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def ValidateSingleFile(input_api, output_api, file_obj, cwd, results):
'Does corresponding validations if histograms.xml or enums.xml is changed.\n\n Args:\n input_api: An input_api instance that contains information about changes.\n output_api: An output_api instance to create results of the PRESUBMIT check.\n file_obj: A file object of one of the changed files.\n cwd: Path to current working directory.\n results: The returned variable which is a list of output_api results.\n\n Returns:\n A boolean that True if a histograms.xml or enums.xml file is changed.\n '
p = file_obj.AbsoluteLocalPath()
if (input_api.os_path.commonprefix([p, cwd]) != cwd):
return False
filepath = input_api.os_path.relpath(p, cwd)
if ('test_data' in filepath):
return False
if ('obsolete_histograms.xml' in filepath):
GetObsoleteXmlErrors(input_api, output_api, cwd, results)
return False
elif (('histograms.xml' in filepath) or ('histogram_suffixes_list.xml' in filepath)):
GetPrettyPrintErrors(input_api, output_api, cwd, filepath, results)
return True
elif ('enums.xml' in filepath):
GetPrettyPrintErrors(input_api, output_api, cwd, filepath, results)
return True
return False | 8,915,752,243,759,788,000 | Does corresponding validations if histograms.xml or enums.xml is changed.
Args:
input_api: An input_api instance that contains information about changes.
output_api: An output_api instance to create results of the PRESUBMIT check.
file_obj: A file object of one of the changed files.
cwd: Path to current working directory.
results: The returned variable which is a list of output_api results.
Returns:
A boolean that True if a histograms.xml or enums.xml file is changed. | tools/metrics/histograms/PRESUBMIT.py | ValidateSingleFile | Ron423c/chromium | python | def ValidateSingleFile(input_api, output_api, file_obj, cwd, results):
'Does corresponding validations if histograms.xml or enums.xml is changed.\n\n Args:\n input_api: An input_api instance that contains information about changes.\n output_api: An output_api instance to create results of the PRESUBMIT check.\n file_obj: A file object of one of the changed files.\n cwd: Path to current working directory.\n results: The returned variable which is a list of output_api results.\n\n Returns:\n A boolean that True if a histograms.xml or enums.xml file is changed.\n '
p = file_obj.AbsoluteLocalPath()
if (input_api.os_path.commonprefix([p, cwd]) != cwd):
return False
filepath = input_api.os_path.relpath(p, cwd)
if ('test_data' in filepath):
return False
if ('obsolete_histograms.xml' in filepath):
GetObsoleteXmlErrors(input_api, output_api, cwd, results)
return False
elif (('histograms.xml' in filepath) or ('histogram_suffixes_list.xml' in filepath)):
GetPrettyPrintErrors(input_api, output_api, cwd, filepath, results)
return True
elif ('enums.xml' in filepath):
GetPrettyPrintErrors(input_api, output_api, cwd, filepath, results)
return True
return False |
def CheckChange(input_api, output_api):
'Checks that histograms.xml is pretty-printed and well-formatted.'
results = []
cwd = input_api.PresubmitLocalPath()
xml_changed = False
for file_obj in input_api.AffectedTextFiles():
is_changed = ValidateSingleFile(input_api, output_api, file_obj, cwd, results)
xml_changed = (xml_changed or is_changed)
if xml_changed:
GetValidateHistogramsError(input_api, output_api, cwd, results)
return results | -6,462,003,374,697,380,000 | Checks that histograms.xml is pretty-printed and well-formatted. | tools/metrics/histograms/PRESUBMIT.py | CheckChange | Ron423c/chromium | python | def CheckChange(input_api, output_api):
results = []
cwd = input_api.PresubmitLocalPath()
xml_changed = False
for file_obj in input_api.AffectedTextFiles():
is_changed = ValidateSingleFile(input_api, output_api, file_obj, cwd, results)
xml_changed = (xml_changed or is_changed)
if xml_changed:
GetValidateHistogramsError(input_api, output_api, cwd, results)
return results |
def test_randomizer_basic(self):
'\n Test functionality of basic randomizer.\n '
randomizer = EnvParameterRandomizer(DummyEnvParameter())
assert (len(randomizer.get_parameters()) == 3)
with self.assertRaises(AssertionError):
randomizer.register_parameter(DummyRandomizerParameter('a', 1))
randomizer.register_parameter(DummyRandomizerParameter('d', 1))
assert (len(randomizer.get_parameters()) == 4)
randomizer.get_parameter('a').set_value(1)
randomizer.get_parameter('b').set_value(0.5)
randomizer.get_parameter('c').set_value(2)
parameters = randomizer.randomize(DummyEnvParameter(), self.random_state)
assert (parameters.a == 1)
assert (parameters.b == 0.5)
assert (parameters.nested.c == 2)
randomizer.disable()
parameters = randomizer.randomize(DummyEnvParameter(), self.random_state)
randomizer.get_parameter('a').set_value(1)
assert (parameters.a == 0) | 6,277,896,458,699,098,000 | Test functionality of basic randomizer. | robogym/randomization/tests/test_randomization.py | test_randomizer_basic | 0xflotus/robogym | python | def test_randomizer_basic(self):
'\n \n '
randomizer = EnvParameterRandomizer(DummyEnvParameter())
assert (len(randomizer.get_parameters()) == 3)
with self.assertRaises(AssertionError):
randomizer.register_parameter(DummyRandomizerParameter('a', 1))
randomizer.register_parameter(DummyRandomizerParameter('d', 1))
assert (len(randomizer.get_parameters()) == 4)
randomizer.get_parameter('a').set_value(1)
randomizer.get_parameter('b').set_value(0.5)
randomizer.get_parameter('c').set_value(2)
parameters = randomizer.randomize(DummyEnvParameter(), self.random_state)
assert (parameters.a == 1)
assert (parameters.b == 0.5)
assert (parameters.nested.c == 2)
randomizer.disable()
parameters = randomizer.randomize(DummyEnvParameter(), self.random_state)
randomizer.get_parameter('a').set_value(1)
assert (parameters.a == 0) |
def __init__(self, model, name=None, category=None, endpoint=None, url=None, static_folder=None, menu_class_name=None, menu_icon_type=None, menu_icon_value=None):
'\n Constructor\n\n :param model:\n Model class\n :param name:\n Display name\n :param category:\n Display category\n :param endpoint:\n Endpoint\n :param url:\n Custom URL\n :param menu_class_name:\n Optional class name for the menu item.\n :param menu_icon_type:\n Optional icon. Possible icon types:\n\n - `flask_admin.consts.ICON_TYPE_GLYPH` - Bootstrap glyph icon\n - `flask_admin.consts.ICON_TYPE_FONT_AWESOME` - Font Awesome icon\n - `flask_admin.consts.ICON_TYPE_IMAGE` - Image relative to Flask static directory\n - `flask_admin.consts.ICON_TYPE_IMAGE_URL` - Image with full URL\n\n :param menu_icon_value:\n Icon glyph name or URL, depending on `menu_icon_type` setting\n '
self._search_fields = []
super(ModelView, self).__init__(model, name, category, endpoint, url, static_folder, menu_class_name=menu_class_name, menu_icon_type=menu_icon_type, menu_icon_value=menu_icon_value)
self._primary_key = self.scaffold_pk() | -1,822,298,911,502,670,000 | Constructor
:param model:
Model class
:param name:
Display name
:param category:
Display category
:param endpoint:
Endpoint
:param url:
Custom URL
:param menu_class_name:
Optional class name for the menu item.
:param menu_icon_type:
Optional icon. Possible icon types:
- `flask_admin.consts.ICON_TYPE_GLYPH` - Bootstrap glyph icon
- `flask_admin.consts.ICON_TYPE_FONT_AWESOME` - Font Awesome icon
- `flask_admin.consts.ICON_TYPE_IMAGE` - Image relative to Flask static directory
- `flask_admin.consts.ICON_TYPE_IMAGE_URL` - Image with full URL
:param menu_icon_value:
Icon glyph name or URL, depending on `menu_icon_type` setting | sleepyenv/lib/python2.7/site-packages/Flask_Admin-1.2.0-py2.7.egg/flask_admin/contrib/mongoengine/view.py | __init__ | hexlism/css_platform | python | def __init__(self, model, name=None, category=None, endpoint=None, url=None, static_folder=None, menu_class_name=None, menu_icon_type=None, menu_icon_value=None):
'\n Constructor\n\n :param model:\n Model class\n :param name:\n Display name\n :param category:\n Display category\n :param endpoint:\n Endpoint\n :param url:\n Custom URL\n :param menu_class_name:\n Optional class name for the menu item.\n :param menu_icon_type:\n Optional icon. Possible icon types:\n\n - `flask_admin.consts.ICON_TYPE_GLYPH` - Bootstrap glyph icon\n - `flask_admin.consts.ICON_TYPE_FONT_AWESOME` - Font Awesome icon\n - `flask_admin.consts.ICON_TYPE_IMAGE` - Image relative to Flask static directory\n - `flask_admin.consts.ICON_TYPE_IMAGE_URL` - Image with full URL\n\n :param menu_icon_value:\n Icon glyph name or URL, depending on `menu_icon_type` setting\n '
self._search_fields = []
super(ModelView, self).__init__(model, name, category, endpoint, url, static_folder, menu_class_name=menu_class_name, menu_icon_type=menu_icon_type, menu_icon_value=menu_icon_value)
self._primary_key = self.scaffold_pk() |
def _refresh_cache(self):
'\n Refresh cache.\n '
if (self.form_subdocuments is None):
self.form_subdocuments = {}
self._form_subdocuments = convert_subdocuments(self.form_subdocuments)
super(ModelView, self)._refresh_cache() | 3,943,640,994,616,751,000 | Refresh cache. | sleepyenv/lib/python2.7/site-packages/Flask_Admin-1.2.0-py2.7.egg/flask_admin/contrib/mongoengine/view.py | _refresh_cache | hexlism/css_platform | python | def _refresh_cache(self):
'\n \n '
if (self.form_subdocuments is None):
self.form_subdocuments = {}
self._form_subdocuments = convert_subdocuments(self.form_subdocuments)
super(ModelView, self)._refresh_cache() |
def _process_ajax_references(self):
'\n AJAX endpoint is exposed by top-level admin view class, but\n subdocuments might have AJAX references too.\n\n This method will recursively go over subdocument configuration\n and will precompute AJAX references for them ensuring that\n subdocuments can also use AJAX to populate their ReferenceFields.\n '
references = super(ModelView, self)._process_ajax_references()
return process_ajax_references(references, self) | 6,137,773,626,474,018,000 | AJAX endpoint is exposed by top-level admin view class, but
subdocuments might have AJAX references too.
This method will recursively go over subdocument configuration
and will precompute AJAX references for them ensuring that
subdocuments can also use AJAX to populate their ReferenceFields. | sleepyenv/lib/python2.7/site-packages/Flask_Admin-1.2.0-py2.7.egg/flask_admin/contrib/mongoengine/view.py | _process_ajax_references | hexlism/css_platform | python | def _process_ajax_references(self):
'\n AJAX endpoint is exposed by top-level admin view class, but\n subdocuments might have AJAX references too.\n\n This method will recursively go over subdocument configuration\n and will precompute AJAX references for them ensuring that\n subdocuments can also use AJAX to populate their ReferenceFields.\n '
references = super(ModelView, self)._process_ajax_references()
return process_ajax_references(references, self) |
def _get_model_fields(self, model=None):
'\n Inspect model and return list of model fields\n\n :param model:\n Model to inspect\n '
if (model is None):
model = self.model
return sorted(iteritems(model._fields), key=(lambda n: n[1].creation_counter)) | 707,176,458,598,894,100 | Inspect model and return list of model fields
:param model:
Model to inspect | sleepyenv/lib/python2.7/site-packages/Flask_Admin-1.2.0-py2.7.egg/flask_admin/contrib/mongoengine/view.py | _get_model_fields | hexlism/css_platform | python | def _get_model_fields(self, model=None):
'\n Inspect model and return list of model fields\n\n :param model:\n Model to inspect\n '
if (model is None):
model = self.model
return sorted(iteritems(model._fields), key=(lambda n: n[1].creation_counter)) |
def get_pk_value(self, model):
'\n Return the primary key value from the model instance\n\n :param model:\n Model instance\n '
return model.pk | 3,359,469,410,447,919,600 | Return the primary key value from the model instance
:param model:
Model instance | sleepyenv/lib/python2.7/site-packages/Flask_Admin-1.2.0-py2.7.egg/flask_admin/contrib/mongoengine/view.py | get_pk_value | hexlism/css_platform | python | def get_pk_value(self, model):
'\n Return the primary key value from the model instance\n\n :param model:\n Model instance\n '
return model.pk |
def scaffold_list_columns(self):
'\n Scaffold list columns\n '
columns = []
for (n, f) in self._get_model_fields():
field_class = type(f)
if ((field_class == mongoengine.ListField) and isinstance(f.field, mongoengine.EmbeddedDocumentField)):
continue
if (field_class == mongoengine.EmbeddedDocumentField):
continue
if (self.column_display_pk or (field_class != mongoengine.ObjectIdField)):
columns.append(n)
return columns | -1,257,183,833,292,583,000 | Scaffold list columns | sleepyenv/lib/python2.7/site-packages/Flask_Admin-1.2.0-py2.7.egg/flask_admin/contrib/mongoengine/view.py | scaffold_list_columns | hexlism/css_platform | python | def scaffold_list_columns(self):
'\n \n '
columns = []
for (n, f) in self._get_model_fields():
field_class = type(f)
if ((field_class == mongoengine.ListField) and isinstance(f.field, mongoengine.EmbeddedDocumentField)):
continue
if (field_class == mongoengine.EmbeddedDocumentField):
continue
if (self.column_display_pk or (field_class != mongoengine.ObjectIdField)):
columns.append(n)
return columns |
def scaffold_sortable_columns(self):
'\n Return a dictionary of sortable columns (name, field)\n '
columns = {}
for (n, f) in self._get_model_fields():
if (type(f) in SORTABLE_FIELDS):
if (self.column_display_pk or (type(f) != mongoengine.ObjectIdField)):
columns[n] = f
return columns | -1,914,602,255,142,035,000 | Return a dictionary of sortable columns (name, field) | sleepyenv/lib/python2.7/site-packages/Flask_Admin-1.2.0-py2.7.egg/flask_admin/contrib/mongoengine/view.py | scaffold_sortable_columns | hexlism/css_platform | python | def scaffold_sortable_columns(self):
'\n \n '
columns = {}
for (n, f) in self._get_model_fields():
if (type(f) in SORTABLE_FIELDS):
if (self.column_display_pk or (type(f) != mongoengine.ObjectIdField)):
columns[n] = f
return columns |
def init_search(self):
'\n Init search\n '
if self.column_searchable_list:
for p in self.column_searchable_list:
if isinstance(p, string_types):
p = self.model._fields.get(p)
if (p is None):
raise Exception('Invalid search field')
field_type = type(p)
if (field_type not in self.allowed_search_types):
raise Exception(('Can only search on text columns. ' + ('Failed to setup search for "%s"' % p)))
self._search_fields.append(p)
return bool(self._search_fields) | -2,954,653,945,820,696,600 | Init search | sleepyenv/lib/python2.7/site-packages/Flask_Admin-1.2.0-py2.7.egg/flask_admin/contrib/mongoengine/view.py | init_search | hexlism/css_platform | python | def init_search(self):
'\n \n '
if self.column_searchable_list:
for p in self.column_searchable_list:
if isinstance(p, string_types):
p = self.model._fields.get(p)
if (p is None):
raise Exception('Invalid search field')
field_type = type(p)
if (field_type not in self.allowed_search_types):
raise Exception(('Can only search on text columns. ' + ('Failed to setup search for "%s"' % p)))
self._search_fields.append(p)
return bool(self._search_fields) |
def scaffold_filters(self, name):
'\n Return filter object(s) for the field\n\n :param name:\n Either field name or field instance\n '
if isinstance(name, string_types):
attr = self.model._fields.get(name)
else:
attr = name
if (attr is None):
raise Exception(('Failed to find field for filter: %s' % name))
visible_name = None
if (not isinstance(name, string_types)):
visible_name = self.get_column_name(attr.name)
if (not visible_name):
visible_name = self.get_column_name(name)
type_name = type(attr).__name__
flt = self.filter_converter.convert(type_name, attr, visible_name)
return flt | -1,826,513,185,149,695,500 | Return filter object(s) for the field
:param name:
Either field name or field instance | sleepyenv/lib/python2.7/site-packages/Flask_Admin-1.2.0-py2.7.egg/flask_admin/contrib/mongoengine/view.py | scaffold_filters | hexlism/css_platform | python | def scaffold_filters(self, name):
'\n Return filter object(s) for the field\n\n :param name:\n Either field name or field instance\n '
if isinstance(name, string_types):
attr = self.model._fields.get(name)
else:
attr = name
if (attr is None):
raise Exception(('Failed to find field for filter: %s' % name))
visible_name = None
if (not isinstance(name, string_types)):
visible_name = self.get_column_name(attr.name)
if (not visible_name):
visible_name = self.get_column_name(name)
type_name = type(attr).__name__
flt = self.filter_converter.convert(type_name, attr, visible_name)
return flt |
def is_valid_filter(self, filter):
'\n Validate if the provided filter is a valid MongoEngine filter\n\n :param filter:\n Filter object\n '
return isinstance(filter, BaseMongoEngineFilter) | -6,644,047,060,746,446,000 | Validate if the provided filter is a valid MongoEngine filter
:param filter:
Filter object | sleepyenv/lib/python2.7/site-packages/Flask_Admin-1.2.0-py2.7.egg/flask_admin/contrib/mongoengine/view.py | is_valid_filter | hexlism/css_platform | python | def is_valid_filter(self, filter):
'\n Validate if the provided filter is a valid MongoEngine filter\n\n :param filter:\n Filter object\n '
return isinstance(filter, BaseMongoEngineFilter) |
def scaffold_form(self):
'\n Create form from the model.\n '
form_class = get_form(self.model, self.model_form_converter(self), base_class=self.form_base_class, only=self.form_columns, exclude=self.form_excluded_columns, field_args=self.form_args, extra_fields=self.form_extra_fields)
return form_class | -5,255,561,762,624,256,000 | Create form from the model. | sleepyenv/lib/python2.7/site-packages/Flask_Admin-1.2.0-py2.7.egg/flask_admin/contrib/mongoengine/view.py | scaffold_form | hexlism/css_platform | python | def scaffold_form(self):
'\n \n '
form_class = get_form(self.model, self.model_form_converter(self), base_class=self.form_base_class, only=self.form_columns, exclude=self.form_excluded_columns, field_args=self.form_args, extra_fields=self.form_extra_fields)
return form_class |
def scaffold_list_form(self, custom_fieldlist=ListEditableFieldList, validators=None):
"\n Create form for the `index_view` using only the columns from\n `self.column_editable_list`.\n\n :param validators:\n `form_args` dict with only validators\n {'name': {'validators': [required()]}}\n :param custom_fieldlist:\n A WTForm FieldList class. By default, `ListEditableFieldList`.\n "
form_class = get_form(self.model, self.model_form_converter(self), base_class=self.form_base_class, only=self.column_editable_list, field_args=validators)
return wrap_fields_in_fieldlist(self.form_base_class, form_class, custom_fieldlist) | 1,362,413,955,699,600,400 | Create form for the `index_view` using only the columns from
`self.column_editable_list`.
:param validators:
`form_args` dict with only validators
{'name': {'validators': [required()]}}
:param custom_fieldlist:
A WTForm FieldList class. By default, `ListEditableFieldList`. | sleepyenv/lib/python2.7/site-packages/Flask_Admin-1.2.0-py2.7.egg/flask_admin/contrib/mongoengine/view.py | scaffold_list_form | hexlism/css_platform | python | def scaffold_list_form(self, custom_fieldlist=ListEditableFieldList, validators=None):
"\n Create form for the `index_view` using only the columns from\n `self.column_editable_list`.\n\n :param validators:\n `form_args` dict with only validators\n {'name': {'validators': [required()]}}\n :param custom_fieldlist:\n A WTForm FieldList class. By default, `ListEditableFieldList`.\n "
form_class = get_form(self.model, self.model_form_converter(self), base_class=self.form_base_class, only=self.column_editable_list, field_args=validators)
return wrap_fields_in_fieldlist(self.form_base_class, form_class, custom_fieldlist) |
def get_query(self):
'\n Returns the QuerySet for this view. By default, it returns all the\n objects for the current model.\n '
return self.model.objects | -4,986,645,289,561,858,000 | Returns the QuerySet for this view. By default, it returns all the
objects for the current model. | sleepyenv/lib/python2.7/site-packages/Flask_Admin-1.2.0-py2.7.egg/flask_admin/contrib/mongoengine/view.py | get_query | hexlism/css_platform | python | def get_query(self):
'\n Returns the QuerySet for this view. By default, it returns all the\n objects for the current model.\n '
return self.model.objects |
def get_list(self, page, sort_column, sort_desc, search, filters, execute=True):
'\n Get list of objects from MongoEngine\n\n :param page:\n Page number\n :param sort_column:\n Sort column\n :param sort_desc:\n Sort descending\n :param search:\n Search criteria\n :param filters:\n List of applied filters\n :param execute:\n Run query immediately or not\n '
query = self.get_query()
if self._filters:
for (flt, flt_name, value) in filters:
f = self._filters[flt]
query = f.apply(query, f.clean(value))
if (self._search_supported and search):
query = self._search(query, search)
count = (query.count() if (not self.simple_list_pager) else None)
if sort_column:
query = query.order_by(('%s%s' % (('-' if sort_desc else ''), sort_column)))
else:
order = self._get_default_order()
if order:
query = query.order_by(('%s%s' % (('-' if order[1] else ''), order[0])))
if (page is not None):
query = query.skip((page * self.page_size))
query = query.limit(self.page_size)
if execute:
query = query.all()
return (count, query) | -6,554,609,987,189,399,000 | Get list of objects from MongoEngine
:param page:
Page number
:param sort_column:
Sort column
:param sort_desc:
Sort descending
:param search:
Search criteria
:param filters:
List of applied filters
:param execute:
Run query immediately or not | sleepyenv/lib/python2.7/site-packages/Flask_Admin-1.2.0-py2.7.egg/flask_admin/contrib/mongoengine/view.py | get_list | hexlism/css_platform | python | def get_list(self, page, sort_column, sort_desc, search, filters, execute=True):
'\n Get list of objects from MongoEngine\n\n :param page:\n Page number\n :param sort_column:\n Sort column\n :param sort_desc:\n Sort descending\n :param search:\n Search criteria\n :param filters:\n List of applied filters\n :param execute:\n Run query immediately or not\n '
query = self.get_query()
if self._filters:
for (flt, flt_name, value) in filters:
f = self._filters[flt]
query = f.apply(query, f.clean(value))
if (self._search_supported and search):
query = self._search(query, search)
count = (query.count() if (not self.simple_list_pager) else None)
if sort_column:
query = query.order_by(('%s%s' % (('-' if sort_desc else ), sort_column)))
else:
order = self._get_default_order()
if order:
query = query.order_by(('%s%s' % (('-' if order[1] else ), order[0])))
if (page is not None):
query = query.skip((page * self.page_size))
query = query.limit(self.page_size)
if execute:
query = query.all()
return (count, query) |
def get_one(self, id):
'\n Return a single model instance by its ID\n\n :param id:\n Model ID\n '
try:
return self.get_query().filter(pk=id).first()
except mongoengine.ValidationError as ex:
flash(gettext('Failed to get model. %(error)s', error=format_error(ex)), 'error')
return None | 7,872,424,681,330,008,000 | Return a single model instance by its ID
:param id:
Model ID | sleepyenv/lib/python2.7/site-packages/Flask_Admin-1.2.0-py2.7.egg/flask_admin/contrib/mongoengine/view.py | get_one | hexlism/css_platform | python | def get_one(self, id):
'\n Return a single model instance by its ID\n\n :param id:\n Model ID\n '
try:
return self.get_query().filter(pk=id).first()
except mongoengine.ValidationError as ex:
flash(gettext('Failed to get model. %(error)s', error=format_error(ex)), 'error')
return None |
def create_model(self, form):
'\n Create model helper\n\n :param form:\n Form instance\n '
try:
model = self.model()
form.populate_obj(model)
self._on_model_change(form, model, True)
model.save()
except Exception as ex:
if (not self.handle_view_exception(ex)):
flash(gettext('Failed to create record. %(error)s', error=format_error(ex)), 'error')
log.exception('Failed to create record.')
return False
else:
self.after_model_change(form, model, True)
return model | 903,978,773,780,675,300 | Create model helper
:param form:
Form instance | sleepyenv/lib/python2.7/site-packages/Flask_Admin-1.2.0-py2.7.egg/flask_admin/contrib/mongoengine/view.py | create_model | hexlism/css_platform | python | def create_model(self, form):
'\n Create model helper\n\n :param form:\n Form instance\n '
try:
model = self.model()
form.populate_obj(model)
self._on_model_change(form, model, True)
model.save()
except Exception as ex:
if (not self.handle_view_exception(ex)):
flash(gettext('Failed to create record. %(error)s', error=format_error(ex)), 'error')
log.exception('Failed to create record.')
return False
else:
self.after_model_change(form, model, True)
return model |
def update_model(self, form, model):
'\n Update model helper\n\n :param form:\n Form instance\n :param model:\n Model instance to update\n '
try:
form.populate_obj(model)
self._on_model_change(form, model, False)
model.save()
except Exception as ex:
if (not self.handle_view_exception(ex)):
flash(gettext('Failed to update record. %(error)s', error=format_error(ex)), 'error')
log.exception('Failed to update record.')
return False
else:
self.after_model_change(form, model, False)
return True | 764,570,345,739,556,700 | Update model helper
:param form:
Form instance
:param model:
Model instance to update | sleepyenv/lib/python2.7/site-packages/Flask_Admin-1.2.0-py2.7.egg/flask_admin/contrib/mongoengine/view.py | update_model | hexlism/css_platform | python | def update_model(self, form, model):
'\n Update model helper\n\n :param form:\n Form instance\n :param model:\n Model instance to update\n '
try:
form.populate_obj(model)
self._on_model_change(form, model, False)
model.save()
except Exception as ex:
if (not self.handle_view_exception(ex)):
flash(gettext('Failed to update record. %(error)s', error=format_error(ex)), 'error')
log.exception('Failed to update record.')
return False
else:
self.after_model_change(form, model, False)
return True |
def delete_model(self, model):
'\n Delete model helper\n\n :param model:\n Model instance\n '
try:
self.on_model_delete(model)
model.delete()
except Exception as ex:
if (not self.handle_view_exception(ex)):
flash(gettext('Failed to delete record. %(error)s', error=format_error(ex)), 'error')
log.exception('Failed to delete record.')
return False
else:
self.after_model_delete(model)
return True | 3,090,824,996,892,932,000 | Delete model helper
:param model:
Model instance | sleepyenv/lib/python2.7/site-packages/Flask_Admin-1.2.0-py2.7.egg/flask_admin/contrib/mongoengine/view.py | delete_model | hexlism/css_platform | python | def delete_model(self, model):
'\n Delete model helper\n\n :param model:\n Model instance\n '
try:
self.on_model_delete(model)
model.delete()
except Exception as ex:
if (not self.handle_view_exception(ex)):
flash(gettext('Failed to delete record. %(error)s', error=format_error(ex)), 'error')
log.exception('Failed to delete record.')
return False
else:
self.after_model_delete(model)
return True |
def estimate(particles, weights):
'returns mean and variance of the weighted particles'
pos = particles
mean = np.average(pos, weights=weights, axis=0)
var = np.average(((pos - mean) ** 2), weights=weights, axis=0)
return (mean, var) | 1,993,265,891,749,680,400 | returns mean and variance of the weighted particles | 002_Particle_Filter/Particle_Filter.py | estimate | zhyongquan/Automotive-Software-Blog | python | def estimate(particles, weights):
pos = particles
mean = np.average(pos, weights=weights, axis=0)
var = np.average(((pos - mean) ** 2), weights=weights, axis=0)
return (mean, var) |
def test_builder_is_pickled(self):
'Unlike most tree builders, HTMLParserTreeBuilder and will\n be restored after pickling.\n '
tree = self.soup('<a><b>foo</a>')
dumped = pickle.dumps(tree, 2)
loaded = pickle.loads(dumped)
self.assertTrue(isinstance(loaded.builder, type(tree.builder))) | -6,423,651,160,975,675,000 | Unlike most tree builders, HTMLParserTreeBuilder and will
be restored after pickling. | virtual/lib/python3.6/site-packages/bs4/tests/test_htmlparser.py | test_builder_is_pickled | AG371/bus-reservation-system | python | def test_builder_is_pickled(self):
'Unlike most tree builders, HTMLParserTreeBuilder and will\n be restored after pickling.\n '
tree = self.soup('<a><b>foo</a>')
dumped = pickle.dumps(tree, 2)
loaded = pickle.loads(dumped)
self.assertTrue(isinstance(loaded.builder, type(tree.builder))) |
def test_error(self):
"Verify that our HTMLParser subclass implements error() in a way\n that doesn't cause a crash.\n "
parser = BeautifulSoupHTMLParser()
parser.error("don't crash") | -6,519,268,211,641,346,000 | Verify that our HTMLParser subclass implements error() in a way
that doesn't cause a crash. | virtual/lib/python3.6/site-packages/bs4/tests/test_htmlparser.py | test_error | AG371/bus-reservation-system | python | def test_error(self):
"Verify that our HTMLParser subclass implements error() in a way\n that doesn't cause a crash.\n "
parser = BeautifulSoupHTMLParser()
parser.error("don't crash") |
async def async_setup_entry(hass, config_entry, async_add_entities):
'Set up the inverter select entities from a config entry.'
inverter = hass.data[DOMAIN][config_entry.entry_id][KEY_INVERTER]
device_info = hass.data[DOMAIN][config_entry.entry_id][KEY_DEVICE_INFO]
entities = []
for description in NUMBERS:
try:
current_value = (await description.getter(inverter))
except InverterError:
_LOGGER.debug('Could not read inverter setting %s', description.key)
continue
entities.append(InverterNumberEntity(device_info, description, inverter, current_value))
async_add_entities(entities) | -6,143,632,953,803,178,000 | Set up the inverter select entities from a config entry. | homeassistant/components/goodwe/number.py | async_setup_entry | kubawolanin/core | python | async def async_setup_entry(hass, config_entry, async_add_entities):
inverter = hass.data[DOMAIN][config_entry.entry_id][KEY_INVERTER]
device_info = hass.data[DOMAIN][config_entry.entry_id][KEY_DEVICE_INFO]
entities = []
for description in NUMBERS:
try:
current_value = (await description.getter(inverter))
except InverterError:
_LOGGER.debug('Could not read inverter setting %s', description.key)
continue
entities.append(InverterNumberEntity(device_info, description, inverter, current_value))
async_add_entities(entities) |
def __init__(self, device_info: DeviceInfo, description: GoodweNumberEntityDescription, inverter: Inverter, current_value: int) -> None:
'Initialize the number inverter setting entity.'
self.entity_description = description
self._attr_unique_id = f'{DOMAIN}-{description.key}-{inverter.serial_number}'
self._attr_device_info = device_info
self._attr_value = float(current_value)
self._inverter: Inverter = inverter | -6,500,139,910,043,170,000 | Initialize the number inverter setting entity. | homeassistant/components/goodwe/number.py | __init__ | kubawolanin/core | python | def __init__(self, device_info: DeviceInfo, description: GoodweNumberEntityDescription, inverter: Inverter, current_value: int) -> None:
self.entity_description = description
self._attr_unique_id = f'{DOMAIN}-{description.key}-{inverter.serial_number}'
self._attr_device_info = device_info
self._attr_value = float(current_value)
self._inverter: Inverter = inverter |
async def async_set_value(self, value: float) -> None:
'Set new value.'
if self.entity_description.setter:
(await self.entity_description.setter(self._inverter, int(value)))
self._attr_value = value
self.async_write_ha_state() | 6,044,406,391,341,202,000 | Set new value. | homeassistant/components/goodwe/number.py | async_set_value | kubawolanin/core | python | async def async_set_value(self, value: float) -> None:
if self.entity_description.setter:
(await self.entity_description.setter(self._inverter, int(value)))
self._attr_value = value
self.async_write_ha_state() |
@runnable
def run_targets(*args):
'Run targets for Python.'
Options.show_coverage = ('coverage' in args)
count = 0
for (count, (command, title, retry)) in enumerate(Options.targets, start=1):
success = call(command, title, retry)
if (not success):
message = (('✅ ' * (count - 1)) + '❌')
show_notification(message, title)
return False
message = ('✅ ' * count)
title = 'All Targets'
show_notification(message, title)
show_coverage()
return True | -73,758,141,938,422,160 | Run targets for Python. | scent.py | run_targets | EazeAI/AI-WS | python | @runnable
def run_targets(*args):
Options.show_coverage = ('coverage' in args)
count = 0
for (count, (command, title, retry)) in enumerate(Options.targets, start=1):
success = call(command, title, retry)
if (not success):
message = (('✅ ' * (count - 1)) + '❌')
show_notification(message, title)
return False
message = ('✅ ' * count)
title = 'All Targets'
show_notification(message, title)
show_coverage()
return True |
def call(command, title, retry):
'Run a command-line program and display the result.'
if Options.rerun_args:
(command, title, retry) = Options.rerun_args
Options.rerun_args = None
success = call(command, title, retry)
if (not success):
return False
print('')
print(('$ %s' % ' '.join(command)))
failure = subprocess.call(command)
if (failure and retry):
Options.rerun_args = (command, title, retry)
return (not failure) | 3,937,803,438,086,842,000 | Run a command-line program and display the result. | scent.py | call | EazeAI/AI-WS | python | def call(command, title, retry):
if Options.rerun_args:
(command, title, retry) = Options.rerun_args
Options.rerun_args = None
success = call(command, title, retry)
if (not success):
return False
print()
print(('$ %s' % ' '.join(command)))
failure = subprocess.call(command)
if (failure and retry):
Options.rerun_args = (command, title, retry)
return (not failure) |
def show_notification(message, title):
'Show a user notification.'
if (notify and title):
notify(message, title=title, group=Options.group) | 1,002,222,407,043,525,500 | Show a user notification. | scent.py | show_notification | EazeAI/AI-WS | python | def show_notification(message, title):
if (notify and title):
notify(message, title=title, group=Options.group) |
def show_coverage():
'Launch the coverage report.'
if Options.show_coverage:
subprocess.call(['make', 'read-coverage'])
Options.show_coverage = False | 2,950,738,002,091,552,300 | Launch the coverage report. | scent.py | show_coverage | EazeAI/AI-WS | python | def show_coverage():
if Options.show_coverage:
subprocess.call(['make', 'read-coverage'])
Options.show_coverage = False |
def download_scripts(destination_dir: Path=Path('ptlflow_scripts')) -> None:
'Download the main scripts and configs to start working with PTLFlow.'
github_url = 'https://raw.githubusercontent.com/hmorimitsu/ptlflow/main/'
script_names = ['datasets.yml', 'infer.py', 'test.py', 'train.py', 'validate.py']
destination_dir.mkdir(parents=True, exist_ok=True)
for sname in script_names:
script_url = (github_url + sname)
data = requests.get(script_url)
if (data.status_code == 200):
with open((destination_dir / sname), 'wb') as f:
f.write(data.content)
else:
logging.warning('Script %s was not found.', script_url)
logging.info('Downloaded scripts to %s.', str(destination_dir)) | -5,417,779,943,224,005,000 | Download the main scripts and configs to start working with PTLFlow. | ptlflow/__init__.py | download_scripts | hmorimitsu/ptlflow | python | def download_scripts(destination_dir: Path=Path('ptlflow_scripts')) -> None:
github_url = 'https://raw.githubusercontent.com/hmorimitsu/ptlflow/main/'
script_names = ['datasets.yml', 'infer.py', 'test.py', 'train.py', 'validate.py']
destination_dir.mkdir(parents=True, exist_ok=True)
for sname in script_names:
script_url = (github_url + sname)
data = requests.get(script_url)
if (data.status_code == 200):
with open((destination_dir / sname), 'wb') as f:
f.write(data.content)
else:
logging.warning('Script %s was not found.', script_url)
logging.info('Downloaded scripts to %s.', str(destination_dir)) |
def get_model(model_name: str, pretrained_ckpt: Optional[str]=None, args: Optional[Namespace]=None) -> BaseModel:
'Return an instance of a chosen model.\n\n The instance can have configured by he arguments, and load some existing pretrained weights.\n\n Note that this is different from get_model_reference(), which returns a reference to the model class. The instance,\n returned by this function, is a class already instantiated. Therefore, the return of this function is equivalent to\n "return get_model_reference()()", which looks confusing. This can be rewritten as\n "model_ref = get_model_reference(); return model_ref()".\n\n Parameters\n ----------\n model_name : str\n Name of the model to get an instance of.\n pretrained_ckpt : Optional[str], optional\n Name of the pretrained weight to load or a path to a local checkpoint file.\n args : Optional[Namespace], optional\n Some arguments that ill be provided to the model.\n\n Returns\n -------\n BaseModel\n The instance of the chosen model.\n\n Raises\n ------\n ValueError\n If the given checkpoint name is not a valid choice.\n ValueError\n If a checkpoint name is given, but the model does not have any pretrained weights available.\n\n See Also\n --------\n get_model_reference : To get a reference to the class of a model.\n '
model_ref = get_model_reference(model_name)
if (args is None):
parser = model_ref.add_model_specific_args()
args = parser.parse_args([])
model = model_ref(args)
if ((pretrained_ckpt is None) and (args is not None) and (args.pretrained_ckpt is not None)):
pretrained_ckpt = args.pretrained_ckpt
if (pretrained_ckpt is not None):
if Path(pretrained_ckpt).exists():
ckpt_path = pretrained_ckpt
elif hasattr(model_ref, 'pretrained_checkpoints'):
ckpt_path = model_ref.pretrained_checkpoints.get(pretrained_ckpt)
if (ckpt_path is None):
raise ValueError(f"Invalid checkpoint name {pretrained_ckpt}. Choose one from {{{','.join(model.pretrained_checkpoints.keys())}}}")
else:
raise ValueError(f'Cannot find checkpoint {pretrained_ckpt} for model {model_name}')
device = ('cuda' if torch.cuda.is_available() else 'cpu')
if Path(ckpt_path).exists():
ckpt = torch.load(ckpt_path, map_location=torch.device(device))
else:
model_dir = ((Path(hub.get_dir()) / 'ptlflow') / 'checkpoints')
ckpt = hub.load_state_dict_from_url(ckpt_path, model_dir=model_dir, map_location=torch.device(device), check_hash=True)
state_dict = ckpt['state_dict']
model.load_state_dict(state_dict)
return model | -7,413,552,895,945,898,000 | Return an instance of a chosen model.
The instance can have configured by he arguments, and load some existing pretrained weights.
Note that this is different from get_model_reference(), which returns a reference to the model class. The instance,
returned by this function, is a class already instantiated. Therefore, the return of this function is equivalent to
"return get_model_reference()()", which looks confusing. This can be rewritten as
"model_ref = get_model_reference(); return model_ref()".
Parameters
----------
model_name : str
Name of the model to get an instance of.
pretrained_ckpt : Optional[str], optional
Name of the pretrained weight to load or a path to a local checkpoint file.
args : Optional[Namespace], optional
Some arguments that ill be provided to the model.
Returns
-------
BaseModel
The instance of the chosen model.
Raises
------
ValueError
If the given checkpoint name is not a valid choice.
ValueError
If a checkpoint name is given, but the model does not have any pretrained weights available.
See Also
--------
get_model_reference : To get a reference to the class of a model. | ptlflow/__init__.py | get_model | hmorimitsu/ptlflow | python | def get_model(model_name: str, pretrained_ckpt: Optional[str]=None, args: Optional[Namespace]=None) -> BaseModel:
'Return an instance of a chosen model.\n\n The instance can have configured by he arguments, and load some existing pretrained weights.\n\n Note that this is different from get_model_reference(), which returns a reference to the model class. The instance,\n returned by this function, is a class already instantiated. Therefore, the return of this function is equivalent to\n "return get_model_reference()()", which looks confusing. This can be rewritten as\n "model_ref = get_model_reference(); return model_ref()".\n\n Parameters\n ----------\n model_name : str\n Name of the model to get an instance of.\n pretrained_ckpt : Optional[str], optional\n Name of the pretrained weight to load or a path to a local checkpoint file.\n args : Optional[Namespace], optional\n Some arguments that ill be provided to the model.\n\n Returns\n -------\n BaseModel\n The instance of the chosen model.\n\n Raises\n ------\n ValueError\n If the given checkpoint name is not a valid choice.\n ValueError\n If a checkpoint name is given, but the model does not have any pretrained weights available.\n\n See Also\n --------\n get_model_reference : To get a reference to the class of a model.\n '
model_ref = get_model_reference(model_name)
if (args is None):
parser = model_ref.add_model_specific_args()
args = parser.parse_args([])
model = model_ref(args)
if ((pretrained_ckpt is None) and (args is not None) and (args.pretrained_ckpt is not None)):
pretrained_ckpt = args.pretrained_ckpt
if (pretrained_ckpt is not None):
if Path(pretrained_ckpt).exists():
ckpt_path = pretrained_ckpt
elif hasattr(model_ref, 'pretrained_checkpoints'):
ckpt_path = model_ref.pretrained_checkpoints.get(pretrained_ckpt)
if (ckpt_path is None):
raise ValueError(f"Invalid checkpoint name {pretrained_ckpt}. Choose one from {{{','.join(model.pretrained_checkpoints.keys())}}}")
else:
raise ValueError(f'Cannot find checkpoint {pretrained_ckpt} for model {model_name}')
device = ('cuda' if torch.cuda.is_available() else 'cpu')
if Path(ckpt_path).exists():
ckpt = torch.load(ckpt_path, map_location=torch.device(device))
else:
model_dir = ((Path(hub.get_dir()) / 'ptlflow') / 'checkpoints')
ckpt = hub.load_state_dict_from_url(ckpt_path, model_dir=model_dir, map_location=torch.device(device), check_hash=True)
state_dict = ckpt['state_dict']
model.load_state_dict(state_dict)
return model |
def get_model_reference(model_name: str) -> BaseModel:
'Return a reference to the class of a chosen model.\n\n Note that this is different from get_model(), which returns an instance of a model. The reference, returned by this\n function, is a class before instantiation. Therefore, the return of this function can be used to instantiate a model as\n "model_ref = get_model_reference(); model_instance = model_ref()".\n\n Parameters\n ----------\n model_name : str\n Name of the model to get a reference of.\n\n Returns\n -------\n BaseModel\n A reference to the chosen model.\n\n Raises\n ------\n ValueError\n If the given name is not a valid choice.\n\n See Also\n --------\n get_model : To get an instance of a model.\n '
try:
return models_dict[model_name]
except KeyError:
raise ValueError(f"Unknown model name: {model_name}. Choose from [{', '.join(models_dict.keys())}]") | -1,848,291,867,390,122,500 | Return a reference to the class of a chosen model.
Note that this is different from get_model(), which returns an instance of a model. The reference, returned by this
function, is a class before instantiation. Therefore, the return of this function can be used to instantiate a model as
"model_ref = get_model_reference(); model_instance = model_ref()".
Parameters
----------
model_name : str
Name of the model to get a reference of.
Returns
-------
BaseModel
A reference to the chosen model.
Raises
------
ValueError
If the given name is not a valid choice.
See Also
--------
get_model : To get an instance of a model. | ptlflow/__init__.py | get_model_reference | hmorimitsu/ptlflow | python | def get_model_reference(model_name: str) -> BaseModel:
'Return a reference to the class of a chosen model.\n\n Note that this is different from get_model(), which returns an instance of a model. The reference, returned by this\n function, is a class before instantiation. Therefore, the return of this function can be used to instantiate a model as\n "model_ref = get_model_reference(); model_instance = model_ref()".\n\n Parameters\n ----------\n model_name : str\n Name of the model to get a reference of.\n\n Returns\n -------\n BaseModel\n A reference to the chosen model.\n\n Raises\n ------\n ValueError\n If the given name is not a valid choice.\n\n See Also\n --------\n get_model : To get an instance of a model.\n '
try:
return models_dict[model_name]
except KeyError:
raise ValueError(f"Unknown model name: {model_name}. Choose from [{', '.join(models_dict.keys())}]") |
def get_trainable_model_names() -> List[str]:
'Return a list of model names that are able to be trained.\n \n This function return the names of the model that have a loss function defined.\n\n Returns\n =======\n List[str]\n The list of the model names that can be trained.\n '
return [mname for mname in models_dict.keys() if (get_model(mname).loss_fn is not None)] | 2,144,936,162,105,759,000 | Return a list of model names that are able to be trained.
This function return the names of the model that have a loss function defined.
Returns
=======
List[str]
The list of the model names that can be trained. | ptlflow/__init__.py | get_trainable_model_names | hmorimitsu/ptlflow | python | def get_trainable_model_names() -> List[str]:
'Return a list of model names that are able to be trained.\n \n This function return the names of the model that have a loss function defined.\n\n Returns\n =======\n List[str]\n The list of the model names that can be trained.\n '
return [mname for mname in models_dict.keys() if (get_model(mname).loss_fn is not None)] |
def blackbody_specific_intensity(wl_nm, T_K):
'Get the monochromatic specific intensity for a blackbody -\n wl_nm = wavelength [nm]\n T_K = temperature [K]\n This is the energy radiated per second per unit wavelength per unit solid angle.\n Reference - Shu, eq. 4.6, p. 78.'
a = ((PLANCK_CONSTANT * SPEED_OF_LIGHT) / BOLTZMAN_CONSTANT)
b = (((2.0 * PLANCK_CONSTANT) * SPEED_OF_LIGHT) * SPEED_OF_LIGHT)
wl_m = (wl_nm * 1e-09)
try:
exponent = (a / (wl_m * T_K))
except ZeroDivisionError:
return 0.0
if (exponent > 500.0):
return 0.0
specific_intensity = (b / (math.pow(wl_m, 5) * (math.exp(exponent) - 1.0)))
return specific_intensity | 2,590,742,495,800,728,600 | Get the monochromatic specific intensity for a blackbody -
wl_nm = wavelength [nm]
T_K = temperature [K]
This is the energy radiated per second per unit wavelength per unit solid angle.
Reference - Shu, eq. 4.6, p. 78. | colorpy/colorpy-0.1.0/blackbody.py | blackbody_specific_intensity | gmweir/QuasiOptics | python | def blackbody_specific_intensity(wl_nm, T_K):
'Get the monochromatic specific intensity for a blackbody -\n wl_nm = wavelength [nm]\n T_K = temperature [K]\n This is the energy radiated per second per unit wavelength per unit solid angle.\n Reference - Shu, eq. 4.6, p. 78.'
a = ((PLANCK_CONSTANT * SPEED_OF_LIGHT) / BOLTZMAN_CONSTANT)
b = (((2.0 * PLANCK_CONSTANT) * SPEED_OF_LIGHT) * SPEED_OF_LIGHT)
wl_m = (wl_nm * 1e-09)
try:
exponent = (a / (wl_m * T_K))
except ZeroDivisionError:
return 0.0
if (exponent > 500.0):
return 0.0
specific_intensity = (b / (math.pow(wl_m, 5) * (math.exp(exponent) - 1.0)))
return specific_intensity |
def blackbody_spectrum(T_K):
'Get the spectrum of a blackbody, as a numpy array.'
spectrum = ciexyz.empty_spectrum()
(num_rows, num_cols) = spectrum.shape
for i in xrange(0, num_rows):
specific_intensity = blackbody_specific_intensity(spectrum[i][0], T_K)
spectrum[i][1] = ((specific_intensity * ciexyz.delta_wl_nm) * 1e-09)
return spectrum | -4,814,021,675,059,225,000 | Get the spectrum of a blackbody, as a numpy array. | colorpy/colorpy-0.1.0/blackbody.py | blackbody_spectrum | gmweir/QuasiOptics | python | def blackbody_spectrum(T_K):
spectrum = ciexyz.empty_spectrum()
(num_rows, num_cols) = spectrum.shape
for i in xrange(0, num_rows):
specific_intensity = blackbody_specific_intensity(spectrum[i][0], T_K)
spectrum[i][1] = ((specific_intensity * ciexyz.delta_wl_nm) * 1e-09)
return spectrum |
def blackbody_color(T_K):
'Given a temperature (K), return the xyz color of a thermal blackbody.'
spectrum = blackbody_spectrum(T_K)
xyz = ciexyz.xyz_from_spectrum(spectrum)
return xyz | -5,026,878,936,742,433,000 | Given a temperature (K), return the xyz color of a thermal blackbody. | colorpy/colorpy-0.1.0/blackbody.py | blackbody_color | gmweir/QuasiOptics | python | def blackbody_color(T_K):
spectrum = blackbody_spectrum(T_K)
xyz = ciexyz.xyz_from_spectrum(spectrum)
return xyz |
def blackbody_patch_plot(T_list, title, filename):
'Draw a patch plot of blackbody colors for the given temperature range.'
xyz_colors = []
color_names = []
for Ti in T_list:
xyz = blackbody_color(Ti)
xyz_colors.append(xyz)
name = ('%g K' % Ti)
color_names.append(name)
plots.xyz_patch_plot(xyz_colors, color_names, title, filename) | 687,483,277,840,238,200 | Draw a patch plot of blackbody colors for the given temperature range. | colorpy/colorpy-0.1.0/blackbody.py | blackbody_patch_plot | gmweir/QuasiOptics | python | def blackbody_patch_plot(T_list, title, filename):
xyz_colors = []
color_names = []
for Ti in T_list:
xyz = blackbody_color(Ti)
xyz_colors.append(xyz)
name = ('%g K' % Ti)
color_names.append(name)
plots.xyz_patch_plot(xyz_colors, color_names, title, filename) |
def blackbody_color_vs_temperature_plot(T_list, title, filename):
'Draw a color vs temperature plot for the given temperature range.'
num_T = len(T_list)
rgb_list = numpy.empty((num_T, 3))
for i in xrange(0, num_T):
T_i = T_list[i]
xyz = blackbody_color(T_i)
rgb_list[i] = colormodels.rgb_from_xyz(xyz)
plots.color_vs_param_plot(T_list, rgb_list, title, filename, plotfunc=pylab.semilogy, tight=True, xlabel='Temperature (K)', ylabel='RGB Color') | 3,642,871,054,975,440,400 | Draw a color vs temperature plot for the given temperature range. | colorpy/colorpy-0.1.0/blackbody.py | blackbody_color_vs_temperature_plot | gmweir/QuasiOptics | python | def blackbody_color_vs_temperature_plot(T_list, title, filename):
num_T = len(T_list)
rgb_list = numpy.empty((num_T, 3))
for i in xrange(0, num_T):
T_i = T_list[i]
xyz = blackbody_color(T_i)
rgb_list[i] = colormodels.rgb_from_xyz(xyz)
plots.color_vs_param_plot(T_list, rgb_list, title, filename, plotfunc=pylab.semilogy, tight=True, xlabel='Temperature (K)', ylabel='RGB Color') |
def blackbody_spectrum_plot(T_K):
'Draw the spectrum of a blackbody at the given temperature.'
spectrum = blackbody_spectrum(T_K)
title = ('Blackbody Spectrum - T %d K' % int(T_K))
filename = ('BlackbodySpectrum-%dK' % int(T_K))
plots.spectrum_plot(spectrum, title, filename, xlabel='Wavelength (nm)', ylabel='Specific Intensity') | -2,097,509,183,976,489,700 | Draw the spectrum of a blackbody at the given temperature. | colorpy/colorpy-0.1.0/blackbody.py | blackbody_spectrum_plot | gmweir/QuasiOptics | python | def blackbody_spectrum_plot(T_K):
spectrum = blackbody_spectrum(T_K)
title = ('Blackbody Spectrum - T %d K' % int(T_K))
filename = ('BlackbodySpectrum-%dK' % int(T_K))
plots.spectrum_plot(spectrum, title, filename, xlabel='Wavelength (nm)', ylabel='Specific Intensity') |
def figures():
'Create some blackbody plots.'
T_list_0 = plots.log_interpolate(1200.0, 20000.0, 48)
T_list_hot = plots.log_interpolate(10000.0, 40000.0, 24)
T_list_cool = plots.log_interpolate(950.0, 1200.0, 24)
blackbody_patch_plot(T_list_0, 'Blackbody Colors', 'Blackbody-Patch')
blackbody_patch_plot(T_list_hot, 'Hot Blackbody Colors', 'Blackbody-HotPatch')
blackbody_patch_plot(T_list_cool, 'Cool Blackbody Colors', 'Blackbody-CoolPatch')
blackbody_color_vs_temperature_plot(range(1200, 16000, 50), 'Blackbody Colors', 'Blackbody-Colors')
blackbody_color_vs_temperature_plot(range(10000, 40000, 100), 'Hot Blackbody Colors', 'Blackbody-HotColors')
blackbody_color_vs_temperature_plot(range(950, 1200, 1), 'Cool Blackbody Colors', 'Blackbody-CoolColors')
blackbody_spectrum_plot(2000.0)
blackbody_spectrum_plot(3000.0)
blackbody_spectrum_plot(SUN_TEMPERATURE)
blackbody_spectrum_plot(11000.0)
blackbody_spectrum_plot(15000.0) | 5,560,260,944,193,342,000 | Create some blackbody plots. | colorpy/colorpy-0.1.0/blackbody.py | figures | gmweir/QuasiOptics | python | def figures():
T_list_0 = plots.log_interpolate(1200.0, 20000.0, 48)
T_list_hot = plots.log_interpolate(10000.0, 40000.0, 24)
T_list_cool = plots.log_interpolate(950.0, 1200.0, 24)
blackbody_patch_plot(T_list_0, 'Blackbody Colors', 'Blackbody-Patch')
blackbody_patch_plot(T_list_hot, 'Hot Blackbody Colors', 'Blackbody-HotPatch')
blackbody_patch_plot(T_list_cool, 'Cool Blackbody Colors', 'Blackbody-CoolPatch')
blackbody_color_vs_temperature_plot(range(1200, 16000, 50), 'Blackbody Colors', 'Blackbody-Colors')
blackbody_color_vs_temperature_plot(range(10000, 40000, 100), 'Hot Blackbody Colors', 'Blackbody-HotColors')
blackbody_color_vs_temperature_plot(range(950, 1200, 1), 'Cool Blackbody Colors', 'Blackbody-CoolColors')
blackbody_spectrum_plot(2000.0)
blackbody_spectrum_plot(3000.0)
blackbody_spectrum_plot(SUN_TEMPERATURE)
blackbody_spectrum_plot(11000.0)
blackbody_spectrum_plot(15000.0) |
@cached_property
def additional_properties_type():
'\n This must be a method because a model may have properties that are\n of type self, this must run after the class is loaded\n '
lazy_import()
return (bool, date, datetime, dict, float, int, list, str, none_type) | 1,702,168,743,392,494,600 | This must be a method because a model may have properties that are
of type self, this must run after the class is loaded | cryptoapis/model/coins_forwarding_success_data.py | additional_properties_type | Crypto-APIs/Crypto_APIs_2.0_SDK_Python | python | @cached_property
def additional_properties_type():
'\n This must be a method because a model may have properties that are\n of type self, this must run after the class is loaded\n '
lazy_import()
return (bool, date, datetime, dict, float, int, list, str, none_type) |
@cached_property
def openapi_types():
'\n This must be a method because a model may have properties that are\n of type self, this must run after the class is loaded\n\n Returns\n openapi_types (dict): The key is attribute name\n and the value is attribute type.\n '
lazy_import()
return {'product': (str,), 'event': (str,), 'item': (CoinsForwardingSuccessDataItem,)} | -2,012,564,197,808,641,800 | This must be a method because a model may have properties that are
of type self, this must run after the class is loaded
Returns
openapi_types (dict): The key is attribute name
and the value is attribute type. | cryptoapis/model/coins_forwarding_success_data.py | openapi_types | Crypto-APIs/Crypto_APIs_2.0_SDK_Python | python | @cached_property
def openapi_types():
'\n This must be a method because a model may have properties that are\n of type self, this must run after the class is loaded\n\n Returns\n openapi_types (dict): The key is attribute name\n and the value is attribute type.\n '
lazy_import()
return {'product': (str,), 'event': (str,), 'item': (CoinsForwardingSuccessDataItem,)} |
@classmethod
@convert_js_args_to_python_args
def _from_openapi_data(cls, product, event, item, *args, **kwargs):
'CoinsForwardingSuccessData - a model defined in OpenAPI\n\n Args:\n product (str): Represents the Crypto APIs 2.0 product which sends the callback.\n event (str): Defines the specific event, for which a callback subscription is set.\n item (CoinsForwardingSuccessDataItem):\n\n Keyword Args:\n _check_type (bool): if True, values for parameters in openapi_types\n will be type checked and a TypeError will be\n raised if the wrong type is input.\n Defaults to True\n _path_to_item (tuple/list): This is a list of keys or values to\n drill down to the model in received_data\n when deserializing a response\n _spec_property_naming (bool): True if the variable names in the input data\n are serialized names, as specified in the OpenAPI document.\n False if the variable names in the input data\n are pythonic names, e.g. snake case (default)\n _configuration (Configuration): the instance to use when\n deserializing a file_type parameter.\n If passed, type conversion is attempted\n If omitted no type conversion is done.\n _visited_composed_classes (tuple): This stores a tuple of\n classes that we have traveled through so that\n if we see that class again we will not use its\n discriminator again.\n When traveling through a discriminator, the\n composed schema that is\n is traveled through is added to this set.\n For example if Animal has a discriminator\n petType and we pass in "Dog", and the class Dog\n allOf includes Animal, we move through Animal\n once using the discriminator, and pick Dog.\n Then in Dog, we will make an instance of the\n Animal class but this time we won\'t travel\n through its discriminator because we passed in\n _visited_composed_classes = (Animal,)\n '
_check_type = kwargs.pop('_check_type', True)
_spec_property_naming = kwargs.pop('_spec_property_naming', False)
_path_to_item = kwargs.pop('_path_to_item', ())
_configuration = kwargs.pop('_configuration', None)
_visited_composed_classes = kwargs.pop('_visited_composed_classes', ())
self = super(OpenApiModel, cls).__new__(cls)
if args:
raise ApiTypeError(('Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments.' % (args, self.__class__.__name__)), path_to_item=_path_to_item, valid_classes=(self.__class__,))
self._data_store = {}
self._check_type = _check_type
self._spec_property_naming = _spec_property_naming
self._path_to_item = _path_to_item
self._configuration = _configuration
self._visited_composed_classes = (_visited_composed_classes + (self.__class__,))
self.product = product
self.event = event
self.item = item
for (var_name, var_value) in kwargs.items():
if ((var_name not in self.attribute_map) and (self._configuration is not None) and self._configuration.discard_unknown_keys and (self.additional_properties_type is None)):
continue
setattr(self, var_name, var_value)
return self | -2,732,593,822,327,209,000 | CoinsForwardingSuccessData - a model defined in OpenAPI
Args:
product (str): Represents the Crypto APIs 2.0 product which sends the callback.
event (str): Defines the specific event, for which a callback subscription is set.
item (CoinsForwardingSuccessDataItem):
Keyword Args:
_check_type (bool): if True, values for parameters in openapi_types
will be type checked and a TypeError will be
raised if the wrong type is input.
Defaults to True
_path_to_item (tuple/list): This is a list of keys or values to
drill down to the model in received_data
when deserializing a response
_spec_property_naming (bool): True if the variable names in the input data
are serialized names, as specified in the OpenAPI document.
False if the variable names in the input data
are pythonic names, e.g. snake case (default)
_configuration (Configuration): the instance to use when
deserializing a file_type parameter.
If passed, type conversion is attempted
If omitted no type conversion is done.
_visited_composed_classes (tuple): This stores a tuple of
classes that we have traveled through so that
if we see that class again we will not use its
discriminator again.
When traveling through a discriminator, the
composed schema that is
is traveled through is added to this set.
For example if Animal has a discriminator
petType and we pass in "Dog", and the class Dog
allOf includes Animal, we move through Animal
once using the discriminator, and pick Dog.
Then in Dog, we will make an instance of the
Animal class but this time we won't travel
through its discriminator because we passed in
_visited_composed_classes = (Animal,) | cryptoapis/model/coins_forwarding_success_data.py | _from_openapi_data | Crypto-APIs/Crypto_APIs_2.0_SDK_Python | python | @classmethod
@convert_js_args_to_python_args
def _from_openapi_data(cls, product, event, item, *args, **kwargs):
'CoinsForwardingSuccessData - a model defined in OpenAPI\n\n Args:\n product (str): Represents the Crypto APIs 2.0 product which sends the callback.\n event (str): Defines the specific event, for which a callback subscription is set.\n item (CoinsForwardingSuccessDataItem):\n\n Keyword Args:\n _check_type (bool): if True, values for parameters in openapi_types\n will be type checked and a TypeError will be\n raised if the wrong type is input.\n Defaults to True\n _path_to_item (tuple/list): This is a list of keys or values to\n drill down to the model in received_data\n when deserializing a response\n _spec_property_naming (bool): True if the variable names in the input data\n are serialized names, as specified in the OpenAPI document.\n False if the variable names in the input data\n are pythonic names, e.g. snake case (default)\n _configuration (Configuration): the instance to use when\n deserializing a file_type parameter.\n If passed, type conversion is attempted\n If omitted no type conversion is done.\n _visited_composed_classes (tuple): This stores a tuple of\n classes that we have traveled through so that\n if we see that class again we will not use its\n discriminator again.\n When traveling through a discriminator, the\n composed schema that is\n is traveled through is added to this set.\n For example if Animal has a discriminator\n petType and we pass in "Dog", and the class Dog\n allOf includes Animal, we move through Animal\n once using the discriminator, and pick Dog.\n Then in Dog, we will make an instance of the\n Animal class but this time we won\'t travel\n through its discriminator because we passed in\n _visited_composed_classes = (Animal,)\n '
_check_type = kwargs.pop('_check_type', True)
_spec_property_naming = kwargs.pop('_spec_property_naming', False)
_path_to_item = kwargs.pop('_path_to_item', ())
_configuration = kwargs.pop('_configuration', None)
_visited_composed_classes = kwargs.pop('_visited_composed_classes', ())
self = super(OpenApiModel, cls).__new__(cls)
if args:
raise ApiTypeError(('Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments.' % (args, self.__class__.__name__)), path_to_item=_path_to_item, valid_classes=(self.__class__,))
self._data_store = {}
self._check_type = _check_type
self._spec_property_naming = _spec_property_naming
self._path_to_item = _path_to_item
self._configuration = _configuration
self._visited_composed_classes = (_visited_composed_classes + (self.__class__,))
self.product = product
self.event = event
self.item = item
for (var_name, var_value) in kwargs.items():
if ((var_name not in self.attribute_map) and (self._configuration is not None) and self._configuration.discard_unknown_keys and (self.additional_properties_type is None)):
continue
setattr(self, var_name, var_value)
return self |
@convert_js_args_to_python_args
def __init__(self, product, event, item, *args, **kwargs):
'CoinsForwardingSuccessData - a model defined in OpenAPI\n\n Args:\n product (str): Represents the Crypto APIs 2.0 product which sends the callback.\n event (str): Defines the specific event, for which a callback subscription is set.\n item (CoinsForwardingSuccessDataItem):\n\n Keyword Args:\n _check_type (bool): if True, values for parameters in openapi_types\n will be type checked and a TypeError will be\n raised if the wrong type is input.\n Defaults to True\n _path_to_item (tuple/list): This is a list of keys or values to\n drill down to the model in received_data\n when deserializing a response\n _spec_property_naming (bool): True if the variable names in the input data\n are serialized names, as specified in the OpenAPI document.\n False if the variable names in the input data\n are pythonic names, e.g. snake case (default)\n _configuration (Configuration): the instance to use when\n deserializing a file_type parameter.\n If passed, type conversion is attempted\n If omitted no type conversion is done.\n _visited_composed_classes (tuple): This stores a tuple of\n classes that we have traveled through so that\n if we see that class again we will not use its\n discriminator again.\n When traveling through a discriminator, the\n composed schema that is\n is traveled through is added to this set.\n For example if Animal has a discriminator\n petType and we pass in "Dog", and the class Dog\n allOf includes Animal, we move through Animal\n once using the discriminator, and pick Dog.\n Then in Dog, we will make an instance of the\n Animal class but this time we won\'t travel\n through its discriminator because we passed in\n _visited_composed_classes = (Animal,)\n '
_check_type = kwargs.pop('_check_type', True)
_spec_property_naming = kwargs.pop('_spec_property_naming', False)
_path_to_item = kwargs.pop('_path_to_item', ())
_configuration = kwargs.pop('_configuration', None)
_visited_composed_classes = kwargs.pop('_visited_composed_classes', ())
if args:
raise ApiTypeError(('Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments.' % (args, self.__class__.__name__)), path_to_item=_path_to_item, valid_classes=(self.__class__,))
self._data_store = {}
self._check_type = _check_type
self._spec_property_naming = _spec_property_naming
self._path_to_item = _path_to_item
self._configuration = _configuration
self._visited_composed_classes = (_visited_composed_classes + (self.__class__,))
self.product = product
self.event = event
self.item = item
for (var_name, var_value) in kwargs.items():
if ((var_name not in self.attribute_map) and (self._configuration is not None) and self._configuration.discard_unknown_keys and (self.additional_properties_type is None)):
continue
setattr(self, var_name, var_value)
if (var_name in self.read_only_vars):
raise ApiAttributeError(f'`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate class with read only attributes.') | -791,863,898,841,504,600 | CoinsForwardingSuccessData - a model defined in OpenAPI
Args:
product (str): Represents the Crypto APIs 2.0 product which sends the callback.
event (str): Defines the specific event, for which a callback subscription is set.
item (CoinsForwardingSuccessDataItem):
Keyword Args:
_check_type (bool): if True, values for parameters in openapi_types
will be type checked and a TypeError will be
raised if the wrong type is input.
Defaults to True
_path_to_item (tuple/list): This is a list of keys or values to
drill down to the model in received_data
when deserializing a response
_spec_property_naming (bool): True if the variable names in the input data
are serialized names, as specified in the OpenAPI document.
False if the variable names in the input data
are pythonic names, e.g. snake case (default)
_configuration (Configuration): the instance to use when
deserializing a file_type parameter.
If passed, type conversion is attempted
If omitted no type conversion is done.
_visited_composed_classes (tuple): This stores a tuple of
classes that we have traveled through so that
if we see that class again we will not use its
discriminator again.
When traveling through a discriminator, the
composed schema that is
is traveled through is added to this set.
For example if Animal has a discriminator
petType and we pass in "Dog", and the class Dog
allOf includes Animal, we move through Animal
once using the discriminator, and pick Dog.
Then in Dog, we will make an instance of the
Animal class but this time we won't travel
through its discriminator because we passed in
_visited_composed_classes = (Animal,) | cryptoapis/model/coins_forwarding_success_data.py | __init__ | Crypto-APIs/Crypto_APIs_2.0_SDK_Python | python | @convert_js_args_to_python_args
def __init__(self, product, event, item, *args, **kwargs):
'CoinsForwardingSuccessData - a model defined in OpenAPI\n\n Args:\n product (str): Represents the Crypto APIs 2.0 product which sends the callback.\n event (str): Defines the specific event, for which a callback subscription is set.\n item (CoinsForwardingSuccessDataItem):\n\n Keyword Args:\n _check_type (bool): if True, values for parameters in openapi_types\n will be type checked and a TypeError will be\n raised if the wrong type is input.\n Defaults to True\n _path_to_item (tuple/list): This is a list of keys or values to\n drill down to the model in received_data\n when deserializing a response\n _spec_property_naming (bool): True if the variable names in the input data\n are serialized names, as specified in the OpenAPI document.\n False if the variable names in the input data\n are pythonic names, e.g. snake case (default)\n _configuration (Configuration): the instance to use when\n deserializing a file_type parameter.\n If passed, type conversion is attempted\n If omitted no type conversion is done.\n _visited_composed_classes (tuple): This stores a tuple of\n classes that we have traveled through so that\n if we see that class again we will not use its\n discriminator again.\n When traveling through a discriminator, the\n composed schema that is\n is traveled through is added to this set.\n For example if Animal has a discriminator\n petType and we pass in "Dog", and the class Dog\n allOf includes Animal, we move through Animal\n once using the discriminator, and pick Dog.\n Then in Dog, we will make an instance of the\n Animal class but this time we won\'t travel\n through its discriminator because we passed in\n _visited_composed_classes = (Animal,)\n '
_check_type = kwargs.pop('_check_type', True)
_spec_property_naming = kwargs.pop('_spec_property_naming', False)
_path_to_item = kwargs.pop('_path_to_item', ())
_configuration = kwargs.pop('_configuration', None)
_visited_composed_classes = kwargs.pop('_visited_composed_classes', ())
if args:
raise ApiTypeError(('Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments.' % (args, self.__class__.__name__)), path_to_item=_path_to_item, valid_classes=(self.__class__,))
self._data_store = {}
self._check_type = _check_type
self._spec_property_naming = _spec_property_naming
self._path_to_item = _path_to_item
self._configuration = _configuration
self._visited_composed_classes = (_visited_composed_classes + (self.__class__,))
self.product = product
self.event = event
self.item = item
for (var_name, var_value) in kwargs.items():
if ((var_name not in self.attribute_map) and (self._configuration is not None) and self._configuration.discard_unknown_keys and (self.additional_properties_type is None)):
continue
setattr(self, var_name, var_value)
if (var_name in self.read_only_vars):
raise ApiAttributeError(f'`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate class with read only attributes.') |
def _activation_summary(x):
'Helper to create summaries for activations.\n\n Creates a summary that provides a histogram of activations.\n Creates a summary that measures the sparsity of activations.\n\n Args:\n x: Tensor\n Returns:\n nothing\n '
tensor_name = re.sub(('%s_[0-9]*/' % TOWER_NAME), '', x.op.name)
tf.summary.histogram((tensor_name + '/activations'), x)
tf.summary.scalar((tensor_name + '/sparsity'), tf.nn.zero_fraction(x)) | 553,231,555,851,507,140 | Helper to create summaries for activations.
Creates a summary that provides a histogram of activations.
Creates a summary that measures the sparsity of activations.
Args:
x: Tensor
Returns:
nothing | examples/cifar10/cifar10.py | _activation_summary | 13927729580/TensorFlowOnSpark | python | def _activation_summary(x):
'Helper to create summaries for activations.\n\n Creates a summary that provides a histogram of activations.\n Creates a summary that measures the sparsity of activations.\n\n Args:\n x: Tensor\n Returns:\n nothing\n '
tensor_name = re.sub(('%s_[0-9]*/' % TOWER_NAME), , x.op.name)
tf.summary.histogram((tensor_name + '/activations'), x)
tf.summary.scalar((tensor_name + '/sparsity'), tf.nn.zero_fraction(x)) |
def _variable_on_cpu(name, shape, initializer):
'Helper to create a Variable stored on CPU memory.\n\n Args:\n name: name of the variable\n shape: list of ints\n initializer: initializer for Variable\n\n Returns:\n Variable Tensor\n '
with tf.device('/cpu:0'):
dtype = (tf.float16 if FLAGS.use_fp16 else tf.float32)
var = tf.get_variable(name, shape, initializer=initializer, dtype=dtype)
return var | 2,365,013,275,469,490,700 | Helper to create a Variable stored on CPU memory.
Args:
name: name of the variable
shape: list of ints
initializer: initializer for Variable
Returns:
Variable Tensor | examples/cifar10/cifar10.py | _variable_on_cpu | 13927729580/TensorFlowOnSpark | python | def _variable_on_cpu(name, shape, initializer):
'Helper to create a Variable stored on CPU memory.\n\n Args:\n name: name of the variable\n shape: list of ints\n initializer: initializer for Variable\n\n Returns:\n Variable Tensor\n '
with tf.device('/cpu:0'):
dtype = (tf.float16 if FLAGS.use_fp16 else tf.float32)
var = tf.get_variable(name, shape, initializer=initializer, dtype=dtype)
return var |
def _variable_with_weight_decay(name, shape, stddev, wd):
'Helper to create an initialized Variable with weight decay.\n\n Note that the Variable is initialized with a truncated normal distribution.\n A weight decay is added only if one is specified.\n\n Args:\n name: name of the variable\n shape: list of ints\n stddev: standard deviation of a truncated Gaussian\n wd: add L2Loss weight decay multiplied by this float. If None, weight\n decay is not added for this Variable.\n\n Returns:\n Variable Tensor\n '
dtype = (tf.float16 if FLAGS.use_fp16 else tf.float32)
var = _variable_on_cpu(name, shape, tf.truncated_normal_initializer(stddev=stddev, dtype=dtype))
if (wd is not None):
weight_decay = tf.multiply(tf.nn.l2_loss(var), wd, name='weight_loss')
tf.add_to_collection('losses', weight_decay)
return var | 8,132,621,707,787,137,000 | Helper to create an initialized Variable with weight decay.
Note that the Variable is initialized with a truncated normal distribution.
A weight decay is added only if one is specified.
Args:
name: name of the variable
shape: list of ints
stddev: standard deviation of a truncated Gaussian
wd: add L2Loss weight decay multiplied by this float. If None, weight
decay is not added for this Variable.
Returns:
Variable Tensor | examples/cifar10/cifar10.py | _variable_with_weight_decay | 13927729580/TensorFlowOnSpark | python | def _variable_with_weight_decay(name, shape, stddev, wd):
'Helper to create an initialized Variable with weight decay.\n\n Note that the Variable is initialized with a truncated normal distribution.\n A weight decay is added only if one is specified.\n\n Args:\n name: name of the variable\n shape: list of ints\n stddev: standard deviation of a truncated Gaussian\n wd: add L2Loss weight decay multiplied by this float. If None, weight\n decay is not added for this Variable.\n\n Returns:\n Variable Tensor\n '
dtype = (tf.float16 if FLAGS.use_fp16 else tf.float32)
var = _variable_on_cpu(name, shape, tf.truncated_normal_initializer(stddev=stddev, dtype=dtype))
if (wd is not None):
weight_decay = tf.multiply(tf.nn.l2_loss(var), wd, name='weight_loss')
tf.add_to_collection('losses', weight_decay)
return var |
def distorted_inputs():
'Construct distorted input for CIFAR training using the Reader ops.\n\n Returns:\n images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.\n labels: Labels. 1D tensor of [batch_size] size.\n\n Raises:\n ValueError: If no data_dir\n '
if (not FLAGS.data_dir):
raise ValueError('Please supply a data_dir')
data_dir = os.path.join(FLAGS.data_dir, 'cifar-10-batches-bin')
(images, labels) = cifar10_input.distorted_inputs(data_dir=data_dir, batch_size=FLAGS.batch_size)
if FLAGS.use_fp16:
images = tf.cast(images, tf.float16)
labels = tf.cast(labels, tf.float16)
return (images, labels) | 5,244,124,816,898,738,000 | Construct distorted input for CIFAR training using the Reader ops.
Returns:
images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
labels: Labels. 1D tensor of [batch_size] size.
Raises:
ValueError: If no data_dir | examples/cifar10/cifar10.py | distorted_inputs | 13927729580/TensorFlowOnSpark | python | def distorted_inputs():
'Construct distorted input for CIFAR training using the Reader ops.\n\n Returns:\n images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.\n labels: Labels. 1D tensor of [batch_size] size.\n\n Raises:\n ValueError: If no data_dir\n '
if (not FLAGS.data_dir):
raise ValueError('Please supply a data_dir')
data_dir = os.path.join(FLAGS.data_dir, 'cifar-10-batches-bin')
(images, labels) = cifar10_input.distorted_inputs(data_dir=data_dir, batch_size=FLAGS.batch_size)
if FLAGS.use_fp16:
images = tf.cast(images, tf.float16)
labels = tf.cast(labels, tf.float16)
return (images, labels) |
def inputs(eval_data):
'Construct input for CIFAR evaluation using the Reader ops.\n\n Args:\n eval_data: bool, indicating if one should use the train or eval data set.\n\n Returns:\n images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.\n labels: Labels. 1D tensor of [batch_size] size.\n\n Raises:\n ValueError: If no data_dir\n '
if (not FLAGS.data_dir):
raise ValueError('Please supply a data_dir')
data_dir = os.path.join(FLAGS.data_dir, 'cifar-10-batches-bin')
(images, labels) = cifar10_input.inputs(eval_data=eval_data, data_dir=data_dir, batch_size=FLAGS.batch_size)
if FLAGS.use_fp16:
images = tf.cast(images, tf.float16)
labels = tf.cast(labels, tf.float16)
return (images, labels) | 5,745,744,370,990,534,000 | Construct input for CIFAR evaluation using the Reader ops.
Args:
eval_data: bool, indicating if one should use the train or eval data set.
Returns:
images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
labels: Labels. 1D tensor of [batch_size] size.
Raises:
ValueError: If no data_dir | examples/cifar10/cifar10.py | inputs | 13927729580/TensorFlowOnSpark | python | def inputs(eval_data):
'Construct input for CIFAR evaluation using the Reader ops.\n\n Args:\n eval_data: bool, indicating if one should use the train or eval data set.\n\n Returns:\n images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.\n labels: Labels. 1D tensor of [batch_size] size.\n\n Raises:\n ValueError: If no data_dir\n '
if (not FLAGS.data_dir):
raise ValueError('Please supply a data_dir')
data_dir = os.path.join(FLAGS.data_dir, 'cifar-10-batches-bin')
(images, labels) = cifar10_input.inputs(eval_data=eval_data, data_dir=data_dir, batch_size=FLAGS.batch_size)
if FLAGS.use_fp16:
images = tf.cast(images, tf.float16)
labels = tf.cast(labels, tf.float16)
return (images, labels) |
def inference(images):
'Build the CIFAR-10 model.\n\n Args:\n images: Images returned from distorted_inputs() or inputs().\n\n Returns:\n Logits.\n '
with tf.variable_scope('conv1') as scope:
kernel = _variable_with_weight_decay('weights', shape=[5, 5, 3, 64], stddev=0.05, wd=0.0)
conv = tf.nn.conv2d(images, kernel, [1, 1, 1, 1], padding='SAME')
biases = _variable_on_cpu('biases', [64], tf.constant_initializer(0.0))
pre_activation = tf.nn.bias_add(conv, biases)
conv1 = tf.nn.relu(pre_activation, name=scope.name)
_activation_summary(conv1)
pool1 = tf.nn.max_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME', name='pool1')
norm1 = tf.nn.lrn(pool1, 4, bias=1.0, alpha=(0.001 / 9.0), beta=0.75, name='norm1')
with tf.variable_scope('conv2') as scope:
kernel = _variable_with_weight_decay('weights', shape=[5, 5, 64, 64], stddev=0.05, wd=0.0)
conv = tf.nn.conv2d(norm1, kernel, [1, 1, 1, 1], padding='SAME')
biases = _variable_on_cpu('biases', [64], tf.constant_initializer(0.1))
pre_activation = tf.nn.bias_add(conv, biases)
conv2 = tf.nn.relu(pre_activation, name=scope.name)
_activation_summary(conv2)
norm2 = tf.nn.lrn(conv2, 4, bias=1.0, alpha=(0.001 / 9.0), beta=0.75, name='norm2')
pool2 = tf.nn.max_pool(norm2, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME', name='pool2')
with tf.variable_scope('local3') as scope:
reshape = tf.reshape(pool2, [FLAGS.batch_size, (- 1)])
dim = reshape.get_shape()[1].value
weights = _variable_with_weight_decay('weights', shape=[dim, 384], stddev=0.04, wd=0.004)
biases = _variable_on_cpu('biases', [384], tf.constant_initializer(0.1))
local3 = tf.nn.relu((tf.matmul(reshape, weights) + biases), name=scope.name)
_activation_summary(local3)
with tf.variable_scope('local4') as scope:
weights = _variable_with_weight_decay('weights', shape=[384, 192], stddev=0.04, wd=0.004)
biases = _variable_on_cpu('biases', [192], tf.constant_initializer(0.1))
local4 = tf.nn.relu((tf.matmul(local3, weights) + biases), name=scope.name)
_activation_summary(local4)
with tf.variable_scope('softmax_linear') as scope:
weights = _variable_with_weight_decay('weights', [192, NUM_CLASSES], stddev=(1 / 192.0), wd=0.0)
biases = _variable_on_cpu('biases', [NUM_CLASSES], tf.constant_initializer(0.0))
softmax_linear = tf.add(tf.matmul(local4, weights), biases, name=scope.name)
_activation_summary(softmax_linear)
return softmax_linear | 4,760,979,126,026,873,000 | Build the CIFAR-10 model.
Args:
images: Images returned from distorted_inputs() or inputs().
Returns:
Logits. | examples/cifar10/cifar10.py | inference | 13927729580/TensorFlowOnSpark | python | def inference(images):
'Build the CIFAR-10 model.\n\n Args:\n images: Images returned from distorted_inputs() or inputs().\n\n Returns:\n Logits.\n '
with tf.variable_scope('conv1') as scope:
kernel = _variable_with_weight_decay('weights', shape=[5, 5, 3, 64], stddev=0.05, wd=0.0)
conv = tf.nn.conv2d(images, kernel, [1, 1, 1, 1], padding='SAME')
biases = _variable_on_cpu('biases', [64], tf.constant_initializer(0.0))
pre_activation = tf.nn.bias_add(conv, biases)
conv1 = tf.nn.relu(pre_activation, name=scope.name)
_activation_summary(conv1)
pool1 = tf.nn.max_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME', name='pool1')
norm1 = tf.nn.lrn(pool1, 4, bias=1.0, alpha=(0.001 / 9.0), beta=0.75, name='norm1')
with tf.variable_scope('conv2') as scope:
kernel = _variable_with_weight_decay('weights', shape=[5, 5, 64, 64], stddev=0.05, wd=0.0)
conv = tf.nn.conv2d(norm1, kernel, [1, 1, 1, 1], padding='SAME')
biases = _variable_on_cpu('biases', [64], tf.constant_initializer(0.1))
pre_activation = tf.nn.bias_add(conv, biases)
conv2 = tf.nn.relu(pre_activation, name=scope.name)
_activation_summary(conv2)
norm2 = tf.nn.lrn(conv2, 4, bias=1.0, alpha=(0.001 / 9.0), beta=0.75, name='norm2')
pool2 = tf.nn.max_pool(norm2, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME', name='pool2')
with tf.variable_scope('local3') as scope:
reshape = tf.reshape(pool2, [FLAGS.batch_size, (- 1)])
dim = reshape.get_shape()[1].value
weights = _variable_with_weight_decay('weights', shape=[dim, 384], stddev=0.04, wd=0.004)
biases = _variable_on_cpu('biases', [384], tf.constant_initializer(0.1))
local3 = tf.nn.relu((tf.matmul(reshape, weights) + biases), name=scope.name)
_activation_summary(local3)
with tf.variable_scope('local4') as scope:
weights = _variable_with_weight_decay('weights', shape=[384, 192], stddev=0.04, wd=0.004)
biases = _variable_on_cpu('biases', [192], tf.constant_initializer(0.1))
local4 = tf.nn.relu((tf.matmul(local3, weights) + biases), name=scope.name)
_activation_summary(local4)
with tf.variable_scope('softmax_linear') as scope:
weights = _variable_with_weight_decay('weights', [192, NUM_CLASSES], stddev=(1 / 192.0), wd=0.0)
biases = _variable_on_cpu('biases', [NUM_CLASSES], tf.constant_initializer(0.0))
softmax_linear = tf.add(tf.matmul(local4, weights), biases, name=scope.name)
_activation_summary(softmax_linear)
return softmax_linear |
def loss(logits, labels):
'Add L2Loss to all the trainable variables.\n\n Add summary for "Loss" and "Loss/avg".\n Args:\n logits: Logits from inference().\n labels: Labels from distorted_inputs or inputs(). 1-D tensor\n of shape [batch_size]\n\n Returns:\n Loss tensor of type float.\n '
labels = tf.cast(labels, tf.int64)
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=labels, logits=logits, name='cross_entropy_per_example')
cross_entropy_mean = tf.reduce_mean(cross_entropy, name='cross_entropy')
tf.add_to_collection('losses', cross_entropy_mean)
return tf.add_n(tf.get_collection('losses'), name='total_loss') | 2,034,962,917,631,843,600 | Add L2Loss to all the trainable variables.
Add summary for "Loss" and "Loss/avg".
Args:
logits: Logits from inference().
labels: Labels from distorted_inputs or inputs(). 1-D tensor
of shape [batch_size]
Returns:
Loss tensor of type float. | examples/cifar10/cifar10.py | loss | 13927729580/TensorFlowOnSpark | python | def loss(logits, labels):
'Add L2Loss to all the trainable variables.\n\n Add summary for "Loss" and "Loss/avg".\n Args:\n logits: Logits from inference().\n labels: Labels from distorted_inputs or inputs(). 1-D tensor\n of shape [batch_size]\n\n Returns:\n Loss tensor of type float.\n '
labels = tf.cast(labels, tf.int64)
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=labels, logits=logits, name='cross_entropy_per_example')
cross_entropy_mean = tf.reduce_mean(cross_entropy, name='cross_entropy')
tf.add_to_collection('losses', cross_entropy_mean)
return tf.add_n(tf.get_collection('losses'), name='total_loss') |
def _add_loss_summaries(total_loss):
'Add summaries for losses in CIFAR-10 model.\n\n Generates moving average for all losses and associated summaries for\n visualizing the performance of the network.\n\n Args:\n total_loss: Total loss from loss().\n Returns:\n loss_averages_op: op for generating moving averages of losses.\n '
loss_averages = tf.train.ExponentialMovingAverage(0.9, name='avg')
losses = tf.get_collection('losses')
loss_averages_op = loss_averages.apply((losses + [total_loss]))
for l in (losses + [total_loss]):
tf.summary.scalar((l.op.name + ' (raw)'), l)
tf.summary.scalar(l.op.name, loss_averages.average(l))
return loss_averages_op | 3,010,989,842,750,706,000 | Add summaries for losses in CIFAR-10 model.
Generates moving average for all losses and associated summaries for
visualizing the performance of the network.
Args:
total_loss: Total loss from loss().
Returns:
loss_averages_op: op for generating moving averages of losses. | examples/cifar10/cifar10.py | _add_loss_summaries | 13927729580/TensorFlowOnSpark | python | def _add_loss_summaries(total_loss):
'Add summaries for losses in CIFAR-10 model.\n\n Generates moving average for all losses and associated summaries for\n visualizing the performance of the network.\n\n Args:\n total_loss: Total loss from loss().\n Returns:\n loss_averages_op: op for generating moving averages of losses.\n '
loss_averages = tf.train.ExponentialMovingAverage(0.9, name='avg')
losses = tf.get_collection('losses')
loss_averages_op = loss_averages.apply((losses + [total_loss]))
for l in (losses + [total_loss]):
tf.summary.scalar((l.op.name + ' (raw)'), l)
tf.summary.scalar(l.op.name, loss_averages.average(l))
return loss_averages_op |
def train(total_loss, global_step):
'Train CIFAR-10 model.\n\n Create an optimizer and apply to all trainable variables. Add moving\n average for all trainable variables.\n\n Args:\n total_loss: Total loss from loss().\n global_step: Integer Variable counting the number of training steps\n processed.\n Returns:\n train_op: op for training.\n '
num_batches_per_epoch = (NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN / FLAGS.batch_size)
decay_steps = int((num_batches_per_epoch * NUM_EPOCHS_PER_DECAY))
lr = tf.train.exponential_decay(INITIAL_LEARNING_RATE, global_step, decay_steps, LEARNING_RATE_DECAY_FACTOR, staircase=True)
tf.summary.scalar('learning_rate', lr)
loss_averages_op = _add_loss_summaries(total_loss)
with tf.control_dependencies([loss_averages_op]):
opt = tf.train.GradientDescentOptimizer(lr)
grads = opt.compute_gradients(total_loss)
apply_gradient_op = opt.apply_gradients(grads, global_step=global_step)
for var in tf.trainable_variables():
tf.summary.histogram(var.op.name, var)
for (grad, var) in grads:
if (grad is not None):
tf.summary.histogram((var.op.name + '/gradients'), grad)
variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, global_step)
variables_averages_op = variable_averages.apply(tf.trainable_variables())
with tf.control_dependencies([apply_gradient_op, variables_averages_op]):
train_op = tf.no_op(name='train')
return train_op | -1,121,517,191,392,497,900 | Train CIFAR-10 model.
Create an optimizer and apply to all trainable variables. Add moving
average for all trainable variables.
Args:
total_loss: Total loss from loss().
global_step: Integer Variable counting the number of training steps
processed.
Returns:
train_op: op for training. | examples/cifar10/cifar10.py | train | 13927729580/TensorFlowOnSpark | python | def train(total_loss, global_step):
'Train CIFAR-10 model.\n\n Create an optimizer and apply to all trainable variables. Add moving\n average for all trainable variables.\n\n Args:\n total_loss: Total loss from loss().\n global_step: Integer Variable counting the number of training steps\n processed.\n Returns:\n train_op: op for training.\n '
num_batches_per_epoch = (NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN / FLAGS.batch_size)
decay_steps = int((num_batches_per_epoch * NUM_EPOCHS_PER_DECAY))
lr = tf.train.exponential_decay(INITIAL_LEARNING_RATE, global_step, decay_steps, LEARNING_RATE_DECAY_FACTOR, staircase=True)
tf.summary.scalar('learning_rate', lr)
loss_averages_op = _add_loss_summaries(total_loss)
with tf.control_dependencies([loss_averages_op]):
opt = tf.train.GradientDescentOptimizer(lr)
grads = opt.compute_gradients(total_loss)
apply_gradient_op = opt.apply_gradients(grads, global_step=global_step)
for var in tf.trainable_variables():
tf.summary.histogram(var.op.name, var)
for (grad, var) in grads:
if (grad is not None):
tf.summary.histogram((var.op.name + '/gradients'), grad)
variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, global_step)
variables_averages_op = variable_averages.apply(tf.trainable_variables())
with tf.control_dependencies([apply_gradient_op, variables_averages_op]):
train_op = tf.no_op(name='train')
return train_op |
def maybe_download_and_extract():
"Download and extract the tarball from Alex's website."
dest_directory = FLAGS.data_dir
if (not os.path.exists(dest_directory)):
os.makedirs(dest_directory)
filename = DATA_URL.split('/')[(- 1)]
filepath = os.path.join(dest_directory, filename)
if (not os.path.exists(filepath)):
def _progress(count, block_size, total_size):
sys.stdout.write(('\r>> Downloading %s %.1f%%' % (filename, ((float((count * block_size)) / float(total_size)) * 100.0))))
sys.stdout.flush()
(filepath, _) = urllib.request.urlretrieve(DATA_URL, filepath, _progress)
print()
statinfo = os.stat(filepath)
print('Successfully downloaded', filename, statinfo.st_size, 'bytes.')
extracted_dir_path = os.path.join(dest_directory, 'cifar-10-batches-bin')
if (not os.path.exists(extracted_dir_path)):
tarfile.open(filepath, 'r:gz').extractall(dest_directory) | -304,177,207,173,734,000 | Download and extract the tarball from Alex's website. | examples/cifar10/cifar10.py | maybe_download_and_extract | 13927729580/TensorFlowOnSpark | python | def maybe_download_and_extract():
dest_directory = FLAGS.data_dir
if (not os.path.exists(dest_directory)):
os.makedirs(dest_directory)
filename = DATA_URL.split('/')[(- 1)]
filepath = os.path.join(dest_directory, filename)
if (not os.path.exists(filepath)):
def _progress(count, block_size, total_size):
sys.stdout.write(('\r>> Downloading %s %.1f%%' % (filename, ((float((count * block_size)) / float(total_size)) * 100.0))))
sys.stdout.flush()
(filepath, _) = urllib.request.urlretrieve(DATA_URL, filepath, _progress)
print()
statinfo = os.stat(filepath)
print('Successfully downloaded', filename, statinfo.st_size, 'bytes.')
extracted_dir_path = os.path.join(dest_directory, 'cifar-10-batches-bin')
if (not os.path.exists(extracted_dir_path)):
tarfile.open(filepath, 'r:gz').extractall(dest_directory) |
@unavailable((not _has_matplotlib), 'matplotlib')
def plot_ellipsoid_3D(p, q, ax, n_points=100):
' Plot an ellipsoid in 3D\n\n Based on\n https://stackoverflow.com/questions/7819498/plotting-ellipsoid-with-matplotlib\n\n TODO: Untested!\n\n Parameters\n ----------\n p: 3x1 array[float]\n Center of the ellipsoid\n q: 3x3 array[float]\n Shape matrix of the ellipsoid\n ax: matplotlib.Axes object\n Ax on which to plot the ellipsoid\n\n Returns\n -------\n ax: matplotlib.Axes object\n The Ax containing the ellipsoid\n\n '
assert (np.shape(p) == (3, 1)), 'p needs to be a 3x1 vector'
assert (np.shape(q) == (3, 3)), 'q needs to be a spd 3x3 matrix'
assert np.allclose(q, (0.5 * (q + q.T)), 'q needs to be spd')
(U, s, rotation) = linalg.svd(q)
assert np.all((s > 0)), 'q needs to be positive definite'
radii = (1.0 / np.sqrt(s))
u = np.linspace(0.0, (2.0 * np.pi), n_points)
v = np.linspace(0.0, np.pi, n_points)
x = (radii[0] * np.outer(np.cos(u), np.sin(v)))
y = (radii[1] * np.outer(np.sin(u), np.sin(v)))
z = (radii[2] * np.outer(np.ones_like(u), np.cos(v)))
for i in range(len(x)):
for j in range(len(x)):
[x[(i, j)], y[(i, j)], z[(i, j)]] = (np.dot([x[(i, j)], y[(i, j)], z[(i, j)]], rotation) + center)
ax.plot_wireframe(x, y, z, rstride=4, cstride=4, color='b', alpha=0.2)
return ax | -1,664,333,436,179,161,300 | Plot an ellipsoid in 3D
Based on
https://stackoverflow.com/questions/7819498/plotting-ellipsoid-with-matplotlib
TODO: Untested!
Parameters
----------
p: 3x1 array[float]
Center of the ellipsoid
q: 3x3 array[float]
Shape matrix of the ellipsoid
ax: matplotlib.Axes object
Ax on which to plot the ellipsoid
Returns
-------
ax: matplotlib.Axes object
The Ax containing the ellipsoid | safe_exploration/visualization/utils_visualization.py | plot_ellipsoid_3D | Pathetiue/safe-exploration | python | @unavailable((not _has_matplotlib), 'matplotlib')
def plot_ellipsoid_3D(p, q, ax, n_points=100):
' Plot an ellipsoid in 3D\n\n Based on\n https://stackoverflow.com/questions/7819498/plotting-ellipsoid-with-matplotlib\n\n TODO: Untested!\n\n Parameters\n ----------\n p: 3x1 array[float]\n Center of the ellipsoid\n q: 3x3 array[float]\n Shape matrix of the ellipsoid\n ax: matplotlib.Axes object\n Ax on which to plot the ellipsoid\n\n Returns\n -------\n ax: matplotlib.Axes object\n The Ax containing the ellipsoid\n\n '
assert (np.shape(p) == (3, 1)), 'p needs to be a 3x1 vector'
assert (np.shape(q) == (3, 3)), 'q needs to be a spd 3x3 matrix'
assert np.allclose(q, (0.5 * (q + q.T)), 'q needs to be spd')
(U, s, rotation) = linalg.svd(q)
assert np.all((s > 0)), 'q needs to be positive definite'
radii = (1.0 / np.sqrt(s))
u = np.linspace(0.0, (2.0 * np.pi), n_points)
v = np.linspace(0.0, np.pi, n_points)
x = (radii[0] * np.outer(np.cos(u), np.sin(v)))
y = (radii[1] * np.outer(np.sin(u), np.sin(v)))
z = (radii[2] * np.outer(np.ones_like(u), np.cos(v)))
for i in range(len(x)):
for j in range(len(x)):
[x[(i, j)], y[(i, j)], z[(i, j)]] = (np.dot([x[(i, j)], y[(i, j)], z[(i, j)]], rotation) + center)
ax.plot_wireframe(x, y, z, rstride=4, cstride=4, color='b', alpha=0.2)
return ax |
@unavailable((not _has_matplotlib), 'matplotlib')
def plot_ellipsoid_2D(p, q, ax, n_points=100, color='r'):
' Plot an ellipsoid in 2D\n\n TODO: Untested!\n\n Parameters\n ----------\n p: 3x1 array[float]\n Center of the ellipsoid\n q: 3x3 array[float]\n Shape matrix of the ellipsoid\n ax: matplotlib.Axes object\n Ax on which to plot the ellipsoid\n\n Returns\n -------\n ax: matplotlib.Axes object\n The Ax containing the ellipsoid\n '
plt.sca(ax)
r = nLa.cholesky(q).T
t = np.linspace(0, (2 * np.pi), n_points)
z = [np.cos(t), np.sin(t)]
ellipse = (np.dot(r, z) + p)
(handle,) = ax.plot(ellipse[0, :], ellipse[1, :], color)
return (ax, handle) | -6,846,811,146,890,000,000 | Plot an ellipsoid in 2D
TODO: Untested!
Parameters
----------
p: 3x1 array[float]
Center of the ellipsoid
q: 3x3 array[float]
Shape matrix of the ellipsoid
ax: matplotlib.Axes object
Ax on which to plot the ellipsoid
Returns
-------
ax: matplotlib.Axes object
The Ax containing the ellipsoid | safe_exploration/visualization/utils_visualization.py | plot_ellipsoid_2D | Pathetiue/safe-exploration | python | @unavailable((not _has_matplotlib), 'matplotlib')
def plot_ellipsoid_2D(p, q, ax, n_points=100, color='r'):
' Plot an ellipsoid in 2D\n\n TODO: Untested!\n\n Parameters\n ----------\n p: 3x1 array[float]\n Center of the ellipsoid\n q: 3x3 array[float]\n Shape matrix of the ellipsoid\n ax: matplotlib.Axes object\n Ax on which to plot the ellipsoid\n\n Returns\n -------\n ax: matplotlib.Axes object\n The Ax containing the ellipsoid\n '
plt.sca(ax)
r = nLa.cholesky(q).T
t = np.linspace(0, (2 * np.pi), n_points)
z = [np.cos(t), np.sin(t)]
ellipse = (np.dot(r, z) + p)
(handle,) = ax.plot(ellipse[0, :], ellipse[1, :], color)
return (ax, handle) |
def __init__(self, p):
'Defines the modulus p which must be a prime\n '
self.F = self
self.p = gmpy.mpz(p)
self.char = self.p
self.q = (self.p + 1)
assert gmpy.is_prime(p)
self.rep = None
self.g = None
'\n g is a random quadratic residue used to compute square roots and it is\n initialized the first time a square root is computed\n '
self.to_fingerprint = ['p']
self.to_export = {'fingerprint': [], 'value': ['p']}
super(Field, self).__init__() | 3,367,812,278,402,845,000 | Defines the modulus p which must be a prime | mathTools/field.py | __init__ | ecuvelier/PPAT | python | def __init__(self, p):
'\n '
self.F = self
self.p = gmpy.mpz(p)
self.char = self.p
self.q = (self.p + 1)
assert gmpy.is_prime(p)
self.rep = None
self.g = None
'\n g is a random quadratic residue used to compute square roots and it is\n initialized the first time a square root is computed\n '
self.to_fingerprint = ['p']
self.to_export = {'fingerprint': [], 'value': ['p']}
super(Field, self).__init__() |
def one(self):
'unit element for multiplication'
return FieldElem(1, self) | -5,955,065,089,090,498,000 | unit element for multiplication | mathTools/field.py | one | ecuvelier/PPAT | python | def one(self):
return FieldElem(1, self) |
def zero(self):
'unit element for addition'
return FieldElem(0, self) | 3,455,417,634,989,473,300 | unit element for addition | mathTools/field.py | zero | ecuvelier/PPAT | python | def zero(self):
return FieldElem(0, self) |
def elem(self, x):
' return an element of value x\n '
if isinstance(x, FieldElem):
assert (x.F == self)
return x
m = gmpy.mpz(1)
assert (isinstance(x, int) or isinstance(x, long) or (type(x) == type(m)))
return FieldElem(x, self) | -8,415,939,500,760,490,000 | return an element of value x | mathTools/field.py | elem | ecuvelier/PPAT | python | def elem(self, x):
' \n '
if isinstance(x, FieldElem):
assert (x.F == self)
return x
m = gmpy.mpz(1)
assert (isinstance(x, int) or isinstance(x, long) or (type(x) == type(m)))
return FieldElem(x, self) |
def random(self, low=1, high=None):
' Return a random element of the Field\n '
if (high == None):
high = int((self.p - 1))
rand = randint(low, high)
return self.elem(rand) | -6,226,670,074,696,561,000 | Return a random element of the Field | mathTools/field.py | random | ecuvelier/PPAT | python | def random(self, low=1, high=None):
' \n '
if (high == None):
high = int((self.p - 1))
rand = randint(low, high)
return self.elem(rand) |
def __eq__(self, other):
'testing if we are working in the same field'
try:
return (self.p == other.p)
except:
return False | -4,467,265,234,563,478,500 | testing if we are working in the same field | mathTools/field.py | __eq__ | ecuvelier/PPAT | python | def __eq__(self, other):
try:
return (self.p == other.p)
except:
return False |
def add(self, a, b):
'\n field operation: addition mod p\n '
return FieldElem(((a.val + b.val) % self.p), self) | -6,150,732,098,473,979,000 | field operation: addition mod p | mathTools/field.py | add | ecuvelier/PPAT | python | def add(self, a, b):
'\n \n '
return FieldElem(((a.val + b.val) % self.p), self) |
def sub(self, a, b):
'\n field operation: substraction mod p\n '
return FieldElem(((a.val - b.val) % self.p), self) | -921,403,297,015,566,600 | field operation: substraction mod p | mathTools/field.py | sub | ecuvelier/PPAT | python | def sub(self, a, b):
'\n \n '
return FieldElem(((a.val - b.val) % self.p), self) |
def neg(self, a):
'\n field operation: opposite mod p\n '
return FieldElem(((self.p - a.val) % self.p), self) | 6,358,035,490,914,622,000 | field operation: opposite mod p | mathTools/field.py | neg | ecuvelier/PPAT | python | def neg(self, a):
'\n \n '
return FieldElem(((self.p - a.val) % self.p), self) |
def mul(self, a, b):
'\n field operation: multiplication of field elements\n '
'\n if isinstance(a,FieldElem) and isinstance(b, FieldElem) and not a.F == b.F :\n raise Exception("multiplication between elements of different fields")\n '
if (not isinstance(b, FieldElem)):
if (b < 0):
return self.smul((- a), (- b))
return self.smul(a, b)
else:
return self.pmul(a, b) | -6,066,617,828,850,303,000 | field operation: multiplication of field elements | mathTools/field.py | mul | ecuvelier/PPAT | python | def mul(self, a, b):
'\n \n '
'\n if isinstance(a,FieldElem) and isinstance(b, FieldElem) and not a.F == b.F :\n raise Exception("multiplication between elements of different fields")\n '
if (not isinstance(b, FieldElem)):
if (b < 0):
return self.smul((- a), (- b))
return self.smul(a, b)
else:
return self.pmul(a, b) |
def smul(self, a, b):
' Return a*b where a or b is scalar\n '
if (not isinstance(b, FieldElem)):
return FieldElem(((gmpy.mpz(b) * a.val) % self.p), self)
else:
return self.smul(b, a) | 4,092,931,305,875,793,000 | Return a*b where a or b is scalar | mathTools/field.py | smul | ecuvelier/PPAT | python | def smul(self, a, b):
' \n '
if (not isinstance(b, FieldElem)):
return FieldElem(((gmpy.mpz(b) * a.val) % self.p), self)
else:
return self.smul(b, a) |
def sm(self, b, a):
' Quick multiplication between a field element a and a scalar b\n '
return FieldElem(((gmpy.mpz(b) * a.val) % self.p), self) | 1,369,684,940,761,127,000 | Quick multiplication between a field element a and a scalar b | mathTools/field.py | sm | ecuvelier/PPAT | python | def sm(self, b, a):
' \n '
return FieldElem(((gmpy.mpz(b) * a.val) % self.p), self) |
def pmul(self, a, b):
' product between two field element in Fp\n '
return FieldElem(((a.val * b.val) % self.p), self) | -1,657,878,191,410,303,200 | product between two field element in Fp | mathTools/field.py | pmul | ecuvelier/PPAT | python | def pmul(self, a, b):
' \n '
return FieldElem(((a.val * b.val) % self.p), self) |
def dbleAndAdd(self, P, Pp, n):
'return n*P using double and add technique'
if (n == 0):
return self.zero()
if (n == 1):
return P
elif ((n % 2) == 1):
Q = self.dbleAndAdd(P, Pp, ((n - 1) / 2))
return ((P + Q) + Q)
elif ((n % 2) == 0):
Q = self.dbleAndAdd(P, Pp, (n / 2))
return (Q + Q) | 304,071,089,569,589,100 | return n*P using double and add technique | mathTools/field.py | dbleAndAdd | ecuvelier/PPAT | python | def dbleAndAdd(self, P, Pp, n):
if (n == 0):
return self.zero()
if (n == 1):
return P
elif ((n % 2) == 1):
Q = self.dbleAndAdd(P, Pp, ((n - 1) / 2))
return ((P + Q) + Q)
elif ((n % 2) == 0):
Q = self.dbleAndAdd(P, Pp, (n / 2))
return (Q + Q) |
def powop(self, a, b):
'return a**b'
m = gmpy.mpz(1)
'exponentiation by a scalar'
if ((not isinstance(b, int)) and (not isinstance(b, long)) and (not (type(b) == type(m)))):
raise Exception('Exponentation by a non integer, long or mpz')
c = b
if ((c > (self.char - 1)) or (c < 0)):
c = (b % (self.char - 1))
if (c == 0):
assert (not ((a.val % self.char) == 0))
return self.one()
elif (c == 1):
return a
else:
return self.sqrtAndMultply(a, a, c) | -2,940,484,699,847,163,000 | return a**b | mathTools/field.py | powop | ecuvelier/PPAT | python | def powop(self, a, b):
m = gmpy.mpz(1)
'exponentiation by a scalar'
if ((not isinstance(b, int)) and (not isinstance(b, long)) and (not (type(b) == type(m)))):
raise Exception('Exponentation by a non integer, long or mpz')
c = b
if ((c > (self.char - 1)) or (c < 0)):
c = (b % (self.char - 1))
if (c == 0):
assert (not ((a.val % self.char) == 0))
return self.one()
elif (c == 1):
return a
else:
return self.sqrtAndMultply(a, a, c) |
def sqrtAndMultply(self, P, Pp, n):
'return P**n using square and multiply technique'
if (n == 0):
return self.one()
elif (n == 1):
return P
elif ((n % 2) == 1):
Q = self.sqrtAndMultply(P, Pp, ((n - 1) / 2))
return (P * self.square(Q))
elif ((n % 2) == 0):
Q = self.sqrtAndMultply(P, Pp, (n / 2))
return self.square(Q) | 781,381,006,290,647,300 | return P**n using square and multiply technique | mathTools/field.py | sqrtAndMultply | ecuvelier/PPAT | python | def sqrtAndMultply(self, P, Pp, n):
if (n == 0):
return self.one()
elif (n == 1):
return P
elif ((n % 2) == 1):
Q = self.sqrtAndMultply(P, Pp, ((n - 1) / 2))
return (P * self.square(Q))
elif ((n % 2) == 0):
Q = self.sqrtAndMultply(P, Pp, (n / 2))
return self.square(Q) |
def square(self, a):
'\n This method returns the square of a\n '
return FieldElem(pow(a.val, 2, self.p), self) | -7,983,734,526,397,552,000 | This method returns the square of a | mathTools/field.py | square | ecuvelier/PPAT | python | def square(self, a):
'\n \n '
return FieldElem(pow(a.val, 2, self.p), self) |
def findnonresidue(self):
'\n find a random non quadratic residue in the Field F,\n that is, find g that is not a square in F, this is\n needed to compute square roots\n '
g = self.random()
while g.isquadres():
g = self.random()
return g | 1,602,797,222,163,183,900 | find a random non quadratic residue in the Field F,
that is, find g that is not a square in F, this is
needed to compute square roots | mathTools/field.py | findnonresidue | ecuvelier/PPAT | python | def findnonresidue(self):
'\n find a random non quadratic residue in the Field F,\n that is, find g that is not a square in F, this is\n needed to compute square roots\n '
g = self.random()
while g.isquadres():
g = self.random()
return g |
def __init__(self, val, F):
'Creating a new field element.\n '
self.F = F
self.val = gmpy.mpz(val)
self.poly = polynom(self.F, [self]) | 5,470,238,425,599,405,000 | Creating a new field element. | mathTools/field.py | __init__ | ecuvelier/PPAT | python | def __init__(self, val, F):
'\n '
self.F = F
self.val = gmpy.mpz(val)
self.poly = polynom(self.F, [self]) |
def isquadres(self):
' This method return True if the element is a quadratic residue mod q\n different than zero\n it returns False otherwhise\n '
if (self + self.F.zero()).iszero():
return False
else:
c = (self ** ((self.F.q - 1) / 2))
return (c == self.F.one()) | -1,876,804,704,010,010,000 | This method return True if the element is a quadratic residue mod q
different than zero
it returns False otherwhise | mathTools/field.py | isquadres | ecuvelier/PPAT | python | def isquadres(self):
' This method return True if the element is a quadratic residue mod q\n different than zero\n it returns False otherwhise\n '
if (self + self.F.zero()).iszero():
return False
else:
c = (self ** ((self.F.q - 1) / 2))
return (c == self.F.one()) |
def squareroot(self):
' This method returns the positive square root of\n an element of the field\n using the Tonelli-Shanks algorithm\n\n Carefull : if the element has no square root, the method does not\n check this case and raises an error. Verification has to be done\n before calling the method.\n '
g = self.F.g
if (g == None):
g = self.F.findnonresidue()
self.F.g = g
q = self.F.q
s = 0
t = (self.F.q - 1)
while ((t % 2) == 0):
s = (s + 1)
t = (t / 2)
e = 0
for i in range(2, (s + 1)):
b = (2 ** (i - 1))
b1 = (b * 2)
c = ((self * (g ** (- e))) ** ((q - 1) / b1))
if (not (c == self.F.one())):
e = (e + b)
h = (self * (g ** (- e)))
b = ((g ** (e / 2)) * (h ** ((t + 1) / 2)))
assert ((b ** 2) == self)
return b | -3,225,873,158,965,586,000 | This method returns the positive square root of
an element of the field
using the Tonelli-Shanks algorithm
Carefull : if the element has no square root, the method does not
check this case and raises an error. Verification has to be done
before calling the method. | mathTools/field.py | squareroot | ecuvelier/PPAT | python | def squareroot(self):
' This method returns the positive square root of\n an element of the field\n using the Tonelli-Shanks algorithm\n\n Carefull : if the element has no square root, the method does not\n check this case and raises an error. Verification has to be done\n before calling the method.\n '
g = self.F.g
if (g == None):
g = self.F.findnonresidue()
self.F.g = g
q = self.F.q
s = 0
t = (self.F.q - 1)
while ((t % 2) == 0):
s = (s + 1)
t = (t / 2)
e = 0
for i in range(2, (s + 1)):
b = (2 ** (i - 1))
b1 = (b * 2)
c = ((self * (g ** (- e))) ** ((q - 1) / b1))
if (not (c == self.F.one())):
e = (e + b)
h = (self * (g ** (- e)))
b = ((g ** (e / 2)) * (h ** ((t + 1) / 2)))
assert ((b ** 2) == self)
return b |
def __init__(self, F, irpoly, g=None, rep=None):
"Define the base Field or extension Field and the irreducible polynomial\n F is the base field on top of which the extension\n field is built\n irpoly is the irreducible polynomial used to build\n the extension field as F/irpoly\n g is a non quadratic residue used to compute square\n roots, if it is set to None, computing a square root\n will initialize g\n rep is the representation of the root of irpoly\n (note that letter 'A' is reserved for the Complex extension field)\n "
self.F = F
self.irpoly = irpoly
self.deg = len(irpoly.coef)
assert (self.deg > 0)
self.q = (self.F.q ** (self.deg - 1))
self.tabular = self.table()
if (rep == None):
self.rep = rd.choice(['B', 'C', 'D', 'E', 'F', 'G', 'H', 'J', 'K', 'L'])
else:
self.rep = rep
self.char = F.char
self.primefield = gmpy.is_prime(self.char)
self.g = g
self.to_fingerprint = ['F', 'irpoly']
self.to_export = {'fingerprint': [], 'value': ['F', 'irpoly']} | 3,613,551,871,637,081,600 | Define the base Field or extension Field and the irreducible polynomial
F is the base field on top of which the extension
field is built
irpoly is the irreducible polynomial used to build
the extension field as F/irpoly
g is a non quadratic residue used to compute square
roots, if it is set to None, computing a square root
will initialize g
rep is the representation of the root of irpoly
(note that letter 'A' is reserved for the Complex extension field) | mathTools/field.py | __init__ | ecuvelier/PPAT | python | def __init__(self, F, irpoly, g=None, rep=None):
"Define the base Field or extension Field and the irreducible polynomial\n F is the base field on top of which the extension\n field is built\n irpoly is the irreducible polynomial used to build\n the extension field as F/irpoly\n g is a non quadratic residue used to compute square\n roots, if it is set to None, computing a square root\n will initialize g\n rep is the representation of the root of irpoly\n (note that letter 'A' is reserved for the Complex extension field)\n "
self.F = F
self.irpoly = irpoly
self.deg = len(irpoly.coef)
assert (self.deg > 0)
self.q = (self.F.q ** (self.deg - 1))
self.tabular = self.table()
if (rep == None):
self.rep = rd.choice(['B', 'C', 'D', 'E', 'F', 'G', 'H', 'J', 'K', 'L'])
else:
self.rep = rep
self.char = F.char
self.primefield = gmpy.is_prime(self.char)
self.g = g
self.to_fingerprint = ['F', 'irpoly']
self.to_export = {'fingerprint': [], 'value': ['F', 'irpoly']} |
def one(self):
'unit element for multiplication'
One = ([self.F.zero()] * (self.deg - 1))
One[(self.deg - 2)] = self.F.one()
return ExtensionFieldElem(self, polynom(self.F, One)) | 8,906,210,674,035,437,000 | unit element for multiplication | mathTools/field.py | one | ecuvelier/PPAT | python | def one(self):
One = ([self.F.zero()] * (self.deg - 1))
One[(self.deg - 2)] = self.F.one()
return ExtensionFieldElem(self, polynom(self.F, One)) |
def zero(self):
'unit element for addition'
Zero = ([self.F.zero()] * (self.deg - 1))
return ExtensionFieldElem(self, polynom(self.F, Zero)) | -6,735,012,592,271,312,000 | unit element for addition | mathTools/field.py | zero | ecuvelier/PPAT | python | def zero(self):
Zero = ([self.F.zero()] * (self.deg - 1))
return ExtensionFieldElem(self, polynom(self.F, Zero)) |
def unit(self):
' root of the irreducible polynomial\n e.g. return element 1*A+0 (or the complex value i) if the irpoly is X**2+1\n '
I = self.zero()
I.poly.coef[(- 2)] = self.F.one()
return I | 8,046,764,757,817,881,000 | root of the irreducible polynomial
e.g. return element 1*A+0 (or the complex value i) if the irpoly is X**2+1 | mathTools/field.py | unit | ecuvelier/PPAT | python | def unit(self):
' root of the irreducible polynomial\n e.g. return element 1*A+0 (or the complex value i) if the irpoly is X**2+1\n '
I = self.zero()
I.poly.coef[(- 2)] = self.F.one()
return I |
def elem(self, x):
' Provided that x belongs to F, return an element of the extension field\n of value x\n '
P = self.zero()
P.poly.coef[(- 1)] = x
return P | 7,193,894,804,630,438,000 | Provided that x belongs to F, return an element of the extension field
of value x | mathTools/field.py | elem | ecuvelier/PPAT | python | def elem(self, x):
' Provided that x belongs to F, return an element of the extension field\n of value x\n '
P = self.zero()
P.poly.coef[(- 1)] = x
return P |
def random(self):
' Return a random element of the Extension Field\n '
polycoef = ([0] * (self.deg - 1))
for i in range((self.deg - 1)):
polycoef[i] = self.F.random()
poly = polynom(self.F, polycoef)
return ExtensionFieldElem(self, poly) | 2,462,468,952,993,113,000 | Return a random element of the Extension Field | mathTools/field.py | random | ecuvelier/PPAT | python | def random(self):
' \n '
polycoef = ([0] * (self.deg - 1))
for i in range((self.deg - 1)):
polycoef[i] = self.F.random()
poly = polynom(self.F, polycoef)
return ExtensionFieldElem(self, poly) |
def __eq__(self, other):
'testing if we are working in the same extension field'
try:
return ((self.F == other.F) and (self.irpoly == other.irpoly))
except:
return False | -6,034,864,863,400,111,000 | testing if we are working in the same extension field | mathTools/field.py | __eq__ | ecuvelier/PPAT | python | def __eq__(self, other):
try:
return ((self.F == other.F) and (self.irpoly == other.irpoly))
except:
return False |
def add(self, a, b):
'\n field operation: addition of polynomial > addition of coefficients in the appropriate field\n '
if (not (a.deg == b.deg)):
a = self.reduc(a)
b = self.reduc(b)
polysum = ([0] * a.deg)
for i in range(a.deg):
polysum[i] = (a.poly.coef[i] + b.poly.coef[i])
P = polynom(self.F, polysum)
return ExtensionFieldElem(self, P) | -4,685,965,467,103,141,000 | field operation: addition of polynomial > addition of coefficients in the appropriate field | mathTools/field.py | add | ecuvelier/PPAT | python | def add(self, a, b):
'\n \n '
if (not (a.deg == b.deg)):
a = self.reduc(a)
b = self.reduc(b)
polysum = ([0] * a.deg)
for i in range(a.deg):
polysum[i] = (a.poly.coef[i] + b.poly.coef[i])
P = polynom(self.F, polysum)
return ExtensionFieldElem(self, P) |
def sub(self, a, b):
'\n field operation: substraction of polynomials > substraction of each coefficient in the appropriate field\n '
if (not (a.deg == b.deg)):
a = self.reduc(a)
b = self.reduc(b)
c = self.neg(b)
return self.add(a, c) | -7,821,902,978,157,330,000 | field operation: substraction of polynomials > substraction of each coefficient in the appropriate field | mathTools/field.py | sub | ecuvelier/PPAT | python | def sub(self, a, b):
'\n \n '
if (not (a.deg == b.deg)):
a = self.reduc(a)
b = self.reduc(b)
c = self.neg(b)
return self.add(a, c) |
def neg(self, a):
'\n field operation: opposite of a polynomial > opposite of each coefficient in appropriate field\n '
ap = ([0] * a.deg)
for i in range(a.deg):
ap[i] = (- a.poly.coef[i])
P = polynom(self.F, ap)
return ExtensionFieldElem(self, P) | -7,593,053,574,250,158,000 | field operation: opposite of a polynomial > opposite of each coefficient in appropriate field | mathTools/field.py | neg | ecuvelier/PPAT | python | def neg(self, a):
'\n \n '
ap = ([0] * a.deg)
for i in range(a.deg):
ap[i] = (- a.poly.coef[i])
P = polynom(self.F, ap)
return ExtensionFieldElem(self, P) |
def smul(self, a, b):
' Return a*b where a or b is scalar\n '
if (not isinstance(b, FieldElem)):
A = a.poly.coef
Pc = ([0] * len(A))
for i in range(len(Pc)):
Pc[i] = (A[i] * gmpy.mpz(b))
return ExtensionFieldElem(self, polynom(self.F, Pc))
else:
return self.smul(b, a) | -2,841,629,317,917,324,300 | Return a*b where a or b is scalar | mathTools/field.py | smul | ecuvelier/PPAT | python | def smul(self, a, b):
' \n '
if (not isinstance(b, FieldElem)):
A = a.poly.coef
Pc = ([0] * len(A))
for i in range(len(Pc)):
Pc[i] = (A[i] * gmpy.mpz(b))
return ExtensionFieldElem(self, polynom(self.F, Pc))
else:
return self.smul(b, a) |
def pmul(self, a, b):
'Multiplication between polynomials\n '
if (not (a.deg == b.deg)):
a = self.reduc(a)
b = self.reduc(b)
A = a.poly.coef
B = b.poly.coef
k = (self.deg - 1)
if ((k == 2) and (self.F.rep == 'A')):
(a0, a1, b0, b1) = (A[0].val, A[1].val, B[0].val, B[1].val)
p = self.char
v0 = (a0 * b0)
v1 = (a1 * b1)
c0 = (((((a0 + a1) * (b0 + b1)) - v0) - v1) % p)
c1 = ((v1 - v0) % p)
c0e = FieldElem(c0, self.F)
c1e = FieldElem(c1, self.F)
cp = polynom(self.F, [c0e, c1e])
C = ExtensionFieldElem(self, cp)
return C
elif (k == 2):
a0 = A[0]
a1 = A[1]
b0 = B[0]
b1 = B[1]
beta = (- self.irpoly.coef[(- 1)])
v0 = self.F.pmul(a0, b0)
v1 = self.F.pmul(a1, b1)
c0 = ((self.F.pmul((a0 + a1), (b0 + b1)) - v0) - v1)
c1 = (v1 + self.F.pmul(v0, beta))
cp = polynom(self.F, [c0, c1])
C = ExtensionFieldElem(self, cp)
return C
elif (k == 3):
(a0, a1, a2) = A
(b0, b1, b2) = B
beta = (- self.irpoly.coef[(- 1)])
(v0, v1, v2) = (self.F.pmul(a0, b0), self.F.pmul(a1, b1), self.F.pmul(a2, b2))
c0 = (((self.F.pmul((a0 + a2), (b0 + b2)) - v0) + v1) - v2)
c1 = (((self.F.pmul((a2 + a1), (b2 + b1)) - v2) - v1) + self.F.pmul(beta, v0))
c2 = (v2 + self.F.pmul(beta, ((self.F.pmul((a1 + a0), (b1 + b0)) - v1) - v0)))
cp = polynom(self.F, [c0, c1, c2])
C = ExtensionFieldElem(self, cp)
return C
else:
prod = convolve(A, B)
return self.reduc2(prod) | -429,399,036,203,879,040 | Multiplication between polynomials | mathTools/field.py | pmul | ecuvelier/PPAT | python | def pmul(self, a, b):
'\n '
if (not (a.deg == b.deg)):
a = self.reduc(a)
b = self.reduc(b)
A = a.poly.coef
B = b.poly.coef
k = (self.deg - 1)
if ((k == 2) and (self.F.rep == 'A')):
(a0, a1, b0, b1) = (A[0].val, A[1].val, B[0].val, B[1].val)
p = self.char
v0 = (a0 * b0)
v1 = (a1 * b1)
c0 = (((((a0 + a1) * (b0 + b1)) - v0) - v1) % p)
c1 = ((v1 - v0) % p)
c0e = FieldElem(c0, self.F)
c1e = FieldElem(c1, self.F)
cp = polynom(self.F, [c0e, c1e])
C = ExtensionFieldElem(self, cp)
return C
elif (k == 2):
a0 = A[0]
a1 = A[1]
b0 = B[0]
b1 = B[1]
beta = (- self.irpoly.coef[(- 1)])
v0 = self.F.pmul(a0, b0)
v1 = self.F.pmul(a1, b1)
c0 = ((self.F.pmul((a0 + a1), (b0 + b1)) - v0) - v1)
c1 = (v1 + self.F.pmul(v0, beta))
cp = polynom(self.F, [c0, c1])
C = ExtensionFieldElem(self, cp)
return C
elif (k == 3):
(a0, a1, a2) = A
(b0, b1, b2) = B
beta = (- self.irpoly.coef[(- 1)])
(v0, v1, v2) = (self.F.pmul(a0, b0), self.F.pmul(a1, b1), self.F.pmul(a2, b2))
c0 = (((self.F.pmul((a0 + a2), (b0 + b2)) - v0) + v1) - v2)
c1 = (((self.F.pmul((a2 + a1), (b2 + b1)) - v2) - v1) + self.F.pmul(beta, v0))
c2 = (v2 + self.F.pmul(beta, ((self.F.pmul((a1 + a0), (b1 + b0)) - v1) - v0)))
cp = polynom(self.F, [c0, c1, c2])
C = ExtensionFieldElem(self, cp)
return C
else:
prod = convolve(A, B)
return self.reduc2(prod) |
def square(self, a):
' This algortihm returns the square of a in the field\n using different methods if the degree of the extension\n is 2,3 or more\n '
assert (a.F == self)
if (not (a.deg == (self.deg - 1))):
a = self.reduc(a)
A = a.poly.coef
k = (self.deg - 1)
if ((k == 2) and (self.F.rep == 'A')):
(a1, a0) = (A[0].val, A[1].val)
p = self.char
v0 = (a0 * a1)
c0 = (((a0 + a1) * (a0 - a1)) % p)
c1 = ((v0 + v0) % p)
c0e = FieldElem(c0, self.F)
c1e = FieldElem(c1, self.F)
cp = polynom(self.F, [c1e, c0e])
C = ExtensionFieldElem(self, cp)
return C
elif (k == 2):
(a1, a0) = A
beta = (- self.irpoly.coef[(- 1)])
v0 = self.F.pmul(a0, a1)
c0 = ((self.F.pmul((a0 + a1), (a0 + self.F.pmul(a1, beta))) - v0) - self.F.pmul(beta, v0))
c1 = (v0 + v0)
cp = polynom(self.F, [c1, c0])
return ExtensionFieldElem(self, cp)
elif (k == 3):
(a2, a1, a0) = A
assert (a0.F == self.F)
beta = (- self.irpoly.coef[(- 1)])
s0 = self.F.square(a0)
t1 = self.F.pmul(a0, a1)
s1 = (t1 + t1)
s2 = self.F.square(((a0 - a1) + a2))
t3 = (a1 * a2)
s3 = (t3 + t3)
s4 = self.F.square(a2)
c0 = (s0 + self.F.pmul(beta, s3))
c1 = (s1 + self.F.pmul(beta, s4))
c2 = ((((s1 + s2) + s3) - s0) - s4)
cp = polynom(self.F, [c2, c1, c0])
return ExtensionFieldElem(self, cp)
else:
return self.F.pmul(a, a) | 392,524,527,384,159,500 | This algortihm returns the square of a in the field
using different methods if the degree of the extension
is 2,3 or more | mathTools/field.py | square | ecuvelier/PPAT | python | def square(self, a):
' This algortihm returns the square of a in the field\n using different methods if the degree of the extension\n is 2,3 or more\n '
assert (a.F == self)
if (not (a.deg == (self.deg - 1))):
a = self.reduc(a)
A = a.poly.coef
k = (self.deg - 1)
if ((k == 2) and (self.F.rep == 'A')):
(a1, a0) = (A[0].val, A[1].val)
p = self.char
v0 = (a0 * a1)
c0 = (((a0 + a1) * (a0 - a1)) % p)
c1 = ((v0 + v0) % p)
c0e = FieldElem(c0, self.F)
c1e = FieldElem(c1, self.F)
cp = polynom(self.F, [c1e, c0e])
C = ExtensionFieldElem(self, cp)
return C
elif (k == 2):
(a1, a0) = A
beta = (- self.irpoly.coef[(- 1)])
v0 = self.F.pmul(a0, a1)
c0 = ((self.F.pmul((a0 + a1), (a0 + self.F.pmul(a1, beta))) - v0) - self.F.pmul(beta, v0))
c1 = (v0 + v0)
cp = polynom(self.F, [c1, c0])
return ExtensionFieldElem(self, cp)
elif (k == 3):
(a2, a1, a0) = A
assert (a0.F == self.F)
beta = (- self.irpoly.coef[(- 1)])
s0 = self.F.square(a0)
t1 = self.F.pmul(a0, a1)
s1 = (t1 + t1)
s2 = self.F.square(((a0 - a1) + a2))
t3 = (a1 * a2)
s3 = (t3 + t3)
s4 = self.F.square(a2)
c0 = (s0 + self.F.pmul(beta, s3))
c1 = (s1 + self.F.pmul(beta, s4))
c2 = ((((s1 + s2) + s3) - s0) - s4)
cp = polynom(self.F, [c2, c1, c0])
return ExtensionFieldElem(self, cp)
else:
return self.F.pmul(a, a) |
def invert(self, a):
" Ths method returns the inverse of a in the field\n The inverse is computed by determining the Bezout coefficient using the\n extended Euclide's algorithm or by specialized algorithms depending\n on the degree of the extension (2 or 3)\n "
assert (a.F == self)
k = (self.deg - 1)
if ((k == 2) and (self.F.rep == 'A')):
A = a.poly.coef
(a1, a0) = (A[0].val, A[1].val)
p = self.char
norm = ((a0 * a0) + (a1 * a1))
invnorm = gmpy.invert(norm, p)
c0 = ((a0 * invnorm) % p)
c1 = (((- a1) * invnorm) % p)
c0e = FieldElem(c0, self.F)
c1e = FieldElem(c1, self.F)
invap = polynom(self.F, [c1e, c0e])
inva = ExtensionFieldElem(self, invap)
return inva
elif (k == 2):
A = a.poly.coef
(a1, a0) = (A[0], A[1])
mod = self.irpoly.coef[(- 1)]
a12 = self.F.square(a1)
mid = self.F.pmul(a12, mod)
norm = (self.F.square(a0) + mid)
invnorm = self.F.invert(norm)
c = self.F.pmul(a0, invnorm)
d = (- self.F.pmul(a1, invnorm))
invap = polynom(self.F, [d, c])
inva = ExtensionFieldElem(self, invap)
return inva
elif (k == 3):
A = a.poly.coef
(a2, a1, a0) = (A[0], A[1], A[2])
mod = (- self.irpoly.coef[(- 1)])
z0 = self.F.zero()
z1 = self.F.one()
if (a0 == z0):
if (a1 == z0):
(c0, c1, c2) = (z0, self.F.invert(self.F.pmul(a2, mod)), z0)
elif (a2 == z0):
(c0, c1, c2) = (z0, z0, self.F.invert(self.F.pmul(a1, mod)))
else:
a22 = self.F.square(a2)
a12 = self.F.square(a1)
c2 = self.F.pmul(a12, self.F.invert((self.F.pmul(self.F.pmul(a22, a2), mod) + self.F.pmul(self.F.pmul(a12, a1), mod))))
c1 = self.F.pmul((z1 - self.F.pmul(self.F.pmul(a1, c2), mod)), self.F.invert(self.F.pmul(a2, mod)))
c0 = self.F.pmul((- self.F.pmul(self.F.pmul(a2, mod), c2)), self.F.invert(a1))
elif ((a1 == z0) and (a2 == z0)):
(c0, c1, c2) = (self.F.invert(a0), z0, z0)
else:
a12 = self.F.pmul(a1, a2)
a12m = self.F.pmul(a12, mod)
a00 = self.F.square(a0)
abis = (a00 - a12m)
if (abis == z0):
a11 = self.F.square(a1)
a22 = self.F.square(a2)
a02 = self.F.pmul(a0, a2)
a01 = self.F.pmul(a0, a1)
c2 = self.F.pmul((- a), self.F.invert(self.F.pmul((a02 - a11), mod)))
c1 = self.F.pmul((- a2), self.F.invert((a01 - self.F.pmul(a22, mod))))
a1c2 = self.F.pmul(a1, c2)
a2c1 = self.F.pmul(a2, c1)
c0 = self.F.pmul((z1 - self.F.pmul((a1c2 + a2c1), mod)), self.F.invert(a0))
elif (a1 == z0):
inva0 = self.F.invert(a0)
a02 = self.F.pmul(a0, a2)
a000 = self.F.pmul(a00, a0)
a22 = self.F.square(a2)
a222 = self.F.pmul(a22, a2)
mm = self.F.square(mod)
a222mm = self.F.pmul(a222, mm)
c2 = self.F.pmul((- a02), self.F.invert((a000 + a222mm)))
a02m = self.F.pmul(a02, mod)
a02mc2 = self.F.pmul(a02m, c2)
inva00 = self.F.square(inva0)
c1 = self.F.pmul((- a02mc2), inva00)
a2m = self.F.pmul(a2, mod)
a2mc1 = self.F.pmul(a2m, c1)
c0 = self.F.pmul((z1 - a2mc1), inva0)
elif (a2 == z0):
a11 = self.F.square(a1)
a111 = self.F.pmul(a11, a1)
a000 = self.F.pmul(a00, a0)
a111m = self.F.pmul(a111, mod)
inva0 = self.F.invert(a0)
c2 = self.F.pmul(a11, self.F.invert((a111m + a000)))
a11m = self.F.pmul(a11, mod)
a11mc2 = self.F.pmul(a11m, c2)
inva00 = self.F.square(inva0)
c1 = self.F.pmul((a11mc2 - a1), inva00)
a1m = self.F.pmul(a1, mod)
a1mc2 = self.F.pmul(a1m, c2)
c0 = self.F.pmul((z1 - a1mc2), inva0)
else:
a01 = self.F.pmul(a0, a1)
a22 = self.F.square(a2)
a22m = self.F.pmul(a22, mod)
a02 = self.F.pmul(a0, a2)
a11 = self.F.square(a1)
abus = (a01 - a22m)
abos = self.F.pmul((a02 - a11), mod)
invabis = self.F.invert(abis)
abb = self.F.pmul(abus, invabis)
abb1 = self.F.pmul(abb, a1)
abbbos = self.F.pmul(abb, abos)
c2 = self.F.pmul((abb1 - a2), self.F.invert((abis - abbbos)))
abosc2 = self.F.pmul(abos, c2)
c1 = self.F.pmul(((- a1) - abosc2), invabis)
a1c2 = self.F.pmul(a1, c2)
a2c1 = self.F.pmul(a2, c1)
c0 = self.F.pmul((z1 - self.F.pmul((a1c2 + a2c1), mod)), self.F.invert(a0))
invap = polynom(self.F, [c2, c1, c0])
inva = ExtensionFieldElem(self, invap)
return inva
else:
P = ExtensionFieldElem(self, self.irpoly)
(r, u, v) = self.extendedeuclide(P, a)
(n, d) = r.poly.truedeg()
assert (n == (self.deg - 2))
c = r.poly.coef[(len(r.poly.coef) - 1)].invert()
cp = polynom(self.F, [c])
ce = ExtensionFieldElem(self, cp)
return (ce * v) | 1,190,864,874,066,247,400 | Ths method returns the inverse of a in the field
The inverse is computed by determining the Bezout coefficient using the
extended Euclide's algorithm or by specialized algorithms depending
on the degree of the extension (2 or 3) | mathTools/field.py | invert | ecuvelier/PPAT | python | def invert(self, a):
" Ths method returns the inverse of a in the field\n The inverse is computed by determining the Bezout coefficient using the\n extended Euclide's algorithm or by specialized algorithms depending\n on the degree of the extension (2 or 3)\n "
assert (a.F == self)
k = (self.deg - 1)
if ((k == 2) and (self.F.rep == 'A')):
A = a.poly.coef
(a1, a0) = (A[0].val, A[1].val)
p = self.char
norm = ((a0 * a0) + (a1 * a1))
invnorm = gmpy.invert(norm, p)
c0 = ((a0 * invnorm) % p)
c1 = (((- a1) * invnorm) % p)
c0e = FieldElem(c0, self.F)
c1e = FieldElem(c1, self.F)
invap = polynom(self.F, [c1e, c0e])
inva = ExtensionFieldElem(self, invap)
return inva
elif (k == 2):
A = a.poly.coef
(a1, a0) = (A[0], A[1])
mod = self.irpoly.coef[(- 1)]
a12 = self.F.square(a1)
mid = self.F.pmul(a12, mod)
norm = (self.F.square(a0) + mid)
invnorm = self.F.invert(norm)
c = self.F.pmul(a0, invnorm)
d = (- self.F.pmul(a1, invnorm))
invap = polynom(self.F, [d, c])
inva = ExtensionFieldElem(self, invap)
return inva
elif (k == 3):
A = a.poly.coef
(a2, a1, a0) = (A[0], A[1], A[2])
mod = (- self.irpoly.coef[(- 1)])
z0 = self.F.zero()
z1 = self.F.one()
if (a0 == z0):
if (a1 == z0):
(c0, c1, c2) = (z0, self.F.invert(self.F.pmul(a2, mod)), z0)
elif (a2 == z0):
(c0, c1, c2) = (z0, z0, self.F.invert(self.F.pmul(a1, mod)))
else:
a22 = self.F.square(a2)
a12 = self.F.square(a1)
c2 = self.F.pmul(a12, self.F.invert((self.F.pmul(self.F.pmul(a22, a2), mod) + self.F.pmul(self.F.pmul(a12, a1), mod))))
c1 = self.F.pmul((z1 - self.F.pmul(self.F.pmul(a1, c2), mod)), self.F.invert(self.F.pmul(a2, mod)))
c0 = self.F.pmul((- self.F.pmul(self.F.pmul(a2, mod), c2)), self.F.invert(a1))
elif ((a1 == z0) and (a2 == z0)):
(c0, c1, c2) = (self.F.invert(a0), z0, z0)
else:
a12 = self.F.pmul(a1, a2)
a12m = self.F.pmul(a12, mod)
a00 = self.F.square(a0)
abis = (a00 - a12m)
if (abis == z0):
a11 = self.F.square(a1)
a22 = self.F.square(a2)
a02 = self.F.pmul(a0, a2)
a01 = self.F.pmul(a0, a1)
c2 = self.F.pmul((- a), self.F.invert(self.F.pmul((a02 - a11), mod)))
c1 = self.F.pmul((- a2), self.F.invert((a01 - self.F.pmul(a22, mod))))
a1c2 = self.F.pmul(a1, c2)
a2c1 = self.F.pmul(a2, c1)
c0 = self.F.pmul((z1 - self.F.pmul((a1c2 + a2c1), mod)), self.F.invert(a0))
elif (a1 == z0):
inva0 = self.F.invert(a0)
a02 = self.F.pmul(a0, a2)
a000 = self.F.pmul(a00, a0)
a22 = self.F.square(a2)
a222 = self.F.pmul(a22, a2)
mm = self.F.square(mod)
a222mm = self.F.pmul(a222, mm)
c2 = self.F.pmul((- a02), self.F.invert((a000 + a222mm)))
a02m = self.F.pmul(a02, mod)
a02mc2 = self.F.pmul(a02m, c2)
inva00 = self.F.square(inva0)
c1 = self.F.pmul((- a02mc2), inva00)
a2m = self.F.pmul(a2, mod)
a2mc1 = self.F.pmul(a2m, c1)
c0 = self.F.pmul((z1 - a2mc1), inva0)
elif (a2 == z0):
a11 = self.F.square(a1)
a111 = self.F.pmul(a11, a1)
a000 = self.F.pmul(a00, a0)
a111m = self.F.pmul(a111, mod)
inva0 = self.F.invert(a0)
c2 = self.F.pmul(a11, self.F.invert((a111m + a000)))
a11m = self.F.pmul(a11, mod)
a11mc2 = self.F.pmul(a11m, c2)
inva00 = self.F.square(inva0)
c1 = self.F.pmul((a11mc2 - a1), inva00)
a1m = self.F.pmul(a1, mod)
a1mc2 = self.F.pmul(a1m, c2)
c0 = self.F.pmul((z1 - a1mc2), inva0)
else:
a01 = self.F.pmul(a0, a1)
a22 = self.F.square(a2)
a22m = self.F.pmul(a22, mod)
a02 = self.F.pmul(a0, a2)
a11 = self.F.square(a1)
abus = (a01 - a22m)
abos = self.F.pmul((a02 - a11), mod)
invabis = self.F.invert(abis)
abb = self.F.pmul(abus, invabis)
abb1 = self.F.pmul(abb, a1)
abbbos = self.F.pmul(abb, abos)
c2 = self.F.pmul((abb1 - a2), self.F.invert((abis - abbbos)))
abosc2 = self.F.pmul(abos, c2)
c1 = self.F.pmul(((- a1) - abosc2), invabis)
a1c2 = self.F.pmul(a1, c2)
a2c1 = self.F.pmul(a2, c1)
c0 = self.F.pmul((z1 - self.F.pmul((a1c2 + a2c1), mod)), self.F.invert(a0))
invap = polynom(self.F, [c2, c1, c0])
inva = ExtensionFieldElem(self, invap)
return inva
else:
P = ExtensionFieldElem(self, self.irpoly)
(r, u, v) = self.extendedeuclide(P, a)
(n, d) = r.poly.truedeg()
assert (n == (self.deg - 2))
c = r.poly.coef[(len(r.poly.coef) - 1)].invert()
cp = polynom(self.F, [c])
ce = ExtensionFieldElem(self, cp)
return (ce * v) |
def invertible(self, a):
' Return True if a is invertible\n '
return (not (self.reduc(a) == self.zero())) | 8,636,644,282,939,494,000 | Return True if a is invertible | mathTools/field.py | invertible | ecuvelier/PPAT | python | def invertible(self, a):
' \n '
return (not (self.reduc(a) == self.zero())) |
def eucldiv(self, a, b):
' Return a/b and a%b\n a and b are of length d-1 where d is the degree of the irreducible polynomial\n '
zero = self.F.zero()
izero = self.zero()
d = self.deg
assert (not b.poly.iszero())
if a.poly.iszero():
return (izero, izero)
elif (a == b):
return (self.one(), izero)
A = a.poly.coef
B = b.poly.coef
(n, da) = a.poly.truedeg()
(m, db) = b.poly.truedeg()
if (da < db):
return (izero, a)
elif (da == db):
deg = max((d - 1), da)
rc = ([zero] * deg)
qc = ([zero] * deg)
q = (A[n] / B[m])
for i in range(1, deg):
rc[i] = (A[(n + i)] - (q * B[(m + i)]))
qc[(deg - 1)] = q
rp = polynom(self.F, rc)
qp = polynom(self.F, qc)
remain = ExtensionFieldElem(self, rp)
quotient = ExtensionFieldElem(self, qp)
return (quotient, remain)
else:
deg = max((d - 1), da)
p = (deg - da)
rc = ([zero] * deg)
qc = ([zero] * deg)
rc[(deg - da):] = A[n:]
pm = 0
while (((p + pm) + db) < (deg + 1)):
k = (((deg - (da - db)) - 1) + pm)
qc[k] = (rc[(p + pm)] / B[m])
for i in range(db):
rc[((i + p) + pm)] = (rc[((i + p) + pm)] - (qc[k] * B[(m + i)]))
pm = (pm + 1)
rp = polynom(self.F, rc)
qp = polynom(self.F, qc)
remain = ExtensionFieldElem(self, rp)
quotient = ExtensionFieldElem(self, qp)
return (quotient, remain) | -7,539,930,612,907,431,000 | Return a/b and a%b
a and b are of length d-1 where d is the degree of the irreducible polynomial | mathTools/field.py | eucldiv | ecuvelier/PPAT | python | def eucldiv(self, a, b):
' Return a/b and a%b\n a and b are of length d-1 where d is the degree of the irreducible polynomial\n '
zero = self.F.zero()
izero = self.zero()
d = self.deg
assert (not b.poly.iszero())
if a.poly.iszero():
return (izero, izero)
elif (a == b):
return (self.one(), izero)
A = a.poly.coef
B = b.poly.coef
(n, da) = a.poly.truedeg()
(m, db) = b.poly.truedeg()
if (da < db):
return (izero, a)
elif (da == db):
deg = max((d - 1), da)
rc = ([zero] * deg)
qc = ([zero] * deg)
q = (A[n] / B[m])
for i in range(1, deg):
rc[i] = (A[(n + i)] - (q * B[(m + i)]))
qc[(deg - 1)] = q
rp = polynom(self.F, rc)
qp = polynom(self.F, qc)
remain = ExtensionFieldElem(self, rp)
quotient = ExtensionFieldElem(self, qp)
return (quotient, remain)
else:
deg = max((d - 1), da)
p = (deg - da)
rc = ([zero] * deg)
qc = ([zero] * deg)
rc[(deg - da):] = A[n:]
pm = 0
while (((p + pm) + db) < (deg + 1)):
k = (((deg - (da - db)) - 1) + pm)
qc[k] = (rc[(p + pm)] / B[m])
for i in range(db):
rc[((i + p) + pm)] = (rc[((i + p) + pm)] - (qc[k] * B[(m + i)]))
pm = (pm + 1)
rp = polynom(self.F, rc)
qp = polynom(self.F, qc)
remain = ExtensionFieldElem(self, rp)
quotient = ExtensionFieldElem(self, qp)
return (quotient, remain) |
def reduc(self, a):
' Return a % self.irpoly\n The polynomial a = [a_0,...,a_n-1] is returned modulo the irreducible polynomial\n The reduced polynomial has length at most d-1 where d is the length\n of the irreducible polynomial\n '
assert (a.F.F == self.F)
if a.poly.iszero():
return self.zero()
elif (a.poly == self.irpoly):
return self.zero()
elif (a.deg < self.deg):
c = ([self.F.zero()] * ((self.deg - 1) - a.deg))
newacoef = (c + a.poly.coef)
newapoly = polynom(self.F, newacoef)
newaelem = ExtensionFieldElem(self, newapoly)
return newaelem
else:
(q, r) = self.eucldiv(a, ExtensionFieldElem(self, self.irpoly))
r = self.trunc(r)
return self.reduc(r) | -6,581,890,971,430,029,000 | Return a % self.irpoly
The polynomial a = [a_0,...,a_n-1] is returned modulo the irreducible polynomial
The reduced polynomial has length at most d-1 where d is the length
of the irreducible polynomial | mathTools/field.py | reduc | ecuvelier/PPAT | python | def reduc(self, a):
' Return a % self.irpoly\n The polynomial a = [a_0,...,a_n-1] is returned modulo the irreducible polynomial\n The reduced polynomial has length at most d-1 where d is the length\n of the irreducible polynomial\n '
assert (a.F.F == self.F)
if a.poly.iszero():
return self.zero()
elif (a.poly == self.irpoly):
return self.zero()
elif (a.deg < self.deg):
c = ([self.F.zero()] * ((self.deg - 1) - a.deg))
newacoef = (c + a.poly.coef)
newapoly = polynom(self.F, newacoef)
newaelem = ExtensionFieldElem(self, newapoly)
return newaelem
else:
(q, r) = self.eucldiv(a, ExtensionFieldElem(self, self.irpoly))
r = self.trunc(r)
return self.reduc(r) |
def reduc2(self, a):
' a is a list of length (d-1)*2-1 (polynomial length)\n this method returns the equivalent element of length d-1\n using the table of equivalences (build from the irreducible polynomial)\n in the function self.table()\n '
As = a[:(self.deg - 2)]
Ad = a[(self.deg - 2):]
b = list((dot(As, self.tabular) + Ad))
newapoly = polynom(self.F, b)
newa = ExtensionFieldElem(self, newapoly)
return newa | 8,261,773,372,050,492,000 | a is a list of length (d-1)*2-1 (polynomial length)
this method returns the equivalent element of length d-1
using the table of equivalences (build from the irreducible polynomial)
in the function self.table() | mathTools/field.py | reduc2 | ecuvelier/PPAT | python | def reduc2(self, a):
' a is a list of length (d-1)*2-1 (polynomial length)\n this method returns the equivalent element of length d-1\n using the table of equivalences (build from the irreducible polynomial)\n in the function self.table()\n '
As = a[:(self.deg - 2)]
Ad = a[(self.deg - 2):]
b = list((dot(As, self.tabular) + Ad))
newapoly = polynom(self.F, b)
newa = ExtensionFieldElem(self, newapoly)
return newa |
def trunc(self, a):
'Return an ExtensionFieldElem of length d-1 where d = deg(irpoly)\n '
d = self.deg
if (a.deg == (d - 1)):
return a
c = a.poly.coef[((a.deg - d) + 1):]
cp = polynom(self.F, c)
return ExtensionFieldElem(self, cp) | -675,842,933,097,962,800 | Return an ExtensionFieldElem of length d-1 where d = deg(irpoly) | mathTools/field.py | trunc | ecuvelier/PPAT | python | def trunc(self, a):
'\n '
d = self.deg
if (a.deg == (d - 1)):
return a
c = a.poly.coef[((a.deg - d) + 1):]
cp = polynom(self.F, c)
return ExtensionFieldElem(self, cp) |
def table(self):
' This method returns a table (usually) stored in self.tabular\n which is used to compute reduction after a multiplication\n between two elements\n '
d = self.deg
T = zeros(((d - 2), (d - 1)), dtype=object_)
Pc = self.irpoly.coef[1:]
for i in range(0, (d - 2)):
Qc = ([self.F.zero()] * ((2 * (d - 1)) - 1))
Qc[(i + 1):(i + d)] = Pc
Qp = polynom(self.F, Qc)
Qe = ExtensionFieldElem(self, Qp)
Q = self.reduc((- Qe))
T[i] = array(Q.poly.coef)
return T | 1,688,632,231,972,374,500 | This method returns a table (usually) stored in self.tabular
which is used to compute reduction after a multiplication
between two elements | mathTools/field.py | table | ecuvelier/PPAT | python | def table(self):
' This method returns a table (usually) stored in self.tabular\n which is used to compute reduction after a multiplication\n between two elements\n '
d = self.deg
T = zeros(((d - 2), (d - 1)), dtype=object_)
Pc = self.irpoly.coef[1:]
for i in range(0, (d - 2)):
Qc = ([self.F.zero()] * ((2 * (d - 1)) - 1))
Qc[(i + 1):(i + d)] = Pc
Qp = polynom(self.F, Qc)
Qe = ExtensionFieldElem(self, Qp)
Q = self.reduc((- Qe))
T[i] = array(Q.poly.coef)
return T |
def extendedeuclide(self, a, b):
'Return s,u,v such as s = ua + vb, s is the gcd of a and b\n This method is used to compute the inverse of a mod b (when s=1)\n '
one = self.one()
zero = self.zero()
s = a
u = one
v = zero
sp = b
up = zero
vp = one
while (not sp.poly.iszero()):
(q, r) = self.eucldiv(s, sp)
(s, u, v, sp, up, vp) = (sp, up, vp, r, (u - (up * q)), (v - (vp * q)))
return (self.reduc(s), self.reduc(u), self.reduc(v)) | 2,513,439,641,807,605,000 | Return s,u,v such as s = ua + vb, s is the gcd of a and b
This method is used to compute the inverse of a mod b (when s=1) | mathTools/field.py | extendedeuclide | ecuvelier/PPAT | python | def extendedeuclide(self, a, b):
'Return s,u,v such as s = ua + vb, s is the gcd of a and b\n This method is used to compute the inverse of a mod b (when s=1)\n '
one = self.one()
zero = self.zero()
s = a
u = one
v = zero
sp = b
up = zero
vp = one
while (not sp.poly.iszero()):
(q, r) = self.eucldiv(s, sp)
(s, u, v, sp, up, vp) = (sp, up, vp, r, (u - (up * q)), (v - (vp * q)))
return (self.reduc(s), self.reduc(u), self.reduc(v)) |
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