EDIT: For some reason I've been downvoted twice for posting this question (it hurts ppl) so I've rejigged it.
How do you combine multiple heatmaps in a stacked way with same color scale like to following image?
Additionally, does anyone know how to create the Augmented suffix tree?
Background:
I've worked through the python jupyter notebooks at the following link on how to create the heatmaps of (any) daily consumption profiles using seaborn
http://www.datadrivenbuilding.org/
...however there's a realllllllly cool combination graphic I'd love to be able to reproduce.
That image is an edited version of an image from this paper:
C. Miller, Z. Nagy, A. Schlueter, Automated daily pattern filtering of
measured building performance data, Automation in Construction 49,
Part A (2015) 1–17. doi:10.1016/j.autcon.2014.09.004. URL
http://www.sciencedirect.com/science/article/pii/S0926580514002015
They came up with the visualisation techniques themselves and describe them there. It looks like C. Miller is the one who wrote the notebook that you already found that shows how to draw the stacked heatmaps.
The augmented suffix tree is a type of visualization called a Sankey Diagram. You can plot these very beautifully using Plotly for example, or pySankey if you want to use matplotlib.
Related
I have this density heatmap generated using Python Plotly package:
Now I need to add wind vectors over the heatmap. I would like to use quiver plots if possible, but currently I want to know how to add any kind of plot to a mapbox.
I have only found this example but the source code in the Plotly Chart Studio doesn't load so I don't know how to do it, also I need it in 2D:
I am not 100% sure if it is helpful but there is a software called QGIS (it's free). It is much easier to work with geospatial data there. Also, try rasterio library. It may have some functions like that (library for geospatial analyze)
I once used the velocity capability of ipyleaflet https://ipyleaflet.readthedocs.io/en/latest/api_reference/velocity.html
It is not using plotly but could be a good replacement for this kind of map until you find a better solution.
I have been reading a lot about Bokeh for visualisations of large datasets. I plan on plotting a heatmap with over 25 million points.
I saw read the page on speeding up WebGL and they mention that any plots with glyphs are accelerated.
Does the Heatmap plot use glyphs? Will there be any benefits in turning on WebGL for heatmap plots?
Pretty much everything that Bokeh draws is a glyph of some type. However, the text on that page you link actually states that "allows rendering some glyph types on graphics hardware." Currently (as of Bokeh 0.12.3) WebGL support only extends to scatter-type markers (e.g. circle, x, etc) and to lines. But HeatMap is implemented using the Rect glyph, so I would not expect WebGL to offer any improvement at the present time.
But I would add: It's good to thoroughly investigate any actual performance hotspots. Bokeh is really two libraries: a Python library and a JavaScript library. If you are seeing performance issues, are you sure it's on the JS side? For example, you have not said what your data sizes are. Are you sure it's not actually the binning/aggregation (that happens on the Python side) that is your issue?
Finally, if you have data sizes that are in the millions-to-billions of points range, you should probably be looking at the separate bokeh/datashader project.
I want the similar visualization as shown below using Bokeh. As I am new to Bokeh , I might be wondering is there any code that is as concise as the below one using Seaborn ?
My main focus is how to write code for same visualization in Bokeh
dataset
data = pd.read_csv('http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv',index_col = 0)
Plotting the dataset
import seaborn as sns
sns.pairplot(data, x_vars = ['TV', 'Radio','Newspaper'], y_vars = ['Sales'],
size =7, aspect =.7, kind = 'reg')
Also the code in Seaborn doesn't require to enter the best fitted line . It automatically generates the best fitted line in the graph with a shadow of confidence interval . Is this kind of plotting possible in Bokeh ?
The above charts are certainly possible with Bokeh, in the sense that Bokeh could draw them, without any question. But it would take some work, and require more code, than with Seaborn. Basically you'd have to compute all the coordinates and set up all the glyphs "by hand". As of Bokeh 0.12.2, there is not currently any comparable "single line" high level function or chart in bokeh.charts.
Adding more high level chart types lie this to bokeh.charts is definitely something that we'd like, but it will probably require motivated new contributors to make that happen. Fortunately, this area of Bokeh is pure-python and probably the most approachable for new contributors. If you are possibly interested in contributing to Bokeh, I encourage you to reach out on the public mailing list or gitter chat channel. We are always happy to answer questions and help people get started.
I'm preparing a set of reports using open source ReportLab. The reports contain a number of charts. Everything works well so far.
I've been asked to take a (working) bar chart that shows two series of data and overlay a fitted curve for each series.
I can see how I could overlay a segmented line on the bar graph by creating both a line chart and bar chart in the same ReportLab drawing. I can't find any reference for fitted curves in ReportLab, however.
Does anyone have any insight into plotting a fitted curve to a series of data in ReportLab or, failing that, a suggestion about how to accomplish this task (I'm thinking that chart would need to be produced in matplotlib instead)?
I would recommend using MatPlotLib. This is exactly the sort of thing it's designed to handle and it will be much easier than trying to piece together something in ReportLab alone, especially since you'll have to do all the calculation of the line on your own and figure out the details of how to draw it in just the right place. MatPlotLib integrates easily with ReportLab; I've used the combination several times with great results.
for a while I've been trying to come up with a good way to graphically represent a data series along with its estimated error.
Recently I saw some graphs where the data was plotted as a line, with a background 'ribbon' filling the area between the lines plotting data +/- sigma.
Is there a name for this type of graph, and is there any python toolkit which has the capability to make such plots?
A simple way to fake it with matplotlib would also be useful - right now I'm just plotting three lines, but I don't know how to fill the area between them.
I would use the fill_between method. Look at the Our Favorite Recipes section of the manual for matplotlib for some good examples. They have one that looks like this:
and another that looks like this: