Is there a way using the python-pptx package to change the base units and major values for a date axis? ie being able to set the X axis for a time series chart to be in years, and to only show every 5th year as a label?
I found this link in the docs, but I can't figure out how to set these attributes using python.
Thanks in advance!
That feature is supported by PowerPoint but unfortunately not yet by python-pptx.
The <c:dateAx> child elements responsible for that behavior are <c:majorUnit> and <c:minorUnit>. If you can edit the c:dateAx subtree to add those (or perhaps just change their attribute values if they already exist) then you might be able to produce the behavior you're after. Note that child element order is significant in PresentationML so you can't just append those elements at the end, you'll need to fit them in at the right place (sequence is specified here: https://github.com/scanny/python-pptx/blob/master/pptx/oxml/chart/axis.py#L128).
Related
I have a question related to ClearML plot logging. We are currently using:
self.task_logger.report_table("TableSpaceName", "Some Info", iteration=0, table_plot=df)
To report tables. They appear under "PLOTS" section. Similarly, we are reporting plotly graphs:
self.task_logger.report_plotly(
title="PlotTitle", iteration=0, series='SeriesName', figure=fig
)
Both work fine. The issue is, each new report_plotly call, instead of replacing the image in the section, creates a new one, and leaves the previous one present too. This cloggs the PLOTS section (tables and figures). The question is, how does one report a plot, so that it's reported in-place (Such as e.g., scalars, where sample plot gets updated in time)?
Turns out that ClearML sorts by "title" param. Hence, having e.g., title="Part 1: someStuff" will be placed before title="Part 2: someOtherStuff".
Disclaimer: I'm part of the ClearML team
Are you providing a fixed title, series and iteration values? If so, this should not happen and is probably a bug
I am trying to plot the availability of my network per hour. So,I have a massive dataframe containing multiple variables including the availability and hour. I can clearly visualise everything I want on my plot I want to plot when I do the following:
mond_data= mond_data.groupby('Hour')['Availability'].mean()
The only problem is, if I bracket the whole code and plot it (I mean this (the code above).plot); I do not get any value on my x-axis that says 'Hour'.How can plot this showing the values of my x-axis (Hour). I should have 24 values as the code above bring an aaverage for the whole day for midnight to 11pm.
Here is how I solved it.
plt.plot(mon_data.index,mond_data.groupby('Hour')['Availability'].mean())
for some reason python was not plotting the index, only if called. I have not tested many cases. So additional explanation to this problem is still welcome.
I've got a three-level bokeh.models.FactorRange which I use to draw tick labels on a vbar-plot. The problem is that there are dozens of factors in total and the lowest-level labels get very cramped.
I can use plot.xaxis.formatter = bokeh.models.PrintfTickFormatter(format='') to suppress drawing of the lowest-level labels, but this seems like an ugly hack. Also, I need to have the second-level tick labels to be rotated, yet plot.xaxis.major_label_orientation = ... only ever affects the lowest-level ticks (just like plot.xaxis.formatter does).
How to control each level of bokeh.models.FactorRange individually?
As of Bokeh 0.12.13, there is no way to control the individual orientations or formatting of different levels.
The basic initial work to revamp categorical support (for multi-level axes, etc) was a large update. Rather than add more even complexity and risk up front for features we were not sure anyone would want or need, we started with basic capability, expecting to hear from users in time what additional features were justified. This seems like it has come up a few times, so it would be reasonable to consider adding, but it would represent new work, so a GitHub feature request issue is the appropriate next step.
For completeness, I will mention that Bokeh is extensible, so it's always technically possible to create a Custom Extension. Axes are some of the most complicated code in Bokeh, and a full custom Axis would be non-trivial to write. However it's possible that would be sufficient to make subclass of CategoricalAxis and just override this one method:
https://github.com/bokeh/bokeh/blob/master/bokehjs/src/coffee/models/axes/categorical_axis.ts#L83-L110
That's where the currently hard-coded 'parallel' orientation are, and also where formatting could be overridden.
In latest Bokeh (2.2.0), the feature #bigreddot was talking about seems to have been implemented: you can call
p.xaxis.group_label_orientation = [angle in radians]
to set orientation of the outer labels, while as in the question
p.xaxis.major_label_orientation = [angle in radians]
sets the orientation of the inner labels.
I am trying write an Abaqus/Python script that will select all the elements that "belong" to a certain face. I.e. taking all the elements that have a connection to one face of a meshed cube (I will calculate the total force acting on that face for force-displacement or stress-strain curves later).
