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GitHub - esheldon/fitsio: A python package for FITS input/output wrapping cfitsio
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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"> Product <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 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"> <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 pb-lg-3" data-analytics-event="{"location":"navbar","action":"github_copilot","context":"product","tag":"link","label":"github_copilot_link_product_navbar"}" href="https://github.com/features/copilot"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-copilot color-fg-subtle mr-3"> <path d="M23.922 16.992c-.861 1.495-5.859 5.023-11.922 5.023-6.063 0-11.061-3.528-11.922-5.023A.641.641 0 0 1 0 16.736v-2.869a.841.841 0 0 1 .053-.22c.372-.935 1.347-2.292 2.605-2.656.167-.429.414-1.055.644-1.517a10.195 10.195 0 0 1-.052-1.086c0-1.331.282-2.499 1.132-3.368.397-.406.89-.717 1.474-.952 1.399-1.136 3.392-2.093 6.122-2.093 2.731 0 4.767.957 6.166 2.093.584.235 1.077.546 1.474.952.85.869 1.132 2.037 1.132 3.368 0 .368-.014.733-.052 1.086.23.462.477 1.088.644 1.517 1.258.364 2.233 1.721 2.605 2.656a.832.832 0 0 1 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0v-2a1 1 0 0 1 1-1Zm-5 0a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Z"></path> </svg> <div> <div class="color-fg-default h4">GitHub Copilot</div> Write better code with AI </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"security","context":"product","tag":"link","label":"security_link_product_navbar"}" href="https://github.com/features/security"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-shield-check color-fg-subtle mr-3"> <path d="M16.53 9.78a.75.75 0 0 0-1.06-1.06L11 13.19l-1.97-1.97a.75.75 0 0 0-1.06 1.06l2.5 2.5a.75.75 0 0 0 1.06 0l5-5Z"></path><path d="m12.54.637 8.25 2.675A1.75 1.75 0 0 1 22 4.976V10c0 6.19-3.771 10.704-9.401 12.83a1.704 1.704 0 0 1-1.198 0C5.77 20.705 2 16.19 2 10V4.976c0-.758.489-1.43 1.21-1.664L11.46.637a1.748 1.748 0 0 1 1.08 0Zm-.617 1.426-8.25 2.676a.249.249 0 0 0-.173.237V10c0 5.46 3.28 9.483 8.43 11.426a.199.199 0 0 0 .14 0C17.22 19.483 20.5 15.461 20.5 10V4.976a.25.25 0 0 0-.173-.237l-8.25-2.676a.253.253 0 0 0-.154 0Z"></path> </svg> <div> <div class="color-fg-default h4">Security</div> Find and fix vulnerabilities </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"actions","context":"product","tag":"link","label":"actions_link_product_navbar"}" href="https://github.com/features/actions"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-workflow color-fg-subtle mr-3"> <path d="M1 3a2 2 0 0 1 2-2h6.5a2 2 0 0 1 2 2v6.5a2 2 0 0 1-2 2H7v4.063C7 16.355 7.644 17 8.438 17H12.5v-2.5a2 2 0 0 1 2-2H21a2 2 0 0 1 2 2V21a2 2 0 0 1-2 2h-6.5a2 2 0 0 1-2-2v-2.5H8.437A2.939 2.939 0 0 1 5.5 15.562V11.5H3a2 2 0 0 1-2-2Zm2-.5a.5.5 0 0 0-.5.5v6.5a.5.5 0 0 0 .5.5h6.5a.5.5 0 0 0 .5-.5V3a.5.5 0 0 0-.5-.5ZM14.5 14a.5.5 0 0 0-.5.5V21a.5.5 0 0 0 .5.5H21a.5.5 0 0 0 .5-.5v-6.5a.5.5 0 0 0-.5-.5Z"></path> </svg> <div> <div class="color-fg-default h4">Actions</div> Automate any workflow </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"codespaces","context":"product","tag":"link","label":"codespaces_link_product_navbar"}" href="https://github.com/features/codespaces"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-codespaces color-fg-subtle mr-3"> <path d="M3.5 3.75C3.5 2.784 4.284 2 5.25 2h13.5c.966 0 1.75.784 1.75 1.75v7.5A1.75 1.75 0 0 1 18.75 13H5.25a1.75 1.75 0 0 1-1.75-1.75Zm-2 12c0-.966.784-1.75 1.75-1.75h17.5c.966 0 1.75.784 1.75 1.75v4a1.75 1.75 0 0 1-1.75 1.75H3.25a1.75 1.75 0 0 1-1.75-1.75ZM5.25 3.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h13.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Zm-2 12a.25.25 0 0 0-.25.25v4c0 .138.112.25.25.25h17.5a.25.25 0 0 0 .25-.25v-4a.25.25 0 0 0-.25-.25Z"></path><path d="M10 17.75a.75.75 0 0 1 .75-.75h6.5a.75.75 0 0 1 0 1.5h-6.5a.75.75 0 0 1-.75-.75Zm-4 0a.75.75 0 0 1 .75-.75h.5a.75.75 0 0 1 0 1.5h-.5a.75.75 0 0 1-.75-.75Z"></path> </svg> <div> <div class="color-fg-default h4">Codespaces</div> Instant dev environments </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"issues","context":"product","tag":"link","label":"issues_link_product_navbar"}" href="https://github.com/features/issues"> <svg aria-hidden="true" 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6.74a.75.75 0 0 1-.04 1.06l-2.908 2.7 2.908 2.7a.75.75 0 1 1-1.02 1.1l-3.5-3.25a.75.75 0 0 1 0-1.1l3.5-3.25a.75.75 0 0 1 1.06.04Zm3.44 1.06a.75.75 0 1 1 1.02-1.1l3.5 3.25a.75.75 0 0 1 0 1.1l-3.5 3.25a.75.75 0 1 1-1.02-1.1l2.908-2.7-2.908-2.7Z"></path><path d="M1.5 4.25c0-.966.784-1.75 1.75-1.75h17.5c.966 0 1.75.784 1.75 1.75v12.5a1.75 1.75 0 0 1-1.75 1.75h-9.69l-3.573 3.573A1.458 1.458 0 0 1 5 21.043V18.5H3.25a1.75 1.75 0 0 1-1.75-1.75ZM3.25 4a.25.25 0 0 0-.25.25v12.5c0 .138.112.25.25.25h2.5a.75.75 0 0 1 .75.75v3.19l3.72-3.72a.749.749 0 0 1 .53-.22h10a.25.25 0 0 0 .25-.25V4.25a.25.25 0 0 0-.25-.25Z"></path> </svg> <div> <div class="color-fg-default h4">Code Review</div> Manage code changes </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" 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2h16.5c.966 0 1.75.784 1.75 1.75v16.5A1.75 1.75 0 0 1 20.25 22H3.75A1.75 1.75 0 0 1 2 20.25Zm1.75-.25a.25.25 0 0 0-.25.25v16.5c0 .138.112.25.25.25h16.5a.25.25 0 0 0 .25-.25V3.75a.25.25 0 0 0-.25-.25Z"></path> </svg> <div> <div class="color-fg-default h4">Code Search</div> Find more, search less </div> </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="product-explore-heading">Explore</span> <ul class="list-style-none f5" aria-labelledby="product-explore-heading"> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"all_features","context":"product","tag":"link","label":"all_features_link_product_navbar"}" href="https://github.com/features"> All features </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative 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data-target="react-partial.embeddedData">{"props":{"initialPayload":{"allShortcutsEnabled":false,"path":"/","repo":{"id":2369386,"defaultBranch":"master","name":"fitsio","ownerLogin":"esheldon","currentUserCanPush":false,"isFork":false,"isEmpty":false,"createdAt":"2011-09-12T05:35:41.000Z","ownerAvatar":"https://avatars.githubusercontent.com/u/621661?v=4","public":true,"private":false,"isOrgOwned":false},"currentUser":null,"refInfo":{"name":"master","listCacheKey":"v0:1737733666.0","canEdit":false,"refType":"branch","currentOid":"8cad13be1ed185daf53b6023da370b369059a806"},"tree":{"items":[{"name":".github","path":".github","contentType":"directory"},{"name":"cfitsio-4.4.1-20240617","path":"cfitsio-4.4.1-20240617","contentType":"directory"},{"name":"fitsio","path":"fitsio","contentType":"directory"},{"name":"patches","path":"patches","contentType":"directory"},{"name":"zlib","path":"zlib","contentType":"directory"},{"name":".gitignore","path":".gitignore","contentType":"file"},{"name":".travis.yml","path":".travis.yml","contentType":"file"},{"name":"CHANGES.md","path":"CHANGES.md","contentType":"file"},{"name":"LICENSE.txt","path":"LICENSE.txt","contentType":"file"},{"name":"MANIFEST.in","path":"MANIFEST.in","contentType":"file"},{"name":"README.md","path":"README.md","contentType":"file"},{"name":"setup.py","path":"setup.py","contentType":"file"}],"templateDirectorySuggestionUrl":null,"readme":null,"totalCount":12,"showBranchInfobar":false},"fileTree":null,"fileTreeProcessingTime":null,"foldersToFetch":[],"treeExpanded":false,"symbolsExpanded":false,"isOverview":true,"overview":{"banners":{"shouldRecommendReadme":false,"isPersonalRepo":false,"showUseActionBanner":false,"actionSlug":null,"actionId":null,"showProtectBranchBanner":false,"publishBannersInfo":{"dismissActionNoticePath":"/settings/dismiss-notice/publish_action_from_repo","releasePath":"/esheldon/fitsio/releases/new?marketplace=true","showPublishActionBanner":false},"interactionLimitBanner":null,"showInvitationBanner":false,"inviterName":null,"actionsMigrationBannerInfo":{"releaseTags":[],"showImmutableActionsMigrationBanner":false,"initialMigrationStatus":null}},"codeButton":{"contactPath":"/contact","isEnterprise":false,"local":{"protocolInfo":{"httpAvailable":true,"sshAvailable":null,"httpUrl":"https://github.com/esheldon/fitsio.git","showCloneWarning":null,"sshUrl":null,"sshCertificatesRequired":null,"sshCertificatesAvailable":null,"ghCliUrl":"gh 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href=\"https://travis-ci.com/esheldon/fitsio\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/fe0dd0efc209704588c0da7427c700fb6b9dedd2ed9bc176a623e7dcfe857e79/68747470733a2f2f7472617669732d63692e636f6d2f657368656c646f6e2f66697473696f2e7376673f6272616e63683d6d6173746572\" alt=\"Build Status (master)\" data-canonical-src=\"https://travis-ci.com/esheldon/fitsio.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/esheldon/fitsio/actions?query=workflow%3Atests\"\u003e\u003cimg src=\"https://github.com/esheldon/fitsio/workflows/tests/badge.svg\" alt=\"tests\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDescription\u003c/h2\u003e\u003ca id=\"user-content-description\" class=\"anchor\" aria-label=\"Permalink: Description\" href=\"#description\"\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\"\u003eThis is a python extension written in c and python. Data are read into\nnumerical python arrays.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eA version of cfitsio is bundled with this package, there is no need to install\nyour own, nor will this conflict with a version you have installed.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSome Features\u003c/h2\u003e\u003ca id=\"user-content-some-features\" class=\"anchor\" aria-label=\"Permalink: Some Features\" href=\"#some-features\"\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\u003eRead from and write to image, binary, and ascii table extensions.\u003c/li\u003e\n\u003cli\u003eRead arbitrary subsets of table columns and rows without loading all the data\nto memory.\u003c/li\u003e\n\u003cli\u003eRead image subsets without reading the whole image. Write subsets to existing images.\u003c/li\u003e\n\u003cli\u003eWrite and read variable length table columns.\u003c/li\u003e\n\u003cli\u003eRead images and tables using slice notation similar to numpy arrays. This is like a more\npowerful memmap, since it is column-aware for tables.\u003c/li\u003e\n\u003cli\u003eAppend rows to an existing table. Delete row sets and row ranges. Resize tables,\nor insert rows.\u003c/li\u003e\n\u003cli\u003eQuery the columns and rows in a table.\u003c/li\u003e\n\u003cli\u003eRead and write header keywords.\u003c/li\u003e\n\u003cli\u003eRead and write images in tile-compressed format (RICE,GZIP,PLIO,HCOMPRESS).\u003c/li\u003e\n\u003cli\u003eRead/write gzip files directly. Read unix compress (.Z,.zip) and bzip2 (.bz2) files.\u003c/li\u003e\n\u003cli\u003eTDIM information is used to return array columns in the correct shape.\u003c/li\u003e\n\u003cli\u003eWrite and read string table columns, including array columns of arbitrary\nshape.\u003c/li\u003e\n\u003cli\u003eRead and write complex, bool (logical), unsigned integer, signed bytes types.\u003c/li\u003e\n\u003cli\u003eWrite checksums into the header and verify them.\u003c/li\u003e\n\u003cli\u003eInsert new columns into tables in-place.\u003c/li\u003e\n\u003cli\u003eIterate over rows in a table. Data are buffered for efficiency.\u003c/li\u003e\n\u003cli\u003epython 3 support, including python 3 strings\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eExamples\u003c/h2\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-label=\"Permalink: Examples\" href=\"#examples\"\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=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"import fitsio\nfrom fitsio import FITS,FITSHDR\n\n# Often you just want to quickly read or write data without bothering to\n# create a FITS object. In that case, you can use the read and write\n# convienience functions.\n\n# read all data from the first hdu that has data\nfilename='data.fits'\ndata = fitsio.read(filename)\n\n# read a subset of rows and columns from a table\ndata = fitsio.read(filename, rows=[35,1001], columns=['x','y'], ext=2)\n\n# read the header\nh = fitsio.read_header(filename)\n# read both data and header\ndata,h = fitsio.read(filename, header=True)\n\n# open the file and write a new binary table extension with the data\n# array, which is a numpy array with fields, or \u0026quot;recarray\u0026quot;.\n\ndata = np.zeros(10, dtype=[('id','i8'),('ra','f8'),('dec','f8')])\nfitsio.write(filename, data)\n\n# Write an image to the same file. By default a new extension is\n# added to the file. use clobber=True to overwrite an existing file\n# instead. To append rows to an existing table, see below.\n\nfitsio.write(filename, image)\n\n#\n# the FITS class gives the you the ability to explore the data, and gives\n# more control\n#\n\n# open a FITS file for reading and explore\nfits=fitsio.FITS('data.fits')\n\n# see what is in here; the FITS object prints itself\nprint(fits)\n\nfile: data.fits\nmode: READONLY\nextnum hdutype hduname\n0 IMAGE_HDU\n1 BINARY_TBL mytable\n\n# at the python or ipython prompt the fits object will\n# print itself\n\u0026gt;\u0026gt;\u0026gt; fits\nfile: data.fits\n... etc\n\n# explore the extensions, either by extension number or\n# extension name if available\n\u0026gt;\u0026gt;\u0026gt; fits[0]\n\nfile: data.fits\nextension: 0\ntype: IMAGE_HDU\nimage info:\n data type: f8\n dims: [4096,2048]\n\n# by name; can also use fits[1]\n\u0026gt;\u0026gt;\u0026gt; fits['mytable']\n\nfile: data.fits\nextension: 1\ntype: BINARY_TBL\nextname: mytable\nrows: 4328342\ncolumn info:\n i1scalar u1\n f f4\n fvec f4 array[2]\n darr f8 array[3,2]\n dvarr f8 varray[10]\n s S5\n svec S6 array[3]\n svar S0 vstring[8]\n sarr S2 array[4,3]\n\n# See bottom for how to get more information for an extension\n\n# [-1] to refers the last HDU\n\u0026gt;\u0026gt;\u0026gt; fits[-1]\n...\n\n# if there are multiple HDUs with the same name, and an EXTVER\n# is set, you can use it. Here extver=2\n# fits['mytable',2]\n\n\n# read the image from extension zero\nimg = fits[0].read()\nimg = fits[0][:,:]\n\n# read a subset of the image without reading the whole image\nimg = fits[0][25:35, 45:55]\n\n\n# read all rows and columns from a binary table extension\ndata = fits[1].read()\ndata = fits['mytable'].read()\ndata = fits[1][:]\n\n# read a subset of rows and columns. By default uses a case-insensitive\n# match. The result retains the names with original case. If columns is a\n# sequence, a numpy array with fields, or recarray is returned\ndata = fits[1].read(rows=[1,5], columns=['index','x','y'])\n\n# Similar but using slice notation\n# row subsets\ndata = fits[1][10:20]\ndata = fits[1][10:20:2]\ndata = fits[1][[1,5,18]]\n\n# Using EXTNAME and EXTVER values\ndata = fits['SCI',2][10:20]\n\n# Slicing with reverse (flipped) striding\ndata = fits[1][40:25]\ndata = fits[1][40:25:-5]\n\n# all rows of column 'x'\ndata = fits[1]['x'][:]\n\n# Read a few columns at once. This is more efficient than separate read for\n# each column\ndata = fits[1]['x','y'][:]\n\n# General column and row subsets.