repository_name stringlengths 5 67 | func_path_in_repository stringlengths 4 234 | func_name stringlengths 0 314 | whole_func_string stringlengths 52 3.87M | language stringclasses 6
values | func_code_string stringlengths 52 3.87M | func_documentation_string stringlengths 1 47.2k | func_code_url stringlengths 85 339 |
|---|---|---|---|---|---|---|---|
threeML/astromodels | astromodels/functions/functions_2D.py | Latitude_galactic_diffuse.get_total_spatial_integral | def get_total_spatial_integral(self, z=None):
"""
Returns the total integral (for 2D functions) or the integral over the spatial components (for 3D functions).
needs to be implemented in subclasses.
:return: an array of values of the integral (same dimension as z).
"""
... | python | def get_total_spatial_integral(self, z=None):
"""
Returns the total integral (for 2D functions) or the integral over the spatial components (for 3D functions).
needs to be implemented in subclasses.
:return: an array of values of the integral (same dimension as z).
"""
... | Returns the total integral (for 2D functions) or the integral over the spatial components (for 3D functions).
needs to be implemented in subclasses.
:return: an array of values of the integral (same dimension as z). | https://github.com/threeML/astromodels/blob/9aac365a372f77603039533df9a6b694c1e360d5/astromodels/functions/functions_2D.py#L99-L115 |
threeML/astromodels | astromodels/core/tree.py | OldNode._get_child_from_path | def _get_child_from_path(self, path):
"""
Return a children below this level, starting from a path of the kind "this_level.something.something.name"
:param path: the key
:return: the child
"""
keys = path.split(".")
this_child = self
for key in keys:
... | python | def _get_child_from_path(self, path):
"""
Return a children below this level, starting from a path of the kind "this_level.something.something.name"
:param path: the key
:return: the child
"""
keys = path.split(".")
this_child = self
for key in keys:
... | Return a children below this level, starting from a path of the kind "this_level.something.something.name"
:param path: the key
:return: the child | https://github.com/threeML/astromodels/blob/9aac365a372f77603039533df9a6b694c1e360d5/astromodels/core/tree.py#L210-L232 |
threeML/astromodels | astromodels/core/tree.py | OldNode._find_instances | def _find_instances(self, cls):
"""
Find all the instances of cls below this node.
:return: a dictionary of instances of cls
"""
instances = collections.OrderedDict()
for child_name, child in self._children.iteritems():
if isinstance(child, cls):
... | python | def _find_instances(self, cls):
"""
Find all the instances of cls below this node.
:return: a dictionary of instances of cls
"""
instances = collections.OrderedDict()
for child_name, child in self._children.iteritems():
if isinstance(child, cls):
... | Find all the instances of cls below this node.
:return: a dictionary of instances of cls | https://github.com/threeML/astromodels/blob/9aac365a372f77603039533df9a6b694c1e360d5/astromodels/core/tree.py#L299-L329 |
threeML/astromodels | astromodels/core/model_parser.py | clone_model | def clone_model(model_instance):
"""
Returns a copy of the given model with all objects cloned. This is equivalent to saving the model to
a file and reload it, but it doesn't require writing or reading to/from disk. The original model is not touched.
:param model: model to be cloned
:return: a clon... | python | def clone_model(model_instance):
"""
Returns a copy of the given model with all objects cloned. This is equivalent to saving the model to
a file and reload it, but it doesn't require writing or reading to/from disk. The original model is not touched.
:param model: model to be cloned
:return: a clon... | Returns a copy of the given model with all objects cloned. This is equivalent to saving the model to
a file and reload it, but it doesn't require writing or reading to/from disk. The original model is not touched.
:param model: model to be cloned
:return: a cloned copy of the given model | https://github.com/threeML/astromodels/blob/9aac365a372f77603039533df9a6b694c1e360d5/astromodels/core/model_parser.py#L40-L53 |
threeML/astromodels | setup.py | sanitize_lib_name | def sanitize_lib_name(library_path):
"""
Get a fully-qualified library name, like /usr/lib/libgfortran.so.3.0, and returns the lib name needed to be
passed to the linker in the -l option (for example gfortran)
:param library_path:
:return:
"""
lib_name = os.path.basename(library_path)
... | python | def sanitize_lib_name(library_path):
"""
Get a fully-qualified library name, like /usr/lib/libgfortran.so.3.0, and returns the lib name needed to be
passed to the linker in the -l option (for example gfortran)
:param library_path:
:return:
"""
lib_name = os.path.basename(library_path)
... | Get a fully-qualified library name, like /usr/lib/libgfortran.so.3.0, and returns the lib name needed to be
passed to the linker in the -l option (for example gfortran)
:param library_path:
:return: | https://github.com/threeML/astromodels/blob/9aac365a372f77603039533df9a6b694c1e360d5/setup.py#L32-L51 |
threeML/astromodels | setup.py | find_library | def find_library(library_root, additional_places=None):
"""
Returns the name of the library without extension
:param library_root: root of the library to search, for example "cfitsio_" will match libcfitsio_1.2.3.4.so
:return: the name of the library found (NOTE: this is *not* the path), and a director... | python | def find_library(library_root, additional_places=None):
"""
Returns the name of the library without extension
:param library_root: root of the library to search, for example "cfitsio_" will match libcfitsio_1.2.3.4.so
:return: the name of the library found (NOTE: this is *not* the path), and a director... | Returns the name of the library without extension
:param library_root: root of the library to search, for example "cfitsio_" will match libcfitsio_1.2.3.4.so
:return: the name of the library found (NOTE: this is *not* the path), and a directory path if the library is not
in the system paths (and None other... | https://github.com/threeML/astromodels/blob/9aac365a372f77603039533df9a6b694c1e360d5/setup.py#L54-L172 |
threeML/astromodels | astromodels/utils/table.py | dict_to_table | def dict_to_table(dictionary, list_of_keys=None):
"""
Return a table representing the dictionary.
:param dictionary: the dictionary to represent
:param list_of_keys: optionally, only the keys in this list will be inserted in the table
:return: a Table instance
"""
# assert len(dictionary.v... | python | def dict_to_table(dictionary, list_of_keys=None):
"""
Return a table representing the dictionary.
:param dictionary: the dictionary to represent
:param list_of_keys: optionally, only the keys in this list will be inserted in the table
:return: a Table instance
"""
# assert len(dictionary.v... | Return a table representing the dictionary.
:param dictionary: the dictionary to represent
:param list_of_keys: optionally, only the keys in this list will be inserted in the table
:return: a Table instance | https://github.com/threeML/astromodels/blob/9aac365a372f77603039533df9a6b694c1e360d5/astromodels/utils/table.py#L6-L49 |
threeML/astromodels | astromodels/utils/table.py | Table._base_repr_ | def _base_repr_(self, html=False, show_name=True, **kwargs):
"""
Override the method in the astropy.Table class
to avoid displaying the description, and the format
of the columns
"""
table_id = 'table{id}'.format(id=id(self))
data_lines, outs = self.formatter._p... | python | def _base_repr_(self, html=False, show_name=True, **kwargs):
"""
Override the method in the astropy.Table class
to avoid displaying the description, and the format
of the columns
"""
table_id = 'table{id}'.format(id=id(self))
data_lines, outs = self.formatter._p... | Override the method in the astropy.Table class
to avoid displaying the description, and the format
of the columns | https://github.com/threeML/astromodels/blob/9aac365a372f77603039533df9a6b694c1e360d5/astromodels/utils/table.py#L60-L78 |
eamigo86/graphene-django-extras | graphene_django_extras/views.py | ExtraGraphQLView.fetch_cache_key | def fetch_cache_key(request):
""" Returns a hashed cache key. """
m = hashlib.md5()
m.update(request.body)
return m.hexdigest() | python | def fetch_cache_key(request):
""" Returns a hashed cache key. """
m = hashlib.md5()
m.update(request.body)
return m.hexdigest() | Returns a hashed cache key. | https://github.com/eamigo86/graphene-django-extras/blob/b27fd6b5128f6b6a500a8b7a497d76be72d6a232/graphene_django_extras/views.py#L42-L47 |
eamigo86/graphene-django-extras | graphene_django_extras/views.py | ExtraGraphQLView.dispatch | def dispatch(self, request, *args, **kwargs):
""" Fetches queried data from graphql and returns cached & hashed key. """
if not graphql_api_settings.CACHE_ACTIVE:
return self.super_call(request, *args, **kwargs)
cache = caches["default"]
operation_ast = self.get_operation_as... | python | def dispatch(self, request, *args, **kwargs):
""" Fetches queried data from graphql and returns cached & hashed key. """
if not graphql_api_settings.CACHE_ACTIVE:
return self.super_call(request, *args, **kwargs)
cache = caches["default"]
operation_ast = self.get_operation_as... | Fetches queried data from graphql and returns cached & hashed key. | https://github.com/eamigo86/graphene-django-extras/blob/b27fd6b5128f6b6a500a8b7a497d76be72d6a232/graphene_django_extras/views.py#L54-L74 |
eamigo86/graphene-django-extras | graphene_django_extras/directives/date.py | _parse | def _parse(partial_dt):
"""
parse a partial datetime object to a complete datetime object
"""
dt = None
try:
if isinstance(partial_dt, datetime):
dt = partial_dt
if isinstance(partial_dt, date):
dt = _combine_date_time(partial_dt, time(0, 0, 0))
if isi... | python | def _parse(partial_dt):
"""
parse a partial datetime object to a complete datetime object
"""
dt = None
try:
if isinstance(partial_dt, datetime):
dt = partial_dt
if isinstance(partial_dt, date):
dt = _combine_date_time(partial_dt, time(0, 0, 0))
if isi... | parse a partial datetime object to a complete datetime object | https://github.com/eamigo86/graphene-django-extras/blob/b27fd6b5128f6b6a500a8b7a497d76be72d6a232/graphene_django_extras/directives/date.py#L73-L94 |
eamigo86/graphene-django-extras | graphene_django_extras/utils.py | get_obj | def get_obj(app_label, model_name, object_id):
"""
Function used to get a object
:param app_label: A valid Django Model or a string with format: <app_label>.<model_name>
:param model_name: Key into kwargs that contains de data: new_person
:param object_id:
:return: instance
"""
try:
... | python | def get_obj(app_label, model_name, object_id):
"""
Function used to get a object
:param app_label: A valid Django Model or a string with format: <app_label>.<model_name>
:param model_name: Key into kwargs that contains de data: new_person
:param object_id:
:return: instance
"""
try:
... | Function used to get a object
:param app_label: A valid Django Model or a string with format: <app_label>.<model_name>
:param model_name: Key into kwargs that contains de data: new_person
:param object_id:
:return: instance | https://github.com/eamigo86/graphene-django-extras/blob/b27fd6b5128f6b6a500a8b7a497d76be72d6a232/graphene_django_extras/utils.py#L104-L130 |
eamigo86/graphene-django-extras | graphene_django_extras/utils.py | create_obj | def create_obj(django_model, new_obj_key=None, *args, **kwargs):
"""
Function used by my on traditional Mutations to create objs
:param django_model: A valid Django Model or a string with format:
<app_label>.<model_name>
:param new_obj_key: Key into kwargs that contains de data: new_person
:para... | python | def create_obj(django_model, new_obj_key=None, *args, **kwargs):
"""
Function used by my on traditional Mutations to create objs
:param django_model: A valid Django Model or a string with format:
<app_label>.<model_name>
:param new_obj_key: Key into kwargs that contains de data: new_person
:para... | Function used by my on traditional Mutations to create objs
:param django_model: A valid Django Model or a string with format:
<app_label>.<model_name>
:param new_obj_key: Key into kwargs that contains de data: new_person
:param args:
:param kwargs: Dict with model attributes values
:return: ins... | https://github.com/eamigo86/graphene-django-extras/blob/b27fd6b5128f6b6a500a8b7a497d76be72d6a232/graphene_django_extras/utils.py#L133-L165 |
eamigo86/graphene-django-extras | graphene_django_extras/utils.py | clean_dict | def clean_dict(d):
"""
Remove all empty fields in a nested dict
"""
if not isinstance(d, (dict, list)):
return d
if isinstance(d, list):
return [v for v in (clean_dict(v) for v in d) if v]
return OrderedDict(
[(k, v) for k, v in ((k, clean_dict(v)) for k, v in list(d... | python | def clean_dict(d):
"""
Remove all empty fields in a nested dict
"""
if not isinstance(d, (dict, list)):
return d
if isinstance(d, list):
return [v for v in (clean_dict(v) for v in d) if v]
return OrderedDict(
[(k, v) for k, v in ((k, clean_dict(v)) for k, v in list(d... | Remove all empty fields in a nested dict | https://github.com/eamigo86/graphene-django-extras/blob/b27fd6b5128f6b6a500a8b7a497d76be72d6a232/graphene_django_extras/utils.py#L168-L179 |
eamigo86/graphene-django-extras | graphene_django_extras/utils.py | _get_queryset | def _get_queryset(klass):
"""
Returns a QuerySet from a Model, Manager, or QuerySet. Created to make
get_object_or_404 and get_list_or_404 more DRY.
