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octicon-link-external HeaderMenu-external-icon color-fg-subtle"> <path d="M3.75 2h3.5a.75.75 0 0 1 0 1.5h-3.5a.25.25 0 0 0-.25.25v8.5c0 .138.112.25.25.25h8.5a.25.25 0 0 0 .25-.25v-3.5a.75.75 0 0 1 1.5 0v3.5A1.75 1.75 0 0 1 12.25 14h-8.5A1.75 1.75 0 0 1 2 12.25v-8.5C2 2.784 2.784 2 3.75 2Zm6.854-1h4.146a.25.25 0 0 1 .25.25v4.146a.25.25 0 0 1-.427.177L13.03 4.03 9.28 7.78a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042l3.75-3.75-1.543-1.543A.25.25 0 0 1 10.604 1Z"></path> </svg> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary Link--external" target="_blank" data-analytics-event="{"location":"navbar","action":"blog","context":"product","tag":"link","label":"blog_link_product_navbar"}" href="https://github.blog"> Blog <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-link-external HeaderMenu-external-icon color-fg-subtle"> <path d="M3.75 2h3.5a.75.75 0 0 1 0 1.5h-3.5a.25.25 0 0 0-.25.25v8.5c0 .138.112.25.25.25h8.5a.25.25 0 0 0 .25-.25v-3.5a.75.75 0 0 1 1.5 0v3.5A1.75 1.75 0 0 1 12.25 14h-8.5A1.75 1.75 0 0 1 2 12.25v-8.5C2 2.784 2.784 2 3.75 2Zm6.854-1h4.146a.25.25 0 0 1 .25.25v4.146a.25.25 0 0 1-.427.177L13.03 4.03 9.28 7.78a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042l3.75-3.75-1.543-1.543A.25.25 0 0 1 10.604 1Z"></path> </svg> </a></li> </ul> </div> </div> </div> </li> <li class="HeaderMenu-item position-relative flex-wrap flex-justify-between flex-items-center d-block d-lg-flex flex-lg-nowrap flex-lg-items-center js-details-container js-header-menu-item"> <button type="button" class="HeaderMenu-link border-0 width-full width-lg-auto px-0 px-lg-2 py-lg-2 no-wrap d-flex flex-items-center flex-justify-between js-details-target" aria-expanded="false"> Solutions <svg opacity="0.5" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-down HeaderMenu-icon ml-1"> <path d="M12.78 5.22a.749.749 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.06 0L3.22 6.28a.749.749 0 1 1 1.06-1.06L8 8.939l3.72-3.719a.749.749 0 0 1 1.06 0Z"></path> </svg> </button> <div class="HeaderMenu-dropdown dropdown-menu rounded m-0 p-0 pt-2 pt-lg-4 position-relative position-lg-absolute left-0 left-lg-n3 d-lg-flex flex-wrap dropdown-menu-wide"> <div class="HeaderMenu-column px-lg-4 border-lg-right mb-4 mb-lg-0 pr-lg-7"> <div class="border-bottom pb-3 pb-lg-0 border-lg-bottom-0 pb-lg-3 mb-3 mb-lg-0"> <span class="d-block h4 color-fg-default my-1" id="solutions-by-company-size-heading">By company size</span> <ul class="list-style-none f5" aria-labelledby="solutions-by-company-size-heading"> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"enterprises","context":"solutions","tag":"link","label":"enterprises_link_solutions_navbar"}" href="https://github.com/enterprise"> Enterprises </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"small_and_medium_teams","context":"solutions","tag":"link","label":"small_and_medium_teams_link_solutions_navbar"}" href="https://github.com/team"> Small and medium teams </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"startups","context":"solutions","tag":"link","label":"startups_link_solutions_navbar"}" href="https://github.com/enterprise/startups"> Startups </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"nonprofits","context":"solutions","tag":"link","label":"nonprofits_link_solutions_navbar"}" href="/solutions/industry/nonprofits"> Nonprofits </a></li> </ul> </div> <div class="border-bottom pb-3 pb-lg-0 border-lg-bottom-0"> <span class="d-block h4 color-fg-default my-1" id="solutions-by-use-case-heading">By use case</span> <ul class="list-style-none f5" aria-labelledby="solutions-by-use-case-heading"> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"devsecops","context":"solutions","tag":"link","label":"devsecops_link_solutions_navbar"}" href="/solutions/use-case/devsecops"> DevSecOps </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"devops","context":"solutions","tag":"link","label":"devops_link_solutions_navbar"}" href="/solutions/use-case/devops"> DevOps </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"ci_cd","context":"solutions","tag":"link","label":"ci_cd_link_solutions_navbar"}" href="/solutions/use-case/ci-cd"> CI/CD </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"view_all_use_cases","context":"solutions","tag":"link","label":"view_all_use_cases_link_solutions_navbar"}" href="/solutions/use-case"> View all use cases </a></li> </ul> </div> </div> <div class="HeaderMenu-column px-lg-4"> <div class="border-bottom pb-3 pb-lg-0 border-lg-bottom-0"> <span class="d-block h4 color-fg-default my-1" id="solutions-by-industry-heading">By industry</span> <ul class="list-style-none f5" aria-labelledby="solutions-by-industry-heading"> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"healthcare","context":"solutions","tag":"link","label":"healthcare_link_solutions_navbar"}" href="/solutions/industry/healthcare"> Healthcare </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"financial_services","context":"solutions","tag":"link","label":"financial_services_link_solutions_navbar"}" href="/solutions/industry/financial-services"> Financial services </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"manufacturing","context":"solutions","tag":"link","label":"manufacturing_link_solutions_navbar"}" href="/solutions/industry/manufacturing"> Manufacturing </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"government","context":"solutions","tag":"link","label":"government_link_solutions_navbar"}" href="/solutions/industry/government"> Government </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"view_all_industries","context":"solutions","tag":"link","label":"view_all_industries_link_solutions_navbar"}" href="/solutions/industry"> View all industries </a></li> </ul> </div> </div> <div class="HeaderMenu-trailing-link rounded-bottom-2 flex-shrink-0 mt-lg-4 px-lg-4 py-4 py-lg-3 f5 text-semibold"> <a href="/solutions"> View all solutions <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-right HeaderMenu-trailing-link-icon"> <path d="M6.22 3.22a.75.75 0 0 1 1.06 0l4.25 4.25a.75.75 0 0 1 0 1.06l-4.25 4.25a.751.751 0 0 1-1.042-.018.751.751 0 0 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d-lg-flex flex-wrap dropdown-menu-wide"> <div class="HeaderMenu-column px-lg-4 border-lg-right mb-4 mb-lg-0 pr-lg-7"> <div class="border-bottom pb-3 pb-lg-0 border-lg-bottom-0"> <span class="d-block h4 color-fg-default my-1" id="resources-topics-heading">Topics</span> <ul class="list-style-none f5" aria-labelledby="resources-topics-heading"> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"ai","context":"resources","tag":"link","label":"ai_link_resources_navbar"}" href="/resources/articles/ai"> AI </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"devops","context":"resources","tag":"link","label":"devops_link_resources_navbar"}" href="/resources/articles/devops"> DevOps </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"security","context":"resources","tag":"link","label":"security_link_resources_navbar"}" href="/resources/articles/security"> Security </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"software_development","context":"resources","tag":"link","label":"software_development_link_resources_navbar"}" href="/resources/articles/software-development"> Software Development </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"view_all","context":"resources","tag":"link","label":"view_all_link_resources_navbar"}" href="/resources/articles"> View all </a></li> </ul> </div> </div> <div class="HeaderMenu-column px-lg-4"> <div class="border-bottom pb-3 pb-lg-0 border-lg-bottom-0 border-bottom-0"> <span class="d-block h4 color-fg-default my-1" id="resources-explore-heading">Explore</span> <ul class="list-style-none f5" aria-labelledby="resources-explore-heading"> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary Link--external" target="_blank" data-analytics-event="{"location":"navbar","action":"learning_pathways","context":"resources","tag":"link","label":"learning_pathways_link_resources_navbar"}" href="https://resources.github.com/learn/pathways"> Learning Pathways <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-link-external HeaderMenu-external-icon color-fg-subtle"> <path d="M3.75 2h3.5a.75.75 0 0 1 0 1.5h-3.5a.25.25 0 0 0-.25.25v8.5c0 .138.112.25.25.25h8.5a.25.25 0 0 0 .25-.25v-3.5a.75.75 0 0 1 1.5 0v3.5A1.75 1.75 0 0 1 12.25 14h-8.5A1.75 1.75 0 0 1 2 12.25v-8.5C2 2.784 2.784 2 3.75 2Zm6.854-1h4.146a.25.25 0 0 1 .25.25v4.146a.25.25 0 0 1-.427.177L13.03 4.03 9.28 7.78a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042l3.75-3.75-1.543-1.543A.25.25 0 0 1 10.604 1Z"></path> </svg> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary Link--external" target="_blank" data-analytics-event="{"location":"navbar","action":"events_amp_webinars","context":"resources","tag":"link","label":"events_amp_webinars_link_resources_navbar"}" href="https://resources.github.com"> Events & Webinars <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-link-external HeaderMenu-external-icon color-fg-subtle"> <path d="M3.75 2h3.5a.75.75 0 0 1 0 1.5h-3.5a.25.25 0 0 0-.25.25v8.5c0 .138.112.25.25.25h8.5a.25.25 0 0 0 .25-.25v-3.5a.75.75 0 0 1 1.5 0v3.5A1.75 1.75 0 0 1 12.25 14h-8.5A1.75 1.75 0 0 1 2 12.25v-8.5C2 2.784 2.784 2 3.75 2Zm6.854-1h4.146a.25.25 0 0 1 .25.25v4.146a.25.25 0 0 1-.427.177L13.03 4.03 9.28 7.78a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042l3.75-3.75-1.543-1.543A.25.25 0 0 1 10.604 1Z"></path> </svg> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"ebooks_amp_whitepapers","context":"resources","tag":"link","label":"ebooks_amp_whitepapers_link_resources_navbar"}" href="https://github.com/resources/whitepapers"> Ebooks & Whitepapers </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"customer_stories","context":"resources","tag":"link","label":"customer_stories_link_resources_navbar"}" href="https://github.com/customer-stories"> Customer Stories </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary Link--external" target="_blank" data-analytics-event="{"location":"navbar","action":"partners","context":"resources","tag":"link","label":"partners_link_resources_navbar"}" href="https://partner.github.com"> Partners <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-link-external HeaderMenu-external-icon color-fg-subtle"> <path d="M3.75 2h3.5a.75.75 0 0 1 0 1.5h-3.5a.25.25 0 0 0-.25.25v8.5c0 .138.112.25.25.25h8.5a.25.25 0 0 0 .25-.25v-3.5a.75.75 0 0 1 1.5 0v3.5A1.75 1.75 0 0 1 12.25 14h-8.5A1.75 1.75 0 0 1 2 12.25v-8.5C2 2.784 2.784 2 3.75 2Zm6.854-1h4.146a.25.25 0 0 1 .25.25v4.146a.25.25 0 0 1-.427.177L13.03 4.03 9.28 7.78a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042l3.75-3.75-1.543-1.543A.25.25 0 0 1 10.604 1Z"></path> </svg> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"executive_insights","context":"resources","tag":"link","label":"executive_insights_link_resources_navbar"}" href="https://github.com/solutions/executive-insights"> Executive Insights </a></li> </ul> </div> </div> </div> </li> <li class="HeaderMenu-item position-relative flex-wrap flex-justify-between flex-items-center d-block d-lg-flex flex-lg-nowrap flex-lg-items-center js-details-container js-header-menu-item"> <button type="button" class="HeaderMenu-link border-0 width-full width-lg-auto px-0 px-lg-2 py-lg-2 no-wrap d-flex flex-items-center flex-justify-between js-details-target" aria-expanded="false"> Open Source <svg opacity="0.5" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-down HeaderMenu-icon ml-1"> <path d="M12.78 5.22a.749.749 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.06 0L3.22 6.28a.749.749 0 1 1 1.06-1.06L8 8.939l3.72-3.719a.749.749 0 0 1 1.06 0Z"></path> </svg> </button> <div class="HeaderMenu-dropdown dropdown-menu rounded m-0 p-0 pt-2 pt-lg-4 position-relative position-lg-absolute left-0 left-lg-n3 pb-2 pb-lg-4 px-lg-4"> <div class="HeaderMenu-column"> <div class="border-bottom pb-3 pb-lg-0 pb-lg-3 mb-3 mb-lg-0 mb-lg-3"> <ul class="list-style-none f5" > <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description" data-analytics-event="{"location":"navbar","action":"github_sponsors","context":"open_source","tag":"link","label":"github_sponsors_link_open_source_navbar"}" href="/sponsors"> <div> <div class="color-fg-default h4">GitHub Sponsors</div> Fund open source developers </div> </a></li> </ul> </div> <div class="border-bottom pb-3 pb-lg-0 pb-lg-3 mb-3 mb-lg-0 mb-lg-3"> <ul class="list-style-none f5" > <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description" data-analytics-event="{"location":"navbar","action":"the_readme_project","context":"open_source","tag":"link","label":"the_readme_project_link_open_source_navbar"}" href="https://github.com/readme"> <div> <div class="color-fg-default h4">The ReadME Project</div> GitHub community articles </div> </a></li> </ul> </div> <div class="border-bottom pb-3 pb-lg-0 border-bottom-0"> <span class="d-block h4 color-fg-default my-1" id="open-source-repositories-heading">Repositories</span> <ul class="list-style-none f5" aria-labelledby="open-source-repositories-heading"> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"topics","context":"open_source","tag":"link","label":"topics_link_open_source_navbar"}" href="https://github.com/topics"> Topics </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" 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dir=\"auto\"\u003ePython for (open) Neuroscience\u003c/h1\u003e\u003ca id=\"user-content-python-for-open-neuroscience\" class=\"anchor\" aria-label=\"Permalink: Python for (open) Neuroscience\" href=\"#python-for-open-neuroscience\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/7e65d27c7c976da081996327a4a92b0a63b79429a6af3a975019f0e9da6b4fc0/68747470733a2f2f696d67732e786b63642e636f6d2f636f6d6963732f707974686f6e2e706e67\"\u003e\u003cimg align=\"right\" width=\"400\" height=\"400\" src=\"https://camo.githubusercontent.com/7e65d27c7c976da081996327a4a92b0a63b79429a6af3a975019f0e9da6b4fc0/68747470733a2f2f696d67732e786b63642e636f6d2f636f6d6963732f707974686f6e2e706e67\" data-canonical-src=\"https://imgs.xkcd.com/comics/python.