body_hash stringlengths 64 64 | body stringlengths 23 109k | docstring stringlengths 1 57k | path stringlengths 4 198 | name stringlengths 1 115 | repository_name stringlengths 7 111 | repository_stars float64 0 191k | lang stringclasses 1 value | body_without_docstring stringlengths 14 108k | unified stringlengths 45 133k |
|---|---|---|---|---|---|---|---|---|---|
625efc26145369688f95433134a44a21d49ba05797bb900fb9906efb77b71067 | def ignored_docstring():
'a => b' | a => b | tests/data/docstring.py | ignored_docstring | Austin-HTTPS/black | 1 | python | def ignored_docstring():
| def ignored_docstring():
<|docstring|>a => b<|endoftext|> |
971f13a20b64ae0ec6f32ac64a61a530b9e626f93c1f3bad302afde030e07d58 | def single_line_docstring_with_whitespace():
'This should be stripped' | This should be stripped | tests/data/docstring.py | single_line_docstring_with_whitespace | Austin-HTTPS/black | 1 | python | def single_line_docstring_with_whitespace():
| def single_line_docstring_with_whitespace():
<|docstring|>This should be stripped<|endoftext|> |
a9ff55578d76d58ad07da070d9eaaefafb44b02127aab0e388b9cb5112fb3c06 | def docstring_with_inline_tabs_and_space_indentation():
'hey\n\n tab\tseparated\tvalue\n tab at start of line and then a tab\tseparated\tvalue\n multiple tabs at the beginning\tand\tinline\n mixed tabs and spaces at beginning. next line has mixed tabs and spaces only.\n\n line ends with some tabs\n ' | hey
tab separated value
tab at start of line and then a tab separated value
multiple tabs at the beginning and inline
mixed tabs and spaces at beginning. next line has mixed tabs and spaces only.
line ends with some tabs | tests/data/docstring.py | docstring_with_inline_tabs_and_space_indentation | Austin-HTTPS/black | 1 | python | def docstring_with_inline_tabs_and_space_indentation():
'hey\n\n tab\tseparated\tvalue\n tab at start of line and then a tab\tseparated\tvalue\n multiple tabs at the beginning\tand\tinline\n mixed tabs and spaces at beginning. next line has mixed tabs and spaces only.\n\n line ends with some tabs\n ' | def docstring_with_inline_tabs_and_space_indentation():
'hey\n\n tab\tseparated\tvalue\n tab at start of line and then a tab\tseparated\tvalue\n multiple tabs at the beginning\tand\tinline\n mixed tabs and spaces at beginning. next line has mixed tabs and spaces only.\n\n line ends with some tabs\n '<|docstring|>hey
tab separated value
tab at start of line and then a tab separated value
multiple tabs at the beginning and inline
mixed tabs and spaces at beginning. next line has mixed tabs and spaces only.
line ends with some tabs<|endoftext|> |
ff5f3b77fa40c12af548d5761ef3cc49fb78615db0b9d29e01a8ec7debd75127 | def docstring_with_inline_tabs_and_tab_indentation():
'hey\n\n tab\tseparated\tvalue\n tab at start of line and then a tab\tseparated\tvalue\n multiple tabs at the beginning\tand\tinline\n mixed tabs and spaces at beginning. next line has mixed tabs and spaces only.\n\n line ends with some tabs\n '
pass | hey
tab separated value
tab at start of line and then a tab separated value
multiple tabs at the beginning and inline
mixed tabs and spaces at beginning. next line has mixed tabs and spaces only.
line ends with some tabs | tests/data/docstring.py | docstring_with_inline_tabs_and_tab_indentation | Austin-HTTPS/black | 1 | python | def docstring_with_inline_tabs_and_tab_indentation():
'hey\n\n tab\tseparated\tvalue\n tab at start of line and then a tab\tseparated\tvalue\n multiple tabs at the beginning\tand\tinline\n mixed tabs and spaces at beginning. next line has mixed tabs and spaces only.\n\n line ends with some tabs\n '
pass | def docstring_with_inline_tabs_and_tab_indentation():
'hey\n\n tab\tseparated\tvalue\n tab at start of line and then a tab\tseparated\tvalue\n multiple tabs at the beginning\tand\tinline\n mixed tabs and spaces at beginning. next line has mixed tabs and spaces only.\n\n line ends with some tabs\n '
pass<|docstring|>hey
tab separated value
tab at start of line and then a tab separated value
multiple tabs at the beginning and inline
mixed tabs and spaces at beginning. next line has mixed tabs and spaces only.
line ends with some tabs<|endoftext|> |
26d488c900d3f33747754c971b7b48b4e0d9cf4a47df197570d9b35c91fa6b2a | def method(self):
'Multiline\n method docstring\n '
pass | Multiline
method docstring | tests/data/docstring.py | method | Austin-HTTPS/black | 1 | python | def method(self):
'Multiline\n method docstring\n '
pass | def method(self):
'Multiline\n method docstring\n '
pass<|docstring|>Multiline
method docstring<|endoftext|> |
4d1340a1d69219a21513c26e843d6636253c03faf90b4431965f7f7f2e9f34d1 | def method(self):
'Multiline\n method docstring\n '
pass | Multiline
method docstring | tests/data/docstring.py | method | Austin-HTTPS/black | 1 | python | def method(self):
'Multiline\n method docstring\n '
pass | def method(self):
'Multiline\n method docstring\n '
pass<|docstring|>Multiline
method docstring<|endoftext|> |
b3bc563d90680badc78d4c8de2b996bf626714e2708a1b04a7399daa6a8ee71b | def test_smoke():
'\n lib and ffi can be imported and looks OK.\n '
from _argon2_cffi_bindings import ffi, lib
assert repr(ffi).startswith('<_cffi_backend.FFI object at')
assert repr(lib).startswith('<Lib object for')
assert (19 == lib.ARGON2_VERSION_NUMBER)
assert (42 == lib.argon2_encodedlen(1, 2, 3, 4, 5, lib.Argon2_id)) | lib and ffi can be imported and looks OK. | tests/test_smoke.py | test_smoke | hynek/argon2-cffi-bindings | 5 | python | def test_smoke():
'\n \n '
from _argon2_cffi_bindings import ffi, lib
assert repr(ffi).startswith('<_cffi_backend.FFI object at')
assert repr(lib).startswith('<Lib object for')
assert (19 == lib.ARGON2_VERSION_NUMBER)
assert (42 == lib.argon2_encodedlen(1, 2, 3, 4, 5, lib.Argon2_id)) | def test_smoke():
'\n \n '
from _argon2_cffi_bindings import ffi, lib
assert repr(ffi).startswith('<_cffi_backend.FFI object at')
assert repr(lib).startswith('<Lib object for')
assert (19 == lib.ARGON2_VERSION_NUMBER)
assert (42 == lib.argon2_encodedlen(1, 2, 3, 4, 5, lib.Argon2_id))<|docstring|>lib and ffi can be imported and looks OK.<|endoftext|> |
dd8ea8a197563697d89f98b4da6a983a4ae414fc870248b1df353bd219e80ea9 | def read_version():
'Read the version number from the VERSION file'
version_file = 'VERSION'
with zipfile.ZipFile(sys.argv[0]) as zf:
try:
with zf.open(version_file) as f:
version = f.read()
version = version.decode('ascii')
version = version.strip()
except KeyError:
version = 'UNKNOWN (this is a non github build)'
return version | Read the version number from the VERSION file | src/__main__.py | read_version | 8cylinder/boss | 0 | python | def read_version():
version_file = 'VERSION'
with zipfile.ZipFile(sys.argv[0]) as zf:
try:
with zf.open(version_file) as f:
version = f.read()
version = version.decode('ascii')
version = version.strip()
except KeyError:
version = 'UNKNOWN (this is a non github build)'
return version | def read_version():
version_file = 'VERSION'
with zipfile.ZipFile(sys.argv[0]) as zf:
try:
with zf.open(version_file) as f:
version = f.read()
version = version.decode('ascii')
version = version.strip()
except KeyError:
version = 'UNKNOWN (this is a non github build)'
return version<|docstring|>Read the version number from the VERSION file<|endoftext|> |
4e4edb3ed5f831a611c4183ef11beea15cd64c7d54f0581cb8a82ce7a713dff5 | @click.group(no_args_is_help=True, context_settings=CONTEXT_SETTINGS)
@click.version_option(version=__version__)
def boss():
"👔 Install various applications and miscellany to set up a dev server.\n\n This can be run standalone or as a Vagrant provider. When run as\n a vagrant provider its recommended that is be run unprivileged.\n This will run as the default user and the script will use sudo\n when necessary (this assumes the default user can use sudo). This\n means that any subsequent uses as the default user will be able to\n update the '$HOME/boss-installed-modules' file. Also if the\n bashrc module is installed during provisioning, then the correct\n home dir will be setup.\n\n \x08\n eg:\n config.vm.provision :shell,\n path: 'boss',\n args: 'install server.local ...'\n\n Its recommended to set up Apt-Cacher NG on the host machine. Once\n that's done adding `aptproxy` to the list of modules will configure\n this server to make use of it." | 👔 Install various applications and miscellany to set up a dev server.
This can be run standalone or as a Vagrant provider. When run as
a vagrant provider its recommended that is be run unprivileged.
This will run as the default user and the script will use sudo
when necessary (this assumes the default user can use sudo). This
means that any subsequent uses as the default user will be able to
update the '$HOME/boss-installed-modules' file. Also if the
bashrc module is installed during provisioning, then the correct
home dir will be setup.
eg:
config.vm.provision :shell,
path: 'boss',
args: 'install server.local ...'
Its recommended to set up Apt-Cacher NG on the host machine. Once
that's done adding `aptproxy` to the list of modules will configure
this server to make use of it. | src/__main__.py | boss | 8cylinder/boss | 0 | python | @click.group(no_args_is_help=True, context_settings=CONTEXT_SETTINGS)
@click.version_option(version=__version__)
def boss():
"👔 Install various applications and miscellany to set up a dev server.\n\n This can be run standalone or as a Vagrant provider. When run as\n a vagrant provider its recommended that is be run unprivileged.\n This will run as the default user and the script will use sudo\n when necessary (this assumes the default user can use sudo). This\n means that any subsequent uses as the default user will be able to\n update the '$HOME/boss-installed-modules' file. Also if the\n bashrc module is installed during provisioning, then the correct\n home dir will be setup.\n\n \x08\n eg:\n config.vm.provision :shell,\n path: 'boss',\n args: 'install server.local ...'\n\n Its recommended to set up Apt-Cacher NG on the host machine. Once\n that's done adding `aptproxy` to the list of modules will configure\n this server to make use of it." | @click.group(no_args_is_help=True, context_settings=CONTEXT_SETTINGS)
@click.version_option(version=__version__)
def boss():
"👔 Install various applications and miscellany to set up a dev server.\n\n This can be run standalone or as a Vagrant provider. When run as\n a vagrant provider its recommended that is be run unprivileged.\n This will run as the default user and the script will use sudo\n when necessary (this assumes the default user can use sudo). This\n means that any subsequent uses as the default user will be able to\n update the '$HOME/boss-installed-modules' file. Also if the\n bashrc module is installed during provisioning, then the correct\n home dir will be setup.\n\n \x08\n eg:\n config.vm.provision :shell,\n path: 'boss',\n args: 'install server.local ...'\n\n Its recommended to set up Apt-Cacher NG on the host machine. Once\n that's done adding `aptproxy` to the list of modules will configure\n this server to make use of it."<|docstring|>👔 Install various applications and miscellany to set up a dev server.
This can be run standalone or as a Vagrant provider. When run as
a vagrant provider its recommended that is be run unprivileged.
This will run as the default user and the script will use sudo
when necessary (this assumes the default user can use sudo). This
means that any subsequent uses as the default user will be able to
update the '$HOME/boss-installed-modules' file. Also if the
bashrc module is installed during provisioning, then the correct
home dir will be setup.
eg:
config.vm.provision :shell,
path: 'boss',
args: 'install server.local ...'
Its recommended to set up Apt-Cacher NG on the host machine. Once
that's done adding `aptproxy` to the list of modules will configure
this server to make use of it.<|endoftext|> |
66c4ee62da9e6c1575ce900e3b6205642d4b133b6022ff3335384b793bcc488c | @boss.command()
@click.argument('servername', type=SERVER)
@click.argument('modules', nargs=(- 1), required=True)
@click.option('-d', '--dry-run', is_flag=True, help='Only print the commands that would be used')
@click.option('-o', '--no-required', is_flag=True, help="Don't install the required modules")
@click.option('-O', '--no-dependencies', is_flag=True, help="Don't install dependent modules")
@click.option('--generate-script', is_flag=True, help='Output suitable for a bash script instead of running them')
@click.option('-n', '--new-user-and-pass', type=USER_PASS, metavar='USERNAME,USERPASS', help="a new unix user's name and password (seperated by a comma), they will be added to the www-data group")
@click.option('-S', '--sql-file', type=click.Path(exists=True, dir_okay=False), metavar='SQLFILE', help='sql file to be run during install')
@click.option('-N', '--db-name', metavar='DB-NAME', required=deps('mysql', 'lamp', 'craft3'), help='the name the schema to create')
@click.option('-P', '--db-root-pass', default='password', metavar='PASSWORD', required=deps('mysql', 'lamp', 'craft3', 'phpmyadmin'), help='password for mysql root user, required for the mysql module')
@click.option('-A', '--new-db-user-and-pass', type=USER_PASS, metavar='USERNAME,PASSWORD', help="a new db user's new username and password (seperated by a comma)")
@click.option('-u', '--new-system-user-and-pass', type=USER_PASS, metavar='USERNAME,PASSWORD', required=deps('newuser'), help="a new system user's new username and password (seperated by a comma)")
@click.option('-s', '--site-name-and-root', type=SITE_DOCROOT, metavar='SITENAME,DOCUMENTROOT[:...]', required=deps('virtualhost'), help='SITENAME, DOCUMENTROOT and CREATEDIR seperated by a comma (doc root will be put in /var/www).\n CREATEDIR is an optional y/n that indicates if to create the dir or not (default:n).\n Multiple sites can be specified by seperating them with a ":", eg: -s site1,root1,y:site2,root2')
@click.option('-c', '--craft-credentials', type=USER_EMAIL_PASS, metavar='USERNAME,EMAIL,PASSWORD', help='Craft admin credentials. If not set, only system requirements for Craft will be installed')
@click.option('-i', '--host-ip', type=IP_ADDRESS, required=deps('aptproxy'), help='Host ip to be used in aptproxy config')
@click.option('--netdata-user-pass', type=USER_PASS, metavar='USERNAME,USERPASS', help="a new user's name and password (seperated by a comma)")
def install(**args):
'Install any modules available from `boss list`'
Args = namedtuple('Args', sorted(args))
args = Args(**args)
available_mods = mods
wanted_mods = [i.lower() for i in args.modules]
required_mods = ['first', 'done']
if args.no_required:
wanted_apps = [i for i in available_mods if (i.__name__.lower() in wanted_mods)]
else:
wanted_apps = [i for i in available_mods if ((i.__name__.lower() in wanted_mods) or (i.__name__.lower() in required_mods))]
mapping_keys = [i.__name__.lower() for i in available_mods]
invalid_modules = [i for i in wanted_mods if (i not in mapping_keys)]
if invalid_modules:
util.error('module(s) "{invalid}" does not exist.\nValid modules are:\n{valid}'.format(valid=', '.join(mapping_keys), invalid=', '.join(invalid_modules)))
if (not args.no_dependencies):
install_reqs = []
for app in wanted_apps:
install_reqs += app.provides
provided = set(install_reqs)
required = set(app.requires)
if len((required - provided)):
util.error('Requirements not met for {}: {}.'.format(app.__name__.lower(), ', '.join(app.requires)))
if args.generate_script:
script_header = ('#!/usr/bin/env bash', '', '# Boss command used to generate this script', '# {}'.format(' '.join(sys.argv)), '', 'set -x')
click.echo('\n'.join(script_header))
for App in wanted_apps:
module_name = App.title
util.title(module_name, script=args.generate_script)
try:
app = App(dry_run=args.dry_run, args=args)
app.pre_install()
app.install()
app.post_install()
app.log(module_name)
except subprocess.CalledProcessError as e:
util.error(e)
except DependencyError as e:
util.error(e)
except PlatformError as e:
util.error(e)
except SecurityError as e:
util.error(e)
except FileNotFoundError as e:
util.error(e.args[0]) | Install any modules available from `boss list` | src/__main__.py | install | 8cylinder/boss | 0 | python | @boss.command()
@click.argument('servername', type=SERVER)
@click.argument('modules', nargs=(- 1), required=True)
@click.option('-d', '--dry-run', is_flag=True, help='Only print the commands that would be used')
@click.option('-o', '--no-required', is_flag=True, help="Don't install the required modules")
@click.option('-O', '--no-dependencies', is_flag=True, help="Don't install dependent modules")
@click.option('--generate-script', is_flag=True, help='Output suitable for a bash script instead of running them')
@click.option('-n', '--new-user-and-pass', type=USER_PASS, metavar='USERNAME,USERPASS', help="a new unix user's name and password (seperated by a comma), they will be added to the www-data group")
@click.option('-S', '--sql-file', type=click.Path(exists=True, dir_okay=False), metavar='SQLFILE', help='sql file to be run during install')
@click.option('-N', '--db-name', metavar='DB-NAME', required=deps('mysql', 'lamp', 'craft3'), help='the name the schema to create')
@click.option('-P', '--db-root-pass', default='password', metavar='PASSWORD', required=deps('mysql', 'lamp', 'craft3', 'phpmyadmin'), help='password for mysql root user, required for the mysql module')
@click.option('-A', '--new-db-user-and-pass', type=USER_PASS, metavar='USERNAME,PASSWORD', help="a new db user's new username and password (seperated by a comma)")
@click.option('-u', '--new-system-user-and-pass', type=USER_PASS, metavar='USERNAME,PASSWORD', required=deps('newuser'), help="a new system user's new username and password (seperated by a comma)")
@click.option('-s', '--site-name-and-root', type=SITE_DOCROOT, metavar='SITENAME,DOCUMENTROOT[:...]', required=deps('virtualhost'), help='SITENAME, DOCUMENTROOT and CREATEDIR seperated by a comma (doc root will be put in /var/www).\n CREATEDIR is an optional y/n that indicates if to create the dir or not (default:n).\n Multiple sites can be specified by seperating them with a ":", eg: -s site1,root1,y:site2,root2')
@click.option('-c', '--craft-credentials', type=USER_EMAIL_PASS, metavar='USERNAME,EMAIL,PASSWORD', help='Craft admin credentials. If not set, only system requirements for Craft will be installed')
@click.option('-i', '--host-ip', type=IP_ADDRESS, required=deps('aptproxy'), help='Host ip to be used in aptproxy config')
@click.option('--netdata-user-pass', type=USER_PASS, metavar='USERNAME,USERPASS', help="a new user's name and password (seperated by a comma)")
def install(**args):
Args = namedtuple('Args', sorted(args))
args = Args(**args)
available_mods = mods
wanted_mods = [i.lower() for i in args.modules]
required_mods = ['first', 'done']
if args.no_required:
wanted_apps = [i for i in available_mods if (i.__name__.lower() in wanted_mods)]
else:
wanted_apps = [i for i in available_mods if ((i.__name__.lower() in wanted_mods) or (i.__name__.lower() in required_mods))]
mapping_keys = [i.__name__.lower() for i in available_mods]
invalid_modules = [i for i in wanted_mods if (i not in mapping_keys)]
if invalid_modules:
util.error('module(s) "{invalid}" does not exist.\nValid modules are:\n{valid}'.format(valid=', '.join(mapping_keys), invalid=', '.join(invalid_modules)))
if (not args.no_dependencies):
install_reqs = []
for app in wanted_apps:
install_reqs += app.provides
provided = set(install_reqs)
required = set(app.requires)
if len((required - provided)):
util.error('Requirements not met for {}: {}.'.format(app.__name__.lower(), ', '.join(app.requires)))
if args.generate_script:
script_header = ('#!/usr/bin/env bash', , '# Boss command used to generate this script', '# {}'.format(' '.join(sys.argv)), , 'set -x')
click.echo('\n'.join(script_header))
for App in wanted_apps:
module_name = App.title
util.title(module_name, script=args.generate_script)
try:
app = App(dry_run=args.dry_run, args=args)
app.pre_install()
app.install()
app.post_install()
app.log(module_name)
except subprocess.CalledProcessError as e:
util.error(e)
except DependencyError as e:
util.error(e)
except PlatformError as e:
util.error(e)
except SecurityError as e:
util.error(e)
except FileNotFoundError as e:
util.error(e.args[0]) | @boss.command()
@click.argument('servername', type=SERVER)
@click.argument('modules', nargs=(- 1), required=True)
@click.option('-d', '--dry-run', is_flag=True, help='Only print the commands that would be used')
@click.option('-o', '--no-required', is_flag=True, help="Don't install the required modules")
@click.option('-O', '--no-dependencies', is_flag=True, help="Don't install dependent modules")
@click.option('--generate-script', is_flag=True, help='Output suitable for a bash script instead of running them')
@click.option('-n', '--new-user-and-pass', type=USER_PASS, metavar='USERNAME,USERPASS', help="a new unix user's name and password (seperated by a comma), they will be added to the www-data group")
@click.option('-S', '--sql-file', type=click.Path(exists=True, dir_okay=False), metavar='SQLFILE', help='sql file to be run during install')
@click.option('-N', '--db-name', metavar='DB-NAME', required=deps('mysql', 'lamp', 'craft3'), help='the name the schema to create')
@click.option('-P', '--db-root-pass', default='password', metavar='PASSWORD', required=deps('mysql', 'lamp', 'craft3', 'phpmyadmin'), help='password for mysql root user, required for the mysql module')
@click.option('-A', '--new-db-user-and-pass', type=USER_PASS, metavar='USERNAME,PASSWORD', help="a new db user's new username and password (seperated by a comma)")
@click.option('-u', '--new-system-user-and-pass', type=USER_PASS, metavar='USERNAME,PASSWORD', required=deps('newuser'), help="a new system user's new username and password (seperated by a comma)")
@click.option('-s', '--site-name-and-root', type=SITE_DOCROOT, metavar='SITENAME,DOCUMENTROOT[:...]', required=deps('virtualhost'), help='SITENAME, DOCUMENTROOT and CREATEDIR seperated by a comma (doc root will be put in /var/www).\n CREATEDIR is an optional y/n that indicates if to create the dir or not (default:n).\n Multiple sites can be specified by seperating them with a ":", eg: -s site1,root1,y:site2,root2')
@click.option('-c', '--craft-credentials', type=USER_EMAIL_PASS, metavar='USERNAME,EMAIL,PASSWORD', help='Craft admin credentials. If not set, only system requirements for Craft will be installed')
@click.option('-i', '--host-ip', type=IP_ADDRESS, required=deps('aptproxy'), help='Host ip to be used in aptproxy config')
@click.option('--netdata-user-pass', type=USER_PASS, metavar='USERNAME,USERPASS', help="a new user's name and password (seperated by a comma)")
def install(**args):
Args = namedtuple('Args', sorted(args))
args = Args(**args)
available_mods = mods
wanted_mods = [i.lower() for i in args.modules]
required_mods = ['first', 'done']
if args.no_required:
wanted_apps = [i for i in available_mods if (i.__name__.lower() in wanted_mods)]
else:
wanted_apps = [i for i in available_mods if ((i.__name__.lower() in wanted_mods) or (i.__name__.lower() in required_mods))]
mapping_keys = [i.__name__.lower() for i in available_mods]
invalid_modules = [i for i in wanted_mods if (i not in mapping_keys)]
if invalid_modules:
util.error('module(s) "{invalid}" does not exist.\nValid modules are:\n{valid}'.format(valid=', '.join(mapping_keys), invalid=', '.join(invalid_modules)))
if (not args.no_dependencies):
install_reqs = []
for app in wanted_apps:
install_reqs += app.provides
provided = set(install_reqs)
required = set(app.requires)
if len((required - provided)):
util.error('Requirements not met for {}: {}.'.format(app.__name__.lower(), ', '.join(app.requires)))
if args.generate_script:
script_header = ('#!/usr/bin/env bash', , '# Boss command used to generate this script', '# {}'.format(' '.join(sys.argv)), , 'set -x')
click.echo('\n'.join(script_header))
for App in wanted_apps:
module_name = App.title
util.title(module_name, script=args.generate_script)
try:
app = App(dry_run=args.dry_run, args=args)
app.pre_install()
app.install()
app.post_install()
app.log(module_name)
except subprocess.CalledProcessError as e:
util.error(e)
except DependencyError as e:
util.error(e)
except PlatformError as e:
util.error(e)
except SecurityError as e:
util.error(e)
except FileNotFoundError as e:
util.error(e.args[0])<|docstring|>Install any modules available from `boss list`<|endoftext|> |
7f972414c61111ec94f4d6833f58a6e9e1e27ea67fbe88f93c128c39590f419d | @boss.command()
def list():
'List available modules'
installed_file = os.path.expanduser('~/boss-installed-modules')
installed = []
if os.path.exists(installed_file):
with open(installed_file) as f:
installed = f.readlines()
installed = [i.lower().strip() for i in installed]
for mod in mods:
name = mod.__name__
module = mod
state = (' ✓ ' if (name in installed) else ' ')
state = click.style(state, fg='green')
description = (module.__doc__ if module.__doc__ else '')
if description:
description = description.splitlines()[0]
click.echo(((state + click.style(name.ljust(13), bold=True)) + description))
sys.stdout.flush() | List available modules | src/__main__.py | list | 8cylinder/boss | 0 | python | @boss.command()
def list():
installed_file = os.path.expanduser('~/boss-installed-modules')
installed = []
if os.path.exists(installed_file):
with open(installed_file) as f:
installed = f.readlines()
installed = [i.lower().strip() for i in installed]
for mod in mods:
name = mod.__name__
module = mod
state = (' ✓ ' if (name in installed) else ' ')
state = click.style(state, fg='green')
description = (module.__doc__ if module.__doc__ else )
if description:
description = description.splitlines()[0]
click.echo(((state + click.style(name.ljust(13), bold=True)) + description))
sys.stdout.flush() | @boss.command()
def list():
installed_file = os.path.expanduser('~/boss-installed-modules')
installed = []
if os.path.exists(installed_file):
with open(installed_file) as f:
installed = f.readlines()
installed = [i.lower().strip() for i in installed]
for mod in mods:
name = mod.__name__
module = mod
state = (' ✓ ' if (name in installed) else ' ')
state = click.style(state, fg='green')
description = (module.__doc__ if module.__doc__ else )
if description:
description = description.splitlines()[0]
click.echo(((state + click.style(name.ljust(13), bold=True)) + description))
sys.stdout.flush()<|docstring|>List available modules<|endoftext|> |
582a7a02c6c59bd1ce7038baa4fd8a89fde3198265fe251a0379d22905f83c47 | @boss.command()
def help():
'Show help for each module'
content = []
w = textwrap.TextWrapper(initial_indent='', subsequent_indent=' ', break_on_hyphens=False)
for app in mods:
content.append('')
title = '{} ({})'.format(app.title, app.__name__.lower())
under = ('-' * len(title))
content.append(click.style(title, fg='yellow', bold=False, underline=True))
if app.__doc__:
lines = app.__doc__.splitlines()
lines = [i.strip() for i in lines]
content.append('\n'.join(lines).strip())
else:
content.append(click.style('(No documentation)', dim=True))
if app.requires:
content.append('')
cont_title = click.style('Required modules:', fg='blue')
content.append('{} {}'.format(cont_title, ', '.join(app.requires)))
content.append('\n')
click.echo_via_pager('\n'.join(content)) | Show help for each module | src/__main__.py | help | 8cylinder/boss | 0 | python | @boss.command()
def help():
content = []
w = textwrap.TextWrapper(initial_indent=, subsequent_indent=' ', break_on_hyphens=False)
for app in mods:
content.append()
title = '{} ({})'.format(app.title, app.__name__.lower())
under = ('-' * len(title))
content.append(click.style(title, fg='yellow', bold=False, underline=True))
if app.__doc__:
lines = app.__doc__.splitlines()
lines = [i.strip() for i in lines]
content.append('\n'.join(lines).strip())
else:
content.append(click.style('(No documentation)', dim=True))
if app.requires:
content.append()
cont_title = click.style('Required modules:', fg='blue')
content.append('{} {}'.format(cont_title, ', '.join(app.requires)))
content.append('\n')
click.echo_via_pager('\n'.join(content)) | @boss.command()
def help():
content = []
w = textwrap.TextWrapper(initial_indent=, subsequent_indent=' ', break_on_hyphens=False)
for app in mods:
content.append()
title = '{} ({})'.format(app.title, app.__name__.lower())
under = ('-' * len(title))
content.append(click.style(title, fg='yellow', bold=False, underline=True))
if app.__doc__:
lines = app.__doc__.splitlines()
lines = [i.strip() for i in lines]
content.append('\n'.join(lines).strip())
else:
content.append(click.style('(No documentation)', dim=True))
if app.requires:
content.append()
cont_title = click.style('Required modules:', fg='blue')
content.append('{} {}'.format(cont_title, ', '.join(app.requires)))
content.append('\n')
click.echo_via_pager('\n'.join(content))<|docstring|>Show help for each module<|endoftext|> |
387a778fc62868d33067ab83a3df7fcfcf0ea13fc15ffa4edcf6d849da63853d | def __init__(self, df=None, temps=None, key_pressures=None, key_uptakes=None, model=None, compname=None, temp_units='C'):
"\n :param df: pd.DataFrame or list[pd.DataFrame]\n Pure-component isotherm data as a pandas dataframe - must be uptake in mmol/g and pressure in bar or\n equivalent. If datasets at different temperatures are required for fitting, the user must specify\n them in the same dataframe. A list of dataframes may be passed for the dual-site Langmuir isotherm\n model procedure where parameter results across different components are utilised. Must be inputted\n in the same order as compname (when passing as a list).\n\n :param temps: list[float]\n List of temperatures corresponding to each dataset within the dataframe for results formatting and\n for calculating heats of adsorption/ binding energies. Must be inputted in the same order as\n key_pressures and key_uptakes.\n\n :param key_pressures: list[str]\n List of unique column key(s) which correspond to each dataset's pressure values within the\n dataframe. Can input any number of keys corresponding to any number of datasets in the dataframe.\n If multiple dataframes are specified, make sure keys are identical across each dataframe for each\n temperature. Must be inputted in the same order as key_uptakes and temps.\n\n :param key_uptakes: list[str]\n List of unique column key(s) which correspond to each dataset's uptake values within the\n dataframe. Can input any number of keys corresponding to any number of datasets in the dataframe.\n If multiple dataframes are specified, make sure keys are identical across each dataframe for each\n temperature. Must be inputted in the same order as key_pressures and temps.\n\n :param model: str\n Model to be fit to dataset(s).\n\n :param compname: str or list[str], optional\n Name of pure component(s) for results formatting. If None is passed, self.compname is instantiated\n as anarbitrary letter or a list of arbitrary letters corresponding to each component. Must be\n inputted in the same order as compname (when passing as a list).\n\n :param temp_units: str, Optional\n Units of temperature input (temps). Default is degrees C. Can accept Kelvin, 'K'.\n\n "
if (df is None):
raise ParameterError('Input Pandas Dataframe with pure-component isotherm data for fitting')
if (temps is None):
raise ParameterError('Input temperature corresponding to each pure-component isotherm dataset within the dataframe for fitting')
if (key_pressures is None):
raise ParameterError("Input list of unique column key(s) which correspond to each dataset's pressure values within the Dataframe")
if (key_uptakes is None):
raise ParameterError("Input list of unique column key(s) which correspond to each dataset's uptake values within the Dataframe")
if (model.lower() is None):
raise ParameterError('Enter a model as a parameter')
if (model.lower() not in _MODELS):
raise ParameterError(('Enter a valid model - List of supported models:\n ' + str(_MODELS)))
len_check = [len(key_uptakes), len(key_pressures), len(temps)]
if (len(temps) != (sum(len_check) / len(len_check))):
raise ParameterError('Lengths of key_uptakes, key_pressures or temps do not match. Check that the length of each list is the same, corresponding to each dataset')
if ((type(df) is list) and (model.lower() != 'dsl')):
raise ParameterError('Enter one dataframe, not a list of dataframes')
if ((compname is None) and (type(compname) is not list)):
self.compname = 'A'
logger.info('No component name passed - giving component an arbitrary name.')
elif ((compname is None) and (type(df) is list)):
letters = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K']
self.compname = [letters[i] for i in range(len(compname))]
logger.info('No component names passed - giving components arbitrary names.')
del letters
logger.info('Checks successfully passed')
self.df = df
self.temps = temps
self.temp_units = temp_units
if (self.temp_units == 'K'):
self.temps = temps
else:
self.temps = [(t + 273) for t in temps]
self.compname = compname
self.key_pressures = key_pressures
self.key_uptakes = key_uptakes
self.model = model.lower()
self.input_model = model
self.x = []
self.y = []
self.params = []
self.df_result = None
self.emod_input = {}
self.henry_params = []
self.rel_pres = False | :param df: pd.DataFrame or list[pd.DataFrame]
Pure-component isotherm data as a pandas dataframe - must be uptake in mmol/g and pressure in bar or
equivalent. If datasets at different temperatures are required for fitting, the user must specify
them in the same dataframe. A list of dataframes may be passed for the dual-site Langmuir isotherm
model procedure where parameter results across different components are utilised. Must be inputted
in the same order as compname (when passing as a list).
:param temps: list[float]
List of temperatures corresponding to each dataset within the dataframe for results formatting and
for calculating heats of adsorption/ binding energies. Must be inputted in the same order as
key_pressures and key_uptakes.
:param key_pressures: list[str]
List of unique column key(s) which correspond to each dataset's pressure values within the
dataframe. Can input any number of keys corresponding to any number of datasets in the dataframe.
If multiple dataframes are specified, make sure keys are identical across each dataframe for each
temperature. Must be inputted in the same order as key_uptakes and temps.
:param key_uptakes: list[str]
List of unique column key(s) which correspond to each dataset's uptake values within the
dataframe. Can input any number of keys corresponding to any number of datasets in the dataframe.
If multiple dataframes are specified, make sure keys are identical across each dataframe for each
temperature. Must be inputted in the same order as key_pressures and temps.
:param model: str
Model to be fit to dataset(s).
:param compname: str or list[str], optional
Name of pure component(s) for results formatting. If None is passed, self.compname is instantiated
as anarbitrary letter or a list of arbitrary letters corresponding to each component. Must be
inputted in the same order as compname (when passing as a list).
:param temp_units: str, Optional
Units of temperature input (temps). Default is degrees C. Can accept Kelvin, 'K'. | src/pyIsoFit/core/fitting.py | __init__ | dominikpantak/pyIsoFit | 5 | python | def __init__(self, df=None, temps=None, key_pressures=None, key_uptakes=None, model=None, compname=None, temp_units='C'):
"\n :param df: pd.DataFrame or list[pd.DataFrame]\n Pure-component isotherm data as a pandas dataframe - must be uptake in mmol/g and pressure in bar or\n equivalent. If datasets at different temperatures are required for fitting, the user must specify\n them in the same dataframe. A list of dataframes may be passed for the dual-site Langmuir isotherm\n model procedure where parameter results across different components are utilised. Must be inputted\n in the same order as compname (when passing as a list).\n\n :param temps: list[float]\n List of temperatures corresponding to each dataset within the dataframe for results formatting and\n for calculating heats of adsorption/ binding energies. Must be inputted in the same order as\n key_pressures and key_uptakes.\n\n :param key_pressures: list[str]\n List of unique column key(s) which correspond to each dataset's pressure values within the\n dataframe. Can input any number of keys corresponding to any number of datasets in the dataframe.\n If multiple dataframes are specified, make sure keys are identical across each dataframe for each\n temperature. Must be inputted in the same order as key_uptakes and temps.\n\n :param key_uptakes: list[str]\n List of unique column key(s) which correspond to each dataset's uptake values within the\n dataframe. Can input any number of keys corresponding to any number of datasets in the dataframe.\n If multiple dataframes are specified, make sure keys are identical across each dataframe for each\n temperature. Must be inputted in the same order as key_pressures and temps.\n\n :param model: str\n Model to be fit to dataset(s).\n\n :param compname: str or list[str], optional\n Name of pure component(s) for results formatting. If None is passed, self.compname is instantiated\n as anarbitrary letter or a list of arbitrary letters corresponding to each component. Must be\n inputted in the same order as compname (when passing as a list).\n\n :param temp_units: str, Optional\n Units of temperature input (temps). Default is degrees C. Can accept Kelvin, 'K'.\n\n "
if (df is None):
raise ParameterError('Input Pandas Dataframe with pure-component isotherm data for fitting')
if (temps is None):
raise ParameterError('Input temperature corresponding to each pure-component isotherm dataset within the dataframe for fitting')
if (key_pressures is None):
raise ParameterError("Input list of unique column key(s) which correspond to each dataset's pressure values within the Dataframe")
if (key_uptakes is None):
raise ParameterError("Input list of unique column key(s) which correspond to each dataset's uptake values within the Dataframe")
if (model.lower() is None):
raise ParameterError('Enter a model as a parameter')
if (model.lower() not in _MODELS):
raise ParameterError(('Enter a valid model - List of supported models:\n ' + str(_MODELS)))
len_check = [len(key_uptakes), len(key_pressures), len(temps)]
if (len(temps) != (sum(len_check) / len(len_check))):
raise ParameterError('Lengths of key_uptakes, key_pressures or temps do not match. Check that the length of each list is the same, corresponding to each dataset')
if ((type(df) is list) and (model.lower() != 'dsl')):
raise ParameterError('Enter one dataframe, not a list of dataframes')
if ((compname is None) and (type(compname) is not list)):
self.compname = 'A'
logger.info('No component name passed - giving component an arbitrary name.')
elif ((compname is None) and (type(df) is list)):
letters = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K']
self.compname = [letters[i] for i in range(len(compname))]
logger.info('No component names passed - giving components arbitrary names.')
del letters
logger.info('Checks successfully passed')
self.df = df
self.temps = temps
self.temp_units = temp_units
if (self.temp_units == 'K'):
self.temps = temps
else:
self.temps = [(t + 273) for t in temps]
self.compname = compname
self.key_pressures = key_pressures
self.key_uptakes = key_uptakes
self.model = model.lower()
self.input_model = model
self.x = []
self.y = []
self.params = []
self.df_result = None
self.emod_input = {}
self.henry_params = []
self.rel_pres = False | def __init__(self, df=None, temps=None, key_pressures=None, key_uptakes=None, model=None, compname=None, temp_units='C'):
"\n :param df: pd.DataFrame or list[pd.DataFrame]\n Pure-component isotherm data as a pandas dataframe - must be uptake in mmol/g and pressure in bar or\n equivalent. If datasets at different temperatures are required for fitting, the user must specify\n them in the same dataframe. A list of dataframes may be passed for the dual-site Langmuir isotherm\n model procedure where parameter results across different components are utilised. Must be inputted\n in the same order as compname (when passing as a list).\n\n :param temps: list[float]\n List of temperatures corresponding to each dataset within the dataframe for results formatting and\n for calculating heats of adsorption/ binding energies. Must be inputted in the same order as\n key_pressures and key_uptakes.\n\n :param key_pressures: list[str]\n List of unique column key(s) which correspond to each dataset's pressure values within the\n dataframe. Can input any number of keys corresponding to any number of datasets in the dataframe.\n If multiple dataframes are specified, make sure keys are identical across each dataframe for each\n temperature. Must be inputted in the same order as key_uptakes and temps.\n\n :param key_uptakes: list[str]\n List of unique column key(s) which correspond to each dataset's uptake values within the\n dataframe. Can input any number of keys corresponding to any number of datasets in the dataframe.\n If multiple dataframes are specified, make sure keys are identical across each dataframe for each\n temperature. Must be inputted in the same order as key_pressures and temps.\n\n :param model: str\n Model to be fit to dataset(s).\n\n :param compname: str or list[str], optional\n Name of pure component(s) for results formatting. If None is passed, self.compname is instantiated\n as anarbitrary letter or a list of arbitrary letters corresponding to each component. Must be\n inputted in the same order as compname (when passing as a list).\n\n :param temp_units: str, Optional\n Units of temperature input (temps). Default is degrees C. Can accept Kelvin, 'K'.\n\n "
if (df is None):
raise ParameterError('Input Pandas Dataframe with pure-component isotherm data for fitting')
if (temps is None):
raise ParameterError('Input temperature corresponding to each pure-component isotherm dataset within the dataframe for fitting')
if (key_pressures is None):
raise ParameterError("Input list of unique column key(s) which correspond to each dataset's pressure values within the Dataframe")
if (key_uptakes is None):
raise ParameterError("Input list of unique column key(s) which correspond to each dataset's uptake values within the Dataframe")
if (model.lower() is None):
raise ParameterError('Enter a model as a parameter')
if (model.lower() not in _MODELS):
raise ParameterError(('Enter a valid model - List of supported models:\n ' + str(_MODELS)))
len_check = [len(key_uptakes), len(key_pressures), len(temps)]
if (len(temps) != (sum(len_check) / len(len_check))):
raise ParameterError('Lengths of key_uptakes, key_pressures or temps do not match. Check that the length of each list is the same, corresponding to each dataset')
if ((type(df) is list) and (model.lower() != 'dsl')):
raise ParameterError('Enter one dataframe, not a list of dataframes')
if ((compname is None) and (type(compname) is not list)):
self.compname = 'A'
logger.info('No component name passed - giving component an arbitrary name.')
elif ((compname is None) and (type(df) is list)):
letters = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K']
self.compname = [letters[i] for i in range(len(compname))]
logger.info('No component names passed - giving components arbitrary names.')
del letters
logger.info('Checks successfully passed')
self.df = df
self.temps = temps
self.temp_units = temp_units
if (self.temp_units == 'K'):
self.temps = temps
else:
self.temps = [(t + 273) for t in temps]
self.compname = compname
self.key_pressures = key_pressures
self.key_uptakes = key_uptakes
self.model = model.lower()
self.input_model = model
self.x = []
self.y = []
self.params = []
self.df_result = None
self.emod_input = {}
self.henry_params = []
self.rel_pres = False<|docstring|>:param df: pd.DataFrame or list[pd.DataFrame]
Pure-component isotherm data as a pandas dataframe - must be uptake in mmol/g and pressure in bar or
equivalent. If datasets at different temperatures are required for fitting, the user must specify
them in the same dataframe. A list of dataframes may be passed for the dual-site Langmuir isotherm
model procedure where parameter results across different components are utilised. Must be inputted
in the same order as compname (when passing as a list).
:param temps: list[float]
List of temperatures corresponding to each dataset within the dataframe for results formatting and
for calculating heats of adsorption/ binding energies. Must be inputted in the same order as
key_pressures and key_uptakes.
:param key_pressures: list[str]
List of unique column key(s) which correspond to each dataset's pressure values within the
dataframe. Can input any number of keys corresponding to any number of datasets in the dataframe.
If multiple dataframes are specified, make sure keys are identical across each dataframe for each
temperature. Must be inputted in the same order as key_uptakes and temps.
:param key_uptakes: list[str]
List of unique column key(s) which correspond to each dataset's uptake values within the
dataframe. Can input any number of keys corresponding to any number of datasets in the dataframe.
If multiple dataframes are specified, make sure keys are identical across each dataframe for each
temperature. Must be inputted in the same order as key_pressures and temps.
:param model: str
Model to be fit to dataset(s).
:param compname: str or list[str], optional
Name of pure component(s) for results formatting. If None is passed, self.compname is instantiated
as anarbitrary letter or a list of arbitrary letters corresponding to each component. Must be
inputted in the same order as compname (when passing as a list).
:param temp_units: str, Optional
Units of temperature input (temps). Default is degrees C. Can accept Kelvin, 'K'.<|endoftext|> |
6ac45837557b29984dbc3de538d33078a0cbcd88790804e2d858102a9776e56d | def info_params(self):
'\n Prints information about the model to be fit\n (WIP)\n\n '
print(f'Parameters for the {self.model} model:')
print(_MODEL_PARAM_LISTS[self.model]) | Prints information about the model to be fit
(WIP) | src/pyIsoFit/core/fitting.py | info_params | dominikpantak/pyIsoFit | 5 | python | def info_params(self):
'\n Prints information about the model to be fit\n (WIP)\n\n '
print(f'Parameters for the {self.model} model:')
print(_MODEL_PARAM_LISTS[self.model]) | def info_params(self):
'\n Prints information about the model to be fit\n (WIP)\n\n '
print(f'Parameters for the {self.model} model:')
print(_MODEL_PARAM_LISTS[self.model])<|docstring|>Prints information about the model to be fit
(WIP)<|endoftext|> |
b000c9ce902803fa41ba779ae2cad377b90181a50dd0c7d0bff39caaa1b7571b | def fit(self, cond=False, meth='leastsq', show_hen=False, hen_tol=0.999, rel_pres=False, henry_off=False, guess=None, cust_bounds=None, fit_report=False, weights=None, dsl_comp_a=None):
"\n Plotting method for the FitIsotherm class.\n Fits model to data using Non-Linear Least-Squares Minimization.\n This method is a generic fitting method for all models included in this package using the lmfit\n Parameters and Models class.\n\n Parameters\n ----------\n\n :param cond : bool\n Input whether to add standardised fitting constraints to fitting procedure. These are different\n for each fitting. Currently only works for Langmuir, Langmuir td, DSL, BDDT. Default is False\n\n :param meth : str\n Input the fitting algorithm which lmfit uses to fit curves. Default is 'leastsq' however lmfit includes\n many fitting algorithms which can be inputted (https://lmfit.github.io/lmfit-py/fitting.html).\n\n :param show_hen : bool\n Input whether to show the henry regime of the datasets approximated by the package. This is False by\n default.\n\n :param hen_tol : float or list[float]\n The henry region approximation function calculates the henry region by finding a line with the highest\n R squared value in the low pressure region of the dataset. This is done with a default R squared\n tolerance value (set to 0.999).\n\n For example, if a float is inputted (a different henry tolerance) this will be the henry tolerance value\n used by the function. i.e if 0.98 is inputted the henry regime will be across a large pressure range\n due to the low tolerance for the R squared value of the henry model fitting.\n\n This function also supports inputting the henry regimes manually. For this, input each henry regime for\n each dataset as a list i.e [1.2, 2.1, ... ]\n\n :param rel_pres : bool\n Input whether to fit the x axis data to relative pressure instead of absolute. Default is False\n\n :param henry_off : bool\n Input whether to turn off the henry regime fitting constraint when using the standardised fitting\n constraint to langmuir or dsl - this is usually done when fitting experimental data which has a messy\n low pressure region. Default is False.\n\n :param guess : dict\n Input custom guess values to override the default guess values. This must be inputted as a dictionary\n with the keys corresponding to the parameter string and the value corresponding to the list of guess\n values corresponding to each dataset.\n i.e for Langmuir: guess = {'q': [5, 5, 6], 'b':[100, 1000, 2000]}\n\n :param cust_bounds : dict\n Input custom bounds for the fitting. These are hard constraints and lmfit will fit only within these\n minimum and maximum values. Input these as a dictionary with the keys corresponding to the parameter\n string and the value corresponding to the list of tuples which include bounds for each dataset in the\n format (min, max).\n i.e for Langmuir: cust_bounds = {'q': [(4,6), (4, None), (5,10)], ... ect.}\n\n :param fit_report : bool\n Display a fitting report generated by lmfit for each dataset. Default is False\n\n :param weights : list[list[float]]\n Weights for fitting\n\n :param dsl_comp_a : str\n Manually input which component is the most adsorbed component (compA) for the dsl constrained\n fitting procedure.\n\n :return Returns a dictionary of fitting results\n\n\n Note:\n ---------\n Because the dsl constrained fitting procedure fits a list of dataframes, the generic fitting method is not\n used when 'dsl' is inputted with the fitting condition as true and the method returns the result from the\n dsl_fit function. This is because the dsl_fit function carries out its' own initial guess calculations and\n henry regime estimations. The user may interact with this model in the same way as with the rest, however guess\n must be inputted as a list of dictionaries (just as with the list of DataFrames and component names).\n Custom bounds cannot yet be inputted into this model as this is a WIP.\n "
if ((self.model == 'dsl') and (cond is True)):
logger.info('DSL Fitting procedure commenced')
if (type(self.df) is not list):
self.df = [self.df]
if (type(self.compname) is not list):
self.compname = [self.compname]
if ((type(guess) is not list) and (guess is not None)):
guess = [guess]
if (type(hen_tol) is not list):
hen_tol = [hen_tol for _ in self.compname]
try:
dsl_result = dsl_fit(self.df, self.key_pressures, self.key_uptakes, self.temps, self.compname, meth, guess, hen_tol, show_hen, henry_off, dsl_comp_a)
except ValueError:
logger.critical(_dsl_error_msg)
return None
(df_dict, results_dict, df_res_dict, params_dict) = dsl_result
self.params = results_dict
for comp in self.compname:
(x_i, y_i) = df_dict[comp]
self.x.append(x_i)
self.y.append(y_i)
self.df_result = df_res_dict
self.emod_input = params_dict
return df_res_dict
logger.info('Generic fitting procedure commenced')
if ((self.model not in _does_something) and (cond is not False)):
logger.warning(f'''WARNING You have set cond={cond} but cond for the model '{self.model}' does nothing.
