Singapore Accountancy Commission accidentally leaked personal data of 6,541 individuals
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The Singapore Accountancy Commission (SAC) revealed today (22 Nov) that it accidentally leaked the personal data of 6,541 individuals over a period of four months this year.
The data was not leaked in a cyber-attack – it was unintentionally disclosed after a folder containing the data was accidentally attached in emails about administrative matters and sent to 41 recipients in 21 accredited training firms and one vendor organisation between 12 June and 22 Oct.
The leaked information includes the names, NRIC numbers, dates of birth, contact information, education and employment records and Singapore chartered accountant qualification examination results of past and current Singapore chartered accountant qualification candidates, accredited training organisation personnel, and others involved in the administration of the Singapore chartered account qualification programme before 17 May.
SAC discovered the data leak on 7 Nov after implementing a new data protection filter, according to the recommendations set out by the Public Sector Data Security Review Committee.
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After discovering the leak, the authority requested all 22 firms that received the confidential data to delete the contents of the folder and investigate whether the data was forwarded to others. The SAC said today that all 22 firms have confirmed that the data folder and forwarded content have all been deleted.
Revealing that it has informed the Personal Data Protection Commission about the incident, the SAC said in a press statement: “SAC takes a serious view of this incident, and deeply regrets this mistake. SAC will set up a panel to review the incident and make any necessary recommendations.”
Affected individuals can contact SAC at<EMAIL_ADDRESS>for further information. -/TISG
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- Chapter 1: JSP Foundations
- Chapter 2: Using JSP
- Chapter 3: Role Separation with JavaBeans
- Chapter 4: Role Separation with Custom Tags
- Chapter 5: Development Using Patterns
- Chapter 6: The Decorating Filter Pattern
- Chapter 7: The Front Controller Pattern
- Chapter 8: The View-Helper Pattern
- Chapter 9: Testing Techniques
- Chapter 10: Deployment Techniques
- Chapter 11: Application Frameworks
- Chapter 12: Putting it all Together


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Loss functions¶
superres-tomo comes with a series of loss functions not found in the ml packages that it is built on. These are primarily useful in cases where there is class imbalance in the training/test set.
Weighted cross entropy¶
The weighted cross entropy (WCE) is an extension of the standard cross entropy. WCE can be used where there is a large class imbalance (excess of a particular label). In WCE all positive examples get weighted by a coefficient, which can be set in inverse proportion to the amount of a given label in the training set.
WCE is defined as
To bias against false positives set \(\beta < 1\) to bias against false positives set \(\beta < 1\)
Balanced cross entropy¶
Balanced cross entropy (BCE) is similar to WCE, but it also biases negatives as well as poitives.
Dice Loss¶
The Dice loss is a loss function that is particularly useful if boundary detection is important in your image analysis. The dice loss is defined as


General info
Site Isolation mode: unknown
Frame Trees
Legend:
Frame[process_id:routing_id:agent_scheduling_group_id]:
SI:site_instance_id, whether process is locked to a
site, site: site_url | url:
last_committed_url


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This example computes matrix-matrix multiplication (sgemm) using WebGPU, future web standard.
When this page is served from milhidaka.github.io
, Chrome enables WebGPU without any config until March 2022 (by origin trial).
As of 2022, you need a beta version web browser, such as Chrome Canary, and enable webgpu feature flag (open chrome://flags/#enable-unsafe-webgpu ).
WebGPU of this browser:
Created by @milhidaka Github
Computes
[1,2 3,4] × [5,6 7,8]
Compares computation result of WebGPU and pure JavaScript implementation.
Note: when m % 32 === 0 && n % 64 === 0 && k % 4 === 0, efficient kernel is used.
M,N,K tuples:


--- title: "\U0001F194 About Me" layout: default ---
Akshay wrote his first code for a construction company at the age of fourteen; his favourite toy in his childhood had been his father’s precious Intel 286. At twenty-three, he built a product that raised seed funding and was later acqui-hired by one of the top five global ad-tech companies; and presently works as a Staff Engineer at Slack.
June 18, 2020
Internet speed is commonly expressed as Mbits/second. It only takes a split second to have information travel thousands of kilometers. But, in some parts of the world it often takes years for technology to reach. However, for people with passion and a genuine desire to learn, nothing can be a barrier. Akshay Deo is one such person...
December 8, 2015
At the age of 23 (2012), I founded a company called RainingClouds along with 3 other co-founders. We built a product, AppSurfer, which in brief, runs Android on the cloud and can stream on any platform. We got a lot of buzz in tech blogs 🗞.
Amazon’s Appstore for Android has long allowed consumers to test apps in the
browser before purchase, but a new startup debuting now wants to offer an
alternative. AppSurfer, as the company is called, has a bigger vision: it wants
to become the “YouTube of Android apps.”
March 21, 2013
AppSurfer, an Indian startup working to bring Android applications to the
browser so users can “try before they buy,” is today launching an Android
application which lets users browse, test, then install the applications they
like.
October 11, 2012
Now, a new service called AppSurfer has arrived on the scene and is taking this
‘try before you buy’ philosophy a step further, with Android apps embeddable on
any website AND in Facebook news feeds.
November
27, 2012
AppSurfer, the online demo site for Android smartphone apps that launched last
October, is now letting developers offer tablet apps, too.  So
try-before-you-buy is a great idea, especially for those apps that you can’t
fully evaluate within the 15-minute Google Play refund period.
January 21, 2013


39 EUR
design magazine
Card Number
CVC
Holder Name
Expiration Date
15
November
2018
Complete payment


U.S.-Employed Chinese Citizen Pleads Guilty to Theft of Trade Secrets
by Associated Press(TULSA, Okla.) — Federal prosecutors say a Chinese national employed by a U.S. petroleum company has pleaded guilty in federal court to stealing trade secrets from his employer.
Authorities say 35-year-old Hongjin Tan pleaded guilty on Tuesday in federal court in Tulsa to theft of a trade secret, unauthorized transmission of a trade secret and unauthorized possession of a trade secret.
A plea agreement calls for Tan to serve up to two years in prison and pay restitution of $150,000.
Tan’s attorney, Ryan Ray, hasn’t returned a telephone message seeking comment.
Court documents indicate Tan worked for Phillips 66 when he stole secrets related to the development of “next generation battery technologies” worth more than $1 billion.
Sentencing is set for February 2020.


--- layout: page title: About header-img: "img/about-bg.png" ---
As a recent graduate in the field of Computer Science, I have the fundamentals down to begin my career. During my undergrad, web development always interested me, but my classes never gave me the tools to confidently dive in. After graduation, I started looking into web development boot camps and came across Flatiron School. I dove in and haven't looked back. I am finally getting the tools I need to be a successful web developer.


getorg package¶
Submodules¶
getorg.core module¶
-
getorg.core.
api_to_web_url
(url)¶
-
getorg.core.
get_org_repos
(github_obj, org_name_or_object)¶
-
getorg.core.
handle_org_name_or_object
(github_obj, org_name_or_object)¶ Takes in either an org name or Github Organization object, and returns a Github Organization object.
getorg.markercluster_output module¶
Files for outputting to a standalone HTML/js markercluster leaflet file Markercluster is released under the MIT license https://github.com/Leaflet/Leaflet.markercluster
Copyright 2012 David Leaver
Leaflet is released under an MIT compatible license Copyright (c) 2010-2016, Vladimir Agafonkin Copyright (c) 2010-2011, CloudMade
https://github.com/Leaflet/Leaflet
-
getorg.markercluster_output.
write_default_css
(folder_name)¶
-
getorg.markercluster_output.
write_htmlmap
(folder_name)¶
-
getorg.markercluster_output.
write_markercluster_css
(folder_name)¶
-
getorg.markercluster_output.
write_markerclusterjs
(folder_name)¶
-
getorg.markercluster_output.
write_markerclustersrc_js
(folder_name)¶
-
getorg.markercluster_output.
write_screen_css
(folder_name)¶
getorg.orgevents module¶
-
getorg.orgevents.
get_org_open_issues
(github_obj, org_name_or_obj, days_open=0, comments=0, debug=1)¶ Get all open issues for an organization.
Optional parameter days_open to only get issues open for longer than a certain number of days (can pass float values for days).
TODO: more debug status messages, like in map_orgs
getorg.orgmap module¶
-
getorg.orgmap.
create_map_obj
(center=[30, 5], zoom=2)¶ Creates a new ipyleaflet map object that defaults to a view of the entire world. Can specify center
-
getorg.orgmap.
csv_to_js_var
(input_file, output_file)¶ Converts a CSV file to a Javascript file with one long list variable.
CSV file must be in the format of: point info, longitude, latitude
-
getorg.orgmap.
get_geolocation
(geocode_obj, loc)¶ Wrapper function around geopy’s geocode function. Used for retry, which will run it at most 3 times to get a non-error return value. It will not retry if it successfully returns a value.
-
getorg.orgmap.
get_org_contributor_locations
(github_obj, org_name_or_object, debug=1, exclude_usernames=[])¶ For a GitHub organization, get location for contributors to any repo in the org.
TODO: Break this into smaller functions. There are a lot of these.
Parameters: github_obj : Pygithub Github() object
org_name_org_object : string or Github org object
exclude_usernames : list of strings, optional
usernames to not include in the returned list
debug : int, optional
Returns: location_dict : dict
dictionary of {username URLS : geopy Locations}
metadata_dict : dict
dictionary of metadata about the org and the query
Notes
- Debug levels:
-
getorg.orgmap.
location_dict_to_csv
(org_location_dict, filename, hashed_usernames=True)¶ Outputs a dict of { user : geopy location } to a CSV file.
parameters and return types:
:org_location_dict: dictionary of {ids : geopy locations}, created by get_org_contributor_locations :filename: string of the filename to write to :hashed_usernames: optional parameter that returns sha-1 hash of username for privacy reasons :return: nothing if successful, error if exception caught
Uses hashes of usernames by default for privacy reasons. Think carefully about publishing location data about uniquely identifiable users. Hashing allows you to check unique users without revealing personal information.
-
getorg.orgmap.
location_dict_to_geocoord_dict
(location_dict)¶ Converts a dictionary of {users : geopy location objects} to {users : [longitude, latitude]}
-
getorg.orgmap.
location_dict_to_geojson
(org_location_dict, filename, hashed_usernames=True)¶ CURRENTLY BROKEN! Outputs a dict of users : locations to a CSV file.
Requires org_location_dict and filename, optional hashed_usernames parameter.
Uses hashes of usernames by default for privacy reasons. Think carefully about publishing location data about uniquely identifiable users. Hashing allows you to check unique users without revealing personal information.
-
getorg.orgmap.
location_dict_to_jsvar
(location_dict, filename, hashed_usernames=True)¶ Writes a dictionary of {users : geopy location objects} to a Javascript file containing a single variable, a list of [user, latitude, longitude]. For use with the clustered leaflets html page in /examples/org_map/.
parameters and return types:
:location_dict: dictionary of {users : geopy location objects} :filename: string of filename to write to :hashed_usernames: replace usernames with sha1 hashes for privacy :returns: success/failure message
-
getorg.orgmap.
location_dict_to_lists
(location_dict)¶ Converts a dictionary of {users : geopy location objects} to three lists of users, longitude, and latitude. Useful for using the zip() function.
-
getorg.orgmap.
map_location_dict
(map_obj, org_location_dict)¶ Maps the locations in a dictionary of {ids : geoPy Locations}.
parameters and return types:
:map_obj: An ipyleaflet map object, can be created with orgmap.create_map_obj :org_location_dict: Dictionary of {ids : geopy locations}, created by get_org_contributor_locations :returns: ipyleaflet map object
-
getorg.orgmap.
map_org
(github_obj, org_name_or_object, debug=1, exclude_usernames=[])¶ Returns a map object, location_dict, and metadata_dict for a Github organization.
-
getorg.orgmap.
map_orgs
(github_obj, org_list_or_object, debug=1, exclude_usernames=[])¶ Returns a map object, location_dict, and metadata_dict for a github organization (name or object) or a list of github organizations (names or objects).
parameters and return types:
:github_obj: pygithub Github object, created with Github() :org_list_or_object: list of Github org objects, list of strings, Github org object, or string. :debug: level of debug/verbosity for printing status updates (see below) :exclude_usernames: list of strings, support is currently broken :returns: set of ipyleaflet map objects, dictionary of {ids:locations}, metadata dictionary for query
debug levels:
:0: quiet :1: (default) is one character per contributor, organization names :2: one character per contributor, organization and repository names :3: full locations for all contributors, organization and repository names
TODO: aggregation for metadata is just summing all the counts. Probably a smarter way to do it.
-
getorg.orgmap.
map_orgs_to_clustermap
(github_obj, org_name_or_object, folder_name='cluster_map', hashed_usernames=True)¶
-
getorg.orgmap.
merge_location_dict
(location_dict_list)¶ Takes a list of location_dicts and returns a single location_dict with all the users and locations. Automatically merges duplicates. Requires unique user ids/names.
-
getorg.orgmap.
output_html_cluster_map
(org_location_dict, folder_name='cluster_map', hashed_usernames=True)¶
getorg.test module¶
-
getorg.test.
test_cluster_map_output
()¶
-
getorg.test.
test_map_org
()¶
-
getorg.test.
test_map_orgs_multiple_orgs
()¶
-
getorg.test.
test_map_orgs_single_org
()¶
-
getorg.test.
test_map_orgs_single_org_with_debug_2
()¶
-
getorg.test.
test_map_orgs_with_excluded_orgs
()¶
-
getorg.test.
test_map_output_js
()¶
-
getorg.test.
test_org_events
()¶


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Detailed course offerings (Time Schedule) are available for
N SCI 101 The Naval Service (3)
General introduction to the U.S. Navy and Marine Corps, with emphasis on organization, missions, roles, tasks, assets, and operations. Offered: A.
View course details in MyPlan: N SCI 101
N SCI 102 History of U.S. Sea Power I (3) I&S
A study of the U.S. Navy and Marine Corps as they fit into the history of the United States, from the American Revolution to the outbreak of World War II. Prerequisite: N SCI 101. Offered: W.
View course details in MyPlan: N SCI 102
N SCI 103 History of U.S. Sea Power II (3) I&S
A study of the U.S. Navy and Marine Corps as they fit into the history of the United States, from World War II to the present day. Prerequisite: N SCI 102. Offered: Sp.
View course details in MyPlan: N SCI 103
N SCI 110 Naval Science Laboratory (*, max. 12)
Evaluates the challenges of leading teams in complex contemporary environments. Topics vary. Required each quarter for NROTC students. Credit/no-credit only. Offered: AWSp.
View course details in MyPlan: N SCI 110
N SCI 201 Naval Leadership and Management (3) I&S
Introduction of the theory and techniques of naval leadership based on principles of behavioral science pertinent to understanding individual and group behavior of adults. Introduces the management process and the relationship of management functions to leadership. Stresses acceptance of a traditional deep sense of moral responsibility on the part of the aspiring leader. Prerequisite: minimum 2.0 grade in N SCI 101. Offered: A.
View course details in MyPlan: N SCI 201
N SCI 202 Navigation I (3) NW
The science and practice of maritime coastal navigation, including visual fixing, dead reckoning, and piloting methods. Computation of tides and currents and nautical rules of the road. Prerequisite: N SCI 101. Offered: W.
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N SCI 203 Navigation II (3) NW
Basic theory and practice of celestial and electronic navigation. Relative motion theory and contact coordination practice in a multiple ship environment. Prerequisite: N SCI 202. Offered: Sp.
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N SCI 301 Naval Ship Systems I (3)
Study of fundamental principles of energy transfer and thermodynamics. An introduction to nuclear propulsion, gas turbines, and auxiliary power systems. Offered: A.
View course details in MyPlan: N SCI 301
N SCI 302 Naval Ship Systems II (3)
Study of ship characteristics, ship design, hydrodynamic forces, stability, damage control, and shipboard electrical systems. Includes introduction to engineering documentation, electrical safety, preventative maintenance, and personnel qualifications. Prerequisite: N SCI 301. Offered: W.
View course details in MyPlan: N SCI 302
N SCI 303 Naval Weapon Systems (3)
Study of fundamental principles of sensor, tracking, weapon delivery subsystems, and current naval weapons. Includes techniques of linear analysis of ballistics and weapons, and dynamics of basic components of weapon-control systems. Prerequisite: N SCI 302. Offered: Sp.
View course details in MyPlan: N SCI 303
N SCI 321 Evolution of Warfare I (3) I&S
Focuses on critical thinking on how and why warfare has changed. Explores how warfare and technology have evolved over time, their relationship, and how innovations promulgate. Topics include maneuver warfare, fourth generational warfare, and various military strategy strategist theories. Designed for future Marine Corps Officers, but open to all students.
View course details in MyPlan: N SCI 321
N SCI 323 USMC Leadership and Administration of Justice I (3)
Concepts, objectives, characteristic qualities, and practical techniques of leadership as exercised by the Marine Corps officer. Emphasizes leadership and management role of the junior officer in the Fleet Marine Forces. Intensive physical activities and outdoor projects to test an individual's physical and mental endurance. Prerequisite: N SCI 110; N SCI 421. Offered: Sp.
View course details in MyPlan: N SCI 323
N SCI 401 Naval Operations (3)
Introduction to naval operations, the employment of naval forces, naval tactics, formulation of operations plans and orders, employment of detection equipment, and meteorology. Prerequisite: N SCI 101. Offered: A.
View course details in MyPlan: N SCI 401
N SCI 402 Naval Leadership and Ethics (3) I&S
Study of leadership and ethics within a military context using applicable case studies. Examines the Law of Armed Conflict and Code of Conduct; the importance of integrity, moral courage, and ethical behavior to effective leadership; and the interrelationship between authority, responsibility, and accountability. Offered: W.
View course details in MyPlan: N SCI 402
N SCI 403 Naval Organization and Management (3)
Study of organization, systems, and techniques employed in the Navy for management of its human, material, and financial resources. Some work relates to the administration of discipline in the Navy under the Uniform Code of Military Justice. Emphasis on the leadership and management role of the junior officer in the fleet. Prerequisite: minimum 2.0 grade in N SCI 402. Offered: Sp.
View course details in MyPlan: N SCI 403
N SCI 421 Amphibious Warfare (3) I&S
Provides basic knowledge of evolution of amphibious warfare from premodern era to present. Strategic, operational, and tactical considerations in planning specific operations and amphibious landings. Prerequisite: either N SCI 321 or minimum two POL S or JSIS courses. Offered: A, even years.
View course details in MyPlan: N SCI 421


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VSL // Feasts For The Senses
* The food at two Michelin-starred restaurant Coi
today’s Very Short List:
food 10 Oct 2014
Mandy Aftel is an internationally known artisan perfumer and the author of six previous books, including Essence and Alchemy: A Natural History of Perfume and most recently Fragrant: The Secret Life of Scent. Aftel lives in Berkeley, California. TWITTER
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* The Cocktail Lab
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culture 8 Oct 2014
Magali Duzant on small spaces and proof of our existence.
style 6 Oct 2014
Jewelry designer Nora Koganshares some of her style inspiration.
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Chris Taillie on some music you have to see to believe.
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The
Adaptive Text Summarizer is a powerful text summarizing tool built to help summarize large essays into shorter ones to give you only the important bits by freeing them of all unnecessary detail. All you have to do is to put in your essay here and leave the rest to us.


