<![CDATA[UX 用户交互设计]]>http://an.forum.genostack.com/category/26RSS for NodeSat, 13 Jun 2026 14:46:19 GMTWed, 16 Oct 2024 07:28:30 GMT60<![CDATA[datawrapper 数据可视化工具]]>https://app.datawrapper.de/chart/1suo3/upload
https://github.com/datawrapper/datawrapper

]]>http://an.forum.genostack.com/topic/1099/datawrapper-数据可视化工具http://an.forum.genostack.com/topic/1099/datawrapper-数据可视化工具Wed, 16 Oct 2024 07:28:30 GMT<![CDATA[d3js-in-action-third-edition]]>https://github.com/d3js-in-action-third-edition/code-files
Anne-Marie Dufour - D3.js in Action, Third Edition (MEAP V15)-Manning Publications (2023).pdf

]]>http://an.forum.genostack.com/topic/1052/d3js-in-action-third-editionhttp://an.forum.genostack.com/topic/1052/d3js-in-action-third-editionWed, 07 Feb 2024 07:53:10 GMT<![CDATA[数据科学可视化组件]]>https://www.blog.dailydoseofds.com/p/9-most-important-plots-in-data-science
f70c7488-240d-4263-9a3d-1c94cd695572-image.png

]]>http://an.forum.genostack.com/topic/971/数据科学可视化组件http://an.forum.genostack.com/topic/971/数据科学可视化组件Wed, 19 Jul 2023 11:48:44 GMT<![CDATA[文献颜色调研]]>https://uicolors.app/create
b967f74e-cd82-4bf9-91a6-41a9bf4b87e9-image.png

]]>http://an.forum.genostack.com/topic/968/文献颜色调研http://an.forum.genostack.com/topic/968/文献颜色调研Fri, 14 Jul 2023 11:24:23 GMT<![CDATA[如何选择可视化组件]]>9c18e60c-af79-46b0-a8fe-14eea11e3f0c-image.pngchoosing-a-good-chart-09.pdf

]]>http://an.forum.genostack.com/topic/965/如何选择可视化组件http://an.forum.genostack.com/topic/965/如何选择可视化组件Fri, 14 Jul 2023 04:07:55 GMT<![CDATA[可视化技术总结]]>ggplot2 是R的一个实现
Vega and Vega-Lite 是JS的实现
Observable Plot 是JS的另一个实现
protovis是另一个JS实现
https://mbostock.github.io/protovis/
Altair 封装了 Vega-Lite 是Python的实现
微软的一个实现
https://github.com/Microsoft/chart-parts
蚂蚁的一个实现 评分还不错
https://github.com/antvis/G2

]]>http://an.forum.genostack.com/topic/935/可视化技术总结http://an.forum.genostack.com/topic/935/可视化技术总结Fri, 30 Jun 2023 08:37:16 GMT<![CDATA[https://orkes.io/blog/long-running-workflows/]]>63acc15a-0316-4ecb-873c-c4ebd4a91800-image.png

]]>http://an.forum.genostack.com/topic/923/https-orkes-io-blog-long-running-workflowshttp://an.forum.genostack.com/topic/923/https-orkes-io-blog-long-running-workflowsWed, 14 Jun 2023 10:22:02 GMT<![CDATA[Violin Plot]]>https://resources.qiagenbioinformatics.com/manuals/clcsinglecellanalysis/current/index.php?manual=_Violin_Plot_output_Create_Expression_Plot.html

]]>http://an.forum.genostack.com/topic/920/violin-plothttp://an.forum.genostack.com/topic/920/violin-plotThu, 08 Jun 2023 08:53:43 GMT<![CDATA[紫色图标]]>a6a9fe4d-45ad-4f50-919b-12955376af88-image.png

]]>http://an.forum.genostack.com/topic/914/紫色图标http://an.forum.genostack.com/topic/914/紫色图标Sat, 03 Jun 2023 03:03:20 GMT<![CDATA[Cell]]>484c17d2-3208-4df9-af8b-ad4ec8120835-image.png

]]>http://an.forum.genostack.com/topic/905/cellhttp://an.forum.genostack.com/topic/905/cellWed, 24 May 2023 11:09:45 GMT<![CDATA[Colorwise]]>5c519d69-d9f6-4fa4-9b7a-fc13c5a7eb92-image.png

]]>http://an.forum.genostack.com/topic/904/colorwisehttp://an.forum.genostack.com/topic/904/colorwiseWed, 24 May 2023 08:58:41 GMT<![CDATA[过滤的设计]]>545cfd93-c89d-4d92-8f6b-69403021ce77-image.png

]]>http://an.forum.genostack.com/topic/768/过滤的设计http://an.forum.genostack.com/topic/768/过滤的设计Wed, 28 Sep 2022 02:44:08 GMT<![CDATA[生信可视化组件]]>https://doc.linkurious.com/ogma/latest/
b0ba59e2-a901-4d11-a489-1c019f9957ef-image.png

]]>http://an.forum.genostack.com/topic/762/生信可视化组件http://an.forum.genostack.com/topic/762/生信可视化组件Thu, 01 Sep 2022 02:53:14 GMT<![CDATA[NN 交互设计研究]]>https://www.nngroup.com/about/

]]>http://an.forum.genostack.com/topic/655/nn-交互设计研究http://an.forum.genostack.com/topic/655/nn-交互设计研究Tue, 17 May 2022 04:23:59 GMT<![CDATA[产品配色]]>https://rzerofour.github.io/ChineseTraditionalColors/#3Page
这个是一个中国传统色的网站 看看这些颜色能不能用