\ncolumns=['index','x','y']\nrows = [1, 5]\ndata = fits[1][columns][rows]\n\n# data are returned in the order requested by the user\n# and duplicates are preserved\nrows = [2, 2, 5]\ndata = fits[1][columns][rows]\n\n# iterate over rows in a table hdu\n# faster if we buffer some rows, let's buffer 1000 at a time\nfits=fitsio.FITS(filename,iter_row_buffer=1000)\nfor row in fits[1]:\n print(row)\n\n# iterate over HDUs in a FITS object\nfor hdu in fits:\n data=hdu.read()\n\n# Note dvarr shows type varray[10] and svar shows type vstring[8]. These\n# are variable length columns and the number specified is the maximum size.\n# By default they are read into fixed-length fields in the output array.\n# You can over-ride this by constructing the FITS object with the vstorage\n# keyword or specifying vstorage when reading. Sending vstorage='object'\n# will store the data in variable size object fields to save memory; the\n# default is vstorage='fixed'. Object fields can also be written out to a\n# new FITS file as variable length to save disk space.\n\nfits = fitsio.FITS(filename,vstorage='object')\n# OR\ndata = fits[1].read(vstorage='object')\nprint(data['dvarr'].dtype)\n dtype('object')\n\n\n# you can grab a FITS HDU object to simplify notation\nhdu1 = fits[1]\ndata = hdu1['x','y'][35:50]\n\n# get rows that satisfy the input expression. See \u0026quot;Row Filtering\n# Specification\u0026quot; in the cfitsio manual (note no temporary table is\n# created in this case, contrary to the cfitsio docs)\nw=fits[1].where(\u0026quot;x \u0026gt; 0.25 \u0026amp;\u0026amp; y \u0026lt; 35.0\u0026quot;)\ndata = fits[1][w]\n\n# read the header\nh = fits[0].read_header()\nprint(h['BITPIX'])\n -64\n\nfits.close()\n\n\n# now write some data\nfits = FITS('test.fits','rw')\n\n\n# create a rec array. Note vstr\n# is a variable length string\nnrows=35\ndata = np.zeros(nrows, dtype=[('index','i4'),('vstr','O'),('x','f8'),\n ('arr','f4',(3,4))])\ndata['index'] = np.arange(nrows,dtype='i4')\ndata['x'] = np.random.random(nrows)\ndata['vstr'] = [str(i) for i in xrange(nrows)]\ndata['arr'] = np.arange(nrows*3*4,dtype='f4').reshape(nrows,3,4)\n\n# create a new table extension and write the data\nfits.write(data)\n\n# can also be a list of ordinary arrays if you send the names\narray_list=[xarray,yarray,namearray]\nnames=['x','y','name']\nfits.write(array_list, names=names)\n\n# similarly a dict of arrays\nfits.write(dict_of_arrays)\nfits.write(dict_of_arrays, names=names) # control name order\n\n# append more rows to the table. The fields in data2 should match columns\n# in the table. missing columns will be filled with zeros\nfits[-1].append(data2)\n\n# insert a new column into a table\nfits[-1].insert_column('newcol', data)\n\n# insert with a specific colnum\nfits[-1].insert_column('newcol', data, colnum=2)\n\n# overwrite rows\nfits[-1].write(data)\n\n# overwrite starting at a particular row. The table will grow if needed\nfits[-1].write(data, firstrow=350)\n\n\n# create an image\nimg=np.arange(2*3,dtype='i4').reshape(2,3)\n\n# write an image in a new HDU (if this is a new file, the primary HDU)\nfits.write(img)\n\n# write an image with rice compression\nfits.write(img, compress='rice')\n\n# control the compression\nfimg=np.random.normal(size=2*3).reshape(2, 3)\nfits.write(img, compress='rice', qlevel=16, qmethod='SUBTRACTIVE_DITHER_2')\n\n# lossless gzip compression for integers or floating point\nfits.write(img, compress='gzip', qlevel=None)\nfits.write(fimg, compress='gzip', qlevel=None)\n\n# overwrite the image\nfits[ext].write(img2)\n\n# write into an existing image, starting at the location [300,400]\n# the image will be expanded if needed\nfits[ext].write(img3, start=[300,400])\n\n# change the shape of the image on disk\nfits[ext].reshape([250,100])\n\n# add checksums for the data\nfits[-1].write_checksum()\n\n# can later verify data integridy\nfits[-1].verify_checksum()\n\n# you can also write a header at the same time. The header can be\n# - a simple dict (no comments)\n# - a list of dicts with 'name','value','comment' fields\n# - a FITSHDR object\n\nhdict = {'somekey': 35, 'location': 'kitt peak'}\nfits.write(data, header=hdict)\nhlist = [{'name':'observer', 'value':'ES', 'comment':'who'},\n {'name':'location','value':'CTIO'},\n {'name':'photometric','value':True}]\nfits.write(data, header=hlist)\nhdr=FITSHDR(hlist)\nfits.write(data, header=hdr)\n\n# you can add individual keys to an existing HDU\nfits[1].write_key(name, value, comment=\u0026quot;my comment\u0026quot;)\n\n# Write multiple header keys to an existing HDU. Here records\n# is the same as sent with header= above\nfits[1].write_keys(records)\n\n# write special COMMENT fields\nfits[1].write_comment(\u0026quot;observer JS\u0026quot;)\nfits[1].write_comment(\u0026quot;we had good weather\u0026quot;)\n\n# write special history fields\nfits[1].write_history(\u0026quot;processed with software X\u0026quot;)\nfits[1].write_history(\u0026quot;re-processed with software Y\u0026quot;)\n\nfits.close()\n\n# using a context, the file is closed automatically after leaving the block\nwith FITS('path/to/file') as fits:\n data = fits[ext].read()\n\n # you can check if a header exists using \u0026quot;in\u0026quot;:\n if 'blah' in fits:\n data=fits['blah'].read()\n if 2 in f:\n data=fits[2].read()\n\n# methods to get more information about extension. For extension 1:\nf[1].get_info() # lots of info about the extension\nf[1].has_data() # returns True if data is present in extension\nf[1].get_extname()\nf[1].get_extver()\nf[1].get_extnum() # return zero-offset extension number\nf[1].get_exttype() # 'BINARY_TBL' or 'ASCII_TBL' or 'IMAGE_HDU'\nf[1].get_offsets() # byte offsets (header_start, data_start, data_end)\nf[1].is_compressed() # for images. True if tile-compressed\nf[1].get_colnames() # for tables\nf[1].get_colname(colnum) # for tables find the name from column number\nf[1].get_nrows() # for tables\nf[1].get_rec_dtype() # for tables\nf[1].get_rec_column_descr() # for tables\nf[1].get_vstorage() # for tables, storage mechanism for variable\n # length columns\n\n# public attributes you can feel free to change as needed\nf[1].lower # If True, lower case colnames on output\nf[1].upper # If True, upper case colnames on output\nf[1].case_sensitive # if True, names are matched case sensitive\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efitsio\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efitsio\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eFITS\u003c/span\u003e,\u003cspan class=\"pl-c1\"\u003eFITSHDR\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e# Often you just want to quickly read or write data without bothering to\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# create a FITS object. In that case, you can use the read and write\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# convienience functions.\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e# read all data from the first hdu that has data\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003efilename\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'data.fits'\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efitsio\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eread\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003efilename\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# read a subset of rows and columns from a table\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efitsio\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eread\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003efilename\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003erows\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e35\u003c/span\u003e,\u003cspan class=\"pl-c1\"\u003e1001\u003c/span\u003e], \u003cspan class=\"pl-s1\"\u003ecolumns\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[\u003cspan class=\"pl-s\"\u003e'x'\u003c/span\u003e,\u003cspan class=\"pl-s\"\u003e'y'\u003c/span\u003e], \u003cspan class=\"pl-s1\"\u003eext\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# read the header\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eh\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efitsio\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eread_header\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003efilename\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e# read both data and header\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e,\u003cspan class=\"pl-s1\"\u003eh\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efitsio\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eread\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003efilename\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eheader\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# open the file and write a new binary table extension with the data\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# array, which is a numpy array with fields, or \"recarray\".\u003c/span\u003e\n\n\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003enp\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003ezeros\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e10\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003edtype\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[(\u003cspan class=\"pl-s\"\u003e'id'\u003c/span\u003e,\u003cspan class=\"pl-s\"\u003e'i8'\u003c/span\u003e),(\u003cspan class=\"pl-s\"\u003e'ra'\u003c/span\u003e,\u003cspan class=\"pl-s\"\u003e'f8'\u003c/span\u003e),(\u003cspan class=\"pl-s\"\u003e'dec'\u003c/span\u003e,\u003cspan class=\"pl-s\"\u003e'f8'\u003c/span\u003e)])\n\u003cspan class=\"pl-s1\"\u003efitsio\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003ewrite\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003efilename\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# Write an image to the same file. By default a new extension is\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# added to the file. use clobber=True to overwrite an existing file\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# instead. To append rows to an existing table, see below.\u003c/span\u003e\n\n\u003cspan class=\"pl-s1\"\u003efitsio\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003ewrite\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003efilename\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eimage\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# the FITS class gives the you the ability to explore the data, and gives\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# more control\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e# open a FITS file for reading and explore\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003efitsio\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eFITS\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e'data.fits'\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# see what is in here; the FITS object prints itself\u003c/span\u003e\n\u003cspan class=\"pl-en\"\u003eprint\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e)\n\n\u003cspan class=\"pl-s1\"\u003efile\u003c/span\u003e: \u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003efits\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003emode\u003c/span\u003e: \u003cspan class=\"pl-smi\"\u003eREADONLY\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eextnum\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ehdutype\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ehduname\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eIMAGE_HDU\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eBINARY_TBL\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emytable\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e# at the python or ipython prompt the fits object will\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# print itself\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003efile\u003c/span\u003e: \u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003efits\u003c/span\u003e\n... \u003cspan class=\"pl-s1\"\u003eetc\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e# explore the extensions, either by extension number or\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# extension name if available\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e]\n\n\u003cspan class=\"pl-s1\"\u003efile\u003c/span\u003e: \u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003efits\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eextension\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003etype\u003c/span\u003e: \u003cspan class=\"pl-smi\"\u003eIMAGE_HDU\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eimage\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003einfo\u003c/span\u003e:\n \u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003etype\u003c/span\u003e: \u003cspan class=\"pl-smi\"\u003ef8\u003c/span\u003e\n \u003cspan class=\"pl-s1\"\u003edims\u003c/span\u003e: [\u003cspan class=\"pl-c1\"\u003e4096\u003c/span\u003e,\u003cspan class=\"pl-c1\"\u003e2048\u003c/span\u003e]\n\n\u003cspan class=\"pl-c\"\u003e# by name; can also use fits[1]\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e[\u003cspan class=\"pl-s\"\u003e'mytable'\u003c/span\u003e]\n\n\u003cspan class=\"pl-s1\"\u003efile\u003c/span\u003e: \u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003efits\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eextension\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003etype\u003c/span\u003e: \u003cspan class=\"pl-smi\"\u003eBINARY_TBL\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eextname\u003c/span\u003e: \u003cspan class=\"pl-smi\"\u003emytable\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003erows\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e4328342\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ecolumn\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003einfo\u003c/span\u003e:\n \u003cspan class=\"pl-s1\"\u003ei1scalar\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eu1\u003c/span\u003e\n \u003cspan class=\"pl-s1\"\u003ef\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ef4\u003c/span\u003e\n \u003cspan class=\"pl-s1\"\u003efvec\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ef4\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003earray\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e]\n \u003cspan class=\"pl-s1\"\u003edarr\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ef8\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003earray\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e3\u003c/span\u003e,\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e]\n \u003cspan class=\"pl-s1\"\u003edvarr\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ef8\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003evarray\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e10\u003c/span\u003e]\n \u003cspan class=\"pl-s1\"\u003es\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eS5\u003c/span\u003e\n \u003cspan class=\"pl-s1\"\u003esvec\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eS6\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003earray\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e3\u003c/span\u003e]\n \u003cspan class=\"pl-s1\"\u003esvar\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eS0\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003evstring\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e8\u003c/span\u003e]\n \u003cspan class=\"pl-s1\"\u003esarr\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eS2\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003earray\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e4\u003c/span\u003e,\u003cspan class=\"pl-c1\"\u003e3\u003c/span\u003e]\n\n\u003cspan class=\"pl-c\"\u003e# See bottom for how to get more information for an extension\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e# [-1] to refers the last HDU\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e\u0026gt;\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e]\n...\n\n\u003cspan class=\"pl-c\"\u003e# if there are multiple HDUs with the same name, and an EXTVER\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# is set, you can use it. Here extver=2\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# fits['mytable',2]\u003c/span\u003e\n\n\n\u003cspan class=\"pl-c\"\u003e# read the image from extension zero\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eimg\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e].\u003cspan class=\"pl-c1\"\u003eread\u003c/span\u003e()\n\u003cspan class=\"pl-s1\"\u003eimg\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e][:,:]\n\n\u003cspan class=\"pl-c\"\u003e# read a subset of the image without reading the whole image\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eimg\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e][\u003cspan class=\"pl-c1\"\u003e25\u003c/span\u003e:\u003cspan class=\"pl-c1\"\u003e35\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e45\u003c/span\u003e:\u003cspan class=\"pl-c1\"\u003e55\u003c/span\u003e]\n\n\n\u003cspan class=\"pl-c\"\u003e# read all rows and columns from a binary table extension\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e].\u003cspan class=\"pl-c1\"\u003eread\u003c/span\u003e()\n\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e[\u003cspan class=\"pl-s\"\u003e'mytable'\u003c/span\u003e].\u003cspan class=\"pl-c1\"\u003eread\u003c/span\u003e()\n\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e][:]\n\n\u003cspan class=\"pl-c\"\u003e# read a subset of rows and columns. By default uses a case-insensitive\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# match. The result retains the names with original case. If columns is a\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# sequence, a numpy array with fields, or recarray is returned\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e].\u003cspan class=\"pl-c1\"\u003eread\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003erows\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e,\u003cspan class=\"pl-c1\"\u003e5\u003c/span\u003e], \u003cspan class=\"pl-s1\"\u003ecolumns\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[\u003cspan class=\"pl-s\"\u003e'index'\u003c/span\u003e,\u003cspan class=\"pl-s\"\u003e'x'\u003c/span\u003e,\u003cspan class=\"pl-s\"\u003e'y'\u003c/span\u003e])\n\n\u003cspan class=\"pl-c\"\u003e# Similar but using slice notation\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# row subsets\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e][\u003cspan class=\"pl-c1\"\u003e10\u003c/span\u003e:\u003cspan class=\"pl-c1\"\u003e20\u003c/span\u003e]\n\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e][\u003cspan class=\"pl-c1\"\u003e10\u003c/span\u003e:\u003cspan class=\"pl-c1\"\u003e20\u003c/span\u003e:\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e]\n\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e][[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e,\u003cspan class=\"pl-c1\"\u003e5\u003c/span\u003e,\u003cspan class=\"pl-c1\"\u003e18\u003c/span\u003e]]\n\n\u003cspan class=\"pl-c\"\u003e# Using EXTNAME and EXTVER values\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e[\u003cspan class=\"pl-s\"\u003e'SCI'\u003c/span\u003e,\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e][\u003cspan class=\"pl-c1\"\u003e10\u003c/span\u003e:\u003cspan class=\"pl-c1\"\u003e20\u003c/span\u003e]\n\n\u003cspan class=\"pl-c\"\u003e# Slicing with reverse (flipped) striding\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e][\u003cspan class=\"pl-c1\"\u003e40\u003c/span\u003e:\u003cspan class=\"pl-c1\"\u003e25\u003c/span\u003e]\n\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e][\u003cspan class=\"pl-c1\"\u003e40\u003c/span\u003e:\u003cspan class=\"pl-c1\"\u003e25\u003c/span\u003e:\u003cspan class=\"pl-c1\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e5\u003c/span\u003e]\n\n\u003cspan class=\"pl-c\"\u003e# all rows of column 'x'\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e][\u003cspan class=\"pl-s\"\u003e'x'\u003c/span\u003e][:]\n\n\u003cspan class=\"pl-c\"\u003e# Read a few columns at once. This is more efficient than separate read for\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# each column\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e][\u003cspan class=\"pl-s\"\u003e'x'\u003c/span\u003e,\u003cspan class=\"pl-s\"\u003e'y'\u003c/span\u003e][:]\n\n\u003cspan class=\"pl-c\"\u003e# General column and row subsets.\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ecolumns\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[\u003cspan class=\"pl-s\"\u003e'index'\u003c/span\u003e,\u003cspan class=\"pl-s\"\u003e'x'\u003c/span\u003e,\u003cspan class=\"pl-s\"\u003e'y'\u003c/span\u003e]\n\u003cspan class=\"pl-s1\"\u003erows\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e [\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e5\u003c/span\u003e]\n\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e][\u003cspan class=\"pl-s1\"\u003ecolumns\u003c/span\u003e][\u003cspan class=\"pl-s1\"\u003erows\u003c/span\u003e]\n\n\u003cspan class=\"pl-c\"\u003e# data are returned in the order requested by the user\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# and duplicates are preserved\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003erows\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e [\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e5\u003c/span\u003e]\n\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e][\u003cspan class=\"pl-s1\"\u003ecolumns\u003c/span\u003e][\u003cspan class=\"pl-s1\"\u003erows\u003c/span\u003e]\n\n\u003cspan class=\"pl-c\"\u003e# iterate over rows in a table hdu\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# faster if we buffer some rows, let's buffer 1000 at a time\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003efitsio\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eFITS\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003efilename\u003c/span\u003e,\u003cspan class=\"pl-s1\"\u003eiter_row_buffer\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e1000\u003c/span\u003e)\n\u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003erow\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e]:\n \u003cspan class=\"pl-en\"\u003eprint\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003erow\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# iterate over HDUs in a FITS object\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ehdu\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e:\n \u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003ehdu\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eread\u003c/span\u003e()\n\n\u003cspan class=\"pl-c\"\u003e# Note dvarr shows type varray[10] and svar shows type vstring[8]. These\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# are variable length columns and the number specified is the maximum size.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# By default they are read into fixed-length fields in the output array.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# You can over-ride this by constructing the FITS object with the vstorage\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# keyword or specifying vstorage when reading. Sending vstorage='object'\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# will store the data in variable size object fields to save memory; the\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# default is vstorage='fixed'. Object fields can also be written out to a\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# new FITS file as variable length to save disk space.\u003c/span\u003e\n\n\u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efitsio\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eFITS\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003efilename\u003c/span\u003e,\u003cspan class=\"pl-s1\"\u003evstorage\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'object'\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e# OR\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e].\u003cspan class=\"pl-c1\"\u003eread\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003evstorage\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'object'\u003c/span\u003e)\n\u003cspan class=\"pl-en\"\u003eprint\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e[\u003cspan class=\"pl-s\"\u003e'dvarr'\u003c/span\u003e].\u003cspan class=\"pl-c1\"\u003edtype\u003c/span\u003e)\n \u003cspan class=\"pl-en\"\u003edtype\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e'object'\u003c/span\u003e)\n\n\n\u003cspan class=\"pl-c\"\u003e# you can grab a FITS HDU object to simplify notation\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ehdu1\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e]\n\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ehdu1\u003c/span\u003e[\u003cspan class=\"pl-s\"\u003e'x'\u003c/span\u003e,\u003cspan class=\"pl-s\"\u003e'y'\u003c/span\u003e][\u003cspan class=\"pl-c1\"\u003e35\u003c/span\u003e:\u003cspan class=\"pl-c1\"\u003e50\u003c/span\u003e]\n\n\u003cspan class=\"pl-c\"\u003e# get rows that satisfy the input expression. See \"Row Filtering\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# Specification\" in the cfitsio manual (note no temporary table is\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# created in this case, contrary to the cfitsio docs)\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ew\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e].\u003cspan class=\"pl-c1\"\u003ewhere\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"x \u0026gt; 0.25 \u0026amp;\u0026amp; y \u0026lt; 35.0\"\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e][\u003cspan class=\"pl-s1\"\u003ew\u003c/span\u003e]\n\n\u003cspan class=\"pl-c\"\u003e# read the header\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eh\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e].\u003cspan class=\"pl-c1\"\u003eread_header\u003c/span\u003e()\n\u003cspan class=\"pl-en\"\u003eprint\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eh\u003c/span\u003e[\u003cspan class=\"pl-s\"\u003e'BITPIX'\u003c/span\u003e])\n \u003cspan class=\"pl-c1\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e64\u003c/span\u003e\n\n\u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eclose\u003c/span\u003e()\n\n\n\u003cspan class=\"pl-c\"\u003e# now write some data\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eFITS\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e'test.fits'\u003c/span\u003e,\u003cspan class=\"pl-s\"\u003e'rw'\u003c/span\u003e)\n\n\n\u003cspan class=\"pl-c\"\u003e# create a rec array. Note vstr\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# is a variable length string\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003enrows\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e35\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003enp\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003ezeros\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003enrows\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003edtype\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[(\u003cspan class=\"pl-s\"\u003e'index'\u003c/span\u003e,\u003cspan class=\"pl-s\"\u003e'i4'\u003c/span\u003e),(\u003cspan class=\"pl-s\"\u003e'vstr'\u003c/span\u003e,\u003cspan class=\"pl-s\"\u003e'O'\u003c/span\u003e),(\u003cspan class=\"pl-s\"\u003e'x'\u003c/span\u003e,\u003cspan class=\"pl-s\"\u003e'f8'\u003c/span\u003e),\n (\u003cspan class=\"pl-s\"\u003e'arr'\u003c/span\u003e,\u003cspan class=\"pl-s\"\u003e'f4'\u003c/span\u003e,(\u003cspan class=\"pl-c1\"\u003e3\u003c/span\u003e,\u003cspan class=\"pl-c1\"\u003e4\u003c/span\u003e))])\n\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e[\u003cspan class=\"pl-s\"\u003e'index'\u003c/span\u003e] \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003enp\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003earange\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003enrows\u003c/span\u003e,\u003cspan class=\"pl-s1\"\u003edtype\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'i4'\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e[\u003cspan class=\"pl-s\"\u003e'x'\u003c/span\u003e] \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003enp\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003erandom\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003erandom\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003enrows\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e[\u003cspan class=\"pl-s\"\u003e'vstr'\u003c/span\u003e] \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e [\u003cspan class=\"pl-en\"\u003estr\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ei\u003c/span\u003e) \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ei\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-en\"\u003exrange\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003enrows\u003c/span\u003e)]\n\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e[\u003cspan class=\"pl-s\"\u003e'arr'\u003c/span\u003e] \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003enp\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003earange\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003enrows\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e*\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e3\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e*\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e4\u003c/span\u003e,\u003cspan class=\"pl-s1\"\u003edtype\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'f4'\u003c/span\u003e).\u003cspan class=\"pl-c1\"\u003ereshape\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003enrows\u003c/span\u003e,\u003cspan class=\"pl-c1\"\u003e3\u003c/span\u003e,\u003cspan class=\"pl-c1\"\u003e4\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# create a new table extension and write the data\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003ewrite\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# can also be a list of ordinary arrays if you send the names\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003earray_list\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[\u003cspan class=\"pl-s1\"\u003exarray\u003c/span\u003e,\u003cspan class=\"pl-s1\"\u003eyarray\u003c/span\u003e,\u003cspan class=\"pl-s1\"\u003enamearray\u003c/span\u003e]\n\u003cspan class=\"pl-s1\"\u003enames\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[\u003cspan class=\"pl-s\"\u003e'x'\u003c/span\u003e,\u003cspan class=\"pl-s\"\u003e'y'\u003c/span\u003e,\u003cspan class=\"pl-s\"\u003e'name'\u003c/span\u003e]\n\u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003ewrite\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003earray_list\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003enames\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003enames\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# similarly a dict of arrays\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003ewrite\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003edict_of_arrays\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003ewrite\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003edict_of_arrays\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003enames\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003enames\u003c/span\u003e) \u003cspan class=\"pl-c\"\u003e# control name order\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e# append more rows to the table. The fields in data2 should match columns\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# in the table. missing columns will be filled with zeros\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e].\u003cspan class=\"pl-c1\"\u003eappend\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003edata2\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# insert a new column into a table\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e].\u003cspan class=\"pl-c1\"\u003einsert_column\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e'newcol'\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# insert with a specific colnum\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e].\u003cspan class=\"pl-c1\"\u003einsert_column\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e'newcol'\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ecolnum\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# overwrite rows\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e].\u003cspan class=\"pl-c1\"\u003ewrite\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# overwrite starting at a particular row. The table will grow if needed\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e].\u003cspan class=\"pl-c1\"\u003ewrite\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003efirstrow\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e350\u003c/span\u003e)\n\n\n\u003cspan class=\"pl-c\"\u003e# create an image\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eimg\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003enp\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003earange\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e*\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e3\u003c/span\u003e,\u003cspan class=\"pl-s1\"\u003edtype\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'i4'\u003c/span\u003e).\u003cspan class=\"pl-c1\"\u003ereshape\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e,\u003cspan class=\"pl-c1\"\u003e3\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# write an image in a new HDU (if this is a new file, the primary HDU)\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003ewrite\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eimg\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# write an image with rice compression\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003ewrite\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eimg\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ecompress\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'rice'\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# control the compression\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003efimg\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003enp\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003erandom\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003enormal\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003esize\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e*\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e3\u003c/span\u003e).\u003cspan class=\"pl-c1\"\u003ereshape\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e3\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003ewrite\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eimg\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ecompress\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'rice'\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eqlevel\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e16\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eqmethod\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'SUBTRACTIVE_DITHER_2'\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# lossless gzip compression for integers or floating point\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003ewrite\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eimg\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ecompress\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'gzip'\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eqlevel\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eNone\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003ewrite\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003efimg\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ecompress\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'gzip'\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eqlevel\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eNone\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# overwrite the image\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e[\u003cspan class=\"pl-s1\"\u003eext\u003c/span\u003e].\u003cspan class=\"pl-c1\"\u003ewrite\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eimg2\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# write into an existing image, starting at the location [300,400]\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# the image will be expanded if needed\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e[\u003cspan class=\"pl-s1\"\u003eext\u003c/span\u003e].\u003cspan class=\"pl-c1\"\u003ewrite\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eimg3\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003estart\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e300\u003c/span\u003e,\u003cspan class=\"pl-c1\"\u003e400\u003c/span\u003e])\n\n\u003cspan class=\"pl-c\"\u003e# change the shape of the image on disk\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e[\u003cspan class=\"pl-s1\"\u003eext\u003c/span\u003e].\u003cspan class=\"pl-c1\"\u003ereshape\u003c/span\u003e([\u003cspan class=\"pl-c1\"\u003e250\u003c/span\u003e,\u003cspan class=\"pl-c1\"\u003e100\u003c/span\u003e])\n\n\u003cspan class=\"pl-c\"\u003e# add checksums for the data\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e].\u003cspan class=\"pl-c1\"\u003ewrite_checksum\u003c/span\u003e()\n\n\u003cspan class=\"pl-c\"\u003e# can later verify data integridy\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e].\u003cspan class=\"pl-c1\"\u003everify_checksum\u003c/span\u003e()\n\n\u003cspan class=\"pl-c\"\u003e# you can also write a header at the same time. The header can be\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# - a simple dict (no comments)\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# - a list of dicts with 'name','value','comment' fields\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# - a FITSHDR object\u003c/span\u003e\n\n\u003cspan class=\"pl-s1\"\u003ehdict\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e {\u003cspan class=\"pl-s\"\u003e'somekey'\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e35\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'location'\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e'kitt peak'\u003c/span\u003e}\n\u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003ewrite\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eheader\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003ehdict\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003ehlist\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e [{\u003cspan class=\"pl-s\"\u003e'name'\u003c/span\u003e:\u003cspan class=\"pl-s\"\u003e'observer'\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'value'\u003c/span\u003e:\u003cspan class=\"pl-s\"\u003e'ES'\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'comment'\u003c/span\u003e:\u003cspan class=\"pl-s\"\u003e'who'\u003c/span\u003e},\n {\u003cspan class=\"pl-s\"\u003e'name'\u003c/span\u003e:\u003cspan class=\"pl-s\"\u003e'location'\u003c/span\u003e,\u003cspan class=\"pl-s\"\u003e'value'\u003c/span\u003e:\u003cspan class=\"pl-s\"\u003e'CTIO'\u003c/span\u003e},\n {\u003cspan class=\"pl-s\"\u003e'name'\u003c/span\u003e:\u003cspan class=\"pl-s\"\u003e'photometric'\u003c/span\u003e,\u003cspan class=\"pl-s\"\u003e'value'\u003c/span\u003e:\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e}]\n\u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003ewrite\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eheader\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003ehlist\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003ehdr\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-en\"\u003eFITSHDR\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ehlist\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003ewrite\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eheader\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003ehdr\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# you can add individual keys to an existing HDU\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e].\u003cspan class=\"pl-c1\"\u003ewrite_key\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ename\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003evalue\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ecomment\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"my comment\"\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# Write multiple header keys to an existing HDU. Here records\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# is the same as sent with header= above\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e].\u003cspan class=\"pl-c1\"\u003ewrite_keys\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003erecords\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# write special COMMENT fields\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e].\u003cspan class=\"pl-c1\"\u003ewrite_comment\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"observer JS\"\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e].\u003cspan class=\"pl-c1\"\u003ewrite_comment\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"we had good weather\"\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# write special history fields\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e].\u003cspan class=\"pl-c1\"\u003ewrite_history\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"processed with software X\"\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e].\u003cspan class=\"pl-c1\"\u003ewrite_history\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"re-processed with software Y\"\u003c/span\u003e)\n\n\u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eclose\u003c/span\u003e()\n\n\u003cspan class=\"pl-c\"\u003e# using a context, the file is closed automatically after leaving the block\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003ewith\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eFITS\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e'path/to/file'\u003c/span\u003e) \u003cspan class=\"pl-k\"\u003eas\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e:\n \u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e[\u003cspan class=\"pl-s1\"\u003eext\u003c/span\u003e].\u003cspan class=\"pl-c1\"\u003eread\u003c/span\u003e()\n\n \u003cspan class=\"pl-c\"\u003e# you can check if a header exists using \"in\":\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e'blah'\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e:\n \u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e[\u003cspan class=\"pl-s\"\u003e'blah'\u003c/span\u003e].\u003cspan class=\"pl-c1\"\u003eread\u003c/span\u003e()\n \u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ef\u003c/span\u003e:\n \u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003efits\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e].\u003cspan class=\"pl-c1\"\u003eread\u003c/span\u003e()\n\n\u003cspan class=\"pl-c\"\u003e# methods to get more information about extension. For extension 1:\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ef\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e].\u003cspan class=\"pl-c1\"\u003eget_info\u003c/span\u003e() \u003cspan class=\"pl-c\"\u003e# lots of info about the extension\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ef\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e].\u003cspan class=\"pl-c1\"\u003ehas_data\u003c/span\u003e() \u003cspan class=\"pl-c\"\u003e# returns True if data is present in extension\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ef\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e].\u003cspan class=\"pl-c1\"\u003eget_extname\u003c/span\u003e()\n\u003cspan class=\"pl-s1\"\u003ef\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e].\u003cspan class=\"pl-c1\"\u003eget_extver\u003c/span\u003e()\n\u003cspan class=\"pl-s1\"\u003ef\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e].\u003cspan class=\"pl-c1\"\u003eget_extnum\u003c/span\u003e() \u003cspan class=\"pl-c\"\u003e# return zero-offset extension number\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ef\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e].\u003cspan class=\"pl-c1\"\u003eget_exttype\u003c/span\u003e() \u003cspan class=\"pl-c\"\u003e# 'BINARY_TBL' or 'ASCII_TBL' or 'IMAGE_HDU'\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ef\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e].\u003cspan class=\"pl-c1\"\u003eget_offsets\u003c/span\u003e() \u003cspan class=\"pl-c\"\u003e# byte offsets (header_start, data_start, data_end)\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ef\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e].\u003cspan class=\"pl-c1\"\u003eis_compressed\u003c/span\u003e() \u003cspan class=\"pl-c\"\u003e# for images. True if tile-compressed\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ef\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e].\u003cspan class=\"pl-c1\"\u003eget_colnames\u003c/span\u003e() \u003cspan class=\"pl-c\"\u003e# for tables\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ef\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e].\u003cspan class=\"pl-c1\"\u003eget_colname\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ecolnum\u003c/span\u003e) \u003cspan class=\"pl-c\"\u003e# for tables find the name from column number\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ef\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e].\u003cspan class=\"pl-c1\"\u003eget_nrows\u003c/span\u003e() \u003cspan class=\"pl-c\"\u003e# for tables\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ef\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e].\u003cspan class=\"pl-c1\"\u003eget_rec_dtype\u003c/span\u003e() \u003cspan class=\"pl-c\"\u003e# for tables\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ef\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e].\u003cspan class=\"pl-c1\"\u003eget_rec_column_descr\u003c/span\u003e() \u003cspan class=\"pl-c\"\u003e# for tables\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ef\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e].\u003cspan class=\"pl-c1\"\u003eget_vstorage\u003c/span\u003e() \u003cspan class=\"pl-c\"\u003e# for tables, storage mechanism for variable\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e# length columns\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e# public attributes you can feel free to change as needed\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ef\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e].