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Photos: Ross Barkley could be in hot water after being filmed topless in Dubai nightclub
by Ron CarneChelsea ace Ross Barkley may have some questions to answer this week after photos emerged of him topless in a Dubai nightclub over the international break.
The 25-year-old has been a key part of Frank Lampard’s plans so far this season as he’s made nine appearances across all competitions.
SEE MORE: Maurizio Sarri wants reunion with Chelsea star at Juventus in January
However, he has been sidelined by an ankle injury in recent weeks and with several of Chelsea’s youngsters impressing this season as well as some of the more senior stars, it remains to be seen whether or not he’ll be able to claim a starting berth when he returns.
This probably won’t help his cause though, as the Daily Mail report how the England international was seen dancing topless and looking ‘worse for wear’ in a Dubai nightclub over the international break.
It’s worth noting that the report adds that Barkley wasn’t misbehaving and was simply just having a good time, and so from that perspective it could be argued that he shouldn’t need to face scrutiny.
Nevertheless, Lampard may not be impressed with what he sees in the report and he’ll surely demand that his Chelsea players conduct themselves in a certain way with class and respect on and off the pitch.
The Mail go on to note that Lampard has already played down an incident with Barkley this season, and so he surely won’t be too happy about having to defend him again and speaking about his antics off the pitch when he simply will want to focus on what he can offer on it.
Time will tell if anything comes of this, but Barkley probably won’t want to be seeing these photos again too often. Fortunately for him though, it doesn’t appear as though he faces a fine after a list of costly infringements at Chelsea went viral this week as seen in the tweet below…


@import gospeak.core.domain.{Cfp, Group, Sponsor, SponsorPack, User} @import gospeak.core.domain.utils.Constants @import gospeak.web.domain.Breadcrumb @import gospeak.web.pages.orga.routes.{GroupCtrl => OrgaGroupCtrl} @import gospeak.web.pages.partials.display.html._ @import gospeak.web.pages.partials.form.html._ @import gospeak.web.pages.published.cfps.routes.CfpCtrl @import gospeak.web.pages.published.groups.routes.GroupCtrl @import gospeak.web.pages.published.speakers.routes.SpeakerCtrl @import gospeak.web.utils.{GsForms, UserAwareReq} @(group: Group.Full, speakerCount: Long, cfps: List[Cfp], sponsors: List[Sponsor.Full], packs: List[SponsorPack], users: List[User], userMembership: Option[Group.Member])(b: Breadcrumb)(implicit req: UserAwareReq[AnyContent])


Who will be responsible for the project finances in your organisation?
This is the person in your organisation responsible for the project’s finances. They will also claim funding once your project is live. We will ask them to provide bank details for your organisation before the project can begin.
23rd March 2016
9 months
Bill Bryson (Dyson)
Algate Solutions 21 Ballygowan Roads Bristol BS2 1RF


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Find Cover
Making use of available cover as you press your attack allows you to avoid unnecessary damage.
${value} Energy Reserved
10
${value}% Chance to Avoid Projectiles
8 10 12 15 17 19 23 25 27 29 32 35
MaxLevel
8
UltimateLevel
12


Privacy policy
We have developed this privacy policy to inform you what personal information we may collect from or about you, what this information will be used for, with whom such information will be shared and the security procedures we have implemented to protect your privacy.
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With whom such information will be shared
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Information and discounts are correct at time of printing and are subject to change without notice.
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Storage and Security
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In summary
STA Travel is committed to protecting your privacy. We use the information we collect on our sites to make our service the best they can be and to enhance your overall experience.
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By using our site, consulting us in one of our branches or over the telephone, or providing your details to make a booking, you consent to the collection and use of this information by STA Travel in accordance with this privacy policy.
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Feedback, access and corrections
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STA Travel reserves the right to alter our privacy policy, as business needs require. Any alterations to this policy will be posted on our website in a timely manner.
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QuantumLibrary
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Parallel execution engine used to run coroutines or IO tasks asynchronously. This class is the main entry point into the library. More...
#include <quantum_dispatcher.h>
Public Member Functions | |
| DEPRECATED | TaskDispatcher (int numCoroutineThreads=-1, int numIoThreads=5, bool pinCoroutineThreadsToCores=false) |
| Constructor. More... | |
| TaskDispatcher (const Configuration &config) | |
| Constructor. More... | |
| ~TaskDispatcher () | |
| Destructor. More... | |
| template<class RET = int, class FUNC , class ... ARGS> | |
| ThreadContext< RET >::Ptr | post (FUNC &&func, ARGS &&... args) |
| Post a coroutine to run asynchronously. More... | |
| template<class RET = int, class FUNC , class ... ARGS> | |
| ThreadContext< RET >::Ptr | post (int queueId, bool isHighPriority, FUNC &&func, ARGS &&... args) |
| Post a coroutine to run asynchronously on a specific queue (thread). More... | |
| template<class RET = int, class FUNC , class ... ARGS> | |
| ThreadContext< RET >::Ptr | postFirst (FUNC &&func, ARGS &&... args) |
| Post the first coroutine in a continuation chain to run asynchronously. More... | |
| template<class RET = int, class FUNC , class ... ARGS> | |
| ThreadContext< RET >::Ptr | postFirst (int queueId, bool isHighPriority, FUNC &&func, ARGS &&... args) |
| Post the first coroutine in a continuation chain to run asynchronously on a specific queue (thread). More... | |
| template<class RET = int, class FUNC , class ... ARGS> | |
| ThreadFuture< RET >::Ptr | postAsyncIo (FUNC &&func, ARGS &&... args) |
| Post a blocking IO (or long running) task to run asynchronously on the IO thread pool. More... | |
| template<class RET = int, class FUNC , class ... ARGS> | |
| ThreadFuture< RET >::Ptr | postAsyncIo (int queueId, bool isHighPriority, FUNC &&func, ARGS &&... args) |
| Post a blocking IO (or long running) task to run asynchronously on a specific thread in the IO thread pool. More... | |
| void | terminate () final |
| Signal all threads to immediately terminate and exit. All other pending coroutines and IO tasks will not complete. Call this function for a fast shutdown of the dispatcher. More... | |
| size_t | size (IQueue::QueueType type=IQueue::QueueType::All, int queueId=(int) IQueue::QueueId::All) const |
| Returns the total number of queued tasks for the specified type and queue id. More... | |
| bool | empty (IQueue::QueueType type=IQueue::QueueType::All, int queueId=(int) IQueue::QueueId::All) const |
| Check if the specified type and queue id is empty (i.e. there are no running tasks) More... | |
| void | drain () |
| Drains all queues on this dispatcher object. More... | |
| int | getNumCoroutineThreads () const |
| Returns the number of underlying coroutine threads as specified in the constructor. If -1 was passed than this number essentially indicates the number of cores. More... | |
| int | getNumIoThreads () const |
| Returns the number of underlying IO threads as specified in the constructor. More... | |
| QueueStatistics | stats (IQueue::QueueType type=IQueue::QueueType::All, int queueId=(int) IQueue::QueueId::All) |
| Returns a statistics object for the specified type and queue id. More... | |
| void | resetStats () |
| Resets all coroutine and IO queue counters. More... | |
| Public Member Functions inherited from Bloomberg::quantum::ITerminate | |
| virtual | ~ITerminate ()=default |
| Virtual destructor. This function is explicitly left empty. More... | |
Parallel execution engine used to run coroutines or IO tasks asynchronously. This class is the main entry point into the library.
|
inline |
Constructor.
This will build two thread pools, one used for running parallel coroutines and another used for running blocking IO tasks.
| [in] | numCoroutineThreads | Number of parallel threads running coroutines. -1 indicates one per core. |
| [in] | numIoThreads | Number of parallel threads running blocking IO calls. |
| [in] | pinCoroutineThreadsToCores | If set to true, it will pin all coroutine threads unto physical cores. provided numCoroutineThreads <= cores. |
|
inlineexplicit |
Constructor.
| config | The configuration for the Quantum dispatcher. |
|
inline |
Destructor.
Destroys the task dispatcher object. This will wait until all coroutines complete, signal all worker threads (coroutine and IO) to exit and join them.
|
inline |
Drains all queues on this dispatcher object.
|
inline |
Check if the specified type and queue id is empty (i.e. there are no running tasks)
| [in] | type | The type of queue. |
| [in] | queueId | The queue number to query. Valid range is [0, numCoroutineThreads) for IQueue::QueueType::Coro, [0, numIoThreads) for IQueue::QueueType::IO and IQueue::QueueId::All for either. |
|
inline |
Returns the number of underlying coroutine threads as specified in the constructor. If -1 was passed than this number essentially indicates the number of cores.
|
inline |
Returns the number of underlying IO threads as specified in the constructor.
| ThreadContext< RET >::Ptr Bloomberg::quantum::TaskDispatcher::post | ( | FUNC && | func, |
| ARGS &&... | args | ||
| ) |
Post a coroutine to run asynchronously.
This method will post the coroutine on any thread available. Typically it will pick one which has the smallest number of concurrent coroutines executing at the time of the post.
| RET | Type of future returned by this coroutine. |
| FUNC | Callable object type which will be wrapped in a coroutine. Can be a standalone function, a method, an std::function, a functor generated via std::bind or a lambda. The signature of the callable object must strictly be 'int f(CoroContext<RET>::Ptr, ...)'. |
| ARGS | Argument types passed to FUNC. |
| [in] | func | Callable object. |
| [in] | args | Variable list of arguments passed to the callable object. |
| ThreadContext< RET >::Ptr Bloomberg::quantum::TaskDispatcher::post | ( | int | queueId, |
| bool | isHighPriority, | ||
| FUNC && | func, | ||
| ARGS &&... | args | ||
| ) |
Post a coroutine to run asynchronously on a specific queue (thread).
| RET | Type of future returned by this coroutine. |
| FUNC | Callable object type which will be wrapped in a coroutine. Can be a standalone function, a method, an std::function, a functor generated via std::bind or a lambda. The signature of the callable object must strictly be 'int f(CoroContext<RET>::Ptr, ...)'. |
| ARGS | Argument types passed to FUNC. |
| [in] | queueId | Id of the queue where this coroutine should run. Note that the user can specify IQueue::QueueId::Any as a value, which is equivalent to running the simpler version of post() above. Valid range is [0, numCoroutineThreads) or IQueue::QueueId::Any. |
| [in] | isHighPriority | If set to true, the coroutine will be scheduled to run immediately after the currently executing coroutine on 'queueId' has completed or has yielded. |
| [in] | func | Callable object. |
| [in] | args | Variable list of arguments passed to the callable object. |
| ThreadFuture< RET >::Ptr Bloomberg::quantum::TaskDispatcher::postAsyncIo | ( | FUNC && | func, |
| ARGS &&... | args | ||
| ) |
Post a blocking IO (or long running) task to run asynchronously on the IO thread pool.
| RET | Type of future returned by this task. |
| FUNC | Callable object type. Can be a standalone function, a method, an std::function, a functor generated via std::bind or a lambda. The signature of the callable object must strictly be 'int f(ThreadPromise<RET>::Ptr, ...)'. |
| ARGS | Argument types passed to FUNC. |
| [in] | func | Callable object. |
| [in] | args | Variable list of arguments passed to the callable object. |
| ThreadFuture< RET >::Ptr Bloomberg::quantum::TaskDispatcher::postAsyncIo | ( | int | queueId, |
| bool | isHighPriority, | ||
| FUNC && | func, | ||
| ARGS &&... | args | ||
| ) |
Post a blocking IO (or long running) task to run asynchronously on a specific thread in the IO thread pool.
| RET | Type of future returned by this task. |
| FUNC | Callable object type. Can be a standalone function, a method, an std::function, a functor generated via std::bind or a lambda. The signature of the callable object must strictly be 'int f(ThreadPromise<RET>::Ptr, ...)'. |
| ARGS | Argument types passed to FUNC. |
| [in] | queueId | Id of the queue where this task should run. Note that the user can specify IQueue::QueueId::Any as a value, which is equivalent to running the simpler version of postAsyncIo() above. Valid range is [0, numCoroutineThreads) or IQueue::QueueId::Any. |
| [in] | isHighPriority | If set to true, the task will be scheduled to run immediately. |
| [in] | func | Callable object. |
| [in] | args | Variable list of arguments passed to the callable object. |
| ThreadContext< RET >::Ptr Bloomberg::quantum::TaskDispatcher::postFirst | ( | FUNC && | func, |
| ARGS &&... | args | ||
| ) |
Post the first coroutine in a continuation chain to run asynchronously.
| RET | Type of future returned by this coroutine. |
| FUNC | Callable object type which will be wrapped in a coroutine. Can be a standalone function, a method, an std::function, a functor generated via std::bind or a lambda. The signature of the callable object must strictly be 'int f(CoroContext<RET>::Ptr, ...)'. |
| ARGS | Argument types passed to FUNC. |
| [in] | func | Callable object. |
| [in] | args | Variable list of arguments passed to the callable object. |
| ThreadContext< RET >::Ptr Bloomberg::quantum::TaskDispatcher::postFirst | ( | int | queueId, |
| bool | isHighPriority, | ||
| FUNC && | func, | ||
| ARGS &&... | args | ||
| ) |
Post the first coroutine in a continuation chain to run asynchronously on a specific queue (thread).
| RET | Type of future returned by this coroutine. |
| FUNC | Callable object type which will be wrapped in a coroutine. Can be a standalone function, a method, an std::function, a functor generated via std::bind or a lambda. The signature of the callable object must strictly be 'int f(CoroContext<RET>::Ptr, ...)'. |
| ARGS | Argument types passed to FUNC. |
| [in] | queueId | Id of the queue where this coroutine should run. Note that the user can specify IQueue::QueueId::Any as a value, which is equivalent to running the simpler version of post() above. Valid range is [0, numCoroutineThreads) or IQueue::QueueId::Any. |
| [in] | isHighPriority | If set to true, the coroutine will be scheduled to run immediately after the currently executing coroutine on 'queueId' has completed or has yielded. |
| [in] | func | Callable object. |
| [in] | args | Variable list of arguments passed to the callable object. |
|
inline |
Resets all coroutine and IO queue counters.
|
inline |
Returns the total number of queued tasks for the specified type and queue id.
| [in] | type | The type of queue. |
| [in] | queueId | The queue number to query. Valid range is [0, numCoroutineThreads) for IQueue::QueueType::Coro, [0, numIoThreads) for IQueue::QueueType::IO and IQueue::QueueId::All for either. |
|
inline |
Returns a statistics object for the specified type and queue id.
| [in] | type | The type of queue. |
| [in] | queueId | The queue number to query. Valid range is [0, numCoroutineThreads) for IQueue::QueueType::Coro, [0, numIoThreads) for IQueue::QueueType::IO and IQueue::QueueId::All for either. |
|
inlinefinalvirtual |
Signal all threads to immediately terminate and exit. All other pending coroutines and IO tasks will not complete. Call this function for a fast shutdown of the dispatcher.
Implements Bloomberg::quantum::ITerminate.


This is the reference for classes (CamelCase
names) and functions
(underscore_case
names) of MNE-Python, grouped thematically by analysis
stage. Functions and classes that are not
below a module heading are found in the mne
namespace.
MNE-Python also provides multiple command-line scripts that can be called directly from a terminal, see Command line tools using Python.
mne
:
MNE software for MEG and EEG data analysis.
io.Raw (fname[, allow_maxshield, preload, …]) |
Raw data in FIF format. |
Epochs (raw, events[, event_id, tmin, tmax, …]) |
Epochs extracted from a Raw instance. |
Evoked (fname[, condition, proj, kind, …]) |
Evoked data. |
Info |
Measurement information. |
IO module for reading raw data.
anonymize_info (info) |
Anonymize measurement information in place. |
find_edf_events (raw) |
Get original EDF events as read from the header. |
read_raw_artemis123 (input_fname[, preload, …]) |
Read Artemis123 data as raw object. |
read_raw_bti (pdf_fname[, config_fname, …]) |
Raw object from 4D Neuroimaging MagnesWH3600 data. |
read_raw_cnt (input_fname, montage[, eog, …]) |
Read CNT data as raw object. |
read_raw_ctf (directory[, system_clock, …]) |
Raw object from CTF directory. |
read_raw_edf (input_fname[, montage, eog, …]) |
Reader function for EDF+, BDF, GDF conversion to FIF. |
read_raw_kit (input_fname[, mrk, elp, hsp, …]) |
Reader function for KIT conversion to FIF. |
read_raw_nicolet (input_fname, ch_type[, …]) |
Read Nicolet data as raw object. |
read_raw_eeglab (input_fname[, montage, eog, …]) |
Read an EEGLAB .set file. |
read_raw_brainvision (vhdr_fname[, montage, …]) |
Reader for Brain Vision EEG file. |
read_raw_egi (input_fname[, montage, eog, …]) |
Read EGI simple binary as raw object. |
read_raw_fif (fname[, allow_maxshield, …]) |
Reader function for Raw FIF data. |
read_raw_eximia (fname[, preload, verbose]) |
Reader for an eXimia EEG file. |
read_raw_fieldtrip (fname, info[, data_name]) |
Load continuous (raw) data from a FieldTrip preprocessing structure. |
Base class:
BaseRaw (info[, preload, first_samps, …]) |
Base class for Raw data. |
KIT module for reading raw data.
read_mrk (fname) |
Marker Point Extraction in MEG space directly from sqd. |
decimate_surface (points, triangles, n_triangles) |
Decimate surface data. |
get_head_surf (subject[, source, …]) |
Load the subject head surface. |
get_meg_helmet_surf (info[, trans, verbose]) |
Load the MEG helmet associated with the MEG sensors. |
get_volume_labels_from_aseg (mgz_fname[, …]) |
Return a list of names and colors of segmented volumes. |
get_volume_labels_from_src (src, subject, …) |
Return a list of Label of segmented volumes included in the src space. |
parse_config (fname) |
Parse a config file (like .ave and .cov files). |
read_labels_from_annot (subject[, parc, …]) |
Read labels from a FreeSurfer annotation file. |
read_bem_solution (fname[, verbose]) |
Read the BEM solution from a file. |
read_bem_surfaces (fname[, patch_stats, …]) |
Read the BEM surfaces from a FIF file. |
read_cov (fname[, verbose]) |
Read a noise covariance from a FIF file. |
read_dipole (fname[, verbose]) |
Read .dip file from Neuromag/xfit or MNE. |
read_epochs (fname[, proj, preload, verbose]) |
Read epochs from a fif file. |
read_epochs_kit (input_fname, events[, …]) |
Reader function for KIT epochs files. |
read_epochs_eeglab (input_fname[, events, …]) |
Reader function for EEGLAB epochs files. |
read_epochs_fieldtrip (fname, info[, …]) |
Load epoched data from a FieldTrip preprocessing structure. |
read_events (filename[, include, exclude, …]) |
Read events from fif or text file. |
read_evokeds (fname[, condition, baseline, …]) |
Read evoked dataset(s). |
read_evoked_fieldtrip (fname, info[, …]) |
Load evoked data from a FieldTrip timelocked structure. |
read_forward_solution (fname[, include, …]) |
Read a forward solution a.k.a. |
read_label (filename[, subject, color]) |
Read FreeSurfer Label file. |
read_morph_map (subject_from, subject_to[, …]) |
Read morph map. |
read_proj (fname) |
Read projections from a FIF file. |
read_reject_parameters (fname) |
Read rejection parameters from .cov or .ave config file. |
read_selection (name[, fname, info, verbose]) |
Read channel selection from file. |
read_source_estimate (fname[, subject]) |
Read a source estimate object. |
read_source_spaces (fname[, patch_stats, verbose]) |
Read the source spaces from a FIF file. |
read_surface (fname[, read_metadata, …]) |
Load a Freesurfer surface mesh in triangular format. |
read_trans (fname[, return_all]) |
Read a -trans.fif file. |
read_tri (fname_in[, swap, verbose]) |
Read triangle definitions from an ascii file. |
write_labels_to_annot (labels[, subject, …]) |
Create a FreeSurfer annotation from a list of labels. |
write_bem_solution (fname, bem) |
Write a BEM model with solution. |
write_bem_surfaces (fname, surfs) |
Write BEM surfaces to a fiff file. |
write_cov (fname, cov) |
Write a noise covariance matrix. |
write_events (filename, event_list) |
Write events to file. |
write_evokeds (fname, evoked) |
Write an evoked dataset to a file. |
write_forward_solution (fname, fwd[, …]) |
Write forward solution to a file. |
write_label (filename, label[, verbose]) |
Write a FreeSurfer label. |
write_proj (fname, projs) |
Write projections to a FIF file. |
write_source_spaces (fname, src[, overwrite, …]) |
Write source spaces to a file. |
write_surface (fname, coords, faces[, …]) |
Write a triangular Freesurfer surface mesh. |
write_trans (fname, trans) |
Write a -trans.fif file. |
io.read_info (fname[, verbose]) |
Read measurement info from a file. |
io.show_fiff (fname[, indent, read_limit, …]) |
Show FIFF information. |
Base class:
BaseEpochs (info, data, events[, event_id, …]) |
Abstract base class for Epochs-type classes. |
EvokedArray (data, info[, tmin, comment, …]) |
Evoked object from numpy array. |
EpochsArray (data, info[, events, tmin, …]) |
Epochs object from numpy array. |
io.RawArray (data, info[, first_samp, verbose]) |
Raw object from numpy array. |
create_info (ch_names, sfreq[, ch_types, …]) |
Create a basic Info instance suitable for use with create_raw. |
Functions for fetching remote datasets.
See datasets for more information.
brainstorm.bst_auditory.data_path ([path, …]) |
Get path to local copy of brainstorm (bst_auditory) dataset. |
brainstorm.bst_resting.data_path ([path, …]) |
Get path to local copy of brainstorm (bst_resting) dataset. |
brainstorm.bst_raw.data_path ([path, …]) |
Get path to local copy of brainstorm (bst_raw) dataset. |
eegbci.load_data (subject, runs[, path, …]) |
Get paths to local copies of EEGBCI dataset files. |
fetch_hcp_mmp_parcellation ([subjects_dir, …]) |
Fetch the HCP-MMP parcellation. |
hf_sef.data_path ([dataset, path, …]) |
Get path to local copy of the high frequency SEF dataset. |
kiloword.data_path ([path, force_update, …]) |
Get path to local copy of the kiloword dataset. |
megsim.data_path (url[, path, force_update, …]) |
Get path to local copy of MEGSIM dataset URL. |
megsim.load_data ([condition, data_format, …]) |
Get path to local copy of MEGSIM dataset type. |
misc.data_path ([path, force_update, …]) |
Get path to local copy of misc dataset. |
mtrf.data_path ([path, force_update, …]) |
Get path to local copy of mtrf dataset. |
multimodal.data_path ([path, force_update, …]) |
Get path to local copy of multimodal dataset. |
opm.data_path ([path, force_update, …]) |
Get path to local copy of opm dataset. |
sample.data_path ([path, force_update, …]) |
Get path to local copy of sample dataset. |
somato.data_path ([path, force_update, …]) |
Get path to local copy of somato dataset. |
spm_face.data_path ([path, force_update, …]) |
Get path to local copy of spm dataset. |
visual_92_categories.data_path ([path, …]) |
Get path to local copy of visual_92_categories dataset. |
phantom_4dbti.data_path ([path, …]) |
Get path to local copy of phantom_4dbti dataset. |
Visualization routines.
ClickableImage (imdata, **kwargs) |
Display an image so you can click on it and store x/y positions. |
add_background_image (fig, im[, set_ratios]) |
Add a background image to a plot. |
compare_fiff (fname_1, fname_2[, fname_out, …]) |
Compare the contents of two fiff files using diff and show_fiff. |
circular_layout (node_names, node_order[, …]) |
Create layout arranging nodes on a circle. |
mne_analyze_colormap ([limits, format]) |
Return a colormap similar to that used by mne_analyze. |
plot_bem ([subject, subjects_dir, …]) |
Plot BEM contours on anatomical slices. |
plot_connectivity_circle (con, node_names[, …]) |
Visualize connectivity as a circular graph. |
plot_cov (cov, info[, exclude, colorbar, …]) |
Plot Covariance data. |
plot_csd (csd[, info, mode, colorbar, cmap, …]) |
Plot CSD matrices. |
plot_dipole_amplitudes (dipoles[, colors, show]) |
Plot the amplitude traces of a set of dipoles. |
plot_dipole_locations (dipoles, trans, subject) |
Plot dipole locations. |
plot_drop_log (drop_log[, threshold, …]) |
Show the channel stats based on a drop_log from Epochs. |
plot_epochs (epochs[, picks, scalings, …]) |
Visualize epochs. |
plot_events (events[, sfreq, first_samp, …]) |
Plot events to get a visual display of the paradigm. |
plot_evoked (evoked[, picks, exclude, unit, …]) |
Plot evoked data using butterfly plots. |
plot_evoked_image (evoked[, picks, exclude, …]) |
Plot evoked data as images. |
plot_evoked_topo (evoked[, layout, …]) |
Plot 2D topography of evoked responses. |
plot_evoked_topomap (evoked[, times, …]) |
Plot topographic maps of specific time points of evoked data. |
plot_evoked_joint (evoked[, times, title, …]) |
Plot evoked data as butterfly plot and add topomaps for time points. |
plot_evoked_field (evoked, surf_maps[, time, …]) |
Plot MEG/EEG fields on head surface and helmet in 3D. |
plot_evoked_white (evoked, noise_cov[, show, …]) |
Plot whitened evoked response. |
plot_filter (h, sfreq[, freq, gain, title, …]) |
Plot properties of a filter. |
plot_head_positions (pos[, mode, cmap, …]) |
Plot head positions. |
plot_ideal_filter (freq, gain[, axes, title, …]) |
Plot an ideal filter response. |
plot_compare_evokeds (evokeds[, picks, gfp, …]) |
Plot evoked time courses for one or more conditions and/or channels. |
plot_ica_sources (ica, inst[, picks, …]) |
Plot estimated latent sources given the unmixing matrix. |
plot_ica_components (ica[, picks, ch_type, …]) |
Project unmixing matrix on interpolated sensor topography. |
plot_ica_properties (ica, inst[, picks, …]) |
Display component properties. |
plot_ica_scores (ica, scores[, exclude, …]) |
Plot scores related to detected components. |
plot_ica_overlay (ica, inst[, exclude, …]) |
Overlay of raw and cleaned signals given the unmixing matrix. |
plot_epochs_image (epochs[, picks, sigma, …]) |
Plot Event Related Potential / Fields image. |
plot_layout (layout[, picks, show]) |
Plot the sensor positions. |
plot_montage (montage[, scale_factor, …]) |
Plot a montage. |
plot_projs_topomap (projs[, layout, cmap, …]) |
Plot topographic maps of SSP projections. |
plot_raw (raw[, events, duration, start, …]) |
Plot raw data. |
plot_raw_psd (raw[, tmin, tmax, fmin, fmax, …]) |
Plot the power spectral density across channels. |
plot_sensors (info[, kind, ch_type, title, …]) |
Plot sensors positions. |
plot_snr_estimate (evoked, inv[, show, verbose]) |
Plot a data SNR estimate. |
plot_source_estimates (stc[, subject, …]) |
Plot SourceEstimates with PySurfer. |
plot_volume_source_estimates (stc, src[, …]) |
Plot Nutmeg style volumetric source estimates using nilearn. |
plot_vector_source_estimates (stc[, subject, …]) |
Plot VectorSourceEstimates with PySurfer. |
plot_sparse_source_estimates (src, stcs[, …]) |
Plot source estimates obtained with sparse solver. |
plot_tfr_topomap (tfr[, tmin, tmax, fmin, …]) |
Plot topographic maps of specific time-frequency intervals of TFR data. |
plot_topo_image_epochs (epochs[, layout, …]) |
Plot Event Related Potential / Fields image on topographies. |
plot_topomap (data, pos[, vmin, vmax, cmap, …]) |
Plot a topographic map as image. |
plot_alignment (info[, trans, subject, …]) |
Plot head, sensor, and source space alignment in 3D. |
snapshot_brain_montage (fig, montage[, …]) |
Take a snapshot of a Mayavi Scene and project channels onto 2d coords. |
plot_arrowmap (data, info_from[, info_to, …]) |
Plot arrow map. |
Projections:
Projection |
Projection vector. |
compute_proj_epochs (epochs[, n_grad, n_mag, …]) |
Compute SSP (spatial space projection) vectors on Epochs. |
compute_proj_evoked (evoked[, n_grad, n_mag, …]) |
Compute SSP (spatial space projection) vectors on Evoked. |
compute_proj_raw (raw[, start, stop, …]) |
Compute SSP (spatial space projection) vectors on Raw. |
read_proj (fname) |
Read projections from a FIF file. |
write_proj (fname, projs) |
Write projections to a FIF file. |
Module dedicated to manipulation of channels.
Can be used for setting of sensor locations used for processing and plotting.
Layout (box, pos, names, ids, kind) |
Sensor layouts. |
Montage (pos, ch_names, kind, selection[, …]) |
Montage for standard EEG electrode locations. |
DigMontage ([hsp, hpi, elp, point_names, …]) |
Montage for digitized electrode and headshape position data. |
fix_mag_coil_types (info) |
Fix magnetometer coil types. |
read_montage (kind[, ch_names, path, unit, …]) |
Read a generic (built-in) montage. |
get_builtin_montages () |
Get a list of all builtin montages. |
read_dig_montage ([hsp, hpi, elp, …]) |
Read subject-specific digitization montage from a file. |
read_layout (kind[, path, scale]) |
Read layout from a file. |
find_layout (info[, ch_type, exclude]) |
Choose a layout based on the channels in the info ‘chs’ field. |
make_eeg_layout (info[, radius, width, …]) |
Create .lout file from EEG electrode digitization. |
make_grid_layout (info[, picks, n_col]) |
Generate .lout file for custom data, i.e., ICA sources. |
find_ch_connectivity (info, ch_type) |
Find the connectivity matrix for the given channels. |
read_ch_connectivity (fname[, picks]) |
Parse FieldTrip neighbors .mat file. |
equalize_channels (candidates[, verbose]) |
Equalize channel picks for a collection of MNE-Python objects. |
rename_channels (info, mapping) |
Rename channels. |
generate_2d_layout (xy[, w, h, pad, …]) |
Generate a custom 2D layout from xy points. |
make_1020_channel_selections (info[, midline]) |
Return dict mapping from ROI names to lists of picks for 10/20 setups. |
Preprocessing with artifact detection, SSP, and ICA.
ICA ([n_components, max_pca_components, …]) |
M/EEG signal decomposition using Independent Component Analysis (ICA). |
Xdawn ([n_components, signal_cov, …]) |
Implementation of the Xdawn Algorithm. |
compute_proj_ecg (raw[, raw_event, tmin, …]) |
Compute SSP/PCA projections for ECG artifacts. |
compute_proj_eog (raw[, raw_event, tmin, …]) |
Compute SSP/PCA projections for EOG artifacts. |
create_ecg_epochs (raw[, ch_name, event_id, …]) |
Conveniently generate epochs around ECG artifact events. |
create_eog_epochs (raw[, ch_name, event_id, …]) |
Conveniently generate epochs around EOG artifact events. |
find_ecg_events (raw[, event_id, ch_name, …]) |
Find ECG peaks. |
find_eog_events (raw[, event_id, l_freq, …]) |
Locate EOG artifacts. |
fix_stim_artifact (inst[, events, event_id, …]) |
Eliminate stimulation’s artifacts from instance. |
ica_find_ecg_events (raw, ecg_source[, …]) |
Find ECG peaks from one selected ICA source. |
ica_find_eog_events (raw[, eog_source, …]) |
Locate EOG artifacts from one selected ICA source. |
infomax (data[, weights, l_rate, block, …]) |
Run (extended) Infomax ICA decomposition on raw data. |
maxwell_filter (raw[, origin, int_order, …]) |
Apply Maxwell filter to data using multipole moments. |
oversampled_temporal_projection (raw[, …]) |
Denoise MEG channels using leave-one-out temporal projection. |
read_ica (fname[, verbose]) |
Restore ICA solution from fif file. |
run_ica (raw, n_components[, …]) |
Run ICA decomposition on raw data and identify artifact sources. |
corrmap (icas, template[, threshold, label, …]) |
Find similar Independent Components across subjects by map similarity. |
EEG referencing:
add_reference_channels (inst, ref_channels[, …]) |
Add reference channels to data that consists of all zeros. |
set_bipolar_reference (inst, anode, cathode) |
Re-reference selected channels using a bipolar referencing scheme. |
set_eeg_reference (inst[, ref_channels, …]) |
Specify which reference to use for EEG data. |
IIR and FIR filtering and resampling functions.
construct_iir_filter (iir_params[, f_pass, …]) |
Use IIR parameters to get filtering coefficients. |
create_filter (data, sfreq, l_freq, h_freq[, …]) |
Create a FIR or IIR filter. |
estimate_ringing_samples (system[, max_try]) |
