Google charts timeline online for non software developers

In this post I provide an online way to create a Google Chart timeline type. Google charts are for software developers or people with some coding skills. Here, I try to allow common citizens use this technology. After reading the instructions you will be able to create a chart like the following:



There are two ways

Way I: Write in the input text field below
The format write in it is:
Category name1, event name1,Start date1, End Date1; Category name2, event name2,Start date2, End Date2; ...Category nameN, event nameN,Start dateN, End DateN

Category name is the row name where the event will be added. According to the example above, it would be "President" or "Vice President".
Event name is the event in colors that will be added to the categories. An event name would be "George Washington".
Start date and end Date are the start and end date/time of the event. Both have the following structure "yyyy/mm/dd hh:mm" . It is possible to specify just some part of these dates like only year:
Category 1, Event1, 2017,2018; Category 2, Event2, 2013,2014

Year and month:
Category 1, Event1, 2017/4,2018/2; Category 2, Event2, 2013/1,2014/11

Also adding day :
Category 1, Event1, 2017/4/11,2018/2/23; Category 2, Event2, 2013/1,2014/11

Or using hour and minute:
Category 1, Event1, 2017/4/01 9:00,2017/4/01 12:00; Category 2, Event2, 2017/4/01 9:00,2017/4/01 15:00

Steps:

  1. Write on the input text field below using the previous format.
  2. Push the "Process" button.
  3. Consider that at the end of the text there is no ";". Add a ";" to separate a for field event only.


Way 2: Import a CSV file
The csv file should follow the previous rules and look something like:
Category name1, event name1,Start date1, End Date1
Category name2, event name2,Start date2, End Date2
.
.
.
Category nameN, event nameN,Start dateN, End DateN

Steps:




  1. Click on the "Choose file" button
  2. Search for the CSV file
  3. Push the "Process" button.
  4. If you want to upload the same file again (Maybe you updated the information), you need to refresh/update/F5 this page before.

Tool



Process

Comentarios

Publicar un comentario

Entradas populares de este blog

Image segmentation by color mean and standard deviation

I present an online segmentation javascript implementation of segmentation given the r,g,b means an standard deviation of each one. Since it is javascript it means it will run on your browser using your computation power, thus, you should test it with small images first.   The pixels that fall in the range [ mean - std; mean + std ] will remain in the visualization. Those who don't, will be turned into black. The std factor will multiply the std value of every channel. It is easier to follow the changes by changing a factor than the std values independently.  Upload Image File: Controls Mean: Red Mean Green Mean Blue Mean Controls Std: Red std Green std Blue std Std factor
Leer más

Google Chart Sankey Diagram online for non software developers

Imagen
I saw lots of tutorials of how to create a Sankey diagram but I couldn't find a quick way to see my data without installing or configuring parameters. Thus, here I present a Sankey Diagram to visualize your data. In case you are a software developer this may result to be something simple for you, but in case you don't have programming skills but want a Sankey diagram, this post is for you. You have to upload a CSV file, comma separated in the format: NodeA, NodeB, Weight An example of a CSV file could be: Root A,Branch X,3 Root B,Branch X,8 Root B,Branch Y,2 Root B,Branch Z,3 Branch X,End 1,19 Branch X,End 2,2 Branch X,End 3,4 Branch Y,End 1,10 Branch Y,End 4,17 Root B,Branch ZX,4 Branch ZX,End 1,1 This will create the following Graph: Instructions of use: Way one Click on Chose File and search for your CSV file. In the text field you will see your data loaded. You should see "NodeX1,NodeY1,N1;NodeX2,NodeY2,N2... Click in the button Process ...
Leer más

Deep neural networks visualization

Imagen
In this publication I present a "toy tool" to see how neurons on a Deep Neural Network (DNN) modify the links between them. These links are also called Weights. For those with technical knowledge, the "bias" unit is not shown (Because of lack of time, I will upgrade it later). In addition I haven't implemented yet the predict option, I would probably add it later. Technical Notes: This DNN uses standard back propagation without optimizations such as Adam optimizer or regularization parameters.   I am using Math.js  for the matrix operations. There are some errors I haven't figured out yet how to solve them, specially for Nan values that will appear when showing the visualization (Sometimes happens, no worry =) ) The last layer, the output layer is a sigmoid layer by default. Maybe later I will implement a linear function for regression analysis. And it has been implemented for binary classification by the moment. The weights are randomly init...
Leer más