''')
if (self.model == 'henry'):
show_hen = True
henry_params = henry_approx(self.df, self.key_pressures, self.key_uptakes, show_hen, hen_tol, self.compname, henry_off)
henry_constants = henry_params[0]
if (self.model == 'henry'):
logger.info('Henry model fitting only chosen')
self.henry_params = henry_params
self.df_result = henry_params[1]
return None
if (guess is None):
guess = get_guess_params(self.model, self.df, self.key_uptakes, self.key_pressures)
logger.info('Guess values successfully obtained')
else:
for (param, guess_val) in guess.items():
if (param not in _MODEL_PARAM_LISTS[self.model]):
raise ParameterError(('%s is not a valid parameter in the %s model.' % (param, self.model)))
guess[param] = guess_val
logger.info('Guess values overridden with custom guess values')
if ('mdr' in self.model):
logger.info('MDR chosen so relative pressure toggle force set')
rel_pres = True
self.rel_pres = rel_pres
(self.x, self.y) = get_xy(self.df, self.key_pressures, self.key_uptakes, self.model, rel_pres)
logger.info('x and y parameters successfully obtained')
if (weights is None):
logger.info('No weights inputted - setting weights to x')
weights = self.x
if ((self.model == 'bddt 2n') or (self.model == 'bddt 2n-1') or (self.model == 'bddt')):
self.model = 'bddt'
(self.params, values_dict) = generic_fit(self.model, weights, self.y, guess, self.temps, cond, meth, cust_bounds, fit_report, henry_constants, henry_off)
logger.info('Generic fit completed successfully')
(final_results_dict, c_list) = get_sorted_results(values_dict, self.model, self.temps)
logger.info('Results sorted successfully')
se = [mse(self.x[i], self.y[i], _MODEL_FUNCTIONS[self.model], c_list[i]) for i in range(len(self.x))]
logger.info('Mean squared error calculated successfully')
final_results_dict['MSE'] = se
df_result = pd.DataFrame.from_dict(final_results_dict)
pd.set_option('display.max_columns', None)
print(f'''
---- Component {self.compname} fitting results -----''')
display(df_result)
self.df_result = df_result
if (len(self.temps) >= 3):
heat_calc(self.model, self.temps, final_results_dict, self.x) | Plotting method for the FitIsotherm class.
Fits model to data using Non-Linear Least-Squares Minimization.
This method is a generic fitting method for all models included in this package using the lmfit
Parameters and Models class.
Parameters
----------
:param cond : bool
Input whether to add standardised fitting constraints to fitting procedure. These are different
for each fitting. Currently only works for Langmuir, Langmuir td, DSL, BDDT. Default is False
:param meth : str
Input the fitting algorithm which lmfit uses to fit curves. Default is 'leastsq' however lmfit includes
many fitting algorithms which can be inputted (https://lmfit.github.io/lmfit-py/fitting.html).
:param show_hen : bool
Input whether to show the henry regime of the datasets approximated by the package. This is False by
default.
:param hen_tol : float or list[float]
The henry region approximation function calculates the henry region by finding a line with the highest
R squared value in the low pressure region of the dataset. This is done with a default R squared
tolerance value (set to 0.999).
For example, if a float is inputted (a different henry tolerance) this will be the henry tolerance value
used by the function. i.e if 0.98 is inputted the henry regime will be across a large pressure range
due to the low tolerance for the R squared value of the henry model fitting.
This function also supports inputting the henry regimes manually. For this, input each henry regime for
each dataset as a list i.e [1.2, 2.1, ... ]
:param rel_pres : bool
Input whether to fit the x axis data to relative pressure instead of absolute. Default is False
:param henry_off : bool
Input whether to turn off the henry regime fitting constraint when using the standardised fitting
constraint to langmuir or dsl - this is usually done when fitting experimental data which has a messy
low pressure region. Default is False.
:param guess : dict
Input custom guess values to override the default guess values. This must be inputted as a dictionary
with the keys corresponding to the parameter string and the value corresponding to the list of guess
values corresponding to each dataset.
i.e for Langmuir: guess = {'q': [5, 5, 6], 'b':[100, 1000, 2000]}
:param cust_bounds : dict
Input custom bounds for the fitting. These are hard constraints and lmfit will fit only within these
minimum and maximum values. Input these as a dictionary with the keys corresponding to the parameter
string and the value corresponding to the list of tuples which include bounds for each dataset in the
format (min, max).
i.e for Langmuir: cust_bounds = {'q': [(4,6), (4, None), (5,10)], ... ect.}
:param fit_report : bool
Display a fitting report generated by lmfit for each dataset. Default is False
:param weights : list[list[float]]
Weights for fitting
:param dsl_comp_a : str
Manually input which component is the most adsorbed component (compA) for the dsl constrained
fitting procedure.
:return Returns a dictionary of fitting results
Note:
---------
Because the dsl constrained fitting procedure fits a list of dataframes, the generic fitting method is not
used when 'dsl' is inputted with the fitting condition as true and the method returns the result from the
dsl_fit function. This is because the dsl_fit function carries out its' own initial guess calculations and
henry regime estimations. The user may interact with this model in the same way as with the rest, however guess
must be inputted as a list of dictionaries (just as with the list of DataFrames and component names).
Custom bounds cannot yet be inputted into this model as this is a WIP. | src/pyIsoFit/core/fitting.py | fit | dominikpantak/pyIsoFit | 5 | python | def fit(self, cond=False, meth='leastsq', show_hen=False, hen_tol=0.999, rel_pres=False, henry_off=False, guess=None, cust_bounds=None, fit_report=False, weights=None, dsl_comp_a=None):
"\n Plotting method for the FitIsotherm class.\n Fits model to data using Non-Linear Least-Squares Minimization.\n This method is a generic fitting method for all models included in this package using the lmfit\n Parameters and Models class.\n\n Parameters\n ----------\n\n :param cond : bool\n Input whether to add standardised fitting constraints to fitting procedure. These are different\n for each fitting. Currently only works for Langmuir, Langmuir td, DSL, BDDT. Default is False\n\n :param meth : str\n Input the fitting algorithm which lmfit uses to fit curves. Default is 'leastsq' however lmfit includes\n many fitting algorithms which can be inputted (https://lmfit.github.io/lmfit-py/fitting.html).\n\n :param show_hen : bool\n Input whether to show the henry regime of the datasets approximated by the package. This is False by\n default.\n\n :param hen_tol : float or list[float]\n The henry region approximation function calculates the henry region by finding a line with the highest\n R squared value in the low pressure region of the dataset. This is done with a default R squared\n tolerance value (set to 0.999).\n\n For example, if a float is inputted (a different henry tolerance) this will be the henry tolerance value\n used by the function. i.e if 0.98 is inputted the henry regime will be across a large pressure range\n due to the low tolerance for the R squared value of the henry model fitting.\n\n This function also supports inputting the henry regimes manually. For this, input each henry regime for\n each dataset as a list i.e [1.2, 2.1, ... ]\n\n :param rel_pres : bool\n Input whether to fit the x axis data to relative pressure instead of absolute. Default is False\n\n :param henry_off : bool\n Input whether to turn off the henry regime fitting constraint when using the standardised fitting\n constraint to langmuir or dsl - this is usually done when fitting experimental data which has a messy\n low pressure region. Default is False.\n\n :param guess : dict\n Input custom guess values to override the default guess values. This must be inputted as a dictionary\n with the keys corresponding to the parameter string and the value corresponding to the list of guess\n values corresponding to each dataset.\n i.e for Langmuir: guess = {'q': [5, 5, 6], 'b':[100, 1000, 2000]}\n\n :param cust_bounds : dict\n Input custom bounds for the fitting. These are hard constraints and lmfit will fit only within these\n minimum and maximum values. Input these as a dictionary with the keys corresponding to the parameter\n string and the value corresponding to the list of tuples which include bounds for each dataset in the\n format (min, max).\n i.e for Langmuir: cust_bounds = {'q': [(4,6), (4, None), (5,10)], ... ect.}\n\n :param fit_report : bool\n Display a fitting report generated by lmfit for each dataset. Default is False\n\n :param weights : list[list[float]]\n Weights for fitting\n\n :param dsl_comp_a : str\n Manually input which component is the most adsorbed component (compA) for the dsl constrained\n fitting procedure.\n\n :return Returns a dictionary of fitting results\n\n\n Note:\n ---------\n Because the dsl constrained fitting procedure fits a list of dataframes, the generic fitting method is not\n used when 'dsl' is inputted with the fitting condition as true and the method returns the result from the\n dsl_fit function. This is because the dsl_fit function carries out its' own initial guess calculations and\n henry regime estimations. The user may interact with this model in the same way as with the rest, however guess\n must be inputted as a list of dictionaries (just as with the list of DataFrames and component names).\n Custom bounds cannot yet be inputted into this model as this is a WIP.\n "
if ((self.model == 'dsl') and (cond is True)):
logger.info('DSL Fitting procedure commenced')
if (type(self.df) is not list):
self.df = [self.df]
if (type(self.compname) is not list):
self.compname = [self.compname]
if ((type(guess) is not list) and (guess is not None)):
guess = [guess]
if (type(hen_tol) is not list):
hen_tol = [hen_tol for _ in self.compname]
try:
dsl_result = dsl_fit(self.df, self.key_pressures, self.key_uptakes, self.temps, self.compname, meth, guess, hen_tol, show_hen, henry_off, dsl_comp_a)
except ValueError:
logger.critical(_dsl_error_msg)
return None
(df_dict, results_dict, df_res_dict, params_dict) = dsl_result
self.params = results_dict
for comp in self.compname:
(x_i, y_i) = df_dict[comp]
self.x.append(x_i)
self.y.append(y_i)
self.df_result = df_res_dict
self.emod_input = params_dict
return df_res_dict
logger.info('Generic fitting procedure commenced')
if ((self.model not in _does_something) and (cond is not False)):
logger.warning(f'WARNING You have set cond={cond} but cond for the model '{self.model}' does nothing.
')
if (self.model == 'henry'):
show_hen = True
henry_params = henry_approx(self.df, self.key_pressures, self.key_uptakes, show_hen, hen_tol, self.compname, henry_off)
henry_constants = henry_params[0]
if (self.model == 'henry'):
logger.info('Henry model fitting only chosen')
self.henry_params = henry_params
self.df_result = henry_params[1]
return None
if (guess is None):
guess = get_guess_params(self.model, self.df, self.key_uptakes, self.key_pressures)
logger.info('Guess values successfully obtained')
else:
for (param, guess_val) in guess.items():
if (param not in _MODEL_PARAM_LISTS[self.model]):
raise ParameterError(('%s is not a valid parameter in the %s model.' % (param, self.model)))
guess[param] = guess_val
logger.info('Guess values overridden with custom guess values')
if ('mdr' in self.model):
logger.info('MDR chosen so relative pressure toggle force set')
rel_pres = True
self.rel_pres = rel_pres
(self.x, self.y) = get_xy(self.df, self.key_pressures, self.key_uptakes, self.model, rel_pres)
logger.info('x and y parameters successfully obtained')
if (weights is None):
logger.info('No weights inputted - setting weights to x')
weights = self.x
if ((self.model == 'bddt 2n') or (self.model == 'bddt 2n-1') or (self.model == 'bddt')):
self.model = 'bddt'
(self.params, values_dict) = generic_fit(self.model, weights, self.y, guess, self.temps, cond, meth, cust_bounds, fit_report, henry_constants, henry_off)
logger.info('Generic fit completed successfully')
(final_results_dict, c_list) = get_sorted_results(values_dict, self.model, self.temps)
logger.info('Results sorted successfully')
se = [mse(self.x[i], self.y[i], _MODEL_FUNCTIONS[self.model], c_list[i]) for i in range(len(self.x))]
logger.info('Mean squared error calculated successfully')
final_results_dict['MSE'] = se
df_result = pd.DataFrame.from_dict(final_results_dict)
pd.set_option('display.max_columns', None)
print(f'
---- Component {self.compname} fitting results -----')
display(df_result)
self.df_result = df_result
if (len(self.temps) >= 3):
heat_calc(self.model, self.temps, final_results_dict, self.x) | def fit(self, cond=False, meth='leastsq', show_hen=False, hen_tol=0.999, rel_pres=False, henry_off=False, guess=None, cust_bounds=None, fit_report=False, weights=None, dsl_comp_a=None):
"\n Plotting method for the FitIsotherm class.\n Fits model to data using Non-Linear Least-Squares Minimization.\n This method is a generic fitting method for all models included in this package using the lmfit\n Parameters and Models class.\n\n Parameters\n ----------\n\n :param cond : bool\n Input whether to add standardised fitting constraints to fitting procedure. These are different\n for each fitting. Currently only works for Langmuir, Langmuir td, DSL, BDDT. Default is False\n\n :param meth : str\n Input the fitting algorithm which lmfit uses to fit curves. Default is 'leastsq' however lmfit includes\n many fitting algorithms which can be inputted (https://lmfit.github.io/lmfit-py/fitting.html).\n\n :param show_hen : bool\n Input whether to show the henry regime of the datasets approximated by the package. This is False by\n default.\n\n :param hen_tol : float or list[float]\n The henry region approximation function calculates the henry region by finding a line with the highest\n R squared value in the low pressure region of the dataset. This is done with a default R squared\n tolerance value (set to 0.999).\n\n For example, if a float is inputted (a different henry tolerance) this will be the henry tolerance value\n used by the function. i.e if 0.98 is inputted the henry regime will be across a large pressure range\n due to the low tolerance for the R squared value of the henry model fitting.\n\n This function also supports inputting the henry regimes manually. For this, input each henry regime for\n each dataset as a list i.e [1.2, 2.1, ... ]\n\n :param rel_pres : bool\n Input whether to fit the x axis data to relative pressure instead of absolute. Default is False\n\n :param henry_off : bool\n Input whether to turn off the henry regime fitting constraint when using the standardised fitting\n constraint to langmuir or dsl - this is usually done when fitting experimental data which has a messy\n low pressure region. Default is False.\n\n :param guess : dict\n Input custom guess values to override the default guess values. This must be inputted as a dictionary\n with the keys corresponding to the parameter string and the value corresponding to the list of guess\n values corresponding to each dataset.\n i.e for Langmuir: guess = {'q': [5, 5, 6], 'b':[100, 1000, 2000]}\n\n :param cust_bounds : dict\n Input custom bounds for the fitting. These are hard constraints and lmfit will fit only within these\n minimum and maximum values. Input these as a dictionary with the keys corresponding to the parameter\n string and the value corresponding to the list of tuples which include bounds for each dataset in the\n format (min, max).\n i.e for Langmuir: cust_bounds = {'q': [(4,6), (4, None), (5,10)], ... ect.}\n\n :param fit_report : bool\n Display a fitting report generated by lmfit for each dataset. Default is False\n\n :param weights : list[list[float]]\n Weights for fitting\n\n :param dsl_comp_a : str\n Manually input which component is the most adsorbed component (compA) for the dsl constrained\n fitting procedure.\n\n :return Returns a dictionary of fitting results\n\n\n Note:\n ---------\n Because the dsl constrained fitting procedure fits a list of dataframes, the generic fitting method is not\n used when 'dsl' is inputted with the fitting condition as true and the method returns the result from the\n dsl_fit function. This is because the dsl_fit function carries out its' own initial guess calculations and\n henry regime estimations. The user may interact with this model in the same way as with the rest, however guess\n must be inputted as a list of dictionaries (just as with the list of DataFrames and component names).\n Custom bounds cannot yet be inputted into this model as this is a WIP.\n "
if ((self.model == 'dsl') and (cond is True)):
logger.info('DSL Fitting procedure commenced')
if (type(self.df) is not list):
self.df = [self.df]
if (type(self.compname) is not list):
self.compname = [self.compname]
if ((type(guess) is not list) and (guess is not None)):
guess = [guess]
if (type(hen_tol) is not list):
hen_tol = [hen_tol for _ in self.compname]
try:
dsl_result = dsl_fit(self.df, self.key_pressures, self.key_uptakes, self.temps, self.compname, meth, guess, hen_tol, show_hen, henry_off, dsl_comp_a)
except ValueError:
logger.critical(_dsl_error_msg)
return None
(df_dict, results_dict, df_res_dict, params_dict) = dsl_result
self.params = results_dict
for comp in self.compname:
(x_i, y_i) = df_dict[comp]
self.x.append(x_i)
self.y.append(y_i)
self.df_result = df_res_dict
self.emod_input = params_dict
return df_res_dict
logger.info('Generic fitting procedure commenced')
if ((self.model not in _does_something) and (cond is not False)):
logger.warning(f'WARNING You have set cond={cond} but cond for the model '{self.model}' does nothing.
')
if (self.model == 'henry'):
show_hen = True
henry_params = henry_approx(self.df, self.key_pressures, self.key_uptakes, show_hen, hen_tol, self.compname, henry_off)
henry_constants = henry_params[0]
if (self.model == 'henry'):
logger.info('Henry model fitting only chosen')
self.henry_params = henry_params
self.df_result = henry_params[1]
return None
if (guess is None):
guess = get_guess_params(self.model, self.df, self.key_uptakes, self.key_pressures)
logger.info('Guess values successfully obtained')
else:
for (param, guess_val) in guess.items():
if (param not in _MODEL_PARAM_LISTS[self.model]):
raise ParameterError(('%s is not a valid parameter in the %s model.' % (param, self.model)))
guess[param] = guess_val
logger.info('Guess values overridden with custom guess values')
if ('mdr' in self.model):
logger.info('MDR chosen so relative pressure toggle force set')
rel_pres = True
self.rel_pres = rel_pres
(self.x, self.y) = get_xy(self.df, self.key_pressures, self.key_uptakes, self.model, rel_pres)
logger.info('x and y parameters successfully obtained')
if (weights is None):
logger.info('No weights inputted - setting weights to x')
weights = self.x
if ((self.model == 'bddt 2n') or (self.model == 'bddt 2n-1') or (self.model == 'bddt')):
self.model = 'bddt'
(self.params, values_dict) = generic_fit(self.model, weights, self.y, guess, self.temps, cond, meth, cust_bounds, fit_report, henry_constants, henry_off)
logger.info('Generic fit completed successfully')
(final_results_dict, c_list) = get_sorted_results(values_dict, self.model, self.temps)
logger.info('Results sorted successfully')
se = [mse(self.x[i], self.y[i], _MODEL_FUNCTIONS[self.model], c_list[i]) for i in range(len(self.x))]
logger.info('Mean squared error calculated successfully')
final_results_dict['MSE'] = se
df_result = pd.DataFrame.from_dict(final_results_dict)
pd.set_option('display.max_columns', None)
print(f'
---- Component {self.compname} fitting results -----')
display(df_result)
self.df_result = df_result
if (len(self.temps) >= 3):
heat_calc(self.model, self.temps, final_results_dict, self.x)<|docstring|>Plotting method for the FitIsotherm class.
Fits model to data using Non-Linear Least-Squares Minimization.
This method is a generic fitting method for all models included in this package using the lmfit
Parameters and Models class.
Parameters
----------
:param cond : bool
Input whether to add standardised fitting constraints to fitting procedure. These are different
for each fitting. Currently only works for Langmuir, Langmuir td, DSL, BDDT. Default is False
:param meth : str
Input the fitting algorithm which lmfit uses to fit curves. Default is 'leastsq' however lmfit includes
many fitting algorithms which can be inputted (https://lmfit.github.io/lmfit-py/fitting.html).
:param show_hen : bool
Input whether to show the henry regime of the datasets approximated by the package. This is False by
default.
:param hen_tol : float or list[float]
The henry region approximation function calculates the henry region by finding a line with the highest
R squared value in the low pressure region of the dataset. This is done with a default R squared
tolerance value (set to 0.999).
For example, if a float is inputted (a different henry tolerance) this will be the henry tolerance value
used by the function. i.e if 0.98 is inputted the henry regime will be across a large pressure range
due to the low tolerance for the R squared value of the henry model fitting.
This function also supports inputting the henry regimes manually. For this, input each henry regime for
each dataset as a list i.e [1.2, 2.1, ... ]
:param rel_pres : bool
Input whether to fit the x axis data to relative pressure instead of absolute. Default is False
:param henry_off : bool
Input whether to turn off the henry regime fitting constraint when using the standardised fitting
constraint to langmuir or dsl - this is usually done when fitting experimental data which has a messy
low pressure region. Default is False.
:param guess : dict
Input custom guess values to override the default guess values. This must be inputted as a dictionary
with the keys corresponding to the parameter string and the value corresponding to the list of guess
values corresponding to each dataset.
i.e for Langmuir: guess = {'q': [5, 5, 6], 'b':[100, 1000, 2000]}
:param cust_bounds : dict
Input custom bounds for the fitting. These are hard constraints and lmfit will fit only within these
minimum and maximum values. Input these as a dictionary with the keys corresponding to the parameter
string and the value corresponding to the list of tuples which include bounds for each dataset in the
format (min, max).
i.e for Langmuir: cust_bounds = {'q': [(4,6), (4, None), (5,10)], ... ect.}
:param fit_report : bool
Display a fitting report generated by lmfit for each dataset. Default is False
:param weights : list[list[float]]
Weights for fitting
:param dsl_comp_a : str
Manually input which component is the most adsorbed component (compA) for the dsl constrained
fitting procedure.
:return Returns a dictionary of fitting results
Note:
---------
Because the dsl constrained fitting procedure fits a list of dataframes, the generic fitting method is not
used when 'dsl' is inputted with the fitting condition as true and the method returns the result from the
dsl_fit function. This is because the dsl_fit function carries out its' own initial guess calculations and
henry regime estimations. The user may interact with this model in the same way as with the rest, however guess
must be inputted as a list of dictionaries (just as with the list of DataFrames and component names).
Custom bounds cannot yet be inputted into this model as this is a WIP.<|endoftext|> |
72cf45dd80fcf69d89b96a0715efe71d94d6a09a63eadb59276d6ac82c3ec84b | def plot(self, logplot=(False, False)):
'\n Plotting method for the FitIsotherm class.\n There are three plotting procedures:\n - For more than one component\n - For henry plots (This requires plotting on individual subplots)\n - The generic plotting procedure for any other model with one component (most models use this)\n\n :param logplot: tuple(bool)\n Whether to have an x and y log axis. Default is off for both x and y axis i.e (False, False) in the order\n (x, y)\n\n '
np.linspace(0, 10, 301)
fit_label = '{temps} K Fit'
data_label = 'Data at {temps} K'
if (type(self.df) is list):
for i in range(len(self.df)):
plot_settings(logplot)
comp_x_params = self.params[self.compname[i]]
plt.title(self.compname[i])
for j in range(len(self.key_pressures)):
plt.plot(self.x[i][j], comp_x_params[j].best_fit, '-', color=colours[j], label=fit_label.format(temps=self.temps[j]))
plt.plot(self.x[i][j], self.y[i][j], 'ko', color='0.75', label=data_label.format(temps=self.temps[j]))
elif (self.model == 'henry'):
henry_const = self.henry_params[0]
xy_dict = self.henry_params[2]
x_hen = xy_dict['x']
y_hen = xy_dict['y']
plot_settings(logplot)
lenx = len(self.x)
for i in range(len(x_hen)):
y_henfit = henry(x_hen[i], henry_const[i])
subplot_size = get_subplot_size(lenx, i)
plt.subplot(subplot_size[0], subplot_size[1], subplot_size[2])
plt.subplots_adjust(wspace=0.3, hspace=0.3)
plt.title((('Henry regime at ' + str(self.temps[i])) + ' K'))
plt.plot(x_hen[i], y_henfit, '-', color=colours[i], label=fit_label.format(temps=self.temps[i]))
plt.plot(x_hen[i], y_hen[i], 'ko', color='0.75', label=data_label.format(temps=self.temps[i]))
plt.legend()
else:
plot_settings(logplot, self.input_model, self.rel_pres)
for i in range(len(self.key_pressures)):
plt.plot(self.x[i], self.params[i].best_fit, '-', color=colours[i], label=fit_label.format(temps=self.temps[i]))
plt.plot(self.x[i], self.y[i], 'ko', color='0.75', label=data_label.format(temps=self.temps[i]))
plt.legend()
plt.show() | Plotting method for the FitIsotherm class.
There are three plotting procedures:
- For more than one component
- For henry plots (This requires plotting on individual subplots)
- The generic plotting procedure for any other model with one component (most models use this)
:param logplot: tuple(bool)
Whether to have an x and y log axis. Default is off for both x and y axis i.e (False, False) in the order
(x, y) | src/pyIsoFit/core/fitting.py | plot | dominikpantak/pyIsoFit | 5 | python | def plot(self, logplot=(False, False)):
'\n Plotting method for the FitIsotherm class.\n There are three plotting procedures:\n - For more than one component\n - For henry plots (This requires plotting on individual subplots)\n - The generic plotting procedure for any other model with one component (most models use this)\n\n :param logplot: tuple(bool)\n Whether to have an x and y log axis. Default is off for both x and y axis i.e (False, False) in the order\n (x, y)\n\n '
np.linspace(0, 10, 301)
fit_label = '{temps} K Fit'
data_label = 'Data at {temps} K'
if (type(self.df) is list):
for i in range(len(self.df)):
plot_settings(logplot)
comp_x_params = self.params[self.compname[i]]
plt.title(self.compname[i])
for j in range(len(self.key_pressures)):
plt.plot(self.x[i][j], comp_x_params[j].best_fit, '-', color=colours[j], label=fit_label.format(temps=self.temps[j]))
plt.plot(self.x[i][j], self.y[i][j], 'ko', color='0.75', label=data_label.format(temps=self.temps[j]))
elif (self.model == 'henry'):
henry_const = self.henry_params[0]
xy_dict = self.henry_params[2]
x_hen = xy_dict['x']
y_hen = xy_dict['y']
plot_settings(logplot)
lenx = len(self.x)
for i in range(len(x_hen)):
y_henfit = henry(x_hen[i], henry_const[i])
subplot_size = get_subplot_size(lenx, i)
plt.subplot(subplot_size[0], subplot_size[1], subplot_size[2])
plt.subplots_adjust(wspace=0.3, hspace=0.3)
plt.title((('Henry regime at ' + str(self.temps[i])) + ' K'))
plt.plot(x_hen[i], y_henfit, '-', color=colours[i], label=fit_label.format(temps=self.temps[i]))
plt.plot(x_hen[i], y_hen[i], 'ko', color='0.75', label=data_label.format(temps=self.temps[i]))
plt.legend()
else:
plot_settings(logplot, self.input_model, self.rel_pres)
for i in range(len(self.key_pressures)):
plt.plot(self.x[i], self.params[i].best_fit, '-', color=colours[i], label=fit_label.format(temps=self.temps[i]))
plt.plot(self.x[i], self.y[i], 'ko', color='0.75', label=data_label.format(temps=self.temps[i]))
plt.legend()
plt.show() | def plot(self, logplot=(False, False)):
'\n Plotting method for the FitIsotherm class.\n There are three plotting procedures:\n - For more than one component\n - For henry plots (This requires plotting on individual subplots)\n - The generic plotting procedure for any other model with one component (most models use this)\n\n :param logplot: tuple(bool)\n Whether to have an x and y log axis. Default is off for both x and y axis i.e (False, False) in the order\n (x, y)\n\n '
np.linspace(0, 10, 301)
fit_label = '{temps} K Fit'
data_label = 'Data at {temps} K'
if (type(self.df) is list):
for i in range(len(self.df)):
plot_settings(logplot)
comp_x_params = self.params[self.compname[i]]
plt.title(self.compname[i])
for j in range(len(self.key_pressures)):
plt.plot(self.x[i][j], comp_x_params[j].best_fit, '-', color=colours[j], label=fit_label.format(temps=self.temps[j]))
plt.plot(self.x[i][j], self.y[i][j], 'ko', color='0.75', label=data_label.format(temps=self.temps[j]))
elif (self.model == 'henry'):
henry_const = self.henry_params[0]
xy_dict = self.henry_params[2]
x_hen = xy_dict['x']
y_hen = xy_dict['y']
plot_settings(logplot)
lenx = len(self.x)
for i in range(len(x_hen)):
y_henfit = henry(x_hen[i], henry_const[i])
subplot_size = get_subplot_size(lenx, i)
plt.subplot(subplot_size[0], subplot_size[1], subplot_size[2])
plt.subplots_adjust(wspace=0.3, hspace=0.3)
plt.title((('Henry regime at ' + str(self.temps[i])) + ' K'))
plt.plot(x_hen[i], y_henfit, '-', color=colours[i], label=fit_label.format(temps=self.temps[i]))
plt.plot(x_hen[i], y_hen[i], 'ko', color='0.75', label=data_label.format(temps=self.temps[i]))
plt.legend()
else:
plot_settings(logplot, self.input_model, self.rel_pres)
for i in range(len(self.key_pressures)):
plt.plot(self.x[i], self.params[i].best_fit, '-', color=colours[i], label=fit_label.format(temps=self.temps[i]))
plt.plot(self.x[i], self.y[i], 'ko', color='0.75', label=data_label.format(temps=self.temps[i]))
plt.legend()
plt.show()<|docstring|>Plotting method for the FitIsotherm class.
There are three plotting procedures:
- For more than one component
- For henry plots (This requires plotting on individual subplots)
- The generic plotting procedure for any other model with one component (most models use this)
:param logplot: tuple(bool)
Whether to have an x and y log axis. Default is off for both x and y axis i.e (False, False) in the order
(x, y)<|endoftext|> |
4ae277885bba38e836b97acabbe70fbcffe156a191817555f79981d89dc49dce | def save(self, directory=None, filestring=None, filetype='.csv'):
'\n Saves the model fitting result and henry region fitting result dataframes to directory as a .csv or .json\n file.\n\n :param directory:\n Full destination directory must be inputted for the user to save a file\n\n :param filestring: list[str] or str\n This is a list of strings corresponding to the file names, first position is fit result, second is\n henry result. Inputting this as a string for the fit result only will also work.\n\n :param filetype: str\n .csv or .json for saving\n\n '
if (directory is None):
raise SaveError('\n\nPlease enter full directory for saving file separated by double dashes i.e C:\\Users\\User\\pyIsoFit-master\\fittingresults\\')
if (filestring is None):
filestring = 'fit_result'
if (type(self.df_result) is dict):
for comp in self.df_result:
save_func(directory, filestring, filetype, self.df_result[comp], comp)
else:
save_func(directory, filestring, filetype, self.df_result) | Saves the model fitting result and henry region fitting result dataframes to directory as a .csv or .json
file.
:param directory:
Full destination directory must be inputted for the user to save a file
:param filestring: list[str] or str
This is a list of strings corresponding to the file names, first position is fit result, second is
henry result. Inputting this as a string for the fit result only will also work.
:param filetype: str
.csv or .json for saving | src/pyIsoFit/core/fitting.py | save | dominikpantak/pyIsoFit | 5 | python | def save(self, directory=None, filestring=None, filetype='.csv'):
'\n Saves the model fitting result and henry region fitting result dataframes to directory as a .csv or .json\n file.\n\n :param directory:\n Full destination directory must be inputted for the user to save a file\n\n :param filestring: list[str] or str\n This is a list of strings corresponding to the file names, first position is fit result, second is\n henry result. Inputting this as a string for the fit result only will also work.\n\n :param filetype: str\n .csv or .json for saving\n\n '
if (directory is None):
raise SaveError('\n\nPlease enter full directory for saving file separated by double dashes i.e C:\\Users\\User\\pyIsoFit-master\\fittingresults\\')
if (filestring is None):
filestring = 'fit_result'
if (type(self.df_result) is dict):
for comp in self.df_result:
save_func(directory, filestring, filetype, self.df_result[comp], comp)
else:
save_func(directory, filestring, filetype, self.df_result) | def save(self, directory=None, filestring=None, filetype='.csv'):
'\n Saves the model fitting result and henry region fitting result dataframes to directory as a .csv or .json\n file.\n\n :param directory:\n Full destination directory must be inputted for the user to save a file\n\n :param filestring: list[str] or str\n This is a list of strings corresponding to the file names, first position is fit result, second is\n henry result. Inputting this as a string for the fit result only will also work.\n\n :param filetype: str\n .csv or .json for saving\n\n '
if (directory is None):
raise SaveError('\n\nPlease enter full directory for saving file separated by double dashes i.e C:\\Users\\User\\pyIsoFit-master\\fittingresults\\')
if (filestring is None):
filestring = 'fit_result'
if (type(self.df_result) is dict):
for comp in self.df_result:
save_func(directory, filestring, filetype, self.df_result[comp], comp)
else:
save_func(directory, filestring, filetype, self.df_result)<|docstring|>Saves the model fitting result and henry region fitting result dataframes to directory as a .csv or .json
file.
:param directory:
Full destination directory must be inputted for the user to save a file
:param filestring: list[str] or str
This is a list of strings corresponding to the file names, first position is fit result, second is
henry result. Inputting this as a string for the fit result only will also work.
:param filetype: str
.csv or .json for saving<|endoftext|> |
2dca7b70edf2e55f4ac8725ab13283b889a60cf020bc372b26d00dd62eb97f19 | def plot_emod(self, yfracs, ext_model='extended dsl', logplot=(False, False)):
'\n Predicts co-adsorption isotherm data and plots it.\n\n :param yfracs: list[float]\n List of component mole fractions within the gas mixture\n\n :param ext_model: str\n Extended model for the method to predict co-adsorption with. Currently extended DSL is the only\n model included\n\n :param logplot: bool\n Whether to have an x and y log axis.\n\n :return:\n Returns a dictionary of co-adsorption uptakes for each component\n '
if (len(self.compname) < 2):
raise ParameterError('Enter 2 components or more to use extended models')
if (self.model != 'dsl'):
raise ParameterError('This isotherm model is not supported for extended models. Currently supported\n models are:\n - DSL ')
q_dict = ext_dsl(self.emod_input, self.temps, self.x, self.compname, yfracs)
for i in range(len(self.compname)):
plot_settings(logplot)
q = q_dict[self.compname[i]]
plt.title(f'Co-adsorption isotherm for component {self.compname[i]} at mol frac of {yfracs[i]}')
for j in range(len(self.temps)):
plt.plot(self.x[i][j], q[j], '--', color=colours[j], label='{temps} K Fit'.format(temps=self.temps[j]))
plt.legend()
plt.show()
return q_dict | Predicts co-adsorption isotherm data and plots it.
:param yfracs: list[float]
List of component mole fractions within the gas mixture
:param ext_model: str
Extended model for the method to predict co-adsorption with. Currently extended DSL is the only
model included
:param logplot: bool
Whether to have an x and y log axis.
:return:
Returns a dictionary of co-adsorption uptakes for each component | src/pyIsoFit/core/fitting.py | plot_emod | dominikpantak/pyIsoFit | 5 | python | def plot_emod(self, yfracs, ext_model='extended dsl', logplot=(False, False)):
'\n Predicts co-adsorption isotherm data and plots it.\n\n :param yfracs: list[float]\n List of component mole fractions within the gas mixture\n\n :param ext_model: str\n Extended model for the method to predict co-adsorption with. Currently extended DSL is the only\n model included\n\n :param logplot: bool\n Whether to have an x and y log axis.\n\n :return:\n Returns a dictionary of co-adsorption uptakes for each component\n '
if (len(self.compname) < 2):
raise ParameterError('Enter 2 components or more to use extended models')
if (self.model != 'dsl'):
raise ParameterError('This isotherm model is not supported for extended models. Currently supported\n models are:\n - DSL ')
q_dict = ext_dsl(self.emod_input, self.temps, self.x, self.compname, yfracs)
for i in range(len(self.compname)):
plot_settings(logplot)
q = q_dict[self.compname[i]]
plt.title(f'Co-adsorption isotherm for component {self.compname[i]} at mol frac of {yfracs[i]}')
for j in range(len(self.temps)):
plt.plot(self.x[i][j], q[j], '--', color=colours[j], label='{temps} K Fit'.format(temps=self.temps[j]))
plt.legend()
plt.show()
return q_dict | def plot_emod(self, yfracs, ext_model='extended dsl', logplot=(False, False)):
'\n Predicts co-adsorption isotherm data and plots it.\n\n :param yfracs: list[float]\n List of component mole fractions within the gas mixture\n\n :param ext_model: str\n Extended model for the method to predict co-adsorption with. Currently extended DSL is the only\n model included\n\n :param logplot: bool\n Whether to have an x and y log axis.\n\n :return:\n Returns a dictionary of co-adsorption uptakes for each component\n '
if (len(self.compname) < 2):
raise ParameterError('Enter 2 components or more to use extended models')
if (self.model != 'dsl'):
raise ParameterError('This isotherm model is not supported for extended models. Currently supported\n models are:\n - DSL ')
q_dict = ext_dsl(self.emod_input, self.temps, self.x, self.compname, yfracs)
for i in range(len(self.compname)):
plot_settings(logplot)
q = q_dict[self.compname[i]]
plt.title(f'Co-adsorption isotherm for component {self.compname[i]} at mol frac of {yfracs[i]}')
for j in range(len(self.temps)):
plt.plot(self.x[i][j], q[j], '--', color=colours[j], label='{temps} K Fit'.format(temps=self.temps[j]))
plt.legend()
plt.show()
return q_dict<|docstring|>Predicts co-adsorption isotherm data and plots it.
:param yfracs: list[float]
List of component mole fractions within the gas mixture
:param ext_model: str
Extended model for the method to predict co-adsorption with. Currently extended DSL is the only
model included
:param logplot: bool
Whether to have an x and y log axis.
:return:
Returns a dictionary of co-adsorption uptakes for each component<|endoftext|> |
0c6ce90f9607add97d44cd13934ab3623417e8541e3760088f1a0aab0b47685a | def get_HNF_diagonals(n):
'Finds the diagonals of the HNF that reach the target n value.\n \n Args:\n n (int): The target determinant for the HNF.\n \n Retruns:\n diags (list of lists): The allowed values of the determinant.\n '
diags = []
for i in range(1, (n + 1)):
if (not ((n % i) == 0)):
continue
else:
q = (n / i)
for j in range(1, (q + 1)):
if (not ((q % j) == 0)):
continue
else:
diags.append([i, j, (q / j)])
return diags | Finds the diagonals of the HNF that reach the target n value.
Args:
n (int): The target determinant for the HNF.
Retruns:
diags (list of lists): The allowed values of the determinant. | support/brute_force/general_approach.py | get_HNF_diagonals | glwhart/autoGR | 17 | python | def get_HNF_diagonals(n):
'Finds the diagonals of the HNF that reach the target n value.\n \n Args:\n n (int): The target determinant for the HNF.\n \n Retruns:\n diags (list of lists): The allowed values of the determinant.\n '
diags = []
for i in range(1, (n + 1)):
if (not ((n % i) == 0)):
continue
else:
q = (n / i)
for j in range(1, (q + 1)):
if (not ((q % j) == 0)):
continue
else:
diags.append([i, j, (q / j)])
return diags | def get_HNF_diagonals(n):
'Finds the diagonals of the HNF that reach the target n value.\n \n Args:\n n (int): The target determinant for the HNF.\n \n Retruns:\n diags (list of lists): The allowed values of the determinant.\n '
diags = []
for i in range(1, (n + 1)):
if (not ((n % i) == 0)):
continue
else:
q = (n / i)
for j in range(1, (q + 1)):
if (not ((q % j) == 0)):
continue
else:
diags.append([i, j, (q / j)])
return diags<|docstring|>Finds the diagonals of the HNF that reach the target n value.
Args:
n (int): The target determinant for the HNF.
Retruns:
diags (list of lists): The allowed values of the determinant.<|endoftext|> |
6b2163971fbfccf3b0b1751fdb978d74ea4c9df5803c54ab7567868f18b0186a | def forms_group(gens, pg):
'Tests if the given generators forms a group.\n \n Args:\n gens (list of list): The generators to check.\n pg (list of list): The group the generators form.\n \n Returns:\n corret_gens (bool): True if the generators form the group.\n '
correct_gens = False
group = []
for i in gens:
for j in gens:
test = np.matmul(i, j)
in_group = False
for k in group:
if np.allclose(test, k):
in_group = True
if (not in_group):
group.append(test)
growing = True
while growing:
nfound = 0
for i in gens:
for j in group:
test = np.matmul(i, j)
in_group = False
for k in group:
if np.allclose(test, k):
in_group = True
if (not in_group):
group.append(test)
nfound += 1
if (nfound == 0):
growing = False
if (not (len(pg) == len(group))):
correct_gens = False
else:
for i in pg:
in_group = False
for k in group:
if np.allclose(i, k):
correct_gens = True
break
if (correct_gens == False):
break
return correct_gens | Tests if the given generators forms a group.
Args:
gens (list of list): The generators to check.
pg (list of list): The group the generators form.
Returns:
corret_gens (bool): True if the generators form the group. | support/brute_force/general_approach.py | forms_group | glwhart/autoGR | 17 | python | def forms_group(gens, pg):
'Tests if the given generators forms a group.\n \n Args:\n gens (list of list): The generators to check.\n pg (list of list): The group the generators form.\n \n Returns:\n corret_gens (bool): True if the generators form the group.\n '
correct_gens = False
group = []
for i in gens:
for j in gens:
test = np.matmul(i, j)
in_group = False
for k in group:
if np.allclose(test, k):
in_group = True
if (not in_group):
group.append(test)
growing = True
while growing:
nfound = 0
for i in gens:
for j in group:
test = np.matmul(i, j)
in_group = False
for k in group:
if np.allclose(test, k):
in_group = True
if (not in_group):
group.append(test)
nfound += 1
if (nfound == 0):
growing = False
if (not (len(pg) == len(group))):
correct_gens = False
else:
for i in pg:
in_group = False
for k in group:
if np.allclose(i, k):
correct_gens = True
break
if (correct_gens == False):
break
return correct_gens | def forms_group(gens, pg):
'Tests if the given generators forms a group.\n \n Args:\n gens (list of list): The generators to check.\n pg (list of list): The group the generators form.\n \n Returns:\n corret_gens (bool): True if the generators form the group.\n '
correct_gens = False
group = []
for i in gens:
for j in gens:
test = np.matmul(i, j)
in_group = False
for k in group:
if np.allclose(test, k):
in_group = True
if (not in_group):
group.append(test)
growing = True
while growing:
nfound = 0
for i in gens:
for j in group:
test = np.matmul(i, j)
in_group = False
for k in group:
if np.allclose(test, k):
in_group = True
if (not in_group):
group.append(test)
nfound += 1
if (nfound == 0):
growing = False
if (not (len(pg) == len(group))):
correct_gens = False
else:
for i in pg:
in_group = False
for k in group:
if np.allclose(i, k):
correct_gens = True
break
if (correct_gens == False):
break
return correct_gens<|docstring|>Tests if the given generators forms a group.
Args:
gens (list of list): The generators to check.
pg (list of list): The group the generators form.
Returns:
corret_gens (bool): True if the generators form the group.<|endoftext|> |
3b2955590f58943fdfc4fb6e746aaf77280cb1c97d235dd38ce562b5d6a3b18b | def find_gens_of_pg(pg):
'This subroutine finds the generators of the point group.\n \n Args:\n pg (list of list): A list of the matrix form of the point group.\n \n Returns:\n gens (list of list): Those operations that will generate the \n remainder of the group.\n '
from itertools import combinations
n_gens = 1
found_gens = False
while (not found_gens):
possible_gens = list(combinations(range(len(pg)), r=n_gens))
for test in possible_gens:
test_gens = []
for i in test:
test_gens.append(pg[i])
if forms_group(test_gens, pg):
gens = test_gens
found_gens = True
break
n_gens += 1
return gens | This subroutine finds the generators of the point group.
Args:
pg (list of list): A list of the matrix form of the point group.
Returns:
gens (list of list): Those operations that will generate the
remainder of the group. | support/brute_force/general_approach.py | find_gens_of_pg | glwhart/autoGR | 17 | python | def find_gens_of_pg(pg):
'This subroutine finds the generators of the point group.\n \n Args:\n pg (list of list): A list of the matrix form of the point group.\n \n Returns:\n gens (list of list): Those operations that will generate the \n remainder of the group.\n '
from itertools import combinations
n_gens = 1
found_gens = False
while (not found_gens):
possible_gens = list(combinations(range(len(pg)), r=n_gens))
for test in possible_gens:
test_gens = []
for i in test:
test_gens.append(pg[i])
if forms_group(test_gens, pg):
gens = test_gens
found_gens = True
break
n_gens += 1
return gens | def find_gens_of_pg(pg):
'This subroutine finds the generators of the point group.\n \n Args:\n pg (list of list): A list of the matrix form of the point group.\n \n Returns:\n gens (list of list): Those operations that will generate the \n remainder of the group.\n '
from itertools import combinations
n_gens = 1
found_gens = False
while (not found_gens):
possible_gens = list(combinations(range(len(pg)), r=n_gens))
for test in possible_gens:
test_gens = []
for i in test:
test_gens.append(pg[i])
if forms_group(test_gens, pg):
gens = test_gens
found_gens = True
break
n_gens += 1
return gens<|docstring|>This subroutine finds the generators of the point group.
Args:
pg (list of list): A list of the matrix form of the point group.
Returns:
gens (list of list): Those operations that will generate the
remainder of the group.<|endoftext|> |
6e12e8ef9b94b3166968760e32de74efc66b7284dc3ab04f7888ac3fbd647edf | def div_HNF(lat, n):
'Finds the HNFs that preserve the symmetry of the lattice.\n \n Args:\n lat (numpy.ndarray): The vectors (as rows) of the parent lattice.\n n (int): The volume factor for the supercell.\n \n Returns:\n HNFs (list of lists): The HNFs the preserve the symmetry.\n '
from phenum.symmetry import _get_lattice_pointGroup
diags = get_HNF_diagonals(n)
pg = _get_lattice_pointGroup(lat)
gens = find_gens_of_pg(pg)
lat = np.transpose(lat)
lat_gens = []
for g in gens:
temp = np.matmul(np.linalg.inv(lat), np.matmul(g, lat))
lat_gens.append(np.transpose(temp))
x11 = []
x12 = []
x13 = []
x21 = []
x22 = []
x23 = []
x31 = []
x32 = []
x33 = []
for g in lat_gens:
x11.append(g[0][0])
x12.append(g[0][1])
x13.append(g[0][2])
x21.append(g[1][0])
x22.append(g[1][1])
x23.append(g[1][2])
x31.append(g[2][0])
x32.append(g[2][1])
x33.append(g[2][2])
x11 = np.array(x11)
x12 = np.array(x12)
x13 = np.array(x13)
x21 = np.array(x21)
x22 = np.array(x22)
x23 = np.array(x23)
x31 = np.array(x31)
x32 = np.array(x32)
x33 = np.array(x33)
count = 0
HNFs = []
for diag in diags:
print('diag', diag)
a = diag[0]
c = diag[1]
f = diag[2]
if np.allclose(((x13 * f) % a), 0):
d = None
e = None
b = None
if (np.allclose(x13, 0) and (not np.allclose(x12, 0))):
if (not np.allclose(((x12 * c) % a), 0)):
continue
b = 0
al1 = ((b * x12) / a)
al2 = ((c * x12) / a)
al3 = ((f * x13) / a)
tHNFs = cdivs(a, b, c, d, e, f, al1, al2, al3, x11, x21, x22, x23, x31, x32, x33)
for t in tHNFs:
HNFs.append(t)
count += 1
elif (np.allclose(x12, 0) and (not np.allclose(x13, 0))):
vals = []
N = 0
xt = x13[np.nonzero(x13)]
val = np.unique(((N * a) / xt))
while any((abs(val) < f)):
for v in val:
if (v < f):
vals.append(v)
N += 1
val = np.unique(((N * a) / xt))
for d in vals:
for e in vals:
al1 = ((d * x13) / a)
al2 = ((e * x13) / a)
al3 = ((f * x13) / a)
tHNFs = cdivs(a, b, c, d, e, f, al1, al2, al3, x11, x21, x22, x23, x31, x32, x33)
for t in tHNFs:
HNFs.append(t)
count += 1
else:
for e in range(f):
if np.allclose((((c * x12) + (e * x13)) % a), 0):
for b in range(c):
for d in range(f):
if np.allclose((((b * x12) + (d * x13)) % a), 0):
al1 = (((b * x12) + (d * x13)) / a)
al2 = (((c * x12) + (e * x13)) / a)
al3 = ((f * x13) / a)
tHNFs = cdivs(a, b, c, d, e, f, al1, al2, al3, x11, x21, x22, x23, x31, x32, x33)
for t in tHNFs:
HNFs.append(t)
count += 1
else:
continue
else:
continue
else:
continue
return HNFs | Finds the HNFs that preserve the symmetry of the lattice.
Args:
lat (numpy.ndarray): The vectors (as rows) of the parent lattice.
n (int): The volume factor for the supercell.