\u003cspan class=\"pl-c1\"\u003elower\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# If True, lower case colnames on output\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ef\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e].\u003cspan class=\"pl-c1\"\u003eupper\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# If True, upper case colnames on output\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ef\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e].\u003cspan class=\"pl-c1\"\u003ecase_sensitive\u003c/span\u003e \u003cspan class=\"pl-c\"\u003e# if True, names are matched case sensitive\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\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\"\u003eThe easiest way is using pip or conda. To get the latest release\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"pip install fitsio\n\n# update fitsio (and everything else)\npip install fitsio --upgrade\n\n# if pip refuses to update to a newer version\npip install fitsio --upgrade --ignore-installed\n\n# if you only want to upgrade fitsio\npip install fitsio --no-deps --upgrade --ignore-installed\n\n# for conda, use conda-forge\nconda install -c conda-forge fitsio\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003epip install fitsio\n\n# update fitsio (and everything else)\npip install fitsio --upgrade\n\n# if pip refuses to update to a newer version\npip install fitsio --upgrade --ignore-installed\n\n# if you only want to upgrade fitsio\npip install fitsio --no-deps --upgrade --ignore-installed\n\n# for conda, use conda-forge\nconda install -c conda-forge fitsio\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eYou can also get the latest source tarball release from\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"https://pypi.python.org/pypi/fitsio\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003ehttps://pypi.python.org/pypi/fitsio\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eor the bleeding edge source from github or use git. To check out\nthe code for the first time\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"git clone https://github.com/esheldon/fitsio.git\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003egit clone https://github.com/esheldon/fitsio.git\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eOr at a later time to update to the latest\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"cd fitsio\ngit update\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003ecd fitsio\ngit update\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eUse tar xvfz to untar the file, enter the fitsio directory and type\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"python setup.py install\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003epython setup.py install\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eoptionally with a prefix\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"python setup.py install --prefix=/some/path\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003epython setup.py install --prefix=/some/path\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eRequirements\u003c/h2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\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\u003epython 2 or python 3\u003c/li\u003e\n\u003cli\u003ea C compiler and build tools like \u003ccode\u003emake\u003c/code\u003e, \u003ccode\u003epatch\u003c/code\u003e, etc.\u003c/li\u003e\n\u003cli\u003enumpy (See the note below. Generally, numpy 1.11 or later is better.)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDo not use numpy 1.10.0 or 1.10.1\u003c/h3\u003e\u003ca id=\"user-content-do-not-use-numpy-1100-or-1101\" class=\"anchor\" aria-label=\"Permalink: Do not use numpy 1.10.0 or 1.10.1\" href=\"#do-not-use-numpy-1100-or-1101\"\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\"\u003eThere is a serious performance regression in numpy 1.10 that results\nin fitsio running tens to hundreds of times slower. A fix may be\nforthcoming in a later release. Please comment here if this\nhas already impacted your work \u003ca class=\"issue-link js-issue-link\" data-error-text=\"Failed to load title\" data-id=\"111188681\" data-permission-text=\"Title is private\" data-url=\"https://github.com/numpy/numpy/issues/6467\" data-hovercard-type=\"issue\" data-hovercard-url=\"/numpy/numpy/issues/6467/hovercard\" href=\"https://github.com/numpy/numpy/issues/6467\"\u003enumpy/numpy#6467\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTests\u003c/h2\u003e\u003ca id=\"user-content-tests\" class=\"anchor\" aria-label=\"Permalink: Tests\" href=\"#tests\"\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\"\u003eThe unit tests should all pass for full support.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"pytest fitsio\"\u003e\u003cpre\u003epytest fitsio\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eSome tests may fail if certain libraries are not available, such\nas bzip2. This failure only implies that bzipped files cannot\nbe read, without affecting other functionality.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eNotes on Usage and Features\u003c/h2\u003e\u003ca id=\"user-content-notes-on-usage-and-features\" class=\"anchor\" aria-label=\"Permalink: Notes on Usage and Features\" href=\"#notes-on-usage-and-features\"\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\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003ecfitsio bundling\u003c/h3\u003e\u003ca id=\"user-content-cfitsio-bundling\" class=\"anchor\" aria-label=\"Permalink: cfitsio bundling\" href=\"#cfitsio-bundling\"\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 bundle cfitsio partly because many deployed versions of cfitsio in the\nwild do not have support for interesting features like tiled image compression.\nBundling a version that meets our needs is a safe alternative.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003earray ordering\u003c/h3\u003e\u003ca id=\"user-content-array-ordering\" class=\"anchor\" aria-label=\"Permalink: array ordering\" href=\"#array-ordering\"\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\"\u003eSince numpy uses C order, FITS uses fortran order, we have to write the TDIM\nand image dimensions in reverse order, but write the data as is. Then we need\nto also reverse the dims as read from the header when creating the numpy dtype,\nbut read as is.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e\u003ccode\u003edistutils\u003c/code\u003e vs \u003ccode\u003esetuptools\u003c/code\u003e\u003c/h3\u003e\u003ca id=\"user-content-distutils-vs-setuptools\" class=\"anchor\" aria-label=\"Permalink: distutils vs setuptools\" href=\"#distutils-vs-setuptools\"\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\"\u003eAs of version \u003ccode\u003e1.0.0\u003c/code\u003e, \u003ccode\u003efitsio\u003c/code\u003e has been transitioned to \u003ccode\u003esetuptools\u003c/code\u003e for packaging\nand installation. There are many reasons to do this (and to not do this). However,\nat a practical level, what this means for you is that you may have trouble uninstalling\nolder versions with \u003ccode\u003epip\u003c/code\u003e via \u003ccode\u003epip uninstall fitsio\u003c/code\u003e. If you do, the best thing to do is\nto manually remove the files manually. See this \u003ca href=\"https://stackoverflow.com/questions/402359/how-do-you-uninstall-a-python-package-that-was-installed-using-distutils\" rel=\"nofollow\"\u003estackoverflow question\u003c/a\u003e\nfor example.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003epython 3 strings\u003c/h3\u003e\u003ca id=\"user-content-python-3-strings\" class=\"anchor\" aria-label=\"Permalink: python 3 strings\" href=\"#python-3-strings\"\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\"\u003eAs of version \u003ccode\u003e1.0.0\u003c/code\u003e, fitsio now supports Python 3 strings natively. This support\nmeans that for Python 3, native strings are read from and written correctly to\nFITS files. All byte string columns are treated as ASCII-encoded unicode strings\nas well. For FITS files written with a previous version of fitsio, the data\nin Python 3 will now come back as a string and not a byte string. Note that this\nsupport is not the same as full unicode support. Internally, fitsio only supports\nthe ASCII character set.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTODO\u003c/h2\u003e\u003ca id=\"user-content-todo\" class=\"anchor\" aria-label=\"Permalink: TODO\" href=\"#todo\"\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\u003eHDU groups: does anyone use these? If so open an issue!\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":2,"text":"Description","anchor":"description","htmlText":"Description"},{"level":2,"text":"Some Features","anchor":"some-features","htmlText":"Some Features"},{"level":2,"text":"Examples","anchor":"examples","htmlText":"Examples"},{"level":2,"text":"Installation","anchor":"installation","htmlText":"Installation"},{"level":2,"text":"Requirements","anchor":"requirements","htmlText":"Requirements"},{"level":3,"text":"Do not use numpy 1.10.0 or 1.10.1","anchor":"do-not-use-numpy-1100-or-1101","htmlText":"Do not use numpy 1.10.0 or 1.10.1"},{"level":2,"text":"Tests","anchor":"tests","htmlText":"Tests"},{"level":2,"text":"Notes on Usage and Features","anchor":"notes-on-usage-and-features","htmlText":"Notes on Usage and Features"},{"level":3,"text":"cfitsio bundling","anchor":"cfitsio-bundling","htmlText":"cfitsio bundling"},{"level":3,"text":"array ordering","anchor":"array-ordering","htmlText":"array ordering"},{"level":3,"text":"distutils vs setuptools","anchor":"distutils-vs-setuptools","htmlText":"distutils vs setuptools"},{"level":3,"text":"python 3 strings","anchor":"python-3-strings","htmlText":"python 3 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data-canonical-src="https://travis-ci.com/esheldon/fitsio.svg?branch=master" style="max-width: 100%;"></a> <a href="https://github.com/esheldon/fitsio/actions?query=workflow%3Atests"><img src="https://github.com/esheldon/fitsio/workflows/tests/badge.svg" alt="tests" style="max-width: 100%;"></a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Description</h2><a id="user-content-description" class="anchor" aria-label="Permalink: Description" href="#description"><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">This is a python extension written in c and python. Data are read into numerical python arrays.</p> <p dir="auto">A version of cfitsio is bundled with this package, there is no need to install your own, nor will this conflict with a version you have installed.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Some Features</h2><a id="user-content-some-features" class="anchor" aria-label="Permalink: Some Features" href="#some-features"><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>Read from and write to image, binary, and ascii table extensions.</li> <li>Read arbitrary subsets of table columns and rows without loading all the data to memory.</li> <li>Read image subsets without reading the whole image. Write subsets to existing images.</li> <li>Write and read variable length table columns.</li> <li>Read images and tables using slice notation similar to numpy arrays. This is like a more powerful memmap, since it is column-aware for tables.</li> <li>Append rows to an existing table. Delete row sets and row ranges. Resize tables, or insert rows.</li> <li>Query the columns and rows in a table.</li> <li>Read and write header keywords.</li> <li>Read and write images in tile-compressed format (RICE,GZIP,PLIO,HCOMPRESS).</li> <li>Read/write gzip files directly. Read unix compress (.Z,.zip) and bzip2 (.bz2) files.</li> <li>TDIM information is used to return array columns in the correct shape.</li> <li>Write and read string table columns, including array columns of arbitrary shape.</li> <li>Read and write complex, bool (logical), unsigned integer, signed bytes types.</li> <li>Write checksums into the header and verify them.</li> <li>Insert new columns into tables in-place.</li> <li>Iterate over rows in a table. Data are buffered for efficiency.</li> <li>python 3 support, including python 3 strings</li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Examples</h2><a id="user-content-examples" class="anchor" aria-label="Permalink: Examples" href="#examples"><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="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="import fitsio from fitsio import FITS,FITSHDR # Often you just want to quickly read or write data without bothering to # create a FITS object. In that case, you can use the read and write # convienience functions. # read all data from the first hdu that has data filename='data.fits' data = fitsio.read(filename) # read a subset of rows and columns from a table data = fitsio.read(filename, rows=[35,1001], columns=['x','y'], ext=2) # read the header h = fitsio.read_header(filename) # read both data and header data,h = fitsio.read(filename, header=True) # open the file and write a new binary table extension with the data # array, which is a numpy array with fields, or "recarray". data = np.zeros(10, dtype=[('id','i8'),('ra','f8'),('dec','f8')]) fitsio.write(filename, data) # Write an image to the same file. By default a new extension is # added to the file. use clobber=True to overwrite an existing file # instead. To append rows to an existing table, see below. fitsio.write(filename, image) # # the FITS class gives the you the ability to explore the data, and gives # more control # # open a FITS file for reading and explore fits=fitsio.FITS('data.fits') # see what is in here; the FITS object prints itself print(fits) file: data.fits mode: READONLY extnum hdutype hduname 0 IMAGE_HDU 1 BINARY_TBL mytable # at the python or ipython prompt the fits object will # print itself >>> fits file: data.fits ... etc # explore the extensions, either by extension number or # extension name if available >>> fits[0] file: data.fits extension: 0 type: IMAGE_HDU image info: data type: f8 dims: [4096,2048] # by name; can also use fits[1] >>> fits['mytable'] file: data.fits extension: 1 type: BINARY_TBL extname: mytable rows: 4328342 column info: i1scalar u1 f f4 fvec f4 array[2] darr f8 array[3,2] dvarr f8 varray[10] s S5 svec S6 array[3] svar S0 vstring[8] sarr S2 array[4,3] # See bottom for how to get more information for an extension # [-1] to refers the last HDU >>> fits[-1] ... # if there are multiple HDUs with the same name, and an EXTVER # is set, you can use it. Here extver=2 # fits['mytable',2] # read the image from extension zero img = fits[0].read() img = fits[0][:,:] # read a subset of the image without reading the whole image img = fits[0][25:35, 45:55] # read all rows and columns from a binary table extension data = fits[1].read() data = fits['mytable'].read() data = fits[1][:] # read a subset of rows and columns. By default uses a case-insensitive # match. The result retains the names with original case. If columns is a # sequence, a numpy array with fields, or recarray is returned data = fits[1].read(rows=[1,5], columns=['index','x','y']) # Similar but using slice notation # row subsets data = fits[1][10:20] data = fits[1][10:20:2] data = fits[1][[1,5,18]] # Using EXTNAME and EXTVER values data = fits['SCI',2][10:20] # Slicing with reverse (flipped) striding data = fits[1][40:25] data = fits[1][40:25:-5] # all rows of column 'x' data = fits[1]['x'][:] # Read a few columns at once. This is more efficient than separate read for # each column data = fits[1]['x','y'][:] # General column