.join('GNX_mod-%s' % self.model_name, 'it-%i' % iteration) #for each species for spp_name in [*self.stats]: #get the subdirectory name and filename for this species subdirname = os.path.join(dirname, 'spp-%s' % spp_name) #make this subdir, and any parent dirs as necessary os.makedirs(subdirname, exist_ok = True) #create the filename and filepath for this spp, for each stat for stat in [*self.stats[spp_name]]: filename = 'mod-%s_it-%i_spp-%s_%s' % (self.model_name, iteration, spp_name, self.file_suffix_dict[stat]) filepath = os.path.join(subdirname, filename) #add the filepath for this stat to self.stats self.stats[spp_name][stat]['filepath'] = filepath #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 def _write_array_to_stack(self, filepath, array, t): _append_array2d_to_array_stack(filepath, array) #wrapper around io.append_row_to_csv def _write_row_to_csv(self, filepath, array, t): _append_row_to_csv(filepath, array, t) #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 def _write_other_stats(self): for spp, spp_stats in self.stats.items(): #get a dictionary of the data values for all stats that are to be #written just once at the end of the iteration data_dict = {k:v['vals'] for k,v in spp_stats.items() if 'OTHER_STATS' in v['filepath']} #they all have the same filepath, so just grab the first filepath = [*spp_stats.values()][0]['filepath'] #write to disk _write_dict_to_csv(filepath, data_dict) #method to write stats to files, in the appropriate directory (by model #and iteration number), and with the appropriate spp names in the filenames def _write_stats(self, t): #for each species for spp_name, spp_stats in self.stats.items(): #for each stat write_list = [k for k,v in spp_stats.items() if t % v['freq'] == 0] for stat, stat_dict in spp_stats.items(): #get the filepath filepath = stat_dict['filepath'] #if the filepath does not contain "OTHER_STATS" then it is a #stat that produces more than a single value per species per #timestep it is collected, so write the data to disk #intermittently and then delete the data from memory (if it was #collected this timestep) if "OTHER_STATS" not in filepath and stat in write_list: #get the correct write_fn for this stat write_fn = self.write_fn_dict[stat] #call the write_fn to write the data to disk write_fn(filepath, stat_dict['vals'][t], t) #then replace the last data collected prior to this #timestep's data with None, to free up memory but still #maintain the latest data in case of plotting rev_nonnull = [n for n, v in enumerate( stat_dict['vals'][::-1]) if (v is not np.nan and v is not None)] nonnull = [range(len( stat_dict['vals']))[::-1][n] for n in rev_nonnull] nonnull = [v for v in nonnull if v != t] for v in nonnull: stat_dict['vals'][v] = None #or write all 'other stats' to disk, if it's the last timestep if t == self.T-1: self._write_other_stats() #method to plot whichever stat as a function of runtime def _plot_stat(self, stat, spp_name=None): #check that the stat argument is valid assert type(stat) is str, "The 'stat' argument must be a string." assert stat in [*self.stats.values()][0].keys(), ("The 'stat' " "argument must name a statistic that was collected. Valid values: " "%s.") % (','.join(["'%s'" % val for val in [*self.stats.values()][0].keys()])) #get the list of spps to plot if spp_name is None: spp_names = [*self.stats] elif (spp_name is not None and type(spp_name) is str and spp_name in [*self.stats]): spp_names = [spp_name] else: raise ValueError(("The 'spp_name' argument, if provided, " "must be a string containing a valid species name.")) #create the figure fig = plt.figure() #plot each species for the chosen statistic for n, spp_name in enumerate(spp_names): #get the stat values to plot vals = self.stats[spp_name][stat]['vals'] #plot 'Nt' or 'mean_fit' if stat in ['Nt', 'mean_fit']: #add axes objects horizontally across ax = fig.add_subplot(1, len(spp_names), n+1) #get the indices of non-NaN values to be plotted indices_to_plot = np.array(np.where( np.invert(np.isnan(vals)))[0]) #get the timesteps at the non-NaN values x = np.arange(0, len(vals))[indices_to_plot] #get the non-NaN values y = np.array(vals)[indices_to_plot] #plot a dotted line (which necessarily linearly interpolates #between collected timesteps if not all timesteps #were collected) plt.plot(x, y, ':') #and plot dots at each of the collected timesteps plt.plot(x, y, '.') #set the title to the stat and the species' name ax.set_title("SPP: '%s'" % (spp_name)) #set the x- and y-labels plt.xlabel('timestep') plt.ylabel(stat) #or plot 'maf' or 'het' elif stat in ['het', 'maf']: #add axes objects horizontally across ax = fig.add_subplot(1, len(spp_names), n+1) #get the reversed-list index of the last set of values #calculated rev_idx_last_vals = [n for n,v in enumerate(vals[::-1]) if ( v is not None and v is not np.nan)][0] #get the last set of values calculated last_vals = vals[::-1][rev_idx_last_vals] #get the timestep of the last set of values t_last_vals = range(len(vals))[::-1][rev_idx_last_vals] #plot the values plt.plot(range(len(last_vals)), last_vals, '-') #set the title to the species' name and timestep of the #values plotted ax.set_title("SPP: '%s'; T: %i" % (spp_name, t_last_vals)) #set the x- and y-labels plt.xlabel('locus') plt.ylabel(stat) #or plot 'ld' elif stat in ['ld']: #get the reversed-list index of the last set of values #calculated rev_idx_last_vals = [n for n,v in enumerate(vals[::-1]) if ( v is not None and v is not np.nan)][0] #get the last set of values (i.e. r^2 array) calculated r2_mat = vals[::-1][rev_idx_last_vals] #get the timestep of the last set of values t_last_vals = range(len(vals))[::-1][rev_idx_last_vals] #add axes objects horizontally across, in two rows ax = fig.add_subplot(2, len(spp_names), n+1) #plot the LD matrix in row 1 plt.imshow(np.clip(r2_mat, a_min = 0, a_max = None), interpolation = 'nearest') plt.colorbar() #set plot title ax.set_title(("SPP: '%s'; T: %i\nLocus-wise " "linkage matrix") % (spp_name, t_last_vals)) #set the x- and y-labels plt.xlabel('locus') plt.ylabel('locus') ax = fig.add_subplot(2, len(spp_names), n+1+len(spp_names)) #plot of mean linkage values r2_list = [r2_mat[0,1]] L = r2_mat.shape[0] for i in range(1,L-1): r2_list.append(np.mean([r2_mat[i-1,i], r2_mat[i,i+1]])) r2_list.append(r2_mat[L-2,L-1]) plt.scatter(range(L), r2_list, c = 'red', marker = 'o', s=25) #set plot title ax.set_title("Locus-wise mean linkage values") #set the x- and y-labels plt.xlabel('locus') plt.ylabel('mean linkage') #or else return informative error message else: raise ValueError(("The value provided for the 'stat' argument " "is not a valid statistic. Valid values include: %s\n\n")%( ','.join(['%s' % k for k in [*self.calc_fn_dict]]))) #set the main title to the stat plotted fig.suptitle('STAT: %s' % stat) #show the image fig.show() ###################################### # -----------------------------------# # FUNCTIONS -------------------------# # -----------------------------------# ###################################### #method to get pop size (NOTE: not actually calculating it) def _calc_Nt(spp): Nt = spp.Nt[-1] return(Nt) def _calc_ld(spp, plot = False): #TODO: I should also include (either as an alternative within this fn, #or as separate fn) the option to calculate D' #TODO: I keep getting warnings like the following, which could just be #due to divison of small floating-point numbers, but I should figure out #exactly what's going on and be sure everything checks out. WARNING: # stats.py:117: RuntimeWarning: invalid value encountered in double_scalars speciome = spp._get_genotypes() n = np.shape(speciome)[0] #num individs x = np.shape(speciome)[2] #ploidy N = n*x L = spp.gen_arch.L assert L == np.shape(speciome)[1], ("The length of the 1st dimension " "of speciome doesn't equal spp.genomic_arch.L") r2_mat = np.zeros([L]*2) * np.nan # vals default to NaN for i in range(L): for j in range(i+1, L): #calculate freq of allele 1 at locus i f1_i = np.sum(speciome[:,i,:], axis = None)/(N) #calculate freq of allele 1 at locus j f1_j = np.sum(speciome[:,j,:], axis = None)/(N) #calculate freq of chroms with 1_1 haplotype at loci i and j f11_ij = float(np.sum(speciome[:,[i,j],:].sum(axis = 1) ==2, axis = None))/(N) D_1_1 = f11_ij - (f1_i * f1_j) r2 = (D_1_1**2)/(f1_i*(1-f1_i)*f1_j*(1-f1_j)) # add to both triangular halves, to produce a symmetric matrix r2_mat[i,j] = r2 r2_mat[j,i] = r2 return(r2_mat) #function to calculate the locus-wise (if mean == False) or mean (if #mean == True) heterozygosity of the species def _calc_het(spp, mean=False): #get pop size N = len(spp) #get the speciome speciome = spp._get_genotypes() #calculate the frequency of heterozygotes, locus-wise het = np.sum(np.mean(speciome, axis = 2) == 0.5, axis = 0)/N #get the mean heterozygosity, if mean argument is True if mean: het = mean(het) return(het) #function to calculate the locus-wise minor allele frequency of the species def _calc_maf(spp): #get two times the pop size two_N = 2*len(spp) #get the speciome speciome = spp._get_genotypes() #get the frequencies of 1-alleles for all loci freqs_1 = np.sum(np.sum(speciome, axis = 2), axis = 0)/two_N #find all loci where the 1-allele is the major allele majors = np.where(freqs_1 > 0.5) #replace the locations where 1 is the major allele with 0-allele freq maf = freqs_1[:] maf[majors] = 1 - freqs_1[majors] return(maf) #function to calculate the mean fitness of the species def _calc_mean_fitness(spp): #calculate the mean fitness, if this species has traits if spp.gen_arch.traits is not None: mean_fit = np.mean(_calc_fitness(spp)) #or else return NaN else: mean_fit = np.nan return(mean_fit)

4d14eb208318979b2b03eac311b90a75532fc290

py

Python

quantz_ground/app.py

zhangyuz/quantz_ground

a3c10aceaa9da537ff5d1fc015f198945bf9d6f0

2020-10-20T15:45:20.000Z

2020-10-20T15:45:20.000Z

quantz_ground/app.py

zhangyuz/quantz_ground

a3c10aceaa9da537ff5d1fc015f198945bf9d6f0

quantz_ground/app.py

zhangyuz/quantz_ground

a3c10aceaa9da537ff5d1fc015f198945bf9d6f0

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)

from eve import Eve from .db_domains import db_domains import os def isInDocker(): return os.environ.get('AM_I_IN_A_DOCKER_CONTAINER', False) 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'] } def exclude_fields(resource, response): excluded_fields = ['_id', '_created', '_updated', '_etag'] for doc in response['_items']: for field in excluded_fields: # Better ask forgiveness than permission try: del doc[field] except KeyError as e: pass def on_fetched_resource(resource_name, response): print('on_fetched_resource:%s' % resource_name) exclude_fields(resource_name, response) app = Eve(settings=SETTINGS) app.on_fetched_resource += on_fetched_resource @app.route('/mnt') def mnt(): return 'This is Maintanance Page' if __name__ == '__main__': app.run(host='0.0.0.0', port=80)

5304b9a306a30a7e2cf57d01ceb4e53e6ccd0bca

py

Python

databird/dtutil.py

jonas-hagen/databird

cfb358e74da62bb9d7ea0e6c7ac984671472120b

2021-11-05T00:12:00.000Z

2021-11-05T00:12:00.000Z

databird/dtutil.py

jonas-hagen/databird

cfb358e74da62bb9d7ea0e6c7ac984671472120b

databird/dtutil.py

jonas-hagen/databird

cfb358e74da62bb9d7ea0e6c7ac984671472120b

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)

import datetime as dt import calendar import time def parse_timedelta(s): valid_units = [ "weeks", "days", "hours", "seconds", "minutes", "miliseconds", "microseconds", ] try: if s == "0": return dt.timedelta() value, unit = s.split(" ") if unit[-1] != "s": unit += "s" value = float(value) delta = dt.timedelta(**{unit: value}) return delta except: raise ValueError( "Could not parse '{}'. Timedelta format is '<number> <unit> | 0', where `unit` is one of {} (tailing 's' is optional).".format( s, ", ".join(valid_units) ) ) def parse_datetime(s): try: date = dt.datetime.strptime(s, "%Y-%m-%d") except: try: date = dt.datetime.strptime(s, "%Y-%m-%d %H:%M:%S") except: raise ValueError( "Could not parse '{}'. Time format is '%Y-%m-%d' or '%Y-%m-%d %H:%M:%S'.".format( s ) ) return date 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) def iso_date(date): return date.strftime("%Y-%m-%d") def normalize_datetime(date): return dt.datetime.fromtimestamp(time.mktime(date.timetuple()))