Returns:
HNFs (list of lists): The HNFs the preserve the symmetry. | support/brute_force/general_approach.py | div_HNF | glwhart/autoGR | 17 | python | def div_HNF(lat, n):
'Finds the HNFs that preserve the symmetry of the lattice.\n \n Args:\n lat (numpy.ndarray): The vectors (as rows) of the parent lattice.\n n (int): The volume factor for the supercell.\n \n Returns:\n HNFs (list of lists): The HNFs the preserve the symmetry.\n '
from phenum.symmetry import _get_lattice_pointGroup
diags = get_HNF_diagonals(n)
pg = _get_lattice_pointGroup(lat)
gens = find_gens_of_pg(pg)
lat = np.transpose(lat)
lat_gens = []
for g in gens:
temp = np.matmul(np.linalg.inv(lat), np.matmul(g, lat))
lat_gens.append(np.transpose(temp))
x11 = []
x12 = []
x13 = []
x21 = []
x22 = []
x23 = []
x31 = []
x32 = []
x33 = []
for g in lat_gens:
x11.append(g[0][0])
x12.append(g[0][1])
x13.append(g[0][2])
x21.append(g[1][0])
x22.append(g[1][1])
x23.append(g[1][2])
x31.append(g[2][0])
x32.append(g[2][1])
x33.append(g[2][2])
x11 = np.array(x11)
x12 = np.array(x12)
x13 = np.array(x13)
x21 = np.array(x21)
x22 = np.array(x22)
x23 = np.array(x23)
x31 = np.array(x31)
x32 = np.array(x32)
x33 = np.array(x33)
count = 0
HNFs = []
for diag in diags:
print('diag', diag)
a = diag[0]
c = diag[1]
f = diag[2]
if np.allclose(((x13 * f) % a), 0):
d = None
e = None
b = None
if (np.allclose(x13, 0) and (not np.allclose(x12, 0))):
if (not np.allclose(((x12 * c) % a), 0)):
continue
b = 0
al1 = ((b * x12) / a)
al2 = ((c * x12) / a)
al3 = ((f * x13) / a)
tHNFs = cdivs(a, b, c, d, e, f, al1, al2, al3, x11, x21, x22, x23, x31, x32, x33)
for t in tHNFs:
HNFs.append(t)
count += 1
elif (np.allclose(x12, 0) and (not np.allclose(x13, 0))):
vals = []
N = 0
xt = x13[np.nonzero(x13)]
val = np.unique(((N * a) / xt))
while any((abs(val) < f)):
for v in val:
if (v < f):
vals.append(v)
N += 1
val = np.unique(((N * a) / xt))
for d in vals:
for e in vals:
al1 = ((d * x13) / a)
al2 = ((e * x13) / a)
al3 = ((f * x13) / a)
tHNFs = cdivs(a, b, c, d, e, f, al1, al2, al3, x11, x21, x22, x23, x31, x32, x33)
for t in tHNFs:
HNFs.append(t)
count += 1
else:
for e in range(f):
if np.allclose((((c * x12) + (e * x13)) % a), 0):
for b in range(c):
for d in range(f):
if np.allclose((((b * x12) + (d * x13)) % a), 0):
al1 = (((b * x12) + (d * x13)) / a)
al2 = (((c * x12) + (e * x13)) / a)
al3 = ((f * x13) / a)
tHNFs = cdivs(a, b, c, d, e, f, al1, al2, al3, x11, x21, x22, x23, x31, x32, x33)
for t in tHNFs:
HNFs.append(t)
count += 1
else:
continue
else:
continue
else:
continue
return HNFs | def div_HNF(lat, n):
'Finds the HNFs that preserve the symmetry of the lattice.\n \n Args:\n lat (numpy.ndarray): The vectors (as rows) of the parent lattice.\n n (int): The volume factor for the supercell.\n \n Returns:\n HNFs (list of lists): The HNFs the preserve the symmetry.\n '
from phenum.symmetry import _get_lattice_pointGroup
diags = get_HNF_diagonals(n)
pg = _get_lattice_pointGroup(lat)
gens = find_gens_of_pg(pg)
lat = np.transpose(lat)
lat_gens = []
for g in gens:
temp = np.matmul(np.linalg.inv(lat), np.matmul(g, lat))
lat_gens.append(np.transpose(temp))
x11 = []
x12 = []
x13 = []
x21 = []
x22 = []
x23 = []
x31 = []
x32 = []
x33 = []
for g in lat_gens:
x11.append(g[0][0])
x12.append(g[0][1])
x13.append(g[0][2])
x21.append(g[1][0])
x22.append(g[1][1])
x23.append(g[1][2])
x31.append(g[2][0])
x32.append(g[2][1])
x33.append(g[2][2])
x11 = np.array(x11)
x12 = np.array(x12)
x13 = np.array(x13)
x21 = np.array(x21)
x22 = np.array(x22)
x23 = np.array(x23)
x31 = np.array(x31)
x32 = np.array(x32)
x33 = np.array(x33)
count = 0
HNFs = []
for diag in diags:
print('diag', diag)
a = diag[0]
c = diag[1]
f = diag[2]
if np.allclose(((x13 * f) % a), 0):
d = None
e = None
b = None
if (np.allclose(x13, 0) and (not np.allclose(x12, 0))):
if (not np.allclose(((x12 * c) % a), 0)):
continue
b = 0
al1 = ((b * x12) / a)
al2 = ((c * x12) / a)
al3 = ((f * x13) / a)
tHNFs = cdivs(a, b, c, d, e, f, al1, al2, al3, x11, x21, x22, x23, x31, x32, x33)
for t in tHNFs:
HNFs.append(t)
count += 1
elif (np.allclose(x12, 0) and (not np.allclose(x13, 0))):
vals = []
N = 0
xt = x13[np.nonzero(x13)]
val = np.unique(((N * a) / xt))
while any((abs(val) < f)):
for v in val:
if (v < f):
vals.append(v)
N += 1
val = np.unique(((N * a) / xt))
for d in vals:
for e in vals:
al1 = ((d * x13) / a)
al2 = ((e * x13) / a)
al3 = ((f * x13) / a)
tHNFs = cdivs(a, b, c, d, e, f, al1, al2, al3, x11, x21, x22, x23, x31, x32, x33)
for t in tHNFs:
HNFs.append(t)
count += 1
else:
for e in range(f):
if np.allclose((((c * x12) + (e * x13)) % a), 0):
for b in range(c):
for d in range(f):
if np.allclose((((b * x12) + (d * x13)) % a), 0):
al1 = (((b * x12) + (d * x13)) / a)
al2 = (((c * x12) + (e * x13)) / a)
al3 = ((f * x13) / a)
tHNFs = cdivs(a, b, c, d, e, f, al1, al2, al3, x11, x21, x22, x23, x31, x32, x33)
for t in tHNFs:
HNFs.append(t)
count += 1
else:
continue
else:
continue
else:
continue
return HNFs<|docstring|>Finds the HNFs that preserve the symmetry of the lattice.
Args:
lat (numpy.ndarray): The vectors (as rows) of the parent lattice.
n (int): The volume factor for the supercell.
Returns:
HNFs (list of lists): The HNFs the preserve the symmetry.<|endoftext|> |
767bff398e874106f1109e6c2210c5dda2bf67e20c4db89023dc76515ee5cd24 | def fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33):
'Finds the f divides conditions for the symmetry preserving HNFs.\n \n Args:\n a (int): a from the HNF.\n b (int): b from the HNF.\n c (int): c from the HNF.\n d (int): d from the HNF.\n e (int): e from the HNF.\n f (int): f from the HNF.\n al1 (numpy.array): array of alpha1 values from write up.\n al2 (numpy.array): array of alpha2 values from write up.\n be1 (numpy.array): array of beta1 values from write up.\n be2 (numpy.array): array of beta2 values from write up.\n x11 (numpy.array): array of pg values for x(1,1) spot.\n x22 (numpy.array): array of pg values for x(2,2) spot.\n x31 (numpy.array): array of pg values for x(3,1) spot.\n x32 (numpy.array): array of pg values for x(3,2) spot.\n x33 (numpy.array): array of pg values for x(3,3) spot.\n \n Returns:\n HNFs (list of lists): The symmetry preserving HNFs.\n '
HNFs = []
if ((b == None) and (d == None) and (e == None)):
xvar1 = ((x33 - x22) - be2)
xvar2 = ((x33 - x11) - al1)
for b in range(c):
for e in range(f):
if (not np.allclose(xvar2, 0)):
N = min(np.round(((((a * x31) + (b * x32)) - (be1 * e)) / f)))
xt = xvar2[np.nonzero(xvar2)]
val = np.unique(np.reshape(np.outer(((((N * f) - (a * x31)) - (b * x32)) + (be1 * e)), (1 / xt)), (len(xt) * len(x32))))
while any((abs(val) < f)):
for v in val:
if ((v < f) and (v >= 0) and np.allclose((v % 1), 0)):
d = v
f1 = ((((a * x31) + (b * x32)) + (d * var2)) - (be1 * e))
f2 = (((c * x32) - (d * al2)) + (e * ((x33 - x33) - be2)))
if (np.allclose((f1 % f), 0) and np.allclose((f2 % f), 0)):
HNF = [[a, 0, 0], [b, c, 0], [d, e, f]]
HNFs.append(HNF)
N += 1
val = np.unique(np.reshape(np.outer(((((N * f) - (a * x31)) - (b * x32)) + (be1 * e)), (1 / xt)), (len(xt) * len(x32))))
elif (not np.allclose(al2, 0)):
N = max(np.round((((c * x32) + (e * var1)) / f)))
at = al2[np.nonzero(al2)]
val = np.unique(np.reshape(np.outer(((((- N) * f) + (c * x32)) + (e * var1)), (1 / at)), (len(x32) * len(at))))
while any((abs(val) < f)):
for v in val:
if ((v < f) and (v >= 0) and np.allclose((v % 1), 0)):
d = v
f1 = ((((a * x31) + (b * x32)) + (d * var2)) - (be1 * e))
f2 = (((c * x32) - (d * al2)) + (e * ((x33 - x33) - be2)))
if (np.allclose((f1 % f), 0) and np.allclose((f2 % f), 0)):
HNF = [[a, 0, 0], [b, c, 0], [d, e, f]]
HNFs.append(HNF)
N -= 1
val = np.unique(np.reshape(np.outer(((((- N) * f) + (c * x32)) + (e * var1)), (1 / at)), (len(x32) * len(at))))
else:
for d in range(f):
f1 = ((((a * x31) + (b * x32)) + (d * var2)) - (be1 * e))
f2 = (((c * x32) - (d * al2)) + (e * ((x33 - x33) - be2)))
if (np.allclose((f1 % f), 0) and np.allclose((f2 % f), 0)):
HNF = [[a, 0, 0], [b, c, 0], [d, e, f]]
HNFs.append(HNF)
elif (b == None):
f2 = (((c * x32) - (d * al2)) + (e * ((x33 - x22) - be2)))
if np.allclose((f2 % f), 0):
if (not np.allclose(x32, 0)):
N = min(np.round(((a * x31) + ((d * ((x33 - x11) - al1)) / f))))
xt = x32[np.nonzero(x32)]
val = np.unique(np.reshape(np.outer((((N * f) - (a * x31)) - (d * ((x33 - x11) - al1))), (1 / xt)), (len(x33) * len(xt))))
while any((abs(val) < c)):
for v in val:
if ((v < c) and (v >= 0) and np.allclose((v % 1), 0)):
b = v
f1 = ((((a * x32) + (b * x32)) + (e * be1)) + (d * ((x33 - x11) - al1)))
if np.allclose((f1 % f), 0):
HNF = [[a, 0, 0], [b, c, 0], [d, e, f]]
HNFs.append(HNF)
N += 1
val = np.unique(np.reshape(np.outer((((N * f) - (a * x31)) - (d * ((x33 - x11) - al1))), (1 / xt)), (len(x33) * len(xt))))
else:
for b in range(c):
f1 = ((((a * x32) + (b * x32)) + (e * be1)) + (d * ((x33 - x11) - al1)))
if np.allclose((f1 % f), 0):
HNF = [[a, 0, 0], [b, c, 0], [d, e, f]]
HNFs.append(HNF)
elif ((d == None) and (e == None)):
for e in range(f):
if (not np.allclose(xvar2, 0)):
N = min(np.round(((((a * x31) + (b * x32)) - (be1 * e)) / f)))
xt = xvar2[np.nonzero(xvar2)]
val = np.unique(np.reshape(np.outer(((((N * f) - (a * x31)) - (b * x32)) + (be1 * e)), (1 / xt)), (len(xt) * len(x32))))
while any((abs(val) < f)):
for v in val:
if ((v < f) and (v >= 0) and np.allclose((v % 1), 0)):
d = v
f1 = ((((a * x31) + (b * x32)) + (d * var2)) - (be1 * e))
f2 = (((c * x32) - (d * al2)) + (e * ((x33 - x33) - be2)))
if (np.allclose((f1 % f), 0) and np.allclose((f2 % f), 0)):
HNF = [[a, 0, 0], [b, c, 0], [d, e, f]]
HNFs.append(HNF)
N += 1
val = np.unique(np.reshape(np.outer(((((N * f) - (a * x31)) - (b * x32)) + (be1 * e)), (1 / xt)), (len(xt) * len(x32))))
elif (not np.allclose(al2, 0)):
N = max(np.round((((c * x32) + (e * var1)) / f)))
at = al2[np.nonzero(al2)]
val = np.unique(np.reshape(np.outer(((((- N) * f) + (c * x32)) + (e * var1)), (1 / at)), (len(x32) * len(at))))
while any((abs(val) < f)):
for v in val:
if ((v < f) and (v >= 0) and np.allclose((v % 1), 0)):
d = v
f1 = ((((a * x31) + (b * x32)) + (d * var2)) - (be1 * e))
f2 = (((c * x32) - (d * al2)) + (e * ((x33 - x33) - be2)))
if (np.allclose((f1 % f), 0) and np.allclose((f2 % f), 0)):
HNF = [[a, 0, 0], [b, c, 0], [d, e, f]]
HNFs.append(HNF)
N -= 1
val = np.unique(np.reshape(np.outer(((((- N) * f) + (c * x32)) + (e * var1)), (1 / at)), (len(x32) * len(at))))
else:
for d in range(f):
f1 = ((((a * x31) + (b * x32)) + (d * var2)) - (be1 * e))
f2 = (((c * x32) - (d * al2)) + (e * ((x33 - x33) - be2)))
if (np.allclose((f1 % f), 0) and np.allclose((f2 % f), 0)):
HNF = [[a, 0, 0], [b, c, 0], [d, e, f]]
HNFs.append(HNF)
elif ((e == None) or (d == None) or (b == None)):
print('*****************ERROR IN fdivs**************')
else:
f2 = (((c * x32) - (d * al2)) + (e * ((x33 - x22) - be2)))
f1 = ((((a * x31) + (b * x32)) + (e * be1)) + (d * ((x33 - x11) - al1)))
if (np.allclose((f1 % f), 0) and np.allclose((f2 % f), 0)):
HNF = [[a, 0, 0], [b, c, 0], [d, e, f]]
HNFs.append(HNF)
return HNFs | Finds the f divides conditions for the symmetry preserving HNFs.
Args:
a (int): a from the HNF.
b (int): b from the HNF.
c (int): c from the HNF.
d (int): d from the HNF.
e (int): e from the HNF.
f (int): f from the HNF.
al1 (numpy.array): array of alpha1 values from write up.
al2 (numpy.array): array of alpha2 values from write up.
be1 (numpy.array): array of beta1 values from write up.
be2 (numpy.array): array of beta2 values from write up.
x11 (numpy.array): array of pg values for x(1,1) spot.
x22 (numpy.array): array of pg values for x(2,2) spot.
x31 (numpy.array): array of pg values for x(3,1) spot.
x32 (numpy.array): array of pg values for x(3,2) spot.
x33 (numpy.array): array of pg values for x(3,3) spot.
Returns:
HNFs (list of lists): The symmetry preserving HNFs. | support/brute_force/general_approach.py | fdivs | glwhart/autoGR | 17 | python | def fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33):
'Finds the f divides conditions for the symmetry preserving HNFs.\n \n Args:\n a (int): a from the HNF.\n b (int): b from the HNF.\n c (int): c from the HNF.\n d (int): d from the HNF.\n e (int): e from the HNF.\n f (int): f from the HNF.\n al1 (numpy.array): array of alpha1 values from write up.\n al2 (numpy.array): array of alpha2 values from write up.\n be1 (numpy.array): array of beta1 values from write up.\n be2 (numpy.array): array of beta2 values from write up.\n x11 (numpy.array): array of pg values for x(1,1) spot.\n x22 (numpy.array): array of pg values for x(2,2) spot.\n x31 (numpy.array): array of pg values for x(3,1) spot.\n x32 (numpy.array): array of pg values for x(3,2) spot.\n x33 (numpy.array): array of pg values for x(3,3) spot.\n \n Returns:\n HNFs (list of lists): The symmetry preserving HNFs.\n '
HNFs = []
if ((b == None) and (d == None) and (e == None)):
xvar1 = ((x33 - x22) - be2)
xvar2 = ((x33 - x11) - al1)
for b in range(c):
for e in range(f):
if (not np.allclose(xvar2, 0)):
N = min(np.round(((((a * x31) + (b * x32)) - (be1 * e)) / f)))
xt = xvar2[np.nonzero(xvar2)]
val = np.unique(np.reshape(np.outer(((((N * f) - (a * x31)) - (b * x32)) + (be1 * e)), (1 / xt)), (len(xt) * len(x32))))
while any((abs(val) < f)):
for v in val:
if ((v < f) and (v >= 0) and np.allclose((v % 1), 0)):
d = v
f1 = ((((a * x31) + (b * x32)) + (d * var2)) - (be1 * e))
f2 = (((c * x32) - (d * al2)) + (e * ((x33 - x33) - be2)))
if (np.allclose((f1 % f), 0) and np.allclose((f2 % f), 0)):
HNF = [[a, 0, 0], [b, c, 0], [d, e, f]]
HNFs.append(HNF)
N += 1
val = np.unique(np.reshape(np.outer(((((N * f) - (a * x31)) - (b * x32)) + (be1 * e)), (1 / xt)), (len(xt) * len(x32))))
elif (not np.allclose(al2, 0)):
N = max(np.round((((c * x32) + (e * var1)) / f)))
at = al2[np.nonzero(al2)]
val = np.unique(np.reshape(np.outer(((((- N) * f) + (c * x32)) + (e * var1)), (1 / at)), (len(x32) * len(at))))
while any((abs(val) < f)):
for v in val:
if ((v < f) and (v >= 0) and np.allclose((v % 1), 0)):
d = v
f1 = ((((a * x31) + (b * x32)) + (d * var2)) - (be1 * e))
f2 = (((c * x32) - (d * al2)) + (e * ((x33 - x33) - be2)))
if (np.allclose((f1 % f), 0) and np.allclose((f2 % f), 0)):
HNF = [[a, 0, 0], [b, c, 0], [d, e, f]]
HNFs.append(HNF)
N -= 1
val = np.unique(np.reshape(np.outer(((((- N) * f) + (c * x32)) + (e * var1)), (1 / at)), (len(x32) * len(at))))
else:
for d in range(f):
f1 = ((((a * x31) + (b * x32)) + (d * var2)) - (be1 * e))
f2 = (((c * x32) - (d * al2)) + (e * ((x33 - x33) - be2)))
if (np.allclose((f1 % f), 0) and np.allclose((f2 % f), 0)):
HNF = [[a, 0, 0], [b, c, 0], [d, e, f]]
HNFs.append(HNF)
elif (b == None):
f2 = (((c * x32) - (d * al2)) + (e * ((x33 - x22) - be2)))
if np.allclose((f2 % f), 0):
if (not np.allclose(x32, 0)):
N = min(np.round(((a * x31) + ((d * ((x33 - x11) - al1)) / f))))
xt = x32[np.nonzero(x32)]
val = np.unique(np.reshape(np.outer((((N * f) - (a * x31)) - (d * ((x33 - x11) - al1))), (1 / xt)), (len(x33) * len(xt))))
while any((abs(val) < c)):
for v in val:
if ((v < c) and (v >= 0) and np.allclose((v % 1), 0)):
b = v
f1 = ((((a * x32) + (b * x32)) + (e * be1)) + (d * ((x33 - x11) - al1)))
if np.allclose((f1 % f), 0):
HNF = [[a, 0, 0], [b, c, 0], [d, e, f]]
HNFs.append(HNF)
N += 1
val = np.unique(np.reshape(np.outer((((N * f) - (a * x31)) - (d * ((x33 - x11) - al1))), (1 / xt)), (len(x33) * len(xt))))
else:
for b in range(c):
f1 = ((((a * x32) + (b * x32)) + (e * be1)) + (d * ((x33 - x11) - al1)))
if np.allclose((f1 % f), 0):
HNF = [[a, 0, 0], [b, c, 0], [d, e, f]]
HNFs.append(HNF)
elif ((d == None) and (e == None)):
for e in range(f):
if (not np.allclose(xvar2, 0)):
N = min(np.round(((((a * x31) + (b * x32)) - (be1 * e)) / f)))
xt = xvar2[np.nonzero(xvar2)]
val = np.unique(np.reshape(np.outer(((((N * f) - (a * x31)) - (b * x32)) + (be1 * e)), (1 / xt)), (len(xt) * len(x32))))
while any((abs(val) < f)):
for v in val:
if ((v < f) and (v >= 0) and np.allclose((v % 1), 0)):
d = v
f1 = ((((a * x31) + (b * x32)) + (d * var2)) - (be1 * e))
f2 = (((c * x32) - (d * al2)) + (e * ((x33 - x33) - be2)))
if (np.allclose((f1 % f), 0) and np.allclose((f2 % f), 0)):
HNF = [[a, 0, 0], [b, c, 0], [d, e, f]]
HNFs.append(HNF)
N += 1
val = np.unique(np.reshape(np.outer(((((N * f) - (a * x31)) - (b * x32)) + (be1 * e)), (1 / xt)), (len(xt) * len(x32))))
elif (not np.allclose(al2, 0)):
N = max(np.round((((c * x32) + (e * var1)) / f)))
at = al2[np.nonzero(al2)]
val = np.unique(np.reshape(np.outer(((((- N) * f) + (c * x32)) + (e * var1)), (1 / at)), (len(x32) * len(at))))
while any((abs(val) < f)):
for v in val:
if ((v < f) and (v >= 0) and np.allclose((v % 1), 0)):
d = v
f1 = ((((a * x31) + (b * x32)) + (d * var2)) - (be1 * e))
f2 = (((c * x32) - (d * al2)) + (e * ((x33 - x33) - be2)))
if (np.allclose((f1 % f), 0) and np.allclose((f2 % f), 0)):
HNF = [[a, 0, 0], [b, c, 0], [d, e, f]]
HNFs.append(HNF)
N -= 1
val = np.unique(np.reshape(np.outer(((((- N) * f) + (c * x32)) + (e * var1)), (1 / at)), (len(x32) * len(at))))
else:
for d in range(f):
f1 = ((((a * x31) + (b * x32)) + (d * var2)) - (be1 * e))
f2 = (((c * x32) - (d * al2)) + (e * ((x33 - x33) - be2)))
if (np.allclose((f1 % f), 0) and np.allclose((f2 % f), 0)):
HNF = [[a, 0, 0], [b, c, 0], [d, e, f]]
HNFs.append(HNF)
elif ((e == None) or (d == None) or (b == None)):
print('*****************ERROR IN fdivs**************')
else:
f2 = (((c * x32) - (d * al2)) + (e * ((x33 - x22) - be2)))
f1 = ((((a * x31) + (b * x32)) + (e * be1)) + (d * ((x33 - x11) - al1)))
if (np.allclose((f1 % f), 0) and np.allclose((f2 % f), 0)):
HNF = [[a, 0, 0], [b, c, 0], [d, e, f]]
HNFs.append(HNF)
return HNFs | def fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33):
'Finds the f divides conditions for the symmetry preserving HNFs.\n \n Args:\n a (int): a from the HNF.\n b (int): b from the HNF.\n c (int): c from the HNF.\n d (int): d from the HNF.\n e (int): e from the HNF.\n f (int): f from the HNF.\n al1 (numpy.array): array of alpha1 values from write up.\n al2 (numpy.array): array of alpha2 values from write up.\n be1 (numpy.array): array of beta1 values from write up.\n be2 (numpy.array): array of beta2 values from write up.\n x11 (numpy.array): array of pg values for x(1,1) spot.\n x22 (numpy.array): array of pg values for x(2,2) spot.\n x31 (numpy.array): array of pg values for x(3,1) spot.\n x32 (numpy.array): array of pg values for x(3,2) spot.\n x33 (numpy.array): array of pg values for x(3,3) spot.\n \n Returns:\n HNFs (list of lists): The symmetry preserving HNFs.\n '
HNFs = []
if ((b == None) and (d == None) and (e == None)):
xvar1 = ((x33 - x22) - be2)
xvar2 = ((x33 - x11) - al1)
for b in range(c):
for e in range(f):
if (not np.allclose(xvar2, 0)):
N = min(np.round(((((a * x31) + (b * x32)) - (be1 * e)) / f)))
xt = xvar2[np.nonzero(xvar2)]
val = np.unique(np.reshape(np.outer(((((N * f) - (a * x31)) - (b * x32)) + (be1 * e)), (1 / xt)), (len(xt) * len(x32))))
while any((abs(val) < f)):
for v in val:
if ((v < f) and (v >= 0) and np.allclose((v % 1), 0)):
d = v
f1 = ((((a * x31) + (b * x32)) + (d * var2)) - (be1 * e))
f2 = (((c * x32) - (d * al2)) + (e * ((x33 - x33) - be2)))
if (np.allclose((f1 % f), 0) and np.allclose((f2 % f), 0)):
HNF = [[a, 0, 0], [b, c, 0], [d, e, f]]
HNFs.append(HNF)
N += 1
val = np.unique(np.reshape(np.outer(((((N * f) - (a * x31)) - (b * x32)) + (be1 * e)), (1 / xt)), (len(xt) * len(x32))))
elif (not np.allclose(al2, 0)):
N = max(np.round((((c * x32) + (e * var1)) / f)))
at = al2[np.nonzero(al2)]
val = np.unique(np.reshape(np.outer(((((- N) * f) + (c * x32)) + (e * var1)), (1 / at)), (len(x32) * len(at))))
while any((abs(val) < f)):
for v in val:
if ((v < f) and (v >= 0) and np.allclose((v % 1), 0)):
d = v
f1 = ((((a * x31) + (b * x32)) + (d * var2)) - (be1 * e))
f2 = (((c * x32) - (d * al2)) + (e * ((x33 - x33) - be2)))
if (np.allclose((f1 % f), 0) and np.allclose((f2 % f), 0)):
HNF = [[a, 0, 0], [b, c, 0], [d, e, f]]
HNFs.append(HNF)
N -= 1
val = np.unique(np.reshape(np.outer(((((- N) * f) + (c * x32)) + (e * var1)), (1 / at)), (len(x32) * len(at))))
else:
for d in range(f):
f1 = ((((a * x31) + (b * x32)) + (d * var2)) - (be1 * e))
f2 = (((c * x32) - (d * al2)) + (e * ((x33 - x33) - be2)))
if (np.allclose((f1 % f), 0) and np.allclose((f2 % f), 0)):
HNF = [[a, 0, 0], [b, c, 0], [d, e, f]]
HNFs.append(HNF)
elif (b == None):
f2 = (((c * x32) - (d * al2)) + (e * ((x33 - x22) - be2)))
if np.allclose((f2 % f), 0):
if (not np.allclose(x32, 0)):
N = min(np.round(((a * x31) + ((d * ((x33 - x11) - al1)) / f))))
xt = x32[np.nonzero(x32)]
val = np.unique(np.reshape(np.outer((((N * f) - (a * x31)) - (d * ((x33 - x11) - al1))), (1 / xt)), (len(x33) * len(xt))))
while any((abs(val) < c)):
for v in val:
if ((v < c) and (v >= 0) and np.allclose((v % 1), 0)):
b = v
f1 = ((((a * x32) + (b * x32)) + (e * be1)) + (d * ((x33 - x11) - al1)))
if np.allclose((f1 % f), 0):
HNF = [[a, 0, 0], [b, c, 0], [d, e, f]]
HNFs.append(HNF)
N += 1
val = np.unique(np.reshape(np.outer((((N * f) - (a * x31)) - (d * ((x33 - x11) - al1))), (1 / xt)), (len(x33) * len(xt))))
else:
for b in range(c):
f1 = ((((a * x32) + (b * x32)) + (e * be1)) + (d * ((x33 - x11) - al1)))
if np.allclose((f1 % f), 0):
HNF = [[a, 0, 0], [b, c, 0], [d, e, f]]
HNFs.append(HNF)
elif ((d == None) and (e == None)):
for e in range(f):
if (not np.allclose(xvar2, 0)):
N = min(np.round(((((a * x31) + (b * x32)) - (be1 * e)) / f)))
xt = xvar2[np.nonzero(xvar2)]
val = np.unique(np.reshape(np.outer(((((N * f) - (a * x31)) - (b * x32)) + (be1 * e)), (1 / xt)), (len(xt) * len(x32))))
while any((abs(val) < f)):
for v in val:
if ((v < f) and (v >= 0) and np.allclose((v % 1), 0)):
d = v
f1 = ((((a * x31) + (b * x32)) + (d * var2)) - (be1 * e))
f2 = (((c * x32) - (d * al2)) + (e * ((x33 - x33) - be2)))
if (np.allclose((f1 % f), 0) and np.allclose((f2 % f), 0)):
HNF = [[a, 0, 0], [b, c, 0], [d, e, f]]
HNFs.append(HNF)
N += 1
val = np.unique(np.reshape(np.outer(((((N * f) - (a * x31)) - (b * x32)) + (be1 * e)), (1 / xt)), (len(xt) * len(x32))))
elif (not np.allclose(al2, 0)):
N = max(np.round((((c * x32) + (e * var1)) / f)))
at = al2[np.nonzero(al2)]
val = np.unique(np.reshape(np.outer(((((- N) * f) + (c * x32)) + (e * var1)), (1 / at)), (len(x32) * len(at))))
while any((abs(val) < f)):
for v in val:
if ((v < f) and (v >= 0) and np.allclose((v % 1), 0)):
d = v
f1 = ((((a * x31) + (b * x32)) + (d * var2)) - (be1 * e))
f2 = (((c * x32) - (d * al2)) + (e * ((x33 - x33) - be2)))
if (np.allclose((f1 % f), 0) and np.allclose((f2 % f), 0)):
HNF = [[a, 0, 0], [b, c, 0], [d, e, f]]
HNFs.append(HNF)
N -= 1
val = np.unique(np.reshape(np.outer(((((- N) * f) + (c * x32)) + (e * var1)), (1 / at)), (len(x32) * len(at))))
else:
for d in range(f):
f1 = ((((a * x31) + (b * x32)) + (d * var2)) - (be1 * e))
f2 = (((c * x32) - (d * al2)) + (e * ((x33 - x33) - be2)))
if (np.allclose((f1 % f), 0) and np.allclose((f2 % f), 0)):
HNF = [[a, 0, 0], [b, c, 0], [d, e, f]]
HNFs.append(HNF)
elif ((e == None) or (d == None) or (b == None)):
print('*****************ERROR IN fdivs**************')
else:
f2 = (((c * x32) - (d * al2)) + (e * ((x33 - x22) - be2)))
f1 = ((((a * x31) + (b * x32)) + (e * be1)) + (d * ((x33 - x11) - al1)))
if (np.allclose((f1 % f), 0) and np.allclose((f2 % f), 0)):
HNF = [[a, 0, 0], [b, c, 0], [d, e, f]]
HNFs.append(HNF)
return HNFs<|docstring|>Finds the f divides conditions for the symmetry preserving HNFs.
Args:
a (int): a from the HNF.
b (int): b from the HNF.
c (int): c from the HNF.
d (int): d from the HNF.
e (int): e from the HNF.
f (int): f from the HNF.
al1 (numpy.array): array of alpha1 values from write up.
al2 (numpy.array): array of alpha2 values from write up.
be1 (numpy.array): array of beta1 values from write up.
be2 (numpy.array): array of beta2 values from write up.
x11 (numpy.array): array of pg values for x(1,1) spot.
x22 (numpy.array): array of pg values for x(2,2) spot.
x31 (numpy.array): array of pg values for x(3,1) spot.
x32 (numpy.array): array of pg values for x(3,2) spot.
x33 (numpy.array): array of pg values for x(3,3) spot.
Returns:
HNFs (list of lists): The symmetry preserving HNFs.<|endoftext|> |
96bee678e06c1c22cbda81d98e778abaadae7a6cf2262c767a55215ba07b482e | def cdivs(a, b, c, d, e, f, al1, al2, al3, x11, x21, x22, x23, x31, x32, x33):
'Finds the c divides conditions for the symmetry preserving HNFs.\n \n Args:\n a (int): a from the HNF.\n b (int): b from the HNF.\n c (int): c from the HNF.\n d (int): d from the HNF.\n e (int): e from the HNF.\n f (int): f from the HNF.\n al1 (numpy.array): array of alpha1 values from write up.\n al2 (numpy.array): array of alpha2 values from write up.\n al3 (numpy.array): array of alpha3 values from write up.\n x11 (numpy.array): array of pg values for x(1,1) spot.\n x21 (numpy.array): array of pg values for x(2,1) spot.\n x22 (numpy.array): array of pg values for x(2,2) spot.\n x23 (numpy.array): array of pg values for x(2,3) spot.\n x31 (numpy.array): array of pg values for x(3,1) spot.\n x32 (numpy.array): array of pg values for x(3,2) spot.\n x33 (numpy.array): array of pg values for x(3,3) spot.\n \n Returns:\n HNFs (list of lists): The symmetry preserving HNFs.\n '
HNFs = []
if np.allclose(x23, 0):
if (b == None):
if (not np.allclose(al3, 0)):
N = 0
at = al3[np.nonzero(al3)]
val = np.unique(((N * c) / at))
while any((abs(val) < c)):
for v in val:
if ((v < c) and (v >= 0) and np.allclose(((v % 1) == 0))):
b = v
c1 = ((a * x21) + (b * ((x22 - al1) - x11)))
c2 = ((- b) * al2)
if (np.allclose((c1 % c), 0) and np.allclose((c2 % c), 0)):
be1 = (c1 / c)
be2 = (c2 / c)
tHNFs = fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33)
for t in tHNFs:
HNFs.append(t)
N += 1
val = np.unique(((N * c) / at))
elif (not np.allclose(al2, 0)):
N = 0
at = al2[np.nonzero(al2)]
val = np.unique(((N * c) / at))
while any((abs(val) < c)):
for v in val:
if ((v < c) and (v >= 0) and np.allclose((v % 1), 0)):
b = v
c1 = ((a * x21) + (b * ((x22 - al1) - x11)))
c3 = ((- b) * al3)
if (np.allclose((c1 % c), 0) and np.allclose((c3 % c), 0)):
be1 = (c1 / c)
be2 = (((- b) * al2) / c)
tHNFs = fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33)
for t in tHNFs:
HNFs.append(t)
N += 1
val = np.unique(((N * c) / at))
elif (not np.allclose(((x22 - x11) - al1), 0)):
N = 0
xt = ((x22 - x11) - al1)
xt = xt[np.nonzero(xt)]
val = np.unique(np.reshape(np.outer(((N * c) - (a * x21)), (1 / xt)), (len(x21) * len(xt))))
while any((abs(val) < c)):
for v in val:
if ((v < c) and (v >= 0) and np.allclose((v % 1), 0)):
b = v
c2 = ((- b) * al2)
c3 = ((- b) * al3)
if (np.allclose((c2 % c), 0) and np.allclose((c3 % c), 0)):
be1 = (((a * x21) + (b * ((x22 - x11) - al1))) / c)
be2 = (((- b) * al2) / c)
tHNFs = fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33)
for t in HNFs:
HNFs.append(t)
N += 1
xt = ((x22 - x11) - al1)
xt = xt[np.nonzero(xt)]
val = np.unique(np.reshape(np.outer(((N * c) - (a * x21)), (1 / xt)), (len(x21) * len(xt))))
else:
c1 = (a * x21)
c2 = 0
c3 = 0
if (np.allclose((c1 % c), 0) and np.allclose((c2 % c), 0) and np.allclose((c3 % c), 0)):
tHNFs = fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33)
for t in HNFs:
HNFs.append(t)
else:
c1 = ((a * x21) + (b * ((x22 - al1) - x11)))
c2 = ((- b) * al2)
c3 = ((- b) * a13)
if (np.allclose((c1 % c), 0) and np.allclose((c2 % c), 0) and np.allclose((c3 % c), 0)):
tHNFs = fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33)
for t in HNFs:
HNFs.append(t)
elif np.allclose(al3, 0):
if np.allclose(((f * x23) % c), 0):
if ((b == None) and (e == None) and (d == None)):
if (np.allclose(al3, 0) and np.allclose(al2, 0) and np.allclose(al3, 0)):
N = 0
xt = x23[np.nonzero(x23)]
val = np.unique(((N * c) / xt))
while any((abs(val) < f)):
for v in val:
if ((v < f) and (v >= 0) and np.allclose((v % 1), 0)):
e = v
for b in range(c):
N2 = 0
xt = x23[np.nonzero(x23)]
val2 = np.unique(np.reshape(np.outer((((N2 * c) - (a * x21)) - (b * (x22 - x11))), (1 / xt)), (len(x22) * len(xt))))
while any((abs(val2) < f)):
for v2 in val2:
if ((v2 < f) and (v2 >= 0) and np.allclose((v2 % 1), 0)):
d = v2
be1 = ((((a * x21) + (b * (x22 - x11))) + (d * x23)) / c)
be2 = ((e * x23) / c)
tHNFs = fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33)
for t in tHNFs:
HNFs.appned(t)
N2 += 1
xt = x23[np.nonzero(x23)]
val2 = np.unique(np.reshape(np.outer((((N2 * c) - (a * x21)) - (b * (x22 - x11))), (1 / xt)), (len(x22) * len(xt))))
N += 1
val = np.unique(((N * c) / xt))
elif (not np.allclose(al3, 0)):
N = max(np.round(((f * x23) / c)))
at = al3[np.nonzero(al3)]
val = np.unique(np.reshape(np.outer((((- N) * c) + (f * x23)), (1 / at)), (len(x23) * len(al3))))
while any((abs(val) < c)):
for v in val:
if ((v < c) and (v >= 0) and np.allclose((v % 1), 0)):
b = v
N2 = min(np.round((((- b) * al2) / c)))
xt = x23[np.nonzero(x23)]
val2 = np.unique(np.reshape(np.outer(((N2 * c) + (b * al2)), (1 / xt)), (len(xt) * len(al2))))
while any((abs(val2) < f)):
for v2 in val2:
if ((v2 < f) and (v2 >= 0) and np.allclose((v2 % 1), 0)):
e = v2
N3 = min(np.round((((a * x21) + (b * ((x22 - x11) - al1))) / c)))
xt = x23[np.nonzero(x23)]
val3 = np.unique(np.reshape(np.outer((((N3 * c) - (a * x21)) - (b * ((x22 - x11) - al1))), (1 / xt)), (len(xt) * len(x22))))
while any((abs(val2) < f)):
for v3 in val3:
if ((v3 < f) and (v3 >= 0) and np.allclose((v3 % 1), 0)):
d = v3
be1 = ((((a * x21) + (b * ((x22 - x11) - al1))) + (d * x23)) / c)
be2 = (((e * x32) - (b * al2)) / c)
tHNFs = fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33)
for t in tHNFs:
HNFs.append(t)
N3 += 1
xt = x23[np.nonzero(x23)]
val3 = np.unique(np.reshape(np.outer((((N3 * c) - (a * x21)) - (b * ((x22 - x11) - al1))), (1 / xt)), (len(xt) * len(x22))))
N2 += 1
xt = x23[np.nonzero(x23)]
val2 = np.unique(np.reshape(np.outer(((N2 * c) + (b * al2)), (1 / xt)), (len(x22) * len(xt))))
N -= 1
at = al3[np.nonzero(al3)]
val = np.unique(np.reshape(np.outer((((- N) * c) + (f * x23)), (1 / at)), (len(x23) * len(at))))
else:
for b in range(c):
N2 = min(np.round((((- b) * al2) / c)))
xt = x23[np.nonzero(x23)]
val2 = np.unique(np.reshape(np.outer(((N2 * c) + (b * al2)), (1 / xt)), (len(xt) * len(al2))))
while any((abs(val2) < f)):
for v2 in val2:
if ((v2 < f) and (v2 >= 0) and np.allclose((v2 % 1), 0)):
e = v2
N3 = min(np.round((((a * x21) + (b * ((x22 - x11) - al1))) / c)))
xt = x23[np.nonzero(x23)]
val3 = np.unique(np.reshape(np.outer((((N3 * c) - (a * x21)) - (b * ((x22 - x11) - al1))), (1 / xt)), (len(x22) * len(xt))))
while any((abs(val2) < f)):
for v3 in val3:
if ((v3 < f) and (v3 >= 0) and np.allclose((v3 % 1), 0)):
d = v3
be1 = ((((a * x21) + (b * ((x22 - x11) - al1))) + (d * x23)) / c)
be2 = (((e * x32) - (b * al2)) / c)
tHNFs = fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33)
for t in tHNFs:
HNFs.append(t)
N3 += 1
xt = x23[np.nonzero(x23)]
val3 = np.unique(np.reshape(np.outer((((N3 * c) - (a * x21)) - (b * ((x22 - x11) - al1))), (1 / xt)), (len(xt) * len(x22))))
N2 += 1
xt = x23[np.nonzero(x23)]
val2 = np.unique(np.reshape(np.outer(((N2 * c) + (b * al2)), (1 / xt)), (len(al2) * len(xt))))
elif (b == None):
if (not np.allclose(al3, 0)):
N = max(np.round(((f * x23) / c)))
at = al3[np.nonzero(al3)]
val = np.unique(np.reshape(np.outer((((- N) * c) + (f * x23)), (1 / at)), (len(x23) * len(at))))
while any((abs(val) < c)):
for v in val:
if ((v < c) and (v >= 0) and np.allclose((v % 1), 0)):
b = v
c1 = (((a * x21) + (b * ((x22 - x11) - al1))) + (d * x23))
c2 = (((- b) * al2) + (e * x23))
if (np.allclose((c1 % c), 0) and np.allclose((c2 % c), 0)):
be1 = (c1 / c)
be2 = (c2 / c)
tHNFs = fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33)
for t in tHNFs:
HNFs.append(t)
N -= 1
at = al3[np.nonzero(al3)]
val = np.unique(np.reshape(np.outer((((- N) * c) + (f * x23)), (1 / at)), (len(x23) * len(at))))
elif (not np.allclose(al2, 0)):
N = max(np.round(((e * x23) / c)))
at = al2[np.nonzero(al2)]
val = np.unique(np.reshape(np.outer((((- N) * c) + (e * x23)), (1 / at)), (len(x23) * len(at))))
while any((abs(val) < c)):
for v in val:
if ((v < c) and (v >= 0) and np.allclose((v % 1), 0)):
b = v
c1 = (((a * x21) + (b * ((x22 - x11) - al1))) + (d * x23))
c2 = (((- b) * al2) + (e * x23))
if (np.allclose((c1 % c), 0) and np.allclose((c2 % c), 0)):
be1 = (c1 / c)
be2 = (c2 / c)
tHNFs = fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33)
for t in tHNFs:
HNFs.append(t)
N -= 1
at = al2[np.nonzero(al2)]
val = np.unique(np.reshape(np.outer((((- N) * c) + (e * x23)), (1 / at)), (len(x23) * len(at))))
elif (not np.allclose(((x22 - x11) - al1), 0)):
N = min(np.round((((a * x21) - (d * x23)) / c)))
xt = ((x22 - x11) - al1)
xt = xt[np.nonzero(xt)]
val = np.unique(np.reshape(np.outer(((N * c) - ((a * x21sd) * x23)), (1 / xt)), (len(x23) * len(xt))))
while any((abs(val) < c)):
for v in val:
if ((v < c) and (v >= 0) and np.allclose((v % 1), 0)):
b = v
c1 = (((a * x21) + (b * ((x22 - x11) - al1))) + (d * x23))
c2 = (((- b) * al2) + (e * x23))
if (np.allclose((c1 % c), 0) and np.allclose((c2 % c), 0)):
be1 = (c1 / c)
be2 = (c2 / c)
tHNFs = fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33)
for t in tHNFs:
HNFs.append(t)
N += 1
xt = ((x22 - x11) - al1)
xt = xt[np.nonzero(xt)]
val = np.unique(np.reshape(np.outer(((N * c) - ((a * x21sd) * x23)), (1 / xt)), (len(x23) * len(xt))))
else:
c1 = ((a * x21) + (d * x23))
c2 = (e * x23)
c3 = (f * x23)
if (np.allclose((c1 % c), 0) and np.allclose((c2 % c), 0) and np.allclose((c3 % c), 0)):
tHNFs = fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33)
for t in tHNFs:
HNFs.append(t)
elif ((d == None) and (e == None)):
N2 = min(np.round((((- b) * al2) / c)))
xt = x23[np.nonzero(x23)]
val2 = np.unique(np.reshape(np.outer(((N2 * c) + (b * al2)), (1 / xt)), (len(xt) * len(al2))))
while any((abs(val2) < f)):
for v2 in val2:
if ((v2 < f) and (v2 >= 0) and np.allclose((v2 % 1), 0)):
e = v2
N3 = min(np.round((((a * x21) + (b * ((x22 - x11) - al1))) / c)))
xt = x23[np.nonzero(x23)]
val3 = np.unique(np.reshape(np.outer((((N3 * c) - (a * x21)) - (b * ((x22 - x11) - al1))), (1 / xt)), (len(x22) * len(xt))))
while any((abs(val3) < f)):
for v3 in val3:
if ((v3 < f) and (v3 >= 0) and np.allclose((v3 % 1), 0)):
d = v3
be1 = ((((a * x21) + (b * ((x22 - x11) - al1))) + (d * x23)) / c)
be2 = (((e * x32) - (b * al2)) / c)
tHNFs = fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33)
for t in tHNFs:
HNFs.append(t)
N3 += 1
xt = x23[np.nonzero(x23)]
val3 = np.unique(np.reshape(np.outer((((N3 * c) - (a * x21)) - (b * ((x22 - x11) - al1))), (1 / xt)), (len(x22) * len(xt))))
N2 += 1
xt = x23[np.nonzero(x23)]
val2 = np.unique(np.reshape(np.outer(((N2 * c) + (b * al2)), (1 / xt)), (len(xt) * len(al2))))
else:
c1 = (((a * x21) + (b * ((x22 - al1) - x11))) + (d * x23))
c2 = (((- b) * al2) + (e * x23))
c3 = (((- b) * al3) + (f * x23))
if (np.allclose((c1 % c), 0) and np.allclose((c2 % c), 0) and np.allclose((c3 % c), 0)):
be1 = (c1 / c)
be2 = (c2 / c)
tHNFs = fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33)
for t in tHNFs:
HNFs.append(t)
elif ((b == None) and (d == None) and (e == None)):
N = max(np.round(((f * x23) / c)))
at = al3[np.nonzero(al3)]
val = np.unique(np.reshape(np.outer((((- N) * c) + (f * x23)), (1 / at)), (len(x23) * len(at))))
while any((abs(val) < c)):
for v in val:
if ((v < c) and (v >= 0) and np.allclose((v % 1), 0)):
b = v
N2 = min(np.round((((- b) * al2) / c)))
xt = x23[np.nonzero(x23)]
val2 = np.unique(np.reshape(np.outer(((N2 * c) + (b * al2)), (1 / xt)), (len(xt) * len(al2))))
while any((abs(val2) < f)):
for v2 in val2:
if ((v2 < f) and (v2 >= 0) and np.allclose((v2 % 1), 0)):
e = v2
N3 = min(np.round((((a * x21) + (b * ((x22 - x11) - al1))) / c)))
xt = x23[np.nonzero(x23)]
val3 = np.unique(np.reshape(np.outer((((N3 * c) - (a * x21)) - (b * ((x22 - x11) - al1))), (1 / xt)), (len(x22) * len(xt))))
while any((abs(val3) < f)):
for v3 in val3:
if ((v3 < f) and (v3 >= 0) and np.allclose((v3 % 1), 0)):
d = v3
c1 = (((a * x21) + (b * ((x22 - x11) - al1))) + (d * x23))
c2 = (((- b) * al2) + (e * x23))
if (np.allclose((c1 % c), 0) and np.allclose((c2 % c), 0)):
be1 = (c1 / c)
be2 = (c2 / c)
tHNFs = fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33)
for t in tHNFs:
HNFs.append(t)
N3 += 1
xt = x23[np.nonzero(x23)]
val3 = np.unique(np.reshape(np.outer((((N3 * c) - (a * x21)) - (b * ((x22 - x11) - al1))), (1 / xt)), (len(x22) * len(xt))))
N2 += 1
xt = x23[np.nonzero(x23)]
val2 = np.unique(np.reshape(np.outer(((N2 * c) + (b * al2)), (1 / xt)), (len(xt) * len(al2))))
N -= 1
at = al3[np.nonzero(al3)]
val = np.unique(np.reshape(np.outer((((- N) * c) + (f * x23)), (1 / at)), (len(x23) * len(at))))
elif (b == None):
N = max(np.round(((f * x23) / c)))
at = al3[np.nonzero(al3)]
val = np.unique(np.reshape(np.outer((((- N) * c) + (f * x23)), (1 / at)), (len(x23) * len(at))))
while any((abs(val) < c)):
for v in val:
if ((v < c) and (v >= 0) and np.allclose((v % 1), 0)):
b = v
c1 = (((a * x21) + (b * ((x22 - x11) - al1))) + (d * x23))
c2 = (((- b) * al2) + (e * x23))
if (np.allclose((c1 % c), 0) and np.allclose((c2 % c), 0)):
be1 = (c1 / c)
be2 = (c2 / c)
tHNFs = fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33)
for t in tHNFs:
HNFs.append(t)
N -= 1
at = al3[np.nonzero(al3)]
val = np.unique(np.reshape(np.outer((((- N) * c) + (f * x23)), (1 / at)), (len(x23) * len(at))))
elif ((d == None) and (e == None)):
N2 = min(np.round((((- b) * al2) / c)))
xt = x23[np.nonzero(x23)]
val2 = np.unique(np.reshape(np.outer(((N2 * c) + (b * al2)), (1 / xt)), (len(xt) * len(al2))))
while any((abs(val2) < f)):
for v2 in val2:
if ((v2 < f) and (v2 >= 0) and np.allclose((v2 % 1), 0)):
e = v2
N3 = min(np.round((((a * x21) + (b * ((x22 - x11) - al1))) / c)))
xt = x23[np.nonzero(x23)]
val3 = np.unique(np.reshape(np.outer((((N3 * c) - (a * x21)) - (b * ((x22 - x11) - al1))), (1 / xt)), (len(x22) * len(xt))))
while any((abs(val3) < f)):
for v3 in val3:
if ((v3 < f) and (v3 >= 0) and np.allclose((v3 % 1), 0)):
d = v3
c1 = (((a * x21) + (b * ((x22 - x11) - al1))) + (d * x23))
c2 = (((- b) * al2) + (e * x23))
if (np.allclose((c1 % c), 0) and np.allclose((c2 % c), 0)):
be1 = (c1 / c)
be2 = (c2 / c)
tHNFs = fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33)
for t in tHNFs:
HNFs.append(t)
N3 += 1
xt = x23[np.nonzero(x23)]
val3 = np.unique(np.reshape(np.outer((((N3 * c) - (a * x21)) - (b * ((x22 - x11) - al1))), (1 / xt)), (len(x22) * len(xt))))
N2 += 1
xt = x23[np.nonzero(x23)]
val2 = np.unique(np.reshape(np.outer(((N2 * c) + (b * al2)), (1 / xt)), (len(xt) * len(al2))))
else:
be1 = (c1 / c)
be2 = (c2 / c)
tHNFs = fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33)
for t in tHNFs:
HNFs.append(t)
return HNFs | Finds the c divides conditions for the symmetry preserving HNFs.