and row subsets. columns=['index','x','y'] rows = [1, 5] data = fits[1][columns][rows] # data are returned in the order requested by the user # and duplicates are preserved rows = [2, 2, 5] data = fits[1][columns][rows] # iterate over rows in a table hdu # faster if we buffer some rows, let's buffer 1000 at a time fits=fitsio.FITS(filename,iter_row_buffer=1000) for row in fits[1]: print(row) # iterate over HDUs in a FITS object for hdu in fits: data=hdu.read() # Note dvarr shows type varray[10] and svar shows type vstring[8]. These # are variable length columns and the number specified is the maximum size. # By default they are read into fixed-length fields in the output array. # You can over-ride this by constructing the FITS object with the vstorage # keyword or specifying vstorage when reading. Sending vstorage='object' # will store the data in variable size object fields to save memory; the # default is vstorage='fixed'. Object fields can also be written out to a # new FITS file as variable length to save disk space. fits = fitsio.FITS(filename,vstorage='object') # OR data = fits[1].read(vstorage='object') print(data['dvarr'].dtype) dtype('object') # you can grab a FITS HDU object to simplify notation hdu1 = fits[1] data = hdu1['x','y'][35:50] # get rows that satisfy the input expression. See "Row Filtering # Specification" in the cfitsio manual (note no temporary table is # created in this case, contrary to the cfitsio docs) w=fits[1].where("x > 0.25 && y < 35.0") data = fits[1][w] # read the header h = fits[0].read_header() print(h['BITPIX']) -64 fits.close() # now write some data fits = FITS('test.fits','rw') # create a rec array. Note vstr # is a variable length string nrows=35 data = np.zeros(nrows, dtype=[('index','i4'),('vstr','O'),('x','f8'), ('arr','f4',(3,4))]) data['index'] = np.arange(nrows,dtype='i4') data['x'] = np.random.random(nrows) data['vstr'] = [str(i) for i in xrange(nrows)] data['arr'] = np.arange(nrows*3*4,dtype='f4').reshape(nrows,3,4) # create a new table extension and write the data fits.write(data) # can also be a list of ordinary arrays if you send the names array_list=[xarray,yarray,namearray] names=['x','y','name'] fits.write(array_list, names=names) # similarly a dict of arrays fits.write(dict_of_arrays) fits.write(dict_of_arrays, names=names) # control name order # append more rows to the table. The fields in data2 should match columns # in the table. missing columns will be filled with zeros fits[-1].append(data2) # insert a new column into a table fits[-1].insert_column('newcol', data) # insert with a specific colnum fits[-1].insert_column('newcol', data, colnum=2) # overwrite rows fits[-1].write(data) # overwrite starting at a particular row. The table will grow if needed fits[-1].write(data, firstrow=350) # create an image img=np.arange(2*3,dtype='i4').reshape(2,3) # write an image in a new HDU (if this is a new file, the primary HDU) fits.write(img) # write an image with rice compression fits.write(img, compress='rice') # control the compression fimg=np.random.normal(size=2*3).reshape(2, 3) fits.write(img, compress='rice', qlevel=16, qmethod='SUBTRACTIVE_DITHER_2') # lossless gzip compression for integers or floating point fits.write(img, compress='gzip', qlevel=None) fits.write(fimg, compress='gzip', qlevel=None) # overwrite the image fits[ext].write(img2) # write into an existing image, starting at the location [300,400] # the image will be expanded if needed fits[ext].write(img3, start=[300,400]) # change the shape of the image on disk fits[ext].reshape([250,100]) # add checksums for the data fits[-1].write_checksum() # can later verify data integridy fits[-1].verify_checksum() # you can also write a header at the same time. The header can be # - a simple dict (no comments) # - a list of dicts with 'name','value','comment' fields # - a FITSHDR object hdict = {'somekey': 35, 'location': 'kitt peak'} fits.write(data, header=hdict) hlist = [{'name':'observer', 'value':'ES', 'comment':'who'}, {'name':'location','value':'CTIO'}, {'name':'photometric','value':True}] fits.write(data, header=hlist) hdr=FITSHDR(hlist) fits.write(data, header=hdr) # you can add individual keys to an existing HDU fits[1].write_key(name, value, comment="my comment") # Write multiple header keys to an existing HDU. Here records # is the same as sent with header= above fits[1].write_keys(records) # write special COMMENT fields fits[1].write_comment("observer JS") fits[1].write_comment("we had good weather") # write special history fields fits[1].write_history("processed with software X") fits[1].write_history("re-processed with software Y") fits.close() # using a context, the file is closed automatically after leaving the block with FITS('path/to/file') as fits: data = fits[ext].read() # you can check if a header exists using "in": if 'blah' in fits: data=fits['blah'].read() if 2 in f: data=fits[2].read() # methods to get more information about extension. For extension 1: f[1].get_info() # lots of info about the extension f[1].has_data() # returns True if data is present in extension f[1].get_extname() f[1].get_extver() f[1].get_extnum() # return zero-offset extension number f[1].get_exttype() # 'BINARY_TBL' or 'ASCII_TBL' or 'IMAGE_HDU' f[1].get_offsets() # byte offsets (header_start, data_start, data_end) f[1].is_compressed() # for images. True if tile-compressed f[1].get_colnames() # for tables f[1].get_colname(colnum) # for tables find the name from column number f[1].get_nrows() # for tables f[1].get_rec_dtype() # for tables f[1].get_rec_column_descr() # for tables f[1].get_vstorage() # for tables, storage mechanism for variable # length columns # public attributes you can feel free to change as needed f[1].lower # If True, lower case colnames on output f[1].upper # If True, upper case colnames on output f[1].case_sensitive # if True, names are matched case sensitive"><pre><span class="pl-k">import</span> <span class="pl-s1">fitsio</span> <span class="pl-k">from</span> <span class="pl-s1">fitsio</span> <span class="pl-k">import</span> <span class="pl-c1">FITS</span>,<span class="pl-c1">FITSHDR</span> <span class="pl-c"># Often you just want to quickly read or write data without bothering to</span> <span class="pl-c"># create a FITS object. In that case, you can use the read and write</span> <span class="pl-c"># convienience functions.</span> <span class="pl-c"># read all data from the first hdu that has data</span> <span class="pl-s1">filename</span><span class="pl-c1">=</span><span class="pl-s">'data.fits'</span> <span class="pl-s1">data</span> <span class="pl-c1">=</span> <span class="pl-s1">fitsio</span>.<span class="pl-c1">read</span>(<span class="pl-s1">filename</span>) <span class="pl-c"># read a subset of rows and columns from a table</span> <span class="pl-s1">data</span> <span class="pl-c1">=</span> <span class="pl-s1">fitsio</span>.<span class="pl-c1">read</span>(<span class="pl-s1">filename</span>, <span class="pl-s1">rows</span><span class="pl-c1">=</span>[<span class="pl-c1">35</span>,<span class="pl-c1">1001</span>], <span class="pl-s1">columns</span><span class="pl-c1">=</span>[<span class="pl-s">'x'</span>,<span class="pl-s">'y'</span>], <span class="pl-s1">ext</span><span class="pl-c1">=</span><span class="pl-c1">2</span>) <span class="pl-c"># read the header</span> <span class="pl-s1">h</span> <span class="pl-c1">=</span> <span class="pl-s1">fitsio</span>.<span class="pl-c1">read_header</span>(<span class="pl-s1">filename</span>) <span class="pl-c"># read both data and header</span> <span class="pl-s1">data</span>,<span class="pl-s1">h</span> <span class="pl-c1">=</span> <span class="pl-s1">fitsio</span>.<span class="pl-c1">read</span>(<span class="pl-s1">filename</span>, <span class="pl-s1">header</span><span class="pl-c1">=</span><span class="pl-c1">True</span>) <span class="pl-c"># open the file and write a new binary table extension with the data</span> <span class="pl-c"># array, which is a numpy array with fields, or "recarray".</span> <span class="pl-s1">data</span> <span class="pl-c1">=</span> <span class="pl-s1">np</span>.<span class="pl-c1">zeros</span>(<span class="pl-c1">10</span>, <span class="pl-s1">dtype</span><span class="pl-c1">=</span>[(<span class="pl-s">'id'</span>,<span class="pl-s">'i8'</span>),(<span class="pl-s">'ra'</span>,<span class="pl-s">'f8'</span>),(<span class="pl-s">'dec'</span>,<span class="pl-s">'f8'</span>)]) <span class="pl-s1">fitsio</span>.<span class="pl-c1">write</span>(<span class="pl-s1">filename</span>, <span class="pl-s1">data</span>) <span class="pl-c"># Write an image to the same file. By default a new extension is</span> <span class="pl-c"># added to the file. use clobber=True to overwrite an existing file</span> <span class="pl-c"># instead. To append rows to an existing table, see below.</span> <span class="pl-s1">fitsio</span>.<span class="pl-c1">write</span>(<span class="pl-s1">filename</span>, <span class="pl-s1">image</span>) <span class="pl-c">#</span> <span class="pl-c"># the FITS class gives the you the ability to explore the data, and gives</span> <span class="pl-c"># more control</span> <span class="pl-c">#</span> <span class="pl-c"># open a FITS file for reading and explore</span> <span class="pl-s1">fits</span><span class="pl-c1">=</span><span class="pl-s1">fitsio</span>.<span class="pl-c1">FITS</span>(<span class="pl-s">'data.fits'</span>) <span class="pl-c"># see what is in here; the FITS object prints itself</span> <span class="pl-en">print</span>(<span class="pl-s1">fits</span>) <span class="pl-s1">file</span>: <span class="pl-s1">data</span>.<span class="pl-c1">fits</span> <span class="pl-s1">mode</span>: <span class="pl-smi">READONLY</span> <span class="pl-s1">extnum</span> <span class="pl-s1">hdutype</span> <span class="pl-s1">hduname</span> <span class="pl-c1">0</span> <span class="pl-c1">IMAGE_HDU</span> <span class="pl-c1">1</span> <span class="pl-c1">BINARY_TBL</span> <span class="pl-s1">mytable</span> <span class="pl-c"># at the python or ipython prompt the fits object will</span> <span class="pl-c"># print itself</span> <span class="pl-c1">></span><span class="pl-c1">>></span> <span class="pl-s1">fits</span> <span class="pl-s1">file</span>: <span class="pl-s1">data</span>.<span class="pl-c1">fits</span> ... <span class="pl-s1">etc</span> <span class="pl-c"># explore the extensions, either by extension number or</span> <span class="pl-c"># extension name if available</span> <span class="pl-c1">>></span><span class="pl-c1">></span> <span class="pl-s1">fits</span>[<span class="pl-c1">0</span>] <span class="pl-s1">file</span>: <span class="pl-s1">data</span>.<span class="pl-c1">fits</span> <span class="pl-s1">extension</span>: <span class="pl-c1">0</span> <span class="pl-s1">type</span>: <span class="pl-smi">IMAGE_HDU</span> <span class="pl-s1">image</span> <span class="pl-s1">info</span>: <span class="pl-s1">data</span> <span class="pl-smi">type</span>: <span class="pl-smi">f8</span> <span class="pl-s1">dims</span>: [<span class="pl-c1">4096</span>,<span class="pl-c1">2048</span>] <span class="pl-c"># by name; can also use fits[1]</span> <span class="pl-c1">></span><span class="pl-c1">>></span> <span class="pl-s1">fits</span>[<span class="pl-s">'mytable'</span>] <span class="pl-s1">file</span>: <span class="pl-s1">data</span>.<span class="pl-c1">fits</span> <span class="pl-s1">extension</span>: <span class="pl-c1">1</span> <span class="pl-s1">type</span>: <span class="pl-smi">BINARY_TBL</span> <span class="pl-s1">extname</span>: <span class="pl-smi">mytable</span> <span class="pl-s1">rows</span>: <span class="pl-c1">4328342</span> <span class="pl-s1">column</span> <span class="pl-s1">info</span>: <span class="pl-s1">i1scalar</span> <span class="pl-smi">u1</span> <span class="pl-s1">f</span> <span class="pl-s1">f4</span> <span class="pl-s1">fvec</span> <span class="pl-s1">f4</span> <span class="pl-s1">array</span>[<span class="pl-c1">2</span>] <span class="pl-s1">darr</span> <span class="pl-s1">f8</span> <span class="pl-s1">array</span>[<span class="pl-c1">3</span>,<span class="pl-c1">2</span>] <span class="pl-s1">dvarr</span> <span class="pl-s1">f8</span> <span class="pl-s1">varray</span>[<span class="pl-c1">10</span>] <span class="pl-s1">s</span> <span class="pl-v">S5</span> <span class="pl-s1">svec</span> <span class="pl-v">S6</span> <span class="pl-s1">array</span>[<span class="pl-c1">3</span>] <span class="pl-s1">svar</span> <span class="pl-v">S0</span> <span class="pl-s1">vstring</span>[<span class="pl-c1">8</span>] <span class="pl-s1">sarr</span> <span class="pl-v">S2</span> <span class="pl-s1">array</span>[<span class="pl-c1">4</span>,<span class="pl-c1">3</span>] <span class="pl-c"># See bottom for how to get more information for an extension</span> <span class="pl-c"># [-1] to refers the last HDU</span> <span class="pl-c1">></span><span class="pl-c1">>></span> <span class="pl-s1">fits</span>[<span class="pl-c1">-</span><span class="pl-c1">1</span>] ... <span class="pl-c"># if there are multiple HDUs with the same name, and an EXTVER</span> <span class="pl-c"># is set, you can use it. Here extver=2</span> <span class="pl-c"># fits['mytable',2]</span> <span class="pl-c"># read the image from extension zero</span> <span class="pl-s1">img</span> <span class="pl-c1">=</span> <span class="pl-s1">fits</span>[<span class="pl-c1">0</span>].<span class="pl-c1">read</span>() <span class="pl-s1">img</span> <span class="pl-c1">=</span> <span class="pl-s1">fits</span>[<span class="pl-c1">0</span>][:,:] <span class="pl-c"># read a subset of the image without reading the whole image</span> <span class="pl-s1">img</span> <span class="pl-c1">=</span> <span class="pl-s1">fits</span>[<span class="pl-c1">0</span>][<span class="pl-c1">25</span>:<span class="pl-c1">35</span>, <span class="pl-c1">45</span>:<span class="pl-c1">55</span>] <span class="pl-c"># read all rows and columns from a binary table extension</span> <span class="pl-s1">data</span> <span class="pl-c1">=</span> <span class="pl-s1">fits</span>[<span class="pl-c1">1</span>].<span class="pl-c1">read</span>() <span class="pl-s1">data</span> <span class="pl-c1">=</span> <span class="pl-s1">fits</span>[<span class="pl-s">'mytable'</span>].<span class="pl-c1">read</span>() <span class="pl-s1">data</span> <span class="pl-c1">=</span> <span class="pl-s1">fits</span>[<span class="pl-c1">1</span>][:] <span class="pl-c"># read a subset of rows and columns. By default uses a case-insensitive</span> <span class="pl-c"># match. The result retains the names with original case. If columns is a</span> <span class="pl-c"># sequence, a numpy array with fields, or recarray is returned</span> <span class="pl-s1">data</span> <span class="pl-c1">=</span> <span class="pl-s1">fits</span>[<span class="pl-c1">1</span>].