5922ee45b768da565c33dd1950061bcdab97ffc8

py

Python

coding_interviews/leetcode/easy/remove_duplicates/remove_duplicates.py

LeandroTk/Algorithms

569ed68eba3eeff902f8078992099c28ce4d7cd6

2018-12-01T17:49:49.000Z

2021-12-22T07:02:27.000Z

coding_interviews/leetcode/easy/remove_duplicates/remove_duplicates.py

LeandroTk/Algorithms

569ed68eba3eeff902f8078992099c28ce4d7cd6

2020-01-01T16:34:29.000Z

2020-04-26T19:11:13.000Z

coding_interviews/leetcode/easy/remove_duplicates/remove_duplicates.py

LeandroTk/Algorithms

569ed68eba3eeff902f8078992099c28ce4d7cd6

2018-11-28T20:51:36.000Z

2021-11-29T04:08:25.000Z

# https://leetcode.com/problems/remove-all-adjacent-duplicates-in-string

# https://leetcode.com/problems/remove-all-adjacent-duplicates-in-string def remove_duplicates(S): if len(S) <= 1: return S start, end = 0, 1 while end < len(S): if S[start] != S[end]: start = end end = start + 1 elif S[start] == S[end] and end + 1 == len(S): S = S[0:start] elif S[start] == S[end]: S = S[0:start] + S[end+1:] start, end = 0, 1 return S def remove_duplicates(S): stack = [] for char in S: if len(stack) and stack[-1] == char: stack.pop() else: stack.append(char) return ''.join(stack)

b82ae840a377927194b91a181787f7527c4df71f

py

Python

py3wirecard/entities/acquirerdetails.py

robertons/py3wirecard

8a9b541a67ee96d75b1c864762fce7148cccb8b4

2019-09-05T20:20:44.000Z

2020-01-14T18:20:45.000Z

py3wirecard/entities/acquirerdetails.py

robertons/py3wirecard

8a9b541a67ee96d75b1c864762fce7148cccb8b4

2020-01-15T12:27:56.000Z

2020-01-16T12:26:13.000Z

py3wirecard/entities/acquirerdetails.py

robertons/py3wirecard

8a9b541a67ee96d75b1c864762fce7148cccb8b4

#-*- coding: utf-8 -*- from py3wirecard.entities.lib.wireentity import * from py3wirecard.entities.taxdocument import TaxDocument

#-*- coding: utf-8 -*- from py3wirecard.entities.lib.wireentity import * from py3wirecard.entities.taxdocument import TaxDocument class AcquirerDetails(WireEntity): @String() def authorizationNumber(self): pass @Object(type=TaxDocument) def taxDocument(self):pass

a55666e686775ea98506356ddf52aca3da3da5cf

py

Python

clpc.py

CnybTseng/LPRNet

5983ae3e3445d121c2ac31ac396287aa134545ab

clpc.py

CnybTseng/LPRNet

5983ae3e3445d121c2ac31ac396287aa134545ab

clpc.py

CnybTseng/LPRNet

5983ae3e3445d121c2ac31ac396287aa134545ab

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

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

fb16e31bf96d01e63ade275800859d1d3efc6eef

py

Python

core/migrations/0001_initial.py

atthana/restapi_q_udemy

f49df5a614ac1b88a3bea975aea9498b8e85d504

core/migrations/0001_initial.py

atthana/restapi_q_udemy

f49df5a614ac1b88a3bea975aea9498b8e85d504

core/migrations/0001_initial.py

atthana/restapi_q_udemy

f49df5a614ac1b88a3bea975aea9498b8e85d504

# Generated by Django 3.1.7 on 2021-04-05 14:35 from django.db import migrations, models import django.db.models.deletion