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003ePython is an \u003cstrong\u003eopen-source\u003c/strong\u003e, \u003cstrong\u003ehigh-level\u003c/strong\u003e, \u003cstrong\u003emultipurpose\u003c/strong\u003e programming language. It offers tools for fast \u003cstrong\u003emanipulation of large matrices\u003c/strong\u003e and datasets (similar to MATLAB) and \u003cstrong\u003epowerful data aggregation\u003c/strong\u003e and statistics (akin to R), together with thousands of packages for \u003cstrong\u003emachine learning\u003c/strong\u003e, \u003cstrong\u003evisualizations\u003c/strong\u003e, simulations, hardware control, and many others. As a result, \u003cstrong\u003ea growing number of labs are adopting it for their workflows\u003c/strong\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThis course will start covering \u003cstrong\u003ethe basics of Python usage\u003c/strong\u003e and build up from there to some \u003cstrong\u003emore advanced topics\u003c/strong\u003e. The aim is to bring participants up to speed in using \u003cstrong\u003ePython to solve some of the common problems\u003c/strong\u003e we face daily in the lab. People with some Python experience are welcome! You can assist other students in the first part, and then learn something new and useful in the later modules.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cbr\u003e\u003cbr\u003e\n\u003cbr\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eOrganization of the course\u003c/h2\u003e\u003ca id=\"user-content-organization-of-the-course\" class=\"anchor\" aria-label=\"Permalink: Organization of the course\" href=\"#organization-of-the-course\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eStructure\u003c/strong\u003e: The course will be organized in four modules. Each module comprises three sessions, two hours each, that will mix frontal lectures and hands-on parts to work on in pairs (peer coding).\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eA schedule for each module could be:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eWeek 0:\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eLecture x.0\u003c/li\u003e\n\u003cli\u003eAssignments\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eWeek 1:\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eLecture x.1\u003c/li\u003e\n\u003cli\u003eAssignments\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eWeek 2:\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eLecture x.2\u003c/li\u003e\n\u003cli\u003eAssignments\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eWeek 3:\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eOffice hours\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eDates\u003c/strong\u003e: the course will run every Monday (tentative time: 17:00-19:00) from February to May 2024. Dates are flexible and we can change them during the course if there's constraints/preferences on the student's side.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eFramework and requirements\u003c/strong\u003e: You will be following the course on your own laptop. The first two modules will be teaching using \u003cstrong\u003eGoogle Colab\u003c/strong\u003e, with \u003cstrong\u003eno setup required\u003c/strong\u003e (you will only need a browser and a working internet connection). In the second part we will move to \u003cstrong\u003eJupyter Notebooks\u003c/strong\u003e, to understand how to \u003cstrong\u003eset up an real-world Python environment\u003c/strong\u003e that can be used in the every day research work. There won't be system requirements, we should be able to set it up on Windows, MacOS, and Linux (you will have instructions and assistance for doing that!).\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eAssignments\u003c/strong\u003e: At the end of each of the first three modules there will be \u003cstrong\u003ea minimal recap assigment\u003c/strong\u003e, and a \u003cstrong\u003etime slot to ask questions\u003c/strong\u003e on the lectures and on the assignment.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eMaterial\u003c/strong\u003e: The material will consist in jupyter notebooks and python scripts with the lecture content and exercises and it will be made available before the lectures using GitHub.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSyllabus\u003c/h2\u003e\u003ca id=\"user-content-syllabus\" class=\"anchor\" aria-label=\"Permalink: Syllabus\" href=\"#syllabus\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eSyllabus for the course. Ideally, its incremental nature should ensure that each core concept that is introduced is then revisited and expanded on in every new lecture.\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eModule 0: the fundamentals 🏗\u003c/h3\u003e\u003ca id=\"user-content-module-0-the-fundamentals-\" class=\"anchor\" aria-label=\"Permalink: Module 0: the fundamentals 🏗\" href=\"#module-0-the-fundamentals-\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eA very basic introduction to the basic syntax and structure of Python code, just a smattering: more will come while exploring other modules.\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003e0.0. Introduction to Python variables and statements\u003c/strong\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cins\u003eTheory:\u003c/ins\u003e Variable types (numbers, strings) and their operators, data structures (lists, dictionaries), basic clauses (conditionals, while/for loops, try-except); simple file reading.\u003c/li\u003e\n\u003cli\u003e\u003cins\u003ePracticals:\u003c/ins\u003eExercises tba.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e0.1. Functions (and modules ?)\u003c/strong\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cins\u003eTheory:\u003c/ins\u003e Reusing code in Python: functions (definition, argument passing, scope), modules (make a new module, import functions from existing library); installing new libraries with \u003ccode\u003epip\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\u003cins\u003ePracticals:\u003c/ins\u003e exercises on functions; create a simple module; \u003ccode\u003epip install\u003c/code\u003e a new library and use it.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e0.2. Fundamentals of classes and objects\u003c/strong\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cins\u003eTheory:\u003c/ins\u003e Definition of classes and their components (methods, attributes, properties); objects and object instantiation.\u003c/li\u003e\n\u003cli\u003e\u003cins\u003ePracticals:\u003c/ins\u003e write a simple custom class. Interact and use previously defined classes, search for their methods and attributes.