<span class="pl-c1">read</span>(<span class="pl-s1">rows</span><span class="pl-c1">=</span>[<span class="pl-c1">1</span>,<span class="pl-c1">5</span>], <span class="pl-s1">columns</span><span class="pl-c1">=</span>[<span class="pl-s">'index'</span>,<span class="pl-s">'x'</span>,<span class="pl-s">'y'</span>]) <span class="pl-c"># Similar but using slice notation</span> <span class="pl-c"># row subsets</span> <span class="pl-s1">data</span> <span class="pl-c1">=</span> <span class="pl-s1">fits</span>[<span class="pl-c1">1</span>][<span class="pl-c1">10</span>:<span class="pl-c1">20</span>] <span class="pl-s1">data</span> <span class="pl-c1">=</span> <span class="pl-s1">fits</span>[<span class="pl-c1">1</span>][<span class="pl-c1">10</span>:<span class="pl-c1">20</span>:<span class="pl-c1">2</span>] <span class="pl-s1">data</span> <span class="pl-c1">=</span> <span class="pl-s1">fits</span>[<span class="pl-c1">1</span>][[<span class="pl-c1">1</span>,<span class="pl-c1">5</span>,<span class="pl-c1">18</span>]] <span class="pl-c"># Using EXTNAME and EXTVER values</span> <span class="pl-s1">data</span> <span class="pl-c1">=</span> <span class="pl-s1">fits</span>[<span class="pl-s">'SCI'</span>,<span class="pl-c1">2</span>][<span class="pl-c1">10</span>:<span class="pl-c1">20</span>] <span class="pl-c"># Slicing with reverse (flipped) striding</span> <span class="pl-s1">data</span> <span class="pl-c1">=</span> <span class="pl-s1">fits</span>[<span class="pl-c1">1</span>][<span class="pl-c1">40</span>:<span class="pl-c1">25</span>] <span class="pl-s1">data</span> <span class="pl-c1">=</span> <span class="pl-s1">fits</span>[<span class="pl-c1">1</span>][<span class="pl-c1">40</span>:<span class="pl-c1">25</span>:<span class="pl-c1">-</span><span class="pl-c1">5</span>] <span class="pl-c"># all rows of column 'x'</span> <span class="pl-s1">data</span> <span class="pl-c1">=</span> <span class="pl-s1">fits</span>[<span class="pl-c1">1</span>][<span class="pl-s">'x'</span>][:] <span class="pl-c"># Read a few columns at once. This is more efficient than separate read for</span> <span class="pl-c"># each column</span> <span class="pl-s1">data</span> <span class="pl-c1">=</span> <span class="pl-s1">fits</span>[<span class="pl-c1">1</span>][<span class="pl-s">'x'</span>,<span class="pl-s">'y'</span>][:] <span class="pl-c"># General column and row subsets.</span> <span class="pl-s1">columns</span><span class="pl-c1">=</span>[<span class="pl-s">'index'</span>,<span class="pl-s">'x'</span>,<span class="pl-s">'y'</span>] <span class="pl-s1">rows</span> <span class="pl-c1">=</span> [<span class="pl-c1">1</span>, <span class="pl-c1">5</span>] <span class="pl-s1">data</span> <span class="pl-c1">=</span> <span class="pl-s1">fits</span>[<span class="pl-c1">1</span>][<span class="pl-s1">columns</span>][<span class="pl-s1">rows</span>] <span class="pl-c"># data are returned in the order requested by the user</span> <span class="pl-c"># and duplicates are preserved</span> <span class="pl-s1">rows</span> <span class="pl-c1">=</span> [<span class="pl-c1">2</span>, <span class="pl-c1">2</span>, <span class="pl-c1">5</span>] <span class="pl-s1">data</span> <span class="pl-c1">=</span> <span class="pl-s1">fits</span>[<span class="pl-c1">1</span>][<span class="pl-s1">columns</span>][<span class="pl-s1">rows</span>] <span class="pl-c"># iterate over rows in a table hdu</span> <span class="pl-c"># faster if we buffer some rows, let's buffer 1000 at a time</span> <span class="pl-s1">fits</span><span class="pl-c1">=</span><span class="pl-s1">fitsio</span>.<span class="pl-c1">FITS</span>(<span class="pl-s1">filename</span>,<span class="pl-s1">iter_row_buffer</span><span class="pl-c1">=</span><span class="pl-c1">1000</span>) <span class="pl-k">for</span> <span class="pl-s1">row</span> <span class="pl-c1">in</span> <span class="pl-s1">fits</span>[<span class="pl-c1">1</span>]: <span class="pl-en">print</span>(<span class="pl-s1">row</span>) <span class="pl-c"># iterate over HDUs in a FITS object</span> <span class="pl-k">for</span> <span class="pl-s1">hdu</span> <span class="pl-c1">in</span> <span class="pl-s1">fits</span>: <span class="pl-s1">data</span><span class="pl-c1">=</span><span class="pl-s1">hdu</span>.<span class="pl-c1">read</span>() <span class="pl-c"># Note dvarr shows type varray[10] and svar shows type vstring[8]. These</span> <span class="pl-c"># are variable length columns and the number specified is the maximum size.</span> <span class="pl-c"># By default they are read into fixed-length fields in the output array.</span> <span class="pl-c"># You can over-ride this by constructing the FITS object with the vstorage</span> <span class="pl-c"># keyword or specifying vstorage when reading. Sending vstorage='object'</span> <span class="pl-c"># will store the data in variable size object fields to save memory; the</span> <span class="pl-c"># default is vstorage='fixed'. Object fields can also be written out to a</span> <span class="pl-c"># new FITS file as variable length to save disk space.</span> <span class="pl-s1">fits</span> <span class="pl-c1">=</span> <span class="pl-s1">fitsio</span>.<span class="pl-c1">FITS</span>(<span class="pl-s1">filename</span>,<span class="pl-s1">vstorage</span><span class="pl-c1">=</span><span class="pl-s">'object'</span>) <span class="pl-c"># OR</span> <span class="pl-s1">data</span> <span class="pl-c1">=</span> <span class="pl-s1">fits</span>[<span class="pl-c1">1</span>].<span class="pl-c1">read</span>(<span class="pl-s1">vstorage</span><span class="pl-c1">=</span><span class="pl-s">'object'</span>) <span class="pl-en">print</span>(<span class="pl-s1">data</span>[<span class="pl-s">'dvarr'</span>].<span class="pl-c1">dtype</span>) <span class="pl-en">dtype</span>(<span class="pl-s">'object'</span>) <span class="pl-c"># you can grab a FITS HDU object to simplify notation</span> <span class="pl-s1">hdu1</span> <span class="pl-c1">=</span> <span class="pl-s1">fits</span>[<span class="pl-c1">1</span>] <span class="pl-s1">data</span> <span class="pl-c1">=</span> <span class="pl-s1">hdu1</span>[<span class="pl-s">'x'</span>,<span class="pl-s">'y'</span>][<span class="pl-c1">35</span>:<span class="pl-c1">50</span>] <span class="pl-c"># get rows that satisfy the input expression. See "Row Filtering</span> <span class="pl-c"># Specification" in the cfitsio manual (note no temporary table is</span> <span class="pl-c"># created in this case, contrary to the cfitsio docs)</span> <span class="pl-s1">w</span><span class="pl-c1">=</span><span class="pl-s1">fits</span>[<span class="pl-c1">1</span>].<span class="pl-c1">where</span>(<span class="pl-s">"x > 0.25 && y < 35.0"</span>) <span class="pl-s1">data</span> <span class="pl-c1">=</span> <span class="pl-s1">fits</span>[<span class="pl-c1">1</span>][<span class="pl-s1">w</span>] <span class="pl-c"># read the header</span> <span class="pl-s1">h</span> <span class="pl-c1">=</span> <span class="pl-s1">fits</span>[<span class="pl-c1">0</span>].<span class="pl-c1">read_header</span>() <span class="pl-en">print</span>(<span class="pl-s1">h</span>[<span class="pl-s">'BITPIX'</span>]) <span class="pl-c1">-</span><span class="pl-c1">64</span> <span class="pl-s1">fits</span>.<span class="pl-c1">close</span>() <span class="pl-c"># now write some data</span> <span class="pl-s1">fits</span> <span class="pl-c1">=</span> <span class="pl-en">FITS</span>(<span class="pl-s">'test.fits'</span>,<span class="pl-s">'rw'</span>) <span class="pl-c"># create a rec array. Note vstr</span> <span class="pl-c"># is a variable length string</span> <span class="pl-s1">nrows</span><span class="pl-c1">=</span><span class="pl-c1">35</span> <span class="pl-s1">data</span> <span class="pl-c1">=</span> <span class="pl-s1">np</span>.<span class="pl-c1">zeros</span>(<span class="pl-s1">nrows</span>, <span class="pl-s1">dtype</span><span class="pl-c1">=</span>[(<span class="pl-s">'index'</span>,<span class="pl-s">'i4'</span>),(<span class="pl-s">'vstr'</span>,<span class="pl-s">'O'</span>),(<span class="pl-s">'x'</span>,<span class="pl-s">'f8'</span>), (<span class="pl-s">'arr'</span>,<span class="pl-s">'f4'</span>,(<span class="pl-c1">3</span>,<span class="pl-c1">4</span>))]) <span class="pl-s1">data</span>[<span class="pl-s">'index'</span>] <span class="pl-c1">=</span> <span class="pl-s1">np</span>.<span class="pl-c1">arange</span>(<span class="pl-s1">nrows</span>,<span class="pl-s1">dtype</span><span class="pl-c1">=</span><span class="pl-s">'i4'</span>) <span class="pl-s1">data</span>[<span class="pl-s">'x'</span>] <span class="pl-c1">=</span> <span class="pl-s1">np</span>.<span class="pl-c1">random</span>.<span class="pl-c1">random</span>(<span class="pl-s1">nrows</span>) <span class="pl-s1">data</span>[<span class="pl-s">'vstr'</span>] <span class="pl-c1">=</span> [<span class="pl-en">str</span>(<span class="pl-s1">i</span>) <span class="pl-k">for</span> <span class="pl-s1">i</span> <span class="pl-c1">in</span> <span class="pl-en">xrange</span>(<span class="pl-s1">nrows</span>)] <span class="pl-s1">data</span>[<span class="pl-s">'arr'</span>] <span class="pl-c1">=</span> <span class="pl-s1">np</span>.<span class="pl-c1">arange</span>(<span class="pl-s1">nrows</span><span class="pl-c1">*</span><span class="pl-c1">3</span><span class="pl-c1">*</span><span class="pl-c1">4</span>,<span class="pl-s1">dtype</span><span class="pl-c1">=</span><span class="pl-s">'f4'</span>).<span class="pl-c1">reshape</span>(<span class="pl-s1">nrows</span>,<span class="pl-c1">3</span>,<span class="pl-c1">4</span>) <span class="pl-c"># create a new table extension and write the data</span> <span class="pl-s1">fits</span>.<span class="pl-c1">write</span>(<span class="pl-s1">data</span>) <span class="pl-c"># can also be a list of ordinary arrays if you send the names</span> <span class="pl-s1">array_list</span><span class="pl-c1">=</span>[<span class="pl-s1">xarray</span>,<span class="pl-s1">yarray</span>,<span class="pl-s1">namearray</span>] <span class="pl-s1">names</span><span class="pl-c1">=</span>[<span class="pl-s">'x'</span>,<span class="pl-s">'y'</span>,<span class="pl-s">'name'</span>] <span class="pl-s1">fits</span>.<span class="pl-c1">write</span>(<span class="pl-s1">array_list</span>, <span class="pl-s1">names</span><span class="pl-c1">=</span><span class="pl-s1">names</span>) <span class="pl-c"># similarly a dict of arrays</span> <span class="pl-s1">fits</span>.<span class="pl-c1">write</span>(<span class="pl-s1">dict_of_arrays</span>) <span class="pl-s1">fits</span>.<span class="pl-c1">write</span>(<span class="pl-s1">dict_of_arrays</span>, <span class="pl-s1">names</span><span class="pl-c1">=</span><span class="pl-s1">names</span>) <span class="pl-c"># control name order</span> <span class="pl-c"># append more rows to the table. The fields in data2 should match columns</span> <span class="pl-c"># in the table. missing columns will be filled with zeros</span> <span class="pl-s1">fits</span>[<span class="pl-c1">-</span><span class="pl-c1">1</span>].<span class="pl-c1">append</span>(<span class="pl-s1">data2</span>) <span class="pl-c"># insert a new column into a table</span> <span class="pl-s1">fits</span>[<span class="pl-c1">-</span><span class="pl-c1">1</span>].<span class="pl-c1">insert_column</span>(<span class="pl-s">'newcol'</span>, <span class="pl-s1">data</span>) <span class="pl-c"># insert with a specific colnum</span> <span class="pl-s1">fits</span>[<span class="pl-c1">-</span><span class="pl-c1">1</span>].<span class="pl-c1">insert_column</span>(<span class="pl-s">'newcol'</span>, <span class="pl-s1">data</span>, <span class="pl-s1">colnum</span><span class="pl-c1">=</span><span class="pl-c1">2</span>) <span class="pl-c"># overwrite rows</span> <span class="pl-s1">fits</span>[<span class="pl-c1">-</span><span class="pl-c1">1</span>].<span class="pl-c1">write</span>(<span class="pl-s1">data</span>) <span class="pl-c"># overwrite starting at a particular row. The table will grow if needed</span> <span class="pl-s1">fits</span>[<span class="pl-c1">-</span><span class="pl-c1">1</span>].<span class="pl-c1">write</span>(<span class="pl-s1">data</span>, <span class="pl-s1">firstrow</span><span class="pl-c1">=</span><span class="pl-c1">350</span>) <span class="pl-c"># create an image</span> <span class="pl-s1">img</span><span class="pl-c1">=</span><span class="pl-s1">np</span>.<span class="pl-c1">arange</span>(<span class="pl-c1">2</span><span class="pl-c1">*</span><span class="pl-c1">3</span>,<span class="pl-s1">dtype</span><span class="pl-c1">=</span><span class="pl-s">'i4'</span>).<span class="pl-c1">reshape</span>(<span class="pl-c1">2</span>,<span class="pl-c1">3</span>) <span class="pl-c"># write an image in a new HDU (if this is a new file, the primary HDU)</span> <span class="pl-s1">fits</span>.<span class="pl-c1">write</span>(<span class="pl-s1">img</span>) <span class="pl-c"># write an image with rice compression</span> <span class="pl-s1">fits</span>.<span class="pl-c1">write</span>(<span class="pl-s1">img</span>, <span class="pl-s1">compress</span><span class="pl-c1">=</span><span class="pl-s">'rice'</span>) <span class="pl-c"># control the compression</span> <span class="pl-s1">fimg</span><span class="pl-c1">=</span><span class="pl-s1">np</span>.<span class="pl-c1">random</span>.<span class="pl-c1">normal</span>(<span class="pl-s1">size</span><span class="pl-c1">=</span><span class="pl-c1">2</span><span class="pl-c1">*</span><span class="pl-c1">3</span>).<span class="pl-c1">reshape</span>(<span class="pl-c1">2</span>, <span class="pl-c1">3</span>) <span class="pl-s1">fits</span>.<span class="pl-c1">write</span>(<span class="pl-s1">img</span>, <span class="pl-s1">compress</span><span class="pl-c1">=</span><span class="pl-s">'rice'</span>, <span class="pl-s1">qlevel</span><span class="pl-c1">=</span><span class="pl-c1">16</span>, <span class="pl-s1">qmethod</span><span class="pl-c1">=</span><span class="pl-s">'SUBTRACTIVE_DITHER_2'</span>) <span class="pl-c"># lossless gzip compression for integers or floating point</span> <span class="pl-s1">fits</span>.<span class="pl-c1">write</span>(<span class="pl-s1">img</span>, <span class="pl-s1">compress</span><span class="pl-c1">=</span><span class="pl-s">'gzip'</span>, <span class="pl-s1">qlevel</span><span class="pl-c1">=</span><span class="pl-c1">None</span>) <span class="pl-s1">fits</span>.<span class="pl-c1">write</span>(<span class="pl-s1">fimg</span>, <span class="pl-s1">compress</span><span class="pl-c1">=</span><span class="pl-s">'gzip'</span>, <span class="pl-s1">qlevel</span><span class="pl-c1">=</span><span class="pl-c1">None</span>) <span class="pl-c"># overwrite the image</span> <span class="pl-s1">fits</span>[<span class="pl-s1">ext</span>].<span class="pl-c1">write</span>(<span class="pl-s1">img2</span>) <span class="pl-c"># write into an existing image, starting at the location [300,400]</span> <span class="pl-c"># the image will be expanded if needed</span> <span class="pl-s1">fits</span>[<span class="pl-s1">ext</span>].<span class="pl-c1">write</span>(<span class="pl-s1">img3</span>, <span class="pl-s1">start</span><span class="pl-c1">=</span>[<span class="pl-c1">300</span>,<span class="pl-c1">400</span>]) <span class="pl-c"># change the shape of the image on disk</span> <span class="pl-s1">fits</span>[<span class="pl-s1">ext</span>].<span class="pl-c1">reshape</span>([<span class="pl-c1">250</span>,<span class="pl-c1">100</span>]) <span class="pl-c"># add checksums for the data</span> <span class="pl-s1">fits</span>[<span class="pl-c1">-</span><span class="pl-c1">1</span>].<span class="pl-c1">write_checksum</span>() <span class="pl-c"># can later verify data integridy</span> <span class="pl-s1">fits</span>[<span class="pl-c1">-</span><span class="pl-c1">1</span>].<span class="pl-c1">verify_checksum</span>() <span class="pl-c"># you can also write a header at the same time. The header can be</span> <span class="pl-c"># - a simple dict (no comments)</span> <span class="pl-c"># - a list of dicts with 'name','value','comment' fields</span> <span class="pl-c"># - a FITSHDR object</span> <span class="pl-s1">hdict</span> <span class="pl-c1">=</span> {<span class="pl-s">'somekey'</span>: <span class="pl-c1">35</span>, <span class="pl-s">'location'</span>: <span class="pl-s">'kitt peak'</span>} <span class="pl-s1">fits</span>.