Args:
a (int): a from the HNF.
b (int): b from the HNF.
c (int): c from the HNF.
d (int): d from the HNF.
e (int): e from the HNF.
f (int): f from the HNF.
al1 (numpy.array): array of alpha1 values from write up.
al2 (numpy.array): array of alpha2 values from write up.
al3 (numpy.array): array of alpha3 values from write up.
x11 (numpy.array): array of pg values for x(1,1) spot.
x21 (numpy.array): array of pg values for x(2,1) spot.
x22 (numpy.array): array of pg values for x(2,2) spot.
x23 (numpy.array): array of pg values for x(2,3) spot.
x31 (numpy.array): array of pg values for x(3,1) spot.
x32 (numpy.array): array of pg values for x(3,2) spot.
x33 (numpy.array): array of pg values for x(3,3) spot.
Returns:
HNFs (list of lists): The symmetry preserving HNFs. | support/brute_force/general_approach.py | cdivs | glwhart/autoGR | 17 | python | def cdivs(a, b, c, d, e, f, al1, al2, al3, x11, x21, x22, x23, x31, x32, x33):
'Finds the c divides conditions for the symmetry preserving HNFs.\n \n Args:\n a (int): a from the HNF.\n b (int): b from the HNF.\n c (int): c from the HNF.\n d (int): d from the HNF.\n e (int): e from the HNF.\n f (int): f from the HNF.\n al1 (numpy.array): array of alpha1 values from write up.\n al2 (numpy.array): array of alpha2 values from write up.\n al3 (numpy.array): array of alpha3 values from write up.\n x11 (numpy.array): array of pg values for x(1,1) spot.\n x21 (numpy.array): array of pg values for x(2,1) spot.\n x22 (numpy.array): array of pg values for x(2,2) spot.\n x23 (numpy.array): array of pg values for x(2,3) spot.\n x31 (numpy.array): array of pg values for x(3,1) spot.\n x32 (numpy.array): array of pg values for x(3,2) spot.\n x33 (numpy.array): array of pg values for x(3,3) spot.\n \n Returns:\n HNFs (list of lists): The symmetry preserving HNFs.\n '
HNFs = []
if np.allclose(x23, 0):
if (b == None):
if (not np.allclose(al3, 0)):
N = 0
at = al3[np.nonzero(al3)]
val = np.unique(((N * c) / at))
while any((abs(val) < c)):
for v in val:
if ((v < c) and (v >= 0) and np.allclose(((v % 1) == 0))):
b = v
c1 = ((a * x21) + (b * ((x22 - al1) - x11)))
c2 = ((- b) * al2)
if (np.allclose((c1 % c), 0) and np.allclose((c2 % c), 0)):
be1 = (c1 / c)
be2 = (c2 / c)
tHNFs = fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33)
for t in tHNFs:
HNFs.append(t)
N += 1
val = np.unique(((N * c) / at))
elif (not np.allclose(al2, 0)):
N = 0
at = al2[np.nonzero(al2)]
val = np.unique(((N * c) / at))
while any((abs(val) < c)):
for v in val:
if ((v < c) and (v >= 0) and np.allclose((v % 1), 0)):
b = v
c1 = ((a * x21) + (b * ((x22 - al1) - x11)))
c3 = ((- b) * al3)
if (np.allclose((c1 % c), 0) and np.allclose((c3 % c), 0)):
be1 = (c1 / c)
be2 = (((- b) * al2) / c)
tHNFs = fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33)
for t in tHNFs:
HNFs.append(t)
N += 1
val = np.unique(((N * c) / at))
elif (not np.allclose(((x22 - x11) - al1), 0)):
N = 0
xt = ((x22 - x11) - al1)
xt = xt[np.nonzero(xt)]
val = np.unique(np.reshape(np.outer(((N * c) - (a * x21)), (1 / xt)), (len(x21) * len(xt))))
while any((abs(val) < c)):
for v in val:
if ((v < c) and (v >= 0) and np.allclose((v % 1), 0)):
b = v
c2 = ((- b) * al2)
c3 = ((- b) * al3)
if (np.allclose((c2 % c), 0) and np.allclose((c3 % c), 0)):
be1 = (((a * x21) + (b * ((x22 - x11) - al1))) / c)
be2 = (((- b) * al2) / c)
tHNFs = fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33)
for t in HNFs:
HNFs.append(t)
N += 1
xt = ((x22 - x11) - al1)
xt = xt[np.nonzero(xt)]
val = np.unique(np.reshape(np.outer(((N * c) - (a * x21)), (1 / xt)), (len(x21) * len(xt))))
else:
c1 = (a * x21)
c2 = 0
c3 = 0
if (np.allclose((c1 % c), 0) and np.allclose((c2 % c), 0) and np.allclose((c3 % c), 0)):
tHNFs = fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33)
for t in HNFs:
HNFs.append(t)
else:
c1 = ((a * x21) + (b * ((x22 - al1) - x11)))
c2 = ((- b) * al2)
c3 = ((- b) * a13)
if (np.allclose((c1 % c), 0) and np.allclose((c2 % c), 0) and np.allclose((c3 % c), 0)):
tHNFs = fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33)
for t in HNFs:
HNFs.append(t)
elif np.allclose(al3, 0):
if np.allclose(((f * x23) % c), 0):
if ((b == None) and (e == None) and (d == None)):
if (np.allclose(al3, 0) and np.allclose(al2, 0) and np.allclose(al3, 0)):
N = 0
xt = x23[np.nonzero(x23)]
val = np.unique(((N * c) / xt))
while any((abs(val) < f)):
for v in val:
if ((v < f) and (v >= 0) and np.allclose((v % 1), 0)):
e = v
for b in range(c):
N2 = 0
xt = x23[np.nonzero(x23)]
val2 = np.unique(np.reshape(np.outer((((N2 * c) - (a * x21)) - (b * (x22 - x11))), (1 / xt)), (len(x22) * len(xt))))
while any((abs(val2) < f)):
for v2 in val2:
if ((v2 < f) and (v2 >= 0) and np.allclose((v2 % 1), 0)):
d = v2
be1 = ((((a * x21) + (b * (x22 - x11))) + (d * x23)) / c)
be2 = ((e * x23) / c)
tHNFs = fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33)
for t in tHNFs:
HNFs.appned(t)
N2 += 1
xt = x23[np.nonzero(x23)]
val2 = np.unique(np.reshape(np.outer((((N2 * c) - (a * x21)) - (b * (x22 - x11))), (1 / xt)), (len(x22) * len(xt))))
N += 1
val = np.unique(((N * c) / xt))
elif (not np.allclose(al3, 0)):
N = max(np.round(((f * x23) / c)))
at = al3[np.nonzero(al3)]
val = np.unique(np.reshape(np.outer((((- N) * c) + (f * x23)), (1 / at)), (len(x23) * len(al3))))
while any((abs(val) < c)):
for v in val:
if ((v < c) and (v >= 0) and np.allclose((v % 1), 0)):
b = v
N2 = min(np.round((((- b) * al2) / c)))
xt = x23[np.nonzero(x23)]
val2 = np.unique(np.reshape(np.outer(((N2 * c) + (b * al2)), (1 / xt)), (len(xt) * len(al2))))
while any((abs(val2) < f)):
for v2 in val2:
if ((v2 < f) and (v2 >= 0) and np.allclose((v2 % 1), 0)):
e = v2
N3 = min(np.round((((a * x21) + (b * ((x22 - x11) - al1))) / c)))
xt = x23[np.nonzero(x23)]
val3 = np.unique(np.reshape(np.outer((((N3 * c) - (a * x21)) - (b * ((x22 - x11) - al1))), (1 / xt)), (len(xt) * len(x22))))
while any((abs(val2) < f)):
for v3 in val3:
if ((v3 < f) and (v3 >= 0) and np.allclose((v3 % 1), 0)):
d = v3
be1 = ((((a * x21) + (b * ((x22 - x11) - al1))) + (d * x23)) / c)
be2 = (((e * x32) - (b * al2)) / c)
tHNFs = fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33)
for t in tHNFs:
HNFs.append(t)
N3 += 1
xt = x23[np.nonzero(x23)]
val3 = np.unique(np.reshape(np.outer((((N3 * c) - (a * x21)) - (b * ((x22 - x11) - al1))), (1 / xt)), (len(xt) * len(x22))))
N2 += 1
xt = x23[np.nonzero(x23)]
val2 = np.unique(np.reshape(np.outer(((N2 * c) + (b * al2)), (1 / xt)), (len(x22) * len(xt))))
N -= 1
at = al3[np.nonzero(al3)]
val = np.unique(np.reshape(np.outer((((- N) * c) + (f * x23)), (1 / at)), (len(x23) * len(at))))
else:
for b in range(c):
N2 = min(np.round((((- b) * al2) / c)))
xt = x23[np.nonzero(x23)]
val2 = np.unique(np.reshape(np.outer(((N2 * c) + (b * al2)), (1 / xt)), (len(xt) * len(al2))))
while any((abs(val2) < f)):
for v2 in val2:
if ((v2 < f) and (v2 >= 0) and np.allclose((v2 % 1), 0)):
e = v2
N3 = min(np.round((((a * x21) + (b * ((x22 - x11) - al1))) / c)))
xt = x23[np.nonzero(x23)]
val3 = np.unique(np.reshape(np.outer((((N3 * c) - (a * x21)) - (b * ((x22 - x11) - al1))), (1 / xt)), (len(x22) * len(xt))))
while any((abs(val2) < f)):
for v3 in val3:
if ((v3 < f) and (v3 >= 0) and np.allclose((v3 % 1), 0)):
d = v3
be1 = ((((a * x21) + (b * ((x22 - x11) - al1))) + (d * x23)) / c)
be2 = (((e * x32) - (b * al2)) / c)
tHNFs = fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33)
for t in tHNFs:
HNFs.append(t)
N3 += 1
xt = x23[np.nonzero(x23)]
val3 = np.unique(np.reshape(np.outer((((N3 * c) - (a * x21)) - (b * ((x22 - x11) - al1))), (1 / xt)), (len(xt) * len(x22))))
N2 += 1
xt = x23[np.nonzero(x23)]
val2 = np.unique(np.reshape(np.outer(((N2 * c) + (b * al2)), (1 / xt)), (len(al2) * len(xt))))
elif (b == None):
if (not np.allclose(al3, 0)):
N = max(np.round(((f * x23) / c)))
at = al3[np.nonzero(al3)]
val = np.unique(np.reshape(np.outer((((- N) * c) + (f * x23)), (1 / at)), (len(x23) * len(at))))
while any((abs(val) < c)):
for v in val:
if ((v < c) and (v >= 0) and np.allclose((v % 1), 0)):
b = v
c1 = (((a * x21) + (b * ((x22 - x11) - al1))) + (d * x23))
c2 = (((- b) * al2) + (e * x23))
if (np.allclose((c1 % c), 0) and np.allclose((c2 % c), 0)):
be1 = (c1 / c)
be2 = (c2 / c)
tHNFs = fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33)
for t in tHNFs:
HNFs.append(t)
N -= 1
at = al3[np.nonzero(al3)]
val = np.unique(np.reshape(np.outer((((- N) * c) + (f * x23)), (1 / at)), (len(x23) * len(at))))
elif (not np.allclose(al2, 0)):
N = max(np.round(((e * x23) / c)))
at = al2[np.nonzero(al2)]
val = np.unique(np.reshape(np.outer((((- N) * c) + (e * x23)), (1 / at)), (len(x23) * len(at))))
while any((abs(val) < c)):
for v in val:
if ((v < c) and (v >= 0) and np.allclose((v % 1), 0)):
b = v
c1 = (((a * x21) + (b * ((x22 - x11) - al1))) + (d * x23))
c2 = (((- b) * al2) + (e * x23))
if (np.allclose((c1 % c), 0) and np.allclose((c2 % c), 0)):
be1 = (c1 / c)
be2 = (c2 / c)
tHNFs = fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33)
for t in tHNFs:
HNFs.append(t)
N -= 1
at = al2[np.nonzero(al2)]
val = np.unique(np.reshape(np.outer((((- N) * c) + (e * x23)), (1 / at)), (len(x23) * len(at))))
elif (not np.allclose(((x22 - x11) - al1), 0)):
N = min(np.round((((a * x21) - (d * x23)) / c)))
xt = ((x22 - x11) - al1)
xt = xt[np.nonzero(xt)]
val = np.unique(np.reshape(np.outer(((N * c) - ((a * x21sd) * x23)), (1 / xt)), (len(x23) * len(xt))))
while any((abs(val) < c)):
for v in val:
if ((v < c) and (v >= 0) and np.allclose((v % 1), 0)):
b = v
c1 = (((a * x21) + (b * ((x22 - x11) - al1))) + (d * x23))
c2 = (((- b) * al2) + (e * x23))
if (np.allclose((c1 % c), 0) and np.allclose((c2 % c), 0)):
be1 = (c1 / c)
be2 = (c2 / c)
tHNFs = fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33)
for t in tHNFs:
HNFs.append(t)
N += 1
xt = ((x22 - x11) - al1)
xt = xt[np.nonzero(xt)]
val = np.unique(np.reshape(np.outer(((N * c) - ((a * x21sd) * x23)), (1 / xt)), (len(x23) * len(xt))))
else:
c1 = ((a * x21) + (d * x23))
c2 = (e * x23)
c3 = (f * x23)
if (np.allclose((c1 % c), 0) and np.allclose((c2 % c), 0) and np.allclose((c3 % c), 0)):
tHNFs = fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33)
for t in tHNFs:
HNFs.append(t)
elif ((d == None) and (e == None)):
N2 = min(np.round((((- b) * al2) / c)))
xt = x23[np.nonzero(x23)]
val2 = np.unique(np.reshape(np.outer(((N2 * c) + (b * al2)), (1 / xt)), (len(xt) * len(al2))))
while any((abs(val2) < f)):
for v2 in val2:
if ((v2 < f) and (v2 >= 0) and np.allclose((v2 % 1), 0)):
e = v2
N3 = min(np.round((((a * x21) + (b * ((x22 - x11) - al1))) / c)))
xt = x23[np.nonzero(x23)]
val3 = np.unique(np.reshape(np.outer((((N3 * c) - (a * x21)) - (b * ((x22 - x11) - al1))), (1 / xt)), (len(x22) * len(xt))))
while any((abs(val3) < f)):
for v3 in val3:
if ((v3 < f) and (v3 >= 0) and np.allclose((v3 % 1), 0)):
d = v3
be1 = ((((a * x21) + (b * ((x22 - x11) - al1))) + (d * x23)) / c)
be2 = (((e * x32) - (b * al2)) / c)
tHNFs = fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33)
for t in tHNFs:
HNFs.append(t)
N3 += 1
xt = x23[np.nonzero(x23)]
val3 = np.unique(np.reshape(np.outer((((N3 * c) - (a * x21)) - (b * ((x22 - x11) - al1))), (1 / xt)), (len(x22) * len(xt))))
N2 += 1
xt = x23[np.nonzero(x23)]
val2 = np.unique(np.reshape(np.outer(((N2 * c) + (b * al2)), (1 / xt)), (len(xt) * len(al2))))
else:
c1 = (((a * x21) + (b * ((x22 - al1) - x11))) + (d * x23))
c2 = (((- b) * al2) + (e * x23))
c3 = (((- b) * al3) + (f * x23))
if (np.allclose((c1 % c), 0) and np.allclose((c2 % c), 0) and np.allclose((c3 % c), 0)):
be1 = (c1 / c)
be2 = (c2 / c)
tHNFs = fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33)
for t in tHNFs:
HNFs.append(t)
elif ((b == None) and (d == None) and (e == None)):
N = max(np.round(((f * x23) / c)))
at = al3[np.nonzero(al3)]
val = np.unique(np.reshape(np.outer((((- N) * c) + (f * x23)), (1 / at)), (len(x23) * len(at))))
while any((abs(val) < c)):
for v in val:
if ((v < c) and (v >= 0) and np.allclose((v % 1), 0)):
b = v
N2 = min(np.round((((- b) * al2) / c)))
xt = x23[np.nonzero(x23)]
val2 = np.unique(np.reshape(np.outer(((N2 * c) + (b * al2)), (1 / xt)), (len(xt) * len(al2))))
while any((abs(val2) < f)):
for v2 in val2:
if ((v2 < f) and (v2 >= 0) and np.allclose((v2 % 1), 0)):
e = v2
N3 = min(np.round((((a * x21) + (b * ((x22 - x11) - al1))) / c)))
xt = x23[np.nonzero(x23)]
val3 = np.unique(np.reshape(np.outer((((N3 * c) - (a * x21)) - (b * ((x22 - x11) - al1))), (1 / xt)), (len(x22) * len(xt))))
while any((abs(val3) < f)):
for v3 in val3:
if ((v3 < f) and (v3 >= 0) and np.allclose((v3 % 1), 0)):
d = v3
c1 = (((a * x21) + (b * ((x22 - x11) - al1))) + (d * x23))
c2 = (((- b) * al2) + (e * x23))
if (np.allclose((c1 % c), 0) and np.allclose((c2 % c), 0)):
be1 = (c1 / c)
be2 = (c2 / c)
tHNFs = fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33)
for t in tHNFs:
HNFs.append(t)
N3 += 1
xt = x23[np.nonzero(x23)]
val3 = np.unique(np.reshape(np.outer((((N3 * c) - (a * x21)) - (b * ((x22 - x11) - al1))), (1 / xt)), (len(x22) * len(xt))))
N2 += 1
xt = x23[np.nonzero(x23)]
val2 = np.unique(np.reshape(np.outer(((N2 * c) + (b * al2)), (1 / xt)), (len(xt) * len(al2))))
N -= 1
at = al3[np.nonzero(al3)]
val = np.unique(np.reshape(np.outer((((- N) * c) + (f * x23)), (1 / at)), (len(x23) * len(at))))
elif (b == None):
N = max(np.round(((f * x23) / c)))
at = al3[np.nonzero(al3)]
val = np.unique(np.reshape(np.outer((((- N) * c) + (f * x23)), (1 / at)), (len(x23) * len(at))))
while any((abs(val) < c)):
for v in val:
if ((v < c) and (v >= 0) and np.allclose((v % 1), 0)):
b = v
c1 = (((a * x21) + (b * ((x22 - x11) - al1))) + (d * x23))
c2 = (((- b) * al2) + (e * x23))
if (np.allclose((c1 % c), 0) and np.allclose((c2 % c), 0)):
be1 = (c1 / c)
be2 = (c2 / c)
tHNFs = fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33)
for t in tHNFs:
HNFs.append(t)
N -= 1
at = al3[np.nonzero(al3)]
val = np.unique(np.reshape(np.outer((((- N) * c) + (f * x23)), (1 / at)), (len(x23) * len(at))))
elif ((d == None) and (e == None)):
N2 = min(np.round((((- b) * al2) / c)))
xt = x23[np.nonzero(x23)]
val2 = np.unique(np.reshape(np.outer(((N2 * c) + (b * al2)), (1 / xt)), (len(xt) * len(al2))))
while any((abs(val2) < f)):
for v2 in val2:
if ((v2 < f) and (v2 >= 0) and np.allclose((v2 % 1), 0)):
e = v2
N3 = min(np.round((((a * x21) + (b * ((x22 - x11) - al1))) / c)))
xt = x23[np.nonzero(x23)]
val3 = np.unique(np.reshape(np.outer((((N3 * c) - (a * x21)) - (b * ((x22 - x11) - al1))), (1 / xt)), (len(x22) * len(xt))))
while any((abs(val3) < f)):
for v3 in val3:
if ((v3 < f) and (v3 >= 0) and np.allclose((v3 % 1), 0)):
d = v3
c1 = (((a * x21) + (b * ((x22 - x11) - al1))) + (d * x23))
c2 = (((- b) * al2) + (e * x23))
if (np.allclose((c1 % c), 0) and np.allclose((c2 % c), 0)):
be1 = (c1 / c)
be2 = (c2 / c)
tHNFs = fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33)
for t in tHNFs:
HNFs.append(t)
N3 += 1
xt = x23[np.nonzero(x23)]
val3 = np.unique(np.reshape(np.outer((((N3 * c) - (a * x21)) - (b * ((x22 - x11) - al1))), (1 / xt)), (len(x22) * len(xt))))
N2 += 1
xt = x23[np.nonzero(x23)]
val2 = np.unique(np.reshape(np.outer(((N2 * c) + (b * al2)), (1 / xt)), (len(xt) * len(al2))))
else:
be1 = (c1 / c)
be2 = (c2 / c)
tHNFs = fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33)
for t in tHNFs:
HNFs.append(t)
return HNFs | def cdivs(a, b, c, d, e, f, al1, al2, al3, x11, x21, x22, x23, x31, x32, x33):
'Finds the c divides conditions for the symmetry preserving HNFs.\n \n Args:\n a (int): a from the HNF.\n b (int): b from the HNF.\n c (int): c from the HNF.\n d (int): d from the HNF.\n e (int): e from the HNF.\n f (int): f from the HNF.\n al1 (numpy.array): array of alpha1 values from write up.\n al2 (numpy.array): array of alpha2 values from write up.\n al3 (numpy.array): array of alpha3 values from write up.\n x11 (numpy.array): array of pg values for x(1,1) spot.\n x21 (numpy.array): array of pg values for x(2,1) spot.\n x22 (numpy.array): array of pg values for x(2,2) spot.\n x23 (numpy.array): array of pg values for x(2,3) spot.\n x31 (numpy.array): array of pg values for x(3,1) spot.\n x32 (numpy.array): array of pg values for x(3,2) spot.\n x33 (numpy.array): array of pg values for x(3,3) spot.\n \n Returns:\n HNFs (list of lists): The symmetry preserving HNFs.\n '
HNFs = []
if np.allclose(x23, 0):
if (b == None):
if (not np.allclose(al3, 0)):
N = 0
at = al3[np.nonzero(al3)]
val = np.unique(((N * c) / at))
while any((abs(val) < c)):
for v in val:
if ((v < c) and (v >= 0) and np.allclose(((v % 1) == 0))):
b = v
c1 = ((a * x21) + (b * ((x22 - al1) - x11)))
c2 = ((- b) * al2)
if (np.allclose((c1 % c), 0) and np.allclose((c2 % c), 0)):
be1 = (c1 / c)
be2 = (c2 / c)
tHNFs = fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33)
for t in tHNFs:
HNFs.append(t)
N += 1
val = np.unique(((N * c) / at))
elif (not np.allclose(al2, 0)):
N = 0
at = al2[np.nonzero(al2)]
val = np.unique(((N * c) / at))
while any((abs(val) < c)):
for v in val:
if ((v < c) and (v >= 0) and np.allclose((v % 1), 0)):
b = v
c1 = ((a * x21) + (b * ((x22 - al1) - x11)))
c3 = ((- b) * al3)
if (np.allclose((c1 % c), 0) and np.allclose((c3 % c), 0)):
be1 = (c1 / c)
be2 = (((- b) * al2) / c)
tHNFs = fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33)
for t in tHNFs:
HNFs.append(t)
N += 1
val = np.unique(((N * c) / at))
elif (not np.allclose(((x22 - x11) - al1), 0)):
N = 0
xt = ((x22 - x11) - al1)
xt = xt[np.nonzero(xt)]
val = np.unique(np.reshape(np.outer(((N * c) - (a * x21)), (1 / xt)), (len(x21) * len(xt))))
while any((abs(val) < c)):
for v in val:
if ((v < c) and (v >= 0) and np.allclose((v % 1), 0)):
b = v
c2 = ((- b) * al2)
c3 = ((- b) * al3)
if (np.allclose((c2 % c), 0) and np.allclose((c3 % c), 0)):
be1 = (((a * x21) + (b * ((x22 - x11) - al1))) / c)
be2 = (((- b) * al2) / c)
tHNFs = fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33)
for t in HNFs:
HNFs.append(t)
N += 1
xt = ((x22 - x11) - al1)
xt = xt[np.nonzero(xt)]
val = np.unique(np.reshape(np.outer(((N * c) - (a * x21)), (1 / xt)), (len(x21) * len(xt))))
else:
c1 = (a * x21)
c2 = 0
c3 = 0
if (np.allclose((c1 % c), 0) and np.allclose((c2 % c), 0) and np.allclose((c3 % c), 0)):
tHNFs = fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33)
for t in HNFs:
HNFs.append(t)
else:
c1 = ((a * x21) + (b * ((x22 - al1) - x11)))
c2 = ((- b) * al2)
c3 = ((- b) * a13)
if (np.allclose((c1 % c), 0) and np.allclose((c2 % c), 0) and np.allclose((c3 % c), 0)):
tHNFs = fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33)
for t in HNFs:
HNFs.append(t)
elif np.allclose(al3, 0):
if np.allclose(((f * x23) % c), 0):
if ((b == None) and (e == None) and (d == None)):
if (np.allclose(al3, 0) and np.allclose(al2, 0) and np.allclose(al3, 0)):
N = 0
xt = x23[np.nonzero(x23)]
val = np.unique(((N * c) / xt))
while any((abs(val) < f)):
for v in val:
if ((v < f) and (v >= 0) and np.allclose((v % 1), 0)):
e = v
for b in range(c):
N2 = 0
xt = x23[np.nonzero(x23)]
val2 = np.unique(np.reshape(np.outer((((N2 * c) - (a * x21)) - (b * (x22 - x11))), (1 / xt)), (len(x22) * len(xt))))
while any((abs(val2) < f)):
for v2 in val2:
if ((v2 < f) and (v2 >= 0) and np.allclose((v2 % 1), 0)):
d = v2
be1 = ((((a * x21) + (b * (x22 - x11))) + (d * x23)) / c)
be2 = ((e * x23) / c)
tHNFs = fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33)
for t in tHNFs:
HNFs.appned(t)
N2 += 1
xt = x23[np.nonzero(x23)]
val2 = np.unique(np.reshape(np.outer((((N2 * c) - (a * x21)) - (b * (x22 - x11))), (1 / xt)), (len(x22) * len(xt))))
N += 1
val = np.unique(((N * c) / xt))
elif (not np.allclose(al3, 0)):
N = max(np.round(((f * x23) / c)))
at = al3[np.nonzero(al3)]
val = np.unique(np.reshape(np.outer((((- N) * c) + (f * x23)), (1 / at)), (len(x23) * len(al3))))
while any((abs(val) < c)):
for v in val:
if ((v < c) and (v >= 0) and np.allclose((v % 1), 0)):
b = v
N2 = min(np.round((((- b) * al2) / c)))
xt = x23[np.nonzero(x23)]
val2 = np.unique(np.reshape(np.outer(((N2 * c) + (b * al2)), (1 / xt)), (len(xt) * len(al2))))
while any((abs(val2) < f)):
for v2 in val2:
if ((v2 < f) and (v2 >= 0) and np.allclose((v2 % 1), 0)):
e = v2
N3 = min(np.round((((a * x21) + (b * ((x22 - x11) - al1))) / c)))
xt = x23[np.nonzero(x23)]
val3 = np.unique(np.reshape(np.outer((((N3 * c) - (a * x21)) - (b * ((x22 - x11) - al1))), (1 / xt)), (len(xt) * len(x22))))
while any((abs(val2) < f)):
for v3 in val3:
if ((v3 < f) and (v3 >= 0) and np.allclose((v3 % 1), 0)):
d = v3
be1 = ((((a * x21) + (b * ((x22 - x11) - al1))) + (d * x23)) / c)
be2 = (((e * x32) - (b * al2)) / c)
tHNFs = fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33)
for t in tHNFs:
HNFs.append(t)
N3 += 1
xt = x23[np.nonzero(x23)]
val3 = np.unique(np.reshape(np.outer((((N3 * c) - (a * x21)) - (b * ((x22 - x11) - al1))), (1 / xt)), (len(xt) * len(x22))))
N2 += 1
xt = x23[np.nonzero(x23)]
val2 = np.unique(np.reshape(np.outer(((N2 * c) + (b * al2)), (1 / xt)), (len(x22) * len(xt))))
N -= 1
at = al3[np.nonzero(al3)]
val = np.unique(np.reshape(np.outer((((- N) * c) + (f * x23)), (1 / at)), (len(x23) * len(at))))
else:
for b in range(c):
N2 = min(np.round((((- b) * al2) / c)))
xt = x23[np.nonzero(x23)]
val2 = np.unique(np.reshape(np.outer(((N2 * c) + (b * al2)), (1 / xt)), (len(xt) * len(al2))))
while any((abs(val2) < f)):
for v2 in val2:
if ((v2 < f) and (v2 >= 0) and np.allclose((v2 % 1), 0)):
e = v2
N3 = min(np.round((((a * x21) + (b * ((x22 - x11) - al1))) / c)))
xt = x23[np.nonzero(x23)]
val3 = np.unique(np.reshape(np.outer((((N3 * c) - (a * x21)) - (b * ((x22 - x11) - al1))), (1 / xt)), (len(x22) * len(xt))))
while any((abs(val2) < f)):
for v3 in val3:
if ((v3 < f) and (v3 >= 0) and np.allclose((v3 % 1), 0)):
d = v3
be1 = ((((a * x21) + (b * ((x22 - x11) - al1))) + (d * x23)) / c)
be2 = (((e * x32) - (b * al2)) / c)
tHNFs = fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33)
for t in tHNFs:
HNFs.append(t)
N3 += 1
xt = x23[np.nonzero(x23)]
val3 = np.unique(np.reshape(np.outer((((N3 * c) - (a * x21)) - (b * ((x22 - x11) - al1))), (1 / xt)), (len(xt) * len(x22))))
N2 += 1
xt = x23[np.nonzero(x23)]
val2 = np.unique(np.reshape(np.outer(((N2 * c) + (b * al2)), (1 / xt)), (len(al2) * len(xt))))
elif (b == None):
if (not np.allclose(al3, 0)):
N = max(np.round(((f * x23) / c)))
at = al3[np.nonzero(al3)]
val = np.unique(np.reshape(np.outer((((- N) * c) + (f * x23)), (1 / at)), (len(x23) * len(at))))
while any((abs(val) < c)):
for v in val:
if ((v < c) and (v >= 0) and np.allclose((v % 1), 0)):
b = v
c1 = (((a * x21) + (b * ((x22 - x11) - al1))) + (d * x23))
c2 = (((- b) * al2) + (e * x23))
if (np.allclose((c1 % c), 0) and np.allclose((c2 % c), 0)):
be1 = (c1 / c)
be2 = (c2 / c)
tHNFs = fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33)
for t in tHNFs:
HNFs.append(t)
N -= 1
at = al3[np.nonzero(al3)]
val = np.unique(np.reshape(np.outer((((- N) * c) + (f * x23)), (1 / at)), (len(x23) * len(at))))
elif (not np.allclose(al2, 0)):
N = max(np.round(((e * x23) / c)))
at = al2[np.nonzero(al2)]
val = np.unique(np.reshape(np.outer((((- N) * c) + (e * x23)), (1 / at)), (len(x23) * len(at))))
while any((abs(val) < c)):
for v in val:
if ((v < c) and (v >= 0) and np.allclose((v % 1), 0)):
b = v
c1 = (((a * x21) + (b * ((x22 - x11) - al1))) + (d * x23))
c2 = (((- b) * al2) + (e * x23))
if (np.allclose((c1 % c), 0) and np.allclose((c2 % c), 0)):
be1 = (c1 / c)
be2 = (c2 / c)
tHNFs = fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33)
for t in tHNFs:
HNFs.append(t)
N -= 1
at = al2[np.nonzero(al2)]
val = np.unique(np.reshape(np.outer((((- N) * c) + (e * x23)), (1 / at)), (len(x23) * len(at))))
elif (not np.allclose(((x22 - x11) - al1), 0)):
N = min(np.round((((a * x21) - (d * x23)) / c)))
xt = ((x22 - x11) - al1)
xt = xt[np.nonzero(xt)]
val = np.unique(np.reshape(np.outer(((N * c) - ((a * x21sd) * x23)), (1 / xt)), (len(x23) * len(xt))))
while any((abs(val) < c)):
for v in val:
if ((v < c) and (v >= 0) and np.allclose((v % 1), 0)):
b = v
c1 = (((a * x21) + (b * ((x22 - x11) - al1))) + (d * x23))
c2 = (((- b) * al2) + (e * x23))
if (np.allclose((c1 % c), 0) and np.allclose((c2 % c), 0)):
be1 = (c1 / c)
be2 = (c2 / c)
tHNFs = fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33)
for t in tHNFs:
HNFs.append(t)
N += 1
xt = ((x22 - x11) - al1)
xt = xt[np.nonzero(xt)]
val = np.unique(np.reshape(np.outer(((N * c) - ((a * x21sd) * x23)), (1 / xt)), (len(x23) * len(xt))))
else:
c1 = ((a * x21) + (d * x23))
c2 = (e * x23)
c3 = (f * x23)
if (np.allclose((c1 % c), 0) and np.allclose((c2 % c), 0) and np.allclose((c3 % c), 0)):
tHNFs = fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33)
for t in tHNFs:
HNFs.append(t)
elif ((d == None) and (e == None)):
N2 = min(np.round((((- b) * al2) / c)))
xt = x23[np.nonzero(x23)]
val2 = np.unique(np.reshape(np.outer(((N2 * c) + (b * al2)), (1 / xt)), (len(xt) * len(al2))))
while any((abs(val2) < f)):
for v2 in val2:
if ((v2 < f) and (v2 >= 0) and np.allclose((v2 % 1), 0)):
e = v2
N3 = min(np.round((((a * x21) + (b * ((x22 - x11) - al1))) / c)))
xt = x23[np.nonzero(x23)]
val3 = np.unique(np.reshape(np.outer((((N3 * c) - (a * x21)) - (b * ((x22 - x11) - al1))), (1 / xt)), (len(x22) * len(xt))))
while any((abs(val3) < f)):
for v3 in val3:
if ((v3 < f) and (v3 >= 0) and np.allclose((v3 % 1), 0)):
d = v3
be1 = ((((a * x21) + (b * ((x22 - x11) - al1))) + (d * x23)) / c)
be2 = (((e * x32) - (b * al2)) / c)
tHNFs = fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33)
for t in tHNFs:
HNFs.append(t)
N3 += 1
xt = x23[np.nonzero(x23)]
val3 = np.unique(np.reshape(np.outer((((N3 * c) - (a * x21)) - (b * ((x22 - x11) - al1))), (1 / xt)), (len(x22) * len(xt))))
N2 += 1
xt = x23[np.nonzero(x23)]
val2 = np.unique(np.reshape(np.outer(((N2 * c) + (b * al2)), (1 / xt)), (len(xt) * len(al2))))
else:
c1 = (((a * x21) + (b * ((x22 - al1) - x11))) + (d * x23))
c2 = (((- b) * al2) + (e * x23))
c3 = (((- b) * al3) + (f * x23))
if (np.allclose((c1 % c), 0) and np.allclose((c2 % c), 0) and np.allclose((c3 % c), 0)):
be1 = (c1 / c)
be2 = (c2 / c)
tHNFs = fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33)
for t in tHNFs:
HNFs.append(t)
elif ((b == None) and (d == None) and (e == None)):
N = max(np.round(((f * x23) / c)))
at = al3[np.nonzero(al3)]
val = np.unique(np.reshape(np.outer((((- N) * c) + (f * x23)), (1 / at)), (len(x23) * len(at))))
while any((abs(val) < c)):
for v in val:
if ((v < c) and (v >= 0) and np.allclose((v % 1), 0)):
b = v
N2 = min(np.round((((- b) * al2) / c)))
xt = x23[np.nonzero(x23)]
val2 = np.unique(np.reshape(np.outer(((N2 * c) + (b * al2)), (1 / xt)), (len(xt) * len(al2))))
while any((abs(val2) < f)):
for v2 in val2:
if ((v2 < f) and (v2 >= 0) and np.allclose((v2 % 1), 0)):
e = v2
N3 = min(np.round((((a * x21) + (b * ((x22 - x11) - al1))) / c)))
xt = x23[np.nonzero(x23)]
val3 = np.unique(np.reshape(np.outer((((N3 * c) - (a * x21)) - (b * ((x22 - x11) - al1))), (1 / xt)), (len(x22) * len(xt))))
while any((abs(val3) < f)):
for v3 in val3:
if ((v3 < f) and (v3 >= 0) and np.allclose((v3 % 1), 0)):
d = v3
c1 = (((a * x21) + (b * ((x22 - x11) - al1))) + (d * x23))
c2 = (((- b) * al2) + (e * x23))
if (np.allclose((c1 % c), 0) and np.allclose((c2 % c), 0)):
be1 = (c1 / c)
be2 = (c2 / c)
tHNFs = fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33)
for t in tHNFs:
HNFs.append(t)
N3 += 1
xt = x23[np.nonzero(x23)]
val3 = np.unique(np.reshape(np.outer((((N3 * c) - (a * x21)) - (b * ((x22 - x11) - al1))), (1 / xt)), (len(x22) * len(xt))))
N2 += 1
xt = x23[np.nonzero(x23)]
val2 = np.unique(np.reshape(np.outer(((N2 * c) + (b * al2)), (1 / xt)), (len(xt) * len(al2))))
N -= 1
at = al3[np.nonzero(al3)]
val = np.unique(np.reshape(np.outer((((- N) * c) + (f * x23)), (1 / at)), (len(x23) * len(at))))
elif (b == None):
N = max(np.round(((f * x23) / c)))
at = al3[np.nonzero(al3)]
val = np.unique(np.reshape(np.outer((((- N) * c) + (f * x23)), (1 / at)), (len(x23) * len(at))))
while any((abs(val) < c)):
for v in val:
if ((v < c) and (v >= 0) and np.allclose((v % 1), 0)):
b = v
c1 = (((a * x21) + (b * ((x22 - x11) - al1))) + (d * x23))
c2 = (((- b) * al2) + (e * x23))
if (np.allclose((c1 % c), 0) and np.allclose((c2 % c), 0)):
be1 = (c1 / c)
be2 = (c2 / c)
tHNFs = fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33)
for t in tHNFs:
HNFs.append(t)
N -= 1
at = al3[np.nonzero(al3)]
val = np.unique(np.reshape(np.outer((((- N) * c) + (f * x23)), (1 / at)), (len(x23) * len(at))))
elif ((d == None) and (e == None)):
N2 = min(np.round((((- b) * al2) / c)))
xt = x23[np.nonzero(x23)]
val2 = np.unique(np.reshape(np.outer(((N2 * c) + (b * al2)), (1 / xt)), (len(xt) * len(al2))))
while any((abs(val2) < f)):
for v2 in val2:
if ((v2 < f) and (v2 >= 0) and np.allclose((v2 % 1), 0)):
e = v2
N3 = min(np.round((((a * x21) + (b * ((x22 - x11) - al1))) / c)))
xt = x23[np.nonzero(x23)]
val3 = np.unique(np.reshape(np.outer((((N3 * c) - (a * x21)) - (b * ((x22 - x11) - al1))), (1 / xt)), (len(x22) * len(xt))))
while any((abs(val3) < f)):
for v3 in val3:
if ((v3 < f) and (v3 >= 0) and np.allclose((v3 % 1), 0)):
d = v3
c1 = (((a * x21) + (b * ((x22 - x11) - al1))) + (d * x23))
c2 = (((- b) * al2) + (e * x23))
if (np.allclose((c1 % c), 0) and np.allclose((c2 % c), 0)):
be1 = (c1 / c)
be2 = (c2 / c)
tHNFs = fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33)
for t in tHNFs:
HNFs.append(t)
N3 += 1
xt = x23[np.nonzero(x23)]
val3 = np.unique(np.reshape(np.outer((((N3 * c) - (a * x21)) - (b * ((x22 - x11) - al1))), (1 / xt)), (len(x22) * len(xt))))
N2 += 1
xt = x23[np.nonzero(x23)]
val2 = np.unique(np.reshape(np.outer(((N2 * c) + (b * al2)), (1 / xt)), (len(xt) * len(al2))))
else:
be1 = (c1 / c)
be2 = (c2 / c)
tHNFs = fdivs(a, b, c, d, e, f, al1, al2, be1, be2, x11, x22, x31, x32, x33)
for t in tHNFs:
HNFs.append(t)
return HNFs<|docstring|>Finds the c divides conditions for the symmetry preserving HNFs.
Args:
a (int): a from the HNF.
b (int): b from the HNF.
c (int): c from the HNF.
d (int): d from the HNF.
e (int): e from the HNF.
f (int): f from the HNF.
al1 (numpy.array): array of alpha1 values from write up.
al2 (numpy.array): array of alpha2 values from write up.
al3 (numpy.array): array of alpha3 values from write up.
x11 (numpy.array): array of pg values for x(1,1) spot.
x21 (numpy.array): array of pg values for x(2,1) spot.
x22 (numpy.array): array of pg values for x(2,2) spot.
x23 (numpy.array): array of pg values for x(2,3) spot.
x31 (numpy.array): array of pg values for x(3,1) spot.
x32 (numpy.array): array of pg values for x(3,2) spot.
x33 (numpy.array): array of pg values for x(3,3) spot.
Returns:
HNFs (list of lists): The symmetry preserving HNFs.<|endoftext|> |
ec0206c7167c91d6f77ce8932f5b6def1d5d8b703e8e206eb097a4fd289c06a3 | def load_configuration(config_info: Dict[(str, str)], extra_vars: Dict[(str, Any)]=None) -> Configuration:
'\n Load the configuration. The `config_info` parameter is a mapping from\n key strings to value as strings or dictionaries. In the former case, the\n value is used as-is. In the latter case, if the dictionary has a key named\n `type` alongside a key named `key`.\n An optional default value is accepted for dictionary value with a key named\n `default`. The default value will be used only if the environment variable\n is not defined.\n\n\n Here is a sample of what it looks like:\n\n ```\n {\n "cert": "/some/path/file.crt",\n "token": {\n "type": "env",\n "key": "MY_TOKEN"\n },\n "host": {\n "type": "env",\n "key": "HOSTNAME",\n "default": "localhost"\n }\n }\n ```\n\n The `cert` configuration key is set to its string value whereas the `token`\n configuration key is dynamically fetched from the `MY_TOKEN` environment\n variable. The `host` configuration key is dynamically fetched from the\n `HOSTNAME` environment variable, but if not defined, the default value\n `localhost` will be used instead.\n\n When `extra_vars` is provided, it must be a dictionnary where keys map\n to configuration key. The values from `extra_vars` always override the\n values from the experiment itself. This is useful to the Chaos Toolkit\n CLI mostly to allow overriding values directly from cli arguments. It\'s\n seldom required otherwise.\n '
logger.debug('Loading configuration...')
env = os.environ
extra_vars = (extra_vars or {})
conf = {}
for (key, value) in config_info.items():
if (isinstance(value, dict) and ('type' in value)):
if (value['type'] == 'env'):
env_key = value['key']
env_default = value.get('default')
if ((env_key not in env) and (env_default is None) and (key not in extra_vars)):
raise InvalidExperiment('Configuration makes reference to an environment key that does not exist: {}'.format(env_key))
conf[key] = extra_vars.get(key, env.get(env_key, env_default))
else:
conf[key] = extra_vars.get(key, value)
return conf | Load the configuration. The `config_info` parameter is a mapping from
key strings to value as strings or dictionaries. In the former case, the
value is used as-is. In the latter case, if the dictionary has a key named
`type` alongside a key named `key`.
An optional default value is accepted for dictionary value with a key named
`default`. The default value will be used only if the environment variable
is not defined.
Here is a sample of what it looks like:
```
{
"cert": "/some/path/file.crt",
"token": {
"type": "env",
"key": "MY_TOKEN"
},
"host": {
"type": "env",
"key": "HOSTNAME",
"default": "localhost"
}
}
```
The `cert` configuration key is set to its string value whereas the `token`
configuration key is dynamically fetched from the `MY_TOKEN` environment
variable. The `host` configuration key is dynamically fetched from the
`HOSTNAME` environment variable, but if not defined, the default value
`localhost` will be used instead.
When `extra_vars` is provided, it must be a dictionnary where keys map
to configuration key. The values from `extra_vars` always override the
values from the experiment itself. This is useful to the Chaos Toolkit
CLI mostly to allow overriding values directly from cli arguments. It's
seldom required otherwise. | chaoslib/configuration.py | load_configuration | roeiK-wix/chaostoolkit-lib | 73 | python | def load_configuration(config_info: Dict[(str, str)], extra_vars: Dict[(str, Any)]=None) -> Configuration:
'\n Load the configuration. The `config_info` parameter is a mapping from\n key strings to value as strings or dictionaries. In the former case, the\n value is used as-is. In the latter case, if the dictionary has a key named\n `type` alongside a key named `key`.\n An optional default value is accepted for dictionary value with a key named\n `default`. The default value will be used only if the environment variable\n is not defined.\n\n\n Here is a sample of what it looks like:\n\n ```\n {\n "cert": "/some/path/file.crt",\n "token": {\n "type": "env",\n "key": "MY_TOKEN"\n },\n "host": {\n "type": "env",\n "key": "HOSTNAME",\n "default": "localhost"\n }\n }\n ```\n\n The `cert` configuration key is set to its string value whereas the `token`\n configuration key is dynamically fetched from the `MY_TOKEN` environment\n variable. The `host` configuration key is dynamically fetched from the\n `HOSTNAME` environment variable, but if not defined, the default value\n `localhost` will be used instead.\n\n When `extra_vars` is provided, it must be a dictionnary where keys map\n to configuration key. The values from `extra_vars` always override the\n values from the experiment itself. This is useful to the Chaos Toolkit\n CLI mostly to allow overriding values directly from cli arguments. It\'s\n seldom required otherwise.\n '
logger.debug('Loading configuration...')
env = os.environ
extra_vars = (extra_vars or {})
conf = {}
for (key, value) in config_info.items():
if (isinstance(value, dict) and ('type' in value)):
if (value['type'] == 'env'):
env_key = value['key']
env_default = value.get('default')
if ((env_key not in env) and (env_default is None) and (key not in extra_vars)):
raise InvalidExperiment('Configuration makes reference to an environment key that does not exist: {}'.format(env_key))
conf[key] = extra_vars.get(key, env.get(env_key, env_default))
else:
conf[key] = extra_vars.get(key, value)
return conf | def load_configuration(config_info: Dict[(str, str)], extra_vars: Dict[(str, Any)]=None) -> Configuration:
'\n Load the configuration. The `config_info` parameter is a mapping from\n key strings to value as strings or dictionaries. In the former case, the\n value is used as-is. In the latter case, if the dictionary has a key named\n `type` alongside a key named `key`.\n An optional default value is accepted for dictionary value with a key named\n `default`. The default value will be used only if the environment variable\n is not defined.\n\n\n Here is a sample of what it looks like:\n\n ```\n {\n "cert": "/some/path/file.crt",\n "token": {\n "type": "env",\n "key": "MY_TOKEN"\n },\n "host": {\n "type": "env",\n "key": "HOSTNAME",\n "default": "localhost"\n }\n }\n ```\n\n The `cert` configuration key is set to its string value whereas the `token`\n configuration key is dynamically fetched from the `MY_TOKEN` environment\n variable. The `host` configuration key is dynamically fetched from the\n `HOSTNAME` environment variable, but if not defined, the default value\n `localhost` will be used instead.\n\n When `extra_vars` is provided, it must be a dictionnary where keys map\n to configuration key. The values from `extra_vars` always override the\n values from the experiment itself. This is useful to the Chaos Toolkit\n CLI mostly to allow overriding values directly from cli arguments. It\'s\n seldom required otherwise.\n '
logger.debug('Loading configuration...')
env = os.environ
extra_vars = (extra_vars or {})
conf = {}
for (key, value) in config_info.items():
if (isinstance(value, dict) and ('type' in value)):
if (value['type'] == 'env'):
env_key = value['key']
env_default = value.get('default')
if ((env_key not in env) and (env_default is None) and (key not in extra_vars)):
raise InvalidExperiment('Configuration makes reference to an environment key that does not exist: {}'.format(env_key))
conf[key] = extra_vars.get(key, env.get(env_key, env_default))
else:
conf[key] = extra_vars.get(key, value)
return conf<|docstring|>Load the configuration. The `config_info` parameter is a mapping from
key strings to value as strings or dictionaries. In the former case, the
value is used as-is. In the latter case, if the dictionary has a key named
`type` alongside a key named `key`.
An optional default value is accepted for dictionary value with a key named
`default`. The default value will be used only if the environment variable
is not defined.
Here is a sample of what it looks like:
```
{
"cert": "/some/path/file.crt",
"token": {
"type": "env",
"key": "MY_TOKEN"
},
"host": {
"type": "env",
"key": "HOSTNAME",
"default": "localhost"
}
}
```
The `cert` configuration key is set to its string value whereas the `token`
configuration key is dynamically fetched from the `MY_TOKEN` environment
variable. The `host` configuration key is dynamically fetched from the
`HOSTNAME` environment variable, but if not defined, the default value
`localhost` will be used instead.
When `extra_vars` is provided, it must be a dictionnary where keys map
to configuration key. The values from `extra_vars` always override the
values from the experiment itself. This is useful to the Chaos Toolkit
CLI mostly to allow overriding values directly from cli arguments. It's
seldom required otherwise.<|endoftext|> |
34116485ed02e983e5612a4fb45c31c14fec1e794c03142b44d697e83784f00b | def test_duplo_tower():
' Tests state machine for duplo tower scenario '
behavior_tree.load_settings_from_file('duplo_state_machine/BT_SETTINGS_TOWER.yaml')
start_positions = []
start_positions.append(Pos((- 0.05), (- 0.1), 0))
start_positions.append(Pos(0, (- 0.1), 0))
start_positions.append(Pos(0.05, (- 0.1), 0))
targets = []
targets.append(Pos(0.0, 0.05, 0))
targets.append(Pos(0.0, 0.05, 0.0192))
targets.append(Pos(0.0, 0.05, (2 * 0.0192)))
environment = duplo_state_machine.environment.Environment(start_positions, targets)
print(environment.get_fitness(['s(', 'f(', '0 at pos (0.0, 0.05, 0.0)?', 's(', 'pick 0!', 'place at (0.0, 0.05, 0.0)!', ')', ')', 'f(', '1 at pos (0.0, 0.05, 0.0192)?', 's(', 'pick 1!', 'place on 0!', ')', ')', ')']))
print(environment.get_fitness(['s(', 'f(', '0 at pos (0.0, 0.05, 0.0)?', 's(', 'pick 0!', 'place at (0.0, 0.05, 0.0)!', ')', ')', 'f(', '1 at pos (0.0, 0.05, 0.0192)?', 's(', 'f(', '1 on 0?', 's(', 'pick 1!', 'place on 0!', ')', ')', 'apply force 1!', ')', ')', ')']))
environment.plot_individual('', 'test', ['s(', 'f(', '0 at pos (0.0, 0.05, 0.0)?', 's(', 'pick 0!', 'place at (0.0, 0.05, 0.0)!', ')', ')', 'f(', '1 at pos (0.0, 0.05, 0.0192)?', 's(', 'f(', '1 on 0?', 's(', 'pick 1!', 'place on 0!', ')', ')', 'apply force 1!', ')', ')', ')'])
environment.plot_individual('', 'test2', ['s(', 'f(', '0 at pos (0.0, 0.05, 0.0)?', 's(', 'f(', 'picked 0?', 'pick 0!', ')', 'place at (0.0, 0.05, 0.0)!', ')', ')', 'f(', '1 at pos (0.0, 0.05, 0.0192)?', 's(', 'f(', 'picked 1?', 'pick 1!', ')', 'place on 0!', ')', ')', 'f(', '2 at pos (0.0, 0.05, 0.0384)?', 's(', 'f(', 'picked 2?', 'pick 2!', ')', 'place on 1!', ')', ')', ')'])
ind = ['s(', 'f(', '0 at pos (0.0, 0.05, 0.0)?', 's(', 'f(', 'picked 0?', 'pick 0!', ')', 'place at (0.0, 0.05, 0.0)!', ')', ')', 'f(', '1 at pos (0.0, 0.05, 0.0192)?', 's(', 'f(', 'picked 1?', 'pick 1!', ')', 'place on 0!', ')', ')', 'f(', '2 at pos (0.0, 0.05, 0.0384)?', 's(', 'f(', 'picked 2?', 'pick 2!', ')', 'place on 1!', ')', ')', ')']
print_and_plot(environment, ind, 'at no force')
ind = ['s(', 'f(', '0 at pos (0.0, 0.05, 0.0)?', 's(', 'f(', 'picked 0?', 'pick 0!', ')', 'place at (0.0, 0.05, 0.0)!', ')', ')', 'f(', '1 on 0?', 's(', 'f(', 'picked 1?', 'pick 1!', ')', 'place on 0!', ')', ')', 'f(', '2 on 1', 's(', 'f(', 'picked 2?', 'pick 2!', ')', 'place on 1!', ')', ')', ')']
print_and_plot(environment, ind, 'on no force')
ind = ['s(', 'f(', '0 at pos (0.0, 0.05, 0.0)?', 's(', 'f(', 'picked 0?', 'pick 0!', ')', 'place at (0.0, 0.05, 0.0)!', ')', ')', 'f(', '1 at pos (0.0, 0.05, 0.0192)?', 's(', 'f(', '1 on 0?', 's(', 'f(', 'picked 1?', 'pick 1!', ')', 'place on 0!', ')', ')', 'apply force 1!', ')', ')', 'f(', '2 at pos (0.0, 0.05, 0.0384)?', 's(', 'f(', '2 on 1?', 's(', 'f(', 'picked 2?', 'pick 2!', ')', 'place on 1!', ')', ')', 'apply force 2!', ')', ')', ')']
print_and_plot(environment, ind, 'on with force')
for i in range(10):
random.seed(i)
ind = ['s(', 'f(', '0 at pos (0.0, 0.05, 0.0)?', 's(', 'pick 0!', 'place at (0.0, 0.05, 0.0)!', ')', ')', 'f(', '1 at pos (0.0, 0.05, 0.0192)?', 's(', 'f(', '1 on 0?', 's(', 'pick 1!', 'place on 0!', ')', ')', 'apply force 1!', ')', ')', 'f(', '2 on 1?', 's(', 'pick 2!', 'place on 1!', ')', ')', 'apply force 2!', ')']
print_and_plot(environment, ind, 'optimal')
ind = ['s(', 'f(', '0 at pos (0.0, 0.05, 0.0)?', 's(', 'f(', 'picked 0?', 'pick 0!', ')', 'place at (0.0, 0.05, 0.0)!', ')', ')', 'f(', '1 at pos (0.0, 0.05, 0.0192)?', 's(', 'f(', '1 on 0?', 's(', 'f(', 'picked 1?', 'pick 1!', ')', 'place on 0!', ')', ')', 'apply force 1!', ')', ')', 'f(', '2 at pos (0.0, 0.05, 0.0384)?', 's(', 'f(', '2 on 1?', 's(', 'f(', 'picked 2?', 'pick 2!', ')', 'place on 1!', ')', ')', 'apply force 2!', ')', ')', ')']
print_and_plot(environment, ind, 'full planned') | Tests state machine for duplo tower scenario | duplo_state_machine/tests/test_duplo.py | test_duplo_tower | jstyrud/planning-and-learning | 8 | python | def test_duplo_tower():
' '
behavior_tree.load_settings_from_file('duplo_state_machine/BT_SETTINGS_TOWER.yaml')
start_positions = []
start_positions.append(Pos((- 0.05), (- 0.1), 0))
start_positions.append(Pos(0, (- 0.1), 0))
start_positions.append(Pos(0.05, (- 0.1), 0))
targets = []
targets.append(Pos(0.0, 0.05, 0))
targets.append(Pos(0.0, 0.05, 0.0192))
targets.append(Pos(0.0, 0.05, (2 * 0.0192)))
environment = duplo_state_machine.environment.Environment(start_positions, targets)
print(environment.get_fitness(['s(', 'f(', '0 at pos (0.0, 0.05, 0.0)?', 's(', 'pick 0!', 'place at (0.0, 0.05, 0.0)!', ')', ')', 'f(', '1 at pos (0.0, 0.05, 0.0192)?', 's(', 'pick 1!', 'place on 0!', ')', ')', ')']))
print(environment.get_fitness(['s(', 'f(', '0 at pos (0.0, 0.05, 0.0)?', 's(', 'pick 0!', 'place at (0.0, 0.05, 0.0)!', ')', ')', 'f(', '1 at pos (0.0, 0.05, 0.0192)?', 's(', 'f(', '1 on 0?', 's(', 'pick 1!', 'place on 0!', ')', ')', 'apply force 1!', ')', ')', ')']))
environment.plot_individual(, 'test', ['s(', 'f(', '0 at pos (0.0, 0.05, 0.0)?', 's(', 'pick 0!', 'place at (0.0, 0.05, 0.0)!', ')', ')', 'f(', '1 at pos (0.0, 0.05, 0.0192)?', 's(', 'f(', '1 on 0?', 's(', 'pick 1!', 'place on 0!', ')', ')', 'apply force 1!', ')', ')', ')'])
environment.plot_individual(, 'test2', ['s(', 'f(', '0 at pos (0.0, 0.05, 0.0)?', 's(', 'f(', 'picked 0?', 'pick 0!', ')', 'place at (0.0, 0.05, 0.0)!', ')', ')', 'f(', '1 at pos (0.0, 0.05, 0.0192)?', 's(', 'f(', 'picked 1?', 'pick 1!', ')', 'place on 0!', ')', ')', 'f(', '2 at pos (0.0, 0.05, 0.0384)?', 's(', 'f(', 'picked 2?', 'pick 2!', ')', 'place on 1!', ')', ')', ')'])
ind = ['s(', 'f(', '0 at pos (0.0, 0.05, 0.0)?', 's(', 'f(', 'picked 0?', 'pick 0!', ')', 'place at (0.0, 0.05, 0.0)!', ')', ')', 'f(', '1 at pos (0.0, 0.05, 0.0192)?', 's(', 'f(', 'picked 1?', 'pick 1!', ')', 'place on 0!', ')', ')', 'f(', '2 at pos (0.0, 0.05, 0.0384)?', 's(', 'f(', 'picked 2?', 'pick 2!', ')', 'place on 1!', ')', ')', ')']
print_and_plot(environment, ind, 'at no force')
ind = ['s(', 'f(', '0 at pos (0.0, 0.05, 0.0)?', 's(', 'f(', 'picked 0?', 'pick 0!', ')', 'place at (0.0, 0.05, 0.0)!', ')', ')', 'f(', '1 on 0?', 's(', 'f(', 'picked 1?', 'pick 1!', ')', 'place on 0!', ')', ')', 'f(', '2 on 1', 's(', 'f(', 'picked 2?', 'pick 2!', ')', 'place on 1!', ')', ')', ')']
print_and_plot(environment, ind, 'on no force')
ind = ['s(', 'f(', '0 at pos (0.0, 0.05, 0.0)?', 's(', 'f(', 'picked 0?', 'pick 0!', ')', 'place at (0.0, 0.05, 0.0)!', ')', ')', 'f(', '1 at pos (0.0, 0.05, 0.0192)?', 's(', 'f(', '1 on 0?', 's(', 'f(', 'picked 1?', 'pick 1!', ')', 'place on 0!', ')', ')', 'apply force 1!', ')', ')', 'f(', '2 at pos (0.0, 0.05, 0.0384)?', 's(', 'f(', '2 on 1?', 's(', 'f(', 'picked 2?', 'pick 2!', ')', 'place on 1!', ')', ')', 'apply force 2!', ')', ')', ')']
print_and_plot(environment, ind, 'on with force')
for i in range(10):
random.seed(i)
ind = ['s(', 'f(', '0 at pos (0.0, 0.05, 0.0)?', 's(', 'pick 0!', 'place at (0.0, 0.05, 0.0)!', ')', ')', 'f(', '1 at pos (0.0, 0.05, 0.0192)?', 's(', 'f(', '1 on 0?', 's(', 'pick 1!', 'place on 0!', ')', ')', 'apply force 1!', ')', ')', 'f(', '2 on 1?', 's(', 'pick 2!', 'place on 1!', ')', ')', 'apply force 2!', ')']
print_and_plot(environment, ind, 'optimal')
ind = ['s(', 'f(', '0 at pos (0.0, 0.05, 0.0)?', 's(', 'f(', 'picked 0?', 'pick 0!', ')', 'place at (0.0, 0.05, 0.0)!', ')', ')', 'f(', '1 at pos (0.0, 0.05, 0.0192)?', 's(', 'f(', '1 on 0?', 's(', 'f(', 'picked 1?', 'pick 1!', ')', 'place on 0!', ')', ')', 'apply force 1!', ')', ')', 'f(', '2 at pos (0.0, 0.05, 0.0384)?', 's(', 'f(', '2 on 1?', 's(', 'f(', 'picked 2?', 'pick 2!', ')', 'place on 1!', ')', ')', 'apply force 2!', ')', ')', ')']
print_and_plot(environment, ind, 'full planned') | def test_duplo_tower():
' '
behavior_tree.load_settings_from_file('duplo_state_machine/BT_SETTINGS_TOWER.yaml')
start_positions = []
start_positions.append(Pos((- 0.05), (- 0.1), 0))
start_positions.append(Pos(0, (- 0.1), 0))
start_positions.append(Pos(0.05, (- 0.1), 0))
targets = []
targets.append(Pos(0.0, 0.05, 0))
targets.append(Pos(0.0, 0.05, 0.0192))
targets.append(Pos(0.0, 0.05, (2 * 0.0192)))
environment = duplo_state_machine.environment.Environment(start_positions, targets)
print(environment.get_fitness(['s(', 'f(', '0 at pos (0.0, 0.05, 0.0)?', 's(', 'pick 0!', 'place at (0.0, 0.05, 0.0)!', ')', ')', 'f(', '1 at pos (0.0, 0.05, 0.0192)?', 's(', 'pick 1!', 'place on 0!', ')', ')', ')']))
print(environment.get_fitness(['s(', 'f(', '0 at pos (0.0, 0.05, 0.0)?', 's(', 'pick 0!', 'place at (0.0, 0.05, 0.0)!', ')', ')', 'f(', '1 at pos (0.0, 0.05, 0.0192)?', 's(', 'f(', '1 on 0?', 's(', 'pick 1!', 'place on 0!', ')', ')', 'apply force 1!', ')', ')', ')']))
environment.plot_individual(, 'test', ['s(', 'f(', '0 at pos (0.0, 0.05, 0.0)?', 's(', 'pick 0!', 'place at (0.0, 0.05, 0.0)!', ')', ')', 'f(', '1 at pos (0.0, 0.05, 0.0192)?', 's(', 'f(', '1 on 0?', 's(', 'pick 1!', 'place on 0!', ')', ')', 'apply force 1!', ')', ')', ')'])
environment.plot_individual(, 'test2', ['s(', 'f(', '0 at pos (0.0, 0.05, 0.0)?', 's(', 'f(', 'picked 0?', 'pick 0!', ')', 'place at (0.0, 0.05, 0.0)!', ')', ')', 'f(', '1 at pos (0.0, 0.05, 0.0192)?', 's(', 'f(', 'picked 1?', 'pick 1!', ')', 'place on 0!', ')', ')', 'f(', '2 at pos (0.0, 0.05, 0.0384)?', 's(', 'f(', 'picked 2?', 'pick 2!', ')', 'place on 1!', ')', ')', ')'])
ind = ['s(', 'f(', '0 at pos (0.0, 0.05, 0.0)?', 's(', 'f(', 'picked 0?', 'pick 0!', ')', 'place at (0.0, 0.05, 0.0)!', ')', ')', 'f(', '1 at pos (0.0, 0.05, 0.0192)?', 's(', 'f(', 'picked 1?', 'pick 1!', ')', 'place on 0!', ')', ')', 'f(', '2 at pos (0.0, 0.05, 0.0384)?', 's(', 'f(', 'picked 2?', 'pick 2!', ')', 'place on 1!', ')', ')', ')']
print_and_plot(environment, ind, 'at no force')
ind = ['s(', 'f(', '0 at pos (0.0, 0.05, 0.0)?', 's(', 'f(', 'picked 0?', 'pick 0!', ')', 'place at (0.0, 0.05, 0.0)!', ')', ')', 'f(', '1 on 0?', 's(', 'f(', 'picked 1?', 'pick 1!', ')', 'place on 0!', ')', ')', 'f(', '2 on 1', 's(', 'f(', 'picked 2?', 'pick 2!', ')', 'place on 1!', ')', ')', ')']
print_and_plot(environment, ind, 'on no force')
ind = ['s(', 'f(', '0 at pos (0.0, 0.05, 0.0)?', 's(', 'f(', 'picked 0?', 'pick 0!', ')', 'place at (0.0, 0.05, 0.0)!', ')', ')', 'f(', '1 at pos (0.0, 0.05, 0.0192)?', 's(', 'f(', '1 on 0?', 's(', 'f(', 'picked 1?', 'pick 1!', ')', 'place on 0!', ')', ')', 'apply force 1!', ')', ')', 'f(', '2 at pos (0.0, 0.05, 0.0384)?', 's(', 'f(', '2 on 1?', 's(', 'f(', 'picked 2?', 'pick 2!', ')', 'place on 1!', ')', ')', 'apply force 2!', ')', ')', ')']
print_and_plot(environment, ind, 'on with force')
for i in range(10):
random.seed(i)
ind = ['s(', 'f(', '0 at pos (0.0, 0.05, 0.0)?', 's(', 'pick 0!', 'place at (0.0, 0.05, 0.0)!', ')', ')', 'f(', '1 at pos (0.0, 0.05, 0.0192)?', 's(', 'f(', '1 on 0?', 's(', 'pick 1!', 'place on 0!', ')', ')', 'apply force 1!', ')', ')', 'f(', '2 on 1?', 's(', 'pick 2!', 'place on 1!', ')', ')', 'apply force 2!', ')']
print_and_plot(environment, ind, 'optimal')
ind = ['s(', 'f(', '0 at pos (0.0, 0.05, 0.0)?', 's(', 'f(', 'picked 0?', 'pick 0!', ')', 'place at (0.0, 0.05, 0.0)!', ')', ')', 'f(', '1 at pos (0.0, 0.05, 0.0192)?', 's(', 'f(', '1 on 0?', 's(', 'f(', 'picked 1?', 'pick 1!', ')', 'place on 0!', ')', ')', 'apply force 1!', ')', ')', 'f(', '2 at pos (0.0, 0.05, 0.0384)?', 's(', 'f(', '2 on 1?', 's(', 'f(', 'picked 2?', 'pick 2!', ')', 'place on 1!', ')', ')', 'apply force 2!', ')', ')', ')']
print_and_plot(environment, ind, 'full planned')<|docstring|>Tests state machine for duplo tower scenario<|endoftext|> |
f785e5271cebe480456540c470435214a07fc2304c03986209b3ea672d7ff220 | def test_duplo_croissant():
' Tests state machine for duplo croissant scenario '
behavior_tree.load_settings_from_file('duplo_state_machine/BT_SETTINGS_CROISSANT.yaml')
start_positions = []
start_positions.append(Pos((- 0.05), (- 0.1), 0))
start_positions.append(Pos(0, (- 0.1), 0))
start_positions.append(Pos(0.05, (- 0.1), 0))
start_positions.append(Pos(0.1, (- 0.1), 0))
targets = []
targets.append(Pos(0.0, 0.0, 0.0))
targets.append(Pos(0.0, 0.0, 0.0192))
targets.append(Pos(0.016, (- 0.032), 0.0))
targets.append(Pos(0.016, 0.032, 0.0))
environment = duplo_state_machine.environment.Environment(start_positions, targets)
best = ['s(', 'f(', '0 at pos (0.0, 0.0, 0.0)?', 's(', 'pick 0!', 'place at (0.0, 0.0, 0.0)!', ')', ')', 'f(', '2 at pos (0.016, -0.032, 0.0)?', 's(', 'pick 2!', 'place at (0.016, -0.032, 0.0)!', ')', ')', 'f(', '3 at pos (0.016, 0.032, 0.0)?', 's(', 'pick 3!', 'place at (0.016, 0.032, 0.0)!', ')', ')', 'f(', '1 at pos (0.0, 0.0, 0.0192)?', 's(', 'f(', '1 on 0?', 's(', 'pick 1!', 'place on 0!', ')', ')', 'apply force 1!', ')', ')', ')']
print(environment.get_fitness(best))
planned = ['s(', 'f(', '0 at pos (0.0, 0.0, 0.0)?', 's(', 'pick 0!', 'place at (0.0, 0.0, 0.0)!', ')', ')', 'f(', '1 at pos (0.0, 0.0, 0.0192)?', 's(', 'f(', '1 on 0?', 's(', 'pick 1!', 'place on 0!', ')', ')', 'apply force 1!', ')', ')', 'f(', '2 at pos (0.016, -0.032, 0.0)?', 's(', 'pick 2!', 'place at (0.016, -0.032, 0.0)!', ')', ')', 'f(', '3 at pos (0.016, 0.032, 0.0)?', 's(', 'pick 3!', 'place at (0.016, 0.032, 0.0)!', ')', ')', ')']
planned = ['s(', 'f(', '0 at pos (0.0, 0.0, 0.0)?', 's(', 'f(', 'picked 0?', 'pick 0!', ')', 'place at (0.0, 0.0, 0.0)!', ')', ')', 'f(', '1 at pos (0.0, 0.0, 0.0192)?', 's(', 'f(', '1 on 0?', 's(', 'f(', 'picked 1?', 'pick 1!', ')', 'place on 0!', ')', ')', 'apply force 1!', ')', ')', 'f(', '2 at pos (0.016, -0.032, 0.0)?', 's(', 'f(', 'picked 2?', 'pick 2!', ')', 'place at (0.016, -0.032, 0.0)!', ')', ')', 'f(', '3 at pos (0.016, 0.032, 0.0)?', 's(', 'f(', 'picked 3?', 'pick 3!', ')', 'place at (0.016, 0.032, 0.0)!', ')', ')', ')']
print(environment.get_fitness(planned))
gp_par = gp.GpParameters()
gp_par.ind_start_length = 8
gp_par.n_population = 16
gp_par.f_crossover = 0.5
gp_par.n_offspring_crossover = 2
gp_par.f_mutation = 0.5
gp_par.n_offspring_mutation = 2
gp_par.parent_selection = gp.SelectionMethods.RANK
gp_par.survivor_selection = gp.SelectionMethods.RANK
gp_par.f_elites = 0.1
gp_par.f_parents = 1
gp_par.mutate_co_offspring = False
gp_par.mutate_co_parents = True
gp_par.mutation_p_add = 0.4
gp_par.mutation_p_delete = 0.3
gp_par.allow_identical = False
gp_par.plot = True
gp_par.n_generations = 50
gp_par.verbose = False
gp_par.fig_last_gen = False
n_logs = 3
for i in range(1, (n_logs + 1)):
gp_par.log_name = ('croissant_baseline_sm_' + str(i))
gp.set_seeds(i)
gp.run(environment, gp_par, baseline=planned) | Tests state machine for duplo croissant scenario | duplo_state_machine/tests/test_duplo.py | test_duplo_croissant | jstyrud/planning-and-learning | 8 | python | def test_duplo_croissant():
' '
behavior_tree.load_settings_from_file('duplo_state_machine/BT_SETTINGS_CROISSANT.yaml')
start_positions = []
start_positions.append(Pos((- 0.05), (- 0.1), 0))
start_positions.append(Pos(0, (- 0.1), 0))
start_positions.append(Pos(0.05, (- 0.1), 0))
start_positions.append(Pos(0.1, (- 0.1), 0))
targets = []
targets.append(Pos(0.0, 0.0, 0.0))
targets.append(Pos(0.0, 0.0, 0.0192))
targets.append(Pos(0.016, (- 0.032), 0.0))
targets.append(Pos(0.016, 0.032, 0.0))
environment = duplo_state_machine.environment.Environment(start_positions, targets)
best = ['s(', 'f(', '0 at pos (0.0, 0.0, 0.0)?', 's(', 'pick 0!', 'place at (0.0, 0.0, 0.0)!', ')', ')', 'f(', '2 at pos (0.016, -0.032, 0.0)?', 's(', 'pick 2!', 'place at (0.016, -0.032, 0.0)!', ')', ')', 'f(', '3 at pos (0.016, 0.032, 0.0)?', 's(', 'pick 3!', 'place at (0.016, 0.032, 0.0)!', ')', ')', 'f(', '1 at pos (0.0, 0.0, 0.0192)?', 's(', 'f(', '1 on 0?', 's(', 'pick 1!', 'place on 0!', ')', ')', 'apply force 1!', ')', ')', ')']
print(environment.get_fitness(best))
planned = ['s(', 'f(', '0 at pos (0.0, 0.0, 0.0)?', 's(', 'pick 0!', 'place at (0.0, 0.0, 0.0)!', ')', ')', 'f(', '1 at pos (0.0, 0.0, 0.0192)?', 's(', 'f(', '1 on 0?', 's(', 'pick 1!', 'place on 0!', ')', ')', 'apply force 1!', ')', ')', 'f(', '2 at pos (0.016, -0.032, 0.0)?', 's(', 'pick 2!', 'place at (0.016, -0.032, 0.0)!', ')', ')', 'f(', '3 at pos (0.016, 0.032, 0.0)?', 's(', 'pick 3!', 'place at (0.016, 0.032, 0.0)!', ')', ')', ')']
planned = ['s(', 'f(', '0 at pos (0.0, 0.0, 0.0)?', 's(', 'f(', 'picked 0?', 'pick 0!', ')', 'place at (0.0, 0.0, 0.0)!', ')', ')', 'f(', '1 at pos (0.0, 0.0, 0.0192)?', 's(', 'f(', '1 on 0?', 's(', 'f(', 'picked 1?', 'pick 1!', ')', 'place on 0!', ')', ')', 'apply force 1!', ')', ')', 'f(', '2 at pos (0.016, -0.032, 0.0)?', 's(', 'f(', 'picked 2?', 'pick 2!', ')', 'place at (0.016, -0.032, 0.0)!', ')', ')', 'f(', '3 at pos (0.016, 0.032, 0.0)?', 's(', 'f(', 'picked 3?', 'pick 3!', ')', 'place at (0.016, 0.032, 0.0)!', ')', ')', ')']
print(environment.get_fitness(planned))
gp_par = gp.GpParameters()
gp_par.ind_start_length = 8
gp_par.n_population = 16
gp_par.f_crossover = 0.5
gp_par.n_offspring_crossover = 2
gp_par.f_mutation = 0.5
gp_par.n_offspring_mutation = 2
gp_par.parent_selection = gp.SelectionMethods.RANK
gp_par.survivor_selection = gp.SelectionMethods.RANK
gp_par.f_elites = 0.1
gp_par.f_parents = 1
gp_par.mutate_co_offspring = False
gp_par.mutate_co_parents = True
gp_par.mutation_p_add = 0.4
gp_par.mutation_p_delete = 0.3
gp_par.allow_identical = False
gp_par.plot = True
gp_par.n_generations = 50
gp_par.verbose = False
gp_par.fig_last_gen = False
n_logs = 3
for i in range(1, (n_logs + 1)):
gp_par.log_name = ('croissant_baseline_sm_' + str(i))
gp.set_seeds(i)
gp.run(environment, gp_par, baseline=planned) | def test_duplo_croissant():
' '
behavior_tree.load_settings_from_file('duplo_state_machine/BT_SETTINGS_CROISSANT.yaml')
start_positions = []
start_positions.append(Pos((- 0.05), (- 0.1), 0))
start_positions.append(Pos(0, (- 0.1), 0))
start_positions.append(Pos(0.05, (- 0.1), 0))
start_positions.append(Pos(0.1, (- 0.1), 0))
targets = []
targets.append(Pos(0.0, 0.0, 0.0))
targets.append(Pos(0.0, 0.0, 0.0192))
targets.append(Pos(0.016, (- 0.032), 0.0))
targets.append(Pos(0.016, 0.032, 0.0))
environment = duplo_state_machine.environment.Environment(start_positions, targets)
best = ['s(', 'f(', '0 at pos (0.0, 0.0, 0.0)?', 's(', 'pick 0!', 'place at (0.0, 0.0, 0.0)!', ')', ')', 'f(', '2 at pos (0.016, -0.032, 0.0)?', 's(', 'pick 2!', 'place at (0.016, -0.032, 0.0)!', ')', ')', 'f(', '3 at pos (0.016, 0.032, 0.0)?', 's(', 'pick 3!', 'place at (0.016, 0.032, 0.0)!', ')', ')', 'f(', '1 at pos (0.0, 0.0, 0.0192)?', 's(', 'f(', '1 on 0?', 's(', 'pick 1!', 'place on 0!', ')', ')', 'apply force 1!', ')', ')', ')']
print(environment.get_fitness(best))
planned = ['s(', 'f(', '0 at pos (0.0, 0.0, 0.0)?', 's(', 'pick 0!', 'place at (0.0, 0.0, 0.0)!', ')', ')', 'f(', '1 at pos (0.0, 0.0, 0.0192)?', 's(', 'f(', '1 on 0?', 's(', 'pick 1!', 'place on 0!', ')', ')', 'apply force 1!', ')', ')', 'f(', '2 at pos (0.016, -0.032, 0.0)?', 's(', 'pick 2!', 'place at (0.016, -0.032, 0.0)!', ')', ')', 'f(', '3 at pos (0.016, 0.032, 0.0)?', 's(', 'pick 3!', 'place at (0.016, 0.032, 0.0)!', ')', ')', ')']
planned = ['s(', 'f(', '0 at pos (0.0, 0.0, 0.0)?', 's(', 'f(', 'picked 0?', 'pick 0!', ')', 'place at (0.0, 0.0, 0.0)!', ')', ')', 'f(', '1 at pos (0.0, 0.0, 0.0192)?', 's(', 'f(', '1 on 0?', 's(', 'f(', 'picked 1?', 'pick 1!', ')', 'place on 0!', ')', ')', 'apply force 1!', ')', ')', 'f(', '2 at pos (0.016, -0.032, 0.0)?', 's(', 'f(', 'picked 2?', 'pick 2!', ')', 'place at (0.016, -0.032, 0.0)!', ')', ')', 'f(', '3 at pos (0.016, 0.032, 0.0)?', 's(', 'f(', 'picked 3?', 'pick 3!', ')', 'place at (0.016, 0.032, 0.0)!', ')', ')', ')']
print(environment.get_fitness(planned))
gp_par = gp.GpParameters()
gp_par.ind_start_length = 8
gp_par.n_population = 16
gp_par.f_crossover = 0.5
gp_par.n_offspring_crossover = 2
gp_par.f_mutation = 0.5
gp_par.n_offspring_mutation = 2
gp_par.parent_selection = gp.SelectionMethods.RANK
gp_par.survivor_selection = gp.SelectionMethods.RANK
gp_par.f_elites = 0.1
gp_par.f_parents = 1
gp_par.mutate_co_offspring = False
gp_par.mutate_co_parents = True
gp_par.mutation_p_add = 0.4
gp_par.mutation_p_delete = 0.3
gp_par.allow_identical = False
gp_par.plot = True
gp_par.n_generations = 50
gp_par.verbose = False
gp_par.fig_last_gen = False
n_logs = 3
for i in range(1, (n_logs + 1)):
gp_par.log_name = ('croissant_baseline_sm_' + str(i))
gp.set_seeds(i)
gp.run(environment, gp_par, baseline=planned)<|docstring|>Tests state machine for duplo croissant scenario<|endoftext|> |
92a40f6c98f501d3d02f5428312789c0c3de25e183c95d5a0a86ad9b368dbd80 | def print_and_plot(environment, bt, name):
' Help function to print and plot bt '
environment.plot_individual('logs/', name, bt)
print(((name + ' ') + str(environment.get_fitness(bt)))) | Help function to print and plot bt | duplo_state_machine/tests/test_duplo.py | print_and_plot | jstyrud/planning-and-learning | 8 | python | def print_and_plot(environment, bt, name):
' '
environment.plot_individual('logs/', name, bt)
print(((name + ' ') + str(environment.get_fitness(bt)))) | def print_and_plot(environment, bt, name):
' '
environment.plot_individual('logs/', name, bt)
print(((name + ' ') + str(environment.get_fitness(bt))))<|docstring|>Help function to print and plot bt<|endoftext|> |
9c715b19b2bb52af46e1f6e9623463164dd275ea92ec0408fa9e02fc38e3f12d | @pytest.mark.skip
def test_check_profile():
'\n Profiles the state machine environment\n '
import cProfile
cProfile.runctx('test_other()', globals=globals(), locals=locals()) | Profiles the state machine environment | duplo_state_machine/tests/test_duplo.py | test_check_profile | jstyrud/planning-and-learning | 8 | python | @pytest.mark.skip
def test_check_profile():
'\n \n '
import cProfile
cProfile.runctx('test_other()', globals=globals(), locals=locals()) | @pytest.mark.skip
def test_check_profile():
'\n \n '
import cProfile
cProfile.runctx('test_other()', globals=globals(), locals=locals())<|docstring|>Profiles the state machine environment<|endoftext|> |
282aeb8d8853620792288d6c72a9140e799ebf0f732414f67eef9c495c98187d | def __init__(self, mode: str, repo_pth: os.PathLike, dataenv: lmdb.Environment, labelenv: lmdb.Environment, *args, **kwargs):
"Developer documentation for init method.\n\n Parameters\n ----------\n mode : str\n 'r' for read-only, 'a' for write-enabled\n repo_pth : os.PathLike\n path to the repository on disk.\n dataenv : lmdb.Environment\n the lmdb environment in which the data records are stored. this is\n the same as the arrayset data record environments.\n labelenv : lmdb.Environment\n the lmdb environment in which the label hash key / values are stored\n permanently. When opened in by this reader instance, no write access\n is allowed.\n "
self._mode = mode
self._path = repo_pth
self._is_conman: bool = False
self._labelenv: lmdb.Environment = labelenv
self._labelTxn: Optional[lmdb.Transaction] = None
self._TxnRegister = TxnRegister()
self._mspecs: Dict[(Union[(str, int)], bytes)] = {}
metaNamesSpec = RecordQuery(dataenv).metadata_records()
for (metaNames, metaSpec) in metaNamesSpec:
labelKey = parsing.hash_meta_db_key_from_raw_key(metaSpec.meta_hash)
self._mspecs[metaNames.meta_name] = labelKey | Developer documentation for init method.
Parameters
----------
mode : str
'r' for read-only, 'a' for write-enabled
repo_pth : os.PathLike
path to the repository on disk.
dataenv : lmdb.Environment
the lmdb environment in which the data records are stored. this is
the same as the arrayset data record environments.
labelenv : lmdb.Environment
the lmdb environment in which the label hash key / values are stored
permanently. When opened in by this reader instance, no write access
is allowed. | src/hangar/metadata.py | __init__ | niranjana687/hangar-py | 1 | python | def __init__(self, mode: str, repo_pth: os.PathLike, dataenv: lmdb.Environment, labelenv: lmdb.Environment, *args, **kwargs):
"Developer documentation for init method.\n\n Parameters\n ----------\n mode : str\n 'r' for read-only, 'a' for write-enabled\n repo_pth : os.PathLike\n path to the repository on disk.\n dataenv : lmdb.Environment\n the lmdb environment in which the data records are stored. this is\n the same as the arrayset data record environments.\n labelenv : lmdb.Environment\n the lmdb environment in which the label hash key / values are stored\n permanently. When opened in by this reader instance, no write access\n is allowed.\n "
self._mode = mode
self._path = repo_pth
self._is_conman: bool = False
self._labelenv: lmdb.Environment = labelenv
self._labelTxn: Optional[lmdb.Transaction] = None
self._TxnRegister = TxnRegister()
self._mspecs: Dict[(Union[(str, int)], bytes)] = {}
metaNamesSpec = RecordQuery(dataenv).metadata_records()
for (metaNames, metaSpec) in metaNamesSpec:
labelKey = parsing.hash_meta_db_key_from_raw_key(metaSpec.meta_hash)
self._mspecs[metaNames.meta_name] = labelKey | def __init__(self, mode: str, repo_pth: os.PathLike, dataenv: lmdb.Environment, labelenv: lmdb.Environment, *args, **kwargs):
"Developer documentation for init method.\n\n Parameters\n ----------\n mode : str\n 'r' for read-only, 'a' for write-enabled\n repo_pth : os.PathLike\n path to the repository on disk.\n dataenv : lmdb.Environment\n the lmdb environment in which the data records are stored. this is\n the same as the arrayset data record environments.\n labelenv : lmdb.Environment\n the lmdb environment in which the label hash key / values are stored\n permanently. When opened in by this reader instance, no write access\n is allowed.\n "
self._mode = mode
self._path = repo_pth
self._is_conman: bool = False
self._labelenv: lmdb.Environment = labelenv
self._labelTxn: Optional[lmdb.Transaction] = None
self._TxnRegister = TxnRegister()
self._mspecs: Dict[(Union[(str, int)], bytes)] = {}
metaNamesSpec = RecordQuery(dataenv).metadata_records()
for (metaNames, metaSpec) in metaNamesSpec:
labelKey = parsing.hash_meta_db_key_from_raw_key(metaSpec.meta_hash)
self._mspecs[metaNames.meta_name] = labelKey<|docstring|>Developer documentation for init method.
Parameters
----------
mode : str
'r' for read-only, 'a' for write-enabled
repo_pth : os.PathLike
path to the repository on disk.
dataenv : lmdb.Environment
the lmdb environment in which the data records are stored. this is
the same as the arrayset data record environments.
labelenv : lmdb.Environment
the lmdb environment in which the label hash key / values are stored
permanently. When opened in by this reader instance, no write access
is allowed.<|endoftext|> |
ad5da069143721010ebc7b0e55e4503071083d6222223bc51d962672049d3337 | def __len__(self) -> int:
'Determine how many metadata key/value pairs are in the checkout\n\n Returns\n -------\n int\n number of metadata key/value pairs.\n '
return len(self._mspecs) | Determine how many metadata key/value pairs are in the checkout
Returns
-------
int
number of metadata key/value pairs. | src/hangar/metadata.py | __len__ | niranjana687/hangar-py | 1 | python | def __len__(self) -> int:
'Determine how many metadata key/value pairs are in the checkout\n\n Returns\n -------\n int\n number of metadata key/value pairs.\n '
return len(self._mspecs) | def __len__(self) -> int:
'Determine how many metadata key/value pairs are in the checkout\n\n Returns\n -------\n int\n number of metadata key/value pairs.\n '
return len(self._mspecs)<|docstring|>Determine how many metadata key/value pairs are in the checkout
Returns
-------
int
number of metadata key/value pairs.<|endoftext|> |
3ec2d082a7757a506cb56db63d019a2701c51fd291d1f3d3a0192ac536bcbb0b | def __getitem__(self, key: Union[(str, int)]) -> str:
'Retrieve a metadata sample with a key. Convenience method for dict style access.\n\n .. seealso:: :meth:`get`\n\n Parameters\n ----------\n key : Union[str, int]\n metadata key to retrieve from the checkout\n\n Returns\n -------\n string\n value of the metadata key/value pair stored in the checkout.\n '
return self.get(key) | Retrieve a metadata sample with a key. Convenience method for dict style access.
.. seealso:: :meth:`get`
Parameters
----------
key : Union[str, int]
metadata key to retrieve from the checkout
Returns
-------
string
value of the metadata key/value pair stored in the checkout. | src/hangar/metadata.py | __getitem__ | niranjana687/hangar-py | 1 | python | def __getitem__(self, key: Union[(str, int)]) -> str:
'Retrieve a metadata sample with a key. Convenience method for dict style access.\n\n .. seealso:: :meth:`get`\n\n Parameters\n ----------\n key : Union[str, int]\n metadata key to retrieve from the checkout\n\n Returns\n -------\n string\n value of the metadata key/value pair stored in the checkout.\n '
return self.get(key) | def __getitem__(self, key: Union[(str, int)]) -> str:
'Retrieve a metadata sample with a key. Convenience method for dict style access.\n\n .. seealso:: :meth:`get`\n\n Parameters\n ----------\n key : Union[str, int]\n metadata key to retrieve from the checkout\n\n Returns\n -------\n string\n value of the metadata key/value pair stored in the checkout.\n '
return self.get(key)<|docstring|>Retrieve a metadata sample with a key. Convenience method for dict style access.
.. seealso:: :meth:`get`
Parameters
----------
key : Union[str, int]
metadata key to retrieve from the checkout
Returns
-------
string
value of the metadata key/value pair stored in the checkout.<|endoftext|> |
32af1caad43adca0ba8bfde5a0023542dd045801e9d11c857663dd5c1ce20a47 | def __contains__(self, key: Union[(str, int)]) -> bool:
'Determine if a key with the provided name is in the metadata\n\n Parameters\n ----------\n key : Union[str, int]\n key to check for containment testing\n\n Returns\n -------\n bool\n True if key exists, False otherwise\n '
if (key in self._mspecs):
return True
else:
return False | Determine if a key with the provided name is in the metadata
Parameters
----------
key : Union[str, int]
key to check for containment testing
Returns
-------
bool
True if key exists, False otherwise | src/hangar/metadata.py | __contains__ | niranjana687/hangar-py | 1 | python | def __contains__(self, key: Union[(str, int)]) -> bool:
'Determine if a key with the provided name is in the metadata\n\n Parameters\n ----------\n key : Union[str, int]\n key to check for containment testing\n\n Returns\n -------\n bool\n True if key exists, False otherwise\n '
if (key in self._mspecs):
return True
else:
return False | def __contains__(self, key: Union[(str, int)]) -> bool:
'Determine if a key with the provided name is in the metadata\n\n Parameters\n ----------\n key : Union[str, int]\n key to check for containment testing\n\n Returns\n -------\n bool\n True if key exists, False otherwise\n '
if (key in self._mspecs):
return True
else:
return False<|docstring|>Determine if a key with the provided name is in the metadata
Parameters
----------
key : Union[str, int]
key to check for containment testing
Returns
-------
bool
True if key exists, False otherwise<|endoftext|> |
14ce8c07c9e6f63556993427c74be17239d9a8fdb1c9f416c3e4f2fa75a91a55 | @property
def iswriteable(self) -> bool:
'Read-only attribute indicating if this metadata object is write-enabled.\n\n Returns\n -------\n bool\n True if write-enabled checkout, Otherwise False.\n '
return (False if (self._mode == 'r') else True) | Read-only attribute indicating if this metadata object is write-enabled.
Returns
-------
bool
True if write-enabled checkout, Otherwise False. | src/hangar/metadata.py | iswriteable | niranjana687/hangar-py | 1 | python | @property
def iswriteable(self) -> bool:
'Read-only attribute indicating if this metadata object is write-enabled.\n\n Returns\n -------\n bool\n True if write-enabled checkout, Otherwise False.\n '
return (False if (self._mode == 'r') else True) | @property
def iswriteable(self) -> bool:
'Read-only attribute indicating if this metadata object is write-enabled.\n\n Returns\n -------\n bool\n True if write-enabled checkout, Otherwise False.\n '
return (False if (self._mode == 'r') else True)<|docstring|>Read-only attribute indicating if this metadata object is write-enabled.
Returns
-------
bool
True if write-enabled checkout, Otherwise False.<|endoftext|> |
3a0e555f4705dad76796d346413268722d9bd73286243120c4c8f2bfedb4b42f | def keys(self) -> Iterator[Union[(str, int)]]:
'generator which yields the names of every metadata piece in the checkout.\n\n For write enabled checkouts, is technically possible to iterate over the\n metadata object while adding/deleting data, in order to avoid internal\n python runtime errors (``dictionary changed size during iteration`` we\n have to make a copy of they key list before beginning the loop.) While\n not necessary for read checkouts, we perform the same operation for both\n read and write checkouts in order in order to avoid differences.\n\n Yields\n ------\n Iterator[Union[str, int]]\n keys of one metadata sample at a time\n '
for name in tuple(self._mspecs.keys()):
(yield name) | generator which yields the names of every metadata piece in the checkout.
For write enabled checkouts, is technically possible to iterate over the
metadata object while adding/deleting data, in order to avoid internal
python runtime errors (``dictionary changed size during iteration`` we
have to make a copy of they key list before beginning the loop.) While
not necessary for read checkouts, we perform the same operation for both
read and write checkouts in order in order to avoid differences.
Yields
------
Iterator[Union[str, int]]
keys of one metadata sample at a time | src/hangar/metadata.py | keys | niranjana687/hangar-py | 1 | python | def keys(self) -> Iterator[Union[(str, int)]]:
'generator which yields the names of every metadata piece in the checkout.\n\n For write enabled checkouts, is technically possible to iterate over the\n metadata object while adding/deleting data, in order to avoid internal\n python runtime errors (``dictionary changed size during iteration`` we\n have to make a copy of they key list before beginning the loop.) While\n not necessary for read checkouts, we perform the same operation for both\n read and write checkouts in order in order to avoid differences.\n\n Yields\n ------\n Iterator[Union[str, int]]\n keys of one metadata sample at a time\n '
for name in tuple(self._mspecs.keys()):
(yield name) | def keys(self) -> Iterator[Union[(str, int)]]:
'generator which yields the names of every metadata piece in the checkout.\n\n For write enabled checkouts, is technically possible to iterate over the\n metadata object while adding/deleting data, in order to avoid internal\n python runtime errors (``dictionary changed size during iteration`` we\n have to make a copy of they key list before beginning the loop.) While\n not necessary for read checkouts, we perform the same operation for both\n read and write checkouts in order in order to avoid differences.\n\n Yields\n ------\n Iterator[Union[str, int]]\n keys of one metadata sample at a time\n '
for name in tuple(self._mspecs.keys()):
(yield name)<|docstring|>generator which yields the names of every metadata piece in the checkout.
For write enabled checkouts, is technically possible to iterate over the
metadata object while adding/deleting data, in order to avoid internal
python runtime errors (``dictionary changed size during iteration`` we
have to make a copy of they key list before beginning the loop.) While
not necessary for read checkouts, we perform the same operation for both
read and write checkouts in order in order to avoid differences.
Yields
------
Iterator[Union[str, int]]
keys of one metadata sample at a time<|endoftext|> |
befa1af6f4bf3f4938eef6b374d6c09702695a90994346876d295e493423b958 | def values(self) -> Iterator[str]:
'generator yielding all metadata values in the checkout\n\n For write enabled checkouts, is technically possible to iterate over the\n metadata object while adding/deleting data, in order to avoid internal\n python runtime errors (``dictionary changed size during iteration`` we\n have to make a copy of they key list before beginning the loop.) While\n not necessary for read checkouts, we perform the same operation for both\n read and write checkouts in order in order to avoid differences.\n\n Yields\n ------\n Iterator[str]\n values of one metadata piece at a time\n '
for name in tuple(self._mspecs.keys()):
(yield self.get(name)) | generator yielding all metadata values in the checkout
For write enabled checkouts, is technically possible to iterate over the
metadata object while adding/deleting data, in order to avoid internal
python runtime errors (``dictionary changed size during iteration`` we
have to make a copy of they key list before beginning the loop.) While
not necessary for read checkouts, we perform the same operation for both
read and write checkouts in order in order to avoid differences.
Yields
------
Iterator[str]
values of one metadata piece at a time | src/hangar/metadata.py | values | niranjana687/hangar-py | 1 | python | def values(self) -> Iterator[str]:
'generator yielding all metadata values in the checkout\n\n For write enabled checkouts, is technically possible to iterate over the\n metadata object while adding/deleting data, in order to avoid internal\n python runtime errors (``dictionary changed size during iteration`` we\n have to make a copy of they key list before beginning the loop.) While\n not necessary for read checkouts, we perform the same operation for both\n read and write checkouts in order in order to avoid differences.\n\n Yields\n ------\n Iterator[str]\n values of one metadata piece at a time\n '
for name in tuple(self._mspecs.keys()):
(yield self.get(name)) | def values(self) -> Iterator[str]:
'generator yielding all metadata values in the checkout\n\n For write enabled checkouts, is technically possible to iterate over the\n metadata object while adding/deleting data, in order to avoid internal\n python runtime errors (``dictionary changed size during iteration`` we\n have to make a copy of they key list before beginning the loop.) While\n not necessary for read checkouts, we perform the same operation for both\n read and write checkouts in order in order to avoid differences.\n\n Yields\n ------\n Iterator[str]\n values of one metadata piece at a time\n '
for name in tuple(self._mspecs.keys()):
(yield self.get(name))<|docstring|>generator yielding all metadata values in the checkout
For write enabled checkouts, is technically possible to iterate over the
metadata object while adding/deleting data, in order to avoid internal
python runtime errors (``dictionary changed size during iteration`` we
have to make a copy of they key list before beginning the loop.) While
not necessary for read checkouts, we perform the same operation for both
read and write checkouts in order in order to avoid differences.
Yields
------
Iterator[str]
values of one metadata piece at a time<|endoftext|> |
b79d16cd89d6f2c082dfb1584bfcd6c4eee4c5d54720cbdea64973d2cadd0dda | def items(self) -> Iterator[Tuple[(Union[(str, int)], str)]]:
'generator yielding key/value for all metadata recorded in checkout.\n\n For write enabled checkouts, is technically possible to iterate over the\n metadata object while adding/deleting data, in order to avoid internal\n python runtime errors (``dictionary changed size during iteration`` we\n have to make a copy of they key list before beginning the loop.) While\n not necessary for read checkouts, we perform the same operation for both\n read and write checkouts in order in order to avoid differences.\n\n Yields\n ------\n Iterator[Tuple[Union[str, int], np.ndarray]]\n metadata key and stored value for every piece in the checkout.\n '
for name in tuple(self._mspecs.keys()):
(yield (name, self.get(name))) | generator yielding key/value for all metadata recorded in checkout.
For write enabled checkouts, is technically possible to iterate over the
metadata object while adding/deleting data, in order to avoid internal
python runtime errors (``dictionary changed size during iteration`` we
have to make a copy of they key list before beginning the loop.) While
not necessary for read checkouts, we perform the same operation for both
read and write checkouts in order in order to avoid differences.
Yields
------
Iterator[Tuple[Union[str, int], np.ndarray]]
metadata key and stored value for every piece in the checkout. | src/hangar/metadata.py | items | niranjana687/hangar-py | 1 | python | def items(self) -> Iterator[Tuple[(Union[(str, int)], str)]]:
'generator yielding key/value for all metadata recorded in checkout.\n\n For write enabled checkouts, is technically possible to iterate over the\n metadata object while adding/deleting data, in order to avoid internal\n python runtime errors (``dictionary changed size during iteration`` we\n have to make a copy of they key list before beginning the loop.) While\n not necessary for read checkouts, we perform the same operation for both\n read and write checkouts in order in order to avoid differences.\n\n Yields\n ------\n Iterator[Tuple[Union[str, int], np.ndarray]]\n metadata key and stored value for every piece in the checkout.\n '
for name in tuple(self._mspecs.keys()):
(yield (name, self.get(name))) | def items(self) -> Iterator[Tuple[(Union[(str, int)], str)]]:
'generator yielding key/value for all metadata recorded in checkout.\n\n For write enabled checkouts, is technically possible to iterate over the\n metadata object while adding/deleting data, in order to avoid internal\n python runtime errors (``dictionary changed size during iteration`` we\n have to make a copy of they key list before beginning the loop.) While\n not necessary for read checkouts, we perform the same operation for both\n read and write checkouts in order in order to avoid differences.\n\n Yields\n ------\n Iterator[Tuple[Union[str, int], np.ndarray]]\n metadata key and stored value for every piece in the checkout.\n '
for name in tuple(self._mspecs.keys()):
(yield (name, self.get(name)))<|docstring|>generator yielding key/value for all metadata recorded in checkout.
For write enabled checkouts, is technically possible to iterate over the
metadata object while adding/deleting data, in order to avoid internal
python runtime errors (``dictionary changed size during iteration`` we
have to make a copy of they key list before beginning the loop.) While
not necessary for read checkouts, we perform the same operation for both
read and write checkouts in order in order to avoid differences.