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eAssignment\u003c/strong\u003e: Exercises tba\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eModule 1: the scientific stack in Python 📚\u003c/h3\u003e\u003ca id=\"user-content-module-1-the-scientific-stack-in-python-\" class=\"anchor\" aria-label=\"Permalink: Module 1: the scientific stack in Python 📚\" href=\"#module-1-the-scientific-stack-in-python-\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWe introduce the Holy Trinity of data analysis: \u003ccode\u003enumpy\u003c/code\u003e, \u003ccode\u003epandas\u003c/code\u003e, and \u003ccode\u003ematplotlib\u003c/code\u003e; and we show how they solve almost all our data analysis problems.\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003e1.0. \u003ccode\u003enumpy\u003c/code\u003e\u003c/strong\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cins\u003eTheory:\u003c/ins\u003e Data types: the \u003ccode\u003enp.array\u003c/code\u003e. initialisation, operators, indexing (numerical and boolean masking); operations with arrays (concatenate, stack, searching extrema, sorting, using sorting indexes). Visualising arrays and matrices with \u003ccode\u003ematplotlib\u003c/code\u003e. Reading and writing \u003ccode\u003e.npy\u003c/code\u003e files.\u003c/li\u003e\n\u003cli\u003e\u003cins\u003ePracticals:\u003c/ins\u003e Exercises tba, ideally with some neuro-related data. Turning \u003ccode\u003efor\u003c/code\u003e loops into vector operations.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e1.1. \u003ccode\u003epandas\u003c/code\u003e\u003c/strong\u003e:\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cins\u003eTheory:\u003c/ins\u003e \u003ccode\u003epd.Series\u003c/code\u003e and \u003ccode\u003epd.DataFrames\u003c/code\u003e; reading and writing \u003ccode\u003e.csv\u003c/code\u003e files. Optimal ways to organize data in dataframes. Working with dataframes: indexing, slicing, selecting, querying, interpolating, mapping. Using \u003ccode\u003ematplotlib\u003c/code\u003e to visualise datasets.\u003c/li\u003e\n\u003cli\u003e\u003cins\u003ePracticals:\u003c/ins\u003e exercises tba, ideally with some neuro-related data. Important: show how labelled data in \u003ccode\u003epandas\u003c/code\u003e make operations easier than when using \u003ccode\u003enumpy\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e1.2. More on \u003ccode\u003epandas\u003c/code\u003e\u003c/strong\u003e:\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cins\u003eTheory:\u003c/ins\u003e Advanced \u003ccode\u003epandas\u003c/code\u003e: aggregated operations using \u003ccode\u003egroupby()\u003c/code\u003e and \u003ccode\u003erolling()\u003c/code\u003e. Group statistics, smoothing, resampling. Mindblowing \u003ccode\u003epandas\u003c/code\u003e (depending on progress/interest): hierarchical indexing with \u003ccode\u003eMultiIndex\u003c/code\u003e, aggregated operations, dataset alignment. Introduction to \u003ccode\u003eseaborn\u003c/code\u003e for dataset visualization.\u003c/li\u003e\n\u003cli\u003e\u003cins\u003ePracticals:\u003c/ins\u003e show how to easily make aggregate statistics and visualisations using \u003ccode\u003eseaborn\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eAssignment\u003c/strong\u003e: Exercise tba\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eModule 2: Python for neuroscientific data 🔬\u003c/h3\u003e\u003ca id=\"user-content-module-2-python-for-neuroscientific-data-\" class=\"anchor\" aria-label=\"Permalink: Module 2: Python for neuroscientific data 🔬\" href=\"#module-2-python-for-neuroscientific-data-\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWe start using all of the above on some real world scenario and neuroscientific data, trying to find common solutions to problems and tasks from different fields.\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003e2.0. Real-world Python for real-world data\u003c/strong\u003e\u003c/li\u003e\n\u003cli\u003e\u003cins\u003eTheory:\u003c/ins\u003e Moving from Google Colab from local Python (using Anaconda) and jupyter notebook; understand where things are in a local installation; install new modules with \u003ccode\u003epip\u003c/code\u003e. Interact with local data: browse and reorganize folders; opening or importing the most common data types that might come from experiments (\u003ccode\u003e.txt\u003c/code\u003e, \u003ccode\u003e.csv\u003c/code\u003e, \u003ccode\u003e.xlsx\u003c/code\u003e, \u003ccode\u003e.mat\u003c/code\u003e, \u003ccode\u003e.tiff\u003c/code\u003e, ...to adjust depending on interest).\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cins\u003ePracticals:\u003c/ins\u003e Set up a working local Python environment, load some data and make some plots.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e2.1. Images\u003c/strong\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cins\u003eTheory:\u003c/ins\u003e Images and stacks data formats. Opening and writing different data formats (\u003ccode\u003e.tiffs\u003c/code\u003e, \u003ccode\u003e.nrrd\u003c/code\u003e, \u003ccode\u003e.nii\u003c/code\u003e). Visualising images with \u003ccode\u003ematplotlib\u003c/code\u003e and videos and stacks with \u003ccode\u003enapari\u003c/code\u003e. Simple image operations (cropping, smoothing, resizing, histogram normalisation...); batch processing of images.\u003c/li\u003e\n\u003cli\u003e\u003cins\u003ePracticals:\u003c/ins\u003e Exercises tba\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e2.2. Time series\u003c/strong\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cins\u003eTheory:\u003c/ins\u003eWorking with time series using \u003ccode\u003enumpy\u003c/code\u003e and \u003ccode\u003epandas\u003c/code\u003e. Reading and writing time series data. Resampling, event detection (eg spike detection or artefact identification), event-triggered cropping. Filtering, smoothing (if there is interest/time, introduction to tools for frequency-domain analysis).\u003c/li\u003e\n\u003cli\u003e\u003cins\u003ePracticals:\u003c/ins\u003e Exercises tba\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eAssignment\u003c/strong\u003e: Exercise tba\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eModule 3: Advanced topics in Python for neuroscience ☄️\u003c/h3\u003e\u003ca id=\"user-content-module-3-advanced-topics-in-python-for-neuroscience-️\" class=\"anchor\" aria-label=\"Permalink: Module 3: Advanced topics in Python for neuroscience ☄️\" href=\"#module-3-advanced-topics-in-python-for-neuroscience-️\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWe see how to bring home the bacon with Python as neuroscientists. Keep your code organised, generate good paper figures, make sure that your code is documented and accessible. Here are some possible topics, but we will choose together and pick up three based on interest.\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003eAdvanced visualisation and data rendering\u003c/strong\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cins\u003eTheory:\u003c/ins\u003e Some basic concepts and rules of data visualisation using \u003ccode\u003ematplotlib\u003c/code\u003e, tips for generating paper quality figures. More on \u003ccode\u003epandas\u003c/code\u003e and \u003ccode\u003eseaborn\u003c/code\u003e. How to create animations with \u003ccode\u003ematplotlib\u003c/code\u003e and \u003ccode\u003enapari\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\u003cins\u003ePracticals:\u003c/ins\u003e Exercises tba.