<span class="pl-c1">write</span>(<span class="pl-s1">data</span>, <span class="pl-s1">header</span><span class="pl-c1">=</span><span class="pl-s1">hdict</span>) <span class="pl-s1">hlist</span> <span class="pl-c1">=</span> [{<span class="pl-s">'name'</span>:<span class="pl-s">'observer'</span>, <span class="pl-s">'value'</span>:<span class="pl-s">'ES'</span>, <span class="pl-s">'comment'</span>:<span class="pl-s">'who'</span>}, {<span class="pl-s">'name'</span>:<span class="pl-s">'location'</span>,<span class="pl-s">'value'</span>:<span class="pl-s">'CTIO'</span>}, {<span class="pl-s">'name'</span>:<span class="pl-s">'photometric'</span>,<span class="pl-s">'value'</span>:<span class="pl-c1">True</span>}] <span class="pl-s1">fits</span>.<span class="pl-c1">write</span>(<span class="pl-s1">data</span>, <span class="pl-s1">header</span><span class="pl-c1">=</span><span class="pl-s1">hlist</span>) <span class="pl-s1">hdr</span><span class="pl-c1">=</span><span class="pl-en">FITSHDR</span>(<span class="pl-s1">hlist</span>) <span class="pl-s1">fits</span>.<span class="pl-c1">write</span>(<span class="pl-s1">data</span>, <span class="pl-s1">header</span><span class="pl-c1">=</span><span class="pl-s1">hdr</span>) <span class="pl-c"># you can add individual keys to an existing HDU</span> <span class="pl-s1">fits</span>[<span class="pl-c1">1</span>].<span class="pl-c1">write_key</span>(<span class="pl-s1">name</span>, <span class="pl-s1">value</span>, <span class="pl-s1">comment</span><span class="pl-c1">=</span><span class="pl-s">"my comment"</span>) <span class="pl-c"># Write multiple header keys to an existing HDU. Here records</span> <span class="pl-c"># is the same as sent with header= above</span> <span class="pl-s1">fits</span>[<span class="pl-c1">1</span>].<span class="pl-c1">write_keys</span>(<span class="pl-s1">records</span>) <span class="pl-c"># write special COMMENT fields</span> <span class="pl-s1">fits</span>[<span class="pl-c1">1</span>].<span class="pl-c1">write_comment</span>(<span class="pl-s">"observer JS"</span>) <span class="pl-s1">fits</span>[<span class="pl-c1">1</span>].<span class="pl-c1">write_comment</span>(<span class="pl-s">"we had good weather"</span>) <span class="pl-c"># write special history fields</span> <span class="pl-s1">fits</span>[<span class="pl-c1">1</span>].<span class="pl-c1">write_history</span>(<span class="pl-s">"processed with software X"</span>) <span class="pl-s1">fits</span>[<span class="pl-c1">1</span>].<span class="pl-c1">write_history</span>(<span class="pl-s">"re-processed with software Y"</span>) <span class="pl-s1">fits</span>.<span class="pl-c1">close</span>() <span class="pl-c"># using a context, the file is closed automatically after leaving the block</span> <span class="pl-k">with</span> <span class="pl-en">FITS</span>(<span class="pl-s">'path/to/file'</span>) <span class="pl-k">as</span> <span class="pl-s1">fits</span>: <span class="pl-s1">data</span> <span class="pl-c1">=</span> <span class="pl-s1">fits</span>[<span class="pl-s1">ext</span>].<span class="pl-c1">read</span>() <span class="pl-c"># you can check if a header exists using "in":</span> <span class="pl-k">if</span> <span class="pl-s">'blah'</span> <span class="pl-c1">in</span> <span class="pl-s1">fits</span>: <span class="pl-s1">data</span><span class="pl-c1">=</span><span class="pl-s1">fits</span>[<span class="pl-s">'blah'</span>].<span class="pl-c1">read</span>() <span class="pl-k">if</span> <span class="pl-c1">2</span> <span class="pl-c1">in</span> <span class="pl-s1">f</span>: <span class="pl-s1">data</span><span class="pl-c1">=</span><span class="pl-s1">fits</span>[<span class="pl-c1">2</span>].<span class="pl-c1">read</span>() <span class="pl-c"># methods to get more information about extension. For extension 1:</span> <span class="pl-s1">f</span>[<span class="pl-c1">1</span>].<span class="pl-c1">get_info</span>() <span class="pl-c"># lots of info about the extension</span> <span class="pl-s1">f</span>[<span class="pl-c1">1</span>].<span class="pl-c1">has_data</span>() <span class="pl-c"># returns True if data is present in extension</span> <span class="pl-s1">f</span>[<span class="pl-c1">1</span>].<span class="pl-c1">get_extname</span>() <span class="pl-s1">f</span>[<span class="pl-c1">1</span>].<span class="pl-c1">get_extver</span>() <span class="pl-s1">f</span>[<span class="pl-c1">1</span>].<span class="pl-c1">get_extnum</span>() <span class="pl-c"># return zero-offset extension number</span> <span class="pl-s1">f</span>[<span class="pl-c1">1</span>].<span class="pl-c1">get_exttype</span>() <span class="pl-c"># 'BINARY_TBL' or 'ASCII_TBL' or 'IMAGE_HDU'</span> <span class="pl-s1">f</span>[<span class="pl-c1">1</span>].<span class="pl-c1">get_offsets</span>() <span class="pl-c"># byte offsets (header_start, data_start, data_end)</span> <span class="pl-s1">f</span>[<span class="pl-c1">1</span>].<span class="pl-c1">is_compressed</span>() <span class="pl-c"># for images. True if tile-compressed</span> <span class="pl-s1">f</span>[<span class="pl-c1">1</span>].<span class="pl-c1">get_colnames</span>() <span class="pl-c"># for tables</span> <span class="pl-s1">f</span>[<span class="pl-c1">1</span>].<span class="pl-c1">get_colname</span>(<span class="pl-s1">colnum</span>) <span class="pl-c"># for tables find the name from column number</span> <span class="pl-s1">f</span>[<span class="pl-c1">1</span>].<span class="pl-c1">get_nrows</span>() <span class="pl-c"># for tables</span> <span class="pl-s1">f</span>[<span class="pl-c1">1</span>].<span class="pl-c1">get_rec_dtype</span>() <span class="pl-c"># for tables</span> <span class="pl-s1">f</span>[<span class="pl-c1">1</span>].<span class="pl-c1">get_rec_column_descr</span>() <span class="pl-c"># for tables</span> <span class="pl-s1">f</span>[<span class="pl-c1">1</span>].<span class="pl-c1">get_vstorage</span>() <span class="pl-c"># for tables, storage mechanism for variable</span> <span class="pl-c"># length columns</span> <span class="pl-c"># public attributes you can feel free to change as needed</span> <span class="pl-s1">f</span>[<span class="pl-c1">1</span>].<span class="pl-c1">lower</span> <span class="pl-c"># If True, lower case colnames on output</span> <span class="pl-s1">f</span>[<span class="pl-c1">1</span>].<span class="pl-c1">upper</span> <span class="pl-c"># If True, upper case colnames on output</span> <span class="pl-s1">f</span>[<span class="pl-c1">1</span>].<span class="pl-c1">case_sensitive</span> <span class="pl-c"># if True, names are matched case sensitive</span></pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Installation</h2><a id="user-content-installation" class="anchor" aria-label="Permalink: Installation" href="#installation"><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">The easiest way is using pip or conda. To get the latest release</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="pip install fitsio # update fitsio (and everything else) pip install fitsio --upgrade # if pip refuses to update to a newer version pip install fitsio --upgrade --ignore-installed # if you only want to upgrade fitsio pip install fitsio --no-deps --upgrade --ignore-installed # for conda, use conda-forge conda install -c conda-forge fitsio"><pre class="notranslate"><code>pip install fitsio # update fitsio (and everything else) pip install fitsio --upgrade # if pip refuses to update to a newer version pip install fitsio --upgrade --ignore-installed # if you only want to upgrade fitsio pip install fitsio --no-deps --upgrade --ignore-installed # for conda, use conda-forge conda install -c conda-forge fitsio </code></pre></div> <p dir="auto">You can also get the latest source tarball release from</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="https://pypi.python.org/pypi/fitsio"><pre class="notranslate"><code>https://pypi.python.org/pypi/fitsio </code></pre></div> <p dir="auto">or the bleeding edge source from github or use git. To check out the code for the first time</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="git clone https://github.com/esheldon/fitsio.git"><pre class="notranslate"><code>git clone https://github.com/esheldon/fitsio.git </code></pre></div> <p dir="auto">Or at a later time to update to the latest</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="cd fitsio git update"><pre class="notranslate"><code>cd fitsio git update </code></pre></div> <p dir="auto">Use tar xvfz to untar the file, enter the fitsio directory and type</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="python setup.py install"><pre class="notranslate"><code>python setup.py install </code></pre></div> <p dir="auto">optionally with a prefix</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="python setup.py install --prefix=/some/path"><pre class="notranslate"><code>python setup.py install --prefix=/some/path </code></pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Requirements</h2><a id="user-content-requirements" class="anchor" aria-label="Permalink: Requirements" href="#requirements"><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>python 2 or python 3</li> <li>a C compiler and build tools like <code>make</code>, <code>patch</code>, etc.</li> <li>numpy (See the note below. Generally, numpy 1.11 or later is better.)</li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Do not use numpy 1.10.0 or 1.10.1</h3><a id="user-content-do-not-use-numpy-1100-or-1101" class="anchor" aria-label="Permalink: Do not use numpy 1.10.0 or 1.10.1" href="#do-not-use-numpy-1100-or-1101"><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">There is a serious performance regression in numpy 1.10 that results in fitsio running tens to hundreds of times slower. A fix may be forthcoming in a later release. Please comment here if this has already impacted your work <a class="issue-link js-issue-link" data-error-text="Failed to load title" data-id="111188681" data-permission-text="Title is private" data-url="https://github.com/numpy/numpy/issues/6467" data-hovercard-type="issue" data-hovercard-url="/numpy/numpy/issues/6467/hovercard" href="https://github.com/numpy/numpy/issues/6467">numpy/numpy#6467</a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Tests</h2><a id="user-content-tests" class="anchor" aria-label="Permalink: Tests" href="#tests"><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">The unit tests should all pass for full support.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="pytest fitsio"><pre>pytest fitsio</pre></div> <p dir="auto">Some tests may fail if certain libraries are not available, such as bzip2. This failure only implies that bzipped files cannot be read, without affecting other functionality.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Notes on Usage and Features</h2><a id="user-content-notes-on-usage-and-features" class="anchor" aria-label="Permalink: Notes on Usage and Features" href="#notes-on-usage-and-features"><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"><h3 tabindex="-1" class="heading-element" dir="auto">cfitsio bundling</h3><a id="user-content-cfitsio-bundling" class="anchor" aria-label="Permalink: cfitsio bundling" href="#cfitsio-bundling"><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 bundle cfitsio partly because many deployed versions of cfitsio in the wild do not have support for interesting features like tiled image compression. Bundling a version that meets our needs is a safe alternative.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">array ordering</h3><a id="user-content-array-ordering" class="anchor" aria-label="Permalink: array ordering" href="#array-ordering"><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">Since numpy uses C order, FITS uses fortran order, we have to write the TDIM and image dimensions in reverse order, but write the data as is. Then we need to also reverse the dims as read from the header when creating the numpy dtype, but read as is.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto"><code>distutils</code> vs <code>setuptools</code></h3><a id="user-content-distutils-vs-setuptools" class="anchor" aria-label="Permalink: distutils vs setuptools" href="#distutils-vs-setuptools"><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">As of version <code>1.0.0</code>, <code>fitsio</code> has been transitioned to <code>setuptools</code> for packaging and installation. There are many reasons to do this (and to not do this). However, at a practical level, what this means for you is that you may have trouble uninstalling older versions with <code>pip</code> via <code>pip uninstall fitsio</code>. If you do, the best thing to do is to manually remove the files manually. See this <a href="https://stackoverflow.com/questions/402359/how-do-you-uninstall-a-python-package-that-was-installed-using-distutils" rel="nofollow">stackoverflow question</a> for example.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">python 3 strings</h3><a id="user-content-python-3-strings" class="anchor" aria-label="Permalink: python 3 strings" href="#python-3-strings"><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">As of version <code>1.0.0</code>, fitsio now supports Python 3 strings natively. This support means that for Python 3, native strings are read from and written correctly to FITS files. All byte string columns are treated as ASCII-encoded unicode strings as well. For FITS files written with a previous version of fitsio, the data in Python 3 will now come back as a string and not a byte string. Note that this support is not the same as full unicode support. Internally, fitsio only supports the ASCII character set.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">TODO</h2><a id="user-content-todo" class="anchor" aria-label="Permalink: TODO" href="#todo"><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>HDU groups: does anyone use these? If so open an issue!</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="6OFWCluaRCHKQ30WrKagpyRMNBAsNpgHH561M16mU1UplM4Yu2Dt3TOx01jZ4wVwi5t+Fmv7Vy09r/f0VllGEA==" /> </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"> A python package for FITS input/output wrapping cfitsio </p> <h3 class="sr-only">Topics</h3> <div class="my-3"> <div class="f6"> <a href="/topics/python" title="Topic: python" data-view-component="true" class="topic-tag topic-tag-link"> python </a> <a href="/topics/astronomy" title="Topic: astronomy" data-view-component="true" class="topic-tag topic-tag-link"> astronomy </a> <a 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class="color-fg-default text-bold mr-1">Other</span> <span>0.5%</span> </span> </li> </ul> </div> </div> </div> </div> </div></div> </div> </div> </turbo-frame> </main> </div> </div> <footer class="footer pt-8 pb-6 f6 color-fg-muted p-responsive" role="contentinfo" > <h2 class='sr-only'>Footer</h2> <div class="d-flex flex-justify-center flex-items-center flex-column-reverse flex-lg-row flex-wrap flex-lg-nowrap"> <div class="d-flex flex-items-center flex-shrink-0 mx-2"> <a aria-label="Homepage" title="GitHub" class="footer-octicon mr-2" href="https://github.com"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-mark-github"> <path d="M12.5.75C6.146.75 1 5.896 1 12.25c0 5.089 3.292 9.387 7.863 10.91.575.101.79-.244.79-.546 0-.273-.014-1.178-.014-2.142-2.889.532-3.636-.704-3.866-1.35-.13-.331-.69-1.352-1.18-1.625-.402-.216-.977-.748-.014-.762.906-.014 1.553.834 1.769 1.179 1.035 1.74 2.688 1.25 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