partition stringclasses 3
values | func_name stringlengths 1 134 | docstring stringlengths 1 46.9k | path stringlengths 4 223 | original_string stringlengths 75 104k | code stringlengths 75 104k | docstring_tokens listlengths 1 1.97k | repo stringlengths 7 55 | language stringclasses 1
value | url stringlengths 87 315 | code_tokens listlengths 19 28.4k | sha stringlengths 40 40 |
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train | popd | Go back to where you once were.
:return: saved directory stack | reusables/cli.py | def popd():
"""Go back to where you once were.
:return: saved directory stack
"""
try:
directory = _saved_paths.pop(0)
except IndexError:
return [os.getcwd()]
os.chdir(directory)
return [directory] + _saved_paths | def popd():
"""Go back to where you once were.
:return: saved directory stack
"""
try:
directory = _saved_paths.pop(0)
except IndexError:
return [os.getcwd()]
os.chdir(directory)
return [directory] + _saved_paths | [
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train | ls | Know the best python implantation of ls? It's just to subprocess ls...
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:param params: options to pass to ls or dir
:param directory: if not this directory
:param printed: If you're using this, you probably wanted it just printed
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:param params: options to pass to ls or dir
:param directory: if not this directory
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train | find | Designed for the interactive interpreter by making default order
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:param name: Part of the file name
:param ext: Extensions of the file you are looking for
:param directory: Top location to recursively search for matching files
:param match_case: If name has to be a direct matc... | reusables/cli.py | def find(name=None, ext=None, directory=".", match_case=False,
disable_glob=False, depth=None):
""" Designed for the interactive interpreter by making default order
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:param name: Part of the file name
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train | head | Read the first N lines of a file, defaults to 10
:param file_path: Path to file to read
:param lines: Number of lines to read in
:param encoding: defaults to utf-8 to decode as, will fail on binary
:param printed: Automatically print the lines instead of returning it
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Read the first N lines of a file, defaults to 10
:param file_path: Path to file to read
:param lines: Number of lines to read in
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Read the first N lines of a file, defaults to 10
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train | tail | A really silly way to get the last N lines, defaults to 10.
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train | cp | Copy files to a new location.
:param src: list (or string) of paths of files to copy
:param dst: file or folder to copy item(s) to
:param overwrite: IF the file already exists, should I overwrite it? | reusables/cli.py | def cp(src, dst, overwrite=False):
"""
Copy files to a new location.
:param src: list (or string) of paths of files to copy
:param dst: file or folder to copy item(s) to
:param overwrite: IF the file already exists, should I overwrite it?
"""
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Copy files to a new location.
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:param overwrite: IF the file already exists, should I overwrite it?
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train | cut | Split a string into a list of N characters each.
.. code:: python
reusables.cut("abcdefghi")
# ['ab', 'cd', 'ef', 'gh', 'i']
trailing gives you the following options:
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"""
Split a string into a list of N characters each.
.. code:: python
reusables.cut("abcdefghi")
# ['ab', 'cd', 'ef', 'gh', 'i']
trailing gives you the following options:
* normal: leaves remaining characters in their own last pos... | def cut(string, characters=2, trailing="normal"):
"""
Split a string into a list of N characters each.
.. code:: python
reusables.cut("abcdefghi")
# ['ab', 'cd', 'ef', 'gh', 'i']
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train | int_to_roman | Convert an integer into a string of roman numbers.
.. code: python
reusables.int_to_roman(445)
# 'CDXLV'
:param integer:
:return: roman string | reusables/string_manipulation.py | def int_to_roman(integer):
"""
Convert an integer into a string of roman numbers.
.. code: python
reusables.int_to_roman(445)
# 'CDXLV'
:param integer:
:return: roman string
"""
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"""
Convert an integer into a string of roman numbers.
.. code: python
reusables.int_to_roman(445)
# 'CDXLV'
:param integer:
:return: roman string
"""
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train | roman_to_int | Converts a string of roman numbers into an integer.
.. code: python
reusables.roman_to_int("XXXVI")
# 36
:param roman_string: XVI or similar
:return: parsed integer | reusables/string_manipulation.py | def roman_to_int(roman_string):
"""
Converts a string of roman numbers into an integer.
.. code: python
reusables.roman_to_int("XXXVI")
# 36
:param roman_string: XVI or similar
:return: parsed integer
"""
roman_string = roman_string.upper().strip()
if "IIII" in roman_... | def roman_to_int(roman_string):
"""
Converts a string of roman numbers into an integer.
.. code: python
reusables.roman_to_int("XXXVI")
# 36
:param roman_string: XVI or similar
:return: parsed integer
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train | int_to_words | Converts an integer or float to words.
.. code: python
reusables.int_to_number(445)
# 'four hundred forty-five'
reusables.int_to_number(1.45)
# 'one and forty-five hundredths'
:param number: String, integer, or float to convert to words. The decimal
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"""
Converts an integer or float to words.