Yields
------
Iterator[Tuple[Union[str, int], np.ndarray]]
metadata key and stored value for every piece in the checkout.<|endoftext|> |
f1b1a1ce31090ca1bf9379e021cbfef510b806192e4ba1c194c133d24ab21722 | def get(self, key: Union[(str, int)]) -> str:
'retrieve a piece of metadata from the checkout.\n\n Parameters\n ----------\n key : Union[str, int]\n The name of the metadata piece to retrieve.\n\n Returns\n -------\n str\n The stored metadata value associated with the key.\n\n Raises\n ------\n ValueError\n If the `key` is not str type or contains whitespace or non\n alpha-numeric characters.\n KeyError\n If no metadata exists in the checkout with the provided key.\n '
try:
tmpconman = (not self._is_conman)
if tmpconman:
self.__enter__()
metaVal = self._labelTxn.get(self._mspecs[key])
meta_val = parsing.hash_meta_raw_val_from_db_val(metaVal)
except KeyError:
raise KeyError(f'The checkout does not contain metadata with key: {key}')
finally:
if tmpconman:
self.__exit__()
return meta_val | retrieve a piece of metadata from the checkout.
Parameters
----------
key : Union[str, int]
The name of the metadata piece to retrieve.
Returns
-------
str
The stored metadata value associated with the key.
Raises
------
ValueError
If the `key` is not str type or contains whitespace or non
alpha-numeric characters.
KeyError
If no metadata exists in the checkout with the provided key. | src/hangar/metadata.py | get | niranjana687/hangar-py | 1 | python | def get(self, key: Union[(str, int)]) -> str:
'retrieve a piece of metadata from the checkout.\n\n Parameters\n ----------\n key : Union[str, int]\n The name of the metadata piece to retrieve.\n\n Returns\n -------\n str\n The stored metadata value associated with the key.\n\n Raises\n ------\n ValueError\n If the `key` is not str type or contains whitespace or non\n alpha-numeric characters.\n KeyError\n If no metadata exists in the checkout with the provided key.\n '
try:
tmpconman = (not self._is_conman)
if tmpconman:
self.__enter__()
metaVal = self._labelTxn.get(self._mspecs[key])
meta_val = parsing.hash_meta_raw_val_from_db_val(metaVal)
except KeyError:
raise KeyError(f'The checkout does not contain metadata with key: {key}')
finally:
if tmpconman:
self.__exit__()
return meta_val | def get(self, key: Union[(str, int)]) -> str:
'retrieve a piece of metadata from the checkout.\n\n Parameters\n ----------\n key : Union[str, int]\n The name of the metadata piece to retrieve.\n\n Returns\n -------\n str\n The stored metadata value associated with the key.\n\n Raises\n ------\n ValueError\n If the `key` is not str type or contains whitespace or non\n alpha-numeric characters.\n KeyError\n If no metadata exists in the checkout with the provided key.\n '
try:
tmpconman = (not self._is_conman)
if tmpconman:
self.__enter__()
metaVal = self._labelTxn.get(self._mspecs[key])
meta_val = parsing.hash_meta_raw_val_from_db_val(metaVal)
except KeyError:
raise KeyError(f'The checkout does not contain metadata with key: {key}')
finally:
if tmpconman:
self.__exit__()
return meta_val<|docstring|>retrieve a piece of metadata from the checkout.
Parameters
----------
key : Union[str, int]
The name of the metadata piece to retrieve.
Returns
-------
str
The stored metadata value associated with the key.
Raises
------
ValueError
If the `key` is not str type or contains whitespace or non
alpha-numeric characters.
KeyError
If no metadata exists in the checkout with the provided key.<|endoftext|> |
60b303d25f7255fec363c8176ccd166b8ab337bfc33d2265de88afa9a78100c3 | def __init__(self, *args, **kwargs):
'Developer documentation of init method\n\n Parameters\n ----------\n *args\n Arguments passed to :class:`MetadataReader`\n **kwargs\n KeyWord arguments passed to :class:`MetadataReader`\n '
super().__init__(*args, **kwargs)
self._dataenv: lmdb.Environment = kwargs['dataenv']
self._dataTxn: Optional[lmdb.Transaction] = None | Developer documentation of init method
Parameters
----------
*args
Arguments passed to :class:`MetadataReader`
**kwargs
KeyWord arguments passed to :class:`MetadataReader` | src/hangar/metadata.py | __init__ | niranjana687/hangar-py | 1 | python | def __init__(self, *args, **kwargs):
'Developer documentation of init method\n\n Parameters\n ----------\n *args\n Arguments passed to :class:`MetadataReader`\n **kwargs\n KeyWord arguments passed to :class:`MetadataReader`\n '
super().__init__(*args, **kwargs)
self._dataenv: lmdb.Environment = kwargs['dataenv']
self._dataTxn: Optional[lmdb.Transaction] = None | def __init__(self, *args, **kwargs):
'Developer documentation of init method\n\n Parameters\n ----------\n *args\n Arguments passed to :class:`MetadataReader`\n **kwargs\n KeyWord arguments passed to :class:`MetadataReader`\n '
super().__init__(*args, **kwargs)
self._dataenv: lmdb.Environment = kwargs['dataenv']
self._dataTxn: Optional[lmdb.Transaction] = None<|docstring|>Developer documentation of init method
Parameters
----------
*args
Arguments passed to :class:`MetadataReader`
**kwargs
KeyWord arguments passed to :class:`MetadataReader`<|endoftext|> |
8f1470bc26d87a2a2adf2059910b056cbf8a13d99acc7f99c30a168bdbc7cf40 | def __setitem__(self, key: Union[(str, int)], value: str) -> Union[(str, int)]:
'Store a key/value pair as metadata. Convenience method to :meth:`add`.\n\n .. seealso:: :meth:`add`\n\n Parameters\n ----------\n key : Union[str, int]\n name of the key to add as metadata\n value : string\n value to add as metadata\n\n Returns\n -------\n Union[str, int]\n key of the stored metadata sample (assuming operation was successful)\n '
return self.add(key, value) | Store a key/value pair as metadata. Convenience method to :meth:`add`.
.. seealso:: :meth:`add`
Parameters
----------
key : Union[str, int]
name of the key to add as metadata
value : string
value to add as metadata
Returns
-------
Union[str, int]
key of the stored metadata sample (assuming operation was successful) | src/hangar/metadata.py | __setitem__ | niranjana687/hangar-py | 1 | python | def __setitem__(self, key: Union[(str, int)], value: str) -> Union[(str, int)]:
'Store a key/value pair as metadata. Convenience method to :meth:`add`.\n\n .. seealso:: :meth:`add`\n\n Parameters\n ----------\n key : Union[str, int]\n name of the key to add as metadata\n value : string\n value to add as metadata\n\n Returns\n -------\n Union[str, int]\n key of the stored metadata sample (assuming operation was successful)\n '
return self.add(key, value) | def __setitem__(self, key: Union[(str, int)], value: str) -> Union[(str, int)]:
'Store a key/value pair as metadata. Convenience method to :meth:`add`.\n\n .. seealso:: :meth:`add`\n\n Parameters\n ----------\n key : Union[str, int]\n name of the key to add as metadata\n value : string\n value to add as metadata\n\n Returns\n -------\n Union[str, int]\n key of the stored metadata sample (assuming operation was successful)\n '
return self.add(key, value)<|docstring|>Store a key/value pair as metadata. Convenience method to :meth:`add`.
.. seealso:: :meth:`add`
Parameters
----------
key : Union[str, int]
name of the key to add as metadata
value : string
value to add as metadata
Returns
-------
Union[str, int]
key of the stored metadata sample (assuming operation was successful)<|endoftext|> |
809e7744b5f7dc786bc5f9cb6ffb1e9b256d60cbb4f4aeb014ffc83e28de9606 | def __delitem__(self, key: Union[(str, int)]) -> Union[(str, int)]:
'Remove a key/value pair from metadata. Convenience method to :meth:`remove`.\n\n .. seealso:: :meth:`remove` for the function this calls into.\n\n Parameters\n ----------\n key : Union[str, int]\n Name of the metadata piece to remove.\n\n Returns\n -------\n Union[str, int]\n Metadata key removed from the checkout (assuming operation successful)\n '
return self.remove(key) | Remove a key/value pair from metadata. Convenience method to :meth:`remove`.
.. seealso:: :meth:`remove` for the function this calls into.
Parameters
----------
key : Union[str, int]
Name of the metadata piece to remove.
Returns
-------
Union[str, int]
Metadata key removed from the checkout (assuming operation successful) | src/hangar/metadata.py | __delitem__ | niranjana687/hangar-py | 1 | python | def __delitem__(self, key: Union[(str, int)]) -> Union[(str, int)]:
'Remove a key/value pair from metadata. Convenience method to :meth:`remove`.\n\n .. seealso:: :meth:`remove` for the function this calls into.\n\n Parameters\n ----------\n key : Union[str, int]\n Name of the metadata piece to remove.\n\n Returns\n -------\n Union[str, int]\n Metadata key removed from the checkout (assuming operation successful)\n '
return self.remove(key) | def __delitem__(self, key: Union[(str, int)]) -> Union[(str, int)]:
'Remove a key/value pair from metadata. Convenience method to :meth:`remove`.\n\n .. seealso:: :meth:`remove` for the function this calls into.\n\n Parameters\n ----------\n key : Union[str, int]\n Name of the metadata piece to remove.\n\n Returns\n -------\n Union[str, int]\n Metadata key removed from the checkout (assuming operation successful)\n '
return self.remove(key)<|docstring|>Remove a key/value pair from metadata. Convenience method to :meth:`remove`.
.. seealso:: :meth:`remove` for the function this calls into.
Parameters
----------
key : Union[str, int]
Name of the metadata piece to remove.
Returns
-------
Union[str, int]
Metadata key removed from the checkout (assuming operation successful)<|endoftext|> |
9d139ef6f6ce72a378d5bb4a5a878344d7967c030b51d43c2951414ae0fc101f | def add(self, key: Union[(str, int)], value: str) -> Union[(str, int)]:
'Add a piece of metadata to the staging area of the next commit.\n\n Parameters\n ----------\n key : Union[str, int]\n Name of the metadata piece, alphanumeric ascii characters only\n value : string\n Metadata value to store in the repository, any length of valid\n ascii characters.\n\n Returns\n -------\n Union[str, int]\n The name of the metadata key written to the database if the\n operation succeeded.\n\n Raises\n ------\n ValueError\n If the `key` contains any whitespace or non alpha-numeric characters.\n ValueError\n If the `value` contains any non ascii characters.\n '
try:
if (not is_suitable_user_key(key)):
raise ValueError(f'Metadata key: {key} of type: {type(key)} invalid. Must be int ascii string with only alpha-numeric / "." "_" "-" characters.')
elif (not (isinstance(value, str) and is_ascii(value))):
raise ValueError(f'Metadata Value: `{value}` not allowed. Must be ascii-only string')
except ValueError as e:
raise e from None
try:
tmpconman = (not self._is_conman)
if tmpconman:
self.__enter__()
val_hash = hashlib.blake2b(value.encode(), digest_size=20).hexdigest()
hashKey = parsing.hash_meta_db_key_from_raw_key(val_hash)
metaRecKey = parsing.metadata_record_db_key_from_raw_key(key)
metaRecVal = parsing.metadata_record_db_val_from_raw_val(val_hash)
existingMetaRecVal = self._dataTxn.get(metaRecKey, default=False)
if existingMetaRecVal:
existingMetaRec = parsing.metadata_record_raw_val_from_db_val(existingMetaRecVal)
if (val_hash == existingMetaRec.meta_hash):
return key
existingHashVal = self._labelTxn.get(hashKey, default=False)
if (existingHashVal is False):
hashVal = parsing.hash_meta_db_val_from_raw_val(value)
self._labelTxn.put(hashKey, hashVal)
self._dataTxn.put(metaRecKey, metaRecVal)
self._mspecs[key] = hashKey
finally:
if tmpconman:
self.__exit__()
return key | Add a piece of metadata to the staging area of the next commit.
Parameters
----------
key : Union[str, int]
Name of the metadata piece, alphanumeric ascii characters only
value : string
Metadata value to store in the repository, any length of valid
ascii characters.
Returns
-------
Union[str, int]
The name of the metadata key written to the database if the
operation succeeded.
Raises
------
ValueError
If the `key` contains any whitespace or non alpha-numeric characters.
ValueError
If the `value` contains any non ascii characters. | src/hangar/metadata.py | add | niranjana687/hangar-py | 1 | python | def add(self, key: Union[(str, int)], value: str) -> Union[(str, int)]:
'Add a piece of metadata to the staging area of the next commit.\n\n Parameters\n ----------\n key : Union[str, int]\n Name of the metadata piece, alphanumeric ascii characters only\n value : string\n Metadata value to store in the repository, any length of valid\n ascii characters.\n\n Returns\n -------\n Union[str, int]\n The name of the metadata key written to the database if the\n operation succeeded.\n\n Raises\n ------\n ValueError\n If the `key` contains any whitespace or non alpha-numeric characters.\n ValueError\n If the `value` contains any non ascii characters.\n '
try:
if (not is_suitable_user_key(key)):
raise ValueError(f'Metadata key: {key} of type: {type(key)} invalid. Must be int ascii string with only alpha-numeric / "." "_" "-" characters.')
elif (not (isinstance(value, str) and is_ascii(value))):
raise ValueError(f'Metadata Value: `{value}` not allowed. Must be ascii-only string')
except ValueError as e:
raise e from None
try:
tmpconman = (not self._is_conman)
if tmpconman:
self.__enter__()
val_hash = hashlib.blake2b(value.encode(), digest_size=20).hexdigest()
hashKey = parsing.hash_meta_db_key_from_raw_key(val_hash)
metaRecKey = parsing.metadata_record_db_key_from_raw_key(key)
metaRecVal = parsing.metadata_record_db_val_from_raw_val(val_hash)
existingMetaRecVal = self._dataTxn.get(metaRecKey, default=False)
if existingMetaRecVal:
existingMetaRec = parsing.metadata_record_raw_val_from_db_val(existingMetaRecVal)
if (val_hash == existingMetaRec.meta_hash):
return key
existingHashVal = self._labelTxn.get(hashKey, default=False)
if (existingHashVal is False):
hashVal = parsing.hash_meta_db_val_from_raw_val(value)
self._labelTxn.put(hashKey, hashVal)
self._dataTxn.put(metaRecKey, metaRecVal)
self._mspecs[key] = hashKey
finally:
if tmpconman:
self.__exit__()
return key | def add(self, key: Union[(str, int)], value: str) -> Union[(str, int)]:
'Add a piece of metadata to the staging area of the next commit.\n\n Parameters\n ----------\n key : Union[str, int]\n Name of the metadata piece, alphanumeric ascii characters only\n value : string\n Metadata value to store in the repository, any length of valid\n ascii characters.\n\n Returns\n -------\n Union[str, int]\n The name of the metadata key written to the database if the\n operation succeeded.\n\n Raises\n ------\n ValueError\n If the `key` contains any whitespace or non alpha-numeric characters.\n ValueError\n If the `value` contains any non ascii characters.\n '
try:
if (not is_suitable_user_key(key)):
raise ValueError(f'Metadata key: {key} of type: {type(key)} invalid. Must be int ascii string with only alpha-numeric / "." "_" "-" characters.')
elif (not (isinstance(value, str) and is_ascii(value))):
raise ValueError(f'Metadata Value: `{value}` not allowed. Must be ascii-only string')
except ValueError as e:
raise e from None
try:
tmpconman = (not self._is_conman)
if tmpconman:
self.__enter__()
val_hash = hashlib.blake2b(value.encode(), digest_size=20).hexdigest()
hashKey = parsing.hash_meta_db_key_from_raw_key(val_hash)
metaRecKey = parsing.metadata_record_db_key_from_raw_key(key)
metaRecVal = parsing.metadata_record_db_val_from_raw_val(val_hash)
existingMetaRecVal = self._dataTxn.get(metaRecKey, default=False)
if existingMetaRecVal:
existingMetaRec = parsing.metadata_record_raw_val_from_db_val(existingMetaRecVal)
if (val_hash == existingMetaRec.meta_hash):
return key
existingHashVal = self._labelTxn.get(hashKey, default=False)
if (existingHashVal is False):
hashVal = parsing.hash_meta_db_val_from_raw_val(value)
self._labelTxn.put(hashKey, hashVal)
self._dataTxn.put(metaRecKey, metaRecVal)
self._mspecs[key] = hashKey
finally:
if tmpconman:
self.__exit__()
return key<|docstring|>Add a piece of metadata to the staging area of the next commit.
Parameters
----------
key : Union[str, int]
Name of the metadata piece, alphanumeric ascii characters only
value : string
Metadata value to store in the repository, any length of valid
ascii characters.
Returns
-------
Union[str, int]
The name of the metadata key written to the database if the
operation succeeded.
Raises
------
ValueError
If the `key` contains any whitespace or non alpha-numeric characters.
ValueError
If the `value` contains any non ascii characters.<|endoftext|> |
b3d4ff0e22bd64828107e76a6d3fcb72890feed3d38dafa0ccf62072273318c4 | def remove(self, key: Union[(str, int)]) -> Union[(str, int)]:
'Remove a piece of metadata from the staging area of the next commit.\n\n Parameters\n ----------\n key : Union[str, int]\n Metadata name to remove.\n\n Returns\n -------\n Union[str, int]\n Name of the metadata key/value pair removed, if the operation was\n successful.\n\n Raises\n ------\n ValueError\n If the key provided is not string type and containing only\n ascii-alphanumeric characters.\n KeyError\n If the checkout does not contain metadata with the provided key.\n '
try:
tmpconman = (not self._is_conman)
if tmpconman:
self.__enter__()
if (not is_suitable_user_key(key)):
msg = f'HANGAR VALUE ERROR:: metadata key: `{key}` not allowed. Must be strcontaining alpha-numeric or "." "_" "-" ascii characters (no whitespace).'
raise ValueError(msg)
metaRecKey = parsing.metadata_record_db_key_from_raw_key(key)
delete_succeeded = self._dataTxn.delete(metaRecKey)
if (delete_succeeded is False):
msg = f'HANGAR KEY ERROR:: No metadata exists with key: {key}'
raise KeyError(msg)
del self._mspecs[key]
except (KeyError, ValueError) as e:
raise e from None
finally:
if tmpconman:
self.__exit__()
return key | Remove a piece of metadata from the staging area of the next commit.
Parameters
----------
key : Union[str, int]
Metadata name to remove.
Returns
-------
Union[str, int]
Name of the metadata key/value pair removed, if the operation was
successful.
Raises
------
ValueError
If the key provided is not string type and containing only
ascii-alphanumeric characters.
KeyError
If the checkout does not contain metadata with the provided key. | src/hangar/metadata.py | remove | niranjana687/hangar-py | 1 | python | def remove(self, key: Union[(str, int)]) -> Union[(str, int)]:
'Remove a piece of metadata from the staging area of the next commit.\n\n Parameters\n ----------\n key : Union[str, int]\n Metadata name to remove.\n\n Returns\n -------\n Union[str, int]\n Name of the metadata key/value pair removed, if the operation was\n successful.\n\n Raises\n ------\n ValueError\n If the key provided is not string type and containing only\n ascii-alphanumeric characters.\n KeyError\n If the checkout does not contain metadata with the provided key.\n '
try:
tmpconman = (not self._is_conman)
if tmpconman:
self.__enter__()
if (not is_suitable_user_key(key)):
msg = f'HANGAR VALUE ERROR:: metadata key: `{key}` not allowed. Must be strcontaining alpha-numeric or "." "_" "-" ascii characters (no whitespace).'
raise ValueError(msg)
metaRecKey = parsing.metadata_record_db_key_from_raw_key(key)
delete_succeeded = self._dataTxn.delete(metaRecKey)
if (delete_succeeded is False):
msg = f'HANGAR KEY ERROR:: No metadata exists with key: {key}'
raise KeyError(msg)
del self._mspecs[key]
except (KeyError, ValueError) as e:
raise e from None
finally:
if tmpconman:
self.__exit__()
return key | def remove(self, key: Union[(str, int)]) -> Union[(str, int)]:
'Remove a piece of metadata from the staging area of the next commit.\n\n Parameters\n ----------\n key : Union[str, int]\n Metadata name to remove.\n\n Returns\n -------\n Union[str, int]\n Name of the metadata key/value pair removed, if the operation was\n successful.\n\n Raises\n ------\n ValueError\n If the key provided is not string type and containing only\n ascii-alphanumeric characters.\n KeyError\n If the checkout does not contain metadata with the provided key.\n '
try:
tmpconman = (not self._is_conman)
if tmpconman:
self.__enter__()
if (not is_suitable_user_key(key)):
msg = f'HANGAR VALUE ERROR:: metadata key: `{key}` not allowed. Must be strcontaining alpha-numeric or "." "_" "-" ascii characters (no whitespace).'
raise ValueError(msg)
metaRecKey = parsing.metadata_record_db_key_from_raw_key(key)
delete_succeeded = self._dataTxn.delete(metaRecKey)
if (delete_succeeded is False):
msg = f'HANGAR KEY ERROR:: No metadata exists with key: {key}'
raise KeyError(msg)
del self._mspecs[key]
except (KeyError, ValueError) as e:
raise e from None
finally:
if tmpconman:
self.__exit__()
return key<|docstring|>Remove a piece of metadata from the staging area of the next commit.
Parameters
----------
key : Union[str, int]
Metadata name to remove.
Returns
-------
Union[str, int]
Name of the metadata key/value pair removed, if the operation was
successful.
Raises
------
ValueError
If the key provided is not string type and containing only
ascii-alphanumeric characters.
KeyError
If the checkout does not contain metadata with the provided key.<|endoftext|> |
7051f8d2ceab3b23b2bd52f8f59f7f823cfe2c3d2956a15b21ac13a51e042c50 | def __init__(self, in_dim=((1024 * 8) * 10), feat_dim=256, num_layers=2, rot_dim=4, norm='none', num_gn_groups=32, act='leaky_relu', num_classes=1):
'\n rot_dim: 4 for quaternion, 6 for rot6d\n num_classes: default 1 (either single class or class-agnostic)\n '
super().__init__()
self.norm = get_norm(norm, feat_dim, num_gn_groups=num_gn_groups)
self.act_func = act_func = get_nn_act_func(act)
self.num_classes = num_classes
self.rot_dim = rot_dim
self.linears = nn.ModuleList()
for _i in range(num_layers):
_in_dim = (in_dim if (_i == 0) else feat_dim)
self.linears.append(nn.Linear(_in_dim, feat_dim))
self.linears.append(get_norm(norm, feat_dim, num_gn_groups=num_gn_groups))
self.linears.append(act_func)
self.fc_r = nn.Linear(feat_dim, (rot_dim * num_classes))
self.fc_t = nn.Linear(feat_dim, (3 * num_classes))
self._init_weights() | rot_dim: 4 for quaternion, 6 for rot6d
num_classes: default 1 (either single class or class-agnostic) | core/deepim/models/heads/fc_rot_trans_head.py | __init__ | THU-DA-6D-Pose-Group/self6dpp | 33 | python | def __init__(self, in_dim=((1024 * 8) * 10), feat_dim=256, num_layers=2, rot_dim=4, norm='none', num_gn_groups=32, act='leaky_relu', num_classes=1):
'\n rot_dim: 4 for quaternion, 6 for rot6d\n num_classes: default 1 (either single class or class-agnostic)\n '
super().__init__()
self.norm = get_norm(norm, feat_dim, num_gn_groups=num_gn_groups)
self.act_func = act_func = get_nn_act_func(act)
self.num_classes = num_classes
self.rot_dim = rot_dim
self.linears = nn.ModuleList()
for _i in range(num_layers):
_in_dim = (in_dim if (_i == 0) else feat_dim)
self.linears.append(nn.Linear(_in_dim, feat_dim))
self.linears.append(get_norm(norm, feat_dim, num_gn_groups=num_gn_groups))
self.linears.append(act_func)
self.fc_r = nn.Linear(feat_dim, (rot_dim * num_classes))
self.fc_t = nn.Linear(feat_dim, (3 * num_classes))
self._init_weights() | def __init__(self, in_dim=((1024 * 8) * 10), feat_dim=256, num_layers=2, rot_dim=4, norm='none', num_gn_groups=32, act='leaky_relu', num_classes=1):
'\n rot_dim: 4 for quaternion, 6 for rot6d\n num_classes: default 1 (either single class or class-agnostic)\n '
super().__init__()
self.norm = get_norm(norm, feat_dim, num_gn_groups=num_gn_groups)
self.act_func = act_func = get_nn_act_func(act)
self.num_classes = num_classes
self.rot_dim = rot_dim
self.linears = nn.ModuleList()
for _i in range(num_layers):
_in_dim = (in_dim if (_i == 0) else feat_dim)
self.linears.append(nn.Linear(_in_dim, feat_dim))
self.linears.append(get_norm(norm, feat_dim, num_gn_groups=num_gn_groups))
self.linears.append(act_func)
self.fc_r = nn.Linear(feat_dim, (rot_dim * num_classes))
self.fc_t = nn.Linear(feat_dim, (3 * num_classes))
self._init_weights()<|docstring|>rot_dim: 4 for quaternion, 6 for rot6d
num_classes: default 1 (either single class or class-agnostic)<|endoftext|> |
42d9d2717b6a7da694d7745a8768636d8a472f2905e3c93b61854ee16b8ad5cc | def forward(self, x):
'\n x: should be flattened\n '
for _layer in self.linears:
x = _layer(x)
rot = self.fc_r(x)
trans = self.fc_t(x)
return (rot, trans) | x: should be flattened | core/deepim/models/heads/fc_rot_trans_head.py | forward | THU-DA-6D-Pose-Group/self6dpp | 33 | python | def forward(self, x):
'\n \n '
for _layer in self.linears:
x = _layer(x)
rot = self.fc_r(x)
trans = self.fc_t(x)
return (rot, trans) | def forward(self, x):
'\n \n '
for _layer in self.linears:
x = _layer(x)
rot = self.fc_r(x)
trans = self.fc_t(x)
return (rot, trans)<|docstring|>x: should be flattened<|endoftext|> |
1dd7284e34d18c974c95692aedfa081c93717fb2269f5fbcdfd649cf9291a97d | def get(self, request, format=None):
' method for handling get request to this class '
an_apiview = ['This is an API View Class', 'We can write http requests like Post, get , put, patch and delete', 'Urls are created in urls.py of the api app , creates using path() ', 'This is used when full control is needed in the API logic', 'Lets have a look, shall we ?']
return Response({'about': 'Hello there ', 'content': an_apiview}) | method for handling get request to this class | profiles_api/views.py | get | john7ric/profiles_rest_api | 0 | python | def get(self, request, format=None):
' '
an_apiview = ['This is an API View Class', 'We can write http requests like Post, get , put, patch and delete', 'Urls are created in urls.py of the api app , creates using path() ', 'This is used when full control is needed in the API logic', 'Lets have a look, shall we ?']
return Response({'about': 'Hello there ', 'content': an_apiview}) | def get(self, request, format=None):
' '
an_apiview = ['This is an API View Class', 'We can write http requests like Post, get , put, patch and delete', 'Urls are created in urls.py of the api app , creates using path() ', 'This is used when full control is needed in the API logic', 'Lets have a look, shall we ?']
return Response({'about': 'Hello there ', 'content': an_apiview})<|docstring|>method for handling get request to this class<|endoftext|> |
18c99d952f37855a50eef8906be281f09508c62176baecd36df192f1d95cf6b2 | def post(self, request):
' method handling the post requests to this view '
serializer = self.serializer_class(data=request.data)
if serializer.is_valid():
name = serializer.validated_data.get('name')
age = serializer.validated_data.get('age')
message_string = (((('Hello ' + name) + ' you are ') + str(age)) + ' years old')
return Response({'message': message_string})
else:
return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST) | method handling the post requests to this view | profiles_api/views.py | post | john7ric/profiles_rest_api | 0 | python | def post(self, request):
' '
serializer = self.serializer_class(data=request.data)
if serializer.is_valid():
name = serializer.validated_data.get('name')
age = serializer.validated_data.get('age')
message_string = (((('Hello ' + name) + ' you are ') + str(age)) + ' years old')
return Response({'message': message_string})
else:
return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST) | def post(self, request):
' '
serializer = self.serializer_class(data=request.data)
if serializer.is_valid():
name = serializer.validated_data.get('name')
age = serializer.validated_data.get('age')
message_string = (((('Hello ' + name) + ' you are ') + str(age)) + ' years old')
return Response({'message': message_string})
else:
return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)<|docstring|>method handling the post requests to this view<|endoftext|> |
39366102ec826ece3114d30a8c279dca1fa2ec7b2354be0776d0650c14913978 | def patch(self, request, pk=None):
' method for handling patch requests to this class'
return Response({'method': 'Patch'}) | method for handling patch requests to this class | profiles_api/views.py | patch | john7ric/profiles_rest_api | 0 | python | def patch(self, request, pk=None):
' '
return Response({'method': 'Patch'}) | def patch(self, request, pk=None):
' '
return Response({'method': 'Patch'})<|docstring|>method for handling patch requests to this class<|endoftext|> |
9069099907d50b887421bf70e936246ac448d0d66c3b9d8469fac98ecf3eafd7 | def put(self, reaction, pk=None):
'method for handling put'
return Response({'message': 'PUT'}) | method for handling put | profiles_api/views.py | put | john7ric/profiles_rest_api | 0 | python | def put(self, reaction, pk=None):
return Response({'message': 'PUT'}) | def put(self, reaction, pk=None):
return Response({'message': 'PUT'})<|docstring|>method for handling put<|endoftext|> |
33483db04edea69ae3eff49eb2a7f3c617aed40af9f0532fd6bc6b155fecdc14 | def delete(self, request, pk=None):
'method to handle delete'
return Response({'message': 'Delete'}) | method to handle delete | profiles_api/views.py | delete | john7ric/profiles_rest_api | 0 | python | def delete(self, request, pk=None):
return Response({'message': 'Delete'}) | def delete(self, request, pk=None):
return Response({'message': 'Delete'})<|docstring|>method to handle delete<|endoftext|> |
0d75c91141200d80548c20bf06c1eca65bef1c2c1b320b9a4abd0aaf4ee81849 | def create(self, request):
' to create a new object '
serializer = self.serializer_class(data=request.data)
if serializer.is_valid():
name = serializer.validated_data.get('name')
age = serializer.validated_data.get('age')
return Response({'message': f'Hello {name} you are {age} years old'})
else:
return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST) | to create a new object | profiles_api/views.py | create | john7ric/profiles_rest_api | 0 | python | def create(self, request):
' '
serializer = self.serializer_class(data=request.data)
if serializer.is_valid():
name = serializer.validated_data.get('name')
age = serializer.validated_data.get('age')
return Response({'message': f'Hello {name} you are {age} years old'})
else:
return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST) | def create(self, request):
' '
serializer = self.serializer_class(data=request.data)
if serializer.is_valid():
name = serializer.validated_data.get('name')
age = serializer.validated_data.get('age')
return Response({'message': f'Hello {name} you are {age} years old'})
else:
return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)<|docstring|>to create a new object<|endoftext|> |
8ad125c15c26098468e3771f3a835c86aeeac10fafc794c4a9ddeb1c162f3914 | def retrieve(self, request, pk=None):
' doc string to fetch an object with an ID'
return Response({'message': 'retreive is called'}) | doc string to fetch an object with an ID | profiles_api/views.py | retrieve | john7ric/profiles_rest_api | 0 | python | def retrieve(self, request, pk=None):
' '
return Response({'message': 'retreive is called'}) | def retrieve(self, request, pk=None):
' '
return Response({'message': 'retreive is called'})<|docstring|>doc string to fetch an object with an ID<|endoftext|> |
93fdd56937209e73b4c1d213fec00f7eea905da338e93b717efd2a567ae8de38 | def update(self, request, pk=None):
' method to update a view '
return Response({'message': 'methode called is update'}) | method to update a view | profiles_api/views.py | update | john7ric/profiles_rest_api | 0 | python | def update(self, request, pk=None):
' '
return Response({'message': 'methode called is update'}) | def update(self, request, pk=None):
' '
return Response({'message': 'methode called is update'})<|docstring|>method to update a view<|endoftext|> |
32f0031182373f4183aa6ec78a127f95c321ce599054b94f513e1451679bd122 | def partial_update(self, request, pk=None):
'method for partial update '
return Response({'Message': 'Method patch'}) | method for partial update | profiles_api/views.py | partial_update | john7ric/profiles_rest_api | 0 | python | def partial_update(self, request, pk=None):
' '
return Response({'Message': 'Method patch'}) | def partial_update(self, request, pk=None):
' '
return Response({'Message': 'Method patch'})<|docstring|>method for partial update<|endoftext|> |
2750bbc26e5ec635163580cadc28f3d31a842865e1c43b92b6011ee81adad56c | def destroy(self, request, pk=None):
' method to delete the object with ID'
return Response({'message': 'HTTP Delete'}) | method to delete the object with ID | profiles_api/views.py | destroy | john7ric/profiles_rest_api | 0 | python | def destroy(self, request, pk=None):
' '
return Response({'message': 'HTTP Delete'}) | def destroy(self, request, pk=None):
' '
return Response({'message': 'HTTP Delete'})<|docstring|>method to delete the object with ID<|endoftext|> |
73ebe0b0253af1c914deced650fdc4ce3fb688573a650a9a7ed0f0d50fbaff98 | def quiz_callback(update: Update, context):
'Ask new question '
client_redis = context.bot_data['r']
if (update.message.text == 'Новый вопрос'):
(quiz_line, quiz_content) = random.choice(list(quiz.items()))
context.bot.send_message(chat_id=update.message.chat_id, text=quiz_content[0])
context.bot.send_message(chat_id=update.message.chat_id, text=quiz_content[1]['q'])
client_redis.rpush(update.effective_user.id, quiz_line, quiz_content[1]['q'], quiz_content[1]['a'])
elif (update.message.text == 'Сдаться'):
context.bot.send_message(chat_id=update.message.chat_id, text='Правильный ответ')
answer = client_redis.lrange(update.effective_user.id, (- 1), (- 1))[0]
context.bot.send_message(chat_id=update.message.chat_id, text=answer)
client_redis.delete(update.effective_user.id, 0, (- 1))
update.message.reply_text('Bye! I hope we can talk again some day.', reply_markup=ReplyKeyboardRemove())
else:
answer = client_redis.lrange(update.effective_user.id, (- 1), (- 1))[0]
if (update.message.text.lower() in answer.lower()):
context.bot.send_message(chat_id=update.message.chat_id, text='Правильно! Поздравляю!')
context.bot.send_message(chat_id=update.message.chat_id, text=answer)
client_redis.delete(update.effective_user.id, 0, (- 1))
return
context.bot.send_message(chat_id=update.message.chat_id, text='Неправильно\n Попробуешь ещё раз?"') | Ask new question | tg_bot.py | quiz_callback | psergal/quiz | 0 | python | def quiz_callback(update: Update, context):
' '
client_redis = context.bot_data['r']
if (update.message.text == 'Новый вопрос'):
(quiz_line, quiz_content) = random.choice(list(quiz.items()))
context.bot.send_message(chat_id=update.message.chat_id, text=quiz_content[0])
context.bot.send_message(chat_id=update.message.chat_id, text=quiz_content[1]['q'])
client_redis.rpush(update.effective_user.id, quiz_line, quiz_content[1]['q'], quiz_content[1]['a'])
elif (update.message.text == 'Сдаться'):
context.bot.send_message(chat_id=update.message.chat_id, text='Правильный ответ')
answer = client_redis.lrange(update.effective_user.id, (- 1), (- 1))[0]
context.bot.send_message(chat_id=update.message.chat_id, text=answer)
client_redis.delete(update.effective_user.id, 0, (- 1))
update.message.reply_text('Bye! I hope we can talk again some day.', reply_markup=ReplyKeyboardRemove())
else:
answer = client_redis.lrange(update.effective_user.id, (- 1), (- 1))[0]
if (update.message.text.lower() in answer.lower()):
context.bot.send_message(chat_id=update.message.chat_id, text='Правильно! Поздравляю!')
context.bot.send_message(chat_id=update.message.chat_id, text=answer)
client_redis.delete(update.effective_user.id, 0, (- 1))
return
context.bot.send_message(chat_id=update.message.chat_id, text='Неправильно\n Попробуешь ещё раз?"') | def quiz_callback(update: Update, context):
' '
client_redis = context.bot_data['r']
if (update.message.text == 'Новый вопрос'):
(quiz_line, quiz_content) = random.choice(list(quiz.items()))
context.bot.send_message(chat_id=update.message.chat_id, text=quiz_content[0])
context.bot.send_message(chat_id=update.message.chat_id, text=quiz_content[1]['q'])
client_redis.rpush(update.effective_user.id, quiz_line, quiz_content[1]['q'], quiz_content[1]['a'])
elif (update.message.text == 'Сдаться'):
context.bot.send_message(chat_id=update.message.chat_id, text='Правильный ответ')
answer = client_redis.lrange(update.effective_user.id, (- 1), (- 1))[0]
context.bot.send_message(chat_id=update.message.chat_id, text=answer)
client_redis.delete(update.effective_user.id, 0, (- 1))
update.message.reply_text('Bye! I hope we can talk again some day.', reply_markup=ReplyKeyboardRemove())
else:
answer = client_redis.lrange(update.effective_user.id, (- 1), (- 1))[0]
if (update.message.text.lower() in answer.lower()):
context.bot.send_message(chat_id=update.message.chat_id, text='Правильно! Поздравляю!')
context.bot.send_message(chat_id=update.message.chat_id, text=answer)
client_redis.delete(update.effective_user.id, 0, (- 1))
return
context.bot.send_message(chat_id=update.message.chat_id, text='Неправильно\n Попробуешь ещё раз?"')<|docstring|>Ask new question<|endoftext|> |
cc3b6a5edfca873467e5b6f85189fc642d1c8fe8217ffa3476ec007899a1d5fb | def log_error(update: Update, context):
'Log Errors caused by Updates.'
logger.warning('Update "%s" caused error "%s"', update, context.error, extra={'Update_err': True}) | Log Errors caused by Updates. | tg_bot.py | log_error | psergal/quiz | 0 | python | def log_error(update: Update, context):
logger.warning('Update "%s" caused error "%s"', update, context.error, extra={'Update_err': True}) | def log_error(update: Update, context):
logger.warning('Update "%s" caused error "%s"', update, context.error, extra={'Update_err': True})<|docstring|>Log Errors caused by Updates.<|endoftext|> |
81f4efeedc05f8903e31e569ed7cb5ea6c9441aaa3a0418d105dcb8aab6825b7 | def generate_img(self, batch):
' Return generated img\n\n :param batch: number of img\n :return: 0~255, uint, numpy array\n [original_a, fake_from_a, cycle_a, identity_a, original_b, fake_from_b, cycle_b, identity_b]\n '
def form_img(target_array):
target_array = (((target_array + 1) / 2) * 255)
target_array = target_array.astype(np.uint8)
return target_array
self.session.run([self.iterator_ini_a, self.iterator_ini_b], feed_dict={self.__tfrecord_a: ('%s/testA.tfrecord' % self.tfrecord_dir), self.__tfrecord_b: ('%s/testB.tfrecord' % self.tfrecord_dir), self.__batch: 1})
result = []
for b in range(batch):
(img_a, img_b) = self.session.run([self.img_a, self.img_b])
imgs = self.session.run([self.fake_img_b, self.cycle_img_a, self.id_a, self.fake_img_a, self.cycle_img_b, self.id_b], feed_dict={self.__original_img_a: img_a, self.__original_img_b: img_b})
result.append([img_a.astype(np.uint8), form_img(imgs[0]), form_img(imgs[1]), form_img(imgs[2]), img_b.astype(np.uint8), form_img(imgs[3]), form_img(imgs[4]), form_img(imgs[5])])
return result | Return generated img
:param batch: number of img
:return: 0~255, uint, numpy array
[original_a, fake_from_a, cycle_a, identity_a, original_b, fake_from_b, cycle_b, identity_b] | cycle_gan/cycle_gan.py | generate_img | asahi417/CycleGAN | 2 | python | def generate_img(self, batch):
' Return generated img\n\n :param batch: number of img\n :return: 0~255, uint, numpy array\n [original_a, fake_from_a, cycle_a, identity_a, original_b, fake_from_b, cycle_b, identity_b]\n '
def form_img(target_array):
target_array = (((target_array + 1) / 2) * 255)
target_array = target_array.astype(np.uint8)
return target_array
self.session.run([self.iterator_ini_a, self.iterator_ini_b], feed_dict={self.__tfrecord_a: ('%s/testA.tfrecord' % self.tfrecord_dir), self.__tfrecord_b: ('%s/testB.tfrecord' % self.tfrecord_dir), self.__batch: 1})
result = []
for b in range(batch):
(img_a, img_b) = self.session.run([self.img_a, self.img_b])
imgs = self.session.run([self.fake_img_b, self.cycle_img_a, self.id_a, self.fake_img_a, self.cycle_img_b, self.id_b], feed_dict={self.__original_img_a: img_a, self.__original_img_b: img_b})
result.append([img_a.astype(np.uint8), form_img(imgs[0]), form_img(imgs[1]), form_img(imgs[2]), img_b.astype(np.uint8), form_img(imgs[3]), form_img(imgs[4]), form_img(imgs[5])])
return result | def generate_img(self, batch):
' Return generated img\n\n :param batch: number of img\n :return: 0~255, uint, numpy array\n [original_a, fake_from_a, cycle_a, identity_a, original_b, fake_from_b, cycle_b, identity_b]\n '
def form_img(target_array):
target_array = (((target_array + 1) / 2) * 255)
target_array = target_array.astype(np.uint8)
return target_array
self.session.run([self.iterator_ini_a, self.iterator_ini_b], feed_dict={self.__tfrecord_a: ('%s/testA.tfrecord' % self.tfrecord_dir), self.__tfrecord_b: ('%s/testB.tfrecord' % self.tfrecord_dir), self.__batch: 1})
result = []
for b in range(batch):
(img_a, img_b) = self.session.run([self.img_a, self.img_b])
imgs = self.session.run([self.fake_img_b, self.cycle_img_a, self.id_a, self.fake_img_a, self.cycle_img_b, self.id_b], feed_dict={self.__original_img_a: img_a, self.__original_img_b: img_b})
result.append([img_a.astype(np.uint8), form_img(imgs[0]), form_img(imgs[1]), form_img(imgs[2]), img_b.astype(np.uint8), form_img(imgs[3]), form_img(imgs[4]), form_img(imgs[5])])
return result<|docstring|>Return generated img
:param batch: number of img
:return: 0~255, uint, numpy array
[original_a, fake_from_a, cycle_a, identity_a, original_b, fake_from_b, cycle_b, identity_b]<|endoftext|> |
5d97b0f084be77649c81d837956be2aa618f73b90fa7226e7554d08f1043565e | def shuffle_data(data, seed=None):
'shuffle array along first axis'
np.random.seed(seed)
np.random.shuffle(data)
return data | shuffle array along first axis | cycle_gan/cycle_gan.py | shuffle_data | asahi417/CycleGAN | 2 | python | def shuffle_data(data, seed=None):
np.random.seed(seed)
np.random.shuffle(data)
return data | def shuffle_data(data, seed=None):
np.random.seed(seed)
np.random.shuffle(data)
return data<|docstring|>shuffle array along first axis<|endoftext|> |
db4591388b26bde78bfdd9b0e6d6ca854c3c11544900a4d619fb49feba9e540d | def learning_rate_scheduler(current_lr, current_epoch):
' heuristic scheduler used in original paper '
bias = 2e-06
if (current_epoch > 100):
return np.max([(current_lr - bias), 0])
else:
return current_lr | heuristic scheduler used in original paper | cycle_gan/cycle_gan.py | learning_rate_scheduler | asahi417/CycleGAN | 2 | python | def learning_rate_scheduler(current_lr, current_epoch):
' '
bias = 2e-06
if (current_epoch > 100):
return np.max([(current_lr - bias), 0])
else:
return current_lr | def learning_rate_scheduler(current_lr, current_epoch):
' '
bias = 2e-06
if (current_epoch > 100):
return np.max([(current_lr - bias), 0])
else:
return current_lr<|docstring|>heuristic scheduler used in original paper<|endoftext|> |
4483c85e5c90508f803cb779ca9905e24e2745e2edc8cb82d344fb8ea023741d | def store_the_master(s):
"Define as 'MASTER_VARIABLES' those variables of the self object that\n do not belong in the list 'SIMULATION_VARIABLES'. Essentially, it includes\n those defined in hamiltonian_class.__init__(s).\n\n "
s.MASTER_VARIABLES = [x for x in s.__dict__.keys() if (x not in s.SIMULATION_VARIABLES) if (x != 'SIMULATION_VARIABLES')]
for (key, arg) in s.model_operators.items():
if (type(s.model_operators[key]) == list):
if (type(s.model_operators[key][0]) == ndarray):
s.model_operators[key] = [csr_matrix(_) for _ in s.model_operators[key]]
if s.STORE_MASTER:
with open(s.MASTER_DATAFILE_PATH, 'wb') as my_file:
dump(s, my_file, HIGHEST_PROTOCOL) | Define as 'MASTER_VARIABLES' those variables of the self object that
do not belong in the list 'SIMULATION_VARIABLES'. Essentially, it includes
those defined in hamiltonian_class.__init__(s). | src/mother_classes.py | store_the_master | lorenzocardarelli/PyTeNC | 0 | python | def store_the_master(s):
"Define as 'MASTER_VARIABLES' those variables of the self object that\n do not belong in the list 'SIMULATION_VARIABLES'. Essentially, it includes\n those defined in hamiltonian_class.__init__(s).\n\n "
s.MASTER_VARIABLES = [x for x in s.__dict__.keys() if (x not in s.SIMULATION_VARIABLES) if (x != 'SIMULATION_VARIABLES')]
for (key, arg) in s.model_operators.items():
if (type(s.model_operators[key]) == list):
if (type(s.model_operators[key][0]) == ndarray):
s.model_operators[key] = [csr_matrix(_) for _ in s.model_operators[key]]
if s.STORE_MASTER:
with open(s.MASTER_DATAFILE_PATH, 'wb') as my_file:
dump(s, my_file, HIGHEST_PROTOCOL) | def store_the_master(s):
"Define as 'MASTER_VARIABLES' those variables of the self object that\n do not belong in the list 'SIMULATION_VARIABLES'. Essentially, it includes\n those defined in hamiltonian_class.__init__(s).\n\n "
s.MASTER_VARIABLES = [x for x in s.__dict__.keys() if (x not in s.SIMULATION_VARIABLES) if (x != 'SIMULATION_VARIABLES')]
for (key, arg) in s.model_operators.items():
if (type(s.model_operators[key]) == list):
if (type(s.model_operators[key][0]) == ndarray):
s.model_operators[key] = [csr_matrix(_) for _ in s.model_operators[key]]
if s.STORE_MASTER:
with open(s.MASTER_DATAFILE_PATH, 'wb') as my_file:
dump(s, my_file, HIGHEST_PROTOCOL)<|docstring|>Define as 'MASTER_VARIABLES' those variables of the self object that
do not belong in the list 'SIMULATION_VARIABLES'. Essentially, it includes
those defined in hamiltonian_class.__init__(s).<|endoftext|> |
a53a1b9c856294e101f585b1e808f8edd9ebcfa592d31292e109015c47e18a2d | def initialize_and_update_simulation_parameters(s, SIM_PARAMS):
'Define (many) simulation parameters as self objects, from SIM_PARAMS.\n Store the algorithm and Hamiltonian input parameters as self objects.\n\n '
if (type(SIM_PARAMS['INIT_STATE_PARAMS']) is list):
s.INITIAL_STATE_MATRIX = SIM_PARAMS['INIT_STATE_PARAMS']
elif (SIM_PARAMS['INIT_STATE_PARAMS'] == 'fixed'):
s.set_standard_initial_state_matrix(SIM_PARAMS)
elif (SIM_PARAMS['INIT_STATE_PARAMS'] == 'random'):
s.INITIAL_STATE_MATRIX = []
OUTPUT_PARAMS = {'LOCAL_RUN': True, 'STORE_STATE': False, 'STORE_MASTER': False, 'INFO_EVERY_SWEEP_STEP': True, 'DISPLAY_RAM': False, 'DISPLAY_TIMERS': False, 'PKL_STORE_TIME_INTERVAL': 1, 'STDOUT_FLUSH_TIME_INTERVAL': 1}
OUTPUT_PARAMS.update(SIM_PARAMS['OUTPUT_PARAMS'])
for key in OUTPUT_PARAMS.keys():
setattr(s, key, OUTPUT_PARAMS[key])
ALG_PARAMS = {'POST_RUN_INSPECTION': False, 'INFINITE_SYSTEM_WARMUP': True, 'REQUIRED_CHAIN_LENGTH': 30, 'NUMBER_SWEEPS': 2, 'BOND_DIMENSION': 50, 'SCHMIDT_TOLERANCE': (10.0 ** (- 15)), 'LANCZOS_ALGORITHM': 'SCIPY', 'SCIPY_EIGSH_TOLERANCE': 0, 'KRYLOV_SPACE_DIMENSION': 200, 'ALWAYS_MINIMIZE': True, 'SELF_ATTRIBUTES': {'rek_value': '%.0E', 'rek_vector': '%.0E'}, 'INFOSTREAM_OPERATORS_SUMMED_OVER_ALL_SITES': {}, 'INFOSTREAM_OPERATORS_ACTING_ON_CENTRAL_SITES': [], 'LOCAL_OPERATORS_SUMMED_OVER_ALL_SITES': [], 'NON_LOCAL_OPERATORS_OR_LIST_LOCAL_EXPECTATION_VALUES': [], 'NAMES_MATRIX_PRODUCT_OPERATORS_ACTING_ON_ALL_SITES': [], 'NAMES_MATRIX_PRODUCT_OPERATORS_ACTING_ON_CENTRAL_SITES': [], 'NAMES_NORMAL_MATRIX_OPERATORS_FOR_CORRELATIONS_AND_LOCAL_EXPECTATION_VALUES': []}
ALG_PARAMS.update(SIM_PARAMS['ALG_PARAMS'])
for key in ALG_PARAMS.keys():
setattr(s, key, ALG_PARAMS[key])
if ALG_PARAMS['INFINITE_SYSTEM_WARMUP']:
s.INITIAL_STATE_LENGTH = 2
else:
s.INITIAL_STATE_LENGTH = s.REQUIRED_CHAIN_LENGTH
s.SELF_ATTRIBUTES_TAGS = list(s.SELF_ATTRIBUTES.keys())
if s.POST_RUN_INSPECTION:
s.NAMES_MATRIX_PRODUCT_OPERATORS_ACTING_ON_ALL_SITES = s.LOCAL_OPERATORS_SUMMED_OVER_ALL_SITES
s.NAMES_NORMAL_MATRIX_OPERATORS_FOR_CORRELATIONS_AND_LOCAL_EXPECTATION_VALUES = s.NON_LOCAL_OPERATORS_OR_LIST_LOCAL_EXPECTATION_VALUES
else:
s.INFOSTREAM_OPERATORS_SUMMED_OVER_ALL_SITES['hamiltonian'] = '%.10f'
s.NAMES_MATRIX_PRODUCT_OPERATORS_ACTING_ON_ALL_SITES = list(s.INFOSTREAM_OPERATORS_SUMMED_OVER_ALL_SITES.keys())
s.NAMES_MATRIX_PRODUCT_OPERATORS_ACTING_ON_CENTRAL_SITES = s.INFOSTREAM_OPERATORS_ACTING_ON_CENTRAL_SITES
s.DATA_COLUMNS_TAG = []
s.DATA_COLUMNS_TAG += s.NAMES_MATRIX_PRODUCT_OPERATORS_ACTING_ON_ALL_SITES
s.DATA_COLUMNS_TAG += [(_ + '_mid') for _ in s.NAMES_MATRIX_PRODUCT_OPERATORS_ACTING_ON_CENTRAL_SITES]
s.DATA_COLUMNS_TAG += s.SELF_ATTRIBUTES_TAGS
s.NAMES_ALL_ACTIVE_MATRIX_PRODUCT_OPERATORS = list((set(s.NAMES_MATRIX_PRODUCT_OPERATORS_ACTING_ON_ALL_SITES) | set(s.NAMES_MATRIX_PRODUCT_OPERATORS_ACTING_ON_CENTRAL_SITES)))
s.H_PARAMS = SIM_PARAMS['H_PARAMS']
if (len(s.H_PARAMS['Commuting_Operators'].keys()) == 0):
s.ABELIAN_SYMMETRIES = False
else:
s.ABELIAN_SYMMETRIES = True
s.TOTAL_CHARGE = {}
s.AVERAGE_CHARGE_PER_SITE = {}
s.LIST_SYMMETRIES_NAMES = list(s.H_PARAMS['Commuting_Operators'].keys())
s.LIST_SYMMETRIC_OPERATORS_NAMES = []
for symmetry_name in s.LIST_SYMMETRIES_NAMES:
if (symmetry_name == 'links_alignment'):
s.LIST_SYMMETRIC_OPERATORS_NAMES.append('links_set_left')
s.AVERAGE_CHARGE_PER_SITE['links_set_left'] = 0
s.LIST_SYMMETRIC_OPERATORS_NAMES.append('links_set_right')
s.AVERAGE_CHARGE_PER_SITE['links_set_right'] = 0
else:
s.LIST_SYMMETRIC_OPERATORS_NAMES.append(symmetry_name)
try:
s.AVERAGE_CHARGE_PER_SITE[symmetry_name] = s.H_PARAMS['Commuting_Operators'][symmetry_name]['Average_Charge']
s.TOTAL_CHARGE[symmetry_name] = int((s.AVERAGE_CHARGE_PER_SITE[symmetry_name] * s.REQUIRED_CHAIN_LENGTH))
except:
s.TOTAL_CHARGE[symmetry_name] = s.H_PARAMS['Commuting_Operators'][symmetry_name]['Total_Charge']
s.AVERAGE_CHARGE_PER_SITE[symmetry_name] = (s.TOTAL_CHARGE[symmetry_name] / s.REQUIRED_CHAIN_LENGTH)
s.number_tensor_contractions = {}
s.number_tensor_contractions['matvec'] = 4
s.number_tensor_contractions['ltm_mpo_update'] = 3
s.number_tensor_contractions['rtm_mpo_update'] = 3
s.number_tensor_contractions['ltm_opt_update'] = 3
s.number_tensor_contractions['rtm_opt_update'] = 3
s.number_tensor_contractions['two_sites_svd'] = 1
s.HALF_REQUIRED_CHAIN_LENGTH = int((s.REQUIRED_CHAIN_LENGTH / 2)) | Define (many) simulation parameters as self objects, from SIM_PARAMS.