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eVersion control using \u003ccode\u003egit\u003c/code\u003e and GitHub\u003c/strong\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cins\u003eTheory:\u003c/ins\u003e Advantages and importance of version control systems. Core \u003ccode\u003egit\u003c/code\u003e concepts: \u003ccode\u003eadd\u003c/code\u003e, \u003ccode\u003ecommit\u003c/code\u003e, \u003ccode\u003ebranch\u003c/code\u003e. Synch code with GitHub: \u003ccode\u003efetch\u003c/code\u003e, \u003ccode\u003epull\u003c/code\u003e, \u003ccode\u003epush\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\u003cins\u003ePracticals:\u003c/ins\u003e Exercises tba\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eOrganising and publishing scientific code.\u003c/strong\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cins\u003eTheory:\u003c/ins\u003e Best practices for clean and readable scripts and notebooks. Design principles for a data processing pipeline; structure of a pip installable repository. How and where to deposit code for a publication.\u003c/li\u003e\n\u003cli\u003e\u003cins\u003ePracticals:\u003c/ins\u003e Exercises tba\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eScripting experiments using Python\u003c/strong\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cins\u003eTheory:\u003c/ins\u003e Use Python to generate visual or auditory stimuli. Brief introduction to Psychopy. Interacting with Arduino and NI boards to read and write digital, analog and serial inputs/outputs\u003c/li\u003e\n\u003cli\u003e\u003cins\u003ePracticals:\u003c/ins\u003e Exercises tba\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eFundamentals of statistics and machine learning with Python\u003c/strong\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cins\u003eTheory:\u003c/ins\u003e Compute basic statistical tests with \u003ccode\u003escipy.statistics\u003c/code\u003e. The \u003ccode\u003escikit-learn\u003c/code\u003e package: Dimensionality reduction and clustering. Using Principal Component Analysis (PCA) to reduce dimensionality on a dataset. Introduction to clustering using the K-means algorithm\u003c/li\u003e\n\u003cli\u003e\u003cins\u003ePracticals:\u003c/ins\u003e Exercises tba\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e...\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eAssignment\u003c/strong\u003e: You will be ask to complete a small Python project of your choice addressing a problem from your daily work at the lab. Could be anything: count cells from images, perform some data analysis on existing datasets, visualize EEG timeseries or MRI stacks, hack the institute coffee machine card...\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eComplimentary soft skills\u003c/h3\u003e\u003ca id=\"user-content-complimentary-soft-skills\" class=\"anchor\" aria-label=\"Permalink: Complimentary soft skills\" href=\"#complimentary-soft-skills\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIdeally, the course will also try to convey some more elusive coding-related soft skills, such as:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003eWrite good data analysis code\u003c/strong\u003e, keeping an eye on reusability, readability, parameterisation...\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eLearn how not to get stuck\u003c/strong\u003e and learn from bugs: find online resources (documentation, StackOverflow, GitHub); and interact with them (asking questions, reporting bugs, raising issues, etc.)\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eUnderstand the value of open source code\u003c/strong\u003e in the scientific endeaviour, and the importance of depositing code and datasets.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eAdditional online resources and exercises\u003c/h2\u003e\u003ca id=\"user-content-additional-online-resources-and-exercises\" class=\"anchor\" aria-label=\"Permalink: Additional online resources and exercises\" href=\"#additional-online-resources-and-exercises\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eIntro level\u003c/h4\u003e\u003ca id=\"user-content-intro-level\" class=\"anchor\" aria-label=\"Permalink: Intro level\" href=\"#intro-level\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThose is mostly aimed at people who have never written a line of Python, or have forgot everything about it.\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://www.datacamp.com/courses/intro-to-python-for-data-science\" rel=\"nofollow\"\u003eDatacamp\u003c/a\u003e: requires registration, but offers free intro courses for basic Python usage. Very boring, as drills should be.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.codecademy.com/learn/learn-python-3\" rel=\"nofollow\"\u003eCodecademy\u003c/a\u003e: has same formula, with free account offers some basic Python tutorials.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://udacity.com/course/cs101\" rel=\"nofollow\"\u003eUdacity\u003c/a\u003e: again the same, more based on videos.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eIntermediate level\u003c/h4\u003e\u003ca id=\"user-content-intermediate-level\" class=\"anchor\" aria-label=\"Permalink: Intermediate level\" href=\"#intermediate-level\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://pyforneuro.com/\" rel=\"nofollow\"\u003ePython for neuroscientists\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://mark-kramer.github.io/Case-Studies-Python/intro.html\" rel=\"nofollow\"\u003eCase Studies in Neural Data Analysis\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://mrgreene09.github.io/computational-neuroscience-textbook/\" rel=\"nofollow\"\u003eComputational Neuroscience in Python textbook\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eOld Lectures recordings\u003c/h2\u003e\u003ca id=\"user-content-old-lectures-recordings\" class=\"anchor\" aria-label=\"Permalink: Old Lectures recordings\" href=\"#old-lectures-recordings\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://youtu.be/ROFbFADkKpU\" rel=\"nofollow\"\u003eLecture 2.0\u003c/a\u003e: Local Python installation, demistified\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://youtu.be/O_rRMRIJdxc\" rel=\"nofollow\"\u003eLecture 2.1\u003c/a\u003e: Jupyter notebooks and working with the filesystem\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://youtu.be/BzLIZx4Y-E8\" rel=\"nofollow\"\u003eLecture 2.2\u003c/a\u003e: Working with images and imaging data\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://youtu.be/UF2rJg5aCYo\" rel=\"nofollow\"\u003eLecture 3.0\u003c/a\u003e: Version control using GitHub\u003c/li\u003e\n\u003cli\u003e(Lecture 3.1 missing - Imaging data analysis)\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://youtu.be/8QhOFkEkhoQ\" rel=\"nofollow\"\u003eLecture 3.2\u003c/a\u003e: Introduction to statistics and machine learning\u003c/li\u003e\n\u003cli\u003e(Lecture 3.3 missing - Running experiments in Python)\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://youtu.be/GjYWyihBAoo\" rel=\"nofollow\"\u003eLecture 3.4\u003c/a\u003e: Data visualization\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"Python for (open) Neuroscience","anchor":"python-for-open-neuroscience","htmlText":"Python for (open) Neuroscience"},{"level":2,"text":"Organization of the course","anchor":"organization-of-the-course","htmlText":"Organization of the course"},{"level":2,"text":"Syllabus","anchor":"syllabus","htmlText":"Syllabus"},{"level":3,"text":"Module 0: the fundamentals 🏗","anchor":"module-0-the-fundamentals-","htmlText":"Module 0: the fundamentals 🏗"},{"level":3,"text":"Module 1: the scientific stack in Python 📚","anchor":"module-1-the-scientific-stack-in-python-","htmlText":"Module 1: the scientific stack in Python 📚"},{"level":3,"text":"Module 2: Python for neuroscientific data 🔬","anchor":"module-2-python-for-neuroscientific-data-","htmlText":"Module 2: Python for neuroscientific data 🔬"},{"level":3,"text":"Module 3: Advanced topics in Python for neuroscience ☄️","anchor":"module-3-advanced-topics-in-python-for-neuroscience-️","htmlText":"Module 3: Advanced topics in Python for neuroscience ☄️"},{"level":3,"text":"Complimentary soft skills","anchor":"complimentary-soft-skills","htmlText":"Complimentary soft skills"},{"level":2,"text":"Additional online resources and exercises","anchor":"additional-online-resources-and-exercises","htmlText":"Additional online resources and exercises"},{"level":4,"text":"Intro level","anchor":"intro-level","htmlText":"Intro level"},{"level":4,"text":"Intermediate level","anchor":"intermediate-level","htmlText":"Intermediate level"},{"level":2,"text":"Old Lectures recordings","anchor":"old-lectures-recordings","htmlText":"Old Lectures recordings"}],"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Fvigji%2Fpython-cimec-2023"}},{"displayName":"LICENSE","repoName":"python-cimec-2023","refName":"main","path":"LICENSE","preferredFileType":"license","tabName":"BSD-3-Clause","richText":null,"loaded":false,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":null,"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Fvigji%2Fpython-cimec-2023"}}],"overviewFilesProcessingTime":0}},"appPayload":{"helpUrl":"https://docs.github.com","findFileWorkerPath":"/assets-cdn/worker/find-file-worker-7d7eb7c71814.js","findInFileWorkerPath":"/assets-cdn/worker/find-in-file-worker-708ec8ade250.js","githubDevUrl":null,"enabled_features":{"copilot_workspace":null,"code_nav_ui_events":false,"overview_shared_code_dropdown_button":true,"react_blob_overlay":false,"accessible_code_button":true,"github_models_repo_integration":false}}}}</script> <div data-target="react-partial.reactRoot"><style data-styled="true" 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Neuroscience</h1><a id="user-content-python-for-open-neuroscience" class="anchor" aria-label="Permalink: Python for (open) Neuroscience" href="#python-for-open-neuroscience"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><a target="_blank" rel="noopener noreferrer nofollow" href="https://camo.githubusercontent.com/7e65d27c7c976da081996327a4a92b0a63b79429a6af3a975019f0e9da6b4fc0/68747470733a2f2f696d67732e786b63642e636f6d2f636f6d6963732f707974686f6e2e706e67"><img align="right" width="400" height="400" src="https://camo.githubusercontent.com/7e65d27c7c976da081996327a4a92b0a63b79429a6af3a975019f0e9da6b4fc0/68747470733a2f2f696d67732e786b63642e636f6d2f636f6d6963732f707974686f6e2e706e67" data-canonical-src="https://imgs.xkcd.com/comics/python.png" style="max-width: 100%;"></a></p> <p dir="auto">Python is an <strong>open-source</strong>, <strong>high-level</strong>, <strong>multipurpose</strong> programming language. It offers tools for fast <strong>manipulation of large matrices</strong> and datasets (similar to MATLAB) and <strong>powerful data aggregation</strong> and statistics (akin to R), together with thousands of packages for <strong>machine learning</strong>, <strong>visualizations</strong>, simulations, hardware control, and many others. As a result, <strong>a growing number of labs are adopting it for their workflows</strong>.</p> <p dir="auto">This course will start covering <strong>the basics of Python usage</strong> and build up from there to some <strong>more advanced topics</strong>. The aim is to bring participants up to speed in using <strong>Python to solve some of the common problems</strong> we face daily in the lab. People with some Python experience are welcome! You can assist other students in the first part, and then learn something new and useful in the later modules.</p> <p dir="auto"><br><br> <br><br></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Organization of the course</h2><a id="user-content-organization-of-the-course" class="anchor" aria-label="Permalink: Organization of the course" href="#organization-of-the-course"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><strong>Structure</strong>: The course will be organized in four modules. Each module comprises three sessions, two hours each, that will mix frontal lectures and hands-on parts to work on in pairs (peer coding).</p> <p dir="auto">A schedule for each module could be:</p> <ul dir="auto"> <li>Week 0: <ul dir="auto"> <li>Lecture x.0</li> <li>Assignments</li> </ul> </li> <li>Week 1: <ul dir="auto"> <li>Lecture x.1</li> <li>Assignments</li> </ul> </li> <li>Week 2: <ul dir="auto"> <li>Lecture x.2</li> <li>Assignments</li> </ul> </li> <li>Week 3: <ul dir="auto"> <li>Office hours</li> </ul> </li> </ul> <p dir="auto"><strong>Dates</strong>: the course will run every Monday (tentative time: 17:00-19:00) from February to May 2024. Dates are flexible and we can change them during the course if there's constraints/preferences on the student's side.</p> <p dir="auto"><strong>Framework and requirements</strong>: You will be following the course on your own laptop. The first two modules will be teaching using <strong>Google Colab</strong>, with <strong>no setup required</strong> (you will only need a browser and a working internet connection). In the second part we will move to <strong>Jupyter Notebooks</strong>, to understand how to <strong>set up an real-world Python environment</strong> that can be used in the every day research work. There won't be system requirements, we should be able to set it up on Windows, MacOS, and Linux (you will have instructions and assistance for doing that!).</p> <p dir="auto"><strong>Assignments</strong>: At the end of each of the first three modules there will be <strong>a minimal recap assigment</strong>, and a <strong>time slot to ask questions</strong> on the lectures and on the assignment.</p> <p dir="auto"><strong>Material</strong>: The material will consist in jupyter notebooks and python scripts with the lecture content and exercises and it will be made available before the lectures using GitHub.