.. code: python
reusables.int_to_number(445)
# 'four hundred forty-five'
reusables.int_to_number(1.45)
# 'one and forty-five hundredths'
:param number: String, integer, or float to co... | def int_to_words(number, european=False):
"""
Converts an integer or float to words.
.. code: python
reusables.int_to_number(445)
# 'four hundred forty-five'
reusables.int_to_number(1.45)
# 'one and forty-five hundredths'
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train | filter_osm_file | Downloads (and compiles) osmfilter tool from web and
calls that osmfilter to only filter out only the road elements. | roadmaptools/osmfilter.py | def filter_osm_file():
""" Downloads (and compiles) osmfilter tool from web and
calls that osmfilter to only filter out only the road elements.
"""
print_info('Filtering OSM file...')
start_time = time.time()
if check_osmfilter():
# params = '--keep="highway=motorway =motorway... | def filter_osm_file():
""" Downloads (and compiles) osmfilter tool from web and
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"""
print_info('Filtering OSM file...')
start_time = time.time()
if check_osmfilter():
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train | task_list | Scans the modules set in RQ_JOBS_MODULES for RQ jobs decorated with @task
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"""
Scans the modules set in RQ_JOBS_MODULES for RQ jobs decorated with @task
Compiles a readable list for Job model task choices
"""
try:
jobs_module = settings.RQ_JOBS_MODULE
except AttributeError:
raise ImproperlyConfigured(_("You have to define RQ_JOBS_MODULE... | def task_list():
"""
Scans the modules set in RQ_JOBS_MODULES for RQ jobs decorated with @task
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"""
try:
jobs_module = settings.RQ_JOBS_MODULE
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train | Job.rq_job | The last RQ Job this ran on | django_rq_jobs/models.py | def rq_job(self):
"""The last RQ Job this ran on"""
if not self.rq_id or not self.rq_origin:
return
try:
return RQJob.fetch(self.rq_id, connection=get_connection(self.rq_origin))
except NoSuchJobError:
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"""The last RQ Job this ran on"""
if not self.rq_id or not self.rq_origin:
return
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return RQJob.fetch(self.rq_id, connection=get_connection(self.rq_origin))
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train | Job.rq_link | Link to Django-RQ status page for this job | django_rq_jobs/models.py | def rq_link(self):
"""Link to Django-RQ status page for this job"""
if self.rq_job:
url = reverse('rq_job_detail',
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"""Link to Django-RQ status page for this job"""
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url = reverse('rq_job_detail',
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train | Job.rq_task | The function to call for this task.
Config errors are caught by tasks_list() already. | django_rq_jobs/models.py | def rq_task(self):
"""
The function to call for this task.
Config errors are caught by tasks_list() already.
"""
task_path = self.task.split('.')
module_name = '.'.join(task_path[:-1])
task_name = task_path[-1]
module = importlib.import_module(module_name... | def rq_task(self):
"""
The function to call for this task.
Config errors are caught by tasks_list() already.
"""
task_path = self.task.split('.')
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train | fix_module | Fix for tasks without a module. Provides backwards compatibility with < 0.1.5 | django_rq_jobs/management/commands/rqjobs.py | def fix_module(job):
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Fix for tasks without a module. Provides backwards compatibility with < 0.1.5
"""
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Fix for tasks without a module. Provides backwards compatibility with < 0.1.5
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train | drop_model_tables | Drop tables for all given models (in the right order). | aioorm/database.py | async def drop_model_tables(models, **drop_table_kwargs):
"""Drop tables for all given models (in the right order)."""
for m in reversed(sort_models_topologically(models)):
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train | model_to_dict | Convert a model instance (and any related objects) to a dictionary.
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:param bool backrefs: Whether lists of related objects should be recursed.
:param only: A list (or set) of field instances indicating which fields
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"""
Convert a model instance (and any related objects) to a dictionary.
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train | RowConverter.extract_rows | Extract `self.sample_size` rows from the CSV file and analyze their
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:param str file_or_name: A string filename or a file handle.
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:param str file_or_name: A string filename or a file handle.
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train | RowConverter.get_checks | Return a list of functions to use when testing values. | aioorm/utils/csv_utils/csv_loader.py | def get_checks(self):
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return [
self.is_date,
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train | RowConverter.analyze | Analyze the given rows and try to determine the type of value stored.
:param list rows: A list-of-lists containing one or more rows from a
csv file.
:returns: A list of peewee Field objects for each column in the CSV. | aioorm/utils/csv_utils/csv_loader.py | def analyze(self, rows):
"""
Analyze the given rows and try to determine the type of value stored.
:param list rows: A list-of-lists containing one or more rows from a
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:returns: A list of peewee Field objects for each column in the CSV.
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... | def analyze(self, rows):
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Analyze the given rows and try to determine the type of value stored.