Store the algorithm and Hamiltonian input parameters as self objects. | src/mother_classes.py | initialize_and_update_simulation_parameters | lorenzocardarelli/PyTeNC | 0 | python | def initialize_and_update_simulation_parameters(s, SIM_PARAMS):
'Define (many) simulation parameters as self objects, from SIM_PARAMS.\n Store the algorithm and Hamiltonian input parameters as self objects.\n\n '
if (type(SIM_PARAMS['INIT_STATE_PARAMS']) is list):
s.INITIAL_STATE_MATRIX = SIM_PARAMS['INIT_STATE_PARAMS']
elif (SIM_PARAMS['INIT_STATE_PARAMS'] == 'fixed'):
s.set_standard_initial_state_matrix(SIM_PARAMS)
elif (SIM_PARAMS['INIT_STATE_PARAMS'] == 'random'):
s.INITIAL_STATE_MATRIX = []
OUTPUT_PARAMS = {'LOCAL_RUN': True, 'STORE_STATE': False, 'STORE_MASTER': False, 'INFO_EVERY_SWEEP_STEP': True, 'DISPLAY_RAM': False, 'DISPLAY_TIMERS': False, 'PKL_STORE_TIME_INTERVAL': 1, 'STDOUT_FLUSH_TIME_INTERVAL': 1}
OUTPUT_PARAMS.update(SIM_PARAMS['OUTPUT_PARAMS'])
for key in OUTPUT_PARAMS.keys():
setattr(s, key, OUTPUT_PARAMS[key])
ALG_PARAMS = {'POST_RUN_INSPECTION': False, 'INFINITE_SYSTEM_WARMUP': True, 'REQUIRED_CHAIN_LENGTH': 30, 'NUMBER_SWEEPS': 2, 'BOND_DIMENSION': 50, 'SCHMIDT_TOLERANCE': (10.0 ** (- 15)), 'LANCZOS_ALGORITHM': 'SCIPY', 'SCIPY_EIGSH_TOLERANCE': 0, 'KRYLOV_SPACE_DIMENSION': 200, 'ALWAYS_MINIMIZE': True, 'SELF_ATTRIBUTES': {'rek_value': '%.0E', 'rek_vector': '%.0E'}, 'INFOSTREAM_OPERATORS_SUMMED_OVER_ALL_SITES': {}, 'INFOSTREAM_OPERATORS_ACTING_ON_CENTRAL_SITES': [], 'LOCAL_OPERATORS_SUMMED_OVER_ALL_SITES': [], 'NON_LOCAL_OPERATORS_OR_LIST_LOCAL_EXPECTATION_VALUES': [], 'NAMES_MATRIX_PRODUCT_OPERATORS_ACTING_ON_ALL_SITES': [], 'NAMES_MATRIX_PRODUCT_OPERATORS_ACTING_ON_CENTRAL_SITES': [], 'NAMES_NORMAL_MATRIX_OPERATORS_FOR_CORRELATIONS_AND_LOCAL_EXPECTATION_VALUES': []}
ALG_PARAMS.update(SIM_PARAMS['ALG_PARAMS'])
for key in ALG_PARAMS.keys():
setattr(s, key, ALG_PARAMS[key])
if ALG_PARAMS['INFINITE_SYSTEM_WARMUP']:
s.INITIAL_STATE_LENGTH = 2
else:
s.INITIAL_STATE_LENGTH = s.REQUIRED_CHAIN_LENGTH
s.SELF_ATTRIBUTES_TAGS = list(s.SELF_ATTRIBUTES.keys())
if s.POST_RUN_INSPECTION:
s.NAMES_MATRIX_PRODUCT_OPERATORS_ACTING_ON_ALL_SITES = s.LOCAL_OPERATORS_SUMMED_OVER_ALL_SITES
s.NAMES_NORMAL_MATRIX_OPERATORS_FOR_CORRELATIONS_AND_LOCAL_EXPECTATION_VALUES = s.NON_LOCAL_OPERATORS_OR_LIST_LOCAL_EXPECTATION_VALUES
else:
s.INFOSTREAM_OPERATORS_SUMMED_OVER_ALL_SITES['hamiltonian'] = '%.10f'
s.NAMES_MATRIX_PRODUCT_OPERATORS_ACTING_ON_ALL_SITES = list(s.INFOSTREAM_OPERATORS_SUMMED_OVER_ALL_SITES.keys())
s.NAMES_MATRIX_PRODUCT_OPERATORS_ACTING_ON_CENTRAL_SITES = s.INFOSTREAM_OPERATORS_ACTING_ON_CENTRAL_SITES
s.DATA_COLUMNS_TAG = []
s.DATA_COLUMNS_TAG += s.NAMES_MATRIX_PRODUCT_OPERATORS_ACTING_ON_ALL_SITES
s.DATA_COLUMNS_TAG += [(_ + '_mid') for _ in s.NAMES_MATRIX_PRODUCT_OPERATORS_ACTING_ON_CENTRAL_SITES]
s.DATA_COLUMNS_TAG += s.SELF_ATTRIBUTES_TAGS
s.NAMES_ALL_ACTIVE_MATRIX_PRODUCT_OPERATORS = list((set(s.NAMES_MATRIX_PRODUCT_OPERATORS_ACTING_ON_ALL_SITES) | set(s.NAMES_MATRIX_PRODUCT_OPERATORS_ACTING_ON_CENTRAL_SITES)))
s.H_PARAMS = SIM_PARAMS['H_PARAMS']
if (len(s.H_PARAMS['Commuting_Operators'].keys()) == 0):
s.ABELIAN_SYMMETRIES = False
else:
s.ABELIAN_SYMMETRIES = True
s.TOTAL_CHARGE = {}
s.AVERAGE_CHARGE_PER_SITE = {}
s.LIST_SYMMETRIES_NAMES = list(s.H_PARAMS['Commuting_Operators'].keys())
s.LIST_SYMMETRIC_OPERATORS_NAMES = []
for symmetry_name in s.LIST_SYMMETRIES_NAMES:
if (symmetry_name == 'links_alignment'):
s.LIST_SYMMETRIC_OPERATORS_NAMES.append('links_set_left')
s.AVERAGE_CHARGE_PER_SITE['links_set_left'] = 0
s.LIST_SYMMETRIC_OPERATORS_NAMES.append('links_set_right')
s.AVERAGE_CHARGE_PER_SITE['links_set_right'] = 0
else:
s.LIST_SYMMETRIC_OPERATORS_NAMES.append(symmetry_name)
try:
s.AVERAGE_CHARGE_PER_SITE[symmetry_name] = s.H_PARAMS['Commuting_Operators'][symmetry_name]['Average_Charge']
s.TOTAL_CHARGE[symmetry_name] = int((s.AVERAGE_CHARGE_PER_SITE[symmetry_name] * s.REQUIRED_CHAIN_LENGTH))
except:
s.TOTAL_CHARGE[symmetry_name] = s.H_PARAMS['Commuting_Operators'][symmetry_name]['Total_Charge']
s.AVERAGE_CHARGE_PER_SITE[symmetry_name] = (s.TOTAL_CHARGE[symmetry_name] / s.REQUIRED_CHAIN_LENGTH)
s.number_tensor_contractions = {}
s.number_tensor_contractions['matvec'] = 4
s.number_tensor_contractions['ltm_mpo_update'] = 3
s.number_tensor_contractions['rtm_mpo_update'] = 3
s.number_tensor_contractions['ltm_opt_update'] = 3
s.number_tensor_contractions['rtm_opt_update'] = 3
s.number_tensor_contractions['two_sites_svd'] = 1
s.HALF_REQUIRED_CHAIN_LENGTH = int((s.REQUIRED_CHAIN_LENGTH / 2)) | def initialize_and_update_simulation_parameters(s, SIM_PARAMS):
'Define (many) simulation parameters as self objects, from SIM_PARAMS.\n Store the algorithm and Hamiltonian input parameters as self objects.\n\n '
if (type(SIM_PARAMS['INIT_STATE_PARAMS']) is list):
s.INITIAL_STATE_MATRIX = SIM_PARAMS['INIT_STATE_PARAMS']
elif (SIM_PARAMS['INIT_STATE_PARAMS'] == 'fixed'):
s.set_standard_initial_state_matrix(SIM_PARAMS)
elif (SIM_PARAMS['INIT_STATE_PARAMS'] == 'random'):
s.INITIAL_STATE_MATRIX = []
OUTPUT_PARAMS = {'LOCAL_RUN': True, 'STORE_STATE': False, 'STORE_MASTER': False, 'INFO_EVERY_SWEEP_STEP': True, 'DISPLAY_RAM': False, 'DISPLAY_TIMERS': False, 'PKL_STORE_TIME_INTERVAL': 1, 'STDOUT_FLUSH_TIME_INTERVAL': 1}
OUTPUT_PARAMS.update(SIM_PARAMS['OUTPUT_PARAMS'])
for key in OUTPUT_PARAMS.keys():
setattr(s, key, OUTPUT_PARAMS[key])
ALG_PARAMS = {'POST_RUN_INSPECTION': False, 'INFINITE_SYSTEM_WARMUP': True, 'REQUIRED_CHAIN_LENGTH': 30, 'NUMBER_SWEEPS': 2, 'BOND_DIMENSION': 50, 'SCHMIDT_TOLERANCE': (10.0 ** (- 15)), 'LANCZOS_ALGORITHM': 'SCIPY', 'SCIPY_EIGSH_TOLERANCE': 0, 'KRYLOV_SPACE_DIMENSION': 200, 'ALWAYS_MINIMIZE': True, 'SELF_ATTRIBUTES': {'rek_value': '%.0E', 'rek_vector': '%.0E'}, 'INFOSTREAM_OPERATORS_SUMMED_OVER_ALL_SITES': {}, 'INFOSTREAM_OPERATORS_ACTING_ON_CENTRAL_SITES': [], 'LOCAL_OPERATORS_SUMMED_OVER_ALL_SITES': [], 'NON_LOCAL_OPERATORS_OR_LIST_LOCAL_EXPECTATION_VALUES': [], 'NAMES_MATRIX_PRODUCT_OPERATORS_ACTING_ON_ALL_SITES': [], 'NAMES_MATRIX_PRODUCT_OPERATORS_ACTING_ON_CENTRAL_SITES': [], 'NAMES_NORMAL_MATRIX_OPERATORS_FOR_CORRELATIONS_AND_LOCAL_EXPECTATION_VALUES': []}
ALG_PARAMS.update(SIM_PARAMS['ALG_PARAMS'])
for key in ALG_PARAMS.keys():
setattr(s, key, ALG_PARAMS[key])
if ALG_PARAMS['INFINITE_SYSTEM_WARMUP']:
s.INITIAL_STATE_LENGTH = 2
else:
s.INITIAL_STATE_LENGTH = s.REQUIRED_CHAIN_LENGTH
s.SELF_ATTRIBUTES_TAGS = list(s.SELF_ATTRIBUTES.keys())
if s.POST_RUN_INSPECTION:
s.NAMES_MATRIX_PRODUCT_OPERATORS_ACTING_ON_ALL_SITES = s.LOCAL_OPERATORS_SUMMED_OVER_ALL_SITES
s.NAMES_NORMAL_MATRIX_OPERATORS_FOR_CORRELATIONS_AND_LOCAL_EXPECTATION_VALUES = s.NON_LOCAL_OPERATORS_OR_LIST_LOCAL_EXPECTATION_VALUES
else:
s.INFOSTREAM_OPERATORS_SUMMED_OVER_ALL_SITES['hamiltonian'] = '%.10f'
s.NAMES_MATRIX_PRODUCT_OPERATORS_ACTING_ON_ALL_SITES = list(s.INFOSTREAM_OPERATORS_SUMMED_OVER_ALL_SITES.keys())
s.NAMES_MATRIX_PRODUCT_OPERATORS_ACTING_ON_CENTRAL_SITES = s.INFOSTREAM_OPERATORS_ACTING_ON_CENTRAL_SITES
s.DATA_COLUMNS_TAG = []
s.DATA_COLUMNS_TAG += s.NAMES_MATRIX_PRODUCT_OPERATORS_ACTING_ON_ALL_SITES
s.DATA_COLUMNS_TAG += [(_ + '_mid') for _ in s.NAMES_MATRIX_PRODUCT_OPERATORS_ACTING_ON_CENTRAL_SITES]
s.DATA_COLUMNS_TAG += s.SELF_ATTRIBUTES_TAGS
s.NAMES_ALL_ACTIVE_MATRIX_PRODUCT_OPERATORS = list((set(s.NAMES_MATRIX_PRODUCT_OPERATORS_ACTING_ON_ALL_SITES) | set(s.NAMES_MATRIX_PRODUCT_OPERATORS_ACTING_ON_CENTRAL_SITES)))
s.H_PARAMS = SIM_PARAMS['H_PARAMS']
if (len(s.H_PARAMS['Commuting_Operators'].keys()) == 0):
s.ABELIAN_SYMMETRIES = False
else:
s.ABELIAN_SYMMETRIES = True
s.TOTAL_CHARGE = {}
s.AVERAGE_CHARGE_PER_SITE = {}
s.LIST_SYMMETRIES_NAMES = list(s.H_PARAMS['Commuting_Operators'].keys())
s.LIST_SYMMETRIC_OPERATORS_NAMES = []
for symmetry_name in s.LIST_SYMMETRIES_NAMES:
if (symmetry_name == 'links_alignment'):
s.LIST_SYMMETRIC_OPERATORS_NAMES.append('links_set_left')
s.AVERAGE_CHARGE_PER_SITE['links_set_left'] = 0
s.LIST_SYMMETRIC_OPERATORS_NAMES.append('links_set_right')
s.AVERAGE_CHARGE_PER_SITE['links_set_right'] = 0
else:
s.LIST_SYMMETRIC_OPERATORS_NAMES.append(symmetry_name)
try:
s.AVERAGE_CHARGE_PER_SITE[symmetry_name] = s.H_PARAMS['Commuting_Operators'][symmetry_name]['Average_Charge']
s.TOTAL_CHARGE[symmetry_name] = int((s.AVERAGE_CHARGE_PER_SITE[symmetry_name] * s.REQUIRED_CHAIN_LENGTH))
except:
s.TOTAL_CHARGE[symmetry_name] = s.H_PARAMS['Commuting_Operators'][symmetry_name]['Total_Charge']
s.AVERAGE_CHARGE_PER_SITE[symmetry_name] = (s.TOTAL_CHARGE[symmetry_name] / s.REQUIRED_CHAIN_LENGTH)
s.number_tensor_contractions = {}
s.number_tensor_contractions['matvec'] = 4
s.number_tensor_contractions['ltm_mpo_update'] = 3
s.number_tensor_contractions['rtm_mpo_update'] = 3
s.number_tensor_contractions['ltm_opt_update'] = 3
s.number_tensor_contractions['rtm_opt_update'] = 3
s.number_tensor_contractions['two_sites_svd'] = 1
s.HALF_REQUIRED_CHAIN_LENGTH = int((s.REQUIRED_CHAIN_LENGTH / 2))<|docstring|>Define (many) simulation parameters as self objects, from SIM_PARAMS.
Store the algorithm and Hamiltonian input parameters as self objects.<|endoftext|> |
c1de0e7f21f498d0733ff5fd000f3344689546c00e82a0fab9c5765229f320ce | @property
def pool(self):
'Create thread pool on first request\n avoids instantiating unused threadpool for blocking clients.\n '
if (self._pool is None):
atexit.register(self.close)
self._pool = ThreadPool(self.pool_threads)
return self._pool | Create thread pool on first request
avoids instantiating unused threadpool for blocking clients. | aylien_news_api/api_client.py | pool | AYLIEN/aylien_newsapi_python | 13 | python | @property
def pool(self):
'Create thread pool on first request\n avoids instantiating unused threadpool for blocking clients.\n '
if (self._pool is None):
atexit.register(self.close)
self._pool = ThreadPool(self.pool_threads)
return self._pool | @property
def pool(self):
'Create thread pool on first request\n avoids instantiating unused threadpool for blocking clients.\n '
if (self._pool is None):
atexit.register(self.close)
self._pool = ThreadPool(self.pool_threads)
return self._pool<|docstring|>Create thread pool on first request
avoids instantiating unused threadpool for blocking clients.<|endoftext|> |
c5c52b83bab6c62c262b0cf7dcecce09b5609dfb7306716d10ea8feac092abe8 | @property
def user_agent(self):
'User agent for this API client'
return self.default_headers['User-Agent'] | User agent for this API client | aylien_news_api/api_client.py | user_agent | AYLIEN/aylien_newsapi_python | 13 | python | @property
def user_agent(self):
return self.default_headers['User-Agent'] | @property
def user_agent(self):
return self.default_headers['User-Agent']<|docstring|>User agent for this API client<|endoftext|> |
bb247c68e3f340cf62644aba05f189a147b17709d1b61b0fdacd87e5e816db7a | def sanitize_for_serialization(self, obj):
'Builds a JSON POST object.\n\n If obj is None, return None.\n If obj is str, int, long, float, bool, return directly.\n If obj is datetime.datetime, datetime.date\n convert to string in iso8601 format.\n If obj is list, sanitize each element in the list.\n If obj is dict, return the dict.\n If obj is OpenAPI model, return the properties dict.\n\n :param obj: The data to serialize.\n :return: The serialized form of data.\n '
if (obj is None):
return None
elif isinstance(obj, self.PRIMITIVE_TYPES):
return obj
elif isinstance(obj, list):
return [self.sanitize_for_serialization(sub_obj) for sub_obj in obj]
elif isinstance(obj, tuple):
return tuple((self.sanitize_for_serialization(sub_obj) for sub_obj in obj))
elif isinstance(obj, (datetime.datetime, datetime.date)):
return obj.isoformat()
if isinstance(obj, dict):
obj_dict = obj
else:
obj_dict = {obj.attribute_map[attr]: getattr(obj, attr) for (attr, _) in six.iteritems(obj.openapi_types) if (getattr(obj, attr) is not None)}
return {key: self.sanitize_for_serialization(val) for (key, val) in six.iteritems(obj_dict)} | Builds a JSON POST object.
If obj is None, return None.
If obj is str, int, long, float, bool, return directly.
If obj is datetime.datetime, datetime.date
convert to string in iso8601 format.
If obj is list, sanitize each element in the list.
If obj is dict, return the dict.
If obj is OpenAPI model, return the properties dict.
:param obj: The data to serialize.
:return: The serialized form of data. | aylien_news_api/api_client.py | sanitize_for_serialization | AYLIEN/aylien_newsapi_python | 13 | python | def sanitize_for_serialization(self, obj):
'Builds a JSON POST object.\n\n If obj is None, return None.\n If obj is str, int, long, float, bool, return directly.\n If obj is datetime.datetime, datetime.date\n convert to string in iso8601 format.\n If obj is list, sanitize each element in the list.\n If obj is dict, return the dict.\n If obj is OpenAPI model, return the properties dict.\n\n :param obj: The data to serialize.\n :return: The serialized form of data.\n '
if (obj is None):
return None
elif isinstance(obj, self.PRIMITIVE_TYPES):
return obj
elif isinstance(obj, list):
return [self.sanitize_for_serialization(sub_obj) for sub_obj in obj]
elif isinstance(obj, tuple):
return tuple((self.sanitize_for_serialization(sub_obj) for sub_obj in obj))
elif isinstance(obj, (datetime.datetime, datetime.date)):
return obj.isoformat()
if isinstance(obj, dict):
obj_dict = obj
else:
obj_dict = {obj.attribute_map[attr]: getattr(obj, attr) for (attr, _) in six.iteritems(obj.openapi_types) if (getattr(obj, attr) is not None)}
return {key: self.sanitize_for_serialization(val) for (key, val) in six.iteritems(obj_dict)} | def sanitize_for_serialization(self, obj):
'Builds a JSON POST object.\n\n If obj is None, return None.\n If obj is str, int, long, float, bool, return directly.\n If obj is datetime.datetime, datetime.date\n convert to string in iso8601 format.\n If obj is list, sanitize each element in the list.\n If obj is dict, return the dict.\n If obj is OpenAPI model, return the properties dict.\n\n :param obj: The data to serialize.\n :return: The serialized form of data.\n '
if (obj is None):
return None
elif isinstance(obj, self.PRIMITIVE_TYPES):
return obj
elif isinstance(obj, list):
return [self.sanitize_for_serialization(sub_obj) for sub_obj in obj]
elif isinstance(obj, tuple):
return tuple((self.sanitize_for_serialization(sub_obj) for sub_obj in obj))
elif isinstance(obj, (datetime.datetime, datetime.date)):
return obj.isoformat()
if isinstance(obj, dict):
obj_dict = obj
else:
obj_dict = {obj.attribute_map[attr]: getattr(obj, attr) for (attr, _) in six.iteritems(obj.openapi_types) if (getattr(obj, attr) is not None)}
return {key: self.sanitize_for_serialization(val) for (key, val) in six.iteritems(obj_dict)}<|docstring|>Builds a JSON POST object.
If obj is None, return None.
If obj is str, int, long, float, bool, return directly.
If obj is datetime.datetime, datetime.date
convert to string in iso8601 format.
If obj is list, sanitize each element in the list.
If obj is dict, return the dict.
If obj is OpenAPI model, return the properties dict.
:param obj: The data to serialize.
:return: The serialized form of data.<|endoftext|> |
92da644dcebc9d93f798594d2a39281b0eeaf90c415ad654d8d1b36f7bee49aa | def deserialize(self, response, response_type):
'Deserializes response into an object.\n\n :param response: RESTResponse object to be deserialized.\n :param response_type: class literal for\n deserialized object, or string of class name.\n\n :return: deserialized object.\n '
if (response_type == 'file'):
return self.__deserialize_file(response)
try:
data = json.loads(response.data)
except ValueError:
data = response.data
return self.__deserialize(data, response_type) | Deserializes response into an object.
:param response: RESTResponse object to be deserialized.
:param response_type: class literal for
deserialized object, or string of class name.
:return: deserialized object. | aylien_news_api/api_client.py | deserialize | AYLIEN/aylien_newsapi_python | 13 | python | def deserialize(self, response, response_type):
'Deserializes response into an object.\n\n :param response: RESTResponse object to be deserialized.\n :param response_type: class literal for\n deserialized object, or string of class name.\n\n :return: deserialized object.\n '
if (response_type == 'file'):
return self.__deserialize_file(response)
try:
data = json.loads(response.data)
except ValueError:
data = response.data
return self.__deserialize(data, response_type) | def deserialize(self, response, response_type):
'Deserializes response into an object.\n\n :param response: RESTResponse object to be deserialized.\n :param response_type: class literal for\n deserialized object, or string of class name.\n\n :return: deserialized object.\n '
if (response_type == 'file'):
return self.__deserialize_file(response)
try:
data = json.loads(response.data)
except ValueError:
data = response.data
return self.__deserialize(data, response_type)<|docstring|>Deserializes response into an object.
:param response: RESTResponse object to be deserialized.
:param response_type: class literal for
deserialized object, or string of class name.
:return: deserialized object.<|endoftext|> |
6f2a08f904947972254855216eea84ba86ce6b8ccd8a98d710745a97cfc84d4c | def __deserialize(self, data, klass):
'Deserializes dict, list, str into an object.\n\n :param data: dict, list or str.\n :param klass: class literal, or string of class name.\n\n :return: object.\n '
if (data is None):
return None
if (type(klass) == str):
if klass.startswith('list['):
sub_kls = re.match('list\\[(.*)\\]', klass).group(1)
return [self.__deserialize(sub_data, sub_kls) for sub_data in data]
if klass.startswith('dict('):
sub_kls = re.match('dict\\(([^,]*), (.*)\\)', klass).group(2)
return {k: self.__deserialize(v, sub_kls) for (k, v) in six.iteritems(data)}
if (klass in self.NATIVE_TYPES_MAPPING):
klass = self.NATIVE_TYPES_MAPPING[klass]
else:
klass = getattr(aylien_news_api.models, klass)
if (klass in self.PRIMITIVE_TYPES):
return self.__deserialize_primitive(data, klass)
elif (klass == object):
return self.__deserialize_object(data)
elif (klass == datetime.date):
return self.__deserialize_date(data)
elif (klass == datetime.datetime):
return self.__deserialize_datetime(data)
else:
return self.__deserialize_model(data, klass) | Deserializes dict, list, str into an object.
:param data: dict, list or str.
:param klass: class literal, or string of class name.
:return: object. | aylien_news_api/api_client.py | __deserialize | AYLIEN/aylien_newsapi_python | 13 | python | def __deserialize(self, data, klass):
'Deserializes dict, list, str into an object.\n\n :param data: dict, list or str.\n :param klass: class literal, or string of class name.\n\n :return: object.\n '
if (data is None):
return None
if (type(klass) == str):
if klass.startswith('list['):
sub_kls = re.match('list\\[(.*)\\]', klass).group(1)
return [self.__deserialize(sub_data, sub_kls) for sub_data in data]
if klass.startswith('dict('):
sub_kls = re.match('dict\\(([^,]*), (.*)\\)', klass).group(2)
return {k: self.__deserialize(v, sub_kls) for (k, v) in six.iteritems(data)}
if (klass in self.NATIVE_TYPES_MAPPING):
klass = self.NATIVE_TYPES_MAPPING[klass]
else:
klass = getattr(aylien_news_api.models, klass)
if (klass in self.PRIMITIVE_TYPES):
return self.__deserialize_primitive(data, klass)
elif (klass == object):
return self.__deserialize_object(data)
elif (klass == datetime.date):
return self.__deserialize_date(data)
elif (klass == datetime.datetime):
return self.__deserialize_datetime(data)
else:
return self.__deserialize_model(data, klass) | def __deserialize(self, data, klass):
'Deserializes dict, list, str into an object.\n\n :param data: dict, list or str.\n :param klass: class literal, or string of class name.\n\n :return: object.\n '
if (data is None):
return None
if (type(klass) == str):
if klass.startswith('list['):
sub_kls = re.match('list\\[(.*)\\]', klass).group(1)
return [self.__deserialize(sub_data, sub_kls) for sub_data in data]
if klass.startswith('dict('):
sub_kls = re.match('dict\\(([^,]*), (.*)\\)', klass).group(2)
return {k: self.__deserialize(v, sub_kls) for (k, v) in six.iteritems(data)}
if (klass in self.NATIVE_TYPES_MAPPING):
klass = self.NATIVE_TYPES_MAPPING[klass]
else:
klass = getattr(aylien_news_api.models, klass)
if (klass in self.PRIMITIVE_TYPES):
return self.__deserialize_primitive(data, klass)
elif (klass == object):
return self.__deserialize_object(data)
elif (klass == datetime.date):
return self.__deserialize_date(data)
elif (klass == datetime.datetime):
return self.__deserialize_datetime(data)
else:
return self.__deserialize_model(data, klass)<|docstring|>Deserializes dict, list, str into an object.
:param data: dict, list or str.
:param klass: class literal, or string of class name.
:return: object.<|endoftext|> |
7378342307cd1c1447649016446df257d866f079f600583bbc2317ac4d5859d6 | def call_api(self, resource_path, method, path_params=None, query_params=None, header_params=None, body=None, post_params=None, files=None, response_type=None, auth_settings=None, async_req=None, _return_http_data_only=None, collection_formats=None, _preload_content=True, _request_timeout=None, _host=None, _request_auth=None):
'Makes the HTTP request (synchronous) and returns deserialized data.\n\n To make an async_req request, set the async_req parameter.\n\n :param resource_path: Path to method endpoint.\n :param method: Method to call.\n :param path_params: Path parameters in the url.\n :param query_params: Query parameters in the url.\n :param header_params: Header parameters to be\n placed in the request header.\n :param body: Request body.\n :param post_params dict: Request post form parameters,\n for `application/x-www-form-urlencoded`, `multipart/form-data`.\n :param auth_settings list: Auth Settings names for the request.\n :param response: Response data type.\n :param files dict: key -> filename, value -> filepath,\n for `multipart/form-data`.\n :param async_req bool: execute request asynchronously\n :param _return_http_data_only: response data without head status code\n and headers\n :param collection_formats: dict of collection formats for path, query,\n header, and post parameters.\n :param _preload_content: if False, the urllib3.HTTPResponse object will\n be returned without reading/decoding response\n data. Default is True.\n :param _request_timeout: timeout setting for this request. If one\n number provided, it will be total request\n timeout. It can also be a pair (tuple) of\n (connection, read) timeouts.\n :param _request_auth: set to override the auth_settings for an a single\n request; this effectively ignores the authentication\n in the spec for a single request.\n :type _request_token: dict, optional\n :return:\n If async_req parameter is True,\n the request will be called asynchronously.\n The method will return the request thread.\n If parameter async_req is False or missing,\n then the method will return the response directly.\n '
if (not async_req):
return self.__call_api(resource_path, method, path_params, query_params, header_params, body, post_params, files, response_type, auth_settings, _return_http_data_only, collection_formats, _preload_content, _request_timeout, _host, _request_auth)
return self.pool.apply_async(self.__call_api, (resource_path, method, path_params, query_params, header_params, body, post_params, files, response_type, auth_settings, _return_http_data_only, collection_formats, _preload_content, _request_timeout, _host, _request_auth)) | Makes the HTTP request (synchronous) and returns deserialized data.
To make an async_req request, set the async_req parameter.
:param resource_path: Path to method endpoint.
:param method: Method to call.
:param path_params: Path parameters in the url.
:param query_params: Query parameters in the url.
:param header_params: Header parameters to be
placed in the request header.
:param body: Request body.
:param post_params dict: Request post form parameters,
for `application/x-www-form-urlencoded`, `multipart/form-data`.
:param auth_settings list: Auth Settings names for the request.
:param response: Response data type.
:param files dict: key -> filename, value -> filepath,
for `multipart/form-data`.
:param async_req bool: execute request asynchronously
:param _return_http_data_only: response data without head status code
and headers
:param collection_formats: dict of collection formats for path, query,
header, and post parameters.
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:param _request_auth: set to override the auth_settings for an a single
request; this effectively ignores the authentication
in the spec for a single request.
:type _request_token: dict, optional
:return:
If async_req parameter is True,
the request will be called asynchronously.
The method will return the request thread.
If parameter async_req is False or missing,
then the method will return the response directly. | aylien_news_api/api_client.py | call_api | AYLIEN/aylien_newsapi_python | 13 | python | def call_api(self, resource_path, method, path_params=None, query_params=None, header_params=None, body=None, post_params=None, files=None, response_type=None, auth_settings=None, async_req=None, _return_http_data_only=None, collection_formats=None, _preload_content=True, _request_timeout=None, _host=None, _request_auth=None):
'Makes the HTTP request (synchronous) and returns deserialized data.\n\n To make an async_req request, set the async_req parameter.\n\n :param resource_path: Path to method endpoint.\n :param method: Method to call.\n :param path_params: Path parameters in the url.\n :param query_params: Query parameters in the url.\n :param header_params: Header parameters to be\n placed in the request header.\n :param body: Request body.\n :param post_params dict: Request post form parameters,\n for `application/x-www-form-urlencoded`, `multipart/form-data`.\n :param auth_settings list: Auth Settings names for the request.\n :param response: Response data type.\n :param files dict: key -> filename, value -> filepath,\n for `multipart/form-data`.\n :param async_req bool: execute request asynchronously\n :param _return_http_data_only: response data without head status code\n and headers\n :param collection_formats: dict of collection formats for path, query,\n header, and post parameters.\n :param _preload_content: if False, the urllib3.HTTPResponse object will\n be returned without reading/decoding response\n data. Default is True.\n :param _request_timeout: timeout setting for this request. If one\n number provided, it will be total request\n timeout. It can also be a pair (tuple) of\n (connection, read) timeouts.\n :param _request_auth: set to override the auth_settings for an a single\n request; this effectively ignores the authentication\n in the spec for a single request.\n :type _request_token: dict, optional\n :return:\n If async_req parameter is True,\n the request will be called asynchronously.\n The method will return the request thread.\n If parameter async_req is False or missing,\n then the method will return the response directly.\n '
if (not async_req):
return self.__call_api(resource_path, method, path_params, query_params, header_params, body, post_params, files, response_type, auth_settings, _return_http_data_only, collection_formats, _preload_content, _request_timeout, _host, _request_auth)
return self.pool.apply_async(self.__call_api, (resource_path, method, path_params, query_params, header_params, body, post_params, files, response_type, auth_settings, _return_http_data_only, collection_formats, _preload_content, _request_timeout, _host, _request_auth)) | def call_api(self, resource_path, method, path_params=None, query_params=None, header_params=None, body=None, post_params=None, files=None, response_type=None, auth_settings=None, async_req=None, _return_http_data_only=None, collection_formats=None, _preload_content=True, _request_timeout=None, _host=None, _request_auth=None):
'Makes the HTTP request (synchronous) and returns deserialized data.\n\n To make an async_req request, set the async_req parameter.\n\n :param resource_path: Path to method endpoint.\n :param method: Method to call.\n :param path_params: Path parameters in the url.\n :param query_params: Query parameters in the url.\n :param header_params: Header parameters to be\n placed in the request header.\n :param body: Request body.\n :param post_params dict: Request post form parameters,\n for `application/x-www-form-urlencoded`, `multipart/form-data`.\n :param auth_settings list: Auth Settings names for the request.\n :param response: Response data type.\n :param files dict: key -> filename, value -> filepath,\n for `multipart/form-data`.\n :param async_req bool: execute request asynchronously\n :param _return_http_data_only: response data without head status code\n and headers\n :param collection_formats: dict of collection formats for path, query,\n header, and post parameters.\n :param _preload_content: if False, the urllib3.HTTPResponse object will\n be returned without reading/decoding response\n data. Default is True.\n :param _request_timeout: timeout setting for this request. If one\n number provided, it will be total request\n timeout. It can also be a pair (tuple) of\n (connection, read) timeouts.\n :param _request_auth: set to override the auth_settings for an a single\n request; this effectively ignores the authentication\n in the spec for a single request.\n :type _request_token: dict, optional\n :return:\n If async_req parameter is True,\n the request will be called asynchronously.\n The method will return the request thread.\n If parameter async_req is False or missing,\n then the method will return the response directly.\n '
if (not async_req):
return self.__call_api(resource_path, method, path_params, query_params, header_params, body, post_params, files, response_type, auth_settings, _return_http_data_only, collection_formats, _preload_content, _request_timeout, _host, _request_auth)
return self.pool.apply_async(self.__call_api, (resource_path, method, path_params, query_params, header_params, body, post_params, files, response_type, auth_settings, _return_http_data_only, collection_formats, _preload_content, _request_timeout, _host, _request_auth))<|docstring|>Makes the HTTP request (synchronous) and returns deserialized data.
To make an async_req request, set the async_req parameter.
:param resource_path: Path to method endpoint.
:param method: Method to call.
:param path_params: Path parameters in the url.
:param query_params: Query parameters in the url.
:param header_params: Header parameters to be
placed in the request header.
:param body: Request body.
:param post_params dict: Request post form parameters,
for `application/x-www-form-urlencoded`, `multipart/form-data`.
:param auth_settings list: Auth Settings names for the request.
:param response: Response data type.
:param files dict: key -> filename, value -> filepath,
for `multipart/form-data`.
:param async_req bool: execute request asynchronously
:param _return_http_data_only: response data without head status code
and headers
:param collection_formats: dict of collection formats for path, query,
header, and post parameters.
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:param _request_auth: set to override the auth_settings for an a single
request; this effectively ignores the authentication
in the spec for a single request.
:type _request_token: dict, optional
:return:
If async_req parameter is True,
the request will be called asynchronously.
The method will return the request thread.
If parameter async_req is False or missing,
then the method will return the response directly.<|endoftext|> |
05ffda3a775a60d420ec75309ae32cef9add19416b4107fe3f0160e24c168375 | def request(self, method, url, query_params=None, headers=None, post_params=None, body=None, _preload_content=True, _request_timeout=None):
'Makes the HTTP request using RESTClient.'
if (method == 'GET'):
return self.rest_client.GET(url, query_params=query_params, _preload_content=_preload_content, _request_timeout=_request_timeout, headers=headers)
elif (method == 'HEAD'):
return self.rest_client.HEAD(url, query_params=query_params, _preload_content=_preload_content, _request_timeout=_request_timeout, headers=headers)
elif (method == 'OPTIONS'):
return self.rest_client.OPTIONS(url, query_params=query_params, headers=headers, _preload_content=_preload_content, _request_timeout=_request_timeout)
elif (method == 'POST'):
return self.rest_client.POST(url, query_params=query_params, headers=headers, post_params=post_params, _preload_content=_preload_content, _request_timeout=_request_timeout, body=body)
elif (method == 'PUT'):
return self.rest_client.PUT(url, query_params=query_params, headers=headers, post_params=post_params, _preload_content=_preload_content, _request_timeout=_request_timeout, body=body)
elif (method == 'PATCH'):
return self.rest_client.PATCH(url, query_params=query_params, headers=headers, post_params=post_params, _preload_content=_preload_content, _request_timeout=_request_timeout, body=body)
elif (method == 'DELETE'):
return self.rest_client.DELETE(url, query_params=query_params, headers=headers, _preload_content=_preload_content, _request_timeout=_request_timeout, body=body)
else:
raise ApiValueError('http method must be `GET`, `HEAD`, `OPTIONS`, `POST`, `PATCH`, `PUT` or `DELETE`.') | Makes the HTTP request using RESTClient. | aylien_news_api/api_client.py | request | AYLIEN/aylien_newsapi_python | 13 | python | def request(self, method, url, query_params=None, headers=None, post_params=None, body=None, _preload_content=True, _request_timeout=None):
if (method == 'GET'):
return self.rest_client.GET(url, query_params=query_params, _preload_content=_preload_content, _request_timeout=_request_timeout, headers=headers)
elif (method == 'HEAD'):
return self.rest_client.HEAD(url, query_params=query_params, _preload_content=_preload_content, _request_timeout=_request_timeout, headers=headers)
elif (method == 'OPTIONS'):
return self.rest_client.OPTIONS(url, query_params=query_params, headers=headers, _preload_content=_preload_content, _request_timeout=_request_timeout)
elif (method == 'POST'):
return self.rest_client.POST(url, query_params=query_params, headers=headers, post_params=post_params, _preload_content=_preload_content, _request_timeout=_request_timeout, body=body)
elif (method == 'PUT'):
return self.rest_client.PUT(url, query_params=query_params, headers=headers, post_params=post_params, _preload_content=_preload_content, _request_timeout=_request_timeout, body=body)
elif (method == 'PATCH'):
return self.rest_client.PATCH(url, query_params=query_params, headers=headers, post_params=post_params, _preload_content=_preload_content, _request_timeout=_request_timeout, body=body)
elif (method == 'DELETE'):
return self.rest_client.DELETE(url, query_params=query_params, headers=headers, _preload_content=_preload_content, _request_timeout=_request_timeout, body=body)
else:
raise ApiValueError('http method must be `GET`, `HEAD`, `OPTIONS`, `POST`, `PATCH`, `PUT` or `DELETE`.') | def request(self, method, url, query_params=None, headers=None, post_params=None, body=None, _preload_content=True, _request_timeout=None):
if (method == 'GET'):
return self.rest_client.GET(url, query_params=query_params, _preload_content=_preload_content, _request_timeout=_request_timeout, headers=headers)
elif (method == 'HEAD'):
return self.rest_client.HEAD(url, query_params=query_params, _preload_content=_preload_content, _request_timeout=_request_timeout, headers=headers)
elif (method == 'OPTIONS'):
return self.rest_client.OPTIONS(url, query_params=query_params, headers=headers, _preload_content=_preload_content, _request_timeout=_request_timeout)
elif (method == 'POST'):
return self.rest_client.POST(url, query_params=query_params, headers=headers, post_params=post_params, _preload_content=_preload_content, _request_timeout=_request_timeout, body=body)
elif (method == 'PUT'):
return self.rest_client.PUT(url, query_params=query_params, headers=headers, post_params=post_params, _preload_content=_preload_content, _request_timeout=_request_timeout, body=body)
elif (method == 'PATCH'):
return self.rest_client.PATCH(url, query_params=query_params, headers=headers, post_params=post_params, _preload_content=_preload_content, _request_timeout=_request_timeout, body=body)
elif (method == 'DELETE'):
return self.rest_client.DELETE(url, query_params=query_params, headers=headers, _preload_content=_preload_content, _request_timeout=_request_timeout, body=body)
else:
raise ApiValueError('http method must be `GET`, `HEAD`, `OPTIONS`, `POST`, `PATCH`, `PUT` or `DELETE`.')<|docstring|>Makes the HTTP request using RESTClient.<|endoftext|> |
95796efc2c32c5f0a63c3023434a7fb8077a70b562341d885353d2b2a18a8d2a | def parameters_to_tuples(self, params, collection_formats):
'Get parameters as list of tuples, formatting collections.\n\n :param params: Parameters as dict or list of two-tuples\n :param dict collection_formats: Parameter collection formats\n :return: Parameters as list of tuples, collections formatted\n '
new_params = []
if (collection_formats is None):
collection_formats = {}
for (k, v) in (six.iteritems(params) if isinstance(params, dict) else params):
if (k in collection_formats):
collection_format = collection_formats[k]
if (collection_format == 'multi'):
new_params.extend(((k, value) for value in v))
else:
if (collection_format == 'ssv'):
delimiter = ' '
elif (collection_format == 'tsv'):
delimiter = '\t'
elif (collection_format == 'pipes'):
delimiter = '|'
else:
delimiter = ','
new_params.append((k, delimiter.join((str(value) for value in v))))
else:
new_params.append((k, v))
return new_params | Get parameters as list of tuples, formatting collections.
:param params: Parameters as dict or list of two-tuples
:param dict collection_formats: Parameter collection formats
:return: Parameters as list of tuples, collections formatted | aylien_news_api/api_client.py | parameters_to_tuples | AYLIEN/aylien_newsapi_python | 13 | python | def parameters_to_tuples(self, params, collection_formats):
'Get parameters as list of tuples, formatting collections.\n\n :param params: Parameters as dict or list of two-tuples\n :param dict collection_formats: Parameter collection formats\n :return: Parameters as list of tuples, collections formatted\n '
new_params = []
if (collection_formats is None):
collection_formats = {}
for (k, v) in (six.iteritems(params) if isinstance(params, dict) else params):
if (k in collection_formats):
collection_format = collection_formats[k]
if (collection_format == 'multi'):
new_params.extend(((k, value) for value in v))
else:
if (collection_format == 'ssv'):
delimiter = ' '
elif (collection_format == 'tsv'):
delimiter = '\t'
elif (collection_format == 'pipes'):
delimiter = '|'
else:
delimiter = ','
new_params.append((k, delimiter.join((str(value) for value in v))))
else:
new_params.append((k, v))
return new_params | def parameters_to_tuples(self, params, collection_formats):
'Get parameters as list of tuples, formatting collections.\n\n :param params: Parameters as dict or list of two-tuples\n :param dict collection_formats: Parameter collection formats\n :return: Parameters as list of tuples, collections formatted\n '
new_params = []
if (collection_formats is None):
collection_formats = {}
for (k, v) in (six.iteritems(params) if isinstance(params, dict) else params):
if (k in collection_formats):
collection_format = collection_formats[k]
if (collection_format == 'multi'):
new_params.extend(((k, value) for value in v))
else:
if (collection_format == 'ssv'):
delimiter = ' '
elif (collection_format == 'tsv'):
delimiter = '\t'
elif (collection_format == 'pipes'):
delimiter = '|'
else:
delimiter = ','
new_params.append((k, delimiter.join((str(value) for value in v))))
else:
new_params.append((k, v))
return new_params<|docstring|>Get parameters as list of tuples, formatting collections.
:param params: Parameters as dict or list of two-tuples
:param dict collection_formats: Parameter collection formats
:return: Parameters as list of tuples, collections formatted<|endoftext|> |
97e3bb67798227f199805ae68b7c5ef4f46a9993b159d67e67de5e22b4cd0fb9 | def files_parameters(self, files=None):
'Builds form parameters.\n\n :param files: File parameters.\n :return: Form parameters with files.\n '
params = []
if files:
for (k, v) in six.iteritems(files):
if (not v):
continue
file_names = (v if (type(v) is list) else [v])
for n in file_names:
with open(n, 'rb') as f:
filename = os.path.basename(f.name)
filedata = f.read()
mimetype = (mimetypes.guess_type(filename)[0] or 'application/octet-stream')
params.append(tuple([k, tuple([filename, filedata, mimetype])]))
return params | Builds form parameters.
:param files: File parameters.
:return: Form parameters with files. | aylien_news_api/api_client.py | files_parameters | AYLIEN/aylien_newsapi_python | 13 | python | def files_parameters(self, files=None):
'Builds form parameters.\n\n :param files: File parameters.\n :return: Form parameters with files.\n '
params = []
if files:
for (k, v) in six.iteritems(files):
if (not v):
continue
file_names = (v if (type(v) is list) else [v])
for n in file_names:
with open(n, 'rb') as f:
filename = os.path.basename(f.name)
filedata = f.read()
mimetype = (mimetypes.guess_type(filename)[0] or 'application/octet-stream')
params.append(tuple([k, tuple([filename, filedata, mimetype])]))
return params | def files_parameters(self, files=None):
'Builds form parameters.\n\n :param files: File parameters.\n :return: Form parameters with files.\n '
params = []
if files:
for (k, v) in six.iteritems(files):
if (not v):
continue
file_names = (v if (type(v) is list) else [v])
for n in file_names:
with open(n, 'rb') as f:
filename = os.path.basename(f.name)
filedata = f.read()
mimetype = (mimetypes.guess_type(filename)[0] or 'application/octet-stream')
params.append(tuple([k, tuple([filename, filedata, mimetype])]))
return params<|docstring|>Builds form parameters.
:param files: File parameters.