Estimate filter ringing. |
filter_data (data, sfreq, l_freq, h_freq[, …]) |
Filter a subset of channels. |
notch_filter (x, Fs, freqs[, filter_length, …]) |
Notch filter for the signal x. |
resample (x[, up, down, npad, axis, window, …]) |
Resample an array. |
Functions for fitting head positions with (c)HPI coils.
filter_chpi (raw[, include_line, t_step, …]) |
Remove cHPI and line noise from data. |
head_pos_to_trans_rot_t (quats) |
Convert Maxfilter-formatted head position quaternions. |
read_head_pos (fname) |
Read MaxFilter-formatted head position parameters. |
write_head_pos (fname, pos) |
Write MaxFilter-formatted head position parameters. |
Helpers for various transformations.
Transform (fro, to[, trans]) |
A transform. |
quat_to_rot (quat) |
Convert a set of quaternions to rotations. |
rot_to_quat (rot) |
Convert a set of rotations to quaternions. |
Annotations (onset, duration, description[, …]) |
Annotation object for annotating segments of raw data. |
AcqParserFIF (info) |
Parser for Elekta data acquisition settings. |
concatenate_events (events, first_samps, …) |
Concatenate event lists to be compatible with concatenate_raws. |
find_events (raw[, stim_channel, output, …]) |
Find events from raw file. |
find_stim_steps (raw[, pad_start, pad_stop, …]) |
Find all steps in data from a stim channel. |
make_fixed_length_events (raw[, id, start, …]) |
Make a set of events separated by a fixed duration. |
merge_events (events, ids, new_id[, …]) |
Merge a set of events. |
parse_config (fname) |
Parse a config file (like .ave and .cov files). |
pick_events (events[, include, exclude, step]) |
Select some events. |
read_annotations (fname[, sfreq, uint16_codec]) |
Read annotations from a file. |
read_events (filename[, include, exclude, …]) |
Read events from fif or text file. |
write_events (filename, event_list) |
Write events to file. |
concatenate_epochs (epochs_list[, add_offset]) |
Concatenate a list of epochs into one epochs object. |
events_from_annotations (raw[, event_id, …]) |
Get events and event_id from an Annotations object. |
IO with fif files containing events.
define_target_events (events, reference_id, …) |
Define new events by co-occurrence of existing events. |
Tools for working with epoched data.
add_channels_epochs (epochs_list[, verbose]) |
Concatenate channels, info and data from two Epochs objects. |
average_movements (epochs[, head_pos, …]) |
Average data using Maxwell filtering, transforming using head positions. |
combine_event_ids (epochs, old_event_ids, …) |
Collapse event_ids from an epochs instance into a new event_id. |
equalize_epoch_counts (epochs_list[, method]) |
Equalize the number of trials in multiple Epoch instances. |
combine_evoked (all_evoked, weights) |
Merge evoked data by weighted addition or subtraction. |
concatenate_raws (raws[, preload, …]) |
Concatenate raw instances as if they were continuous. |
equalize_channels (candidates[, verbose]) |
Equalize channel picks for a collection of MNE-Python objects. |
grand_average (all_inst[, interpolate_bads, …]) |
Make grand average of a list evoked or AverageTFR data. |
pick_channels (ch_names, include[, exclude]) |
Pick channels by names. |
pick_channels_cov (orig[, include, exclude]) |
Pick channels from covariance matrix. |
pick_channels_forward (orig[, include, …]) |
Pick channels from forward operator. |
pick_channels_regexp (ch_names, regexp) |
Pick channels using regular expression. |
pick_types (info[, meg, eeg, stim, eog, ecg, …]) |
Pick channels by type and names. |
pick_types_forward (orig[, meg, eeg, …]) |
Pick by channel type and names from a forward operator. |
pick_info (info[, sel, copy, verbose]) |
Restrict an info structure to a selection of channels. |
read_epochs (fname[, proj, preload, verbose]) |
Read epochs from a fif file. |
read_reject_parameters (fname) |
Read rejection parameters from .cov or .ave config file. |
read_selection (name[, fname, info, verbose]) |
Read channel selection from file. |
rename_channels (info, mapping) |
Rename channels. |
Covariance (data, names, bads, projs, nfree) |
Noise covariance matrix. |
compute_covariance (epochs[, …]) |
Estimate noise covariance matrix from epochs. |
compute_raw_covariance (raw[, tmin, tmax, …]) |
Estimate noise covariance matrix from a continuous segment of raw data. |
cov.regularize (cov, info[, mag, grad, eeg, …]) |
Regularize noise covariance matrix. |
cov.compute_whitener (noise_cov, info[, …]) |
Compute whitening matrix. |
make_ad_hoc_cov (info[, std, verbose]) |
Create an ad hoc noise covariance. |
read_cov (fname[, verbose]) |
Read a noise covariance from a FIF file. |
write_cov (fname, cov) |
Write a noise covariance matrix. |
Step by step instructions for using gui.coregistration()
:
gui.coregistration ([tabbed, split, width, …]) |
Coregister an MRI with a subject’s head shape. |
gui.fiducials ([subject, fid_file, subjects_dir]) |
Set the fiducials for an MRI subject. |
create_default_subject ([fs_home, update, …]) |
Create an average brain subject for subjects without structural MRI. |
scale_mri (subject_from, subject_to, scale[, …]) |
Create a scaled copy of an MRI subject. |
scale_bem (subject_to, bem_name[, …]) |
Scale a bem file. |
scale_labels (subject_to[, pattern, …]) |
Scale labels to match a brain that was previously created by scaling. |
scale_source_space (subject_to, src_name[, …]) |
Scale a source space for an mri created with scale_mri(). |
Forward |
Forward class to represent info from forward solution. |
SourceSpaces (source_spaces[, info]) |
Represent a list of source space. |
add_source_space_distances (src[, …]) |
Compute inter-source distances along the cortical surface. |
apply_forward (fwd, stc, info[, start, stop, …]) |
Project source space currents to sensor space using a forward operator. |
apply_forward_raw (fwd, stc, info[, start, …]) |
Project source space currents to sensor space using a forward operator. |
average_forward_solutions (fwds[, weights]) |
Average forward solutions. |
convert_forward_solution (fwd[, surf_ori, …]) |
Convert forward solution between different source orientations. |
forward.restrict_forward_to_label (fwd, labels) |
Restrict forward operator to labels. |
forward.restrict_forward_to_stc (fwd, stc) |
Restrict forward operator to active sources in a source estimate. |
make_bem_model (subject[, ico, conductivity, …]) |
Create a BEM model for a subject. |
make_bem_solution (surfs[, verbose]) |
Create a BEM solution using the linear collocation approach. |
make_forward_dipole (dipole, bem, info[, …]) |
Convert dipole object to source estimate and calculate forward operator. |
make_forward_solution (info, trans, src, bem) |
Calculate a forward solution for a subject. |
make_field_map (evoked[, trans, subject, …]) |
Compute surface maps used for field display in 3D. |
make_sphere_model ([r0, head_radius, info, …]) |
Create a spherical model for forward solution calculation. |
morph_source_spaces (src_from, subject_to[, …]) |
Morph an existing source space to a different subject. |
read_bem_surfaces (fname[, patch_stats, …]) |
Read the BEM surfaces from a FIF file. |
read_forward_solution (fname[, include, …]) |
Read a forward solution a.k.a. |
read_trans (fname[, return_all]) |
Read a -trans.fif file. |
read_source_spaces (fname[, patch_stats, verbose]) |
Read the source spaces from a FIF file. |
read_surface (fname[, read_metadata, …]) |
Load a Freesurfer surface mesh in triangular format. |
sensitivity_map (fwd[, projs, ch_type, mode, …]) |
Compute sensitivity map. |
setup_source_space (subject[, spacing, …]) |
Set up bilateral hemisphere surface-based source space with subsampling. |
setup_volume_source_space ([subject, pos, …]) |
Set up a volume source space with grid spacing or discrete source space. |
surface.complete_surface_info (surf[, …]) |
Complete surface information. |
use_coil_def (fname) |
Use a custom coil definition file. |
write_bem_surfaces (fname, surfs) |
Write BEM surfaces to a fiff file. |
write_trans (fname, trans) |
Write a -trans.fif file. |
ConductorModel |
BEM or sphere model. |
fit_sphere_to_headshape (info[, dig_kinds, …]) |
Fit a sphere to the headshape points to determine head center. |
get_fitting_dig (info[, dig_kinds, verbose]) |
Get digitization points suitable for sphere fitting. |
make_watershed_bem (subject[, subjects_dir, …]) |