:param list rows: A list-of-lists containing one or more rows from a
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:returns: A list of peewee Field objects for each column in the CSV.
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train | BleachField.clean_text | Clean text using bleach. | bleachfields/bleachfield.py | def clean_text(self, text):
'''Clean text using bleach.'''
if text is None:
return ''
text = re.sub(ILLEGAL_CHARACTERS_RE, '', text)
if '<' in text or '<' in text:
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return unescape(text) | def clean_text(self, text):
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return ''
text = re.sub(ILLEGAL_CHARACTERS_RE, '', text)
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train | LoadFromDisk.path | Constructs the path to categories, images and features.
This path function assumes that the following storage scheme is used on
the hard disk to access categories, images and features:
- categories: /impath/category
- images: /impath/category/category_image.png
-... | ocupy/loader.py | def path(self, category = None, image = None, feature = None):
"""
Constructs the path to categories, images and features.
This path function assumes that the following storage scheme is used on
the hard disk to access categories, images and features:
- categories: /impath/categor... | def path(self, category = None, image = None, feature = None):
"""
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train | LoadFromDisk.get_image | Loads an image from disk. | ocupy/loader.py | def get_image(self, cat, img):
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train | LoadFromDisk.get_feature | Load a feature from disk. | ocupy/loader.py | def get_feature(self, cat, img, feature):
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train | SaveToDisk.save_image | Saves a new image. | ocupy/loader.py | def save_image(self, cat, img, data):
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train | SaveToDisk.save_feature | Saves a new feature. | ocupy/loader.py | def save_feature(self, cat, img, feature, data):
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train | factorise_field | This removes a common beginning from the data of the fields, placing
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if parameter_name is None, then it will be <field_name>_common.
So far, it's probably only useful for the file_name.
TODO: remove field entirely ... | ocupy/datamat_tools.py | def factorise_field(dm, field_name, boundary_char = None, parameter_name=None):
"""This removes a common beginning from the data of the fields, placing
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if parameter_name is None, then it will be <field_name>_common.
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train | randsample | Draws nr_samples random samples from vec. | ocupy/utils.py | def randsample(vec, nr_samples, with_replacement = False):
"""
Draws nr_samples random samples from vec.
"""
if not with_replacement:
return np.random.permutation(vec)[0:nr_samples]
else:
return np.asarray(vec)[np.random.randint(0, len(vec), nr_samples)] | def randsample(vec, nr_samples, with_replacement = False):
"""
Draws nr_samples random samples from vec.
"""
if not with_replacement:
return np.random.permutation(vec)[0:nr_samples]
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train | calc_resize_factor | Calculates how much prediction.shape and image_size differ. | ocupy/utils.py | def calc_resize_factor(prediction, image_size):
"""
Calculates how much prediction.shape and image_size differ.
"""
resize_factor_x = prediction.shape[1] / float(image_size[1])
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train | dict_2_mat | Creates a NumPy array from a dictionary with only integers as keys and
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or ignored.
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data : dict
a dictionary whose values are adequate input to the second argument
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train | snip_string_middle | >>> snip_string_middle('this is long', 8)
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>>> snip_string_middle('this is long', 12)
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>>> snip_string_middle('this is long', 8, '~')
'thi~long' | ocupy/utils.py | def snip_string_middle(string, max_len=20, snip_string='...'):
"""
>>> snip_string_middle('this is long', 8)
'th...ong'
>>> snip_string_middle('this is long', 12)
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>>> snip_string_middle('this is long', 8, '~')
'thi~long'
"""
#warn('use snip_string() instead', Dep... | def snip_string_middle(string, max_len=20, snip_string='...'):
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>>> snip_string_middle('this is long', 12)
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"""
Snips a string so that it is no longer than max_len, replacing deleted
characters with the snip_string.
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boundary_char defines a boundary that must be preserved, so that the
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train | factorise_strings | Given a list of strings, finds the longest string that is common
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boundary_char defines a boundary that must be preserved, so that the
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train | load | Load datamat at path.
Parameters:
path : string
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"""
Load datamat at path.
Parameters:
path : string
Absolute path of the file to load from.
"""
f = h5py.File(path,'r')
try:
dm = fromhdf5(f[variable])
finally:
f.close()
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"""
Load datamat at path.
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path : string
Absolute path of the file to load from.
"""
f = h5py.File(path,'r')
try:
dm = fromhdf5(f[variable])
finally:
f.close()
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fields: Dictionary
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parameters: Dictionary
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fields: Dictionary
The values will be used as fields of the datamat and the keys
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train | Datamat.filter | Filters a datamat by different aspects.
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Filters a datamat by different aspects.
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train | Datamat.copy | Returns a copy of the datamat. | ocupy/datamat.py | def copy(self):
"""
Returns a copy of the datamat.
"""
return self.filter(np.ones(self._num_fix).astype(bool)) | def copy(self):
"""
Returns a copy of the datamat.