:return: Form parameters with files.<|endoftext|> |
67e460fe35972cffd5484e31890892031d5bd4a32baf7f5717be1b353dcd6260 | def select_header_accept(self, accepts):
'Returns `Accept` based on an array of accepts provided.\n\n :param accepts: List of headers.\n :return: Accept (e.g. application/json).\n '
if (not accepts):
return
accepts = [x.lower() for x in accepts]
if ('application/json' in accepts):
return 'application/json'
else:
return ', '.join(accepts) | Returns `Accept` based on an array of accepts provided.
:param accepts: List of headers.
:return: Accept (e.g. application/json). | aylien_news_api/api_client.py | select_header_accept | AYLIEN/aylien_newsapi_python | 13 | python | def select_header_accept(self, accepts):
'Returns `Accept` based on an array of accepts provided.\n\n :param accepts: List of headers.\n :return: Accept (e.g. application/json).\n '
if (not accepts):
return
accepts = [x.lower() for x in accepts]
if ('application/json' in accepts):
return 'application/json'
else:
return ', '.join(accepts) | def select_header_accept(self, accepts):
'Returns `Accept` based on an array of accepts provided.\n\n :param accepts: List of headers.\n :return: Accept (e.g. application/json).\n '
if (not accepts):
return
accepts = [x.lower() for x in accepts]
if ('application/json' in accepts):
return 'application/json'
else:
return ', '.join(accepts)<|docstring|>Returns `Accept` based on an array of accepts provided.
:param accepts: List of headers.
:return: Accept (e.g. application/json).<|endoftext|> |
a9d569d0f0bd6a8e47f52567aa35c9cf4726a04df122971a70f45f918bed128a | def select_header_content_type(self, content_types):
'Returns `Content-Type` based on an array of content_types provided.\n\n :param content_types: List of content-types.\n :return: Content-Type (e.g. application/json).\n '
if (not content_types):
return 'application/json'
content_types = [x.lower() for x in content_types]
if (('application/json' in content_types) or ('*/*' in content_types)):
return 'application/json'
else:
return content_types[0] | Returns `Content-Type` based on an array of content_types provided.
:param content_types: List of content-types.
:return: Content-Type (e.g. application/json). | aylien_news_api/api_client.py | select_header_content_type | AYLIEN/aylien_newsapi_python | 13 | python | def select_header_content_type(self, content_types):
'Returns `Content-Type` based on an array of content_types provided.\n\n :param content_types: List of content-types.\n :return: Content-Type (e.g. application/json).\n '
if (not content_types):
return 'application/json'
content_types = [x.lower() for x in content_types]
if (('application/json' in content_types) or ('*/*' in content_types)):
return 'application/json'
else:
return content_types[0] | def select_header_content_type(self, content_types):
'Returns `Content-Type` based on an array of content_types provided.\n\n :param content_types: List of content-types.\n :return: Content-Type (e.g. application/json).\n '
if (not content_types):
return 'application/json'
content_types = [x.lower() for x in content_types]
if (('application/json' in content_types) or ('*/*' in content_types)):
return 'application/json'
else:
return content_types[0]<|docstring|>Returns `Content-Type` based on an array of content_types provided.
:param content_types: List of content-types.
:return: Content-Type (e.g. application/json).<|endoftext|> |
dd7565f679c2afa0d10fffdf4ac6ad5ac6cdf777b010bde532b34e1ed4da96f5 | def update_params_for_auth(self, headers, querys, auth_settings, request_auth=None):
'Updates header and query params based on authentication setting.\n\n :param headers: Header parameters dict to be updated.\n :param querys: Query parameters tuple list to be updated.\n :param auth_settings: Authentication setting identifiers list.\n :param request_auth: if set, the provided settings will\n override the token in the configuration.\n '
if (not auth_settings):
return
if request_auth:
self._apply_auth_params(headers, querys, request_auth)
return
for auth in auth_settings:
auth_setting = self.configuration.auth_settings().get(auth)
if auth_setting:
self._apply_auth_params(headers, querys, auth_setting) | Updates header and query params based on authentication setting.
:param headers: Header parameters dict to be updated.
:param querys: Query parameters tuple list to be updated.
:param auth_settings: Authentication setting identifiers list.
:param request_auth: if set, the provided settings will
override the token in the configuration. | aylien_news_api/api_client.py | update_params_for_auth | AYLIEN/aylien_newsapi_python | 13 | python | def update_params_for_auth(self, headers, querys, auth_settings, request_auth=None):
'Updates header and query params based on authentication setting.\n\n :param headers: Header parameters dict to be updated.\n :param querys: Query parameters tuple list to be updated.\n :param auth_settings: Authentication setting identifiers list.\n :param request_auth: if set, the provided settings will\n override the token in the configuration.\n '
if (not auth_settings):
return
if request_auth:
self._apply_auth_params(headers, querys, request_auth)
return
for auth in auth_settings:
auth_setting = self.configuration.auth_settings().get(auth)
if auth_setting:
self._apply_auth_params(headers, querys, auth_setting) | def update_params_for_auth(self, headers, querys, auth_settings, request_auth=None):
'Updates header and query params based on authentication setting.\n\n :param headers: Header parameters dict to be updated.\n :param querys: Query parameters tuple list to be updated.\n :param auth_settings: Authentication setting identifiers list.\n :param request_auth: if set, the provided settings will\n override the token in the configuration.\n '
if (not auth_settings):
return
if request_auth:
self._apply_auth_params(headers, querys, request_auth)
return
for auth in auth_settings:
auth_setting = self.configuration.auth_settings().get(auth)
if auth_setting:
self._apply_auth_params(headers, querys, auth_setting)<|docstring|>Updates header and query params based on authentication setting.
:param headers: Header parameters dict to be updated.
:param querys: Query parameters tuple list to be updated.
:param auth_settings: Authentication setting identifiers list.
:param request_auth: if set, the provided settings will
override the token in the configuration.<|endoftext|> |
0f666636f6238cf696a0b3f26690e3554e5cb65fa7da719fdf6be36131b143a0 | def _apply_auth_params(self, headers, querys, auth_setting):
'Updates the request parameters based on a single auth_setting\n\n :param headers: Header parameters dict to be updated.\n :param querys: Query parameters tuple list to be updated.\n :param auth_setting: auth settings for the endpoint\n '
if (auth_setting['in'] == 'cookie'):
headers['Cookie'] = auth_setting['value']
elif (auth_setting['in'] == 'header'):
headers[auth_setting['key']] = auth_setting['value']
elif (auth_setting['in'] == 'query'):
querys.append((auth_setting['key'], auth_setting['value']))
else:
raise ApiValueError('Authentication token must be in `query` or `header`') | Updates the request parameters based on a single auth_setting
:param headers: Header parameters dict to be updated.
:param querys: Query parameters tuple list to be updated.
:param auth_setting: auth settings for the endpoint | aylien_news_api/api_client.py | _apply_auth_params | AYLIEN/aylien_newsapi_python | 13 | python | def _apply_auth_params(self, headers, querys, auth_setting):
'Updates the request parameters based on a single auth_setting\n\n :param headers: Header parameters dict to be updated.\n :param querys: Query parameters tuple list to be updated.\n :param auth_setting: auth settings for the endpoint\n '
if (auth_setting['in'] == 'cookie'):
headers['Cookie'] = auth_setting['value']
elif (auth_setting['in'] == 'header'):
headers[auth_setting['key']] = auth_setting['value']
elif (auth_setting['in'] == 'query'):
querys.append((auth_setting['key'], auth_setting['value']))
else:
raise ApiValueError('Authentication token must be in `query` or `header`') | def _apply_auth_params(self, headers, querys, auth_setting):
'Updates the request parameters based on a single auth_setting\n\n :param headers: Header parameters dict to be updated.\n :param querys: Query parameters tuple list to be updated.\n :param auth_setting: auth settings for the endpoint\n '
if (auth_setting['in'] == 'cookie'):
headers['Cookie'] = auth_setting['value']
elif (auth_setting['in'] == 'header'):
headers[auth_setting['key']] = auth_setting['value']
elif (auth_setting['in'] == 'query'):
querys.append((auth_setting['key'], auth_setting['value']))
else:
raise ApiValueError('Authentication token must be in `query` or `header`')<|docstring|>Updates the request parameters based on a single auth_setting
:param headers: Header parameters dict to be updated.
:param querys: Query parameters tuple list to be updated.
:param auth_setting: auth settings for the endpoint<|endoftext|> |
9b86180dd535039229f5641d433347368798a645d1087ea786c8a27498886a5d | def __deserialize_file(self, response):
'Deserializes body to file\n\n Saves response body into a file in a temporary folder,\n using the filename from the `Content-Disposition` header if provided.\n\n :param response: RESTResponse.\n :return: file path.\n '
(fd, path) = tempfile.mkstemp(dir=self.configuration.temp_folder_path)
os.close(fd)
os.remove(path)
content_disposition = response.getheader('Content-Disposition')
if content_disposition:
filename = re.search('filename=[\\\'"]?([^\\\'"\\s]+)[\\\'"]?', content_disposition).group(1)
path = os.path.join(os.path.dirname(path), filename)
with open(path, 'wb') as f:
f.write(response.data)
return path | Deserializes body to file
Saves response body into a file in a temporary folder,
using the filename from the `Content-Disposition` header if provided.
:param response: RESTResponse.
:return: file path. | aylien_news_api/api_client.py | __deserialize_file | AYLIEN/aylien_newsapi_python | 13 | python | def __deserialize_file(self, response):
'Deserializes body to file\n\n Saves response body into a file in a temporary folder,\n using the filename from the `Content-Disposition` header if provided.\n\n :param response: RESTResponse.\n :return: file path.\n '
(fd, path) = tempfile.mkstemp(dir=self.configuration.temp_folder_path)
os.close(fd)
os.remove(path)
content_disposition = response.getheader('Content-Disposition')
if content_disposition:
filename = re.search('filename=[\\\'"]?([^\\\'"\\s]+)[\\\'"]?', content_disposition).group(1)
path = os.path.join(os.path.dirname(path), filename)
with open(path, 'wb') as f:
f.write(response.data)
return path | def __deserialize_file(self, response):
'Deserializes body to file\n\n Saves response body into a file in a temporary folder,\n using the filename from the `Content-Disposition` header if provided.\n\n :param response: RESTResponse.\n :return: file path.\n '
(fd, path) = tempfile.mkstemp(dir=self.configuration.temp_folder_path)
os.close(fd)
os.remove(path)
content_disposition = response.getheader('Content-Disposition')
if content_disposition:
filename = re.search('filename=[\\\'"]?([^\\\'"\\s]+)[\\\'"]?', content_disposition).group(1)
path = os.path.join(os.path.dirname(path), filename)
with open(path, 'wb') as f:
f.write(response.data)
return path<|docstring|>Deserializes body to file
Saves response body into a file in a temporary folder,
using the filename from the `Content-Disposition` header if provided.
:param response: RESTResponse.
:return: file path.<|endoftext|> |
7c794fb8b3b38a2148831d9bd56333b28469bdcce6b43073a1e095b7702c7691 | def __deserialize_primitive(self, data, klass):
'Deserializes string to primitive type.\n\n :param data: str.\n :param klass: class literal.\n\n :return: int, long, float, str, bool.\n '
try:
return klass(data)
except UnicodeEncodeError:
return six.text_type(data)
except TypeError:
return data | Deserializes string to primitive type.
:param data: str.
:param klass: class literal.
:return: int, long, float, str, bool. | aylien_news_api/api_client.py | __deserialize_primitive | AYLIEN/aylien_newsapi_python | 13 | python | def __deserialize_primitive(self, data, klass):
'Deserializes string to primitive type.\n\n :param data: str.\n :param klass: class literal.\n\n :return: int, long, float, str, bool.\n '
try:
return klass(data)
except UnicodeEncodeError:
return six.text_type(data)
except TypeError:
return data | def __deserialize_primitive(self, data, klass):
'Deserializes string to primitive type.\n\n :param data: str.\n :param klass: class literal.\n\n :return: int, long, float, str, bool.\n '
try:
return klass(data)
except UnicodeEncodeError:
return six.text_type(data)
except TypeError:
return data<|docstring|>Deserializes string to primitive type.
:param data: str.
:param klass: class literal.
:return: int, long, float, str, bool.<|endoftext|> |
26b7ee7e8810fec6b49476dc902020e0c0d471cfc3e57c520e10d0c85fca7e5c | def __deserialize_object(self, value):
'Return an original value.\n\n :return: object.\n '
return value | Return an original value.
:return: object. | aylien_news_api/api_client.py | __deserialize_object | AYLIEN/aylien_newsapi_python | 13 | python | def __deserialize_object(self, value):
'Return an original value.\n\n :return: object.\n '
return value | def __deserialize_object(self, value):
'Return an original value.\n\n :return: object.\n '
return value<|docstring|>Return an original value.
:return: object.<|endoftext|> |
6e1afd52baab9845bc695f1a514eb003454e7ca5f724602e39314566e2077929 | def __deserialize_date(self, string):
'Deserializes string to date.\n\n :param string: str.\n :return: date.\n '
try:
return parse(string).date()
except ImportError:
return string
except ValueError:
raise rest.ApiException(status=0, reason='Failed to parse `{0}` as date object'.format(string)) | Deserializes string to date.
:param string: str.
:return: date. | aylien_news_api/api_client.py | __deserialize_date | AYLIEN/aylien_newsapi_python | 13 | python | def __deserialize_date(self, string):
'Deserializes string to date.\n\n :param string: str.\n :return: date.\n '
try:
return parse(string).date()
except ImportError:
return string
except ValueError:
raise rest.ApiException(status=0, reason='Failed to parse `{0}` as date object'.format(string)) | def __deserialize_date(self, string):
'Deserializes string to date.\n\n :param string: str.\n :return: date.\n '
try:
return parse(string).date()
except ImportError:
return string
except ValueError:
raise rest.ApiException(status=0, reason='Failed to parse `{0}` as date object'.format(string))<|docstring|>Deserializes string to date.
:param string: str.
:return: date.<|endoftext|> |
3ddc2cfd7081465c841052febd2f0257a6963ce5e23ffd7e2e477b018136cfad | def __deserialize_datetime(self, string):
'Deserializes string to datetime.\n\n The string should be in iso8601 datetime format.\n\n :param string: str.\n :return: datetime.\n '
try:
return parse(string)
except ImportError:
return string
except ValueError:
raise rest.ApiException(status=0, reason='Failed to parse `{0}` as datetime object'.format(string)) | Deserializes string to datetime.
The string should be in iso8601 datetime format.
:param string: str.
:return: datetime. | aylien_news_api/api_client.py | __deserialize_datetime | AYLIEN/aylien_newsapi_python | 13 | python | def __deserialize_datetime(self, string):
'Deserializes string to datetime.\n\n The string should be in iso8601 datetime format.\n\n :param string: str.\n :return: datetime.\n '
try:
return parse(string)
except ImportError:
return string
except ValueError:
raise rest.ApiException(status=0, reason='Failed to parse `{0}` as datetime object'.format(string)) | def __deserialize_datetime(self, string):
'Deserializes string to datetime.\n\n The string should be in iso8601 datetime format.\n\n :param string: str.\n :return: datetime.\n '
try:
return parse(string)
except ImportError:
return string
except ValueError:
raise rest.ApiException(status=0, reason='Failed to parse `{0}` as datetime object'.format(string))<|docstring|>Deserializes string to datetime.
The string should be in iso8601 datetime format.
:param string: str.
:return: datetime.<|endoftext|> |
35d1a459f1b1340b2e8f76869c05c06aa0510c10aa302f7254f22a2ace5da974 | def __deserialize_model(self, data, klass):
'Deserializes list or dict to model.\n\n :param data: dict, list.\n :param klass: class literal.\n :return: model object.\n '
has_discriminator = False
if (hasattr(klass, 'get_real_child_model') and klass.discriminator_value_class_map):
has_discriminator = True
if ((not klass.openapi_types) and (has_discriminator is False)):
return data
kwargs = {}
if ((data is not None) and (klass.openapi_types is not None) and isinstance(data, (list, dict))):
for (attr, attr_type) in six.iteritems(klass.openapi_types):
if (klass.attribute_map[attr] in data):
value = data[klass.attribute_map[attr]]
kwargs[attr] = self.__deserialize(value, attr_type)
instance = klass(**kwargs)
if has_discriminator:
klass_name = instance.get_real_child_model(data)
if klass_name:
instance = self.__deserialize(data, klass_name)
return instance | Deserializes list or dict to model.
:param data: dict, list.
:param klass: class literal.
:return: model object. | aylien_news_api/api_client.py | __deserialize_model | AYLIEN/aylien_newsapi_python | 13 | python | def __deserialize_model(self, data, klass):
'Deserializes list or dict to model.\n\n :param data: dict, list.\n :param klass: class literal.\n :return: model object.\n '
has_discriminator = False
if (hasattr(klass, 'get_real_child_model') and klass.discriminator_value_class_map):
has_discriminator = True
if ((not klass.openapi_types) and (has_discriminator is False)):
return data
kwargs = {}
if ((data is not None) and (klass.openapi_types is not None) and isinstance(data, (list, dict))):
for (attr, attr_type) in six.iteritems(klass.openapi_types):
if (klass.attribute_map[attr] in data):
value = data[klass.attribute_map[attr]]
kwargs[attr] = self.__deserialize(value, attr_type)
instance = klass(**kwargs)
if has_discriminator:
klass_name = instance.get_real_child_model(data)
if klass_name:
instance = self.__deserialize(data, klass_name)
return instance | def __deserialize_model(self, data, klass):
'Deserializes list or dict to model.\n\n :param data: dict, list.\n :param klass: class literal.\n :return: model object.\n '
has_discriminator = False
if (hasattr(klass, 'get_real_child_model') and klass.discriminator_value_class_map):
has_discriminator = True
if ((not klass.openapi_types) and (has_discriminator is False)):
return data
kwargs = {}
if ((data is not None) and (klass.openapi_types is not None) and isinstance(data, (list, dict))):
for (attr, attr_type) in six.iteritems(klass.openapi_types):
if (klass.attribute_map[attr] in data):
value = data[klass.attribute_map[attr]]
kwargs[attr] = self.__deserialize(value, attr_type)
instance = klass(**kwargs)
if has_discriminator:
klass_name = instance.get_real_child_model(data)
if klass_name:
instance = self.__deserialize(data, klass_name)
return instance<|docstring|>Deserializes list or dict to model.
:param data: dict, list.
:param klass: class literal.
:return: model object.<|endoftext|> |
32b31629f1f194a0081462ee1bb154726dae358c8f8f45b17de524fe1c4fc52d | def parse_tagdata_to_rawdata(self, tag_bsobject):
'\n 解析接口返回的标签数据，返回原数据列表\n '
if tag_bsobject:
li_array = tag_bsobject.find_all('li')
category = li_array[0].text
growth = li_array[1].text
ref_avg_growth = li_array[2].text
ref_HS300_growth = li_array[3].text
current_range = li_array[4].text
range_update_content = li_array[5]
range_update_prefix = ''
range_update_flag = range_update_content.find('font')
if (range_update_flag['class'][0] == 'grn'):
range_update_prefix = '-'
range_update_content.font.extract()
range_updage = (range_update_prefix + range_update_content.text)
four_division_grange = '---'
four_division_grange_content = li_array[6].find('p')
if four_division_grange_content:
four_division_grange = four_division_grange_content.text
rawdata = ResultRawDataModel(category, growth, ref_avg_growth, ref_HS300_growth, current_range, range_updage, four_division_grange)
return rawdata
else:
return ResultRawDataModel('---', '---', '---', '---', '---', '---', '---') | 解析接口返回的标签数据，返回原数据列表 | funds/src/service/easymoney/result_rawdata.py | parse_tagdata_to_rawdata | biztudio/richlab | 0 | python | def parse_tagdata_to_rawdata(self, tag_bsobject):
'\n \n '
if tag_bsobject:
li_array = tag_bsobject.find_all('li')
category = li_array[0].text
growth = li_array[1].text
ref_avg_growth = li_array[2].text
ref_HS300_growth = li_array[3].text
current_range = li_array[4].text
range_update_content = li_array[5]
range_update_prefix =
range_update_flag = range_update_content.find('font')
if (range_update_flag['class'][0] == 'grn'):
range_update_prefix = '-'
range_update_content.font.extract()
range_updage = (range_update_prefix + range_update_content.text)
four_division_grange = '---'
four_division_grange_content = li_array[6].find('p')
if four_division_grange_content:
four_division_grange = four_division_grange_content.text
rawdata = ResultRawDataModel(category, growth, ref_avg_growth, ref_HS300_growth, current_range, range_updage, four_division_grange)
return rawdata
else:
return ResultRawDataModel('---', '---', '---', '---', '---', '---', '---') | def parse_tagdata_to_rawdata(self, tag_bsobject):
'\n \n '
if tag_bsobject:
li_array = tag_bsobject.find_all('li')
category = li_array[0].text
growth = li_array[1].text
ref_avg_growth = li_array[2].text
ref_HS300_growth = li_array[3].text
current_range = li_array[4].text
range_update_content = li_array[5]
range_update_prefix =
range_update_flag = range_update_content.find('font')
if (range_update_flag['class'][0] == 'grn'):
range_update_prefix = '-'
range_update_content.font.extract()
range_updage = (range_update_prefix + range_update_content.text)
four_division_grange = '---'
four_division_grange_content = li_array[6].find('p')
if four_division_grange_content:
four_division_grange = four_division_grange_content.text
rawdata = ResultRawDataModel(category, growth, ref_avg_growth, ref_HS300_growth, current_range, range_updage, four_division_grange)
return rawdata
else:
return ResultRawDataModel('---', '---', '---', '---', '---', '---', '---')<|docstring|>解析接口返回的标签数据，返回原数据列表<|endoftext|> |
51ef500d1a78fcd8a937347bb7714909c082b476878ea10e2c843ee734ca86a3 | def fetch_archivedata(self, code):
'\n fetch data via fund code from api at eastmoney site\n '
page = urllib.request.urlopen((('http://fund.eastmoney.com/f10/FundArchivesDatas.aspx?type=jdzf&code=' + code) + '&rt=0.5686106265556327'))
lines = page.readlines()
page.close()
document = ''
for line in lines:
document = (document + line.decode('utf-8'))
contentjson = document.replace
contentjson = document.replace('var apidata=', '').replace('};', '}').replace('content:', '"content":')
acdata = json.loads(contentjson)['content']
soup = BeautifulSoup(acdata, 'html.parser')
uls_array = soup.find_all('ul')[1:]
return [self.parse_tagdata_to_rawdata(d) for d in uls_array] | fetch data via fund code from api at eastmoney site | funds/src/service/easymoney/result_rawdata.py | fetch_archivedata | biztudio/richlab | 0 | python | def fetch_archivedata(self, code):
'\n \n '
page = urllib.request.urlopen((('http://fund.eastmoney.com/f10/FundArchivesDatas.aspx?type=jdzf&code=' + code) + '&rt=0.5686106265556327'))
lines = page.readlines()
page.close()
document =
for line in lines:
document = (document + line.decode('utf-8'))
contentjson = document.replace
contentjson = document.replace('var apidata=', ).replace('};', '}').replace('content:', '"content":')
acdata = json.loads(contentjson)['content']
soup = BeautifulSoup(acdata, 'html.parser')
uls_array = soup.find_all('ul')[1:]
return [self.parse_tagdata_to_rawdata(d) for d in uls_array] | def fetch_archivedata(self, code):
'\n \n '
page = urllib.request.urlopen((('http://fund.eastmoney.com/f10/FundArchivesDatas.aspx?type=jdzf&code=' + code) + '&rt=0.5686106265556327'))
lines = page.readlines()
page.close()
document =
for line in lines:
document = (document + line.decode('utf-8'))
contentjson = document.replace
contentjson = document.replace('var apidata=', ).replace('};', '}').replace('content:', '"content":')
acdata = json.loads(contentjson)['content']
soup = BeautifulSoup(acdata, 'html.parser')
uls_array = soup.find_all('ul')[1:]
return [self.parse_tagdata_to_rawdata(d) for d in uls_array]<|docstring|>fetch data via fund code from api at eastmoney site<|endoftext|> |
5d912f042f010e32421ffdbb32d870faa380e1aa818742648c29fb2549fd90c0 | def import_class(module, cls_name, file_location=None):
'Import and return the given class from the given module.\n\n File location can be given to import the class from a location that\n is not accessible through the PYTHONPATH.\n This works from python 2.6 to python 3.\n '
try:
module = importlib.import_module(module)
except NameError:
module = __import__(module, globals(), locals(), ['object'], (- 1))
try:
cls = getattr(module, cls_name)
except AttributeError:
loader = importlib.machinery.SourceFileLoder('module', file_location)
spec = importlib.machinery.ModuleSpec('module', loader, origin=file_location)
module = importlib.util.module_from_spec(spec)
cls = getattr(module, cls_name)
return cls | Import and return the given class from the given module.
File location can be given to import the class from a location that
is not accessible through the PYTHONPATH.
This works from python 2.6 to python 3. | flowpipe/utilities.py | import_class | osynge/flowpipe | 139 | python | def import_class(module, cls_name, file_location=None):
'Import and return the given class from the given module.\n\n File location can be given to import the class from a location that\n is not accessible through the PYTHONPATH.\n This works from python 2.6 to python 3.\n '
try:
module = importlib.import_module(module)
except NameError:
module = __import__(module, globals(), locals(), ['object'], (- 1))
try:
cls = getattr(module, cls_name)
except AttributeError:
loader = importlib.machinery.SourceFileLoder('module', file_location)
spec = importlib.machinery.ModuleSpec('module', loader, origin=file_location)
module = importlib.util.module_from_spec(spec)
cls = getattr(module, cls_name)
return cls | def import_class(module, cls_name, file_location=None):
'Import and return the given class from the given module.\n\n File location can be given to import the class from a location that\n is not accessible through the PYTHONPATH.\n This works from python 2.6 to python 3.\n '
try:
module = importlib.import_module(module)
except NameError:
module = __import__(module, globals(), locals(), ['object'], (- 1))
try:
cls = getattr(module, cls_name)
except AttributeError:
loader = importlib.machinery.SourceFileLoder('module', file_location)
spec = importlib.machinery.ModuleSpec('module', loader, origin=file_location)
module = importlib.util.module_from_spec(spec)
cls = getattr(module, cls_name)
return cls<|docstring|>Import and return the given class from the given module.
File location can be given to import the class from a location that
is not accessible through the PYTHONPATH.
This works from python 2.6 to python 3.<|endoftext|> |
0ac06f6d8b80b6b48237733b93adbbd9832ef19216bd8964dbd028c9ad2062e9 | def deserialize_node(data):
'De-serialize a node from the given json data.'
node = import_class(data['module'], data['cls'], data['file_location'])(graph=None)
node.post_deserialize(data)
return node | De-serialize a node from the given json data. | flowpipe/utilities.py | deserialize_node | osynge/flowpipe | 139 | python | def deserialize_node(data):
node = import_class(data['module'], data['cls'], data['file_location'])(graph=None)
node.post_deserialize(data)
return node | def deserialize_node(data):
node = import_class(data['module'], data['cls'], data['file_location'])(graph=None)
node.post_deserialize(data)
return node<|docstring|>De-serialize a node from the given json data.<|endoftext|> |
2530cff75309005839266ccd3d40003d79c2dd701037e73162993d05b26780d3 | def deserialize_graph(data):
'De-serialize from the given json data.'
graph = import_class(data['module'], data['cls'])()
graph.name = data['name']
graph.nodes = []
for node in data['nodes']:
deserialized_node = deserialize_node(node)
graph.nodes.append(deserialized_node)
deserialized_node.graph = graph
nodes = {n.identifier: n for n in graph.nodes}
all_nodes = [n for n in data['nodes']]
subgraphs = []
for sub_data in data.get('subgraphs', []):
subgraph = import_class(sub_data['module'], sub_data['cls'])()
subgraph.name = sub_data['name']
subgraph.nodes = []
for node in sub_data['nodes']:
deserialized_node = deserialize_node(node)
subgraph.nodes.append(deserialized_node)
deserialized_node.graph = subgraph
all_nodes += sub_data['nodes']
subgraphs.append(subgraph)
nodes.update({n.identifier: n for n in subgraph.nodes})
for node in all_nodes:
this = nodes[node['identifier']]
for (name, input_) in node['inputs'].items():
for (identifier, plug) in input_['connections'].items():
upstream = nodes[identifier]
(upstream.outputs[plug] >> this.inputs[name])
for (sub_plug_name, sub_plug) in input_['sub_plugs'].items():
sub_plug_name = sub_plug_name.split('.')[(- 1)]
for (identifier, plug) in sub_plug['connections'].items():
upstream = nodes[identifier]
upstream.outputs[plug].connect(this.inputs[name][sub_plug_name])
return graph | De-serialize from the given json data. | flowpipe/utilities.py | deserialize_graph | osynge/flowpipe | 139 | python | def deserialize_graph(data):
graph = import_class(data['module'], data['cls'])()
graph.name = data['name']
graph.nodes = []
for node in data['nodes']:
deserialized_node = deserialize_node(node)
graph.nodes.append(deserialized_node)
deserialized_node.graph = graph
nodes = {n.identifier: n for n in graph.nodes}
all_nodes = [n for n in data['nodes']]
subgraphs = []
for sub_data in data.get('subgraphs', []):
subgraph = import_class(sub_data['module'], sub_data['cls'])()
subgraph.name = sub_data['name']
subgraph.nodes = []
for node in sub_data['nodes']:
deserialized_node = deserialize_node(node)
subgraph.nodes.append(deserialized_node)
deserialized_node.graph = subgraph
all_nodes += sub_data['nodes']
subgraphs.append(subgraph)
nodes.update({n.identifier: n for n in subgraph.nodes})
for node in all_nodes:
this = nodes[node['identifier']]
for (name, input_) in node['inputs'].items():
for (identifier, plug) in input_['connections'].items():
upstream = nodes[identifier]
(upstream.outputs[plug] >> this.inputs[name])
for (sub_plug_name, sub_plug) in input_['sub_plugs'].items():
sub_plug_name = sub_plug_name.split('.')[(- 1)]
for (identifier, plug) in sub_plug['connections'].items():
upstream = nodes[identifier]
upstream.outputs[plug].connect(this.inputs[name][sub_plug_name])
return graph | def deserialize_graph(data):
graph = import_class(data['module'], data['cls'])()
graph.name = data['name']
graph.nodes = []
for node in data['nodes']:
deserialized_node = deserialize_node(node)
graph.nodes.append(deserialized_node)
deserialized_node.graph = graph
nodes = {n.identifier: n for n in graph.nodes}
all_nodes = [n for n in data['nodes']]
subgraphs = []
for sub_data in data.get('subgraphs', []):
subgraph = import_class(sub_data['module'], sub_data['cls'])()
subgraph.name = sub_data['name']
subgraph.nodes = []
for node in sub_data['nodes']:
deserialized_node = deserialize_node(node)
subgraph.nodes.append(deserialized_node)
deserialized_node.graph = subgraph
all_nodes += sub_data['nodes']
subgraphs.append(subgraph)
nodes.update({n.identifier: n for n in subgraph.nodes})
for node in all_nodes:
this = nodes[node['identifier']]
for (name, input_) in node['inputs'].items():
for (identifier, plug) in input_['connections'].items():
upstream = nodes[identifier]
(upstream.outputs[plug] >> this.inputs[name])
for (sub_plug_name, sub_plug) in input_['sub_plugs'].items():
sub_plug_name = sub_plug_name.split('.')[(- 1)]
for (identifier, plug) in sub_plug['connections'].items():
upstream = nodes[identifier]
upstream.outputs[plug].connect(this.inputs[name][sub_plug_name])
return graph<|docstring|>De-serialize from the given json data.<|endoftext|> |
0ae6d2e2a76beded111c88420ec1752be4ad7cc61b1a554d57c45c31df35ca11 | def get_hash(obj, hash_func=(lambda x: sha256(x).hexdigest())):
'Safely get the hash of an object.\n\n This function tries to compute the hash as safely as possible, dealing with\n json data and strings in a well-defined manner.\n\n Args:\n obj: The object to hash\n hash_func (func(obj) -> str): The hashing function to use\n\n Returns:\n (str): A hash of the obj\n\n '
try:
return hash_func(obj)
except (TypeError, ValueError):
try:
js = json.dumps(obj, sort_keys=True)
except TypeError:
pass
else:
obj = js
if isinstance(obj, str):
return hash_func(obj.encode('utf-8'))
if (sys.version_info.major > 2):
try:
return hash_func(bytes(obj))
except TypeError:
return None
else:
return None | Safely get the hash of an object.
This function tries to compute the hash as safely as possible, dealing with
json data and strings in a well-defined manner.
Args:
obj: The object to hash
hash_func (func(obj) -> str): The hashing function to use
Returns:
(str): A hash of the obj | flowpipe/utilities.py | get_hash | osynge/flowpipe | 139 | python | def get_hash(obj, hash_func=(lambda x: sha256(x).hexdigest())):
'Safely get the hash of an object.\n\n This function tries to compute the hash as safely as possible, dealing with\n json data and strings in a well-defined manner.\n\n Args:\n obj: The object to hash\n hash_func (func(obj) -> str): The hashing function to use\n\n Returns:\n (str): A hash of the obj\n\n '
try:
return hash_func(obj)
except (TypeError, ValueError):
try:
js = json.dumps(obj, sort_keys=True)
except TypeError:
pass
else:
obj = js
if isinstance(obj, str):
return hash_func(obj.encode('utf-8'))
if (sys.version_info.major > 2):
try:
return hash_func(bytes(obj))
except TypeError:
return None
else:
return None | def get_hash(obj, hash_func=(lambda x: sha256(x).hexdigest())):
'Safely get the hash of an object.\n\n This function tries to compute the hash as safely as possible, dealing with\n json data and strings in a well-defined manner.\n\n Args:\n obj: The object to hash\n hash_func (func(obj) -> str): The hashing function to use\n\n Returns:\n (str): A hash of the obj\n\n '
try:
return hash_func(obj)
except (TypeError, ValueError):
try:
js = json.dumps(obj, sort_keys=True)
except TypeError:
pass
else:
obj = js
if isinstance(obj, str):
return hash_func(obj.encode('utf-8'))
if (sys.version_info.major > 2):
try:
return hash_func(bytes(obj))
except TypeError:
return None
else:
return None<|docstring|>Safely get the hash of an object.
This function tries to compute the hash as safely as possible, dealing with
json data and strings in a well-defined manner.
Args:
obj: The object to hash
hash_func (func(obj) -> str): The hashing function to use
Returns:
(str): A hash of the obj<|endoftext|> |
0fa544f78e9536eb610cd015c4ea7c45b041625cc5b49ed29e3b5c6cc15492ed | def default(self, o):
'Encode the object, handling type errors by encoding into sha256.'
try:
return super(NodeEncoder, self).default(o)
except TypeError:
try:
return sha256(o).hexdigest()
except TypeError:
return str(o)
except ValueError:
return sha256(bytes(o)).hexdigest() | Encode the object, handling type errors by encoding into sha256. | flowpipe/utilities.py | default | osynge/flowpipe | 139 | python | def default(self, o):
try:
return super(NodeEncoder, self).default(o)
except TypeError:
try:
return sha256(o).hexdigest()
except TypeError:
return str(o)
except ValueError:
return sha256(bytes(o)).hexdigest() | def default(self, o):
try:
return super(NodeEncoder, self).default(o)
except TypeError:
try:
return sha256(o).hexdigest()
except TypeError:
return str(o)
except ValueError:
return sha256(bytes(o)).hexdigest()<|docstring|>Encode the object, handling type errors by encoding into sha256.<|endoftext|> |
62ec93afcbccd7b2ada6d764c43ce931ab4c81d86eeee7fdf78d031c826f9888 | def fourthPower(x):
'\n x: int or float.\n '
return square(square(x)) | x: int or float. | fingerExercises/fingerExercises-02/02.4-finger.fourth-power.py | fourthPower | sodaPhix/MITx-6.00.1x | 1 | python | def fourthPower(x):
'\n \n '
return square(square(x)) | def fourthPower(x):
'\n \n '
return square(square(x))<|docstring|>x: int or float.<|endoftext|> |
eb29bd242d07e0767ea170df155298eb7b2b3f8e72f281a29fa55a1578f284b1 | def log_error(f):
'Отлавливание ошибок'
def inner(*args, **kwargs):
try:
return f(*args, **kwargs)
except Exception as e:
error = f'ERROR {e} in '
print(error)
update = args[0]
if (update and hasattr(update, 'message')):
update.message.bot.send_message(chat_id=ADMIN_ID, text=error)
raise e
return inner | Отлавливание ошибок | main.py | log_error | xm4dn355x/tg_artyukhov_today | 0 | python | def log_error(f):
def inner(*args, **kwargs):
try:
return f(*args, **kwargs)
except Exception as e:
error = f'ERROR {e} in '
print(error)
update = args[0]
if (update and hasattr(update, 'message')):
update.message.bot.send_message(chat_id=ADMIN_ID, text=error)
raise e
return inner | def log_error(f):
def inner(*args, **kwargs):
try:
return f(*args, **kwargs)
except Exception as e:
error = f'ERROR {e} in '
print(error)
update = args[0]
if (update and hasattr(update, 'message')):
update.message.bot.send_message(chat_id=ADMIN_ID, text=error)
raise e
return inner<|docstring|>Отлавливание ошибок<|endoftext|> |
1d033015c7554ea4803bd4e66cd348c6042547ead846b23c4ee5957a10e6b798 | @log_error
def post_in_channel(data: dict) -> None:
'Постит пост в канал'
bot.send_message(parse_mode='markdown', chat_id=CHAT_ID, text=f'''**{data['title']}**
{data['description']}
{data['url']}''') | Постит пост в канал | main.py | post_in_channel | xm4dn355x/tg_artyukhov_today | 0 | python | @log_error
def post_in_channel(data: dict) -> None:
bot.send_message(parse_mode='markdown', chat_id=CHAT_ID, text=f'**{data['title']}**
{data['description']}
{data['url']}') | @log_error
def post_in_channel(data: dict) -> None:
bot.send_message(parse_mode='markdown', chat_id=CHAT_ID, text=f'**{data['title']}**
{data['description']}
{data['url']}')<|docstring|>Постит пост в канал<|endoftext|> |
4d00dfb33343d6eadb60aab1f063a64a07add388d84fa38b28908bb795ba5181 | def setUp(self):
'Run at the begining of every test to setup the gui'
self.test_obj = LamSlotWind(Rint=0.1, Rext=0.2)
self.test_obj.slot = SlotW25(H1=0.11, H2=0.12, W3=0.14, W4=0.15)
self.widget = PWSlot25(self.test_obj) | Run at the begining of every test to setup the gui | Tests/GUI/DMachineSetup/test_PWSlot25.py | setUp | Kelos-Zhu/pyleecan | 2 | python | def setUp(self):
self.test_obj = LamSlotWind(Rint=0.1, Rext=0.2)
self.test_obj.slot = SlotW25(H1=0.11, H2=0.12, W3=0.14, W4=0.15)
self.widget = PWSlot25(self.test_obj) | def setUp(self):
self.test_obj = LamSlotWind(Rint=0.1, Rext=0.2)
self.test_obj.slot = SlotW25(H1=0.11, H2=0.12, W3=0.14, W4=0.15)
self.widget = PWSlot25(self.test_obj)<|docstring|>Run at the begining of every test to setup the gui<|endoftext|> |
0175da663076db20a7276aa6c316b47546ce5dd77c219d9c1794522fde599b8e | @classmethod
def setUpClass(cls):
'Start the app for the test'
print('\nStart Test PWSlot25')
cls.app = QtWidgets.QApplication(sys.argv) | Start the app for the test | Tests/GUI/DMachineSetup/test_PWSlot25.py | setUpClass | Kelos-Zhu/pyleecan | 2 | python | @classmethod
def setUpClass(cls):
print('\nStart Test PWSlot25')
cls.app = QtWidgets.QApplication(sys.argv) | @classmethod
def setUpClass(cls):
print('\nStart Test PWSlot25')
cls.app = QtWidgets.QApplication(sys.argv)<|docstring|>Start the app for the test<|endoftext|> |
033a316ab8de57809551c33ebf4e118bfcad5c1960e9590ebcdbc0b93187d15f | @classmethod
def tearDownClass(cls):
'Exit the app after the test'
cls.app.quit() | Exit the app after the test | Tests/GUI/DMachineSetup/test_PWSlot25.py | tearDownClass | Kelos-Zhu/pyleecan | 2 | python | @classmethod
def tearDownClass(cls):
cls.app.quit() | @classmethod
def tearDownClass(cls):
cls.app.quit()<|docstring|>Exit the app after the test<|endoftext|> |
d4b76de49709936b2fc99b9cff85c0952781996ab4dd390cf56623b6079f1227 | def test_init(self):
'Check that the Widget spinbox initialise to the lamination value'
self.assertEqual(self.widget.lf_H1.value(), 0.11)
self.assertEqual(self.widget.lf_H2.value(), 0.12)
self.assertEqual(self.widget.lf_W3.value(), 0.14)
self.assertEqual(self.widget.lf_W4.value(), 0.15)
self.test_obj.slot = SlotW25(H1=0.21, H2=0.22, W3=0.24, W4=0.25)
self.widget = PWSlot25(self.test_obj)
self.assertEqual(self.widget.lf_H1.value(), 0.21)
self.assertEqual(self.widget.lf_H2.value(), 0.22)
self.assertEqual(self.widget.lf_W3.value(), 0.24)
self.assertEqual(self.widget.lf_W4.value(), 0.25) | Check that the Widget spinbox initialise to the lamination value | Tests/GUI/DMachineSetup/test_PWSlot25.py | test_init | Kelos-Zhu/pyleecan | 2 | python | def test_init(self):
self.assertEqual(self.widget.lf_H1.value(), 0.11)
self.assertEqual(self.widget.lf_H2.value(), 0.12)
self.assertEqual(self.widget.lf_W3.value(), 0.14)
self.assertEqual(self.widget.lf_W4.value(), 0.15)
self.test_obj.slot = SlotW25(H1=0.21, H2=0.22, W3=0.24, W4=0.25)
self.widget = PWSlot25(self.test_obj)
self.assertEqual(self.widget.lf_H1.value(), 0.21)
self.assertEqual(self.widget.lf_H2.value(), 0.22)
self.assertEqual(self.widget.lf_W3.value(), 0.24)
self.assertEqual(self.widget.lf_W4.value(), 0.25) | def test_init(self):
self.assertEqual(self.widget.lf_H1.value(), 0.11)
self.assertEqual(self.widget.lf_H2.value(), 0.12)
self.assertEqual(self.widget.lf_W3.value(), 0.14)
self.assertEqual(self.widget.lf_W4.value(), 0.15)
self.test_obj.slot = SlotW25(H1=0.21, H2=0.22, W3=0.24, W4=0.25)
self.widget = PWSlot25(self.test_obj)
self.assertEqual(self.widget.lf_H1.value(), 0.21)
self.assertEqual(self.widget.lf_H2.value(), 0.22)
self.assertEqual(self.widget.lf_W3.value(), 0.24)
self.assertEqual(self.widget.lf_W4.value(), 0.25)<|docstring|>Check that the Widget spinbox initialise to the lamination value<|endoftext|> |
fd2d0b12fd5ced9659c5a1c7f0a5c441d4e8c3e7ff1a34ce7a2992626a0c8b27 | def test_set_W3(self):
'Check that the Widget allow to update W3'
self.widget.lf_W3.clear()
QTest.keyClicks(self.widget.lf_W3, '0.32')
self.widget.lf_W3.editingFinished.emit()
self.assertEqual(self.widget.slot.W3, 0.32)
self.assertEqual(self.test_obj.slot.W3, 0.32) | Check that the Widget allow to update W3 | Tests/GUI/DMachineSetup/test_PWSlot25.py | test_set_W3 | Kelos-Zhu/pyleecan | 2 | python | def test_set_W3(self):
self.widget.lf_W3.clear()
QTest.keyClicks(self.widget.lf_W3, '0.32')
self.widget.lf_W3.editingFinished.emit()
self.assertEqual(self.widget.slot.W3, 0.32)
self.assertEqual(self.test_obj.slot.W3, 0.32) | def test_set_W3(self):
self.widget.lf_W3.clear()
QTest.keyClicks(self.widget.lf_W3, '0.32')
self.widget.lf_W3.editingFinished.emit()
self.assertEqual(self.widget.slot.W3, 0.32)
self.assertEqual(self.test_obj.slot.W3, 0.32)<|docstring|>Check that the Widget allow to update W3<|endoftext|> |
13decc1a9dd1ee9560679b289fcfc82ab75eb18ec2d48038fdb8730ec52847e6 | def test_set_W4(self):
'Check that the Widget allow to update W4'
self.widget.lf_W4.clear()
QTest.keyClicks(self.widget.lf_W4, '0.33')
self.widget.lf_W4.editingFinished.emit()
self.assertEqual(self.widget.slot.W4, 0.33)
self.assertEqual(self.test_obj.slot.W4, 0.33) | Check that the Widget allow to update W4 | Tests/GUI/DMachineSetup/test_PWSlot25.py | test_set_W4 | Kelos-Zhu/pyleecan | 2 | python | def test_set_W4(self):
self.widget.lf_W4.clear()
QTest.keyClicks(self.widget.lf_W4, '0.33')
self.widget.lf_W4.editingFinished.emit()
self.assertEqual(self.widget.slot.W4, 0.33)
self.assertEqual(self.test_obj.slot.W4, 0.33) | def test_set_W4(self):
self.widget.lf_W4.clear()
QTest.keyClicks(self.widget.lf_W4, '0.33')
self.widget.lf_W4.editingFinished.emit()
self.assertEqual(self.widget.slot.W4, 0.33)
self.assertEqual(self.test_obj.slot.W4, 0.33)<|docstring|>Check that the Widget allow to update W4<|endoftext|> |
799e2d540e477f92b240a3fe9c98f541cba0dbaa28fa2d3f68daccfca8134ce6 | def test_set_H1(self):
'Check that the Widget allow to update H1'
self.widget.lf_H1.clear()
QTest.keyClicks(self.widget.lf_H1, '0.35')
self.widget.lf_H1.editingFinished.emit()
self.assertEqual(self.widget.slot.H1, 0.35)
self.assertEqual(self.test_obj.slot.H1, 0.35) | Check that the Widget allow to update H1 | Tests/GUI/DMachineSetup/test_PWSlot25.py | test_set_H1 | Kelos-Zhu/pyleecan | 2 | python | def test_set_H1(self):
self.widget.lf_H1.clear()
QTest.keyClicks(self.widget.lf_H1, '0.35')
self.widget.lf_H1.editingFinished.emit()
self.assertEqual(self.widget.slot.H1, 0.35)
self.assertEqual(self.test_obj.slot.H1, 0.35) | def test_set_H1(self):
self.widget.lf_H1.clear()
QTest.keyClicks(self.widget.lf_H1, '0.35')
self.widget.lf_H1.editingFinished.emit()
self.assertEqual(self.widget.slot.H1, 0.35)
self.assertEqual(self.test_obj.slot.H1, 0.35)<|docstring|>Check that the Widget allow to update H1<|endoftext|> |
0bb20219f3660e7cc11980835ad88f96f052c00250bacb83596ff19e0efb1bae | def test_set_H2(self):
'Check that the Widget allow to update H2'
self.widget.lf_H2.clear()
QTest.keyClicks(self.widget.lf_H2, '0.36')
self.widget.lf_H2.editingFinished.emit()
self.assertEqual(self.widget.slot.H2, 0.36)
self.assertEqual(self.test_obj.slot.H2, 0.36) | Check that the Widget allow to update H2 | Tests/GUI/DMachineSetup/test_PWSlot25.py | test_set_H2 | Kelos-Zhu/pyleecan | 2 | python | def test_set_H2(self):
self.widget.lf_H2.clear()
QTest.keyClicks(self.widget.lf_H2, '0.36')
self.widget.lf_H2.editingFinished.emit()
self.assertEqual(self.widget.slot.H2, 0.36)
self.assertEqual(self.test_obj.slot.H2, 0.36) | def test_set_H2(self):
self.widget.lf_H2.clear()
QTest.keyClicks(self.widget.lf_H2, '0.36')
self.widget.lf_H2.editingFinished.emit()
self.assertEqual(self.widget.slot.H2, 0.36)
self.assertEqual(self.test_obj.slot.H2, 0.36)<|docstring|>Check that the Widget allow to update H2<|endoftext|> |
dce49d320e80a67d3f5b1894f1faeda95099a4e3e3eb35a63aa7e7a1b960dcc8 | def test_output_txt(self):
'Check that the Output text is computed and correct\n '
self.test_obj = LamSlotWind(Rint=0, Rext=0.5, is_internal=True, is_stator=False, L1=0.9, Nrvd=1, Wrvd=0.1)
self.test_obj.slot = SlotW25(Zs=12, W4=0.15, W3=0.075, H1=0.03, H2=0.15)
self.widget = PWSlot25(self.test_obj)
self.assertEqual(self.widget.w_out.out_slot_height.text(), 'Slot height: 0.1789 m') | Check that the Output text is computed and correct | Tests/GUI/DMachineSetup/test_PWSlot25.py | test_output_txt | Kelos-Zhu/pyleecan | 2 | python | def test_output_txt(self):
'\n '
self.test_obj = LamSlotWind(Rint=0, Rext=0.5, is_internal=True, is_stator=False, L1=0.9, Nrvd=1, Wrvd=0.1)
self.test_obj.slot = SlotW25(Zs=12, W4=0.15, W3=0.075, H1=0.03, H2=0.15)
self.widget = PWSlot25(self.test_obj)
self.assertEqual(self.widget.w_out.out_slot_height.text(), 'Slot height: 0.1789 m') | def test_output_txt(self):
'\n '
self.test_obj = LamSlotWind(Rint=0, Rext=0.5, is_internal=True, is_stator=False, L1=0.9, Nrvd=1, Wrvd=0.1)
self.test_obj.slot = SlotW25(Zs=12, W4=0.15, W3=0.075, H1=0.03, H2=0.15)
self.widget = PWSlot25(self.test_obj)
self.assertEqual(self.widget.w_out.out_slot_height.text(), 'Slot height: 0.1789 m')<|docstring|>Check that the Output text is computed and correct<|endoftext|> |
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