# Generated by Django 3.1.7 on 2021-04-05 14:35 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Customer', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=50)), ('address', models.CharField(max_length=50)), ('active', models.BooleanField(default=True)), ('doc_num', models.CharField(blank=True, max_length=12, null=True, unique=True)), ], ), migrations.CreateModel( name='DataSheet', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('description', models.CharField(max_length=50)), ('historical_data', models.TextField()), ], ), migrations.CreateModel( name='Profession', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('description', models.CharField(max_length=50)), ], ), migrations.CreateModel( name='Document', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('dtype', models.CharField(choices=[('PP', 'Passport'), ('ID', 'Identity card'), ('OT', 'Others')], max_length=2)), ('doc_number', models.CharField(max_length=50)), ('customer', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='core.customer')), ], ), migrations.AddField( model_name='customer', name='datasheet', field=models.OneToOneField(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='core.datasheet'), ), migrations.AddField( model_name='customer', name='profession', field=models.ManyToManyField(to='core.Profession'), ), ]

9f663f59a9673a49aadc92ab4dc19bf0f2475490

py

Python

virtool_workflow/workflow.py

eroberts9789/virtool-workflow

18219eec2b9b934cedd3770ac319f40305c165f2

2020-09-24T20:29:08.000Z

2022-03-17T14:50:56.000Z

virtool_workflow/workflow.py

eroberts9789/virtool-workflow

18219eec2b9b934cedd3770ac319f40305c165f2

2020-10-01T23:38:34.000Z

2022-03-31T08:26:28.000Z

virtool_workflow/workflow.py

eroberts9789/virtool-workflow

18219eec2b9b934cedd3770ac319f40305c165f2

2020-09-29T21:29:46.000Z

2021-07-27T20:34:58.000Z

"""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

"""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

daa5b4decdea31a15e43a756422b8f10fbdeb296

py

Python

test/test_pool.py

viverlxl/resource_pool

6fa226e0ac504df604362bf0ef84cb3a9f21109c

2019-10-09T10:30:23.000Z

2020-01-20T01:36:10.000Z

test/test_pool.py

viverlxl/resource_pool

6fa226e0ac504df604362bf0ef84cb3a9f21109c

test/test_pool.py

viverlxl/resource_pool

6fa226e0ac504df604362bf0ef84cb3a9f21109c

2020-07-28T22:57:50.000Z

2020-07-28T22:57:50.000Z

#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" } }

#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" } } class JJApp: def __init__(self): self.obj_pool = {} self.init_mysql_pool() def init_mysql_pool(self): debug = False for key in DATABASECONFIG: mysql_pool = ConnPool() mysql_pool.add_obj(DataBase, DATABASECONFIG[key], debug) self.obj_pool.setdefault(key, mysql_pool) def __getattr__(self, name): obj = None if name in DATABASECONFIG: pool = self.obj_pool[name] obj = pool.get_obj() if not obj: time.sleep(10) obj = pool.get_obj() return obj def release(self, name): if name in self.obj_pool: pool = self.obj_pool[name] pool.release_obj() def print_func(lock): global app sql = u""" select * from test limit 10; """ data = app.test.query(sql) if lock.acquire(): for item in data: print(item['name']) lock.release() app.release("test") time.sleep(20) def test_pool(): global app app = JJApp() lock = threading.Lock() with ThreadPoolExecutor(3) as executor: for _ in range(5): executor.submit(print_func, lock)

43335eeb60e279b37dceda2e87c76453a8540cc6

py

Python

nlppack/__init__.py

swordsbird/Jx3Price

03663665fe9c712268368e77145640d8228ae3b0

2019-12-20T12:51:33.000Z

2021-06-05T13:35:40.000Z

nlppack/__init__.py

swordsbird/Jx3Price

03663665fe9c712268368e77145640d8228ae3b0

2019-12-20T13:21:53.000Z

2022-03-08T23:06:27.000Z

nlppack/__init__.py

swordsbird/Jx3Price

03663665fe9c712268368e77145640d8228ae3b0

2020-11-13T15:29:01.000Z

2020-11-13T15:29:01.000Z

from . import parseutil

from . import parseutil

5dbd873944ee57a896246371918c17eb040e68d8

py

Python

org/apache/helix/HelixProperty.py

davzhang/helix-python-binding

11a9ecf730bce07720e0b0bcf7f0ec1cd2b25878

2015-04-08T22:51:04.000Z

2015-05-03T06:42:35.000Z