"""
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train | Datamat.save | Saves Datamat to path.
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path : string
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"""
Saves Datamat to path.
Parameters:
path : string
Absolute path of the file to save to.
"""
f = h5py.File(path, 'w')
try:
fm_group = f.create_group('Datamat')
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path : string
Absolute path of the file to save to.
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train | Datamat.set_param | Set the value of a parameter. | ocupy/datamat.py | def set_param(self, key, value):
"""
Set the value of a parameter.
"""
self.__dict__[key] = value
self._parameters[key] = value | def set_param(self, key, value):
"""
Set the value of a parameter.
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self.__dict__[key] = value
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train | Datamat.by_field | Returns an iterator that iterates over unique values of field
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field : string
Filters the datamat for every unique value in field and yields
the filtered datamat.
Returns:
datamat : Datamat that is filtered according to one of the uniqu... | ocupy/datamat.py | def by_field(self, field):
"""
Returns an iterator that iterates over unique values of field
Parameters:
field : string
Filters the datamat for every unique value in field and yields
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Returns:
datamat : Datamat... | def by_field(self, field):
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Returns an iterator that iterates over unique values of field
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field : string
Filters the datamat for every unique value in field and yields
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train | Datamat.add_field_like | Add a new field to the Datamat with the dtype of the
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train | Datamat.rm_field | Remove a field from the datamat.
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"""
Remove a field from the datamat.
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name : string
Name of the field to be removed
"""
if not name in self._fields:
raise ValueError
self._fields.remove(name)
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Remove a field from the datamat.
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name : string
Name of the field to be removed
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train | Datamat.add_parameter | Adds a parameter to the existing Datamat.
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Adds a parameter to the existing Datamat.
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train | Datamat.parameter_to_field | Promotes a parameter to a field by creating a new array of same
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value of the parameter, and then removing that parameter. | ocupy/datamat.py | def parameter_to_field(self, name):
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train | makeAngLenHist | Histograms and performs a spline fit on the given data,
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ad : array
The data to be histogrammed along the x-axis.
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ld : array
The data to be histogrammed along the y-axis.
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ad : array
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train | firstSacDist | Computes the distribution of angle and length
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fm : ocupy.fixmat
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"""
Computes the distribution of angle and length
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Parameters:
fm : ocupy.fixmat
The fixation data to be analysed
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ang, leng, ad, ld = anglendiff(fm, return_abs=True)
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"""
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fm : ocupy.fixmat
The fixation data to be analysed
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train | trajLenDist | Computes the distribution of trajectory lengths, i.e.
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Parameters:
fm : ocupy.fixmat
The fixation data to be analysed | ocupy/simulator.py | def trajLenDist(fm):
"""
Computes the distribution of trajectory lengths, i.e.
the number of saccades that were made as a part of one trajectory
Parameters:
fm : ocupy.fixmat
The fixation data to be analysed
"""
trajLen = np.roll(fm.fix, 1... | def trajLenDist(fm):
"""
Computes the distribution of trajectory lengths, i.e.
the number of saccades that were made as a part of one trajectory
Parameters:
fm : ocupy.fixmat
The fixation data to be analysed
"""
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train | reshift | Transforms the given number element into a range of [-180, 180],
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"""
Transforms the given number element into a range of [-180, 180],
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Prepares the data to be replicated. Calculates the second-order length
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Prepares the data to be replicated. Calculates the second-order length
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train | FixGen._calc_xy | Calculates the coordinates after a specific saccade was made.
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The coordinates before the saccade was made
angle : float or int
The angle that the next saccade encloses with the
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"""
Calculates the coordinates after a specific saccade was made.
Parameters:
(x,y) : tuple of floats or ints
The coordinates before the saccade was made
angle : float or int
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Calculates the coordinates after a specific saccade was made.
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(x,y) : tuple of floats or ints
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train | FixGen._draw | Draws a new length- and angle-difference pair and calculates
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prev_angle : float, optional
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prev_length : float, optional
... | ocupy/simulator.py | def _draw(self, prev_angle = None, prev_length = None):
"""
Draws a new length- and angle-difference pair and calculates
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prev_angle : float, optional
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Draws a new length- and angle-difference pair and calculates
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train | FixGen.sample_many | Generates a given number of trajectories, using the method sample().
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num_samples : int, optional
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"""
Generates a given number of trajectories, using the method sample().
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num_samples : int, optional
The number of trajectories that shall be generated.
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Generates a given number of trajectories, using the method sample().
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num_samples : int, optional
The number of trajectories that shall be generated.
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train | FixGen.sample | Draws a trajectory length, first coordinates, lengths, angles and
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"""
Draws a trajectory length, first coordinates, lengths, angles and
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Each call creates one complete trajectory.
"""
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Draws a trajectory length, first coordinates, lengths, angles and
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train | FixGen.drawFrom | Draws a value from a cumulative sum.