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Syllabus</h2><a id="user-content-syllabus" class="anchor" aria-label="Permalink: Syllabus" href="#syllabus"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Syllabus for the course. Ideally, its incremental nature should ensure that each core concept that is introduced is then revisited and expanded on in every new lecture.</p> <hr> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Module 0: the fundamentals 🏗</h3><a id="user-content-module-0-the-fundamentals-" class="anchor" aria-label="Permalink: Module 0: the fundamentals 🏗" href="#module-0-the-fundamentals-"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">A very basic introduction to the basic syntax and structure of Python code, just a smattering: more will come while exploring other modules.</p> <ul dir="auto"> <li><strong>0.0. Introduction to Python variables and statements</strong> <ul dir="auto"> <li><ins>Theory:</ins> Variable types (numbers, strings) and their operators, data structures (lists, dictionaries), basic clauses (conditionals, while/for loops, try-except); simple file reading.</li> <li><ins>Practicals:</ins>Exercises tba.</li> </ul> </li> <li><strong>0.1. Functions (and modules ?)</strong> <ul dir="auto"> <li><ins>Theory:</ins> Reusing code in Python: functions (definition, argument passing, scope), modules (make a new module, import functions from existing library); installing new libraries with <code>pip</code>.</li> <li><ins>Practicals:</ins> exercises on functions; create a simple module; <code>pip install</code> a new library and use it.</li> </ul> </li> <li><strong>0.2. Fundamentals of classes and objects</strong> <ul dir="auto"> <li><ins>Theory:</ins> Definition of classes and their components (methods, attributes, properties); objects and object instantiation.</li> <li><ins>Practicals:</ins> write a simple custom class. Interact and use previously defined classes, search for their methods and attributes.</li> </ul> </li> </ul> <p dir="auto"><strong>Assignment</strong>: Exercises tba</p> <hr> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Module 1: the scientific stack in Python 📚</h3><a id="user-content-module-1-the-scientific-stack-in-python-" class="anchor" aria-label="Permalink: Module 1: the scientific stack in Python 📚" href="#module-1-the-scientific-stack-in-python-"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">We introduce the Holy Trinity of data analysis: <code>numpy</code>, <code>pandas</code>, and <code>matplotlib</code>; and we show how they solve almost all our data analysis problems.</p> <ul dir="auto"> <li><strong>1.0. <code>numpy</code></strong> <ul dir="auto"> <li><ins>Theory:</ins> Data types: the <code>np.array</code>. initialisation, operators, indexing (numerical and boolean masking); operations with arrays (concatenate, stack, searching extrema, sorting, using sorting indexes). Visualising arrays and matrices with <code>matplotlib</code>. Reading and writing <code>.npy</code> files.</li> <li><ins>Practicals:</ins> Exercises tba, ideally with some neuro-related data. Turning <code>for</code> loops into vector operations.</li> </ul> </li> <li><strong>1.1. <code>pandas</code></strong>: <ul dir="auto"> <li><ins>Theory:</ins> <code>pd.Series</code> and <code>pd.DataFrames</code>; reading and writing <code>.csv</code> files. Optimal ways to organize data in dataframes. Working with dataframes: indexing, slicing, selecting, querying, interpolating, mapping. Using <code>matplotlib</code> to visualise datasets.</li> <li><ins>Practicals:</ins> exercises tba, ideally with some neuro-related data. Important: show how labelled data in <code>pandas</code> make operations easier than when using <code>numpy</code>.</li> </ul> </li> <li><strong>1.2. More on <code>pandas</code></strong>: <ul dir="auto"> <li><ins>Theory:</ins> Advanced <code>pandas</code>: aggregated operations using <code>groupby()</code> and <code>rolling()</code>. Group statistics, smoothing, resampling. Mindblowing <code>pandas</code> (depending on progress/interest): hierarchical indexing with <code>MultiIndex</code>, aggregated operations, dataset alignment. Introduction to <code>seaborn</code> for dataset visualization.</li> <li><ins>Practicals:</ins> show how to easily make aggregate statistics and visualisations using <code>seaborn</code>.</li> </ul> </li> </ul> <p dir="auto"><strong>Assignment</strong>: Exercise tba</p> <hr> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Module 2: Python for neuroscientific data 🔬</h3><a id="user-content-module-2-python-for-neuroscientific-data-" class="anchor" aria-label="Permalink: Module 2: Python for neuroscientific data 🔬" href="#module-2-python-for-neuroscientific-data-"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">We start using all of the above on some real world scenario and neuroscientific data, trying to find common solutions to problems and tasks from different fields.</p> <ul dir="auto"> <li><strong>2.0. Real-world Python for real-world data</strong></li> <li><ins>Theory:</ins> Moving from Google Colab from local Python (using Anaconda) and jupyter notebook; understand where things are in a local installation; install new modules with <code>pip</code>. Interact with local data: browse and reorganize folders; opening or importing the most common data types that might come from experiments (<code>.txt</code>, <code>.csv</code>, <code>.xlsx</code>, <code>.mat</code>, <code>.tiff</code>, ...to adjust depending on interest). <ul dir="auto"> <li><ins>Practicals:</ins> Set up a working local Python environment, load some data and make some plots.</li> </ul> </li> <li><strong>2.1. Images</strong> <ul dir="auto"> <li><ins>Theory:</ins> Images and stacks data formats. Opening and writing different data formats (<code>.tiffs</code>, <code>.nrrd</code>, <code>.nii</code>). Visualising images with <code>matplotlib</code> and videos and stacks with <code>napari</code>. Simple image operations (cropping, smoothing, resizing, histogram normalisation...); batch processing of images.</li> <li><ins>Practicals:</ins> Exercises tba</li> </ul> </li> <li><strong>2.2. Time series</strong> <ul dir="auto"> <li><ins>Theory:</ins>Working with time series using <code>numpy</code> and <code>pandas</code>. Reading and writing time series data. Resampling, event detection (eg spike detection or artefact identification), event-triggered cropping. Filtering, smoothing (if there is interest/time, introduction to tools for frequency-domain analysis).</li> <li><ins>Practicals:</ins> Exercises tba</li> </ul> </li> </ul> <p dir="auto"><strong>Assignment</strong>: Exercise tba</p> <hr> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Module 3: Advanced topics in Python for neuroscience ☄️</h3><a id="user-content-module-3-advanced-topics-in-python-for-neuroscience-️" class="anchor" aria-label="Permalink: Module 3: Advanced topics in Python for neuroscience ☄️" href="#module-3-advanced-topics-in-python-for-neuroscience-️"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">We see how to bring home the bacon with Python as neuroscientists. Keep your code organised, generate good paper figures, make sure that your code is documented and accessible. Here are some possible topics, but we will choose together and pick up three based on interest.