Raises a ValueError if klass is not a Model, Manager, or QuerySet.
"""
if isinstance(klass, QuerySet):
return klass
elif isinstance(kl... | python | def _get_queryset(klass):
"""
Returns a QuerySet from a Model, Manager, or QuerySet. Created to make
get_object_or_404 and get_list_or_404 more DRY.
Raises a ValueError if klass is not a Model, Manager, or QuerySet.
"""
if isinstance(klass, QuerySet):
return klass
elif isinstance(kl... | Returns a QuerySet from a Model, Manager, or QuerySet. Created to make
get_object_or_404 and get_list_or_404 more DRY.
Raises a ValueError if klass is not a Model, Manager, or QuerySet. | https://github.com/eamigo86/graphene-django-extras/blob/b27fd6b5128f6b6a500a8b7a497d76be72d6a232/graphene_django_extras/utils.py#L224-L246 |
eamigo86/graphene-django-extras | graphene_django_extras/utils.py | get_Object_or_None | def get_Object_or_None(klass, *args, **kwargs):
"""
Uses get() to return an object, or None if the object does not exist.
klass may be a Model, Manager, or QuerySet object. All other passed
arguments and keyword arguments are used in the get() query.
Note: Like with get(), an MultipleObjectsReturn... | python | def get_Object_or_None(klass, *args, **kwargs):
"""
Uses get() to return an object, or None if the object does not exist.
klass may be a Model, Manager, or QuerySet object. All other passed
arguments and keyword arguments are used in the get() query.
Note: Like with get(), an MultipleObjectsReturn... | Uses get() to return an object, or None if the object does not exist.
klass may be a Model, Manager, or QuerySet object. All other passed
arguments and keyword arguments are used in the get() query.
Note: Like with get(), an MultipleObjectsReturned will be raised
if more than one object is found.
... | https://github.com/eamigo86/graphene-django-extras/blob/b27fd6b5128f6b6a500a8b7a497d76be72d6a232/graphene_django_extras/utils.py#L249-L267 |
Proteus-tech/tormor | tormor/schema.py | find_schema_paths | def find_schema_paths(schema_files_path=DEFAULT_SCHEMA_FILES_PATH):
"""Searches the locations in the `SCHEMA_FILES_PATH` to
try to find where the schema SQL files are located.
"""
paths = []
for path in schema_files_path:
if os.path.isdir(path):
paths.append(path)
if paths:
... | python | def find_schema_paths(schema_files_path=DEFAULT_SCHEMA_FILES_PATH):
"""Searches the locations in the `SCHEMA_FILES_PATH` to
try to find where the schema SQL files are located.
"""
paths = []
for path in schema_files_path:
if os.path.isdir(path):
paths.append(path)
if paths:
... | Searches the locations in the `SCHEMA_FILES_PATH` to
try to find where the schema SQL files are located. | https://github.com/Proteus-tech/tormor/blob/3083b0cd2b9a4d21b20dfd5c27678b23660548d7/tormor/schema.py#L41-L51 |
Proteus-tech/tormor | tormor/connection.py | Connection.execute | def execute(self, cmd, *args, **kwargs):
""" Execute the SQL command and return the data rows as tuples
"""
self.cursor.execute(cmd, *args, **kwargs) | python | def execute(self, cmd, *args, **kwargs):
""" Execute the SQL command and return the data rows as tuples
"""
self.cursor.execute(cmd, *args, **kwargs) | Execute the SQL command and return the data rows as tuples | https://github.com/Proteus-tech/tormor/blob/3083b0cd2b9a4d21b20dfd5c27678b23660548d7/tormor/connection.py#L66-L69 |
Proteus-tech/tormor | tormor/connection.py | Connection.select | def select(self, cmd, *args, **kwargs):
""" Execute the SQL command and return the data rows as tuples
"""
self.cursor.execute(cmd, *args, **kwargs)
return self.cursor.fetchall() | python | def select(self, cmd, *args, **kwargs):
""" Execute the SQL command and return the data rows as tuples
"""
self.cursor.execute(cmd, *args, **kwargs)
return self.cursor.fetchall() | Execute the SQL command and return the data rows as tuples | https://github.com/Proteus-tech/tormor/blob/3083b0cd2b9a4d21b20dfd5c27678b23660548d7/tormor/connection.py#L71-L75 |
plivo/sharq-server | runner.py | run | def run():
"""Exposes a CLI to configure the SharQ Server and runs the server."""
# create a arg parser and configure it.
parser = argparse.ArgumentParser(description='SharQ Server.')
parser.add_argument('-c', '--config', action='store', required=True,
help='Absolute path of the ... | python | def run():
"""Exposes a CLI to configure the SharQ Server and runs the server."""
# create a arg parser and configure it.
parser = argparse.ArgumentParser(description='SharQ Server.')
parser.add_argument('-c', '--config', action='store', required=True,
help='Absolute path of the ... | Exposes a CLI to configure the SharQ Server and runs the server. | https://github.com/plivo/sharq-server/blob/9f4c50eb5ee28d1084591febc4a3a34d7ffd0556/runner.py#L38-L97 |
plivo/sharq-server | sharq_server/server.py | setup_server | def setup_server(config_path):
"""Configure SharQ server, start the requeue loop
and return the server."""
# configure the SharQ server
server = SharQServer(config_path)
# start the requeue loop
gevent.spawn(server.requeue)
return server | python | def setup_server(config_path):
"""Configure SharQ server, start the requeue loop
and return the server."""
# configure the SharQ server
server = SharQServer(config_path)
# start the requeue loop
gevent.spawn(server.requeue)
return server | Configure SharQ server, start the requeue loop
and return the server. | https://github.com/plivo/sharq-server/blob/9f4c50eb5ee28d1084591febc4a3a34d7ffd0556/sharq_server/server.py#L204-L212 |
plivo/sharq-server | sharq_server/server.py | SharQServer.requeue | def requeue(self):
"""Loop endlessly and requeue expired jobs."""
job_requeue_interval = float(
self.config.get('sharq', 'job_requeue_interval'))
while True:
self.sq.requeue()
gevent.sleep(job_requeue_interval / 1000.00) | python | def requeue(self):
"""Loop endlessly and requeue expired jobs."""
job_requeue_interval = float(
self.config.get('sharq', 'job_requeue_interval'))
while True:
self.sq.requeue()
gevent.sleep(job_requeue_interval / 1000.00) | Loop endlessly and requeue expired jobs. | https://github.com/plivo/sharq-server/blob/9f4c50eb5ee28d1084591febc4a3a34d7ffd0556/sharq_server/server.py#L57-L63 |
plivo/sharq-server | sharq_server/server.py | SharQServer._view_enqueue | def _view_enqueue(self, queue_type, queue_id):
"""Enqueues a job into SharQ."""
response = {
'status': 'failure'
}
try:
request_data = json.loads(request.data)
except Exception, e:
response['message'] = e.message
return jsonify(**re... | python | def _view_enqueue(self, queue_type, queue_id):
"""Enqueues a job into SharQ."""
response = {
'status': 'failure'
}
try:
request_data = json.loads(request.data)
except Exception, e:
response['message'] = e.message
return jsonify(**re... | Enqueues a job into SharQ. | https://github.com/plivo/sharq-server/blob/9f4c50eb5ee28d1084591febc4a3a34d7ffd0556/sharq_server/server.py#L69-L91 |
plivo/sharq-server | sharq_server/server.py | SharQServer._view_dequeue | def _view_dequeue(self, queue_type):
"""Dequeues a job from SharQ."""
response = {
'status': 'failure'
}
request_data = {
'queue_type': queue_type
}
try:
response = self.sq.dequeue(**request_data)
if response['status'] == '... | python | def _view_dequeue(self, queue_type):
"""Dequeues a job from SharQ."""
response = {
'status': 'failure'
}
request_data = {
'queue_type': queue_type
}
try:
response = self.sq.dequeue(**request_data)
if response['status'] == '... | Dequeues a job from SharQ. | https://github.com/plivo/sharq-server/blob/9f4c50eb5ee28d1084591febc4a3a34d7ffd0556/sharq_server/server.py#L93-L110 |
plivo/sharq-server | sharq_server/server.py | SharQServer._view_finish | def _view_finish(self, queue_type, queue_id, job_id):
"""Marks a job as finished in SharQ."""
response = {
'status': 'failure'
}
request_data = {
'queue_type': queue_type,
'queue_id': queue_id,
'job_id': job_id
}
try:
... | python | def _view_finish(self, queue_type, queue_id, job_id):
"""Marks a job as finished in SharQ."""
response = {
'status': 'failure'
}
request_data = {
'queue_type': queue_type,
'queue_id': queue_id,
'job_id': job_id
}
try:
... | Marks a job as finished in SharQ. | https://github.com/plivo/sharq-server/blob/9f4c50eb5ee28d1084591febc4a3a34d7ffd0556/sharq_server/server.py#L112-L131 |
plivo/sharq-server | sharq_server/server.py | SharQServer._view_interval | def _view_interval(self, queue_type, queue_id):
"""Updates the queue interval in SharQ."""
response = {
'status': 'failure'
}
try:
request_data = json.loads(request.data)
interval = request_data['interval']
except Exception, e:
resp... | python | def _view_interval(self, queue_type, queue_id):
"""Updates the queue interval in SharQ."""
response = {
'status': 'failure'
}
try:
request_data = json.loads(request.data)
interval = request_data['interval']
except Exception, e:
resp... | Updates the queue interval in SharQ. | https://github.com/plivo/sharq-server/blob/9f4c50eb5ee28d1084591febc4a3a34d7ffd0556/sharq_server/server.py#L133-L159 |
plivo/sharq-server | sharq_server/server.py | SharQServer._view_metrics | def _view_metrics(self, queue_type, queue_id):
"""Gets SharQ metrics based on the params."""
response = {
'status': 'failure'
}
request_data = {}
if queue_type:
request_data['queue_type'] = queue_type
if queue_id:
request_data['queue_id... | python | def _view_metrics(self, queue_type, queue_id):
"""Gets SharQ metrics based on the params."""
response = {
'status': 'failure'
}
request_data = {}
if queue_type:
request_data['queue_type'] = queue_type
if queue_id:
request_data['queue_id... | Gets SharQ metrics based on the params. | https://github.com/plivo/sharq-server/blob/9f4c50eb5ee28d1084591febc4a3a34d7ffd0556/sharq_server/server.py#L161-L178 |
plivo/sharq-server | sharq_server/server.py | SharQServer._view_clear_queue | def _view_clear_queue(self, queue_type, queue_id):
"""remove queueu from SharQ based on the queue_type and queue_id."""