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Cumulative sum from which shall be drawn.
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Draws a value from a cumulative sum.
Parameters:
cumsum : array
Cumulative sum from which shall be drawn.
Returns:
int : Index of the cumulative sum element drawn.
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Draws a value from a cumulative sum.
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Cumulative sum from which shall be drawn.
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train | load | Load fixmat at path.
Parameters:
path : string
Absolute path of the file to load from. | ocupy/fixmat.py | def load(path):
"""
Load fixmat at path.
Parameters:
path : string
Absolute path of the file to load from.
"""
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"""
Load fixmat at path.
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path : string
Absolute path of the file to load from.
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train | compute_fdm | Computes a fixation density map for the calling fixmat.
Creates a map the size of the image fixations were recorded on.
Every pixel contains the frequency of fixations
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Gaussian kernel to approximate the area with highest processi... | ocupy/fixmat.py | def compute_fdm(fixmat, fwhm=2, scale_factor=1):
"""
Computes a fixation density map for the calling fixmat.
Creates a map the size of the image fixations were recorded on.
Every pixel contains the frequency of fixations
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... | def compute_fdm(fixmat, fwhm=2, scale_factor=1):
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train | relative_bias | Computes the relative bias, i.e. the distribution of saccade angles
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Parameters:
fm : DataMat
The fixation data to use
scale_factor : double
Returns:
2D probability distribution of saccade angles and amplitudes. | ocupy/fixmat.py | def relative_bias(fm, scale_factor = 1, estimator = None):
"""
Computes the relative bias, i.e. the distribution of saccade angles
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Parameters:
fm : DataMat
The fixation data to use
scale_factor : double
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Computes the relative bias, i.e. the distribution of saccade angles
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fm : DataMat
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train | DirectoryFixmatFactory | Concatenates all fixmats in dir and returns the resulting single
fixmat.
Parameters:
directory : string
Path from which the fixmats should be loaded
categories : instance of stimuli.Categories, optional
If given, the resulting fixmat provides direct access
... | ocupy/fixmat.py | def DirectoryFixmatFactory(directory, categories = None, glob_str = '*.mat', var_name = 'fixmat'):
"""
Concatenates all fixmats in dir and returns the resulting single
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Parameters:
directory : string
Path from which the fixmats should be loaded
categories : instan... | def DirectoryFixmatFactory(directory, categories = None, glob_str = '*.mat', var_name = 'fixmat'):
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train | FixmatFactory | Loads a single fixmat (fixmatfile).
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fixmatfile : string
The matlab fixmat that should be loaded.
categories : instance of stimuli.Categories, optional
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Loads a single fixmat (fixmatfile).
Parameters:
fixmatfile : string
The matlab fixmat that should be loaded.
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Loads a single fixmat (fixmatfile).
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fixmatfile : string
The matlab fixmat that should be loaded.
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train | FixMat.add_feature_values | Adds feature values of feature 'feature' to all fixations in
the calling fixmat.
For fixations out of the image boundaries, NaNs are returned.
The function generates a new attribute field named with the
string in features that contains an np.array listing feature
values... | ocupy/fixmat.py | def add_feature_values(self, features):
"""
Adds feature values of feature 'feature' to all fixations in
the calling fixmat.
For fixations out of the image boundaries, NaNs are returned.
The function generates a new attribute field named with the
string in featu... | def add_feature_values(self, features):
"""
Adds feature values of feature 'feature' to all fixations in
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For fixations out of the image boundaries, NaNs are returned.
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"""
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train | get_velocity | Compute velocity of eye-movements.
Samplemat must contain fields 'x' and 'y', specifying the x,y coordinates
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'''
Compute velocity of eye-movements.
Samplemat must contain fields 'x' and 'y', specifying the x,y coordinates
of gaze location. The function assumes that the values in x,y are sampled
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'''
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Compute velocity of eye-movements.
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of gaze location. The function assumes that the values in x,y are sampled
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train | saccade_detection | Detect saccades in a stream of gaze location samples.
Coordinates in samplemat are assumed to be in degrees.
Saccades are detect by a velocity/acceleration threshold approach.
A saccade starts when a) the velocity is above threshold, b) the
acceleration is above acc_thresh at least once during the int... | ocupy/samples2fix.py | def saccade_detection(samplemat, Hz=200, threshold=30,
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ignore_blinks=False):
'''
Detect saccades in a stream of gaze location samples.
Coordinates in samplemat are assumed to be in degrees.
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Detect saccades in a stream of gaze location samples.
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train | fixation_detection | Detect Fixation from saccades.
Fixations are defined as intervals between saccades. This function
also calcuates start and end times (in ms) for each fixation.
Input:
samplemat: datamat
Contains the recorded samples and associated metadata.
saccades: ndarray
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respect_trial_borders=False, sample_times=None):
'''
Detect Fixation from saccades.