If I do it using the GUI I get:
mdb.models['Model-1'].rootAssembly.Set(elements=
mdb.models['Model-1'].rootAssembly.instances['Part-1-1'].elements.getSequenceFromMask(
mask=('[#0:5 #fff80000 #ff #f #ffe00000 #f000000f #3f',
' #0:6 #fffe #c0003f00 #3 #3fff8 #ffc00 ]', ), ), name='Set-1')
But, getSequenceFromMask does not work in a general case. I tried using findat with no luck.
Is there a way to do that?
define a face set on the part or assembly:
part.Set('facename',faces=part.faces.findAt(((1,0,0),),))
where (1,0,0) is a coordinate anywhere on the face. (Don't use a point on a edge/corner though)
then after meshing you can access the elements attached to that face, something like:
instance.sets['facename'].elements
note if you want to get those elements on the odb after running an analysis it is a little different:
instance.elementSets['FACENAME'].elements
note that the set name is upcased on the odb..
One can select an specific element from its label by using:
mdb.models['model-name'].parts['part_name'].elements.getFromLabel(lable=element_id)
This way it is not necessary to have information about the coordinate of the element. Only the element id is enough to access to it.
You are apparently using a Macro in order to get the location of your surface in order to pick the set using Python. The issue is: the Macro facility uses getSequenceFromMask() by default and isn't very user-friendly...
Fortunately, this default option can be changed! One just needs to run the following line of code:
session.journalOptions.setValues(replayGeometry=COORDINATE,recoverGeometry=COORDINATE)
Now when you record a macro using the MacroManager, you get findAt() which is what you want.
Extra TIP:
You can include this piece of code in the onCaeStartup() function in your custom_v6.env file. It will then run every time you open CAE.
C:\Program Files\Dassault Systemes\SimulationServices\V6R2018x\win_b64\SMA\site\custom_v6.env
I had this issue myself a few days ago. Maybe I'm wrong but as far as I know, there is no way to directly select particular elements. You can select them with a "Bounding Box" or a "Bounding Sphere" or you can get them by your parts/ instances faces and cells. If you need to select the elements in a more specific way then you can get them by the nodes with which they are connected. You can use the "findAt" command with these nodes and get the elements by the "getElements()" command.
That is how I solved it and it works pretty fine. If there are other ways to solve that I will be happy to hear them because this is sometimes really frustrating.
Cheers
Note from maintainers: this question is about the obsolete bokeh.charts API removed several years ago. For an example of timeseries charts in modern Bokeh, see here:
https://docs.bokeh.org/en/latest/docs/gallery/range_tool.html
I'm trying to create a timeseries graph with bokeh. This is my first time using bokeh, and my first time dealing with pandas as well. Our customers receive reviews on their products. I'm trying to create a graph which shows how their average review rating has changed over time.
Our database contains the dates of each review. We also have the average review value for that date. I need to plot a line with the x axis being dates and the y axis being the review value range (1 through 10).
When I accepted this project I thought it would be easy. How wrong I was. I found a timeseries example that looks good. Unfortunately, the example completely glosses over what is the most difficult part about creating a solution. Specifically, it does not show how to create an appropriate data structure from your source data. The example is retrieving pre-built datastructures from the yahoo api. I've tried examining these structures, but they don't exactly look straightforward to me.
I found a page explaining pandas structs. It is a little difficult for me to understand. Particularly confusing to me is how to represent points in the graph without necessarily labeling those points. For example the y axis should display whole numbers, but data points need not intersect with the whole number value. The page I found is linked below:
http://pandas.pydata.org/pandas-docs/stable/dsintro.html
Does anyone know of a working example for the timeseries chart type which exemplifies how to build the necessary data structure?
UPDATE:
Thanks to the answer below I toyed around with just passing lists into lines. It didn't occur to me that I could do this, but it works very well. For example:
date = [1/11/2011, 1/12/2011. 1/13/2011, 4/5/2014]
rating = [4, 4, 5, 2]
line(
date, # x coordinates
rating, # y coordinates
color='#A6CEE3', # set a color for the line
x_axis_type = "datetime", # NOTE: only needed on first
tools="pan,wheel_zoom,box_zoom,reset,previewsave" # NOTE: only needed on first
)
You don't have to use Pandas, you simply need to supply a sequence of x-values and a sequence of y-values. These can be plain Python lists of numbers, or NumPy arrays, or Pandas Series. Here is another time series example that uses just NumPy arrays:
http://docs.bokeh.org/en/latest/docs/gallery/color_scatter.html
EDIT: link updated