response = {
'status': 'failure'
}
try:
request_data = json.loads(request.data)
except Exception, e:
response['message'] = e... | python | def _view_clear_queue(self, queue_type, queue_id):
"""remove queueu from SharQ based on the queue_type and queue_id."""
response = {
'status': 'failure'
}
try:
request_data = json.loads(request.data)
except Exception, e:
response['message'] = e... | remove queueu from SharQ based on the queue_type and queue_id. | https://github.com/plivo/sharq-server/blob/9f4c50eb5ee28d1084591febc4a3a34d7ffd0556/sharq_server/server.py#L180-L201 |
depop/python-automock | automock/base.py | _get_from_path | def _get_from_path(import_path):
# type: (str) -> Callable
"""
Kwargs:
import_path: full import path (to a mock factory function)
Returns:
(the mock factory function)
"""
module_name, obj_name = import_path.rsplit('.', 1)
module = import_module(module_name)
return getatt... | python | def _get_from_path(import_path):
# type: (str) -> Callable
"""
Kwargs:
import_path: full import path (to a mock factory function)
Returns:
(the mock factory function)
"""
module_name, obj_name = import_path.rsplit('.', 1)
module = import_module(module_name)
return getatt... | Kwargs:
import_path: full import path (to a mock factory function)
Returns:
(the mock factory function) | https://github.com/depop/python-automock/blob/8a02acecd9265c8f9a00d7b8e097cae87cdf28bd/automock/base.py#L32-L43 |
depop/python-automock | automock/base.py | register | def register(func_path, factory=mock.MagicMock):
# type: (str, Callable) -> Callable
"""
Kwargs:
func_path: import path to mock (as you would give to `mock.patch`)
factory: function that returns a mock for the patched func
Returns:
(decorator)
Usage:
automock.regis... | python | def register(func_path, factory=mock.MagicMock):
# type: (str, Callable) -> Callable
"""
Kwargs:
func_path: import path to mock (as you would give to `mock.patch`)
factory: function that returns a mock for the patched func
Returns:
(decorator)
Usage:
automock.regis... | Kwargs:
func_path: import path to mock (as you would give to `mock.patch`)
factory: function that returns a mock for the patched func
Returns:
(decorator)
Usage:
automock.register('path.to.func.to.mock') # default MagicMock
automock.register('path.to.func.to.mock', Cu... | https://github.com/depop/python-automock/blob/8a02acecd9265c8f9a00d7b8e097cae87cdf28bd/automock/base.py#L46-L72 |
depop/python-automock | automock/base.py | start_patching | def start_patching(name=None):
# type: (Optional[str]) -> None
"""
Initiate mocking of the functions listed in `_factory_map`.
For this to work reliably all mocked helper functions should be imported
and used like this:
import dp_paypal.client as paypal
res = paypal.do_paypal_expre... | python | def start_patching(name=None):
# type: (Optional[str]) -> None
"""
Initiate mocking of the functions listed in `_factory_map`.
For this to work reliably all mocked helper functions should be imported
and used like this:
import dp_paypal.client as paypal
res = paypal.do_paypal_expre... | Initiate mocking of the functions listed in `_factory_map`.
For this to work reliably all mocked helper functions should be imported
and used like this:
import dp_paypal.client as paypal
res = paypal.do_paypal_express_checkout(...)
(i.e. don't use `from dp_paypal.client import x` import s... | https://github.com/depop/python-automock/blob/8a02acecd9265c8f9a00d7b8e097cae87cdf28bd/automock/base.py#L88-L121 |
depop/python-automock | automock/base.py | stop_patching | def stop_patching(name=None):
# type: (Optional[str]) -> None
"""
Finish the mocking initiated by `start_patching`
Kwargs:
name (Optional[str]): if given, only unpatch the specified path, else all
defined default mocks
"""
global _patchers, _mocks
if not _patchers:
... | python | def stop_patching(name=None):
# type: (Optional[str]) -> None
"""
Finish the mocking initiated by `start_patching`
Kwargs:
name (Optional[str]): if given, only unpatch the specified path, else all
defined default mocks
"""
global _patchers, _mocks
if not _patchers:
... | Finish the mocking initiated by `start_patching`
Kwargs:
name (Optional[str]): if given, only unpatch the specified path, else all
defined default mocks | https://github.com/depop/python-automock/blob/8a02acecd9265c8f9a00d7b8e097cae87cdf28bd/automock/base.py#L124-L145 |
matousc89/padasip | padasip/preprocess/standardize_back.py | standardize_back | def standardize_back(xs, offset, scale):
"""
This is function for de-standarization of input series.
**Args:**
* `xs` : standardized input (1 dimensional array)
* `offset` : offset to add (float).
* `scale` : scale (float).
**Returns:**
* `x` : original (destandardised) ser... | python | def standardize_back(xs, offset, scale):
"""
This is function for de-standarization of input series.
**Args:**
* `xs` : standardized input (1 dimensional array)
* `offset` : offset to add (float).
* `scale` : scale (float).
**Returns:**
* `x` : original (destandardised) ser... | This is function for de-standarization of input series.
**Args:**
* `xs` : standardized input (1 dimensional array)
* `offset` : offset to add (float).
* `scale` : scale (float).
**Returns:**
* `x` : original (destandardised) series | https://github.com/matousc89/padasip/blob/c969eadd7fa181a84da0554d737fc13c6450d16f/padasip/preprocess/standardize_back.py#L33-L62 |
matousc89/padasip | padasip/preprocess/standardize.py | standardize | def standardize(x, offset=None, scale=None):
"""
This is function for standarization of input series.
**Args:**
* `x` : series (1 dimensional array)
**Kwargs:**
* `offset` : offset to remove (float). If not given, \
the mean value of `x` is used.
* `scale` : scale (float). If... | python | def standardize(x, offset=None, scale=None):
"""
This is function for standarization of input series.
**Args:**
* `x` : series (1 dimensional array)
**Kwargs:**
* `offset` : offset to remove (float). If not given, \
the mean value of `x` is used.
* `scale` : scale (float). If... | This is function for standarization of input series.
**Args:**
* `x` : series (1 dimensional array)
**Kwargs:**
* `offset` : offset to remove (float). If not given, \
the mean value of `x` is used.
* `scale` : scale (float). If not given, \
the standard deviation of `x` is used.... | https://github.com/matousc89/padasip/blob/c969eadd7fa181a84da0554d737fc13c6450d16f/padasip/preprocess/standardize.py#L62-L100 |
matousc89/padasip | padasip/preprocess/input_from_history.py | input_from_history | def input_from_history(a, n, bias=False):
"""
This is function for creation of input matrix.
**Args:**
* `a` : series (1 dimensional array)
* `n` : size of input matrix row (int). It means how many samples \
of previous history you want to use \
as the filter input. It also repres... | python | def input_from_history(a, n, bias=False):
"""
This is function for creation of input matrix.
**Args:**
* `a` : series (1 dimensional array)
* `n` : size of input matrix row (int). It means how many samples \
of previous history you want to use \
as the filter input. It also repres... | This is function for creation of input matrix.
**Args:**
* `a` : series (1 dimensional array)
* `n` : size of input matrix row (int). It means how many samples \
of previous history you want to use \
as the filter input. It also represents the filter length.
**Kwargs:**
* `bias`... | https://github.com/matousc89/padasip/blob/c969eadd7fa181a84da0554d737fc13c6450d16f/padasip/preprocess/input_from_history.py#L34-L72 |
matousc89/padasip | padasip/filters/base_filter.py | AdaptiveFilter.init_weights | def init_weights(self, w, n=-1):
"""
This function initialises the adaptive weights of the filter.
**Args:**
* `w` : initial weights of filter. Possible values are:
* array with initial weights (1 dimensional array) of filter size
* "random" : ... | python | def init_weights(self, w, n=-1):
"""
This function initialises the adaptive weights of the filter.
**Args:**
* `w` : initial weights of filter. Possible values are:
* array with initial weights (1 dimensional array) of filter size
* "random" : ... | This function initialises the adaptive weights of the filter.
**Args:**
* `w` : initial weights of filter. Possible values are:
* array with initial weights (1 dimensional array) of filter size
* "random" : create random weights
* "zer... | https://github.com/matousc89/padasip/blob/c969eadd7fa181a84da0554d737fc13c6450d16f/padasip/filters/base_filter.py#L16-L56 |
matousc89/padasip | padasip/filters/base_filter.py | AdaptiveFilter.predict | def predict(self, x):
"""
This function calculates the new output value `y` from input array `x`.
**Args:**
* `x` : input vector (1 dimension array) in length of filter.
**Returns:**
* `y` : output value (float) calculated from input array.
"""
y = np... | python | def predict(self, x):
"""
This function calculates the new output value `y` from input array `x`.
**Args:**
* `x` : input vector (1 dimension array) in length of filter.
**Returns:**
* `y` : output value (float) calculated from input array.
"""
y = np... | This function calculates the new output value `y` from input array `x`.
**Args:**
* `x` : input vector (1 dimension array) in length of filter.
**Returns:**
* `y` : output value (float) calculated from input array. | https://github.com/matousc89/padasip/blob/c969eadd7fa181a84da0554d737fc13c6450d16f/padasip/filters/base_filter.py#L58-L72 |
matousc89/padasip | padasip/filters/base_filter.py | AdaptiveFilter.pretrained_run | def pretrained_run(self, d, x, ntrain=0.5, epochs=1):
"""
This function sacrifices part of the data for few epochs of learning.
**Args:**
* `d` : desired value (1 dimensional array)
* `x` : input matrix (2-dimensional array). Rows are samples,
columns are inp... | python | def pretrained_run(self, d, x, ntrain=0.5, epochs=1):
"""
This function sacrifices part of the data for few epochs of learning.
**Args:**
* `d` : desired value (1 dimensional array)
* `x` : input matrix (2-dimensional array). Rows are samples,
columns are inp... | This function sacrifices part of the data for few epochs of learning.
**Args:**
* `d` : desired value (1 dimensional array)
* `x` : input matrix (2-dimensional array). Rows are samples,
columns are input arrays.
**Kwargs:**
* `ntrain` : train to test... | https://github.com/matousc89/padasip/blob/c969eadd7fa181a84da0554d737fc13c6450d16f/padasip/filters/base_filter.py#L74-L110 |
matousc89/padasip | padasip/filters/base_filter.py | AdaptiveFilter.explore_learning | def explore_learning(self, d, x, mu_start=0, mu_end=1., steps=100,
ntrain=0.5, epochs=1, criteria="MSE", target_w=False):
"""
Test what learning rate is the best.
**Args:**
* `d` : desired value (1 dimensional array)
* `x` : input matrix (2-dimensional array). Rows... | python | def explore_learning(self, d, x, mu_start=0, mu_end=1., steps=100,
ntrain=0.5, epochs=1, criteria="MSE", target_w=False):
"""
Test what learning rate is the best.
**Args:**
* `d` : desired value (1 dimensional array)
* `x` : input matrix (2-dimensional array). Rows... | Test what learning rate is the best.
**Args:**
* `d` : desired value (1 dimensional array)
* `x` : input matrix (2-dimensional array). Rows are samples,
columns are input arrays.
**Kwargs:**
* `mu_start` : starting learning rate (float)
... | https://github.com/matousc89/padasip/blob/c969eadd7fa181a84da0554d737fc13c6450d16f/padasip/filters/base_filter.py#L112-L168 |
matousc89/padasip | padasip/filters/base_filter.py | AdaptiveFilter.check_float_param | def check_float_param(self, param, low, high, name):
"""
Check if the value of the given parameter is in the given range
and a float.
Designed for testing parameters like `mu` and `eps`.
To pass this function the variable `param` must be able to be converted
into a float ... | python | def check_float_param(self, param, low, high, name):
"""
Check if the value of the given parameter is in the given range
and a float.
Designed for testing parameters like `mu` and `eps`.
To pass this function the variable `param` must be able to be converted
into a float ... | Check if the value of the given parameter is in the given range
and a float.
Designed for testing parameters like `mu` and `eps`.
To pass this function the variable `param` must be able to be converted
into a float with a value between `low` and `high`.
**Args:**
* `par... | https://github.com/matousc89/padasip/blob/c969eadd7fa181a84da0554d737fc13c6450d16f/padasip/filters/base_filter.py#L170-L203 |
matousc89/padasip | padasip/filters/base_filter.py | AdaptiveFilter.check_int | def check_int(self, param, error_msg):
"""
This function check if the parameter is int.
If yes, the function returns the parameter,
if not, it raises error message.
**Args:**
* `param` : parameter to check (int or similar)
* `error_ms` : lowest ... | python | def check_int(self, param, error_msg):
"""
This function check if the parameter is int.
If yes, the function returns the parameter,
if not, it raises error message.
**Args:**
* `param` : parameter to check (int or similar)
* `error_ms` : lowest ... | This function check if the parameter is int.
If yes, the function returns the parameter,
if not, it raises error message.
**Args:**
* `param` : parameter to check (int or similar)
* `error_ms` : lowest allowed value (int), or None
**Ret... | https://github.com/matousc89/padasip/blob/c969eadd7fa181a84da0554d737fc13c6450d16f/padasip/filters/base_filter.py#L205-L224 |
matousc89/padasip | padasip/filters/base_filter.py | AdaptiveFilter.check_int_param | def check_int_param(self, param, low, high, name):
"""
Check if the value of the given parameter is in the given range
and an int.
Designed for testing parameters like `mu` and `eps`.
To pass this function the variable `param` must be able to be converted
into a float wit... | python | def check_int_param(self, param, low, high, name):
"""
Check if the value of the given parameter is in the given range
and an int.
Designed for testing parameters like `mu` and `eps`.
To pass this function the variable `param` must be able to be converted
into a float wit... | Check if the value of the given parameter is in the given range
and an int.
Designed for testing parameters like `mu` and `eps`.
To pass this function the variable `param` must be able to be converted
into a float with a value between `low` and `high`.
**Args:**
* `para... | https://github.com/matousc89/padasip/blob/c969eadd7fa181a84da0554d737fc13c6450d16f/padasip/filters/base_filter.py#L226-L259 |
matousc89/padasip | padasip/filters/nlmf.py | FilterNLMF.adapt | def adapt(self, d, x):
"""
Adapt weights according one desired value and its input.
**Args:**
* `d` : desired value (float)
* `x` : input array (1-dimensional array)
"""
y = np.dot(self.w, x)
e = d - y
nu = self.mu / (self.eps + np.dot(x, x))
... | python | def adapt(self, d, x):
"""
Adapt weights according one desired value and its input.
**Args:**
* `d` : desired value (float)
* `x` : input array (1-dimensional array)
"""
y = np.dot(self.w, x)
e = d - y
nu = self.mu / (self.eps + np.dot(x, x))
... | Adapt weights according one desired value and its input.
**Args:**
* `d` : desired value (float)
* `x` : input array (1-dimensional array) | https://github.com/matousc89/padasip/blob/c969eadd7fa181a84da0554d737fc13c6450d16f/padasip/filters/nlmf.py#L123-L136 |
matousc89/padasip | padasip/filters/nlmf.py | FilterNLMF.run | def run(self, d, x):
"""
This function filters multiple samples in a row.
**Args:**
* `d` : desired value (1 dimensional array)
* `x` : input matrix (2-dimensional array). Rows are samples,
columns are input arrays.
**Returns:**
* `y` : output value... | python | def run(self, d, x):
"""
This function filters multiple samples in a row.
**Args:**
* `d` : desired value (1 dimensional array)
* `x` : input matrix (2-dimensional array). Rows are samples,
columns are input arrays.
**Returns:**
* `y` : output value... | This function filters multiple samples in a row.
**Args:**
* `d` : desired value (1 dimensional array)
* `x` : input matrix (2-dimensional array). Rows are samples,
columns are input arrays.
**Returns:**
* `y` : output value (1 dimensional array).
The siz... | https://github.com/matousc89/padasip/blob/c969eadd7fa181a84da0554d737fc13c6450d16f/padasip/filters/nlmf.py#L138-L183 |
matousc89/padasip | padasip/misc/error_evaluation.py | get_valid_error | def get_valid_error(x1, x2=-1):
"""
Function that validates:
* x1 is possible to convert to numpy array
* x2 is possible to convert to numpy array (if exists)
* x1 and x2 have the same length (if both exist)
"""
# just error
if type(x2) == int and x2 == -1:
try:
... | python | def get_valid_error(x1, x2=-1):
"""
Function that validates:
* x1 is possible to convert to numpy array
* x2 is possible to convert to numpy array (if exists)
* x1 and x2 have the same length (if both exist)
"""
# just error
if type(x2) == int and x2 == -1:
try:
... | Function that validates:
* x1 is possible to convert to numpy array
* x2 is possible to convert to numpy array (if exists)
* x1 and x2 have the same length (if both exist) | https://github.com/matousc89/padasip/blob/c969eadd7fa181a84da0554d737fc13c6450d16f/padasip/misc/error_evaluation.py#L99-L125 |
matousc89/padasip | padasip/misc/error_evaluation.py | logSE | def logSE(x1, x2=-1):
"""
10 * log10(e**2)
This function accepts two series of data or directly
one series with error.
**Args:**
* `x1` - first data series or error (1d array)
**Kwargs:**
* `x2` - second series (1d array) if first series was not error directly,\\
then thi... | python | def logSE(x1, x2=-1):
"""
10 * log10(e**2)
This function accepts two series of data or directly
one series with error.
**Args:**
* `x1` - first data series or error (1d array)
**Kwargs:**
* `x2` - second series (1d array) if first series was not error directly,\\
then thi... | 10 * log10(e**2)
This function accepts two series of data or directly
one series with error.
**Args:**
* `x1` - first data series or error (1d array)
**Kwargs:**
* `x2` - second series (1d array) if first series was not error directly,\\
then this should be the second series
... | https://github.com/matousc89/padasip/blob/c969eadd7fa181a84da0554d737fc13c6450d16f/padasip/misc/error_evaluation.py#L127-L149 |
matousc89/padasip | padasip/misc/error_evaluation.py | MAE | def MAE(x1, x2=-1):
"""
Mean absolute error - this function accepts two series of data or directly
one series with error.
**Args:**
* `x1` - first data series or error (1d array)
**Kwargs:**
* `x2` - second series (1d array) if first series was not error directly,\\
then this sho... | python | def MAE(x1, x2=-1):
"""
Mean absolute error - this function accepts two series of data or directly
one series with error.
**Args:**
* `x1` - first data series or error (1d array)
**Kwargs:**
* `x2` - second series (1d array) if first series was not error directly,\\
then this sho... | Mean absolute error - this function accepts two series of data or directly
one series with error.
**Args:**
* `x1` - first data series or error (1d array)
**Kwargs:**
* `x2` - second series (1d array) if first series was not error directly,\\
then this should be the second series
**... | https://github.com/matousc89/padasip/blob/c969eadd7fa181a84da0554d737fc13c6450d16f/padasip/misc/error_evaluation.py#L152-L173 |
matousc89/padasip | padasip/misc/error_evaluation.py | MSE | def MSE(x1, x2=-1):
"""
Mean squared error - this function accepts two series of data or directly
one series with error.
**Args:**
* `x1` - first data series or error (1d array)
**Kwargs:**
* `x2` - second series (1d array) if first series was not error directly,\\
then this shou... | python | def MSE(x1, x2=-1):
"""
Mean squared error - this function accepts two series of data or directly
one series with error.
**Args:**
* `x1` - first data series or error (1d array)
**Kwargs:**
* `x2` - second series (1d array) if first series was not error directly,\\
then this shou... | Mean squared error - this function accepts two series of data or directly
one series with error.
**Args:**
* `x1` - first data series or error (1d array)
**Kwargs:**
* `x2` - second series (1d array) if first series was not error directly,\\
then this should be the second series
**R... | https://github.com/matousc89/padasip/blob/c969eadd7fa181a84da0554d737fc13c6450d16f/padasip/misc/error_evaluation.py#L175-L196 |
matousc89/padasip | padasip/misc/error_evaluation.py | RMSE | def RMSE(x1, x2=-1):
"""
Root-mean-square error - this function accepts two series of data
or directly one series with error.
**Args:**
* `x1` - first data series or error (1d array)
**Kwargs:**
* `x2` - second series (1d array) if first series was not error directly,\\
then this... | python | def RMSE(x1, x2=-1):
"""
Root-mean-square error - this function accepts two series of data
or directly one series with error.
**Args:**
* `x1` - first data series or error (1d array)
**Kwargs:**
* `x2` - second series (1d array) if first series was not error directly,\\
then this... | Root-mean-square error - this function accepts two series of data
or directly one series with error.
**Args:**
* `x1` - first data series or error (1d array)
**Kwargs:**
* `x2` - second series (1d array) if first series was not error directly,\\
then this should be the second series
... | https://github.com/matousc89/padasip/blob/c969eadd7fa181a84da0554d737fc13c6450d16f/padasip/misc/error_evaluation.py#L198-L219 |
matousc89/padasip | padasip/misc/error_evaluation.py | get_mean_error | def get_mean_error(x1, x2=-1, function="MSE"):
"""
This function returns desired mean error. Options are: MSE, MAE, RMSE
**Args:**
* `x1` - first data series or error (1d array)
**Kwargs:**
* `x2` - second series (1d array) if first series was not error directly,\\
then this shou... | python | def get_mean_error(x1, x2=-1, function="MSE"):
"""
This function returns desired mean error. Options are: MSE, MAE, RMSE
**Args:**
* `x1` - first data series or error (1d array)
**Kwargs:**
* `x2` - second series (1d array) if first series was not error directly,\\
then this shou... | This function returns desired mean error. Options are: MSE, MAE, RMSE
**Args:**
* `x1` - first data series or error (1d array)
**Kwargs:**
* `x2` - second series (1d array) if first series was not error directly,\\
then this should be the second series
**Returns:**
* `e` - mean... | https://github.com/matousc89/padasip/blob/c969eadd7fa181a84da0554d737fc13c6450d16f/padasip/misc/error_evaluation.py#L221-L246 |
matousc89/padasip | padasip/detection/elbnd.py | ELBND | def ELBND(w, e, function="max"):
"""
This function estimates Error and Learning Based Novelty Detection measure
from given data.
**Args:**
* `w` : history of adaptive parameters of an adaptive model (2d array),
every row represents parameters in given time index.
* `e` : error of adapti... | python | def ELBND(w, e, function="max"):
"""
This function estimates Error and Learning Based Novelty Detection measure
from given data.
**Args:**
* `w` : history of adaptive parameters of an adaptive model (2d array),
every row represents parameters in given time index.
* `e` : error of adapti... | This function estimates Error and Learning Based Novelty Detection measure
from given data.
**Args:**
* `w` : history of adaptive parameters of an adaptive model (2d array),
every row represents parameters in given time index.
* `e` : error of adaptive model (1d array)
**Kwargs:**
* `... | https://github.com/matousc89/padasip/blob/c969eadd7fa181a84da0554d737fc13c6450d16f/padasip/detection/elbnd.py#L93-L138 |
matousc89/padasip | padasip/filters/rls.py | FilterRLS.adapt | def adapt(self, d, x):
"""
Adapt weights according one desired value and its input.
**Args:**
* `d` : desired value (float)
* `x` : input array (1-dimensional array)
"""
y = np.dot(self.w, x)
e = d - y
R1 = np.dot(np.dot(np.dot(self.R,x),x.T),se... | python | def adapt(self, d, x):
"""
Adapt weights according one desired value and its input.
**Args:**
* `d` : desired value (float)
* `x` : input array (1-dimensional array)
"""
y = np.dot(self.w, x)
e = d - y
R1 = np.dot(np.dot(np.dot(self.R,x),x.T),se... | Adapt weights according one desired value and its input.
**Args:**
* `d` : desired value (float)
* `x` : input array (1-dimensional array) | https://github.com/matousc89/padasip/blob/c969eadd7fa181a84da0554d737fc13c6450d16f/padasip/filters/rls.py#L208-L224 |
matousc89/padasip | padasip/filters/rls.py | FilterRLS.run | def run(self, d, x):
"""
This function filters multiple samples in a row.
**Args:**
* `d` : desired value (1 dimensional array)
* `x` : input matrix (2-dimensional array). Rows are samples,
columns are input arrays.
**Returns:**
* `y` : output val... | python | def run(self, d, x):
"""
This function filters multiple samples in a row.
**Args:**
* `d` : desired value (1 dimensional array)
* `x` : input matrix (2-dimensional array). Rows are samples,
columns are input arrays.
**Returns:**
* `y` : output val... | This function filters multiple samples in a row.
**Args:**
* `d` : desired value (1 dimensional array)
* `x` : input matrix (2-dimensional array). Rows are samples,
columns are input arrays.
**Returns:**
* `y` : output value (1 dimensional array).
The s... | https://github.com/matousc89/padasip/blob/c969eadd7fa181a84da0554d737fc13c6450d16f/padasip/filters/rls.py#L226-L273 |
matousc89/padasip | padasip/filters/ap.py | FilterAP.adapt | def adapt(self, d, x):
"""
Adapt weights according one desired value and its input.
**Args:**
* `d` : desired value (float)
* `x` : input array (1-dimensional array)
"""
# create input matrix and target vector
self.x_mem[:,1:] = self.x_mem[:,:-1]
... | python | def adapt(self, d, x):
"""
Adapt weights according one desired value and its input.
**Args:**
* `d` : desired value (float)
* `x` : input array (1-dimensional array)
"""
# create input matrix and target vector
self.x_mem[:,1:] = self.x_mem[:,:-1]
... | Adapt weights according one desired value and its input.
**Args:**
* `d` : desired value (float)
* `x` : input array (1-dimensional array) | https://github.com/matousc89/padasip/blob/c969eadd7fa181a84da0554d737fc13c6450d16f/padasip/filters/ap.py#L205-L227 |
matousc89/padasip | padasip/filters/ap.py | FilterAP.run | def run(self, d, x):
"""
This function filters multiple samples in a row.
**Args:**
* `d` : desired value (1 dimensional array)
* `x` : input matrix (2-dimensional array). Rows are samples,
columns are input arrays.
**Returns:**
* `y` : output value... | python | def run(self, d, x):
"""
This function filters multiple samples in a row.
**Args:**
* `d` : desired value (1 dimensional array)
* `x` : input matrix (2-dimensional array). Rows are samples,
columns are input arrays.
**Returns:**
* `y` : output value... | This function filters multiple samples in a row.
**Args:**
* `d` : desired value (1 dimensional array)
* `x` : input matrix (2-dimensional array). Rows are samples,
columns are input arrays.
**Returns:**
* `y` : output value (1 dimensional array).
The siz... | https://github.com/matousc89/padasip/blob/c969eadd7fa181a84da0554d737fc13c6450d16f/padasip/filters/ap.py#L229-L285 |
matousc89/padasip | padasip/preprocess/lda.py | LDA_base | def LDA_base(x, labels):
"""
Base function used for Linear Discriminant Analysis.
**Args:**
* `x` : input matrix (2d array), every row represents new sample
* `labels` : list of labels (iterable), every item should be label for \
sample with corresponding index
**Returns:**
* ... | python | def LDA_base(x, labels):
"""
Base function used for Linear Discriminant Analysis.
**Args:**
* `x` : input matrix (2d array), every row represents new sample
* `labels` : list of labels (iterable), every item should be label for \
sample with corresponding index
**Returns:**
* ... | Base function used for Linear Discriminant Analysis.
**Args:**
* `x` : input matrix (2d array), every row represents new sample
* `labels` : list of labels (iterable), every item should be label for \
sample with corresponding index
**Returns:**
* `eigenvalues`, `eigenvectors` : eigen... | https://github.com/matousc89/padasip/blob/c969eadd7fa181a84da0554d737fc13c6450d16f/padasip/preprocess/lda.py#L104-L144 |
matousc89/padasip | padasip/preprocess/lda.py | LDA | def LDA(x, labels, n=False):
"""
Linear Discriminant Analysis function.
**Args:**
* `x` : input matrix (2d array), every row represents new sample
* `labels` : list of labels (iterable), every item should be label for \
sample with corresponding index
**Kwargs:**
* `n` : number of... | python | def LDA(x, labels, n=False):
"""
Linear Discriminant Analysis function.
**Args:**
* `x` : input matrix (2d array), every row represents new sample
* `labels` : list of labels (iterable), every item should be label for \
sample with corresponding index
**Kwargs:**
* `n` : number of... | Linear Discriminant Analysis function.
**Args:**
* `x` : input matrix (2d array), every row represents new sample
* `labels` : list of labels (iterable), every item should be label for \
sample with corresponding index
**Kwargs:**
* `n` : number of features returned (integer) - how many c... | https://github.com/matousc89/padasip/blob/c969eadd7fa181a84da0554d737fc13c6450d16f/padasip/preprocess/lda.py#L146-L181 |
matousc89/padasip | padasip/preprocess/lda.py | LDA_discriminants | def LDA_discriminants(x, labels):
"""
Linear Discriminant Analysis helper for determination how many columns of
data should be reduced.
**Args:**
* `x` : input matrix (2d array), every row represents new sample
* `labels` : list of labels (iterable), every item should be label for \
s... | python | def LDA_discriminants(x, labels):
"""
Linear Discriminant Analysis helper for determination how many columns of
data should be reduced.
**Args:**
* `x` : input matrix (2d array), every row represents new sample
* `labels` : list of labels (iterable), every item should be label for \
s... | Linear Discriminant Analysis helper for determination how many columns of
data should be reduced.
**Args:**
* `x` : input matrix (2d array), every row represents new sample
* `labels` : list of labels (iterable), every item should be label for \
sample with corresponding index
**Returns:... | https://github.com/matousc89/padasip/blob/c969eadd7fa181a84da0554d737fc13c6450d16f/padasip/preprocess/lda.py#L184-L208 |
matousc89/padasip | padasip/filters/gngd.py | FilterGNGD.adapt | def adapt(self, d, x):
"""
Adapt weights according one desired value and its input.
**Args:**
* `d` : desired value (float)
* `x` : input array (1-dimensional array)
"""
y = np.dot(self.w, x)
e = d - y
self.eps = self.eps - self.ro * self.mu * e... | python | def adapt(self, d, x):
"""
Adapt weights according one desired value and its input.
**Args:**
* `d` : desired value (float)
* `x` : input array (1-dimensional array)
"""
y = np.dot(self.w, x)
e = d - y
self.eps = self.eps - self.ro * self.mu * e... | Adapt weights according one desired value and its input.
**Args:**
* `d` : desired value (float)
* `x` : input array (1-dimensional array) | https://github.com/matousc89/padasip/blob/c969eadd7fa181a84da0554d737fc13c6450d16f/padasip/filters/gngd.py#L158-L175 |
matousc89/padasip | padasip/filters/ocnlms.py | FilterOCNLMS.adapt | def adapt(self, d, x):
"""
Adapt weights according one desired value and its input.
Args:
* `d` : desired value (float)
* `x` : input array (1-dimensional array)
"""
self.update_memory_x(x)
m_d, m_x = self.read_memory()
# estimate
y = np... | python | def adapt(self, d, x):
"""
Adapt weights according one desired value and its input.
Args:
* `d` : desired value (float)
* `x` : input array (1-dimensional array)
"""
self.update_memory_x(x)
m_d, m_x = self.read_memory()
# estimate
y = np... | Adapt weights according one desired value and its input.
Args:
* `d` : desired value (float)
* `x` : input array (1-dimensional array) | https://github.com/matousc89/padasip/blob/c969eadd7fa181a84da0554d737fc13c6450d16f/padasip/filters/ocnlms.py#L54-L72 |
matousc89/padasip | padasip/filters/ocnlms.py | FilterOCNLMS.read_memory | def read_memory(self):
"""
This function read mean value of target`d`
and input vector `x` from history
"""
if self.mem_empty == True:
if self.mem_idx == 0:
m_x = np.zeros(self.n)
m_d = 0
else:
m_x = np.mean(... | python | def read_memory(self):
"""
This function read mean value of target`d`
and input vector `x` from history
"""
if self.mem_empty == True:
if self.mem_idx == 0:
m_x = np.zeros(self.n)
m_d = 0
else:
m_x = np.mean(... | This function read mean value of target`d`
and input vector `x` from history | https://github.com/matousc89/padasip/blob/c969eadd7fa181a84da0554d737fc13c6450d16f/padasip/filters/ocnlms.py#L86-L105 |
matousc89/padasip | padasip/filters/__init__.py | filter_data | def filter_data(d, x, model="lms", **kwargs):
"""
Function that filter data with selected adaptive filter.
**Args:**
* `d` : desired value (1 dimensional array)
* `x` : input matrix (2-dimensional array). Rows are samples, columns are
input arrays.
**Kwargs:**
* An... | python | def filter_data(d, x, model="lms", **kwargs):
"""
Function that filter data with selected adaptive filter.
**Args:**
* `d` : desired value (1 dimensional array)
* `x` : input matrix (2-dimensional array). Rows are samples, columns are
input arrays.
**Kwargs:**
* An... | Function that filter data with selected adaptive filter.
**Args:**
* `d` : desired value (1 dimensional array)
* `x` : input matrix (2-dimensional array). Rows are samples, columns are
input arrays.
**Kwargs:**
* Any key argument that can be accepted with selected filter m... | https://github.com/matousc89/padasip/blob/c969eadd7fa181a84da0554d737fc13c6450d16f/padasip/filters/__init__.py#L179-L228 |
matousc89/padasip | padasip/filters/__init__.py | AdaptiveFilter | def AdaptiveFilter(model="lms", **kwargs):
"""
Function that filter data with selected adaptive filter.
**Args:**
* `d` : desired value (1 dimensional array)
* `x` : input matrix (2-dimensional array). Rows are samples, columns are
input arrays.
**Kwargs:**
* Any ... | python | def AdaptiveFilter(model="lms", **kwargs):
"""
Function that filter data with selected adaptive filter.
**Args:**
* `d` : desired value (1 dimensional array)
* `x` : input matrix (2-dimensional array). Rows are samples, columns are
input arrays.
**Kwargs:**
* Any ... | Function that filter data with selected adaptive filter.
**Args:**
* `d` : desired value (1 dimensional array)
* `x` : input matrix (2-dimensional array). Rows are samples, columns are
input arrays.
**Kwargs:**
* Any key argument that can be accepted with selected filter ... | https://github.com/matousc89/padasip/blob/c969eadd7fa181a84da0554d737fc13c6450d16f/padasip/filters/__init__.py#L230-L281 |
matousc89/padasip | padasip/detection/le.py | learning_entropy | def learning_entropy(w, m=10, order=1, alpha=False):
"""
This function estimates Learning Entropy.
**Args:**
* `w` : history of adaptive parameters of an adaptive model (2d array),
every row represents parameters in given time index.
**Kwargs:**
* `m` : window size (1d array) - how man... | python | def learning_entropy(w, m=10, order=1, alpha=False):
"""
This function estimates Learning Entropy.
**Args:**
* `w` : history of adaptive parameters of an adaptive model (2d array),
every row represents parameters in given time index.
**Kwargs:**
* `m` : window size (1d array) - how man... | This function estimates Learning Entropy.
**Args:**
* `w` : history of adaptive parameters of an adaptive model (2d array),
every row represents parameters in given time index.
**Kwargs:**
* `m` : window size (1d array) - how many last samples are used for
evaluation of every sample.
... | https://github.com/matousc89/padasip/blob/c969eadd7fa181a84da0554d737fc13c6450d16f/padasip/detection/le.py#L145-L200 |
matousc89/padasip | padasip/ann/mlp.py | Layer.activation | def activation(self, x, f="sigmoid", der=False):
"""
This function process values of layer outputs with activation function.
**Args:**
* `x` : array to process (1-dimensional array)
**Kwargs:**
* `f` : activation function
* `der` : normal output, or its deri... | python | def activation(self, x, f="sigmoid", der=False):
"""
This function process values of layer outputs with activation function.
**Args:**
* `x` : array to process (1-dimensional array)
**Kwargs:**
* `f` : activation function
* `der` : normal output, or its deri... | This function process values of layer outputs with activation function.
**Args:**
* `x` : array to process (1-dimensional array)
**Kwargs:**
* `f` : activation function
* `der` : normal output, or its derivation (bool)
**Returns:**
* values processed with ... | https://github.com/matousc89/padasip/blob/c969eadd7fa181a84da0554d737fc13c6450d16f/padasip/ann/mlp.py#L126-L152 |
matousc89/padasip | padasip/ann/mlp.py | Layer.predict | def predict(self, x):
"""
This function make forward pass through this layer (no update).
**Args:**
* `x` : input vector (1-dimensional array)
**Returns:**
* `y` : output of MLP (float or 1-diemnsional array).
Size depends on number of nodes in thi... | python | def predict(self, x):
"""
This function make forward pass through this layer (no update).
**Args:**
* `x` : input vector (1-dimensional array)
**Returns:**
* `y` : output of MLP (float or 1-diemnsional array).
Size depends on number of nodes in thi... | This function make forward pass through this layer (no update).
**Args:**
* `x` : input vector (1-dimensional array)
**Returns:**
* `y` : output of MLP (float or 1-diemnsional array).
Size depends on number of nodes in this layer. | https://github.com/matousc89/padasip/blob/c969eadd7fa181a84da0554d737fc13c6450d16f/padasip/ann/mlp.py#L154-L170 |
matousc89/padasip | padasip/ann/mlp.py | Layer.update | def update(self, w, e):
"""
This function make update according provided target
and the last used input vector.
**Args:**
* `d` : target (float or 1-dimensional array).
Size depends on number of MLP outputs.
**Returns:**
* `w` : weights of the laye... | python | def update(self, w, e):
"""
This function make update according provided target
and the last used input vector.
**Args:**
* `d` : target (float or 1-dimensional array).
Size depends on number of MLP outputs.
**Returns:**
* `w` : weights of the laye... | This function make update according provided target
and the last used input vector.
**Args:**
* `d` : target (float or 1-dimensional array).
Size depends on number of MLP outputs.
**Returns:**
* `w` : weights of the layers (2-dimensional layer).
Every ... | https://github.com/matousc89/padasip/blob/c969eadd7fa181a84da0554d737fc13c6450d16f/padasip/ann/mlp.py#L172-L198 |
matousc89/padasip | padasip/ann/mlp.py | NetworkMLP.train | def train(self, x, d, epochs=10, shuffle=False):
"""
Function for batch training of MLP.
**Args:**
* `x` : input array (2-dimensional array).
Every row represents one input vector (features).
* `d` : input array (n-dimensional array).
Every row represen... | python | def train(self, x, d, epochs=10, shuffle=False):
"""
Function for batch training of MLP.
**Args:**
* `x` : input array (2-dimensional array).
Every row represents one input vector (features).
* `d` : input array (n-dimensional array).
Every row represen... | Function for batch training of MLP.
**Args:**
* `x` : input array (2-dimensional array).
Every row represents one input vector (features).
* `d` : input array (n-dimensional array).
Every row represents target for one input vector.
Target can be one or more... | https://github.com/matousc89/padasip/blob/c969eadd7fa181a84da0554d737fc13c6450d16f/padasip/ann/mlp.py#L267-L334 |
matousc89/padasip | padasip/ann/mlp.py | NetworkMLP.run | def run(self, x):
"""
Function for batch usage of already trained and tested MLP.
**Args:**
* `x` : input array (2-dimensional array).
Every row represents one input vector (features).
**Returns:**
* `y`: output vector (n-dimensional array). Every ... | python | def run(self, x):
"""
Function for batch usage of already trained and tested MLP.
**Args:**
* `x` : input array (2-dimensional array).
Every row represents one input vector (features).
**Returns:**
* `y`: output vector (n-dimensional array). Every ... | Function for batch usage of already trained and tested MLP.
**Args:**
* `x` : input array (2-dimensional array).
Every row represents one input vector (features).
**Returns:**
* `y`: output vector (n-dimensional array). Every row represents
output (out... | https://github.com/matousc89/padasip/blob/c969eadd7fa181a84da0554d737fc13c6450d16f/padasip/ann/mlp.py#L336-L365 |
matousc89/padasip | padasip/ann/mlp.py | NetworkMLP.predict | def predict(self, x):
"""
This function make forward pass through MLP (no update).
**Args:**
* `x` : input vector (1-dimensional array)
**Returns:**
* `y` : output of MLP (float or 1-diemnsional array).
Size depends on number of MLP outputs.
... | python | def predict(self, x):
"""
This function make forward pass through MLP (no update).
**Args:**
* `x` : input vector (1-dimensional array)
**Returns:**
* `y` : output of MLP (float or 1-diemnsional array).
Size depends on number of MLP outputs.
... | This function make forward pass through MLP (no update).
**Args:**
* `x` : input vector (1-dimensional array)
**Returns:**
* `y` : output of MLP (float or 1-diemnsional array).
Size depends on number of MLP outputs. | https://github.com/matousc89/padasip/blob/c969eadd7fa181a84da0554d737fc13c6450d16f/padasip/ann/mlp.py#L413-L436 |
matousc89/padasip | padasip/ann/mlp.py | NetworkMLP.update | def update(self, d):
"""
This function make update according provided target
and the last used input vector.
**Args:**
* `d` : target (float or 1-dimensional array).
Size depends on number of MLP outputs.
**Returns:**
* `e` : error used for... | python | def update(self, d):
"""
This function make update according provided target
and the last used input vector.
**Args:**
* `d` : target (float or 1-dimensional array).
Size depends on number of MLP outputs.
**Returns:**
* `e` : error used for... | This function make update according provided target
and the last used input vector.
**Args:**
* `d` : target (float or 1-dimensional array).
Size depends on number of MLP outputs.
**Returns:**
* `e` : error used for update (float or 1-diemnsional array).
... | https://github.com/matousc89/padasip/blob/c969eadd7fa181a84da0554d737fc13c6450d16f/padasip/ann/mlp.py#L438-L467 |
matousc89/padasip | padasip/preprocess/pca.py | PCA_components | def PCA_components(x):
"""
Principal Component Analysis helper to check out eigenvalues of components.
**Args:**
* `x` : input matrix (2d array), every row represents new sample
**Returns:**
* `components`: sorted array of principal components eigenvalues
"""
# validat... | python | def PCA_components(x):
"""
Principal Component Analysis helper to check out eigenvalues of components.
**Args:**
* `x` : input matrix (2d array), every row represents new sample
**Returns:**
* `components`: sorted array of principal components eigenvalues
"""
# validat... | Principal Component Analysis helper to check out eigenvalues of components.
**Args:**
* `x` : input matrix (2d array), every row represents new sample
**Returns:**
* `components`: sorted array of principal components eigenvalues | https://github.com/matousc89/padasip/blob/c969eadd7fa181a84da0554d737fc13c6450d16f/padasip/preprocess/pca.py#L68-L91 |
matousc89/padasip | padasip/preprocess/pca.py | PCA | def PCA(x, n=False):
"""
Principal component analysis function.
**Args:**
* `x` : input matrix (2d array), every row represents new sample
**Kwargs:**
* `n` : number of features returned (integer) - how many columns
should the output keep
**Returns:**
* `new_x` : matrix ... | python | def PCA(x, n=False):
"""
Principal component analysis function.
**Args:**
* `x` : input matrix (2d array), every row represents new sample
**Kwargs:**
* `n` : number of features returned (integer) - how many columns
should the output keep
**Returns:**
* `new_x` : matrix ... | Principal component analysis function.
**Args:**
* `x` : input matrix (2d array), every row represents new sample
**Kwargs:**
* `n` : number of features returned (integer) - how many columns
should the output keep
**Returns:**
* `new_x` : matrix with reduced size (lower number o... | https://github.com/matousc89/padasip/blob/c969eadd7fa181a84da0554d737fc13c6450d16f/padasip/preprocess/pca.py#L94-L127 |
widdowquinn/pyani | pyani/pyani_graphics.py | clean_axis | def clean_axis(axis):
"""Remove ticks, tick labels, and frame from axis"""
axis.get_xaxis().set_ticks([])
axis.get_yaxis().set_ticks([])
for spine in list(axis.spines.values()):
spine.set_visible(False) | python | def clean_axis(axis):
"""Remove ticks, tick labels, and frame from axis"""
axis.get_xaxis().set_ticks([])
axis.get_yaxis().set_ticks([])
for spine in list(axis.spines.values()):
spine.set_visible(False) | Remove ticks, tick labels, and frame from axis | https://github.com/widdowquinn/pyani/blob/2b24ec971401e04024bba896e4011984fe3f53f0/pyani/pyani_graphics.py#L63-L68 |
widdowquinn/pyani | pyani/pyani_graphics.py | get_seaborn_colorbar | def get_seaborn_colorbar(dfr, classes):
"""Return a colorbar representing classes, for a Seaborn plot.
The aim is to get a pd.Series for the passed dataframe columns,
in the form:
0 colour for class in col 0
1 colour for class in col 1
... colour for class in col ...
n colour for ... | python | def get_seaborn_colorbar(dfr, classes):
"""Return a colorbar representing classes, for a Seaborn plot.
The aim is to get a pd.Series for the passed dataframe columns,
in the form:
0 colour for class in col 0
1 colour for class in col 1
... colour for class in col ...
n colour for ... | Return a colorbar representing classes, for a Seaborn plot.
The aim is to get a pd.Series for the passed dataframe columns,
in the form:
0 colour for class in col 0
1 colour for class in col 1
... colour for class in col ...
n colour for class in col n | https://github.com/widdowquinn/pyani/blob/2b24ec971401e04024bba896e4011984fe3f53f0/pyani/pyani_graphics.py#L72-L98 |
widdowquinn/pyani | pyani/pyani_graphics.py | get_safe_seaborn_labels | def get_safe_seaborn_labels(dfr, labels):
"""Returns labels guaranteed to correspond to the dataframe."""
if labels is not None:
return [labels.get(i, i) for i in dfr.index]
return [i for i in dfr.index] | python | def get_safe_seaborn_labels(dfr, labels):
"""Returns labels guaranteed to correspond to the dataframe."""
if labels is not None:
return [labels.get(i, i) for i in dfr.index]
return [i for i in dfr.index] | Returns labels guaranteed to correspond to the dataframe. | https://github.com/widdowquinn/pyani/blob/2b24ec971401e04024bba896e4011984fe3f53f0/pyani/pyani_graphics.py#L102-L106 |
widdowquinn/pyani | pyani/pyani_graphics.py | get_seaborn_clustermap | def get_seaborn_clustermap(dfr, params, title=None, annot=True):
"""Returns a Seaborn clustermap."""
fig = sns.clustermap(
dfr,
cmap=params.cmap,
vmin=params.vmin,
vmax=params.vmax,
col_colors=params.colorbar,
row_colors=params.colorbar,
figsize=(params.fi... | python | def get_seaborn_clustermap(dfr, params, title=None, annot=True):
"""Returns a Seaborn clustermap."""
fig = sns.clustermap(
dfr,
cmap=params.cmap,
vmin=params.vmin,
vmax=params.vmax,
col_colors=params.colorbar,
row_colors=params.colorbar,
figsize=(params.fi... | Returns a Seaborn clustermap. | https://github.com/widdowquinn/pyani/blob/2b24ec971401e04024bba896e4011984fe3f53f0/pyani/pyani_graphics.py#L110-L134 |
widdowquinn/pyani | pyani/pyani_graphics.py | heatmap_seaborn | def heatmap_seaborn(dfr, outfilename=None, title=None, params=None):
"""Returns seaborn heatmap with cluster dendrograms.
- dfr - pandas DataFrame with relevant data
- outfilename - path to output file (indicates output format)
"""
# Decide on figure layout size: a minimum size is required for
... | python | def heatmap_seaborn(dfr, outfilename=None, title=None, params=None):
"""Returns seaborn heatmap with cluster dendrograms.
- dfr - pandas DataFrame with relevant data
- outfilename - path to output file (indicates output format)
"""
# Decide on figure layout size: a minimum size is required for
... | Returns seaborn heatmap with cluster dendrograms.
- dfr - pandas DataFrame with relevant data
- outfilename - path to output file (indicates output format) | https://github.com/widdowquinn/pyani/blob/2b24ec971401e04024bba896e4011984fe3f53f0/pyani/pyani_graphics.py#L138-L175 |
widdowquinn/pyani | pyani/pyani_graphics.py | add_mpl_dendrogram | def add_mpl_dendrogram(dfr, fig, heatmap_gs, orientation="col"):
"""Return a dendrogram and corresponding gridspec, attached to the fig
Modifies the fig in-place. Orientation is either 'row' or 'col' and
determines location and orientation of the rendered dendrogram.
"""
# Row or column axes?
i... | python | def add_mpl_dendrogram(dfr, fig, heatmap_gs, orientation="col"):
"""Return a dendrogram and corresponding gridspec, attached to the fig
Modifies the fig in-place. Orientation is either 'row' or 'col' and
determines location and orientation of the rendered dendrogram.
"""
# Row or column axes?
i... | Return a dendrogram and corresponding gridspec, attached to the fig
Modifies the fig in-place. Orientation is either 'row' or 'col' and
determines location and orientation of the rendered dendrogram. | https://github.com/widdowquinn/pyani/blob/2b24ec971401e04024bba896e4011984fe3f53f0/pyani/pyani_graphics.py#L179-L215 |
widdowquinn/pyani | pyani/pyani_graphics.py | get_mpl_heatmap_axes | def get_mpl_heatmap_axes(dfr, fig, heatmap_gs):
"""Return axis for Matplotlib heatmap."""
# Create heatmap axis
heatmap_axes = fig.add_subplot(heatmap_gs[1, 1])
heatmap_axes.set_xticks(np.linspace(0, dfr.shape[0] - 1, dfr.shape[0]))
heatmap_axes.set_yticks(np.linspace(0, dfr.shape[0] - 1, dfr.shape[... | python | def get_mpl_heatmap_axes(dfr, fig, heatmap_gs):
"""Return axis for Matplotlib heatmap."""
# Create heatmap axis
heatmap_axes = fig.add_subplot(heatmap_gs[1, 1])
heatmap_axes.set_xticks(np.linspace(0, dfr.shape[0] - 1, dfr.shape[0]))
heatmap_axes.set_yticks(np.linspace(0, dfr.shape[0] - 1, dfr.shape[... | Return axis for Matplotlib heatmap. | https://github.com/widdowquinn/pyani/blob/2b24ec971401e04024bba896e4011984fe3f53f0/pyani/pyani_graphics.py#L219-L228 |
widdowquinn/pyani | pyani/pyani_graphics.py | add_mpl_colorbar | def add_mpl_colorbar(dfr, fig, dend, params, orientation="row"):
"""Add class colorbars to Matplotlib heatmap."""
for name in dfr.index[dend["dendrogram"]["leaves"]]:
if name not in params.classes:
params.classes[name] = name
# Assign a numerical value to each class, for mpl
classdi... | python | def add_mpl_colorbar(dfr, fig, dend, params, orientation="row"):
"""Add class colorbars to Matplotlib heatmap."""
for name in dfr.index[dend["dendrogram"]["leaves"]]:
if name not in params.classes:
params.classes[name] = name
# Assign a numerical value to each class, for mpl
classdi... | Add class colorbars to Matplotlib heatmap. | https://github.com/widdowquinn/pyani/blob/2b24ec971401e04024bba896e4011984fe3f53f0/pyani/pyani_graphics.py#L231-L269 |
widdowquinn/pyani | pyani/pyani_graphics.py | add_mpl_labels | def add_mpl_labels(heatmap_axes, rowlabels, collabels, params):
"""Add labels to Matplotlib heatmap axes, in-place."""
if params.labels:
# If a label mapping is missing, use the key text as fall back
rowlabels = [params.labels.get(lab, lab) for lab in rowlabels]
collabels = [params.label... | python | def add_mpl_labels(heatmap_axes, rowlabels, collabels, params):
"""Add labels to Matplotlib heatmap axes, in-place."""
if params.labels:
# If a label mapping is missing, use the key text as fall back
rowlabels = [params.labels.get(lab, lab) for lab in rowlabels]
collabels = [params.label... | Add labels to Matplotlib heatmap axes, in-place. | https://github.com/widdowquinn/pyani/blob/2b24ec971401e04024bba896e4011984fe3f53f0/pyani/pyani_graphics.py#L273-L285 |
widdowquinn/pyani | pyani/pyani_graphics.py | add_mpl_colorscale | def add_mpl_colorscale(fig, heatmap_gs, ax_map, params, title=None):
"""Add colour scale to heatmap."""
# Set tick intervals
cbticks = [params.vmin + e * params.vdiff for e in (0, 0.25, 0.5, 0.75, 1)]
if params.vmax > 10:
exponent = int(floor(log10(params.vmax))) - 1
cbticks = [int(round... | python | def add_mpl_colorscale(fig, heatmap_gs, ax_map, params, title=None):
"""Add colour scale to heatmap."""
# Set tick intervals
cbticks = [params.vmin + e * params.vdiff for e in (0, 0.25, 0.5, 0.75, 1)]
if params.vmax > 10:
exponent = int(floor(log10(params.vmax))) - 1
cbticks = [int(round... | Add colour scale to heatmap. | https://github.com/widdowquinn/pyani/blob/2b24ec971401e04024bba896e4011984fe3f53f0/pyani/pyani_graphics.py#L289-L308 |
widdowquinn/pyani | pyani/pyani_graphics.py | heatmap_mpl | def heatmap_mpl(dfr, outfilename=None, title=None, params=None):
"""Returns matplotlib heatmap with cluster dendrograms.
- dfr - pandas DataFrame with relevant data
- outfilename - path to output file (indicates output format)
- params - a list of parameters for plotting: [colormap, vmin, vmax]
- l... | python | def heatmap_mpl(dfr, outfilename=None, title=None, params=None):
"""Returns matplotlib heatmap with cluster dendrograms.
- dfr - pandas DataFrame with relevant data
- outfilename - path to output file (indicates output format)
- params - a list of parameters for plotting: [colormap, vmin, vmax]
- l... | Returns matplotlib heatmap with cluster dendrograms.
- dfr - pandas DataFrame with relevant data
- outfilename - path to output file (indicates output format)
- params - a list of parameters for plotting: [colormap, vmin, vmax]
- labels - dictionary of alternative labels, keyed by default sequence
... | https://github.com/widdowquinn/pyani/blob/2b24ec971401e04024bba896e4011984fe3f53f0/pyani/pyani_graphics.py#L312-L375 |
widdowquinn/pyani | pyani/run_multiprocessing.py | run_dependency_graph | def run_dependency_graph(jobgraph, workers=None, logger=None):
"""Creates and runs pools of jobs based on the passed jobgraph.
- jobgraph - list of jobs, which may have dependencies.
- verbose - flag for multiprocessing verbosity
- logger - a logger module logger (optional)
The strategy here is to... | python | def run_dependency_graph(jobgraph, workers=None, logger=None):
"""Creates and runs pools of jobs based on the passed jobgraph.
- jobgraph - list of jobs, which may have dependencies.
- verbose - flag for multiprocessing verbosity
- logger - a logger module logger (optional)
The strategy here is to... | Creates and runs pools of jobs based on the passed jobgraph.
- jobgraph - list of jobs, which may have dependencies.
- verbose - flag for multiprocessing verbosity
- logger - a logger module logger (optional)
The strategy here is to loop over each job in the list of jobs (jobgraph),
and create/pop... | https://github.com/widdowquinn/pyani/blob/2b24ec971401e04024bba896e4011984fe3f53f0/pyani/run_multiprocessing.py#L22-L48 |
widdowquinn/pyani | pyani/run_multiprocessing.py | populate_cmdsets | def populate_cmdsets(job, cmdsets, depth):
"""Creates a list of sets containing jobs at different depths of the
dependency tree.
This is a recursive function (is there something quicker in the itertools
module?) that descends each 'root' job in turn, populating each
"""
if len(cmdsets) < depth:... | python | def populate_cmdsets(job, cmdsets, depth):
"""Creates a list of sets containing jobs at different depths of the
dependency tree.
This is a recursive function (is there something quicker in the itertools
module?) that descends each 'root' job in turn, populating each
"""
if len(cmdsets) < depth:... | Creates a list of sets containing jobs at different depths of the
dependency tree.
This is a recursive function (is there something quicker in the itertools
module?) that descends each 'root' job in turn, populating each | https://github.com/widdowquinn/pyani/blob/2b24ec971401e04024bba896e4011984fe3f53f0/pyani/run_multiprocessing.py#L51-L65 |
widdowquinn/pyani | pyani/run_multiprocessing.py | multiprocessing_run | def multiprocessing_run(cmdlines, workers=None):
"""Distributes passed command-line jobs using multiprocessing.
- cmdlines - an iterable of command line strings
Returns the sum of exit codes from each job that was run. If
all goes well, this should be 0. Anything else and the calling
function shou... | python | def multiprocessing_run(cmdlines, workers=None):
"""Distributes passed command-line jobs using multiprocessing.
- cmdlines - an iterable of command line strings
Returns the sum of exit codes from each job that was run. If
all goes well, this should be 0. Anything else and the calling
function shou... | Distributes passed command-line jobs using multiprocessing.
- cmdlines - an iterable of command line strings
Returns the sum of exit codes from each job that was run. If
all goes well, this should be 0. Anything else and the calling
function should act accordingly. | https://github.com/widdowquinn/pyani/blob/2b24ec971401e04024bba896e4011984fe3f53f0/pyani/run_multiprocessing.py#L69-L89 |
widdowquinn/pyani | pyani/pyani_files.py | get_input_files | def get_input_files(dirname, *ext):
"""Returns files in passed directory, filtered by extension.
- dirname - path to input directory
- *ext - list of arguments describing permitted file extensions
"""
filelist = [f for f in os.listdir(dirname) if
os.path.splitext(f)[-1] in ext]
... | python | def get_input_files(dirname, *ext):
"""Returns files in passed directory, filtered by extension.
- dirname - path to input directory
- *ext - list of arguments describing permitted file extensions
"""
filelist = [f for f in os.listdir(dirname) if
os.path.splitext(f)[-1] in ext]
... | Returns files in passed directory, filtered by extension.
- dirname - path to input directory
- *ext - list of arguments describing permitted file extensions | https://github.com/widdowquinn/pyani/blob/2b24ec971401e04024bba896e4011984fe3f53f0/pyani/pyani_files.py#L27-L35 |
widdowquinn/pyani | pyani/pyani_files.py | get_sequence_lengths | def get_sequence_lengths(fastafilenames):
"""Returns dictionary of sequence lengths, keyed by organism.
Biopython's SeqIO module is used to parse all sequences in the FASTA
file corresponding to each organism, and the total base count in each
is obtained.
NOTE: ambiguity symbols are not discounted... | python | def get_sequence_lengths(fastafilenames):
"""Returns dictionary of sequence lengths, keyed by organism.
Biopython's SeqIO module is used to parse all sequences in the FASTA
file corresponding to each organism, and the total base count in each
is obtained.
NOTE: ambiguity symbols are not discounted... | Returns dictionary of sequence lengths, keyed by organism.
Biopython's SeqIO module is used to parse all sequences in the FASTA
file corresponding to each organism, and the total base count in each
is obtained.
NOTE: ambiguity symbols are not discounted. | https://github.com/widdowquinn/pyani/blob/2b24ec971401e04024bba896e4011984fe3f53f0/pyani/pyani_files.py#L39-L52 |
widdowquinn/pyani | bin/average_nucleotide_identity.py | parse_cmdline | def parse_cmdline():
"""Parse command-line arguments for script."""
parser = ArgumentParser(prog="average_nucleotide_identity.py")
parser.add_argument(
"--version", action="version", version="%(prog)s: pyani " + VERSION
)
parser.add_argument(
"-o",
"--outdir",
dest="o... | python | def parse_cmdline():
"""Parse command-line arguments for script."""
parser = ArgumentParser(prog="average_nucleotide_identity.py")
parser.add_argument(
"--version", action="version", version="%(prog)s: pyani " + VERSION
)
parser.add_argument(
"-o",
"--outdir",
dest="o... | Parse command-line arguments for script. | https://github.com/widdowquinn/pyani/blob/2b24ec971401e04024bba896e4011984fe3f53f0/bin/average_nucleotide_identity.py#L185-L435 |
widdowquinn/pyani | bin/average_nucleotide_identity.py | last_exception | def last_exception():
""" Returns last exception as a string, or use in logging.
"""
exc_type, exc_value, exc_traceback = sys.exc_info()
return "".join(traceback.format_exception(exc_type, exc_value, exc_traceback)) | python | def last_exception():
""" Returns last exception as a string, or use in logging.
"""
exc_type, exc_value, exc_traceback = sys.exc_info()
return "".join(traceback.format_exception(exc_type, exc_value, exc_traceback)) | Returns last exception as a string, or use in logging. | https://github.com/widdowquinn/pyani/blob/2b24ec971401e04024bba896e4011984fe3f53f0/bin/average_nucleotide_identity.py#L439-L443 |
widdowquinn/pyani | bin/average_nucleotide_identity.py | make_outdir | def make_outdir():
"""Make the output directory, if required.
This is a little involved. If the output directory already exists,
we take the safe option by default, and stop with an error. We can,
however, choose to force the program to go on, in which case we can
either clobber the existing dire... | python | def make_outdir():
"""Make the output directory, if required.
This is a little involved. If the output directory already exists,
we take the safe option by default, and stop with an error. We can,
however, choose to force the program to go on, in which case we can
either clobber the existing dire... | Make the output directory, if required.
This is a little involved. If the output directory already exists,
we take the safe option by default, and stop with an error. We can,
however, choose to force the program to go on, in which case we can
either clobber the existing directory, or not. The option... | https://github.com/widdowquinn/pyani/blob/2b24ec971401e04024bba896e4011984fe3f53f0/bin/average_nucleotide_identity.py#L447-L490 |
widdowquinn/pyani | bin/average_nucleotide_identity.py | compress_delete_outdir | def compress_delete_outdir(outdir):
"""Compress the contents of the passed directory to .tar.gz and delete."""
# Compress output in .tar.gz file and remove raw output
tarfn = outdir + ".tar.gz"
logger.info("\tCompressing output from %s to %s", outdir, tarfn)
with tarfile.open(tarfn, "w:gz") as fh:
... | python | def compress_delete_outdir(outdir):
"""Compress the contents of the passed directory to .tar.gz and delete."""
# Compress output in .tar.gz file and remove raw output
tarfn = outdir + ".tar.gz"
logger.info("\tCompressing output from %s to %s", outdir, tarfn)
with tarfile.open(tarfn, "w:gz") as fh:
... | Compress the contents of the passed directory to .tar.gz and delete. | https://github.com/widdowquinn/pyani/blob/2b24ec971401e04024bba896e4011984fe3f53f0/bin/average_nucleotide_identity.py#L494-L502 |
widdowquinn/pyani | bin/average_nucleotide_identity.py | calculate_anim | def calculate_anim(infiles, org_lengths):
"""Returns ANIm result dataframes for files in input directory.
- infiles - paths to each input file
- org_lengths - dictionary of input sequence lengths, keyed by sequence
Finds ANI by the ANIm method, as described in Richter et al (2009)
Proc Natl Acad S... | python | def calculate_anim(infiles, org_lengths):
"""Returns ANIm result dataframes for files in input directory.
- infiles - paths to each input file
- org_lengths - dictionary of input sequence lengths, keyed by sequence
Finds ANI by the ANIm method, as described in Richter et al (2009)
Proc Natl Acad S... | Returns ANIm result dataframes for files in input directory.
- infiles - paths to each input file
- org_lengths - dictionary of input sequence lengths, keyed by sequence
Finds ANI by the ANIm method, as described in Richter et al (2009)
Proc Natl Acad Sci USA 106: 19126-19131 doi:10.1073/pnas.09064121... | https://github.com/widdowquinn/pyani/blob/2b24ec971401e04024bba896e4011984fe3f53f0/bin/average_nucleotide_identity.py#L506-L599 |
widdowquinn/pyani | bin/average_nucleotide_identity.py | calculate_tetra | def calculate_tetra(infiles):
"""Calculate TETRA for files in input directory.
- infiles - paths to each input file
- org_lengths - dictionary of input sequence lengths, keyed by sequence
Calculates TETRA correlation scores, as described in:
Richter M, Rossello-Mora R (2009) Shifting the genomic ... | python | def calculate_tetra(infiles):
"""Calculate TETRA for files in input directory.
- infiles - paths to each input file
- org_lengths - dictionary of input sequence lengths, keyed by sequence
Calculates TETRA correlation scores, as described in:
Richter M, Rossello-Mora R (2009) Shifting the genomic ... | Calculate TETRA for files in input directory.
- infiles - paths to each input file
- org_lengths - dictionary of input sequence lengths, keyed by sequence
Calculates TETRA correlation scores, as described in:
Richter M, Rossello-Mora R (2009) Shifting the genomic gold standard for
the prokaryotic... | https://github.com/widdowquinn/pyani/blob/2b24ec971401e04024bba896e4011984fe3f53f0/bin/average_nucleotide_identity.py#L603-L632 |
widdowquinn/pyani | bin/average_nucleotide_identity.py | unified_anib | def unified_anib(infiles, org_lengths):
"""Calculate ANIb for files in input directory.
- infiles - paths to each input file
- org_lengths - dictionary of input sequence lengths, keyed by sequence
Calculates ANI by the ANIb method, as described in Goris et al. (2007)
Int J Syst Evol Micr 57: 81-91... | python | def unified_anib(infiles, org_lengths):
"""Calculate ANIb for files in input directory.
- infiles - paths to each input file
- org_lengths - dictionary of input sequence lengths, keyed by sequence
Calculates ANI by the ANIb method, as described in Goris et al. (2007)
Int J Syst Evol Micr 57: 81-91... | Calculate ANIb for files in input directory.
- infiles - paths to each input file
- org_lengths - dictionary of input sequence lengths, keyed by sequence
Calculates ANI by the ANIb method, as described in Goris et al. (2007)
Int J Syst Evol Micr 57: 81-91. doi:10.1099/ijs.0.64483-0. There are
some... | https://github.com/widdowquinn/pyani/blob/2b24ec971401e04024bba896e4011984fe3f53f0/bin/average_nucleotide_identity.py#L636-L752 |
widdowquinn/pyani | bin/average_nucleotide_identity.py | write | def write(results):
"""Write ANIb/ANIm/TETRA results to output directory.
- results - results object from analysis
Each dataframe is written to an Excel-format file (if args.write_excel is
True), and plain text tab-separated file in the output directory. The
order of result output must be reflecte... | python | def write(results):
"""Write ANIb/ANIm/TETRA results to output directory.
- results - results object from analysis
Each dataframe is written to an Excel-format file (if args.write_excel is
True), and plain text tab-separated file in the output directory. The
order of result output must be reflecte... | Write ANIb/ANIm/TETRA results to output directory.
- results - results object from analysis
Each dataframe is written to an Excel-format file (if args.write_excel is
True), and plain text tab-separated file in the output directory. The
order of result output must be reflected in the order of filestems... | https://github.com/widdowquinn/pyani/blob/2b24ec971401e04024bba896e4011984fe3f53f0/bin/average_nucleotide_identity.py#L756-L780 |
widdowquinn/pyani | bin/average_nucleotide_identity.py | draw | def draw(filestems, gformat):
"""Draw ANIb/ANIm/TETRA results
- filestems - filestems for output files
- gformat - the format for output graphics
"""
# Draw heatmaps
for filestem in filestems:
fullstem = os.path.join(args.outdirname, filestem)
outfilename = fullstem + ".%s" % gf... | python | def draw(filestems, gformat):
"""Draw ANIb/ANIm/TETRA results
- filestems - filestems for output files
- gformat - the format for output graphics
"""
# Draw heatmaps
for filestem in filestems:
fullstem = os.path.join(args.outdirname, filestem)
outfilename = fullstem + ".%s" % gf... | Draw ANIb/ANIm/TETRA results
- filestems - filestems for output files
- gformat - the format for output graphics | https://github.com/widdowquinn/pyani/blob/2b24ec971401e04024bba896e4011984fe3f53f0/bin/average_nucleotide_identity.py#L784-L809 |
widdowquinn/pyani | bin/average_nucleotide_identity.py | subsample_input | def subsample_input(infiles):
"""Returns a random subsample of the input files.
- infiles: a list of input files for analysis
"""
logger.info("--subsample: %s", args.subsample)
try:
samplesize = float(args.subsample)
except TypeError: # Not a number
logger.error(
"-... | python | def subsample_input(infiles):
"""Returns a random subsample of the input files.
- infiles: a list of input files for analysis
"""
logger.info("--subsample: %s", args.subsample)
try:
samplesize = float(args.subsample)
except TypeError: # Not a number
logger.error(
"-... | Returns a random subsample of the input files.
- infiles: a list of input files for analysis | https://github.com/widdowquinn/pyani/blob/2b24ec971401e04024bba896e4011984fe3f53f0/bin/average_nucleotide_identity.py#L813-L842 |
widdowquinn/pyani | pyani/pyani_jobs.py | Job.wait | def wait(self, interval=SGE_WAIT):
"""Wait until the job finishes, and poll SGE on its status."""
finished = False
while not finished:
time.sleep(interval)
interval = min(2 * interval, 60)
finished = os.system("qstat -j %s > /dev/null" % (self.name)) | python | def wait(self, interval=SGE_WAIT):
"""Wait until the job finishes, and poll SGE on its status."""
finished = False
while not finished:
time.sleep(interval)
interval = min(2 * interval, 60)
finished = os.system("qstat -j %s > /dev/null" % (self.name)) | Wait until the job finishes, and poll SGE on its status. | https://github.com/widdowquinn/pyani/blob/2b24ec971401e04024bba896e4011984fe3f53f0/pyani/pyani_jobs.py#L77-L83 |
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