]]>http://an.forum.genostack.com/topic/503/产品配色http://an.forum.genostack.com/topic/503/产品配色Wed, 29 Dec 2021 14:39:01 GMT<![CDATA[holoviz 相关包梳理]]>单细胞开发中　图表渲染涉及holoviz中的几个关键包　梳理如下：
1.Holoviz包括的包
ee0c8a83-510f-4196-9b63-3fdfc51d6bea-image.png
Holoviz的目标是整合已有工具　对图表开发进行简化
a set of open-source Python packages to streamline the entire process of working with small and large datasets (from a few datapoints to billions or more) in a web browser, whether doing exploratory analysis, making simple widget-based tools, or building multipage standalone dashboards. Building on existing plotting libraries like Bokeh, Matplotlib, and Plotly, the HoloViz ecosystem includes a set of special-purpose tools designed to fill in the gaps and solve the whole problem of visualization:

Panel: Assembling objects from many different libraries into a layout or app, whether in a Jupyter notebook or in a standalone servable dashboard

hvPlot: Quickly return interactive HoloViews, GeoViews or Panel objects from Pandas, Xarray, or other data structures

HoloViews: Declarative objects for instantly visualizable data, building Bokeh plots from convenient high-level specifications

GeoViews: Visualizable geographic data that that can be mixed and matched with HoloViews objects

Datashader: Rasterizing huge datasets quickly as fixed-size images

Colorcet: A wide range of perceptually uniform or large-number categorical colormaps for use with the other libraries
594a4ca7-1f3e-4339-acc8-4724c2183d37-image.png

Holoviz对python的可视化组件进行了有机整合
a7fdc032-80d8-4110-b2de-109f1492be32-image.png
0365b9c2-5bd8-49ab-ba6a-0e1047d6ebc5-image.png
HoloViz Goals:
Full functionality in browsers (not desktop)
Full interactivity (inside and out of plots)
Focus on Python users, not web programmers
Start with data, not coding
Work with data of any size
Exploit general-purpose SciPy/PyData tools
Focus on 2D primarily, with some 3D
Avoid entangling your data, code, and viz:
Same viz/analysis code in Jupyter, Python, HPC, ...
Widgets/apps in Jupyter, standalone servers, web pages
Jupyter as a tool, not part of the results
2.

]]>http://an.forum.genostack.com/topic/417/holoviz-相关包梳理http://an.forum.genostack.com/topic/417/holoviz-相关包梳理Mon, 27 Sep 2021 11:55:39 GMT<![CDATA[如何选择Python可视化方案]]>beeimgtmp-20210914-100246.png

]]>http://an.forum.genostack.com/topic/415/如何选择python可视化方案http://an.forum.genostack.com/topic/415/如何选择python可视化方案Tue, 14 Sep 2021 02:04:27 GMT<![CDATA[plotly python vs plotly js]]>https://github.com/tonio73/dash-to-react

和bokeh 类似　plotly 也涉及到用Python开发图表还是用完全用js开发图表的问题
上面的项目对两种实现方法做了详细的对比

]]>http://an.forum.genostack.com/topic/396/plotly-python-vs-plotly-jshttp://an.forum.genostack.com/topic/396/plotly-python-vs-plotly-jsSat, 21 Aug 2021 10:15:57 GMT<![CDATA[python的后台渲染和angular集成]]>https://towardsdatascience.com/reactive-event-and-python-backend-driven-bokeh-charts-in-angular-6fc23d608eeb

]]>http://an.forum.genostack.com/topic/395/python的后台渲染和angular集成http://an.forum.genostack.com/topic/395/python的后台渲染和angular集成Sat, 21 Aug 2021 07:42:05 GMT<![CDATA[Bokeh实战]]>d7d9f810-234e-491c-ae88-6d1b56146129-image.png

]]>http://an.forum.genostack.com/topic/393/bokeh实战http://an.forum.genostack.com/topic/393/bokeh实战Fri, 20 Aug 2021 04:34:57 GMT<![CDATA[大文件上传用户交互设计]]>大文件上传功能的用户界面 第一步 会让用户选择本地文件，然后哈希，并提示哈希的进度：
9324ffc8-9643-45fc-80fc-0011627061c7-image.png
点评：1.客户不可能知道什么是哈希，提示建议改为“正在准备上传...”
第二步 当哈希完成时 进度变为0 真正开始上传
c1f3cd9e-d352-45ed-9fdd-e8cf981ca02b-image.png
点评：1.哈希这个过程属于多余的提示进度 造成和实际上传的进度带来的困扰，建议删除哈希进度提示
2.选择文件和开始上传 作为两个按钮功能 文件选择后 如果一次只能传一个文件 就把选择文件功能灰化
某客户在此时 以为上传发生了故障 又点击了一次选择文件
94302b12-a0bb-40e6-a55f-9f5000e0dbd7-image.png

当上传完成时，提示功能性错误，上传错误：
2ae2cd54-99d2-4d4e-bd4d-9761451768ba-image.png
但是通过和xshell两次上传的对比，发现实际已经上传完毕：
51a9f4da-b130-4fab-8468-b8e9d2161401-image.png

]]>http://an.forum.genostack.com/topic/196/大文件上传用户交互设计http://an.forum.genostack.com/topic/196/大文件上传用户交互设计Tue, 02 Feb 2021 01:34:57 GMT