Create BEM surfaces using the FreeSurfer watershed algorithm. |
make_flash_bem (subject[, overwrite, show, …]) |
Create 3-Layer BEM model from prepared flash MRI images. |
convert_flash_mris (subject[, flash30, …]) |
Convert DICOM files for use with make_flash_bem. |
Linear inverse solvers based on L2 Minimum Norm Estimates (MNE).
InverseOperator |
InverseOperator class to represent info from inverse operator. |
apply_inverse (evoked, inverse_operator[, …]) |
Apply inverse operator to evoked data. |
apply_inverse_epochs (epochs, …[, method, …]) |
Apply inverse operator to Epochs. |
apply_inverse_raw (raw, inverse_operator, lambda2) |
Apply inverse operator to Raw data. |
compute_source_psd (raw, inverse_operator[, …]) |
Compute source power spectrum density (PSD). |
compute_source_psd_epochs (epochs, …[, …]) |
Compute source power spectrum density (PSD) from Epochs. |
compute_rank_inverse (inv) |
Compute the rank of a linear inverse operator (MNE, dSPM, etc.). |
estimate_snr (evoked, inv[, verbose]) |
Estimate the SNR as a function of time for evoked data. |
make_inverse_operator (info, forward, noise_cov) |
Assemble inverse operator. |
prepare_inverse_operator (orig, nave, lambda2) |
Prepare an inverse operator for actually computing the inverse. |
read_inverse_operator (fname[, verbose]) |
Read the inverse operator decomposition from a FIF file. |
source_band_induced_power (epochs, …[, …]) |
Compute source space induced power in given frequency bands. |
source_induced_power (epochs, …[, label, …]) |
Compute induced power and phase lock. |
write_inverse_operator (fname, inv[, verbose]) |
Write an inverse operator to a FIF file. |
point_spread_function (inverse_operator, …) |
Compute point-spread functions (PSFs) for linear estimators. |
cross_talk_function (inverse_operator, …[, …]) |
Compute cross-talk functions (CTFs) for linear estimators. |
Non-Linear sparse inverse solvers.
mixed_norm (evoked, forward, noise_cov, alpha) |
Mixed-norm estimate (MxNE) and iterative reweighted MxNE (irMxNE). |
tf_mixed_norm (evoked, forward, noise_cov[, …]) |
Time-Frequency Mixed-norm estimate (TF-MxNE). |
gamma_map (evoked, forward, noise_cov, alpha) |
Hierarchical Bayes (Gamma-MAP) sparse source localization method. |
make_stc_from_dipoles (dipoles, src[, verbose]) |
Convert a list of spatio-temporal dipoles into a SourceEstimate. |
Beamformers for source localization.
Beamformer |
A computed beamformer. |
read_beamformer (fname) |
Read a beamformer filter. |
make_lcmv (info, forward, data_cov[, reg, …]) |
Compute LCMV spatial filter. |
apply_lcmv (evoked, filters[, max_ori_out, …]) |
Apply Linearly Constrained Minimum Variance (LCMV) beamformer weights. |
apply_lcmv_epochs (epochs, filters[, …]) |
Apply Linearly Constrained Minimum Variance (LCMV) beamformer weights. |
apply_lcmv_raw (raw, filters[, start, stop, …]) |
Apply Linearly Constrained Minimum Variance (LCMV) beamformer weights. |
make_dics (info, forward, csd[, reg, label, …]) |
Compute a Dynamic Imaging of Coherent Sources (DICS) spatial filter. |
apply_dics (evoked, filters[, verbose]) |
Apply Dynamic Imaging of Coherent Sources (DICS) beamformer weights. |
apply_dics_csd (csd, filters[, verbose]) |
Apply Dynamic Imaging of Coherent Sources (DICS) beamformer weights. |
apply_dics_epochs (epochs, filters[, …]) |
Apply Dynamic Imaging of Coherent Sources (DICS) beamformer weights. |
rap_music (evoked, forward, noise_cov[, …]) |
RAP-MUSIC source localization method. |
tf_dics (epochs, forward, noise_csds, tmin, …) |
5D time-frequency beamforming based on DICS. |
tf_lcmv (epochs, forward, noise_covs, tmin, …) |
5D time-frequency beamforming based on LCMV. |
Dipole (times, pos, amplitude, ori, gof[, …]) |
Dipole class for sequential dipole fits. |
DipoleFixed (info, data, times, nave, …[, …]) |
Dipole class for fixed-position dipole fits. |
fit_dipole (evoked, cov, bem[, trans, …]) |
Fit a dipole. |
Single-dipole functions and classes.
get_phantom_dipoles ([kind]) |
Get standard phantom dipole locations and orientations. |
BiHemiLabel (lh, rh[, name, color]) |
A freesurfer/MNE label with vertices in both hemispheres. |
Label (vertices[, pos, values, hemi, …]) |
A FreeSurfer/MNE label with vertices restricted to one hemisphere. |
MixedSourceEstimate (data[, vertices, tmin, …]) |
Container for mixed surface and volume source estimates. |
SourceEstimate (data[, vertices, tmin, …]) |
Container for surface source estimates. |
VectorSourceEstimate (data[, vertices, tmin, …]) |
Container for vector surface source estimates. |
VolSourceEstimate (data[, vertices, tmin, …]) |
Container for volume source estimates. |
SourceMorph (subject_from, subject_to, kind, …) |
Morph source space data from one subject to another. |
compute_source_morph (src[, subject_from, …]) |
Create a SourceMorph from one subject to another. |
head_to_mni (pos, subject, mri_head_t[, …]) |
Convert pos from head coordinate system to MNI ones. |
head_to_mri (pos, subject, mri_head_t[, …]) |
Convert pos from head coordinate system to MRI ones. |
extract_label_time_course (stcs, labels, src) |
Extract label time course for lists of labels and source estimates. |
grade_to_tris (grade[, verbose]) |
Get tris defined for a certain grade. |
grade_to_vertices (subject, grade[, …]) |
Convert a grade to source space vertices for a given subject. |
grow_labels (subject, seeds, extents, hemis) |
Generate circular labels in source space with region growing. |
label_sign_flip (label, src) |
Compute sign for label averaging. |
random_parcellation (subject, n_parcel, hemi) |
Generate random cortex parcellation by growing labels. |
read_labels_from_annot (subject[, parc, …]) |
Read labels from a FreeSurfer annotation file. |
read_dipole (fname[, verbose]) |
Read .dip file from Neuromag/xfit or MNE. |
read_label (filename[, subject, color]) |
Read FreeSurfer Label file. |
read_source_estimate (fname[, subject]) |
Read a source estimate object. |
read_source_morph (fname) |
Load the morph for source estimates from a file. |
split_label (label[, parts, subject, …]) |
Split a Label into two or more parts. |
stc_to_label (stc[, src, smooth, connected, …]) |
Compute a label from the non-zero sources in an stc object. |
transform_surface_to (surf, dest, trans[, copy]) |
Transform surface to the desired coordinate system. |
vertex_to_mni (vertices, hemis, subject[, …]) |
Convert the array of vertices for a hemisphere to MNI coordinates. |
write_labels_to_annot (labels[, subject, …]) |
Create a FreeSurfer annotation from a list of labels. |
write_label (filename, label[, verbose]) |
Write a FreeSurfer label. |
Time frequency analysis tools.
AverageTFR (info, data, times, freqs, nave[, …]) |
Container for Time-Frequency data. |
EpochsTFR (info, data, times, freqs[, …]) |
Container for Time-Frequency data on epochs. |
CrossSpectralDensity (data, ch_names, …[, …]) |
Cross-spectral density. |
Functions that operate on mne-python objects:
csd_fourier (epochs[, fmin, fmax, tmin, …]) |
Estimate cross-spectral density from an array using short-time fourier. |
csd_multitaper (epochs[, fmin, fmax, tmin, …]) |
Estimate cross-spectral density from epochs using Morlet wavelets. |
csd_morlet (epochs, frequencies[, tmin, …]) |
Estimate cross-spectral density from epochs using Morlet wavelets. |
pick_channels_csd (csd[, include, exclude]) |
Pick channels from covariance matrix. |
read_csd (fname) |
Read a CrossSpectralDensity object from an HDF5 file. |
fit_iir_model_raw (raw[, order, picks, tmin, …]) |
Fit an AR model to raw data and creates the corresponding IIR filter. |
psd_welch (inst[, fmin, fmax, tmin, tmax, …]) |
Compute the power spectral density (PSD) using Welch’s method. |
psd_multitaper (inst[, fmin, fmax, tmin, …]) |
Compute the power spectral density (PSD) using multitapers. |
tfr_morlet (inst, freqs, n_cycles[, use_fft, …]) |
Compute Time-Frequency Representation (TFR) using Morlet wavelets. |
tfr_multitaper (inst, freqs, n_cycles[, …]) |
Compute Time-Frequency Representation (TFR) using DPSS tapers. |
tfr_stockwell (inst[, fmin, fmax, n_fft, …]) |
Time-Frequency Representation (TFR) using Stockwell Transform. |
read_tfrs (fname[, condition]) |
Read TFR datasets from hdf5 file. |
write_tfrs (fname, tfr[, overwrite]) |
Write a TFR dataset to hdf5. |
Functions that operate on np.ndarray
objects:
csd_array_fourier (X, sfreq[, t0, fmin, …]) |
