{"id":6620,"date":"2021-01-30T15:42:57","date_gmt":"2021-01-30T15:42:57","guid":{"rendered":"https:\/\/www.blopig.com\/blog\/?p=6620"},"modified":"2021-02-02T16:34:34","modified_gmt":"2021-02-02T16:34:34","slug":"plotly-for-interactive-3d-plotting","status":"publish","type":"post","link":"https:\/\/www.blopig.com\/blog\/2021\/01\/plotly-for-interactive-3d-plotting\/","title":{"rendered":"Plotly for interactive 3D plotting"},"content":{"rendered":"\n

An recently wrote a post on how to use the seaborn library<\/a>. I really like seaborn and use it a lot for 2D plots. However, recently I have been dealing with 3D data and have found plotly<\/a> to be best. When used in a jupyter notebook, it allows you to easily generate 3D interactive plots. This is extremely useful to visualize structural data.<\/p>\n<\/div>\n\n\n\n\n\n\n\n

Plotly is best used in combination with the pandas library<\/a>, most of the tutorials in its documentations use this library. In the following example they plot data from the iris flower data set<\/a>.<\/p>\n<\/div>\n\n\n\n

import plotly.express as px\ndf = px.data.iris()\nfig = px.scatter_3d(df, x='sepal_length', y='sepal_width', z='petal_width',\n              color='species')\nfig.show()<\/pre>\n\n\n\n
And this will return the following plot (I tried to make it interactive but I am not sure if that worked. If it didn’t, an interactive version can be found by clicking on the image)<\/p>\n<\/div>\n\n\n\n
\n    \"Iris\"<\/a>\n    \n<\/div>\n\n\n\n
Personally, I don’t always use pandas data frames for everything, so here is how to plot the position of residues in the H3 loop stored as a numpy array. <\/p>\n\n\n\n
<\/p>\n\n\n\n
import plotly.graph_objects as go\ntrueH3 = go.Scatter3d(x=truth[0], y=truth[1], z=truth[2], name = \"Truth\", mode = \"lines\")\nprediction = go.Scatter3d(x=pred[0], y=pred[1], z=pred[2], name = \"Prediction\", mode = \"lines\")\nfig  = go.Figure(data=[trueH3, prediction])\nfig.update_layout(title = \"CDRH3 Truth Vs Prediction\")\nfig.show()<\/pre>\n\n\n\n
And that would return the following plot (interactive version here<\/a>):<\/p>\n<\/div>\n\n\n\n\n\n
\"\"<\/a><\/figure>\n\n\n\n
Plotly can be used for a large number of plotting tasks, so I hope I have convinced you that it is at least worth looking at. If you have read this far I hope this was not a complete waste of your time.<\/p>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":71,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"nf_dc_page":"","wikipediapreview_detectlinks":true,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"ngg_post_thumbnail":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[29,361,227],"tags":[256],"ppma_author":[552],"class_list":["post-6620","post","type-post","status-publish","format-standard","hentry","category-code","category-data-science","category-python-code","tag-plotting"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"authors":[{"term_id":552,"user_id":71,"is_guest":0,"slug":"brennan","display_name":"Brennan Abanades Kenyon","avatar_url":"https:\/\/secure.gravatar.com\/avatar\/5c85dcbb5b1499e82ecfc264ec387c8302ac238c786e68cc5c92e9c21904d260?s=96&d=mm&r=g","0":null,"1":"","2":"","3":"","4":"","5":"","6":"","7":"","8":""}],"_links":{"self":[{"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/posts\/6620","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/users\/71"}],"replies":[{"embeddable":true,"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/comments?post=6620"}],"version-history":[{"count":5,"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/posts\/6620\/revisions"}],"predecessor-version":[{"id":6638,"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/posts\/6620\/revisions\/6638"}],"wp:attachment":[{"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/media?parent=6620"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/categories?post=6620"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/tags?post=6620"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=6620"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}