</p> <ul dir="auto"> <li><strong>Advanced visualisation and data rendering</strong> <ul dir="auto"> <li><ins>Theory:</ins> Some basic concepts and rules of data visualisation using <code>matplotlib</code>, tips for generating paper quality figures. More on <code>pandas</code> and <code>seaborn</code>. How to create animations with <code>matplotlib</code> and <code>napari</code>.</li> <li><ins>Practicals:</ins> Exercises tba.</li> </ul> </li> <li><strong>Version control using <code>git</code> and GitHub</strong> <ul dir="auto"> <li><ins>Theory:</ins> Advantages and importance of version control systems. Core <code>git</code> concepts: <code>add</code>, <code>commit</code>, <code>branch</code>. Synch code with GitHub: <code>fetch</code>, <code>pull</code>, <code>push</code>.</li> <li><ins>Practicals:</ins> Exercises tba</li> </ul> </li> <li><strong>Organising and publishing scientific code.</strong> <ul dir="auto"> <li><ins>Theory:</ins> Best practices for clean and readable scripts and notebooks. Design principles for a data processing pipeline; structure of a pip installable repository. How and where to deposit code for a publication.</li> <li><ins>Practicals:</ins> Exercises tba</li> </ul> </li> <li><strong>Scripting experiments using Python</strong> <ul dir="auto"> <li><ins>Theory:</ins> Use Python to generate visual or auditory stimuli. Brief introduction to Psychopy. Interacting with Arduino and NI boards to read and write digital, analog and serial inputs/outputs</li> <li><ins>Practicals:</ins> Exercises tba</li> </ul> </li> <li><strong>Fundamentals of statistics and machine learning with Python</strong> <ul dir="auto"> <li><ins>Theory:</ins> Compute basic statistical tests with <code>scipy.statistics</code>. The <code>scikit-learn</code> package: Dimensionality reduction and clustering. Using Principal Component Analysis (PCA) to reduce dimensionality on a dataset. Introduction to clustering using the K-means algorithm</li> <li><ins>Practicals:</ins> Exercises tba</li> </ul> </li> <li><strong>...</strong></li> </ul> <p dir="auto"><strong>Assignment</strong>: You will be ask to complete a small Python project of your choice addressing a problem from your daily work at the lab. Could be anything: count cells from images, perform some data analysis on existing datasets, visualize EEG timeseries or MRI stacks, hack the institute coffee machine card...</p> <hr> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Complimentary soft skills</h3><a id="user-content-complimentary-soft-skills" class="anchor" aria-label="Permalink: Complimentary soft skills" href="#complimentary-soft-skills"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Ideally, the course will also try to convey some more elusive coding-related soft skills, such as:</p> <ul dir="auto"> <li><strong>Write good data analysis code</strong>, keeping an eye on reusability, readability, parameterisation...</li> <li><strong>Learn how not to get stuck</strong> and learn from bugs: find online resources (documentation, StackOverflow, GitHub); and interact with them (asking questions, reporting bugs, raising issues, etc.)</li> <li><strong>Understand the value of open source code</strong> in the scientific endeaviour, and the importance of depositing code and datasets.</li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Additional online resources and exercises</h2><a id="user-content-additional-online-resources-and-exercises" class="anchor" aria-label="Permalink: Additional online resources and exercises" href="#additional-online-resources-and-exercises"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Intro level</h4><a id="user-content-intro-level" class="anchor" aria-label="Permalink: Intro level" href="#intro-level"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Those is mostly aimed at people who have never written a line of Python, or have forgot everything about it.</p> <ul dir="auto"> <li><a href="https://www.datacamp.com/courses/intro-to-python-for-data-science" rel="nofollow">Datacamp</a>: requires registration, but offers free intro courses for basic Python usage. Very boring, as drills should be.</li> <li><a href="https://www.codecademy.com/learn/learn-python-3" rel="nofollow">Codecademy</a>: has same formula, with free account offers some basic Python tutorials.</li> <li><a href="http://udacity.com/course/cs101" rel="nofollow">Udacity</a>: again the same, more based on videos.</li> </ul> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Intermediate level</h4><a id="user-content-intermediate-level" class="anchor" aria-label="Permalink: Intermediate level" href="#intermediate-level"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><a href="https://pyforneuro.com/" rel="nofollow">Python for neuroscientists</a></li> <li><a href="https://mark-kramer.github.io/Case-Studies-Python/intro.html" rel="nofollow">Case Studies in Neural Data Analysis</a></li> <li><a href="https://mrgreene09.github.io/computational-neuroscience-textbook/" rel="nofollow">Computational Neuroscience in Python textbook</a></li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Old Lectures recordings</h2><a id="user-content-old-lectures-recordings" class="anchor" aria-label="Permalink: Old Lectures recordings" href="#old-lectures-recordings"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><a href="https://youtu.be/ROFbFADkKpU" rel="nofollow">Lecture 2.0</a>: Local Python installation, demistified</li> <li><a href="https://youtu.be/O_rRMRIJdxc" rel="nofollow">Lecture 2.1</a>: Jupyter notebooks and working with the filesystem</li> <li><a href="https://youtu.be/BzLIZx4Y-E8" rel="nofollow">Lecture 2.2</a>: Working with images and imaging data</li> <li><a href="https://youtu.be/UF2rJg5aCYo" rel="nofollow">Lecture 3.0</a>: Version control using GitHub</li> <li>(Lecture 3.1 missing - Imaging data analysis)</li> <li><a href="https://youtu.be/8QhOFkEkhoQ" rel="nofollow">Lecture 3.2</a>: Introduction to statistics and machine learning</li> <li>(Lecture 3.3 missing - Running experiments in Python)</li> <li><a href="https://youtu.be/GjYWyihBAoo" rel="nofollow">Lecture 3.4</a>: Data visualization</li> </ul> </article></div></div></div></div></div> <!-- --> <!-- --> <script type="application/json" id="__PRIMER_DATA_:R0:__">{"resolvedServerColorMode":"day"}</script></div> </react-partial> <input type="hidden" data-csrf="true" value="Gcl3Cy11dHXoyhZseFciM/6xCFTuFtqCy/s7UPVwsXAmgPC7Qeoorw2tHdfC184r7vfXK4SWP50wX1p1o6tL7A==" /> </div> <div data-view-component="true" class="Layout-sidebar"> <div class="BorderGrid about-margin" data-pjax> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <div class="hide-sm hide-md"> <h2 class="mb-3 h4">About</h2> <p class="f4 my-3"> Python 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