plotly VS lisa

Github: https://github.com/plotly/plotly.py

First we need to install the Plotly. To create some very dynamic graphics, this tool helps a lot.

my good dude wake up and smell the plotly. Knowing the ins and outs of matplotlib is helpful but doing interactive stuff with jupyter I always use plotly.

Plotly documentation (https://plotly.com/python/)

I don´t know your workflow, but I use 2 markdown based presentations: obsidian advance slides and Quarto presentations. The former is a plugin for Obsidian, which is the software I use to take all my notes, write my thesis, etc., so It makes it extremely easy to make presentations since all my information is in Obsidian. In the other hand, Quarto is a publishing system (articles, presentations, websites books) that can be easily integrated with python and R. This makes it supper convenient for showing my data to my PI since I can analyze my data and at the same time make a presentation for the data. Besides this, Quarto also integrates with my Zotero library, so I can insert citations. Lastly, one thing that made my Quarto presentations infinitely better that the powerpoints, Is that I can insert interactive graphs with plotly, so when I'm showing my data, my PI is able to explore the data inside the presentation.

Plotly GitHub repository: https://github.com/plotly/plotly.py

I'm using plotly.py now. This is why I love this community.

Depending on the nature of your reporting requirements, you could output a formatted Excel document with Python and a library such as openpyxl, and shove that into your SharePoint environment. This would be less dynamic than PBI reports can be, but may be sufficient. If you want viz as well, you can use something like ggplot or Plotly. Again, less dynamic than PBI for the same effort.

You might be interested in that: https://github.com/ARM-software/lisa/blob/master/lisa/monad.py

This is to replace the current flow purely based on pandas dataframe and offline trace.dat parsing used in LISA: https://github.com/ARM-software/lisa (collecting a trace.dat is nice for debugging but limits to small durations, and pandas does not allow running computations in constant memory, which is an issue for very big traces)

The module added by this PR seems to be a pretty good fit: https://github.com/ARM-software/lisa/pull/1722

I made that thing taking some vague inspiration from SML module system: https://github.com/ARM-software/lisa/pull/1722/files

import builtins from operator import add import functools # These functions can be found at: # https://github.com/ARM-software/lisa/blob/master/lisa/utils.py#L147 # Note: my implementation of curry() seems to be broken wrt named parameters (or for parameters with defaults, haven't looked at the details) for some reason but for this example it does not matter from lisa.utils import compose, curry def even(x): return x % 2 == 0 # The builtin functions don't have a signature, which will upset curry() so we # redefine it here def map(f, iterable): return builtins.map(f, iterable) def filter(f, iterable): return builtins.filter(f, iterable) # Swapped init and iterable to be curry-friendly def reduce(f, init, iterable): return functools.reduce(f, iterable, init) def pipeline(*items): # Add a currying layer so that we spare the user the need to do it return compose(*(curry(f)(*args) for (f, *args) in items)) # x = filter(even, list) |> map(lambda x: x+1) |> reduce(+) f = pipeline( (filter, even), (map, lambda x: x+1), (reduce, add, 0), ) l = [1,2,3,4] x = f(l) print(x)