This is the third assignment from the DSCI 550 class. This assignment is collaborated and completed by Team 3.
Team menbers: Jimin Ding, Mingyu Zong, Hui Qi, Xiaoyu Dong
Jimin Ding
Mingyu Zong
Hui Qi
Took charge of two D3 visualizations. (Index Chart, Zoomable Sunburst)
Wrote reports about visualizations of Radial Stacked Bar Chart, Index Chart, and Zoomable Sunburst.
Took charge of Apache Solr (step 3 & 5)
Xiaoyu Dong
A list of all of the dependencies used, included their version number.
pandas==1.4.4
pysolr==3.9.0
requests==2.23.0
tqdm==4.64.1
Install the requirements necessary to run this project:
pip install -r requirements.txt
All d3 visualizaions are in the D3 folder. If you want to run and open the html files, please look at the step2 in Methodology section becasue you need to use a webserver, like python -m http.server in the directory where the html file locates.
In the step3_Apache_Solr folder, open the pixstory folder and take a look at the jupyter notebook and two .sh files. If you want to run it and re-do the same process, please look at the step3 in Methodology section.
.ipynb
Select the plots we want from https://observablehq.com/@d3/gallery . Then, we adjusted our data to fit and input into the existing visualization examples. After, completing drawing the chart in the d3 website, we downloaded the codes of our d3 visualizations from the observable websites, unzipped the document and opened the terminal of this directory. In the terminal, we typed python -m http.server and opened http://localhost:8000/.
Notice that : all D3 visualizations need to the webpage locally by using a webserver (to test) like python -m http.server in the directory where the downloaded observable html and JSON locate.
python -m http.server
First, we need to use "git clone" from https://github.com/nasa-jpl-memex/GeoParser. So, "git clone https://github.com/nasa-jpl-memex/GeoParser.git". Then, take a look at this example in https://github.com/nasa-jpl-memex/GeoParser/wiki/Sample:-COVID19-publication-data-parsing. Follow the instruction in this covid example. Notice that possibly need to do the installation in the Docker folder. Then, do the pre-requisties and "Get Data". We need to adjust the jupyter notebook, create_core.sh, and add_fields.sh from the covid example to our date before "Get Data". The order is create_core.sh (changing the folder name), jupyter notebook, add_fields.sh.
At last, open http://localhost:8983/solr/ to see the result.