Fixations are defined as intervals between saccades. This function
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train | parse | Parse a subscription list and return a dict containing the results.
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:type parse_obj: str or file
:param agent: User-Agent header to be sent when requesting a URL
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train | FixmatStimuliFactory | Constructs an categories object for all image / category
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Parameters:
fm: FixMat
Used for extracting valid category/image combination.
loader: loader
Loader that accesses the stimuli for this fixmat
Returns:
Categories object | ocupy/stimuli.py | def FixmatStimuliFactory(fm, loader):
"""
Constructs an categories object for all image / category
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Parameters:
fm: FixMat
Used for extracting valid category/image combination.
loader: loader
Loader that accesses the stimuli for th... | def FixmatStimuliFactory(fm, loader):
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Constructs an categories object for all image / category
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Used for extracting valid category/image combination.
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train | DirectoryStimuliFactory | Takes an input path to the images folder of an experiment and generates
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loader : Loader object which contains
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Takes an input path to the images folder of an experiment and generates
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Parameters :
loader : Loader object which contains
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Takes an input path to the images folder of an experiment and generates
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train | Categories.fixations | Filter the fixmat such that it only contains fixations on images
in categories that are also in the categories object | ocupy/stimuli.py | def fixations(self):
''' Filter the fixmat such that it only contains fixations on images
in categories that are also in the categories object'''
if not self._fixations:
raise RuntimeError('This Images object does not have'
+' an associated fixmat')
if len(lis... | def fixations(self):
''' Filter the fixmat such that it only contains fixations on images
in categories that are also in the categories object'''
if not self._fixations:
raise RuntimeError('This Images object does not have'
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train | Image.data | Saves a new image to disk | ocupy/stimuli.py | def data(self, value):
"""
Saves a new image to disk
"""
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"""
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train | Image.fixations | Returns all fixations that are on this image.
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"""
Returns all fixations that are on this image.
A precondition for this to work is that a fixmat
is associated with this Image object.
"""
if not self._fixations:
raise RuntimeError('This Images object does not have'
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"""
Returns all fixations that are on this image.
A precondition for this to work is that a fixmat
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train | SimpleXValidation.generate | Generator for creating the cross-validation slices.
Returns
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"""
Generator for creating the cross-validation slices.
Returns
A tuple of that contains two fixmats (training and test)
and two Category objects (test and train).
"""
for _ in range(0, self.num_slices):
#1. separate fixm... | def generate(self):
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Generator for creating the cross-validation slices.
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train | prepare_data | Computes angle and length differences up to given order and deletes
suspiciously long fixations.
Input
fm: Fixmat
Fixmat for which to comput angle and length differences
max_back: Int
Computes delta angle and amplitude up to order max_back.
dur_cap: Int
... | ocupy/saccade_geometry.py | def prepare_data(fm, max_back, dur_cap=700):
'''
Computes angle and length differences up to given order and deletes
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Input
fm: Fixmat
Fixmat for which to comput angle and length differences
max_back: Int
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'''
Computes angle and length differences up to given order and deletes
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fm: Fixmat
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train | saccadic_momentum_effect | Computes the mean fixation duration at forward angles. | ocupy/saccade_geometry.py | def saccadic_momentum_effect(durations, forward_angle,
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"""
Computes the mean fixation duration at forward angles.
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durations_per_da = np.nan * np.ones((len(e_angle) - 1,))
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Computes the mean fixation duration at forward angles.
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train | ior_effect | Computes a measure of fixation durations at delta angle and delta
length combinations. | ocupy/saccade_geometry.py | def ior_effect(durations, angle_diffs, length_diffs,
summary_stat=np.mean, parallel=True, min_samples=20):
"""
Computes a measure of fixation durations at delta angle and delta
length combinations.
"""
raster = np.empty((len(e_dist) - 1, len(e_angle) - 1), dtype=object)
for a, (a_... | def ior_effect(durations, angle_diffs, length_diffs,
summary_stat=np.mean, parallel=True, min_samples=20):
"""
Computes a measure of fixation durations at delta angle and delta
length combinations.
"""
raster = np.empty((len(e_dist) - 1, len(e_angle) - 1), dtype=object)
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train | predict_fixation_duration | Fits a non-linear piecewise regression to fixtaion durations for a fixmat.
Returns corrected fixation durations. | ocupy/saccade_geometry.py | def predict_fixation_duration(
durations, angles, length_diffs, dataset=None, params=None):
"""
Fits a non-linear piecewise regression to fixtaion durations for a fixmat.
Returns corrected fixation durations.
"""
if dataset is None:
dataset = np.ones(durations.shape)
corrected_d... | def predict_fixation_duration(
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"""
Fits a non-linear piecewise regression to fixtaion durations for a fixmat.
Returns corrected fixation durations.
"""
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train | subject_predictions | Calculates the saccadic momentum effect for individual subjects.
Removes any effect of amplitude differences.