Estimate cross-spectral density from an array using short-time fourier. |
csd_array_multitaper (X, sfreq[, t0, fmin, …]) |
Estimate cross-spectral density from an array using Morlet wavelets. |
csd_array_morlet (X, sfreq, frequencies[, …]) |
Estimate cross-spectral density from an array using Morlet wavelets. |
dpss_windows (N, half_nbw, Kmax[, low_bias, …]) |
Compute Discrete Prolate Spheroidal Sequences. |
morlet (sfreq, freqs[, n_cycles, sigma, …]) |
Compute Morlet wavelets for the given frequency range. |
stft (x, wsize[, tstep, verbose]) |
STFT Short-Term Fourier Transform using a sine window. |
istft (X[, tstep, Tx]) |
ISTFT Inverse Short-Term Fourier Transform using a sine window. |
stftfreq (wsize[, sfreq]) |
Frequencies of stft transformation. |
psd_array_multitaper (x, sfreq[, fmin, fmax, …]) |
Compute power spectrum density (PSD) using a multi-taper method. |
psd_array_welch (x, sfreq[, fmin, fmax, …]) |
Compute power spectral density (PSD) using Welch’s method. |
tfr_array_morlet (epoch_data, sfreq, freqs[, …]) |
Compute time-frequency transform using Morlet wavelets. |
tfr_array_multitaper (epoch_data, sfreq, freqs) |
Compute time-frequency transforms using wavelets and multitaper windows. |
tfr_array_stockwell (data, sfreq[, fmin, …]) |
Compute power and intertrial coherence using Stockwell (S) transform. |
A module which implements the time-frequency estimation.
Morlet code inspired by Matlab code from Sheraz Khan & Brainstorm & SPM
cwt (X, Ws[, use_fft, mode, decim]) |
Compute time freq decomposition with continuous wavelet transform. |
morlet (sfreq, freqs[, n_cycles, sigma, …]) |
Compute Morlet wavelets for the given frequency range. |
Spectral and effective connectivity measures.
seed_target_indices (seeds, targets) |
Generate indices parameter for seed based connectivity analysis. |
spectral_connectivity (data[, method, …]) |
Compute frequency- and time-frequency-domain connectivity measures. |
phase_slope_index (data[, indices, sfreq, …]) |
Compute the Phase Slope Index (PSI) connectivity measure. |
Functions for statistical analysis.
Parametric statistics (see scipy.stats
and statsmodels
for more
options):
ttest_1samp_no_p (X[, sigma, method]) |
Perform one-sample t-test. |
f_oneway (*args) |
Perform a 1-way ANOVA. |
f_mway_rm (data, factor_levels[, effects, …]) |
Compute M-way repeated measures ANOVA for fully balanced designs. |
f_threshold_mway_rm (n_subjects, factor_levels) |
Compute F-value thresholds for a two-way ANOVA. |
linear_regression (inst, design_matrix[, names]) |
Fit Ordinary Least Squares regression (OLS). |
linear_regression_raw (raw, events[, …]) |
Estimate regression-based evoked potentials/fields by linear modeling. |
Mass-univariate multiple comparison correction:
bonferroni_correction (pval[, alpha]) |
P-value correction with Bonferroni method. |
fdr_correction (pvals[, alpha, method]) |
P-value correction with False Discovery Rate (FDR). |
Non-parametric (clustering) resampling methods:
permutation_cluster_test (X[, threshold, …]) |
Cluster-level statistical permutation test. |
permutation_cluster_1samp_test (X[, …]) |
Non-parametric cluster-level paired t-test. |
permutation_t_test (X[, n_permutations, …]) |
One sample/paired sample permutation test based on a t-statistic. |
spatio_temporal_cluster_test (X[, threshold, …]) |
Non-parametric cluster-level test for spatio-temporal data. |
spatio_temporal_cluster_1samp_test (X[, …]) |
Non-parametric cluster-level paired t-test for spatio-temporal data. |
summarize_clusters_stc (clu[, p_thresh, …]) |
Assemble summary SourceEstimate from spatiotemporal cluster results. |
Compute connectivity
matrices for cluster-level statistics:
channels.find_ch_connectivity (info, ch_type) |
Find the connectivity matrix for the given channels. |
channels.read_ch_connectivity (fname[, picks]) |
Parse FieldTrip neighbors .mat file. |
spatial_dist_connectivity (src, dist[, verbose]) |
Compute connectivity from distances in a source space. |
spatial_src_connectivity (src[, dist, verbose]) |
Compute connectivity for a source space activation. |
spatial_tris_connectivity (tris[, …]) |
Compute connectivity from triangles. |
spatial_inter_hemi_connectivity (src, dist[, …]) |
Get vertices on each hemisphere that are close to the other hemisphere. |
spatio_temporal_src_connectivity (src, n_times) |
Compute connectivity for a source space activation over time. |
spatio_temporal_tris_connectivity (tris, n_times) |
Compute connectivity from triangles and time instants. |
spatio_temporal_dist_connectivity (src, …) |
Compute connectivity from distances in a source space and time instants. |
Data simulation code.
simulate_evoked (fwd, stc, info, cov[, nave, …]) |
Generate noisy evoked data. |
simulate_raw (raw, stc, trans, src, bem[, …]) |
Simulate raw data. |
simulate_stc (src, labels, stc_data, tmin, tstep) |
Simulate sources time courses from waveforms and labels. |
simulate_sparse_stc (src, n_dipoles, times[, …]) |
Generate sparse (n_dipoles) sources time courses from data_fun. |
select_source_in_label (src, label[, …]) |
Select source positions using a label. |
Decoding and encoding, including machine learning and receptive fields.
CSP ([n_components, reg, log, cov_est, …]) |
M/EEG signal decomposition using the Common Spatial Patterns (CSP). |
EMS |
Transformer to compute event-matched spatial filters. |
FilterEstimator (info, l_freq, h_freq[, …]) |
Estimator to filter RtEpochs. |
LinearModel ([model]) |
Compute and store patterns from linear models. |
PSDEstimator ([sfreq, fmin, fmax, bandwidth, …]) |
Compute power spectrum density (PSD) using a multi-taper method. |
Scaler ([info, scalings, with_mean, with_std]) |
Standardize channel data. |
TemporalFilter ([l_freq, h_freq, sfreq, …]) |
Estimator to filter data array along the last dimension. |
TimeFrequency (freqs[, sfreq, method, …]) |
Time frequency transformer. |
UnsupervisedSpatialFilter (estimator[, average]) |
Use unsupervised spatial filtering across time and samples. |
Vectorizer |
Transform n-dimensional array into 2D array of n_samples by n_features. |
ReceptiveField (tmin, tmax, sfreq[, …]) |
Fit a receptive field model. |
TimeDelayingRidge (tmin, tmax, sfreq[, …]) |
Ridge regression of data with time delays. |
SlidingEstimator (base_estimator[, scoring, …]) |
Search Light. |
GeneralizingEstimator (base_estimator[, …]) |
Generalization Light. |
SPoC ([n_components, reg, log, …]) |
Implementation of the SPoC spatial filtering. |
Functions that assist with decoding and model fitting:
compute_ems (epochs[, conditions, picks, …]) |
Compute event-matched spatial filter on epochs. |
cross_val_multiscore (estimator, X[, y, …]) |
Evaluate a score by cross-validation. |
get_coef (estimator[, attr, inverse_transform]) |
Retrieve the coefficients of an estimator ending with a Linear Model. |
Realtime MEG data processing with servers and clients.
RtEpochs (client, event_id, tmin, tmax[, …]) |
Realtime Epochs. |
RtClient (host[, cmd_port, data_port, …]) |
Realtime Client. |
MockRtClient (raw[, verbose]) |
Mock Realtime Client. |
FieldTripClient ([info, host, port, …]) |
Realtime FieldTrip client. |
StimServer ([port, n_clients]) |
Stimulation Server. |
StimClient (host[, port, timeout, verbose]) |
Stimulation Client. |
mne
:
Report ([info_fname, subjects_dir, subject, …]) |
Object for rendering HTML. |
open_report (fname, **params) |
Read a saved report or, if it doesn’t exist yet, create a new one. |
get_config_path ([home_dir]) |
Get path to standard mne-python config file. |
get_config ([key, default, raise_error, home_dir]) |
Read MNE-Python preferences from environment or config file. |
open_docs ([kind, version]) |
Launch a new web browser tab with the MNE documentation. |
set_log_level ([verbose, return_old_level]) |
Set the logging level. |
set_log_file ([fname, output_format, overwrite]) |
Set the log to print to a file. |
set_config (key, value[, home_dir, set_env]) |
Set a MNE-Python preference key in the config file and environment. |
sys_info ([fid, show_paths]) |
Print the system information for debugging. |
verbose (function, *args, **kwargs) |
Verbose decorator to allow functions to override log-level. |
get_cuda_memory ([kind]) |
Get the amount of free memory for CUDA operations. |
init_cuda ([ignore_config, verbose]) |
Initialize CUDA functionality. |