The parameters are fitted on unbinned data. The effects are
computed on binned data. See e_dist and e_angle for the binning
parameter. | ocupy/saccade_geometry.py | def subject_predictions(fm, field='SUBJECTINDEX',
method=predict_fixation_duration, data=None):
'''
Calculates the saccadic momentum effect for individual subjects.
Removes any effect of amplitude differences.
The parameters are fitted on unbinned data. The effects are
comp... | def subject_predictions(fm, field='SUBJECTINDEX',
method=predict_fixation_duration, data=None):
'''
Calculates the saccadic momentum effect for individual subjects.
Removes any effect of amplitude differences.
The parameters are fitted on unbinned data. The effects are
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train | intersubject_scores | Calculates how well the fixations from a set of subjects on a set of
images can be predicted with the fixations from another set of subjects
on another set of images.
The prediction is carried out by computing a fixation density map from
fixations of predicting_subjects subjects on predicting_images im... | ocupy/bounds.py | def intersubject_scores(fm, category, predicting_filenumbers,
predicting_subjects, predicted_filenumbers,
predicted_subjects, controls = True, scale_factor = 1):
"""
Calculates how well the fixations from a set of subjects on a set of
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predicted_subjects, controls = True, scale_factor = 1):
"""
Calculates how well the fixations from a set of subjects on a set of
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train | intersubject_scores_random_subjects | Calculates how well the fixations of n random subjects on one image can
be predicted with the fixations of m other random subjects.
Notes
Function that uses intersubject_auc for computing auc.
Parameters
fm : fixmat instance
category : int
Category from which the fixati... | ocupy/bounds.py | def intersubject_scores_random_subjects(fm, category, filenumber, n_train,
n_predict, controls=True,
scale_factor = 1):
"""
Calculates how well the fixations of n random subjects on one image can
be predicted with the fixations ... | def intersubject_scores_random_subjects(fm, category, filenumber, n_train,
n_predict, controls=True,
scale_factor = 1):
"""
Calculates how well the fixations of n random subjects on one image can
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train | upper_bound | compute the inter-subject consistency upper bound for a fixmat.
Input:
fm : a fixmat instance
nr_subs : the number of subjects used for the prediction. Defaults
to the total number of subjects in the fixmat minus 1
scale_factor : the scale factor of the FDMs. Default is 1.... | ocupy/bounds.py | def upper_bound(fm, nr_subs = None, scale_factor = 1):
"""
compute the inter-subject consistency upper bound for a fixmat.
Input:
fm : a fixmat instance
nr_subs : the number of subjects used for the prediction. Defaults
to the total number of subjects in the fixmat minus 1... | def upper_bound(fm, nr_subs = None, scale_factor = 1):
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compute the inter-subject consistency upper bound for a fixmat.
Input:
fm : a fixmat instance
nr_subs : the number of subjects used for the prediction. Defaults
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train | lower_bound | Compute the spatial bias lower bound for a fixmat.
Input:
fm : a fixmat instance
nr_subs : the number of subjects used for the prediction. Defaults
to the total number of subjects in the fixmat minus 1
nr_imgs : the number of images used for prediction. If given, the
... | ocupy/bounds.py | def lower_bound(fm, nr_subs = None, nr_imgs = None, scale_factor = 1):
"""
Compute the spatial bias lower bound for a fixmat.
Input:
fm : a fixmat instance
nr_subs : the number of subjects used for the prediction. Defaults
to the total number of subjects in the fixmat minu... | def lower_bound(fm, nr_subs = None, nr_imgs = None, scale_factor = 1):
"""
Compute the spatial bias lower bound for a fixmat.
Input:
fm : a fixmat instance
nr_subs : the number of subjects used for the prediction. Defaults
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train | ind2sub | Calculates subscripts for indices into regularly spaced matrixes. | ocupy/parallel.py | def ind2sub(ind, dimensions):
"""
Calculates subscripts for indices into regularly spaced matrixes.
"""
# check that the index is within range
if ind >= np.prod(dimensions):
raise RuntimeError("ind2sub: index exceeds array size")
cum_dims = list(dimensions)
cum_dims.reverse()
m =... | def ind2sub(ind, dimensions):
"""
Calculates subscripts for indices into regularly spaced matrixes.
"""
# check that the index is within range
if ind >= np.prod(dimensions):
raise RuntimeError("ind2sub: index exceeds array size")
cum_dims = list(dimensions)
cum_dims.reverse()
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train | sub2ind | An exemplary sub2ind implementation to create randomization
scripts.
This function calculates indices from subscripts into regularly spaced
matrixes. | ocupy/parallel.py | def sub2ind(indices, dimensions):
"""
An exemplary sub2ind implementation to create randomization
scripts.
This function calculates indices from subscripts into regularly spaced
matrixes.
"""
# check that none of the indices exceeds the size of the array
if any([i > j for i, j in zip(... | def sub2ind(indices, dimensions):
"""
An exemplary sub2ind implementation to create randomization
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This function calculates indices from subscripts into regularly spaced
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"""
# check that none of the indices exceeds the size of the array
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train | RestoreTaskStoreFactory | Restores a task store from file. | ocupy/parallel.py | def RestoreTaskStoreFactory(store_class, chunk_size, restore_file, save_file):
"""
Restores a task store from file.
"""
intm_results = np.load(restore_file)
intm = intm_results[intm_results.files[0]]
idx = np.isnan(intm).flatten().nonzero()[0]
partitions = math.ceil(len(idx) / float(chunk_si... | def RestoreTaskStoreFactory(store_class, chunk_size, restore_file, save_file):
"""
Restores a task store from file.
"""
intm_results = np.load(restore_file)
intm = intm_results[intm_results.files[0]]
idx = np.isnan(intm).flatten().nonzero()[0]
partitions = math.ceil(len(idx) / float(chunk_si... | [
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train | TaskManager.xmlrpc_reschedule | Reschedule all running tasks. | ocupy/parallel.py | def xmlrpc_reschedule(self):
"""
Reschedule all running tasks.
"""
if not len(self.scheduled_tasks) == 0:
self.reschedule = list(self.scheduled_tasks.items())
self.scheduled_tasks = {}
return True | def xmlrpc_reschedule(self):
"""
Reschedule all running tasks.
"""
if not len(self.scheduled_tasks) == 0:
self.reschedule = list(self.scheduled_tasks.items())
self.scheduled_tasks = {}
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train | TaskManager.xmlrpc_get_task | Return a new task description: ID and necessary parameters,
all are given in a dictionary | ocupy/parallel.py | def xmlrpc_get_task(self):
"""
Return a new task description: ID and necessary parameters,
all are given in a dictionary
"""
try:
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(task_id, cur_task) = next(self.task_iterator)
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(task_id, ... | def xmlrpc_get_task(self):
"""
Return a new task description: ID and necessary parameters,
all are given in a dictionary
"""
try:
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(task_id, cur_task) = next(self.task_iterator)
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(task_id, ... | [
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train | TaskManager.xmlrpc_task_done | Take the results of a computation and put it into the results list. | ocupy/parallel.py | def xmlrpc_task_done(self, result):
"""
Take the results of a computation and put it into the results list.
"""
(task_id, task_results) = result
del self.scheduled_tasks[task_id]
self.task_store.update_results(task_id, task_results)
self.results += 1
retur... | def xmlrpc_task_done(self, result):
"""
Take the results of a computation and put it into the results list.
"""
(task_id, task_results) = result
del self.scheduled_tasks[task_id]
self.task_store.update_results(task_id, task_results)
self.results += 1
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train | TaskManager.xmlrpc_status | Return a status message | ocupy/parallel.py | def xmlrpc_status(self):
"""
Return a status message
"""
return ("""
%i Jobs are still wating for execution
%i Jobs are being processed
%i Jobs are done
""" %(self.task_store.partitions -
self.results -
len(self.scheduled_... | def xmlrpc_status(self):
"""
Return a status message
"""
return ("""
%i Jobs are still wating for execution
%i Jobs are being processed
%i Jobs are done
""" %(self.task_store.partitions -
self.results -
len(self.scheduled_... | [
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train | TaskManager.xmlrpc_save2file | Save results and own state into file. | ocupy/parallel.py | def xmlrpc_save2file(self, filename):
"""
Save results and own state into file.
"""
savefile = open(filename,'wb')
try:
pickle.dump({'scheduled':self.scheduled_tasks,
'reschedule':self.reschedule},savefile)
except pickle.PicklingError... | def xmlrpc_save2file(self, filename):
"""
Save results and own state into file.
"""
savefile = open(filename,'wb')
try:
pickle.dump({'scheduled':self.scheduled_tasks,
'reschedule':self.reschedule},savefile)
except pickle.PicklingError... | [
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train | Worker.run | This function needs to be called to start the computation. | ocupy/parallel.py | def run(self):
"""This function needs to be called to start the computation."""
(task_id, tasks) = self.server.get_task()
self.task_store.from_dict(tasks)
for (index, task) in self.task_store:
result = self.compute(index, task)
self.results.append(result)
... | def run(self):
"""This function needs to be called to start the computation."""
(task_id, tasks) = self.server.get_task()
self.task_store.from_dict(tasks)
for (index, task) in self.task_store:
result = self.compute(index, task)
self.results.append(result)
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train | TaskStore.from_dict | Configures the task store to be the task_store described
in description | ocupy/parallel.py | def from_dict(self, description):
"""Configures the task store to be the task_store described
in description"""
assert(self.ident == description['ident'])
self.partitions = description['partitions']
self.indices = description['indices'] | def from_dict(self, description):
"""Configures the task store to be the task_store described
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assert(self.ident == description['ident'])
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